Juliana-Andrea Alzate-Gómez, Hélène Roux, L. Cassan, Thomas Bonometti, Jorge Alberto Escobar Vargas, Luis-Javier Montoya Jaramillo
This paper presents an analysis of air–water exchange in a Colombian tropical reservoir. A coupled thermal-3D hydrodynamic model using TELEMAC-3D and WAQTEL is implemented to evaluate the dynamics of thermal processes in the reservoir. A sensitivity analysis is carried out on various modeling parameters, such as turbulence models, temperature diffusion coefficients, and heat exchange at the free surface based on observations. In particular, three different approaches have been tested to study the impact of air–water exchanges at the free surface: a constant water temperature, constant meteorological forcing, and time-varying meteorological forcing. All the simulations correctly represent the constant heating at the free surface for the first meters. However, no simulation has been able to correctly reproduce the amplitude of temperature oscillations in the surface layers: only the simulations with time-varying meteorological forcing show temperature oscillations, but their amplitude is greatly overestimated. Eventually, the analysis shows that the most crucial parameters for a correct representation of the observed temperature behavior are the heat exchange coefficient and the wind. The different approaches tested all have limitations, but they can reproduce reservoir temperature trends at different depths with a maximum standard deviation ranging from 3 to 8 °C.
{"title":"Analysis of different hypotheses for modeling air–water exchange and temperature evolution in a tropical reservoir","authors":"Juliana-Andrea Alzate-Gómez, Hélène Roux, L. Cassan, Thomas Bonometti, Jorge Alberto Escobar Vargas, Luis-Javier Montoya Jaramillo","doi":"10.2166/wcc.2023.567","DOIUrl":"https://doi.org/10.2166/wcc.2023.567","url":null,"abstract":"\u0000 This paper presents an analysis of air–water exchange in a Colombian tropical reservoir. A coupled thermal-3D hydrodynamic model using TELEMAC-3D and WAQTEL is implemented to evaluate the dynamics of thermal processes in the reservoir. A sensitivity analysis is carried out on various modeling parameters, such as turbulence models, temperature diffusion coefficients, and heat exchange at the free surface based on observations. In particular, three different approaches have been tested to study the impact of air–water exchanges at the free surface: a constant water temperature, constant meteorological forcing, and time-varying meteorological forcing. All the simulations correctly represent the constant heating at the free surface for the first meters. However, no simulation has been able to correctly reproduce the amplitude of temperature oscillations in the surface layers: only the simulations with time-varying meteorological forcing show temperature oscillations, but their amplitude is greatly overestimated. Eventually, the analysis shows that the most crucial parameters for a correct representation of the observed temperature behavior are the heat exchange coefficient and the wind. The different approaches tested all have limitations, but they can reproduce reservoir temperature trends at different depths with a maximum standard deviation ranging from 3 to 8 °C.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"129 24","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453326","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}
A traditional hydrologic water infrastructure design assumes that the climate is stationary, and that historic data reflect future conditions. The traditional approach may no longer be applicable since the earth's climate is not stationary. Thus, there is a need for a new way of designing water infrastructure that accounts for the effects of climate change by shifting the current static design paradigm to a more dynamic paradigm. Researchers have proposed several approaches accounting for climate change. In this paper, we group the approaches into five groups (adaptive management, inverse climate change impact, machine learning, flood frequency analysis, and soft computing approaches), outline each approach's strengths and weaknesses, and assess their applicability to the water infrastructure design. We find that the flood frequency analysis approach is most applicable to the water infrastructure design as it is the least disruptive in terms of standard hydrological analysis methods, is cost-effective, and adaptable to most basins. However, adaptive management approaches are best suited for uncertainty reductions since they provide opportunities to constantly adjust decisions based on improved climate change data. Combining these two approaches could provide an optimal way of accounting for non-stationarity.
