P. van Thienen, G. A. Chatzistefanou, Christos Makropoulos, L. Vamvakeridou-Lyroudia
The world grapples with immediate crises like COVID-19, Russia's invasion of Ukraine, floods, droughts and wildfires. However, a longer-term crisis looms due to humanity's overstepping of planetary boundaries and its disruptive consequences. Growing awareness of the potential collapse of societies due to planetary boundary violations has prompted increased attention in the scientific literature. In the water sector, where infrastructure built today might persist during a future collapse, we must therefore ask ourselves how a (basic) level of water supply can be maintained in a collapsing society. This paper explores this question and proposes research directions to address it in the short to medium term. Despite the seeming remoteness of a societal collapse scenario, it is imperative to incorporate it urgently into water infrastructure research and planning.
{"title":"What water supply system research is needed in the face of a conceivable societal collapse?","authors":"P. van Thienen, G. A. Chatzistefanou, Christos Makropoulos, L. Vamvakeridou-Lyroudia","doi":"10.2166/wcc.2023.351","DOIUrl":"https://doi.org/10.2166/wcc.2023.351","url":null,"abstract":"\u0000 \u0000 The world grapples with immediate crises like COVID-19, Russia's invasion of Ukraine, floods, droughts and wildfires. However, a longer-term crisis looms due to humanity's overstepping of planetary boundaries and its disruptive consequences. Growing awareness of the potential collapse of societies due to planetary boundary violations has prompted increased attention in the scientific literature. In the water sector, where infrastructure built today might persist during a future collapse, we must therefore ask ourselves how a (basic) level of water supply can be maintained in a collapsing society. This paper explores this question and proposes research directions to address it in the short to medium term. Despite the seeming remoteness of a societal collapse scenario, it is imperative to incorporate it urgently into water infrastructure research and planning.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"100 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138975515","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}
Urban heat islands are hotter than rural places. Sustainable urban growth and improving urban environments need understanding Urban Heat Island (UHI) causes and finding effective mitigation techniques. This research examines the seasonal deviations in surface temperatures for the UHI effect in Pune, India, focusing on land use patterns and water body cooling. Land use categorization included residential, commercial, industrial, vegetation, and open spaces. The research studied the cooling potential and temperature variance by distance from water bodies in the form of lakes, rivers, and ponds. These aquatic bodies have surface and ambient temperature sensors. Roads, soil, commercial areas, residential areas, industrial areas, and vegetation have all shown increases in NDBI, ranging from 15.84 to 36.45%. Urban regions with heat accumulation and dissipation have been revealed by DEM and contour maps. The research found that the water bodies have a cooling effect on LST till the distance of 350 m. The research finds hotter places and shows how natural features mitigate UHI by analyzing land use patterns and water body cooling. The findings emphasize the significance of green areas and water bodies in urban design and development to improve Pune's climate resilience and inhabitability.
{"title":"Geo-physical seasonal deviations of land use, terrain analysis, and water cooling effect on the surface temperature of Pune city","authors":"Kul Vaibhav Sharma, Vijendra Kumar, Lilesh Gautam, Sumit Choudhary, Aneesh Mathew","doi":"10.2166/wcc.2023.432","DOIUrl":"https://doi.org/10.2166/wcc.2023.432","url":null,"abstract":"\u0000 \u0000 Urban heat islands are hotter than rural places. Sustainable urban growth and improving urban environments need understanding Urban Heat Island (UHI) causes and finding effective mitigation techniques. This research examines the seasonal deviations in surface temperatures for the UHI effect in Pune, India, focusing on land use patterns and water body cooling. Land use categorization included residential, commercial, industrial, vegetation, and open spaces. The research studied the cooling potential and temperature variance by distance from water bodies in the form of lakes, rivers, and ponds. These aquatic bodies have surface and ambient temperature sensors. Roads, soil, commercial areas, residential areas, industrial areas, and vegetation have all shown increases in NDBI, ranging from 15.84 to 36.45%. Urban regions with heat accumulation and dissipation have been revealed by DEM and contour maps. The research found that the water bodies have a cooling effect on LST till the distance of 350 m. The research finds hotter places and shows how natural features mitigate UHI by analyzing land use patterns and water body cooling. The findings emphasize the significance of green areas and water bodies in urban design and development to improve Pune's climate resilience and inhabitability.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"89 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008363","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}
K. Sahu, S. Chandniha, Manish Kumar Nema, G. K. Das, Haritha Lekshmi V., Pratibha Wadware
Rainfall is the key weather element which regulates the hydrological cycle, availability of water resources and crop production. In this study, spatial and temporal variability of rainfall has been investigated on seasonal and annual time scales of 149 blocks of Chhattisgarh State using 120 years (1901–2020) of rainfall data. Non-parametric, and Theil and Sen's slope estimator were used to identify possible trends and ascertain the variability in the magnitude. The results revealed that there exists a well-marked spatial variability in rainfall over Chhattisgarh in annual and seasonal time scales. Out of 149 blocks a significant negative rainfall was noticed in 105 blocks. Annual rainfall showed a significant positive trend in a few blocks like Bhopalpattnam, Bijapur, Usur, Konta. A similar pattern of trend was noticed in monsoon season. The results of the study demand the urgent need to formulate policies and strategies for water resource management and planning. The blocks which showed the positive rainfall trends can be identified to intensify the cultivation of more water requiring crops based on the suitability to that region. The findings of this study can be used as valuable information for crop planning, policy-making and preparation of contingency plans.
