APPLICATIONS OF DOWNSCALING: HYDROLOGY AND WATER RESOURCES EXAMPLES

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1 APPLICATIONS OF DOWNSCALING: HYDROLOGY AND WATER RESOURCES EXAMPLES Dennis P. Lettenmaier Department of Civil and Environmental Engineering For presentation at Workshop on Regional Climate Research NCAR April 4, 2001

2 OUTLINE Hydrologic models macroscale (VIC) and explicit (DHSVM) Example 1: Seasonal ensemble forecasting Example 2: Flood forecasting Example 3: Climate change assessment Final comments where are the weak links?

4 Variable Infiltration Capacity (VIC) Macroscale Hydrology Model Key features: Parameterization of runoff via saturation excess and base flow Explicit vegetation, including subgrid variability in vegetation classes Energy balance snow model, elevation bands for increased vertical resolution Streamflow routing Subgrid parameterizations for spatial precipitation variability, frozen soils, lakes and wetlands (new!) Full surface energy balance Spatial resolution typically 1/8-2 degrees lat-long

6 Distributed Hydrology-Soil- Vegetation Model (DHSVM) Key features: Explicit representation of runoff production via saturation excess mechanism, and base flow Explicit vegetation Energy balance snow model Streamflow routing to any identified location on stream channels Optional representation of roads and road drainage networks High spatial resolution (typically m)

10 DHSVM Snow Accumulation and Melt Model

Land surface characterization required by DHSVM Terrain - 150 m. aggregated from 10 m.

11 Land surface characterization required by DHSVM Terrain m. aggregated from 10 m. resolution DEM Land Cover - 19 classes aggregated from over 200 GAP classes Soils - 3 layers aggregated from 13 layers (31 different classes); variable soil depth from 1-3 meters Stream Network - based on 0.25 km 2 source area

12 Example 1: Ensemble Seasonal streamflow forecasting

13 Overview of Modeling Linkages Global Climate Models downscaling temp precip wind Hydrology Models streamflow Water Resources Models Regional Climate Models water demand

14 SST Forecast Coupled Global CM Embedded Regional CM Water Management Decisions Macro-Scale Hydrologic Model Water Management Model

MM5 Meso-Scale Climate Model ColSim Reservoir

Melt Vegetation and Soil Characteristics Runoff

15 MM5 Meso-Scale Climate Model ColSim Reservoir Model VIC Hydrology Model Precipitation, Temperature, Wind, Humidity Evapotransporation (canopy and soil layers) Snow Accumulation and Melt Vegetation and Soil Characteristics Runoff Routing Model Base Flow Inter-layer Infiltration Surface Infiltration

16 Constructing the Driving Data for the Perpetual January Streamflow Forecasting Experiments Jan MM5 MM5 Jan MM5 Jan 24 MM5 MM5 Jan MM5 Jan MM5 Jan Forecasts MM5 Jan MM5 Jan MM5 Jan MM5 Jan MM5 Jan MM5 Jan MM5 Jan MM5 Jan Jan... Oct Nov Dec Feb Mar Apr May Jun Jul... Repeating 1960 Observed Temp and Precip Time Series with January s Replaced by MM5 Forecasts

17 Hydrologic Simulations Using RCM Normal January Ensembles Before and After Bias Correction B) Uncorrected RCM Simulated Ensemble Hydrograph for The Dalles RCM Normal Met Data for January 1960 Water Year Met Data for Other Months Flow (cfs) A) Retrospective Ensemble Hydrograph for The Dalles Observed Normal Met Data for January 1960 Water Year Met Data for Other Months oct nov dec jan feb mar apr may jun jul aug sep Month Flow (cfs) C) oct nov dec jan feb mar apr may jun jul aug sep Month Bias Corrected Ensemble Hydrograph for The Dalles RCM Simulated Normal Met Data for January 1960 Water Year Met Data for Other Months Flow (cfs) oct nov dec jan feb mar apr may jun jul aug sep Month

