Pub Date : 2024-05-23DOI: 10.3389/frwa.2024.1405603
Michael Lewis, Hamed Moftakhari, Paola Passalacqua
Compound flooding (CF) events, driven by coincident/concurrent and mutually reinforcing factors such as heavy rainfall, storm surges, and river discharge, pose severe threats to coastal communities around the Globe. Moreover, the exacerbating influence of climate change and sea-level rise further amplifies these risks. This study delves into the complex and multifaceted issue of compound coastal flooding in two freshwater-influenced systems on the Gulf Coast of the United States – Southeast Texas and South Alabama. We first conduct a robust statistical analysis to evaluate the significance of non-stationarity, multi-dimensionality, and non-linearity of interactions among various drivers of CF. Second, to assess the extent to which current flood resilience policies and guidelines account for these characteristics of CF events, we perform a critical review of existing policy documents. The results of the statistical analysis reveal significant compounding and shifts in the statistics of flood drivers that emphasize the pressing need for a multi-mechanism, non-stationary approach to flood hazard assessment. We also found an evident lack of appropriate language/recommendation in policy documents of solid tools that systematically take non-stationarity, multi-dimensionality, and non-linearity of CF into account. By identifying the gaps between current policy measures and the detected complexities of CF, we seek to provide insights that can inform more effective flood resilience policies and design guidelines. Through this robust analysis, we aspire to bridge the divide between research and policy.
{"title":"Challenges for compound coastal flood risk management in a warming climate: a case study of the Gulf Coast of the United States","authors":"Michael Lewis, Hamed Moftakhari, Paola Passalacqua","doi":"10.3389/frwa.2024.1405603","DOIUrl":"https://doi.org/10.3389/frwa.2024.1405603","url":null,"abstract":"Compound flooding (CF) events, driven by coincident/concurrent and mutually reinforcing factors such as heavy rainfall, storm surges, and river discharge, pose severe threats to coastal communities around the Globe. Moreover, the exacerbating influence of climate change and sea-level rise further amplifies these risks. This study delves into the complex and multifaceted issue of compound coastal flooding in two freshwater-influenced systems on the Gulf Coast of the United States – Southeast Texas and South Alabama. We first conduct a robust statistical analysis to evaluate the significance of non-stationarity, multi-dimensionality, and non-linearity of interactions among various drivers of CF. Second, to assess the extent to which current flood resilience policies and guidelines account for these characteristics of CF events, we perform a critical review of existing policy documents. The results of the statistical analysis reveal significant compounding and shifts in the statistics of flood drivers that emphasize the pressing need for a multi-mechanism, non-stationary approach to flood hazard assessment. We also found an evident lack of appropriate language/recommendation in policy documents of solid tools that systematically take non-stationarity, multi-dimensionality, and non-linearity of CF into account. By identifying the gaps between current policy measures and the detected complexities of CF, we seek to provide insights that can inform more effective flood resilience policies and design guidelines. Through this robust analysis, we aspire to bridge the divide between research and policy.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106575","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}
Pub Date : 2024-05-23DOI: 10.3389/frwa.2024.1386303
Sultan Kotb, Li Cheng, Mohamed Amin, Mohamed Monir Elzoghby, Ahmed Nasr
Water scarcity presents significant challenges to sustainable development, particularly in arid regions like Fayoum City, Egypt, which faces particular water challenges due to its unique topography. This study explores the pivotal role of pump stations and wastewater reuse in mitigating water scarcity and promoting sustainable water management practices. Utilizing a comprehensive mixed-method approach, including desk research, field surveys, stakeholder interviews, and integrating the Analytic Hierarchy Process (AHP) into a decision-making framework. The study categorizes pump stations into priority ranking groups based on the evaluation of the following criteria: efficiency, operating hours, working unit ratio, and discharge capacity. The investigation reveals that the 72 pump stations in Fayoum City play a vital role in regulating water levels, optimizing distribution, and facilitating the reuse of irrigation wastewater. Despite operational challenges, such as manpower shortages and maintenance issues, these stations are crucial for sustaining agricultural productivity and addressing water scarcity concerns, including the prevention of recurring inundation events like the one in 2012. Moreover, the study underscores the potential of wastewater reuse as a sustainable solution to water scarcity, particularly in meeting agricultural water demands and mitigating water balance issues, such as inundation. Based on the findings, the study proposes actionable recommendations, including upgrading high-priority pump stations, evaluating lower-priority ones, enhancing canal infrastructure, and promoting water-efficient irrigation methods. In conclusion, this study provides valuable insights into the pivotal role of pump stations and wastewater reuse in addressing water scarcity challenges in arid regions. By implementing the proposed recommendations, Fayoum City can optimize its water management practices, ensure water security, and support the long-term development of the region.
