Using a global sensitivity analysis to estimate the appropriate length of calibration period in the presence of high hydrological model uncertainty

Shin, Mun-Ju; Jung, Yong

doi:10.1016/j.jhydrol.2022.127546

Cited by 15 publications

(7 citation statements)

References 43 publications

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“…Morbidelli et al(2021) demonstrated that lower resolution affected the trend of extreme rainfall prediction. By analyzing appropriate calibration data lengths in three hydrological models(GR4J, IHACRES, and Sacramento models) for three dam catchments in Korea, Shin & Jung(2022) found that calibration data lengths of more than 8 years yielded relatively stable simulation results.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Gumbel-Logistic Model’s performance in modeling long-term rainfall series in a high-latitude plain region

Wang,

Kong,

Lan

et al. 2024

Theor Appl Climatol

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The frequency of extreme events is increasing on a global scale due to various factors, accounting for natural disaster such as landslides, oods and droughts. Heilongjiang province, which is agriculturally important for China, has suffered from several extreme rainfall events in the past as a high-latitude plain region (43°26' N-53°33'N). Analyzing historical rainfall characteristics of Heilongjiang region is useful for understanding and predicting the behavior of extreme rainfall events, providing effective strategy of reducing the economic loss of agriculture. In this paper, precipitation data from 1974 to 2017 is selected to estimate whether the monthly extreme rainfall and annual rainfall series in Heilongjiang Province obey the Gumbel-logistic model. Then we compare the Gumbel-Logistic distribution based on the data length of 5 years, 10 years, and 20 years with the original 44-year data to determine the appropriate data partition length and explore the reasons for the errors. Combining the empirical frequency and KS test, this paper concludes that the proposed model is appropriate for the representation of the joint distribution of annual monthly rainfall and corresponding amounts, which improves the rainfall assessment of these two positively correlated variables in this region. Univariate and bivariate return periods and correlation coe cients are derived as the basis for assessing the risk of extreme rainfall. The proposed model obtained from the 5-year data length has the largest error, while the distribution curve is progressively smoother as the data length increases.

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Section: Introductionmentioning

confidence: 99%

Assessing the Gumbel-Logistic Model’s performance in modeling long-term rainfall series in a high-latitude plain region

Wang,

Kong,

Lan

et al. 2024

Theor Appl Climatol

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“…Previous studies have been conducted to understand the uncertainty caused by the selection of the calibration period [24,25]. However, most of these studies do not consider the variety of evaluation metrics used for quantification.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Uncertainty in Runoff Simulation According to Multiple Evaluation Metrics and Varying Calibration Data Length

Ziarh,

Kim,

Song

et al. 2024

Water

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In this study, the uncertainty in runoff simulations using hydrological models was quantified based on the selection of five evaluation metrics and calibration data length. The calibration data length was considered to vary from 1 to 11 years, and runoff analysis was performed using a soil and water assessment tool (SWAT). SWAT parameter optimization was then performed using R-SWAT. The results show that the uncertainty was lower when using a calibration data length of five to seven years, with seven years achieving the lowest uncertainty. Runoff simulations using a calibration data length of more than seven years yielded higher uncertainty overall but lower uncertainty for extreme runoff simulations compared to parameters with less than five years of calibration data. Different uncertainty evaluation metrics show different levels of uncertainty, which means it is necessary to consider multiple evaluation metrics rather than relying on any one single metric. Among the evaluation metrics, the Nash–Sutcliffe model efficiency coefficient (NSE) and normalized root-mean-squared error (NRMSE) had large uncertainties at short calibration data lengths, whereas the Kling–Gupta efficiency (KGE) and Percent Bias (Pbias) had large uncertainties at long calibration data lengths.

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“…A comprehensive sensitivity analysis, as provided by global methods such as Sobol indices, is essential for a precise understanding of a model (Saltelli et al, 2008;Saltelli, 2013). It can help to improve parameter calibration efficiency and avoid overparameterization (e.g., Shin and Jung, 2022;Tang et al, 2007a, b). It is also an efficient tool to better understand the model structure (Saltelli et al, 2008), its uncertainties (e.g., Pheulpin et al, 2022) and the dominant processes under various conditions (e.g., Huang et al, 2021;Zhang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain

et al. 2023

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Abstract. The prediction of water resource evolution is considered to be a major challenge for the coming century, particularly in the context of climate change and increasing demographic pressure. Water resources are directly linked to the continental water cycle, and the main processes modulating changes can be represented by global hydrological models. However, anthropogenic impacts on water resources, and in particular the effects of dams-reservoirs on river flows, are still poorly known and generally neglected in coupled land surface–river routing models. This paper presents a parameterized reservoir model, DROP (Dam-Reservoir OPeration), based on Hanasaki's scheme to compute monthly releases given inflows, water demands and the management purpose. With its significantly anthropized river basins, Spain has been chosen as a study case for which simulated outflows and water storage variations are evaluated against in situ observations over the period 1979–2014. Using a default configuration of the reservoir model, results reveal its positive contribution in representing the seasonal cycle of discharge and storage variation, specifically for large-storage capacity irrigation reservoirs. Based on a bounded version of the Nash–Sutcliffe efficiency (NSE) index, called C2M, the overall outflow representation is improved by 43 % in the median. For irrigation reservoirs, the improvement rate reaches 80 %. A comprehensive sensitivity analysis of DROP model parameters was conducted based on the performance of C2M on outflows and volumes using the Sobol method. The results show that the most influential parameter is the threshold coefficient describing the demand-controlled release level. The analysis also reveals the parameters that need to be focused on in order to improve river flow or reservoir water storage modeling by highlighting the difference in the individual effects of the parameters and their interactions depending on whether one focuses on outflows or volume mean seasonal patterns. The results of this generic reservoir scheme show promise for modeling present and future reservoir impacts on the continental hydrology within global land surface–river routing models.

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Using a global sensitivity analysis to estimate the appropriate length of calibration period in the presence of high hydrological model uncertainty

Cited by 15 publications

References 43 publications

Assessing the Gumbel-Logistic Model’s performance in modeling long-term rainfall series in a high-latitude plain region

Assessing the Gumbel-Logistic Model’s performance in modeling long-term rainfall series in a high-latitude plain region

Quantifying Uncertainty in Runoff Simulation According to Multiple Evaluation Metrics and Varying Calibration Data Length

Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain

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