Aldona Dobrzycka-Krahel, Michał E. Skóra, Michał Raczyński, Katarzyna Magdoń
Various biological traits support the invasive success of different organisms. The osmoregulatory capacity and food preferences of the signal crayfish Pacifastacus leniusculus were experimentally tested to determine if they contribute to its invasive success. The osmotic concentrations of haemolymph were determined after acclimation of the crustaceans to seven salinities from 0 to 20 PSU. Food preferences were tested using Canadian pondweed Elodea canadensis, and rainbow trout Oncorhynchus mykiss. The results showed that the signal crayfish exhibits a hyper-hypoosmotic regulation pattern in the salinity range from 0 to 20 PSU, enabling them to inhabit both freshwater and brackish environments. Furthermore, the study found signal crayfish to have non-specific food preferences, although fish muscle tissue is more beneficial as a source of energy. Both features, osmoregulatory ability and food preferences, can increase the invasive success of this species as it expands into new areas. The ability to survive in higher salinities compared to the coastal waters of the Baltic Sea along the Polish coastline should be considered in targeted management strategies to control the spread of this invasive species.
{"title":"Osmoregulatory Capacity and Non-Specific Food Preferences as Strengths Contributing to the Invasive Success of the Signal Crayfish Pacifastacus leniusculus: Management Implications","authors":"Aldona Dobrzycka-Krahel, Michał E. Skóra, Michał Raczyński, Katarzyna Magdoń","doi":"10.3390/w16182657","DOIUrl":"https://doi.org/10.3390/w16182657","url":null,"abstract":"Various biological traits support the invasive success of different organisms. The osmoregulatory capacity and food preferences of the signal crayfish Pacifastacus leniusculus were experimentally tested to determine if they contribute to its invasive success. The osmotic concentrations of haemolymph were determined after acclimation of the crustaceans to seven salinities from 0 to 20 PSU. Food preferences were tested using Canadian pondweed Elodea canadensis, and rainbow trout Oncorhynchus mykiss. The results showed that the signal crayfish exhibits a hyper-hypoosmotic regulation pattern in the salinity range from 0 to 20 PSU, enabling them to inhabit both freshwater and brackish environments. Furthermore, the study found signal crayfish to have non-specific food preferences, although fish muscle tissue is more beneficial as a source of energy. Both features, osmoregulatory ability and food preferences, can increase the invasive success of this species as it expands into new areas. The ability to survive in higher salinities compared to the coastal waters of the Baltic Sea along the Polish coastline should be considered in targeted management strategies to control the spread of this invasive species.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Floods are normal components of many river regimes and, as such, they exert a significant influence at the ecosystem level. In recent decades, however, climate change has increased the frequency and intensity of floods, with serious consequences for lotic biota, particularly benthic macroinvertebrates, due to their limited mobility and sensitivity to disturbance. The impact of floods varies according to different biological parameters including the characteristics of the macrobenthic communities (taxonomic composition, morphology, behaviour, and life history traits) on one hand and various nonbiological parameters such as flood intensity, artificialisation of the river bed, the presence of dams, and many other factors on the other. Understanding these dynamics is pivotal to improve the effective management and conservation of aquatic ecosystems in the context of current climate change. The aim of this short communication is to evaluate the impact of a catastrophic flood on the macroinvertebrate community of a low-order Appennine stream (NW Italy). This will provide data regarding the varying impacts on different taxa and the recovery pattern of this significant component of the ecosystem.
{"title":"The Impact of Catastrophic Floods on Macroinvertebrate Communities in Low-Order Streams: A Study from the Apennines (Northwest Italy)","authors":"Anna Marino, Stefano Fenoglio, Tiziano Bo","doi":"10.3390/w16182646","DOIUrl":"https://doi.org/10.3390/w16182646","url":null,"abstract":"Floods are normal components of many river regimes and, as such, they exert a significant influence at the ecosystem level. In recent decades, however, climate change has increased the frequency and intensity of floods, with serious consequences for lotic biota, particularly benthic macroinvertebrates, due to their limited mobility and sensitivity to disturbance. The impact of floods varies according to different biological parameters including the characteristics of the macrobenthic communities (taxonomic composition, morphology, behaviour, and life history traits) on one hand and various nonbiological parameters such as flood intensity, artificialisation of the river bed, the presence of dams, and many other factors on the other. Understanding these dynamics is pivotal to improve the effective management and conservation of aquatic ecosystems in the context of current climate change. The aim of this short communication is to evaluate the impact of a catastrophic flood on the macroinvertebrate community of a low-order Appennine stream (NW Italy). This will provide data regarding the varying impacts on different taxa and the recovery pattern of this significant component of the ecosystem.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee
Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea.
