A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales

Assessing the time-varying sensitivity of environmental models has become a common approach to understand both the value of different data periods for estimating specific parameters, and as part of a diagnostic analysis of the model structure itself (i.e. whether dominant processes are emerging in the model at the right times and over the appropriate time periods). It is not straightforward to visualize these results though, given that the window size over which the time-varying sensitivity is best integrated generally varies for different parameters. In this short communication we present a new approach to visualizing such time-varying sensitivity across time scales of integration. As a case study, we estimate first order sensitivity indices with the FAST (Fourier Amplitude Sensitivity Test) method for a typical conceptual rainfall-runoff model. The resulting plots can guide data selection for model calibration, support diagnostic model evaluation and help to define the timing and length of spot gauging campaigns in places where long-term calibration data are not yet available.

[1]  P. Reed,et al.  Characterization of watershed model behavior across a hydroclimatic gradient , 2008 .

[2]  Florian Pappenberger,et al.  Multi‐method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes , 2008 .

[3]  Thorsten Wagener,et al.  Ten guidelines for effective data visualization in scientific publications , 2011, Environ. Model. Softw..

[4]  Thorsten Wagener,et al.  Dynamic identifiability analysis of the transient storage model for solute transport in rivers , 2002, Journal of Hydroinformatics.

[5]  Florian Pappenberger,et al.  Multi-method global sensitivity analysis of flood inundation models. , 2008 .

[6]  Patrick M. Reed,et al.  Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior , 2013 .

[7]  Soroosh Sorooshian,et al.  A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.

[8]  Brian J. Wagner,et al.  Experimental design for estimating parameters of rate‐limited mass transfer: Analysis of stream tracer studies , 1997 .

[9]  S. Uhlenbrook,et al.  Sensitivity analyses of a distributed catchment model to verify the model structure , 2005 .

[10]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[11]  K.,et al.  Nonlinear sensitivity analysis of multiparameter model systems , 1977 .

[12]  N. Fohrer,et al.  How to improve the representation of hydrological processes in SWAT for a lowland catchment – temporal analysis of parameter sensitivity and model performance , 2014 .

[13]  Erwin Zehe,et al.  Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity , 2011 .

[14]  Erwin Zehe,et al.  Temporal dynamics of model parameter sensitivity for computationally expensive models with the Fourier amplitude sensitivity test , 2011 .

[15]  Thorsten Wagener,et al.  Influence of constant rate versus slug injection experiment type on parameter identifiability in a 1‐D transient storage model for stream solute transport , 2013 .

[16]  Thorsten Wagener.  Evaluation of catchment models , 2003 .

[17]  Luca Palmerini,et al.  A Wavelet-Based Approach to Fall Detection , 2015, Sensors.

[18]  Neil McIntyre,et al.  Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis , 2003 .

[19]  Yuqiong Liu,et al.  A wavelet-based approach to assessing timing errors in hydrologic predictions , 2011 .

[20]  K. Shuler,et al.  Nonlinear sensitivity analysis of multiparameter model systems , 1977 .

[21]  Patrick M. Reed,et al.  Multiobjective sensitivity analysis to understand the information content in streamflow observations for distributed watershed modeling , 2009 .