Weissling, BP, Xie, H (2009). MODIS BIOPHYSICAL STATES AND NEXRAD PRECIPITATION IN A STATISTICAL EVALUATION OF ANTECEDENT MOISTURE CONDITION AND STREAMFLOW. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, 45(2), 419-433.
The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km(2) watershed in south-central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight-day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r(2) = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log-transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base-flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash-Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log-space) for the full 15-month period, -0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of -32%. The contribution of parameter uncertainties to model discrepancy was evaluated.