Probabilistic numerical assessment of seawater intrusion overshoot in heterogeneous coastal aquifers

Ketabchi, Hamed; Jahangir, Mohammad Sina

doi:10.1007/s00477-019-01744-5

Cited by 8 publications

(1 citation statement)

References 37 publications

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“…Precipitation forecasting is used in this study to explore temporal hierarchical reconciliation, as precipitation is one of the most important variables in water resources systems. Thus, any advancement in the capability to accurately forecast precipitation may also result in improved: hydrological models (Goodarzi et al., 2019; Kobold & Sušelj, 2005; J. Li et al., 2019; Yao et al., 2019), climate change scenario analysis, coastal risk management (Ketabchi & Jahangir, 2019, 2021) reservoir and water supply management, flood risk evaluation, hydro‐power generation forecasting, agricultural planning, and so on (Choubin et al., 2018; Field et al., 2014; Gouda et al., 2020). Of note, each of the previously mentioned applications requires accurate precipitation forecasts across different timescales.…”

Section: Introductionmentioning

confidence: 99%

Temporal Hierarchical Reconciliation for Consistent Water Resources Forecasting Across Multiple Timescales: An Application to Precipitation Forecasting

Jahangir

Quilty

2022

Water Resources Research

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Accurate forecasts of key water resources variables (e.g., precipitation, streamflow, evaporation) across multiple timescales (e.g., daily, monthly, yearly) are integral for the successful planning, management, and operation of water resources systems (van Den Hurk et al., 2016). However, in water resources, it is often the case that different models are applied for forecasting different timescales of a particular variable (Peters-Lidard et al., 2019), without ensuring that the forecasts are consistent across timescales (e.g., that monthly forecasts aggregate to seasonal or yearly forecasts), providing decision-makers with disparate information that may lead to sub-optimal planning, management, and operation of water resources systems. Further, "Bottom-Up" (BU) forecasts, which aggregate finer scale forecasts to a coarser scale (see, e.g., E. Wang et al., 2011), miss an opportunity to exploit low-and high-frequency characteristics of the data that may prove helpful in improving forecast quality (Spiliotis et al., 2020). To overcome these significant issues, that have been mostly ignored in the water resources forecasting literature, this paper explores the use of temporal hierarchical reconciliation (Athanasopoulos et al., 2017), a development in the time-series forecasting community, for generating consistent forecasts of water resources variables across multiple timescales. Briefly, temporal hierarchical reconciliation is a statistical procedure used to ensure that forecasts produced from different models, each at a specific timescale, are consistent with the forecasts at all other timescales (see Section 2 for more details). The approach explored here applies to any variable that can be aggregated across timescales (e.g., precipitation, evaporation, streamflow volumes) and thus, should prove useful in numerous water resources applications (reservoir operation, irrigation scheduling, water supply, etc.).Since temporal hierarchical reconciliation has yet to be explored within the water resources domain, a brief explanation is given on the differences between two common reconciliation methods and two broader approaches, more commonly adopted in the hydrology and water resources domains, when working with time-series at multiple

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Section: Introductionmentioning

confidence: 99%

Temporal Hierarchical Reconciliation for Consistent Water Resources Forecasting Across Multiple Timescales: An Application to Precipitation Forecasting

Jahangir

Quilty

2022

Water Resources Research

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Reliability framework for characterizing heat wave and cold spell events

Moghim

Jahangir

2022

Nat Hazards

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Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty

Zheng

Fan

et al. 2021

Water Resour Manage

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Linked simulation-optimization (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties in seawater intrusion (SI) simulation models often undermine the reliability of the derived solutions. In this study, a stochastic S/O framework is presented and applied to a real-world case of the Longkou coastal aquifer in China. The three conflicting objectives of maximizing the total pumping rate, minimizing the total injection rate, and minimizing the solute mass increase are considered in the optimization model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is utilized to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimization model. EN-NSGA2 overcomes some inherent limitations in the traditional nondominated sorting genetic algorithm-II (NSGA-II) by introducing information entropy theory. The comparison results indicate that EN-NSGA2 can effectively ameliorate the diversity in Pareto-optimal solutions. For the computational challenge in the stochastic S/O process, a surrogate model based on the multigene genetic programming (MGGP) method is developed to substitute for the numerical simulation model. The results show that the MGGP surrogate model can tremendously reduce the computational burden while ensuring an acceptable level of accuracy.

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Probabilistic numerical assessment of seawater intrusion overshoot in heterogeneous coastal aquifers

Cited by 8 publications

References 37 publications

Temporal Hierarchical Reconciliation for Consistent Water Resources Forecasting Across Multiple Timescales: An Application to Precipitation Forecasting

Temporal Hierarchical Reconciliation for Consistent Water Resources Forecasting Across Multiple Timescales: An Application to Precipitation Forecasting

Reliability framework for characterizing heat wave and cold spell events

Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty

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