Instream ecological flow (IEF) provides a flow reference value for maintaining the stability of river ecosystems and enriching biodiversity. Existing methods for determining the IEF often consider only one of the intra-annual runoff distribution characteristics or the inter-annual runoff distribution characteristics. In this study, a copula-based ecological runoff determination method is proposed to consider both annual and monthly runoff magnitudes. The marginal distribution functions of annual and monthly runoff are first determined using the great likelihood method. Then the copula function is used to construct the joint distribution function of annual and monthly runoff series. Using the flow duration curve method, the IEF under different annual and monthly runoff exceedance probabilities is calculated. The probability values are combined with the initial IEF values to determine the final IEF process. A case study is conducted using real-world data from the Tongtian River in China. The results show that the total annual basic ecological water demands of the Tongtian River under abundant, flat, and dry water years are 138, 102 and 72. 2 billion m3. The proposed method effectively avoids the disturbance of extreme runoff.
{"title":"A copula-based approach to instream ecological flow determination considering inter- and intra-annual runoff variability","authors":"Xin Liu, Weixing Yang, Yu Zhang, Guohua Fang","doi":"10.2166/wcc.2024.025","DOIUrl":"https://doi.org/10.2166/wcc.2024.025","url":null,"abstract":"\u0000 Instream ecological flow (IEF) provides a flow reference value for maintaining the stability of river ecosystems and enriching biodiversity. Existing methods for determining the IEF often consider only one of the intra-annual runoff distribution characteristics or the inter-annual runoff distribution characteristics. In this study, a copula-based ecological runoff determination method is proposed to consider both annual and monthly runoff magnitudes. The marginal distribution functions of annual and monthly runoff are first determined using the great likelihood method. Then the copula function is used to construct the joint distribution function of annual and monthly runoff series. Using the flow duration curve method, the IEF under different annual and monthly runoff exceedance probabilities is calculated. The probability values are combined with the initial IEF values to determine the final IEF process. A case study is conducted using real-world data from the Tongtian River in China. The results show that the total annual basic ecological water demands of the Tongtian River under abundant, flat, and dry water years are 138, 102 and 72. 2 billion m3. The proposed method effectively avoids the disturbance of extreme runoff.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994774","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}
This study aims to investigate variability in precipitation and air temperature data of all provinces in Turkey. The innovative trend pivot analysis method (ITPAM) was used to analyze the trend in precipitation (mm) and air temperature (°C) data of Turkey in this study. Analyzing the country as a whole using this method is one of the strengths of the study. In the study, 30-year datasets between 1991 and 2020 were analyzed. As a result of the study, trend analysis results of precipitation and air temperature data of 81 provinces in Turkey were obtained. The significance level in trends was determined as 5%. In this study, 41% of the monthly total precipitation data of Turkey showed an increasing trend, 41% showed a decreasing trend, while 18% showed no trend. In the standard deviation of precipitation data, 44% of the data showed an increasing trend, 42% showed a decreasing trend and 14% showed no trend. In addition, it was concluded that there was an increasing trend of 67% in the monthly average air temperature data and an increasing trend of 46% in the standard deviation data. According to these results, it is concluded that monthly average air temperature data have a largely increasing trend.
