Sijal Dangol, Xuesong Zhang, Chao Sun, Kang Liang, Xin-Zhong Liang
The cultivation of bioenergy feedstocks, such as miscanthus, or energycane, on marginal lands helps alleviate the competition between food and fuel. However, such land use conversion could lead to complex interactions among climate, vegetation, and water resources, resulting in positive or negative environmental impacts. In this study, we used the Climate-Weather Research and Forecasting (CWRF) model to simulate the feedback from growing bioenergy crops on marginal lands to regional climate for the present and future scenarios and used the Soil and Water Assessment Tool (SWAT) to evaluate the climate and land use change impacts on the hydrological cycle and water quality in the U.S. Northern High Plains Aquifer region. CWRF projects a wetter and cooler regional climate by considering climate-crop feedback as compared to a control scenario (no land use change and climate feedback), highlighting the importance of land-atmosphere interactions in regional climate assessment. Our watershed-scale assessment shows that although growing miscanthus increases local evapotranspiration and decreases surface runoff, soil moisture, and percolation on marginal lands, watershed-scale streamflow substantially increases during the growing season in both present and future conditions due to increases in regional precipitation. The differences in the extent of marginal land use change between the Platte (4%) and Republican (20%) river basins result in different responses in streamflow and nitrogen loading. Overall, our study highlights the importance of assessing the regional climate-crop feedback and environmental quality impacts of using marginal lands for future sustainable bioenergy production.
{"title":"Impacts of Bioenergy Crop Cultivation on Regional Climate, Hydrology, and Water Quality in the U.S. Northern High Plains","authors":"Sijal Dangol, Xuesong Zhang, Chao Sun, Kang Liang, Xin-Zhong Liang","doi":"10.1029/2024wr037782","DOIUrl":"https://doi.org/10.1029/2024wr037782","url":null,"abstract":"The cultivation of bioenergy feedstocks, such as miscanthus, or energycane, on marginal lands helps alleviate the competition between food and fuel. However, such land use conversion could lead to complex interactions among climate, vegetation, and water resources, resulting in positive or negative environmental impacts. In this study, we used the Climate-Weather Research and Forecasting (CWRF) model to simulate the feedback from growing bioenergy crops on marginal lands to regional climate for the present and future scenarios and used the Soil and Water Assessment Tool (SWAT) to evaluate the climate and land use change impacts on the hydrological cycle and water quality in the U.S. Northern High Plains Aquifer region. CWRF projects a wetter and cooler regional climate by considering climate-crop feedback as compared to a control scenario (no land use change and climate feedback), highlighting the importance of land-atmosphere interactions in regional climate assessment. Our watershed-scale assessment shows that although growing miscanthus increases local evapotranspiration and decreases surface runoff, soil moisture, and percolation on marginal lands, watershed-scale streamflow substantially increases during the growing season in both present and future conditions due to increases in regional precipitation. The differences in the extent of marginal land use change between the Platte (4%) and Republican (20%) river basins result in different responses in streamflow and nitrogen loading. Overall, our study highlights the importance of assessing the regional climate-crop feedback and environmental quality impacts of using marginal lands for future sustainable bioenergy production.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"12 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143192362","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}
The physics-informed neural network (PINN) method has been applied to solve water hammer equations in pipeline systems due to its ability of seamlessly integrate measurement data with conservation laws, offering advantages over traditional numerical method. However, existing PINN approaches require multiple neural networks to construct composite models for complex water distribution systems (WDS). This situation treats nodal information as boundary condition or labeled data during training, leading to a weaker robustness and a high demand for data. To address these issues, a hybrid water hammer model based on eXtended Physics-Informed Neural Networks for WDS (WDS-XPINN) is developed in this study. Unlike the standard PINN, WDS-XPINN incorporates the nodal mechanistic model directly into the loss function, enabling to synchronously train a unified neural network jointly through sparse augmented measurement data for pipeline system. Additionally, an adaptive weights method is introduced to improve model robustness by balancing the contributions of flowrates and pressures. The proposed WDS-XPINN is evaluated in two case studies: a series pipeline system with different operational events and noise perturbation, as well as a topological structure with looped and branched pipe. According to the simulation results and uncertainty analysis, the WDS-XPINN model demonstrates its excellent capacity of modeling fluid transient accurately in pipeline system, even without exact operational conditions or true pipe parameters.
