Yao Blé Anouma Fhorest, G. Soro, I. Larbi, A. Limantol, B. Goula
The study focuses on assessing the individual and combined impacts of climate variability and land use change on hydrological responses. The results indicate that the basin, urban area, cropland, degraded forest, and open forest shows an increasing trend, while gallery forest show a decreasing trend over the period 1992–2015. Climatic variability is marked by two climatic periods (a wet one from 1980 to 1996 and a dry one from 1997 to 2016) with a 35% decrease in rainfall. Regardless of the state of the landcover used, the simulated mean annual runoff decreases by 67.92% between the wet and dry climate periods. Changes in land use between 1992 and 2015 reduce mean annual runoff by 4.71%. Analysis of the joint effect of climatic and LU variation shows a 68.96% reduction in runoff. In this catchment, the joint impact has a clearly greater effect on runoff than the climatic impact, which is greater than that of human activities. There is a need for policymakers to prioritise sustainable land use practices and integrated water resource management strategies in the area to mitigate the combined effects of climate variability and anthropogenic activities, ensuring the long-term resilience of the ecosystem and water availability for local communities.
{"title":"Effects of climate variability and/or land use dynamics on the hydrological balance of the Cavally river catchment at Toulepleu, West Africa","authors":"Yao Blé Anouma Fhorest, G. Soro, I. Larbi, A. Limantol, B. Goula","doi":"10.2166/wcc.2024.512","DOIUrl":"https://doi.org/10.2166/wcc.2024.512","url":null,"abstract":"\u0000 \u0000 The study focuses on assessing the individual and combined impacts of climate variability and land use change on hydrological responses. The results indicate that the basin, urban area, cropland, degraded forest, and open forest shows an increasing trend, while gallery forest show a decreasing trend over the period 1992–2015. Climatic variability is marked by two climatic periods (a wet one from 1980 to 1996 and a dry one from 1997 to 2016) with a 35% decrease in rainfall. Regardless of the state of the landcover used, the simulated mean annual runoff decreases by 67.92% between the wet and dry climate periods. Changes in land use between 1992 and 2015 reduce mean annual runoff by 4.71%. Analysis of the joint effect of climatic and LU variation shows a 68.96% reduction in runoff. In this catchment, the joint impact has a clearly greater effect on runoff than the climatic impact, which is greater than that of human activities. There is a need for policymakers to prioritise sustainable land use practices and integrated water resource management strategies in the area to mitigate the combined effects of climate variability and anthropogenic activities, ensuring the long-term resilience of the ecosystem and water availability for local communities.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
André Teixeira da Silva Hucke, Mateus Nardini Menegaz, Jorge Manuel Guieiro Pereira Isidoro, Rafael de Oliveira Tiezzi
Climate change has the potential to fundamentally transform landscapes on a global scale. Leveraging advanced predictive modeling to enhance water resource management within the Alto Paranapanema Basin (Brazil), holds the potential to proactively anticipate challenges and alleviate the impacts and conflicts arising from this phenomenon. This is particularly important in a region boasting over 1,600 center-pivot irrigation systems. This study employs the Soil Moisture Accounting Procedure, a physical model, to simulate long-term climate datasets and flows. Future climate scenarios, rooted in the Representative Concentration Pathways, are developed through the downscaling of Global Climate Models. The findings reveal a temporal shift in rainfall patterns, characterized by a reduction during the wet season of up to −40% compared to the average historical rainfall, and an increase throughout the dry season up to 40% compared to the same historical, estimated by the Eta–BESM model. These changes present challenges regarding to water availability, hydroelectric generation, and agricultural sustainability. By fostering collaboration among different governmental entities responsible for the managements of basins and harnessing the potential of predictive models, this research advocates for the adoption of proactive strategies in management of water resources. These strategies are imperative to effectively counteract the far-reaching effects of climate change.
