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Overview of UW Surface Water Monitor

Overview of UW Surface Water Monitor. Theodore J. Bohn Dennis P. Lettenmaier August 27, 2009. Outline. Basic Overview Daily Process Flow Forcings – Method Time Periods and State Files Models Outputs Percentiles & Multi-Model Average Case Studies. UW Surface Water Monitor. Purpose :

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Overview of UW Surface Water Monitor

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  1. Overview of UW Surface Water Monitor Theodore J. Bohn Dennis P. Lettenmaier August 27, 2009

  2. Outline • Basic Overview • Daily Process Flow • Forcings – Method • Time Periods and State Files • Models • Outputs • Percentiles & Multi-Model Average • Case Studies

  3. UW Surface Water Monitor Purpose: • Real-Time Estimates of Hydrologic Conditions • Daily Nowcasts • 0.5 Degree • US & Mexico • Multiple LSMs • Initial State for Drought Forecasts

  4. Daily Process Flow Previous day’s meteorological observations from index stations, gridded to 0.5 degree All models use same input forcings, different formats Average Percentiles Model results expressed as percentiles of historical output Compute Percentiles Make Plots

  5. Forcings - Method • Daily obs from ~2300 index stations • Same source as for 1/8-deg forecast system, but gridded separately Gridding: • Compare to stations’ monthly climatology • Precip: Grid the Percentiles • Tmin/max: Grid the Anomalies

  6. Temporal Organization & State Files Trusted state is result of retrospective simulations using “good” forcings Trusted state is advanced by 1 month on the 25th of every month

  7. Models • VIC 4.0.6 • CLM 3.5 • Noah 2.8 • Sacramento/Snow-17 (SAC) • Catchment (in progress)

  8. Outputs • Total Column Soil Moisture • SWE • Total Moisture • Cumulative Runoff

  9. Model i Cumulative Probability, 1916-2004 100 % Multi-Model Cumulative Probability, 1916-2004 0 100 50 Soil Moisture (mm) 800 % 0 0 Avg Percentile (%) 100 Percentiles & Multi-Model Model i soil moisture For each model, re-express current soil moisture as percentile of climatology for this day of year Grand distribution from 30-day moving window centered on current day Average all models’ percentiles = 1/N Σ (i=1 to N) percentile i Model i percentile Multi-Model percentile Multi-model ensemble result is the percentile of the average of model percentiles This procedure occurs separately for each grid cell

  10. Examples – Winter 2008-09 Soil Moisture Percentiles – January 2009

  11. SWE Percentiles – January 2009

  12. Total Moisture Percentiles – January 2009

  13. Soil Moisture Percentiles – February 2009

  14. SWE Percentiles – February 2009

  15. Total Moisture Percentiles – February 2009

  16. Soil Moisture Percentiles – March 2009

  17. Total Moisture Percentiles – March 2009

  18. Model Agreement • Correlation and Response Times • In general, long response times (West) correspond to poor model agreement • Response times may affect uncertainty Average Model Correlation • Eastern US • Strong agreement • Smaller uncertainty • Western US • Poor agreement • Larger uncertainty

  19. Comparison with US Drought Monitor (UNL/NOAA/USDA) Soil Moisture Percentiles w.r.t. 1920-2003 2008-07-01 VIC CLM SAC NOAH Multi-Model US Drought Monitor

  20. US Drought Monitor UW Surface Water Monitor Multimodel Average Jul 1 Agreement: Dry west coast Aug 5 Disagreement: Dry conditions in N.,S. Carolina? Sep 2 Agreement: WI drying trend Agreement: Gulf wetting trend

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