{"title":"A Hybrid Model Coupling Data and Hydraulic Transient Laws for Water Distribution Systems","authors":"Hexiang Yan, Shixun Li, Wenchong Tian, Jiaying Wang, Fei Li, Huanfeng Duan, Tao Tao, Kunlun Xin","doi":"10.1029/2023wr036641","DOIUrl":"https://doi.org/10.1029/2023wr036641","url":null,"abstract":"The physics-informed neural network (PINN) method has been applied to solve water hammer equations in pipeline systems due to its ability of seamlessly integrate measurement data with conservation laws, offering advantages over traditional numerical method. However, existing PINN approaches require multiple neural networks to construct composite models for complex water distribution systems (WDS). This situation treats nodal information as boundary condition or labeled data during training, leading to a weaker robustness and a high demand for data. To address these issues, a hybrid water hammer model based on eXtended Physics-Informed Neural Networks for WDS (WDS-XPINN) is developed in this study. Unlike the standard PINN, WDS-XPINN incorporates the nodal mechanistic model directly into the loss function, enabling to synchronously train a unified neural network jointly through sparse augmented measurement data for pipeline system. Additionally, an adaptive weights method is introduced to improve model robustness by balancing the contributions of flowrates and pressures. The proposed WDS-XPINN is evaluated in two case studies: a series pipeline system with different operational events and noise perturbation, as well as a topological structure with looped and branched pipe. According to the simulation results and uncertainty analysis, the WDS-XPINN model demonstrates its excellent capacity of modeling fluid transient accurately in pipeline system, even without exact operational conditions or true pipe parameters.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124898","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}
S. Baccour, A. Tilmant, J. Albiac, V. Espanmanesh, T. Kahil
Pressures on water resources are fueling conflicts between sectors. This trend will likely worsen under future climate-induced water stress, jeopardizing food, energy and human water security in most arid and semi-arid regions. Probabilistic analysis using stochastic optimization modeling can characterize multi-sector vulnerabilities and risks associated with future water stress. This study identifies the probabilistic trade-offs between agricultural, urban and energy sectors in the Ebro Basin (Spain). Two intervention policies have been examined and compared: (a) agricultural priority, and (b) energy priority, for two planning horizons 2040–2070 and 2070–2100. Results show that the human water security goal is achieved under both intervention policies. However, the achievement of the food and energy security goals depends on the policy objectives and on the spatial location of irrigation schemes and hydropower plants, which result in different stream flows across the basin. The policy choice results in substantially different benefit gains and losses by sector and therefore by location. None of the sectoral production priority policy provides an equitable sharing of benefits among all sectors and locations under climate change, which is an important issue, because the success or failure of policy interventions would depend on the distribution of the gains and losses of benefits across the basin. Policy uptake by stakeholders would depend on reaching win-win outcomes where losers are compensated, while delivering acceptable levels of food, energy and human water security in large river basins. Information on the probabilistic trade-offs contributes to the design of water management strategies capable of addressing the multi-sector vulnerability.
