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STREAMFLOW FORECAST iNG
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FINAL REPORT
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SUSlf NA JOlNT VENTURE Docmsnt No1 2832
SUSITNW HPDRBELECTWHC PROJECT
STREAMFLQH FQRECASTIBG FEASIBXEITP STUDY
Report by
Hydex Corporation
Eugene L. Peck
Thoma% N, Keefer
Under Contract ta
Harza-Eba sco Sus i t na Jo int Venture
Prepared for
Alaska Power Authority
t..- 4 *< rq 3 .L.i~ objec"ii7e of the Susi trla Hydroelectric Project is to
e <~~~c~r.:~ze the iise of water for power generation while meeting
environmental constraints, suck as requirements for fisheries and
>~ildlife, The purpose of this study is to provide information on
hyd-o? ogical forecasting nrtetlnods (including basic liata reqcire-
fner?ts) to assist the Alaska Power Authority in devc:loping an
eiFjiectj - "j,, water supply foxezasting program for the 2rajecte
Only about half of the annual runoff from the Susitna Basin
origina':es from snowfall during the winter months (October-April)
and acurate forecasts of this snowmelt contributiean to tile
sessonal runoff are possible. The question of how accurately
icunoff from summer (Hay-September) precipitation can be
forecasted is more difficult, out foracasts of runoff for two to
three weeks in advance are possible.
For accurate long-term forecasts of the total seasonal
runoff, it is necessary to have a knowledge of;
(a) the amount of runoff that will be contributed from
rainfall and snowfall (including glacier ice) that
have occurred in the basin,
(b) short-per iod weather forecasts,
[c) current seasonal trends of precipitation and
temperature, and
(d) statistical indices of climatological cond itions
for the rest of the forecast period.
An operational forecast program can be developed that will
provide short and long-term forecasts of runoff of sufficient
accuracy to successfully operate the Susitna Hydroelectric
Project and achieve the objective stated above. This will require
i:he collection of additional information on the precipi tatinn
regime sf the basin and application of hydralogical models, To
cost effectively accomplish the collection of required basic data
and ts develop an operational forecasting service for the
project, the follow~ny recommendations should be followed.
2. Basic Data Collection Program
A. Findings
The current data collection program does not provide
sufficient inEormatian to support either the development nor the
operation of an hydrological foreczsting service.
13 ,, ~ecsrz~~qendat ions
In order to have sufficient information for c?evelopn:ent of an
o;jsrz"Lional forecasting service "ie following actions should be
taken during the period until the project becomes operational;
(1) Discontinue measuring at meteorological
stations (except at Matarla base camp and
for other specialized purposes) all.
meteorologicaL variables except preci-
pitation, teinperature and wind movement-, ,
(2) Operate and collect in real time measure-
ments from 12 meteorological stations
shown on Figure 2, page 24,
(3) Continue to coliect snow survey informa-
tion (in cooperation with SCS) for sites
shown on Figure 3, page 26,
(4) Continue to collect streamf low records
from the three 'JSGS gaging stations, and
(5) Summarize and archive all basic data
collected fsr the forecast program,
When the project becomes operational do the follo~.iing;
(1) Collect in realtime streamflow, reservoir
stage and information on reservoir re-
lease sf water,
(2) Continue collection in real time measure-
ments from the 12 meteorological stations,
and
(3) Continue collection of snow survey measure-
ments from those sites whose data are found
to be required for the operational forecast
pragzam*
It is recommended that data collection be accomplished usin52
a satellite telemetering system.
3. Development af Operational Forecasting Service
PA. Even with the additiorlal basic data proposed above, it
would not be possible, using presently available niodels and
imataloij ica1 informat ion, to forecast the runoff for the
Susitna Basin with sufficient accuracy. Additional studies need
to be accomplished to im2rove the utility of models that arc
currently used for forecasting runoff: from other glacier izec-i
basins in Alaska. Actions ta be taken incl-ude;
."'. CI in~aisological Stud ies
(I) Develop seasonal isohyetal (aver3ge ppreci-
pitati011 maps) for the Susitna aasin.
(2) Develop climatological statistics of Play-
September weather conditons for Susitna
Basin required for ex tending short-term
supply forecasts to seasonal forecast
(semi-conceptual model) .
B, Model Studies
(I) Arrange for application of hydrological
forecasting models to basin as recom-
mended in Chapter 3. Models to be con-
side~ed for use are:
(a) Wnder son NWS snomeE t: model
(b] Quasi-conceptual seasonal runoff model
(e) NWSRFS and the SSARR soil moisture
accounting models
Other actions to be considered in the future for improving
the forecasting service include:
(1) Adjusting precipitation records (espe-
cially snowfall) to reduce variability and
bias introduced by wind sction on gage catch,
(2) Incorporating satellite techniques for
enhancing the knowledge of the magnitude
and distr ibutiomn of summer: thunderstsrm
precipitation,
(3) Using aerial gamma radiation surveys to
obtain better knowledge of the areal
distribution and magnitude of the snow
cover on non-glacierized arsas,
(4) Using meteorological i~formation (such
as upper air measurements of moisture and
wind) to improve knowledge of precipita-
tion over the high areas of the basin, and
(5) Obtaining reliable measurements of the
axes% extant and af the seasonal water
balance af the primary glaciers in the
bash~.rr,
TABLE Oi7 CON2'ENTS
r>;F"" "rn' .-JLLj~aVE SUMMARY,...,,,., ,.,.e,,-,,,.,,,,,,,,s,,s,,,,eaosei
1, QBJE@TIVE,~,eeo*~~~oslfo~kyIes~biaosocpoaia~~~aoe~*~~~-
2, BASXC DATA COLLECTION PROGRAF4, ts e e e e e a 0 e e 9 Cs e 14 I
3, DEVELOPMENT OF OPERATIONAL FORECASTING
* I
SERVICEeaoe+eetr;ecalaaaa~cseeh-ea,eoiJo~adevaaeazsaso~~
4, OTHER CONSIDERATIONS, , , , , 8 a w1 i&i ?P ea 18 "4 ii -e"
CHAPTER 1, PNTRODLT,FGTION,,,..,,..,,.,,,,,,,.,.,,.,,,,.~e,..~.
1, BACKGROUND, , , . , , , . . . , . . , , ,, , , . . , , . , . , , , . , , , , , , b
2, PURPOSE AND SC~IPE,~.,.,,,,.,,,,,,,,..,,~~.,~~~
CHnFTER 2. DATA REQUIREMENTS.. . . . . . iQ qb %11 rn 4P B) QP a i&i iPd bB , B, I a , Ib a a 4
2, RESULTS OF FIELD TRIP TO ACA%KA,,..,.......,.d
2, ACCURACY AND REPRESENTATIVENESS OF aASH@
DW'~A~s+rn890eBC~.0~e98b.~0BI~~~~0)(h8tIs~*IIa41)e~~~e~6
3, EVALUATION OF EXISTING DWT8"A CBLIL8ECTIBN
PROGRWMB6m88e~QaiQOgIPIB~~~e4Bb~~gCB)Bdl,D~OOs~~~
4. DATA REQUIREMENTS FOR HYDROLOGIC
FORECASTIMG,,D,,,,,~~rsC69,(g,Q~$,~,or?rO~r)~'6F~Is~~~~f~
5, CLIMATOLOGICAL DATA REQUIREMENTS FOR
MODEL CA&&BAATfQNaeeese,~esrB)eaill.Bs~*eaoIea9po1eIj*I1"3"
6, OPERATIONAL DATA RE~UIREMEMTS,,,,,,,(iiUeO(irsbsaI~8
7, RECOiqMEMDATf ONS FOR CHANGES IN DATA
COLLECTION PR6GRAMrB)e*eeeea*eaaa~Vre.*e~r~e~22
8, OTHER RE@OMMENDATPONS,,e,rB..iJiOIRBOeD8~(IIaDOQ0~.&,O28
CHAPTER 3, MODEL TECHNOLOGUeaea,*,,e~pIoLa~ceaa,aeeeaa~e01:*p1a~O
L, FACTORS FOR SELECTING FORECAST MODELS* B, a *. :30
2, MODEL WLTERMATHVES,,,I,,,Qmr)Ls~B1,~s~eCaD,~~dXU~37
3, FORECAST ERRORS,,~,ge.~,B,..,ssQDO~Osdee.t(b~OIR~TO
4, MODEL REGOMMEMDATIONS,a,Ba~iQ*.~p~e91a1P,ussdiC.El.S3
CHAPTER 4, OPERATIONAL CONSEDEWATIONS,,,,,(ZQ(D,PJr,09$IBe.*Qd55
l, INTRODUGTIQN, . , . , , . . . ., . , , . , . , , . , ,. . , . . , . . , .!55
2, OPERATION OF TEIE FORECAST MODEL,, , , , , , , , , , , ,55
3, DATW GOGLECTIBN RNB REEAUeea.a,~s~,,,,,0re4L~!55
4, RECOMMENDATION ~~,,~~**~~~~~m~a~*~4r~Saayo~eaVra4S3
5, ALTERNATIVES FOR TEiE SUSITWW BASIN,, , , , , , , , ,58
5, COOPERATION ON DATW RECEPTIBN,,,,..,,,,e,,*06:3
7, PURCHASE OF DEDICATED GROUND STArrC"ISN, .. . . . , .63
BUDGET iCST&MWTION FICISRESu BL I$ + * 0) BL ,#. . rp, k. q, ap 64
9, ADDXTIONAL RECOMMEMDATIBNS,*e,~e~*$ieweaspJ~nrg,6~$
FfAPS OF MONTHLY PRECIPITATION, SUSTTNA RIVER BASIN
PIaOTS OF DAILY DISCHARGE AND DAILY BRECIEK'3TArEC"ION,
SUSITNA RIVER BASIN
RESULTS QF MULTIPLE REGRESSION ANALYSTS, SUSITMA
RIVER BASIN AT DENBEI, ALASKA
EXPLWWATORY INFORMATIONd DFFPNfTfONS OF STATES
WMD PARAMETERS, AND SCHEMATIC BXAGWAHS FQK
CONCEPTUAL HYDROLOGICAL MQQECS
R & M CONSULTANTS LETTER OF ILWRCH 8, 2985,
The S&'isitna Hydroelectric Project includes the construction
of two tla~i~s, Watana and Devil Canyon, on the main channel of the
Sustina River, The 8atana damsite's drainage area is 5,180 square
mi?zs, the Devil Canyonss, 5,810. Tkie project is designed to
p?.ovide eltzctriclty to the Railbelt area of Alaska Ear well iflto
the rlexc zrzrrtury and to have Elow regimes that will minimize
impact on fisheries and other environmental resources,
The prc3posed Watana Reservoir will have 9.7 million acre feet
of storac;t? of which 3.7 million will be active storage, The
Devil Can:ron Reservoir will not have any significant amount of
active storage, Since power requirements are greatest during the
winter, tl~e psoject is designed to have as much water in storage
as possrble by September of each year.
Durint~ visits by Eugene L. Peck and Thomas N. Keefer to
Anchorage, Alaska, and Chicago, Illinois, in December 1984,
discussi.o~2s were held with representatives of Warza-Ebascu and
the Alask.-i Power Authority (Mr. Eric A. Mari-hegiani). The
following :nformation perzinent to hydrological forecasting for
the pxoje#:t: was received.
Based on the discussions held in December 1984, the following
bydrologir:al forecasts are required for the operation af the
project:
t 1) Forecasts of seasonal May-September inflow to
Matana Reserve ir (long-term forecasts) ,
-2) Forecasts of inflow to Flatana Reservoir for
two-week periods (short-term forecasts) .
The i$~ng-term seasanal forecasts are necessary to schedule
releases i'rom the reservoirs to ensure the maximum water storage
hy S~zptemkjer. These flarecasts are required at semi-monthly
jntervals starting on April 1 but possibly as early as January L
of each year. Each forecast should be a probability forecast for
the May-S~ptember runoff (or fox forecasts after May 1. for tile
period frcrn the date of the forecast through September), These
forecasts, used in canjunction with standard reservoir
operational. ru1.e c~rves, will percmit the reservair operators k.3
plan for having maxisum water storage in the Watana Reservoir
prior to the winter.
The $tort-term forecasts (inflow to Watana Reser'~oir for the
next two wgzeks] are rctquired daily from May through September,
The Susi tna Bydroelectxic Project includes the construction
of two dams, Watana and Devil Canyon, on the main channel of the
Sustina River, The Watana damsite's drainage area is 5,180 square
ritiles, the Devil Canyon"s, 5,810. The project is designed to
provide electricity to the Railbelt area of Alaska for well into
the next century and to have flow regimes that will mini-mize
impact on fisheries and other environmental resources,
The? proposed Watana Reservoir will have 9.7 million acre Eoet
of storage of which 3.7 million will be active storage. The
Devil Canyon Reservoir will not have any significant amount of
active storage. Since pcwer requirements are greatest during the
winter, the project is designed to have as much water in storage
as possible by September of each year,
During visits by Eugene i. Peck and Thomas N. Keefer to
Anchorage, Alaska, and Chicago, Illinois, in December 1904,
discussions were held with representatives of Warza-Gbasco and
the Alaska Pc~der Authority (Mr. Eric A. Marchegiani). The
following informat ion pertinent t3 hydrological forecasting for
the project was received.
Based on the discussions held in December 1984, the following
hydroLogical forecasts are required for the operation of the
project:
(1) Forecasts of seasonal May-September inflow to
Watana Reservoir (long-term forecasts),
(2) Forecasts of inflow to Watana Reservoir for
two-week period;^ (short-term forecasts] .
The long-term seasonal forecasts are necessary to schedule
releases from the reservairs to ensure the maximum water storage
by September. These forecasts are r.equi red at semi-monthly
intervals starting on April I b~t possibly as early as January 1
of each year. Each forecast should be a probability farecast for
the May-September runoff (or for forecasts after May I Eor the
period from the date of tl7e foreeast througl~ September). These
forecasts, used in conjurlct ion with standard reservoir
operational rule curves, will permit the reservoir operators to
plan for naving maxiinurn water storage in the Watana Reservoir
prior to the winter.
The short-term forecasts (inflow to Mc~tana Reservoir for the
ne,:'r. two weeks) are reyuir~d Slai1.y from May throucjh Septemis6:r.
,z . st L;z::ei.asw swill ~rovide "ihe necessary infc?xn;?tion "c \jdj
k- b; P_j
% - sca!:onal inf Itow Eorecasts, to prevent enc~:oachme!~i 3r. dam
" - "" "*-** p> - "I ..... 1 udrd req:li~;emeflirs - and to sa"iisfy doxnstzeaa i- iver
*Fp-iX '1 - . C~L~J remenks v~i'ch a mirlimum reduction in storage,
--a Ij-h
4 e k~:, 2:p(3t se and Scope
a1:.. " AS stuljy is to provide information on hydralog ical forecast
r:?4?:5i-,ods (and on basic data requirements) that will help the
,TI% l z s ha *.- Power 8ulii~;ri ty develop an effective farecast inq service
- ',- ? 4-
-&:GC will ensure that the project's objectives are achieved (i.
6% ,-- -- to maximize the use of the reservoir" inflows for energy
genexati~r~ and to provide flow mregimes in the reach of the river
belo%> the reservoirs that will minimize the impact on fisheries
arid sther environmental ~~SOUTC~S) .
The scope of the study is:
(I) Ta review sets of alternative forecast syst~ms
that could be used for operational forecasti~g
fog the project;
(2) To evaluate each method's ability to provide the
required type, accuracy, frequeczy and lead time
of fsreeasts; and
(3) To p~ovide information on the basic data re-
quirements associated with each alternative
forecast system.
The approach to the study is:
(If To rneet with Harza-Ebasco and Alaska Power
Authority personnel to more fully understand the
needs for the operational forecasting service;
(2) To meet with state, federal and other agencies
in Alaska to evaluate the benefits that can be
achieved from cooperation with such agencies for
developing and operating a forecast service;
(3) To review the methodologies and the basic data
requirements for a forecasting service;
(4) To review specific forecasticg methods and
evaluate their usefulness far the project;
(5) To provide a list of alternative sets of forz-
cast methods together with information on the
accuracy, sensitivity and data requiremenks for
tzheir op~hrational use; and
(6) To present a draft of z final report and to meet
wi?h Hazzil-Ebrtsco ofEi:~i~Ls to revie?w the draft
and sublnit a final xeport,
*- U A j: scuss jon of the basic data needs for preparirrig 5'-or" ihfqid
1 sr.g-- term hydrological forecasts for the Susi tima Bas! n and
r;:;eci f ic rceomn~endations for cllanges in the existing data
collection network are presented in Chapter 2, Forecast model
technology is discussed in Chapter 3, with reviek~s of alternative
corecasting methods. Enformation aad recommendations for an
cpe;:ating hydrological forecasting service for the project and
(3 d.LuLussions ; c s4 on collection and psocessing 3f dat.a, computer and
manpqwer requirements and cost factors are presented in Chapter
4,
The executive summary foilowing the Table of Contents
contains recommendations for collecting and processing basrc data
foe the operational forecasting service for the Susi tna
Hydroelectric Project.
Durirlg the week of December 10, 1984, Eugene L, Peck and
'i'homas N. Keefer visited Anchorage and disccssed the preserlt da. ta
ccllection networ;~ of the Susitna River Basin with many agencies
anCi a~ganizations in Alaska. They also surveyed much of the
upper Susi tna Basin by he! icopter. Considerable information was
furnished by Harza-Ebasco prior to the field trip. Jeffrey He
Coffin, H & M Consultants, Xnc., furnished additional information
with his letter of December 19, 1984.
Hydex personnel were impressed with the attention given to
Cne develapment of the data network for the basin. The
cooperative attitude expressed by federal and state repre-
s~:ntatbves, by R & M Consultants and by personnel of Harza-Ebasco
was appreciated.
The major meetings held during the visit in Alaska with brief
comments on items discussed are listed below:
General overview of the project and the cooperating agencies
was given by E. J, Gemperline al!d Khalid Jawed. Other Harza-
Ebasco personnel participated. The primary discussion items cere
forecast needs, wildlife habitat and other environmental
requirements. Wayne Dyok provided information on power
requizements and the reservoir levels that can be maintained at
different times of the year. Eric Marckegiani, Alaska Power
Authority, provided specific information on forecast needs. It
was clear that the primary forecast need is for accurate
estimates of the seasonal inflow from early in the calendar year
through August 1. A secondary need is for short-term wa*ter
supply forecasts (inflow to reservoir for next two weeks) from
May through September. No specific need is foreseen at the
present time for river fo~ecasts for ti^^ river below the Devil
Canyon Reservoir, It was felt that the operation of this portion
of the river could be accomplished by krlowledcje aE reservoir
f ei.ea~e~*
M Consultants,
Jeff Coffin and Steve Bredthauer advised us of their data
collection program and provided information on R & Mgs overall
activities, They have considerable first: haild knowledgo of 'rile
Susitna River Rasirl and the dai:a collection program,
Clagett provided complete records of ail $no;; survey
%-:a~--.rds L b % s-~ "ihat haye been collected by the SCS in A1.a~ki.i~ Geor~e is
;;:.lowLedgeable of snow conditions in Alaska and is very wil1irr.j tJ
t,LId cooperate. George classif ik2d each snow survey course in "LA-
Susi tna Basin on the basis of representativenes~:.
D, Alaska State Climatologist.
Jim Wise, Artic Environmental Information and Datz Center,
supplied a photocopy of portions of an annual precipitation map
fo -6- L Alaska that covers the Susitna River Basin, The office has
printouts of all climatological records that have been collected
by the National Climatic Center in Asheville, N. G, , an6 provided
copies of records for some stations that operated for a short
period of time in the Susitna Basin.
E, U, S. Geological Survey.
Raymond S. Geozge, Larry Leveen and others discussed lnt-eeent
creamfl flaw measuring stations and the proposal to establish a
station upstream of the high water level of the major reservcir,
F, Nat ianal Weather Service,
The hydrologist in charge, Jerry NibLer, and members of his
staff and the meteorologist in charge, Edward Diemer, provided
several reports and personal information on precipitation,
snowfall and thunderstorm climatology of Alaska. These reparts
will be of value for understanding the precipitation regime of
klaska. Jerry also provided complete sets of parameters for the
eleven basins in Alaska that have been calibrated using the
Platianal Weather Service River Forecast System's (WSRFS)
canueptual model.
C, University of Alaska.
Will Harrison, glaciologist, Geophysical Institue of the
University of Alaska, presey-d an interesting discussion on
glaciers and their relation to streamflow and sediment discharge
in the Susltna Basin. He disccssed the effects of surges in the
discharge of rufiaff from the qiaciers and the resulting very
large changes in the sediment discharge. Fie supplied consi-
derable information on the lo-.lg term changes in the glaciers thai-.
indicate ttle glaciers have, cn the average, been wasting for th.2
last 30 years and therefore 3 substarltial portion of the
streamflaw of the Susitna River has originated from this source:.
From his observations of thf2 glaciers and snow cover on the
glaciers d~ring the past three years he llas computed the wlnter
and summer balance (loss o: gain af the glacier during the
season) and the annual bal anee. T';.iese estimates ind cake that
some of the glaciers in t31e Susitna Basin have grown while others
appear to have wastled during the past three years.
rn . ;.; ie ,.. information f~rn-~shed by Dr, Harrison, especially cn !:hi?
pc::csntage of the stream discharge for specific sub basin:^ tha T:
= a p:zr.iglniite from the glaciated areas, will help in evaluating
i.-izr iaus r~odel s for forecasting snomel t runoff, iiowever , GUT
upinzorr, a,:3 one that is apparently shared by Dr, Harrison, is
.chat his observations are more impoetant fax estimati.ng the
r) lr" sxreuir of glacier contribution to the long-term (33-year)
strearnflow "clan for ilse in forecastin9 water supply during a
specific seasan.
