Trade-offs between parameter constraints and model realism: a case study

Jehn, Florian Ulrich; Chamorro, Alejandro; Houska, Tobias

doi:10.1038/s41598-019-46963-6

Cited by 16 publications

(9 citation statements)

References 55 publications

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“…If we consider the factor C of Sherman's formula, as a parameter associated with the hydrological behavior of the site, then we can say that C is a hydrological signature. Taken together, these results suggest that realism is expressed as the performance of a model to simulate a variety of hydrologic signatures [68].…”

Section: Fact Values Referencementioning

confidence: 72%

“…It is important to note that one of the main purposes of scientific hydrology is to develop better predictive models of rainfall-runoff processes where IDF curves are of very high significance [67]. In this way, models that incorporate relevant hydrological processes are able to simulate more realistically concerning hydrological signatures [68]. If we consider the factor C of Sherman's formula, as a parameter associated with the hydrological behavior of the site, then we can say that C is a hydrological signature.…”

Section: Fact Values Referencementioning

confidence: 99%

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Physical Parameterization of IDF Curves Based on Short-Duration Storms

Gutiérrez-López

Hernandez

Sandoval

2019

Water

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Intensity-duration-frequency (IDF) curves are empirical mathematical formulations that have been used for years in engineering for planning, design, and operation of hydraulic projects. The expression proposed by Sherman (1931) has been validated and used largely by many researchers. In all cases, the four parameters of this formulation are obtained through a numerical procedure. Although these parameters are obtained from historical rainfall observations, the optimization of these parameters implies an infinite combination between them and all those solutions would be valid. Of the four parameters, only one of them (C) has units, and for this reason, a physical sense of parameter C is searched for. Having certainty that some of them can be measured in situ would represent a great advance for modern hydrology. With data from 523 storms monitored every minute, a parametric adjustment was made to the Sherman equation and the typical duration of storms at each site was also obtained. To demonstrate how rainfall intensities vary with the change in C value, rainfall intensities calculations for of 5, 10, 15, and 20 min rainfall duration are used to validate the proposed methodology. The results show that typical storm duration is correlated with the additive parameter of Sherman's formula. the rainfall depths of 5, 10, 15, and 30 min are inversely correlated with the rainfall depth of one hour, establishing empirical factors 0.29, 0.45, 0.57, and 0.79 respectively. Using data from nearly 200 rain gauges, Hershfield found values for non-dimensional factors that allowed the transformation of daily rainfall data to calculate IDF curves. In this research, frequency analysis was performed by fitting the Gumbel and Lognormal probability functions, using the maximum rainfall depth accumulated in 24 h to provide functional mathematical relationships for desegregation of rainfall in minutes. Even today, the same distributions are used for the analysis of the maximum annual precipitation associated with duration of 1, 2, 3, 6, 12, 18, 24, 48, and 72 h [4]. There are different methods or approaches to obtain IDF curves. A considerable literature on IDF curves has been published. The reference studies for IDF curves for Australia were conducted by [5][6][7]. In Belgium, the reference IDF curves were presented by [8,9]. In Canada, a number of investigations in the field of IDF curves were published by [10][11][12][13][14][15][16]. In Denmark [17,18] used Sherman's formulation to define IDF curves. In Sweden, detailed studies have also been formalized to define IDF curves [19][20][21]. To date, various methods have been developed and introduced in Spain to compute IDF curves [22][23][24]. In Taiwan, IDF curves were prepared according to the procedure used by [25][26][27]. Reference [28] have recently developed a methodology for select IDF curves in China. In Peninsular Malaysia, a software tool for estimated IDF curves has been developed as an Excel add-in by using Visual Basics for Applications [29]. A site in Java, Indonesia, wa...

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Section: Fact Values Referencementioning

confidence: 72%

Section: Fact Values Referencementioning

confidence: 99%

Physical Parameterization of IDF Curves Based on Short-Duration Storms

Gutiérrez-López

Hernandez

Sandoval

2019

Water

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“…Structural uncertainty can also be manifested by parameters residing at the edge of the permissible parameter range [160]. Indeed, calibrated parameter values lying outside their physical ranges serve as an indicator of structural uncertainty [161]. Similarly, different studies showed that the tradeoff between these two sources [162,163].…”

Section: Uncertainty Source Interaction and Reductionmentioning

confidence: 99%

Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis

Moges

Demissie

Larsen

et al. 2020

Water

114

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Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.

