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Martyn Clark, David Gochis , Ethan Gutmann , and Roy Rasmussen

Suggestions for research to fill critical capability gaps to support short-term water management decisions . Martyn Clark, David Gochis , Ethan Gutmann , and Roy Rasmussen NCAR Research Applications Laboratory. Outline.

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Martyn Clark, David Gochis , Ethan Gutmann , and Roy Rasmussen

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  1. Suggestions for research to fill critical capability gapsto support short-term water management decisions Martyn Clark, David Gochis, Ethan Gutmann, and Roy Rasmussen NCAR Research Applications Laboratory

  2. Outline • The myriad of uncertainties in hydrologic monitoring and prediction products • Critical capability gaps – and research that is needed to fill them • Summary of research suggestions

  3. Barriers to prediction hydrological uncertainty meteorological uncertainty • Hydrological uncertainty: • How well can we estimate the amount of water stored in the snow and soil? • Accuracy of precipitation estimates • Fidelity of hydro model simulations • Effectiveness of hydrologic data assimilation methods Meteorological uncertainty: How well can we forecast the weather? Water Cycle (from NASA)

  4. Typical streamflow forecasting method… Historical Data SNOW-17 / SAC Historical Simulation SWE SM Q Past Future • Run hydrologic model up to the start of the forecast period to estimate basin initial conditions;

  5. Typical streamflow forecasting method… Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q Past Future • Run hydrologic model up to the start of the forecast period to estimate basin initial conditions; • Run hydrologic model into the future, using an ensemble of local-scale weather and climate forecasts.

  6. Past Future Forecast Uncertainties Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  7. Past Future Uncertainties in model inputs Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  8. Past Future Uncertainties in the hydrological model Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  9. Past Future Uncertainties in initial conditions Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation DA TO REDUCE UNCERTAINTIES SWE SM Q

  10. Past Future Uncertainties in weather/climate forecasts Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  11. Past Future The whole enchilada Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  12. Short-term forecasts merged radar & high-res NWP Meteorological Analyses ensemble QPE, etc. Medium-range forecasts global NWP models (from outside NCAR) Seasonal forecasts statistical and dynamical (from outside NCAR) Hydrologic model Integrated hydro-LSM (from WRF-Hydro) Probabilistic downscaling Conditioned weather generators Hydrologic data assimilation Forecast blending methods Local-scale probabilistic meteorological forecasts Hydrologic model Integrated Hydrologic model—LSM (from WRF-Hydro) Local-scale hydrologic analyses Statistical post-processing methods Local-scale probabilistic hydrologic forecasts

  13. Monitoring and forecast products reviewed in the ST-doc Also: NRCS NWCC water supply forecasts Monitoring products: USGS stream gauging NRCS SNOTEL NRCS snowcourse NWS COOP observer RFC Precipitation analysis

  14. Outline • The myriad of uncertainties in hydrologic monitoring and prediction products • Critical capability gaps – and research that is needed to fill them • Summary of research suggestions

  15. Past Future Uncertainties in model inputs Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  16. Step 1: Estimate precipitation CDF at each grid cell Step 2: Synthesize ensembles from the CDF Uncertainties in model inputs Case study over the Colorado Headwaters • Gap: CONUS-domain ensemble hyper-resolution forcing data (Precip, Temp, RH, wind, SW, LW) • Use all available data networks, remote sensing products and NWP reanalyses • Ensure consistency among variables and realistic space-time variability corresponding observations Clark & Slater, 2006 – JHM

  17. Past Future Uncertainties in the hydrological model Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  18. The quest for physically realistic streamflow simulations • Current assessments: DMIP • A well-calibrated conceptual model can perform just as well as a calibrated physics-based model (when evaluating streamflow at the calibration point) • Kirchner’s “mathematical marionettes”? • Contemplating stationarity… • Parameters in conceptual models are often assigned unrealistic values to compensate for structural weaknesses • Conceptual models subject to the same stationarity predicament that plagues statistical streamflow forecasting systems • Treat the symptom or the disease? Can develop increasingly elegant methods for data assimilation and statistical post-processing, but does not address the root cause of forecast errors • (Go as far as you can with physics and do the rest with statistics)

  19. Research under the WRF-Hydro umbrella:Improve simulations of hydrology from local-continental scales Global Earth System Models Atmospheric (Re)analyses Regional Weather/ Climate models Multi-scale WRF-Hydro Driver/Coupler Model library: Multi-model Multi-physics Multi-scale • Gap: Research needs similar to those required to support long-term planning • Improved “fit-for-purpose” hydrologic simulations and analyses using multi-model / multi-physics / multi-scale hydrologic modeling capabilities • Improved estimation of hydrologic model parameters from local-continental scales • Improved estimates of hydrologic uncertainty process complexity HRU-based Lumped horizontal complexity 2-d surface and sub-surface flow