In establishing a basic data network for a hydrological
Corecasting service several factors are importznt. For precip-
itation and sncw cover measurements two important factors are
sometimes not given the attention they deser-~e for the data to be
of maximum value, The first, and most critical of these factors,
4s the effect of wind (gage exposure) in reducing the gage catch
and causing variation in the measurements relative to the true
pr~cipitatlon at the si%* The second factor is the need to have
the site "exposed" to the general paths of storms so that the
records represent the average true precipitation for the general
area {representativeness). Information on these two factors are
presented in tte follvwing sections.
A, Gage Exposure
Hydrologists have long recognized that deficiencies ex ist in
precipitation measurements, especially for snow fall. Errors in
precipitation data account for a Large portion of the
inaccuracies in precipitation-runoff relationships and in the
simclaeted streamflow from operatianal hydrological modelas, Our
inability to evali,,ate adequately the areal average water
equivalent of the snow cover is the greatest limitation on
inlproving the re1 iability of silomelt forecasting (1) .
Snowfall is the most difficult form of precipitation to
measure. Most precipitation gages have an orifice that is
exposed in a horizontal plane for intercepting failing
precipitation. The effect of wind on a gage is the largest: cause
of measunement. error (2). Wilson (3) demonstrated that snowfall
and snow cover measurements that were most representative of the
actual snowfall had the highest correlation with streamflow
measurements of small basins in the high Sierra Mountains, Brown
and Peck (4) showed that the reliability of a precipitation
measurement (snowfall) was related to how well the site of the
gage was protected from wind movement. Lai~~on and Peck (5)
showed that; the accuracy of conceptual models for simulating
s~omel t streamflow was enhanced when precipi tation measurements
are adjusted far bias in catch due to %find action on the
precipi.tation gage. Figure I, from Larson and Peck (5) , presents
a summary of gage catch deficiencies that have been observed in
measuring rainzall arid snowfall jn relation to the average irlind
measlired at tkje gage or i Eice,
r*- * L" i2 2 precipitation measurement problez h%:s receivel consi-
ciitabi~ attention by international melreoroLsgical cind hyd~o-
li~j ica2 organizations, Dux ing the 1912 World ~etearolog ic.1
Ora-ri%n-izetionn symposium on Distzibution of Precipitation in
v m >!i.iini:alnaus Areas he1.d in Geilo, Norway, "ce pprbblsrns uf
w* - i.tescsl- ing precipitation in mountainous areas were discussed (61 .
1'fB ;.- -.tie conclusions stated that the mast xeP iable and consistent:
i;?.i.ascremen"i those obtained at sites th~t are well protected
from adverse wi 3 action. It was also repo~ted that adjusting
preci;>itation records using wind measurements was a good method
t Lu sr reduce the bias and variability induced by wind on the
6,
OPX fxee,,
F*?easurements of the average water equivalent of the :snow
cave*: may also be greatly affected by wind. Snoi~fall on the site
may be blown from the location by very low (less than 5 mph) rgind
speeds, Sites located at the edge of forests or other ccver may
col lect amounts of snow greater than the average snowf all.
ii. Representativeness of Precipitation and Snow Cover.
Heasurements,
AL?:hough it may seem obvious, it is not always recognitzed
that :cipi tation (snowfall) and snow survey measu~ements'~ are
most valuable for forecasting snomel t runoff whon the k
measurements are representative of the true snowfall at the site,
Ideal exposure for observational sites for other meteoroloyi-
cal parameters such as wind, temperature, humidity and
evaporation are different than those for precipitation. For
these measurements the best sites are those./that are sanewhat
open to wind movement. f
B
For snowfall and snow survey measurements the gaging sites
should be away from the immediate influence of trees, buildings
and water bodies and in such a position as to afford a fair
representation r E surrounding condtions. A station should not be
~ited upon, or close to, steep slopes, ridges, cliffs or k~ollows.
Pol- some purposes (e. g., modeling crop conditions) the station
location should be situtated to lneasure the conditions in the
agricultural field. However, for use in modeling rjver basin
eond itians the site location should represent average conditions
i rr t:he basin rather than those of Local conditions.
The Location af the site with respect to the general
direction of storm movement during periods of precipitaticn is an
important factor as to how well the preci~i tation at the site
eelates to tbc average precipitation in tile gcirreral area. En
this report this is referred to as the repres~ntstiveness of the
si- te. Dased on experience in developing procethure sfor
forecasting water supply for the mountainous areas of the western
United States, Lo{::,tinq gages a"ci;ites that axe '%exposedw ta the
gentzral direction of stab-m..; is an irnpor:tant factor for the data
t.ci bf: of mos";\ri~lue, RPCXF~?~ @ark b)Oi;$CIe:". in dcvslopinq
8
+. s ,as ,- ,. . for nehwork design in mountainous zreas hzz also \.
'a, Iemonstraf.ed that a measure of how well a site is "e::~~~~ed'"-*~ ks,3
{jt5:?e1:31 storm movement is an important parameter fcr *\
-!ldez.s"ianding the relation of average precipitatl-on at one Z
r~cation with average values at other locations, h
\
In summaryi for pxecipitation or snow survey information to \-,
be cf most value for forecasting the seasonal sncwmelk i:unoff in
mountainous areas, it is important to know the relation af the
rrecipitation catch to the true precipitation and how w~iL the
s; Le is '"exposedw ((representativeness) to the general d irect ion
of ansv~rnent of ~'k~rms,
For high elevation basins like the Susitna there may not be
zany sites that provide proteetion from wirld and are also wel.1.
F W. exposed" to the general direction of storm movement. In the
f~ll owing discussians the need for protected and re2resentat ive
sites k~here precipitation and snow survey measurements are made
is a major consideration.
Consideration is also given to the techniques for adjusting
precipitation measurements to correct for wind effects and to
obtaining enhanced knowledge of precipitation at high elevations
by using meteorological parameters to extrapolate measuremtnts
observed at lower elevations. The advantages of remote sensing
are also presented.
~ 3, EVALtlATPON OF EiXXSTPMG DATA COCLECTZOH FROG
The data requirements for the forecasting service depends to
same extent sn the models selected, Haw wcZl the data eollecyt2d
meets the forecasting program needs depends upon the network
design and on the accuracy and representativeness of the
sbsgrvations,
~ A. Data Requirements for Long-term Seao~al Forecast iny
Data requirements far preparing long- term seasonal water
supply forecasts may be different than those for short-term
forecasts during the May-September period, For seasonal
farecasts issued on or before May L a knowledqe of the winter
precipitatia~ (and/or water equivalent of the snow cover) Is
required. Comments on the usefulness of the measurements fccs
the established meteorolag ical stat ians in tl.15 basin for lt~.:~q~--
term seasonal foreeast ing are given below.
(1) Air Temperature. Ternperatur~ measurernoilks are rjot
specifically required for most forecast mode2.s
unless there are periods of winter snowmelt, Tem-
perature measurements have been used along with wind
movement for adjusting precipitation records for
deficiencies in gage catch (7),
Obse~vatisns now xecerded in the basj-n axe $!YE$
maximum, minimum and mean air temperature in deg.:ses
@czJ-sius,
(2) Wind Measurements. Like temperature, wind iileasure-.
ments are not required for forecasting long-term
seasonal runsf f, Wind measu~ements are used to
adjust precipitation records for def ieiencies in
gage catch and ta estimate precipitation at high
elevations using meteorological models,
Six daily values for wind are recarded at the
meteorological stations: average wind speed in m/s,
maximum gust speed in m/s, direction of maximum
gust in degreesl zssuitant wind direction in
degrees, resultant wind speed in m/s, and pre-
vailing wind direction to 16 points of the compass,
(3) Moisture (humidity and dew point] Measurements.
Generally models that forecast seasonal water supply
generally do not require input of moisture
measurements. Measuremenrs of moisture could have
some value for computing sublimation in snowmelt
models.
Daily values of the mean daily relatire humidity
in percent and the mean daily dew point in degree
Celsius are recorded at the meteorolog icaL
stations,
'. -. (4) Precipitation. Precipitation, along with measure-
ments of the water equivalent of the snow cover, is
\
\ the most important meteorolog ical input for models
'\ forecasting seasonal water su~~ply during the winter ,a
\ Preci.pitation is generally not measured at the
meteorolog isal stations during the winter \. aycept oceasi.onally for the month of April,
~iecipitation measurements are available from the
Wyoming gage at the Wa tana rrleteorolog ical s"lation.
(5) Radiatiqn Measurements. Measurements of sal.ar
(short wave) and long wave 2:adlation arc? not
generally required for seasonal wcl ter supp'ly
forecasts m~de during the winter. The nftec2
for rad iatioo measuretnenksr especially dilr ing the
winter, is not clear,
division of the Naticna!- Weather Service, NORA, h:a3
19,ade no evaluation of the accuracy or reliability
of this instrument. The following facts sake the
~ePisbility of the recorded data during the winter
months of questionable value:
(a) The information on the pyxanomewer states
that the cgsine response uE the instrument
is true over a range of 70 degrees, it is
not clear if this is 35 degree each side of
vertical or 70 degrees from the vertical.
In any event, during the winter months the
sun is at a very low angle and the response
of the instrument to the solar radiation
would not be accurate,
(b) Most of the instruments observed during
the field survey were covered with snow and
ice. This prabably occurs ofter~ duxing thd
winter esp2cially following periods of
srlowfaLl and the sensors remain in this
condition until the snow and ice are blown
off , melted off, or are removed duuing an
inspection visit.
The brouchure on the instrument furnish-
ed by R & M Consultants provides some
information on the effects of temperature on
the change in output for constant radiant
input. However, this information is furnish-
ed relative to 28 degrees Celsius and only
dawn to O degrees Celsius. How the instru-
ment responds to the very cold temperatures
of Alaska is not known,
(6) Snow Course Meesurrmsnts. The depths and water
equivalent af the snow cover are read near five
stakes that are installed in the ground along a line
extending north from the instrument shelter at some
of the meteorological stations. Because these si tes
are generally open to wind movement, the depth of
the snow cover is primarily limited to the height af
the stubble along the snow course. Except for those
stations which have natural protection from wind
movement: (such as at the Sherman station) these
measurements are of no value to the water supply
forecasting program.
Sojne aflotl courses estatlisi-ngd by Soil
Conservation Service and by & M Consultants ha..
aerial markers tilat are read frrtrr. :;e'licapters or
light aircraft, This techciaae bas proven success-*
ful ic other high elevation snow areas where same
water equivalent measurements for computing scow
density are available at similarly located sta-
tions. The value of the readings is related to tile
windiness (exposure) of the aerial marker snow stsite
site. If the immediate area is protected from iqind
movement, such as by trees or by terrain featcres,
the readings can be of considerable value fur
seasonal water supply forecasting,
B, Results of Field Survey.
Comments on exposure of precipitation and snow survey
measurements sites visited during the field trip on December 13,
1984, follow. The exposure classification for the immediate area
of the gage or snow course is based on the system developed by
?xuwn arid Peck (4).
(I) Watana Camp Meteorological Station.
Exposure. The site is open and is classified as a
windy site. There is a slight rise in the terrain
ta the north of the measurement site. The location
is exposed to the west and the site is cansidered as
representative for storms in the lower portion of
the basin,
Pzecipi tation. A Wyoming precipitation gage has
been installed. However, the maintenance of gage
is poor. The wind vanes dip in the middle
of each section and the northern seetion was
disconnected at top and hanging down. The precipi-
tation gage catch in properly constructed Wyoming
gages is considered to be more consistent (approx-
imately PO percent of true value) than a gage in a
windy location without such protection,
Snow cover survey. Records of water equivalent or
of snow depths Erom the five-stake snow course at
the station are of little ox no valve far a water
supply forecasting program.
(2) Marlahan Flat Snow Co~~rse Station (SCS)
Exposure. The present sic@ of che snow course is
in trees southeast af lalre, The classif icatioil of
the exposure sf the site is rated as fair1.y wstl
protect;ed t:o moderiltely windy, Prior to 1983 the
SCF sllow course ~23s located in i mucir inove ogotin site
arid khprcforr rec(~r(js for chi? f;r~io periods arc? not
$+* p am* f- . fSieasurerqen,s 'nave bg>e!l zade
both sj-tes, ::II published recaras arc C L Erom a-e -:
4"Q The SC.S plans to publish22 sa~ustec! va?;cs
for the entire period when a sufficien;; overlap of
data is availalzie. Snow appezrance in 3x23
during the field sarvey indicated that the wind
movement at the Monahan survey site is much less
than 5 miles to the south near the De~iali Highway.
The station location is representative for the
central portion of the upper Susitna basin
13) Susi tna Glacier Meteorological Station
Exposure, The measuremr:nt site is sn a ridge be-
tweerr two Eoarks of the Susitna Gl.acier, 'The
primary drainage wind movement is below the eleva-
tian of the station. Rcrwevez, during storms, the
site could be subject to strong wind movement.
Therefore the exposure of the gage site is classi-
fied as windy. Snow course measurements at the
site would be poor.
The site is open towaxds the southwest. The site
is considered good for representing the upper
portion of the basin. In factr true precipitation
os adjusted precipitation values from the site
would be nearly ideal for lrepxesenting the high
elevation area of the basin.
(4) Caxibou Snow Course
Exposure. The site is not protected from wind
accompanying storms but is protected from genexai
winds, especially the major drainage winds in the
area, The site is classified as fairly well pro-
tected to windy. The measurement site is located
in a saddle at a hig:~ elevation south of the Susitrta
Glacier. The snow course information is probably as
good as one could obtain from a site at: a high
elevation in the basin.
The site is opened to the soutl'lwest and measurements
should be representative of snowfall at high
elevations in the basin.
(5) Dcaal i Mcteorologicar Station
Exposure, The gage site is completely open with
no protection from wind. Due to this and the fact
that the site is subject to strung drainage winds,
the gage site is classified ss vsxy windy.
The precipitation gage was in need of
rerdair and was also unlcvc+1, Tila prc~ipi"c:tbni?
gage in use does not have sufficient
strength fox the winds experienced at this site,
Snow course data observed at five surface stakes
e3rtcnding north from "Le instrument shelter are
considered useless. A location a half mile to the
northeast of the present site would place the gage
in trees and out of the path of the strong drainage
winds that originate in the glaciers upstream from
the station. Relocation of the gage would reduce
the bias and variability of the precipitation mea-
surements in relation to true precipitation.
The location of the present site (or the proposed
one) is considered ta be such that true information
on the alztual precipitation at the site would be
representative of the precipitation at the middle
elevations of the basin.
(6) Tyone Meteorological Station (Discontinued)
Exposure,. The site is in thinly scattered trees
near the Tyone River. No strong wind movement
was evident from snow patterns. The gage ~ite
is classified as fairly well protected to
moderately windy.
The measurements would be considered as repre-
sentative of a large portion of the lower drainages
of the Tyone and Osbetna River Basins.
(7) Kosina Meteorological Station
Exposure. The location of the gage is open with
little or no pl-otection by trees. The appearance
of snow in the area of the gage indicates fairly
strong wind action. The gage site is classified
as windy. A location to the north at lowar
elevation in trees would provide a much better
measurement of precipitation and snow cover.
Accurate measurements at this site would be
representative of a large area of the middle
Susitna Basin around the proposed Watana Reservoir.
(8) Devil Canyon Meteorological Station
Exposure. The station is Located on a small ridge
with protection from wind movement afforded by the
general terrain. The gage can be affected by wind
during snowfall. Although the exposure of the
present gage site is fairly good (classified as
fairly well protected) relocating the precipitation
gage just to the north off the ridge an^? in trees
would enhance the protection for the gage (to well
protected) .
The site is considered to be representative cf a
large portion of the lower middle basin.
(9) Sherman F'ieteorolog ical Station
Exposure. The gage can be affected only by strong
downdrafts during storms. The site is classified
as well protected. In fact, because of the good
proteetion the srlow builds up on the wind sensors,
During the field visit the wind sensors were com-
pletely inoperative because of snow and ice
accumulation. Snow survey measurements at the site
are considered to be very good.
The station location with respect to general storms
makes the site very good for representing precipi-
tation and/or snow cover for the lower portions of
the basin near the village of Gold Creek,
6, FieEd Bbservatisn of Wind Movement in Basin,
The light snow cover that existed over the basin during the
aerial suPvey on Decembes 13, 1484, pxovided an unusual
opportunity to observe the general wind movement patterns of the
basin, The snow cover for regions with light wind movement was
swath and unbroken, Ir1 other areas the snow was blown into
ridges or showed other evidence of wind action. The area having
the most effect of wind action was approximately 10 miles west of
DenaLi near Butte take.. Strong wind effects were seen south of
Butte Lake as far as Deadman Lake. Flying north from Butte Lake
over the Denali Highway towards Monahan Flat the wind effects
became less observable. There was little evidence of strang wj.nci
mavernest in the imimediate area sf Mornahart. Plat,
Drainage winds originate over the Susitna, West Fork Susitna
and East Fork glaciers and flow dcwnhill towards the confluence
sf" the West and EasP: Forks af the Susi tnla River, The wind f'law
tends to split near DenaLi with a major flow westward towards
Butte Lake and the rest moving down the rnain Susitna River
ck~annel,
D. Evaluation of Snow Course Information
The locrati~n sf all snow course sites (SC*- -nd R & Mj nlc4t
visited during the field survey were located on O. S. Geological
Survey maps and the terrain features fur each site were reviewed.
Tkrf- stat$-ons have been well located and arc considered to be
ri7cesentative of the snow cover in the general, area of each
sitelr
The following evaluation of exposures Eoz snow survey
courses in the Susitna Basin was pravided by George Clagett, SCS
snow survey supcrvisnx, based on iiis personal experience:
Classification of Snow Course Exposures
by George Clagett
Snow Co~anse Classification
Cathedral Creek (new)
GPcarwater Lake [reactivated)
Devils Canyon (aerial marker in trees)
Fog Lakes
Worsepasture Pass [aerial markey)
Jatu Pass
Lake Louise
Malemute 2 (new)
Plonahan Flat (and precip gage)
Monsoon Lake (new)
Square Lake
Tyone River (reactivated)
PJest Pork Glacier
Watana Camp (Wyoming gage)
pss r
good
fa FP
fair
fair
exeellent
-?
fair
?
exeelient
good
excellent
4, Data Requirements for Hydrological Forecasting.
The data used in hydrolcgical forecasts can be divided inko
two groups: the material required to develop the forecasting
method (cal ihration or parameter estimation) and the informat ion
needed to operate the forecast (operation). Data requirements
depend upon the forecast method used, the time period of the
forecas"cand the hydrological characteristics of the basin.
Factors affecting the decision to use different hydrological
forecasting models are discussed in Chapter 3, Model Technology,
Although available data may restrict the actual choice of
forecasting methods? the recommendations for changes in the
network presented in this chapter assume that the project will
support a newwork that will meet lcealistic requirements and that
a physically based (conceptual) model will be an alternative (at
least for headwater areas).
Cal ibration of a model requires conventional time-ser ies o f
hydrological information e p~ecipitation, temperature and
streamflow) as well as information on constant basin and river
characteristics, such as subcatchment areas, area of woodland,
soiltype, channel dimensions and slopes, and, in the case of the
Susitna Basin, on glaciers.
For operational forecasting, data requirements include the
hydrometeorological data specified by the forecastiilg scheme to
characterize the state of the catchment irnmed iately befare the
issue of the forecast and may include a measurement of the
farecast elelnent itsel f for monitoring the forecast perform;lnce
updating the farecast mt~ciel,
anly the hydrometeorolog ical data requized to cal ihrate nd
c]?s@rz k2 7-3 forecast models are discussed in this chapter, aecat~se
he unraalkial climatic condi tiana in the SusHtna Wil~ex Bacil,
special attention has been given to problems cf collecting
accurate and representative measurements and to evaluating the
e: ist ing collection network.
P" 2 e3 ClimataPogical Data Reqni~ements for Model Calibration
A primary data nced for calibration of a forecast model is
time series of precipitation, water equivalent of the snow cover,
s,ir temperature and streamflow. Care must be taken to insure
tha3:here is not a bias between the data used to develop the
forecast procedure and the data used for operational forecastiipigw
Consiskeney of the records is as important for calibration as
having records of sufficient length of the required data.
The application af any hydrologic model to a basin requires
exper lence and hydralog ic expertise for best results. W
statistical model (commonly referred to as a black-box model) can
be adopted to a basin without considerable knowledge of the
hydrological characteristics of the watershed. The overall
operational accuracy of a black-box model may not greatly differ
whether applied by someone with experience or by one without much
experience. However, E02: application of a conceptual model the
operational accuracy from an expe::ienced hydrologist and f ram one
without much experience can be greatly different.
