Pub Date : 2025-01-04DOI: 10.1016/j.jhydrol.2025.132664
Filip Stanić, Nenad Jaćimović, Željko Vasilić, Anja Ranđelović
Hydrological models use methods of varying complexity to compute vertical infiltration described by Richards equation, which lacks an analytical solution, and is often solved using time-consuming, iterative numerical models. For continuous hydrological simulations these models are often replaced by simpler, yet less accurate models for greater computational efficiency. Seeking a compromise between accuracy and efficiency, a new semi-numerical infiltration model, combining conceptual and physically based approaches is developed and presented in this paper. The model assumes dividing the computational domain into computational cells that retain a differential form of the mass balance equation. After linearizing the input and output flux in each cell, an analytical solution of the mass balance equation is obtained. The solution is similar to a “linear reservoir” function, and it is valid only for a discrete time interval. By combining such solutions for each computational cell, a tridiagonal system of linear equations is obtained and solved directly without iterations. This non-iterative approach to solving Richards equation is reminiscent of the Ross model, with a key difference in the “linear reservoir” exponential term, contributing to the accuracy and stability of the presented semi-numerical model. Comparison between this model and the Ross model on four numerical examples shows that, except in strictly unsaturated conditions when the soil is exposed to low-intensity precipitation, the semi-numerical model achieves more stable results with considerably smaller number of computational steps and reduced mass balance errors. This indicates a clear potential for effective application of the proposed approach in distributed hydrological models.
{"title":"A novel semi-numerical infiltration model combining conceptual and physically based approaches","authors":"Filip Stanić, Nenad Jaćimović, Željko Vasilić, Anja Ranđelović","doi":"10.1016/j.jhydrol.2025.132664","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132664","url":null,"abstract":"Hydrological models use methods of varying complexity to compute vertical infiltration described by Richards equation, which lacks an analytical solution, and is often solved using time-consuming, iterative numerical models. For continuous hydrological simulations these models are often replaced by simpler, yet less accurate models for greater computational efficiency. Seeking a compromise between accuracy and efficiency, a new semi-numerical infiltration model, combining conceptual and physically based approaches is developed and presented in this paper. The model assumes dividing the computational domain into computational cells that retain a differential form of the mass balance equation. After linearizing the input and output flux in each cell, an analytical solution of the mass balance equation is obtained. The solution is similar to a “linear reservoir” function, and it is valid only for a discrete time interval. By combining such solutions for each computational cell, a tridiagonal system of linear equations is obtained and solved directly without iterations. This non-iterative approach to solving Richards equation is reminiscent of the Ross model, with a key difference in the “linear reservoir” exponential term, contributing to the accuracy and stability of the presented semi-numerical model. Comparison between this model and the Ross model on four numerical examples shows that, except in strictly unsaturated conditions when the soil is exposed to low-intensity precipitation, the semi-numerical model achieves more stable results with considerably smaller number of computational steps and reduced mass balance errors. This indicates a clear potential for effective application of the proposed approach in distributed hydrological models.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"89 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968029","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.2025.132665
Yihang Wang, Nan Cong, Yu Zhong, Yongshuo Fu, Nan Wang, Lijian Ouyang, Weiwei Yao
The Jinsha River (JSR) serves as a crucial ecological corridor in the upper Yangtze River Basin, hosting one of the world’s largest cascade hydropower dam (CHD) developments. However, systematic quantitative research on riparian vegetation (RV) ecosystems along JSR response to CHD construction and climate change is lacking. Using multi-source datasets from 2000 to 2022, we quantified the effects of CHD on key climatic factors (temperature, precipitation, vapor pressure deficit, and soil moisture) and analyzed their contributions to RV dynamics. Results indicate that RV in the JSR exhibited a more pronounced “greening” trend after CHD construction (The NDVI trend is rising from 0.0021 yr−1 to 0.0096 yr−1, by more than four times). CHD operations led to decreased temperature and vapor pressure deficit, while increasing precipitation and soil moisture, significantly improving the growth conditions for RV. Structural equation modeling further revealed that CHD not only directly promoted RV growth but also exerted significant positive indirect effects by regulating the regional microclimate. Importantly, the cumulative effects of CHD resulted in a long-term positive impact on RV growth that outweighed the initial short-term negative impacts during construction. This study underscores the importance of integrating hydropower development into long-term riparian ecosystem monitoring and management, providing valuable insights for sustainable river basin management amid global climate change.
