Simultaneously determining global sensitivities of model parameters and model structure

Mai, Juliane; Craig, James R.; Tolson, Bryan A.

doi:10.5194/hess-24-5835-2020

Cited by 30 publications

(39 citation statements)

References 48 publications

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“…The lack of sensitivity studies conducted over large domains and the novelty of the sensitivity method presented by Mai et al 47 in estimating sensitivities of processes rather than model parameters lead to challenges in comparing results to those of previous studies. However, a large-scale sensitivity study across the continental US was performed by Markstrom et al 34 using the US Geological Survey’s Precipitation-Runoff Modeling System (PRMS) 60 .…”

Section: Resultsmentioning

confidence: 99%

“…This work applies the extended Sobol’ Sensitivity Analysis (xSSA) method of Mai et al 47 to a set of more than 3000 modelled locations across North America. The novelty of the xSSA method is that it generates process sensitivities in addition to the traditionally derived parameter sensitivities (addressing limitation 1).…”

Section: Introductionmentioning

confidence: 99%

“…The continental-scale deployment allows for conclusions that hold over large domains (addressing limitation 2). The method is applied to the “Blended Model” for streamflow simulation introduced by Mai et al 47 , which enables a seamless analysis of model parameters and model structures, reducing the dependence of the results on specific modeling choices (addressing limitation 3). The input data, model setups, and SA results are shared on a map-based interactive website for members of the hydrologic research community to browse, explore, download and use for their specific regions of interest (addressing limitation 4).…”

Section: Introductionmentioning

confidence: 99%

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The sensitivity of simulated streamflow to individual hydrologic processes across North America

et al. 2022

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Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties. This enables seamless quantification of model process sensitivities and parameter sensitivities across a continuous set of models. It also leads to high-level conclusions about the importance of water cycle components on streamflow predictions, such as quickflow being the most sensitive process for streamflow simulations across the North American continent. The results of the 3000 basins are used to derive an approximation of sensitivities based on physiographic and climatologic data without the need to perform expensive sensitivity analyses. Detailed spatio-temporal inputs and results are shared through an interactive website.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The sensitivity of simulated streamflow to individual hydrologic processes across North America

et al. 2022

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“…Similar performance was observed for soils data by Quinn et al (2005) using RHESSys and by Anderson et al (2006) using a SAC-SMA model. This lends support to future analyses that consider sensitivity analysis of alternative model structures and parameters to discover dominant processes, as in Mai et al (2020) and Koo et al (2020a). The selected parameters across water quantity and quality-focused metrics would likely be different if TN concentrations were estimated from a process-based model, as in the dynamic mode of RHESSys, instead of statistically as a function of streamflow using WRTDS (e.g., RHESSys and WRTDS estimations are compared in Son et al (2019)).…”

Section: Determining Opportunities For Parameter Reductionmentioning

confidence: 76%

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Smith

Lin

Quinn

et al. 2021

Preprint

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Abstract. Spatially distributed hydrologic models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing decision objectives. Identifying parameters that control physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using parameter multipliers to further reduce dimensionality. We evaluate six sensitivity metrics that align with four decision objectives; two metrics consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results to those obtained by four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that 1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, 2) evaluating sensitivity metrics at only the basin outlet does not capture many parameters that control streamflows in hillslopes, and 3) for some parameter multipliers, calibration of just one of the parameters being adjusted may be the preferred approach for reducing dimensionality. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.

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“…J. Mai et al. (2020) calculates that approximately 8 × 10 12 hydrologic model configurations may be setup using Raven. For this study, the benefits of using Raven include (1) the large number of available model structures, (2) the computational efficiency of the software, and (3) the unique support for blended model structures, which is necessary to support the proposed calibration approach.…”

Section: Methodsmentioning

confidence: 99%

Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model

Chlumsky

Mai

Craig

et al. 2021

Water Resources Research

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Hydrologic models have been used for numerous applications in the last number of decades. These applications include streamflow prediction, flood forecasting, or reservoir level forecasting, or in a scientific capacity to advance our understanding of hydrologic systems. Whether used in a predictive or scientific capacity, models are an abstraction of the complex natural system being simulated, and necessarily simplify the treatment of hydrological processes occurring in a watershed, either to facilitate computational expediency or in recognition of the large degree of uncertainty regarding how the watershed functions. In most practical cases, model calibration is required in order to reconcile model output with historical observation; conventionally, this calibration process focuses on tuning only model parameters. However, hydrologic models are typically considered to have four main components that contribute to model uncertainty (e.g., Beven, 2005;Butts et al., 2004;Gupta, Clark, et al., 2012): (1) input forcing data, such as precipitation and temperature, (2) model structure, which includes the algorithmic functions used to simulate the hydrologic cycle, (3) model parameters related to the selected hydrologic process algorithms (often the only degree of freedom used to tune model performance), and (4) an error term, which represents any uncertainty or deviation from reality not captured in the other three components of model uncertainty.This study focuses upon the simultaneous calibration of model structure and model parameters. We refer to model structure here as a combination of (a) the number and type of stores represented in the model and (b) the collection of algorithms used to describe the relationships between fluxes and storage in each hydrological response unit (HRU). Model structure can be further extended to other modeling decisions such as spatial discretization, but this extended definition is not evaluated here. Historically, most hydrologic models have been designed with a fixed model structure while the input data and model parameters may vary from watershed to watershed. These fixed model structures were typically chosen because they (1) adequately represented the hydrologic response of one or more watersheds, (2) generally respected the physics of water flow and storage, (3) were consistent with conceptual models of the water cycle garnered from field investigation, and/or (4) convention (Addor & Melsen, 2019). In many cases, trial and error was used to determine final model structure (e.g., Perrin et al., 2003). However, there are many such model structures that can generally meet these criteria, and as such, there exist dozens of fixed structure models which used different algorithms to represent various components of the water cycle. The fixed model structure concept

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Simultaneously determining global sensitivities of model parameters and model structure

Cited by 30 publications

References 48 publications

The sensitivity of simulated streamflow to individual hydrologic processes across North America

The sensitivity of simulated streamflow to individual hydrologic processes across North America

Guidance on evaluating parametric model uncertainty at decision-relevant scales

Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model

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