{"title":"Assessment of climate change impacts on rainfall and streamflow in the Alto Paranapanema Basin, Brazil","authors":"André Teixeira da Silva Hucke, Mateus Nardini Menegaz, Jorge Manuel Guieiro Pereira Isidoro, Rafael de Oliveira Tiezzi","doi":"10.2166/wcc.2024.549","DOIUrl":"https://doi.org/10.2166/wcc.2024.549","url":null,"abstract":"\u0000 \u0000 Climate change has the potential to fundamentally transform landscapes on a global scale. Leveraging advanced predictive modeling to enhance water resource management within the Alto Paranapanema Basin (Brazil), holds the potential to proactively anticipate challenges and alleviate the impacts and conflicts arising from this phenomenon. This is particularly important in a region boasting over 1,600 center-pivot irrigation systems. This study employs the Soil Moisture Accounting Procedure, a physical model, to simulate long-term climate datasets and flows. Future climate scenarios, rooted in the Representative Concentration Pathways, are developed through the downscaling of Global Climate Models. The findings reveal a temporal shift in rainfall patterns, characterized by a reduction during the wet season of up to −40% compared to the average historical rainfall, and an increase throughout the dry season up to 40% compared to the same historical, estimated by the Eta–BESM model. These changes present challenges regarding to water availability, hydroelectric generation, and agricultural sustainability. By fostering collaboration among different governmental entities responsible for the managements of basins and harnessing the potential of predictive models, this research advocates for the adoption of proactive strategies in management of water resources. These strategies are imperative to effectively counteract the far-reaching effects of climate change.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140693500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wetland ecosystems are vital in maintaining the ecological balance of the wider area. The increase of frequent and intense droughts due to accelerated climate changes poses a threat to wetlands as fragile ecosystems which further require a holistic approach and cooperation between stakeholders to define long-term sustainable solutions. This paper focuses on identifying nature-based solutions to mitigate drought in Ramsar-designated sites through understanding the preferences of stakeholders for effective implementation. An interval version of the analytic hierarchy process is proposed as a systematic framework for selecting solutions considering multiple objectives (climate change mitigation, biodiversity preservation, and human welfare) and six alternatives applicable to Ramsar wetlands. The Serbian case study demonstrates the evaluation of alternatives using interval values in pairwise comparison matrices and priority weights were computed by linear programming. Top-ranked measures identified by three experts involve increasing water availability, supporting agroforestry practices, and utilizing natural reservoirs. Mulch and wastewater reuse are excluded due to additional implementation investments. The added value of the proposed approach is that the results can be used by managers and decision-makers in many ways; for example, weights of the alternatives could indicate resource allocation, while rankings serve as valuable indicators for optimizing the number of applied solutions.
{"title":"A stakeholder-driven holistic approach to mitigate drought in Ramsar wetlands: evaluation of nature-based solutions using interval analytic hierarchy process","authors":"Senka Ždero, B. Srđević, Z. Srđević","doi":"10.2166/wcc.2024.705","DOIUrl":"https://doi.org/10.2166/wcc.2024.705","url":null,"abstract":"\u0000 Wetland ecosystems are vital in maintaining the ecological balance of the wider area. The increase of frequent and intense droughts due to accelerated climate changes poses a threat to wetlands as fragile ecosystems which further require a holistic approach and cooperation between stakeholders to define long-term sustainable solutions. This paper focuses on identifying nature-based solutions to mitigate drought in Ramsar-designated sites through understanding the preferences of stakeholders for effective implementation. An interval version of the analytic hierarchy process is proposed as a systematic framework for selecting solutions considering multiple objectives (climate change mitigation, biodiversity preservation, and human welfare) and six alternatives applicable to Ramsar wetlands. The Serbian case study demonstrates the evaluation of alternatives using interval values in pairwise comparison matrices and priority weights were computed by linear programming. Top-ranked measures identified by three experts involve increasing water availability, supporting agroforestry practices, and utilizing natural reservoirs. Mulch and wastewater reuse are excluded due to additional implementation investments. The added value of the proposed approach is that the results can be used by managers and decision-makers in many ways; for example, weights of the alternatives could indicate resource allocation, while rankings serve as valuable indicators for optimizing the number of applied solutions.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140695871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The capabilities of 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 were evaluated for six extreme precipitation indices from 1961 to 2010 using interannual variability and Taylor skill scores in the Yellow River Basin and its eight subregions. The temporal variations and spatial distributions of extreme precipitation indices were projected from 2021 to 2050 under the shared socioeconomic pathway scenarios (SSP2–4.5 and SSP5–8.5). The results show that most GCMs perform well in simulating extreme values (1-day maximum precipitation (RX1day) and 5-day maximum precipitation (RX5day)), duration (consecutive dry days), and intensity index (simple daily intensity index (SDII)), and perform poor in simulating the threshold indices (precipitation on very wet days (R95p) and number of heavy precipitation days (R10mm)). The projected changes in extreme precipitation indicate that under the SSP2-4.5 scenario, future extreme precipitation will increase by 15.7% (RX1day), 15.8% (RX5day), 30.3% (R95p), 1d (R10mm), and 6.6% (SDII), respectively, decrease by 2.1d (CDD). The aforementioned changes are further enhanced under the SSP5-8.5 scenario. Extreme precipitation changes widely in Hekou Town to Longmen, in the northeastern part of the region from Longmen to Sanmenxia, below Huayuankou, and in the interflow basin.