{"title":"Accounting for climate change in the water infrastructure design: evaluating approaches and recommending a hybrid framework","authors":"Kenneth Hunu, S. A. Conrad, M. DePue","doi":"10.2166/wcc.2023.611","DOIUrl":"https://doi.org/10.2166/wcc.2023.611","url":null,"abstract":"\u0000 \u0000 A traditional hydrologic water infrastructure design assumes that the climate is stationary, and that historic data reflect future conditions. The traditional approach may no longer be applicable since the earth's climate is not stationary. Thus, there is a need for a new way of designing water infrastructure that accounts for the effects of climate change by shifting the current static design paradigm to a more dynamic paradigm. Researchers have proposed several approaches accounting for climate change. In this paper, we group the approaches into five groups (adaptive management, inverse climate change impact, machine learning, flood frequency analysis, and soft computing approaches), outline each approach's strengths and weaknesses, and assess their applicability to the water infrastructure design. We find that the flood frequency analysis approach is most applicable to the water infrastructure design as it is the least disruptive in terms of standard hydrological analysis methods, is cost-effective, and adaptable to most basins. However, adaptive management approaches are best suited for uncertainty reductions since they provide opportunities to constantly adjust decisions based on improved climate change data. Combining these two approaches could provide an optimal way of accounting for non-stationarity.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"46 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138946442","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 article presents the results of the dike breach for the La Giang dike, in the Ha Tinh province, Vietnam. The study combined a field survey and mathematical simulation to assess the consequences of the dike breach. Through the field survey, potential dike breach locations were specifically identified. This minimizes the number of calculation scenarios. The mathematical model was calibrated and validated with large floods in the area. The results show that the model is consistent with the observation data, with the Nash index at good to very good levels. A series of simulations were performed to assess the dike breach consequence. In each case, the study provided details on the inundation area and the number of affected residents for each inundation level by an administrative unit. Based on the calculated results, the degree and scope of consequence varied depending on the locations of the dike breach. This is very useful information for the decision-makers to establish different response plans for different emergency cases.
文章介绍了越南河静省 La Giang 堤坝决口的结果。该研究结合了实地调查和数学模拟来评估决堤的后果。通过实地调查，具体确定了潜在的决堤位置。这最大限度地减少了计算方案的数量。数学模型通过该地区的大洪水进行了校准和验证。结果表明，模型与观测数据一致，纳什指数处于良好到非常好的水平。为评估决堤后果，进行了一系列模拟。在每种情况下，研究提供了按行政单位划分的每个淹没等级的淹没面积和受影响居民人数的详细信息。根据计算结果，堤坝决口的位置不同，后果的程度和范围也不同。这对于决策者针对不同的紧急情况制定不同的应对方案是非常有用的信息。
{"title":"Consequence assessment of the La Giang dike breach in the Ca river system, Vietnam","authors":"Chau Kim Tran, Thai Canh Nguyen","doi":"10.2166/wcc.2023.380","DOIUrl":"https://doi.org/10.2166/wcc.2023.380","url":null,"abstract":"\u0000 \u0000 The article presents the results of the dike breach for the La Giang dike, in the Ha Tinh province, Vietnam. The study combined a field survey and mathematical simulation to assess the consequences of the dike breach. Through the field survey, potential dike breach locations were specifically identified. This minimizes the number of calculation scenarios. The mathematical model was calibrated and validated with large floods in the area. The results show that the model is consistent with the observation data, with the Nash index at good to very good levels. A series of simulations were performed to assess the dike breach consequence. In each case, the study provided details on the inundation area and the number of affected residents for each inundation level by an administrative unit. Based on the calculated results, the degree and scope of consequence varied depending on the locations of the dike breach. This is very useful information for the decision-makers to establish different response plans for different emergency cases.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"124 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138994560","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 quality assessment plays a crucial role in various aspects, including human health, environmental impact, agricultural productivity, and industrial processes. Machine learning (ML) algorithms offer the ability to automate water quality evaluation and allow for effective and rapid assessment of parameters associated with water quality. This article proposes an ML-based classification model for water quality prediction. The model was tested with 14 ML algorithms and considers 20 features that represent various substances present in water samples and their concentrations. The dataset used in the study comprises 7,996 samples, and the model development involves several stages, including data preprocessing, Yeo–Johnson transformation for data normalization, principal component analysis (PCA) for feature selection, and the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Performance metrics, such as accuracy, precision, recall, and F1 score, are provided for each algorithm with and without SMOTE. LightGBM, XGBoost, CatBoost, and Random Forest were identified as the best-performing algorithms. LightGBM achieved the highest accuracy of 96.25% without SMOTE, while XGBoost attained the highest precision of 0.933. The application of SMOTE enhanced the performance of CatBoost. These findings provide valuable insights for ML-based water quality assessment, aiding researchers and professionals in decision-making and management.
