Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2024.12.002
Abhijeet Das
<div><div>Baitarani River, Odisha, faces serious deterioration due to massive human intervention. It is particularly susceptible to degradation because it receives industrial and waste water emissions from surrounding organizations and municipal bodies. The current condition of the river is deplorable, leaving behind only minimal economic and ecological values. In this Baitarani Watershed, Odisha, this study emphasizes on analysing the seasonal variation (post-monsoon) of the water quality rating of the river in terms of the Water Quality Index (WQI). Study assessed the hydro-chemical variables, collected from thirteen sampling sites, during 2021–2024; and the whole river was investigated for 15 physicochemical parameters. Again, environ-metrics techniques, such as principal component analysis (PCA), and hierarchical (H) cluster analysis (CA), were used to assess the hydro-chemical variables. In all sites, the indicator Turbidity did not meet the drinking water quality limits (< 5NTU). During the post-monsoon season, the obtained WA-WQI value scored as 21.7 to 191, signifying excellent to unsuitable water quality. In this context, the WAWQI (Weighed Arithmetic Water Quality Index) values show that almost 61.54 % sampling sites have poor to unsuitable quality of water. On the contrary, the computed CCMEWQI (Canadian Council of Ministers of Environment Water Quality Index) value of the present research, varied between 23 and 97. These values indicate that water quality ranges from excellent to very poor water quality. Spanning a spectrum, the values of Integrated Weight (I)-WQI oscillated between 14 and 97. About 23.08 % remained within the excellent-good category, suggesting low pollution. These values also indicate 76.92 % of samples renders poor water and thus, significant contamination of the research zone by elements like turbidity, EC, and TDS indicates that the water quality in these areas is below drinkable limits and requires purification before use. The method, CA grouped four zones into three clusters, i.e., relatively low-polluted, medium-polluted, and high polluted. During post-monsoon season, most of the water quality characteristics were lower owing to dilution by monsoon rainfall, while pollutants were relatively higher in at some places, which might be due to reduced river flow and concentrated pollutants. The PCA resulted into 4 components namely PC-1 (51.31 %), PC-2 (16.044 %), PC-3 (11.799 %) and PC-4 (9.04 %) and indicated that particularly PC-1 contributes parameters such as turbidity, EC, TDS, Na<sup>+</sup>, K<sup>+</sup>, Ca<sup>2+</sup>, and Mg<sup>2+</sup>, were mostly influenced by mineralization, ions dissolution, and rock weathering. Ultimately, this innovative study from both indexing techniques, concludes that out of the 13 sampling sites, around 61.54 % (WA), 76.92 % (IWQI) and 53.85 % (CCME) is observed to be the most polluted site. CA and PCA identified that natural phenomena, along with agricultural, municipal
{"title":"Surface water quality evaluation, apportionment of pollution sources and aptness testing for drinking using water quality indices and multivariate modelling in Baitarani River basin, Odisha","authors":"Abhijeet Das","doi":"10.1016/j.hydres.2024.12.002","DOIUrl":"10.1016/j.hydres.2024.12.002","url":null,"abstract":"<div><div>Baitarani River, Odisha, faces serious deterioration due to massive human intervention. It is particularly susceptible to degradation because it receives industrial and waste water emissions from surrounding organizations and municipal bodies. The current condition of the river is deplorable, leaving behind only minimal economic and ecological values. In this Baitarani Watershed, Odisha, this study emphasizes on analysing the seasonal variation (post-monsoon) of the water quality rating of the river in terms of the Water Quality Index (WQI). Study assessed the hydro-chemical variables, collected from thirteen sampling sites, during 2021–2024; and the whole river was investigated for 15 physicochemical parameters. Again, environ-metrics techniques, such as principal component analysis (PCA), and hierarchical (H) cluster analysis (CA), were used to assess the hydro-chemical variables. In all sites, the indicator Turbidity did not meet the drinking water quality limits (< 5NTU). During the post-monsoon season, the obtained WA-WQI value scored as 21.7 to 191, signifying excellent to unsuitable water quality. In this context, the WAWQI (Weighed Arithmetic Water Quality Index) values show that almost 61.54 % sampling sites have poor to unsuitable quality of water. On the contrary, the computed CCMEWQI (Canadian Council of Ministers of Environment Water Quality Index) value of the present research, varied between 23 and 97. These values indicate that water quality ranges from excellent to very poor water quality. Spanning a spectrum, the values of Integrated Weight (I)-WQI oscillated between 14 and 97. About 23.08 % remained within the