To best adapt a conceptual model to a basin, the modeler
should understand the mathematical representation of the model
and how this relates to the real world. In applying a conceptual
hydrologic model to a basin the model representation must relate?
to the xeal world conditioils. Thus, the components of the basic
vatex balance equation
Discharge = Precipitation minus Basin Lasses plus
Change in Storage (1)
must be xealistic. If this condition is not met: the calibration
fpalrameter estimation) may be biased and the model, while
demovstratitag a goad relationship for the calibration periad,
migilt not be of much value for conditions not covered by the
calibration period. This has been demonstrated in the paper
"Advantages of Conceptual Models far Northern Research Basins
Studies" by Eugene L. Peck and Thomas K. Carroll (8). This paper
axso demonstrated that additional information not used in the
calibration can not be used at a later time to improve the
mode19s aecuzacy if the calibration is no: in line with real
world cand it ions,
To insure that the caxibratian af a nodeX in mauntainaus
areas is irn $ir..ne with real. mx$d ce3r74dit ions IL;he model~r rnust have
3 5;rjad !;nowledye of the basin" average pl-ecipitatzicn. Tkij-c3
requirm.is accurate maps ( isohyetal) of ';he seasonal precipi k; z.i.03
A- - Lg-hat E~FS mast xelated with stxeamfffsw, At the present time ;here
t3 'a"l 3- ~tjree 1' isohyetal maps of annual preeipi tation for the Susi tiia
_11-0 + @:iver Basin, These were prepared by N'AA in @arch 1974 (9) by
the SCS in August 1981 (10) 2nd by the State Climatologist for
Alaska (11). As far as can be determined ,, the maps were
c'reyeloped using only station means for %$hatever periods of r;.cord
were available -- not specifically taking into account
"iopocjraphic effects and probably not making maximum use of snow
A good method for developing isohyetal maps in cold cl.imat:2
mountainous areas is the anomaly technique developed by Peck and
Brown (12). In this technique the authors develop maps foe
seasonal. periods during which storm paths are fairly consis~ent
and siqqificant relationships can be established between average
preciplcation valuer: and topographic features. These rela-
tionships are used in preparing the seasonal isohyetal maps,
These seasonal maps are graphically added to obtain annual maps,
The technique has provisions for using snow cover measurements to
provide additional estimates of winter season precipitation.
Relationships between annual precipitation values and topographic
characteristics are not generally adequate for preparing annual
maps directly.
A substantial portion of the May-SeptemGer streamflow in the
Susitna River Basin results from precipitation that occurs during
the winter months of October-April. The lnost useful application
of any conceptual (and even statistical) model for forecasting
the long-term water supply runoff for the basin could be achieved
only with the use of an accurate and representative isohyetal map
for the October-April winter season.
Fleasurements of year-round precipitation are very Ximite6
for the Susitna Basin. The actual calibration of any model to
the basin should be delayed until this lack of data is improved.
As discussed in Chapter 3, the period of time that records are
required for calibration depends to some extent on the range af
conditions that are covered by the records available for use in
calibration. Most ideally, as wide a range (from very high
precipitation years to years with very Light precipitation)
should be available. If a wide range of conditions does exist,
four to five years of records may be sufficient to obtain a good
calibration far a conceptual model, as many as 20 to 30 years of
records may be required for application of a statistical (black
bax) model.
6, Operational Data Requirements
The primary concern ir~ forecasting with any model is the
uncertainty of the forecasts, Measurement errors, model errors
and the natural varriability o: meteorological inputs are causes
nf uncertainty in the forecasts. Methods exist to evaluate the
accu:acy of hydrolay ical instrumentation, to quantify the natural
:-?yc?rolog ica? va-iabil ity of meteoroloy ical inpets ar:d to asse: c:
,- 'L iitr accuracy of the hydolog ical models by empir ical iy compar ing
s i-nj-18ak:ed xest~lts with observed data,
ii major source of uncertainty for forecasts whose lead tine
i time f rorrl forecast issuance to observed flows) is greater than
f~ bga .. ... tirne of concentration (time from occurrence of rainfall to
observed flows) is the uncertainty in future weather conditions,
especially the occurrence of precipitation. Thus an early
seasonal forecast issued on February I has a high degree of
uncertainty since precipitation for a considerable portion of the
season has not been observed,
Since the Susitna Hydraelectrical Project forecasting need
is primarill for short- and long-term water supply forecasts from
May tinrough September the primary data requir~ment is for
measurements that adequately describe the magnitude and spatial
distribution of precipitation (especially snowfall) in the basin.
Accurate and representative measurements of the snow cover as of
April 1 have been found to be very useful in describing the
average winten: snowfall and for forecasting the runoff that
results from that snow cover in basins where there is little
snowmelt prior to April 1. This is in part due to the fact that
the snow cover as of April 1 is generally near or at the maximum
water equivalent of the snow cover for the season.
Far forecasting seasonal water supply runoff on dates other
than April L (using the April snow course data) , accurate and
representative measurements of precipitation (rain and snowfall)
are required. Therefore, for seasonal forecasts prior ta April
1, fur seasonal forecasts after April 1, and for short-term water
supply forecasts, precipitation measurements are essential for an
operational forecast program for the Susitna River Basin.
Data required to support a hydrological forecast program in
the Susitna River Basin are discussed by data type in the
following paragraphs:
a, Streamf low. Streamflow measurelnents are made by
the U. S. Geological Sgrvey. Ideally, observations
of river stages (for conversion into discharge
values) should be available in real time for aLZ
forecast points.
b, Reservoir Storage, To compute reservoir inflow,
lake stage values (for converting into reservoir
storage) should be available in real time for 311
reservoirs far which inflow water supply forecasts
are to be issued,
c rS Reservoir Releases. As for reservoir storage,
real time information on the amount of water
released from reservoirs should be available for all
reservoirs Eor which forecasts are prepared, The
formation would also be required to torecast rivei-
csnddtions belaw the reservoirs fo~ environmental
and river operations,
d, Snow Cover. The primary snow measurements re-
quired are water equivalent for selected snow
courses. The Soil. Conservation Service (SCS) has
recornended procedures for establ isbing and oper-
ating snow survey stations. Measurements of the
water equivalent of the snow cover should be
available either in real time or at set times durinc
the season, The SCS also establishes snaw markers
for reading the depth of the snow cover from
aircraft. Some of these are in use in the Susitna
Basin, The National Weather Service has an
operational program to measure the average water
equivalent of the snow cover by means of aerial
gamma radiation surveys (13). These measurements
provide average values of the water equivalent (or
measure the sail moisture) of the snow cover for an
area about 1500 feet wide and from 6 to 10 miles in
length compared to the small area of a single snow
course, Such measurements wsuPd be of value fox the
Susitna Rivea Basin,
e, rive^ Ice, Measurenents aE ice covex sn rivers
is not critical for the water supply forecasting
program. They are of value for other hydrological
or environmental purposes.
f * Glacier Measurements, Information on magnitude
of snow and ice making up the glaciers in the
Susitna Basin would be of potential value for use in
the water supply forecasting program. Knowledge of
the areal extent is an important factor that could
be obtained Esom aerial (or satellite) photographs
prior to the beginning nf snowfall in each season.
Other factors that could be sf value include
periodic measurements of the albedo of the snow
cover on tho glQciers, measurements to determine the
winter halance (what comes in during the winter
period) and the summer balance (what snow and ice
melts during the summer) and measurements to
determinec'furing a seasan if a glacier is
accumulating additional ice or is wasting.
Except for measurements of the areal extent of the
glaciers prior to winter and possibly remote sensing
measurements sf the albedo of the snow GBV~~~ no
othez i~easGrements listed above are presently
available or useful for operational forecasting.
Techniques for handling snow and ice melt from
glaciers in an operational forecasting program will
be covered in Chapter 3, Model Technology.
r1 Trecipi3:ation. Measurements of preeip~ kai;ic5 slid
snow cover are "the primary inputs far models that
forecast water supply for basins such as the Scsitna
River Basin, For seasunal forecasts ~SSC~P~ mi3fithly I
oniy measuremsnts of monthly precipitation and/er
the water equivalent of the snew cover may be
suf t &cienZ to provide generalized outLooks prior to
beginning of the snamtei t season, Short-tee~n
forecasts of water supply (i.e. inflow to a major
reservoir for the next two weeks) require daily
measurements of precipitation (sncwfall and rain-
fall), air temperature and forecasts of future
precipitation and temperature conditions, The same
measlrremer~ts would also be required to piaduee
revised forecasts of the subseqcient resrcxvair inficw
fsx the rest of the season,
Meteorological. Various hydrological forecast
models require metecrolog ical measurements of
temperaturer wind, radiation and evaporation, 1x1
add ition, knowledge of meteorological cond i tions
(i.e., parameters relating to precipitation and
storm wind direction) can be used to improve the
knowledge sf the magnitude and variation of
precipitation in a basin. Remote sensing measure-
ments of precipitation, snow cover and soil moisture
may also be used as inputs or for updating some
hydrological forecast models. Cornments of need far
aeteoroLogical measurements are given below:
(1) Temperature. Measurements of air temper-
ature are required in most: snowmelt forecast
models. Air temperature is also used in many
conceptual hydrolog ical model s to est imaize
the form of the precipitation (sol id or
liquid). It is ass~med that daily temper-
ature measurements (maximum and minimua
temperature) will be required for the
recommended opera tionaX pragram.
Evaporation. Estimates of the rate uE daily
evaporation in a basir, are not: generally
required for season31 water supply fore-
casting but ~k3y be used for short-term
forecasting, Evaporation estimates can be
based on pan measurements or derived from
met~oralog ical rneasuren~ents of wind, tem-
perature and some measrJre of incoming solar
radiation (i.e,, such 21s percentage of
cloud iness) ,
(3) Wind. Wind measurements $ie rhak used
direce;ly ill most hydroioqical models, Mow-
Ever, wind ar;asurert~ents car1 be asefu"!* in
adjusting precipi tat ion m@F~surements to
account for adverse wind a~tion that redu<zes
the precipitation catch, Wind measurerc;entr
may also be used in @$timating evaporat*inn
and for computing wirrd surges ir, reservoi~:~.
(4) Radiation, Some sncG me1 t raadela use
radiation as an inpbc, ilowever, based on
experience of tha National Weather Sf2rvjce
River Forecasting Service, it is very
difficult to obtain and ta tfiaintain can-
sistency in radiation measurements far
operational. forecasting, There 73rz alter-
native methods for obtaining estimates of
radiation. Unless there is a clear need for
radiation measu:rements, their us~i should be
avoided,
(5) Meteorological Pclrameterr;. Upper air
parameters can be used to enhance the
knowledge of the magnitude and distribu-
tian of precipitation in relatian to topo-
graphy. Such parameters call be used for
this purpose in conjunction with point
precipitation measurements or with infor-
mation obtained by remote sensing (e,g,,
radar ox satellite sbservakisns) ,
endatisns for Changes in Data Callection Proyraa
R & M Consultant Stephen Bredthauer's letter cE March 8,
1985, (Appendix El comments on the preliminacy reeommendat ions
for changes in the location and operation of the meteorological
statiorls in the basin have been considered in the followincj
xeecmmendatisns:
Caribou Snaw Survey site
We agree with Bredthauer" srecommendation to operate a
meteorolegical station at the Caribou snow survey si be,
However, it is believed that May-September precipitntbon
measurements at a site with similar exposure as that for
the Susitna Glacier station would be very valuable for
the prajeck,
Tyone site
c r We agree with relocating tn a site near 2 lake, ~f ~~~gerl $$ fl
we feel the station should not be moved tocj far since its
location fills a major. gap in ci-ie prapased ne::gqcrk.
A. Changes in Winter Measureaient Program
rq ii., $+-< a? location of the meteorolagical and snow survey (snow
a"b .ak~+*~d.s2s A a * Ye sr:d aerial markers) statiofis are well selected tcl
~~,*cp:~*::~sent the var igus areas of the Susitna River Basinqb ~-~e~~~~~~
tbs aa3ata colfection program does not provide sufficient accuzacy
i*P u, 3M quantity of information on the precipitation for khe entire
area. As indicated in the previous sections same of the d3t.a
beir:g cn3,Iected during the winter months are Rat required far
$zu;?pnr.:t- of a water supply farecast ing program. Recommends t ions
X LOX in the data collection program during the wrntcr
mt3nths are:
(9) Discontinue a11 measurements at meteorological.
stalkions except precipitation, wind movement atid =i i Y
ti2mpt2rature at: observation time.
(2) Incraase the number of stations at which met-
eoralagical data are collected during the win!:#er to
those shown an Figure 2 and as indicated 43 TCobie 2,
(3) Instail equipment for telemeteriny metearalogieab
msasuzements%
(4) Qevelap or purchase (see chapter 4) computer and
software capability for collecting and processinrj
the meteorological measurements azd to monitor
and evaluate the data in real tia~e,
(5) Maintain March I, April I and May 1 snow surveys
at snow couxses and of aerial rna~icezs
Ear the stations shown on Figure 3 and as listed
in Table 3. Possible improvements in locations oE
snow survey stations should he discussed with George
Clagett, SCS snow survey supervisor, based on his
evaluation of tht2 sdtes as listed in Table k, TI73
number of snow survey stations required far ti-re
operational Eorecast ing may be reduced when actual
forecast procedures are developed.
fbos ina
&fa t a %?a
Devil Canyon
7:f~nahan Flats
a r p+-R
I t Discontinue, ~e a precxpx ta-
tion station to operate Slay-Sep
with similar exposure and distance a
X to high mountains and not requlrlng
aver-glac ier serv ice f l ight s.
Relocate to protected site (precip
itation, temperature and \*rind n?oxTe-
ment)
Reactivate at protected site (pre-
cipitation,, temperature and s~ind
movement)
Relocate to protected site (precip-
itation only)
Rehabil i tate !?iyorning gage (~3reu.p-
itation, temperature and wind move:-
ent for water supply forecasting,
other variables as required)
Re?iocate to Nigh Lake (precipi t;-i--
tion only)
Establish at SCS location (precip-
itation, and temperarturc and
w i nd movement)
Esstablish at snow survey site
(preeipi tation, temperature and
wind) Consider installation aE
Wyoming shielded gage.
Establish a precipitation gage
only in protected site with
exposure to southwest and r:ot
requiring over-glacier service
flights for May-Sep
period only.
Establish at snaw cause lacatla~s
in protected site if passiblc
ar consider Wyoming Sage install-
at ion, (preeipi tati<,n, tempera-
t:a~:e atr~ti ib~i;*ind rnavej~er~t)
Xs~k6: Ebauiwsle EstakjZ ish at snaw course Sc~catiaws
in prarected site if possible
(precipitation only)
Stgw taite E~;t~i'k~~?IF,fst') at ssr-eow cejurse? lZ,caeat:io~m
in praccrted site if possible
(prcc i pi t;t t ion crn! y) .
Sheg~~an Gt:a% ion ~S~~;.~C:IOO~*~.~~IPU~~ if tak>c+ded tor
other purposes,
Fiaore d 3 Snow Survey and Meteorolggica: Stations, Present
235 Pr~)pr,~~d ; Susitna Ri.*-- VGJ, Bt%~in
--ulx, ------- --par*ri .a~rrl~~mv*mmw -mw-- *-m--- *----*-
Snow Survey Courses
West Fork Gbaeier
Caribou
Valdez Creek
BsaPder Naxth
Susitna Gbacie~r Main Fsxk
East Fs~k %usjA ta Gf a@ ier
Pyramid
Jatu Bass
Monahan Flat
Butte Cxeek
Cathedral Creek
Clearwater Lake
Tyone Rivez
Lake Louise
Horsepasture Pass
Square Lake
Fog Cakes
Devil Canyon
-- a, h,?-omnended ehailqrs in datz ccailectisn progra&: dur
Zumrner months (?lay-September) .
'T'he fo~ec~ssting prugraro will require dai;y streamflow
ii.Eormation for the Susitna River at Denali, the NacCaren River
;;ear Paxson anti the station to be establishzd in lieu of the
+2i3sib kna River ilt. Cantwell. Recorninendat ions Ear tho- sclminer
o;>cia kions for streamf low gaging stations are;
(1) Ii~stall equipment at the three U, S, GeoLogicaI
Suvey stream gaging stations listed above for
telemeter ing river stages. When reservoirs are
in operation, information an reservoirs stages
and reservoirs releases will also need to be
telemetexed,
(2) Maintain the meteorological stations istnd xn
Table (2).
(3) Develop computer and software capability for
collecting and processing the streamfLow and
meteorol.oy ical data and to monitor and evaluate the
data in real time,
(4) Continue pan evaporation measurements at the Watana
Camp, The equik2ment consists of a Class A point
mear;jirenent evaporstion pan equipped with %a teil
temperature sensors. A pan anemometer should be
installed. The site should be an opea site and
as free as 2assibie Eronr influence of wet areas,
(5) Coalecfr at the Watana Camp station (and possibly
at other sites arn%,nd the Watzna Reseirvoir)
additional metearoiog isax measurementc required
for other purpases. These may iaclude long and
shcrrt wavlf* rahfi.stion measuzerirenta, humidity
me~m~re~enks an23 aBdcKiti~naX wind mezls\,aretner:t%,
insure that the fo1:ecasting proyra:n will. upr2rate at
maximum eff icierlcy when the reslarv\2ir is complet~>f3, the "ci13wing
actisns m:lrit be taken in the near :iuture tto have ariey~.~;it~
informatian fur cal ibratio~~ of the foramst models.
The da td collect j.013 prograris xecommended in {:he pre-v iot.is
sec"kon shuuLd be commenced as :ioc317 as pract iaablc ,
8, Preparation of Seasonal Xsohyetal Maps
Isohyetal maps (~ctober-Ap~~il, May-September and annual)
should be developed for the Susitna Basin. The aaps should be
based on physiographic relations and make maximum use of the
large amount of information available on precipitation patterns
for Alaska, This includes studies on meteorolog icaL patterns
asso; late6 with snowfall (La) , studies on frequency of occurrence
of thunderstorms (15) and studies on storm movements (16).
C, Techniques for Adjusting Snowfall Measusements
'Techniques to adjust precipitation (snowfall) measurements
using wind movement measurements to improve their accuracy and
representativeness for use in the hydrological farecastinq models
(as discussed in seetion 2 of this chapter) should be developed
so that the techniques can be applied prior to calibration of the
%208eI* s 0
9. Future Items for Consideration
Depending upon the success of the calibration and
operational use of the hydrolog isal forecasting models, othfsr
actions may be considered in the future. These may include
reviewing the use of satellite information to improve knowleclge
of the temporal and spatial distribution of precipitation, the
use of aerial gamma radiation surveys to measure water egirivalent
of the snow cover over non glacierized areas and the use of
meteorological models to improve the knowledge of precipitation
at khe higher elevations of the basin.
CHAPTER 3
ibfany excellent hydrometeorological reports on the Susi tna
Basin have been furnished by Warza-Ebasco and no effort is made
ZO sunmar ize the findings of these reports. Xowever, there are
several factors that have an important bearing on the
1- irldroloyical . . regime of the basin that need to be given special
consideration in selecting models for forecasting the short- and
Song-term streamflow for the basin.
A Data Limitations
A primary consideration as to the type of model that can be
used is the adequacy and availability of basic data for applying
the model ta a basii. Th3 only hydrometeorological basic data
tiohat has been recorded continuously in the basin for a fairly
long period of time have been streamflow measurements by the U.
S, Gealacjical Survey and snow survey measurczments by the Soil
Consexwat-.idn Service. Records of other hydrometeorolog ical
parameters (precipitation, temperature, wind, radation, etc.)
have been colLceted by B & M Consultants in the basin since 1980
and are discussed in the Chapter 2.
One result of a sparse data base is a limitation on the use
of statistical models for forecasting seasonal runoff.
Correlations between long-record, consistent precipitation and/or
snow course obacr~~ations and streamflow records are required for
t"n deeveLopment of thz.c;e procedures. The only records in the
Susitna Basin of sufficeat length for developing a statistical
relationship with the streamflow records are snow course
me23surements. Even these are l imi ted in the ir usefulness because
sorrle of the snow corjrses are subject to considerable wind actic~rl
and sorne have been moved and do not pr~vitle a consistetnt index to
the average snow caver,
Correlations between April 1 snow course records and May-
September runoEf for subdrainage areas in the Susitna Basin
indicate that less than half of the variabil.ity (coefficient of
2 deteri%ination, r jess than 0.50) of the runoff is attributed to
tl-rc winter snowfall. Marchegiani (171 found a coefficient of
deterntination of O,6Z between a May 1 inclex (weighted April water
equivalent from four snow course in the basin and the April
precipi tat~ion at Gulkana, Alaska) and the April-Septembex runoff
fur the Susi tna River at Gold Creek, Alaska, Since precipitation
has been measured far only a few yeasx% in the basin it is
difficult: to develop a statistical index procedure using ex ist-.i :jq
records that would be an improveinent over that fuund by
~archegiani.. It ~;viclunt: from the low April b watc-r
g+j .-<~ivalents A+-s u ., observred at most SCS snow cdurses and fronz the Lsi:
e~efficients of determinatiun reported above that a Large portion
I-X + che total runoff from the basin resalts from precipitation
chr in9 the months of May through September,
B, Glaciers
Infosmation on glaciers in the basin is limited to a short
1 -a? p~eziod of time and no consistent: periodical measurements of the
areal extent sf the glaciers, of the depth of finn ice ar of the
perennial snow aecumulaticil on the glaciers are available.
William Harrison of the University of Alaska and R & M Consula~ts
(18) hajie determined that wasting of the glaciers during %he past
30 years was a cocsiderablk percentage of the average runoff
du-incj that period. Recent calculations by Harrison (private
communication) indicate that 32 percent of the runoff of the
Sinsitna River at Denali is from summer melt: from the glaciers and
that approximately 11 percent of the flow at Watana damsi te is
from summer melt from glaciers.
Larry Mayo, U. S. G~ological Survey, Fairbanks, Alaska,
furnished the schematic diagram shown in Figure 5 depicting the
relative values of the annual precipitation and runoff at various
elevations for the Yukon Tanana Uplands and the Alaska Range are2
east and southeast of Fairbanks (private communication) . The
upper line on Figure 4 indicates the total precipitation wbi-ch is
divided into the arrlount that is from rainfall, snowfall and the
amount that is perennial snow accumulation, The lower dashed
line indicates the total water losses fxom the basin whieh is
divided into the evaporation loss, snowmelt runoff, rain sunoff
and the perennial ice ablation. Although the relative amounts of
the various portions of the precipitation and runoff would be
different for the Susitna Basin, the diagram provides information
thzt is important ta consider in selecting or developing a rnodel
for forecasting the runoff from the glaciated portion of the
basin,
The information on Figure 4 indicates that an the average
there is no runoff from rain on the glacier at elevations above
2100 meters. Mayo has stated that rain water above this
elevation is retained in the snow cover of the glacier and
becames part of the perennial snow cover. This snow cover either
csntxibutes .$o the rrunogf at a later time as snsmelt ablation at:
becomes part of the firn ice of the glacier.
