Yangming Ou, Ran Li, Jingjie Feng, Hang Wan, Yanpeng Cai, Zhifeng Yang, Guoyu Zhu, Shengyun Liu, Abd El-Fatah Abomohra, Juping Huang
Hydropower provides continuous and clean energy for human consumption but also brings a series of environmental concerns to local watersheds. Gas bubble disease or mass mortality in fish can be attributed to total dissolved gas (TDG) supersaturation, which occurs when water is released from dams. It is possible to create temporary refuges for fish suffering from supersaturated total dissolved gas (STDG) by strategically arranging aeration facilities along rivers or reservoirs and using the bubbles generated by aeration to increase the dissipation of STDG. The critical limitation to the widespread application of this approach in engineering is the insufficient understanding of the mass transfer mechanisms of STDG under aerated conditions and the transport characteristics of STDG in water flows. In this work, the mass transfer (MT) mechanisms of STDG under aerated conditions were systematically studied via experiments, image processing, and numerical simulation. An innovative three-dimensional numerical model was established to forecast the MT process of STDG under aerated conditions. The determination of STDG MT in the model incorporated a sophisticated approach that accounted for the dynamic changes in bubble sizes resulting from diverse mechanisms of bubble coalescence and breakup. To validate and calibrate the model, precise aeration experiments were executed at various aeration intensities to gather data on the bubble size distribution, total gas holdup, and STDG dissipation rates. Furthermore, a numerical model was used to quantitatively investigate the impact of the aerator installation depth on STDG dissipation performance. The results revealed that the relationship between the dissipation coefficients of STDG and the aerator installation depth followed a power function. This research can enhance the understanding of the MT characteristics of STDG under aeration conditions while also providing a useful tool for studying the design and optimization of facilities related to STDG engineering treatment via aeration measures.
{"title":"A Numerical Model to Simulate the Mass Transfer Process of Supersaturated Total Dissolved Gas in Aerated Conditions","authors":"Yangming Ou, Ran Li, Jingjie Feng, Hang Wan, Yanpeng Cai, Zhifeng Yang, Guoyu Zhu, Shengyun Liu, Abd El-Fatah Abomohra, Juping Huang","doi":"10.1029/2024wr037745","DOIUrl":"https://doi.org/10.1029/2024wr037745","url":null,"abstract":"Hydropower provides continuous and clean energy for human consumption but also brings a series of environmental concerns to local watersheds. Gas bubble disease or mass mortality in fish can be attributed to total dissolved gas (TDG) supersaturation, which occurs when water is released from dams. It is possible to create temporary refuges for fish suffering from supersaturated total dissolved gas (STDG) by strategically arranging aeration facilities along rivers or reservoirs and using the bubbles generated by aeration to increase the dissipation of STDG. The critical limitation to the widespread application of this approach in engineering is the insufficient understanding of the mass transfer mechanisms of STDG under aerated conditions and the transport characteristics of STDG in water flows. In this work, the mass transfer (MT) mechanisms of STDG under aerated conditions were systematically studied via experiments, image processing, and numerical simulation. An innovative three-dimensional numerical model was established to forecast the MT process of STDG under aerated conditions. The determination of STDG MT in the model incorporated a sophisticated approach that accounted for the dynamic changes in bubble sizes resulting from diverse mechanisms of bubble coalescence and breakup. To validate and calibrate the model, precise aeration experiments were executed at various aeration intensities to gather data on the bubble size distribution, total gas holdup, and STDG dissipation rates. Furthermore, a numerical model was used to quantitatively investigate the impact of the aerator installation depth on STDG dissipation performance. The results revealed that the relationship between the dissipation coefficients of STDG and the aerator installation depth followed a power function. This research can enhance the understanding of the MT characteristics of STDG under aeration conditions while also providing a useful tool for studying the design and optimization of facilities related to STDG engineering treatment via aeration measures.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"5 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742798","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}
Traditional analytical models in groundwater studies often simplify the complexities arising from spatial variations in aquifer geometry and anisotropy, limiting their ability to capture the full theoretical nuances of groundwater flow. In this study, we present a novel methodology that integrates geodesic distances within the intrinsic geometry of confined, constant-thickness aquifers, while also accounting for directional anisotropy in hydraulic properties. This approach provides a rigorous mathematical framework for accurately capturing the true distances along the aquifer geometry between pumping and observation wells, in contrast to traditional Euclidean distances. Our methodology is compatible with various analytical solutions, including the Theis (1935, https://doi.org/10.1111/jawr.1965.1.3.9) and Papadopulos and Cooper (1967, https://doi.org/10.1029/wr003i001p00241) solutions, extending their theoretical applicability to more complex aquifer geometries and anisotropic conditions. Numerical simulations of synthetic examples illustrate the theoretical consistency of the proposed approach, aligning drawdown patterns within this advanced framework. While primarily focused on enhancing existing analytical models, this methodology sets the stage for future theoretical advances in groundwater modeling, offering a conceptual expansion of analytical solutions to better address geometric and anisotropic complexities.
