Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological–hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins.
{"title":"Prediction of flash flood peak discharge in hilly areas with ungauged basins based on machine learning","authors":"Weilin Wang, Guoqing Sang, Qiang Zhao, Yang Liu, Guangwen Shao, Longbin Lu, Mintian Xu","doi":"10.2166/nh.2024.004","DOIUrl":"https://doi.org/10.2166/nh.2024.004","url":null,"abstract":"\u0000 \u0000 Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological–hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927904","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}
Hongwu Tang, Kang Chen, Saiyu Yuan, Lei Xu, Jiajian Qiu, Qingwei Lin, Carlo Gualtieri
Flooding propagation is a crucial aspect of hydrological monitoring and forecasting. Previous studies have focused on hysteresis in the rating curve, caused by energy loss during flood propagation. However, the impact of tributary inflow on hysteresis downstream remains unclear, leading to inconsistent field observations on whether it strengthens or weakens hysteresis. In this study, we conducted flume experiments to identify the relationship between hysteresis in unsteady flow and the discharge magnitude of the tributary and the unsteady flow period in the mainstream. It was found that the discharge variations in the tributary had a larger influence on hysteresis compared to the periodical variations in the mainstream unsteady flow. Interestingly, the hysteresis of the unsteady flow had an initial strengthening followed by weakening as the tributary discharge increased. When the tributary inflow was low, the widening of the downstream cross-section sharpened the flood wave, increasing the hysteresis. However, as the tributary discharge increased to generate a backwater effect on the mainstream, the pressure gradient flattened flood waves, thereby weakening the hysteresis. This study improves our understanding of how tributary inflow affects flood propagation in the mainstream, offering new insights for flood prediction and control.
{"title":"Effects of tributary inflows on unsteady flow hysteresis and hydrodynamics in the mainstream","authors":"Hongwu Tang, Kang Chen, Saiyu Yuan, Lei Xu, Jiajian Qiu, Qingwei Lin, Carlo Gualtieri","doi":"10.2166/nh.2024.018","DOIUrl":"https://doi.org/10.2166/nh.2024.018","url":null,"abstract":"\u0000 Flooding propagation is a crucial aspect of hydrological monitoring and forecasting. Previous studies have focused on hysteresis in the rating curve, caused by energy loss during flood propagation. However, the impact of tributary inflow on hysteresis downstream remains unclear, leading to inconsistent field observations on whether it strengthens or weakens hysteresis. In this study, we conducted flume experiments to identify the relationship between hysteresis in unsteady flow and the discharge magnitude of the tributary and the unsteady flow period in the mainstream. It was found that the discharge variations in the tributary had a larger influence on hysteresis compared to the periodical variations in the mainstream unsteady flow. Interestingly, the hysteresis of the unsteady flow had an initial strengthening followed by weakening as the tributary discharge increased. When the tributary inflow was low, the widening of the downstream cross-section sharpened the flood wave, increasing the hysteresis. However, as the tributary discharge increased to generate a backwater effect on the mainstream, the pressure gradient flattened flood waves, thereby weakening the hysteresis. This study improves our understanding of how tributary inflow affects flood propagation in the mainstream, offering new insights for flood prediction and control.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647009","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}
Severe droughts typically last for extended periods and result in substantial water shortages, posing challenges for water conservancy projects. This study proposed a framework for coordinating drought mitigation operations across projects of various scales. First, the regulation and drought mitigation capacities of each project were analyzed, and thus critical reservoirs was identified. Subsequently, a joint regulation model for water supply, prioritizing projects based on their regulatory capacity from weak to strong, was established. An optimization model is then developed to determine the drought-limited levels for critical reservoirs, aiming to minimize water shortages. This model facilitates temporal coordination of water resources to prevent severe water shortages with frequent mild water shortages. Results in the Chuxionglucheng District of Chuxiong, Yunnan Province, during the severe drought period from 2009 to 2013, demonstrates significant reductions in water shortage. Specifically, the maximum shortage ratio decreased from 59 to 45% for agriculture and from 52 to 8% for industry. Moreover, emergency measures for drought mitigation were compared and recommend for regions with weak projects regulation. Overall, this framework offers a systematic approach to enhancing drought resilience across diverse water conservancy projects in severe drought conditions.
