Abstract
Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input—precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better than using traditional interpolated observations can be obtained. These realizations show how much rainfall runoff models may profit from better precipitation input and how much remains for the parameter and model concept uncertainty. The methodology is applied using two hydrological models with a contrasting basis, SHETRAN and HBV, for three different mesoscale sub-catchments of the Neckar basin in Germany. Results show that up to 50% of the model error can be attributed to precipitation uncertainty. Further, inverting precipitation using hydrological models can improve model performance even in neighboring catchments which are not considered explicitly.
Highlights
Hydrological modeling is affected by high uncertainty due to problems with: process description. process parametrization. observation uncertainty. spatial variability of static or slowly changing spatial fields. spatial and temporal variability of meteorological forcing—precipitation and temperature.The quantification of the uncertainty of the outputs of hydrological models is very important both theoretically and for applications
In order to see to what extent the consideration of the discharge of several catchments constrains the adjustments of the precipitation fields, a joint inversion was carried out
The improvement is less than that with the same number of iterations for the individual catchments, but one can find precipitation fields which fulfill the conditions that lead to a significant improvement for all catchments
Summary
Hydrological modeling is affected by high uncertainty due to problems with:. process description. process parametrization. observation uncertainty. spatial variability of static or slowly changing spatial fields (soil properties, land cover). spatial and temporal variability of meteorological forcing—. There are multiple sources of uncertainty in the modeling process to be considered, which may be more important than parameter estimation These include the observation of inputs to the model (typically precipitation, potential evapotranspiration), the specification of inputs to the model (e.g., time and space resolution), the model structure (e.g., physics-based or lumped), and the observed discharge data used for model validation (or calibration). In these cases most frequently sensitivity analyses have been carried out to show how large differences in the simulated discharge can be as a consequence of changing precipitation input. A methodology to find appropriate plausible space-time precipitation fields is developed The use of this methodology leads to further research questions: Is this methodology general or specific to different hydrological models? A similar approach for the analysis of a single event was presented in Bárdossy et al (2020)
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