{"title":"Impacts of cascade dam construction on riparian vegetation in an alpine region","authors":"Yihang Wang, Nan Cong, Yu Zhong, Yongshuo Fu, Nan Wang, Lijian Ouyang, Weiwei Yao","doi":"10.1016/j.jhydrol.2025.132665","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132665","url":null,"abstract":"The Jinsha River (JSR) serves as a crucial ecological corridor in the upper Yangtze River Basin, hosting one of the world’s largest cascade hydropower dam (CHD) developments. However, systematic quantitative research on riparian vegetation (RV) ecosystems along JSR response to CHD construction and climate change is lacking. Using multi-source datasets from 2000 to 2022, we quantified the effects of CHD on key climatic factors (temperature, precipitation, vapor pressure deficit, and soil moisture) and analyzed their contributions to RV dynamics. Results indicate that RV in the JSR exhibited a more pronounced “greening” trend after CHD construction (The NDVI trend is rising from 0.0021 yr<ce:sup loc=\"post\">−1</ce:sup> to 0.0096 yr<ce:sup loc=\"post\">−1</ce:sup>, by more than four times). CHD operations led to decreased temperature and vapor pressure deficit, while increasing precipitation and soil moisture, significantly improving the growth conditions for RV. Structural equation modeling further revealed that CHD not only directly promoted RV growth but also exerted significant positive indirect effects by regulating the regional microclimate. Importantly, the cumulative effects of CHD resulted in a long-term positive impact on RV growth that outweighed the initial short-term negative impacts during construction. This study underscores the importance of integrating hydropower development into long-term riparian ecosystem monitoring and management, providing valuable insights for sustainable river basin management amid global climate change.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"21 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968016","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.2025.132675
Somaye Imani, Mohammad Hossein Niksokhan, Reza Safari shali
Just water resource allocation is a critical policy issue, particularly in water-scarce areas. Governments claim that water allocation policies are adopted and implemented based on justice. However, justice is a value that still requires clear explanations and rules for implementation. This study mainly explores the perception of Iranian policy-makers in water resources concerning the distributive justice. For this purpose, the empirical evidence was collected from semi-structured, in-depth interviews, in which the interviewees answered exploratory open-ended questions. These interviews were conducted from November to February 2021 with 28 water policy-makers. They believed the current water allocation system should be improved to ensure justice, particularly to adapt to emerging challenges like climate change. Results showed that the prevailing understandings of policy-makers about distributive justice were based on principles like liberty, human right to water (drinking, sanitation and the environment), and “different principles” of Rawls. The latter emphasizes supporting the deprived and poor water users. Furthermore, policy-makers regarded “water-use efficiency” as the fairest criterion for water resource allocation. The general mindset of Iranian policy-makers, similar to other water-scarce countries, was consistent with the current trend of market-based approaches. However, to incorporate the prevailing perspective of policy-makers into water reform policies, we argued that water market development requires solving some fundamental issues first. These are the explicit identification of distributive justice conceptions, a clear legal definition of water ownership, and the entitlement to revise the permits based on water scarcity for fair water reallocation.