{"title":"An Automated Machine Learning Approach to the Retrieval of Daily Soil Moisture in South Korea Using Satellite Images, Meteorological Data, and Digital Elevation Model","authors":"Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee","doi":"10.3390/w16182661","DOIUrl":"https://doi.org/10.3390/w16182661","url":null,"abstract":"Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karl-Erich Lindenschmidt, Robert Briggs, Amir Ali Khan, Thomas Puestow
This article provides a comprehensive overview of ice-jam flood forecasting methodologies applicable to rivers during freezing. It emphasizes the importance of understanding river ice processes and fluvial geomorphology for developing a freeze-up ice-jam flood forecasting system. The article showcases a stochastic modelling approach, which involves simulating a deterministic river ice model multiple times with varying parameters and boundary conditions. This approach has been applied to the Exploits River at Badger in Newfoundland, Canada, a river that has experienced several freeze-up ice-jam floods. The forecasting involves two approaches: predicting the extent of the ice cover during river freezing and using an ensemble method to determine backwater flood level elevations. Other examples of current ice-jam flood forecasting systems for the Kokemäenjoki River (Pori, Finland), Saint John River (Edmundston, NB, Canada), and Churchill River (Mud Lake, NL, Canada) that are operational are also presented. The text provides a detailed explanation of the processes involved in river freeze-up and ice-jam formation, as well as the methodologies used for freeze-up ice-jam flood forecasting. Ice-jam flood forecasting systems used for freeze-up were compared to those employed for spring breakup. Spring breakup and freeze-up ice-jam flood forecasting systems differ in their driving factors and methodologies. Spring breakup, driven by snowmelt runoff, typically relies on deterministic and probabilistic approaches to predict peak flows. Freeze-up, driven by cold temperatures, focuses on the complex interactions between atmospheric conditions, river flow, and ice dynamics. Both systems require air temperature forecasts, but snowpack data are more crucial for spring breakup forecasting. To account for uncertainty, both approaches may employ ensemble forecasting techniques, generating multiple forecasts using slightly different initial conditions or model parameters. The objective of this review is to provide an overview of the current state-of-the-art in ice-jam flood forecasting systems and to identify gaps and areas for improvement in existing ice-jam flood forecasting approaches, with a focus on enhancing their accuracy, reliability, and decision-making potential. In conclusion, an effective freeze-up ice-jam flood forecasting system requires real-time data collection and analysis, historical data analysis, ice jam modeling, user interface design, alert systems, and integration with other relevant systems. This combination allows operators to better understand ice jam behavior and make informed decisions about potential risks or mitigation measures to protect people and property along rivers. The key findings of this review are as follows: (i) Ice-jam flood forecasting systems are often based on simple, empirical models that rely heavily on historical data and limited real-time monitoring information. (ii) There is a need for more sophisticated modeling tech
{"title":"Requirements for the Development and Operation of a Freeze-Up Ice-Jam Flood Forecasting System","authors":"Karl-Erich Lindenschmidt, Robert Briggs, Amir Ali Khan, Thomas Puestow","doi":"10.3390/w16182648","DOIUrl":"https://doi.org/10.3390/w16182648","url":null,"abstract":"This article provides a comprehensive overview of ice-jam flood forecasting methodologies applicable to rivers during freezing. It emphasizes the importance of understanding river ice processes and fluvial geomorphology for developing a freeze-up ice-jam flood forecasting system. The article showcases a stochastic modelling approach, which involves simulating a deterministic river ice model multiple times with varying parameters and boundary conditions. This approach has been applied to the Exploits River at Badger in Newfoundland, Canada, a river that has experienced several freeze-up ice-jam floods. The forecasting involves two approaches: