Arpan Pradhan, K. Khatua, Kirtikanta Sahoo, A. Mohanta, M. Beulah, M. Sudhir
Laboratory experimentation for bed shear stress distribution has been carried out in two sets of meandering channels. The channels have crossover angles of 110° and 60° constructed by ‘sine-generated’ curves over a flume of 4 m width. Variations in bed roughness were studied for the meandering main channel. Bed shear stress distribution across a meandering length for the 110° and 60° channels was examined for different sinuosities and roughnesses. The boundary shear stress study illustrated the position of maximum shear along the apex section and across the meandering path. These variations were observed for different flow depths. A comparison of the bed shear among the three experimental channels was conducted, and the results were analyzed.
{"title":"Bed shear stress distribution across a meander path","authors":"Arpan Pradhan, K. Khatua, Kirtikanta Sahoo, A. Mohanta, M. Beulah, M. Sudhir","doi":"10.2166/wcc.2024.682","DOIUrl":"https://doi.org/10.2166/wcc.2024.682","url":null,"abstract":"\u0000 Laboratory experimentation for bed shear stress distribution has been carried out in two sets of meandering channels. The channels have crossover angles of 110° and 60° constructed by ‘sine-generated’ curves over a flume of 4 m width. Variations in bed roughness were studied for the meandering main channel. Bed shear stress distribution across a meandering length for the 110° and 60° channels was examined for different sinuosities and roughnesses. The boundary shear stress study illustrated the position of maximum shear along the apex section and across the meandering path. These variations were observed for different flow depths. A comparison of the bed shear among the three experimental channels was conducted, and the results were analyzed.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"14 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ricky Anak Kemarau, Wee Hin Boo, Zaini Sakawi, Ramzah Dambul, S. A. Suab, W. S. Wan Mohd Jaafar, O. V. Eboy, Muhammad Ammar Fakhry Norzin
The severe El Niño events of 1997/1998 and 2015/2016 caused significant disruptions in Southeast Asia, particularly in Borneo, resulting in hazardous haze and acute water shortages. This study examines the influence of El Niño, the Indian Ocean Dipole (IOD), and the Madden–Julian oscillation (MJO) on regional climate, using time-series data from February 1993 to December 2020. Data from El Niño, IOD, and MJO indices were integrated with Landsat 5 and 8 land surface temperature records, allowing for a detailed analysis of their combined effects on regional temperature and precipitation patterns. Time-series trend decomposition and the generalized linear mixed model approach identified the Oceanic Niño Index (ONI) as a significant driver of temperature increases and dry spell occurrences ONLY during the peak El Niño years. On the other hand, ONI correlated strongly with mean monthly temperatures, underscoring its dominant influence. In addition, the IOD was found to significantly affect regional temperatures with a regression coefficient of 0.38867 (p = 0.0455), indicating its significant but less pronounced impact compared with ONI. These findings clarify the dynamics between key climate indices and their impact on regional climate extremes, offering critical insights for improving climate resilience and adaptation in tropical regions.
{"title":"Impact of El Niño, Indian Ocean dipole, and Madden–Julian oscillation on land surface temperature in Kuching City Sarawak, during the periods of 1997/1998 and 2015/2016: a pilot study","authors":"Ricky Anak Kemarau, Wee Hin Boo, Zaini Sakawi, Ramzah Dambul, S. A. Suab, W. S. Wan Mohd Jaafar, O. V. Eboy, Muhammad Ammar Fakhry Norzin","doi":"10.2166/wcc.2024.022","DOIUrl":"https://doi.org/10.2166/wcc.2024.022","url":null,"abstract":"\u0000 The severe El Niño events of 1997/1998 and 2015/2016 caused significant disruptions in Southeast Asia, particularly in Borneo, resulting in hazardous haze and acute water shortages. This study examines the influence of El Niño, the Indian Ocean Dipole (IOD), and the Madden–Julian oscillation (MJO) on regional climate, using time-series data from February 1993 to December 2020. Data from El Niño, IOD, and MJO indices were integrated with Landsat 5 and 8 land surface temperature records, allowing for a detailed analysis of their combined effects on regional temperature and precipitation patterns. Time-series trend decomposition and the generalized linear mixed model approach identified the Oceanic Niño Index (ONI) as a significant driver of temperature increases and dry spell occurrences ONLY during the peak El Niño years. On the other hand, ONI correlated strongly with mean monthly temperatures, underscoring its dominant influence. In addition, the IOD was found to significantly affect regional temperatures with a regression coefficient of 0.38867 (p = 0.0455), indicating its significant but less pronounced impact compared with ONI. These findings clarify the dynamics between key climate indices and their impact on regional climate extremes, offering critical insights for improving climate resilience and adaptation in tropical regions.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"34 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141645267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bairu Chen, Zhiguo He, Li Li, Qian Chen, Junyu He, Feixiang Li, Dongdong Chu, Zeng Cao, Xuchao Yang