{"title":"Probabilistic Trade-Offs Analysis for Sustainable and Equitable Management of Climate-Induced Water Risks","authors":"S. Baccour, A. Tilmant, J. Albiac, V. Espanmanesh, T. Kahil","doi":"10.1029/2024wr038514","DOIUrl":"https://doi.org/10.1029/2024wr038514","url":null,"abstract":"Pressures on water resources are fueling conflicts between sectors. This trend will likely worsen under future climate-induced water stress, jeopardizing food, energy and human water security in most arid and semi-arid regions. Probabilistic analysis using stochastic optimization modeling can characterize multi-sector vulnerabilities and risks associated with future water stress. This study identifies the probabilistic trade-offs between agricultural, urban and energy sectors in the Ebro Basin (Spain). Two intervention policies have been examined and compared: (a) agricultural priority, and (b) energy priority, for two planning horizons 2040–2070 and 2070–2100. Results show that the human water security goal is achieved under both intervention policies. However, the achievement of the food and energy security goals depends on the policy objectives and on the spatial location of irrigation schemes and hydropower plants, which result in different stream flows across the basin. The policy choice results in substantially different benefit gains and losses by sector and therefore by location. None of the sectoral production priority policy provides an equitable sharing of benefits among all sectors and locations under climate change, which is an important issue, because the success or failure of policy interventions would depend on the distribution of the gains and losses of benefits across the basin. Policy uptake by stakeholders would depend on reaching win-win outcomes where losers are compensated, while delivering acceptable levels of food, energy and human water security in large river basins. Information on the probabilistic trade-offs contributes to the design of water management strategies capable of addressing the multi-sector vulnerability.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"16 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083640","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}
Ning Liu, Ge Sun, Yun Yang, Maricar Aguilos, Gregory Starr, Thomas L. O’Halloran, Devendra M. Amatya, A. Christopher Oishi, Yulong Zhang, Carl Trettin
Over 95% of original longleaf pine (Pinus palustris) (LLP) forests have been converted to other land uses, including loblolly pine (Pinus taeda L) (LOP), croplands, urban uses during the past two centuries in the southeastern United States (U.S.) for socioeconomic developments. Restoring the LLP forests represents a contemporary forest management objective to improve wildlife habitat, water yield, and overall ecosystem services and resilience to a changing climate. Given the importance of understanding ecohydrological processes for guiding restoration efforts, this study compared evapotranspiration (ET) measurements at eight eddy covariance flux sites dominated by LLP or LOP forests in the southeastern U.S. In addition, we developed a “paired stands” approach to compare remote sensing based ET estimates and associated site biophysical properties for approximately 1,600 LLP-LOP pairs. We found significant differences in ET, ET/Precipitation ratio, and water yield/precipitation ratio between the two types of pine forests, and these differences are explained by surface properties and management histories. Compared to LOP, the LLP forests generally had lower ET due to their significantly (p < 0.05) lower leaf area index but higher land surface temperature and albedo. Regionally, forest ET differences increased with the increase in atmospheric dryness index (reference ET/precipitation ratio). Therefore, we conclude that large-scale restoration of LLP forests has the potential to reduce ET and augment water yield in the long run, especially in relatively drier watersheds. Maintaining low stand tree density and understory leaf area characteristic of natural LLP ecosystems through active forest management is critical for enhancing forest water supply. Our study provides the scientific basis for large scale restoration of a diminishing ecosystem for benefiting water resources in the southeastern U.S.