{"title":"Strategic water resource management: pump stations in Fayoum City, Egypt","authors":"Sultan Kotb, Li Cheng, Mohamed Amin, Mohamed Monir Elzoghby, Ahmed Nasr","doi":"10.3389/frwa.2024.1386303","DOIUrl":"https://doi.org/10.3389/frwa.2024.1386303","url":null,"abstract":"Water scarcity presents significant challenges to sustainable development, particularly in arid regions like Fayoum City, Egypt, which faces particular water challenges due to its unique topography. This study explores the pivotal role of pump stations and wastewater reuse in mitigating water scarcity and promoting sustainable water management practices. Utilizing a comprehensive mixed-method approach, including desk research, field surveys, stakeholder interviews, and integrating the Analytic Hierarchy Process (AHP) into a decision-making framework. The study categorizes pump stations into priority ranking groups based on the evaluation of the following criteria: efficiency, operating hours, working unit ratio, and discharge capacity. The investigation reveals that the 72 pump stations in Fayoum City play a vital role in regulating water levels, optimizing distribution, and facilitating the reuse of irrigation wastewater. Despite operational challenges, such as manpower shortages and maintenance issues, these stations are crucial for sustaining agricultural productivity and addressing water scarcity concerns, including the prevention of recurring inundation events like the one in 2012. Moreover, the study underscores the potential of wastewater reuse as a sustainable solution to water scarcity, particularly in meeting agricultural water demands and mitigating water balance issues, such as inundation. Based on the findings, the study proposes actionable recommendations, including upgrading high-priority pump stations, evaluating lower-priority ones, enhancing canal infrastructure, and promoting water-efficient irrigation methods. In conclusion, this study provides valuable insights into the pivotal role of pump stations and wastewater reuse in addressing water scarcity challenges in arid regions. By implementing the proposed recommendations, Fayoum City can optimize its water management practices, ensure water security, and support the long-term development of the region.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141103227","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}
Pub Date : 2024-05-22DOI: 10.3389/frwa.2024.1380877
Dima Al Atawneh, J Sreekanth, Nick Cartwright, Edoardo Bertone, Rebecca Doble
Groundwater in the Middle East and North Africa region is a critical component of the water supply budget due to a (semi-)arid climate and hence limited surface water resources. Despite the significance, factors affecting the groundwater balance and overall sustainability of the resource are often poorly understood. This often includes recharge and discharge characteristics, groundwater extraction and impacts of climate change. The present study investigates the groundwater balance in the Dead Sea Basin aquifer in Jordan using a groundwater flow model developed using the MODFLOW.The study aimed to simulate groundwater balance components and their effect on estimation of the aquifer's safe yield, and to also undertake a preliminary analysis of the impact of climate change on groundwater levels in the aquifer. Model calibration and predictive analysis was undertaken using a probabilistic modeling workflow. Spatially heterogeneous groundwater recharge for the historical period was estimated as a function of rainfall by simultaneously calibrating the recharge and aquifer hydraulic property parameters.The model indicated that annual average recharge constituted 5.1% of the precipitation over a simulation period of 6 years. The effect of groundwater recharge and discharge components were evaluated in the context of estimation of safe yield of the aquifer. The average annual safe yield is estimated as ~8.0 mm corresponding to the 80% of the calibrated recharge value. Simulated groundwater levels matched well with the declining trends in observed water levels which are indicative of unsustainable use. Long-term simulation of groundwater levels indicated that current conditions would result in large drawdown in groundwater levels by the end of the century. Simulation of climate change scenarios using projected estimates of rainfall and evaporation indicates that climate change scenarios would further exacerbate groundwater levels by relatively small amounts. These findings highlight the need to simulate the groundwater balance to better understand the water availability and future sustainability.