{"title":"Changes in precipitation and air temperature over Turkey using innovative trend pivot analysis method","authors":"Ahmet Iyad Ceyhunlu, Gokmen Ceribasi","doi":"10.2166/wcc.2024.041","DOIUrl":"https://doi.org/10.2166/wcc.2024.041","url":null,"abstract":"\u0000 \u0000 This study aims to investigate variability in precipitation and air temperature data of all provinces in Turkey. The innovative trend pivot analysis method (ITPAM) was used to analyze the trend in precipitation (mm) and air temperature (°C) data of Turkey in this study. Analyzing the country as a whole using this method is one of the strengths of the study. In the study, 30-year datasets between 1991 and 2020 were analyzed. As a result of the study, trend analysis results of precipitation and air temperature data of 81 provinces in Turkey were obtained. The significance level in trends was determined as 5%. In this study, 41% of the monthly total precipitation data of Turkey showed an increasing trend, 41% showed a decreasing trend, while 18% showed no trend. In the standard deviation of precipitation data, 44% of the data showed an increasing trend, 42% showed a decreasing trend and 14% showed no trend. In addition, it was concluded that there was an increasing trend of 67% in the monthly average air temperature data and an increasing trend of 46% in the standard deviation data. According to these results, it is concluded that monthly average air temperature data have a largely increasing trend.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"52 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141009946","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}
In this research, the authors aim to evaluate the damage caused by floods in Vinh Phuc Province. The study incorporates the construction of hydro-hydraulic models and the development of damage functions for the main elements at risk. In order to ensure the accuracy of the calculated results, the hydro-hydraulic models and damage curves are verified for their reasonableness. The outcomes of this study give us an overview of flood damage in the research area. It indicates that the potential level of flood damage is a matter of significant concern, with an annual damage estimate of approximately 6.9 million USD. The research also reveals the distribution of damage by sector and by space. According to the research findings, the greatest damage was in Vinh Tuong District, where most of the damage was caused by flooding of agricultural land. In contrast, residential buildings in the area suffered relatively little damage compared to agricultural land. This information is crucial for decision-makers to implement effective and targeted mitigation measures.
{"title":"Flood damage assessment for the Phan-Ca Lo River basin in Vinh Phuc Province, Vietnam","authors":"Hung Manh Phan, Chau Kim Tran, Hue Thi Minh Vu","doi":"10.2166/wcc.2024.036","DOIUrl":"https://doi.org/10.2166/wcc.2024.036","url":null,"abstract":"\u0000 \u0000 In this research, the authors aim to evaluate the damage caused by floods in Vinh Phuc Province. The study incorporates the construction of hydro-hydraulic models and the development of damage functions for the main elements at risk. In order to ensure the accuracy of the calculated results, the hydro-hydraulic models and damage curves are verified for their reasonableness. The outcomes of this study give us an overview of flood damage in the research area. It indicates that the potential level of flood damage is a matter of significant concern, with an annual damage estimate of approximately 6.9 million USD. The research also reveals the distribution of damage by sector and by space. According to the research findings, the greatest damage was in Vinh Tuong District, where most of the damage was caused by flooding of agricultural land. In contrast, residential buildings in the area suffered relatively little damage compared to agricultural land. This information is crucial for decision-makers to implement effective and targeted mitigation measures.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"147 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141013474","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}
Vikneswari Someetheram, Muhammad Fadhil Marsani, Mohd Shareduwan Mohd Kasihmuddin, Siti Zulaikha Mohd Jamaludin, M. Mansor
As global climates undergo changes, the frequency of water-related disasters rises, leading to significant economic losses and safety hazards. During flood events, river water levels exhibit unpredictable fluctuations, introducing considerable noise that poses challenges for accurate prediction. A prediction of water level by using existing water level data makes a major contribution to forecasting flood. Enhanced least-squares support vector machine (ELSSVM) is utilized by integrating an additional extra bias error control term. In this study, least-squares support vector machine (LSSVM) and ELSSVM optimized by the genetic algorithm (GA) were chosen to be compared with the help of data decomposition methods to improve daily water level prediction accuracy. Double empirical mode decomposition (DEMD) will be integrated with LSSVM and ELSSVM. Thus, the models are named LSSVM-GA, ELSSVM-GA, empirical mode decomposition (EMD)-LSSVM-GA, EMD-ELSSVM-GA, DEMD-LSSVM-GA, and DEMD-ELSSVM-GA. The proposed models are used in forecasting the water level of Klang River in Sri Muda, Malaysia. The behavior proposed models are investigated and compared based on several performance metrics such as root-mean-square error (RMSE) and squared correlation coefficient (R2). The results demonstrated that the DEMD-ELSSVM-GA model outperformed the other models based on the performance analysis in forecasting the water level with RMSE = 0.2536 m and R2 = 0.8596 for testing data that indicate the forecasting accuracy.
