The significance of sediment-laden river discharges is strongly related to climate change and rainfall intensity, resulting in severe erosion of the catchment areas and riverbanks. The combination of tides and waves considerably influence the sediment transport and distribution patterns of an estuary, inducing the sedimentary processes of the coastal area. This study aims to analyze the impacts of the La Niña event in 2022 and high river discharges in the Bojong Salawe Beach, Pangandaran. This area has a large estuary with several tributaries, with a high potential for erosion and sedimentation. Furthermore, its location directly faces the Indian Ocean, posing the risk of wind-induced high waves. The methods used in this research are descriptive analysis (using dataset ERA-5 taken from the Copernicus Climate Change Service) and numerical models (using Mike21) with an identification of erosion and accretion processes. The results show that the boreal autumn 2022 significantly impacted the study area, compared to the boreal winter 2022. Higher precipitation levels during boreal autumn substantially increased the river discharges, transferring the total load of sediment of about 1.48 m3/s/m. Moreover, shoreline change analysis using digital shoreline analysis system confirmed that Bojong Salawe Beach was indicated to experience high erosion, particularly around the mouth of the estuary.
{"title":"Morpho-hydrodynamic processes impacted by the 2022 extreme La Niña event and high river discharge conditions in the southern coast of West Java, Indonesia","authors":"Fahmi Amanulloh, Andhy Romdani","doi":"10.2166/wcc.2024.343","DOIUrl":"https://doi.org/10.2166/wcc.2024.343","url":null,"abstract":"\u0000 \u0000 The significance of sediment-laden river discharges is strongly related to climate change and rainfall intensity, resulting in severe erosion of the catchment areas and riverbanks. The combination of tides and waves considerably influence the sediment transport and distribution patterns of an estuary, inducing the sedimentary processes of the coastal area. This study aims to analyze the impacts of the La Niña event in 2022 and high river discharges in the Bojong Salawe Beach, Pangandaran. This area has a large estuary with several tributaries, with a high potential for erosion and sedimentation. Furthermore, its location directly faces the Indian Ocean, posing the risk of wind-induced high waves. The methods used in this research are descriptive analysis (using dataset ERA-5 taken from the Copernicus Climate Change Service) and numerical models (using Mike21) with an identification of erosion and accretion processes. The results show that the boreal autumn 2022 significantly impacted the study area, compared to the boreal winter 2022. Higher precipitation levels during boreal autumn substantially increased the river discharges, transferring the total load of sediment of about 1.48 m3/s/m. Moreover, shoreline change analysis using digital shoreline analysis system confirmed that Bojong Salawe Beach was indicated to experience high erosion, particularly around the mouth of the estuary.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Abdisa Alemu Tolossa, Diriba Korecha Dadi, L. W. Mirkena, Z. Erena, Feyera Liben
Examining the characteristics of drought indices in the context of climate variability and change, particularly in semi-arid water-stressed regions, requires adaptation. Observed climate data of the Babile station from 1980 to 2009 were used as a baseline for climate projection. Future climate projection was established under two Representative Concentration Pathway (RCP4.5 and RCP8.5) climate scenarios for the 21st century. Two drought indices, namely standard precipitation index and standard evapotranspiration index (SPI and SPEI) were employed based on temperature and rainfall to characterize droughts. Our study revealed that drought severity and intensity are more likely to increase under RCP4.5 climate forcing in the middle of the 21st century. While the average drought severity (S) were 1.1, 1.53, 1.55, and 1.8 in SPEI 3-month time scale; 1.51, 2.1, 2.38, and 2.29 in SPEI 4-month time scale; and 2.15, 2.77, 3.44, and 2.91 in SPEI 6-month time scale, whereas, the drought severity (S) were 1.33, 1.37, and 1.79 in SPEI 3-month time scale; 1.79, 2.05, and 2.19 in SPEI 4-month time scale; and 2.47, 3.19, and 2.69 in SPEI 6-month time scale in observed, near, mid and end of the 21st century under RCP4.5 and 8.5 scenarios, respectively. High drought frequency occurrences and unprecedented severity under RCP4.5, which is highly likely to negatively impacted sorghum crop productivity and recommended for further instructive and practical soil water conservation to drought management in the study area.
