Search

Copyright of Annals of the Association of American Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Extreme weather and climate events, especially short-term excessive drought and wet periods over agricultural areas, have received increased attention. The Southern Great Plains (SGP) is one of the largest agricultural regions in North America and features the underlying Ogallala-High Plains Aquifer system worth great economic value in large part due to production gains from groundwater. Climate research over the SGP is needed to better understand complex coupled climate-hydrology-socioeconomic interactions critical to the sustainability of this region, especially under extreme climate scenarios. Here the authors studied growing-season extreme conditions using the Weather Research and Forecasting (WRF) Model. The six most extreme recent years, both wet and dry, were simulated to investigate the impacts of land surface model and cumulus parameterization on the simulated hydroclimate. The results show that under short-term climate extremes, the land surface model plays a more important role modulating the land-atmosphere water budget, and thus the entire regional climate, than the cumulus parameterization under current model configurations. Between the two land surface models tested, the more sophisticated land surface model produced significantly larger wet bias in large part due to overestimation of moisture flux convergence, which is attributed mainly to an overestimation of the surface evapotranspiration during the simulated period. The deficiencies of the cumulus parameterizations resulted in the model's inability to depict the diurnal rainfall variability. Both land surface processes and cumulus parameterizations remain the most challenging parts of regional climate modeling under extreme climates over the SGP, with the former strongly affecting the precipitation amount and the latter strongly affecting the precipitation pattern. [ABSTRACT FROM AUTHOR]

Disastrous impacts of recent drought events around the world have led to extensive efforts in drought monitoring and prediction. Various drought information systems have been developed with different indicators to provide early drought warning. The climate forecast from North American Multimodel Ensemble (NMME) has been among the most salient progress in climate prediction and its application for drought prediction has been considerably growing. Since its development in 1999, the U.S. Drought Monitor (USDM) has played a critical role in drought monitoring with different drought categories to characterize drought severity, which has been employed to aid decision making by a wealth of users such as natural resource managers and authorities. Due to wide applications of USDM, the development of drought prediction with USDM drought categories would greatly aid decision making. This study presented a categorical drought prediction system for predicting USDM drought categories in the U.S., based on the initial conditions from USDM and seasonal climate forecasts from NMME. Results of USDM drought categories predictions in the U.S. demonstrate the potential of the prediction system, which is expected to contribute to operational early drought warning in the U.S. [ABSTRACT FROM AUTHOR]