Notes

Outline

Soil Moisture Estimation Using Remote Sensing Jeffrey Walker Dept Civil and Env Eng The  University of Melbourne Paul Houser Hydrological Sciences Branch, Head NASA Goddard Space Flight Center http://www.civag.unimelb.edu.au/~jwalker

Importance of Soil Moisture Early warning systems Flood forecasting Socio-economic activities Agriculture management – yield forecasting, pesticides etc Water management – irrigation Policy planning Drought relief Global change Weather and climate Evapotranspiration

Soil Moisture vs Sea Surface Temp Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST).

The Situation

The Problem With LSMs Same forcing and initial conditions but different predictions of soil moisture!

Importance of Soil Moisture

Soil Moisture Coverage: Veg (Mean PR)

Data Assimilation Defined Definition 1: using data to force a model ie. precipitation and evapotranspiration to force a LSM Analogy: passenger giving instructions to a blindfolded driver on the M1 at peak hour

The Kalman Filter (sequential assimilation)

Catchment-based LSM

Catchment Discretisation

Bias Reduced Forcing Observational Data Sets NCAR Northern Hemisphere Sea Level Pressure  01/1899 – present; 5 x 5 degrees Climate Research Unit (University of East Anglia) Temperature and Precipitation 01/01 - 12/98; 0.5 x 0.5 degrees Center for Climatic Research (University of Delaware) Terrestrial Temperature and Precipitation 01/50 - 12/96 ; 0.5 x 0.5 degree Global Precipitation Climatology Project (GPCP) 01/86 - 03/95; 2.5 x 2.5 degree Langley Eight Year Shortwave and Longwave Surface Radiation Budget 07/83 - 06/91 - in process of being extended; 2.5 x 2.5 degree Re-Analysis Atmospheric Forcing Data Sets ECMWF Re-analysis Advanced Global Data  4x/day, 01/79 - 12/93 1.125 degrees  (Gaussian) NCEP/NCAR Re-analysis 4x/day, 01/48 – 12/99 2.5 x 2.5 degrees Bias Correct Using Monthly Mean Observational Data Sets      Berg, Famiglietti, Walker and Houser, AMS 2001

SMMR Soil Moisture Observations

Soil Moisture Time Series: Illinois

Animation

Slide 16

Evaluation of Assimilation How can we evaluate the soil moisture assimilation? Soil moisture – ideal but limited data available. United states has 19 stations in Illinois, 6 stations in Iowa and transect of 89 points in New Mexico for SMMR period. Analysis increments – only provides a check for systematic biases. Runoff - data available but assumes that soil moisture is the only reason for poor estimates. Evapotranspiration – data not available and assumes that soil moisture is the only reason for poor estimates. Precipitation forecasts – assumes soil moisture is the only reason for poor forecasts. Other ?

Soil Moisture: Iowa

Conclusions First known study to assimilate space-borne soil moisture measurements Soil moisture estimate with assimilation was an improvement when compared to point measurements