{"title":"Potential for Augmenting Water Yield by Restoring Longleaf Pine (Pinus palustris) Forests in the Southeastern United States","authors":"Ning Liu, Ge Sun, Yun Yang, Maricar Aguilos, Gregory Starr, Thomas L. O’Halloran, Devendra M. Amatya, A. Christopher Oishi, Yulong Zhang, Carl Trettin","doi":"10.1029/2024wr037444","DOIUrl":"https://doi.org/10.1029/2024wr037444","url":null,"abstract":"Over 95% of original longleaf pine (<i>Pinus palustris</i>) (LLP) forests have been converted to other land uses, including loblolly pine (<i>Pinus taeda L</i>) (LOP), croplands, urban uses during the past two centuries in the southeastern United States (U.S.) for socioeconomic developments. Restoring the LLP forests represents a contemporary forest management objective to improve wildlife habitat, water yield, and overall ecosystem services and resilience to a changing climate. Given the importance of understanding ecohydrological processes for guiding restoration efforts, this study compared evapotranspiration (ET) measurements at eight eddy covariance flux sites dominated by LLP or LOP forests in the southeastern U.S. In addition, we developed a “paired stands” approach to compare remote sensing based ET estimates and associated site biophysical properties for approximately 1,600 LLP-LOP pairs. We found significant differences in ET, ET/Precipitation ratio, and water yield/precipitation ratio between the two types of pine forests, and these differences are explained by surface properties and management histories. Compared to LOP, the LLP forests generally had lower ET due to their significantly (<i>p</i> < 0.05) lower leaf area index but higher land surface temperature and albedo. Regionally, forest ET differences increased with the increase in atmospheric dryness index (reference ET/precipitation ratio). Therefore, we conclude that large-scale restoration of LLP forests has the potential to reduce ET and augment water yield in the long run, especially in relatively drier watersheds. Maintaining low stand tree density and understory leaf area characteristic of natural LLP ecosystems through active forest management is critical for enhancing forest water supply. Our study provides the scientific basis for large scale restoration of a diminishing ecosystem for benefiting water resources in the southeastern U.S.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"10 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083681","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}
Confluence-induced river discontinuities pose several challenges for flood control, navigation, and maintenance of ecological integrity. River-connected lakes in lowland regions represent a unique type of confluence, fulfilling significant floodplain functions in fluvial processes and forms of main streams, yet they remain insufficiently studied. This study utilized Dongting Lake in the Middle Yangtze River as a representative case to examine the confluence effects of lake-type tributary on the fluvial processes of the main stream and to identify key driving factors. The findings revealed that during the equilibrium period, both the flow frequency curves and sediment-transport rating curves exhibited segmentation characteristics due to the floodplain functions of river-lake system. The confluence effects of the lake amplified the segmentation behaviors across spatial scales, which inherently depended on the river-lake flow combination. Following the Three Gorges Reservoir impoundment, alterations in the confluence effects of the lake led to recent abnormal local erosion-deposition adjustments under sediment subsaturation conditions, whereas upstream reservoir regulation dominated a long-term reduction in channel-forming discharge with a channel shrinkage trend downstream of the confluence. Despite the altered water-sediment regimes, tributary inflow qualitatively remains a critical factor influencing the discontinuity of fluvial processes in the main stream. Our research demonstrated that the hydrological characteristics of confluence, rather than sediment transport, significantly influence the fluvial processes of lowland alluvial river. This insight enhances our understanding of the evolutionary mechanisms of large alluvial rivers with complex river-lake relationships and offers important implications for the management of water resource systems.
{"title":"Confluence Effects of Dongting Lake in the Middle Yangtze River: Discontinuous Fluvial Processes and Their Driving Mechanisms","authors":"Weixing Zhou, Zhaohua Sun, Zhonghua Yang, Ge Guo","doi":"10.1029/2024wr039030","DOIUrl":"https://doi.org/10.1029/2024wr039030","url":null,"abstract":"Confluence-induced river discontinuities pose several challenges for flood control, navigation, and maintenance of ecological integrity. River-connected lakes in lowland regions represent a unique type of confluence, fulfilling significant floodplain functions in fluvial processes and forms of main streams, yet they remain insufficiently studied. This study utilized Dongting Lake in the Middle Yangtze River as a representative case to examine the confluence effects of lake-type tributary on the fluvial processes of the main stream and to identify key driving factors. The findings revealed that during the equilibrium period, both the flow frequency curves and sediment-transport rating curves exhibited segmentation characteristics due to the floodplain functions of river-lake system. The confluence effects of the lake amplified the segmentation behaviors across spatial scales, which inherently depended on the river-lake flow combination. Following the Three Gorges Reservoir impoundment, alterations in the confluence effects of the lake led to recent abnormal local erosion-deposition adjustments under sediment subsaturation conditions, whereas upstream reservoir regulation dominated a long-term reduction in channel-forming discharge with a channel shrinkage trend downstream of the confluence. Despite the altered water-sediment regimes, tributary inflow qualitatively remains a critical factor influencing the discontinuity of fluvial processes in the main stream. Our research demonstrated that the hydrological characteristics of confluence, rather than sediment transport, significantly influence the fluvial processes of lowland alluvial river. This insight enhances our understanding of the evolutionary mechanisms of large alluvial rivers with complex river-lake relationships and offers important implications for the management of water resource systems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"133 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083682","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}
