|LORENZ, D - University Of Wisconsin|
|OTKIN, J. - University Of Wisconsin|
|SVOBODA, M. - University Of Nebraska|
|HAIN, C. - University Of Maryland|
|ZHONG, Y - University Of Wisconsin|
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/3/2014
Publication Date: 7/3/2017
Citation: Lorenz, D., Otkin, J., Svoboda, M., Hain, C., Anderson, M.C., Zhong, Y. 2017. Predicting the US Drought Monitor (USDM) using precipitation, soil noisture, and evapotranspiration anomalies, Part II: Intraseasonal drought intensification forecasts. Journal of Hydrometeorology. 18:1943-1962. https://doi.org/10.1175/JHM-D-16-0067.1.
Interpretive Summary: The ability to reasonably forecast drought conditions, even over periods of a few weeks to months, will have significant benefit to the agricultural community and other drought-vulnerable groups by allowing them to implement proactive measures in a timely manner to lessen the detrimental impacts of drought. This paper describes statistical methods for generating forecasts of US Drought Monitor (USDM) intensification over two, four and eight week time periods, based on recent anomalies in precipitation, evapotranspiration and soil moisture observed on the ground and with spaceborne sensors. The 2- and 4-week forecasts show good accuracy in predicting drought intensification, particularly over the north-central U.S. The model was able to predict a month or so in advance several flash drought events that occurred over the U.S. during the past decade. Future work will combine predictions from the method presented in this paper with data from numerical weather forecasts to further increase the accuracy of the USDM drought intensification forecasts.
Technical Abstract: Probabilistic forecasts of US Drought Monitor (USDM) intensification over two, four and eight 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 non-discrete 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 further from this threshold. The method shows skill over most of the US, but is most skillful over the north-central US where the cross-validated Brier Skill Score averages 0.20 for both two and four week forecasts. The eight-week forecasts are less skillful in most locations. The two and four week probabilities have very good reliability. The eight-week probabilities, on the other hand, are noticeably over-confident. For individual drought events, the method shows the most skill when forecasting high amplitude flash droughts and when large regions of the US are experiencing intensifying drought.