A. Kathirvelpandian, Ahamed Rasheeq, Ganesan Kantharajan, T. Kumawat, T. T. A. Ajith Kumar, U. Sarkar
Mangrove-inhabited coastal areas are seriously threatened by climate change that disturbs the sustenance of dependent native communities. This study assesses the status of climate variables to identify and evaluate climate change–induced vulnerabilities to fishers and recommend mitigation measures at a regional scale in the Pichavaram Mangroves, a Ramsar wetland of international importance in India. The time series climate data (1951–2021) revealed an insignificant increase in annual rainfall (p< 0.05) and a significant increasing trend (p> 0.05) for mean annual temperature. The results of the stakeholder-based approach (n = 240) indicate that mangrove dwellers experience ecological and health-based vulnerabilities and estuarine fishers face community and nutritional issues. Fishers of this wetland are prone to resources (60.5) and user-based vulnerabilities (59.03). The overall vulnerability index places fishers of the mangrove/estuary (T.S.Pettai: 54.13; Killai: 53.04; Pichavaram: 52.91) more vulnerable than the marine region (Mudasalodai: 49.84). This study suggests social networking, skill development, awareness of welfare schemes, ecotourism and sustainable fishing, climate research for developing mitigation strategies, and fisheries enhancement to combat climate change impacts. Furthermore, building resilience among the stakeholders and resource management through a citizen science approach is crucial to lessen climate change vulnerability in coastal wetlands in India, and elsewhere.
{"title":"Evaluating the climate change–induced vulnerability of the Pichavaram Mangrove ecosystem through a stakeholder-centric multiscale approach","authors":"A. Kathirvelpandian, Ahamed Rasheeq, Ganesan Kantharajan, T. Kumawat, T. T. A. Ajith Kumar, U. Sarkar","doi":"10.2166/wcc.2024.373","DOIUrl":"https://doi.org/10.2166/wcc.2024.373","url":null,"abstract":"\u0000 \u0000 Mangrove-inhabited coastal areas are seriously threatened by climate change that disturbs the sustenance of dependent native communities. This study assesses the status of climate variables to identify and evaluate climate change–induced vulnerabilities to fishers and recommend mitigation measures at a regional scale in the Pichavaram Mangroves, a Ramsar wetland of international importance in India. The time series climate data (1951–2021) revealed an insignificant increase in annual rainfall (p< 0.05) and a significant increasing trend (p> 0.05) for mean annual temperature. The results of the stakeholder-based approach (n = 240) indicate that mangrove dwellers experience ecological and health-based vulnerabilities and estuarine fishers face community and nutritional issues. Fishers of this wetland are prone to resources (60.5) and user-based vulnerabilities (59.03). The overall vulnerability index places fishers of the mangrove/estuary (T.S.Pettai: 54.13; Killai: 53.04; Pichavaram: 52.91) more vulnerable than the marine region (Mudasalodai: 49.84). This study suggests social networking, skill development, awareness of welfare schemes, ecotourism and sustainable fishing, climate research for developing mitigation strategies, and fisheries enhancement to combat climate change impacts. Furthermore, building resilience among the stakeholders and resource management through a citizen science approach is crucial to lessen climate change vulnerability in coastal wetlands in India, and elsewhere.","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":"141821719","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}
Linyan Chen, Xuan Ji, Zhangchao Xu, Peng Qin, Chuangjian Yang, Siyi Yan, Cezong Sun, Yangfan Zheng, Yuanping Zhang
This study compares simulations from 13 CMIP5 and CMIP6 homologous models and their multi-model ensemble (MME) for temperature and precipitation over the Yarlung Tsangpo-Brahmaputra River Basin (YBRB). The results showed that (1) the MME estimations are superior to most single models, indicating TCH is effective for reducing model uncertainty. (2) CMIP5 and CMIP6 were more applicable during spring and autumn for maximum and minimum temperatures (Tasmax, Tasmin) and precipitation over the YBRB. Moreover, CMIP5 and CMIP6 showed better performance for precipitation in the downstream floodplain, for Tasmax on the Tibetan Plateau, and for Tasmin in the whole YBRB. (3) CMIP5 and CMIP6 can better simulate the spatial distribution of temperature rather than precipitation (temporal correlation coefficient (TCC) of Tasmax and Tasmin: 0.72–0.89; TCC of precipitation: 0.43–0.6). Both perform poorly in simulating interannual variation in temperature and precipitation (all anomaly correlation coefficients (ACC) <0.60). (4) The bias of CMIP6 for temperature and precipitation is mostly lower than CMIP5, but still has a cold bias over YBRB (Tasmax: −7.98 to −14.88 °C, Tasmin: −6.24 to −21.45 °C) and wet bias on Tibetan Plateau (0.56–2.00 mm), dry bias on Himalayan belt (−0.69 to −7.56 mm), and floodplain (−0.46 to −6.98 mm).