{"title":"Predictive analysis of groundwater balance and assessment of safe yield using a probabilistic groundwater model for the Dead Sea Basin","authors":"Dima Al Atawneh, J Sreekanth, Nick Cartwright, Edoardo Bertone, Rebecca Doble","doi":"10.3389/frwa.2024.1380877","DOIUrl":"https://doi.org/10.3389/frwa.2024.1380877","url":null,"abstract":"Groundwater in the Middle East and North Africa region is a critical component of the water supply budget due to a (semi-)arid climate and hence limited surface water resources. Despite the significance, factors affecting the groundwater balance and overall sustainability of the resource are often poorly understood. This often includes recharge and discharge characteristics, groundwater extraction and impacts of climate change. The present study investigates the groundwater balance in the Dead Sea Basin aquifer in Jordan using a groundwater flow model developed using the MODFLOW.The study aimed to simulate groundwater balance components and their effect on estimation of the aquifer's safe yield, and to also undertake a preliminary analysis of the impact of climate change on groundwater levels in the aquifer. Model calibration and predictive analysis was undertaken using a probabilistic modeling workflow. Spatially heterogeneous groundwater recharge for the historical period was estimated as a function of rainfall by simultaneously calibrating the recharge and aquifer hydraulic property parameters.The model indicated that annual average recharge constituted 5.1% of the precipitation over a simulation period of 6 years. The effect of groundwater recharge and discharge components were evaluated in the context of estimation of safe yield of the aquifer. The average annual safe yield is estimated as ~8.0 mm corresponding to the 80% of the calibrated recharge value. Simulated groundwater levels matched well with the declining trends in observed water levels which are indicative of unsustainable use. Long-term simulation of groundwater levels indicated that current conditions would result in large drawdown in groundwater levels by the end of the century. Simulation of climate change scenarios using projected estimates of rainfall and evaporation indicates that climate change scenarios would further exacerbate groundwater levels by relatively small amounts. These findings highlight the need to simulate the groundwater balance to better understand the water availability and future sustainability.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113173","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}
Pub Date : 2024-05-22DOI: 10.3389/frwa.2024.1393226
Moslem Savari, Ahmad Jafari, Abbas Sheheytavi
Floods have significantly affected many regions worldwide, imposing economic, social, and psychological consequences on human societies, in recent decades. Rural communities in Iran are particularly vulnerable to floods, and without effective risk reduction systems, the impact can be exacerbated. In this context, this study aims to investigate the role of social capital in enhancing the resilience of rural households against floods in the southwest of Iran. The statistical population includes all rural households in Shushtar County that have experienced floods at least once. The primary tool for data collection was a questionnaire and obtained data were analyzed using structural equation modeling. In examining the situation of confrontation between different groups of people based on the state of social capital and resilience, it can be said that men, older people and people with higher income had more resilience and social capital to deal with floods. In addition, the results revealed that components of social capital (social networks, social solidarity, social trust, social awareness, participation and collection action) explained 68.1% of the variance in the resilience of rural households against floods. Overall, our findings can provide new insights for policymakers in the area, contributing to the reduction of flood impacts and promoting safer living conditions in flood-prone areas.
