Description and Evaluation of a Hydrometeorological

250 VOLUME 17W E A T H E R A N D F O R E C A S T I N G

q 2002 American Meteorological Society

Description and Evaluation of a Hydrometeorological Forecast System forMountainous Watersheds

KENNETH J. WESTRICK,* PASCAL STORCK, AND CLIFFORD F. MASS

Department of Atmospheric Science, University of Washington, Seattle, Washington

(Manuscript received 3 May 2001, in final form 10 September 2001)

ABSTRACT

This paper describes and evaluates an automated riverflow forecasting system for the prediction of peak flowsduring the cool season of 1998–99 over six watersheds in western Washington. The forecast system is basedon the Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) fifth-generation Mesoscale Model (MM5) and the University of Washington Distributed-Hydrology-Soil-VegetationModel (DHSVM). The control simulation used the forecasts produced by the University of Washington’s real-time MM5 forecasts system as input to the hydrologic model. A second set of simulations applied a correctionscheme that reduced the long-term precipitation bias identified in the MM5 precipitation field. A third set ofsimulations used only those observations that are available in real time for forcing the hydrologic model. Thevarious MM5–DHSVM forecasts are also compared with those issued by the National Weather Service NorthwestRiver Forecast Center. Results showed that the observations-based simulation produced the most accurate peakflow forecasts, although it was susceptible to inadequate input data and the overdependence on the few availableobservations. The control simulation performed remarkably well, although several poor synoptic (MM5) forecasts,in addition to a model wet bias, produced a significant overprediction of peak flows over one watershed. Thebias correction scheme did not prove worthwhile for peak flow forecasting, but may be useful for longer-termmodeling studies where the emphasis is on long-term discharge rather than peak flow forecasting. A real-timeupdating procedure that incorporated meteorologic observations in the creation of initial hydrologic states showedconsiderable promise for forecast peak streamflow error reduction.

1. Introduction

Real-time streamflow forecasting remains a formi-dable challenge in the mountainous western UnitedStates, where flooding and its effects represent the singlemost damaging weather-driven phenomenon in the re-gion. The scarcity of real-time rain gauge data (Grois-man and Legates 1994), poor radar coverage for pre-cipitation estimation (Westrick et al. 1999), and largeorographically induced precipitation gradients (Tang-born and Rasmussen 1976) greatly complicate the fore-casting of streamflow. In addition, mesoscale effects inand near complex terrain provide additional challenges.For example, downwind of major gaps in the CascadeMountain range, temperatures can be more than 108Ccolder than in nearby nongap locations (Steenburgh etal. 1997), a gradient that is often crucial for forecastingrain-on-snow (ROS) flood events.

A limited number of studies have examined whether

* Current affiliation: 3TIER Environmental Forecasting Group Inc.,Seattle, Washington.

Corresponding author address: K. J. Westrick, 2366 Eastlake Ave.East, Suite 415, Seattle, WA 98102.E-mail: [email protected]

hydrologic models driven by mesoscale atmosphericmodels can provide reasonable streamflow forecasts inregions of complex terrain given that the precipitationforecasts used as input are accurate (Westrick and Mass2001; Miller and Kim 1996). The capability to produceaccurate forecasts in real time has yet to be demonstrat-ed. Mesoscale models can simulate the orographicallyinfluenced spatial distribution of rainfall accurately, al-though biases in the precipitation fields exist (Colle etal. 1999, 2000). Mesoscale models can also capture thecomplex three-dimensional structure in the temperatureand wind fields provided that they possess horizontalresolution fine enough to resolve major orographic fea-tures (Steenburgh et al. 1997; Westrick and Mass 2001).

This study evaluates the performance of a high-res-olution streamflow forecast system configured for real-time application in western Washington State during thecool season of 1998–99. Moderate La Nina conditionsprevailed throughout this period and provided a rangeof medium-to-high-flow events ideal for assessing theperformance of the system. Given the importance offlood forecasting throughout this region, this paper willconcentrate on the prediction of peak streamflows for anumber of significant runoff events.

The next section describes the atmospheric and hy-

APRIL 2002 251W E S T R I C K E T A L .

drologic models, provides a brief description of the re-gional geography and climate, and describes the mod-eling and evaluation method. Section 3 provides resultsfrom the real-time system, with a sensitivity experimentdetailed in section 4. This is followed by the summaryand conclusions in section 5.

2. Geographic description and event overview

The six watersheds configured in this study are lo-cated on the western (windward) flanks of the CascadeMountain range (Figs. 1a, b) and consequently receivecopious amounts of precipitation, ranging between 2 and3 m of precipitation annually (Daly et al. 1994). Thesizes of these watersheds range from 106 to 1849 km2

(Table 1), thus providing a range of sizes for assessingthe accuracy of a streamflow forecast system. In addi-tion, a large fraction of the total basin area of eachwatershed is located in or near the transient rain–snowzone, thereby making accurate prediction of precipitatetype crucial during significant runoff events. There area number of meteorological observation locations withinthe region (Fig. 1c), including seven Natural ResourcesConservation Service Snowpack Telemetry (SNOTEL),seven Surface Airway Observations (SAO), and 19hourly National Weather Service Cooperative ObserverProgram (CO-OP) sites. Data from all the sites wereused for the calibration and validation of the Distrib-uted-Hydrology-Soil-Vegetation Model (DHSVM) sys-tem prior to its application in real-time flood forecasting.