  20. Past Future Uncertainties in initial conditions Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation DA TO REDUCE UNCERTAINTIES SWE SM Q

  21. EnKF Sample Results: snow data assimilation Interpolated SWE Mean & Std. Dev Model Truth Slater & Clark, JHM 2006 Data withholding experiments at 53 stations in Colorado -- stations represent high-resolution model grid cells without any data

  22. no DA with DA EnKF Sample Results: streamflow assimilation Barnett’s Bank • Gap: Integrated hydrologic data assimilation system • Multiple ground-based and remotely sensed observations • Evaluate different assimilation strategies (esp. the role of the particle filter) Use of the ensemble Kalman filter to use mis-match between observed and simulated streamflow to update hydrologic states (Clark et al., AdWR 2008)

  23. Past Future Uncertainties in weather/climate forecasts Historical Data Forecasts SNOW-17 / SAC SNOW-17 / SAC Historical Simulation SWE SM Q

  24. Integrated set of forecast inputs from minutes to seasons • Different models for different forecast lead times • 0-6 hours  Radar extrapolation • 6-72 hours  High-resolution regional NWP model • 3-14 days  Global-scale NWP model • Seasonal  Disaggregated seasonal climate outlooks • Requirements • An ensemble of sub-daily weather sequences • Preserve inter-site correlations, temporal persistence, and correlations between variables • Minimize abrupt changes when a new model is introduced • Methods: The “Schaake Shuffle” (Clark et al., 2004) • Estimate CDF at a given forecast lead time (using the most appropriate forecasting method at that lead time) • Sample ensemble members from the CDF in a way that preserves observed space-time correlation structure (e.g., use rank of observed weather patterns to resample).

  25. Forecasts based on NWP model output Forecasts based on historical data Example Results: Cle Elum River Basin (Central Washington) Clark and Hay (2004) – Journal of Hydrometeorology

  26. Operational Implementation: NWSRFS Hourly instantaneous flow ensembles are created by ESP and saved. MRF shows higher flows than historical when it is warmer (during the first week). These may be converted into probabilistic forecasts… Werner et al. (2005) – Journal of Hydrometeorology

  27. Operational Implementation: Probabilistic skill (20-year hindcast) Improvements in forecast skill is most pronounced during the rising limb of the hydrograph. Ranked Probability Skill Score (RPSS) Werner et al. (2005) – Journal of Hydrometeorology

  28. New products • Gap: Integrate best-available forecast products to construct conditioned weather sequences suitable for hydrologic models • Post-processing methods to improve statistical reliability of model ensembles • Stochastic methods to reproduce observed space-time variability in local-scale climate • Seamlessly merge forecasts of different type and resolution.

  29. Outline • The myriad of uncertainties in hydrologic monitoring and prediction products • Critical capability gaps – and research that is needed to fill them • Summary of research suggestions

  30. Summary of research suggestions • CONUS-domain ensemble hyper-resolution forcing data (Precip, Temp, RH, wind, SW, LW) • Use all available data networks, remote sensing products and NWP reanalyses • Ensure consistency among variables and realistic space-time variability • Hydrologic modeling • Improved “fit-for-purpose” hydrologic simulations and analyses using multi-model / multi-physics / multi-scale hydrologic modeling capabilities • Improved estimation of hydrologic model parameters from local-continental scales • Improved estimates of hydrologic uncertainty • Integrated hydrologic data assimilation system • Multiple ground-based and remotely sensed observations • Evaluate different assimilation strategies (esp. the role of the particle filter) • Integrate best-available forecast products to construct conditioned weather sequences at forecast lead times from minutes to seasons, suitable for hydrologic models • Post-processing methods to improve statistical reliability of model ensembles • Stochastic methods to reproduce observed space-time variability in local-scale climate • Seamlessly merge forecasts of different type and resolution.

  31. Reclamation-USACE-NCARStreamflow predictability project • Assess performance of current hydrologic models used by the NWS, and assess dependence of model performance on • Physical characteristics of the basins (climate, vegetation, soils, topography) • Reliability of quantitative precipitation estimates (e.g., station density, radar) • Assess the relative importance of hydrologic and meteorological/ climatological information in determining forecast skill • Conduct research to improve estimates of uncertainty • During model spin-up • During the forecast period • Conduct research to reduce forecast uncertainty • Better hydrologic models • Better weather forecasts and climate outlooks • Adoption of hydrologic data assimilation methods and statistical post-processing methods • Examine impact of different sources of uncertainty in water management decisions

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