水质评估在人类健康、环境影响、农业生产力和工业流程等各个方面都发挥着至关重要的作用。机器学习（ML）算法能够自动进行水质评价，并能有效、快速地评估与水质相关的参数。本文提出了一种基于 ML 的水质预测分类模型。该模型使用 14 种 ML 算法进行了测试，并考虑了代表水样中各种物质及其浓度的 20 个特征。研究中使用的数据集包括 7,996 个样本，模型开发涉及多个阶段，包括数据预处理、用于数据归一化的 Yeo-Johnson 转换、用于特征选择的主成分分析 (PCA)，以及用于解决类不平衡问题的合成少数过度采样技术 (SMOTE)。每种算法在有 SMOTE 和没有 SMOTE 的情况下，都有准确度、精确度、召回率和 F1 分数等性能指标。LightGBM、XGBoost、CatBoost 和随机森林被认为是性能最好的算法。在没有 SMOTE 的情况下，LightGBM 的准确率最高，达到 96.25%，而 XGBoost 的精度最高，达到 0.933。SMOTE 的应用提高了 CatBoost 的性能。这些发现为基于 ML 的水质评估提供了宝贵的见解，有助于研究人员和专业人员进行决策和管理。
{"title":"Water quality prediction: A data-driven approach exploiting advanced machine learning algorithms with data augmentation","authors":"Karthick K, S. Krishnan, R. Manikandan","doi":"10.2166/wcc.2023.403","DOIUrl":"https://doi.org/10.2166/wcc.2023.403","url":null,"abstract":"\u0000 \u0000 Water quality assessment plays a crucial role in various aspects, including human health, environmental impact, agricultural productivity, and industrial processes. Machine learning (ML) algorithms offer the ability to automate water quality evaluation and allow for effective and rapid assessment of parameters associated with water quality. This article proposes an ML-based classification model for water quality prediction. The model was tested with 14 ML algorithms and considers 20 features that represent various substances present in water samples and their concentrations. The dataset used in the study comprises 7,996 samples, and the model development involves several stages, including data preprocessing, Yeo–Johnson transformation for data normalization, principal component analysis (PCA) for feature selection, and the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Performance metrics, such as accuracy, precision, recall, and F1 score, are provided for each algorithm with and without SMOTE. LightGBM, XGBoost, CatBoost, and Random Forest were identified as the best-performing algorithms. LightGBM achieved the highest accuracy of 96.25% without SMOTE, while XGBoost attained the highest precision of 0.933. The application of SMOTE enhanced the performance of CatBoost. These findings provide valuable insights for ML-based water quality assessment, aiding researchers and professionals in decision-making and management.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"104 10","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138958792","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}
Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.