predicting the extent of the ice cover during river freezing and using an ensemble method to determine backwater flood level elevations. Other examples of current ice-jam flood forecasting systems for the Kokemäenjoki River (Pori, Finland), Saint John River (Edmundston, NB, Canada), and Churchill River (Mud Lake, NL, Canada) that are operational are also presented. The text provides a detailed explanation of the processes involved in river freeze-up and ice-jam formation, as well as the methodologies used for freeze-up ice-jam flood forecasting. Ice-jam flood forecasting systems used for freeze-up were compared to those employed for spring breakup. Spring breakup and freeze-up ice-jam flood forecasting systems differ in their driving factors and methodologies. Spring breakup, driven by snowmelt runoff, typically relies on deterministic and probabilistic approaches to predict peak flows. Freeze-up, driven by cold temperatures, focuses on the complex interactions between atmospheric conditions, river flow, and ice dynamics. Both systems require air temperature forecasts, but snowpack data are more crucial for spring breakup forecasting. To account for uncertainty, both approaches may employ ensemble forecasting techniques, generating multiple forecasts using slightly different initial conditions or model parameters. The objective of this review is to provide an overview of the current state-of-the-art in ice-jam flood forecasting systems and to identify gaps and areas for improvement in existing ice-jam flood forecasting approaches, with a focus on enhancing their accuracy, reliability, and decision-making potential. In conclusion, an effective freeze-up ice-jam flood forecasting system requires real-time data collection and analysis, historical data analysis, ice jam modeling, user interface design, alert systems, and integration with other relevant systems. This combination allows operators to better understand ice jam behavior and make informed decisions about potential risks or mitigation measures to protect people and property along rivers. The key findings of this review are as follows: (i) Ice-jam flood forecasting systems are often based on simple, empirical models that rely heavily on historical data and limited real-time monitoring information. (ii) There is a need for more sophisticated modeling tech","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuelong Su, Xiangdong Xu, Meng Dai, Yan Hu, Qianna Li, Shumiao Shu
The Han River Ecological Economic Belt (HREEB) has a substantial amount of water resources; however, its distribution is uneven, and issues such as seasonal and engineering water shortages are prevalent. This necessitates a thorough assessment of the current water resource situation and trends in water resource carrying capacity (WRCC) to provide scientific support for the rational allocation of water resources. This study employed the RAGA-PP model to establish a WRCC evaluation index system composed of four subsystems: water resources, economy, society, and the ecological environment. The WRCC of the 17 major cities in the HREEB was evaluated from 2008 to 2022. The differentiation method was introduced to compare the reliability of the RAGA-PP model with three evaluation methods: the entropy weight TOPSIS method, the rank sum ratio method, and the principal component analysis method. In addition, an obstacle degree model was introduced to analyze the factors influencing WRCC enhancement. The results indicated the following. (1) In the differentiation test of the four models, the RAGA-PP model was found to have the highest differentiation value, and the results showed that it was more reliable in the WRCC evaluation of HREEB. (2) WRCC in the HREEB underwent significant changes between 2008 and 2022. (3) The WRCC in Shiyan and Wuhan, which are located in the eastern part of the HREEB, were high in Hubei, low in four cities in Henan, and satisfactory in three cities in Shaanxi. (4) The carrying capacity of the subsystems of the cities in the HREEB exhibited fluctuating changes with obvious internal variations. (5) The problems in the WRCC guideline layer were consistent across all cities in the HREEB, with limited per capita water resources being the primary issue in the indicator layer. Assessing WRCC is essential for achieving sustainable water resource use and high-quality regional development.
