Precipitation-Runoff and Streamflow-Routing Models for the ...

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observed flow at the Willamette River at Wilsonvillestream-gaging station (14198000, discontinued),where only 1 year of record was available for comparisonpurposes. PRMS was used to simulate 350 mi 2 ofintervening drainage area (4 percent of the basin).Statistical results for the calibration period indicate anabsolute error of 3.3 percent and a small bias of about-1.5 percent (table 12).AN EXAMPLE WATER-QUALITYAPPLICATIONThe ultimate goal of precipitation-runoff simulationand channel-flow routing is to model water qualityin a basin context. There was no intent during thisphase of the project to calibrate a water-quality modelfor a particular set of constituents; however, a linkagefrom the constructed hydrologic models to a waterqualitymodel was developed. The following sectiondescribes an application of the model used for waterqualitysimulation. Flow data were supplied toDAFLOW by both measured inflows and by using therainfall-runoff simulation of the Pudding River, a subbasinof the Willamette River. The output of theDAFLOW model was used to drive the BLTM modelby Jobson and Schoellhamer (1987) to simulate theinjection and subsequent dispersion of dye. At thispoint, the model is ready to simulate other conservativeconstituents. Decay rates are required for the simulationof nonconservative constituents, such asnutrients or dissolved oxygen, whose fate and transportare linked to their consumption by biota and bychemical interactions. Field studies made to measurethese decay rates are a prerequisite to nutrient or dissolvedoxygen modeling.Description of the Example BasinThe Pudding River is a highly sinuous streamincised in silt. The river drains primarily agriculturallands on the eastern, lower-elevation side of the MolallaRiver Basin (fig. 21). Basin characteristics are illustratedin figure 6. There is considerable State andFederal interest in major nonpoint sources of contaminationfrom urban and agricultural areas in the WillametteRiver Basin, and the Pudding River Basinprovides a good example of water-quality problemsfrom these sources. The Pudding River has been targetedfor intensive study by both ODEQ and theUSGS. Point sources of contamination are also importantin the basin, and although these sources have beenidentified, their effect on stream water quality is notwell known.During summer and fall periods, dissolved oxygenconcentration in the Pudding River frequently isbelow 90 percent of saturation and at times is below70 percent of saturation (Oregon Department of EnvironmentalQuality, 1994). Excessive growth of algaecontributes to dissolved oxygen problems in the mainstem. In the summer, high concentrations of nutrients,warm temperatures, favorable light conditions, andlow flows promote algal growth. In the late fall, winter,and spring, when high flow occurs, excessivelyhigh counts of bacteria and sediment concentrationsoccur frequently.Sources of nutrients, sediment, bacteria, andoxygen-consuming substances in the Pudding RiverBasin are not well identified. Some of these contaminantscome from identified point sources, includingwastewater-treatment plants and food-processingindustries. Nonpoint sources, however, also are knownto be important. The major sources of sediment andbacteria in the Pudding River are probably nonpointsources; the contaminants are the result of activitiesthat disturb the land surface, which include farming,logging, and urbanization. Inputs of nutrients probablyderive from similar sources; however, during the summermonths, when nutrient inputs promote algalgrowth, shallow and deep ground water may also be aproportionally major nutrient source to the main-stemPudding River and its tributaries. Water-quality transportmodeling of this basin can provide valuableinsight to the complex processes that occur and providea tool for management.Use of the Branched-Lagrangian TransportModelA one-dimensional water-quality model basedon the Lagrangian reference frame was developed foruse in simulating the transport of conservative andnonconservative constituents and for applying reactionsbetween constituents for branched river systems,tidal canal systems, and deltaic channels (Jobson andSchoellhamer, 1987). BLTM solves the convectivedispersionequation by using a Lagrangian referenceframe in which the computational nodes move withthe flow. Unsteady flow hydraulics must be supplied tothe model, and constituent concentrations are assumedto have no effect on the hydraulics. A flow model suchas DAFLOW can be used to supply the hydraulic58
information to the model. Reaction kinetics for nonconservativeconstituents can be supplied by the user.One-dimensional transport theory is thoroughlyexplained in the BLTM user’s manual (Jobson andSchoellhamer, 1987). The advantages of a Lagrangianapproach are (1) the scheme accurately models theconvection and dispersion terms in comparison to theusual Eulerian approach (Jobson, 1980; Thomson andothers, 1984), and (2) the model directly represents theactual transport processes by using the concept of afluid parcel, where chemical and biological reactionsoccur and move with the flow. The solution schemestarts with a series of parcels in the river (initial conditionsat various input locations) and adds a new parcelat each external boundary node with flow into the systemduring each time step (boundary conditions).Model CalibrationData were collected from time-of-travel studiesthat defined the travel and dispersion of dye in thePudding River for July 7–12, 1993 (Lee, 1995). Thesedata were used to calibrate a solute transport model forthe transport of conservative constituents. Figures 37–39 show simulation results of the calibrated BLTMmodel compared to observed data. Appendix 14 liststhe input files used in DAFLOW and BLTM modeling.For model calibration, DAFLOW input flows onthe main stem and tributaries were interpolated frommeasurements of discharge made at the time of the dyestudies. These discharge measurements were usedrather than discharge simulations from PRMS precipitation-runoffmodeling (table 13) because they provideda more accurate calibration of the BLTM model.On July 2, a convection storm