{"title":"Impacts of climate change and variability on drought characteristics and challenges on sorghum productivity in Babile District, Eastern Ethiopia","authors":"Abdisa Alemu Tolossa, Diriba Korecha Dadi, L. W. Mirkena, Z. Erena, Feyera Liben","doi":"10.2166/wcc.2024.012","DOIUrl":"https://doi.org/10.2166/wcc.2024.012","url":null,"abstract":"\u0000 \u0000 Examining the characteristics of drought indices in the context of climate variability and change, particularly in semi-arid water-stressed regions, requires adaptation. Observed climate data of the Babile station from 1980 to 2009 were used as a baseline for climate projection. Future climate projection was established under two Representative Concentration Pathway (RCP4.5 and RCP8.5) climate scenarios for the 21st century. Two drought indices, namely standard precipitation index and standard evapotranspiration index (SPI and SPEI) were employed based on temperature and rainfall to characterize droughts. Our study revealed that drought severity and intensity are more likely to increase under RCP4.5 climate forcing in the middle of the 21st century. While the average drought severity (S) were 1.1, 1.53, 1.55, and 1.8 in SPEI 3-month time scale; 1.51, 2.1, 2.38, and 2.29 in SPEI 4-month time scale; and 2.15, 2.77, 3.44, and 2.91 in SPEI 6-month time scale, whereas, the drought severity (S) were 1.33, 1.37, and 1.79 in SPEI 3-month time scale; 1.79, 2.05, and 2.19 in SPEI 4-month time scale; and 2.47, 3.19, and 2.69 in SPEI 6-month time scale in observed, near, mid and end of the 21st century under RCP4.5 and 8.5 scenarios, respectively. High drought frequency occurrences and unprecedented severity under RCP4.5, which is highly likely to negatively impacted sorghum crop productivity and recommended for further instructive and practical soil water conservation to drought management in the study area.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
L. Enamoto, Andre Rufino Arsenio Santos, Weigang Li, Rodolfo Meneguette, G. P. Rocha Filho
Climate change, driven by greenhouse gas (GHG) emissions, causes extreme weather events, impacting ecosystems, biodiversity, population health, and the economy. Predicting GHG emissions is crucial for mitigating these impacts and planning sustainable policies. This research proposes a novel machine learning model for GHG emission forecasting. Our model, named the meta-learning applied to multivariate single-step fusion model, utilizes historical GHG data from Brazil over the past 60 years. It predicts multivariate time series, meaning it can consider multiple factors simultaneously, leading to more accurate forecasts. Additionally, the model employs two innovative techniques: (i) fusion model aligns different data sources to ensure compatibility and improve prediction accuracy and (ii) meta-learning allows the model to learn from past prediction tasks, generalizing better to new data and reducing the need for large training datasets. Compared to the widely used Bidirectional Long Short-Term Memory (BiLSTM) model, our approach achieves significantly better results. On the same dataset, it reduces the mean absolute percentage error by 116.84% with 95% confidence, demonstrating its superior performance. Furthermore, the model's flexibility allows it to be adapted for predicting other multivariate substances, making it a valuable tool for various environmental studies.
{"title":"Meta-learning applied to a multivariate single-step fusion model for greenhouse gas emission forecasting in Brazil","authors":"L. Enamoto, Andre Rufino Arsenio Santos, Weigang Li, Rodolfo Meneguette, G. P. Rocha Filho","doi":"10.2166/wcc.2024.252","DOIUrl":"https://doi.org/10.2166/wcc.2024.252","url":null,"abstract":"\u0000 \u0000 Climate change, driven by greenhouse gas (GHG) emissions, causes extreme weather events, impacting ecosystems, biodiversity, population health, and the economy. Predicting GHG emissions is crucial for mitigating these impacts and planning sustainable policies. This research proposes a novel machine learning model for GHG emission forecasting. Our model, named the meta-learning applied to multivariate single-step fusion model, utilizes historical GHG data from Brazil over the past 60 years. It predicts multivariate time series, meaning it can consider multiple factors simultaneously, leading to more accurate forecasts. Additionally, the model employs two innovative techniques: (i) fusion model aligns different data sources to ensure compatibility and improve prediction accuracy and (ii) meta-learning allows the model to learn from past prediction tasks, generalizing better to new data and reducing the need for large training datasets. Compared to the widely used Bidirectional Long Short-Term Memory (BiLSTM) model, our approach achieves significantly better results. On the same dataset, it reduces the mean absolute percentage error by 116.84% with 95% confidence, demonstrating its superior performance. Furthermore, the model's flexibility allows it to be adapted for predicting other multivariate substances, making it a valuable tool for various environmental studies.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Soorya Sudesan, Ickkshaanshu Sonkar, Hari Prasad K. S., Ojha Chandra Shekhar Prasad
Given the challenges posed by climate change and the scarcity of water, it is essential to adopt sustainable irrigation practices that do not compromise crop yields. Research studies are crucial to determine the optimal deficit soil moisture levels to be maintained for cultivation in different soil types. This study examines the response of maize grown on loamy sand soil under different water deficit moisture contents by monitoring the variation of the crop growth in terms of the leaf area index, biomass weight, root depth and yield. The daily soil moisture is measured to understand the actual evapotranspiration from the study plots. From the experiments, the optimal moisture content identified is 13%, and the plot maintained at this moisture content has shown the highest evapotranspiration, yield and biomass. The yield response factor of the maize grown in water deficit conditions is also observed to be very close to the value reported by FAO. As expected, the yield response factor is found to be sensitive to water stress. The deficit irrigation at the optimal moisture content of 13% could be recommended for maize cultivation in loamy sand soil in North Indian climatic conditions. Such considerations will be vital for achieving sustainable irrigation goals.