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“…the model is relevant for the application and fits the purpose to the extent possible. To constrain and improve the realism of a model ensemble we may use multiple sources of hard and soft data/information (Pool et al, 2019;, various performance metrics and/or diagnostic signatures (Addor et al, 2018;Jehn et al, 2019;Pool et al, 2018), define some limits of acceptability Blazkova & Beven, 2009;Schaefli, 2016), elicit expert knowledge , or apply various methods of uncertainty Beven & Binley, 2014; and sensitivity analysis (Gupta & Razavi, 2018;Kelleher et al, 2015;, information theoretic analyses Gong et al, 2013;Goodwell & Kumar, 2017;Nearing & Gupta, 2015), or process-based model evaluation Melsen et al, 2016). Through an iterative and revisionary process, we continuously generate/refine plausible models (hypotheses) to better approximate reality, while improving our approximate understanding of the real-world processes.…”

Section: Equifinality and Multiple Working Hypothesesmentioning

confidence: 99%

Evaluating Catchment Models as Multiple Working Hypotheses under Uncertainty

Khatami

2020

Preprint

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Catchment models are conventionally evaluated in terms of their response surface or likelihood surface constructed from model runs using different sets of model parameters. Model evaluation methods are mainly based upon the concept of the equifinality of model structures or parameter sets. The operational definition of equifinality is that multiple model structures/parameters are equally capable of producing acceptable simulations of catchment processes such as runoff. Examining various aspects of this convention, in this thesis I demonstrate their shortcomings and introduce improvements including new approaches and insights for evaluating catchment models as multiple working hypotheses (MWH). First (Chapter 2), arguing that there is more to equifinality than just model structures/parameters, I propose a theoretical framework to conceptualise various facets of equifinality, based on a meta-synthesis of a broad range of literature across geosciences, system theory, and philosophy of science. I distinguish between process-equifinality (equifinality within the real-world systems/processes) and model-equifinality (equifinality within models of real-world systems), explain various aspects of each of these two facets, and discuss their implications for hypothesis testing and modelling of hydrological systems under uncertainty. Second (Chapter 3), building up on this theoretical framework, I propose that characterising model-equifinality based on model internal fluxes — instead of model parameters which is the current approach to account for model-equifinality — provides valuable insights for evaluating catchment models. I developed a new method for model evaluation — called flux mapping — based on the equifinality of runoff generating fluxes of large ensembles of catchment model simulations (1 million model runs for each catchment). Evaluating the model behaviour within the flux space is a powerful approach, beyond the convention, to formulate testable hypotheses for runoff generation processes at the catchment scale. Third (Chapter 4), I further explore the dependency of the flux map of a catchment model upon the choice of model structure and parameterisation, error metric, and data information content. I compare two catchment models (SIMHYD and SACRAMENTO) across 221 Australian catchments (known as Hydrologic Reference Stations, HRS) using multiple error metrics. I particularly demonstrate the fundamental shortcomings of two widely used error metrics — i.e. Nash–Sutcliffe efficiency and Willmott’s refined index of agreement — in model evaluation. I develop the skill score version of Kling–Gupta efficiency (KGEss), and argue it is a more reliable error metric that the other metrics. I also compare two strategies of random sampling (Latin Hypercube Sampling) and guided search (Shuffled Complex Evolution) for model parameterisation, and discuss their implications in evaluating catchment models as MWH. Finally (Chapter 5), I explore how catchment characteristics (physiographic, climatic, and streamflow response characteristics) control the flux map of catchment models (i.e. runoff generation hypotheses). To this end, I formulate runoff generating hypotheses from a large ensemble of SIMHYD simulations (1 million model runs in each catchment). These hypotheses are based on the internal runoff fluxes of SIMHYD — namely infiltration excess overland flow, interflow and saturation excess overland flow, and baseflow — which represent runoff generation at catchment scale. I examine the dependency of these hypotheses on 22 different catchment attributes across 186 of the HRS catchments with acceptable model performance and sufficient parameter sampling. The model performance of each simulation is evaluated using KGEss metric benchmarked against the catchment-specific calendar day average observed flow model, which is more informative than the conventional benchmark of average overall observed flow. I identify catchment attributes that control the degree of equifinality of model runoff fluxes. Higher degree of flux equifinality implies larger uncertainties associated with the representation of runoff processes at catchment scale, and hence pose a greater challenge for reliable and realistic simulation and prediction of streamflow. The findings of this chapter provides insights into the functional connectivity of catchment attributes and the internal dynamics of model runoff fluxes.

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Trade-offs between parameter constraints and model realism: a case study

Cited by 16 publications

References 55 publications

Physical Parameterization of IDF Curves Based on Short-Duration Storms

Physical Parameterization of IDF Curves Based on Short-Duration Storms

Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis

Evaluating Catchment Models as Multiple Working Hypotheses under Uncertainty

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