Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale

Abstract

[1] The requirements for hydrological models have increased considerably during the previous decades to cope with the resolution of extensive remotely sensed data sets and a number of demanding applications. Existing models exhibit deficiencies such as overparameterization, the lack of an effective technique to integrate the spatial heterogeneity of physiographic characteristics, and the nontransferability of parameters across scales and locations. A multiscale parameter regionalization (MPR) technique is proposed as a way to address these issues simultaneously. Using this technique, parameters at a coarser scale, in which the dominant hydrological processes are represented, are linked with their corresponding ones at a finer resolution in which input data sets are available. The linkage is done with upscaling operators such as the harmonic mean, among others. Parameters at the finer scale are regionalized through nonlinear transfer functions which link basin predictors with global parameters to be determined through calibration. MPR was compared with a standard regionalization (SR) method in which basin predictors instead of model parameters are first aggregated. Both methods were tested in a basin located in Germany using a distributed hydrologic model. Results indicate that MPR is superior to SR in many respects, especially if global parameters are transferred from coarser to finer scales. Furthermore, MPR, as opposed to SR, preserves the spatial variability of state variables and conserves the mass balance with respect to a control scale. Cross-validation tests indicate that the transferability of the global parameters to ungauged locations is possible.Citation: Samaniego, L., R. Kumar, and S. Attinger (2010), Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523,

Groundwater and Surface Water Interaction at the Regional-scale – A Review with Focus on Regional Integrated Models

“…The same is true for regionalisation, i.e. the generalisation of data or model parameters obtained from distinct points or small spatial entities to larger areas (Diekkrüger et al 1999;Kleeberg et al 1999;Parajka et al 2005;Samaniego et al 2010). Few authors, however, deal explicitly with the scale dependency of GW-SW (CSIRO 2008;Dahl et al 2007;Kollet and Maxwell 2008;Levy and Xu 2012).…”

“…& How should we deal with heterogeneity, how do we scale up processes, properties and model parameters (see, e.g. Bárdossy and Singh 2011;de Marsily et al 2005;Fleckenstein et al 2006;Götzinger and Bardossy 2007;McDonnell et al 2007;Noetinger et al 2005;Samaniego et al 2010;Vermeulen et al 2006)?…”

Model parameter transfer for streamflow and sediment loss prediction with SWAT in a tropical watershed

“…The spatial distribution of hydrological variables simulated with those models is achieved by accounting for spatial variability of typical physical characteristics like topography, land use/land cover, soil types and meteorological variables such as temperature and precipitation. Recurrent challenges in modelling medium to large scale watersheds (102 to 105 km 2 ) are typically overparameterization, parameter non-identifiability, non-transferability of parameters across calibration scales and across spatial scales and locations and last but not least, increasing computing time (Beven, 1993;Haddeland, 2002;Samaniego et al, 2010;Kumar et al, 2013). Because distributed hydrological models are spatially complex and deal with large numbers of unknown parameters, parameterization techniques have to be applied.…”

“…Other major challenges when applying distributed hydrological models are the non-transferability of model parameters through spatial resolution and transferability of parameters across scale and space. Several studies have shown that shifting model parameters across calibration scale generates bias in simulation of water fluxes and state variables (Haddeland, 2002;Liang et al, 2004;Samaniego et al, 2010). Similarly, discrepancies occur when parameters are transferred across locations (Merz and Blöschl, 2004;Samaniego et al, 2010;Smith et al, 2012;Singh et al, 2012).…”

“…Several studies have shown that shifting model parameters across calibration scale generates bias in simulation of water fluxes and state variables (Haddeland, 2002;Liang et al, 2004;Samaniego et al, 2010). Similarly, discrepancies occur when parameters are transferred across locations (Merz and Blöschl, 2004;Samaniego et al, 2010;Smith et al, 2012;Singh et al, 2012). However, relatively few researchers have attempted to model parameter transfer so far and none, to our knowledge, have ever tried it in Ethiopia.…”