{"title":"Experimental study to understand the effects of deficit irrigation in maize","authors":"Soorya Sudesan, Ickkshaanshu Sonkar, Hari Prasad K. S., Ojha Chandra Shekhar Prasad","doi":"10.2166/wcc.2024.079","DOIUrl":"https://doi.org/10.2166/wcc.2024.079","url":null,"abstract":"\u0000 \u0000 Given the challenges posed by climate change and the scarcity of water, it is essential to adopt sustainable irrigation practices that do not compromise crop yields. Research studies are crucial to determine the optimal deficit soil moisture levels to be maintained for cultivation in different soil types. This study examines the response of maize grown on loamy sand soil under different water deficit moisture contents by monitoring the variation of the crop growth in terms of the leaf area index, biomass weight, root depth and yield. The daily soil moisture is measured to understand the actual evapotranspiration from the study plots. From the experiments, the optimal moisture content identified is 13%, and the plot maintained at this moisture content has shown the highest evapotranspiration, yield and biomass. The yield response factor of the maize grown in water deficit conditions is also observed to be very close to the value reported by FAO. As expected, the yield response factor is found to be sensitive to water stress. The deficit irrigation at the optimal moisture content of 13% could be recommended for maize cultivation in loamy sand soil in North Indian climatic conditions. Such considerations will be vital for achieving sustainable irrigation goals.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
S. S. Hassan, M. A. Goheer, Humera Farah, Momana Nadeem, Aleeza Muazzam, Jahan Ara Munir, Saba Fatima
Pakistan's geographic position and socioeconomic profile make it one of the nations that are particularly susceptible to the negative effects of climate change. The Tharparkar district in Pakistan is of particular importance in this regard as it is an arid region with serious environmental issues like drought, desertification, and soil degradation. Therefore, the purpose of this study is to examine how topographic and climatic factors affect vegetation indicators in the Tharparkar. The study utilizes spatiotemporal data spanning over 20 years (2001–2020) collected from the satellites MOD11A2 and MOD13A3. The collected data are processed using a range of tools in ArcGIS 10.4.1, and the impact of topographic and climatic conditions is analyzed based on different vegetation indices, including EVI, NDVI, STVI, OSAVI, and SAVI. The findings reveal that temperature and precipitation, both of which are controlled by topographic features, such as elevation and slope, are the key elements affecting vegetation in Tharparkar. At high elevations, rainfall (>440 mm) and LST (>39 °C) are also high and where the slope is low the density of vegetation indices is high.