{"title":"Comparison of CMIP5 and CMIP6 models for temperature and precipitation simulation over the Yarlung Tsangpo-Brahmaputra River Basin","authors":"Linyan Chen, Xuan Ji, Zhangchao Xu, Peng Qin, Chuangjian Yang, Siyi Yan, Cezong Sun, Yangfan Zheng, Yuanping Zhang","doi":"10.2166/wcc.2024.393","DOIUrl":"https://doi.org/10.2166/wcc.2024.393","url":null,"abstract":"\u0000 \u0000 This study compares simulations from 13 CMIP5 and CMIP6 homologous models and their multi-model ensemble (MME) for temperature and precipitation over the Yarlung Tsangpo-Brahmaputra River Basin (YBRB). The results showed that (1) the MME estimations are superior to most single models, indicating TCH is effective for reducing model uncertainty. (2) CMIP5 and CMIP6 were more applicable during spring and autumn for maximum and minimum temperatures (Tasmax, Tasmin) and precipitation over the YBRB. Moreover, CMIP5 and CMIP6 showed better performance for precipitation in the downstream floodplain, for Tasmax on the Tibetan Plateau, and for Tasmin in the whole YBRB. (3) CMIP5 and CMIP6 can better simulate the spatial distribution of temperature rather than precipitation (temporal correlation coefficient (TCC) of Tasmax and Tasmin: 0.72–0.89; TCC of precipitation: 0.43–0.6). Both perform poorly in simulating interannual variation in temperature and precipitation (all anomaly correlation coefficients (ACC) <0.60). (4) The bias of CMIP6 for temperature and precipitation is mostly lower than CMIP5, but still has a cold bias over YBRB (Tasmax: −7.98 to −14.88 °C, Tasmin: −6.24 to −21.45 °C) and wet bias on Tibetan Plateau (0.56–2.00 mm), dry bias on Himalayan belt (−0.69 to −7.56 mm), and floodplain (−0.46 to −6.98 mm).","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824205","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}
M. O’Driscoll, Charles P. Humphrey, G. Iverson, Jared Bowden, Jane Harrison
Onsite wastewater treatment systems (OWTS) are a common wastewater treatment approach in coastal communities. Vertical separation distance (VSD) requirements between the drainfield and groundwater aim to ensure aerated soils for wastewater treatment. When the VSD declines, OWTS can fail. This study evaluated groundwater response to sea level rise (SLR) and the implications for OWTS. A groundwater monitoring network (13 wells) was used to evaluate groundwater depth in Dare County, North Carolina. Groundwater levels were measured with water level meters and pressure transducers. Trends in groundwater depth and SLR were analyzed to evaluate the influence of SLR on groundwater depth. From 1984 to 2022, mean groundwater levels have risen (∼7.6 mm/year) in response to SLR. Currently, sites at <2.7 m land elevation are most likely to have groundwater depths <1 m and inadequate VSD. Based on current precipitation and NOAA intermediate SLR projections, groundwater depth projections suggest that OWTS at lower elevations are more likely to experience groundwater inundation by 2040–2060. SLR has resulted in reduced VSD causing diminished wastewater treatment capacity in low-lying areas. OWTS VSD requirements are typically static due to regulatory constraints. Future management approaches should consider adapting to rising coastal groundwater levels because of increasing wastewater contamination risks.