{"title":"Drought mitigation operation of water conservancy projects under severe droughts","authors":"Wei Ding, Aimei Bao, Jie Lin, Chengxin Luo, Hui Cao, Dongjie Zhang","doi":"10.2166/nh.2024.034","DOIUrl":"https://doi.org/10.2166/nh.2024.034","url":null,"abstract":"\u0000 \u0000 Severe droughts typically last for extended periods and result in substantial water shortages, posing challenges for water conservancy projects. This study proposed a framework for coordinating drought mitigation operations across projects of various scales. First, the regulation and drought mitigation capacities of each project were analyzed, and thus critical reservoirs was identified. Subsequently, a joint regulation model for water supply, prioritizing projects based on their regulatory capacity from weak to strong, was established. An optimization model is then developed to determine the drought-limited levels for critical reservoirs, aiming to minimize water shortages. This model facilitates temporal coordination of water resources to prevent severe water shortages with frequent mild water shortages. Results in the Chuxionglucheng District of Chuxiong, Yunnan Province, during the severe drought period from 2009 to 2013, demonstrates significant reductions in water shortage. Specifically, the maximum shortage ratio decreased from 59 to 45% for agriculture and from 52 to 8% for industry. Moreover, emergency measures for drought mitigation were compared and recommend for regions with weak projects regulation. Overall, this framework offers a systematic approach to enhancing drought resilience across diverse water conservancy projects in severe drought conditions.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674644","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}
Mahesh Tr, Surbhi Bhatia Khan, A. Balajee, Ahlam Almusharraf, T. Gadekallu, Eid Albalawi, Vinoth Kumar
Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensemble the random forest and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%.
{"title":"Water quality level estimation using IoT sensors and probabilistic machine learning model","authors":"Mahesh Tr, Surbhi Bhatia Khan, A. Balajee, Ahlam Almusharraf, T. Gadekallu, Eid Albalawi, Vinoth Kumar","doi":"10.2166/nh.2024.048","DOIUrl":"https://doi.org/10.2166/nh.2024.048","url":null,"abstract":"\u0000 \u0000 Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters including the PH level, hardness, TDS, etc. The outcome of such methods analyzes whether the environment provides potable water or not. Potable denotes the purified water that is free from all contaminations. This analysis gives an absolute solution whereas the demand for drinking water is a growing problem where the multiple-level estimations are essential to use the available water resources efficiently. In this article, we used a benchmark water quality assessment dataset for analysis. To perform a level assessment, we computed three major features namely correlation-entropy, dynamic scaling, and estimation levels, and annexed with the earlier feature vector. The assessment of the available data was performed using the statistical machine learning model that ensemble the random forest and light gradient boost model (GBM). The probability of the ensemble model was done by the Kullback Libeler Divergence model. The proposed probabilistic model has achieved an accuracy of 96.8%, a sensitivity of 94.55%, and a specificity of 98.29%.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677765","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}
Rosario Balbastre-Soldevila, Ignacio Andrés-Doménech, R. García-Bartual
A significant amount of information on regional rainfall characteristics is available nowadays, allowing its use in hydrological applications. This article is motivated by the availability of regional studies regarding maximum daily rainfall and intensity–duration–frequency curves that can be coupled with the design storm concept for urban hydrology studies. This is accomplished through a convenient index describing temporal variability of rainfall. More precisely, a methodology for regionalising the two parameters (i0, φ) of the two-parameter gamma design storm (G2P) is developed herein. A three-step methodology is proposed for obtaining the two parameters (i0, φ) for a given location. The results obtained in a case study show coherence with previous studies concerning maximum rainfall statistics.