excellent-good category, suggesting low pollution. These values also indicate 76.92 % of samples renders poor water and thus, significant contamination of the research zone by elements like turbidity, EC, and TDS indicates that the water quality in these areas is below drinkable limits and requires purification before use. The method, CA grouped four zones into three clusters, i.e., relatively low-polluted, medium-polluted, and high polluted. During post-monsoon season, most of the water quality characteristics were lower owing to dilution by monsoon rainfall, while pollutants were relatively higher in at some places, which might be due to reduced river flow and concentrated pollutants. The PCA resulted into 4 components namely PC-1 (51.31 %), PC-2 (16.044 %), PC-3 (11.799 %) and PC-4 (9.04 %) and indicated that particularly PC-1 contributes parameters such as turbidity, EC, TDS, Na<sup>+</sup>, K<sup>+</sup>, Ca<sup>2+</sup>, and Mg<sup>2+</sup>, were mostly influenced by mineralization, ions dissolution, and rock weathering. Ultimately, this innovative study from both indexing techniques, concludes that out of the 13 sampling sites, around 61.54 % (WA), 76.92 % (IWQI) and 53.85 % (CCME) is observed to be the most polluted site. CA and PCA identified that natural phenomena, along with agricultural, municipal","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 244-264"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2024.11.007
V.C. Deivayanai, P. Thamarai, P.R. Yaashikaa, A. Saravanan, A.S. Vickram
Growing anthropological inhabitants and unorganized cleaning and disposal leads to the widespread garbage and usable residues witnessed every here and there in the ecosystem, especially Polycyclic Aromatic Hydrocarbons (PAHs). This waste of PAHs and its various forms is a great disaster that is been mixed in right from the air all respirate into the water organisms drink plus food intake is also contaminated. The pollution made by this bio-cumulative pollutant not only cause damage to land and water but directly alter the metabolisms of living beings and causes life-threatening disease and sometimes takes the life of immune defective groups. Whereas short communication sharply and crisply explains the PAHs in society and their worsening body impacts other than it this brief about the degradation techniques with latest techno advancements. Differently, this paper concentrates on the cleaning up or removal methods, policy discussion and making by governmental organizations.
{"title":"A mini-review on clinical and epidemiological assessment of PAHs toxicity: Process of policy-making on a global scale","authors":"V.C. Deivayanai, P. Thamarai, P.R. Yaashikaa, A. Saravanan, A.S. Vickram","doi":"10.1016/j.hydres.2024.11.007","DOIUrl":"10.1016/j.hydres.2024.11.007","url":null,"abstract":"<div><div>Growing anthropological inhabitants and unorganized cleaning and disposal leads to the widespread garbage and usable residues witnessed every here and there in the ecosystem, especially Polycyclic Aromatic Hydrocarbons (PAHs). This waste of PAHs and its various forms is a great disaster that is been mixed in right from the air all respirate into the water organisms drink plus food intake is also contaminated. The pollution made by this bio-cumulative pollutant not only cause damage to land and water but directly alter the metabolisms of living beings and causes life-threatening disease and sometimes takes the life of immune defective groups. Whereas short communication sharply and crisply explains the PAHs in society and their worsening body impacts other than it this brief about the degradation techniques with latest techno advancements. Differently, this paper concentrates on the cleaning up or removal methods, policy discussion and making by governmental organizations.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 237-243"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2025.06.001
Satish Bastola
A distributed hydrological model (BTOPMC) provides a distributed estimation of water balance components imperative for sustainable water resource management. The model employs numerous calibration parameters to represent the complex river system. The model uses a transfer function relating the basin scale parameters to estimate grid scale parameters. However, in large and heterogeneous river basin systems, more hydrological response units and subsequently more calibration parameters are required. The study developed the functional relationship between model parameters and catchment attributes at multiple scales to reduce the calibration parameters. The functional relationship developed from one of the test basins from Nepal with extant observations is analyzed by endogenizing the relationship and calibrating its coefficient rather than directly calibrating parameters. The results show that calibration of the regional relationship coefficient rather than the model parameters resulted in comparable model performance to direct calibration, but with more identifiable model parameters.
