Abstract
Abstract. State-of-the-art hydrological applications require a process-based, spatially distributed hydrological model. Runoff characteristics are demanded to be well reproduced by the model. Despite that, the model should be able to describe the processes at a subcatchment scale in a physically credible way. The objective of this study is to present a robust procedure to generate various sets of parameterisations of soil hydraulic functions for the description of soil heterogeneity on a subgrid scale. Relations between Rosetta-generated values of saturated hydraulic conductivity (Ks) and van Genuchten's parameters of soil hydraulic functions were statistically analysed. An universal function that is valid for the complete bandwidth of Ks values could not be found. After concentrating on natural texture classes, strong correlations were identified for all parameters. The obtained regression results were used to parameterise sets of hydraulic functions for each soil class. The methodology presented in this study is applicable on a wide range of spatial scales and does not need input data from field studies. The developments were implemented into a hydrological modelling system.
Highlights
IntroductionOne of the major challenges in hydrological process modelling is to minimise the discrepancy between model and data scale as described e.g. by Blöschl and Sivapalan (1995) or Hopmans et al (2002)
One of the major challenges in hydrological process modelling is to minimise the discrepancy between model and data scale as described e.g. by Blöschl and Sivapalan (1995) or Hopmans et al (2002).State-of-the-art hydrological applications require a process-based, spatially distributed hydrological model
Since the objective of this paper is the consideration of subgrid variability of the parameterisation of soil hydraulic functions at the meso- and macroscale, the model for the description of the soil hydraulic functions has to be determined in the first place
Summary
One of the major challenges in hydrological process modelling is to minimise the discrepancy between model and data scale as described e.g. by Blöschl and Sivapalan (1995) or Hopmans et al (2002). State-of-the-art hydrological applications require a process-based, spatially distributed hydrological model. Even for large-scale applications, the model should be able to describe the processes at a subcatchment scale in a physically credible way. Following Blöschl and Sivapalan (1995), hydrological processes that are dominant at spatial scales larger than the smallest calculation unit (hydrological response unit respective elementary grid size) of the model are assumed to be described directly by the model. Small-scale processes below the smallest spatial calculation unit are assumed to be described indirectly by the model, e.g. by calibration
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