{"title":"Level long-term rainfall variability using trend analysis in a state of central India","authors":"K. Sahu, S. Chandniha, Manish Kumar Nema, G. K. Das, Haritha Lekshmi V., Pratibha Wadware","doi":"10.2166/wcc.2023.047","DOIUrl":"https://doi.org/10.2166/wcc.2023.047","url":null,"abstract":"\u0000 \u0000 Rainfall is the key weather element which regulates the hydrological cycle, availability of water resources and crop production. In this study, spatial and temporal variability of rainfall has been investigated on seasonal and annual time scales of 149 blocks of Chhattisgarh State using 120 years (1901–2020) of rainfall data. Non-parametric, and Theil and Sen's slope estimator were used to identify possible trends and ascertain the variability in the magnitude. The results revealed that there exists a well-marked spatial variability in rainfall over Chhattisgarh in annual and seasonal time scales. Out of 149 blocks a significant negative rainfall was noticed in 105 blocks. Annual rainfall showed a significant positive trend in a few blocks like Bhopalpattnam, Bijapur, Usur, Konta. A similar pattern of trend was noticed in monsoon season. The results of the study demand the urgent need to formulate policies and strategies for water resource management and planning. The blocks which showed the positive rainfall trends can be identified to intensify the cultivation of more water requiring crops based on the suitability to that region. The findings of this study can be used as valuable information for crop planning, policy-making and preparation of contingency plans.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"32 21","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009006","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 study on the hydrologic regionalization was taken up to evaluate the utility of hierarchical cluster analysis for the delineation of hydrologically homogeneous regions and multiple linear regression (MLR) models for information transfer to derive flow duration curve (FDC) in ungauged basins. For this purpose, 50 catchments with largely unregulated flows located in South India were identified and a dataset of historical streamflow records and 16 catchment attributes was created. Using selected catchment attributes, three hydrologically homogenous regions were delineated using a hierarchical agglomerative cluster approach, and nine flow quantiles (10–90%) for each of the catchments in the respective clusters was derived. Regionalization approach was then adopted, whereby using step-wise regression, flow quantiles were related with readily derived basin-physical characteristics through MLR models. Cluster-wise performance analysis of the developed models indicated excellent performance with an average coefficient of determination (R2) values of 0.85, 0.97, and 0.8 for Cluster-1, -2, and -3, respectively, in comparison to poor performance when all 50 stations were considered to be in a single region. However, Jackknife cross-validation showed mixed performances with regard to the reliability of developed models with performance being good for high-flow quantiles and poor for low-flow quantiles.