C, Climatic Regime
The drainage area of the Susitna Basin is subject to
considerable difference in climatic r=onditi9=ssns, The eastelrm
portion of the drainage area (lower elevations of thr E-lacLare-n
River basin and the Tyone River basir?) are subject to a
~ontincn~ial climate as is the upper portion of the Copper Rivcr
Basin (repesented hy the Gulkana Meteoralog ical Stat ion nf the
National Weather Service), The cl irnat~? of the '1~:d~'r rt?;iehe:; of
METERS EtEVAf ION
with eleua- Figure 4. Distribution of prccipi tat ion anrl runocf =.-
tion - Yukon-Tanana Dplands cast and southeast of
!?a i rbanks, nlaska (L, Pfnyo)
the Susitna River Basin west of the Talkeetna Mountains can be
eiassli. fed as mnsd ifid mar itirne f represented by the Talkeetna
t*?ereorolog ical Station of the National Weather Service) .
Most of the drainage area of the Susitna River Basin above
the Watana damsite is in a transition zone which is subject to
both modified maritime and continental climatic cand i"%i~ns,
The precipitation data that has been collected by R & M
GsnsuBtants f~sm 1980 ts 1884 were reviewed $a evaluate the
effects of the varying climatic conditions over the basin. Maps
showing monthly val.ues of recorded precipitation for stztions
located i:~ and near the upper Susitna River Basin are ?resented
in Appendix R. R and M Consultants have indicated that
measurements from the meteorological stat ions operated in the
ba~in may not include all snowfall. In general, it appears that
precipitation in October and April (the only maps available for
the k~ir~ter months) is in line with the amounts that occur over
the Cooper Ri.ver Basin and is representative of continental.
c% imate eand itians,
During the summer, the precipitation over the upper Susitna
River Basin ia mare representative of a maritime climate. The
Large amounts of precipitation recorded during some July and
August months at the Susitna Glacier station (Maps A-4, A-6 and
A-7) are a result of the inflow of moisture from the Gulf of
Alaska, Studies sf thunderstorm activity in Alaska as discussed
in Chapter 2 (15) ~Lso show that this area of the basin is
snbjeet: to considerable thunderstorm activity in July, August and
sometimes September, The thunderstorms foxm over the southern
and western Talkeetna ~ountains and move to the norlheast,
It is evident that a large percentage of the upper Susitna
Basin, especial-ly the primary and west forks of the Susitna
River, recieve a major porti.on of their annual precipitation
during these summer months. Studies on the Gulkana Glacier east
of the Susitna Basin have shown that fairly heavy preciptiation
does occur on this basin during the late summer months. However,
the upper elevations of the Susitna Basin may receive more surnmer
thunderstorm precipitation than does the upper elevations of the
Gulkana Glacier. One reason is that the high mountains of the
RLaskan Range are oriented as to receive maximum precipi tat iorl
Peom thunders"corms that move frwm the southwest and that
originate over the Talkeetna Mountains. The mountains above the
GuLkana Basin are also similarly oriented, Novever, the overall
summer thunderstorm activity in this avea is probably somewhat
less since the mountains where the thunderstorms originate arE
I.nwer in elevation and are also more glaciexized,
' "*, Ronof f and Precipi tation-Runof f Analyses
(1) Runoff Analyses.
Daily discharge records from streamgag ing stat ions in the
t~g~pei Susitna Basins were plotted for the summer months for the
years 1981, 1982, 1983 and 1984. Daily precipitation as recorded
elr the Paxson and the Talkeetna weather stations operated for and
by the National Weather Service were also plotted for the same
periods as the daily discharges. For each year the following
plots are presented in Appendix B:
Susitna River near Dendi, Alaska
2, Placlaren Mver near Paxson, Alaska
3, Susitna River near Cantwell by solid line and
combined daily flow for Denali plcs Baxson by
dashed line
4, Daily precipitation for Talkeetna and Paxson
A subjective review of these plots for the Susitna River at
Denali an2 of the temperature variations that occurcd each year
provide a good view of the runoff characteristics of this basic.
During the months of May and June rises in air tempe.:ature result
in an increase in the snow and ice melt with only a small lag
between the rise in temperature and a rise in the discharge,
Following heavy rain durinq the later months of July, August and
September there are also fairly rapid responses in the discharge.
Far the Maclaren River near Faxson, increases in discharge
resulting from increases in air temperature during May and June
appear to be less noticable and generally start later. The rises
resulting from rainfall during the latter part of the summer are
i3sre in line with those for the Susf tna abwe Dena%i, RevSew sf
the combined plots (Cantwell and Denali plus Paxson) sho\qs that
the contribution from the ifitervening area below the upper gaging
stations and above the Cantwell gaging station (indicated by the
spacing between the dashed and solid lines) during May and June
varies*conslderably in relation to the amount of runoff from the
glacierized areas above the upstream gaging stations.
Semilog plots of the daily discharges for May-September for
1981-E984 for "Lbe DenaSi and Paxsom streamflow stations and far
the runoff contributed by the intervening area are presented in
Apendix B (Figures B17 through 8-28). Review of these plots for
May and June each year shows that the discharge peaks resulting
from snowmelt at the Denali and Paxson gaging stations are fairly
well correlated with possibly a longer delay of one to two days
in the time to peak for the Uenali station hydrograph.
iin intrrxestinq fact observed from the Cenal i and Pa:;sor;
i>?ats is the gradual decay in the flows during August and
S~~ptenber (day 93 fro day 153). The decay in runoff xesults f rain
she deexease in the areal extent af kke snow cover, Peaks in the
runoff resulting from nain are superimposed on the decay curve.
2-'" - ' ~'nss is sn indication that a snomelt madel that aceaunts for
variability in the axe31 extent of the snow cover should model
the basin snown~elt very well.
(2) Precipitation-rbnoff relationships
Studies on Probable Max imum Prec ipi ti2 t ion for the Sus i tna
giver Basin were received f xom Haarrza-Ebasces (19 and 20) . These
reports contained results of studies on unit hydrographs for
subbasins of tile area and some application of the HEC-1 rainfall-
runoff procedure. In the report prepared by Acres (20) the SSARR
(Streamflow Synthesis and Reservoir Regulation) model developed
by ?:he U. S. Army Corps of Engineers was adapted to the entire
basin above Gold Creek for part of the years of 1964, 1971 and
1932, The above applications of precipitation-runoff models to
the basira are sufficient ts demonstrate that the basin 'can be
reasonab:ly fitted with models. The accuracy of the relationships
was limited because of the lack of precipitation and temperature
data fxsm within the basin,
although it was not planned to fit any model during this
study, in crder to better understand precipitation-runoff
sebationships for the area and to obtain a knowledge of the
relativc importance of different basic data, some statistical
,analyses were performed. These included correlations cf snow
course, p4:ecipi tation and temperature data with streangag ing
zeco~ds for the Srasitrsa River at Denali, the Maezlaresl Riven at
Paxsan, the Susitna River near Cantwell and for the lower
intervening area of the basin (the area above Cantwell gaging
station excluding the area above the Denali and Paxson stations) .
The August ~~noff of the Susitna River at Cantwell has a
0,77 correlation with the combined runoff observed during August
at the Denali and Paxson streamgaging stations. Using the
precipitation recorded during August at Talk ree$_~~a as an
additional variable increases this correlation to 0,84 even
though Talkeetna is over one hundred miles away. It is also
interesting that the July-September TaLkcetna precipitation has a
corcelation of 0.77 with the July-Sept~mbsr runoff from the lower
intervening area above Cantwell.
Summer runoff Ear the glacierized basins is not as well
rt3lated ta precipitation observations. The best single cor-
rglation with the May-September runoff of the Susi ~na River at
Denali is with the April 1 snow survey data observed at Monahnn
Flats (r = 0.60 or in coefficient at determjnation of only 0.36).
This indicates that the winter precipitation accounts f~r
8% b:ilv a small. f;orfion of the Hay-Septembex runoff of the Susitna
River above Denali, Other factors that influence how much runof i
%:ill occur dillring the May-September period for this basin are:
T, Amount of energy received in the basin duririg the
summer months
2. The sun-iiier precipitation over the basin
3, The amount of snow carryover in the basin
A multiple correlation analysis was made to determine if the
effect sf each of the above factors could be faund using the
ax4faialahle data base. Monthly departures from the average
temperatures at the Talkeetna meteorological station for five
inonths [14ay through September) were summed each year for an index
02 the amount of energy received during the runoff period. The
May-September precipitation at Talkeetna was used as an index of
the summer precipitation in the basin. The May-September
precipitation of the previcos year was used as an index af the
amobnt of snow that was carried over from the previous year.
Most of this is seen as patches of sncw on non-glacierized areas
at higher elevations prior to the snow cover of the current year.
The data used in the correlation anlysis and the statistical
results are shown in Appendix C. The final correlation using all
four variables was 0,.6 1 and the separate contribution ind icated
for each of the four variables appears to be significznt from the
t and F test statistics,
Although the correlation is not high, tne results are very
good sir3ce the analysis was made using only seasonal values for
data t-eeo~ded a very long distance from the basin, The results
suppart the hypotheses that the winter precipitation for the
Susitna basin above DenaBi accounts for abcut half of the total
ranoff from the basin during the period from May to September.
A trend analysis was made on the basic data. This gave a
carwelation of 0.52 between the May-September runoff and time
(year to 2 digits)) with a trend towards increasing amounts.
This could be related to changes in the discharge measurements,
changes in the amount of wasting of the glacier, a real trend
towards wetter years or a combination of these factors.
During meetings with the various scientists in Anchorage an
interesting statement was made by William Harrisan. It was his
observation that during years with very heavy precipitation there
seemed to be less runoff than would be expected for *e Smsitn?
River at Detilali, Pje also stated that there seemed ts b? a
negative correlation between summer precipitation and the May-
September runoff. Al thc ugh no conclusive ev idence has been
develop--d to prove Harr ir;on8s statements, the resul ts of the
correlation studies using monthly means values seein to
substantiate to some degree his observations. In addition
"'9 "^ k8tecipitati,sn recoxds cjbserved in the Power Susikwa Wasin ds
carrelate negatriv4%ly ufith the May-September runoff of ",he Susitna
:?ivef at Denali,.
There is na way to prove the interactive role played by
~jrecipitation and temperatune in the Susi tna Rivez basin above
Denzbl i without detailed measurementti from the basin. However,
assuming that the zesults of the corxelations analyses made using
zlvailahle monthly values are related to the true cond it ions, some
observations can be made. When heavy precipitation occurs during
the sunlmer nronths the cloud eover is much greater anfj the amount:
of energy reaching the basin is reduced. Although the total
runoff from the basin may he greater than it would have been with
Less sumR7ex precipitation, he amount added to the perennial snow
covey: above elevation 2180 cnuld be greater than the amQunt
ablated. Thus the aniount of snow added to the perennial snow
cover (iind on the notl-glacie.:~zed areas) during those years with
heavy summer precipitation (and consequentlv proportional less
snowmelt) should be much greater than durin9 years with light
summer p~eeipitation. This supports the use of the previous May-
September pzecipiati~n to account far the apparent increase in
runoff during the current year.
T1ne method selected for forecasting the May-September runoff
of the St~sitna River at Denali and the Maclaren Rivet. near 2axson
shot~ld take into account the importance of the summer
precipitation and the carryover effects. If accurate and
consistent measurements of the perennial snow cover on the
glaciers and of the caxryover snow -over of the non-qlacierized
areas of the basir: were available they could be used to account
for this factor. Howevex, such measurements are difficult to
obta i il.. Without such ~~sasurements, techniques can be develci?ed
to hzlndle this effect for forecasting the seasonal water supply.
This can be done by finding a variable, such 2s the previous yay-
September precipitation, that is an index to the amount of
carryover and correlating this variable with the errors in the
farecas t procedure for the seasonal runoff,
The factgsrs discussed in the previous section are the
prim;iry ones to be considered in selecting the models for cse in
the forecasting program for the Susi tna River Hydrnelectx ic
Project.. Anokher important factor for consider.3t ion is the
abil ity of ttlc. r~iodel to be updated using real time streamflod
mea!;uremenfFs and/or otrher hydrorncteorol og ical measuremerrts tld,at
zay become available in real time in the future.
I.
P* " ine first part of this section consists of a review of the
modeis that could be used for forecasting short- and long-terxri
xunof f in the basin, This inclt~des disci1ssi~ns on acctlxacy af
4- ** ,fie forecasts far the various models and an the seqsitivi ty cf
the dodels to the basic data input.
The tern '"modelling of hydrolag ical systemsqp generally means
% 1- .4~ a appL ication af mathemat leal and logical expresrjiuns wjhich
define the quantitative relationships between the flow
chaxac-teristies (output) ar,d the flow-forming factors f input] .
This is a very general definition which covers an entire spectrum
of approaches. At one extreme are the purely empirical 4
statistical "black boxwP techniques that make no atteinpt to modelf
the intorr~al structlare and response of i.he catchmerat: system. At
the other extreme are techniques involving complex systems of
equations based on physical laws and concepts, the so-called
conceptual models, Bath %!~e statistical $Pack bax and the
conceptual models axe deterministic models and any classification
of a partic~l~ar model to one cl,ass or the other forces a decision
on the degree of empiricism. Xn this review the terms black box
and conceptual will be used to classify the models,
Most deterministic models musue calibrated ts a basin
using past records of hydrometeorological variables to determine
a set of paramters to fit the model to a particular basin. In
general, black bax models require a Longer length of record to
determine the ~iost suitable parameter set. Por conceptual models
some of the parameters can be determined directly from physical
eharactcristics of the basin fie soil type] and from ana1yst.s
of streamflow recolds (i.e., coefficients for ground water
d ischarge) .
Because the Susitna basin has a large percentage of runoff
~esulting from melting of snow and glacier ice, special attention
is gj.ven to review of approaches for forecasting the melting of
SI:OW and ice*
Water supply forecasts for basins with considerable snow
cover (and glaciers) are made by three basic techniques:
a, SnowmeX t forecasts,
Conceptual models
c. Time series analysis.
The modc1.s for forecasting the snowmelt will be considered
first, follow~d by n review of conceptual models for forecasting
the entire runoff from a basin (complete or in conjunction with a
snowmel t model) . Many snowmel t model.s, use a simple degree day
approach as an index of snowmelt,
lqBDELS FOR FORECASTING SNOWMELT AND RUNOFF FROM
CLACXERPED BASINS
VAL Kss\ovalov 621, 22) an6 ethers in the USSR have
-i~:vc?oped tec,?niques for forecesting snowmelt and glacier melt, - 2'ar r~ountainous areas, most of the models compute snowmelt by
ale!i;lt ion zones and require eoi3siderable input of snow
IneasGrements, FOP glaeiei meit forecasting, Konovalav (21) has
discussed the rel~tive importance of knowing the ratio of the
acc~niulation and ablation areas of the glacier. En addition, he
has stressed the need for computing the sepatate estimates of the
voiu~~s of melting of pure ice, ice under the moraine, old firn
and a*.intem: and surmer snokf making up the total volume of melting
from a glacier region. The data requirements for these
;%ppra.lches s.re deemed to be mote than necessary for the Susitna
??rojece,
~ (2) Models for Predicting Runoff from Glacierized Basins
Wodrew Fountain (U. S. Geological Survey, Tacoma, Washington)
ktas ptovided a copy of a chapter (23) prepared by him and Werldell
J Tangborn (WyMET Company, Seattle, Washing ton) for an
~npublished report by the Working Group an Snow and ice Hydr~l~gy
of Glacierized Basins, International Commission on Snow and Ice,
International Association of Hydrological Sciences. This report,
"Contemporary Techniques for Predicting Runoff from Glacierized
Basins," provides comments on ,he advantages and disadvantages of
several models and includes a table summarizing the main features
of each mondel,
Most of the models reviewed in the report are nesearch
models that have been applied to only a single basin. Exceptions
t-.o this are the model by E. A. Anderson (National Weather
Service, Si lver Springs, Maryland) which has been appl ied
operationally by the national forecasting services in the United
States and Canada (including 11 basins in Alaska) and the
Tangborn model which was reported as being applied to three
hissins in Canada,
(3) Eric Anderson Model {NWSFRS Snowmelt Model)
The Sno9~ Accumu%ation and Ahlation MoSel of the National
Weather Service (NWS) has been in operational use by the NWS
since 1913 (21), Nibler, National Weather Service River Forecast
Center, Anchor3gc, Alaska, has modified the model to include the
presence of glsciers in the basin. T$& snowpack depletion curve
(area of snowc3ver as a function of znowpack water equivalent)
was altered so the minimum snowcovered area is equal to the
basin's glacie~ ized area. This area is then assigned a snowpack
thickness greater than what cou1.d be lost by summer ablation.
Nibler reports that this modification $$arks satisEactori2y
fiur estimates of seasonal runoff f ro~n glacier izeci basins, bii t it
is d5sappointini~ for daily forecasts. Calculated runoff has a
very quick and "flashy" response relative to the more slowly
rzsponding cbsenved runoff, which he suggests is caused by
neglectang storage faeturs of the glaciers. Parameter values far
eleven basins that are forecasted operationally by the NWS in
alaska have been furnished by Gerald NibLes {private
c3nrmunication), For most of these basins the model has been
appa ied with two zones (based on elevation) ,
Although the NWS Anderson snowmelt model has generally
satisfactory zesults, certain deficiencies do exist. Insuffi-
cient knowledge of the actual occurrence (and of the noriBa1
precipitation during the various seasons of the year) of
precipitation at higher elevations c%n result in runoff volume
errurs. There is also a problem in cia-sifying precipitation as
to rain or snow based solely on rnax~mv3-minimum temperatures.
This is mainly a peoblem in basins where both rain and snow
freqiaently occur during the same event. The above are
deficiencies in basic data and not: in the structure of the model.
and wo d be applicable to all models.
The Anderson Model uses temperature as the sole index to
- sttowme1.t but nat in a degree day approach. Under certain
temperature conditions the use of air temperature as the sole
inde:r of snowmelt h2s proven to be inadequate. This can occur
under clear skies with abnormally cold temperatures (under-
predicts), under very warm temperatures with little or no wind
(ovexpredicts) and with very high dew points and high winds
(underp~edicts). The latter two conditions would not be a factor
in thrt Susitna Basin. The first condition should not be a major
arie because of the high amount of cloudiness that occurs during
snowmelt period in the basin. In any case, these conditions
affect th~ timing and magnitude of the peak runoff more than the
total volume for a period.
The Anderson model was reviewed in a report prepared by the
authors for the National Aeronautics and Space Aministation
(NASA:, a diagram from that report (25) illustrating the model is
incl~ded in Appendix D (Figure D-1) . Also included in Appendix D
are listing of the states and pauam~t~rs for the model (Table D-l
and D-2). Informatian on the schen1;tic diagram and on the
definitions of states and parramckers as used in the NASA report
is included in Appendix D.
The paxameters foi the Anderson model have been found to be
related to cl imatic and physiographic characteristics and
reasonable initial parameter values can be obtained from a
kllowledge of typical wand i t ions over a watershed.
A discussion nn the possible appl ication of hi s model to the
Susitna Basin was he1.d with Eric Andersan, Anderson believes
that fc~r ylacifzrlzed biisin~: "the daily forecast values of runcff
wauld have mane vaxiance than obse~ved values but that this
c.Efeci: would tend to average out for longer pelricds [such as for
b7i t:k~a-week fore~ast) He also staed that reb% iable seasanal :naps
05 23:ecipi tation would be required to properly calibrate the
mo2el. He agreed with Gerald Nibler that a separate zone should
he ineluded and assigned a large water equivalent value for the
glacierized area of the basin. With an areal depletion curve as
used in his model, he believes that you would not need many zones
(probably 2 but a maximum of 3). For models not having an areal
depletion curve many zones would be required as is done in the
USSR, Anderson furnished an unpubl ished report summarizing the
findings of the recent comparison study on snowrnelt models
discussed in the next section,
Anderson has fitted his mqdel in Al?ska. An interesting
result is the difference in the shape of his curves for
distributing melt factors through the year as shown in Figure 5.
In the contiguous United States the curve is a sine wave; in
Alaska the curve stays at a minimum until it starts to rise
sharply during April. Anderson attributes this to the
differences in length of sunlight and is a reason why the streams
in Alaska do not: have a significant increase in snowmelt runoff
until after May 1 as may be seen in the report on Daily Flow
Statistics of Alaskan Streams by Chapman of the NWS (26).
(4) WE40 Comparison of Snowmelt Models
During the period 1978-1983 the World Meteorological
Organization (WMO) carried out an international comparison of
canceptuai models of snowmelt runoff (27). 'The final report of
this study has not beec released by WMO but comparison figures
have beer: furnished to the participants and were reviewed in
Anderson's office. Three models developed in the Uni ted States
were included in the WMO comparison study and the calibration
used in the comparison study were those fitted to eackl basin by
the authors of the models,
The unpublished paper furnished by Anderson was p1:epared for
WHO by two Canadiazs (28). The statistics for the results of the
cakibra"%;":on and verification runs far the "ezhsee Ui, S, msdels are
summarized below and are considered to be more represents t ive
than most comparison studies where the models are often
calibrated by hydrologists who may not be experienced in
cal ibrating the ~aodels compared.