{"title":"Rising groundwater levels in Dare County, North Carolina: implications for onsite wastewater management for coastal communities","authors":"M. O’Driscoll, Charles P. Humphrey, G. Iverson, Jared Bowden, Jane Harrison","doi":"10.2166/wcc.2024.735","DOIUrl":"https://doi.org/10.2166/wcc.2024.735","url":null,"abstract":"\u0000 \u0000 Onsite wastewater treatment systems (OWTS) are a common wastewater treatment approach in coastal communities. Vertical separation distance (VSD) requirements between the drainfield and groundwater aim to ensure aerated soils for wastewater treatment. When the VSD declines, OWTS can fail. This study evaluated groundwater response to sea level rise (SLR) and the implications for OWTS. A groundwater monitoring network (13 wells) was used to evaluate groundwater depth in Dare County, North Carolina. Groundwater levels were measured with water level meters and pressure transducers. Trends in groundwater depth and SLR were analyzed to evaluate the influence of SLR on groundwater depth. From 1984 to 2022, mean groundwater levels have risen (∼7.6 mm/year) in response to SLR. Currently, sites at <2.7 m land elevation are most likely to have groundwater depths <1 m and inadequate VSD. Based on current precipitation and NOAA intermediate SLR projections, groundwater depth projections suggest that OWTS at lower elevations are more likely to experience groundwater inundation by 2040–2060. SLR has resulted in reduced VSD causing diminished wastewater treatment capacity in low-lying areas. OWTS VSD requirements are typically static due to regulatory constraints. Future management approaches should consider adapting to rising coastal groundwater levels because of increasing wastewater contamination risks.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825015","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}
Akram Elghouat, A. Algouti, Abdellah Algouti, Soukaina Baid, Salma Ezzahzi, Salma Kabili, Saloua Agli
Flash floods are highly destructive disasters, posing severe threats to lives and infrastructure. In this study, we conducted a comparative analysis of bivariate and multivariate statistical models and machine learning to predict flash flood susceptibility in the flood-prone Rheraya watershed. Six models were utilized, including frequency ratio (FR), logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), K-nearest neighbors (KNN), and naïve Bayes (NB). We considered 12 flash flood conditioning variables, such as slope, elevation, distance to the river, and others, as independent variables and 246 flash flood inventory points recorded over the past 40 years as dependent variables in the modeling process. The area under the curve (AUC) of the receiver operating characteristic was used to validate and compare the performance of the models. The results indicated that distance to the river was the most contributing factor to flash floods in the study area. Moreover, the RF outperformed all the other models, achieving an AUC of 0.86, followed by XGBoost (AUC = 0.85), LR (AUC = 0.83), NB (AUC = 0.76), KNN (AUC = 0.75), and FR (AUC = 0.72). The RF model effectively pinpoints highly susceptible zones, which is critical for establishing precise flash flood mitigation strategies within the region.
{"title":"Integrated approaches for flash flood susceptibility mapping: spatial modeling and comparative analysis of statistical and machine learning models. A case study of the Rheraya watershed, Morocco","authors":"Akram Elghouat, A. Algouti, Abdellah Algouti, Soukaina Baid, Salma Ezzahzi, Salma Kabili, Saloua Agli","doi":"10.2166/wcc.2024.726","DOIUrl":"https://doi.org/10.2166/wcc.2024.726","url":null,"abstract":"\u0000 \u0000 Flash floods are highly destructive disasters, posing severe threats to lives and infrastructure. In this study, we conducted a comparative analysis of bivariate and multivariate statistical models and machine learning to predict flash flood susceptibility in the flood-prone Rheraya watershed. Six models were utilized, including frequency ratio (FR), logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), K-nearest neighbors (KNN), and naïve Bayes (NB). We considered 12 flash flood conditioning variables, such as slope, elevation, distance to the river, and others, as independent variables and 246 flash flood inventory points recorded over the past 40 years as dependent variables in the modeling process. The area under the curve (AUC) of the receiver operating characteristic was used to validate and compare the performance of the models. The results indicated that distance to the river was the most contributing factor to flash floods in the study area. Moreover, the RF outperformed all the other models, achieving an AUC of 0.86, followed by XGBoost (AUC = 0.85), LR (AUC = 0.83), NB (AUC = 0.76), KNN (AUC = 0.75), and FR (AUC = 0.72). The RF model effectively pinpoints highly susceptible zones, which is critical for establishing precise flash flood mitigation strategies within the region.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141831138","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}
High climate change stress escalates agriculture risks, particularly in nations like India heavily reliant on farming. Previous studies focused on Coupled Model Intercomparison Project Phase (CMIP3) and (CMIP5) scenarios for large river basins, but the heightened risk of local climate changes poses a significant threat to smaller basins, notably affecting crops. This study investigates the spatiotemporal dynamics of climate change impacts on paddy crop irrigation in India's Lower Mahanadi Basin, utilizing the latest general circulation models (GCMs) from the CMIP6, focuses on two emission scenarios, SSP585 and SSP370. Thirteen models were analysed, top six were selected based on statistical criteria like PBIAS, NSE, R2, RSR, and RMSE. Models project climate changes for near- (2025–2050), mid- (2051–2075), and far-future (2076–2100) periods against a baseline (1981–2014), investigating spatiotemporal variations in rainfall, temperature, and irrigation water requirements (IWRs) in the region. In both scenarios, future mean seasonal rainfall is expected to increase compared with the baseline. SSP370 projects a 23.7% rise in minimum rainfall, while maximum rainfall varies by 11.5%. SSP585, on the other hand, projects a 9.53% decrease in maximum IWR and a 28.9% increase in maximum rainfall compared with the baseline. Both scenarios anticipate a 3–4 °C temperature increase in the far-future.
