Toward computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme



[1] 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 ungauged basins, and (2) as a tool for upscaling high-fidelity hydrologic simulations closer to a general circulation model (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 United States. 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 toward 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.