Sensitivity analysis and multi‐response, multi‐criteria evaluation of a physically based distributed model

By S. Anderton, J. Latron, F. Gallart. 2002

123 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/148bd4637c63ffc58cd8962396880f4f858789c0

How researchers see this paper

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

In Civil and Structural Engineering

1. Indrajeet Chaubey
2. Hoshin V. Gupta
3. Y. Her

In Environmental Engineering

1. G. Martinez
2. Koray K. Yilmaz
3. H. Kling
4. Hoshin V. Gupta
5. Indrajeet Chaubey
6. Y. Her
7. Thorsten Wagener
8. Jasper A. Vrugt

In Water Science and Technology

1. Hoshin V. Gupta
2. H. Kling
3. G. Martinez
4. Koray K. Yilmaz
5. Indrajeet Chaubey
6. Y. Her
7. William H. Farmer
8. Jasper A. Vrugt

Citations in the corpus

• Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling (2009, background): “More generally, however, different types of model performance criteria – such as NSE, coefficient of correlation, and bias – can be computed from multiple variables and/or at multiple sites (see Anderton et al., 2002; Beldring , 2002; Rojanschi et al., 2005; Cao et al., 2006; and others).”
• Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty (2015, background): “For instance, flow and soil moisture measurements made at points other than the main outlet within a watershed and/or in longer periodsmay provide useful information in filtering out parameter sets in the calibration (Refsgaard, 1997; Anderton et al., 2002; Carpenter and Georgakakos, 2004, 2006).”