Pursuing the method of multiple working hypotheses for hydrological modeling

By M. Clark, Dmitri Kavetski, Fabrizio Fenicia. 2011

557 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/66aa266a94723f0925bc03cdf2b5e29e633b35b9

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, methodology): “Hypothesis testing has traditionally been the realm of statistical inference and probability, including the recent application of Bayesian statistical theory to hydrological modelling (e.g. Clark et al. 2011).”
Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems (2020, background): “This makes the comparison of model predictions and projections difficult and frequently uninformative (Clark et al., 2011), a feature which was noted in early model intercomparison efforts (Koster & Milly, 1997) and remains true today.”
Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification (2012, background): “Epistemic and aleatory prediction uncertainty is caused by data errors and scarcity, parameter uncertainty, model structure uncertainty, and scenario uncertainty [Morgan and Henrion, 1990; Neuman and Wierenga, 2003; Meyer et al., 2007; Renard et al., 2011; Clark et al., 2011].”
A philosophical basis for hydrological uncertainty (2016, methodology): “If we can only test models that predict integrated responses of complex systems, then we cannot directly test hypotheses about the components of those systems, and we cannot arrive at generalities (Clark et al. 2011).”
Integrated environmental modeling: A vision and roadmap for the future (2013, background): “Several textbooks on these subjects have been produced in recent years (Menke, 2012; Aster et al., 2005 from geophysics; Beven, 2009; Clark et al., 2011; Hill and Tiedeman, 2007 from hydrology; Saltelli et al., 2008 from econometrics), and an active scienti ﬁ c community continues to explore new…”
Physically based modeling in catchment hydrology at 50: Survey and outlook (2015, background): “…and the increasing number and complexity of processes represented in integrated physically based models, it becomes imperative to conduct sensitivity analyses and to assess model performance against multiresponse data [e.g., Clark et al., 2011b; Brunner et al., 2012; Rakovec et al., 2014].”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “The original GLUE methodology has been subject of debate for using a subjectively set threshold of behavioral models (e.g. Mantovan and Todini, 2006; Stedinger et al., 2008; Clark et al., 2011; Nearing et al., 2016).”
Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior (2013, methodology): “Opportunities remain for novel diagnostic methods to evaluate models according to process-level behavior in addition to output performance [Kuczera and Franks, 2002; Wagener et al., 2003; Gupta et al., 2008; Clark et al., 2011a].”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, methodology): “Crucially, the top–down approach is based on understanding and testing different models as competing alternative hypotheses of system functioning (e.g. Clark et al., 2011).”
Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, methodology): “Such modeling assumptions require careful testing, as demonstrated in previous studies that explored model structure adequacy (Bulygina & Gupta, 2010; Clark et al., 2008; Clark et al., 2011; Fenicia et al., 2008; Fenicia et al., 2011; Wagener et al., 2001), the quality of hydrological observations u”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, background): “Typically, process-driven models are sometimes called conceptual (lumped) models and physically-based (distributed) models, which are complicated and need more sophisticated mathematical expressions to describe the observed real hydrological processes by applying some hypotheses for hydrologic syste”