The U. S. models included in the WMO comparison study were:
a, MWS Anderson Model
b. The Precipitation-runef f Modeling System
(PRHS) of the U. S. Geological Survey
c, The Streamflow Synthesis and Reservoir
Regulation Modcb (SSRRR] of the O, S, army
Corps of Engineers
4 1
$8
& # *
gj
Figure 5 Seasanal variation in melt fitctors used durir.8 non-rain periods.
Th@ statistical vaf ues aQ the simulated and obse~ved
s::reamflow were compared in the unpublished study for
~:.gi?i lficanee using four statistical vau iables, A valtle fox each
02 the four statisticd vasiabfes was computed fur the
r:zi ilsrat ion-complete year, cal ibration-snobnneltr period , vex i f ica-
<:ion-complete yeax, and vex ification-snotg~nel t period) fonr each f
the three river basins that were calibrated by all three of the
0. 3, models. This provided a total. of 48 cases. For each case,
each model's results were compared with the results r" the model
having the best statistical value f;r that case. 3 ?hat wore
found to be as significant as the best model for that c ;e
(withit? a 95 percent confidence interval) were considered to be
as good as the model that had the best statistical value and were
considered as a tie for first place.
Tables showing the ranking of the models for each of the 48
cases were presented in the unpublished report. The following
indicates how often each of the three U. S, models were ranked in
first place;
NWS Ranked first in all 48 cases
PRMS Ranked first in 22 cases
SSARR Rarrslsed first in 21 cases
The Anderson model is the most physically based snowmelt
model in use today and the results of the test program indieate
its accuracy and reliabity for forecasting basins in different
cl imates, The model was developed f ram physical concepts using
detailed measurements of all measurable variabies yet was
designed to use only standard measurements of preeipi ta tion and
tentirperakure.
B, Conceptual Soil Moisture Accounting Models
Five conceptual soil moisture accounting models were
selected to be considered as alternatives for the operational
system for the Susi tna Hydroelectric Project. These are:
Tangborn Model (HyMet)
National Weather .C,irvice River Forecast System (NWSRFS)
Precipi tat ion-Runof f Madel ing System (PRMS)
Stanf srd Watershed Model (SiqM)
Streamfow Synthesis and Reservoir Regulation (SSARR)
Three of the above models we:re included in the review oE
lnodels completed by Hyclex for NASA (25) and by the WMO (30) .
This study provided a yaod methad to study and canpare models.
Since informatian on three of the models is available in the form
vlse+l in the NASA study it was felt it would be beneficial to have
all mode3.s in !:he s2r9ae format,
The at~thors 0% $ha two models tnat wexe nat included in th~
i4aSA study were contacted to obtain more complete information cn
the podels and their structure. Wendell V. Tangborn (HyMet
Company, Seattle, Washing ton) furnished infoxmation on the HyMet
iaodei and George PI, Ceavesley (U. S. Geological Survey, Denver,
Coio~ado) on the PRMS model. Schematic diagrams and gefinitions
of states and parameters for the two models were prepared and*
submitted to Tangborn and Leavesley for review. The schematic
diagrams and lis- sf definitions for all five models are
presented in Appendix D along with the same material for the
Anderssn Snowlelt Model,
k glossary of terms used in review of the models for NASA
and a Legend for the seheniatic diagrams as ori2inally published
in ,the NASA xeport (25) are reproduced in Appendix D. By
developing the diagrams and studying the states and parameters
used in the models, a reasonable understanding of the structure
and operation of each model can be achieved. Based on this
knowledge, and on our experience, comments on the models for
possible selection for the project are given below.
(1) HyMet Model (Diagram D-2, Tables D-3 and D-4)
(a) Data Requirements
The aperation of the model requires only the
standard measurements of precipitation,
temperature,
(b) Field Experience
The HyMet model is being used operationally
for seasonal forecasting in the Pacific
Northwest and in Central Wtiasna, As far as
can be determined there is no complete
documentation for calibration or operational
use af the model,
Short term streamflaw forecasts sf 1-3 days
duration require weather forecasts of preci-
pi tation arid temperature. Seasonal forecasts
are based on regressing indices of the total
water in storage (in snow cover, soil, surface
and groundwater] in the basin on the day of ti7e
forezast with subsequent runoff for a specified
ti~ne period.
The model has not been included in any af the
WMO comparison tests and no results of
comparison with other models have been found.
The only available results arc those published
by the author with little or ns supporti\lg
inEormation for evalulating the model perfor--
n3:aan"ree,
[d) Use in Snow and Glacierized areas
It is evident from the Diagram in Figure 0-2
that the original Tangboxn model has been
adapted 'for Lase in glacierized areas. Tangborn
furnished 2 paper "Prediction of Glacier
Deri~ed Runof: for Hydroelectric Development"
(29) which presents the basis for the glacier
protion of the model. The paper reports on the
analyses made fox two small basins in Bnitisb
Columbia, Canada. T12e assumption is ~.zde that
englacial storage of water (from ablation of
snow, firn and ice, and precipitation) occurs
during the period from November L to July 14
and that outflow from this stored watex islt%ceurs
from July 15 to October 31. From the
information furnished on the model it was not
clear exactly how the model handles the states
in the model for firn ice and glacier net
balance. Bscause the precipitation input to the
glaciers in the Susitna Basin is considerably
different than for glaciers along the west
coast of ?J~r%h Werica (Less than haif of the
inj?ut during the winter compared to a very
large percentage for glaciers along the west
coast of the United States), it is not certain
if the adjustment technique for englacial
storage as used in the paper would be of direct
value for the Susitna Basin,
For glacierized areas the HyMet model uses up
ts five elevation, zones and snaw accumulation
for each altitude is determined using standard
precipitation measurements from one ar two
selected weather stations, The snaw, firn and
ice melt axe calculated from the mean ai~
temperature and the range of daily temperature,
(el Updating
A technique using tht. error far a short-term
forecast as an index to the exrar in the
seasonal forecast is used for ad just ing the
seasonal forecast an the basis of comparison of
simulated and observed flows. For example, a
seasonal forecast prepared as of May 1 uses the
exrosr in the simulated versus okserved flows
during May LJ~ revising the seasonal forecast.
This has meare vaLilae for basins where the major.
portion of the input for the s9asonal flow
occurs prior to the date of the forecast. It
is no% ezrtain how it would wo;:k fox the
Sasi tna Basin, The updating prucedure has
not been incorporated into the shor t- term
forecasting method (311,
The soil mositure accounting structure of the
msdel is sirnilax ts other madels and it is
clear that the model was not developed with the
concept of having states of tbe model to re? ate
directly to measureable hydrolog j>cal variables
in the real woxld, The~e is no indication that
techniques have been developed that would
objectively use remote measurements (i, e.,
water equivalent of the snow cover) for
updating the model,
(f) Operational Factors
Unless the model was applied by the HyMet
Company the lack of documentatioa for the model
would be a serious Iimitatisn, Thexe is
insufficient information ta determine how the
madel would be used fox short and long-term
forecasting.
(2) NWSRFS Model (Diagram D-3, Tables D-5 and D-6)
(a) Data requirements.
The operation of the model requires only
G tandaxd measurements.
jb) Field Experience
The NWSRFS is used operationally throughout the
United States and Canada. There is complete
documentation for the NWS River Forecast System
and for each sf the models used, A report an
techniques for catchment mo3el ing and initial
parameter estimation for the NWSRFS soil
moistare accounting model was published by Peck
(3ZL
The MWSHFS was i~cluded in the WM8 comparison
study on soil moisture accounting models and
although no formal comparison was roported on
the results, a review of the published
ecmpariaon indicates that the model performed
as well or better than any of the models
tested. The Hydrolog ic Research Labor3tory of
t,he i\iWS did an extensive testirlg program in
1975. Three models were comk3ared with a
teen prepared onlk- for prediction use where ax1
data for one year (or a long period of time) is
imput at one time. There is nc operational
model fox use in Es~ecastlng but Leavesley
states that they plan to prepare one.
The only known evdluation of the model that has
been published is that reported above from the
WMO snowmel t compar ison study,
(d) Use in Snow and Glacierized Areas
The model has a component for handling sno~xnel t
runoff as may be seen on the schematic diagram
(Figure D-4) 'and it is based on energy balance
equations. The model has not been adapted for
glacier areas.
(e) Updating
Since there is no forecasting mode there axe no
updating techniques.
(E) Operation Use
The model is well. dacaniqsented (3"$ ).
(4) SWN Model (Diagram D-5, Tables D-9 and D-10)
(3) Data Requirements
Same as far NWSRFS model
/b) Field Experience
The model has been used throughout the world by
the autk~xs but it is not certain where the
model is presently being used.
The SWM was included in the test cs~lducted by
the NWS Hydroingic Research L2')oratory and in
the WMO comparison tests. In both studies the
results were very camparable to those for the
NWSRFS,
Uf3e in Snow and Glacier ized Areas
TI"$ model has been used in ~120~ melt areas but
no information is ava'lable for glacier ized
areas, 'S"k~e i."sndcnr~ion mode% is used for snowme1 t
model irig ,
[e) Updating
The authors have updating tect:rliques us in2
observed streamflow but no speci f it informat ior;
is available,
(f) Opexat ional Factors
Similar to those indicated for the NWSRFS,
(5) SSARR Model (Diagram D-6, Tables D-TI and D-12)
(a) Data Requirements
St.awdard measurements,
(b) Field Experience
The SSARR model has been ixsed throughout the
world and is used ~peratj~onally by the joink
WS-Corps of Engineers river forecast center in
Portland, Oxegon. Excellent documentat ion
exiets foe the model,
The SSARR model is primarily a river routing
and reservoir regulation model. The components
for its soil moisture accounting and sno~melt
components are not as conceptual as for the
other models considered. Tests conducted by
the NWS and the WMO (on both the soil moisture
aceaunt ing an3 the snowmel t compar isons) have
shown the model to have more limitations in
forecasting for individual basins.
(d) Use in Snow and Glacierized Areas
The model has been used in many snowmel t areas.
There is no component for accounting for
glaciers, The model has been used ta calibrate
the Susitna River Basin for the months of
August and September using standard tables by
Bredthauer of R and M Consultants.
Some subjective methods for updating the model
operat,iooally have been used. However, the
structure cf the model (large use nf tables)
makes it very d i ff icul t to develop procedures
for objective updating the model, Rnalysis by
Hydex for NASA has detertniner3 that the use of
(1") Operational Factors
There are no known problems Eor using the SS?lRR
model operationally.
Knowledge of the sour-es of error that would occur in
forecasting short- and long-term runoff of the Susikna River i::
?:in important factor for determinin, the forecast models that
sl3aukd be used, Possible sources sf foxecast errors are shown by
the schembtic diagram in Figure 6; rbese are model, basic data
and cl ima tolog ical,
!%ladel erroar results from the fae"i:hat the madel (and its
parameterization) does not accurately represent the real world,
The magnitude of the model error in forecasts of short- and long-
tern seasonal runoff would be apgroximately the same for either a
statistical or a conceptual approach,
The accuracy of a seasonal forecast for water supply depends
on the ability to knew the true basin averages of
hydro~et~orologid parameters used in the model. Even if a
perfect model and a perfect set of parameters were available,
inadequacies in basic data would introduce i.al:ge errors in thc.
forecasts of the May-September runoff far the Susi tna River
Basin. The basic data error For the winter precipitation (due to
fairly good correlations agnong the April I snaw course
measurements) is probably less t.han the data error fop forecasts
du2: ing the summer thunderstorm period. Improvement in the
knowledge of the actual precipitation by proper locating gages,
by adjusting precipitation measurements for gage catch defi-
ciencies, and by improvements in the network (additional stations
and representative exposures as recommended above) all help to
reduce the basic error component of the total forecast error.
The climatological poxtian of the forecast error results
from futurc weather conditions which are unknown at the time the
forecast is issued. The percentage of the total error due to
cl imatolog ical error for forecasts for the Susi tna Basin above
~enali is directly related to the percentage of the total input
that has occurred up to *.he date of the foreczst. Since this is
approximately less than &~alf of the total seasonal input as of
April 1, the climatoloai--1 error is probably much greater for
the Susitna River Basin ct~an for basins where the percentage of
the seasonal input is much larger prior to April I.
Climatological Error
Peb Mar
pigure 6. Schematic diagram of sources of forecsst errors
Jan Feb
~iglrre 6. Schematic diagram of soirrces of forecast e+rorr;
Sep
It is not possible to determine the relative errors
t cil imatolog ical , basic data and model.) that c~ou'id be assoc ia ted
.;it23 -eacn model without completely applying the models to the
S7.sicna River Basin. However, the climatological error should be
approximately the same for each model used. The basic data errsr
~i5sociated with the use of the five models reviewed is prbmar lly
dependent upon the basic data and only marginally on bow the data
axe p~seessed in the saodel's, It is assumed that the basic data
error and the model erxon may be approximately the same fur
dz A-srecasts prepared using the PRMS, SWM, HyMet or NWSRFS models
.A somewhat greater for the SSARR model,
The actual error of the forecasts finally developed for the
project will depend much more on other factors than an the
sePeel:isn of the model or models to be used,, Basic data error
~esul t:i ng fzom inconsistency {change in time or l neonsistencies
in the data used for calibration and for operational forecasting)
can be very great. The successful application (and minimum
forecast error) using any model depends to a larye extent on the
experience of the modeler and on the characteristics of t:?e
basin,
One advantage of using a conceptual model over a black box
regression model is the increased ability to forecast extreme
(dry or wet) conditions that were nct experienced in the set of
data used for calibration of the model. For a conceptual model to
realize this advantage the input data for the n~~del should be as
representative of "truew values as possible. For calibration an
aceurate knowledge af cl imatolog ical averages of the magnitude
and distribution of the seasonal precipikation over the basin is
requi red (seasonal isohyet31 maps) . For operational use
information on the climatol~gical averages of the possible
weather conditions for the rest of the season are required
(statistical indicies of rlimatological conditions].
The only way to reduce the climatological error of forecast
is by using forecasts (that have information content) of future
weather condl tions. Availability of short- and long-range
weather forecasts and monthly outlooks have been summarized by
Nibler (38). Forecasts of temperature and quantitative preci-
pitat ion forecasts (QPF) are available from the National
~eteorolog ical Center, National Weather Service, Sui tland ,
Maryland, and are received directly by the NWS off ices in
Anchorage. Specif ie forecasts of precipitation and temperature
conditiorls are issued for 12-hour periods for the next 48 hours.
Other prognostic information issued for op to 72-hours periods
and for 6-20 days ahead can be used to derive forecasts for
precipitation and temperature for up to 10 days in the future. In
addition, information on expected average monthly values is
avsilf3b2e frorn pub1 ished monthly outlooks for Alaska,
Sevr?ral individuals with whom the study has betn disct~ssed
'3 3 . ."A indicated that forecasting the snow and ice meit from the
7.i-zcierized portions of the basin ~~olnld be difficult, iiowever,
ba:?d on the review uf the hydrometeorological data for the basin
and thri results of previous model studies, it is bel ieved tha:
the Ariderson model will provide the accuracy and relitibility fa;
suc:n forecasting when used with available updating procedures 2nd
real-tjme measurements af the streamflow. Thus, the Anderson
i~1odil.1 is recommended for use in forecasting sncw and ice mnir for
?he project.
The Xack sf accurate and consistent data Ear a number of
years (except for some snow survl2y and streamflow data) makes i E
difficult, if not impossible, to use regression type "black-hcsw
approaches for forecasting the long-term water supply runoff.
J*ssuming a real ist ic knowledge of the magnitude and
variability of seasonal precipitation is provided by the propn~ed
basic data coLlection program, simple quasi-conceptual models can
be usg:;d ta prepare Long-term farecasts of the water supply
runoff, Forecasts of the long-term water supply runoff can als43
be prepared by extending the forecast periods for the conceptual
models discussed in this report. This zan be done by
initializing the model as of the date of the forecast by using
measurements of the water in storage (in the snow cover and
englacial) , by using forecast weather conditions for the rleair
filirurcl and hy applying Extended Stre3mfEaw Prediction (ESP)
procedurks used by the National Weather Sevice (39) far time
periods fax which weather fo~ecasts are available,
The long-term water supply forecasts can be issued in terrns
of conditional probability that will provide considerably more
information for the reservoir operations than a simple volumetric
sr'orecas t. For example, probabil ity distr ibtltion relations can he
produced providing the expected probability oE occurrence of thz
ral7ge of water supply that could be expected. Such information
is valuable for reservoir operational decision making.
The time and expense to develop a data base and process all
data for use in forecast modeling is considerably greater than
the time required to test and ,evaluate the use aE different:
Fsreeast models, "Peerefcare, it is ri-acornmended tbjat mo&e thaa3 arcre
lnodel be tested for the soil moisture 2ccounting and the long-
term forecast models for the project. This is considered viable
in view of the tart that the actual operation of the reservoirs
will not- commence for several yEars, Re<-ommendations for mode-1s
that should be investigated for use in the project arc.:
A, For short-tern forecasting:
(1) NWS A~--aderssa %now ablation and k~t:cusnul-c~t ion itaodcl
for forecasting snob] anrl rjliicicr mc!l? fur kt-!F. bnsir-i.
(2) Test the SSARR and kl\qzj models for soil :~iojst~jre
accollnting and use as a routing model.
R, Pox long- te~m foretasting,;
t,*j Use the conceptual ionecast model selected for
issuing short-term water supply runoff to forecast
~sn~-tkrm (from date of forecast to September 30)
wzter supply using weather forecasts and
E-c teilded Streamf low Prediction procedures.
(2) For early season forecasts [prior to May 1)
def~el op a simple quasi-conceptual yodel :. ssed
on wiitershed conditions as cf April 1 si-3:~ sta-
istic:% of climatological conditions dux ~ilg
the ilay-September per iod.
The seetion includes a shost summary of current data
~3llecti.on snd relay methods, a discussion of requirements for
operational forecasting, several alternative communications
schemes, and rece0rnmenda"L-,~r1~~
A. Existing Data ColLection Methods
The existing data collection methods are well summarized in
previous portions of this report, At the present time all data
from the Susitna Basin is recovered by helicopter and to a
limited extent by ground transportation after having been
recorded by automated weather or stream gaging stations. Data
from the basin are thus available at approximately monthly
intervals. The time delay from collection until the data arc
plzoeessed into useful farm can run from one to seven weeks,
B. Data Callection Requirements for Operational Modeling
Discussions held during the field trip to Alaska indicate
that operational forecasts will be required as often as daily
dueing the summer months. The forecast tilne interval dictates
the minimum time which can elapse between recording of data in
the field and recovery and processing.
Models can obviously be run at any time, However, if the
sophistication of rainfall-runoff models is to be used to maximum
advantage (i.e., if short time interval runoff from thunder
starms is to be modeled) then data must be available at short
intervals and soon after the events occur. Hourly 2ata in real
or near-real time would not be an unreasonable requirement,
C. Alternative Data Gathering Systems
(I) Con"einue Present Methods
One alternative which must be given considerati3n is tc
continue data gathering just as it is done now. This rtay, i?
fact, be a satisfactory alternative during the winter months
where forecasts will not be made as often. During the summer the
frequency of site visits could be increased to weekly or
biweekly. Fuxther automation of the data handlintj process could
take place so that data can proceed directly frovt the field tapes
in a farm suitable for input to the models. Current methods can
not provide the timely information needed far rainfall/runoff
modeling in the late summer period.
(2) Automated Telemetry Systems
There are several types of automated telemetry systeias
available for the Susitnn Basin Project, Each system requires
field processing and teletnetry klardwarc, a communications link or
I = : inlis, central receiving equipaent, and data processing
ey9l i pne~,
(39 Field Site Hardware
Most current automated telemetry systems make use of
field hardware called "Data Col lection Platforms" (DCPs) . DCFs
incorporate microprocessors, optional tapes or other form of
storL3ge, and one or more telemetrl- modules. The telemetry
modules can be line of sight radio transmitters, satellite
transmitters, or telephone modems, some even including voice
capability. The meteor burst system (to be discussed later) uses
remote data terminals that operate only with that system.
DCP's are designed tc interface with a wide variety of
strea~u gaging and meteorological equipment. They are widely used
by the U.S. Geological Survey, Corps of ~n~ineerk and other
agencies, They are normally small in size and powered by 12-volt
batteries charged by solar pan5ls. Currently there are three
U,f. firms actively manufacturing DCPs. They are Sutron
Corporaticn, Nandar Corporation, and Synergetic3 Corporation.
All of the firms have sizeable numbers of platforms in service
and all offer similar capabilities i ~4 6istinctly different
pae:kages,
Considerable experience by government agencies indicates
t:22t the use of DCPs and real-time data obviates the need for an-
site recording. This is contrary to the suggestion by R&M
Concultants (W&M review memo dated March 8, 2985) that additional
mechanical recorders be added at each site. The lemetry
equipment has been found to be more reliable than on-site
reccrd ing instruments. X t makes l ittle sense to make dupl icste
recordings of had data on-site. No goad data is no good data
whether recorded on-site 0s: telemetered to some centnak Xscatics~
and recordcd there* Not only that, but the adds an the central
site computer functioning properly are much higher than for any
field recorder, The less moving parts in the field, the better,
However, there would be some value in retaining on site
precipitation recorder during the period when data are being
collected for model cal ibration purposes.
(4) Central Receiving Stations
Central receiving equipment is specific to the
communictions link used. Cine of sight radio systems require an
antenna, (usually a whip or Yagi mounted on a tower) a receiver,
and an RF' modem (modulator-demodui-ator) for data recovery in
computer compatible form, Satellite systems also require an
antenna, but this must be a 3 to 5 meter dish permanantly focused
on the satellj te beitlg used, A receiver, demodulators, and
inul- t iplexiny kquipment are also required to reduce incoming data
to computer compatible form, Telephone systems require only a
roodern to receive incoming cal.1~ and interface with a computer.
