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Title (Primary) Towards computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme
Author Kumar, R.; Livneh, B.; Samaniego, L.;
Journal Water Resources Research
Year 2013
Department CHS;
Volume 49
Issue 9
Language englisch;
POF III (all) T34;
Keywords MPR; PUB; Hydrologic model; upscaling; downscaling
UFZ wide themes RU5;
Abstract We present an assessment of a framework to reduce computational expense required for hydrologic prediction over new domains. A common problem in computational hydrology arises when a Hydrologist seeks to model a new domain and is subsequently required to estimate representative model parameters for that domain. Our focus is to extend previous development of the Multiscale Parameter Regionalization (MPR) technique, to a broader set of climatic regimes and spatial scales to demonstrate the utility of this approach. We hypothesize that this technique will be applicable for (1) improving predictions in un-gauged basins, and (2) as a tool for up-scaling high-fidelity hydrologic simulations closer to GCM scales, while appreciably reducing computational expense in parameter estimation. We transfer hydrologic model parameters from a single central European basin, to 80 candidate basins within the U.S. The regionalization is further tested across a range of climatic and land-cover conditions to identify potential biases in transferability. The results indicate a high degree of success in transferring parameters from central Europe to North America. Parameter scaling from 1/8° up to 1° confirms that MPR can produce a set of quasi-scale independent parameters, with only modest differences in model performance across scales (< 3%). Model skill generally decreases approximately 10-20% when transferring parameters towards alternate climatic and land-cover conditions. Finally, we show that the success of model parameter transfer is contingent upon soil, land-cover, and climatic regimes relative to those used during calibration, particularly going from high-to-low clay content and from dense-to-sparse forest.
ID 13894
Persistent UFZ Identifier
Kumar, R., Livneh, B., Samaniego, L. (2013):
Towards computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme
Water Resour. Res. 49 (9), 5700 - 5714