{"title":"The impact of social capital to improve rural households’ resilience against flooding: evidence from Iran","authors":"Moslem Savari, Ahmad Jafari, Abbas Sheheytavi","doi":"10.3389/frwa.2024.1393226","DOIUrl":"https://doi.org/10.3389/frwa.2024.1393226","url":null,"abstract":"Floods have significantly affected many regions worldwide, imposing economic, social, and psychological consequences on human societies, in recent decades. Rural communities in Iran are particularly vulnerable to floods, and without effective risk reduction systems, the impact can be exacerbated. In this context, this study aims to investigate the role of social capital in enhancing the resilience of rural households against floods in the southwest of Iran. The statistical population includes all rural households in Shushtar County that have experienced floods at least once. The primary tool for data collection was a questionnaire and obtained data were analyzed using structural equation modeling. In examining the situation of confrontation between different groups of people based on the state of social capital and resilience, it can be said that men, older people and people with higher income had more resilience and social capital to deal with floods. In addition, the results revealed that components of social capital (social networks, social solidarity, social trust, social awareness, participation and collection action) explained 68.1% of the variance in the resilience of rural households against floods. Overall, our findings can provide new insights for policymakers in the area, contributing to the reduction of flood impacts and promoting safer living conditions in flood-prone areas.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111225","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}
Pub Date : 2024-05-22DOI: 10.3389/frwa.2024.1378598
Mounia El Hafyani, Khalid El Himdi, Salah-Eddine El Adlouni
This research paper explores the implementation of machine learning (ML) techniques in weather and climate forecasting, with a specific focus on predicting monthly precipitation. The study analyzes the efficacy of six multivariate machine learning models: Decision Tree, Random Forest, K-Nearest Neighbors (KNN), AdaBoost, XGBoost, and Long Short-Term Memory (LSTM). Multivariate time series models incorporating lagged meteorological variables were employed to capture the dynamics of monthly rainfall in Rabat, Morocco, from 1993 to 2018. The models were evaluated based on various metrics, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). XGBoost showed the highest performance among the six individual models, with an RMSE of 40.8 (mm). In contrast, Decision Tree, AdaBoost, Random Forest, LSTM, and KNN showed relatively lower performances, with specific RMSEs ranging from 47.5 (mm) to 51 (mm). A novel multi-view stacking learning approach is introduced, offering a new perspective on various ML strategies. This integrated algorithm is designed to leverage the strengths of each individual model, aiming to substantially improve the precision of precipitation forecasts. The best results were achieved by combining Decision Tree, KNN, and LSTM to build the meta-base while using XGBoost as the second-level learner. This approach yielded a RMSE of 17.5 millimeters. The results show the potential of the proposed multi-view stacking learning algorithm to refine predictive results and improve the accuracy of monthly precipitation forecasts, setting a benchmark for future research in this field.
{"title":"Improving monthly precipitation prediction accuracy using machine learning models: a multi-view stacking learning technique","authors":"Mounia El Hafyani, Khalid El Himdi, Salah-Eddine El Adlouni","doi":"10.3389/frwa.2024.1378598","DOIUrl":"https://doi.org/10.3389/frwa.2024.1378598","url":null,"abstract":"This research paper explores the implementation of machine learning (ML) techniques in weather and climate forecasting, with a specific focus on predicting monthly precipitation. The study analyzes the efficacy of six multivariate machine learning models: Decision Tree, Random Forest, K-Nearest Neighbors (KNN), AdaBoost, XGBoost, and Long Short-Term Memory (LSTM). Multivariate time series models incorporating lagged meteorological variables were employed to capture the dynamics of monthly rainfall in Rabat, Morocco, from 1993 to 2018. The models were evaluated based on various metrics, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). XGBoost showed the highest performance among the six individual models, with an RMSE of 40.8 (mm). In contrast, Decision Tree, AdaBoost, Random Forest, LSTM, and KNN showed relatively lower performances, with specific RMSEs ranging from 47.5 (mm) to 51 (mm). A novel multi-view stacking learning approach is introduced, offering a new perspective on various ML strategies. This integrated algorithm is designed to leverage the strengths of each individual model, aiming to substantially improve the precision of precipitation forecasts. The best results were achieved by combining Decision Tree, KNN, and LSTM to build the meta-base while using XGBoost as the second-level learner. This approach yielded a RMSE of 17.5 millimeters. The results show the potential of the proposed multi-view stacking learning algorithm to refine predictive results and improve the accuracy of monthly precipitation forecasts, setting a benchmark for future research in this field.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110211","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}
Pub Date : 2024-05-17DOI: 10.3389/frwa.2024.1359109
Alison M. Franklin, Daniel L. Weller, Lisa M. Durso, Mark Bagley, Benjamin C. Davis, Jonathan G. Frye, Christopher J. Grim, Abasiofiok M. Ibekwe, Michael A. Jahne, Scott P. Keely, Autumn L Kraft, Betty R. McConn, Richard M. Mitchell, Andrea R. Ottesen, Manan Sharma, Errol A. Strain, Daniel A. Tadesse, Heather Tate, Jim E. Wells, Clinton F. Williams, Kim L. Cook, Claudine Kabera, Patrick F. McDermott, Jay L. Garland
Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990’s. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.