{"title":"Design storm parameterisation for urban drainage studies derived from regional rainfall datasets: A case study in the Spanish Mediterranean region","authors":"Rosario Balbastre-Soldevila, Ignacio Andrés-Doménech, R. García-Bartual","doi":"10.2166/nh.2024.056","DOIUrl":"https://doi.org/10.2166/nh.2024.056","url":null,"abstract":"\u0000 \u0000 A significant amount of information on regional rainfall characteristics is available nowadays, allowing its use in hydrological applications. This article is motivated by the availability of regional studies regarding maximum daily rainfall and intensity–duration–frequency curves that can be coupled with the design storm concept for urban hydrology studies. This is accomplished through a convenient index describing temporal variability of rainfall. More precisely, a methodology for regionalising the two parameters (i0, φ) of the two-parameter gamma design storm (G2P) is developed herein. A three-step methodology is proposed for obtaining the two parameters (i0, φ) for a given location. The results obtained in a case study show coherence with previous studies concerning maximum rainfall statistics.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141681653","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}
Tsnat Tsegay Woldu, T. Ayenew, Belete Baychken, Behailu Birhanu
Sustainable groundwater management decisions require an understanding of the spatial distribution and seasonal fluctuations of site-specific water budget computations. This study aims to estimate the spatiotemporal distribution of recharge in the upper Awash sub-basin where the groundwater is experiencing intensive abstraction for domestic, industrial, and irrigation water uses. We estimated the spatial and long-term average monthly, seasonal, and annual groundwater recharge using a GIS-based spatially distributed water balance WetSpass-M model. Distributed grid maps of physical parameters (land-use land cover, soil, and slope) and monthly climatological records (rainfall, maximum and minimum temperature, wind speed) were used as model inputs. The WetSpass-M model estimated recharge is validated with the independently computed recharge using the automated digital filtering baseflow separation method. Attributed mainly to variability in soil texture and land use, the annual precipitation (1,032 mm) is distributed as evapotranspiration (45%), surface runoff (42%), and groundwater recharge (11%). Forest and grass areas with loamy sand, have high recharge, while built-up areas with clay soil have low recharge. August to September is estimated to have the largest recharge, while November to December has the lowest. Understanding the spatial and seasonal variability of groundwater recharge is important for sustainable utilization, proper management, and planning of groundwater resources.
{"title":"Spatiotemporal recharge estimation in the upper Awash sub-basin, central Ethiopia","authors":"Tsnat Tsegay Woldu, T. Ayenew, Belete Baychken, Behailu Birhanu","doi":"10.2166/nh.2024.164","DOIUrl":"https://doi.org/10.2166/nh.2024.164","url":null,"abstract":"\u0000 \u0000 Sustainable groundwater management decisions require an understanding of the spatial distribution and seasonal fluctuations of site-specific water budget computations. This study aims to estimate the spatiotemporal distribution of recharge in the upper Awash sub-basin where the groundwater is experiencing intensive abstraction for domestic, industrial, and irrigation water uses. We estimated the spatial and long-term average monthly, seasonal, and annual groundwater recharge using a GIS-based spatially distributed water balance WetSpass-M model. Distributed grid maps of physical parameters (land-use land cover, soil, and slope) and monthly climatological records (rainfall, maximum and minimum temperature, wind speed) were used as model inputs. The WetSpass-M model estimated recharge is validated with the independently computed recharge using the automated digital filtering baseflow separation method. Attributed mainly to variability in soil texture and land use, the annual precipitation (1,032 mm) is distributed as evapotranspiration (45%), surface runoff (42%), and groundwater recharge (11%). Forest and grass areas with loamy sand, have high recharge, while built-up areas with clay soil have low recharge. August to September is estimated to have the largest recharge, while November to December has the lowest. Understanding the spatial and seasonal variability of groundwater recharge is important for sustainable utilization, proper management, and planning of groundwater resources.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358431","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}
Yuanyi Liang, Xingjun Zhang, Yigao Sun, Linlin Yao, Lin Gan, Jialin Wu, Si Chen, Junyi Li, Jian Wang