{"title":"Fair water re-allocation: Lessons learnt from the perception of Iranian policy-makers about distributive justice","authors":"Somaye Imani, Mohammad Hossein Niksokhan, Reza Safari shali","doi":"10.1016/j.jhydrol.2025.132675","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132675","url":null,"abstract":"Just water resource allocation is a critical policy issue, particularly in water-scarce areas. Governments claim that water allocation policies are adopted and implemented based on justice. However, justice is a value that still requires clear explanations and rules for implementation. This study mainly explores the perception of Iranian policy-makers in water resources concerning the distributive justice. For this purpose, the empirical evidence was collected from semi-structured, in-depth interviews, in which the interviewees answered exploratory open-ended questions. These interviews were conducted from November to February 2021 with 28 water policy-makers. They believed the current water allocation system should be improved to ensure justice, particularly to adapt to emerging challenges like climate change. Results showed that the prevailing understandings of policy-makers about distributive justice were based on principles like liberty, human right to water (drinking, sanitation and the environment), and “different principles” of Rawls. The latter emphasizes supporting the deprived and poor water users. Furthermore, policy-makers regarded “water-use efficiency” as the fairest criterion for water resource allocation. The general mindset of Iranian policy-makers, similar to other water-scarce countries, was consistent with the current trend of market-based approaches. However, to incorporate the prevailing perspective of policy-makers into water reform policies, we argued that water market development requires solving some fundamental issues first. These are the explicit identification of distributive justice conceptions, a clear legal definition of water ownership, and the entitlement to revise the permits based on water scarcity for fair water reallocation.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"29 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968018","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.2025.132673
Chi Zhang, Di Long, Xizhi Nong, Kourosh Behzadian, Dongguo Shao, Luiza C. Campos
Achieving reasonable and effective nutrient management requires a comprehensive framework that seamlessly integrates modelling outcomes for both present conditions and future projections. Due to the diversity of basin attributes and variations of removal processes in large-scale basins, it remains difficult to understand nutrient budgets in basins with complex stream networks. Additionally, external environmental changes induced by climate change and socioeconomic development, will also bring uncertainty to water management policies based on current assessments. This study develops a new holistic framework based on the SPARROW (SPAtially Referenced Regression On Watershed attributes) model coupling with the interaction of climate change and socioeconomic development. The framework can integrate multi-source information and model present and future scenarios to evaluate the nutrient status comprehensively. The application of this methodology is demonstrated through a case study in the Danjiangkou Reservoir basin (DJKRB) in China. Findings revealed that atmospheric deposition emerged as the predominant total nitrogen (TN) source in the DJKRB, contributing over 60% on average; while total phosphorus (TP) sources were more diversified, with untreated urban wastewater being a significant contributor, accounting for roughly 37%. The analysis of future uncertainties based on scenario simulations and sensitivity analyses further shows the need for prioritising efforts to mitigate atmospheric nitrogen pollution and promote precipitation-induced runoff management within the DJKRB. This study not only serves as a scientific basis for nutrient modelling in the context of evolving environmental conditions but also proposes a practical methodological framework for water resources management in expansive basins through a real-world case study in the DJKRB.
{"title":"Modelling of basin-scale nutrient loading variations under the synergistic influences of climate change and socioeconomic development","authors":"Chi Zhang, Di Long, Xizhi Nong, Kourosh Behzadian, Dongguo Shao, Luiza C. Campos","doi":"10.1016/j.jhydrol.2025.132673","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2025.132673","url":null,"abstract":"Achieving reasonable and effective nutrient management requires a comprehensive framework that seamlessly integrates modelling outcomes for both present conditions and future projections. Due to the diversity of basin attributes and variations of removal processes in large-scale basins, it remains difficult to understand nutrient budgets in basins with complex stream networks. Additionally, external environmental changes induced by climate change and socioeconomic development, will also bring uncertainty to water management policies based on current assessments. This study develops a new holistic framework based on the SPARROW (SPAtially Referenced Regression On Watershed attributes) model coupling with the interaction of climate change and socioeconomic development. The framework can integrate multi-source information and model present and future scenarios to evaluate the nutrient status comprehensively. The application of this methodology is demonstrated through a case study in the Danjiangkou Reservoir basin (DJKRB) in China. Findings revealed that atmospheric deposition emerged as the predominant total nitrogen (TN) source in the DJKRB, contributing over 60% on average; while total phosphorus (TP) sources were more diversified, with untreated urban wastewater being a significant contributor, accounting for roughly 37%. The analysis of future uncertainties based on scenario simulations and sensitivity analyses further shows the need for prioritising efforts to mitigate atmospheric nitrogen pollution and promote precipitation-induced runoff management within the DJKRB. This study not only serves as a scientific basis for nutrient modelling in the context of evolving environmental conditions but also proposes a practical methodological framework for water resources management in expansive basins through a real-world case study in the DJKRB.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"22 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968019","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.2024.132662
Katarzyna Samborska-Goik, Simon Bottrell
The study encompasses three cases: a segment of the pristine carbonate aquifer with relatively low dissolved sulphate concentrations, often below 20 mg/L; a chalk aquifer contaminated by hydrocarbons, where microbial sulphate reduction was confirmed; and an experiment involving dissimilatory sulphate reduction. Bayesian models were developed using Python and its libraries, alongside the open-source probabilistic programming framework PYMC3, to assess the sulphate reduction hypothesis, ascertain the initial conditions of these processes, and demonstrate the applicability of this probabilistic method in elucidating hydrogeochemical processes, surpassing the widely used Rayleigh distillation model. The model produced robust and dependable results for both aquifer cases. Additionally, a model was devised to validate controllable and known conditions. The findings hold promise and may find relevance in similar hydrogeochemical processes. This study underscores the effectiveness of Bayesian modelling in revealing initial process conditions, particularly in situations where knowledge and data are limited. This limitation is often encountered with isotopic data, which remain relatively scarce due to the considerable effort required for material preparation and the costs associated with such investigations. Moreover, this study contributes to the identification of redox zonation, which holds significant implications for water quality-related concerns such as pollutant migration, remediation of hydrocarbon-contaminated sites, and meeting potable water quality standards regarding metal concentrations.