As climate change continues to worsen, coastal areas are increasingly vulnerable to more frequent and severe storm surges. This poses a significant risk to economic entities, particularly in areas that have undergone rapid development. However, quantitative assessment of economic losses from storm surge disasters in China has been challenging due to limited exposure and vulnerability data. This study proposes a framework for comprehensive economic losses assessment of storm surge disasters using open data, focusing on Zhoushan City as an example. The study quantifies economic loss ratios caused by storm surges by identifying essential urban land use/cover (EULUC) and considering the water depth of different EULUC types for quantitative vulnerability assessment. The study then calculates direct economic losses using the loss ratio maps and gridded gross domestic product data and quantifies indirect economic losses (IEL) using an input–output model to account for inter-industry correlation. Results show that under the scenario of a super typhoon intensity (915 hPa), the total economic loss can reach 131 million CNY, with IEL accounting for 60% of the total. The construction and industrial sectors experience higher IEL due to excessive dependence on upstream and downstream industries, with IEL accounting for approximately 70%.
{"title":"Comprehensive economic losses assessment of storm surge disasters using open data: a case study of Zhoushan, China","authors":"Bairu Chen, Zhiguo He, Li Li, Qian Chen, Junyu He, Feixiang Li, Dongdong Chu, Zeng Cao, Xuchao Yang","doi":"10.2166/wcc.2024.731","DOIUrl":"https://doi.org/10.2166/wcc.2024.731","url":null,"abstract":"\u0000 \u0000 As climate change continues to worsen, coastal areas are increasingly vulnerable to more frequent and severe storm surges. This poses a significant risk to economic entities, particularly in areas that have undergone rapid development. However, quantitative assessment of economic losses from storm surge disasters in China has been challenging due to limited exposure and vulnerability data. This study proposes a framework for comprehensive economic losses assessment of storm surge disasters using open data, focusing on Zhoushan City as an example. The study quantifies economic loss ratios caused by storm surges by identifying essential urban land use/cover (EULUC) and considering the water depth of different EULUC types for quantitative vulnerability assessment. The study then calculates direct economic losses using the loss ratio maps and gridded gross domestic product data and quantifies indirect economic losses (IEL) using an input–output model to account for inter-industry correlation. Results show that under the scenario of a super typhoon intensity (915 hPa), the total economic loss can reach 131 million CNY, with IEL accounting for 60% of the total. The construction and industrial sectors experience higher IEL due to excessive dependence on upstream and downstream industries, with IEL accounting for approximately 70%.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"45 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141652359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The growing population contributes to an increase in greenhouse gases and affects environment in a negative manner. To determine and predict the impact of climate change on hydrological processes, and to examine the status of our current/future water resources, hydrological modeling is of great importance and various models are utilized in this regard. In this study, the hydrological impact of climate change on a river in Türkiye, located in the Mediterranean Basin, has been revealed using a hydrological model, Hydrologiska Byråns Vattenbalansavdelning (HBV). Precipitation, temperature, and streamflow data from 1979 to 2021 were used for model calibration, validation, and warming processes, and model performance was assessed using the Nash–Sutcliffe efficiency (NSE) coefficient criterion. The established model's NSE performance has been determined as 0.66 for calibration and 0.69 for validation. The hydrological model was run with climate projection data (representative concentration pathways (RCP4.5 and RCP8.5)) to predict streamflows for the projection period (2023–2092). The evaluations conducted using the hydrological model for the period between 2023 and 2092 under the RCP8.5 scenario indicate a statistically significant decreasing trend of streamflows due to climate change. However, for RCP4.5, no trend was detected in streamflows for the projection period. From a seasonal perspective, while the greatest decrease in trends is expected to occur in autumn according to the RCP 8.5 scenario, all seasons are anticipated to exhibit significant decreasing.