The hydrologic system was evaluated for the period1 October 1998–30 March 1999, a period during whichthis region typically receives more than 80% of its an-nual precipitation. During this particular period, mod-erate La Nina (cold episode) conditions prevailed. Inthe Pacific Northwest La Nina periods are usually as-sociated with colder temperatures and heavier precipi-tation (Mote et al. 1999); consistently, during the studyperiod Washington State had the fourth wettest Decem-ber–March on record, with unusually high winter snowaccumulations in the Cascade Mountains. Mount Baker(Fig. 1b) set a world record for annual snowfall withover 1200 in. (Redmond 2000), and river dischargesaveraged above normal throughout much of the winterseason. Despite the above-average precipitationthroughout this evaluation period, all of the significantpeak runoff events were at or below the 2-yr recurrencelevel (Sumioka et al. 1998). The observed hydrographfor the Snoqualmie River at Snoqualmie Falls revealsthat there was six well-defined high runoff events duringthe cool season (Fig. 2), all within the 2-month periodbetween mid-November 1998 and mid-January 1999.The performance of the coupled modeling system inforecasting these peak flows is the focus of this paper.

3. Model descriptions and forecast analysismethod

a. Atmospheric model

The University of Washington (UW) real-time mod-eling system is based on the Pennsylvania State Uni-versity–National Center for Atmospheric Research(Penn State–NCAR) fifth-generation Mesoscale Model(MM5), and has been used at the UW for real-timeregional weather forecasting since 1995. The MM5 isa sigma-coordinate mesoscale atmospheric model (Grellet al. 1995) that has been used extensively for bothresearch and forecasting throughout the world. Sinceautumn 1997 the system has used a three-domain con-figuration: a large 36-km outer domain covering theeastern Pacific and western North America; a 12-kmnest over southern British Columbia, Washington, andOregon; and a 4-km high-resolution nest over westernWashington. Only the 4-km MM5 output is used for theMM5–DHSVM simulations. Thirty-two vertical layersare used, with increased resolution in the planetaryboundary layer (PBL). Initial and boundary conditions,available at 6-h intervals, were interpolated from theNational Centers for Environmental Prediction (NCEP)Eta Model 104 grids (80-km horizontal 3 25-hPa ver-tical resolution) to the MM5 grid. The suite of modelphysics used during the period of this assessment in-cludes

• the explicit ice microphysics scheme of Dudhia(1989),

• the Kain–Fritsch cumulus parameterization scheme(Kain and Fritsch 1990) (36- and 12-km nests only),and

• the Medium-Range Forecast (MRF) (Hong and Pan1996) planetary boundary layer scheme.

The model physics have remained relatively un-changed over the past several years, facilitating the gath-ering and assessment of long-term statistics. One majorfinding is that the MM5, using the explicit ice micro-physics scheme, overpredicts precipitation upwind ofmajor orographic barriers throughout the region for lightto moderate amounts (Colle et al. 1999, 2000). This biasis especially relevant for this study, as all of the eval-uated watersheds are located on the western (upwind)side of the Cascade Mountain barrier. A procedure toremove this bias prior to forcing the hydrologic modelis discussed in the appendix.

b. Hydrologic model

The hydrologic model used in this project is theDHSVM, which is a physically based distributed hy-drologic model designed for use in a variety of geo-graphical and environmental settings (Wigmosta et al.1994) that has been extensively tested in regions ofcomplex terrain (Bowling et al. 2000; Storck et al.1995). The version used in this study incorporates ex-


FIG. 1. Maps of (a) the Pacific Northwest, (b) western Washington, and (c) the western flanks of theWashington Cascade Mountains. The watersheds evaluated in this study are outlined in (b) and (c), withnumbered locations in (c) identified in the legend.

plicit channel routing, allowing for the forecasting ofstreamflow at any point within the channel network.DHSVM includes an energy balance snowpack modelthat explicitly represents the effect of a forest canopyon snow accumulation and snowmelt (Storck and Let-tenmaier 1999). The hydrologic model was configuredat 150-m horizontal resolution and all hydrologic fore-casts used a 1-h time step. A variable depth soil withthree vertical layers was used, with the soil character-istics aggregated from the Conterminous U.S. Soil Da-taset (Miller and White 1998). The land cover is derivedfrom the 1991 Gap Analysis Program (GAP; Scott and

Jennings 1998) and allows for 19 separate land covertypes.

DHSVM was calibrated with observed meteorologi-cal data using a 3-h time step for water years 1989–92.The 33 observation locations described in the previoussection were used as meteorological input to DHSVMduring the calibration. These observational data werethoroughly quality controlled (QC) with missing and/orbad data replaced using a variety of physically and sta-tistically based methods (Westrick and Mass 2001). TheParameter-Elevation Regressions on Independent SlopesModel (PRISM) climatologic precipitation fields (Daly


FIG. 2. Observed runoff on the Snoqualmie River at Snoqualmie Falls for the winter season 1998–99. The six peak flow events assessedin this study are event 1 (13–14 Nov 1998), event 2 (19–21 Nov 1998), event 3 (25–27 Nov 1998), event 4 (13–14 Dec 1998), event 5 (27–30 Dec 1998), and event 6 (13–15 Jan 1999).

TABLE 1. Streamflow gauge locations, National Weather Service ID number, USGS ID number, drainage size, and peak flow for the sixsignificant runoff events for water year 1999 [bold 5 2–10-yr recurrence interval (Sumioka et al. 1998)].

Name

NWSgaugeID No.

USGSgaugeID No.