{"title":"Bayesian modelling of sulphate isotopic composition in pristine, contaminated, and experimental environments for investigating microbial bacterial reduction","authors":"Katarzyna Samborska-Goik, Simon Bottrell","doi":"10.1016/j.jhydrol.2024.132662","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132662","url":null,"abstract":"The study encompasses three cases: a segment of the pristine carbonate aquifer with relatively low dissolved sulphate concentrations, often below 20 mg/L; a chalk aquifer contaminated by hydrocarbons, where microbial sulphate reduction was confirmed; and an experiment involving dissimilatory sulphate reduction. Bayesian models were developed using Python and its libraries, alongside the open-source probabilistic programming framework PYMC3, to assess the sulphate reduction hypothesis, ascertain the initial conditions of these processes, and demonstrate the applicability of this probabilistic method in elucidating hydrogeochemical processes, surpassing the widely used Rayleigh distillation model. The model produced robust and dependable results for both aquifer cases. Additionally, a model was devised to validate controllable and known conditions. The findings hold promise and may find relevance in similar hydrogeochemical processes. This study underscores the effectiveness of Bayesian modelling in revealing initial process conditions, particularly in situations where knowledge and data are limited. This limitation is often encountered with isotopic data, which remain relatively scarce due to the considerable effort required for material preparation and the costs associated with such investigations. Moreover, this study contributes to the identification of redox zonation, which holds significant implications for water quality-related concerns such as pollutant migration, remediation of hydrocarbon-contaminated sites, and meeting potable water quality standards regarding metal concentrations.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"16 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968022","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 : 2024-12-31DOI: 10.1016/j.jhydrol.2024.132573
Diogo Costa, Andrea Spolaor, Elena Barbaro, Juan I. López-Moreno, John W. Pomeroy
Circumpolar and high-elevation cold regions receive a large portion of their annual precipitation as snowfall, which accumulates in snowpacks that can store many contaminants. The discharge of chemical eluent during snowmelt can alter the chemical composition of local streams and have a detrimental effect on aquatic ecosystems.
{"title":"Improving snowpack chemistry simulations through improved representation of liquid water movement through layered snow and rain-on-snow (ROS) episodes: Application to Svalbard, Norway","authors":"Diogo Costa, Andrea Spolaor, Elena Barbaro, Juan I. López-Moreno, John W. Pomeroy","doi":"10.1016/j.jhydrol.2024.132573","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132573","url":null,"abstract":"Circumpolar and high-elevation cold regions receive a large portion of their annual precipitation as snowfall, which accumulates in snowpacks that can store many contaminants. The discharge of chemical eluent during snowmelt can alter the chemical composition of local streams and have a detrimental effect on aquatic ecosystems.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"78 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929768","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 : 2024-12-29DOI: 10.1016/j.jhydrol.2024.132601
Min Lv, Zhinan Su, Guanglong Qiu, Kam W. Tang, Yan Hong, Yifei Zhang, Yingyi Chen, Jiafang Huang, Wanyi Zhu, Hong Yang, Ping Yang
Aquaculture contributes to global CO2 emission, but the intensity varies across different aquaculture systems. In this study, we compared the CO2 emission between earthen aquaculture ponds (EAP), plastic-lined ponds (PLAP), and mangrove wetland eco-aquaculture systems (MWEAS) in the coastal region of the Guangxi Province, South China. Results showed that CO2 emissions varied between −27.28 and 12.39 mg m−2h−1 from PLAP and between −17.78 and 14.18 mg m−2h−1 from MWEAS, both significantly lower than those from EAP (2.76 to 19.30 mg m−2h−1). The largest emission fluxes were observed during the growth stage of the farming period. On average, PLAP and MWEAS acted as CO2 sinks, whereas EAP acted as a source, and Chlorophyll-a, phosphorus and carbon substrates were the main environmental factors influencing the variation in CO2 emissions. The overall average CO2 emission flux from our aquaculture ponds was 2.54 ± 1.11 mg m−2h−1, which was much lower than those observed in freshwater and brackish coastal aquaculture ponds, but higher than certain high-salinity aquaculture environments. In summary, enhancing water salinity levels and encouraging the adoption of plastic liners alongside ecological aquaculture systems could serve as effective strategies for mitigating CO2 emissions in coastal aquaculture.