Zhonghang Wu, Xiao Yan, Jingjie Feng, Jingcheng Zuo, Ran Li, Xiaolong Cheng, Kefeng Li
Withdrawal of fish eggs and larvae through a river intake (entrainment) may damage the river's early fish resources. To investigate how the hydraulics (flow velocities, directions, and magnitudes) around and within the water intake structure influence entrainment, this study focused on a typical river-pump intake. A turbulence model was developed based on the Euler-Lagrange method and the variable of helicity was introduced to define the zone of the river from which water is withdrawn and organisms are entrained. The process of simulated hydraulics on organism withdrawal was validated by physical experiments using artificial fish eggs under various river flow and intake flow conditions. The simulated results indicated that when the intake-to-river flow ratio ranged from 3 to 7‰, the width of the planar entrainment area was approximately 1.2 to 1.4 times the width of the intake structure, and the entrainment quantity of fish eggs and larvae accounted for 0.12% to 0.49% of the incoming flux. The entrainment quantity increased with the intake flow to the river flow ratio. The absolute helicity threshold under different conditions ranged from 0.004 to 0.047 m/s2, which was inversely proportional to the intake flow rate and unaffected by river flow and water depth. An optimized intake structure design with stepped side walls was promoted, which can minimize the impact of river sedimentation and reduce the fish entrainment quantity by an average of 11%. This research provides valuable insights for water intake safety and fish resource protection.
{"title":"Estimated Hydraulic Characteristics and the Entrainment of Fish Eggs and Larvae at a Pumped River-Water Intake","authors":"Zhonghang Wu, Xiao Yan, Jingjie Feng, Jingcheng Zuo, Ran Li, Xiaolong Cheng, Kefeng Li","doi":"10.1029/2024wr038894","DOIUrl":"https://doi.org/10.1029/2024wr038894","url":null,"abstract":"Withdrawal of fish eggs and larvae through a river intake (entrainment) may damage the river's early fish resources. To investigate how the hydraulics (flow velocities, directions, and magnitudes) around and within the water intake structure influence entrainment, this study focused on a typical river-pump intake. A turbulence model was developed based on the Euler-Lagrange method and the variable of helicity was introduced to define the zone of the river from which water is withdrawn and organisms are entrained. The process of simulated hydraulics on organism withdrawal was validated by physical experiments using artificial fish eggs under various river flow and intake flow conditions. The simulated results indicated that when the intake-to-river flow ratio ranged from 3 to 7‰, the width of the planar entrainment area was approximately 1.2 to 1.4 times the width of the intake structure, and the entrainment quantity of fish eggs and larvae accounted for 0.12% to 0.49% of the incoming flux. The entrainment quantity increased with the intake flow to the river flow ratio. The absolute helicity threshold under different conditions ranged from 0.004 to 0.047 m/s<sup>2</sup>, which was inversely proportional to the intake flow rate and unaffected by river flow and water depth. An optimized intake structure design with stepped side walls was promoted, which can minimize the impact of river sedimentation and reduce the fish entrainment quantity by an average of 11%. This research provides valuable insights for water intake safety and fish resource protection.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"56 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124407","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}
Reihaneh Zarrabi, Riley McDermott, Seyed Mohammad Hassan Erfani, Sagy Cohen
Widely adopted models for estimating hydraulic geometry attributes rely on simplistic power-law equations, which can introduce inaccuracy due to their inability to capture spatial variability. This study introduces a new model for predicting channel geometry utilizing advanced tree-based Machine Learning (ML) algorithms. The research enhances the quality of the extensive HYDRoacoustic data set supporting Surface Water Oceanographic Topography (HYDRoSWOT) through a proposed preprocessing method. Observations of bankfull and mean-flow conditions at each gauge site are identified and extracted as target variables for model development. HYDRoSWOT-extracted attributes, along with other predictors from various sources, such as National Hydrography Data set Plus (NHDPlusV2.1), are used to train and validate predictive models. The models achieve average R2 values of 0.85 for channel width and 0.69 for channel depth, demonstrating high accuracy in capturing spatial variability in hydraulic geometry attributes. Independent evaluations further test the models' performance in predicting reach-averaged conditions at locations outside the training and testing data sets. The results show that the proposed model significantly outperforms existing regional hydraulic geometry relations, with accuracy improvements of 30% for bankfull width and 76% for bankfull depth. The proposed model is then utilized to generate channel width and depth under bankfull and mean-flow conditions data set across approximately 2.7 million streams within NHDPlusV2.1 data set across the CONtiguous United State (CONUS). This data set is a valuable resource for water-related sciences, including hydrology, geomorphology, flood modeling, water quality assessment, and flood management.
