|Wagener, T. - PENN STATE UNIVERSITY|
|Gupta, H. - UNIVERSITY OF ARIZONA|
|Yatheendradas, S. - UNIVERSITY OF ARIZONA|
|Schaffner, M. - NATIONAL WEATHER SERVICE|
Submitted to: International Association of Hydrological Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 10, 2007
Publication Date: July 13, 2007
Citation: Wagener, T., Gupta, H., Yatheendradas, S., Goodrich, D.C., Unkrich, C.L., Schaffner, M. 2007. Understanding sources of uncertainty in flash-flood forecasting for semi-arid regions. International Association of Hydrological Science. 313:204-212. Interpretive Summary: One-third of the earth’s surface can currently be classified as arid or semi-arid. This fraction may increase in the future for example due to global warming effects. Many arid and semi-arid regions are particularly affected by flash floods, caused mainly by convective storm systems, and they often result in substantial damages to life and property. The short duration and the small geographic extent of these events make predicting the subsequent floods extremely difficult. To improve our predictive flood capability, we have developed a semi-arid specific modeling system based on the well-established USDA-ARS event-based rainfall-runoff model KINEROS2. The model is driven by high-resolution, near real-time radar rainfall inputs. The ability of this modeling system to predict floods was investigated as well as how uncertainty in the rainfall data and the model affects these predictions. The model was found to make good predictions when calibrated but it was found that the predictive uncertainty of the model is dominated by the radar-rainfall depth estimates. This highlights the need to use radar rainfall data and ground-based rain gauge data to improve predictions.
Technical Abstract: About one-third of the earth’s landsurface is located in arid or semi-arid regions, often in areas suffering severely from the negative impacts of desertification and population pressure. Reliable hydrological forecasts across spatial and temporal scales are crucial in order to achieve water security – protection from excess and lack of water – for the population and ecosystems in these areas. At short temporal scales, flash floods are extremely dangerous hazards accounting, for example, for more than 80% of all flood related deaths in the USA. Forecasting of these floods requires a connected spatially-distributed hydro-meteorological modeling system which accounts for the specific meteorological and hydrological characteristics of semi-arid watersheds, e.g. convective events and transmission losses. The spatially highly heterogeneous nature of the precipitation and the non-linear response behavior of the system demand the explicit accounting and propagation of uncertainties into the model predictions. This short paper presents the results of a multi-year study in which such a system was developed for flash-flood forecasting in the semi-arid southwestern US. In particular, we discuss our effort to understand and estimate underlying uncertainties in such a modeling system.