{"title":"Determination of climate change impacts on Mediterranean streamflows: a case study of Edremit Eybek Creek, Türkiye","authors":"Ayşenur Iltas, Mesut Demircan, Erkan Gokdag, Hakan Aksu","doi":"10.2166/wcc.2024.491","DOIUrl":"https://doi.org/10.2166/wcc.2024.491","url":null,"abstract":"\u0000 The growing population contributes to an increase in greenhouse gases and affects environment in a negative manner. To determine and predict the impact of climate change on hydrological processes, and to examine the status of our current/future water resources, hydrological modeling is of great importance and various models are utilized in this regard. In this study, the hydrological impact of climate change on a river in Türkiye, located in the Mediterranean Basin, has been revealed using a hydrological model, Hydrologiska Byråns Vattenbalansavdelning (HBV). Precipitation, temperature, and streamflow data from 1979 to 2021 were used for model calibration, validation, and warming processes, and model performance was assessed using the Nash–Sutcliffe efficiency (NSE) coefficient criterion. The established model's NSE performance has been determined as 0.66 for calibration and 0.69 for validation. The hydrological model was run with climate projection data (representative concentration pathways (RCP4.5 and RCP8.5)) to predict streamflows for the projection period (2023–2092). The evaluations conducted using the hydrological model for the period between 2023 and 2092 under the RCP8.5 scenario indicate a statistically significant decreasing trend of streamflows due to climate change. However, for RCP4.5, no trend was detected in streamflows for the projection period. From a seasonal perspective, while the greatest decrease in trends is expected to occur in autumn according to the RCP 8.5 scenario, all seasons are anticipated to exhibit significant decreasing.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"20 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weslley de Brito Gomes, Praky Satyamurty, F. W. Correia, S. C. Chou, A. Fleischmann, F. Papa, Leonardo Alves Vergasta, A. Lyra
We developed and analyzed the performance of an ensemble forecasting system for the Madeira River basin, the largest sub-basin of the Amazon, with forecasts up to 30 days under different hydrometeorological conditions. We used outputs from the regional Eta model of precipitation and global climatological data as inputs to a large-scale hydrological model. Bias correction of precipitation through quantile mapping significantly improved the results, achieving a hit rate >70%. The system demonstrated the ability to discriminate between high, medium, and low flow conditions. Forecast performance is better for larger catchment areas. This system is expected to increase decision-making efficiency for flood and drought situations in the largest Amazon tributary.
我们开发并分析了马德拉河流域的集合预报系统的性能，该流域是亚马逊河最大的子流域，在不同的水文气象条件下可进行长达 30 天的预报。我们将区域 Eta 降水模型的输出结果和全球气候数据作为大规模水文模型的输入。通过量子图对降水量进行偏差校正，显著改善了结果，命中率大于 70%。该系统展示了区分大、中、小流量条件的能力。对于较大的集水区，预测效果更好。该系统有望提高亚马逊最大支流洪水和干旱情况下的决策效率。
{"title":"Ensemble hydrological predictions at an intraseasonal scale through a statistical–dynamical downscaling approach over southwestern Amazonia","authors":"Weslley de Brito Gomes, Praky Satyamurty, F. W. Correia, S. C. Chou, A. Fleischmann, F. Papa, Leonardo Alves Vergasta, A. Lyra","doi":"10.2166/wcc.2024.262","DOIUrl":"https://doi.org/10.2166/wcc.2024.262","url":null,"abstract":"\u0000 \u0000 We developed and analyzed the performance of an ensemble forecasting system for the Madeira River basin, the largest sub-basin of the Amazon, with forecasts up to 30 days under different hydrometeorological conditions. We used outputs from the regional Eta model of precipitation and global climatological data as inputs to a large-scale hydrological model. Bias correction of precipitation through quantile mapping significantly improved the results, achieving a hit rate >70%. The system demonstrated the ability to discriminate between high, medium, and low flow conditions. Forecast performance is better for larger catchment areas. This system is expected to increase decision-making efficiency for flood and drought situations in the largest Amazon tributary.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"52 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study was conducted under two water qualities (fresh water (FW), recycled wastewater (RWW)) and two biochar treatments (no biochar (No-B) and biochar (B)). It was determined that B reduced the actual evapotranspiration by saving irrigation water and that biomass yield increased in RWW and B; thus, RWW and B provided higher WPirrigation and WP. RWW and B increased OM, TN, P2O5, K2O, CEC, porosity, and aggregate stability, thus encouraging the development of the physical–physiological properties, ADF-NDF content, and biomass yields of the crop, but causing EC to increase. RWW and B resulted in higher macro–microelement contents and heavy metal (HM) contamination in the soil; thus, increases were observed in the macro–microelements and the HM content of the crop grown in RWW and B, but the absorption and buffering capacity of B limited the Na–Cd–Cr–Ni uptake of maize. However, the HM contents of the soil–crop did not exceed international standards in all treatments except the Cd content of maize. It was found that the use of B in irrigation with RWW can be recommended, considering the productivity-increasing contribution and the effectiveness of B in reducing the possible HM risks of RWW in maize cultivation, but monitoring the Cd content of maize and the EC of the soil.