{"title":"Plastic-lined ponds and eco-aquaculture systems had lower CO2 emissions than earthen aquaculture ponds","authors":"Min Lv, Zhinan Su, Guanglong Qiu, Kam W. Tang, Yan Hong, Yifei Zhang, Yingyi Chen, Jiafang Huang, Wanyi Zhu, Hong Yang, Ping Yang","doi":"10.1016/j.jhydrol.2024.132601","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132601","url":null,"abstract":"Aquaculture contributes to global CO<ce:inf loc=\"post\">2</ce:inf> emission, but the intensity varies across different aquaculture systems. In this study, we compared the CO<ce:inf loc=\"post\">2</ce:inf> emission between earthen aquaculture ponds (EAP), plastic-lined ponds (PLAP), and mangrove wetland eco-aquaculture systems (MWEAS) in the coastal region of the Guangxi Province, South China. Results showed that CO<ce:inf loc=\"post\">2</ce:inf> emissions varied between −27.28 and 12.39 mg m<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup> from PLAP and between −17.78 and 14.18 mg m<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup> from MWEAS, both significantly lower than those from EAP (2.76 to 19.30 mg m<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>). The largest emission fluxes were observed during the growth stage of the farming period. On average, PLAP and MWEAS acted as CO<ce:inf loc=\"post\">2</ce:inf> sinks, whereas EAP acted as a source, and Chlorophyll-<ce:italic>a</ce:italic>, phosphorus and carbon substrates were the main environmental factors influencing the variation in CO<ce:inf loc=\"post\">2</ce:inf> emissions. The overall average CO<ce:inf loc=\"post\">2</ce:inf> emission flux from our aquaculture ponds was 2.54 ± 1.11 mg m<ce:sup loc=\"post\">−2</ce:sup>h<ce:sup loc=\"post\">−1</ce:sup>, which was much lower than those observed in freshwater and brackish coastal aquaculture ponds, but higher than certain high-salinity aquaculture environments. In summary, enhancing water salinity levels and encouraging the adoption of plastic liners alongside ecological aquaculture systems could serve as effective strategies for mitigating CO<ce:inf loc=\"post\">2</ce:inf> emissions in coastal aquaculture.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"27 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929769","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}
With the frequent occurrence of extreme rainfall and the acceleration of urbanization, the issue of urban flooding worldwide has gained increasing prominence. City scale flooding risk assessment is critical for urban safety and renovation, yet faces challenges such as data complexity, accuracy and interpretability. In this study, a machine learning approach incorporated with multi-source big data was developed to perform a city-scale urban flooding assessment. The developed approach was demonstrated in China’s largest city of Shanghai. Five ensemble learning models including categorical boosting (CatBoost), extreme gradient boosting, random forest, light gradient boosting machine and adaptive boosting, were employed for establishing the relationship among a variety of geological, natural, social-economical factors and urban flooding events. It was found that all the ensemble learning models achieved prediction reliability of over 80% for the city-scale flooding events; specially, the CatBoost model had the relatively best performance, offering 95% prediction of the actual flooding events. With the CatBoost model, Shapley additive explanations, partial dependency plot and individual conditional expectation plot were further employed to probe the quantitative effects of a variety of factors on urban flooding. It was revealed that areas with higher road network density, slighter topography gradient, closer distance to rivers, higher gross domestic product and population density are more prone to urban flooding. Further, city-scale risk map was generated, showing downtown areas exhibits higher flooding risk than the suburban areas. Therefore, urban flooding prevention strategies were provided.
