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Real-time Hydrologic Forecasting for Seasonal Analysis: A Novel Approach

Explore the experimental project by Andrew Wood and Dennis P. Lettenmaier from the University of Washington on real-time hydrologic forecasting presented at the 2002 AMS Conference on Applied Climatology. The innovative methodology integrates climate and hydrologic models to generate monthly to seasonal forecasts for river basins on a continental scale. It leverages global numerical weather prediction models and aims to improve snowpack, streamflow, runoff, and soil moisture predictions. The project involves bias correction, downscaling, and hydrologic simulation techniques to enhance forecast accuracy. Witness the research objective and findings, supported by a detailed overview of the comprehensive approach and ongoing work. Gain insights into the integration of Global Spectral Model (GSM) ensemble forecasts and Variable Infiltration Capacity (VIC) Hydrologic Model for robust flow routing. Learn about bias correction methodologies, spatial interpolation, and temporal disaggregation processes to refine climate model outputs. Follow the project's forecast products and simulations for the Columbia River Basin application, emphasizing the assessment and expansion of hydrologic forecasts. Explore the potential and challenges of this climate-hydrology forecasting method and its significance for real-time applications. Access the latest advancements in hydrologic modeling and forecasting techniques for improved water resource management.

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Real-time Hydrologic Forecasting for Seasonal Analysis: A Novel Approach

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  1. Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis P. Lettenmaier University of Washington presented: AMS Conference on Applied Climatology, 2002 Portland, OR May 2002

  2. Project Overview • Research Objective: • To produce monthly to seasonal snowpack, streamflow, runoff & soil moisture forecasts for continental scale river basins • Underlying rationale/motivation: • Global numerical weather prediction / climate models (e.g. GSM) take advantage of SST – atmosphere teleconnections • Hydrologic models add soil-moisture – streamflow influence (persistence)

  3. Topics • Approach • Columbia River basin (summer 2001) results • Ongoing Work • Comments

  4. General Approach • climate model forecast • meteorological outputs • ~1.9 degree resolution (T62) • monthly total P, avg T • Use 3 steps: 1) statistical bias correction • 2) downscaling and disaggregation • 3) hydrologic simulation hydrologic model inputs • streamflow, soil moisture, snowpack, • runoff • 1/8-1/4 degree resolution • daily P, Tmin, Tmax

  5. Models: 1. Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC • forecast ensembles available near beginning of each month, extend 6 months beginning in following month • each month: • 210 ensemble members define GSM climatology for monthly Ptot & Tavg • 20 ensemble members define GSM forecast

  6. Models: 2. VIC Hydrologic Model

  7. Flow Routing Network domain slide

  8. TOBS a. b. c. TGSM One Way Coupling of GSM and VIC models a) bias correction: climate model climatology  observed climatology b) spatial interpolation: GSM (1.8-1.9 deg.)  VIC (1/8 deg) c) temporal disaggregation (via resampling of observed patterns): monthly  daily

  9. Bias Example:JFM precipitation from Parallel Climate Model (DOE)climate model vs. “observed” distributions at climate model scale (T42)

  10. Dealing with bias using a climatology-based correction Note: we apply correction to both forecast ensemble and climatology ensemble itself (to use as a baseline)

  11. monthly GSM anomaly (T62) interpolated to VIC scale VIC-scale monthly forecast observed mean fields (1/8-1/4 degree) note: month m, m = 1-6 ens e, e = 1-20 Downscaling: add spatial VIC-scale variability

  12. Lastly, temporal disaggregation… for each VIC-scale monthly forecast value, e.g.:

  13. 1-2 years back VIC forecast ensemble VIC model spin-up VIC climatology ensemble NCDC met. station obs. up to 2-4 months from current LDAS/other met. forcings for remaining spin-up climate forecast information (from GSM) data sources Forecast Products streamflow soil moisture runoff snowpack Simulations start of month 0 end of month 6

  14. Columbia River Basin Application

  15. Initial Conditionslate-May SWE &water balance

  16. Initial Conditionslate-May SWE &water balance(percentiles)

  17. forecast observed May climate forecast forecast medians

  18. May snowpack forecast hindcast “observed” forecast forecast medians

  19. forecast May runoff & soil moisture forecast hindcast “observed” forecast medians

  20. May streamflow forecast

  21. Ongoing Work: Assessment and Expansion

  22. Tercile Prediction “Hit Rate”e.g.,GSM Ensemble “Forecast” Average, January(based on retrospectiveperfect-SST ensemble forecasts)Masked for local significance

  23. U.S. West-wide Hydrologic Forecasting

  24. Summary Comments • climate-hydrology model forecasting method has potential • hydrologic persistence was most important in the CRB example • bias-correction of climate model outputs (using a climate model hindcast climatology) is critical • access to quality met data for hydrologic model initialization is also essential

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