{"title":"Monitoring the effects of climate change and topography on vegetation health in Tharparkar, Pakistan","authors":"S. S. Hassan, M. A. Goheer, Humera Farah, Momana Nadeem, Aleeza Muazzam, Jahan Ara Munir, Saba Fatima","doi":"10.2166/wcc.2024.398","DOIUrl":"https://doi.org/10.2166/wcc.2024.398","url":null,"abstract":"\u0000 \u0000 Pakistan's geographic position and socioeconomic profile make it one of the nations that are particularly susceptible to the negative effects of climate change. The Tharparkar district in Pakistan is of particular importance in this regard as it is an arid region with serious environmental issues like drought, desertification, and soil degradation. Therefore, the purpose of this study is to examine how topographic and climatic factors affect vegetation indicators in the Tharparkar. The study utilizes spatiotemporal data spanning over 20 years (2001–2020) collected from the satellites MOD11A2 and MOD13A3. The collected data are processed using a range of tools in ArcGIS 10.4.1, and the impact of topographic and climatic conditions is analyzed based on different vegetation indices, including EVI, NDVI, STVI, OSAVI, and SAVI. The findings reveal that temperature and precipitation, both of which are controlled by topographic features, such as elevation and slope, are the key elements affecting vegetation in Tharparkar. At high elevations, rainfall (>440 mm) and LST (>39 °C) are also high and where the slope is low the density of vegetation indices is high.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Van Truong Tran, H. Nguyen, D. Ngoc, Du Vu Viet Quan, Nguyen Cao Huan, Pham Viet Thanh, Ngo Van Liem, Q. Nguyen
The objective of this study was the development of a new machine learning model using a radial basis function neural network (RBFNN) to build flood susceptibility maps and damage assessment for the Phu Yen province of Vietnam. The built model will be optimized by five algorithms, namely Giant Trevally Optimization (GTO), Golden Jackal Optimization (GJO), Brown-Bear Optimization (BBO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) to find out the best model to establish the flood susceptibility map. These models were evaluated using the statistical indices such as root mean square error (RMSE), mean absolute error (MAE), receiver operating characteristic (ROC), area under the curve (AUC), and coefficient of determination (COD). The result showed that all five optimization algorithms were successfully improving the performance of the RBFNN model, among them the hybrid model RBFNN–BBO has the highest performance with AUC = 0.998 and R2 = 0.8 and the RBFNN–GTO model has the lowest performance with AUC = 0.755 and R2 = 0.65. The regions identified with a high- and very-high flood susceptibility area (1,075 km2) were concentrated on the plain and along three of the largest rivers in Phu Yen province.
{"title":"Flood assessment using machine learning and its implications for coastal spatial planning in Phu Yen Province, Vietnam","authors":"Van Truong Tran, H. Nguyen, D. Ngoc, Du Vu Viet Quan, Nguyen Cao Huan, Pham Viet Thanh, Ngo Van Liem, Q. Nguyen","doi":"10.2166/wcc.2024.035","DOIUrl":"https://doi.org/10.2166/wcc.2024.035","url":null,"abstract":"\u0000 The objective of this study was the development of a new machine learning model using a radial basis function neural network (RBFNN) to build flood susceptibility maps and damage assessment for the Phu Yen province of Vietnam. The built model will be optimized by five algorithms, namely Giant Trevally Optimization (GTO), Golden Jackal Optimization (GJO), Brown-Bear Optimization (BBO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) to find out the best model to establish the flood susceptibility map. These models were evaluated using the statistical indices such as root mean square error (RMSE), mean absolute error (MAE), receiver operating characteristic (ROC), area under the curve (AUC), and coefficient of determination (COD). The result showed that all five optimization algorithms were successfully improving the performance of the RBFNN model, among them the hybrid model RBFNN–BBO has the highest performance with AUC = 0.998 and R2 = 0.8 and the RBFNN–GTO model has the lowest performance with AUC = 0.755 and R2 = 0.65. The regions identified with a high- and very-high flood susceptibility area (1,075 km2) were concentrated on the plain and along three of the largest rivers in Phu Yen province.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141817859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
This study improves snow identification and snowmelt simulation in the source regions of the Yellow River basin (SYRB). By establishing a response function between elevation and snowfall, snow cover, and temperature, it reveals dynamic relationships and a significant decrease in the snowfall ratio. Snowmelt is negatively correlated with snowfall and cover, more so at higher altitudes. The study enhances the accuracy of snow identification and simulation in the WEP-L model, contributing to better water resource understanding and utilization in high-altitude cold regions. It provides valuable insights for managing water resources and can aid the development of more accurate models for similar areas.