研究在两种水质（淡水（FW）、再生废水（RWW））和两种生物炭处理（无生物炭（No-B）和生物炭（B））下进行。结果表明，生物炭通过节约灌溉用水减少了实际蒸散量，而在 RWW 和生物炭处理中生物量产量增加；因此，RWW 和生物炭处理提供了更高的灌溉水量和可耕地面积。RWW 和 B 增加了 OM、TN、P2O5、K2O、CEC、孔隙度和集料稳定性，从而促进了作物物理生理特性、ADF-NDF 含量和生物量产量的发展，但导致 EC 增加。RWW 和 B 导致土壤中的大微量元素含量和重金属（HM）污染增加；因此，在 RWW 和 B 中生长的作物的大微量元素和 HM 含量都有所增加，但 B 的吸收和缓冲能力限制了玉米对 Na-Cd-Cr-Ni 的吸收。不过，除玉米的镉含量外，所有处理中土壤-作物的 HM 含量均未超过国际标准。研究发现，考虑到硼对提高生产力的贡献以及硼在降低玉米种植中 RWW 可能带来的 HM 风险方面的有效性，可以建议在 RWW 灌溉中使用硼，但要监测玉米的镉含量和土壤的导电率。
{"title":"Determination of the effects of irrigation with recycled wastewater and biochar treatments on crop and soil properties in maize cultivation","authors":"C. Yerli","doi":"10.2166/wcc.2024.072","DOIUrl":"https://doi.org/10.2166/wcc.2024.072","url":null,"abstract":"\u0000 The study was conducted under two water qualities (fresh water (FW), recycled wastewater (RWW)) and two biochar treatments (no biochar (No-B) and biochar (B)). It was determined that B reduced the actual evapotranspiration by saving irrigation water and that biomass yield increased in RWW and B; thus, RWW and B provided higher WPirrigation and WP. RWW and B increased OM, TN, P2O5, K2O, CEC, porosity, and aggregate stability, thus encouraging the development of the physical–physiological properties, ADF-NDF content, and biomass yields of the crop, but causing EC to increase. RWW and B resulted in higher macro–microelement contents and heavy metal (HM) contamination in the soil; thus, increases were observed in the macro–microelements and the HM content of the crop grown in RWW and B, but the absorption and buffering capacity of B limited the Na–Cd–Cr–Ni uptake of maize. However, the HM contents of the soil–crop did not exceed international standards in all treatments except the Cd content of maize. It was found that the use of B in irrigation with RWW can be recommended, considering the productivity-increasing contribution and the effectiveness of B in reducing the possible HM risks of RWW in maize cultivation, but monitoring the Cd content of maize and the EC of the soil.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"75 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The decision support indicators (DSIs) are specifically designed to inform local and regional stakeholders on the characteristics of a predicted event to facilitate decision-making. They can be classified as conventional, impact-based and event-based DSIs. This study aims to develop methodologies for calculating event-based DSIs and to evaluate the usefulness of different classes of DSIs for climate impact assessment and climate actions by learning about users' perceptions. The DSIs are calculated based on an ensemble of hydrological projections in western Norway under two representative concentration pathway (RCP) scenarios. The definitions, methodologies and results of the indicators are summarized in questionnaires and evaluated by key stakeholders in terms of understandability, importance, plausibility and applicability. Based on the feedback, we conclude that the conventional DSIs are still preferred by stakeholders and an appropriate selection of conventional DSIs may overcome the understanding problems between the scientists and stakeholders. The DSIs based on well-known historical events are easy to understand and can be a useful tool to convey climate information to the public. However, they are not readily implemented by stakeholders in the decision-making process. The impact-based DSI is generally easy to understand and important but it can be restricted to specific impact sectors.
