Remote Sensing of Hydrological Variables over the Red Arkansas Eric Wood Matthew McCabe Rafal Wojcik Hongbo Su Huilin Gao Justin Sheffield Princeton University

Introduction Recent progress has seen the development on continental and global scale data products. These products include both meteorological forcing and model output. However, there is a lack in assessing the accuracy of these predictions – particularly hydrological variables modeled over regions where in-situ data is not available! Remote sensing approaches can bridge this gap, offering a means to both assess model output and potentially improve predictions through assimilation techniques.

Presentation Overview Here, remote sensing estimates of evapotranspiration (SEBS) and soil moisture (LSMEM) are made for the continental US. Data from the NLDAS are used to force these process models and results are compared with NLDAS-VIC surrogates. Focus is maintained on the Red Arkansas basin, with daily and weekly remote sensing predictions determined for August, Both AMSR-E and MODIS-Terra data are employed. Are process models more effective in modeling hydrological variables than land surface schemes? Can they be used as validation sources?

SEBS Model Description SEBS was run using both VIC and 5km MODIS-Terra temp, allowing a direct comparison of land surface / process model prediction of ET. SEBS INPUTS Use SEBS to determine daily/weekly ET predictions (limited by surface temperature).

Land Surface Microwave Emission Model (LSMEM) is used with AMSR-E GHz brightness temperatures and NLDAS forcing to predict the near surface soil moisture Soil Emission Vegetation Emission Atmosphere Emission Surface Refection LSMEM Model Description LSMEM Model Requirements

NDLAS – Model Inputs The Land Data Assimilation System (LDAS) is a collaborative effort towards improving the initialization of land surface state variables for numerical weather prediction. The Land Information System (LIS) offers potential for further continental and global scale modeling.

Modeling Overview NDLAS Forcing SEBS LSMEM MODIS-Ts VIC-Ts 0.125° AMSR-E 10GHz BT 25 km Temporally Correct Latent Heat Soil Moisture Compare process model output to equivalent VIC results across the Red Arkansas

MODIS Daily Evapotranspiration

Large scale MODIS based estimates are restricted by cloud cover. Over the Red Arkansas (August, 2003) cloud cover generally exceeds 50% - not a problem for AMSR-E. Compare predictions at weekly scale to determine equivalence – taking average of available measurements to produce a composite prediction. Large differences in the surface-air temperature gradient (H→Hmax) also limits spatial coverage

Weekly MODIS Evapotranspiration

Weekly SEBS-VIC Evapotranspiration

Weekly LDAS-VIC Evapotranspiration

Areal Average ET Comparison MODIS instantaneous estimates for August, 2003 indicate a greater degree of variability than equivalent LDAS-VIC predictions. Responses are also well reflected in SEBS-VIC. MODIS values represent a more realistic reproduction of expected fluxes over the time scale examined. MODIS: Avg: W/m 2 Stdev: 42.5 W/m 2 SEBS-VIC: Avg: W/m 2 Stdev: 14.5 W/m 2 LDAS-VIC: Avg: W/m 2 Stdev: 35.4 W/m 2

AMSR-E Near Surface Soil Moisture

VIC 10 cm Soil Moisture

Comparison between VIC-10 cm soil moisture and AMSR-E (0.5 cm) soil moisture is inevitably limited! Predictions have different response times – AMSR-E (storm response) – VIC (inter-storm response) VIC values illustrate a strong relationship between soil properties – hydraulic conductivity/porosity AMSR responses strongly linked with precipitation patterns offering an approach towards qualitative assessment (see McCabe et al., 2005 GRL) AMSR-E/VIC Soil Moisture Comparison

Soil Moisture – Rainfall Comparison Precipitation events on Aug 29/31: …strong influence on soil storage in both the VIC and AMSR responses. Delayed response evident in VIC estimates.

Model Evaluation Issues of measurement accuracy, frequency, type… Intensive field experiments offer excellent detail, but are temporally limited Continuous measurements are usually spatially sparse… What is the best / most efficient combination of these. Global products need to be locally validated… Predictions are only as good as the evaluation (and input) data!!!

Remote sensing offers useful insight into both prediction and model assessment - effort is required to fuse measurements to models and observations (calibration??). Both observations and measurements require careful use – do models perform correctly – do they conform to observations. There is clear coherence between rainfall patterns and soil moisture distributions. Can we use remotely sensed soil moisture to improve rainfall characterization…ungauged basins. Independent observations allow independent examination of model results – role of assimilation in improving model representation. Discussion and Conclusion