{"title":"Assessing climate change impacts on irrigation water requirements in the Lower Mahanadi Basin: A CMIP6-based spatiotemporal analysis and future projections","authors":"Pushpanjali Kumari, Rahul Kumar Jaiswal, Harendra Prasad Singh","doi":"10.2166/wcc.2024.152","DOIUrl":"https://doi.org/10.2166/wcc.2024.152","url":null,"abstract":"\u0000 \u0000 High climate change stress escalates agriculture risks, particularly in nations like India heavily reliant on farming. Previous studies focused on Coupled Model Intercomparison Project Phase (CMIP3) and (CMIP5) scenarios for large river basins, but the heightened risk of local climate changes poses a significant threat to smaller basins, notably affecting crops. This study investigates the spatiotemporal dynamics of climate change impacts on paddy crop irrigation in India's Lower Mahanadi Basin, utilizing the latest general circulation models (GCMs) from the CMIP6, focuses on two emission scenarios, SSP585 and SSP370. Thirteen models were analysed, top six were selected based on statistical criteria like PBIAS, NSE, R2, RSR, and RMSE. Models project climate changes for near- (2025–2050), mid- (2051–2075), and far-future (2076–2100) periods against a baseline (1981–2014), investigating spatiotemporal variations in rainfall, temperature, and irrigation water requirements (IWRs) in the region. In both scenarios, future mean seasonal rainfall is expected to increase compared with the baseline. SSP370 projects a 23.7% rise in minimum rainfall, while maximum rainfall varies by 11.5%. SSP585, on the other hand, projects a 9.53% decrease in maximum IWR and a 28.9% increase in maximum rainfall compared with the baseline. Both scenarios anticipate a 3–4 °C temperature increase in the far-future.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340919","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}
Majid Niazkar, M. Zakwan, M. Goodarzi, Mohammad Azamathulla Hazi
{"title":"Editorial: Assessment of Climate Change Impact on Water Resources Using Machine Learning Algorithms","authors":"Majid Niazkar, M. Zakwan, M. Goodarzi, Mohammad Azamathulla Hazi","doi":"10.2166/wcc.2024.002","DOIUrl":"https://doi.org/10.2166/wcc.2024.002","url":null,"abstract":"","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344020","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}
Peiyi Peng, Yiming Zhang, Jie Chen, Xunchang J. Zhang, Xiuzhen Li, Di Xu
Extreme precipitation in eastern China (EC) is closely related to the diversity of the decaying phases of El Niño (warm-pool El Niño, i.e., WP El Niño and cold-tongue El Niño, i.e., CT El Niño), but little attention is paid to how the El Niño event variability influences precipitation sources for EC from an isotopic perspective. Stable isotopes are ideal physical tracers that can distinguish different sources of precipitation and quantify their relative contributions to precipitation. Accordingly, this study investigates spatiotemporal variations of water vapor flux and oceanic fraction to precipitation during different ENSO events by an isotopic mixing model. The results show that spatiotemporal patterns of moisture divergence for the decaying phase of WP El Niño are different from that of CT El Niño. The oceanic fraction anomalies present similar spatiotemporal trends with advection fraction anomalies. The spatiotemporal variations of precipitation source anomalies for different El Niño events are closely related to atmospheric circulations, i.e., the intensity and location of the western Pacific subtropical high (WPSH). These findings provide isotopic insights into the precipitation sources by El Niño events in EC. Future studies may further focus on the mechanisms producing extreme precipitation between the two kinds of El Niño.
