Overtopping risk of high-hazard embankment dam under climate change condition

Methods Methodologies developed for assessing the safety of embankment dams in the context of cli- mate change. The objective is to assess the risk of overtopping of the dam. In the context of dam safety, the risk is the combination of three elements: the possibility of impact (dam fail- ure), the likelihood of the event (failure probability), and the potential consequences (effects/ failure consequences) [36]. Eq (1) can be employed for estimating risk [36]. Risk can be calcu- lated using Eq (1): R ¼ X e pðeÞ � pðf jeÞ � Cðf jeÞ ð1Þ If the risk is expresses in term of social or economic consequences per year, the summation is determined for all event of an incident, e, where p(e) is the likelihood of an incident, Here, p (e) represents the probability of the incident happening, p(f|e) represents the probability of failure resulting from the incident e, and C(f|e) represents the consequences resulting from the risk Climate change is expected to increase the frequency of heavy rainfall events, which will affect the factors contributing to dam failure [24]. These effects are expected to change over time. The climate projections for the end of the twenty-first century will be different from those for the historical period, and therefore may be subject to change. Fig 3 outlines the meth- odology for applying the steps based on the available hydroclimate observations for historical climate projections. This methodology aims to assess the impact of climate change on the risk of embankment dam safety due to overtopping. Bias correction of climate projections

Data for future projected rainfall is corrected using the Linear Scaling bias correction method as shown in Eq (2); it was the best-performing correction method for the correction of long- term trends [12]. This method is mainly used to correct errors to improve the accuracy of cli- mate change data by offering a simple and flexible approach to correcting model biases. Addi- tionally, it helps to reduce uncertainty by improving the reliability of climate change predictions. Specifically, the linear scaling method adjusts monthly mean values resulting in corrected data that closely aligns with the original GCM data. CF ¼ ð�xoÞ ð�xpÞ ð2Þ Where CF is Correction Factor, �xo is Mean of monthly observed data, �xp is mean of monthly projected data, and Corrected Projected Data is Raw Projected Data × Correction Factor (CF) [37].This procedure is independently applied to each climate forecast data under three future Periods (1, 2, and 3), with 2010–2020 data calibrating for the correction component. Table 1. Global Climate Models (GCM) for the 16 future projections from 2006 to 2100. (NAHRIM, 2021). The GCM that have all the scenario in RCP2.5, RCP4.5 and RCP6.0 and RCP8.0 are highlighted in yellow. GCM Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 CCSM4 x - x - x MIROC5 x x x x x MRI-CGCM3 x - x - x GFDL-ESM2M x x x x x IPSL-CM5A-LR x x x x x https://doi.org/10.1371/journal.pone.0311181.t001 PLOS ONE Climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0311181 February 27, 2025 6 / 32