{"title":"Double decomposition with enhanced least-squares support vector machine to predict water level","authors":"Vikneswari Someetheram, Muhammad Fadhil Marsani, Mohd Shareduwan Mohd Kasihmuddin, Siti Zulaikha Mohd Jamaludin, M. Mansor","doi":"10.2166/wcc.2024.558","DOIUrl":"https://doi.org/10.2166/wcc.2024.558","url":null,"abstract":"\u0000 As global climates undergo changes, the frequency of water-related disasters rises, leading to significant economic losses and safety hazards. During flood events, river water levels exhibit unpredictable fluctuations, introducing considerable noise that poses challenges for accurate prediction. A prediction of water level by using existing water level data makes a major contribution to forecasting flood. Enhanced least-squares support vector machine (ELSSVM) is utilized by integrating an additional extra bias error control term. In this study, least-squares support vector machine (LSSVM) and ELSSVM optimized by the genetic algorithm (GA) were chosen to be compared with the help of data decomposition methods to improve daily water level prediction accuracy. Double empirical mode decomposition (DEMD) will be integrated with LSSVM and ELSSVM. Thus, the models are named LSSVM-GA, ELSSVM-GA, empirical mode decomposition (EMD)-LSSVM-GA, EMD-ELSSVM-GA, DEMD-LSSVM-GA, and DEMD-ELSSVM-GA. The proposed models are used in forecasting the water level of Klang River in Sri Muda, Malaysia. The behavior proposed models are investigated and compared based on several performance metrics such as root-mean-square error (RMSE) and squared correlation coefficient (R2). The results demonstrated that the DEMD-ELSSVM-GA model outperformed the other models based on the performance analysis in forecasting the water level with RMSE = 0.2536 m and R2 = 0.8596 for testing data that indicate the forecasting accuracy.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"3 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141014385","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}
Phattrasuda Phosri, Sitang Pilailar, S. Chittaladakorn
The expansion of Bangkok Metropolitan, driven by economic growth, includes extending metro systems.These changes, alongside a denser population and higher waste generation, have compromised drainage efficiency, amplifying flood vulnerability. Focused on the Pink Line's 34.5-km stretch from Khaerai intersection to Minburi, this study aims to evaluate the Flood Vulnerability Index (FVI) near the metro line, identify critical factors influencing vulnerability, and propose mitigation strategies. The analysis divided the study area into five distinct zones based on area characteristics, then evaluated the FVI according to existing conditions of population density (PO), drainage efficiency (DE), impervious ratio (IR), garbage management (GB), and pond area ratio (PA). It was found that the FVI values ranged from 0.41 to 0.55, and the sensitivity analysis indicated Lat Pha Khao and Ramindra km.4 stations have minor FVI impact due to DE and IR. Conversely, improvements in PA and GB reduced the FVI at Khubon and Eastern Ring Road stations. Consistent FVI is observed with PA and IR enhancements but fluctuates with GB and DE changes. At the Minburi Market Station, the increased IR has a minor FVI impact, while enhanced DE and PA significantly lower the FVI.The degree of factor sensitivity is advantageous for planning local mitigation strategies.