{"title":"Impacts of different El Niño events in the decaying summer on the oceanic source of summer rainfall for eastern China: A perspective from stable isotope","authors":"Peiyi Peng, Yiming Zhang, Jie Chen, Xunchang J. Zhang, Xiuzhen Li, Di Xu","doi":"10.2166/wcc.2024.062","DOIUrl":"https://doi.org/10.2166/wcc.2024.062","url":null,"abstract":"\u0000 \u0000 Extreme precipitation in eastern China (EC) is closely related to the diversity of the decaying phases of El Niño (warm-pool El Niño, i.e., WP El Niño and cold-tongue El Niño, i.e., CT El Niño), but little attention is paid to how the El Niño event variability influences precipitation sources for EC from an isotopic perspective. Stable isotopes are ideal physical tracers that can distinguish different sources of precipitation and quantify their relative contributions to precipitation. Accordingly, this study investigates spatiotemporal variations of water vapor flux and oceanic fraction to precipitation during different ENSO events by an isotopic mixing model. The results show that spatiotemporal patterns of moisture divergence for the decaying phase of WP El Niño are different from that of CT El Niño. The oceanic fraction anomalies present similar spatiotemporal trends with advection fraction anomalies. The spatiotemporal variations of precipitation source anomalies for different El Niño events are closely related to atmospheric circulations, i.e., the intensity and location of the western Pacific subtropical high (WPSH). These findings provide isotopic insights into the precipitation sources by El Niño events in EC. Future studies may further focus on the mechanisms producing extreme precipitation between the two kinds of El Niño.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141346077","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}
Mathurin François, Maria Carolina Gonçalves Pontes, R. N. De Vasconcelos, Ulisses Costa de Oliveira, Heraldo Peixoto da Silva, Deborah Faria, E. Mariano‐Neto
Erosion is a worldwide threat to biodiversity conservation and agricultural yield, and it is linked to deforestation. In this study, we aim to assess soil loss in landscapes of the Cachoeira River watershed, in southern Bahia, northeastern Brazil. We estimate the role of forests in diminishing soil erosion using the Revised Universal Soil Loss Equation (RUSLE). We compare real and simulated scenarios in which the forest was replaced by agricultural use, also comparing estimates of erosivity factor (R factor) derived from remote sensing and climatological station data. Real and simulated annual soil losses varied from 0 to 167.87 t/year and from 0 to 351.81 t/year along the watershed, respectively. However, only 0.04 and 1.67% of this area is highly and severely exposed to erosion, using data from climatological stations and remote sensing, respectively. We showed that soil loss in the simulated deforested scenario was approximately two times higher than the real annual soil loss, indicating the importance of forest cover to mitigate soil erosion. Moreover, soil loss was 10.5 times greater when using precipitation data from remote sensing compared to climatological stations. Conclusively, the practice of agroforestry can be used as an alternative to avoid erosion.
{"title":"Assessing soil erosion and its drivers in agricultural landscapes: a case study in southern Bahia, Brazil","authors":"Mathurin François, Maria Carolina Gonçalves Pontes, R. N. De Vasconcelos, Ulisses Costa de Oliveira, Heraldo Peixoto da Silva, Deborah Faria, E. Mariano‐Neto","doi":"10.2166/wcc.2024.147","DOIUrl":"https://doi.org/10.2166/wcc.2024.147","url":null,"abstract":"\u0000 Erosion is a worldwide threat to biodiversity conservation and agricultural yield, and it is linked to deforestation. In this study, we aim to assess soil loss in landscapes of the Cachoeira River watershed, in southern Bahia, northeastern Brazil. We estimate the role of forests in diminishing soil erosion using the Revised Universal Soil Loss Equation (RUSLE). We compare real and simulated scenarios in which the forest was replaced by agricultural use, also comparing estimates of erosivity factor (R factor) derived from remote sensing and climatological station data. Real and simulated annual soil losses varied from 0 to 167.87 t/year and from 0 to 351.81 t/year along the watershed, respectively. However, only 0.04 and 1.67% of this area is highly and severely exposed to erosion, using data from climatological stations and remote sensing, respectively. We showed that soil loss in the simulated deforested scenario was approximately two times higher than the real annual soil loss, indicating the importance of forest cover to mitigate soil erosion. Moreover, soil loss was 10.5 times greater when using precipitation data from remote sensing compared to climatological stations. Conclusively, the practice of agroforestry can be used as an alternative to avoid erosion.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354069","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}
The present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land-use land cover (LULC), rainfall, and digital elevation model (DEM) parameters, were processed and analysed within a geographic information system (GIS) framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model analysis. Subsequently, the sediment yield (SY) is estimated based on the sediment delivery ratio (SDR) in the watershed. The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is a high soil erosion risk. The model's accuracy suggests a very good (82.1%) outcome for the RUSLE model results. Moreover, the study region revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the outlet. The investigation region revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual SY. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model anticipated a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.
