Overview of UW Surface Water Monitor

Theodore J. Bohn

Dennis P. Lettenmaier

August 27, 2009

Outline

1. Basic Overview2. Daily Process Flow3. Forcings – Method4. Time Periods and State Files5. Models6. Outputs7. Percentiles & Multi-Model Average8. Case Studies

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

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

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

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

Models

• VIC 4.0.6

• CLM 3.5

• Noah 2.8

• Sacramento/Snow-17 (SAC)

• Catchment (in progress)

Outputs

• Total Column Soil Moisture

• SWE

• Total Moisture

• Cumulative Runoff

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

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

SWE Percentiles – January 2009

Total Moisture Percentiles – January 2009

Soil Moisture Percentiles – February 2009

SWE Percentiles – February 2009

Total Moisture Percentiles – February 2009

Soil Moisture Percentiles – March 2009

Total Moisture Percentiles – March 2009

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

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)

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