Author Description

About 7,000 people lose their lives and nearly 100 million people are adversely affected by floods each year worldwide. Severe flooding also costs billions of dollars each year in damage and economic losses. This new volume focuses on two detailed studies that employ physically based hydrologic models to predict flooding in the particularly challenging environment of small watersheds with mountainous terrain and high intensity/high variability rainfall. The first study, by Dr. Alejandra Rojas Gonzalez, discusses flood prediction limitations in small watersheds with mountainous terrain and high rainfall variability. The hypothesis of the study is that it is possible to perform a small-scale, affordable model calibration, and then scale-up the parameters to a larger basin-scale model. The study specifically addresses the following scientific questions: How is flow prediction affected by the spatial variability of point rainfall at scales below that of the typical resolution of radar-based products? How does parameter and hydrological model resolution affect the model's predictive capabilities and the errors of the hydrologic model? Would the assumptions developed for the small scale enhance the hydrologic predictability at larger scales? The second study, by Dr. Luz E. Torres Molina, describes the development of a stochastic model to forecast short-term rainfall for a tropical basin. The high-resolution rainfall data (≈ 100-m) was derived using the TropiNet radar system at the University of Puerto Rico-Mayaguez Campus, representing possibly the only study of its kind in a tropical environment. The predicted short-term rainfall data was input into a hydrologic model, and flood inundation levels were estimated at selected locations within the basin. Results of the rainfall and hydrologic forecasts are compared with observed data. The study also provides a prototype for a flood forecast alarm system. Book jacket.