Current basin-scale patterns in riverine total-phosphorus (TP) retention and flux for predicting water quality remain unclear, when considering TP as a key water quality indicator. We modeled TP retention and fluxes from the largest Changjiang River network in China based on high-frequency monitoring data of TP concentrations at 55 monitoring stations during Jan.1, 2021-Dec.31, 2022. We emphasized basin-scale control of the TP loss rate (also called the first-order rate constant) in response to variations in discharge and total suspended solids (TSS) under climate change. We found that the TP loss rates ranged from 0.008 h−1 to 0.032 h−1 and declined with water discharge but increased with the TSS content. The ratio of TP retention is negatively related to Strahler river orders, and was ∼ 0.55 for the streams with low orders (1–3) and ∼ 0.25 for the highest order river (8). TP concentrations at 55 stations ranged from 0.008 ∼ 0.145 mg·L-1 for annual average and 0.03 ∼ 4.89 mg·L-1 for daily maximum. TP fluxes demonstrated significant spatial pattern from the source area to the estuary with an increasing trend, with the highest flux of 64150 t·year−1 at Datong. We also found the urban disturbance index (UDI) was significantly positively correlated with the TP input load and output flux (input: r = 0.78; output: r = 0.62), suggesting that basin-scale urbanization, together with hydrology and climate change, controls the river TP concentration and retention in the entire Changjiang River network. Our results can help to understand TP retention process, and are useful for TP water quality planning and management.
{"title":"Dependence of riverine total phosphorus retention and fluxes on hydrology and river size at river network scale","authors":"Fang Wang, Shengyi Li, Weijin Yan, Qibiao Yu, Siyu Tian, Jun Yan, Demin Zhou, Yulai Shao","doi":"10.1016/j.jhydrol.2025.132676","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132676","url":null,"abstract":"Current basin-scale patterns in riverine total-phosphorus (TP) retention and flux for predicting water quality remain unclear, when considering TP as a key water quality indicator. We modeled TP retention and fluxes from the largest Changjiang River network in China based on high-frequency monitoring data of TP concentrations at 55 monitoring stations during Jan.1, 2021-Dec.31, 2022. We emphasized basin-scale control of the TP loss rate (also called the first-order rate constant) in response to variations in discharge and total suspended solids (TSS) under climate change. We found that the TP loss rates ranged from 0.008 h<ce:sup loc=\"post\">−1</ce:sup> to 0.032 h<ce:sup loc=\"post\">−1</ce:sup> and declined with water discharge but increased with the TSS content. The ratio of TP retention is negatively related to Strahler river orders, and was ∼ 0.55 for the streams with low orders (1–3) and ∼ 0.25 for the highest order river (8). TP concentrations at 55 stations ranged from 0.008 ∼ 0.145 mg·L<ce:sup loc=\"post\">-1</ce:sup> for annual average and 0.03 ∼ 4.89 mg·L<ce:sup loc=\"post\">-1</ce:sup> for daily maximum. TP fluxes demonstrated significant spatial pattern from the source area to the estuary with an increasing trend, with the highest flux of 64150 t·year<ce:sup loc=\"post\">−1</ce:sup> at Datong. We also found the urban disturbance index (UDI) was significantly positively correlated with the TP input load and output flux (input: <ce:italic>r = 0.78</ce:italic>; output: <ce:italic>r = 0.62</ce:italic>), suggesting that basin-scale urbanization, together with hydrology and climate change, controls the river TP concentration and retention in the entire Changjiang River network. Our results can help to understand TP retention process, and are useful for TP water quality planning and management.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"7 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliable spatial–temporal simulation and forecast of flood are crucial for accurate flood control. Profile soil moisture (PSM) is a key intermediate variable in rainfall-runoff transformation, its spatial heterogeneity affects the spatial–temporal processes of floods. However, soil moisture data in the current flood modeling researches are only the intermediately simulated values that lack spatial validation, how spatial heterogeneity in soil moisture affects high-resolution flood processes is still unclear. In contrast, soil moisture data derived from satellites are spatial observed values that can better represent real spatial distribution. This study is first to incorporate satellite-based data and hydrological modeled data to identify the effects of different spatial patterns of profile soil moisture on spatial–temporal processes of floods at 1 km × 1 km resolution. The distributed hydrological model (DDRM) and the European Space Agency Climate Change Initiative (ESA CCI) products are chosen to provide different PSM spatial patterns, under which, the DDRM is used to investigate corresponding spatial–temporal processes of floods. Four modeling scenarios (two DDRM-only scenarios and two assimilation scenarios) in two humid and one semi-arid catchments are set to see the different effects, and the spatial–temporal statistical analysis and the semi-variogram method are used to identify the spatial heterogeneity. Results indicate that both PSM and runoff show strong spatial heterogeneity with the values of C/(C0 + C) in the semi-variogram method higher than 0.8; for different periods, the decrease of spatial heterogeneity in PSM corresponds to the decrease of spatial heterogeneity in runoff; for different scenarios, soil moisture storage capacity (SMC) affects spatial heterogeneity of runoff more in the semi-arid catchment, and the change of sensitive parameters of spatial soil moisture is the main driver to change the spatial heterogeneity of runoff. The findings of the study would enhance the development of refined modeling of floods, and have practical implications for flood risk management and early flood warning systems.
