Model identification for hydrological forecasting under uncertainty

By Thorsten Wagener, Hoshin V. Gupta. 2005

358 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/939bf473a28ff0eb3d11301f60879fa7e0f275c8

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Citations in the corpus

Reconciling theory with observations: elements of a diagnostic approach to model evaluation (2008, methodology): “However, it is increasingly recognized that such measures of average model/data similarity inherently lack the ‘power’ to provide a meaningful comparative evaluation (more on this later) (Gupta et al. 1998, 2005; Wagener and Gupta, 2005 ).”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, background): “due to urbanization, deforestation, stream lining and other human interventions (Beven & Binley, 1992 ; Wagener & Gupta, 2005 ; Rosbjerg & Madsen, 2005 ; Engeland et al.”
Uncertainty and sensitivity assessments of an agricultural–hydrological model (RZWQM2) using the GLUE method (2016, methodology): “When a series of assumptions and generalizations were used on agricultural–hydrological models to describe the nature system, some possible errors or lacks of model input prior information, result model uncertainty (Wagener and Gupta, 2005).”
A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model (2008, methodology): “As discussed by Gupta et al. [1998, 2008] and Wagener and Gupta [2005] , automated model evaluation strategies that rely on the use of a single regression-based aggregate measure of performance (e.g., Mean squared error or the corresponding normalized Nash Sutcliffe efficiency) are, in general, wea”
A spatial regularization approach to parameter estimation for a distributed watershed model (2008, methodology): “…minimum of a parameter estimation problem even for complex watershed models [e.g., Duan et al., 1992, 1993, 1994; Sorooshian et al., 1993; Luce and Cundy, 1994; Gan and Biftu, 1996; Tanakamaru, 1995; Kuczera, 1997; Hogue et al., 2000; Boyle et al., 2000; Wagener and Gupta, 2005; among others].”
Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management (2008, background): “To enable key integration via modeling, a multi-disciplinary strategy is necessary so that the biophysical, chemical, engineering, socio-economic, and institutional dimensions of the couple human-environmental system can be considered within a single, coordinated framework (Wagener et al., 2005).”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, background): “It is now being broadly recognized that proper consideration of uncertainty in hydrologic predictions is essential for purposes of both research and operational modeling [Wagener and Gupta, 2005].”
Sensitivity analysis of environmental models: A systematic review with practical workflow (2014, background): “Then, the modeller may also want to verify the consistency of the model behaviour with his/her perception of the real-world system (Wagener and Gupta, 2005).”
Using model averaging to combine soil property rasters from legacy soil maps and from point data (2014, neutral): “This type of situation is common in atmospheric and hydrologic research fields (Wagener and Gupta, 2005) where multiple forecasts of a given process are derived from a number of competing predictive models.”
Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior (2013, background): “…applications has been motivated by the idea that the consistency of modeled and observed behavior and process controls, rather than model optimality with respect to some measures of performance, should be the primary objective of environmental model identification [Wagener and Gupta, 2005].”
A formal framework for scenario development in support of environmental decision-making (2009, background): “There exists an extensive literature on understanding and estimating uncertainties in environmental studies (e.g. Morgan and Henrion, 1990; Beven and Freer, 2001; Wagener and Gupta, 2005).”
Assessment of parametric uncertainty for groundwater reactive transport modeling (2014, methodology): “…is caused by propagation of various sources of uncertainty in model structures, parameters, driving forces, and data used in modeling [Neuman, 2003; Wagener and Gupta, 2005; Srinivasan et al., 2007; Meyer et al., 2007; Clark et al., 2011; Refsgaard et al., 2012; Gupta et al., 2012; Tartakovsky,…”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, background): “…case important to realize that any distributed model application, irrespective of the spatial resolution and scale of its individual model units, is an assemblage of lumped representations of the system at the scale of the individual model units (Grayson and Blöschl, 2001; Wagener and Gupta, 2005).”
On the use of spatial regularization strategies to improve calibration of distributed watershed models (2010, background): “By extension, this sensitivity should be evaluated for both spatial and temporal variations (rather than treated as fixed, as in this study), to improve overall information extraction (suggested also by Gupta et al. [2006] and Wagener and Gupta [2005] ).”