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|Title:||Semi-Distributed Calibration of a Rainfall-Runoff Model for the Morava Catchment Using Global Optimization|
|Authors:||KALAS MILAN; FEYEN LUC; VRUGT Jasper A.|
|Citation:||The 23 Conference of the Danubian Countries on the Hydrological Forecasting and Hydrological Basis of the Water Management - Conference Abstracts p. 27|
|Publisher:||Committee for International Hydrological Programme, Serbia & Montenegro|
|Type:||Articles in periodicals and books|
|Abstract:||In this paper we address the problem of parameter identification and parameter uncertainty estimation for the rainfall-runoff model LISFLOOD. The model is driven by meteorological input data and simulates river discharges in large drainage basins as a function of spatial information on topography, soils and land cover. Even though LISFLOOD is physically-based to a certain extent, some processes are only represented in a lumped conceptual way. As a result, some parameters lack physical basis and cannot be directly inferred from quantities that can be measured. In the current LISFLOOD version five parameters need to be determined by calibration. We employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm [Vrugt et al., 2003] to automatically calibrate the model against discharge observations. The resulting posterior parameter distribution reflects the residual uncertainty about the model parameters and forms the basis for making probabilistic flow predictions. Typically, uniform values are identified for the unknown parameters by calibration against discharge observations at the catchment outlet. We assess the value of semi-distributing the calibration parameters by comparing three different calibration strategies. In the first calibration strategy uniform values over the entire area of interest are adopted for the unknown parameters, which are calibrated against discharge observations at the downstream outlet of the catchment. In the second calibration strategy the parameters are also uniformly distributed, but they are calibrated against the discharge at the catchment outlet and at internal discharge stations. In the third strategy a semi-distributed approach is adopted. Starting from upstream, parameters in each subcatchment are calibrated against the observed discharges at the outlet of the subcatchment. In order not to propagate upstream errors in the calibration process, observed discharges at upstream catchment outlets are used as inflow when calibrating downstream subcatchments. As an illustrative example, we demonstrate the methodology for a part of the Morava catchment, covering an area of approximately 10.000 km2. The calibration results reveal that the additional value of the internal discharge stations is limited when applying a lumped parameter approach. Moving from a lumped to a semi-distributed parameter approach (i) improves the flow predictions, especially in the upstream subcatchments; and (ii) reduces parameter uncertainty, and consequently flow prediction uncertainty. The results show the clear need to spatially vary the calibration parameters, especially in large catchments characterized by spatially varying hydrological processes and responses.|
|JRC Institute:||Sustainable Resources|
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