Retrieving precipitation data from raingauge network is a classical and common practice in hydrology and climatology. These data represent the key input in hydrological modeling to reproduce, for example, the characteristics of a flood phenomenon. The accuracy of the model results is strongly dependent on the consistency of the monitoring network in terms of spatial scale, i.e. network density and location of raingauges, and time resolution. In this context, several studies have been carried out to analyze how the rainfall sampling influences the estimation of total runoff volume.The aim of this paper is to use a physically based and distributed-parameter hydrologic model to investigate how the number and the spatial distribution of a raingauge network influence the estimation of the hydrograph and its characteristics, in conjunction with different spatial and temporal characteristics of rainfall forcing and different soil type characteristics. The tRIBS hydrologic model was used to simulate hydrologic response at Baron Fork basin, Oklahoma. Downscaled NEXRAD radar measurements were assumed to represent the true precipitation distribution over the basin. Additional precipitation fields have been derived from interpolation of eight fictitious raingauges randomly placed in the area. The hydrological response from tRIBS that is driven by these precipitation fields has been compared with the response of the model forced with NEXRAD precipitation. The analysis has been carried out assuming first simplified spatial distributions of soil characteristics, and then the real soil-type distribution. Results have shown the dependence of the best raingauges configuration for the estimation of runoff on the spatiotemporal characteristics of storm events and the soil-type distribution.
|Numero di pagine||10|
|Rivista||JOURNAL OF HYDROLOGIC ENGINEERING|
|Stato di pubblicazione||Published - 2014|
All Science Journal Classification (ASJC) codes
- Environmental Chemistry
- Civil and Structural Engineering
- Water Science and Technology