Runoff data knowledge is of fundamental importance for a wide range of hydrological, ecological, and socioeconomic applications. The reconstruction of annual runoff is a fundamental task for several activities related to water resources management, especially for ungauged basins. At catchment scales, the Budyko's framework provides an extremely useful and, in some cases, accurate estimation of the long-term partitioning of precipitation into evapotranspiration and runoff as a function of the prevailing climatic conditions. Recently the same long-term partitioning rules have been successfully used to describe water partitioning also at the annual scale and calculate the annual runoff distribution within a simple analytic framework in arid and semi-arid basins. One of the main advantages of the latter method is that only annual precipitation and potential evapotranspiration statistics, and the Fu's equation parameter omega are required to obtain the annual runoff probability distribution. The aim of this study is to test the limit and potentialities of the aforementioned method under different climatic conditions. To this aim, the model is applied to more than four hundred basins located in the United States. Catchments were grouped into five different samples, following the subdivision of the continental region in five homogeneous climatic zones according to Koppen-Geiger classification. The theoretical probability distribution of annual runoff at each basin has been compared with that derived from historical observations. The results confirm the capability of the tested technique to reproduce the empirical annual runoff distributions with similar and satisfactory performances across different areas, revealing a good option also in cases characterized by climate and hydrological conditions very different from those hypothesized during the original analytical model design, thus extending the geographical and conceptual limits of applicability of the framework.
|Numero di pagine||14|
|Rivista||Water Resources Management|
|Stato di pubblicazione||Published - 2018|
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