{"title":"Model parameterization scheme for a distributed hydrological model, BTOPMC","authors":"Satish Bastola","doi":"10.1016/j.hydres.2025.06.001","DOIUrl":"10.1016/j.hydres.2025.06.001","url":null,"abstract":"<div><div>A distributed hydrological model (BTOPMC) provides a distributed estimation of water balance components imperative for sustainable water resource management. The model employs numerous calibration parameters to represent the complex river system. The model uses a transfer function relating the basin scale parameters to estimate grid scale parameters. However, in large and heterogeneous river basin systems, more hydrological response units and subsequently more calibration parameters are required. The study developed the functional relationship between model parameters and catchment attributes at multiple scales to reduce the calibration parameters. The functional relationship developed from one of the test basins from Nepal with extant observations is analyzed by endogenizing the relationship and calibrating its coefficient rather than directly calibrating parameters. The results show that calibration of the regional relationship coefficient rather than the model parameters resulted in comparable model performance to direct calibration, but with more identifiable model parameters.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 384-393"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329812","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}
Most river basins across the world are ungauged, and just a handful are gauged. As a result, predicting runoff in an unmeasured watershed is a difficult problem for the researchers. This research takes into account the tropical monsoon region, which is primarily covered by mountains and has a changing climate. This research is also carried out by creating a model with a machine learning technique that comprises Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The hybrid model considerably improves runoff forecast accuracy, with the CNN-LSTM model reaching an overall accuracy of 99.29 % across many datasets. The study uses 25 years of meteorological data from gauged stations to calculate runoff predictions for four ungauged sites: Katigora, Subhang, Sonai, and Morang. The findings highlight the necessity of combining machine learning and classical approaches to improve flood forecasting skills, which are critical for successful water resource management in flood-prone areas. This novel technique not only fills a vital vacuum in hydrological research, but it also has practical implications for catastrophe risk mitigation initiatives worldwide.
{"title":"Prediction of runoff at ungauged areas employing interpolation techniques and deep learning algorithm","authors":"Vinay Mahakur , Vijay Kumar Mahakur , Sandeep Samantaray , Dillip K. Ghose","doi":"10.1016/j.hydres.2024.12.001","DOIUrl":"10.1016/j.hydres.2024.12.001","url":null,"abstract":"<div><div>Most river basins across the world are ungauged, and just a handful are gauged. As a result, predicting runoff in an unmeasured watershed is a difficult problem for the researchers. This research takes into account the tropical monsoon region, which is primarily covered by mountains and has a changing climate. This research is also carried out by creating a model with a machine learning technique that comprises Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The hybrid model considerably improves runoff forecast accuracy, with the CNN-LSTM model reaching an overall accuracy of 99.29 % across many datasets. The study uses 25 years of meteorological data from gauged stations to calculate runoff predictions for four ungauged sites: Katigora, Subhang, Sonai, and Morang. The findings highlight the necessity of combining machine learning and classical approaches to improve flood forecasting skills, which are critical for successful water resource management in flood-prone areas. This novel technique not only fills a vital vacuum in hydrological research, but it also has practical implications for catastrophe risk mitigation initiatives worldwide.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 265-275"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate change, recognized as the long-term change in atmospheric conditions is one of the most evident feature of 21st century. Limited studies on Saurashtra's rainfall variability underscore the need to explore temporal and spatial patterns, vital for sustainable water resource management and agricultural planning in the region. This study examines seasonal and sub-seasonal rainfall trends (1981–2020) using high-resolution IMD data, applying Z-score, Mann-Kendall test, Sen's slope estimator, and Rainfall Anomaly Index for analysis. The region has experienced increased variability in rainfall, particularly at the upper extremes. Statistical analysis shows a significant increase in mean rainfall during 2001–2020, with distinct trends compared to earlier decades. Notably, September exhibits the largest upward trend in rainfall at +1.53 mm/year. Seasonal RAI highlights a shift from predominantly negative rainfall anomalies (1981–2000) to positive anomalies in the later period (2001−2020). These findings emphasize studying atmospheric dynamics to understand future rainfall changes.
