TY  - JOUR
A1  - Guse, B.
A1  - Pfannerstill, M.
A1  - Fohrer, N.
A1  - Gupta, H.
T1  - Improving Information Extraction From Simulated Discharge Using Sensitivity-Weighted Performance Criteria
Y1  - 2020
VL  - 56
IS  - 9
JF  - Water Resources Research
DO  - 10.1029/2019WR025605
DO  - 10.23689/fidgeo-4498
N2  - Due to seasonal or interannual variability, the relevance of hydrological processes and of the associated model parameters can vary significantly throughout the simulation period. To achieve accurately identified model parameters, temporal variations in parameter dominance should be taken into account. This is not achieved if performance criteria are applied to the entire model output time series. Even when using complementary performance criteria, it is often only possible to identify some of the model parameters precisely. We present an innovative approach to improve parameter identifiability that exploits the information available regarding temporal variations in parameter dominance. Using daily parameter sensitivity time series, we construct a set of sensitivity-weighted performance criteria, one for each parameter, whereby periods of higher dominance of a model parameter and its corresponding process are assigned higher weights in the calculation of the associated performance criterion. These criteria are used to impose constraints on parameter values. We demonstrate this approach by constraining 12 model parameters for three catchments and examine ensemble hydrological simulations generated using these constrained parameter sets. The sensitivity-weighted approach improves in particular the identifiability for parameters whose corresponding processes are dominant only for short periods of time or have strong seasonal patterns. This results overall in slight improvement of model performance for a set of 10 contrasting performance criteria. We conclude that the sensitivity-weighted approach improves the extraction of hydrologically relevant information from data, thereby resulting in improved parameter identifiability and better representation of model parameters.
UR  - http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8844
ER  -