Drainagesize

(km2)

Significant events peak flow (m3 s21)

113–14

Nov 1998

219–21

Nov 1998

325–27

Nov 1998

413–14

Dec 1998

527–30

Dec 1998

613–15

Jan 1999

Sauk River near SaukSkykomish River near

Gold BarNorth Fork of the Sno-

qualmie River nearSnoqualmie

Snoqualmie River nearSnoqualmie Falls

Middle Fork of theSnoqualmie Rivernear Tanner

Cedar River near Ced-er Falls

SAKGLB

SNQ

SQU

TAN

CRR

1218950012134500

12142000

12144500

12141300

12115000

18491385

166

971

399

106

333902

179

728

479

41

481661

147

553

364

52

397520

127

565

299

54

714557

99

183

375

19

8271757

209

889

507

121

7841206

196

658

422

59

et al. 1994) were used for spatially scaling the pointprecipitation data between observation points. Under-catchment of precipitation due to wind speed and pre-cipitate type was corrected for using formulas fromYang et al. (1998).

The U.S. Geological Survey (USGS) gauge locations

on the Skykomish River at Gold Bar (not shown) andthe Snoqualmie River at Carnation (Fig. 3a), Washing-ton, were used for calibration. Calibration was limitedto parameters controlling the distribution of soil mois-ture storage such as the total soil depth and saturatedhydraulic conductivity, the distributions of which are


FIG. 3. Observed (solid) and model-predicted (dashed) hydrographsfor the calibration period for the USGS gauge site at (a) Carnation,WA, and representative validation results for the cool season of 1995–96 at an uncalibrated location within (b) the same watershed (MiddleFork of the Snoqualmie River) and (c) in a nearby watershed (SaukRiver at Sauk).

largely unknown at watershed scale. Previous studies(Storck et al. 1998), as well as sensitivity experimentsconducted by the authors, have shown that these hy-drological parameters had the greatest impact on peakrunoff. Using the calibration parameters for Gold Barand Carnation, the DHSVM model was then validatedagainst observed runoff for water years 1992–96 at 17USGS gauge locations on six river systems throughoutwestern Washington. Analysis of the validation period

with the available daily mean flows at all USGS gaugesites revealed that the parameters developed for the twocalibration sites transferred well to the validation wa-tersheds. Representative results are shown for locationswithin (Fig. 3b) and outside (Fig. 3c) the calibrationwatershed for the winter of 1996 (during which twomajor flood events occurred, November 1995 and Feb-ruary 1996).

c. Forecast and analysis methods

This study evaluates the performance of the MM5–DHSVM coupled system for the period 1 October 1998–30 March 1999. Initial hydrologic states1 for 1 October1998 were created from an observation-driven DHSVMsimulations conducted from 1 June to 30 September1998. Prognostic model fields from the UW real-timeMM5 forecast system were subsequently used to pro-vide the meteorological fields for DHSVM. The requiredhydrologic fields, including soil moisture, snowpack,and interception, are carried forward in time with noupdating by observations. All MM5-based hydrologicalforecasts use the 13–24-h forecast fields. The variousconfigurations tested for this evaluation are as follows.

• Direct MM5 input—This forecast uses the MM5 fore-cast meteorological fields after they have been down-scaled (interpolated) to the 150-m resolution of thehydrologic model. Downscaling of the various 4-kmhorizontal-resolution MM5 forecast fields is accom-plished through either biparabolic interpolation (non-precipitation fields) or modified Cressman (precipi-tation fields) algorithms (Westrick and Mass 2001).This forecast will be referred to as the control sim-ulation throughout this paper. The MM5 forecast oflowest sigma level (0.995s) air temperature was ad-justed using a model-derived lapse rate to compensatefor the difference between the atmospheric model(MM5 at 4-km horizontal resolution) and hydrologicmodel (DSHVM at 150-m horizontal resolution) ter-rain heights.

• Long-term MM5 precipitation bias removed—In thissensitivity forecast a correction field that removes thelong-term model precipitation bias is applied to allMM5 forecast precipitation fields. The bias correctionmethod is described in the appendix.

• DHSVM forced with observations only—There is atime lag between when precipitation occurs over awatershed and when it arrives at a particular gaugesite. This lag often allows for short-term hydrologicforecasts to be created based on observations, al-though the forecast period is limited to the time lag,which may be only a few hours. Because these fore-casts use actual, rather than predicted meteorological

1 The initial hydrologic states that DHSVM requires are soil mois-ture, snow quantity and quality, water intercepted by various vege-tative layers, and quantity of water in the channel network.


conditions, this approach is likely to have greater pre-dictive skill than a hydrologic forecast that is basedon a meteorologic forecast. This configuration of thesystem uses observations from available SAO, SNO-TEL, and hourly CO-OP precipitation sites to createthe meteorological forcing for DHSVM (with noMM5). This configuration was performed with twoobjectives in mind: first, to assure that DHSVM canreproduce a similar degree of accuracy as was evidentfor the calibration–validation period (Figs. 3b, 3c),and second, to assess how the system performs whenforced with observations available in real time. Itshould be noted that the number of observations usedfor this forecast was smaller than that used for cali-bration–validation, as fewer observations are availablein real time.

To provide a performance benchmark for the UW sys-tem, the runoff forecasts produced by the National Weath-er Service Northwest River Forecast Center (NWS-NWRFC) are included for comparison. These forecastswere created using the National Weather Service RiverForecast System (NWSRFS) with quantitative precipi-tation forecasts (QPFs) provided by the National WeatherService Forecast Offices, in this case, Seattle (NWSFO-SEA). The NWRFC forecasts used in this comparisonwere the forecasts valid 18–24 h from the time of issuanceand are, therefore, comparable to the lead time of theMM5–DHSVM forecasts (13–24 h).

