Objective Blends of Multiple NLDAS Drought Indices over the Continental United States (CONUS): Development and Application

An objective blending approach of multiple drought indices is developed through collaboration between NOAA National Centers for Environmental Prediction (NCEP), NASA Hydrological Sciences Laboratory, and Princeton University. This study is jointly sponsored by CPO's Modeling Analyses, Prediction and Projection (MAPP) and Climate Testbed Program. The drought indices used in this study include monthly top 1-meter soil moisture and total column soil moisture (SM) percentile, total runoff percentile (Q), Evapotranspiration (ET) percentile, snow water equivalent (SWE) percentile, and 3-month and 6-month standard precipitation index percentile (spi3 and spi6). The blending equations are assumed to be a linear combination of multiple drought indices, and the weights of the equations are estimated by an optimization approach (called very fast simulated annealing) through minimizing root-mean-square error between drought extent derived from the US drought Monitor (USDM) and the blends. The calibrated spatial scales are CONUS, six USDM regions, and 48 states. The results show that for most cases, SM plays a dominant role for most regions and states, followed by Q, spi6, ET, and spi3 over the continental United States. Over the western regions, the SWE plays a dominant role, followed by SM and ET. This work will be expanded by adding operational drought indices used in the Climate Prediction Center and remotely-sensed drought indices through a collaboration with CPC scientists. Spatial scales will be expanded from states to 345 climate divisions, and temporal scales will be expanded from monthly to weekly. The purpose of this study is to enhance the objectivity of the USDM and efficiency of generating the USDM draft map from the NLDAS products and other drought indices.