{"title":"Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach","authors":"C. G. Hiremath, L. Nandagiri","doi":"10.2166/wcc.2023.467","DOIUrl":"https://doi.org/10.2166/wcc.2023.467","url":null,"abstract":"\u0000 \u0000 The present study on the hydrologic regionalization was taken up to evaluate the utility of hierarchical cluster analysis for the delineation of hydrologically homogeneous regions and multiple linear regression (MLR) models for information transfer to derive flow duration curve (FDC) in ungauged basins. For this purpose, 50 catchments with largely unregulated flows located in South India were identified and a dataset of historical streamflow records and 16 catchment attributes was created. Using selected catchment attributes, three hydrologically homogenous regions were delineated using a hierarchical agglomerative cluster approach, and nine flow quantiles (10–90%) for each of the catchments in the respective clusters was derived. Regionalization approach was then adopted, whereby using step-wise regression, flow quantiles were related with readily derived basin-physical characteristics through MLR models. Cluster-wise performance analysis of the developed models indicated excellent performance with an average coefficient of determination (R2) values of 0.85, 0.97, and 0.8 for Cluster-1, -2, and -3, respectively, in comparison to poor performance when all 50 stations were considered to be in a single region. However, Jackknife cross-validation showed mixed performances with regard to the reliability of developed models with performance being good for high-flow quantiles and poor for low-flow quantiles.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"7 5","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980547","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 compares the capability of Sentinel-1, Sentinel-2, and PlanetScope (PS) satellites in monitoring the variations of surface water of Dai Lai Lake, located in North Vietnam, for the 2018–2023 period. The analysis involves the utilization of Google Earth Engine to partially process Sentinel-1 and Sentinel-2 observations, while PS observations are processed using local computers, to generate VH-polarized backscatter coefficient, Normalized Difference Water Index (NDWI), and Modified of Normalized Difference Water Index (MNDWI) maps. The method for making binary water/non-water maps primarily employs the Otsu algorithm on each single map derived from the previous step. Findings reveal that the lake's water extent remains relatively stable over the 6-year period, and is not strongly affected by the seasonal cycle. Although the spatial distribution patterns of the lake exhibit significant similarity, average water extent of the lake derived from 3-m resolution PS imagery is about 2.17 and 5.60% more than that obtained from 10-m resolution Sentinel-2 and Sentinel-1 imagery, respectively. PS observations are effective for monitoring small lakes, but it is advised to check the quality of its NIR band. Sentinel-2 observations prove great effectiveness for lake monitoring, using both NDWI and MNDWI. For Sentinel-1 observations, potential misclassifications could arise due to similarities in VH-polarized backscatter coefficients between water surfaces and other flat surfaces.
{"title":"Comparison of multi-source satellite remote sensing observations for monitoring the variations of small lakes: a case study of Dai Lai Lake (Vietnam)","authors":"Binh Pham-Duc","doi":"10.2166/wcc.2023.505","DOIUrl":"https://doi.org/10.2166/wcc.2023.505","url":null,"abstract":"\u0000 \u0000 This study compares the capability of Sentinel-1, Sentinel-2, and PlanetScope (PS) satellites in monitoring the variations of surface water of Dai Lai Lake, located in North Vietnam, for the 2018–2023 period. The analysis involves the utilization of Google Earth Engine to partially process Sentinel-1 and Sentinel-2 observations, while PS observations are processed using local computers, to generate VH-polarized backscatter coefficient, Normalized Difference Water Index (NDWI), and Modified of Normalized Difference Water Index (MNDWI) maps. The method for making binary water/non-water maps primarily employs the Otsu algorithm on each single map derived from the previous step. Findings reveal that the lake's water extent remains relatively stable over the 6-year period, and is not strongly affected by the seasonal cycle. Although the spatial distribution patterns of the lake exhibit significant similarity, average water extent of the lake derived from 3-m resolution PS imagery is about 2.17 and 5.60% more than that obtained from 10-m resolution Sentinel-2 and Sentinel-1 imagery, respectively. PS observations are effective for monitoring small lakes, but it is advised to check the quality of its NIR band. Sentinel-2 observations prove great effectiveness for lake monitoring, using both NDWI and MNDWI. For Sentinel-1 observations, potential misclassifications could arise due to similarities in VH-polarized backscatter coefficients between water surfaces and other flat surfaces.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"295 ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138983282","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}
H. Nguyen, Dinh Kha Dang, Y. N. Nguyen, Chien Pham Van, Thi Thao Van Nguyen, Q. Nguyen, Xuan Linh Nguyen, Le Tuan Pham, Viet Thanh Pham, Quang-Thanh Bui
Flood prediction is an important task, which helps local decision-makers in taking effective measures to reduce damage to the people and economy. Currently, most studies use machine learning to predict flooding in a given region; however, the extrapolation problem is considered a major challenge when using these techniques and is rarely studied. Therefore, this study will focus on an approach to resolve the extrapolation problem in flood depth prediction by integrating machine learning (XGBoost, Extra-Trees (EXT), CatBoost (CB), and light gradient boost machines (LightGBM)) and hydraulic modeling under MIKE FLOOD. The results show that the hydraulic model worked well in providing the flood depth data needed to build the machine learning model. Among the four proposed machine learning models, XGBoost was found to be the best at solving the extrapolation problem in the estimation of flood depth, followed by EXT, CB, and LightGBM. Quang Binh province was hit by floods with depths ranging from 0 to 3.2 m. Areas with high flood depths are concentrated along and downstream of the two major rivers (Gianh and Nhat Le – Kien Giang).
