The solution of many practical water problems is strictly connected to the availability of reliable and widespread information about runoff. The estimation of mean annual runoff and its interannual variability for any basin over a wide region, even if ungauged, would be fundamental for both water resources assessment and planning and for water quality analysis. Starting from these premises, the main aim of this work is to show a new approach, based on the Budyko's framework, for mapping the mean annual surface runoff and deriving the probability distribution of the annual runoff in arid and semiarid watersheds. As a case study, the entire island of Sicily, Italy, is here proposed. First, time series data of annual rainfall, runoff, and reconstructed series of potential evapotranspiration have been combined within the Budyko's curve framework to obtain regional rules for rainfall partitioning between evapotranspiration and runoff. Then this knowledge has been used to infer long-term annual runoff at the point scale by means of interpolated rainfall and potential evapotranspiration. The long-term annual runoff raster layer has been obtained at each pixel of the drainage network, averaging the upstream runoff using advanced spatial analysis techniques within a GIS environment. Furthermore, 2 alternative methods are here proposed to derive the distribution of annual runoff, under the assumption of negligible interannual variations of basin water storage. The first method uses Monte Carlo simulations, combining rainfall and potential evapotranspiration randomly extracted from independent distributions. The second method is based on a simplification of the Budyko's curve and analytically provides the annual runoff distribution as the derived distribution of annual rainfall and potential evapotranspiration. Results are very encouraging: long-term annual runoff and its distribution have been derived and compared with historical records at several gauged stations, obtaining satisfactory matching.
|Numero di pagine||13|
|Stato di pubblicazione||Published - 2017|
All Science Journal Classification (ASJC) codes