{"title":"Resource to risk: Inter-decadal and sub-seasonal rainfall modulation over Saurashtra region in Western India","authors":"Bhanu Parmar , Suvarna Shah , Hiteshri Shastri , Indra Mani Tripathi","doi":"10.1016/j.hydres.2025.01.002","DOIUrl":"10.1016/j.hydres.2025.01.002","url":null,"abstract":"<div><div>Climate change, recognized as the long-term change in atmospheric conditions is one of the most evident feature of 21st century. Limited studies on Saurashtra's rainfall variability underscore the need to explore temporal and spatial patterns, vital for sustainable water resource management and agricultural planning in the region. This study examines seasonal and sub-seasonal rainfall trends (1981–2020) using high-resolution IMD data, applying <em>Z</em>-score, Mann-Kendall test, Sen's slope estimator, and Rainfall Anomaly Index for analysis. The region has experienced increased variability in rainfall, particularly at the upper extremes. Statistical analysis shows a significant increase in mean rainfall during 2001–2020, with distinct trends compared to earlier decades. Notably, September exhibits the largest upward trend in rainfall at +1.53 mm/year. Seasonal RAI highlights a shift from predominantly negative rainfall anomalies (1981–2000) to positive anomalies in the later period (2001−2020). These findings emphasize studying atmospheric dynamics to understand future rainfall changes.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 351-360"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2024.12.007
Maximo Basheija Twinomuhangi , Yazidhi Bamutaze , Isa Kabenge , Joshua Wanyama , Michael Kizza , Geoffrey Gabiri , Pascal Emanuel Egli
Research on hydrological extremes has increased due to their increasing frequency and destructive power, with their non-stationarity attributed to human activities and climate change. To understand current advances in analyzing extremes, a systematic review of online literature was conducted using PRISMA framework. The review covered several aspects of analysis considered in literature like time series types, non-stationarity detection techniques, frequency analysis (FA) category, probability distribution types, covariates used, parameter estimation and model selection techniques. Results indicate that AMS (71.7 %), Mann-Kendall non-stationarity detection test (70.8 %), GEV distribution (41.4 %), ML parameter estimation (34.6 %) and model selection AIC (30.0 %) were mostly applied. Non-stationary alongside stationary FA was carried out most (82 %) and non-stationary models outperformed the stationary ones. Time was used as a covariate in most studies (50.5 %) compared to anthropogenic (7.1 %), local-scale (11.4 %) and large-scale (31.0 %) climate covariates. Effective hydrological extremes management requires an understanding of their non-stationarity in a changing environment.
{"title":"Analysis of stationary and non-stationary hydrological extremes under a changing environment: A systematic review","authors":"Maximo Basheija Twinomuhangi , Yazidhi Bamutaze , Isa Kabenge , Joshua Wanyama , Michael Kizza , Geoffrey Gabiri , Pascal Emanuel Egli","doi":"10.1016/j.hydres.2024.12.007","DOIUrl":"10.1016/j.hydres.2024.12.007","url":null,"abstract":"<div><div>Research on hydrological extremes has increased due to their increasing frequency and destructive power, with their non-stationarity attributed to human activities and climate change. To understand current advances in analyzing extremes, a systematic review of online literature was conducted using PRISMA framework. The review covered several aspects of analysis considered in literature like time series types, non-stationarity detection techniques, frequency analysis (FA) category, probability distribution types, covariates used, parameter estimation and model selection techniques. Results indicate that AMS (71.7 %), Mann-Kendall non-stationarity detection test (70.8 %), GEV distribution (41.4 %), ML parameter estimation (34.6 %) and model selection AIC (30.0 %) were mostly applied. Non-stationary alongside stationary FA was carried out most (82 %) and non-stationary models outperformed the stationary ones. Time was used as a covariate in most studies (50.5 %) compared to anthropogenic (7.1 %), local-scale (11.4 %) and large-scale (31.0 %) climate covariates. Effective hydrological extremes management requires an understanding of their non-stationarity in a changing environment.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 332-350"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2024.12.005
Yiwei Du , Xiaoguang Li , Siru Liu , Tian Lin
The construction of nutrient flux can provide a scientific basis for understanding the fate in the aquaculture systems to facilitate sustainable coastal environment management. In this study, the tailwater of the mariculture pond from 65 sampling points in the Xiangshan Bay were measured. The average concentrations of total nitrogen and total phosphorus of the samples were 2.16 mg/L and 0.11 mg/L, which were lower than Category I of Mariculture Tailwater Discharge Standards of Zhejiang Province. Two methods were applied to estimate the discharge flux of the total nitrogen and total phosphorus from mariculture ponds in the Xiangshan Bay. The application of chemical analysis method was technically optimized by means of area data obtained by high-density sampling campaign and GIS method. It was estimated that the annual discharge flux of total nitrogen and total phosphorus via chemical analysis method were 271.2–338.9 tones and 7.6–9.5 tones, respectively. Potential environmental effects were further assessed according to monthly discharge flux combined with seasonal variations of hydrodynamic environment. In comparison, the estimated annual fluxes of chemical analysis method were much lower than the ones of pollution discharging coefficient method which were 357.56 tones/year and 22.00 tones/year, respectively. It is considered that part of nitrogen and phosphorus nutrients deposited into the bottom sediment of the ponds or coastal environment. The continuous loading of nitrogen and phosphorus nutrients into sediment can increase the risk of secondary release, especially in such semi-enclosed bay.