4. Results

For brevity, the various forecasts will be referred toas control (runoff forecasts that directly use MM5 fore-cast output), no bias (forecasts using the MM5 precip-itation fields after processing with the bias reducingscheme described in the appendix), and observationbased (using observations for meteorological forcing,as described in the previous section). The forecasts is-sued by the Northwest River Forecast Center will bereferred to as RFC forecasts. The statistics shown belowdo not include forecast peak timing errors. This is dueto the inherent difficulty in quantifying these types oferrors, especially when updated forecasts are often made(in the case of the RFC forecasts) and in the case ofcomplicated flood peaks (e.g., the case where two peaksare separated by 36 h but only differ by less than 1%in peak discharge). Unless otherwise noted, timing er-rors were at most 18 h and often much less. To providemore detail for the events of interest, the hydrographfigures will only encompass the time period from 10November 1998 through 20 January 1999.

Figures 4 and 5 show the predicted and observedhydrographs for the various watersheds, and Table 2provides detailed results for the peak flow events bywatershed and event. For brevity, discussion of resultswill focus on major discrepancies or errors as well asnoteworthy successes in the various forecasts.

The hydrograph for the Sauk River (Fig. 4a) revealsa significant overprediction in peak streamflow by boththe control and no-bias forecasts for the first four events.For example, the observed peak streamflow for event 1has a statistical recurrence of less than 2 yr, while thepredicted peak has a recurrence of approximately 10 yr(Sumioka et al. 1998). Analysis of the hourly precipi-tation record from Darrington, the only rain gauge avail-able within the Sauk River watershed (Fig. 1c, ‘‘5’’),reveals that for the 4-day period from 12 to 15 Novem-ber 1998, 74 mm (2.9 in.) of precipitation was observed,while the MM5 predicted 295 mm (11.3 in.) for thesame time period. The peaks from the no-bias forecastare also significantly overpredicted. The substantial ov-erprediction of this first event, and the subsequent over-saturation of the soils, adversely impacted the two sub-sequent events. The peak flows for events 2 and 3 arealso substantially overpredicted because of both the soiloversaturation and a substantial overprediction of pre-cipitation by MM5 (165 mm observed versus 427 mmpredicted for the period 20–26 November). Also evidenton the Sauk River is the substantial streamflow over-prediction of event 4 by the control simulation due toexcessive precipitation.

Of interest on the Skykomish River forecasts (Fig.4b) is the inability of any of the forecasts to accuratelycapture event 5, with all forecasts capturing less than60% of the peak flow. It is also worth noting both thetiming and magnitude error for the observation-basedforecast for event 5. Analysis of the Skykomish Riverrain gauge site (Fig. 1c, ‘‘4’’) reveals that 200 mm ofprecipitation fell from 24 to 27 December, while only33 mm fell in the period 28–30 December, immediatelybefore the observed flood peak on 30 December. Thisis in contrast to the SNOTEL site at Stevens Pass, whichrecorded 233 mm of precipitation during the 3-day pe-riod 28–30 December. It is likely that the precipitationvalues for the Skykomish River site were not represen-tative of much of the watershed. Because of its centrallocation within the Skykomish River watershed and theparticular precipitation interpolation method used (in-versed distance weighting), this site heavily affected theprecipitation distribution.

Forecasts were better for the larger events on theNorth Fork of the Snoqualmie River (Fig. 4c), especiallyfor the control simulation, although significant under-forecasts for event 6 are noted. Substantial overpred-iction of event 5 for the North Fork of the SnoqualmieRiver is also evident in the RFC forecast. The resultsfor the Middle Fork of the Snoqualmie River are quitesimilar to those of the North Fork (Fig. 5a), and thepreviously noted deficiency with the observation-basedforecast for event 5 is still apparent. The SnoqualmieRiver at Snoqualmie Falls (Fig. 5b) is predicted quitewell by all the forecast methods; this is especially ap-parent in the control simulation where all peak stream-flow events forecast were within 22% of the observed.

The most accurate forecasts are produced for the Ce-


FIG. 4. Hydrographs of the observed and model-predicted hourly flows (m3 s21) for the period 10 Nov 1998–20 Jan 1999 for USGS gaugesat (a) the Sauk River at Sauk, (b) the Skykomish River at Gold Bar, and (c) the North Fork of the Snoqualmie River near Snoqualmie. Thebold numbers refer to the six events described in the text. For comparison, the 18- and 24-h runoff forecasts issued by the NWRFC are alsoplotted (solid circles).


FIG. 5. As in Fig. 4 but for the following gauge sites: (a) the Middle Fork of the Snoqualmie River near Tanner, (b) the Snoqualmie Riverat Snoqualmie Falls, and (c) the Cedar River above Cedar Falls. NWRFC forecasts are not available for the Cedar River near Cedar Falls(c).


TA

BL

E2.

Det

aile

dbr

eakd

own

ofob

serv

edan

dpr

edic

ted

peak

flow

sby

even

tan

dw

ater

shed

.A

lso

show

nar

eth

epe

akre

lati

veer

ror

and

the

aver

age

abso

lute

erro

rby

wat

ersh

ed.

