Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior

By Jonathan D. Herman, Patrick M. Reed, Thorsten Wagener. 2013

155 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/177cce4a5b9f2b917140799d5fedb8300867013b

How researchers see this paper

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

In Environmental Engineering

1. H. Dai
2. Ming Ye
3. Robert L. Wilby
4. T. Matthews

In Water Science and Technology

1. H. Dai
2. Ming Ye
3. C. Murphy
4. Robert L. Wilby
5. Satish Bastola
6. T. Matthews
7. C. Broderick

Citations in the corpus

• Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods (2016, background): “…important given how the dynamics of intraseasonal processes during training (the rate, timing, and distribution of storage recharge and reduction through the year) may affect the model response when used to simulate more extreme wetting-up and drying episodes [Wagener, 2003; Herman et al., 2013].”
• Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, background): “…considers entire ranges of model parameters and takes into account the interactions between different parameters (van Griensven et al., 2006; Herman et al., 2013; Mishra et al., 2009; Nossent et al., 2011; Pan et al., 2011; Saltelli, 2000; Saltelli et al., 2010; Saltelli and Sobol, 1995;…”
• Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, unknown): “Model calibration using long historical records, which usually include contrasting climatic periods, will generally not be able to reproduce catchment behavior with similar accuracy for every period [Coron et al., 2012; Herman et al., 2013].”
• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “Example studies based on time-varying and time-aggregate SA results include Wagener et al. (2003), Herman et al. (2013), Haghnegahdar et al. (2017) and Razavi and Gupta (2019).”