养分通量的构建可以为了解水产养殖系统的命运提供科学依据，促进沿海环境的可持续管理。本研究对象山湾65个样点的海水养殖池尾水进行了测量。样品中总氮和总磷的平均浓度分别为2.16 mg/L和0.11 mg/L，均低于浙江省海水养殖尾水排放第一类标准。采用两种方法对象山湾海水养殖池塘的总氮和总磷排放通量进行了估算。利用高密度采样和GIS方法获得的面积数据，对化学分析法的应用进行了技术优化。通过化学分析方法估算出全氮和全磷年排放通量分别为271.2 ~ 338.9 t和7.6 ~ 9.5 t。根据月流量结合水动力环境的季节变化，进一步评价潜在的环境影响。化学分析法的年通量预测值分别为357.56 t /年和22.00 t /年，明显低于排放系数法的年通量预测值。认为部分氮磷营养物沉积在池塘底泥或海岸环境中。氮磷营养物持续向沉积物中加载会增加二次释放的风险，尤其是在这种半封闭的海湾中。
{"title":"Construction of nitrogen and phosphorus flux of land-based pond mariculture: Taking the Xiangshan Bay as an example","authors":"Yiwei Du , Xiaoguang Li , Siru Liu , Tian Lin","doi":"10.1016/j.hydres.2024.12.005","DOIUrl":"10.1016/j.hydres.2024.12.005","url":null,"abstract":"<div><div>The construction of nutrient flux can provide a scientific basis for understanding the fate in the aquaculture systems to facilitate sustainable coastal environment management. In this study, the tailwater of the mariculture pond from 65 sampling points in the Xiangshan Bay were measured. The average concentrations of total nitrogen and total phosphorus of the samples were 2.16 mg/L and 0.11 mg/L, which were lower than Category I of Mariculture Tailwater Discharge Standards of Zhejiang Province. Two methods were applied to estimate the discharge flux of the total nitrogen and total phosphorus from mariculture ponds in the Xiangshan Bay. The application of chemical analysis method was technically optimized by means of area data obtained by high-density sampling campaign and GIS method. It was estimated that the annual discharge flux of total nitrogen and total phosphorus via chemical analysis method were 271.2–338.9 tones and 7.6–9.5 tones, respectively. Potential environmental effects were further assessed according to monthly discharge flux combined with seasonal variations of hydrodynamic environment. In comparison, the estimated annual fluxes of chemical analysis method were much lower than the ones of pollution discharging coefficient method which were 357.56 tones/year and 22.00 tones/year, respectively. It is considered that part of nitrogen and phosphorus nutrients deposited into the bottom sediment of the ponds or coastal environment. The continuous loading of nitrogen and phosphorus nutrients into sediment can increase the risk of secondary release, especially in such semi-enclosed bay.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 286-293"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2024.12.003
George Bennett
Groundwater recharge maps developed using GIS techniques are important tools that aid the identification of recharge zones. This map must be validated with field data. This study reviewed 63 articles published between 2007 and 2024 to examine the data used to validate these maps and identify the appropriate data for validation. About 50 % of articles reviewed contain non-validated maps, suggesting that many researchers did not do map validation. A total of 12 types of data have been identified to validate groundwater recharge maps, including electrical conductivity, total dissolved solids, stable isotope, and the magnitude of groundwater level fluctuations. Moreover, this review shows that 50 % (n = 6) of the identified data have high uncertainty and are therefore inappropriate for validation, including groundwater level, nitrate concentration, well yield, and aquifer transmissivity. Data reflecting groundwater residence time are more appropriate for validation.
