Abstract
Abstract. In this study, land surface related parameter distributions of a conceptual semi-distributed hydrological model are constrained by employing time series of satellite-based evaporation estimates during the dry season as explanatory information. The approach has been applied to the ungauged Luangwa river basin (150 000 (km)2) in Zambia. The information contained in these evaporation estimates imposes compliance of the model with the largest outgoing water balance term, evaporation, and a spatially and temporally realistic depletion of soil moisture within the dry season. The model results in turn provide a better understanding of the information density of remotely sensed evaporation. Model parameters to which evaporation is sensitive, have been spatially distributed on the basis of dominant land cover characteristics. Consequently, their values were conditioned by means of Monte-Carlo sampling and evaluation on satellite evaporation estimates. The results show that behavioural parameter sets for model units with similar land cover are indeed clustered. The clustering reveals hydrologically meaningful signatures in the parameter response surface: wetland-dominated areas (also called dambos) show optimal parameter ranges that reflect vegetation with a relatively small unsaturated zone (due to the shallow rooting depth of the vegetation) which is easily moisture stressed. The forested areas and highlands show parameter ranges that indicate a much deeper root zone which is more drought resistent. Clustering was consequently used to formulate fuzzy membership functions that can be used to constrain parameter realizations in further calibration. Unrealistic parameter ranges, found for instance in the high unsaturated soil zone values in the highlands may indicate either overestimation of satellite-based evaporation or model structural deficiencies. We believe that in these areas, groundwater uptake into the root zone and lateral movement of groundwater should be included in the model structure. Furthermore, a less distinct parameter clustering was found for forested model units. We hypothesize that this is due to the presence of two dominant forest types that differ substantially in their moisture regime. This could indicate that the spatial discretization used in this study is oversimplified.
Highlights
Hydrological models in data sparse areas are often oversimplified
It is likely that the model structure applied, may not be suitable for these areas: first, the Miombo woodlands may tap from groundwater, and second, there may be a lateral influx of groundwater, which cannot be modelled with a 1-dimensional box-type model such as the one we present here
We presented a method with which remotely sensed evaporation can be employed to construct posterior parameter distributions of land-surface related parameters in hydrological models
Summary
Hydrological models in data sparse areas are often oversimplified. This is partly due to the lack of observational data to justify more complexity. Parsimony in model parameters is often advocated to prevent the undesirable occurrence of equifinality Parsimony results in simple and to a certain extent identifiable models, their predictive capacity, for instance of land cover changes, is rather small, because parameters usually have little physical meaning and cannot represent the variability inherent in the landscape. Uhlenbrook et al, 1999) and cannot be justifiably distributed in space in view of the problem of equifinality.
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