抗菌素耐药性（AMR）是一个全球性的公共卫生威胁，预计到 2050 年，每年将导致全球 1000 万人死亡。AMR这一公共卫生问题促使人们制定了抗击AMR的行动计划，包括改进抗菌药物管理、开发新的抗菌药物和先进的监测手段。自 20 世纪 90 年代中期以来，由美国食品和药物管理局、美国疾病控制中心和美国农业部领导的国家抗菌素耐药性监测系统（NARMS）一直在监测零售肉类、人类和食用动物中的抗菌素耐药性细菌。NARMS 目前正在探索一种综合的 "一个健康 "监测模式，该模式认识到人类、动物、植物和环境系统与公共健康息息相关。自 2020 年以来，美国环境保护局一直领导着一个跨机构的 NARMS 环境工作组 (EWG)，在流域和国家范围内实施地表水 AMR 监测计划 (SWAM)。NARMS 环境工作组将环境监测工作的发展分为五个方面：(i) 确定目标和问题，(ii) 设计研究/采样设计，(iii) 选择 AMR 指标，(iv) 建立分析方法，以及 (v) 制定数据管理/分析/元数据计划。在制定该环境监测计划之前，我们对科学界和文献中的共识进行了审查和仔细考虑。SWAM 项目产生的数据将有助于制定强有力的地表水监测计划，目的是评估与地表水中 AMR 病原体相关的公共卫生风险（如娱乐用水暴露），全面了解耐药菌株在流域内的空间和时间关系，并帮助评估人为因素和干预策略如何影响 AMR 在人类、动物和环境系统中的传播。
{"title":"A one health approach for monitoring antimicrobial resistance: developing a national freshwater pilot effort","authors":"Alison M. Franklin, Daniel L. Weller, Lisa M. Durso, Mark Bagley, Benjamin C. Davis, Jonathan G. Frye, Christopher J. Grim, Abasiofiok M. Ibekwe, Michael A. Jahne, Scott P. Keely, Autumn L Kraft, Betty R. McConn, Richard M. Mitchell, Andrea R. Ottesen, Manan Sharma, Errol A. Strain, Daniel A. Tadesse, Heather Tate, Jim E. Wells, Clinton F. Williams, Kim L. Cook, Claudine Kabera, Patrick F. McDermott, Jay L. Garland","doi":"10.3389/frwa.2024.1359109","DOIUrl":"https://doi.org/10.3389/frwa.2024.1359109","url":null,"abstract":"Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990’s. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140962004","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}
Pub Date : 2024-05-16DOI: 10.3389/frwa.2024.1374961
Ravindra Dwivedi, J. Biederman, P. Broxton, Jessie K. Pearl, Kangsan Lee, B. Svoma, Willem J. D. van Leeuwen, Marcos D. Robles
Across the western United States, forests are changing rapidly, with uncertain impacts on snowmelt water resources. Snow partitioning is controlled by forest effects on interception, radiation, and sublimation. Yet, models often lack snow measurements with sufficiently high spatial and temporal resolution across gradients of forest structure to accurately represent these fine-scale processes. Here, we utilize four Snowtography stations in Arizona, in the lower Colorado River Basin, with daily measurements over 3–5 years at ~110 positions distributed across gradients of forest structure resulting from wildfires and mechanical thinning. We combine Snowtography with lidar snapshots of forest and snow to train a high-resolution snow model and run it for 6 years to quantify how forest structure regulates snowpack and snowmelt. These study sites represent a climate gradient from lower/warmer ephemeral snowpack (~2,100 m asl) to higher/colder seasonal snowpack (~2,800 m asl). Forest cover reduced snowpack and snowmelt through canopy sublimation. Forest advanced snowmelt timing at lower/warmer sites but delayed it at higher/colder sites. Within canopy gaps, shaded cool edges had the greatest peak snow water equivalent (SWE). Surprisingly, sunny/warm gap edges produced more snowmelt than cool edges, because high radiation melted snow quickly, reducing exposure to sublimation. Therefore, peak SWE is not an ideal proxy for snowmelt volume from ephemeral snowpacks, which are becoming more prevalent due to warming. The results imply that forest management can influence the amount and timing of snowmelt, and that there may be decision trade-offs between enhancing forest resilience through delayed snowmelt and maximizing snowmelt volumes for downstream water resources.