Groundwater vulnerability to nitrate assessment serves as a measure of potential groundwater nitrate pollution in a target area. The primary objective of this study is to utilize the traditional DRASTIC-land use assessment framework, groundwater nitrate distribution data, and three machine learning models (random forest (RF), XGBoost, and support vector machine) to classify whether groundwater nitrate exceeds a threshold (10 mg/L as nitrogen) in Chongqing, southwest China. Model evaluation is conducted using accuracy and F1 score metrics, and ultimately, the classification probabilities are employed as the groundwater vulnerability to nitrate index. The results indicate that the RF model outperforms the other two models, achieving the highest accuracy (92.9% for testing), kappa value (0.857 for testing), and area under the curve (0.948 for testing). Furthermore, the SHapley Additive exPlanations (SHAP) interpreter revealed that aquifer conductivity, lithology, agricultural activities, areas with high-intensity development, and groundwater recharge are the most influential indicators of groundwater vulnerability. The final groundwater vulnerability level distribution map, with a resolution of 1 km × 1 km, reveals that high and extremely high vulnerability levels are concentrated in areas with high-intensity urban development and karst trough valleys in the southeastern, northeastern, and central urban areas. This work represents the first attempt at using machine learning models for groundwater vulnerability assessment in Chongqing.
{"title":"Enhanced groundwater vulnerability assessment to nitrate contamination in Chongqing, Southwest China: Integrating novel explainable machine learning algorithms with DRASTIC-LU","authors":"Yuanyi Liang, Xingjun Zhang, Yigao Sun, Linlin Yao, Lin Gan, Jialin Wu, Si Chen, Junyi Li, Jian Wang","doi":"10.2166/nh.2024.036","DOIUrl":"https://doi.org/10.2166/nh.2024.036","url":null,"abstract":"\u0000 \u0000 Groundwater vulnerability to nitrate assessment serves as a measure of potential groundwater nitrate pollution in a target area. The primary objective of this study is to utilize the traditional DRASTIC-land use assessment framework, groundwater nitrate distribution data, and three machine learning models (random forest (RF), XGBoost, and support vector machine) to classify whether groundwater nitrate exceeds a threshold (10 mg/L as nitrogen) in Chongqing, southwest China. Model evaluation is conducted using accuracy and F1 score metrics, and ultimately, the classification probabilities are employed as the groundwater vulnerability to nitrate index. The results indicate that the RF model outperforms the other two models, achieving the highest accuracy (92.9% for testing), kappa value (0.857 for testing), and area under the curve (0.948 for testing). Furthermore, the SHapley Additive exPlanations (SHAP) interpreter revealed that aquifer conductivity, lithology, agricultural activities, areas with high-intensity development, and groundwater recharge are the most influential indicators of groundwater vulnerability. The final groundwater vulnerability level distribution map, with a resolution of 1 km × 1 km, reveals that high and extremely high vulnerability levels are concentrated in areas with high-intensity urban development and karst trough valleys in the southeastern, northeastern, and central urban areas. This work represents the first attempt at using machine learning models for groundwater vulnerability assessment in Chongqing.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141386928","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}
Machine learning algorithms are increasingly applied in hydrological studies with promising results. However, these algorithms generally lack the ability for easy interpretability of the results by users. In this study, we compare six different explainable artificial intelligence (XAI) algorithms that help understand the effect of input data on the simulation results. The methods are explored on two distinct approaches for streamflow modeling using the long short-term memory (LSTM) model: a single model approach using only meteorological forcing data and a regional approach including also static catchment attributes. To gain further insight into the internal dynamics of the LSTM models, the relationship between cell states and soil moisture is investigated. A strong correlation suggests that the LSTM models inherently capture the concept of soil moisture as a catchment-scale storage mechanism. The XAI methods are applied to derive a timestep of influence, revealing how many days of input data are relevant for the model output. All XAI methods result in similar seasonal patterns in the timestep of influence, suggesting that the methods are comparable. Setting soil moisture dynamics in context to seasonal development of the timestep of influence suggests resetting LSTM as soon as soil moisture saturation occurs.