{"title":"A Comprehensive Evaluation of Water Resource Carrying Capacity Based on the Optimized Projection Pursuit Regression Model: A Case Study from China","authors":"Yuelong Su, Xiangdong Xu, Meng Dai, Yan Hu, Qianna Li, Shumiao Shu","doi":"10.3390/w16182650","DOIUrl":"https://doi.org/10.3390/w16182650","url":null,"abstract":"The Han River Ecological Economic Belt (HREEB) has a substantial amount of water resources; however, its distribution is uneven, and issues such as seasonal and engineering water shortages are prevalent. This necessitates a thorough assessment of the current water resource situation and trends in water resource carrying capacity (WRCC) to provide scientific support for the rational allocation of water resources. This study employed the RAGA-PP model to establish a WRCC evaluation index system composed of four subsystems: water resources, economy, society, and the ecological environment. The WRCC of the 17 major cities in the HREEB was evaluated from 2008 to 2022. The differentiation method was introduced to compare the reliability of the RAGA-PP model with three evaluation methods: the entropy weight TOPSIS method, the rank sum ratio method, and the principal component analysis method. In addition, an obstacle degree model was introduced to analyze the factors influencing WRCC enhancement. The results indicated the following. (1) In the differentiation test of the four models, the RAGA-PP model was found to have the highest differentiation value, and the results showed that it was more reliable in the WRCC evaluation of HREEB. (2) WRCC in the HREEB underwent significant changes between 2008 and 2022. (3) The WRCC in Shiyan and Wuhan, which are located in the eastern part of the HREEB, were high in Hubei, low in four cities in Henan, and satisfactory in three cities in Shaanxi. (4) The carrying capacity of the subsystems of the cities in the HREEB exhibited fluctuating changes with obvious internal variations. (5) The problems in the WRCC guideline layer were consistent across all cities in the HREEB, with limited per capita water resources being the primary issue in the indicator layer. Assessing WRCC is essential for achieving sustainable water resource use and high-quality regional development.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oscar Yáñez-Hernández, Armando Javier Ríos-Lira, Yaquelin Verenice Pantoja-Pacheco, Edgar Augusto Ruelas-Santoyo, Martha Laura Asato-España, Esveidi Montserrat Valdovinos-García
Recently, water has become a resource that generates controversy due to shortage alarms and high consumption by production companies. Making good use of water has become the main objective of government institutions. The food industry generates a large amount of wastewater, concentrating the largest number of contaminants originated in its processes. Wastewater from the food industry is characterized by having a large amount of organic matter, especially fats and oils, as well as suspended solids. The objective of this research is to carry out a characterization of effluents generated in a wastewater treatment plant in the food sector based on Mexican and international regulations to determine whether it is reusable. This article addresses Mexico’s lag in the reuse of treated wastewater in the face of the water crisis, highlighting the urgency of adopting these practices to mitigate water scarcity. For the development of this investigation, samples were collected at the discharge point produced by the company’s effluents, followed by an evaluation of their physical, chemical, and biological parameters, and finally, it was determined that the effluent follows the regulatory standards for discharge into the city sewer but outside the range for reuse in productive processes or irrigation of green areas.
{"title":"Characterization of Wastewater in an Activated Sludge Treatment Plant of the Food Sector","authors":"Oscar Yáñez-Hernández, Armando Javier Ríos-Lira, Yaquelin Verenice Pantoja-Pacheco, Edgar Augusto Ruelas-Santoyo, Martha Laura Asato-España, Esveidi Montserrat Valdovinos-García","doi":"10.3390/w16182647","DOIUrl":"https://doi.org/10.3390/w16182647","url":null,"abstract":"Recently, water has become a resource that generates controversy due to shortage alarms and high consumption by production companies. Making good use of water has become the main objective of government institutions. The food industry generates a large amount of wastewater, concentrating the largest number of contaminants originated in its processes. Wastewater from the food industry is characterized by having a large amount of organic matter, especially fats and oils, as well as suspended solids. The objective of this research is to carry out a characterization of effluents generated in a wastewater treatment plant in the food sector based on Mexican and international regulations to determine whether it is reusable. This article addresses Mexico’s lag in the reuse of treated wastewater in the face of the water crisis, highlighting the urgency of adopting these practices to mitigate water scarcity. For the development of this investigation, samples were collected at the discharge point produced by the company’s effluents, followed by an evaluation of their physical, chemical, and biological parameters, and finally, it was determined that the effluent follows the regulatory standards for discharge into the city sewer but outside the range for reuse in productive processes or irrigation of green areas.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%.
