Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor Journal of Hydrometeorology

Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Köppen climate zones. This reveals 1) agreement between some ratings and our MI values [high for example indicators like standardized precipitation evapotranspiration index (SPEI)]; 2) some divergences (e.g., soil moisture has high ratings but near-zero MIs for ESA Climate Change Initiative (CCI) soil moisture in the Western United States, indicating the need of another remotely sensed soil moisture source); and 3) new insights into the importance of variables such as snow water equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to 1) hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); 2) hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures, respectively); and 3) predictability (high for the California 2012–17 event, with longer-time scale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.