在经济增长的推动下，曼谷大都市不断扩大，其中包括地铁系统的延伸。这些变化，加上人口密集和废物产生量增加，影响了排水效率，加剧了洪水的脆弱性。本研究以粉红线从 Khaerai 十字路口到 Minburi 的 34.5 公里路段为重点，旨在评估地铁线附近的洪水脆弱性指数 (FVI)，确定影响脆弱性的关键因素，并提出缓解策略。分析根据区域特征将研究区域划分为五个不同的区域，然后根据人口密度 (PO)、排水效率 (DE)、不透水率 (IR)、垃圾管理 (GB) 和池塘面积比 (PA) 等现有条件评估洪水脆弱性指数。敏感性分析表明，由于 DE 和 IR 的影响，Pha Khao 站和 Ramindra km.4 站对 FVI 的影响较小。相反，PA 和 GB 的改善降低了 Khubon 站和东环路站的 FVI 值。PA 和 IR 增强后，FVI 保持一致，但 GB 和 DE 变化后，FVI 有所波动。在 Minburi Market 站，IR 的增加对 FVI 的影响较小，而 DE 和 PA 的增加则显著降低了 FVI。
{"title":"Effect of metro rail extension on flood risks of Bangkok Metropolitan Authority outskirt due to climate and land use land cover changes","authors":"Phattrasuda Phosri, Sitang Pilailar, S. Chittaladakorn","doi":"10.2166/wcc.2024.691","DOIUrl":"https://doi.org/10.2166/wcc.2024.691","url":null,"abstract":"\u0000 \u0000 The expansion of Bangkok Metropolitan, driven by economic growth, includes extending metro systems.These changes, alongside a denser population and higher waste generation, have compromised drainage efficiency, amplifying flood vulnerability. Focused on the Pink Line's 34.5-km stretch from Khaerai intersection to Minburi, this study aims to evaluate the Flood Vulnerability Index (FVI) near the metro line, identify critical factors influencing vulnerability, and propose mitigation strategies. The analysis divided the study area into five distinct zones based on area characteristics, then evaluated the FVI according to existing conditions of population density (PO), drainage efficiency (DE), impervious ratio (IR), garbage management (GB), and pond area ratio (PA). It was found that the FVI values ranged from 0.41 to 0.55, and the sensitivity analysis indicated Lat Pha Khao and Ramindra km.4 stations have minor FVI impact due to DE and IR. Conversely, improvements in PA and GB reduced the FVI at Khubon and Eastern Ring Road stations. Consistent FVI is observed with PA and IR enhancements but fluctuates with GB and DE changes. At the Minburi Market Station, the increased IR has a minor FVI impact, while enhanced DE and PA significantly lower the FVI.The degree of factor sensitivity is advantageous for planning local mitigation strategies.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"118 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017335","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}
Hedayatullah Shams, Asif Khan, Kashif Haleem, Saqib Mahmood
This study critically assesses the combined effects of climate and land-use change on flood recurrence in the Kokcha River, Afghanistan, spanning the period from 2010 to 2021 and projecting forward to 2088–2099. Through the application of a bias-corrected model, we achieved high accuracy in temperature and precipitation simulations, with notable NSE values of 0.9 and 0.69, and R2 values of 0.92 and 0.78, respectively. Future streamflow simulations under different scenarios highlight climate change as the major driver influencing flood recurrence in the Kokcha River, contributing to 101.1% of the total variation, while land-use change has a minimal contribution of −1.1%. Our analysis of precipitation, average temperature, and streamflow data reveals significant patterns and changes, with future projections indicating a gradual decline in precipitation levels, mean temperature, and streamflow. Flood frequency analysis for return periods of 10, 50, 100, 200, and 500 years, considering different scenarios, underscores the likelihood of floods of varying magnitudes. Notably, the highest streamflow spikes under both scenarios highlight the impact of futuristic air temperature and precipitation on flood recurrence. The study advocates prioritizing climate change adaptation and resilient land-use strategies to ensure sustainable water resource management, emphasizing the mitigation of potential flood risks.
