Learning from hydrological models’ challenges: A case study from the Nelson basin model intercomparison project

Ahmed, Mansoor; Stadnyk, Tricia A.; Pietroniro, Alain; Awoye, Hervé; Bajracharya, A. R.; Mai, Juliane; Tolson, Bryan A.; Shen, Helen C.; Craig, James R.; Gervais, Melissa; Sagan, Kevin; Wruth, Shane G.; Koenig, Kristina; Lilhare, Rajtantra; Déry, Stephen J.; Pokorny, Scott; Venema, Henry David; Muhammad, Ameer; Taheri, Mahkameh

doi:10.1016/j.jhydrol.2023.129820

Cited by 9 publications

(1 citation statement)

References 68 publications

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“…This is likely due to a lack of clear guidelines on which models to use when, where and why, as well as the considerable effort needed to test different potential model candidates. While dedicated model intercomparison studies exist (e.g., Ahmed et al., 2023; Boer‐Euser et al., 2016; Breuer et al., 2009; Butts et al., 2004; Duan et al., 2006; Perrin et al., 2001; Refsgaard & Knudsen, 1996; Seiller et al., 2012), our understanding of between‐model differences still remains somewhat limited. A common criticism of such studies is that differences in data handling, calibration routines or their numerical environment hinder the comparability of results (Ceola et al., 2015; Clark, Kavetski, & Fenicia, 2011; Fenicia et al., 2008).…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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Assessment of the impact of climate change on current and future flows of the ungauged Aga-Foua-Djilas watershed: a comparative study of hydrological models CWatM under ISIMIP and HMF-WA

Dione,

Faye,

Mohamed

et al. 2024

Appl Water Sci

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Studying the pressing impacts of climate change on runoff is vital for the sustainable functioning of society and ecosystems. In Senegal, there is insufficient consideration given to the magnitude of the decrease in water resources caused by climate change and the potential impact of this decrease on both society and the environment. The objective of this study was to evaluate the hydrological effects of climate change in the Aga-Foua-Djilas basin by employing CWatM hydrological models inside the frameworks of ISIMIP and HMF-WA. Over the historical period (1981–2019) in the Aga-Foua-Djilas basin, the analysis of all hydrological parameters indicates positive trends, although not statistically significant (except for runoff). Over the future period, unlike temperatures and PET, which show an upward trend in all scenarios, precipitation and runoff show downward trends, which are more significant under SSP 585. For precipitation, Kendall’s Tau shows a downward trend of − 0.157 mm/yr, − 0.321 mm/yr, and − 0.472 mm/yr under SSP 126, SSP 370 and SSP 585, respectively. For runoff, the trends are negative and of the order of − 0.207 m3/s/yr, − 0.44 m3/s/yr, and − 0.565 m3/s/yr, respectively, under SSP 126, SSP 370 and SSP 585 with CwatM and − 0.248 m3/s/yr (SSP 126), − 0.389 m3/s/yr (SSP 245) and − 0.579 m3/s/yr (SSP585) with HMF-WA. Compared with the decrease in precipitation toward the end of the century, the decrease in runoff noted for the distant future (2081–2100) will be of the order of − 32.8% (SSP 126), − 80.8% (SSP 370) and − 94.6% (SSP 585) with CwatM and − 22.3% (SSP 126), − 19.6% (SSP 245) and − 50.9% (SSP 585) with HMF-WA. This study could help policymakers and stakeholders to develop adaptation strategies for the Aga-Foua-Djilas basin.

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Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics

Jaffar,

Hadri,

El Khalki

et al. 2024

Journal of Hydrology: Regional Studies

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Learning from hydrological models’ challenges: A case study from the Nelson basin model intercomparison project

Cited by 9 publications

References 68 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

Assessment of the impact of climate change on current and future flows of the ungauged Aga-Foua-Djilas watershed: a comparative study of hydrological models CWatM under ISIMIP and HMF-WA

Assessment of hydrological model performance in Morocco in relation to model structure and catchment characteristics

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