Towards The Improvement Of Drought Monitoring And Prediction In The United States PDF Download

Drought and heat waves resulted in economic losses of $171 billion in the United States between 1980 and 2007, losses which emphasize the need for a proactive risk management approach for drought management. The motivation for this study is to develop and evaluate approaches and tools to improve drought monitoring and prediction in the U.S., through the use of advanced macroscale hydrologic models and weather/climate forecasts. The value of a macroscale hydrologic model-based Drought Monitoring System (DMS) was assessed in terms of its potential use as a drought-monitoring tool in Washington State. The results show that had the DMS indicators been available during four major droughts from 1976 on, they would have detected the onset and recovery of drought conditions, in many cases, up to four months before state drought declarations. Subsequently, the relative contributions of the sources of seasonal hydrologic and drought predictability (i.e., initial hydrologic conditions (IHCs) and climate forecast skill) were identified over the Contiguous U.S. (CONUS). This analysis indicated that improvement in hydrologic forecasts can result from better knowledge of IHCs in the western U.S. during spring and summer. On the other hand, improved climate forecast skill is needed to improve hydrologic and drought forecast skill in most parts of the northeastern and southeastern U.S. throughout the year and in the western U.S. during fall and winter months. The effect of medium range weather forecast skill (i.e. 14 days) on seasonal hydrologic/drought forecast skill was then investigated. The analysis indicated that medium-range weather forecasts have the potential to improve seasonal hydrologic forecast skill beyond the initial hydrologic condition effect at 1-month leadtime and, in some cases, up to 3 months. Finally, the value of dynamical in contrast with statistical downscaling of seasonal climate forecasts was evaluated in terms of resulting improvement in seasonal hydrologic forecast skill. This analysis identified that dynamical downscaling does somewhat increase the seasonal hydrologic forecast skill over some parts of the Northwestern and North Central U.S.