A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model

By Koray K. Yilmaz, Hoshin V. Gupta, Thorsten Wagener. 2008

579 citations recorded by the source corpus.

Distributed hydrological models have the potential to provide improved streamflow forecasts along the entire channel network, while also simulating the spatial dynamics of evapotranspiration, soil moisture content, water quality, soil erosion, and land use change impacts. However, they are perceived as being difficult to parameterize and evaluate, thus translating into significant predictive uncertainty in the model results. Although a priori parameter estimates derived from observable watershed characteristics can help to minimize obstacles to model implementation, there exists a need for powerful automated parameter estimation strategies that incorporate diagnostic information regarding the causes of poor model performance. This paper investigates a diagnostic approach to model evaluation that exploits hydrological context and theory to aid in the detection and resolution of watershed model inadequacies, through consideration of three of the four major behavioral functions of any watershed system; overall water balance, vertical redistribution, and temporal redistribution (spatial redistribution was not addressed). Instead of using classical statistical measures (such as mean squared error), we use multiple hydrologically relevant “signature measures” to quantify the performance of the model at the watershed outlet in ways that correspond to the functions mentioned above and therefore help to guide model improvements in a meaningful way. We apply the approach to the Hydrology Laboratory Distributed Hydrologic Model (HL‐DHM) of the National Weather Service and show that diagnostic evaluation has the potential to provide a powerful and intuitive basis for deriving consistent estimates of the parameters of watershed models.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/c593afb1d21623daeba25df8ae308053716541b7

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

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, background): “Examples of flow indices include average annual and monthly flows (Peñas et al., 2014), runoff coefficient (Almeida et al., 2016), quantiles and slope of the flow duration curves (Westerberg et al., 2014; Yilmaz et al., 2008), and the base flow index (Bulygina et al., 2009).”
Sensitivity analysis of environmental models: A systematic review with practical workflow (2014, background): “The modeller has to decide though whether this problem could be caused by limited calibration data variability and whether there is a potential for future, maybe more extreme, conditions to still trigger these processes (Gupta et al., 2008; Yilmaz et al., 2008).”
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling (2009, background): “From a hydrological perspective, 'good' values for each of these three components are highly desirable, since in general we aim at matching the overall volume of flow, the spread of flows (e.g. flow duration curve), and the timing and shape of (for example ) the hydrograph (Yilmaz et al., 2008).”
A multiobjective response surface approach for improved water quality planning in lakes and reservoirs (2010, background): “A great research effort has been directed in recent years toward this target [e.g., Yilmaz et al., 2008; Bulygina and Gupta, 2009]: in fact the need for reducing structural model uncertainty is obviously independent of the use of the LPRS procedure but affects any decision‐making process based on…”
Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior (2013, background): “One weakness of this approach is that optimal performance does not necessarily signify the proper representation of underlying system processes [Beven, 2001; Wagener, 2003; Clark et al., 2008; Yilmaz et al., 2008].”
Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency (2018, methodology): “hydrograph signatures provide information on the major catchment functions by linking rainfall input to the flow response of a catchment (Yilmaz et al. 2008).”
A spatial regularization approach to parameter estimation for a distributed watershed model (2008, methodology): “…relationships, future work will explore the use of signature type objective functions selected specifically for their enhanced diagnostic power and information extraction ability [Gupta et al., 2008; Yilmaz et al., 2008], and the use of parallel computing to enable the use of larger data sets.”
Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States (2011, background): “In general, for broad hydrological application, particularly for scientific assessments aimed at understanding and improving model inadequacies, it will be important that the model be able to reproduce overall characteristics of both flow distribution and timing [Yilmaz et al., 2008].”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, background): “…percentile flows) and either construct objective measures (i.e., volumetric bias, error in the slope) for each segment [van Werkhoven et al., 2008; Yilmaz et al., 2008; Zhang et al., 2008] or select comparison points along the FDC (e.g., based on equal intervals of discharge or of volume of…”
A fully multiple-criteria implementation of the Sobol' method for parameter sensitivity analysis (2012, methodology): “Attempts to identify influential parameters have also been conducted through the analysis of “signature measures” [Yilmaz et al., 2008] but the approach can also become subjective, in the sense that a standardized list of signature measures is not available for different applications (e.g.,…”
Characterising performance of environmental models (2013, background): “Yilmaz et al. (2008) present a number of hydrological “signature measures” making use of the floweduration curve.”