{"title":"Assessing the effects of climate and land-use change on flood recurrence in Kokcha River, Afghanistan","authors":"Hedayatullah Shams, Asif Khan, Kashif Haleem, Saqib Mahmood","doi":"10.2166/wcc.2024.043","DOIUrl":"https://doi.org/10.2166/wcc.2024.043","url":null,"abstract":"\u0000 \u0000 This study critically assesses the combined effects of climate and land-use change on flood recurrence in the Kokcha River, Afghanistan, spanning the period from 2010 to 2021 and projecting forward to 2088–2099. Through the application of a bias-corrected model, we achieved high accuracy in temperature and precipitation simulations, with notable NSE values of 0.9 and 0.69, and R2 values of 0.92 and 0.78, respectively. Future streamflow simulations under different scenarios highlight climate change as the major driver influencing flood recurrence in the Kokcha River, contributing to 101.1% of the total variation, while land-use change has a minimal contribution of −1.1%. Our analysis of precipitation, average temperature, and streamflow data reveals significant patterns and changes, with future projections indicating a gradual decline in precipitation levels, mean temperature, and streamflow. Flood frequency analysis for return periods of 10, 50, 100, 200, and 500 years, considering different scenarios, underscores the likelihood of floods of varying magnitudes. Notably, the highest streamflow spikes under both scenarios highlight the impact of futuristic air temperature and precipitation on flood recurrence. The study advocates prioritizing climate change adaptation and resilient land-use strategies to ensure sustainable water resource management, emphasizing the mitigation of potential flood risks.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141015307","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}
S. S. Kolukula, P. L. N. Murty, Balaji Baduru, D. Sharath, Francis P. A.
Downscaling is reconstructing data from low-resolution to high-resolution, capturing local effects and magnitudes. Widely employed methods for downscaling are dynamic and statistical methods with pros and cons. With ample data, ML and DL techniques can be employed to learn the mapping from low-resolution to high-resolution. The current article investigates convolutional neural network capabilities for the downscaling of winds. The speed and direction of the wind are guided by a complex relation among pressure, Coriolis force, friction, and temperature, which leads to highly nonlinear wind patterns and poses a significant challenge for downscaling. The problem can be formulated as a super-resolution technique called a super-resolution convolutional neural network (SRCNN) for data reconstruction. Few variations of SRCNN are studied for wind downscaling. Six years of ECMWF wind datasets along the east coast of India are used in the current study and are downscaled up to four times. Downscaled winds provide better results than traditional interpolation methods. Simulations for an extreme event are conducted with SRCNN downscaled winds and are compared against interpolation methods and original data. The numerical simulation results show that DL-based methods provide results closer to the ground truth than the interpolation methods.
{"title":"Downscaling of wind fields on the east coast of India using deep convolutional neural networks and their applications in storm surge computations","authors":"S. S. Kolukula, P. L. N. Murty, Balaji Baduru, D. Sharath, Francis P. A.","doi":"10.2166/wcc.2024.507","DOIUrl":"https://doi.org/10.2166/wcc.2024.507","url":null,"abstract":"\u0000 Downscaling is reconstructing data from low-resolution to high-resolution, capturing local effects and magnitudes. Widely employed methods for downscaling are dynamic and statistical methods with pros and cons. With ample data, ML and DL techniques can be employed to learn the mapping from low-resolution to high-resolution. The current article investigates convolutional neural network capabilities for the downscaling of winds. The speed and direction of the wind are guided by a complex relation among pressure, Coriolis force, friction, and temperature, which leads to highly nonlinear wind patterns and poses a significant challenge for downscaling. The problem can be formulated as a super-resolution technique called a super-resolution convolutional neural network (SRCNN) for data reconstruction. Few variations of SRCNN are studied for wind downscaling. Six years of ECMWF wind datasets along the east coast of India are used in the current study and are downscaled up to four times. Downscaled winds provide better results than traditional interpolation methods. Simulations for an extreme event are conducted with SRCNN downscaled winds and are compared against interpolation methods and original data. The numerical simulation results show that DL-based methods provide results closer to the ground truth than the interpolation methods.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140212750","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}
Fernanda Custodio Pereira do Carmo, Jeenu John, L. Sushama, Muhammad Naveed Khaliq
Generating continuous streamflow information through integrated climate-hydrology modeling at fine spatial scales of the order of a few kilometers is often challenged by computational costs associated with running high-resolution (HR) climate models. To address this challenge, the present study explores deep learning approaches to generate HR streamflow information from that at low resolution (LR), based on runoff generated by climate models. To this end, two sets of daily streamflow simulations spanning 10 years (2011–2020), at LR (50 km) and HR (5 km), for the Ottawa River basin in Canada are employed. The proposed deep learning model is trained using upscaled features derived from LR streamflow simulation for the 2011–2018 period as input and the corresponding HR streamflow simulation as the target; data for 2019 are used for validation. The model estimates for the year 2020, when compared with unseen HR data for the same year, suggest good performance, with differences in monthly mean values for different accumulation area categories in the −0.7–5% range and correlation coefficients for streamflow values for the same accumulation area categories in the 0.92–0.96 range. The developed framework can be ported to other watersheds for generating similar information, which is often required in climate change adaptation studies.