{"title":"City scale urban flooding risk assessment using multi-source data and machine learning approach","authors":"Qing Wei, Huijin Zhang, Yongqi Chen, Yifan Xie, Hailong Yin, Zuxin Xu","doi":"10.1016/j.jhydrol.2024.132626","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132626","url":null,"abstract":"With the frequent occurrence of extreme rainfall and the acceleration of urbanization, the issue of urban flooding worldwide has gained increasing prominence. City scale flooding risk assessment is critical for urban safety and renovation, yet faces challenges such as data complexity, accuracy and interpretability. In this study, a machine learning approach incorporated with multi-source big data was developed to perform a city-scale urban flooding assessment. The developed approach was demonstrated in China’s largest city of Shanghai. Five ensemble learning models including categorical boosting (CatBoost), extreme gradient boosting, random forest, light gradient boosting machine and adaptive boosting, were employed for establishing the relationship among a variety of geological, natural, social-economical factors and urban flooding events. It was found that all the ensemble learning models achieved prediction reliability of over 80% for the city-scale flooding events; specially, the CatBoost model had the relatively best performance, offering 95% prediction of the actual flooding events. With the CatBoost model, Shapley additive explanations, partial dependency plot and individual conditional expectation plot were further employed to probe the quantitative effects of a variety of factors on urban flooding. It was revealed that areas with higher road network density, slighter topography gradient, closer distance to rivers, higher gross domestic product and population density are more prone to urban flooding. Further, city-scale risk map was generated, showing downtown areas exhibits higher flooding risk than the suburban areas. Therefore, urban flooding prevention strategies were provided.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"4 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929766","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 : 2024-12-28DOI: 10.1016/j.jhydrol.2024.132617
Cheng Huang, Long Zhao, Yingying Chen, Jinyan Chen, Kun Yang
Soil moisture plays a key role in regulating water and energy cycle in the Tibetan Plateau, which further impacts regional climate. However, the quality of existing soil moisture products over the central to west TP (CWTP) remains unclear due to the lack of in-situ observations. Using rain gauge data from a recently established rainfall network in the CWTP region and by checking the hydrological consistency between surface soil moisture and precipitation, this study performed a first evaluation of several satellite soil moisture products, including those from the Soil Moisture Active Passive (SMAP), the European Space Agency Climate Change Initiative (ESA CCI), and the artificial neural network reproduced AMSR-E/2 retrievals (NNsm) in this area. Results show that: (1) both descending and ascending orbits of the SMAP product generally outperform ESA CCI and NNsm, with more robust hydrological consistency across all rain gauge stations; (2) ESA CCI is subjected to low availability of effective retrievals in this area, with the combined one performs slightly more robust than the active and passive channels; and (3) NNsm possesses the most effective soil moisture retrievals owing to less revisit time of AMSR2, but its hydrological consistency is the lowest as compared to the other two products. Further analysis suggests that large topography and dense vegetation can potentially impact the retrieval accuracy of surface soil moisture and thus hydrological consistency. Besides, relatively large amounts of rainfall is likely to impose more positive increments in surface soil moisture, whereas extremely heavy rainfall may further degrade hydrological consistency. These findings are complementary to existing soil moisture evaluations in the eastern TP, and are expected to contribute to improving soil moisture retrieval algorithms and understanding land–atmosphere interactions over the entire plateau.