{"title":"Elevation-dependent effects of snowfall and snow cover changes on runoff variations at the source regions of the Yellow River basin","authors":"Yuanhui Yu, Yuyan Zhou, Meihua Li, Wei Xue, Jianwei Liu, Yingying Hu","doi":"10.2166/wcc.2024.658","DOIUrl":"https://doi.org/10.2166/wcc.2024.658","url":null,"abstract":"\u0000 This study improves snow identification and snowmelt simulation in the source regions of the Yellow River basin (SYRB). By establishing a response function between elevation and snowfall, snow cover, and temperature, it reveals dynamic relationships and a significant decrease in the snowfall ratio. Snowmelt is negatively correlated with snowfall and cover, more so at higher altitudes. The study enhances the accuracy of snow identification and simulation in the WEP-L model, contributing to better water resource understanding and utilization in high-altitude cold regions. It provides valuable insights for managing water resources and can aid the development of more accurate models for similar areas.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141814475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Nga Thi Thanh Pham, Thi The Doan, Thuc Duy Tran, Kien Ba Truong, Hao Thi Phuong Nguyen, Hang Vu-Thanh, Ha Pham-Thanh, Nam Pham-Quang, Hang Thu Nguyen, Quan Tran-Anh, Long Trinh-Tuan
Tropical cyclones (TCs) contribute significantly to rainfall along Vietnam's coast, yet their complex precipitation structures remain poorly resolved, hindering forecast skill. This study analyzes TC rainfall distributions over the Vietnam East Sea from 2000 to 2020. The Global Satellite Mapping of Precipitation (GSMaP) product provides precipitation estimates with 0.1° resolution at hourly intervals, enabling detailed structural characterization. Rainfall features are analyzed across TC intensities, motion vectors, landfall locations, and interactions with cold surge (CS) air masses. Results show that total coverage differences are less significant than the intensity variations in narrow inner core rainbands. Asymmetric rainfall distributions concentrate in the front-right quadrant but shift after landfall. Northern Vietnam observes higher TC frequencies, but southern regions experience heavier extreme rains. Additionally, CS intrusions substantially intensify eyewall convection and redirect TC precipitation. These structural sensitivities visible in GSMaP observations elucidate the dynamics modulating TC rainfall. Characterizing multi-scale interactions and precipitation processes aids in forecasting and impact assessment for these high-risk storms with complex regional behavior.
{"title":"Characteristics of rainfall distribution induced by tropical cyclones using GSMaP data over the Vietnam region","authors":"Nga Thi Thanh Pham, Thi The Doan, Thuc Duy Tran, Kien Ba Truong, Hao Thi Phuong Nguyen, Hang Vu-Thanh, Ha Pham-Thanh, Nam Pham-Quang, Hang Thu Nguyen, Quan Tran-Anh, Long Trinh-Tuan","doi":"10.2166/wcc.2024.210","DOIUrl":"https://doi.org/10.2166/wcc.2024.210","url":null,"abstract":"\u0000 \u0000 Tropical cyclones (TCs) contribute significantly to rainfall along Vietnam's coast, yet their complex precipitation structures remain poorly resolved, hindering forecast skill. This study analyzes TC rainfall distributions over the Vietnam East Sea from 2000 to 2020. The Global Satellite Mapping of Precipitation (GSMaP) product provides precipitation estimates with 0.1° resolution at hourly intervals, enabling detailed structural characterization. Rainfall features are analyzed across TC intensities, motion vectors, landfall locations, and interactions with cold surge (CS) air masses. Results show that total coverage differences are less significant than the intensity variations in narrow inner core rainbands. Asymmetric rainfall distributions concentrate in the front-right quadrant but shift after landfall. Northern Vietnam observes higher TC frequencies, but southern regions experience heavier extreme rains. Additionally, CS intrusions substantially intensify eyewall convection and redirect TC precipitation. These structural sensitivities visible in GSMaP observations elucidate the dynamics modulating TC rainfall. Characterizing multi-scale interactions and precipitation processes aids in forecasting and impact assessment for these high-risk storms with complex regional behavior.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Changes on the land surface caused due to human activities or natural events generate changes in land cover, which directly affect the availability of water in watersheds. This article evaluates the case study regarding the effects on the hydrological regime of the Andean mountain basin on the Coello river basin in Colombia due to changes in land use/land cover during the 2000–2019 period by the use of the Hydro-BID tool. The physical analysis of the land surface included the processing of Landsat 7 ETM and Landsat 8 OLI satellite images for the years 2001, 2003, 2015, and 2019. Seven types of coverage were determined based on these data using the Mixed Gaussian Method. The changes between each year were evaluated, after which the land use/land cover change for the year 2050 was predicted using a Markov chain. The multi-temporal analysis showed a decrease in forested areas during the studied period, while low vegetation significantly increased within the watershed. This trend was shown to continue in the future scenario for the year 2050, with an increase in flow on the watershed of 59.6%. Additionally, the climate change scenarios were modeled with the changes in land use. The combined effects established a progressive decrease in the modal flow.