{"title":"Developing and evaluating decision support indicators (DSIs) of climate change impacts on flood and drought: a case study in Western Norway","authors":"Shaochun Huang, Stephanie Eisner, S. Beldring","doi":"10.2166/wcc.2024.198","DOIUrl":"https://doi.org/10.2166/wcc.2024.198","url":null,"abstract":"\u0000 \u0000 The decision support indicators (DSIs) are specifically designed to inform local and regional stakeholders on the characteristics of a predicted event to facilitate decision-making. They can be classified as conventional, impact-based and event-based DSIs. This study aims to develop methodologies for calculating event-based DSIs and to evaluate the usefulness of different classes of DSIs for climate impact assessment and climate actions by learning about users' perceptions. The DSIs are calculated based on an ensemble of hydrological projections in western Norway under two representative concentration pathway (RCP) scenarios. The definitions, methodologies and results of the indicators are summarized in questionnaires and evaluated by key stakeholders in terms of understandability, importance, plausibility and applicability. Based on the feedback, we conclude that the conventional DSIs are still preferred by stakeholders and an appropriate selection of conventional DSIs may overcome the understanding problems between the scientists and stakeholders. The DSIs based on well-known historical events are easy to understand and can be a useful tool to convey climate information to the public. However, they are not readily implemented by stakeholders in the decision-making process. The impact-based DSI is generally easy to understand and important but it can be restricted to specific impact sectors.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"140 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate hydrological modeling is essential for understanding and managing water resources. This study conducts a comparative analysis of hydrological modeling strategies in a date-scarce region. This study examines lumped (IHACRES), semi-distributed (HEC-HMS), and hybrid-lumped/long short-term memory (LSTM) models, aiming to assess their performance and accuracy in a data-scarce region. It investigates whether lump models can accurately simulate flow and evaluates the impact of combining lump models with machine learning to enhance accuracy, compared to semi-distributed models. The IHACRES model underestimates discharge, but its commendable NSE during calibration (0.628) and validation (0.681) signifies reliable simulation. The HEC-HMS model accurately depicts daily streamflow but struggles with extreme events, showcasing limitations in predicting maximum flows. The hybrid-lumped/LSTM model, exhibits improved accuracy over IHACRES. Despite some underestimation, it mitigates IHACRES limitations during extreme events. However, challenges persist in simulating high flows, emphasizing the necessity for further refinement. The findings contribute to the discourse on merging machine learning with traditional hydrological models in data-scarce regions. The hybrid model offers promise but underscores the need for ongoing research to optimize performance, especially during extreme events. This study provides valuable insights for advancing hydrological modeling capabilities in complex watersheds.
{"title":"Comparing the hybrid-lumped-LSTM model with a semi-distributed model for improved hydrological modeling","authors":"Erfan Zarei, Farzin Nasiri Saleh, Afsaneh Nobakht Dalir","doi":"10.2166/wcc.2024.269","DOIUrl":"https://doi.org/10.2166/wcc.2024.269","url":null,"abstract":"\u0000 Accurate hydrological modeling is essential for understanding and managing water resources. This study conducts a comparative analysis of hydrological modeling strategies in a date-scarce region. This study examines lumped (IHACRES), semi-distributed (HEC-HMS), and hybrid-lumped/long short-term memory (LSTM) models, aiming to assess their performance and accuracy in a data-scarce region. It investigates whether lump models can accurately simulate flow and evaluates the impact of combining lump models with machine learning to enhance accuracy, compared to semi-distributed models. The IHACRES model underestimates discharge, but its commendable NSE during calibration (0.628) and validation (0.681) signifies reliable simulation. The HEC-HMS model accurately depicts daily streamflow but struggles with extreme events, showcasing limitations in predicting maximum flows. The hybrid-lumped/LSTM model, exhibits improved accuracy over IHACRES. Despite some underestimation, it mitigates IHACRES limitations during extreme events. However, challenges persist in simulating high flows, emphasizing the necessity for further refinement. The findings contribute to the discourse on merging machine learning with traditional hydrological models in data-scarce regions. The hybrid model offers promise but underscores the need for ongoing research to optimize performance, especially during extreme events. This study provides valuable insights for advancing hydrological modeling capabilities in complex watersheds.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"136 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Global climate change is a phenomenon resulting from the complex interaction of human influences and natural factors. These changes lead to imbalances in climate systems as human activities such as greenhouse gas emissions increase atmospheric gas concentrations. This situation affects the frequency and intensity of climate events worldwide, with floods being one of them. Floods manifest as water inundations due to factors such as changes in rainfall patterns, rising temperatures, erosion, and sea level rise. These floods cause significant damage to sensitive areas such as residential areas, agricultural lands, river valleys, and coastal regions, adversely impacting people's lives, economies, and environments. Therefore, flood risk has been investigated in decision-making processes in the Diyarbakır province using the Analytical Hierarchy Process (AHP) method and future disintegration of global climate model data. HadGEM-ES, GFDL-ESM2M, and MPI-ESM-MR models with RCP4.5 and RCP8.5 scenarios were used. Model data were disintegrated using the equivalent quantile matching method. The study reveals flood risk findings in the HadGEM-ES model while no flood risk was found in the GFDL-ESM2M and MPI-ESM-MR models. In the AHP method, flood risk analysis was conducted based on historical data across Diyarbakır and interpreted alongside future rainfall data.
