Based on the confluence principle, the confluence should represent the aggregate of net rainfall confluences within each distinct basic watershed unit (BWU) of a basin. The BWUs are categorized as V-type and Horseshoe-type based on their shape characteristics and two types of time–area curves of slope convergence have been derived separately. The cascade river convergence is modeled using a lagged linear reservoir, resulting in the development of a distributed CLARK convergence model based on the BWUs of a basin (BWU-DCLARK). The key findings are as follows: (1) The BWU-DCLARK model effectively captures the runoff convergence process and has been successfully applied in the Yanduhe River basin. Modeling results demonstrate high simulation accuracy. (2)The time of slope convergence indicate that the regulatory and storage effects on runoff of BWUs cannot be overlooked. (3)The BWU-DCLARK confluence model not only enables the calculation of flow at the basin outlet but also facilitates the computation of flow at any node along the river chain which is of great significance for hydrological forecasting in un-gauged basins but the application effect will need further verification.
根据汇流原理，汇流应代表流域内每个不同基本流域单元（BWU）内净降雨汇流的总和。根据基本流域单元的形状特征，将其分为 V 型和马蹄型，并分别推导出两种类型的坡面汇流时空曲线。利用滞后线性水库对河流级联汇流进行建模，从而建立了基于流域 BWU 的分布式 CLARK 汇流模型（BWU-DCLARK）。主要结论如下(1) BWU-DCLARK 模型有效地捕捉了径流汇聚过程，并成功地应用于盐渡河流域。建模结果表明模拟精度较高。(2）坡度收敛时间表明，BWU 对径流的调节和调蓄作用不容忽视。(3)BWU-DCLARK 汇流模型不仅可以计算流域出口的流量，还可以计算河道链上任意节点的流量，对无测站流域的水文预报具有重要意义，但应用效果有待进一步验证。
{"title":"Research on the distributed Clark confluence model based on basic watershed units","authors":"Junjun Zhu, Jingru Liu, Hui Zhou, Xuheng Che","doi":"10.2166/wcc.2024.638","DOIUrl":"https://doi.org/10.2166/wcc.2024.638","url":null,"abstract":"\u0000 Based on the confluence principle, the confluence should represent the aggregate of net rainfall confluences within each distinct basic watershed unit (BWU) of a basin. The BWUs are categorized as V-type and Horseshoe-type based on their shape characteristics and two types of time–area curves of slope convergence have been derived separately. The cascade river convergence is modeled using a lagged linear reservoir, resulting in the development of a distributed CLARK convergence model based on the BWUs of a basin (BWU-DCLARK). The key findings are as follows: (1) The BWU-DCLARK model effectively captures the runoff convergence process and has been successfully applied in the Yanduhe River basin. Modeling results demonstrate high simulation accuracy. (2)The time of slope convergence indicate that the regulatory and storage effects on runoff of BWUs cannot be overlooked. (3)The BWU-DCLARK confluence model not only enables the calculation of flow at the basin outlet but also facilitates the computation of flow at any node along the river chain which is of great significance for hydrological forecasting in un-gauged basins but the application effect will need further verification.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"25 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rainfall forecasting is pivotal in improving the lead time for issuing flood warnings and flood management. Machine learning (ML) models are popular as they can effectively manage extensive data and non-stationarity of the data series with improved performance and cost-effective solutions. However, more studies are required to understand the dynamic characteristics of rainfall. This study proposes a hybrid model and demonstrates its efficiency in improving the daily rainfall forecast. Singular spectrum analysis (SSA) was used as a data pre-processing technique (successfully removing and identifying the nature of noise) and coupled with ML models (artificial neural network (ANN) and support vector machine (SVM)) improving daily scale forecast. Since the current response of the hydrological system depends on previous responses, rainfall at the next time step was derived with the previous 2, 3, 5 and 7 days of rainfall. Study shows that the first eigen vector derived through SSA is the trend component which has a maximum contribution of 18.75%, suggesting it can explain 18.75% of the given rainfall series. The 16.42% (eigen vector 2–9) contributes to periodicity, with period of 1 year, 6 months, and 4 months within the data. Conclusively, the hybrid SSA–ML model outperformed the single model for daily rainfall forecasts.