{"title":"Effects of climate change and land use on the hydrologic regime using the Hydro-BID tool: a case study of the Andean mountain basin in Colombia","authors":"Mena Darwin, C. Peña-Guzmán, Manuel Rodriguez","doi":"10.2166/wcc.2024.197","DOIUrl":"https://doi.org/10.2166/wcc.2024.197","url":null,"abstract":"\u0000 \u0000 Changes on the land surface caused due to human activities or natural events generate changes in land cover, which directly affect the availability of water in watersheds. This article evaluates the case study regarding the effects on the hydrological regime of the Andean mountain basin on the Coello river basin in Colombia due to changes in land use/land cover during the 2000–2019 period by the use of the Hydro-BID tool. The physical analysis of the land surface included the processing of Landsat 7 ETM and Landsat 8 OLI satellite images for the years 2001, 2003, 2015, and 2019. Seven types of coverage were determined based on these data using the Mixed Gaussian Method. The changes between each year were evaluated, after which the land use/land cover change for the year 2050 was predicted using a Markov chain. The multi-temporal analysis showed a decrease in forested areas during the studied period, while low vegetation significantly increased within the watershed. This trend was shown to continue in the future scenario for the year 2050, with an increase in flow on the watershed of 59.6%. Additionally, the climate change scenarios were modeled with the changes in land use. The combined effects established a progressive decrease in the modal flow.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
Sanal Kumar Aditya, A. Krishnakumar, K. AnoopKrishnan
In this study, an extensive and methodical investigation was carried out to comprehend the different geochemical processes, factors governing the hydrochemical composition and water suitability for drinking, irrigation and industrial usage in the Periyar River Basin (PRB). A total of 300 samples were collected from the mainstream, tributaries and dams of the river during PREM (Pre-Monsoon), POM (Post Monsoon), NEM (North-East Monsoon) and SWM (South-West Monsoon). The results suggested that the cationic composition is chiefly characterized by the predominant presence of Ca2+ and Mg2+ while Cl− dominates the anionic composition followed by HCO3-. The results identified transitional waters. Gibb's diagram revealed that the ionic composition dominance in the study area is influenced by the chemistry of the host rock rather than precipitation and evaporation. A comparatively greater pCO2 (>10−3.5 atm) shows an atmospheric disequilibrium in natural waterbodies due to both anthropogenic activities and input of baseflow to stream discharge. The Water Quality Index showed excellent (0–25) to unsuitable (>300) category during NEM, POM and PREM with significant spatial variation along the river. Integrated irrigational suitability indices illustrated the suitability of the samples for agricultural use, except for a few samples in the lowland region.
{"title":"Analysis of seasonal spatio-temporal variations in the quality of river waters and its influencing factors in the Periyar River Basin, southern Western Ghats, India","authors":"Sanal Kumar Aditya, A. Krishnakumar, K. AnoopKrishnan","doi":"10.2166/wcc.2024.136","DOIUrl":"https://doi.org/10.2166/wcc.2024.136","url":null,"abstract":"\u0000 \u0000 In this study, an extensive and methodical investigation was carried out to comprehend the different geochemical processes, factors governing the hydrochemical composition and water suitability for drinking, irrigation and industrial usage in the Periyar River Basin (PRB). A total of 300 samples were collected from the mainstream, tributaries and dams of the river during PREM (Pre-Monsoon), POM (Post Monsoon), NEM (North-East Monsoon) and SWM (South-West Monsoon). The results suggested that the cationic composition is chiefly characterized by the predominant presence of Ca2+ and Mg2+ while Cl− dominates the anionic composition followed by HCO3-. The results identified transitional waters. Gibb's diagram revealed that the ionic composition dominance in the study area is influenced by the chemistry of the host rock rather than precipitation and evaporation. A comparatively greater pCO2 (>10−3.5 atm) shows an atmospheric disequilibrium in natural waterbodies due to both anthropogenic activities and input of baseflow to stream discharge. The Water Quality Index showed excellent (0–25) to unsuitable (>300) category during NEM, POM and PREM with significant spatial variation along the river. Integrated irrigational suitability indices illustrated the suitability of the samples for agricultural use, except for a few samples in the lowland region.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：