{"title":"Bankfull and Mean-Flow Channel Geometry Estimation Through Machine Learning Algorithms Across the CONtiguous United States (CONUS)","authors":"Reihaneh Zarrabi, Riley McDermott, Seyed Mohammad Hassan Erfani, Sagy Cohen","doi":"10.1029/2024wr037997","DOIUrl":"https://doi.org/10.1029/2024wr037997","url":null,"abstract":"Widely adopted models for estimating hydraulic geometry attributes rely on simplistic power-law equations, which can introduce inaccuracy due to their inability to capture spatial variability. This study introduces a new model for predicting channel geometry utilizing advanced tree-based Machine Learning (ML) algorithms. The research enhances the quality of the extensive HYDRoacoustic data set supporting Surface Water Oceanographic Topography (HYDRoSWOT) through a proposed preprocessing method. Observations of bankfull and mean-flow conditions at each gauge site are identified and extracted as target variables for model development. HYDRoSWOT-extracted attributes, along with other predictors from various sources, such as National Hydrography Data set Plus (NHDPlusV2.1), are used to train and validate predictive models. The models achieve average <i>R</i><sup>2</sup> values of 0.85 for channel width and 0.69 for channel depth, demonstrating high accuracy in capturing spatial variability in hydraulic geometry attributes. Independent evaluations further test the models' performance in predicting reach-averaged conditions at locations outside the training and testing data sets. The results show that the proposed model significantly outperforms existing regional hydraulic geometry relations, with accuracy improvements of 30% for bankfull width and 76% for bankfull depth. The proposed model is then utilized to generate channel width and depth under bankfull and mean-flow conditions data set across approximately 2.7 million streams within NHDPlusV2.1 data set across the CONtiguous United State (CONUS). This data set is a valuable resource for water-related sciences, including hydrology, geomorphology, flood modeling, water quality assessment, and flood management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"81 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077305","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}
Ludovic Schorpp, Julien Straubhaar, Philippe Renard
Sedimentary formations that compose most aquifers are difficult to model as a result of the nature of their deposition. Their formation generally involves multiple processes (alluvial, glacial, lacustrine, etc.) that contribute to the complex organization of these deposits. Representative models can be obtained using process-based or rule-based methods. However, such methods have several drawbacks: complicated parameterization, large computing time, and challenging, if not impossible, conditioning. To address these problems, we propose a new simple hierarchical surface-based algorithm, named EROSim. First, a predefined number of stochastic surfaces are simulated in a given order (from older to younger). These surfaces are simulated independently but interact with each other through erosion rules. Each surface is either an erosive or a deposition surface. The deposition surfaces represent the boundaries of depositional events, whereas the erosive surfaces can remove parts of the previously simulated deposits. Finally, these surfaces delimit sedimentary regions that are filled with facies. The approach is quite simple, general, flexible, and can be conditioned to borehole data. The applicability of the method is illustrated using data from fluvio-glacial sedimentary deposits observed in the Bümberg quarry in Switzerland.