{"title":"Performance assessment of rainfall forecasting models for urban Guwahati City using machine learning techniques and singular spectrum analysis","authors":"P. Shejule, S. Pekkat","doi":"10.2166/wcc.2024.465","DOIUrl":"https://doi.org/10.2166/wcc.2024.465","url":null,"abstract":"\u0000 \u0000 Rainfall forecasting is pivotal in improving the lead time for issuing flood warnings and flood management. Machine learning (ML) models are popular as they can effectively manage extensive data and non-stationarity of the data series with improved performance and cost-effective solutions. However, more studies are required to understand the dynamic characteristics of rainfall. This study proposes a hybrid model and demonstrates its efficiency in improving the daily rainfall forecast. Singular spectrum analysis (SSA) was used as a data pre-processing technique (successfully removing and identifying the nature of noise) and coupled with ML models (artificial neural network (ANN) and support vector machine (SVM)) improving daily scale forecast. Since the current response of the hydrological system depends on previous responses, rainfall at the next time step was derived with the previous 2, 3, 5 and 7 days of rainfall. Study shows that the first eigen vector derived through SSA is the trend component which has a maximum contribution of 18.75%, suggesting it can explain 18.75% of the given rainfall series. The 16.42% (eigen vector 2–9) contributes to periodicity, with period of 1 year, 6 months, and 4 months within the data. Conclusively, the hybrid SSA–ML model outperformed the single model for daily rainfall forecasts.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"28 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140245092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agilambigai Arumugam, M. F. Sigid, Azimah Ab Rahman, Widad Fadhullah
Tropical highland regions are susceptible to climate change and natural disasters due to their geographical location and hilly terrain. The objectives of this study are to determine land use land cover (LULC) changes in Cameron Highlands and analyse the climatic parameters of Cameron Highlands. This study integrates LULC analysis using remote sensing techniques and 10-year climatic parameters data to evaluate the impact of climate variability on the sustainability of Cameron Highlands. Based on the validation results, the overall accuracy of LULC was 95.42% in 2016, 96.60% in 2018, and 97.40% in 2020. The results show an 18% rise in agriculture, a 16% increase in urban growth, and an 8.14% decline in forest coverage in Tanah Rata and Ringlet, Cameron Highlands, from 2016 to 2020. The Mann–Kendall and Sen slope indicated a statistically significant increasing trend in rainfall (Kendall's Tau, Z = 0.102, p < 0.0001 and Sen value = 0.131, p < 0.001, respectively) and temperature (Kendall's Tau, Z = 0.151, p < 0.001 and Sen value = 0.294, p < 0.001, respectively) from 2012 to 2021, increasing the area's susceptibility towards climate change impact and natural disasters. This study highlights the vulnerability of Cameron Highlands to natural disasters, emphasizing the crucial need for efficient land management in slope areas to minimize the impact of climate change.