{"title":"Investigating the effects of spatial heterogeneity of multi-source profile soil moisture on spatial–temporal processes of high-resolution floods","authors":"Han Yang, Xiaoqi Zhang, Zhe Yuan, Xiaofeng Hong, Liqiang Yao, Xiuping Zhang","doi":"10.1016/j.jhydrol.2025.132672","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132672","url":null,"abstract":"Reliable spatial–temporal simulation and forecast of flood are crucial for accurate flood control. Profile soil moisture (PSM) is a key intermediate variable in rainfall-runoff transformation, its spatial heterogeneity affects the spatial–temporal processes of floods. However, soil moisture data in the current flood modeling researches are only the intermediately simulated values that lack spatial validation, how spatial heterogeneity in soil moisture affects high-resolution flood processes is still unclear. In contrast, soil moisture data derived from satellites are spatial observed values that can better represent real spatial distribution. This study is first to incorporate satellite-based data and hydrological modeled data to identify the effects of different spatial patterns of profile soil moisture on spatial–temporal processes of floods at 1 km × 1 km resolution. The distributed hydrological model (DDRM) and the European Space Agency Climate Change Initiative (ESA CCI) products are chosen to provide different PSM spatial patterns, under which, the DDRM is used to investigate corresponding spatial–temporal processes of floods. Four modeling scenarios (two DDRM-only scenarios and two assimilation scenarios) in two humid and one semi-arid catchments are set to see the different effects, and the spatial–temporal statistical analysis and the semi-variogram method are used to identify the spatial heterogeneity. Results indicate that both PSM and runoff show strong spatial heterogeneity with the values of C/(C0 + C) in the semi-variogram method higher than 0.8; for different periods, the decrease of spatial heterogeneity in PSM corresponds to the decrease of spatial heterogeneity in runoff; for different scenarios, soil moisture storage capacity (SMC) affects spatial heterogeneity of runoff more in the semi-arid catchment, and the change of sensitive parameters of spatial soil moisture is the main driver to change the spatial heterogeneity of runoff. The findings of the study would enhance the development of refined modeling of floods, and have practical implications for flood risk management and early flood warning systems.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"16 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1016/j.jhydrol.2025.132671
Siqi Zhang, Daoyuan Tan, Honghu Zhu, Chao Zhou
Soil-water characteristic curves (SWCCs) are a family of water content values versus soil suction, illustrating the hysteresis in natural soil. The accurate and efficient estimation of SWCCs is indispensable in geotechnical engineering and hydrology. This paper aims to develop a comprehensive model for accurately estimating the family of SWCCs for sandy soils using their unimodal grain size distribution (GSD). First, an improved model is developed by combining the physical-based MV model with statistical estimation to predict the initial drying SWCC. This model explicitly quantified the effects of soil uniformity and residual water content on estimated SWCC based on GSD. Second, the model is further developed to predict the main wetting curve from the estimated drying curve by introducing residual air content, contact angle hysteresis, and “ink bottle” effects. The drying and wetting scanning curves are predicted for any given transition point. Then, the model’s performance is evaluated through comparison with experimental data from various sandy soils, cross-validation, sensitivity analysis, and uncertainty quantification. Results show that the model stands out for its superior accuracy and convenience compared to classical models. It provides reliable predictions of the entire family of SWCCs for a wide range of sandy soils, from gravelly to clayey sand. Finally, the potential application in other fields and limitations of the model are discussed. The model also demonstrates the potential to be extended to various soil types, including gap-graded soils, fine-grained soils, and soils with organic matter.
