Browsing by Author "Das, Tapash"

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The impact of spatial variability of precipitation on the predictive uncertainty of hydrological models
(2006) Das, Tapash; Bárdossy, András (Prof. Dr. rer.nat. Dr.-Ing.)
Hydrological models are simplified representations of a part of the hydrological cycle. The fact that natural processes are described with mathematical equations and the corresponding parameters are estimated using observations leads to uncertainties. The uncertainty stems from the parameters, the model structure and measurements of input and output data. Precipitation is one of the most important hydrological model inputs. Precipitation is often significantly variable in space and time within a catchment. The main aim of this dissertation was to investigate and quantify the impact of spatial variability of precipitation on the predictive uncertainty of hydrological model simulations. Given the importance of the role of the precipitation input in hydrological applications, the following research questions were addressed: (a) how does the spatial variability of precipitation influence the hydrological simulation results? (b) will a higher spatial resolution of model input data necessarily lead to a better model performance? (c) what is the impact on the simulated hydrographs of interpolated precipitation at different spatial resolutions through varying raingauge networks? (d) what is the benefit of using conditionally simulated precipitation in hydrological modeling? (e) how does uncertainty in precipitation affect parameter identification of a conceptual model? The modified rainfall-runoff model HBV was applied to investigate the majority of the objectives. Based on the HBV model concept, four different structures namely, fully-lumped, semi-lumped, semi-distributed and distributed were developed. The physically-based spatially-distributed modeling system SHETRAN was also used to investigate how uncertainty in precipitation affects parameter identification of a conceptual model? The upper Neckar catchment, located in south-west Germany, was selected as test catchment. A number of simulation experiments were carried out in line with the objectives and scope of this study. The study aimed to investigate the influence of spatial variability of precipitation in a rainfall-runoff model indicated no significant differences in the model performance when the model was run using averaged precipitation at different spatial scales. However, there was a clear deterioration in the model performance during the summer season. The results also highlight that there can be a significant deterioration in the model performance when the model calibrated using detailed precipitation is run using relatively less detailed input precipitation. The study on the comparison of modelling performance using different representations of spatial variability indicates that for the present study catchment semi-distributed and semi-lumped model structures out-perform the distributed and fully-lumped model structures for the given level of information. The results indicate that using interpolated precipitation on finer resolution does not improve the simulation accuracy in either the calibration or validation periods at the subcatchments’ outlets. The study suggests that there can be a trade-off among the model complexity and available observations. The study related to assess the impacts of raingauge density on the simulation results showed that the number and spatial distribution of raingauges affect the simulation results. It was found that the model performances worsen radically with an excessive reduction of raingauges. However, the performances were not significantly improved by increasing the number of raingauges more than a certain threshold number. The analysis also indicates that models using different raingauge networks might need their parameters recalibrated. Specifically, models calibrated with dense input precipitation information fail when run with sparse information. However, the models calibrated with sparse input precipitation information can perform well when run with dense information. Also the model calibrated with complete set of observed precipitation and being run with incomplete observed precipitation data associated with data estimated at the locations with missing measurements using multiple linear regression technique, performed well. Conditional spatial rainfall simulation indicates significantly more spatial variability in the simulated rainfall than interpolated rainfall. The model performs better for modeling the peak discharges using conditionally simulated rainfall than the model using interpolated rainfall. Thus conditional rainfall simulation is reasonable for flood modeling. The analysis also indicates that inadequate representation of spatial variability of precipitation in modeling is partly responsible for modeling errors and also this leads to the problems in parameter estimation of a conceptual hydrological model. Thus spatial variability must be captured and used as an input to the hydrological model in order to eliminate the errors due to input rainfall data.