{"title":"Evaluation and projection of extreme precipitation using CMIP6 model simulations in the Yellow River Basin","authors":"Heng Xiao, Yue Zhuo, Peng Jiang, Yan Zhao, Kaiwen Pang, Xiuyu Zhang","doi":"10.2166/wcc.2024.696","DOIUrl":"https://doi.org/10.2166/wcc.2024.696","url":null,"abstract":"\u0000 The capabilities of 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 were evaluated for six extreme precipitation indices from 1961 to 2010 using interannual variability and Taylor skill scores in the Yellow River Basin and its eight subregions. The temporal variations and spatial distributions of extreme precipitation indices were projected from 2021 to 2050 under the shared socioeconomic pathway scenarios (SSP2–4.5 and SSP5–8.5). The results show that most GCMs perform well in simulating extreme values (1-day maximum precipitation (RX1day) and 5-day maximum precipitation (RX5day)), duration (consecutive dry days), and intensity index (simple daily intensity index (SDII)), and perform poor in simulating the threshold indices (precipitation on very wet days (R95p) and number of heavy precipitation days (R10mm)). The projected changes in extreme precipitation indicate that under the SSP2-4.5 scenario, future extreme precipitation will increase by 15.7% (RX1day), 15.8% (RX5day), 30.3% (R95p), 1d (R10mm), and 6.6% (SDII), respectively, decrease by 2.1d (CDD). The aforementioned changes are further enhanced under the SSP5-8.5 scenario. Extreme precipitation changes widely in Hekou Town to Longmen, in the northeastern part of the region from Longmen to Sanmenxia, below Huayuankou, and in the interflow basin.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Naqibah Aminuddin Jafry, J. Suhaila, Fadhilah Yusof, Siti Rohani Mohd Nor, Nor Eliza Alias
Copulas are a vital statistical tool, particularly in hydrology, for understanding complex relationships among flood characteristics. This study focuses on three key flood features: peak discharge, flood volume, and flood duration, using trivariate copulas to capture their interdependencies. This is crucial because bivariate and univariate analyses fall short in considering all three factors simultaneously. To handle extreme flood values, L-moment is proposed over maximum likelihood estimation and inference function margin due to its enhanced reliability and susceptibility to outliers and extreme values. Akaike information criterion was employed to identify the best-fit marginal distribution and copula. The Lognormal distribution effectively models peak discharge, while Weibull and generalized extreme value distributions fit flood volume and duration best, respectively. Various copula families, including elliptical and Archimedean, are assessed, where Clayton copula emerge as the most suitable. This analysis demonstrates that when more flood features are considered together, the return period increases, indicating the reduced likelihood of occurrence. The trivariate case of the AND-joint return period surpasses the trivariate case of the OR-joint return period where the TP, V, DAND=5, 405.93 years, while TP, V, DOR=500.46 years. This comprehensive approach enhances hydrological modeling and decision-making for water resource management and flood mitigation projects.