{"title":"How three-dimensional forest structure regulates the amount and timing of snowmelt across a climatic gradient of snow persistence","authors":"Ravindra Dwivedi, J. Biederman, P. Broxton, Jessie K. Pearl, Kangsan Lee, B. Svoma, Willem J. D. van Leeuwen, Marcos D. Robles","doi":"10.3389/frwa.2024.1374961","DOIUrl":"https://doi.org/10.3389/frwa.2024.1374961","url":null,"abstract":"Across the western United States, forests are changing rapidly, with uncertain impacts on snowmelt water resources. Snow partitioning is controlled by forest effects on interception, radiation, and sublimation. Yet, models often lack snow measurements with sufficiently high spatial and temporal resolution across gradients of forest structure to accurately represent these fine-scale processes. Here, we utilize four Snowtography stations in Arizona, in the lower Colorado River Basin, with daily measurements over 3–5 years at ~110 positions distributed across gradients of forest structure resulting from wildfires and mechanical thinning. We combine Snowtography with lidar snapshots of forest and snow to train a high-resolution snow model and run it for 6 years to quantify how forest structure regulates snowpack and snowmelt. These study sites represent a climate gradient from lower/warmer ephemeral snowpack (~2,100 m asl) to higher/colder seasonal snowpack (~2,800 m asl). Forest cover reduced snowpack and snowmelt through canopy sublimation. Forest advanced snowmelt timing at lower/warmer sites but delayed it at higher/colder sites. Within canopy gaps, shaded cool edges had the greatest peak snow water equivalent (SWE). Surprisingly, sunny/warm gap edges produced more snowmelt than cool edges, because high radiation melted snow quickly, reducing exposure to sublimation. Therefore, peak SWE is not an ideal proxy for snowmelt volume from ephemeral snowpacks, which are becoming more prevalent due to warming. The results imply that forest management can influence the amount and timing of snowmelt, and that there may be decision trade-offs between enhancing forest resilience through delayed snowmelt and maximizing snowmelt volumes for downstream water resources.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969860","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 significance of this study involves the optimisation of the aeration efficiency (AE) of the venturi aerator using an artificial neural network (ANN) technique integrated with an optimisation algorithm, i.e., particle swarm optimisation (PSO) and genetic algorithm (GA). To optimise the effects of operational factors on aeration efficiency by utilising a venturi aeration system, aeration experiments were conducted in an experimental tank with dimensions of 90cm×55cm×45cm. The operating parameters of the venturi aerator include throat length (TL), effective outlet pipe (EOP), and flow rate (Q) to estimate the efficacy of the venturi aerator in terms of AE. A 3–6-1 ANN model was developed and integrated with the PSO and GA techniques to find out the best possible optimal operating variables of the venturi aerator. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) determined from the experimental and estimated data were used to assess and compare the performance of the ANN-PSO and ANN-GA modelling. It is shown that ANN-PSO provides a better result as compared to ANN-GA. The operational parameters, TL, EOP, and Q, were determined to have the most optimum values at 50 mm, 6 m, and 0.6 L/s, respectively. The optimised aeration efficiency of the venturi was found to be 0.105 kg O2/kWh at optimum operational circumstances. In fact, the neural network having an ideal design of (3-6-1) and a correlation coefficient value that is extremely close to unity has validated the results indicated above.