{"title":"Integration of machine learning and hydrodynamic modeling to solve the extrapolation problem in flood depth estimation","authors":"H. Nguyen, Dinh Kha Dang, Y. N. Nguyen, Chien Pham Van, Thi Thao Van Nguyen, Q. Nguyen, Xuan Linh Nguyen, Le Tuan Pham, Viet Thanh Pham, Quang-Thanh Bui","doi":"10.2166/wcc.2023.573","DOIUrl":"https://doi.org/10.2166/wcc.2023.573","url":null,"abstract":"\u0000 Flood prediction is an important task, which helps local decision-makers in taking effective measures to reduce damage to the people and economy. Currently, most studies use machine learning to predict flooding in a given region; however, the extrapolation problem is considered a major challenge when using these techniques and is rarely studied. Therefore, this study will focus on an approach to resolve the extrapolation problem in flood depth prediction by integrating machine learning (XGBoost, Extra-Trees (EXT), CatBoost (CB), and light gradient boost machines (LightGBM)) and hydraulic modeling under MIKE FLOOD. The results show that the hydraulic model worked well in providing the flood depth data needed to build the machine learning model. Among the four proposed machine learning models, XGBoost was found to be the best at solving the extrapolation problem in the estimation of flood depth, followed by EXT, CB, and LightGBM. Quang Binh province was hit by floods with depths ranging from 0 to 3.2 m. Areas with high flood depths are concentrated along and downstream of the two major rivers (Gianh and Nhat Le – Kien Giang).","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"336 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138983270","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}
Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.
{"title":"Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset","authors":"Sandra Maria Cherian, Rajitha K","doi":"10.2166/wcc.2023.238","DOIUrl":"https://doi.org/10.2166/wcc.2023.238","url":null,"abstract":"\u0000 \u0000 Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"273 ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010766","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}
Chennai is a rapidly urbanizing Indian megacity and experiences flooding frequently. Literature state that climate change and land use change have a significant impact on the runoff generated every year making it essential to study the historical trend and forecast changes in LULC and climate to model runoff. This study considered Adyar watershed for LULC change detection, climate change analysis, and flood forecasting for 2030 and 2050 based on LULC and runoff of 2005 and 2015. A coupled hydrologic–hydraulic model (HEC-HMS and HEC-RAS) was developed to assess flooding for future LULC and climate scenarios. LULC analysis shows an increase in built-up cover by 6%, and climate analysis shows a 74% probability of an increase in precipitation intensity between 2015 and 2050 compared to 2015. It was observed that depth of flooding increased by 19.4% in 2030 and 60.4% in 2050 compared to 2015. This study makes a structural proposition for flood mitigation through flood carrier canals on the downstream reach of the river, which flows through Chennai city. The canals were found to prevent overbanking, thereby providing complete protection against flooding. It is proved that this is the best possible measure that provides the highest flood reduction for the study area.
{"title":"Modelling and forecasting of urban flood under changing climate and land use land cover","authors":"S. Anuthaman, Saravanan R., Balamurugan R., B. L.","doi":"10.2166/wcc.2023.164","DOIUrl":"https://doi.org/10.2166/wcc.2023.164","url":null,"abstract":"\u0000 Chennai is a rapidly urbanizing Indian megacity and experiences flooding frequently. Literature state that climate change and land use change have a significant impact on the runoff generated every year making it essential to study the historical trend and forecast changes in LULC and climate to model runoff. This study considered Adyar watershed for LULC change detection, climate change analysis, and flood forecasting for 2030 and 2050 based on LULC and runoff of 2005 and 2015. A coupled hydrologic–hydraulic model (HEC-HMS and HEC-RAS) was developed to assess flooding for future LULC and climate scenarios. LULC analysis shows an increase in built-up cover by 6%, and climate analysis shows a 74% probability of an increase in precipitation intensity between 2015 and 2050 compared to 2015. It was observed that depth of flooding increased by 19.4% in 2030 and 60.4% in 2050 compared to 2015. This study makes a structural proposition for flood mitigation through flood carrier canals on the downstream reach of the river, which flows through Chennai city. The canals were found to prevent overbanking, thereby providing complete protection against flooding. It is proved that this is the best possible measure that provides the highest flood reduction for the study area.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"2 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597079","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}
Sinuhé Sánchez, Fernando J. González Villarreal, Ramón Domínguez Mora, M. L. Arganis Juárez
The aim of this study was to investigate the existence and the magnitude of trend in different areas and durations of TCR. To achieve this objective, a mixed-method approach was employed using depth–area–duration and areal reduction factor (ARFs) curves that can be described as a logarithm equation to generate time series that allows the application of statistical methods such as the Mann–Kendall (MK) and Spearman Rho (SR) to detect trends. Time series are generated by substituting different areas in the logarithmic equations. The evidence presented shows that in Mexico, the TCR lasting 24 h shows an increasing trend for maximum areas between 300 and 1,700 km2 according to the MK and SR tests, respectively; according to these same tests for durations of 48 h, upward trends were observed up to maximum areas between 5,700 and 6,900 km2. The Sen slope reports annual increases between 0.76 and 1.32 mm and between 1.15 and 2.06 for a duration of 24 and 48 h, respectively. In contrast, no trends were observed in the time series obtained from the ARFs. Finally, the Pettitt test reports an abrupt jump from the year 1997 in all cases.