{"title":"Enhancing flood risk assessment in the Johor River Basin through trivariate copula","authors":"Naqibah Aminuddin Jafry, J. Suhaila, Fadhilah Yusof, Siti Rohani Mohd Nor, Nor Eliza Alias","doi":"10.2166/wcc.2024.624","DOIUrl":"https://doi.org/10.2166/wcc.2024.624","url":null,"abstract":"\u0000 Copulas are a vital statistical tool, particularly in hydrology, for understanding complex relationships among flood characteristics. This study focuses on three key flood features: peak discharge, flood volume, and flood duration, using trivariate copulas to capture their interdependencies. This is crucial because bivariate and univariate analyses fall short in considering all three factors simultaneously. To handle extreme flood values, L-moment is proposed over maximum likelihood estimation and inference function margin due to its enhanced reliability and susceptibility to outliers and extreme values. Akaike information criterion was employed to identify the best-fit marginal distribution and copula. The Lognormal distribution effectively models peak discharge, while Weibull and generalized extreme value distributions fit flood volume and duration best, respectively. Various copula families, including elliptical and Archimedean, are assessed, where Clayton copula emerge as the most suitable. This analysis demonstrates that when more flood features are considered together, the return period increases, indicating the reduced likelihood of occurrence. The trivariate case of the AND-joint return period surpasses the trivariate case of the OR-joint return period where the TP, V, DAND=5, 405.93 years, while TP, V, DOR=500.46 years. This comprehensive approach enhances hydrological modeling and decision-making for water resource management and flood mitigation projects.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present research evaluated the prospects of utilizing rainfall and temperature combined with Landsat-8 derived HANTS (Harmonic ANalysis of Time Series) reconstructed NDVI for estimating the metrics of the mangrove phenology. The selected period of the study was from 2013 to 2020 for the Pichavaram mangroves of Tamil Nadu. The NDVI and ERA5 (ECMWF Re-Analysis) datasets of rainfall and temperature were the input datasets for developing the new algorithm. The ‘z-score sum’ provided a measure of the cumulative impact of rainfall and temperature, displaying its most negative value coinciding with the peak positive value of the NDVI time series datasets. The algorithm developed for phenological metrics estimation identified the common inflection points of the z-score sum and NDVI curves. The temporal analysis of metrics revealed the average Length of Season (LoS) as 230 days. The metrics also identified the drought year 2016 with the shortest LoS and the least Gross Primary Productivity (GPP) values. The analysis showed the influences of the preceding year’s monsoon rainfall on the GPP values of the later part of the phenological cycle. The temperatures during the days of PoS were found to be the optimum temperature for the growth of mangroves.
{"title":"An inflection point-based method for estimating metrics of mangrove phenology combining climatic factors and Landsat NDVI time series","authors":"Mounika Manne, R. K.","doi":"10.2166/wcc.2024.463","DOIUrl":"https://doi.org/10.2166/wcc.2024.463","url":null,"abstract":"\u0000 \u0000 The present research evaluated the prospects of utilizing rainfall and temperature combined with Landsat-8 derived HANTS (Harmonic ANalysis of Time Series) reconstructed NDVI for estimating the metrics of the mangrove phenology. The selected period of the study was from 2013 to 2020 for the Pichavaram mangroves of Tamil Nadu. The NDVI and ERA5 (ECMWF Re-Analysis) datasets of rainfall and temperature were the input datasets for developing the new algorithm. The ‘z-score sum’ provided a measure of the cumulative impact of rainfall and temperature, displaying its most negative value coinciding with the peak positive value of the NDVI time series datasets. The algorithm developed for phenological metrics estimation identified the common inflection points of the z-score sum and NDVI curves. The temporal analysis of metrics revealed the average Length of Season (LoS) as 230 days. The metrics also identified the drought year 2016 with the shortest LoS and the least Gross Primary Productivity (GPP) values. The analysis showed the influences of the preceding year’s monsoon rainfall on the GPP values of the later part of the phenological cycle. The temperatures during the days of PoS were found to be the optimum temperature for the growth of mangroves.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate change is altering flood risk globally, with local variations prompting the necessity for regional assessments to guide the planning and management of water-related infrastructures. This study details an integrated framework for assessing future changes in flood frequencies, using the case of Bitlis Creek (Turkey). The precipitation and temperature simulations of 21 global circulation models (GCMs) from the coupled model intercomparison project phase 6 (CMIP6) are used to drive the developed soil and water assessment tool (SWAT) model in generating daily streamflow projections under the CMIP6 historical experiment and the shared socio-economic pathway (SSP) scenarios of SSP245 and SSP585. Five probability distribution functions are considered to calculate the 5-, 10-, 25-, 50-, 100-, and 500-year flood discharges for the historical period 1955–2010 and the future periods 2025–2074 and 2025–2099. The quantification of climate change impacts on the design discharges is based on the medians of the flood discharges obtained for the climate data of each GCM, using the best-fitted distribution functions according to the Kolmogorov–Smirnov test results. The findings illustrate significant increases in discharge rates, ranging from 21.1 to 31.7% for the 2025–2099 period under the SSP585 scenario, highlighting the necessity of considering changing climate conditions in designing water-related infrastructures.