{"title":"Land use changes and climate parameters assessments in a tropical highland region of Cameron Highlands, Malaysia","authors":"Agilambigai Arumugam, M. F. Sigid, Azimah Ab Rahman, Widad Fadhullah","doi":"10.2166/wcc.2024.552","DOIUrl":"https://doi.org/10.2166/wcc.2024.552","url":null,"abstract":"\u0000 \u0000 Tropical highland regions are susceptible to climate change and natural disasters due to their geographical location and hilly terrain. The objectives of this study are to determine land use land cover (LULC) changes in Cameron Highlands and analyse the climatic parameters of Cameron Highlands. This study integrates LULC analysis using remote sensing techniques and 10-year climatic parameters data to evaluate the impact of climate variability on the sustainability of Cameron Highlands. Based on the validation results, the overall accuracy of LULC was 95.42% in 2016, 96.60% in 2018, and 97.40% in 2020. The results show an 18% rise in agriculture, a 16% increase in urban growth, and an 8.14% decline in forest coverage in Tanah Rata and Ringlet, Cameron Highlands, from 2016 to 2020. The Mann–Kendall and Sen slope indicated a statistically significant increasing trend in rainfall (Kendall's Tau, Z = 0.102, p < 0.0001 and Sen value = 0.131, p < 0.001, respectively) and temperature (Kendall's Tau, Z = 0.151, p < 0.001 and Sen value = 0.294, p < 0.001, respectively) from 2012 to 2021, increasing the area's susceptibility towards climate change impact and natural disasters. This study highlights the vulnerability of Cameron Highlands to natural disasters, emphasizing the crucial need for efficient land management in slope areas to minimize the impact of climate change.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"230 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140256387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study was conducted to identify variability in precipitation entropy and specify the water resource zones of Iran. Precipitation data with a spatial resolution of 0.25° during the period from 01/01/1962 to 31/12/2019 were used. For the investigation of variability in precipitation entropy over Iran, two indices were applied: entropy and disorder. The results demonstrated that the maximum occurred at the Caspian coasts and the minimum observed at the southern coasts of Iran. Most areas over the country have encountered negative trends in the entropy index. The rates of the entropy index have decreased, and the mean rate of the disorder index has increased. An analysis of variability in the extension of water resource zones in terms of the entropy index demonstrated that Iran could be divided into four zones: abundant and permanent; deficient and permanent; deficient and concentrate; and abundant and concentrate. After 1998, the abundant, permanent zone in the northern, high-altitude half of the country, the abundant, concentrated zone in the Southwest, and the zone with deficient and permanent precipitation in the northern half of the central parts have become less extensive, while the zone with deficient and concentrate precipitation has become more extensive toward the northern latitudes.
{"title":"Spatiotemporal analysis of precipitation variability based on entropy over Iran","authors":"M. Darand, Farshad Pazhoh","doi":"10.2166/wcc.2024.440","DOIUrl":"https://doi.org/10.2166/wcc.2024.440","url":null,"abstract":"\u0000 \u0000 This study was conducted to identify variability in precipitation entropy and specify the water resource zones of Iran. Precipitation data with a spatial resolution of 0.25° during the period from 01/01/1962 to 31/12/2019 were used. For the investigation of variability in precipitation entropy over Iran, two indices were applied: entropy and disorder. The results demonstrated that the maximum occurred at the Caspian coasts and the minimum observed at the southern coasts of Iran. Most areas over the country have encountered negative trends in the entropy index. The rates of the entropy index have decreased, and the mean rate of the disorder index has increased. An analysis of variability in the extension of water resource zones in terms of the entropy index demonstrated that Iran could be divided into four zones: abundant and permanent; deficient and permanent; deficient and concentrate; and abundant and concentrate. After 1998, the abundant, permanent zone in the northern, high-altitude half of the country, the abundant, concentrated zone in the Southwest, and the zone with deficient and permanent precipitation in the northern half of the central parts have become less extensive, while the zone with deficient and concentrate precipitation has become more extensive toward the northern latitudes.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"147 S286","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140256511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Minh Anh Vu, D. N. Quang, Tinh Xuan Nguyen, L. Ribbe
Consistent monitoring of surface water dynamics is essential for water resources, flood risk management, and addressing the challenges posed by climate change, urbanization. Located in Central Vietnam, Nhat Le River Basin witnesses significant and noticeable dynamics in surface water on a yearly basis due to water-related disasters like floods and droughts. This article presents the first comprehensive study to systematically map and analyse the long-term (2016–2022) spatiotemporal dynamics of surface water in the Nhat Le River Basin of Vietnam, utilizing Sentinel-1 data. The results reveal that the optimal threshold for separating water from non-water pixels is −19 dB, with an overall accuracy of 0.93–0.94 and a Kappa coefficient of 0.77–0.82. Through quantitative analysis, the study characterizes seasonal and interannual variations in the surface water extent, contributing to an enhanced understanding of flood patterns and associated risks in a data-scarce region. Our analysis reveals the Kien Giang river delta as the most flooding-vulnerable sub-region, underscoring the importance of targeted risk management and adaptation planning in this area. A Google Earth Engine Tool is developed for automatic detecting, monitoring, and accessing the spatiotemporal dynamics of surface water in Nhat Le River Basin over the period 2016–2022 and is freely available on GitHub (https://github.com/MinhVu25/Surface_Water_Dynamics_2023).