{"title":"Deep learning modeling framework for multi-resolution streamflow generation","authors":"Fernanda Custodio Pereira do Carmo, Jeenu John, L. Sushama, Muhammad Naveed Khaliq","doi":"10.2166/wcc.2024.706","DOIUrl":"https://doi.org/10.2166/wcc.2024.706","url":null,"abstract":"\u0000 \u0000 Generating continuous streamflow information through integrated climate-hydrology modeling at fine spatial scales of the order of a few kilometers is often challenged by computational costs associated with running high-resolution (HR) climate models. To address this challenge, the present study explores deep learning approaches to generate HR streamflow information from that at low resolution (LR), based on runoff generated by climate models. To this end, two sets of daily streamflow simulations spanning 10 years (2011–2020), at LR (50 km) and HR (5 km), for the Ottawa River basin in Canada are employed. The proposed deep learning model is trained using upscaled features derived from LR streamflow simulation for the 2011–2018 period as input and the corresponding HR streamflow simulation as the target; data for 2019 are used for validation. The model estimates for the year 2020, when compared with unseen HR data for the same year, suggest good performance, with differences in monthly mean values for different accumulation area categories in the −0.7–5% range and correlation coefficients for streamflow values for the same accumulation area categories in the 0.92–0.96 range. The developed framework can be ported to other watersheds for generating similar information, which is often required in climate change adaptation studies.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"87 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223972","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 solar sterilization treatment innovation method can be implemented within households to address water pollution, especially those contaminants caused by microorganisms that may lead to waterborne illnesses. During review, water sources in the local area were assessed for turbidity and microbial quality. It was found that river water had turbidity levels of less than 30 NTU (Nephelometric Turbidity Units) during the dry season and typically up to 50 NTU during the wet season. This review aimed to understand the impact of these findings on the local area and its technological context. This study researched the utilization of sun-based radiation to clean the microbial mass tracked down in untreated water. Temperature and length of the openness are the two normal factors that were distinguished. The first water with a microbial heap of 40–55 (CFU) was presented to solar radiation at a temperature of above 44.5 °C outcome 0 (CFU) at the openness season of 6 h. The size and kind of materials utilized for this process were PET bottles of 1.5 L capacity with a half-dark tone. The factual examination at a 95% certainty information stretch has a p-value of <0.0001.