occurred that was notlogged on all precipitation-gage records used in thePRMS simulation. The differences between the measuredand PRMS simulated flows shown in table 13are typical of what can be expected from simulationswhen convection storms dominate the weather andirrigation occurs within the basin. Simulated flowswere lower than measured flows at most locationsbecause the rainfall distribution was not accuratelyportrayed by data from the precipitation-gage networkused in modeling. Simulated flows were higher thanmeasured flows on Mill and Zollner Creeks becausesimulations were not adjusted for irrigation of agriculturalcrops (Appendix 14 contains the FLOW.IN fileused in calibration and lists the WDM files of interpolatedflows that were used).Table 13. Measured and Precipitation-Runoff ModelingSystem simulated discharge used to define flowdistribution in the main stem of the Pudding River forBranch Lagrangian Transport Model modeling[ft 3 /s, cubic feet per second; RM, river mile]____________________________________________________Measured SimulatedRiver Stream Date of discharge, discharge, 1mile name measurement in ft 3 /s in ft 3 /s____________________________________________________6.2 Mill Creek atRM 0.1 07/08/93 6.2 19.48.1 Pudding Riverat Aurora(14202000) 07/07/93 279 21507/08/93 259 21020.2 Butte Creek atRM 2.3 07/07/93 52.8 32.207/09/93 35.7 31.427.0 Pudding Riverat bridge 07/07/93 187 13029.1 Zollner Creek atRM 0.4 07/07/93 2.1 6.335.8 Pudding Riverat bridge 07/08/93 135 11845.5 Abiqua Creek atRM 1.0 07/09/93 68.2 30.348.7 Pudding Riverat bridge 07/09/93 57.8 53.0____________________________________________________1 Simulated by rainfall-runoff modeling using Precipitation-Runoffmodeling System. Flows were underestimated because the source of therain was a convection storm that affected only part of the basin.Dye concentrations were simulated for threereaches on the main stem of the Pudding River fromRM 45.5 to 31.5, RM 31.5 to 17.6, and RM 17.6 to5.4 to coincide with the observed dye-study data.Observed dye-concentration data were adjusted tosimulate a conservative substance by applying a recoveryratio. The recovery ratio was computed by techniquesdescribed by Hubbard and others (1982). The15-minute flow data needed in BLTM modeling werelinearly interpolated in the BLTM.FLW file from dailydata. This is an appropriate method if flows are not rapidlychanging. The input boundary used in modeling isnot the point at which dye is injected, but rather thefirst downstream location where the dye is mixed laterally.This input is used because BLTM is a one-dimensionalmodel that assumes total mixing in the crosssection. In model calibration, peak timing is matchedbetween observed and simulated values by adjustmentof the A 0 parameter (cross-section area storage) in the59
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Precipitation-Runoff and Streamflow
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U.S. DEPARTMENT OF THE INTERIORBRUC
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Determining Travel Time and Dilutio
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2. Stream-gaging stations used to c
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network-routing models where availa
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made on the main stem at base-flow
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Figure 1. Willamette River Basin, O
Page 18 and 19: EvapotranspirationINPUTSAirtemperat
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Page 22 and 23: Table 2. Stream-gaging stations use
Page 24 and 25: Figure 4. Mean annual precipitation
Page 26 and 27: Major Land Use MapHydrologic Soil G
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Page 34 and 35: Table 8. Selected monthly basinwide
Page 36 and 37: Figure 9. Location of Precipitation
Page 38 and 39: Table 11. Statistical analyses of P
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Page 46 and 47: 3,0002,500June 21-30, 1993September
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Page 56 and 57: YamhillRiver123˚45´ 30´ 15´123
Page 58 and 59: 123˚07´30´´ 123˚00´ 45´ 30´
Page 60 and 61: 123˚07´30´´ 123˚00´ 45´ 30´
Page 62 and 63: 44˚15´14166000122˚07´30´´Will
Page 64 and 65: 122˚15´ 122˚00´121˚52´30´´4
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Page 70 and 71: DAFLOW model, and peak amplitude is
Page 72 and 73: 20DYE CONCENTRATION, IN MICROGRAMS
Page 74 and 75: ARC-INFO and Geographic Information
Page 76 and 77: WILL.WDMData-base file(Observed pre
Page 78 and 79: WILL.WDMData-base fileSWSTATSFreque
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Page 84 and 85: APPENDIX 1. STREAM GEOMETRY FOR MAI
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APPENDIX 2. ARC MACRO LANGUAGE (AML
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APPENDIX 2. ARC MACRO LANGUAGE (AML
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APPENDIX 3. DEFINITIONS OF PARAMETE
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APPENDIX 3. DEFINITIONS OF PARAMETE
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APPENDIX 4. MEASUREMENTS USED TO DE
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APPENDIX 5. EXAMPLE PRECIPITATION-R
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APPENDIX 5. EXAMPLE PRECIPITATION-R
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APPENDIX 6. DIFFUSION ANALOGY FLOW
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APPENDIX 7. PROGRAMMING STEPS TO IN
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APPENDIX 8. PROGRAMMING STEPS TO DE
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150Climatemaxtempmintempprecipitati
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APPENDIX 10. DIRECTORY TREE AND DES
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APPENDIX 11. DIRECTORY TREES AND DE
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APPENDIX 11. DIRECTORY TREES AND DE
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APPENDIX 12. DIRECTORY FOR will.wdm
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APPENDIX 13. PROGRAMMING STEPS FOR
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APPENDIX 14. INPUT FILES FOR BRANCH
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