{"title":"Geodesic Distance Integration in Analytical Frameworks for Aquifer Hydraulic Modeling","authors":"Zhang Wen, Eungyu Park, Peipei Xue, Huali Chen","doi":"10.1029/2024wr038316","DOIUrl":"https://doi.org/10.1029/2024wr038316","url":null,"abstract":"Traditional analytical models in groundwater studies often simplify the complexities arising from spatial variations in aquifer geometry and anisotropy, limiting their ability to capture the full theoretical nuances of groundwater flow. In this study, we present a novel methodology that integrates geodesic distances within the intrinsic geometry of confined, constant-thickness aquifers, while also accounting for directional anisotropy in hydraulic properties. This approach provides a rigorous mathematical framework for accurately capturing the true distances along the aquifer geometry between pumping and observation wells, in contrast to traditional Euclidean distances. Our methodology is compatible with various analytical solutions, including the Theis (1935, https://doi.org/10.1111/jawr.1965.1.3.9) and Papadopulos and Cooper (1967, https://doi.org/10.1029/wr003i001p00241) solutions, extending their theoretical applicability to more complex aquifer geometries and anisotropic conditions. Numerical simulations of synthetic examples illustrate the theoretical consistency of the proposed approach, aligning drawdown patterns within this advanced framework. While primarily focused on enhancing existing analytical models, this methodology sets the stage for future theoretical advances in groundwater modeling, offering a conceptual expansion of analytical solutions to better address geometric and anisotropic complexities.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"50 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742797","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}
Xiaolong Geng, Holly A. Michael, James W. Heiss, Michel C. Boufadel, Hailong Li, Xuejing Wang
The interactions between the atmosphere, ocean, and beach in the swash zone are dynamic, influencing water flux and solute exchange across the land-sea interface. This study employs groundwater simulations to examine the combined effects of waves and evaporation on subsurface flow and salinity dynamics in a shallow beach environment. Our simulations reveal that wave motion generates a saline plume beneath the swash zone, where evaporation induces hypersalinity near the sand surface. This leads to the formation of a hypersaline plume beneath the swash zone during periods of wave recession, which extends vertically downward to a maximum depth of 30 cm, driven by the resulting vertical density gradients. This hypersaline plume moves approximately 2 m landward to the top of the swash zone and down the beachface due to wave-induced seawater infiltration and is subsequently diluted by the surrounding saline groundwater. Furthermore, swash motion increases near-surface moisture, leading to an elevated evaporation rate, with dynamic fluctuations in both moisture and evaporation rate due to high-frequency surface inundation caused by individual waves. Notably, the highest evaporation rates on the swash zone surface do not always correspond to the greatest elevations of salt concentration within the swash zone. This is because optimal moisture is also required—neither too low to impede evaporation nor too high to dilute accumulated salt near the surface. These insights are crucial for enhancing our understanding of coastal groundwater flow, biogeochemical conditions, and the subsequent nutrient cycling and contaminant transport in coastal zones.