{"title":"Estimating family of soil–water characteristic curves for sandy soils from unimodal grain size distribution and void ratio","authors":"Siqi Zhang, Daoyuan Tan, Honghu Zhu, Chao Zhou","doi":"10.1016/j.jhydrol.2025.132671","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132671","url":null,"abstract":"Soil-water characteristic curves (SWCCs) are a family of water content values versus soil suction, illustrating the hysteresis in natural soil. The accurate and efficient estimation of SWCCs is indispensable in geotechnical engineering and hydrology. This paper aims to develop a comprehensive model for accurately estimating the family of SWCCs for sandy soils using their unimodal grain size distribution (GSD). First, an improved model is developed by combining the physical-based MV model with statistical estimation to predict the initial drying SWCC. This model explicitly quantified the effects of soil uniformity and residual water content on estimated SWCC based on GSD. Second, the model is further developed to predict the main wetting curve from the estimated drying curve by introducing residual air content, contact angle hysteresis, and “ink bottle” effects. The drying and wetting scanning curves are predicted for any given transition point. Then, the model’s performance is evaluated through comparison with experimental data from various sandy soils, cross-validation, sensitivity analysis, and uncertainty quantification. Results show that the model stands out for its superior accuracy and convenience compared to classical models. It provides reliable predictions of the entire family of SWCCs for a wide range of sandy soils, from gravelly to clayey sand. Finally, the potential application in other fields and limitations of the model are discussed. The model also demonstrates the potential to be extended to various soil types, including gap-graded soils, fine-grained soils, and soils with organic matter.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"50 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1016/j.jhydrol.2025.132667
Saman Shahnazi, Kiyoumars Roushangar, Hossein Hashemi
Groundwater drought, as a form of hydrological drought, embodies the distinctive characteristics of the aquifer and human-induced disruptions within the hydrological system. The intricate nature of groundwater flow systems, coupled with challenges in acquiring field observations related to aquifers, poses significant challenges in quantitatively characterizing groundwater drought. The present paper presents a novel contribution to the time series forecasting of groundwater drought through state-of-the-art integrated GWO-SVM models. The Standardized Groundwater Level Index (SGI) was employed to monitor groundwater drought in one of the critical aquifers in Iran, and forecasts were conducted for various horizons, including short-term (3 months: t + 3), mid-term (9 months: t + 9), and long-term (12 months: t + 12) periods. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variation Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), Empirical Fourier Decomposition (EFD), and Discrete Wavelet Transform (DWT) were further incorporated as pre-processing techniques to enhance forecasting accuracy. The trend analysis findings indicated that out of the 20 observation wells assessed, 15 observation wells (P1–P15) located in the western part of the aquifer showed a negative trend. The SOM method clustered the aquifer into five clusters, with well P8, representing cluster 1, demonstrating the highest accuracy in forecasting groundwater drought. The overall results demonstrated the significant impact of pre-processing on enhancing the forecasting accuracy of groundwater drought. The VMD-GWO-SVM model provided superior performance compared to all employed models in short to long-term horizons, achieving NSE values of 0.955, 0.915, and 0.838 for short-term, mid-term, and long-term periods, respectively.
