The effects of the spatial variability of soil infiltration capacity in distributed flood modelling

✍ Scribed by Armando Brath; Alberto Montanari

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 279 KB
• Volume: 14
• Category: Article
• ISSN: 0885-6087
• DOI: 10.1002/1099-1085(20001030)14:15<2779::aid-hyp91>3.0.co;2-x

✦ Synopsis

Spatial variability is recognized to exert a remarkable in¯uence on the catchment hydrological response over a wide range of scales. Accordingly, one of the main advantages of distributed rainfall±runo models is their capability of accounting for its eects. The application of these models, however, is often limited by the large amount of data required. Particularly, the spatial characterization of both the vegetation cover and soil type is needed in order to describe properly the spatial distribution of the soil in®ltration properties. Lack of these data, which are often quite dicult to obtain in practice, is one of the reasons which may induce hydrologists to prefer the application of lumped models, despite their lower capability of describing hydrological processes. A possible way out for preserving the distributed nature of the model is neglecting the spatial variability of the soil in®ltration process by lumping the correspondent model parameters at the basin scale, and describing the transfer of runo production at the basin outlet through a distributed approach. Lumping the in®ltration parameters might be a reasonable assumption if one is interested in modelling those situations where the spatial variability of the in®ltration process is expected to exert a marginal eect on the river discharge. Accordingly, the present analysis is aimed at verifying the eects of the spatial variability of the soil in®ltration parameters on the ¯ood ¯ows modelled by a distributed model, with special reference to a case study of a river basin located in northern Italy. The main purpose of this work is to derive indications in order to possibly reduce the data requirements of distributed models, thus encouraging their application even in the case of limited data availability.