Eve

nt1

Pea

kfl

owE

rror

(%)

Eve

nt2

Pea

kfl

owE

rror

(%)

Eve

nt3

Pea

kfl

owE

rror

(%)

Eve

nt4

Pea

kfl

owE

rror

(%)

Eve

nt5

Pea

kfl

owE

rror

(%)

Eve

nt6

Pea

kfl

owE

rror

(%)

Avg

abso

lute

erro

r(%

)

Sau

kR

iver

near

Sau

kO

bser

ved

Obs

forc

edM

M5

forc

edM

M5

forc

edno

bias

RF

Cfo

reca

st

609

784

1381 93

954

4

28.7

126.

854

.22

10.7

480

320

1030 70

388

1

233

.311

4.6

46.5

83.5

397

503

844

596

421

26.7

112.

650

.1 6.0

714

581

1263 88

856

1

218

.676

.924

.42

21.4

827

429

559

407

1140

248

.12

32.4

250

.837

.8

784

844

803

534

544

7.7

2.4

231

.92

30.6

32.6

93.1

51.6

38.0

Sky

kom

ish

Riv

erne

arG

old

Bar

Obs

erve

dO

bsfo

rced

MM

5fo

rced

MM

5fo

rced

nobi

asR

FC

fore

cast

902

823

1136 84

867

5

28.

825

.92

6.0

225

.2

661

715

713

537

1126

8.2

7.9

218

.870

.3

520

709

604

467

610

36.3

16.2

210

.217

.3

557

361

635

482

561

235

.214

.02

13.5 0.7

1757 73

079

259

010

72

258

.52

54.9

266

.42

39.0

1206

1040 67

049

692

3

213

.82

44.4

258

.92

23.5

32.1

32.7

34.7

35.2

Nor

thF

ork

ofth

eS

noqu

alm

ieR

iv-

erne

arS

noqu

al-

mie

Obs

erve

dO

bsfo

rced

MM

5fo

rced

MM

5fo

rced

nobi

asR

FC

fore

cast

179

133

233

127

199

225

.730

.22

29.1

11.2

147 71 131 82 180

251

.72

10.9

244

.222

.4

127 82 93 65 135

235

.42

26.8

248

.8 6.3

99 65 108 70 135

234

.3 9.1

229

.336

.4

209

123

198

116

330

241

.12

5.3

244

.557

.9

196

121

124 83 156

238

.32

36.7

257

.72

20.4

45.3

23.8

50.7

30.9

Mid

dle

For

kof

the

Sno

qual

mie

Riv

-er

near

Tann

er

Obs

erve

dO

bsfo

rced

MM

5fo

rced

MM

5fo

rced

nobi

asR

FC

fore

cast

478

494

436

309

328

3.3

28.

82

35.4

231

.4

364

349

264

197

370

24.

12

27.5

245

.9 1.6

299

195

175

138

321

234

.82

41.5

253

.8 7.4

183

103

143

107

345

243

.72

21.9

241

.588

.5

507

260

448

315

568

248

.72

11.6

237

.912

.0

421

415

235

180

353

21.

42

44.2

257

.22

16.2

27.2

31.1

54.3

31.4

Sno

qual

mie

Riv

erne

arS

noqu

alm

ieF

alls

Obs

erve

dO

bsfo

rced

MM

5fo

rced

MM

5fo

rced

nobi

asR

FC

fore

cast

727

814

886

600

687

12.0

21.9

217

.52

5.5

553

628

585

427

760

13.6 5.8

222

.837

.4

564

469

473

359

504

216

.82

16.1

236

.32

10.6

375

282

363

263

475

224

.82

3.2

229

.926

.7

889

567

939

637

836

236

.2 5.3

228

.32

6.0

658

721

561

417

564

56.3 9.6

214

.72

36.6

214

.3

22.6

13.4

34.3

20.1

Ced

arR

iver

near

Ced

arF

alls

Obs

erve

dO

bsfo

rced

MM

5fo

rced

MM

5fo

rced

nobi

as

41 38 72 69

27.

375

.668

.3

51 60 58 52

17.6

13.7 2.0

54 57 41 37

5.6

224

.12

31.5

19 22 19 18

15.8 0.0

25.

3

121 98 137

121

219

.013

.2 0.0

59 46 63 57

222

.0 6.8

23.

4

17.5

26.7

22.1


FIG. 6. The average absolute error in peak runoff forecast for sixmoderate runoff events during water year 1999. The errors aregrouped by watershed. The forecasts are (a) observations based, (b)control, (c) no bias, and (d) the 18–24-h Northwest River ForecastCenter model predictions, which are not available for the Cedar wa-tershed.

dar River at Cedar Falls, where only event 1 has sizableerrors. The results are especially encouraging when con-sidering that the hydrologic system was calibrated fortwo watersheds that were an order of magnitude largerthan the Cedar River system. Unfortunately, comparisonRFC forecasts are not available for this watershed.

An assessment of the average peak error for the sixevents (Fig. 6) reveals that the observation-based con-figuration produced the most accurate forecasts, with anaverage error of 31%. This is not unexpected as it isthe only forecast based on observed meteorological data.The fact that it is not significantly better than either theRFC or control forecasts suggests that uncertainties dueto instrument error, areal representativeness of point ob-servations, and the choice of interpolation method canbe nearly as large as the uncertainty in the meteorolog-ical forecast, at least for a 13–24-h forecast.