{"title":"Linking explainable artificial intelligence and soil moisture dynamics in a machine learning streamflow model","authors":"Alexander Ley, Helge Bormann, Markus Casper","doi":"10.2166/nh.2024.003","DOIUrl":"https://doi.org/10.2166/nh.2024.003","url":null,"abstract":"\u0000 \u0000 Machine learning algorithms are increasingly applied in hydrological studies with promising results. However, these algorithms generally lack the ability for easy interpretability of the results by users. In this study, we compare six different explainable artificial intelligence (XAI) algorithms that help understand the effect of input data on the simulation results. The methods are explored on two distinct approaches for streamflow modeling using the long short-term memory (LSTM) model: a single model approach using only meteorological forcing data and a regional approach including also static catchment attributes. To gain further insight into the internal dynamics of the LSTM models, the relationship between cell states and soil moisture is investigated. A strong correlation suggests that the LSTM models inherently capture the concept of soil moisture as a catchment-scale storage mechanism. The XAI methods are applied to derive a timestep of influence, revealing how many days of input data are relevant for the model output. All XAI methods result in similar seasonal patterns in the timestep of influence, suggesting that the methods are comparable. Setting soil moisture dynamics in context to seasonal development of the timestep of influence suggests resetting LSTM as soon as soil moisture saturation occurs.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102179","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}
Water level adjustment downstream of dams significantly impacts river regimes and flood control. However, due to constant strong scouring, our quantitative understanding of the characteristics of water level variations and their causes in the Chenglingji–Jiujiang Reach of the Yangtze River remains limited. Here, we analyzed the water level change trend via the Mann–Kendall method and analyzed geomorphic change and river resistance using 406 cross-sectional profiles as well as data on discharge and water levels from 1991 to 2022. Results showed that the critical conversion discharges (CCD) in the Chenglingji-Hankou Reach and the Hankou-Jiujiang Reach were approximately 35,000 and 30,000 m3/s, respectively, after the operation of the Three Gorges Dam. The water level exhibited an overall decline mainly due to river erosion when the discharge was lower than the CCD. The water level exhibited a nonsignificant upward trend mainly due to increased river resistance (7–20%) when the discharge was higher than the CCD. The obvious increase in the floodwater level in individual years was caused by the effect of downstream water level increase. Our findings further the understanding of downstream geomorphic response to dam operation and their impacts on water levels and have important implications for flood management in such rivers worldwide.