{"title":"Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley","authors":"Hongzhuan Chen, Xinhuai Yin, Xiaoyu Huang, Enrong Zhao, Xiaofeng Ou, Chengzhi Ye","doi":"10.2166/wcc.2023.545","DOIUrl":"https://doi.org/10.2166/wcc.2023.545","url":null,"abstract":"\u0000 \u0000 Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"49 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138954590","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}
N’da Jocelyne Maryse Christine Amichiatchi, Jean Hounkpè, G. Soro, Ojelabi Oluwatoyin Khadijat, I. Larbi, A. Limantol, A. M. Alhassan, T. A. G. Bi, A. E. Lawin
The purpose of this study is to analyse trends in annual rainfall extremes over five watersheds within Côte d'Ivoire using observed data (1976–2017) and projected (2020–2050) rainfall data from the fourth version of the Rossby Centre regional atmospheric model, RCA4, for the representative concentration pathways RCP 4.5 and RCP 8.5. Four rainfall extreme indices, namely, the consecutive dry days (CDD), maximum annual rainfall (Pmaxan), very wet day (R95p), and maximum 5-day rainfall (Rx5days), were considered for trend analysis by using the non-parametric modified Mann–Kendall test and the distribution mapping bias-correction technique to adjust the simulated regional climate model climate of the simulated daily precipitation. As a result, it is found that during the period 1976–2017, there was a significant downward trend in the drought-related index (CDD) at the Bagoue, Baya, Agneby, and Lobo watersheds. The Baya and N'zo watersheds also experienced a significant downward trend under the RCP 4.5 and RCP 8.5 scenarios. The flood-related indices (Pmaxan, R95p, and Rx5days) show a clear downward trend in the recorded data for almost all the considered watersheds and generally a significant upward trend for both cases. These findings indicate that the watersheds are vulnerable to climate-induced disasters.
{"title":"Analyse of past and projected changes in extreme precipitation indices in some watersheds in côte d'Ivoire","authors":"N’da Jocelyne Maryse Christine Amichiatchi, Jean Hounkpè, G. Soro, Ojelabi Oluwatoyin Khadijat, I. Larbi, A. Limantol, A. M. Alhassan, T. A. G. Bi, A. E. Lawin","doi":"10.2166/wcc.2023.365","DOIUrl":"https://doi.org/10.2166/wcc.2023.365","url":null,"abstract":"\u0000 The purpose of this study is to analyse trends in annual rainfall extremes over five watersheds within Côte d'Ivoire using observed data (1976–2017) and projected (2020–2050) rainfall data from the fourth version of the Rossby Centre regional atmospheric model, RCA4, for the representative concentration pathways RCP 4.5 and RCP 8.5. Four rainfall extreme indices, namely, the consecutive dry days (CDD), maximum annual rainfall (Pmaxan), very wet day (R95p), and maximum 5-day rainfall (Rx5days), were considered for trend analysis by using the non-parametric modified Mann–Kendall test and the distribution mapping bias-correction technique to adjust the simulated regional climate model climate of the simulated daily precipitation. As a result, it is found that during the period 1976–2017, there was a significant downward trend in the drought-related index (CDD) at the Bagoue, Baya, Agneby, and Lobo watersheds. The Baya and N'zo watersheds also experienced a significant downward trend under the RCP 4.5 and RCP 8.5 scenarios. The flood-related indices (Pmaxan, R95p, and Rx5days) show a clear downward trend in the recorded data for almost all the considered watersheds and generally a significant upward trend for both cases. These findings indicate that the watersheds are vulnerable to climate-induced disasters.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":" 116","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138962047","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}
Sonali Swagatika, Jagadish Chandra Paul, B. B. Sahoo, Sushindra Kumar Gupta, P. K. Singh
Accurate prediction of monthly runoff is critical for effective water resource management and flood forecasting in river basins. In this study, we developed a hybrid deep learning (DL) model, Fourier transform long short-term memory (FT-LSTM), to improve the prediction accuracy of monthly discharge time series in the Brahmani river basin at Jenapur station. We compare the performance of FT-LSTM with three popular DL models: LSTM, recurrent neutral network, and gated recurrent unit, considering different lag periods (1, 3, 6, and 12). The lag period, representing the interval between the observed data points and the predicted data points, is crucial for capturing the temporal relationships and identifying patterns within the hydrological data. The results of this study show that the FT-LSTM model consistently outperforms other models across all lag periods in terms of error metrics. Furthermore, the FT-LSTM model demonstrates higher Nash–Sutcliffe efficiency and R2 values, indicating a better fit between predicted and actual runoff values. This work contributes to the growing field of hybrid DL models for hydrological forecasting. The FT-LSTM model proves effective in improving the accuracy of monthly runoff forecasts and offers a promising solution for water resource management and river basin decision-making processes.