It can also be seen that the observation-based forecastperforms best on those river networks where the densityof observations is highest (Fig. 1c), such as the Cedarand Snoqualmie Rivers, although it also performed re-markably well on the Sauk River watershed. Of theremaining forecasts, the control forecast had an averageerror for the six significant peaks of 38%, which wasslightly worse than the RFC average forecast error of32%. If the poor forecast on the Sauk River basin isexcluded, the control forecast becomes the most accu-rate, with a forecast error of only 25%. The no-biasforecast had an average error of 45%, and nearly alwayserred on the low side. The poor performance of the no-bias forecast is due to the dependence of the precipi-tation bias on the synoptic-scale forcing (Colle et al.1999, 2000). The precipitation bias is much more pro-nounced during weakly forced events and becomes

much less apparent during strongly forced events.Therefore, application of a single precipitation bias cor-rection field reduces precipitation by a fixed fractionduring all events and results in a degraded peak runoffforecast. Long-term mass balance is much better withthe no-bias configuration though, as this configurationproduced total 6-month runoff volumes that were muchmore accurate than the control and nearly as accurateas the observation-based forecast.

The significant overpredictions in streamflow on theSauk River watershed were overwhelmingly due to poorMM5 meteorological forecasts. Since subsequent hy-drological forecasts rely on previous forecasts for hy-drological initial states, significant error can accumulate;thus a single poor MM5 forecast can adversely affectsubsequent hydrological forecasts. To assess the benefitsof more accurate hydrologic initial states in the forecasta test was conducted. In this test the hydrological fore-casts are based on MM5-predicted meteorological con-ditions, but the initial hydrologic conditions for eachforecast period are obtained from the observation-basedhydrologic forecast. Although such a hybrid observa-tion–MM5 forecast system can be implemented in realtime, the value of such a system is highly dependent onthe quality, representativeness, and real-time availabilityof the meteorological observations.

A significant improvement in streamflow forecasts isapparent when MM5-forced hydrologic states are reg-ularly updated with an observation-based hydrologicforecast (Fig. 7). The dashed line indicates the originalcontrol forecast, and the open circles are the hourlyforecasts from the updated initial state forecast. Theerror reduction is significant. For example, the peak er-ror in the forecast for event 1 drops from greater than120% to under 60% when the hydrologic model is ini-tialized with observations-based initial states. There isalso an indication of a second peak occurring early on16 November, information that was not evident in thecontrol forecast because of the severity of the peak flowerror. Events 2 and 3 also show considerable improve-ment, and the relative error in the peak forecast errordrops from 115% to 68% for event 2 and from 113%to 81% for event 3. Although overprediction of peakflow is still evident, this experiment does show thatconsiderable reduction in peak error can result fromusing an observations-based initial hydrologic state.

5. Summary and conclusions

A coupled, fully automated atmospheric–hydrologicstreamflow forecasting system using the Penn State–NCAR MM5 and the University of Washington distrib-uted hydrologic model DHSVM is described. Three con-figurations of the system are evaluated for their abilityto correctly forecast the peak discharge for six high-flow events during the cool season of 1998–99. Thecontrol configuration uses meteorological forecast fieldsfrom the 4-km-resolution nest of the MM5 as input to


FIG. 7. Hydrographs of observed streamflow (heavy solid line), the control forecast (light dashed line), RFC forecast (solid black circles),and the test simulation that incorporated hydrologic initial states from the observations-based simulation (series of open circles denoted‘‘sensitivity’’) for the Sauk River near the Sauk gauge site.

the distributed hydrological model. The second config-uration uses the same system but applies a spatial-cor-rection algorithm to remove the long-term precipitationbias when using this particular mesoscale model andmicrophysics scheme. The third configuration uses real-time meteorological observations rather than mesoscalemodel predictions to force the hydrological model. Riv-erflow forecasts issued by the National Weather ServiceNorthwest River Forecast Center are also provided asa benchmark of performance for the system.

The hydrologic model was initially calibrated andthen validated using observed meteorological data fora recent 8-yr period. The parameters developed for thetwo calibration watersheds transferred well to both larg-er and smaller watersheds within and outside of theoriginal calibration watershed, with no further refine-ments to the original calibration parameters required.

Forecast results for the six high-flow events showedthat the observations-based forecast performed best overall the basins, with an average error in peak flow of31%.2 The control, no-bias, and RFC forecasts had av-erage errors of 38%, 45%, and 32%, respectively. Onthe Sauk River watershed the control forecast signifi-cantly overpredicted the peak flow for four of the sixevaluated runoff events. The error was due to a seriesof poor synoptic weather forecasts by the mesoscalemodel, which overpredicted the observed precipitationby 200%–500% over this particular watershed. In ad-

2 The observation-based forecast obviously has less uncertaintythan the other forecasts evaluated, since the others are based on pre-dicted rather than observed meteorological conditions.

dition to the synoptic error, the overprediction in pre-cipitation was likely exacerbated by overproduction ofprecipitation by the mesoscale model (Colle et al. 1999).Since initial hydrologic states are carried forward forsubsequent forecasts, this error accumulated and af-fected subsequent hydrologic forecasts. An experimentthat evaluated the effect of using observation-based hy-drological initial states, rather than those from previousmesoscale model forecasts, revealed that peak flow er-rors were reduced by approximately 50% by the use ofthese more accurate hydrologic initial states. This is avery important point, as this procedure can be performedin real time and can significantly reduce the error in therunoff forecast. Certainly, subsequent assessments of theUW system will include a more thorough validation ofthis as well as other real-time updating procedures.