{"title":"Drought Quantification in Africa Using Remote Sensing, Gaussian Kernel, and Machine Learning","authors":"Fred Sseguya, Kyung-Soo Jun","doi":"10.3390/w16182656","DOIUrl":"https://doi.org/10.3390/w16182656","url":null,"abstract":"Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Nenninger, James R. Mihelcic, Jeffrey A. Cunningham
Shallow groundwater is an important resource, especially in low- and middle-income countries; however, shallow groundwater is particularly vulnerable to point sources of pollution such as latrines or unlined waste disposal ponds. The objective of this paper is to derive a quantitative criterion for siting an extraction well and an upgradient point source of pollution to ensure that they are hydraulically disconnected, i.e., that no water flows from the point source to the well. To achieve this objective, we modeled the flow of shallow groundwater considering uniform regional flow, a single point source of pollution, and a single extraction well. For any set of flow rates and upgradient point source distance, we sought the minimum “off-center distance” ymin (i.e., the distance in the direction perpendicular to regional flow) that ensures the well and the point source are hydraulically disconnected. For constituencies with access to computing resources and coding expertise, we used a computer-based method for determining ymin that is exact to within the accuracy of a root-finding algorithm; this approach is recommended when computer access is available. For constituencies lacking these resources, we determined a simple, closed-form, approximate solution for ymin that has an average error of less than 3% for the conditions we tested. For a subset of scenarios in which the point source is sufficiently far upgradient of the well (n = 77), the root mean square relative error of the approximate solution is only 0.52%. We found that ymin depends on a length parameter (Qw + Qps)/QR, where Qw is the extraction rate of the well, Qps is the injection rate of the point source, and QR is the regional groundwater flow rate per unit of perpendicular length. Either the exact solution or the closed-form approximation can help to site wells near point sources of pollution, or to site point sources near wells, in a manner that protects the health of the well user. The approximate solution is valuable because many constituencies that rely on shallow wells for water supply and latrines for sanitation also lack access to the computer resources necessary to apply the exact solution.
{"title":"Ensuring the Safety of an Extraction Well from an Upgradient Point Source of Pollution in a Computationally Constrained Setting","authors":"Christopher Nenninger, James R. Mihelcic, Jeffrey A. Cunningham","doi":"10.3390/w16182645","DOIUrl":"https://doi.org/10.3390/w16182645","url":null,"abstract":"Shallow groundwater is an important resource, especially in low- and middle-income countries; however, shallow groundwater is particularly vulnerable to point sources of pollution such as latrines or unlined waste disposal ponds. The objective of this paper is to derive a quantitative criterion for siting an extraction well and an upgradient point source of pollution to ensure that they are hydraulically disconnected, i.e., that no water flows from the point source to the well. To achieve this objective, we modeled the flow of shallow groundwater considering uniform regional flow, a single point source of pollution, and a single extraction well. For any set of flow rates and upgradient point source distance, we sought the minimum “off-center distance” ymin (i.e., the distance in the direction perpendicular to regional flow) that ensures the well and the point source are hydraulically disconnected. For constituencies with access to computing resources and coding expertise, we used a computer-based method for determining ymin that is exact to within the accuracy of a root-finding algorithm; this approach is recommended when computer access is available. For constituencies lacking these resources, we determined a simple, closed-form, approximate solution for ymin that has an average error of less than 3% for the conditions we tested. For a subset of scenarios in which the point source is sufficiently far upgradient of the well (n = 77), the root mean square relative error of the approximate solution is only 0.52%. We found that ymin depends on a length parameter (Qw + Qps)/QR, where Qw is the extraction rate of the well, Qps is the injection rate of the point source, and QR is the regional groundwater flow rate per unit of perpendicular length. Either the exact solution or the closed-form approximation can help to site wells near point sources of pollution, or to site point sources near wells, in a manner that protects the health of the well user. The approximate solution is valuable because many constituencies that rely on shallow wells for water supply and latrines for sanitation also lack access to the computer resources necessary to apply the exact solution.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolau Chirinza, Federico A. Leon Zerpa, Paulino Muguirrima, Tania del Pino García, Gilberto Martel Rodriguez, Camila Gutierrez, Carlos A. Mendieta Pino