{"title":"Influence of Evaporation and High-Frequency Seawater Inundation on Salinity Dynamics in Swash Zones","authors":"Xiaolong Geng, Holly A. Michael, James W. Heiss, Michel C. Boufadel, Hailong Li, Xuejing Wang","doi":"10.1029/2024wr037427","DOIUrl":"https://doi.org/10.1029/2024wr037427","url":null,"abstract":"The interactions between the atmosphere, ocean, and beach in the swash zone are dynamic, influencing water flux and solute exchange across the land-sea interface. This study employs groundwater simulations to examine the combined effects of waves and evaporation on subsurface flow and salinity dynamics in a shallow beach environment. Our simulations reveal that wave motion generates a saline plume beneath the swash zone, where evaporation induces hypersalinity near the sand surface. This leads to the formation of a hypersaline plume beneath the swash zone during periods of wave recession, which extends vertically downward to a maximum depth of 30 cm, driven by the resulting vertical density gradients. This hypersaline plume moves approximately 2 m landward to the top of the swash zone and down the beachface due to wave-induced seawater infiltration and is subsequently diluted by the surrounding saline groundwater. Furthermore, swash motion increases near-surface moisture, leading to an elevated evaporation rate, with dynamic fluctuations in both moisture and evaporation rate due to high-frequency surface inundation caused by individual waves. Notably, the highest evaporation rates on the swash zone surface do not always correspond to the greatest elevations of salt concentration within the swash zone. This is because optimal moisture is also required—neither too low to impede evaporation nor too high to dilute accumulated salt near the surface. These insights are crucial for enhancing our understanding of coastal groundwater flow, biogeochemical conditions, and the subsequent nutrient cycling and contaminant transport in coastal zones.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"19 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713159","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}
Representing and preserving complex (non-Gaussian) spatial patterns in aquifer flow properties during model calibration are challenging. Conventional parameterization methods that rely on linear/Gaussian assumptions are not suitable for representation of property maps with more complex spatial patterns. Deep learning techniques, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), have recently been proposed to address this difficulty by learning complex spatial patterns from prior training images and synthesizing similar realizations using low-dimensional latent variables with Gaussian distributions. The resulting Gaussian latent variables lend themselves to calibration with the ensemble Kalman filter-based updating schemes that are suitable for parameters with Gaussian distribution. Despite their superior performance in generating complex spatial patterns, these generative models may not provide desirable properties that are needed for parameterization of model calibration problems, including robustness, smoothness in the latent domain, and reconstruction fidelity. This paper introduces the second generation of style-based Generative Adversarial Networks (StyleGAN) for parameterization of complex subsurface flow properties and compares its model calibration properties and performance with those of the convolutional VAE and GAN architectures. Numerical experiments involving model calibration with the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in single-phase and two-phase fluid flow examples are used to assess the capabilities and limitations of these methods. The results show that parameterization with StyleGANs provides superior performance in terms of reconstruction fidelity and flexibility, underscoring its potential for improving the representation and reconstruction of complex spatial patterns in subsurface flow model calibration problems.