The method of removing the long-term precipitationbias from the MM5 precipitation fields did not proveworthwhile for peak flow forecasting, at least in its cur-rent form. It appears that the MM5 precipitation bias istime (regime) dependent; furthermore, as noted in Colleet al. (1999, 2000), during the heaviest events the ov-erprediction bias may be much smaller than during otherperiods. The no-bias forecast had the lowest averagemass balance error, defined as the ratio of the model-predicted to observed discharge integrated over the 6-month period. The use of this correction scheme maytherefore be useful in the prediction of other hydrolog-ical phenomena, such as maximum snowpack, summerlow flow, and monthly or seasonal runoff. This couldbe especially useful for longer-term climate-relatedstudies, where the accurate forecasting of significant


flow events may be secondary to determining the long-term (monthly or longer) discharge from a watershed.

The results from the control forecast are especiallyencouraging, particularly when one considers that thesystem as described in this study was not updated withobservations, hence allowing MM5 forecast errors toaccumulate and adversely affect later forecasts. It is not-ed that the statistics for the control forecast were sig-nificantly degraded by the poor meteorological forecastover the Sauk River during the three November events.In fact, if the Sauk River system is excluded from theoverall statistics, the control forecasts become the mostaccurate of the four tested, with an average error of25%.

The study also revealed other relevant results regard-ing peak flow forecasting. For example, the successfultranslation of calibration parameters to other gaugeswithin the calibration basin and to adjacent basins (with-out recalibration) underscores a capability for forecast-ing in regions where gauge data are absent. This ca-pability could be extremely beneficial not only in data-sparse regions throughout the western United States, butalso in various regions throughout the world. The strongdependence of forecast runoff to input precipitation alsohighlights the necessity of accurate quantitative precip-itation forecasts and the associated need to identify andrectify the deficiencies of atmospheric modeling sys-tems, such as problems in microphysical schemes. Sim-ple correction schemes based on long-term biases of theatmospheric model reduced the forecast skill of the sys-tem for individual events. Alternatively, more sophis-ticated corrections schemes (e.g., schemes that couldfurther subdivide precipitation biases and spatial pat-terns based on mid-tropospheric wind speed and direc-tion) may provide better bias correction. Assimilationof observations in real time continues to be problematic,but inclusion of these observations can lead to consid-erable (e.g., 50% reduction in forecast error on the SaukRiver) improvement of forecast skill during major run-off events.

The synoptic skill of any particular atmospheric fore-cast can be a major source of error in the streamflowforecast. Methods that account for this atmospheric un-certainty, such as ensemble-based atmospheric fore-casts, may significantly reduce the errors due to a singlepoor synoptic forecast, as occurred over the Sauk Riverwatershed in November. The integration of mesoscaleensembles, and the subsequent reduced dependence onany single set of forecast conditions, would help to min-imize the effect of a single poor forecast. Long-termtesting of this type of system is currently being con-ducted at the University of Washington.

Acknowledgments. This research was supported bythe National Oceanic and Atmospheric Administration(NOAA Cooperative Agreement NA67RJ0155) as wellas the University of Washington Puget Sound RegionalSynthesis Model (PRISM) project, which is supported

by the University Initiative Fund (UIF). Use of the MM5was made possible by the Microscale and MesoscaleMeteorological Division of the National Center for At-mospheric Research, which is supported by the NationalScience Foundation. Special thanks to Mr. DougMcDonnal of the National Weather Service ForecastOffice—Seattle, Dr. Bart Nijssen of the University ofWashington Civil and Environmental Engineering De-partment, and Mr. Harvey Greenberg of the Universityof Washington Geological Sciences Department.

APPENDIX

Method of Determining the Long-Term Precipita-tion Bias in MM5 Spatial Precipitation Fields

The 13–24-h forecasts from all the real-time MM5forecasts between October 1997 and May 1998 wereaggregated. An analysis region was defined encom-passing the MM5 4-km-resolution domain. The long-term precipitation bias was determined for 104 precip-itation gauges within the analysis region. The area-weighted model precipitation bias B for the analysisregion was given by

N1B 5 p b a ,O i i iAP i51

where b is the bias at the precipitation gauge, a is thearea of a Thiessen polygon, p is the total volume ofannual precipitation within the polygon, P is the totalvolume of water that falls over all the Thiessen regions,and A is the total area of all the Thiessen regions. Theassumption in this approach is that each individual sitebias is valid for a region surrounding the observation,which is a reasonable approach given sufficient gaugedensity. Since undercatchment of precipitation due tovarious environmental and instrumental factors was notaccounted for, a regionwide adjustment where a 15%undercatchment was assumed was applied. Using thisapproach, it was determined that the MM5 model hadan area-weighted bias of 1.52 throughout the integrationregion.

Determining the long-term precipitation bias in theMM5 precipitation fields requires accurate precipitationclimatology of the region. The PRISM-derived precip-itation climatology (Daly et al. 1994) is felt to be thebest available for the region. A mass balance analysisof the PRISM-produced climatology fields with histor-ical hydrographs revealed that there could be substantialunderestimate of precipitation in many of the headwaterregions in the western Cascades. It was also found thatthe MM5 tended to capture the distribution of precip-itation in many of these headwater regions. Therefore,in an effort to retain some of this information from theMM5, the estimated ‘‘true’’ precipitation climatologyused a weighted average of the PRISM-derived precip-itation and the climatology produced by the MM5 for


the winter seasons from October 1997 through March1999. Here, Ptrue is given by

P 5 aP 1 bP ,true PRISM MM5

where Pprism is the 1961–90 PRISM-derived climatol-ogy and PMM5 is the normalized, bias-corrected MM5climatology, and the coefficients a and b are given by2/3 and 1/3, respectively. The long-term MM5 correc-tion field is then given by

PMM5C 5Ptrue

for any given grid point. The approach assumes that theareal region of integration is large enough that signifi-cant biases in the relative mass of water are insignificant.It should also be noted that this approach produced abias field that agreed closely with the point values notedin Colle et al. (1999).

