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

Abstract

[1] Lumped rainfall-runoff models are widely used for flow prediction, but a longrecognized need exists for diagnostic tools to determine whether the process-level behavior of a model aligns with the expectations inherent in its formulation. To this end, we develop a comprehensive exploration of dominant parameters in the Hymod, HBV, and Sacramento Soil Moisture Accounting (SAC-SMA) model structures. Model controls are isolated using time-varying Sobol 0 sensitivity analysis for twelve MOPEX watersheds in the eastern United States over a 10 year period. Sensitivity indices are visualized along gradients of observed precipitation and streamflow to identify key behavioral differences between the three models and to connect these back to the models' underlying assumptions. Results indicate that the models' dominant parameters strongly depend on time-varying hydroclimatic conditions. Parameters associated with surface processes such as evapotranspiration and runoff generally dominate under dry conditions, when high evaporative fluxes are required for accurate simulation. Parameters associated with routing processes typically dominate under high-flow conditions, when performance depends on the timing of flow events. The results highlight significant inter-model differences in performance controls, even in cases where the models share similar process formulations. The dominant parameters identified can be counterintuitive; even these simple models represent complex, nonlinear systems, and the links between formulation and behavior are difficult to discern a priori as complexity increases. Scrutinizing the links between model formulation and behavior becomes an important diagnostic approach, particularly in applications such as predictions under change where dominant model controls will shift under hydrologic extremes.

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

“…The contribution of interactions is larger for middle-magnitude flows than for small and large discharges. The different contributions of interactions for various magnitude flows may be because different magnitude rainfall data could trigger different hydrological processes (Herman et al, 2013). Small discharges mainly come from base flows which are relatively stable and do not need much rainfall to be triggered, and large discharges are mainly controlled by overland flows when heavy precipitation occurs.…”

Isotope‐based Fluvial Organic Carbon (ISOFLOC) Model: Model formulation, sensitivity, and evaluation

“…ISOFLOC sensitivity analysis uses quantitative apportionment based on a variance-based sensitivity analysis. Variance based methods are widely used in hydrologic and water quality modeling studies [Wang et al, 2006;Nossent et al, 2011;Herman et al, 2013]. ISOFLOC sensitivity uses low discrepancy, quasi-random Sobol sequences to generate a sample and resample (N3d) matrix (A and B respectively) with N rows of sequences, and d columns of sensitive parameters following from the methods of Sobol' [2001] and Salteli et al [2010].…”

Regional analysis of parameter sensitivity for simulation of streamflow and hydrological fingerprints

“…In this context, Herman et al (2013) showed that the longterm water balance is dominated by only very few parameters, irrespective of the hydrological conditions and of the model. Cuntz et al (2015) performed a global Sobol sensitivity analysis on the hydrologic model mHM.…”

“…Guse et al (2016b) argued that the study of temporal variations in sensitivity is essential to learn about the relation between dominant parameters and governing processes under changing hydrological conditions. The characterisation of temporal dynamics of parameter sensitivity (TEDPAS) has been accomplished in diverse ways (Cloke et al, 2008;Cibin et al, 2010;Herman et al, 2013;Sanadhya et al, 2013;Guse et al, 2014;Pfannerstill et al, 2015;Pianosi and Wagener, 2016). The choice of the temporal resolution is an important factor which clearly influences the way parameters are identified and how inferences on related processes are made (Tang et al, 2007;Massmann and Holzmann, 2012;O'Loughlin et al, 2013).…”

“…Sensitivity analysis to assess the influence of parameters can be directed to (i) simulated streamflow, (ii) different objective functions (e.g. Wagener et al, 2009;Herman et al, 2013;Sanadhya et al, 2013), (iii) simulated hydrological processes (e.g. Massmann and Holzmann, 2015;Pfannerstill et al, 2015;Guse et al, 2016a), or (iv) different hydrological fingerprints (this study).…”