Background/Objectives
The spatial and temporal distribution of rainfall strongly influences the behaviors of surface water and groundwater systems. Rainfall gauges measure rainfall at local point locations. In the last three decades, Doppler radar has been providing high-resolution rainfall data throughout most of the United States but such data are not accurate because they were not direct rainfall measurements. Therefore, the variability of future rainfall is highly uncertain which makes managing reliability, resilience, and vulnerability of water system challenging, especially in the events of extreme drought and flood situations.
Approach/Activities
In the northern Tampa Bay region, a Bayesian data fusion approach has been applied to integrate rain gauge and radar rainfall data to improve resolution and accuracy of rainfall estimation in the last 25 years. Together with rain gauges with long-term rainfall data, the improved results were used to generate Monte Carlo realizations of the spatial and temporal variation of future rainfall. Such realizations were used in an integrated groundwater-surface water model to analyze the hydrological responses in the region to future rainfall uncertainty under different water management strategies. The results were used to develop plans for improving the management of reliability, resilience, and vulnerability of the water system.
Results/Lessons Learned
This presentation will describe the technical basis of the analysis and the development of strategy for managing the water system in the future.