{"title":"Spatio-temporal dynamics monitoring of surface water bodies in Nhat Le River Basin, Vietnam, by Google Earth Engine","authors":"Minh Anh Vu, D. N. Quang, Tinh Xuan Nguyen, L. Ribbe","doi":"10.2166/wcc.2024.574","DOIUrl":"https://doi.org/10.2166/wcc.2024.574","url":null,"abstract":"\u0000 \u0000 Consistent monitoring of surface water dynamics is essential for water resources, flood risk management, and addressing the challenges posed by climate change, urbanization. Located in Central Vietnam, Nhat Le River Basin witnesses significant and noticeable dynamics in surface water on a yearly basis due to water-related disasters like floods and droughts. This article presents the first comprehensive study to systematically map and analyse the long-term (2016–2022) spatiotemporal dynamics of surface water in the Nhat Le River Basin of Vietnam, utilizing Sentinel-1 data. The results reveal that the optimal threshold for separating water from non-water pixels is −19 dB, with an overall accuracy of 0.93–0.94 and a Kappa coefficient of 0.77–0.82. Through quantitative analysis, the study characterizes seasonal and interannual variations in the surface water extent, contributing to an enhanced understanding of flood patterns and associated risks in a data-scarce region. Our analysis reveals the Kien Giang river delta as the most flooding-vulnerable sub-region, underscoring the importance of targeted risk management and adaptation planning in this area. A Google Earth Engine Tool is developed for automatic detecting, monitoring, and accessing the spatiotemporal dynamics of surface water in Nhat Le River Basin over the period 2016–2022 and is freely available on GitHub (https://github.com/MinhVu25/Surface_Water_Dynamics_2023).","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"162 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140256454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Dar, AL Ramanathan, R. A. Mir, Rayees Ahmad Pir
Climate change and human interventions over the past few decades have significantly affected the groundwater resources in Ladakh Himalaya. Sparse or lack of suitable data and knowledge gaps are a major challenge in evaluating these impacts. Here, we synthesize the available data to assess the status of groundwater quantity, quality, withdrawal, and contamination in the Leh district of India. The study shows that glacier area has decreased by 40% whereas its volume has reduced by 25% since the Little Ice Age (∼1650 AD). The glacier melt, which influences the recharge, has reduced significantly. The growth of population by 15% per year, expansion of build-up area by 50%, and changes in the socio-ecology have further stressed the groundwater. The bore wells and groundwater draft have increased at ∼115 wells/year and ∼7 MCM/year, respectively. The increase of groundwater development by ∼26 times has reduced the reserves. Hence, for the sustainability of the resource, modeling and managing the impacts is imperatively required. In this direction, this paper provides guidelines for researchers, policymakers, and water users to develop an integrative consortium management strategy for the sustainable utilization of the groundwater.