{"title":"Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model","authors":"Sonali Swagatika, Jagadish Chandra Paul, B. B. Sahoo, Sushindra Kumar Gupta, P. K. Singh","doi":"10.2166/wcc.2023.487","DOIUrl":"https://doi.org/10.2166/wcc.2023.487","url":null,"abstract":"\u0000 Accurate prediction of monthly runoff is critical for effective water resource management and flood forecasting in river basins. In this study, we developed a hybrid deep learning (DL) model, Fourier transform long short-term memory (FT-LSTM), to improve the prediction accuracy of monthly discharge time series in the Brahmani river basin at Jenapur station. We compare the performance of FT-LSTM with three popular DL models: LSTM, recurrent neutral network, and gated recurrent unit, considering different lag periods (1, 3, 6, and 12). The lag period, representing the interval between the observed data points and the predicted data points, is crucial for capturing the temporal relationships and identifying patterns within the hydrological data. The results of this study show that the FT-LSTM model consistently outperforms other models across all lag periods in terms of error metrics. Furthermore, the FT-LSTM model demonstrates higher Nash–Sutcliffe efficiency and R2 values, indicating a better fit between predicted and actual runoff values. This work contributes to the growing field of hybrid DL models for hydrological forecasting. The FT-LSTM model proves effective in improving the accuracy of monthly runoff forecasts and offers a promising solution for water resource management and river basin decision-making processes.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"98 4","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998162","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}
J. Kopáček, Stanislav Grill, J. Hejzlar, P. Porcal, Jan Turek
The water temperature of many lakes has recently risen as a result of climate change. We evaluated trends in the cloudiness, solar radiation, wind, air and water temperatures, ice cover, thermocline depth, transparency, and composition of two Bohemian Forest lakes (Czech Republic) from 1998 to 2022. Lake water temperatures increased by 0.32–0.47 °C decade−1, and the ice cover periods decreased by 11.7–14.8 days decade−1. These changes were mostly associated with increasing air temperatures during most months and increasing solar radiation (due to decreasing cloudiness) especially in March and November (the months preceding ice-on/off). Decreasing snow cover in winter (by 3.8 cm decade−1) further accelerated the earlier ice melt. The number of days with water temperature ≥4 °C increased similarly in both lakes by 12–13 days decade−1. However, the number of days with water temperature ≥20 °C increased and the depth of the summer thermocline decreased more in the lake with tree dieback in its catchment. Tree dieback accelerated the leaching of organic carbon and phosphorus, increasing water brownification, algal production, and decreasing water transparency. Solar radiation was absorbed in shallower water layers. Changes in catchment forest thus contributed to the variability in the response of lake water temperatures to climate change.
{"title":"Tree dieback and subsequent changes in water quality accelerated the climate-related warming of a central European forest lake","authors":"J. Kopáček, Stanislav Grill, J. Hejzlar, P. Porcal, Jan Turek","doi":"10.2166/wcc.2023.581","DOIUrl":"https://doi.org/10.2166/wcc.2023.581","url":null,"abstract":"\u0000 \u0000 The water temperature of many lakes has recently risen as a result of climate change. We evaluated trends in the cloudiness, solar radiation, wind, air and water temperatures, ice cover, thermocline depth, transparency, and composition of two Bohemian Forest lakes (Czech Republic) from 1998 to 2022. Lake water temperatures increased by 0.32–0.47 °C decade−1, and the ice cover periods decreased by 11.7–14.8 days decade−1. These changes were mostly associated with increasing air temperatures during most months and increasing solar radiation (due to decreasing cloudiness) especially in March and November (the months preceding ice-on/off). Decreasing snow cover in winter (by 3.8 cm decade−1) further accelerated the earlier ice melt. The number of days with water temperature ≥4 °C increased similarly in both lakes by 12–13 days decade−1. However, the number of days with water temperature ≥20 °C increased and the depth of the summer thermocline decreased more in the lake with tree dieback in its catchment. Tree dieback accelerated the leaching of organic carbon and phosphorus, increasing water brownification, algal production, and decreasing water transparency. Solar radiation was absorbed in shallower water layers. Changes in catchment forest thus contributed to the variability in the response of lake water temperatures to climate change.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"14 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998086","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 paper focuses on exploring the potential of climate-resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyze the spatial pattern of the climate variables (precipitation, Tmax, and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021–2100 was conducted using the CMIP6-based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway SSP2-4.5, SSP5-8.5, and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from CSR and JPL data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).