Enormous variatians are poscible in central site data processing
i 1 Opt ions vary from desktop personal compilters ko
s s ri- computeus us capable of not only receiving the data butt
pA-?cessinq i t in real-time and updating the inod+zls.
- r Fk,i ke:xnati~~~s EQK the Susitna Basiw
At the current time the writers believe that there are
tkt:es alternatives fax !:he SusJ tna Basi n:
(1) A combined line-af-sight radio (with or
without voice capability) and telephone
systgrn d,
(2) A satellite system with a variety of central
site options, and
(3) A Meteor Burst system.
The alternatives are discussed below,
A. Line-of-Sight and Telephone System
The wide open nature of the Susitna Basin makes a line-
of-sight system reasonably attractive. It should be possibls tto
relay data fro21 virtually any part of the basin to a central
locntio~? such as Watana using a minimum number of repeaters* Two
or three should be sufficient. The systerct call be designed
strictly for data purposes or it may be ccrrnbined with a s.aice
network for use in managing the reservoirs, A small minicomputer
at Watana can save the data and relay it to Anchorage on a daily
basis by telephone. The assumption being made, of course, is
when the reservoirs are built that phone service will foilow. If
such is not the case then line-of-sight is probably not a
practical alternative.
Discussions during the field trip indicate that
thunderstorms are a frequent acculrxrence in the Scrsikrra basin,
Thunderstorms are the most frequent cause of faiaure in line-of-
sight systems. Lightning strikes near repeaters nearly always
cause failures, Any system should probably be designed with dual
daka paths (multiple repeaters) to minimize data loss and allow
for tirne to repair. Hot standby repeaters could also be
considered. Hat: standby uses duplicate transmi t/receive equip-
ment with a spare ready to take over the minute the aperational
unit fails. The cost for hot standby is fairly substantial since
ik doubles the amount of equipment at each repeater.
It: is possible to design a line-of-sight system using
polling, wherein the cent:rol sight queries the field sites for
data, This requires more complex equipment on both ends 2nd is
rlot rec~3nmtznded. DCPs are easily capable of determining when
data should be sent and with great reliability.
Do Satzellite Data Relay System
A17 attrackive way to recover diita from the Suslhna Basin is
by %;cans of the GOES (Geostationary, Operational Environment81
Si'telliete) system. The GOES system, owned by tqY'OAA, is the szme
satellites wh'ch provide the satellite wesLher pictures seen on
tel evision newscasts. The Data Collection Systen~ (DCS) on the
sate1 1 ites provide 256 channels far environmental data
trai~smission. The two U, S. GOES satellites provide sufficient
aeegrayhic coverage to send data from Florida to Alaska with no -i ~n'iermediafe repeaters. GOES is truly the "ultimate" line-of-
aiqht repeater,
The GOES satellite data collection system has been in use
for many years, Tbe U. S, Bureau of Reclamation operates a
network of over 150 DCP" in the upper Snake River Basin for
which part of the area has cold temperature extremes similar to
those experienced in the Suisitna River Basin (near Yellowstone
Park Wyoming). The contractor in charge of the system
maintenance is paid based on the percentage of data received
(percentage of 15 minutes readings from many types of sensors).
The Eormula is such ?hat the contractor receives full payment for
data reception of 95 percent or greater, 90 percent payment for
90 to 95 percent receptios and so on until no payment is received
If reception falls below 50 percent, Ta date the contractor has
xJ.ceived LOO percent payment over the life of the contract. The
U. S, Bureau of Reclamation officials in charge of the project
using the GOES system is Dan Lute, DSBOR, Box 043, 500 West Fort
Street, Boise, Idaho, 83724, telephone 208-334-1976.
A newly installed GOES collection system at high elevations
along the Ro-ky Mountains in Colorado for the Office of the
Colorado State Engineer has been demonstrating similar
performance as that for the USBOR in Idaho after initial
instal latiox1 bugs were worlted out.
The "hid method for relaying data from the Susitna River
Basin is the Meteor Burst or Meteor Trail technique. Meteor
Barst conu~unications make use of ionization trails in the
atmasphere to reflect high fcequency (HF) radio signals. The
trails take the place of the satellite in the GOES system or the
repeaters in the l ine-of-si te systems.
Meteor Burst systems are, by nature, more complex than
satellite or line of site systems. First, the systen is two-way.
A base station sends aut signals to one or more remotes
requesting them to report stored data. The remotes then send out
data signals, The requirement for a polling scheduler and 017-
site receiver^ more than doubles the cost of the hardware over a
one-way system. Second, the meteor gurst field sites require 2
to 3 times the average power to operate as a satellite or other
one-way system, The receiver at the site must be sn at all. times
d 3 20 5 times the transmitter power (25 to 40 :~at*ks) is
d 1-0 bounce signals off a :neteor trail as compared to a
sateiiite (8 watts) or line-of-site repeater (2 to 4 rqatts),
""- kJ::gresed in a different way, that battery will. last 3 to six
d- " ~~rnes as long as the same battery powering a meteor burst station
.hen both transmit the same message length, Finally, meteor
Djrrst hardware 3re larger in physical size tnan satellie or Line-
f-.- s; te systems. The transmissian units are large^, and much
la3:ger solar panels and more batteries are required,
Meteor burst's one true advaz~tage is the preserrc . of 2
:;.cciver at the remote sites. It is possible to send and receive
text messages at the remotes which may offer some further safety
for field crews in remote lacatins,
The basic cost of a meteor burst remote station is roughly
twice the cost of a satellite DCP, exelusive of the accessories
and iilstallatian. Accessory cost is also higher with antennas
costing 2 to 3 times as much as GOES ($500 to $700 compared to
S250B er line-of*site,
At a meeting with Narza-Ebasco and Alaska Power Authority
officials in Anchorage, Alaska, on May 30, 1905, George Clagget,
SCS, Anchorage Alaska, indicated that considexable improvement
has heen experienced in the operation of the SNOTEL data
collection program (meteor burst system] of the SCS. However,
CLagget did not have detailed information on the performance of
their system. "ontact was made with the SCS offices in
Washington, D. C. and in Portland, Oregon to obtain first hand
information. Art Crook, Water Supply Forecasting Staff, Soil
Consexvat ion Service , Portland, Orgeon (503-221-28~3) supervises
the SNOTEL program for the SCS and provided information on the
syst:,em,
Figure 7 and Table 4 were provided by Crook and provides
specific information on the SNOTEL system performance (for data
collected by two master stations, one in Boise, Idaho, and one in
Ogden, Utah) from February 1981 to May 1985. The sclid line on
Figure 7 represents the pe,centage (by a four-month moving
average) of sites that successfully responded during the once-a-
day colletion period (from 0500 to 0800 local time). The dashed
line represents what the SCS believes the collection percentage
would have been assuming that the collection would have been
sccompl ished with present day software (5 percent improvement)
and without a master station failure during the 1983-84 winter.
D, Comparison of Meteor Rurst and GOES Satellite Systems
As indicated in tt3e abave diesussion a radio line-of-site
system is not recommended primarily because of the need for
repeater stations and the thunderstorm activity of the area.
There are many factors to be consideved in making a decisisn
between Meteor Burst and the GOES Satellite system. Cost of the
initial hardware and accessories (iracbudirrg antennas) 3"s much
OBSERVE ',hi) JUSTF~P' -- LI
OBSEj;VED
- -- ADJUST. DATA LOSS
----I( ADDNL. ADJUST
--..-=--. PREDICTED
Table 4
nonthly Wve~age Percentage oE Sites Repo~ting
Daily Miomial Pa:LLs
dazauary
Febr~ary
p@< a c pfi
April
$4" i 4ay
June
July
August
Segtenben
October
kJavember
December
less far the GOES system. Installation costs would be very
eompa~able. Maintenance costs would also favor the GOES system
since the power requirements for the Meteor Burst system is much
qxeater,
The reliability of the two systems for once a day collection
of reports is assumed tc be about the same based on recent
performance information. However, the reliability of ebtaining
15 minutes or hourly readings far an entire day would be much
greater using the GOES system.
The two way communication potential of the Meteor Burst
system was indicated as a plus by the federal agencies in Alaska
that attended the Play 30, 1985, meeting. However, this would be
of advantage only during the maintenance visits, For the Bureau
of Reclamation net~rork for the Snake River Basin this is often
only once a year. The GOES system does allow for text ("related
textN) aqd emergency messages to be sent from the DCP. In fact,
NOAA, owner of the GOES satellite, currently operates a program
called SEAS (Shipboard Environmental data Acquisi t ion System)
which prefaces each data transmission with text detail ing weather
and ship locations. The message are keyed in by the shipboard
operators. If it is truly important to have the meteor burst 2-
way messages it may be best to buy a single transmittor for the
Meteor Burst system for use by the servicing field crews to carry
and operate it in cooperation with the SCS ineteor burst system.
When operation of the hydroelectric system commences there
will undoubtedly be a greater need for data for short periods of
& * &nrne, i, e,, for information on resexvair releases and dawnriver
streamflow to ensure that envirornmental requirements below the
reservoirs are satisfie$. Th2 added reliability of the GOES
system for collecting 15 minute or hourly data for the entire day
would be of vzlue,
based on the abo~~e cons idexat ions, it is reccmmended that
"; '";? GOES satellite system be investigated for ilse in ti?e prt~ject~
" )"* ' : 81% .r?~ould include an ansite field survey to ensure thar ail
:;r?lected sites requiring collection of data can be seen by the
$:x;3 $:Q 1 1 i ke,
6. Coeperration on Data ~eceptian
A Large number of firms and agencies awn and maintain GOES
!+.a -. b, :a receiving stations. The U. S. Geological. Survey, for
tixampie, operates a portion of its own gaging network by means of
a receiving station in Anchorage. Any such site can collect the
nccessasy data and hold it for telephone transfee to the comput<+r
chti:re the models will be run. The geographic coverage of GOES is
emphasized by the fact that nydex Corporation using the Sutron
Csrpoxation downlink in Herndon, Virginia could easily receive
the data, xun the models, and return the answers to Anchorage.
In the initial stages, however, the U.S.G.S. site will certainly
be attractive,
A satellite system using the U.S.G.S. site would require
only an investment in DCPs and sufficient telephone equipment and
software to take the data from the ground station to the computsr
isihere the models will be run. Larger, moxe nlodern ground
stations in t,he $50,000 to $100,000 range conre with complete data
base software and model inter faces. This rnore sophisticated
system should be considered if caoperatior~ with II. S.G.5. is not
desirable,
I, Prrrehase of ddicated ground station
Purchase of a dedicated ground station will have to be
weighed on the basis of cost effectiveness. Such stations are
avairable with a wide range of capabilities and corresponding
wide range in prices,
There isre rur-rent%y sszy ewe manc2ac%urer% sf qrauz2-&
stations in the U,S. - Sutron Carpazstion sncZ Synsrgetics
Corpauation. Sutron is the newes of the two coxparations and
offers mare data handling capability and smaller, fully digital
electronics, Synergetics has rnore stations in place, The markci-
is highly competitive, The simplest receiving site from either
manufacturer costs approximately $27,000. Far that ?rice the
user receives an antenna, frequency downconverter, receij:sr,
cab1 ing , and demodulators for monitoring the satel l i tc chann6sks.
Also included is a pr:rsonal eompiiter to select channels nn ;1 t ivic
t;chedule loasis and to store and print: out: data. Stlch botton. end
receis~e sites are minimally useful and are usually ussd as "frot*ttr
essds" for h~rgera sys%e~%,
To increase the receiving site ~apa'uj.1 ity is primarily .1
matter of add incj computer capacity, H<~nufacturers 0t:fer
2 :q;i;ased --* d_i processor and disk space in nouyhly 510,000 doila:
ii;cremcnts, realjstically useful receive site with a cc.tnpu?r.er *. czgJa122e 0: runllinq the real-time m~d~ls and a goad data base i*.lLl
cLc;st approx inately $60,000 without such extras as an
~i>i*Ote~ruptible power supply or installation. Additional maney
Tg: ..:zA - d- *- be ccrrsidered for data handling software. Sutron offers a
1 icense data base package designed to provide real-time data
** -8 :r:spiays and to interface with models. Xt cilrrntly 1 ists for
525,000. This is approximately the cost for single-purpose
cus"im software if a "hare bones" receiving site is interfaced tra
a!: EX lsting computer.
8. Kgudget Estimation Figures
Detailed budgeting at this time is not practical, However,
cst inates of telemetry system costs axe needed for planning
porposes. The following are reasonable price estimates in 1985
dQ$lans Ear V~P~QK~IS items wkPch would be ;.meelled far a satefli te
systd:?rn:
a Data coLlection platforms - satellite $4,500 ea,
(includes antenna, solar panels cables,
batteries and insulated csrxtainer) , arad
o Minimum satellite grcund station for
interface to existing computer
$ Snelude% S2S,OOaS custom softwar@ , ar
a Sakellite ground station capable of
running real-time models as well as
receiving data (includes data base
saftware) ar
9, Add i kisnal Reeoamendatians
If the Susitna Hydroelectric Project will have a line-of-
sight cammunications system installed for: other seasons it: would
msjce sense to multiplex in the data gathering and combine the
maintenance. However, if no such system is planned a satellite
system as recommended above seems highly atfractive,
If suff icianl: funds are available a gcourld station ::~~ould be
puxchased. The USGS site is fajrly old technology and the data
handling software for it is primitive by today's standards, A
new receive site could be purchased with an-line quality aontral,
;! camp'lcte data hase, and sljfficient capability to run all 1:lie
~OXEC:SS?S*
rn the mean time, it wot~ld make a great deal of sense to buy
UCPs and place them at the existing data gathering network sitt~s.
Data shouLd be manitored daily and as-needed maintenance
under takex-i. By usj.ilig UCPs and the USGS grourld station it is
gmnsslble to knai.4 ilnmed late1 y when px~b;lre:ns OCCUI- and cnfould
- - cJ~..:s!.~c.~sE,1y up the rtzl iability ~"ata ret.rieval, if DCPs jre
s. ~~ed i; will be possible to get rid of t'-9 on-si:e recorders,
S:IC~ t;lcire wculd axso up the reliability of the sibs. ALL dat~,
r<:&ccrding ;*.auld take place in data fifes on the computer that
P pcrz *j ~ps - *- & the data,
The conside~able experience of R&M Consultants in the - *.l -2 s, 9 :gi.-e~iiliatior: artd maintenance of the currznt system rgould seer3 :a
4 -* ?~io~cate them as a logical choice to install and mainkain a
satel'{ ice system. They have accurately defined the cost of
s*aa-j !i,.c_i.;ea - :instruments a and varying kinds of maintenance schedules,
iwy P ?:?ray w + --, could easily be trained in the installation and setup of
r$6, u?, LAq* P 5 *
1. E. L, Peck 1972, Snow Maasure~srat Predicamen.%;, Wantei-
!?e5wr~e5 RQS~B~C~, Val, 8, N0, Feb, A972
2, C, E, lsraalsen 1967, R~liability ~4' Can-Pyps Precl-
pitaef cn Gage Heaeurenents, Techn Repu 2, Utah State
Water Re$. Lab., Utah State U,, Lerqan, Utah, Jetiy
3 U, T, Wilson 1954, Rnalysis of Winter Pre~~p~~tatlan in
*$he &oapesa%ive Snow 11% tss$%ga$iona, Manth1 y Weac Re\#-
82, pp, f83-%99, July
4, M. 5, Braun and E, &, Peck 1962, Reliability a+' P~eci-
pita'kion MeasurnenSs as Related ta Expasure, Sour. af
Rppl. Metea~s, J143e
5, &* Larson and E, h, Peek %974, Rewsaey of Preeipitataan
Msa~ursmen+% for Hydro1 sgic Hodel ing, Water Retiaurcea
Research, Vol, 18, NQ. 4, kguat
6, E. L, Peck 1973, Discussion sf Problems in Measuring
Prccl pi tat ion ir~ F"lraun*%;ainous Areas, Symp~sium art
Oistrf bu%icbn of Pracipi-katian in Motantairlous Areas,
EeiPo, Yoruay, Nav, I, WMBIBMM, Ma, 32S, Genava,
Swi tzarland
7.. Ea G. Bsgdaaova 1968, Es$imat~ of tf-se RelixabiEity of
the Charactariskica of $h~ Shortage in Solid Preci-
pitation he to Wind, Sor. Hydral. S~&ae%ed Papers, Esagl.
fsansf,, Ma* 2
8, El L, Peck and T. R. Carfmal1 1979, Advantages af
Coxlceptue% Madal a for Mlsr*.ft;t"aarrn Research Bafaan 5tridsz?s,
lhdrd Symp~si~m EHP Rege Idorkrng Group 017 Marl;hef-n
Rt3aearplh Basins, hebe&: Crty, Canada, Jurle 11-15,
9, G,, 0, Klilday 1974, Mea8.o M~nChly and Rnneral Prsei-.
pitatxan, Alaska, WAR Vaeh, Hema, NU5 AR-18, Anchr~raqc,
Alaska Mar,
18. 5C5 1991, Maarr Al.snuaX Precip~Gat~~n, Susltria Rrver-
Baszn, filaskt3, Prepared by SCS WSTC Casts Unx t ,
H'7-M-24165-1, X3u~ust
11. J. W~ss $9Q49., Annual f"%reorpafat~or-r Map frlr- HI,-s5shti
rdrrpu b2 l sfled, Per sasial Crrr*reuporrdcncu
12 El L, PCC~ AI?~ M, r7, 8r~~9"t 1962, Ai7 A~praach ti) the
Dcveiaj~~~aaat a$" S3ahyatal Maps for Hsur~tal rraus fire~95
T, R, Gar~aIl and K. 6. Vadnais f 980, Operatione31
fiarborne Heaauranen-8; sf Snaw Wakes Eqi~ i val ent Uai ng
Natural Terr~strial Gam~a Radiatzon, Prac, 48th
RnrlediaP Weatern Snow Cansf,, taramis, Wya,, fipril
D, &, Fineh and J, P Ualker 6979, Snouf a1 l at
@nchorage, Alaska Rssociated with Cold Advecti an,
NOBa reek, Mema* NWS AR-25, Oct,
D, Blanche* 1983, Chugach Mat ional E~visonmental fit las,
Chugach Natianal Fares$, Alaskan Ragisn, Rep, NQ. 124,
Ee A* Ma~chegiani 1385, A Relalianship Between Snow
Cwrse Infsrmatfe~~.r and R*lnguiB.'f, Unwblisk-sed Paper.
R & M Can~ul$ants and U, Q, Hz3rsisan 1381, elaska Pawer
RuPharity Susitna WydraaekacSr~,c Prsj ect , Task 3 -
Hydrslagy G1aci er Skudi es RG:~O~B prepared far Acres
American Xncarporated, bffafa, New Yark,
Hasza-Ebascs Susi %na Saint Venture 1984, Watsna and
Devil Canyen Sites Prabeble Maxxwn Flaod, Draf d Regasrt,
Docamsnt No, 457 Jan.
Rcres American Inc, 698B. Appendix AZ, Watana and
Devil. Canyon Sites P~cubable Fvtexlb~gum FIOC)~, Subf ask 3,85
a$* G. Kaolovalov 2988, i"E@l%lrag! and Runoff in G~acaer
Basin%, Ssviet Hydrology, Selected Papers, Val 19, Na, %
V, G6 P(onsve1av H98B. Madel sf fPle Hovement aC *the Snow
Line an a Gaaciss =ring $he flblatzan Period, SovxeP
Wydrslagy, Sele~ted Papers, 901. 19, No, Z
A. 6, Fountain ab-~d W, kd, Tangtjharrr 1985, Cantampssrary
Tacfrrrnwas Csr Pr~dlctiprg hra~~ff from Glacleri~cd Bas] na,
esnptdhliahed by Working Grcaup on Snow and Ice Hydrningy of
Glacieri ncd Basins, international Eanm an Snaw and X ce,
T AHS ( pri vata csmmu ral e:a%nat.1)
E. W. Rr-icdersan 1973* %dat;rr~na,% WeatP-\er* Sera vt:e Rn ver
Forecast System - Snuu Accanmulatzon and Ahll3tlurr ?.ladel*
NQfiB Tec5-I, Hexuao. NUS Hydf-0- %'7s Snlbver S~rl rng, Rd. N(3\ .
E. I,, Peck, 8, S, FBcQurvegt, 'F, N, Muefer, E, t-4- LIcxE-tr~ciarrr,
and J, I,, E~aksruag 1,981. Revrcw of b4ydr-alagic tq'%ocJ;:ls Ftti.
Evaluad lt mg Uss a43~ar~ro-t;~ Sen42 rag Capabr 1 n t A ns, tJHSi"i
CR 1666'74 Mar
8-s i c. D, L, Chapman 1902. Daily Flow Statistics of Alaskan
Streams, NOAA Tech. Memo. NWS AR-35, Anchorage, Alaska,
oc t
2'*7, PJMO, Unpubl ishedl rep~xt on Intercompar ison of Snowmel t
Model (private communication f lorn E. A. Anderson)
20, G. C-vadias and 6;. Morin 1985. Approximate Confidence
Intervals "~r Numerical Verification Critexia used in
Hydrologic, . Models. Applicatipon to the WMO Inter-
comparison of Conceptual Models of Snowmelt Runoff.