{"title":"Groundwater scenario under climate change and anthropogenic stress in Ladakh Himalaya, India","authors":"F. Dar, AL Ramanathan, R. A. Mir, Rayees Ahmad Pir","doi":"10.2166/wcc.2024.307","DOIUrl":"https://doi.org/10.2166/wcc.2024.307","url":null,"abstract":"\u0000 \u0000 Climate change and human interventions over the past few decades have significantly affected the groundwater resources in Ladakh Himalaya. Sparse or lack of suitable data and knowledge gaps are a major challenge in evaluating these impacts. Here, we synthesize the available data to assess the status of groundwater quantity, quality, withdrawal, and contamination in the Leh district of India. The study shows that glacier area has decreased by 40% whereas its volume has reduced by 25% since the Little Ice Age (∼1650 AD). The glacier melt, which influences the recharge, has reduced significantly. The growth of population by 15% per year, expansion of build-up area by 50%, and changes in the socio-ecology have further stressed the groundwater. The bore wells and groundwater draft have increased at ∼115 wells/year and ∼7 MCM/year, respectively. The increase of groundwater development by ∼26 times has reduced the reserves. Hence, for the sustainability of the resource, modeling and managing the impacts is imperatively required. In this direction, this paper provides guidelines for researchers, policymakers, and water users to develop an integrative consortium management strategy for the sustainable utilization of the groundwater.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"47 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The present study focused on evaluating the separate and combined response of land use land cover and climate change (CC) on future water balance components of a Subarnarekha River basin, spanning between the latitudes 21°33′N–23°18′N and longitudes 85°11′E–87°23′E, situated in the eastern India. The Soil and Water Assessment Tool is used for single-site calibration and multi-site calibration (MSC) of the model to characterize the future water balance components of the basin using the Cellular Automata-Markov model and climate projections under two representative concentration pathway (RCP) scenarios (4.5 and 8.5). The findings indicate that the model parameters obtained through MSC better represent spatial heterogeneity, making it the preferred calibration approach for model simulations. In the middle region of the basin, future annual water yield, groundwater recharge (GWR), and streamflow showed a reduction, respectively, by 46–47%, 29–30%, and 13–15%, while evapotranspiration showed an increase by 5–7% following projected CC under both RCP scenarios. The findings are relevant for policy-makers to mitigate the adverse effects of reduced GWR for sustainable water resources management. Future research may integrate reservoir operation framework to effectively address the water management issues of the basin.
{"title":"Response of climate change and land use land cover change on catchment-scale water balance components: a multi-site calibration approach","authors":"Shashi Bhushan Kumar, Ashok Mishra, S. S. Dash","doi":"10.2166/wcc.2024.581","DOIUrl":"https://doi.org/10.2166/wcc.2024.581","url":null,"abstract":"\u0000 \u0000 The present study focused on evaluating the separate and combined response of land use land cover and climate change (CC) on future water balance components of a Subarnarekha River basin, spanning between the latitudes 21°33′N–23°18′N and longitudes 85°11′E–87°23′E, situated in the eastern India. The Soil and Water Assessment Tool is used for single-site calibration and multi-site calibration (MSC) of the model to characterize the future water balance components of the basin using the Cellular Automata-Markov model and climate projections under two representative concentration pathway (RCP) scenarios (4.5 and 8.5). The findings indicate that the model parameters obtained through MSC better represent spatial heterogeneity, making it the preferred calibration approach for model simulations. In the middle region of the basin, future annual water yield, groundwater recharge (GWR), and streamflow showed a reduction, respectively, by 46–47%, 29–30%, and 13–15%, while evapotranspiration showed an increase by 5–7% following projected CC under both RCP scenarios. The findings are relevant for policy-makers to mitigate the adverse effects of reduced GWR for sustainable water resources management. Future research may integrate reservoir operation framework to effectively address the water management issues of the basin.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianqun Guo, Zhonglian Jiang, Xiao Chu, Wenyuan Wang
The management of port water quality is crucial to marine ecological balance and has been of great concern. In the present study, the water quality monitoring data in Zhanjiang Port from 2015 to 2022 were utilized to analyze the spatiotemporal characteristics and reveal the correlation between different parameters. The structural equation model has been applied to profile the dominant factors of water quality level. The results showed that the port water quality was generally worse in summer and better in winter. Variations in total phosphorus (TP), chemical oxygen demand (COD) and total nitrogen (TN) content directly led to water quality changes in Zhanjiang Port, where an increase in TP content resulted in a significant decrease in water quality level (path coefficient is 2.87). Permanganate index (CODMn) and ammonia nitrogen content indirectly affected the water quality level, while changes in pH and dissolved oxygen (DO) showed no impact. Ammonia nitrogen, pH and DO contents were significantly associated with TP. Human activities and industrial production were identified as the main sources of water quality pollution. The increasing trend of certain water quality parameters highlights the urgency of implementing timely measures to improve water quality conditions in Zhanjiang Bay, China.