{"title":"Impacts of hydroclimate change on climate-resilient agriculture at the river basin management","authors":"C. Singha, Satiprasad Sahoo, Ajit Govind, Biswajeet Pradhan, Shatha Alrawashdeh, Taghreed Hamdi Aljohani, Hussein Almohamad, Abu Reza Md Towfiqul Islam, Hazam Ghassan Abdo","doi":"10.2166/wcc.2023.656","DOIUrl":"https://doi.org/10.2166/wcc.2023.656","url":null,"abstract":"\u0000 \u0000 This paper focuses on exploring the potential of climate-resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyze the spatial pattern of the climate variables (precipitation, Tmax, and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021–2100 was conducted using the CMIP6-based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway SSP2-4.5, SSP5-8.5, and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from CSR and JPL data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"29 11","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997406","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 resources and flood hazards in global watersheds are heavily influenced by climate change. In this study, the impact of climate change on the streamflow of the Qinglong River located in northern China is predicted. The streamflow of the Qinglong River (2021–2100) under two climate change scenarios (RCP 4.5 and RCP 8.5) was mainly synthesized over multiple timescales. The meteorological data from 31 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used as inputs of the Hydrological Simulation Program-Fortran (HSPF) for hydrological simulation. Results show that the peak flood flow and average daily streamflow for the RCP4.5 scenario are at least 101.15 and 110.14% of the historical phase, and at least 108.89 and 121.88% of the historical phase for the RCP8.5 scenario. Under both scenarios, the proportion of summer streamflow to annual total streamflow is projected to increase from 61.46% (historical phase) to over 85%, while the proportion of winter streamflow to annual total streamflow is projected to decrease from 8.84% (historical phase) to below 0.5%. Compared to the historical period, the maximum increase in future multi-year average annual streamflow for the RCP4.5 and RCP8.5 scenarios is 30.34 and 31.48%, respectively.
{"title":"Impacts of climate change on streamflow of Qinglong River, China","authors":"Xingpo Liu, Zixuan Tang","doi":"10.2166/wcc.2023.568","DOIUrl":"https://doi.org/10.2166/wcc.2023.568","url":null,"abstract":"\u0000 \u0000 Water resources and flood hazards in global watersheds are heavily influenced by climate change. In this study, the impact of climate change on the streamflow of the Qinglong River located in northern China is predicted. The streamflow of the Qinglong River (2021–2100) under two climate change scenarios (RCP 4.5 and RCP 8.5) was mainly synthesized over multiple timescales. The meteorological data from 31 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used as inputs of the Hydrological Simulation Program-Fortran (HSPF) for hydrological simulation. Results show that the peak flood flow and average daily streamflow for the RCP4.5 scenario are at least 101.15 and 110.14% of the historical phase, and at least 108.89 and 121.88% of the historical phase for the RCP8.5 scenario. Under both scenarios, the proportion of summer streamflow to annual total streamflow is projected to increase from 61.46% (historical phase) to over 85%, while the proportion of winter streamflow to annual total streamflow is projected to decrease from 8.84% (historical phase) to below 0.5%. Compared to the historical period, the maximum increase in future multi-year average annual streamflow for the RCP4.5 and RCP8.5 scenarios is 30.34 and 31.48%, respectively.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"220 1‐2","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139002295","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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