{"title":"Assessing future changes in flood frequencies under CMIP6 climate projections using SWAT modeling: a case study of Bitlis Creek, Turkey","authors":"Emrah Yalcin","doi":"10.2166/wcc.2024.646","DOIUrl":"https://doi.org/10.2166/wcc.2024.646","url":null,"abstract":"\u0000 Climate change is altering flood risk globally, with local variations prompting the necessity for regional assessments to guide the planning and management of water-related infrastructures. This study details an integrated framework for assessing future changes in flood frequencies, using the case of Bitlis Creek (Turkey). The precipitation and temperature simulations of 21 global circulation models (GCMs) from the coupled model intercomparison project phase 6 (CMIP6) are used to drive the developed soil and water assessment tool (SWAT) model in generating daily streamflow projections under the CMIP6 historical experiment and the shared socio-economic pathway (SSP) scenarios of SSP245 and SSP585. Five probability distribution functions are considered to calculate the 5-, 10-, 25-, 50-, 100-, and 500-year flood discharges for the historical period 1955–2010 and the future periods 2025–2074 and 2025–2099. The quantification of climate change impacts on the design discharges is based on the medians of the flood discharges obtained for the climate data of each GCM, using the best-fitted distribution functions according to the Kolmogorov–Smirnov test results. The findings illustrate significant increases in discharge rates, ranging from 21.1 to 31.7% for the 2025–2099 period under the SSP585 scenario, highlighting the necessity of considering changing climate conditions in designing water-related infrastructures.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Greenhouse gases affect climate system disturbances. This research employs sixth generation CMIP6 models in the SSP5.85 scenario and extends the use of the neural wavelet network to predict precipitation variations for the future (2025–2065). Kendall's trend test is used to assess changes in precipitation trends for observed and projected periods. An analysis of variance (ANOVA) validates models under SSP5.85 by comparing observed precipitation with model predictions. A multi-layer perceptron neural network assesses climate change's impact on future precipitation. Findings indicate future precipitation is projected to fluctuate from −0.146 to over −2.127 mm compared to the baseline period. The observed period showed a significant 3.37% monthly precipitation decrease within the watershed. The CanESM5 model predicts a 3.916 reduction in precipitation with 95% confidence, while INM-CM4-8 and MRI-ESM2-0 models are less certain. The minor difference between CanESM5's predicted (−5.91) and observed (−5.05) precipitation suggests a slight variance. On the other hand, the wavelet neural network (WNN) model predicts that precipitation in this region will increase in the future. In general, this study predicts a decrease in precipitation for the Aji-Chay watershed in Iran over the next decade, could lead to serious issues like lower crop yields, rising food prices, and even droughts.