{"title":"Dynamic analysis of port water quality: insights from Zhanjiang Port, China","authors":"Jianqun Guo, Zhonglian Jiang, Xiao Chu, Wenyuan Wang","doi":"10.2166/wcc.2024.623","DOIUrl":"https://doi.org/10.2166/wcc.2024.623","url":null,"abstract":"\u0000 The management of port water quality is crucial to marine ecological balance and has been of great concern. In the present study, the water quality monitoring data in Zhanjiang Port from 2015 to 2022 were utilized to analyze the spatiotemporal characteristics and reveal the correlation between different parameters. The structural equation model has been applied to profile the dominant factors of water quality level. The results showed that the port water quality was generally worse in summer and better in winter. Variations in total phosphorus (TP), chemical oxygen demand (COD) and total nitrogen (TN) content directly led to water quality changes in Zhanjiang Port, where an increase in TP content resulted in a significant decrease in water quality level (path coefficient is 2.87). Permanganate index (CODMn) and ammonia nitrogen content indirectly affected the water quality level, while changes in pH and dissolved oxygen (DO) showed no impact. Ammonia nitrogen, pH and DO contents were significantly associated with TP. Human activities and industrial production were identified as the main sources of water quality pollution. The increasing trend of certain water quality parameters highlights the urgency of implementing timely measures to improve water quality conditions in Zhanjiang Bay, China.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"14 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140263086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Dorjsuren, V. A. Zemtsov, Nyamdavaa Batsaikhan, Otgonbayar Demberel, Denghua Yan, Hongfei Zhou, Otgonbayar Yadamjav, S. Chonokhuu, Altanbold Enkhbold, Bolorjargal Ganzorig, Erdenebayar Bavuu, Oyunchimeg Namsrai, Liu Xiang, Yingjie Yan, Wang Siyu
Arid and semi-arid regions are the first to be affected by hydro-climatic changes. The Great Lakes Depression Basin in western Mongolia is the most notable example of such a region. Therefore, analyzing hydro-climatic changes in the Great Lakes Depression region is essential for future climate, hydrological, eco-hydrological processes, and ecosystem studies in similar areas and basins. In this study, Mann–Kendall (MK), innovative trend analysis method (ITAM), and Sen's slope estimator test (SSET) were used to determine the interrelationship between climate and river discharge changes and lake water level changes through statistical analysis. During the last 30 years, the air temperature has increased by 1.2 °C (Z = 1.16). Total annual precipitation decreased by 23.44 mm, resulting in 134.16 mm (Z = −0.79). The river discharge of the major rivers, such as Khovd River (Z = −3.51) and Zavkhan River (Z = −6.01), has significantly decreased. In Uvs (Z = 0.30) and Khyargas (Z = 2.03) lakes, the water level has also dropped. This study confirms that the increase in air temperature in the depression area of the Great Lakes reduces the amount of precipitation, and the decrease in precipitation affects the decrease in river discharge, which further affects the water level of the inflowing lakes.