太阳能杀菌处理创新方法可在家庭中实施，以解决水污染问题，尤其是那些由微生物引起的、可能导致水传播疾病的污染物。在审查期间，对当地水源的浑浊度和微生物质量进行了评估。结果发现，在旱季，河水的浊度低于 30 NTU（奈米浊度单位），而在雨季，河水的浊度通常高达 50 NTU。本综述旨在了解这些发现对当地及其技术背景的影响。这项研究探讨了如何利用太阳辐射来净化未经处理的水中的微生物。温度和开放时间是两个不同的正常因素。首先将微生物量为 40-55 (CFU) 的水置于温度高于 44.5 °C 的太阳辐射下，结果为 0 (CFU)，开放时间为 6 小时。在 95% 的确定性信息范围内进行的事实检验的 P 值小于 0.0001。
{"title":"Assessment of microbial water treatment by direct solar radiation disinfection approach","authors":"Firaol Bedada Kopesa, Miliyon Dida Feye, Ashenafi Dechasa, Mezgebu Game Worku, Oftau Sorsa","doi":"10.2166/wcc.2024.013","DOIUrl":"https://doi.org/10.2166/wcc.2024.013","url":null,"abstract":"\u0000 The solar sterilization treatment innovation method can be implemented within households to address water pollution, especially those contaminants caused by microorganisms that may lead to waterborne illnesses. During review, water sources in the local area were assessed for turbidity and microbial quality. It was found that river water had turbidity levels of less than 30 NTU (Nephelometric Turbidity Units) during the dry season and typically up to 50 NTU during the wet season. This review aimed to understand the impact of these findings on the local area and its technological context. This study researched the utilization of sun-based radiation to clean the microbial mass tracked down in untreated water. Temperature and length of the openness are the two normal factors that were distinguished. The first water with a microbial heap of 40–55 (CFU) was presented to solar radiation at a temperature of above 44.5 °C outcome 0 (CFU) at the openness season of 6 h. The size and kind of materials utilized for this process were PET bottles of 1.5 L capacity with a half-dark tone. The factual examination at a 95% certainty information stretch has a p-value of <0.0001.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220864","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 objective of this study is to determine the optimum size of stone revetment for different water standing durations and critical drawdown rates for 80% height of flood. The model bank was constructed using soil sourced from the Parlalpur ferry ghat of the Ganga River in Kaliachak, West Bengal, with a field density of 1.5 g/cc achieved by maintaining a 10% moisture content (MC) in the model bank soil. The model river bank was prepared considering Froude (F) similitude having a distorted depth scale of 1/20 and a linear scale of 1/200. In this study, effective stone sizes (D10) – 2.3, 3.22, 4.58, 6.31, and 8.84 mm – were used. These stone sizes were investigated in conjunction with three water standing durations: 15, 30, and 45 min. The bank slope was prepared at 1V:1.5H, and a drawdown ratio of 80% was maintained. The effectiveness of stone revetment size was analysed in terms of the percentage loss of stone revetment and the percentage loss of the model bank's cross-sectional area. The outcomes of this study indicate that the 6.31 mm stone size exhibits optimal performance.
{"title":"Experimental investigation of the efficiency of stone revetment for different temporal variations with the static water condition","authors":"Mohsin Jamal, Supia Khatun, Shivendra Jha, Darshan Mehta, Sudhanshu Dixit, Vijendra Kumar, Deepak Kumar Tiwari","doi":"10.2166/wcc.2024.126","DOIUrl":"https://doi.org/10.2166/wcc.2024.126","url":null,"abstract":"\u0000 The objective of this study is to determine the optimum size of stone revetment for different water standing durations and critical drawdown rates for 80% height of flood. The model bank was constructed using soil sourced from the Parlalpur ferry ghat of the Ganga River in Kaliachak, West Bengal, with a field density of 1.5 g/cc achieved by maintaining a 10% moisture content (MC) in the model bank soil. The model river bank was prepared considering Froude (F) similitude having a distorted depth scale of 1/20 and a linear scale of 1/200. In this study, effective stone sizes (D10) – 2.3, 3.22, 4.58, 6.31, and 8.84 mm – were used. These stone sizes were investigated in conjunction with three water standing durations: 15, 30, and 45 min. The bank slope was prepared at 1V:1.5H, and a drawdown ratio of 80% was maintained. The effectiveness of stone revetment size was analysed in terms of the percentage loss of stone revetment and the percentage loss of the model bank's cross-sectional area. The outcomes of this study indicate that the 6.31 mm stone size exhibits optimal performance.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":" 61","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221091","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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