Impacts of GCM credibility on hydropower production robustness under climate change: CMIP5 vs CMIP6

Guo, Yuxue; Xu, Yue‐Ping; Yu, Xinting; Xie, Jingkai; Chen, Hao; Si, Yuan

doi:10.1016/j.jhydrol.2023.129233

Cited by 8 publications

(2 citation statements)

References 60 publications

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“…However, because of inherent systematic deviation and coarse spatial resolution, the simulation accuracy needs to be improved when using a global climate model to study the impact of climate change. To better study the climate analysis and future predictions of the Qinghai Lake Basin, it is necessary to statistically downscale the global climate model [37]. The basic idea of statistical downscaling is to establish a statistical relationship between large-scale climate conditions (mainly atmospheric circulation) and regional climate elements using years of observation data.…”

Section: Cmip6-related Settings and Datamentioning

confidence: 99%

Research on Climate Change in Qinghai Lake Basin Based on WRF and CMIP6

Luo,

Liu,

Zhang

et al. 2023

Remote Sensing

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Climate change directly affects water resources by changing temperature and precipitation and the responses of inland basins on plateaus to climate change show a certain pattern. To systematically evaluate the changing facts and evolution trend of temperature and precipitation in the Qinghai Lake Basin, the Weather Research and Forecasting Model (WRF) was used to simulate the spatial distribution of temperature and precipitation in typical periods of the current year based on the observations of hydrological and meteorological stations. Based on the output results of different climate models in CMIP6, the temporal changing trends of temperature and precipitation were predicted. The results showed that precipitation and runoff significantly increased compared to the past, and the lake level first decreased and then increased. In August 2020, the temperature and precipitation near the lake were higher than those in the other areas of the basin. In the future, temperature and precipitation will increase under the influence of different forcing scenarios with the temperature change being more significant. A close combination of observations and simulations will provide quantitative spatiotemporal data and technical support for future climate change adaptability research in the Qinghai Lake Basin.

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Section: Cmip6-related Settings and Datamentioning

confidence: 99%

Research on Climate Change in Qinghai Lake Basin Based on WRF and CMIP6

Luo,

Liu,

Zhang

et al. 2023

Remote Sensing

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“…Many recent studies have integrated MRO into reservoir operations, demonstrating its capacity to enhance robustness under uncertainty. However, most literature primarily focuses on maximizing water allocation or hydropower generation (Guo et al., 2023; Park & Bayraksan, 2023; Xu et al., 2022; Yan et al., 2017). Concerning flood control, considerable challenges persist in effectively guiding decision‐making processes amidst uncertain flood forecasts and in the pursuit of identifying robust strategies.…”

Section: Introductionmentioning

confidence: 99%

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

Guo,

Xu,

et al. 2024

Water Resources Research

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Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.

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Investigating the skills of HighResMIP in capturing historical and future mean precipitation shifts over Pakistan

Eman,

Chung,

Ayugi

2024

Intl Journal of Climatology

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Climatic change over the globe due to global warming affects the characteristics of climate variables that have critical implications on large fraction of population that depends on agriculture for livelihood like Pakistan. Consequently, this study examined how high horizontal grid resolution CMIP6 models simulate the observed precipitation variability during 1981–2014 and further explored the future changes during 2017–2050 under high emission scenario SSP5‐8.5 over Pakistan region. The performances of 12 (CMIP6) High Resolution Model Inter‐comparison Project version 1.0 (hereafter; HighResMIP) GCMs and their ensemble means in reproducing the observed climate were calculated at each station in the study domain and formed the basis for deriving HighResMIP ranking. Further, the study employed Shannon's Entropy and a modified version of Criteria Importance through Inter‐Criteria Correlation (D‐CRITIC) method to build an ensemble mean from the best performing models. Evaluation of HighResMIP GCMs performance revealed that most models showed mixed signals in the region, with fewer models such as HadGEM3‐GC31‐HH, HadGEM3‐GC31‐HM and HadGEM3‐GC31‐MM showing good agreement with the observed precipitation. Overall, HighResMIP multi‐model ensemble outperforms precipitation distribution over individual models. D‐CRITIC based ensemble mean implies higher increase in precipitation than entropy approach. Future changes depict an increase in mean annual in the northern region relative to the historical period. A pronounced increase of about 16%–18% in precipitation was noted in HadGEM3‐GC31‐HH and HiRAM‐SIT‐HR. Conversely, FGOAL‐f3‐H project noteworthy reduction (21%) in precipitation in the near future (2017–2050). The projected seasonal precipitation shows upsurge pattern of 5%–28% in pre‐monsoon season, whereas the reduction in monsoon precipitation is projected to be 29%–40%. The findings of this study can help in building future climate resilience and developing strategic policies in Pakistan.

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Impacts of GCM credibility on hydropower production robustness under climate change: CMIP5 vs CMIP6

Cited by 8 publications

References 60 publications

Research on Climate Change in Qinghai Lake Basin Based on WRF and CMIP6

Research on Climate Change in Qinghai Lake Basin Based on WRF and CMIP6

AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control

Investigating the skills of HighResMIP in capturing historical and future mean precipitation shifts over Pakistan

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