{"title":"Assessment of soil erosion and sediment yield in the Peddavagu watershed, India, using a revised universal soil loss equation model (RUSLE) and GIS techniques","authors":"Padala Raja Shekar, Aneesh Mathew","doi":"10.2166/wcc.2024.010","DOIUrl":"https://doi.org/10.2166/wcc.2024.010","url":null,"abstract":"\u0000 \u0000 The present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land-use land cover (LULC), rainfall, and digital elevation model (DEM) parameters, were processed and analysed within a geographic information system (GIS) framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model analysis. Subsequently, the sediment yield (SY) is estimated based on the sediment delivery ratio (SDR) in the watershed. The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is a high soil erosion risk. The model's accuracy suggests a very good (82.1%) outcome for the RUSLE model results. Moreover, the study region revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the outlet. The investigation region revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual SY. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model anticipated a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354889","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}
Jie Liu, Ke Chang, Xueping Zhu, Xueyao Wang, Binbin Lin, Wenjun Cai
High-precision simulation of runoff–sediment is a significant challenge due to the combined impacts of climate change and human activities. In this paper, runoff–sediment processes were simulated, and their impact attribution was analyzed using the Soil and Water Assessment Tool (SWAT) model in the upper Fenhe River basin of the Loess Plateau, China. A SWAT model was constructed to assess its applicability during the historical baseline period, which reflects low human activity. However, the simulation results for the comprehensive impact period, using the calibrated historical baseline model, were unsatisfactory. Consequently, a method was proposed to enhance the accuracy of simulation results by considering the presence of soil-retaining dams. This method incorporates large and small soil-retaining dams as reservoirs and ponds, respectively, into SWAT. The results indicate that the accuracy of runoff and sediment simulation reaches a satisfactory level. The attribution analysis results show that human activities have a greater impact on runoff and sediment than climate change, with land use change and soil-retaining dams being particularly significant. The construction of soil-retaining dams plays a more significant role in reducing runoff and sediment. These findings provide valuable insights into the management and utilization of runoff and sediment in river basins.
{"title":"Simulation and attribution analysis of runoff–sediment in the Upper Basin of Fenhe River, China","authors":"Jie Liu, Ke Chang, Xueping Zhu, Xueyao Wang, Binbin Lin, Wenjun Cai","doi":"10.2166/wcc.2024.518","DOIUrl":"https://doi.org/10.2166/wcc.2024.518","url":null,"abstract":"\u0000 \u0000 High-precision simulation of runoff–sediment is a significant challenge due to the combined impacts of climate change and human activities. In this paper, runoff–sediment processes were simulated, and their impact attribution was analyzed using the Soil and Water Assessment Tool (SWAT) model in the upper Fenhe River basin of the Loess Plateau, China. A SWAT model was constructed to assess its applicability during the historical baseline period, which reflects low human activity. However, the simulation results for the comprehensive impact period, using the calibrated historical baseline model, were unsatisfactory. Consequently, a method was proposed to enhance the accuracy of simulation results by considering the presence of soil-retaining dams. This method incorporates large and small soil-retaining dams as reservoirs and ponds, respectively, into SWAT. The results indicate that the accuracy of runoff and sediment simulation reaches a satisfactory level. The attribution analysis results show that human activities have a greater impact on runoff and sediment than climate change, with land use change and soil-retaining dams being particularly significant. The construction of soil-retaining dams plays a more significant role in reducing runoff and sediment. These findings provide valuable insights into the management and utilization of runoff and sediment in river basins.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360967","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}
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