REFERENCES

Bowling, L. C., P. Storck, and D. P. Lettenmaier, 2000: Hydrologiceffects of logging in western Washington, United States. WaterResour. Res., 36, 3223–3240.

Colle, B. A., K. J. Westrick, and C. F. Mass, 1999: Evaluation of theMM5 and Eta-10 precipitation forecasts over the Pacific North-west during the cool season. Wea. Forecasting, 14, 137–154.

——, C. F. Mass, and K. J. Westrick, 2000: MM5 precipitation ver-ification over the Pacific Northwest during the 1997–99 coolseasons. Wea. Forecasting, 15, 730–744.

Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-to-pographic model for mapping climatological precipitation overmountainous terrain. J. Appl. Meteor., 33, 140–158.

Dudhia, J., 1989: Numerical study of convection observed during theWinter Monsoon Experiment using a mesoscale two-dimensionalmodel. J. Atmos. Sci., 46, 3077–3107.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1995: A description ofthe fifth-generation Penn State/NCAR Mesoscale Model (MM5).NCAR Tech. Note NCAR/TN-3981STR, 122 pp.

Groisman, P. Y., and D. R. Legates, 1994: The accuracy of UnitedStates precipitation data. Bull. Amer. Meteor. Soc., 75, 215–227.

Hong, S. Y., and H. L. Pan, 1996: Nonlocal boundary layer verticaldiffusion in a medium-range forecast model. Mon. Wea. Rev.,124, 2332–2339.

Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective param-eterization. J. Atmos. Sci., 47, 2784–2802.

Miller, D. A., and R. A. White, 1998: A conterminous United Statesmultilayer soil characteristics dataset for regional climate and

hydrology modeling. Earth Interactions, 2. [Available online athttp://EarthInteractions.org.]

Miller, N. L., and J. Kim, 1996: Numerical prediction of precipitationand river flow over the Russian River watershed during the Jan-uary 1995 storms. Bull. Amer. Meteor. Soc., 77, 101–105.

Mote, P. M., and Coauthors, 1999: Impacts of climate variability andchange on the Pacific Northwest. JISAO/SMA, 108 pp. [Avail-able from JISAO/SMA Climate Impacts Group, University ofWashington, Box 354235, Seattle, WA 98195-4235.]

Redmond, K., 2000: Verification of the Mt. Baker world record snow-fall in 1999. Proc. 68th Annual Meeting of the Western SnowConference, Port Angeles, WA, 58–63.

Scott, J. M., and M. D. Jennings, 1998: Large-area mapping of bio-diversity. Ann. Mo. Bot. Gard., 85, 34–47.

Steenburgh, J. W., C. F. Mass, and S. A. Ferguson, 1997: The influenceof terrain-induced circulations on wintertime temperature andsnow level in the Washington Cascades. Wea. Forecasting, 12,208–227.

Storck, P., and D. P. Lettenmaier, 1999: Predicting the effect of aforest canopy on ground snow pack accumulation and ablationin maritime climates. Proc. 67th Annual Western Snow Conf.,South Lake Tahoe, CA, 1–12.

——, ——, B. A. Connelly, and T. W. Cundy, 1995: Implications offorest practices on downstream flooding, Phase II final report.Washington Forest Protection Association TFW-SH20-96-001,100 pp. [Available from WFPA, 724 Columbia St. NW, Suite250, Olympia, WA 98501.]

——, L. Bowling, P. Wetherbee, and D. Lettenmaier, 1998: Appli-cation of a GIS-based distributed hydrology model for the pre-diction of forest harvest effects on peak streamflow in the PacificNorthwest. Hydrol. Processes, 12, 889–904.

Sumioka, S. S., D. L. Kresch, and K. D. Kasnick, 1998: Magnitudeand frequency of floods in Washington. Resources InvestigationsRep. 97-4277, United States Geological Survey Water, 91 pp.[Available from USGS Information Services, Box 25286, Den-ver Federal Center, Denver, CO 80225-0046.]

Tangborn, W. V., and L. A. Rasmussen, 1976: Hydrology of the northCascades region, Washington 1, Runoff, precipitation, and stor-age characteristics. Water Resour. Res., 12, 187–202.

Westrick, K. J., and C. F. Mass, 2001: An evaluation of a high-resolution hydrometeorological modeling system for predictionof a cool-season flood event in a coastal mountainous watershed.J. Hydrometeor., 2, 161–180.

——, ——, and B. A. Colle, 1999: The limitations of the WSR-88Dradar network for quantitative precipitation estimation over thecoastal western United States. Bull. Amer. Meteor. Soc., 80,2289–2298.

Wigmosta, M. S., L. W. Vail, and D. P. Lettenmaier, 1994: A dis-tributed hydrology–soil–vegetation model for complex terrain.Water Resour. Res., 30, 1665–1679.

Yang, D., B. E. Goodison, J. R. Metcalfe, V. S. Golubev, R. Bates,T. Pangburn, and C. L. Hanson, 1998: Accuracy of NWS 80standard nonrecording precipitation gauge: Results and appli-cation of WMO intercomparison. J. Atmos. Oceanic Technol.,15, 54–68.

Documents

Description and Evaluation of a Hydrometeorological