{"title":"A novel implementation of pre-processing approaches and hybrid kernel-based model for short- and long-term groundwater drought forecasting","authors":"Saman Shahnazi, Kiyoumars Roushangar, Hossein Hashemi","doi":"10.1016/j.jhydrol.2025.132667","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132667","url":null,"abstract":"Groundwater drought, as a form of hydrological drought, embodies the distinctive characteristics of the aquifer and human-induced disruptions within the hydrological system. The intricate nature of groundwater flow systems, coupled with challenges in acquiring field observations related to aquifers, poses significant challenges in quantitatively characterizing groundwater drought. The present paper presents a novel contribution to the time series forecasting of groundwater drought through state-of-the-art integrated GWO-SVM models. The Standardized Groundwater Level Index (SGI) was employed to monitor groundwater drought in one of the critical aquifers in Iran, and forecasts were conducted for various horizons, including short-term (3 months: t + 3), mid-term (9 months: t + 9), and long-term (12 months: t + 12) periods. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variation Mode Decomposition (VMD), Empirical Wavelet Transform (EWT), Empirical Fourier Decomposition (EFD), and Discrete Wavelet Transform (DWT) were further incorporated as pre-processing techniques to enhance forecasting accuracy. The trend analysis findings indicated that out of the 20 observation wells assessed, 15 observation wells (P1–P15) located in the western part of the aquifer showed a negative trend. The SOM method clustered the aquifer into five clusters, with well P8, representing cluster 1, demonstrating the highest accuracy in forecasting groundwater drought. The overall results demonstrated the significant impact of pre-processing on enhancing the forecasting accuracy of groundwater drought. The VMD-GWO-SVM model provided superior performance compared to all employed models in short to long-term horizons, achieving NSE values of 0.955, 0.915, and 0.838 for short-term, mid-term, and long-term periods, respectively.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"36 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1016/j.jhydrol.2024.132639
Jinjun Zhou, Tianyi Huang, Hao Wang, Wei Du, Yi Zhan, Aochuan Duan, Guangtao Fu
This study explores the performance of Phycically-based modelling (PBM), Machine learning (ML), and Hybrid modelling (HM) in soil water movement. Three types of models were tested on experiments under different soils and external pressure head conditions. In PBM, we proposed an adaptive step-length model named Time Cellular Automata (TCA), achieving an RMSE of 5.91, which outperforms HYDRUS (RMSE 7.92). In ML, Root Mean Square Error (RMSE) of all four tested models was below 1.5, with eXtreme Gradient Boosting (XGBoost) performing the best. The predictive accuracy of ML significantly outperformed PBM. SHapley Additive exPlanation was used to interpret the data and feature importance of machine learning. Middle-layer soil temperature, surface-layer soil salinity, water head and air temperature were identified as important parameters for ML. Heuristic algorithm can assist in searching for optimal parameters for TCA (Optimized TCA) and improve RMSE from 5.91 to 4.79. By integrating PBM and ML, developed a hybrid modeling strategy named HM. The HM was constructed using XGB and TCA, and achieved an error rate falling between Non-Optimized TCA (5.91) and Optimized TCA (5.51). This study presents a method for constructing HM from PBM and ML which is guided by data-driven approaches to make the analysis of soil water movement more efficient and economical.
{"title":"Using physical method, machine learning and hybrid method to model soil water movement","authors":"Jinjun Zhou, Tianyi Huang, Hao Wang, Wei Du, Yi Zhan, Aochuan Duan, Guangtao Fu","doi":"10.1016/j.jhydrol.2024.132639","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132639","url":null,"abstract":"This study explores the performance of Phycically-based modelling (PBM), Machine learning (ML), and Hybrid modelling (HM) in soil water movement. Three types of models were tested on experiments under different soils and external pressure head conditions. In PBM, we proposed an adaptive step-length model named Time Cellular Automata (TCA), achieving an RMSE of 5.91, which outperforms HYDRUS (RMSE 7.92). In ML, Root Mean Square Error (RMSE) of all four tested models was below 1.5, with eXtreme Gradient Boosting (XGBoost) performing the best. The predictive accuracy of ML significantly outperformed PBM. SHapley Additive exPlanation was used to interpret the data and feature importance of machine learning. Middle-layer soil temperature, surface-layer soil salinity, water head and air temperature were identified as important parameters for ML. Heuristic algorithm can assist in searching for optimal parameters for TCA (Optimized TCA) and improve RMSE from 5.91 to 4.79. By integrating PBM and ML, developed a hybrid modeling strategy named HM. The HM was constructed using XGB and TCA, and achieved an error rate falling between Non-Optimized TCA (5.91) and Optimized TCA (5.51). This study presents a method for constructing HM from PBM and ML which is guided by data-driven approaches to make the analysis of soil water movement more efficient and economical.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"105 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-05DOI: 10.1016/j.jhydrol.2024.132660
Yanyan Li, Yuanyuan Chen, Hongrui Ding, Di Cui, Xiang Ji, Chuanye Zhou, Yan Li, Hongwei Jing, Anhuai Lu
Groundwater is a crucial water supply resource for Beijing, the capital of China. However, high fluoride (F) concentrations in groundwater have been reported previously on the southern Beijing Plain. In this study, F distribution in groundwater and aquifers of the southern Beijing Plain is comprehensively elucidated and its controlling factors are analyzed by integrating multiple approaches, including hydrogeochemical and isotopic analysis of groundwater, and chemical, lithological, and mineralogical studies of borehole sediment. Groundwater F− concentrations ranged from 0.01 to 0.95 mg/L, and were below the permissible limit in drinking water recommended by the Chinese government (1.0 mg/L). Relatively high F−− concentrations in groundwater were primarily distributed in the alluvial plain rather than those in alluvial fan. The spatial pattern of total F contents in the aquifer sediments was similar to that of groundwater F. The results of mineralogy, microstructure, and lithology also indicated that clay and F-bearing minerals (such as apatite, biotite, muscovite, fluorite, clinochlorite, and illite) in sediments provided geogenic sources of groundwater F. Various hydrogeochemical and mineralogical analyses provided corroborating evidence that sediment weathering (particularly silicates), dissolution of F-bearing minerals, and desorption from clay and iron (oxy)hydroxides were important processes that mobilize water-soluble and absorbed F into groundwater. Enrichment of groundwater F in the alluvial plain was attributed to high pH, groundwater flow path, long residence time, and the corresponding enhanced cation exchange of Ca2+ and Na+, reprecipitation of Ca2+, and substitution of F− by OH− under these conditions. Our findings highlight the combined effects of hydrogeochemical and mineralogical processes on F behavior in groundwater and have important implications for guiding the scientific control of high F groundwater.