{"title":"Characteristics and causes of water level variations in the Chenglingji–Jiujiang reach of the Yangtze River following the operation of the Three Gorges Dam","authors":"Guangyue Zhang, Guangming Tan, Wei Zhang, Yuanfang Chai, Jingwen Wang, Zhi Yin, Yong Hu","doi":"10.2166/nh.2024.010","DOIUrl":"https://doi.org/10.2166/nh.2024.010","url":null,"abstract":"\u0000 \u0000 Water level adjustment downstream of dams significantly impacts river regimes and flood control. However, due to constant strong scouring, our quantitative understanding of the characteristics of water level variations and their causes in the Chenglingji–Jiujiang Reach of the Yangtze River remains limited. Here, we analyzed the water level change trend via the Mann–Kendall method and analyzed geomorphic change and river resistance using 406 cross-sectional profiles as well as data on discharge and water levels from 1991 to 2022. Results showed that the critical conversion discharges (CCD) in the Chenglingji-Hankou Reach and the Hankou-Jiujiang Reach were approximately 35,000 and 30,000 m3/s, respectively, after the operation of the Three Gorges Dam. The water level exhibited an overall decline mainly due to river erosion when the discharge was lower than the CCD. The water level exhibited a nonsignificant upward trend mainly due to increased river resistance (7–20%) when the discharge was higher than the CCD. The obvious increase in the floodwater level in individual years was caused by the effect of downstream water level increase. Our findings further the understanding of downstream geomorphic response to dam operation and their impacts on water levels and have important implications for flood management in such rivers worldwide.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111092","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}
Valentin Brice Ebodé, Raphael Onguéné, Jean Jacques Braun
Flooding constitutes a major problem for the inhabitants of Douala City in general and those of the Tongo Bassa watershed (TBW) in particular. Faced with this situation, public authorities need to put in place measures to mitigate the vulnerability of populations to these disasters. This article aims to map flooding risk areas in the TBW using the geographic information system, field data (historical flood points), remote sensing data (Sentinel II image) and the frequency ratio model. The map produced shows that 1.41, 8.88, 28.51, 33.86 and 27.33% of the basin area are respectively delimited into very low, low, medium, high and very high flood vulnerability classes. High and very high flooding risk areas (those where flooding is most likely to occur) occupy more than half of the basin (61.19%). These areas are characterized by significant imperviousness, low altitudes, weak slopes, significant proximity to watercourses and clayey soils. Most of the houses in the basin (66.92%) are located in areas affected by these two levels of exposure (high and very high). With respective success and prediction accuracy rates of 89 and 96.78%, a certain confidence deserves to be placed on the map of flooding risk areas produced.
洪水是杜阿拉市居民，特别是通戈巴萨流域（TBW）居民面临的一个主要问题。面对这种情况，公共当局需要制定措施，减轻居民在这些灾害面前的脆弱性。本文旨在利用地理信息系统、实地数据（历史洪水点）、遥感数据（哨兵 II 图像）和频率比模型绘制通戈巴萨流域洪水风险区域图。绘制的地图显示，流域面积的 1.41%、8.88%、28.51%、33.86% 和 27.33%分别被划分为极低、低、中、高和极高洪水脆弱性等级。高洪水风险区和极高洪水风险区（最有可能发生洪水的地区）占流域面积的一半以上（61.19%）。这些地区的特点是严重不透水、海拔低、坡度弱、非常靠近水道以及土壤粘重。盆地中的大部分房屋（66.92%）都位于受这两种程度（高和极高）影响的地区。由于成功率和预测准确率分别为 89% 和 96.78%，因此对所绘制的洪水风险区地图有一定的信心。
{"title":"Flood susceptibility mapping in the Tongo Bassa watershed through the GIS, remote sensing and the frequency ratio model","authors":"Valentin Brice Ebodé, Raphael Onguéné, Jean Jacques Braun","doi":"10.2166/nh.2024.152","DOIUrl":"https://doi.org/10.2166/nh.2024.152","url":null,"abstract":"\u0000 \u0000 Flooding constitutes a major problem for the inhabitants of Douala City in general and those of the Tongo Bassa watershed (TBW) in particular. Faced with this situation, public authorities need to put in place measures to mitigate the vulnerability of populations to these disasters. This article aims to map flooding risk areas in the TBW using the geographic information system, field data (historical flood points), remote sensing data (Sentinel II image) and the frequency ratio model. The map produced shows that 1.41, 8.88, 28.51, 33.86 and 27.33% of the basin area are respectively delimited into very low, low, medium, high and very high flood vulnerability classes. High and very high flooding risk areas (those where flooding is most likely to occur) occupy more than half of the basin (61.19%). These areas are characterized by significant imperviousness, low altitudes, weak slopes, significant proximity to watercourses and clayey soils. Most of the houses in the basin (66.92%) are located in areas affected by these two levels of exposure (high and very high). With respective success and prediction accuracy rates of 89 and 96.78%, a certain confidence deserves to be placed on the map of flooding risk areas produced.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232049","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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