Unpublished report (private communication) Jan
29, W, V. Tangborn 1984. Prediction of Glacier Derived
Runoff for Hyd-qelectric Development, Geografiska,
668, 3
30, WMO 1975. Intercompar ison of Conceptual Models used
in Operational Hydrological Forecasting, Operational
Hydrology Report Ha. 7, WMO - No. 429, Geneva,
Swi tzexland
31. W. V. Tangborn 1977. Application of a New Hydro-
meteorological Streamflow Prediction Model, Western
Snow CanE, Apr
32. E, L. Peck 1976. Catchment Modeling and Initital
Parameter Estimaeion for the National Weather Service
River Forecast System, NOAA Tech. Memo. NWS Hydro-31,
Jun
33. E. L. Peck, E. R. Johnson, K. M. Krouse, T. R. Cirroll
and J. C. Schaake, Jr 1980. Hydrological Update Tech-
niques rJsed by the US National Weather Servi.cer
Proceedings of the Oxford Symposium, IAHS-AISH Publ.
No 129 Rpn
34, W, T, Sittner and K, M, Krsuse E979, Improvement sf
Hydrologic Simulation by Utilizing Observed Discharge as
an Indirect Input (Computed Hydrograph Adjustment
Technique - CHAT), NOAA Tech. Memo. NWS Hydro-38, Feb
35. T. R. Carroll and K. G. Vadnais 1980, Operational &AT-
borne-Measurement of Snow Water Equivalent Using Na'rual
Terrestxial Gamma Radiation, Western Snow Conf, Apr
36, E. R, Johnson, E. L. Peck and T, N. Keefer 1983. Creating
a Bridge Between Hcmote Sensing and Hydrologic Models,
NASA CR 170517 Jan
37. G. He Leavcsley, N. W. Lichty, 8. M. Txoutman and L. G.
Saindon 1983. Precipitation-Runof f Modcling System:
User's Manual, Water-Resources Investigations Report
@,-4238, U. S. Geoloy ical Surveyr Denver, Cola.
38, 6. J. Nibler. Information Content of River Forecasts,
Unpublished Paper (private communication]
7 Q r-3
id. L. W. Twedt, J. C. Ssbaake, Jr,, and E, L. Peck 1977.
National Weather Service Extended Streamflow Fred iction,
We.:teprn Snow Conf. Apr
APPENDICES
Appendix A Maps of Monthly Precipitation, Susitna River Basin
(Figures A-l to A-9)
Appendix B Plots of Daily Discharge and Daily Pzecipitation,
Susitna River Basin (Figures B-l to 8-16) and
Semilog plots of Daily Discharge, Susitna River
Basin (Figures B-17 to B-28)
Appcndix C Results of Multiple Regression Analysis, Susitna
River at Denali, Alaska
Appendix B Explanatory Information (Figure D) , Definitions of
States and Parameters (Tables D-l to D-12), and
Schematic Diagrams for Conceptual Hydrological
Models (Figures D-l to D-12)
Appendix E R and M Consultants letter of March 8, 1985
s bpbds
Marawnan
8 -
Pax?S9T?i "
Coppr Canter
C)Ld Edgerton
Figure A-.I Precipitation April 1983
"$%rims Camp
B
Figure A-2 Precipitation May 1982
Figure A-3 Precipitation June 1984
~i.gbre A-4 l>reci.p~talrion July 1.981.
Center
Edgar tan
Tg.i&w Camp
0
V; Galkana L / lcb -- o Cetpwr Canter
8 :, @&- - - - 1.7+* @ -
*r*a Glerutallen x&% 01x3 %:dqer%an
Figure A-5 Precipitation July 1983
Bek*ll.y Par* 0 . wrly
1, SBS~&%$ Re+ P6,ta mw%&
2, Bkgaxen 8, w, Paxwn 3*).~3 x$l
Figure A-6 Precipi.tat ion August 1931
Figure A-7 Precipitation August 1983
Sw$&qaging Smasns . waxy
1, gasi-a B, are Wnalf esnley Park dg @
2, Hemen R. ~ar, ~X~IB . .
3, SUS%.%~~ 8, AZP. an-*%%
Figure A-8 Precipitation September 1983
Figure A-9 Precipitation October 1983
DISCHARGE
DENALI 1981
DAY OF MAY-SEPT, PERIOD
Figure 8-1
Figure B-2 DAY OF MAY-SEPT. PERIOD
Figure 13-3 DAY OF MAY-SEPT, PERIOD
Figure B-4
HM&&&ddddM4v4
DAY OF k4AY-SEPT, PERIOD
Figure B-5
DISCHARGE DATA
PMmN IQB2
Figure 13-6 DAY OF bjAY--SEPT, PERIOD
DISCHARGE COMPARISON I982
Figure B-7 DAY OF MAY-SEPV, PERIOD
1982 PRECIPITATION DATA
Figure B-8
DAY OF MAY-SEPT. PERIOD
Figure B-9
DISCHARGE 25888
248&16
23888
223@@
21@88
28888
19888
188168
1 "%go
A 16888 " fS&1PIB % " 14808 w
w f 308651
12888
< $.arm@ nr~l~B g leagg
&-@
0 9888
8888
aBaB
6888
58B8
4BBB
3888
28@B
l BBB
B
~~~~~MB~3mMmu3mmBmmMmb3~Lnmmmmmm~b2mm ~~~~mmtfmm~~bRmmrn~~~~-NN~~~W~~ MMM~~~WVMM~~M
Figure B-i~ DAMOF MAYwSE$$, PER I OD
DISCHARGE COMPARISON 1983
pigwe B-1.1- DAY OF HAY-SEPT. PERIOD
DISCHARGE
I1
DAY OF MAY-SEPT. PERIOD
Figure E.-13
DISCHARGE
Figure 13-14
DISCHARGE COMPARISON 1984
Figure B-16
F-4
Figure B-18
--
DAY OF MAY-SEPB. PERIOD * .-i
Figure B-19
6
Figue B-20 DAY OF MAY-SEPY, PERIOD
r----------b
MAY-SEPf. PEW I08
~;~rs-p..a n-39 8A't OF MAY-SEPT. PERIOD
I i I
I
I --.J
Figure B-24
-mr*.-nnux..rr..rr^r-o~~.w-.~-e -"w.bw*"--I.- ! -,,- L-a--.-"-LIA-- -- -k . .-.Y--n"-"-' -5-- ---,.---.""-'--
I 1 ! I I . *
---* I i -- ----7.
-.- * ll-O-*..i.. -----"-.-'--------'
1
4 I 3 i... I 1 j -- t
I I ! 1 i .. ..! . :-; ----.--p.------.~--.".----".. -po,p--P--P--P---
I , . C -- -.".-..-tpL ----- id --I--- ----."------.---- i
I t
1 - I- j I - i
"-.rrc-"..---.--."-""----.----L
Figure 8-25
. - / - ....
- I.. .- 1
B
Figure B-26 DAY OF MAY-SEPT. PERIOD
1
-*-- - - ---- --- --- -- ---
4 - --I
1 -
L ! ! -.- ---- _..d
I
+
I 1 --_-- -? Iy.-------LI --- ---- -1- --- I , -~---I
I 1
I i
I
I
I
0 Figure 5-27 DAY OF MAY-SEPT, PERIOD
- .. "-".- 1.. . [ ... -. I-" -..-.--.- 1
-,-: -.-,-.-.-. ,. .. 1 .-.-. -...- ...--..
Figure B-28 DAY OF MAY-SEPT. PERIOD
PROBLEM NUMBER
REPLACEMENT BND
DEPENDENT VARIABLE 16 NOW 2
NUMBER 66 VARIABLES DELETED 1
VAR %ABLE5 DELETED. . . f
AMALY%%G OF VARIAYCE FOR REGRESSION
SOURCE OF V&WXATBBM B. F. SUM QF HE AN F
GGUAREf BGUAAES VALUE
DUE f8 REGRESSTON. 4 3388. 7030B 1397. %7&2f 8.07810
DEW r AT ro~ ~asur ~~bkkbki bk: : a 4 2426.41992 8~2.93836
TQTAC.. , ie 808~. as300
INTERCEPT [A VALUE! IS 89,04"i)27
-- -
lea. baJsao
COMPARE CHECK ON FINAL COEFFICIENT. .,... 0. $0182 ,
INCWEHENTS FBB INDEPENDENT VARIABLES Q CUMULATIVE REGRESSIONS +a
WAR SABLE SWHS Of PROP. F VALUE @ SBD. ERROR SWHS OF PRDB. WAR. . P H&T l PL6
ND. MARE SQUARES VAR . EACH TERH a 6F ESPEHATE 58WCtRES . " R 60. VALUE R
3 x 4 ~~83. 37861 8. 36~49 0. 82448 17.314329 . a~ias.~legn o.=lisosc% 43.82448 8. &Ql24
4 X2 159&. 311928 8. 19432 8, 98847 8%. "31230 4432, s397&& 0. 93'981 30. $91026 0. $4333
9 X3 &SO. 371983 0.07997 3, 29333 33,94820 3092. &TAT8 0.83378 8. ;Pa7 Bb 0.7913&
k3 x 4 466. a2869 s, oar 9s z.eb790 53. as137 ~SSB.~OSQB 0. &OO'P~ 8.87913 0. @a339
BR9PQRT16N OF VARIANCE 5PE-
ClFttEBTDLBfiXTWACifAiOLES O.008LBQ .
1 Year Twa digits far year ( 3 962 = 62 1
iI 'sJ Hay-September runaf f @ S~b~itna RIVBP at
Benali, 1,888 acre fee$
3 X1 + Water ewivalent, Bp~il 1 MsnahanFla$
snou causse, 1bdB inch86
4 Xil Sum of nonthl y departures f ran average
%snpera%~nre f6r Hw-Sept~rnb~r TaPkast na
degree6 F b plus dB degrees F!
5 H3 May-September precipitation, Palkeatna,
1Bb8 iarsche~
6 X4 Value of X3 far psavisus year
Regressian equaf ion
PLlVHC
SBA ESC
PARAPaq ETERS
STATES
Figure D LEGEND FOR SCHEWIA'TIC DIAGRAk4S OF MODELS
Inouts A-
The set of driving forces required periodically by
the model, Co on examples are precipitation poken-
tiaL evapotranspiration, and temperature. For most
hydrologic models the inputs are all m&teorologic
factors, but some require inpnta describing man's
activities (cropping practices).
The key phrase in the definition of the inputs
of a model is "required periodically." If it is
possible to run the model without providing a valve
for a particular item, that item is not an input.
Likewise, if the model can be run with a particular
item provided only once or perhaps intermittently,
that item is not an input. Some models, however,
may have default values for certain inputs (e.g.,
precipitation is zero if not entered).
Parameters
The set of values that are changed to make a general
hydrologic model apply to a particuJar location.
Parameters ar cans%ant with time BP at most, vary
only slightly with time as compared to inputs.
/.
States
The set sf intsxnak model values sufficient to start
the model. The states of the model completely define
the past history of inputs. These are usually values
of moistQre stored in various model components (e.g.
upper zone tension water contents), indices to model
status(e.g.,API), or computational carryover values
(e.g.,the carryover values of a unit hydragraph opera-
tion). In each time step of ope~ation~the model uses
the initial values of the states along with parameters
and inputs for that time step in order to compute the
state for the next time step.
OutpI-:ts
Variables of interest that can be computed from knowl-
edge of the states and inputs. Usual examples are
streamflow and actual evapotranspiration. In many
cases an output will be identical to some state of
the model, but such .does not have to be the case. The
mdel may produce an output that is of vital
interest to the mode% user but 2s not necessary to
the model computations.
PRECIPITATION, MA
$42 MI@ TBEBtqPERAVU
Figure D-1NIMSRFS (ANDERSON) SMCBWMELT MODEL
SCHEMATiC DlAGRAM
PA TERS {DEFINITIONS) N\#Qi$SRFS SNOmELT MODEL
AmAL - DEPLETION CURE - Curve that defines the areal extent
of the snow cover as a functian sf how
much of the original snow coverremains.
It also implicitly accounts for the re-
duction in the melt rake that OCCUXS
with a decrease in the areal e:4tewt of
SCF
Constant amount of mehk that occurs at
the snow-soil inkerface whenever snow is
present,
Base temperature for snowmelt computations
during nonrain periods.
Maximum melt factor during nonrain periods;
asswed to occur c,n June 21,
Minimum melt factor during nonrain periods;
assumed to occur an Dece&er 21,
The maximum negative melt factor.
Percent (decimal) liquid water holding
capacity; indicates the maximum amount of
liquid water that can be held against
gravity drainage in the snow cover.
The temperature that delineates rain from
snow,
A multiplying factor that adjusts pre-
cipitation data for gage catch deficiencies
during periods of snowfall and implicitly
accounts for net vapor transfer and inter-
ception losses. At a point, it also
implicitly accounts for gains or losses
from drifting.
The mean areal water-equivalent ~Sove which
there is always 100 percent areal snow cover.
Antecedent temperature index
(range is Oel;TIPM<l. - - 01 . parameter
The average wind funetion during rain-on-
snow periods.
Table D-2 STALTES (DEFINITIONS ) NWSWFS SINQWIIEET $3QBEL
~ NEGHS
antecedent Tempcsrature Index; represents
the temperature within the snow cover,
EAGRQ and S together define the amount of
excess liquii water in transit in the
snowpack.
The amount of liquid-water held against
gravity drainage.
The maximurn water-equival.ent that has
occurred over the area since scow began
to aecmulate,
Heat Deficit; the amount 0% heat thak must
be added to return thg snow cover to an
fsothemal state at 0 C with the same
liquidwater content as when the heat defi~ix
was previously zero.
S and LAGRO together define the amount of
excess liquid water.in transit in the snow-
pack.
The areal water equivalent just prior to
the new snowfall,
The areal extent of SFPOW cover from the
areal depletion curve just prior to the
new snowfall,
The amount of water equivalent above which
100 percent areal snow cover temporarily
exists,
Water equivalent of the solid portion of
the snowpack.
"These states are onby used when there is a new snowfall an a
basin with a partial snowcover.
Pl :lGblRE D-2. WYFIET MODEL XMEmTIC DP bGN&M HYDEX, April 1985
TROS
T:x 1 K
TIIAX
S $3 N
S?,X
EFF
TFF
QAm
SBMF
S U B-I,
U2IN
UZOUT
GW1 E
GROUT
GLW
TF
GEf N
GLOUT
TFF
GL S PK
THRESBOD TEWEUT'dRE FOR MIN OR SMOW
%WESEBLD "$IEMBEMTgWE FOR WA2OMTXOH
TBRESBOLD TEWEM-TUBE FOR ShOIvWELT
m$SWX OF FIN TO SOIL MOISTVRE
MOWT OF SN8 LT TO SOIL PiOXSTUWE
VARIATION EN MELT UTE DUE TO SW ANGLE
VMXBATXON ZN IELT 'UTE DUE TO U35ATION
SNObXELT PRODUCED BY AIR TEWEUTUEE
DEPLETTOM OF SNOW CWEREB AREA
SUBLZMTIOM FRQH SN6WAa
IRPUT TO UPPER ZONE STBMGE
OUTP5T FROM WPEW ZQHE STOMGE
INPUT TO GROUNDVATEM STOMGE
B3TPUT FROM GROUNDWATER STOMGE
GUCTER CWER FPACTPBE
GUCIER $.EL$ DUE TO UBIATION
INFLOW TO ENGUCXAL STBWGE
OUTFLOW FRlIM ENGUCEU STOUGE
GUCIAL MELT DUE TO AIR TEWEMTURE
GLACIAL m%T FACTOR DUE TO QRIENTATXON
'ff"&le D.-4 BYWT SLMUUTION MODEL STATES
SNACC
SOIL MOISTURE
SNOW CWERED AREA FUCTION
GBAE GLACIER BALANCE
PERVIOUS AREA
INf ERf LOW
Figure D-3RIWSRFS (SACRAMENTO) MODEL SCHEMATIC DIAGRAM
That fraction sf LT-ke basin that becomes
impervious as all tension water require-
ments axe met,
Maximum capacity of I~wer zone primary
free water stswage,
Lateral drainage rate of lower zone prirnary
free water expressed as a fraction of contents
per day,
P/Iaximum capacity of lower zone supplemental
f%ee water storage*
Lateral drainage rate of lrwer zone supple-
mental free water expressed as a fraction of
contents per day.
Maximum capacity of lower zone tension water.
Fraction of ilnpervinus basin contiguous with
strea*m channels,
The percentage of percolation water that direct-
ly enters the lower zone free water without a
prior elaim by lower zone tension water.
Fraction of lower zone free water not available
for transpiration purposes (incapable of re-
supplying lower zone tension water).
An exponent determining the rate of change of
the percolation rate as the lower zone deficiency
ratio varies from 1 to 0 (1 = c~mpletely dry; 0 -
lower zone storage completely full)
Fraction of basin covered by riparian vegetation.
The ratio of unobserved to observed baseflow. SIDE
Maximum capacity of upper zone free water.
Lateral drainage rate of upper zone free water
expressed as a fraction sf contents per day.
Msximum capacity upper zone tension water.
W fraction used to define the proportional in-
crease in percolation from saturated-to-dry lower
zone soil moisture conditions. This parameter,
when used with other parameters, indicates the
maximum percolation rate possible when upper
zone storages are full and the lower zone soil
moisture is 100 percent deficient.
% Table B-6 STATES (DEFINITTONS) NwSRFS MODEL
Additional impervious area.
Lower zone free primary water storage.
IIZFSC ----- Lower zone free supplemental water storage.
Lower zone tension water storage.
Upper zone free water storage.
UZTWC -- Upper zone tension water storage.
Figure D*~P~s ElODEi SCIEEIATIC DIAGRAM
13;b-f Tmprature k10tq wait:? ppr-ipitation is snow ard
above r*rhich it is rain (dqrees F or C),
C2ECIg Convection-Codensastion eqery coefficient for
months I-12 (cal/degree abve OOC)
~~~~~ r cover density for major vqetation far
each (decimal prcent]
Winter cc er density for major vqetation for
each HRU (decimal percent)
Air tanperature coefficient for ET emputation
for mn~s 1-12
Air tgnperature coefficient for ET camputation
fon each
Impervious drainage area for each HRU (acres)
Initial density of new-fallen snow fdshal
prcent)
average maximum density of snow pack (decimal
pxcent)
Ehissivity of air on days without precipitation
maporation pan coefficient for montk~s 1-12
Evaporation loss from hpervious area for each
(imkes)
Free water holding capacity of snowpack (Decimal
percent of snowpack water
Coefficient to compute seepage from each ground-
wate~ reservoir to a ground-water sink
Vegetation cover t fcr each mU (14.. Bare,
1= Grasses, 22 Shurbs, 3= Trees)
of rout iny for each surface reservoir
(8= ReaPs; S= Linear)
Julian date to start i.ooking for spring snow
me$ t stage
Julian date to force snowpack to spring snow
me1 t stage
Month that transpiration ends for each HRU
Proprtion of rain in ~.ain~'sr?ok~ event &ave
bfiniriich snow albedo is not reset for sraoclpack
mglt stage
Inte~eeption storage capacity of unit area af
vwyetation for rain during stxmer priod, for
each (inches)
Interception storage capacity of unit area of
vegetation for rain during winter perid for each
(ivchesf
Sepg~: rate frm each sU&~SUE~~C~ neservoir to
ground water reservoir ( ir~hes/day)
Miniman contributing area for surface runoff when
IS=:= 0; Cwffirient in contrij2utirig area - soil
moisture idex relation when $SSR1= 1
Maximm possible contributing area for surface
runoff as proprtion of each HR&I
Coefficient in surface runoff contributing area -
soil misture idex relation
Seepge rate frm soil moisture excess to each
ground9later reservoiz ( inchesiday]
Snofqck set tlment the cans tant
Paximm available water holding capacity of soil
profile for each ( inches)
Interception storage capacity of unit area of
vegetation for snow, for each HRU (inches, water
ivalewt)
Maximm daily srlo It infiltration capcity of
soil profile at field capacity for each kPU
( inches j
Storage values in ou"cflow/storage table for Puls
routing (aS days)
hpse rate for minimm daily temprature for
months 1-32 (degrees C or F)
Lapse rate for maxirr,un daily air tfmperature for
months 1-12 (deqrees C or F)
Trawm~issisw coefficient for shortwave radiacisn
through vegetation canopy for each HRU
P?cnl:l1 to bs~in ckclsi~ for start of trar~.-
spisatiew fox each
Tranpsiration switch for each .lhU (0 = v8y-
etstion donoat; 1 = vegetation transpiring)
Month that thurderstom :y[* events end
Outflow-storage table values for Puls rrouting
F4aximm1 air tanperature, kihich when exceedeo,
forces precipitation to be all rain
,Ujusted snowpack water
obsev~d SMW course data
Rntlting coefficient for each grouMi~ater
reservo i P
Linear routing coefficient for ezch absurface
rewrvoir
Mon-l inear routing coefficient for each s
surface resexvsir
Surface storage reservoir linear routing
coefficient for each reservoir
Y - Intercept for velation &tween tmerature
(XI and 1) degree day (Y) or 2) sky cover (Y)
*en ==lor2
Slope fox relation between tanperahure (X) and
1) degree day (Y) or 2) sky cover (Y) when
=POP%
Maximm Rrcent of potential solar radiation
(decimal)
mximm value of SfR for each (in~l=~)
Coefficient for routing water from each sub
surface reservoir to groundwater reservoir
Maximum retention storage on hyx3rvious area for
each [ ineks)
Cwfficient for routing water frm each sub-
surface reservoir to groundwater reservoir
Proportion of rain in rain/snow event above which
snow albdo is not reset for snobpack arcmula-
tion stage
TABLE ~-8 STAT1:S (DEFINITIONS) PWMS MODEL
IJET
e-
PICE
PKDEF
PSS
RES
SMAV
Computed ALBEDO for each Hydrologic Response Unit
[HRUl
Density of snowpaek on each HRU
Depth of snowpack on each NRU (inches]
Fxee water eontent sf snow on eacl2 MRU (inches)
Storage in each ground-water reservoir (acrec-
inches)
Total seepage to ground water sink for each
ground-water reservoir (acre-inches)
Snowpack temperature (Degrees C) , each HRU
Potential Evapoxanspiration, computed by model
( inche,.s)
Porticon of snowpack existing as ice on each HRU
( inches)
Calozies required to bring pack to isothermal
state, .each HRU
Accumulated sum of net precipitation beginning on
the first day of snowpack formation
Water equivalent of snowpack on each HRU (Inches)
Storage in upper part of soil profile where
losses occur as evaporation and transpiration
( incf-a~zs)
Stosraqje in each subsuaf ace reseHvsi H (acre-
inches)
Retention storage on imprevious area for each
HRU (inches)
Number of days since last snowfall on each HRU
Daily available water in soil profile for each
HRU (inches)
Depth of new snow on each HRU (inches)
Initial storage in each surface reservoir
(CFS-Da ys)
Intercept ion for each HRU (inches)
P""a kable -D--9 p TERS (DEFINITIONS 18 STWFQRD WATERSHED MODEL 171
A -- Percent impervious area.
