Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #330500

Title: Predicting US Drought Monitor (USDM) states using precipitation, soil moisture, and evapotranspiration anomalies, Part I: Development of a non-discrete USDM index

item LORENZ, D. - University Of Wisconsin
item OTKIN, J. - University Of Wisconsin
item SVOBODA, M. - University Of Nebraska
item HAIN, C. - University Of Maryland
item Anderson, Martha
item ZHONG, Y. - University Of Wisconsin

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/3/2017
Publication Date: 7/3/2017
Citation: Lorenz, D., Otkin, J., Svoboda, M., Hain, C., Anderson, M.C., Zhong, Y. 2017. Predicting US Drought Monitor (USDM) states using precipitation, soil moisture, and evapotranspiration anomalies, Part I: Development of a non-discrete USDM index. Journal of Hydrometeorology. 18:1963-1982.

Interpretive Summary: The ability to forecast drought conditions, both in the short term (several weeks) and the long term (months), is a topic at the forefront of current drought research. Several recent severe drought events, such as the flash drought that afflicted the central United States in 2012, were not well-predicted by current drought forecasting tools, and did not provide sufficient warning for adaptive management and decision-making within the agricultural community. This paper is the first part in a two-part series discussing methods for predicting future states of the United States Drought Monitor (USDM) - the state-of-the-art depiction of current drought conditions over the U.S. The USDM is a categorical (discrete) drought classification, with categories ranging from "no drought" to "exceptional drought", and as such is not conducive to forecasting methods which typically work with continuous (non-discrete) datasets. In Part I of this series, we present a technique for converting the USDM into a continous variable using predictors based on anomalies in precipitation, soil moisture and evapotranspiration. This new continuous form of the USDM also includes "wet" categories, as well as better information on where, within a specific drought category, current conditions lie. In Part II, the improved current drought state estimates generated using the method described here are used to compute probabilistic intensification forecasts over sub-seasonal time scales.

Technical Abstract: The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a non-discrete U.S. Drought Monitor (USDM) index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be optimally reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th and 2nd percentiles – corresponding with USDM definitions for the D4-D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the Probability Density Function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness of fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method provides accurate estimates of the current USDM analyses. In a follow-on paper, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next higher drought category.