{"title":"A Novel Surface-Based Approach to Represent Aquifer Heterogeneity in Sedimentary Formations","authors":"Ludovic Schorpp, Julien Straubhaar, Philippe Renard","doi":"10.1029/2024wr038364","DOIUrl":"https://doi.org/10.1029/2024wr038364","url":null,"abstract":"Sedimentary formations that compose most aquifers are difficult to model as a result of the nature of their deposition. Their formation generally involves multiple processes (alluvial, glacial, lacustrine, etc.) that contribute to the complex organization of these deposits. Representative models can be obtained using process-based or rule-based methods. However, such methods have several drawbacks: complicated parameterization, large computing time, and challenging, if not impossible, conditioning. To address these problems, we propose a new simple hierarchical surface-based algorithm, named EROSim. First, a predefined number of stochastic surfaces are simulated in a given order (from older to younger). These surfaces are simulated independently but interact with each other through erosion rules. Each surface is either an erosive or a deposition surface. The deposition surfaces represent the boundaries of depositional events, whereas the erosive surfaces can remove parts of the previously simulated deposits. Finally, these surfaces delimit sedimentary regions that are filled with facies. The approach is quite simple, general, flexible, and can be conditioned to borehole data. The applicability of the method is illustrated using data from fluvio-glacial sedimentary deposits observed in the Bümberg quarry in Switzerland.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"29 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071606","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}
Monitoring groundwater dynamics within the vadose zone is important for the investigation of many hydrological and ecological processes. Nuclear magnetic resonance (NMR) technology can be utilized to reveal these dynamics due to its unique sensitivity to water. The correlation between water content and distribution with NMR signals (relaxation times and amplitude) aids in discerning water retention patterns in porous media. However, interpreting NMR data to understand unsaturated pore-water dynamics is challenging due to complex pore environments and fluid-rock-air interactions. Especially, previous studies often misinterpret the increased amplitude of shorter relaxation T2 components of unsaturated T2 to erroneously imply that the small pore exceeds their maximum saturation capacity. We develop a simulation framework to accurately track pore-water dynamics and NMR responses (T2 and T1-T2) in unsaturated multi-pore systems. Dual-spherical and dual-triangular pore systems with different pore size distributions modeled the imbibition process. Simulations clarified the decrease in shorter relaxation T2 components in unsaturated states, revealing that unsaturated macropores and saturated micropores can exhibit the same short relaxation times when their fluid volume to fluid occupied surface area ratio is identical. Our simulation also demonstrates that different pore shapes and pore size distributions lead to distinct unsaturated NMR signals. Furthermore, we propose a new method evaluating water distribution in various natural porous media at various saturations by interpreting experimental unsaturated NMR data. Our work enhances unsaturated NMR data interpretation, providing accurate insights into water distribution and pore structure in unsaturated porous media.