{"title":"Mineralogical and hydrogeochemical insights into the distribution and source of groundwater fluoride in the southern Beijing plain","authors":"Yanyan Li, Yuanyuan Chen, Hongrui Ding, Di Cui, Xiang Ji, Chuanye Zhou, Yan Li, Hongwei Jing, Anhuai Lu","doi":"10.1016/j.jhydrol.2024.132660","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132660","url":null,"abstract":"Groundwater is a crucial water supply resource for Beijing, the capital of China. However, high fluoride (F) concentrations in groundwater have been reported previously on the southern Beijing Plain. In this study, F distribution in groundwater and aquifers of the southern Beijing Plain is comprehensively elucidated and its controlling factors are analyzed by integrating multiple approaches, including hydrogeochemical and isotopic analysis of groundwater, and chemical, lithological, and mineralogical studies of borehole sediment. Groundwater F<ce:sup loc=\"post\">−</ce:sup> concentrations ranged from 0.01 to 0.95 mg/L, and were below the permissible limit in drinking water recommended by the Chinese government (1.0 mg/L). Relatively high F<ce:sup loc=\"post\">−</ce:sup>− concentrations in groundwater were primarily distributed in the alluvial plain rather than those in alluvial fan. The spatial pattern of total F contents in the aquifer sediments was similar to that of groundwater F. The results of mineralogy, microstructure, and lithology also indicated that clay and F-bearing minerals (such as apatite, biotite, muscovite, fluorite, clinochlorite, and illite) in sediments provided geogenic sources of groundwater F. Various hydrogeochemical and mineralogical analyses provided corroborating evidence that sediment weathering (particularly silicates), dissolution of F-bearing minerals, and desorption from clay and iron (oxy)hydroxides were important processes that mobilize water-soluble and absorbed F into groundwater. Enrichment of groundwater F in the alluvial plain was attributed to high pH, groundwater flow path, long residence time, and the corresponding enhanced cation exchange of Ca<ce:sup loc=\"post\">2+</ce:sup> and Na<ce:sup loc=\"post\">+</ce:sup>, reprecipitation of Ca<ce:sup loc=\"post\">2+</ce:sup>, and substitution of F<ce:sup loc=\"post\">−</ce:sup> by OH<ce:sup loc=\"post\">−</ce:sup> under these conditions. Our findings highlight the combined effects of hydrogeochemical and mineralogical processes on F behavior in groundwater and have important implications for guiding the scientific control of high F groundwater.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"10 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-04DOI: 10.1016/j.jhydrol.2025.132669
Degavath Vinod, Amai Mahesha
India’s complex topography and the increasing influence of climate change have exacerbated the challenges of modeling 1-hour non-stationary extreme rainfall events. Prior studies have indicated rising intensities of such events, particularly in coastal and urban areas. This study addresses these issues by developing 155 basin-specific non-stationary surface response models, incorporating geographical, climatic, and temporal covariates. Using 13 Max-Stable Process (MSP) characterizations, extreme rainfall variability across 11 major river basins and three-time scales were effectively modeled. The Brown-Resnick, Geometric-Gaussian, and Extremal-t models demonstrated varying effectiveness across regions. The findings emphasize the critical role of region-specific analysis in water resource management and disaster preparedness, where the high temporal resolution datasets are limited for the point process-based models. The global processes and regional climate change are found to predominantly influence 1-hour extreme rainfall across the majority of river basins in India.