{"title":"A review of the data used to validate groundwater recharge maps created with GIS techniques over the past two decades","authors":"George Bennett","doi":"10.1016/j.hydres.2024.12.003","DOIUrl":"10.1016/j.hydres.2024.12.003","url":null,"abstract":"<div><div>Groundwater recharge maps developed using GIS techniques are important tools that aid the identification of recharge zones. This map must be validated with field data. This study reviewed 63 articles published between 2007 and 2024 to examine the data used to validate these maps and identify the appropriate data for validation. About 50 % of articles reviewed contain non-validated maps, suggesting that many researchers did not do map validation. A total of 12 types of data have been identified to validate groundwater recharge maps, including electrical conductivity, total dissolved solids, stable isotope, and the magnitude of groundwater level fluctuations. Moreover, this review shows that 50 % (<em>n</em> = 6) of the identified data have high uncertainty and are therefore inappropriate for validation, including groundwater level, nitrate concentration, well yield, and aquifer transmissivity. Data reflecting groundwater residence time are more appropriate for validation.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 276-285"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2025.01.001
Md. Imran Hosen , Md. Mahmudul Hasan , Md. Talha , Most. Mitu Akter , N.M. Refat Nasher
The historical and contemporary development of Dhaka city is predominantly unplanned, resulting in significant environmental deterioration. The lack of green and blue spaces and the built areas make it hard for people to stay in comfortable living conditions. The impacts of urban water bodies on the local thermal environment are examined in this study. Landsat 5, 7, and 8 images of 1991, 1996, 2001, 2006, 2011, 2016, and 2021 years were analyzed using ArcGIS (V.10.8) to look at the seasonal changes of normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surrounding lake area. The LST was obtained at a spatial resolution of 30 m using Landsat TM and TIRS imagery, particularly in cloud-free conditions within the study area. The lake water controls the temperature of the adjacent area within 100 to 150 m. The presence of the lake mitigated the disparity between the maximum and minimum temperatures in the hinterland thermal conditions. With time, there was a gradual but consistent rise in temperatures across the board, encompassing the monthly peaks, lows, and averages. Notably, the highest temperatures increased at a faster rate compared to lower and average temperature ranges. It's important to highlight that the rate of temperature rise was more pronounced at 200 m from the lake compared to the increase observed at 100 m. The rise in global temperature due to climate change has made cooling effects less effective for blue bodies in cities. This study's outcomes will help urban planners understand the blue and green space planning in cities to create a comfortable living environment.
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Pub Date : 2025-01-01DOI: 10.1016/j.hydres.2024.12.004
Gaurav Pakhale, Rakesh Khosa, A.K. Gosain
Analysing the spatiotemporal changes in long-term rainfall and extreme events at a river basin scale is crucial for optimal water resource management. This study examines trends in long-term rainfall and extreme event indices in the Krishna River Basin (KRB) using Indian Meteorological Department (IMD) 0.25° resolution daily precipitation data from 1951 to 2019. Methods include the Mann–Kendall trend test, Innovative Trend Analysis (ITA), Hurst's Rescaled Range (He) Analysis, and Wavelets.
The results show variations in rainfall and extreme event patterns across KRB subsystems. Overall, Annual Rainfall (AR) is decreasing, with significant trends in Ghatprabha (K3), Lower Bhima (K6), and Vedavati (K9). Seasonal and monthly rainfall trends are not significant. Extreme event indices (Daily rainfall greater than 10 mm [R10], Daily rainfall greater than 20 mm [R20], and Daily rainfall greater than 40 mm [R40], the annual maximum for 1 to 7 days and starting days of such events showed the non-significant trend in some of the subsystems when analysed with statistical methods; however, graphical analysis using ITA indicates clear trends. He construed a sustainable decreasing trend and predicted a future rainfall reduction. The wavelet power spectra for different indices infer periods between 2 and 16, predominantly concentrated around 2–4 year bands. Decreasing annual rainfall in most headwater catchments, captured by most methods, suggests that KRB will experience less rainfall and fewer rainy days in the future.
