overview of uw surface water monitor
DESCRIPTION
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 : - PowerPoint PPT PresentationTRANSCRIPT
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Overview of UW Surface Water Monitor
Theodore J. Bohn
Dennis P. Lettenmaier
August 27, 2009
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Outline
1. Basic Overview2. Daily Process Flow3. Forcings – Method4. Time Periods and State Files5. Models6. Outputs7. Percentiles & Multi-Model Average8. Case Studies
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UW Surface Water MonitorPurpose:
1. Real-Time Estimates of Hydrologic Conditions
– Daily Nowcasts– 0.5 Degree– US & Mexico– Multiple LSMs
2. Initial State for Drought Forecasts
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Daily Process FlowPrevious day’s meteorological observations from index stations, gridded to 0.5 degree
All models use same input forcings, different formats
Model results expressed as percentiles of historical output
Average Percentiles
Compute Percentiles
Make Plots
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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
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Temporal Organization & State Files
Trusted state is advanced by 1 month on the 25th of every month
Trusted state is result of retrospective simulations using “good” forcings
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Models
• VIC 4.0.6
• CLM 3.5
• Noah 2.8
• Sacramento/Snow-17 (SAC)
• Catchment (in progress)
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Outputs
• Total Column Soil Moisture
• SWE
• Total Moisture
• Cumulative Runoff
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0
100
Multi-ModelCumulative Probability,
1916-2004
0 100Avg Percentile (%)
%
Percentiles & Multi-Model
100
0
Model iCumulative Probability,
1916-2004
50 800Soil Moisture (mm)
%
For each model, re-express current soil moisture as percentile of climatology for this day of year
Model isoil moisture
Model ipercentile
Average all models’ percentiles
= 1/N Σ (i=1 to N) percentile i
Multi-Modelpercentile
Multi-model ensemble result is the percentile of the average of model percentiles
This procedure occurs separately for each grid cell
Grand distribution from 30-day moving window centered on current day
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Examples – Winter 2008-09Soil Moisture Percentiles – January 2009
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SWE Percentiles – January 2009
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Total Moisture Percentiles – January 2009
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Soil Moisture Percentiles – February 2009
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SWE Percentiles – February 2009
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Total Moisture Percentiles – February 2009
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Soil Moisture Percentiles – March 2009
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Total Moisture Percentiles – March 2009
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Model Agreement
Eastern US•Strong agreement•Smaller uncertainty
Western US•Poor agreement•Larger uncertainty
Average Model Correlation
Correlation and Response Times•In general, long response times (West) correspond to poor model agreement•Response times may affect uncertainty
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Soil Moisture Percentiles w.r.t. 1920-20032008-07-01
CLM
SAC NOAH
Multi-Model
VIC
US Drought Monitor
Comparison with US Drought Monitor (UNL/NOAA/USDA)
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US Drought Monitor UW Surface Water MonitorMultimodel Average
Jul 1
Aug 5
Sep 2
Agreement: WI drying trend
Agreement: Gulf wetting trend
Disagreement: Dry conditions in N.,S. Carolina?
Agreement: Dry west coast