{"title":"Modeling non-stationary 1-hour extreme rainfall for Indian river basins under changing climate","authors":"Degavath Vinod, Amai Mahesha","doi":"10.1016/j.jhydrol.2025.132669","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132669","url":null,"abstract":"India’s complex topography and the increasing influence of climate change have exacerbated the challenges of modeling 1-hour non-stationary extreme rainfall events. Prior studies have indicated rising intensities of such events, particularly in coastal and urban areas. This study addresses these issues by developing 155 basin-specific non-stationary surface response models, incorporating geographical, climatic, and temporal covariates. Using 13 Max-Stable Process (MSP) characterizations, extreme rainfall variability across 11 major river basins and three-time scales were effectively modeled. The Brown-Resnick, Geometric-Gaussian, and Extremal-t models demonstrated varying effectiveness across regions. The findings emphasize the critical role of region-specific analysis in water resource management and disaster preparedness, where the high temporal resolution datasets are limited for the point process-based models. The global processes and regional climate change are found to predominantly influence 1-hour extreme rainfall across the majority of river basins in India.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"25 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-04DOI: 10.1016/j.jhydrol.2024.132648
Ziwei Wang, Xiaohong Ruan, Fan Le, Shuai Chen, Tong Chen
Inland aquatic systems play a crucial role in the global nitrogen (N) cycle. This study focused on the Shaying River Basin, which is characterized by intensive damming, base-flow deficiencies and high N loading. Metagenomics sequencing and molecular ecological network analysis were used to conduct a comparative analysis of N cycling and its coupling effects with carbon (C) and sulfur (S) cycling in different stagnant habitats. Our major findings are listed below. (1) Compared with free-flowing habitats, overlying water in stagnant habitats (reservoirs and sluices) had higher abundances of mineralization and organic nitrogen synthesis genes, and lower abundances of denitrification and nitrification genes. These results indicate that damming enhances the conversion between organic and inorganic N but weakens inorganic N removal. The superposition of high N concentrations also led to inhibition of N removal. (2) The topology of molecular ecological networks showed differentiated coupling effects between cycling of N with C or S. Specifically, methane metabolism in reservoirs with low N concentrations promoted N removal, and the co-occurrence between N and S cycling enhanced the simultaneous removal of N and S. Conversely, co-exclusion between N with C or S cycling functional groups hindered nitrification and denitrification in sluices with high N concentrations. (3) For sediment, a high N concentration enhanced the potential of mineralization, nitrification, denitrification, and anaerobic ammonium oxidation to facilitate inorganic N removal. The co-exclusion between C and N cycling consistently inhibited denitrification, anaerobic ammonium oxidation, and N fixation in different habitats. Co-occurrence of organic S transformation, S mineralization, S reduction genes and N mineralization, organic N synthesis, and denitrification genes promoted N removal. In conclusion, high N concentrations of overlying water and dam-induced stagnation impeded N removal, while the coupling of N with C or S cycling showed different effects on N removal in different stagnant habitats.