The objective of the described activity is to develop technologies or proposals that can be implemented within the cycle to enhance the relationship between climate change, water, energy, and food. The focus is on analyzing natural treatment systems for wastewater (NTSW) within the context of Macaronesia, considering factors such as life-cycle assessment (LCA), carbon footprint, impacts, and mitigation capacity. The analysis of real case data from the Canary Islands and Cape Verde will inform the development of appropriate technologies tailored to different areas and scales within Macaronesia. This work includes a comprehensive life-cycle analysis of the Santa Catarina (Cape Verde) NTSW. This analysis encompasses: (a) Inventory analysis of the construction phase: This involves the assessment of inputs and outputs associated with the construction of the NTSW, including materials, energy consumption, transportation, and waste generation. The maintenance and operation phases are then evaluated, with a focus on the ongoing maintenance and operation activities required for the NTSW, including energy consumption, water usage, chemical inputs (if any), labor, and equipment maintenance. (b) Finally, the impacts of the NTSW are evaluated. The environmental, social, and economic impacts generated by the NTSW are assessed. This includes an analysis of factors such as carbon emissions, water usage, land use, ecosystem impacts, human health effects, and economic costs. By conducting a comprehensive analysis of the Santa Catarina NTSW, the document aims to provide insights into the environmental performance and sustainability of the system. This information can then be used as a tool and experience of educational innovation for final-year undergraduate students to identify areas for improvement, develop mitigation strategies in the water sector, and inform decision-making processes regarding wastewater treatment technologies in Macaronesia. Furthermore, lessons learned from real case studies in the Canary Islands and Cape Verde can be applied to similar regions within the Macaronesia archipelago (IDIWATER project).
{"title":"Life-Cycle Analysis of Natural Treatment Systems for Wastewater (NTSW) Applied to Municipal Effluents","authors":"Nicolau Chirinza, Federico A. Leon Zerpa, Paulino Muguirrima, Tania del Pino García, Gilberto Martel Rodriguez, Camila Gutierrez, Carlos A. Mendieta Pino","doi":"10.3390/w16182653","DOIUrl":"https://doi.org/10.3390/w16182653","url":null,"abstract":"The objective of the described activity is to develop technologies or proposals that can be implemented within the cycle to enhance the relationship between climate change, water, energy, and food. The focus is on analyzing natural treatment systems for wastewater (NTSW) within the context of Macaronesia, considering factors such as life-cycle assessment (LCA), carbon footprint, impacts, and mitigation capacity. The analysis of real case data from the Canary Islands and Cape Verde will inform the development of appropriate technologies tailored to different areas and scales within Macaronesia. This work includes a comprehensive life-cycle analysis of the Santa Catarina (Cape Verde) NTSW. This analysis encompasses: (a) Inventory analysis of the construction phase: This involves the assessment of inputs and outputs associated with the construction of the NTSW, including materials, energy consumption, transportation, and waste generation. The maintenance and operation phases are then evaluated, with a focus on the ongoing maintenance and operation activities required for the NTSW, including energy consumption, water usage, chemical inputs (if any), labor, and equipment maintenance. (b) Finally, the impacts of the NTSW are evaluated. The environmental, social, and economic impacts generated by the NTSW are assessed. This includes an analysis of factors such as carbon emissions, water usage, land use, ecosystem impacts, human health effects, and economic costs. By conducting a comprehensive analysis of the Santa Catarina NTSW, the