{"title":"A first evaluation of satellite soil moisture products over the Central-Western Tibetan Plateau using rain gauge observations","authors":"Cheng Huang, Long Zhao, Yingying Chen, Jinyan Chen, Kun Yang","doi":"10.1016/j.jhydrol.2024.132617","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132617","url":null,"abstract":"Soil moisture plays a key role in regulating water and energy cycle in the Tibetan Plateau, which further impacts regional climate. However, the quality of existing soil moisture products over the central to west TP (CWTP) remains unclear due to the lack of in-situ observations. Using rain gauge data from a recently established rainfall network in the CWTP region and by checking the hydrological consistency between surface soil moisture and precipitation, this study performed a first evaluation of several satellite soil moisture products, including those from the Soil Moisture Active Passive (SMAP), the European Space Agency Climate Change Initiative (ESA CCI), and the artificial neural network reproduced AMSR-E/2 retrievals (NNsm) in this area. Results show that: (1) both descending and ascending orbits of the SMAP product generally outperform ESA CCI and NNsm, with more robust hydrological consistency across all rain gauge stations; (2) ESA CCI is subjected to low availability of effective retrievals in this area, with the combined one performs slightly more robust than the active and passive channels; and (3) NNsm possesses the most effective soil moisture retrievals owing to less revisit time of AMSR2, but its hydrological consistency is the lowest as compared to the other two products. Further analysis suggests that large topography and dense vegetation can potentially impact the retrieval accuracy of surface soil moisture and thus hydrological consistency. Besides, relatively large amounts of rainfall is likely to impose more positive increments in surface soil moisture, whereas extremely heavy rainfall may further degrade hydrological consistency. These findings are complementary to existing soil moisture evaluations in the eastern TP, and are expected to contribute to improving soil moisture retrieval algorithms and understanding land–atmosphere interactions over the entire plateau.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"27 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929729","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 : 2024-12-28DOI: 10.1016/j.jhydrol.2024.132604
Kevin K.W. Cheung, Ugur Ozturk, Nishant Malik, Ankit Agarwal, Raghavan Krishnan, Balaji Rajagopalan
Monsoon precipitation is the critical source of freshwater for some of the world’s most densely populated areas, yet extreme precipitation events in these regions present significant risks, including devastating floods and damage to agriculture and infrastructure. Recent events, such as the severe flooding and landslides during the 2023 North India monsoon and the 2022 Pakistan floods,11For example, https://foreignpolicy.com/2022/09/01/pakistan-flooding-crisis-climate-change-governance/ and https://www.theguardian.com/world/2023/jul/10/india-floods-new-delhi-rain-record-deaths (last assessed 30 September 2024). underscore the pressing need to better understand and predict these hazards. While the science of monsoons has been studied for decades, with theories centered on global dynamics and moist energy budgets to explain the zonal mean state of monsoon and factors leading to regional differences, one key theme of all these analyses is the spatiotemporal variability of rainfall from the dry to wet seasons. A key challenge is understanding and predicting extreme rainfall incidents during monsoon seasons to help mitigate dire undesired consequences.
{"title":"A review of synchronization of extreme precipitation events in monsoons from complex network perspective","authors":"Kevin K.W. Cheung, Ugur Ozturk, Nishant Malik, Ankit Agarwal, Raghavan Krishnan, Balaji Rajagopalan","doi":"10.1016/j.jhydrol.2024.132604","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132604","url":null,"abstract":"Monsoon precipitation is the critical source of freshwater for some of the world’s most densely populated areas, yet extreme precipitation events in these regions present significant risks, including devastating floods and damage to agriculture and infrastructure. Recent events, such as the severe flooding and landslides during the 2023 North India monsoon and the 2022 Pakistan floods,<ce:cross-ref ref><ce:sup loc=\"post\">1</ce:sup></ce:cross-ref><ce:footnote><ce:label>1</ce:label><ce:note-para view=\"all\">For example, <ce:inter-ref xlink:href=\"https://foreignpolicy.com/2022/09/01/pakistan-flooding-crisis-climate-change-governance/\" xlink:type=\"simple\">https://foreignpolicy.com/2022/09/01/pakistan-flooding-crisis-climate-change-governance/</ce:inter-ref> and <ce:inter-ref xlink:href=\"https://www.theguardian.com/world/2023/jul/10/india-floods-new-delhi-rain-record-deaths\" xlink:type=\"simple\">https://www.theguardian.com/world/2023/jul/10/india-floods-new-delhi-rain-record-deaths</ce:inter-ref> (last assessed 30 September 2024).</ce:note-para></ce:footnote> underscore the pressing need to better understand and predict these hazards. While the science of monsoons has been studied for decades, with theories centered on global dynamics and moist energy budgets to explain the zonal mean state of monsoon and factors leading to regional differences, one key theme of all these analyses is the spatiotemporal variability of rainfall from the dry to wet seasons. A key challenge is understanding and predicting extreme rainfall incidents during monsoon seasons to help mitigate dire undesired consequences.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"38 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929730","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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