By discerning long-term rainfall trends and periodic patterns in the KRB, future water availability can be predicted, and extreme events can be better analysed. This analysis will be the basis for devising robust flood control measures, mitigating flood risks, and optimizing water resource allocation across sectors, thereby enhancing resilience to climate variability in future.
在流域尺度上分析长期降雨和极端事件的时空变化对优化水资源管理具有重要意义。本研究利用1951年至2019年印度气象局（IMD） 0.25°分辨率日降水数据，研究了克里希纳河流域（KRB）长期降雨和极端事件指数的趋势。方法包括Mann-Kendall趋势检验、创新趋势分析（ITA）、Hurst's rescaledrange （He）分析和小波分析。结果表明，降水和极端事件模式在KRB子系统之间存在差异。总体而言，年降雨量（AR）正在减少，其中Ghatprabha （K3）、下Bhima （K6）和Vedavati （K9）的趋势显著。季节和月降雨量趋势不显著。极端事件指数（日降雨量大于10 mm [R10]、日降雨量大于20 mm [R20]、日降雨量大于40 mm [R40]）、极端事件1 ~ 7天的年最大值和事件发生日在部分子系统中呈不显著趋势；然而，使用ITA的图形分析显示了清晰的趋势。他解释了一个持续减少的趋势，并预测了未来的降雨量减少。不同指数的小波功率谱推断周期在2 ~ 16年之间，主要集中在2 ~ 4年的波段。通过大多数方法捕获的大多数水源集水区的年降雨量减少表明，KRB未来将经历更少的降雨量和更少的雨天。通过识别KRB的长期降雨趋势和周期性模式，可以预测未来的水可用性，并可以更好地分析极端事件。这一分析将成为制定强有力的洪水控制措施、减轻洪水风险和优化各部门水资源分配的基础，从而增强未来应对气候变化的能力。
{"title":"Trends and periodicities in Krishna Basin rainfall/extremes found via hierarchical analysis","authors":"Gaurav Pakhale, Rakesh Khosa, A.K. Gosain","doi":"10.1016/j.hydres.2024.12.004","DOIUrl":"10.1016/j.hydres.2024.12.004","url":null,"abstract":"<div><div>Analysing the spatiotemporal changes in long-term rainfall and extreme events at a river basin scale is crucial for optimal water resource management. This study examines trends in long-term rainfall and extreme event indices in the Krishna River Basin (KRB) using Indian Meteorological Department (IMD) 0.25° resolution daily precipitation data from 1951 to 2019. Methods include the Mann–Kendall trend test, Innovative Trend Analysis (ITA), Hurst's Rescaled Range (H<sub>e</sub>) Analysis, and Wavelets.</div><div>The results show variations in rainfall and extreme event patterns across KRB subsystems. Overall, Annual Rainfall (AR) is decreasing, with significant trends in Ghatprabha (K3), Lower Bhima (K6), and Vedavati (K9). Seasonal and monthly rainfall trends are not significant. Extreme event indices (Daily rainfall greater than 10 mm [R10], Daily rainfall greater than 20 mm [R20], and Daily rainfall greater than 40 mm [R40], the annual maximum for 1 to 7 days and starting days of such events showed the non-significant trend in some of the subsystems when analysed with statistical methods; however, graphical analysis using ITA indicates clear trends. H<sub>e</sub> construed a sustainable decreasing trend and predicted a future rainfall reduction. The wavelet power spectra for different indices infer periods between 2 and 16, predominantly concentrated around 2–4 year bands. Decreasing annual rainfall in most headwater catchments, captured by most methods, suggests that KRB will experience less rainfall and fewer rainy days in the future.</div><div>By discerning long-term rainfall trends and periodic patterns in the KRB, future water availability can be predicted, and extreme events can be better analysed. This analysis will be the basis for devising robust flood control measures, mitigating flood risks, and optimizing water resource allocation across sectors, thereby enhancing resilience to climate variability in future.</div></div>","PeriodicalId":100615,"journal":{"name":"HydroResearch","volume":"8 ","pages":"Pages 316-331"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143154689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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