18 ENSO Neutral Simulated Ensemble Hydrograph for The Dalles Observed Normal Met Data for January 1960 Water Year Met Data for Other Months ENSO Warm Simulated Ensemble Hydrograph for The Dalles Observed El Nino Met Data for January 1960 Water Year Met Data for Other Months Flow (cfs) Flow (cfs) oct nov dec jan feb mar apr may jun jul aug sep 0 oct nov dec jan feb mar apr may jun jul aug sep Month Month Simulated Ensemble Hydrograph for The Dalles Normal SST Meso-Scale Met Simulations for January 1960 Water Year Met Data for Other Months Simulated Ensemble Hydrograph for The Dalles Warm SST Meso-Scale Met Simulations for January 1960 Water Year Met Data for Other Months Flow (cfs) Flow (cfs) oct nov dec jan feb mar apr may jun jul aug sep Month 0 oct nov dec jan feb mar apr may jun jul aug sep Month

20 Experimental Hydrologic Forecasting for East Coast US River Basins during Summer 2000 Andrew Wood Ed Maurer Dennis Lettenmaier University of Washington Arun Kumar NCEP/EMC/CMB

21 Model forecasting domain

22 General Method Climate model forecast outputs 2.81 degree resolution monthly total P, avg T hydrologic model inputs 1/8 degree resolution daily P, Tmin, Tmax streamflow, hydrologic variables Use 3 step approach: a) statistical bias correction b) downscaling c) hydrologic simulation of ensembles

23 Coupling GSM to VIC Step 1: Statistical Bias Correction at the GSM scale (~2.8 deg), use a quantile to quantile mapping from GSM climatology to observed historical climatology, for precip & temperature separately, e.g.,

24 GSM ensemble forecasts from NCEP/EMC rolling climatology based on historical SSTs roughly 2.8 degree resolution ensembles available around 10 th of month, extend 6 months beginning in following month each month: new set of 210 climatology ensemble members for monthly total precip & average temperature derived from 10 initial condition perturbations for each year of the 21 year climatology period 20 forecast ensemble members

25 Kanawha River Basin Simulations Using Observed Data and Bias Corrected GCM Output Mean Observations Bias-Corrected GCM Variance

26 Soil Moisture example: April `00 forecast for summer forecast ensemble median shown as percentile of climatology ensemble

27 Streamflow example: April `00 forecast for summer

28 Example 2: Flood forecasting

29 2000/2001 Real-time Streamflow Forecast System 26 basins 48,896 km 2 2,173, m resolution

Calibration (Snohomish River) From 1987-1991

30 Calibration-Validation with all available meteorological observations (50 sites) Calibration (Snohomish River) From (USGS gauges at Gold Bar and Carnation only ) Validation

31 DHSVM Calibration (Snoqualmie at Carnation) Flood of record Principal calibration locations were the Skykomish at Gold Bar and the Snoqualmie at Carnation

32 DHSVM Parameter Transferability Application of Snohomish River parameters to Bull Run Reservoir (Portland, OR Water Supply) (124 km 2 )

33 Sauk Accurate precipitation forecasts are key!!! Snoqualmie Observed MM5-DHSVM NWRFC

34 Deschutes Nisqually MM5-DHSVM Observed NWRFC

35 Example 3: Climate change assessment

36 April 1 Average Snow Cover Extent Hadley Centre Model Base Case ~2025 ~2095

37 April 1 Average Snow Cover Extent MPI Model Base Case ~2025 ~2045

38 Average Virgin Streamflow at The Dalles Hadley Centre (CO2 plus aerosols) 1/8 Degree Model Streamflow (cfs) Base Decade 2 Decade Calendar Month

39 Average Virgin Streamflow at The Dalles MPI (CO2 plus aerosols) 1/8 Degree Model Streamflow (cfs) Base MPI Dec2 MPI Dec Calendar Month

40 Final comments where are the weak links? Downscaling problem isn t likely to go away, especially for local (explicit) model applications Bias (in the surface forcings) is the zero order problem in linking weather/climate and hydrology models For S/I forecasting, outstanding question is whether there is value added in downscaling of climate signal (e.g., via RCM) v direct downscaling (e.g., via PDF approach) to hydrology model For climate change applications, downscaling remains problematic esp. failure to preserve properties of large scale forcings

Impacts of climate change on flooding in the river Meuse

Impacts of climate change on flooding in the river Meuse Martijn Booij University of Twente,, The Netherlands m.j.booij booij@utwente.nlnl 2003 in the Meuse basin Model appropriateness Appropriate model