{"title":"Investigation of the flood event under global climate change with different analysis methods for both historical and future periods","authors":"Burak Gül, N. Kayaalp","doi":"10.2166/wcc.2024.196","DOIUrl":"https://doi.org/10.2166/wcc.2024.196","url":null,"abstract":"\u0000 \u0000 Global climate change is a phenomenon resulting from the complex interaction of human influences and natural factors. These changes lead to imbalances in climate systems as human activities such as greenhouse gas emissions increase atmospheric gas concentrations. This situation affects the frequency and intensity of climate events worldwide, with floods being one of them. Floods manifest as water inundations due to factors such as changes in rainfall patterns, rising temperatures, erosion, and sea level rise. These floods cause significant damage to sensitive areas such as residential areas, agricultural lands, river valleys, and coastal regions, adversely impacting people's lives, economies, and environments. Therefore, flood risk has been investigated in decision-making processes in the Diyarbakır province using the Analytical Hierarchy Process (AHP) method and future disintegration of global climate model data. HadGEM-ES, GFDL-ESM2M, and MPI-ESM-MR models with RCP4.5 and RCP8.5 scenarios were used. Model data were disintegrated using the equivalent quantile matching method. The study reveals flood risk findings in the HadGEM-ES model while no flood risk was found in the GFDL-ESM2M and MPI-ESM-MR models. In the AHP method, flood risk analysis was conducted based on historical data across Diyarbakır and interpreted alongside future rainfall data.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"120 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141667781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The analysis of rainfall variability has significant implications for environmental studies since it influences the agrarian economy of regions such as the western Himalayas. The main objective of this research is to identify future precipitation trends in parts of the Beas River basin using modeled data from three models employed in the Climate Model Intercomparison Project Phase 6. The ACCESS, CanESM, and NorESM models were utilized to obtain modeled meteorological data from 2015 to 2100 (86 years). Data from global climate models were downscaled to the regional level and validated with the India Meteorological Department (IMD). Mention that the modeled data were downscaled from the regional level to the local level. The nonparametric trends test, modified Mann–Kendall, and Sen's slope estimator (Q) were employed to detect the trend and magnitude. Furthermore, the sub-trends of the data series were evaluated utilizing the innovative trend analysis (ITA) approach. Results have shown a significant increasing trend in future timescales, indicating the more frequent extreme events in the basin under all scenarios. The basin has shown a maximum slope of 24.9 (ITA) and 12.2 (Sen's slope).This study findings hold significant implications for policymakers and water resource managers.
{"title":"Projection of future rainfall events over the Beas River basin, Western Himalaya, using shared socioeconomic pathways (SSPs) from CMIP6","authors":"Chander Kant, Raysing Meena","doi":"10.2166/wcc.2024.627","DOIUrl":"https://doi.org/10.2166/wcc.2024.627","url":null,"abstract":"\u0000 The analysis of rainfall variability has significant implications for environmental studies since it influences the agrarian economy of regions such as the western Himalayas. The main objective of this research is to identify future precipitation trends in parts of the Beas River basin using modeled data from three models employed in the Climate Model Intercomparison Project Phase 6. The ACCESS, CanESM, and NorESM models were utilized to obtain modeled meteorological data from 2015 to 2100 (86 years). Data from global climate models were downscaled to the regional level and validated with the India Meteorological Department (IMD). Mention that the modeled data were downscaled from the regional level to the local level. The nonparametric trends test, modified Mann–Kendall, and Sen's slope estimator (Q) were employed to detect the trend and magnitude. Furthermore, the sub-trends of the data series were evaluated utilizing the innovative trend analysis (ITA) approach. Results have shown a significant increasing trend in future timescales, indicating the more frequent extreme events in the basin under all scenarios. The basin has shown a maximum slope of 24.9 (ITA) and 12.2 (Sen's slope).This study findings hold significant implications for policymakers and water resource managers.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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