{"title":"How Does Pore Structure Affect the NMR Relaxation in Unsaturated Porous Media: A Simulation Study","authors":"Junwen Zhou, Chi Zhang","doi":"10.1029/2024wr038139","DOIUrl":"https://doi.org/10.1029/2024wr038139","url":null,"abstract":"Monitoring groundwater dynamics within the vadose zone is important for the investigation of many hydrological and ecological processes. Nuclear magnetic resonance (NMR) technology can be utilized to reveal these dynamics due to its unique sensitivity to water. The correlation between water content and distribution with NMR signals (relaxation times and amplitude) aids in discerning water retention patterns in porous media. However, interpreting NMR data to understand unsaturated pore-water dynamics is challenging due to complex pore environments and fluid-rock-air interactions. Especially, previous studies often misinterpret the increased amplitude of shorter relaxation <i>T</i><sub>2</sub> components of unsaturated <i>T</i><sub>2</sub> to erroneously imply that the small pore exceeds their maximum saturation capacity. We develop a simulation framework to accurately track pore-water dynamics and NMR responses (<i>T</i><sub>2</sub> and <i>T</i><sub>1</sub>-<i>T</i><sub>2</sub>) in unsaturated multi-pore systems. Dual-spherical and dual-triangular pore systems with different pore size distributions modeled the imbibition process. Simulations clarified the decrease in shorter relaxation <i>T</i><sub>2</sub> components in unsaturated states, revealing that unsaturated macropores and saturated micropores can exhibit the same short relaxation times when their fluid volume to fluid occupied surface area ratio is identical. Our simulation also demonstrates that different pore shapes and pore size distributions lead to distinct unsaturated NMR signals. Furthermore, we propose a new method evaluating water distribution in various natural porous media at various saturations by interpreting experimental unsaturated NMR data. Our work enhances unsaturated NMR data interpretation, providing accurate insights into water distribution and pore structure in unsaturated porous media.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"122 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071608","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 flooding events expected to increase in both intensity and frequency in the future due to climate change, ensuring the safety of river embankments is vital to withstand flood disasters. Piping is one of the most harmful river embankment hazards in the flood season, and recent advances in unmanned aerial vehicles (UAVs) and deep learning-based object detection have enabled efficient and automated hazard detection. In this study, a novel approach that integrates a UAV with deep learning-based object detection and edge computing was proposed for rapid and automatic piping detection. First, a total of 104 field simulation experiments were conducted across 12 different sites in flood-prone areas to fill gaps in the high-quality data set, and the UAV thermal infrared and visible data sets of river embankment piping were produced, including various times (forenoon, afternoon, and night), weather conditions (clear-sky, cloudy, and rainy), locations (bare land, paddy, grassland, and pond) and flight altitudes (10, 20, and 30 m). Second, the deep learning-based object detection model was selected and trained on the thermal infrared and visible data sets. The well-trained infrared and visible models have detection precisions of 92.7% and 70.4%, respectively, with recalls of 84.9% and 69.7%. Furthermore, both models exhibited great resistance to interference from several types of aquatic vegetation and could effectively detect piping on rainy days. The integration of a UAV and edge computing enabled real-time detection of piping. The proposed method enhances hazard detection efficiency, contributing to intelligent emergency embankment management.
{"title":"Rapid and Automatic UAV Detection of River Embankment Piping","authors":"Quntao Duan, Baili Chen, Lihui Luo","doi":"10.1029/2024wr038931","DOIUrl":"https://doi.org/10.1029/2024wr038931","url":null,"abstract":"With flooding events expected to increase in both intensity and frequency in the future due to climate change, ensuring the safety of river embankments is vital to withstand flood disasters. Piping is one of the most harmful river embankment hazards in the flood season, and recent advances in unmanned aerial vehicles (UAVs) and deep learning-based object detection have enabled efficient and automated hazard detection. In this study, a novel approach that integrates a UAV with deep learning-based object detection and edge computing was proposed for rapid and automatic piping detection. First, a total of 104 field simulation experiments were conducted across 12 different sites in flood-prone areas to fill gaps in the high-quality data set, and the UAV thermal infrared and visible data sets of river embankment piping were produced, including various times (forenoon, afternoon, and night), weather conditions (clear-sky, cloudy, and rainy), locations (bare land, paddy, grassland, and pond) and flight altitudes (10, 20, and 30 m). Second, the deep learning-based object detection model was selected and trained on the thermal infrared and visible data sets. The well-trained infrared and visible models have detection precisions of 92.7% and 70.4%, respectively, with recalls of 84.9% and 69.7%. Furthermore, both models exhibited great resistance to interference from several types of aquatic vegetation and could effectively detect piping on rainy days. The integration of a UAV and edge computing enabled real-time detection of piping. The proposed method enhances hazard detection efficiency, contributing to intelligent emergency embankment management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"60 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071607","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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