{"title":"The prediction of precipitation changes in the Aji-Chay watershed using CMIP6 models and the wavelet neural network","authors":"Farahnaz Khoramabadi, Sina Fard Moradinia","doi":"10.2166/wcc.2024.607","DOIUrl":"https://doi.org/10.2166/wcc.2024.607","url":null,"abstract":"\u0000 \u0000 Greenhouse gases affect climate system disturbances. This research employs sixth generation CMIP6 models in the SSP5.85 scenario and extends the use of the neural wavelet network to predict precipitation variations for the future (2025–2065). Kendall's trend test is used to assess changes in precipitation trends for observed and projected periods. An analysis of variance (ANOVA) validates models under SSP5.85 by comparing observed precipitation with model predictions. A multi-layer perceptron neural network assesses climate change's impact on future precipitation. Findings indicate future precipitation is projected to fluctuate from −0.146 to over −2.127 mm compared to the baseline period. The observed period showed a significant 3.37% monthly precipitation decrease within the watershed. The CanESM5 model predicts a 3.916 reduction in precipitation with 95% confidence, while INM-CM4-8 and MRI-ESM2-0 models are less certain. The minor difference between CanESM5's predicted (−5.91) and observed (−5.05) precipitation suggests a slight variance. On the other hand, the wavelet neural network (WNN) model predicts that precipitation in this region will increase in the future. In general, this study predicts a decrease in precipitation for the Aji-Chay watershed in Iran over the next decade, could lead to serious issues like lower crop yields, rising food prices, and even droughts.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140725076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic assessment of water scarcity utilising blue water (BW) and green water (GW) can enhance water resource management. The traditional water scarcity assessment mainly considers blue water, ignoring GW, for static evaluation. The improvement objective of this study is dynamically quantifying water scarcity, integrated BW and GW. This study proposed a framework to present an overview of water scarcity within multiple indicators and pinpoint water-stressed areas within an ever-changing process. The framework is based on the theorem of mutual change of quality and quantity to assess the spatiotemporal variability of BW and GW availability and to quantify the water scarcity in watersheds. A case study was carried out in Taoer River Basin, a semiarid region of China, to demonstrate the use of the framework. The anthropogenic elements (such as water demand) and natural conditions were combined to quantify water scarcity, as measured by BW and GW scarcity indices. This study also analysed the variation of water scarcity on different spatiotemporal scales. The findings demonstrate that severe water scarcity has been occurring downstream with a tendency towards upstream of the watershed. Collectively, this study provides a useful tool for dynamic water scarcity assessment, helping develop policies to promote sustainable development.
{"title":"A synergistic framework for dynamic water scarcity assessment: integrated blue and green water","authors":"Jianwei Liu, Xiaoteng Pang, Xiaohui Yan, Xiaoqiang Chen, Mingwei Wang, Ruixue Ma, Liguo Ma","doi":"10.2166/wcc.2024.728","DOIUrl":"https://doi.org/10.2166/wcc.2024.728","url":null,"abstract":"\u0000 \u0000 Dynamic assessment of water scarcity utilising blue water (BW) and green water (GW) can enhance water resource management. The traditional water scarcity assessment mainly considers blue water, ignoring GW, for static evaluation. The improvement objective of this study is dynamically quantifying water scarcity, integrated BW and GW. This study proposed a framework to present an overview of water scarcity within multiple indicators and pinpoint water-stressed areas within an ever-changing process. The framework is based on the theorem of mutual change of quality and quantity to assess the spatiotemporal variability of BW and GW availability and to quantify the water scarcity in watersheds. A case study was carried out in Taoer River Basin, a semiarid region of China, to demonstrate the use of the framework. The anthropogenic elements (such as water demand) and natural conditions were combined to quantify water scarcity, as measured by BW and GW scarcity indices. This study also analysed the variation of water scarcity on different spatiotemporal scales. The findings demonstrate that severe water scarcity has been occurring downstream with a tendency towards upstream of the watershed. Collectively, this study provides a useful tool for dynamic water scarcity assessment, helping develop policies to promote sustainable development.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shimalis Sishah Dagne, Zenebe Reta Roba, Mitiku Badasa Moisa, Kiros Tsegay Deribew, D. O. Gemeda, Hurgesa Hundera Hirpha
In African nations with complex topographies, alternative rainfall estimation methods such as satellites are crucial. This study is aimed at predicting the spatial and temporal distribution of rainfall in the Lake Tana sub-basin from 1990 to 2020. A satellite-based rainfall estimate of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) was used with the same spanning period (1990–2020). The validation process employs point-to-pixel analysis, comparing CHIRPS estimates with observed data at specific gauge stations. The findings showed that CHIRPS had well-estimated rainfall incidence in the highland areas and significantly overestimated it in the lowland areas. The Mann–Kendall trends for January, June, and August indicate decreasing trends, while the Bega and spring seasons show notable declines. Regression analysis reveals a non-significant decrease in annual rainfall with the highest rainfall in the summer and relatively dry winters. In addition, the coefficient of variation value of 26.37% suggests a moderate level of variability around the mean annual rainfall. In conclusion, the CHIRPS satellite exhibited varied performance across the Tana Sub-basin, with site-specific discrepancies and notable inaccuracies at certain stations. The study underscores the importance of considering local factors and topography in satellite-based rainfall assessments, providing valuable insights for agricultural planning in the region.
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