Open AccessPredicting U.S. Drought Monitor States Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part I: Development of a Nondiscrete USDM Index
Author(s) -
David J. Lorenz,
Jason A. Otkin,
Mark D. Svoboda,
Christopher Hain,
Martha C. Anderson,
Yafang Zhong
Publication year - 2017
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/jhm-d-16-0066.1
Subject(s) - dryness , evapotranspiration , environmental science , precipitation , probabilistic logic , water content , percentile , probability density function , index (typography) , statistics , mathematics , climatology , meteorology , computer science , geography , medicine , ecology , surgery , geotechnical engineering , geology , world wide web , biology , engineering
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 nondiscrete 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 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 is well correlated with the current USDM analyses. In Part II, 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.