Structural calibration of an semi-distributed hydrological model of the Liard River basin

Brown, Genevieve; Craig, James R.

doi:10.1080/07011784.2020.1803143

Cited by 8 publications

(3 citation statements)

References 64 publications

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“…It is an open-source tool that comes with several useful resources like a Raven R package (Chlumsky et al, 2022), a Python package (Arsenault et al, 2023) or the Basin Maker (Han et al, 2023). More information on how Raven works and may be used is Water Resources Research 10.1029/2023WR036199 available in Craig et al (2020) or Brown and Craig (2020). In this study, we built only lumped conceptual models and used Version 3.0.1 of Raven (http://raven.uwaterloo.ca/Downloads.html).…”

Section: Raven: the Self-defined Model Hypothesis Space (Sd-mhs)mentioning

confidence: 99%

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Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

Spieler,

Schütze

2024

Water Resources Research

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Selecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro‐climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best‐performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling‐Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high‐accuracy models among the 7,488 options, with Kling‐Gupta‐Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.

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Section: Raven: the Self-defined Model Hypothesis Space (Sd-mhs)mentioning

confidence: 99%

“…More information on how Raven works and may be used is available in Craig et al. (2020) or Brown and Craig (2020). In this study, we built only lumped conceptual models and used Version 3.0.1 of Raven (http://raven.uwaterloo.ca/Downloads.html).…”

Section: Model Hypothesis Spaces Modeling Tools and Datamentioning

confidence: 99%

Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

Spieler,

Schütze

2024

Water Resources Research

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“…Butts et al. (2004) outline other reasons to advocate for flexible model structures including the ability for modelers to improve and tailor their models as their understanding of the functioning of a study site improves (e.g., Brown & Craig, 2020), the complexity of a model is justified, and/or as new data becomes available. An additional benefit of flexible model structures is the ability to build model structures which are purpose‐driven in order to meet specific needs (Andréassian et al., 2009; Coron et al., 2012), or to enable scientific studies (e.g., J. O. Remmers et al., 2020).…”

Section: Introductionmentioning

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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The PAVICS-Hydro platform: A virtual laboratory for hydroclimatic modelling and forecasting over North America

Arsenault,

Huard,

Martel

et al. 2023

Environmental Modelling & Software

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Structural calibration of an semi-distributed hydrological model of the Liard River basin

Cited by 8 publications

References 64 publications

Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

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

The PAVICS-Hydro platform: A virtual laboratory for hydroclimatic modelling and forecasting over North America

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