A distributed real-time semiarid flash-flood forecasting model utilizing radar data

One-third of the Earth's surface can currently be classified as arid or semiarid. This fraction may increase in the future for example due to global warming effects. Many arid and semiarid regions are particularly affected by flash floods, caused mainly by convective storm systems, and often resulting in significant damages to property and even loss of life. The short duration and the small geographic extent of these events make predicting the subsequent floods extremely difficult. To improve our predictive capability, we are currently developing a semiarid specific model based on the well-established event-based rainfall-runoff model KINEROS2, capable of continuously simulating the response of a specific basin and driven by high-resolution precipitation measurements. This spatially distributed kinematic wave model represents the basin as a cascade of planes and channels. The dynamic infiltration algorithm is particularly well suited for simulation of semiarid hydrological processes. Adjustments to the original model include restructuring the code in a modular fashion, adding long-term soil moisture storage and evapotranspiration algorithms, and including optimization tools for parameter estimation. The project aims towards more accurate, reliable and probabilistic flood warnings, for semiarid flash-flood forecasting, risk assessment and decision making. This paper outlines the model and some associated data processing tools, and represents some initial results of applying the model to a small semiarid basin in the southwestern USA.