document aims to provide insights into the environmental performance and sustainability of the system. This information can then be used as a tool and experience of educational innovation for final-year undergraduate students to identify areas for improvement, develop mitigation strategies in the water sector, and inform decision-making processes regarding wastewater treatment technologies in Macaronesia. Furthermore, lessons learned from real case studies in the Canary Islands and Cape Verde can be applied to similar regions within the Macaronesia archipelago (IDIWATER project).","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lucky Baloyi, Thokozani Kanyerere, Innocent Muchingami, Harrison Pienaar
The application of hydrogeophysical techniques to delineating aquifers was conducted in De Aar, the eastern part of the Karoo region, Northern, South Africa. Previously, recharge estimations in this region assumed a uniform aquifer type, overlooking the presence of diverse aquifer systems. This study identified both unconfined and confined aquifers to improve recharge potential assessments. Vertical electrical resistivity sounding (VES) and ground telluric methods were applied. Six VES stations and eleven profiles were measured using a 1D Wenner array configuration. The VES data, processed with IPI2win software, generated a 2D subsurface model. In contrast, the telluric data were analyzed using an automated algorithm to create a 2D profile. The electric potential difference curve was interpreted in comparison with lithological cross-sections. The VES results revealed three to four distinct layers of low-resistivity (0.9–8.1 Ωm), moderate-resistivity (22.4–125 Ωm), and high-resistivity (68–177 Ωm) values, indicating three lithological formations. The telluric data suggested that shallow groundwater boreholes were located in areas with groundwater levels above 50 m. These findings, which matched the lithological data, pointed to a double-layer aquifer system, suggesting that recharge estimates should be carried out to different aquifer layers. The study demonstrated how hydrogeophysical methods can effectively delineate aquifer systems and enhance the identification of recharge areas.
在南非北部卡鲁地区东部的德阿尔，应用水文地质物理技术划定了含水层。此前，该地区的补给量估算假定含水层类型一致，忽略了多种含水层系统的存在。这项研究确定了无压含水层和承压含水层，以改进补给潜力评估。采用了垂直电阻率探测（VES）和地面碲化方法。使用一维温纳阵列配置测量了六个 VES 站和十一个剖面。使用 IPI2win 软件处理的 VES 数据生成了二维地下模型。碲化镉数据则通过自动算法进行分析，生成二维剖面图。电位差曲线与岩性横截面进行了对比解释。VES 结果显示了三至四个不同的层，分别为低电阻率（0.9-8.1 Ωm）、中等电阻率（22.4-125 Ωm）和高电阻率（68-177 Ωm）值，表明有三种岩性。碲化镉数据表明，浅层地下水钻孔位于地下水位高于 50 米的区域。这些发现与岩性数据相吻合，表明存在双层含水层系统，建议对不同含水层进行补给估算。这项研究表明，水文地质物理方法可以有效地划分含水层系统，并加强补给区的确定。
{"title":"Application of Hydrogeophysical Techniques in Delineating Aquifers to Enhancing Recharge Potential Areas in Groundwater-Dependent Systems, Northern Cape, South Africa","authors":"Lucky Baloyi, Thokozani Kanyerere, Innocent Muchingami, Harrison Pienaar","doi":"10.3390/w16182652","DOIUrl":"https://doi.org/10.3390/w16182652","url":null,"abstract":"The application of hydrogeophysical techniques to delineating aquifers was conducted in De Aar, the eastern part of the Karoo region, Northern, South Africa. Previously, recharge estimations in this region assumed a uniform aquifer type, overlooking the presence of diverse aquifer systems. This study identified both unconfined and confined aquifers to improve recharge potential assessments. Vertical electrical resistivity sounding (VES) and ground telluric methods were applied. Six VES stations and eleven profiles were measured using a 1D Wenner array configuration. The VES data, processed with IPI2win software, generated a 2D subsurface model. In contrast, the telluric data were analyzed using an automated algorithm to create a 2D profile. The electric potential difference curve was interpreted in comparison with lithological cross-sections. The VES results revealed three to four distinct layers of low-resistivity (0.9–8.1 Ωm), moderate-resistivity (22.4–125 Ωm), and high-resistivity (68–177 Ωm) values, indicating three lithological formations. The telluric data suggested that shallow groundwater boreholes were located in areas with groundwater levels above 50 m. These findings, which matched the lithological data, pointed to a double-layer aquifer system, suggesting that recharge estimates should be carried out to different aquifer layers. The study demonstrated how hydrogeophysical methods can effectively delineate aquifer systems and enhance the identification of recharge areas.","PeriodicalId":23788,"journal":{"name":"Water","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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