The Use of Satellite Rainfall Estimates with Real-Time Flow Prediction Models for Large Catchments

使用卫星降雨量估算和大型流域实时流量预测模型

• 批准号: 9207943
• 负责人: Anastasios Tsonis
• 金额: $ 16.16万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-01 至 1995-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9207943&HistoricalAwards=false
• 关键词: Use; Satellite; Rainfall; Estimates; Real

The main research objective of this work is to integrate meteorological procedures for obtaining estimates of mean areal rainfall from satellite Visible and InfraRed data with hydrological procedures and models for real-time flow prediction. In particular the goal is to characterize flow-prediction errors when using satellite rainfall estimates in the real-time prediction of streamflows from large catchments. Uncertainty due to rainfall estimation errors is explicitly taken into account and is propagated through the meteorological and hydrological models and procedures to become uncertainty in the real-time flow predictions. This is a joint project in which the satellite data analysis will be performed at the University of Wisconsin-Milwaukee and the hydrological part will be performed at the University of Iowa. The results of this research will benefit experts, administrators and other users because the developed methods for integrating rainfall satellite data and hydrologic data will be used in predicting flows in real-time and thus, the quality of real-time flood prediction will be enhanced.

这项工作的主要研究目标是将用于从卫星可见光和红外数据获取平均面积降雨量估计的气象程序与用于实时流量预测的水文程序和模型相结合。 特别是，目标是在使用卫星降雨估计来实时预测大型流域的水流时，表征流量预测误差。 由于降雨估计误差引起的不确定性被明确考虑在内，并通过气象和水文模型和程序传播，成为实时流量预测的不确定性。 这是一个联合项目，其中卫星数据分析将在威斯康星大学密尔沃基分校进行，水文部分将在爱荷华大学进行。 这项研究的成果将使专家、管理人员和其他用户受益，因为所开发的降雨卫星数据和水文数据相结合的方法将用于实时预测流量，从而提高实时洪水预测的质量。