Christopher R. Hain

Probabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the “distance” from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought.

Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.