CB -- -- Infiltration index,
Interflow index, which determines the ratio of
interflow to surface runoff.
Maximum amount of interception storage.
Ratio of total stream and lake area to the total
watershed area,
Daily interflow rececsion coefficient.
Daily groundwater recession coefficient.
Weighting factor to allow variable groundwater
secession rates,
Percent of watershed stxeam surfaces and riparian
vegetation.
Percent of groundwater recharge assigned to deep
. percolation,
Evaporation loss index for the lower zc-lne.
Overland flow length.
Manning's "n" for overland flow.
Nominal lower zone storage, an index to the
magnitude of lower zone capacity.
Nominal upper Zone storage, an index to the
magnitude of upper zone capacity.
Overland flow slope.
2. -0 STA-TES (DEFINITIONS) STmFBRB PJATERSHE1D MODEL PV
SRGX
--*
EPX
Surface depth.
Interflow storage.
Active groundwate:~ starage.
Groundwater inflow index,
Upper zone storage.
tower zone storage.
Interception storage.
UPPER ZONE
LEGEND
PW PERIOD kENOPH
RGP QENERATED RUNOFF %
ROS GENERATED RUNOFF, SURFACE
W I RAINFALL . 1TENSlTY
RS SURFACE db9N8FF
WP WEIGHTED PRECIPIWTION
GROUNDWATER LOWER ZOHE
ROUTlNG PHASE ,p
BASE FHOka
TSBF, W STORAGE
h
TERS ~DEFINHTIBNS) SSARR HODEL
BFL
ETI --
KSS --
ROP
TSS
TSBF
_yl__
Base flow infiltration limit,
Base flow, percent.
Evapotranspikaticn index.
Percent effectiveness of EXI (function
of rainfall intensity, HI] .
Limiting subsurface infiltration rate.
er of routing phases (surface flow)
er of routing phases (subsurface flow)
er of routing phases (baseflow).
Runoff percent:.
Surface runoff percent, function of
WSlXS table-
Time of storage; surface flow.
Time of storage; subsurface flow (interflow).
Time of storage; baseflow.
Tab,Le D-1% STATES (DEFPMITIDNS) SSARR MODEL
-a- -
Sail Moisture Index.
Base Flow Infiltration Index,
Phzse storage (discharge or stage) for
surface flow
Phase storage (discharge) for subsurface
d.%,ew*
Phase storage (discharge) for baseflow.
~ Marsh 8, 1985
Warra-Ebasco Susitna Joint Venture
7 I "8% 'Xtreet
Anchorage, Alaska 99501
~ Attention: Dr. Larry Gilbertson
R &M Nos. 45241 9 452443
~ Re: Review of Hydex Streamflaw Forecasting Feasibility Draft Report
We have examined Chapter 2 of Dr. Peck's draft report, dealing with data
requirements for streamflow forecasting, and have several comments.
These comments primarily address the major conclusions and recommenda-
tions in the report. A few additional recommendations are given for your
cosssideratiar~:
1 , The objective of the hydrological-meteoroDogicaI data-collectian network
will be to obtain input data for a streamfiow forecasting system for
the hydroelectric project. Other purposes of the data collection
should be kept in mind, such as terrestrial game studies and impact
monitoring. Many of the existing stations were established to support
such efforts during the feasibility assessment. Future data require-
ments will be oriented toward project operation and monitoring.
2. An additional application currently being made of' meteoroiogical data
is air quality modelling for the Watana site. Required parameters
there ate wind speed, wind direction, and standard deviation of the
wind direction (known as sigma theta). The sigma theta has been
measured since October 1954. At least one year of data is felt to be
necessary for satisfactorily modelling of the air quality.
3. The network of existing and proposed met. (meteorological) stations
in Figure 2 and Table 2 of the report recammends that data be col-
lected at a total of 11 sites in the basin .aboxt 3evii Canyon. Five
stations would be at or near existing siaL.sns, one would be
re-established at a former meteorological station site, and the other
five wouid be new stations established at existing snow course sites.
Factors which we consider important in selecting specific station
Iscations at each site are:
a. Ability for ~eteorological data from the site to be represeritative
of a large area.
~ b. Protection afforded by nattlral objects, such as trees.
3 14 a 8e x i3 .is Ebaseo Susitrta Jaif~t Venture
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c. Relationship to location where long-term data at the site have
previously been collected.
d. Logistical advantages offered, such as accessibility by fixed-wing
aircraft f~r winter maintenance and Eocatisn on an efficient
transportation route relative to the other statioils to economize
aircraft logistics.
e. Availability of and access by a local observer to provide "con-
trol" data in the event of a system failure.
4, The 97 total proposed sites, shown in Figure 2 of the report, fairly
well cover the expanse of the upper basin. However, a few advan-
tages may bc offered by shifts in some of the iocations. Considering
each of the sites in numerical order, with report recommendations
shown in parenthesis:
1) Susitna Glacier (retain existing site). Relocation to near a small
lake, a Few miles southwest of the existing site at the existing
"Caribou" snow marker location would permit access by
fixed-wing aircraft and still be exposed to much of the "glacier"
weather conditions prevalent at the higher elevations.
2) Denali (relocate to protected site). This sounds favorable,
likely utilizing the vegetated area to the north of the present
site.
3) Tyone [reactivate). While this would be a good location, satis-
factory data for the area could probably be obtained from sites
9, 30 and 13 around it.
4) Kosina (ml~cate Ps prsteicted site). a mare favorable facation
may be higher up in the Kosina Creek basin, where a greater
percentage of the annual precipitation falls. As with site 1,
above, a lake is present which would permit fixed-wing access.
5) Watana (rehabilitate Wyoming gage). Agree that this site's
proximity to the base camp makes it favorable. Perhaps relocate
to the north near a small lake and eatiiize a loeat observer. .
6) Devil Canyon (consider small relocation). Rather than simply
relocating to the trees near the present site, a complete move to
near a lake on the other site of the Sarsitsla River would offer
fixed-wing access as well as natural wind protection. High
Lake, which has a lodge and an airstrip, is a possible site, with
potential for a local observer as well.
~ 7) Monahan Flat (establish at SCS location). Agree.
8) Cathedral hake (establish at snow course location in protected
site if possible). Agree.
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* 9) Clearwater Lake {estabiish at snow course location ir, protec"led
site if possible). Agree.
13) I.ake Louise (establish at snow course location in protected site
if possible). Agree. Use local observer, if possible.
41) Square Lake (establish at snow course location in protected site
is possible). Agree.
if site 3 is deemed superfluous, as discussed above. a new installa-
tion could be made in the upper Tsusena Creek basin. There is a
fairly large "hole" in the data network in this area. The site would '
represent the upper elevations of the Tsusena and Deadman Creek
watersheds and to same extent Watana Crcek, each of which contrib-
utes to one of the two reservoirs- I$ is alss ~10563 to the headwaters
of Portage Creek, the largest tributary to the Middle Susitna River
and an important salmcn stream.
5, A few additional sites, besides the 71 discussed above, may ofler
advantages for other specific data-collection purposes. These ate
locations downstream of the project and would thus not be directly
applicable to forecasting of streamflow into the reservoirs. The sites
aped suggested uses of the data are described below:
1) Middle Susitna River - Since mainstem Susitna flows in this
reach, from Devil Canyon to Talkeetna, are of concern for
fisheries habitat and spawning, knowledge of the meteorologic
conditions influencing river inflow fron, below Devil Canyon Dam
is important for forecasting and impact analysis. The ex isring
station at Sherman could be continued, or a never station could be
established at Gold Creek. Each site wouBd offer shelter by
surrounding trees. Sherman would offer arl existing record
which would be extended; Gold Creek would offer potentiti1 for a
laca0 observer.
2) Lower Susitna River - Temperature conditions in the lower basin
are of concern because of their effect on river freeze-up and ice
conditions. Some data have been collected fcrr one season at
Susitna River Mile 61. 7 his station could be continued if
des i red.
6, Dr. Peck has noted that the important meteorological inputs for
modelling and forecasting streamflow are precipitation, temperature,
and wind speed. The cne most important is precipitation, both
summer and winter, The mast efficient and economics!
hydro-meteorologicaI system would consist of a series of weather
stations at il~tervals throughout the watershed to record and u ltirnate-
ly transmit representative data to a central location. The system
shou!d provide the data for calibration and operation of the forecast-
ing model, yet remain flexible enough to accommodate additional future
requirements. By the time the hydroelectric project has become
operational, installations would most likely be "ransmitting r-c.ala.time
data to be utilized in the forecasting model to a central location.
7, The discussion of ~lhich parameters are necessary to measure at each
site focused on precipitation, temperature, and wind speed, and min-
imized the importance of other data currently reported: wind direc-
tion, relative humidity, shortwave radiation, and longwave radiation.
We fee! also that these latter variables would not be cri"kical for
streamf low forecasting and could be dropped from the d3ta-collection
program except possibly for continued efforts during the summer at
Watana. Relative humidity, in particular, currently poses gr-eat
prablems in assurance of reliable data. if not required, its omission
would ease the data-reduction effort conriderabiy . As noted, the
t-adiation measurements are also difficult to oh%tain reliably, especial1 y
in the irinter, and are not very applicable to streamflow forecasting
models. Wind direction sensors have occasional winter problems, too,
but the data would be useful for analyzing storm movements if it is
not too difficult to continue the measurements. It is possible that
measurement of temperature may be satisfactorily performed by re-
cording only the daily maximum and minimum temperatures.
Continued measurement of all the parameters could be desirable dur-
ing the summer at Watana Camp to expand the data base for reservoir
temperature modelling. Sensors could be more easily maintained on a
daily basis once a camp is operated continuously.
8. Where installation of new equipment is planned for one or more sites,
consideration should be given to installing mechanical stations (rather
than the electronic digital instruments now in use). Advantages that
could be offered include the following:
" bower initial cost. Since the stations record fewer paranders
and are less technically sophisticated, they are less expensive.
Less expensive repairs. Sophisticated electronics in the existing
stations are difficult ts repair in the field.
' Greater reliability.
' No need for a special enclosure since artificial heat is not
required. Existing stations do require a shelter and heat
source,
63 An immediate visual record of the data provided on strip charts.
This aids equipment troubleshooting at the site and can make
data-reduction less costly, since the complete range of
meteoroiogic analysis would not be required.
9. In consideration of the desire to improve reliability of the data reccrd
and reduce the amount of missing data, one of two approaches could
be taken. The first would be to instail a back-try system of sensors
and recorder at each station, which would provide a partially
redundant record with the primary system. The data from the
secondary would not need to be reduced unless a problem caused
some of the primary data to be lost. Then only the data to replace
the missing recards would need tc be obtained. Back-up equipment
would consist of' a thermograph to record contini.jous temperature and
a precipitation can - to permit measurem~*nt of accumulated
psecipi$atisn.
The sther alterna"live WOIJ~~ be to instal1 data-collection platforms at
the station and tefemeter the data for daily monitoring and review at
the office. In this configuration, the redundant recorders would be
useful but not necessary, but a commitment would have to be made to
immediately maintain the field stations when data-collection or trans -
mission problems are indicated.
90. The report recor3mendation to install data-collection platforms (DCP's)
at data sites as soon as possible is very agreeable in concept. This
would permit opportunity to get the system up and running before it
is critically needed, would make real-time data available to benefit
ongoing field operations, and wouid increase the reliability of the da-
ta-collection system. However, the ability to limit instrument
down-time whell problems are revealed via the telemetry depends on
the ability to visit the site immediately. This is naturally subject "to
weather, daylight, and helicopter or other logistical considerations.
Along the same line as comment number 10, we do not agree with the
report suggestion to remove on-site recorders after DCP's have been
installed. Even with the telemetry, back-up recording systems are
needed, especially if reliability of data-collection is a concern. Ex pe-
sience with data-collection in the Susitna and other remote Alaskan
basins has shown that data can easily be lost from problems besides
just sensor malfunctions. Losses can also occur due to malfunctions
of' the transmitter, receiver, or communication link or due to deizys
in access to the site caused by weather, daylight, or helicopter
availability. Back-up data does not necessarily need to be reduced
unless data gaps occur in the telemetry system.
Measurement of pan evaporation at \l'atana has been recommended in
the report. These measurements have been collected at Watana since
1951. A pan anemometer, which aids in applying the data, has not
been part of the instaliation but will be installed this season. Daily
observations are recorded by hydrology staff or camp logistics per-
son nel .
There are several statements in the report that snow surveys at
existing weather stations are of no value. The reason the surveys
were initiated, even though some of the areas are extreme!y
windblown, was because game biologists were very interested in snow
conditions in sibu, 1.e. , the snow depths that moose and caribou
actually had to contend with in the open areas. Since regular visits
are made ta the sites, the data are very easily obtained. The state-
ment is correct, however, in describing the windblo\*rn snow courses
as of little value to streamflow forecasting, so perhaps snow surveys
couf:2 be performed in nearby protected areas as well.
14. As was mentioned above, retention of observers to record daily
observations at selected sites would increase system reliability and
provide data at times when the instrumentation goes down. Such
information could even be transmitted by radio or telephone to a
processing center if necessary. Observers would probably be avail -
able at the foilowing sites:
12) Denali (probably less than 100% of the year)
(5) VJatana (as long as the camp is occupied)
(6) Devil Canyon (if moved to High Lake Lodge)
$90) Lake Louise
An additional alternative would te relocation of the Devil Canyon
station to Gold Creek instead of to High Lake. Goid Creek is beiow
both damsites but would represent Middie Susitna areas (in place of
the existing Sherman statics) and would offer reliable railroad per-
sonnet as observers.
15. Rough cost estimates have been developed for various instrumentation
alternatives considered. These are listed below:
a. Approximate cost sf continuing existing stations (including
recommended improvements to each): 84,400 per station
$38,860 for 7 stations
Costs for reduction, review, handling, editing, and reporting of
the electronic station data are estimted to be $1,230 per month
per station, including labor and computer costs.
Reported data would include many of the same precipitation,
temperature, and wind parameters currently reported (since the
software al~eady exists) :
0 Precipitation (hour*ly and daily totals)
~ €3 Temperature (daily min/max/average)
0 Wind (daily resultant speed and direction, daiiy average
speed, daily peak gust speed and direction, and daily
prevailing direction. Wind roses could also continue to be
prepared if desired. Wind sigma theta should continue to
be measured at Watana, and reported when needed.)
The back-up data system would permit reporting cf daily
min/max/average temperatures and total accumulated precipitatjon
since the: last inspection.
The above estimate includes constructiibn of new shelters,
relocation where necessary, construction of \Vyoming wind gages,
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h il rv.,i;::*cF8 8, "Ega5
Page 7
a and installaticrn of a bac1.a:-up data recorder at each site.
Elimination sf the back-up recorder would redldce the per-station
cast approximately $1,200 (58,400 for 7 stations).
The estimate does not include telejnetry costs or helicopter costs
Crpr sling-loading tc new sites.
These stations could be improved in turn, over several years if
des i red,
b. Approximate cost of acquiring mechanical weather stations:
86,600 per station
$26,480 fw 4 sPa$lsns
Costs for reduction, review, handling, editing, and reporting of
the mechanical station data are estimated to be $1,250 per month
per station.
Reported data would include:
Q% Precipitation (dai,ly total)
o Temperature (daily min/max/average)
8 Wind (daily total wind run or daily average speed)
The back-up system would permit reporting of daily
minimax/average temperatures and total accumulated precipitation
since the fast inspection,
he estimate includes purchase of mechanical instruments
(precipitation, temperature, wind), purchase of a backup system
to provide redundant measurements, instailation of both systems,
and construction of a Wyoming wind gage at each site. As
above, elimination of the back-up system would reduce the
per-station cost approximately $1,200 (54.800 for 4 stations).
The estimate does not include telemetry costs or helicopter
logistics for slil~g-loading to site.
c. Approximate cost of hiring local observer for daily observation:
$1,500 + $300/month
at each station
Costs for review, handling, and reporting of the observer data
reports are estimated to be an additional 8250 per month per
station.
Reported data. would include the same values listed above for the
mechanical shtians :
EB Precipitation (daily tetal)
o lemperatu re (daily min/max/average)
$B Wind (daily total wind run or daily average speed)
The estimate includes purchase 04 instruments (min/max
thermometer, totalizing anemometer, and accumldlating
precipitation can), instrument shelter, Alter wind screen. and
installation. Observers to be paid $10 per day.
76. Another consideration for measurement of precipitation at higher
elevations, where greater amounts of precipitation fall, is installation
of large-volume storage gages and recorders. The advantage of the
larger site is that the danger of the gage overflowing and losing data
during large rainstorms and snowstorms is reduced. The SCS
currently operates one of these at the Monahan Flat site. The cost of
installing additional storage gages is estimated to be approximately
55,000 per site, not including helicopter logistics. Additional
installations would be beneficial at sites 1, 4, 8, and 11 in the list
under comment 4, above, and at the Upper Tsusena site if
established.
Operation of the 11-station network would increase field labor time
and helicopter time during maintenance trips. The six existing
stations can normally be maintained on a 1-2 day trip. A total of two
to three days would probably be required to maintain the full 11
staticlns. Helicopter usage would be approximately four flight hours
per day. The stations should continue to be inspected and main-
tained once per month to verify their proper operation. The logistics
costs for maintenance trips would be slightly higher when Watana
Camp is closed, since helicopter flights would originate and end in
Taikeetwa instead sf at the camp.
-8. An alternative jogistics plan which should be considered in the net-
work planning process is use of fixed-wing aircraft for access to the
meteorologic stations. All stations recommended above for future
station locations should be accessible by fixed-wing airplane most of
the time, with the exception of the Upper Tsusena site suggested.
For estimation purposes, using a helicopter cost of S320 per hour (Jet
Ranger) and a fixed-wing cost of S2OO per hour (Cessna 2061, the
foilowing are approximate monthly costs for each alternative:
1) Helicopter (2 days @ 6 hours per day 12 hours per month)
2) Fixed-wing (2 days @ 6 hours per day = 12 hours per month,
plus 1 hour from Watana Camp by helicopter)
19.- As is emphasized in the report, data for streamflow forecasting will
have to be available in a timely manner. This will best be accom-
plished by telemetry of the data from the stations, either by the
telephone-repeater network or the GOES satellite system, and then
incorporation into the data storage and n~odeliing system, as Dr. Peck
hss discussed. The alternative selected depends primarily on other
communication requirements of the project.
In summary, our recommendations at the present are:
7) To make sure field data needs of other environmental and engineering
studies are considered and coordinated in design of the data-collection
natwark. This wpuld include terrestrial studies, air quality
management, river ice monitoring, and fisheries monitoring, as well as
the water s~pply forecasting.
2) Operate a total of eleven meteorological stations i~ the basin above
the Devil Canyon darnsite. Select sites in the vicinity of those
proposed in the report in Figure 2 and Table 2, modified as discussed
above in ccsmments number 3 and 4.
3) Continue precipitation, temperature, wind speed, and wind direction
measu sements ,
4) if not required for other purposes, measurement of relative humidity,
solar radiatiop. and longwave radiation should be discontinued (with
the possible excQption of Watana).
5) The existing electronic digital recording mete~rolagical stations should
be maintained, There are seven rec~rders and associated sensors
available for field use. The eighth unit also currently available
should Be retained as a spare. The field installations and she1"rer.s
should be upgraded as described above.
6) Mechanical recording meteorological ;tations should be acquired and
installed at 'Four new sites to measure precipitation, "temperature, and
wind speed.
7) Hire local observers where possible to improve data reliability. This
could be done at Denali, Watana, Dwii Canyo11 and Lake Louise, and
Gsid Creek.
Bb install telemetry at one or more meteorologic stations to start develop-
ment of the real-time data-collection system. Recorders should be
retained at the stations even after installation sf data-coIIect'ron
platforms.
91 Install back-up sensor and recorder systems to provide redundant
measurements at stations ivfaich do not have either a locat observer- or
a data-collection platform.
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a [: 8, 198s
1"". r;ge 10
10) Continue snow surveys at existing weather sta4:lons but also make
measurements in nearby protected areas to imprave the data quality
for forecasting and for development of isohyetal maps.
If) Consider conversion of the data-collection program to at least a
partial fixed-wing aircraft network by selecting compatible station
Iscations ,
It is expected that these recommendations will be considered along with
those of Dr. Peck in his report, and the data-coilection programs for FY86
and beyond kill be refined over the next several months. We look forward
to working with you in the system planning effort. Thank you for "ihe
opportunity to comment on the report. If you have questions or comments
on any of the above material, please do not hesitate to contact Jeff Coffin
or myself.
Very truly yours,
Rdf4 CONSULTANTS, INC.
Stephen Bredthauer, P.E.
Susif na Hydt-ology Coordinator
SB:JHC; bje
cc: 8.H.Wang
G. Gemperline