内陆水生系统在全球氮（N）循环中发挥着至关重要的作用。本研究以沙颍河流域为重点，该流域的特点是密集筑坝、基流不足和高氮负荷。我们利用元基因组测序和分子生态网络分析，对不同积水生境的氮循环及其与碳（C）和硫（S）循环的耦合效应进行了比较分析。我们的主要发现如下。(1）与自由流动生境相比，滞洪生境（水库和水闸）上覆水的矿化和有机氮合成基因丰度较高，而反硝化和硝化基因丰度较低。这些结果表明，筑坝加强了有机氮和无机氮之间的转化，但削弱了无机氮的去除。高浓度氮的叠加也导致了对氮去除的抑制。(2) 分子生态网络拓扑结构显示了 N 与 C 或 S 循环之间不同的耦合效应。具体而言，低 N 浓度水库中的甲烷代谢促进了 N 的去除，N 与 S 循环之间的共生增强了 N 和 S 的同时去除；相反，N 与 C 或 S 循环功能群之间的共排斥阻碍了高 N 浓度水闸中的硝化和反硝化。(3) 就沉积物而言，高浓度的 N 会增强矿化、硝化、反硝化和厌氧铵氧化的潜力，从而促进无机 N 的去除。在不同的生境中，碳和氮循环之间的共同排斥始终抑制反硝化、厌氧铵氧化和氮固定。有机 S 转化、S 矿化、S 还原基因与 N 矿化、有机 N 合成和反硝化基因的共存促进了 N 的去除。总之，上层水的高浓度氮和水坝引起的停滞阻碍了氮的去除，而氮与碳或硫循环的耦合在不同的停滞生境中对氮的去除有不同的影响。
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Surface water bodies provide essential sources of fresh water for drinking, agriculture and industrial purposes. The hyporheic zone beneath and around surface water bodies plays a crucial role in maintaining the health and functionality of aquatic ecosystems. In recent times, surface water bodies have been observed to dry up rapidly in many parts of the globe due to groundwater drought. Hence, examining the sustenance of the surface water bodies over time is essential, which includes examining whether they are in a gaining or losing state and acting as perennial or non-perennial. In this context, this research aimed to analyze the behavioural changes in surface water bodies due to groundwater drought and stream-aquifer interaction in the Godavari Basin, India. This study proposes a new index called the Potential Stream-Aquifer Interaction Index (PSAII) or the Potential River Perenniality Index (PRPI) to define the nature of surface water bodies. This study employs satellite-derived bathymetry to determine water depth in ungauged lakes and Standardized Ground Water Table Index (SGWTI) to ascertain the magnitude of groundwater drought. The influence of groundwater drought on surface water bodies is determined based on the correlation of the head difference between groundwater table (GWT) and water surface elevation, as well as the SGWTI. The analysis reveals that PRPI for the upper Godavari River ranges from 0 to −0.4, and the lower Godavari River lies between 2.03 and 2.04 from 2009 to 2018, validated by the flow duration curves. The correlation between head difference and SGWTI lies between 0.8 and 0.9 for the upper Godavari and 0.08 to 0.48 for the lower Godavari River. The results imply that the upper Godavari river basin has become non-perennial due to groundwater drought. Based on the correlation analysis, it is also found that several ungauged lakes in the vicinity of the upper Godavari River are highly influenced by groundwater drought.
{"title":"A novel approach to analyze the impact of groundwater drought on the perennial environment and hyporheic zone of surface water bodies","authors":"Thallam Prashanth, Sayantan Ganguly, Gummadi Manoj, Dharmaraj Teppala","doi":"10.1016/j.jhydrol.2025.132668","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132668","url":null,"abstract":"Surface water bodies provide essential sources of fresh water for drinking, agriculture and industrial purposes. The hyporheic zone beneath and around surface water bodies plays a crucial role in maintaining the health and functionality of aquatic ecosystems. In recent times, surface water bodies have been observed to dry up rapidly in many parts of the globe due to groundwater drought. Hence, examining the sustenance of the surface water bodies over time is essential, which includes examining whether they are in a gaining or losing state and acting as perennial or non-perennial. In this context, this research aimed to analyze the behavioural changes in surface water bodies due to groundwater drought and stream-aquifer interaction in the Godavari Basin, India. This study proposes a new index called the Potential Stream-Aquifer Interaction Index (<ce:italic>PSAII</ce:italic>) or the Potential River Perenniality Index (<ce:italic>PRPI</ce:italic>) to define the nature of surface water bodies. This study employs satellite-derived bathymetry to determine water depth in ungauged lakes and Standardized Ground Water Table Index (<ce:italic>SGWTI</ce:italic>) to ascertain the magnitude of groundwater drought. The influence of groundwater drought on surface water bodies is determined based on the correlation of the head difference between groundwater table (GWT) and water surface elevation, as well as the <ce:italic>SGWTI</ce:italic>. The analysis reveals that <ce:italic>PRPI</ce:italic> for the upper Godavari River ranges from 0 to −0.4, and the lower Godavari River lies between 2.03 and 2.04 from 2009 to 2018, validated by the flow duration curves. The correlation between head difference and <ce:italic>SGWTI</ce:italic> lies between 0.8 and 0.9 for the upper Godavari and 0.08 to 0.48 for the lower Godavari River. The results imply that the upper Godavari river basin has become non-perennial due to groundwater drought. Based on the correlation analysis, it is also found that several ungauged lakes in the vicinity of the upper Godavari River are highly influenced by groundwater drought.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"2 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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