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United States Department of Agriculture

Agricultural Research Service

Title: Adapting Seasonal Precipitation Forecasts for Agricultural and Natural Resource Management: Assessment of Utility, Downscaling, and Initial Strategy for Implementation

Authors
item Schneider, Jeanne
item Garbrecht, Jurgen

Submitted to: Climate and Weather Research Workshop Proceedings
Publication Type: Proceedings
Publication Acceptance Date: October 1, 2002
Publication Date: October 1, 2002
Citation: Schneider, J.M., Garbrecht, J.D., Adapting seasonal precipitation forecasts for agricultural and natural resource management: assessment of utility, downscaling, and initial strategy for implementation. Proceedings of NOAA's Climate Prediction Assessment Workshop: Research and Applications on Use and Impacts. 2002. Abstract p. 37.

Interpretive Summary: The NOAA/Climate Prediction Center seasonal precipitation forecasts offer potentially important information for practical applications. However, given the format of the forecasts, it is not obvious to agricultural and natural resource managers when or how to apply the forecast information. Scientists at the Grazinglands Research Laboratory are working to incorporate seasonal precipitation forecasts in risk-based decision support tools. Early assessments include forecast utility, the initial strategy for developing the correspondence between forecasts and agricultural or hydrologic impacts, and methodologies to downscale the forecasts to watershed to field scale at monthly increments. Forecast performance over the period 1995-2001 was evaluated using several application-oriented measures of utility. These analyses reveal a wide disparity across the coterminous United States in potential utility of the precipitation forecasts, with best results generally across the southern tier of states. Since forecast information will be used on smaller scales, methodologies were developed to downscale the precipitation forecasts in space and time using only local climatological information. But successful utilization of the forecasts requires more than just an understanding of forecast characteristics and performance. The key effort will be the definition of the potential impact of variations in monthly to yearly precipitation on the managed systems. An initial strategy for the development of 1-to-1 correspondence between forecast sequences and agricultural or hydrologic impacts has been designed in accordance with current practice in crop and hydrologic modeling.

Technical Abstract: The NOAA/Climate Prediction Center seasonal precipitation forecasts offer potentially important information for practical applications. However, given the nature of the forecasts, it is not obvious to agricultural and natural resource managers when or how the forecast information could or should be applied. Scientists at the Grazinglands Research Laboratory are working to incorporate seasonal precipitation forecasts in risk-based decision support tools, with intent to preserve the inherent uncertainty of the precipitation forecasts and include information on forecast reliability. Forecast performance at the forecast division scale over the period 1995-2001 has been evaluated using several application-oriented measures of utility. These analyses reveal a wide disparity across the contiguous United States in potential utility of the precipitation forecasts. Since forecast information will be used on smaller scales (watershed to field scale at monthly increments), methodologies have been developed to downscale the precipitation forecasts in space and time using only local climatological information. But successful utilization of the forecasts requires more than just an understanding of forecast characteristics and performance. The key effort will be the definition of the potential impact of variations in monthly to yearly precipitation on the managed systems. An initial strategy for the development of 1-to-1 mapping between forecast sequences and agricultural or hydrologic impacts has been developed, designed in accordance with current practice in crop and hydrologic modeling.

Last Modified: 4/23/2014
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