{"title":"Trend in rainfall associated with tropical cyclones in Mexico attributed to climate change and variability","authors":"Sinuhé Sánchez, Fernando J. González Villarreal, Ramón Domínguez Mora, M. L. Arganis Juárez","doi":"10.2166/wcc.2023.300","DOIUrl":"https://doi.org/10.2166/wcc.2023.300","url":null,"abstract":"\u0000 \u0000 The aim of this study was to investigate the existence and the magnitude of trend in different areas and durations of TCR. To achieve this objective, a mixed-method approach was employed using depth–area–duration and areal reduction factor (ARFs) curves that can be described as a logarithm equation to generate time series that allows the application of statistical methods such as the Mann–Kendall (MK) and Spearman Rho (SR) to detect trends. Time series are generated by substituting different areas in the logarithmic equations. The evidence presented shows that in Mexico, the TCR lasting 24 h shows an increasing trend for maximum areas between 300 and 1,700 km2 according to the MK and SR tests, respectively; according to these same tests for durations of 48 h, upward trends were observed up to maximum areas between 5,700 and 6,900 km2. The Sen slope reports annual increases between 0.76 and 1.32 mm and between 1.15 and 2.06 for a duration of 24 and 48 h, respectively. In contrast, no trends were observed in the time series obtained from the ARFs. Finally, the Pettitt test reports an abrupt jump from the year 1997 in all cases.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"36 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601246","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}
T. V. Tra, Van Thi Hang, Ngo Thi Thuy, Dang Thi Lan Phuong, Phan Van Thanh
Due to the hydrologic non-stationarity and uncertainty related to the probability assignment of flood peaks under climate change, the use of flood statistics may no longer be applicable. Therefore, a sensitivity analysis (i.e., a scenario-neutral approach) is used to examine the impacts of climate change on flooding in the Ba River Basin. A Delphi method with a set of KAMET rules was used to obtain a representative and a threshold flood event. These inputs are used for hydraulic simulation using a MIKE FLOOD model package. Flood simulations were performed using parametrically varied rainfall and temperature conditions. In total, 22 conditions were explored and are in line with CMIP5 and CMIP6. The results obtained have several implications. Firstly, rainfall change is the primary factor affecting flood impact in the Ba River Basin. Secondly, the flood peak in the Ba River Basin is highly sensitive to an increase in rainfall by up to 10%. Thirdly, the flooded threshold is reached when rainfall increases beyond 20%. Fourthly, the flood extent and depth are expected to increase as rainfall increases. Further research could improve the study using satellite rainfall data, satellite digital elevation models, and stochastic weather generators.
{"title":"Using a scenario-neutral approach to assess the impacts of climate change on flooding in the Ba River Basin, Viet Nam","authors":"T. V. Tra, Van Thi Hang, Ngo Thi Thuy, Dang Thi Lan Phuong, Phan Van Thanh","doi":"10.2166/wcc.2023.569","DOIUrl":"https://doi.org/10.2166/wcc.2023.569","url":null,"abstract":"\u0000 \u0000 Due to the hydrologic non-stationarity and uncertainty related to the probability assignment of flood peaks under climate change, the use of flood statistics may no longer be applicable. Therefore, a sensitivity analysis (i.e., a scenario-neutral approach) is used to examine the impacts of climate change on flooding in the Ba River Basin. A Delphi method with a set of KAMET rules was used to obtain a representative and a threshold flood event. These inputs are used for hydraulic simulation using a MIKE FLOOD model package. Flood simulations were performed using parametrically varied rainfall and temperature conditions. In total, 22 conditions were explored and are in line with CMIP5 and CMIP6. The results obtained have several implications. Firstly, rainfall change is the primary factor affecting flood impact in the Ba River Basin. Secondly, the flood peak in the Ba River Basin is highly sensitive to an increase in rainfall by up to 10%. Thirdly, the flooded threshold is reached when rainfall increases beyond 20%. Fourthly, the flood extent and depth are expected to increase as rainfall increases. Further research could improve the study using satellite rainfall data, satellite digital elevation models, and stochastic weather generators.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"34 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601253","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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