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Title: The impact of spatial soil variability on simulating soil moisture deficit : an application of Topmodel
Author: Ciaccio, M.
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2002
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Abstract:
The present research aims to illustrate and evaluate the effects of spatially variable soil data on the modelling of catchment rainfall-runoff transformations, using the hydrological model Topmodel. The soil-topographic wetness index used in Topmodel has always allowed for a spatially variable T0 - lateral saturated transmissivity - yet very little published research has focussed on the use of spatial soil datasets to derive To. In recent years the availability of soil hydrologic parameters, either from soil classification and/or from new measurement techniques has increased significantly and, especially with regards to remote sensing, there is still great potential for further advances. It is therefore important that models like Topmodel should be able to incorporate such distributed soil data and to assess if its' inclusion may allow a better representation of rainfall-runoff transformation processes. In particular, one of the key issues is the need to use distributed data to predict internal catchment conditions - such as runoff source areas - and not only global volumetric outflows. This aspects is of importance both a the catchment scale, for improved integrated catchment management (i.e. in the presence of land-use changes), and at the GCM modelling scale for the simulation of regional land-atmosphere interactions. With regard to the soil data, particular importance is associated to soil hydraulic parameters such as porosity and saturated conductivities. Traditionally, such data have only been available from measurements on single soil samples. But in recent years, various analytical methods and hydromorphic classification schemes have been developed which allow us to estimate the above parameters or, alternatively, provide qualitative indeces of the soils behaviour in terms of runoff generation. The present research has therefore evaluated the effect of different soil classification schemes with respect to their ability to improve the prediction of soil moisture deficit using TOPMODEL. Given the strengths of GIS in storing and analysing spatial data, the research has also evaluated if and how GIS can be used to better understand the effect of spatial classification schemes applied to the soil input data. Though GIS cannot substitute the theoretical knowledge of the processes occurring, it can certainly provide the spatial functionalities often lacking in hydrological models. It is this spiral perspective that can allow us to visualise synoptically the phenomena being studied, while at the same time exploring, highlighting, and verifying the prominent spatial variables that control the rainfall-runoff transformation processes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.643174  DOI: Not available
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