{"title":"Trend analysis of hydro-climatic variables in the Great Lakes Depression region of Mongolia","authors":"B. Dorjsuren, V. A. Zemtsov, Nyamdavaa Batsaikhan, Otgonbayar Demberel, Denghua Yan, Hongfei Zhou, Otgonbayar Yadamjav, S. Chonokhuu, Altanbold Enkhbold, Bolorjargal Ganzorig, Erdenebayar Bavuu, Oyunchimeg Namsrai, Liu Xiang, Yingjie Yan, Wang Siyu","doi":"10.2166/wcc.2024.379","DOIUrl":"https://doi.org/10.2166/wcc.2024.379","url":null,"abstract":"\u0000 \u0000 Arid and semi-arid regions are the first to be affected by hydro-climatic changes. The Great Lakes Depression Basin in western Mongolia is the most notable example of such a region. Therefore, analyzing hydro-climatic changes in the Great Lakes Depression region is essential for future climate, hydrological, eco-hydrological processes, and ecosystem studies in similar areas and basins. In this study, Mann–Kendall (MK), innovative trend analysis method (ITAM), and Sen's slope estimator test (SSET) were used to determine the interrelationship between climate and river discharge changes and lake water level changes through statistical analysis. During the last 30 years, the air temperature has increased by 1.2 °C (Z = 1.16). Total annual precipitation decreased by 23.44 mm, resulting in 134.16 mm (Z = −0.79). The river discharge of the major rivers, such as Khovd River (Z = −3.51) and Zavkhan River (Z = −6.01), has significantly decreased. In Uvs (Z = 0.30) and Khyargas (Z = 2.03) lakes, the water level has also dropped. This study confirms that the increase in air temperature in the depression area of the Great Lakes reduces the amount of precipitation, and the decrease in precipitation affects the decrease in river discharge, which further affects the water level of the inflowing lakes.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"19 S1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Safaa AL Shamayleh, M. Tan, N. Samat, Michel Rahbeh, Fei Zhang
The evaluation of open-source precipitation data is crucial to enable the selection of the most appropriate product for a specific research or operational application. This study aims to evaluate the capability of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a spatial resolution of 0.05° for estimating monthly and annual precipitation in the Wala basin, Jordan, from 1987 to 2017 using a point-to-pixel comparison approach. Eleven precipitation extreme indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used in this study. The findings show that CHIRPS correlated moderately with stations in monthly precipitation estimation, with the Pearson correlation coefficient values ranging from 0.50 to 0.73. However, CHIRPS had low correlations with stations in most of the extreme indices, except PRCPTOT, R10mm, and R20mm. The CHIRPS, particularly in the extreme years, overestimated low precipitation amounts and underestimated high ones. Moreover, CHIRPS underestimated the calculation of consecutive dry days, consecutive wet days, R10mm, R20mm, and R30mm, while an overestimation was found for the R95p, R99p, and Rx1day. The trend analysis and Wilcox text showed a lack of resemblance between the CHIRPS and gauges, showing a bias correction is needed before applying an extreme analysis in this region.
{"title":"Performance of CHIRPS for estimating precipitation extremes in the Wala Basin, Jordan","authors":"Safaa AL Shamayleh, M. Tan, N. Samat, Michel Rahbeh, Fei Zhang","doi":"10.2166/wcc.2024.611","DOIUrl":"https://doi.org/10.2166/wcc.2024.611","url":null,"abstract":"\u0000 \u0000 The evaluation of open-source precipitation data is crucial to enable the selection of the most appropriate product for a specific research or operational application. This study aims to evaluate the capability of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a spatial resolution of 0.05° for estimating monthly and annual precipitation in the Wala basin, Jordan, from 1987 to 2017 using a point-to-pixel comparison approach. Eleven precipitation extreme indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used in this study. The findings show that CHIRPS correlated moderately with stations in monthly precipitation estimation, with the Pearson correlation coefficient values ranging from 0.50 to 0.73. However, CHIRPS had low correlations with stations in most of the extreme indices, except PRCPTOT, R10mm, and R20mm. The CHIRPS, particularly in the extreme years, overestimated low precipitation amounts and underestimated high ones. Moreover, CHIRPS underestimated the calculation of consecutive dry days, consecutive wet days, R10mm, R20mm, and R30mm, while an overestimation was found for the R95p, R99p, and Rx1day. The trend analysis and Wilcox text showed a lack of resemblance between the CHIRPS and gauges, showing a bias correction is needed before applying an extreme analysis in this region.","PeriodicalId":506949,"journal":{"name":"Journal of Water and Climate Change","volume":"13 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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