{"title":"Modelling and prediction of aeration efficiency of the venturi aeration system using ANN-PSO and ANN-GA","authors":"Anamika Yadav, Subha M. Roy, Abhijit Biswas, Bhagaban Swain, Sudipta Majumder","doi":"10.3389/frwa.2024.1401689","DOIUrl":"https://doi.org/10.3389/frwa.2024.1401689","url":null,"abstract":"The significance of this study involves the optimisation of the aeration efficiency (AE) of the venturi aerator using an artificial neural network (ANN) technique integrated with an optimisation algorithm, i.e., particle swarm optimisation (PSO) and genetic algorithm (GA). To optimise the effects of operational factors on aeration efficiency by utilising a venturi aeration system, aeration experiments were conducted in an experimental tank with dimensions of 90cm×55cm×45cm. The operating parameters of the venturi aerator include throat length (TL), effective outlet pipe (EOP), and flow rate (Q) to estimate the efficacy of the venturi aerator in terms of AE. A 3–6-1 ANN model was developed and integrated with the PSO and GA techniques to find out the best possible optimal operating variables of the venturi aerator. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) determined from the experimental and estimated data were used to assess and compare the performance of the ANN-PSO and ANN-GA modelling. It is shown that ANN-PSO provides a better result as compared to ANN-GA. The operational parameters, TL, EOP, and Q, were determined to have the most optimum values at 50 mm, 6 m, and 0.6 L/s, respectively. The optimised aeration efficiency of the venturi was found to be 0.105 kg O2/kWh at optimum operational circumstances. In fact, the neural network having an ideal design of (3-6-1) and a correlation coefficient value that is extremely close to unity has validated the results indicated above.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977552","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}
Pub Date : 2024-05-15DOI: 10.3389/frwa.2024.1424944
P. Célicourt, Alain N. Rousseau, S. Gumiere, Matteo Camporese
{"title":"Editorial: Hydro-informatics for sustainable water management in agrosystems, volume II","authors":"P. Célicourt, Alain N. Rousseau, S. Gumiere, Matteo Camporese","doi":"10.3389/frwa.2024.1424944","DOIUrl":"https://doi.org/10.3389/frwa.2024.1424944","url":null,"abstract":"","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140977150","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 Netherlands has traditionally focused on managing flood risk. However, the frequent occurrence of droughts in recent years has brought attention to managing both extremes. Transitions between these opposite extremes pose additional challenges to water management, requiring a trade-off between water storage during dry periods and flood control during wet periods. In this study, we develop a framework to define wet and dry meteorological events and study their transitions using timeseries of meteorological data namely, precipitation, temperature and potential evapotranspiration. The magnitudes of event characteristics are retained, which presents a different approach to the normalized climate indices (like the Standardized Precipitation Index) commonly used in literature. We apply this framework to the Dutch part of the Meuse River basin in northwestern Europe using climate observations between 1951 and 2022. Our analysis shows a statistically significant increase in the amount of water lost from potential evapotranspiration compared to water gained from precipitation between April and September of the water year and an increase in the length of this drying period over the past decades. Such trends in the drying period are related to variability in potential evapotranspiration caused by rising temperatures in the region, indicating the potential for increased water shortage in Spring and Summer due to future temperature increases. We also identify abrupt transitions between opposite extreme events where there is a lack of water at the end of the second event as meteorological situations that challenge water management due to overlapping impacts like flash flooding, less time for water storage, and reduced water availability. We see such conditions occur in 6% of the wet-dry transitions and 20% of the dry-wet transitions, highlighting meteorological scenarios to which the hydrological response of the catchment can be simulated to increase our understanding of the combined risk of floods and droughts.
{"title":"Investigating meteorological wet and dry transitions in the Dutch Meuse River basin","authors":"Srividya Hariharan Sudha, Elisa Ragno, Oswaldo Morales-Nápoles, Matthijs Kok","doi":"10.3389/frwa.2024.1394563","DOIUrl":"https://doi.org/10.3389/frwa.2024.1394563","url":null,"abstract":"The Netherlands has traditionally focused on managing flood risk. However, the frequent occurrence of droughts in recent years has brought attention to managing both extremes. Transitions between these opposite extremes pose additional challenges to water management, requiring a trade-off between water storage during dry periods and flood control during wet periods. In this study, we develop a framework to define wet and dry meteorological events and study their transitions using timeseries of meteorological data namely, precipitation, temperature and potential evapotranspiration. The magnitudes of event characteristics are retained, which presents a different approach to the normalized climate indices (like the Standardized Precipitation Index) commonly used in literature. We apply this framework to the Dutch part of the Meuse River basin in northwestern Europe using climate observations between 1951 and 2022. Our analysis shows a statistically significant increase in the amount of water lost from potential evapotranspiration compared to water gained from precipitation between April and September of the water year and an increase in the length of this drying period over the past decades. Such trends in the drying period are related to variability in potential evapotranspiration caused by rising temperatures in the region, indicating the potential for increased water shortage in Spring and Summer due to future temperature increases. We also identify abrupt transitions between opposite extreme events where there is a lack of water at the end of the second event as meteorological situations that challenge water management due to overlapping impacts like flash flooding, less time for water storage, and reduced water availability. We see such conditions occur in 6% of the wet-dry transitions and 20% of the dry-wet transitions, highlighting meteorological scenarios to which the hydrological response of the catchment can be simulated to increase our understanding of the combined risk of floods and droughts.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976406","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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