Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops

By Q. Duana, J. Schaakeb, V. Andréassianc, S. Franksd, G. Gotetie, H. V. Guptaf, Y. M. Gusevg, F. Habetsh, A. Halli, L. Hayj and 10 more. 2006

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jasper A. Vrugt
2. Mojtaba Sadegh
3. Wei Gong
4. G. Schoups
5. Keith Beven
6. Hoshin V. Gupta
7. Wei Chu
8. Qingyun Duan

Citations in the corpus

• Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, background): “…regionalization relationships are difficult or impossible to derive (Blöschl et al., 2013), in no small part due to difficulties in establishing correspondence between model parameters and physical catchment attributes (Duan et al., 2006; Koren et al., 2003; see also Fenicia et al., 2014).”
• Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models (2009, methodology): “Study watersheds The watersheds used in this study are part of the Model Parameter Estimation Experiment (MOPEX), an initiative which has developed datasets for hundreds of watersheds across the US [11].”
• A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model (2014, methodology): “How to specify system model parameters properly is not a trivial issue (Sorooshian and Gupta, 1983; Duan et al., 1992, 2006; Kavetski et al., 2003).”
• Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, methodology): “This is the wettest watershed of the original 12 MOPEX basins described in the study by Duan et al. [2006]. We use a lumped conceptual hydrologic model, described in detail by Schoups and Vrugt [2010], and part of the DREAM software package.”
• Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models (2010, methodology): “As a test, we applied a global optimization algorithm (SCE‐UA of Duan et al. [1993] ) to our problem with the aim of finding the global optimum (located near Smax = 100 mm in Figure 2) when integrating the hydrologic model with the S1 method.”
• Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis (2013, background): “I think it could make a valuable contribution to HESS.”