USDA-NRCS, Portland, Oregon

Transcription

1 Hydrologic Simulation Modeling for Streamflow Forecasting and Evaluation of Land and Water Management Practices in the Sprague River, Upper Klamath Basin, Oregon, USA David Garen John Risley Jolyne Lea USDA-NRCS, Portland, Oregon USGS, Portland, Oregon USDA-NRCS, Portland, Oregon

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3 Resource Issues and Conflicts in the Upper Klamath Basin Irrigated agriculture Juniper, wetlands, riparian areas Fish survival, endangered species Water management, conservation, overallocation Hydropower production Complex hydrology, climate variability / change, drought Competing interests: farmers, Native Americans, federal agencies, PacifiCorp, wildlife refuges

4 Agricultural Sustainability in the Upper Klamath Basin Requires... Integrated consideration of resource issues and conflicts Cooperation among the many interested people Understanding of the physical system Models to predict effects of land management and conservation practices Models to forecast streamflow

5 Sprague River Basin

6 Sprague River Basin Dry and wet

7 Cattle Grazing In the hills and pastures but sometimes too close to the river

8 Irrigated Pasture

9 Juniper Encroachment and removal

10 Restoration of: Riparian areas and wetlands

11 Fish 2002 fish kill in lower Klamath Fish survival and passage within upper Klamath system

12 Hydrologic Modeling Goals Evaluate hydrologic impacts of juniper removal Evaluate hydrologic impacts of riparian zone restoration Evaluate hydrologic impacts of switching from flood to sprinkler irrigation Evaluate ability to simulate and forecast streamflow using a commonly used conceptual model (PRMS) Evaluate ability of newer, more physically explicit gridbased models to simulate and forecast snowpack and streamflow (ISNOBAL, DHSVM) Obtain better understanding of hydrology and water balance of basin

13 Hydrologic Models Used Distributed Hydrology-Soil-Vegetation Model (DHSVM) MIKE 11 / MIKE SHE Precipitation-Runoff Modeling System (PRMS) ISNOBAL

14 Spatially Distributed Hydrologic Modeling i Elevation Elevation Vegetation Land Use Soil Soil Slope Slope

15 Structure of ISNOBAL Model Solar reflectance Solar irradiance Thermal irradiance Advective heat flux Thermal exitance Latent heat flux Sensible heat flux Sublimation, Evaporation, Condensation Rain, Snow Snow layer 1 Snow layer 2 Conductive heat flux Soil Melt water outflow

16 Watershed Characteristics Basic data layers Elevation Land use / Vegetation Soil texture Soil Elevation Derived data layers Land Use Slope Slope Aspect Topo. index Flow distance LAI Root depth ET crop coeff. Porosity Wilting point Sat. hyd. cond.

17 Meteorological Forcing Data Required Precipitation Air temperature Solar radiation Relative humidity Wind speed Thermal radiation

18 Preparation of Meteorological Forcing Input Data Conceptual semi-distributed model (PRMS) requires inputs for Hydrologic Response Units (HRUs) Fully distributed models (ISNOBAL, DHSVM) require inputs for spatial grids In both cases, spatial interpolation of station data is required Time step is daily to hourly

19 40 35 Silver Creek Precipitation Precipitation (mm) Crazyman Flat Summer Rim Taylor Butte Strawberry Agency Lake Quartz Mountain Klamath Falls Gerber Reservoir 1 January 2004 Precipitation-elevation relationship Elevation (m) Interpolated spatial field

20 0.0 Klamath Falls Gerber Reservoir -1.0 Agency Lake Taylor Butte Temperature Strawberry -2.0 Crazyman Flat 1 January 2004 Temperature ( o C) Quartz Mountain Silver Creek Summer Rim Elevation (m) Temperature-elevation relationship Interpolated spatial field

21 Solar Radiation Data Sprague River Basin Solar Radiation, WY 2004, KFLO AGKO Quartz Silver Solar radiation (w/m 2 ) 12-Oct 26-Oct 9-Nov 23-Nov 7-Dec 21-Dec 4-Jan 18-Jan 1-Feb 15-Feb 29-Feb 14-Mar 28-Mar 11-Apr 25-Apr 9-May 23-May 6-Jun 20-Jun 4-Jul 18-Jul 1-Aug 15-Aug 29-Aug 12-Sep 26-Sep

22 Solar Radiation Processing Compute cosine of local illumination angle based on solar zenith angle for location and time of day and topographic slope and aspect Run elevrad for visible and near infrared to get clear sky radiation on horizontal surface at ground level Multiply by cloud correction factor Direct / diffuse split Run toporad with above inputs to get topographically-corrected beam-visible, beam-infrared, diffusevisible, and diffuse-infrared radiation Apply canopy corrections to four components Run ialbedo to get visible and near infrared clean snow albedo Compute additional albedo reduction (melt period only) and subtract from clean snow albedo Subtract reflected radiation based on albedo for four components Add four components to get total net radiation

23 Jackson Peak (2155 m)... and snow depth at a station Observed Simulated Snow Model Verification Snow water equivalent... Jackson Peak (2155 m) Observed Simulated Snow water equivalent (mm) 9-Oct 29-Oct 18-Nov 8-Dec 28-Dec 17-Jan 6-Feb 26-Feb 18-Mar 7-Apr 27-Apr 17-May 6-Jun 26-Jun Snow depth (cm) 26-Jun 9-Oct 29-Oct 18-Nov 8-Dec 28-Dec 17-Jan 6-Feb 26-Feb 18-Mar 7-Apr 27-Apr 17-May 6-Jun

24 Snow Model Verification Model simulated snow water equivalent field Satellite snow covered area (white) and forested area for which model indicated snow but satellite did not (green) Example shown is for Boise River basin, 21 May 1998

25 Snow Model Verification Snow water equivalent spatial fields from remote sensing and modeling

26 Hydrologic Model Verification Simulated Observed Flow (mm/d) 1-Mar 11-Mar 21-Mar 31-Mar 10-Apr 20-Apr 30-Apr 10-May 20-May 30-May 9-Jun 19-Jun 29-Jun 9-Jul 19-Jul 29-Jul 8-Aug 18-Aug 28-Aug

27 Ensemble Streamflow Forecasting with Hydrologic Model /1/2004 1/8/2004 1/15/2004 1/22/2004 1/29/2004 2/5/2004 2/12/2004 2/19/2004 2/26/2004 3/4/2004 3/11/2004 3/18/2004 3/25/2004 4/1/2004 4/8/2004 4/15/2004 4/22/2004 4/29/2004 5/6/2004 5/13/2004 5/20/2004 5/27/2004 Flow, in cubic feet per second

28 Conclusion The complex issues and high political visibility of the Upper Klamath Basin provide much interest in our hydrologic modeling and forecasting work. Complex geology and hydrology coupled with high spatial and temporal variability of climate make for a challenging environment in which to test our models. These modeling efforts are significant both for the scientific issues involved as well as for the practical relevance of the results.