2015
DOI: 10.1002/2015wr017780
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Physically based modeling in catchment hydrology at 50: Survey and outlook

Abstract: Integrated, process‐based numerical models in hydrology are rapidly evolving, spurred by novel theories in mathematical physics, advances in computational methods, insights from laboratory and field experiments, and the need to better understand and predict the potential impacts of population, land use, and climate change on our water resources. At the catchment scale, these simulation models are commonly based on conservation principles for surface and subsurface water flow and solute transport (e.g., the Ric… Show more

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Cited by 245 publications
(186 citation statements)
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References 638 publications
(706 reference statements)
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“…The first solution, and the most obvious, is to exploit advances in massively parallel (e.g., exa-scale) computation (Kollet et al, 2010;Wood et al, 2011;Paniconi and Putti, 2015;Fatichi et al, 2016). This solution is often implemented by running a complex model at the finest grid resolution possible over the domain of interest (e.g., Maxwell et al, 2015;Maxwell and Condon, 2016).…”
Section: Computing Solutionsmentioning
confidence: 99%
“…The first solution, and the most obvious, is to exploit advances in massively parallel (e.g., exa-scale) computation (Kollet et al, 2010;Wood et al, 2011;Paniconi and Putti, 2015;Fatichi et al, 2016). This solution is often implemented by running a complex model at the finest grid resolution possible over the domain of interest (e.g., Maxwell et al, 2015;Maxwell and Condon, 2016).…”
Section: Computing Solutionsmentioning
confidence: 99%
“…Currently, researchers approach distributed modeling and parameter set selection in a number of different ways, all of which have implications for reducing equifinality. Many studies avoid the topics of uncertainty or equifinality by assigning parameter values from measurements (e.g., Du et al, 2014), though the sheer number of parameters, heterogeneity of the catchment environment, uncertainty in model structure and inputs, and problems translating measurements from the point to the grid scale can make this difficult (Grayson et al, 1992;Surfleet et al, 2010;Paniconi et al, 2015). Other approaches involve fixing some parameters, while letting others vary, often through manual calibration.…”
Section: Introductionmentioning
confidence: 99%
“…While there are examples where different observations, including snow accumulation and melt (Thyer et al, 2004;Whitaker et al, 2003), soil moisture (Koren et al, 2008;Graeff et al, 2012), and catchment chemistry (Birkel et al, 2014), have been incorporated into the calibration and parameter set selection process, many catchments lack measurements beyond streamflow, suggesting that other sources of information are needed Grayson et al, 2002;Paniconi et and Putti, 2015). Finally, while examples exist where model simulations are compared to internal measurements of different hydrologic processes at a few points, there are fewer examples where researchers holistically evaluate the patterns of these simulated processes (Franks et al, 1998;Lamb et al, 1998;Grayson et al, 2002;Wealands et al, 2005;Koch et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
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“…Models at the lowresolution and low-complexity end of the continuum are criticized for lacking a robust physical or theoretical basis and for their inability to meaningfully represent spatial patterns (e.g. Paniconi and Putti, 2015;Fatichi et al, 2016), whereas models at the high-resolution and high-complexity end are often viewed as having inferior representations of sub-grid variability (e.g. Beven and Cloke, 2012) and as being not sufficiently agile to represent the dominant processes in different environments (e.g.…”
Section: Introductionmentioning
confidence: 99%