在模型校准过程中，表示和保留含水层流动特性的复杂（非高斯）空间模式是一项挑战。传统的参数化方法依赖于线性/高斯假设，不适合表示具有更复杂空间模式的属性图。最近提出的深度学习技术，如变异自动编码器（VAE）和生成对抗网络（GAN），通过从先前的训练图像中学习复杂的空间模式，并使用具有高斯分布的低维潜在变量合成类似的现实，解决了这一难题。由此产生的高斯潜变量可通过基于集合卡尔曼滤波器的更新方案进行校准，该方案适用于高斯分布参数。尽管这些生成模型在生成复杂空间模式方面性能优越，但它们可能无法提供模型校准问题参数化所需的理想特性，包括鲁棒性、潜域平滑性和重建保真度。本文介绍了用于复杂地下流动特性参数化的第二代基于样式的生成对抗网络（StyleGAN），并比较了其与卷积 VAE 和 GAN 架构的模型校准特性和性能。在单相和两相流体流动示例中，使用多数据同化集合平滑器（ES-MDA）进行了模型校准的数值实验，以评估这些方法的能力和局限性。结果表明，使用 StyleGANs 进行参数化可在重建保真度和灵活性方面提供更优越的性能，凸显了其在改进地下流动模型校准问题中复杂空间模式的表示和重建方面的潜力。
{"title":"Improving the Parameterization of Complex Subsurface Flow Properties With Style-Based Generative Adversarial Network (StyleGAN)","authors":"Wei Ling, Behnam Jafarpour","doi":"10.1029/2024wr037630","DOIUrl":"https://doi.org/10.1029/2024wr037630","url":null,"abstract":"Representing and preserving complex (non-Gaussian) spatial patterns in aquifer flow properties during model calibration are challenging. Conventional parameterization methods that rely on linear/Gaussian assumptions are not suitable for representation of property maps with more complex spatial patterns. Deep learning techniques, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), have recently been proposed to address this difficulty by learning complex spatial patterns from prior training images and synthesizing similar realizations using low-dimensional latent variables with Gaussian distributions. The resulting Gaussian latent variables lend themselves to calibration with the ensemble Kalman filter-based updating schemes that are suitable for parameters with Gaussian distribution. Despite their superior performance in generating complex spatial patterns, these generative models may not provide desirable properties that are needed for parameterization of model calibration problems, including robustness, smoothness in the latent domain, and reconstruction fidelity. This paper introduces the second generation of style-based Generative Adversarial Networks (StyleGAN) for parameterization of complex subsurface flow properties and compares its model calibration properties and performance with those of the convolutional VAE and GAN architectures. Numerical experiments involving model calibration with the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in single-phase and two-phase fluid flow examples are used to assess the capabilities and limitations of these methods. The results show that parameterization with StyleGANs provides superior performance in terms of reconstruction fidelity and flexibility, underscoring its potential for improving the representation and reconstruction of complex spatial patterns in subsurface flow model calibration problems.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713161","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}
This discussion is a reply to the comments made by Dr. Jasper Vrugt on the Metropolis-Hastings (M-H) algorithm with multiple independent Markov chains proposed by Huang and Merwade (2023a), https://doi.org/10.1029/2023wr034947 concerning the validity of the methodology in estimating Bayesian model averaging (BMA) parameters (weights and variances) of the framework proposed by Raftery et al. (2005), https://doi.org/10.1175/mwr2906.1. In this reply, we address his concerns by emphasizing the motivation of applying the proposed M-H algorithm to BMA analysis and the applicability of the effective sample size that accounts for the autocorrelation across samples in evaluating the efficiency of Markov chain Monte Carlo sampling. Moreover, the details of sampling procedure for BMA prediction distribution are clarified. On the other hand, we present a fair comparison of the default Expectation-Maximization, M-H, and differential evolution adaptive Metropolis (DREAM) algorithms in estimating BMA parameters based on a numerical experiment. Results reinforce the findings obtained from Huang and Merwade (2023a) https://doi.org/10.1029/2023wr034947 and further indicate that the proposed M-H algorithm is better than the DREAM algorithm in terms of sampling efficiency and prediction accuracy. Accordingly, we raise concerns on the use of DREAM algorithm in BMA analysis and suggest conducting peer reviews on the MODELAVG toolbox.
{"title":"Reply to Comment on “Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling” by Jasper Vrugt","authors":"Tao Huang, Venkatesh Merwade","doi":"10.1029/2024wr037387","DOIUrl":"https://doi.org/10.1029/2024wr037387","url":null,"abstract":"This discussion is a reply to the comments made by Dr. Jasper Vrugt on the Metropolis-Hastings (M-H) algorithm with multiple independent Markov chains proposed by Huang and Merwade (2023a), https://doi.org/10.1029/2023wr034947 concerning the validity of the methodology in estimating Bayesian model averaging (BMA) parameters (weights and variances) of the framework proposed by Raftery et al. (2005), https://doi.org/10.1175/mwr2906.1. In this reply, we address his concerns by emphasizing the motivation of applying the proposed M-H algorithm to BMA analysis and the applicability of the effective sample size that accounts for the autocorrelation across samples in evaluating the efficiency of Markov chain Monte Carlo sampling. Moreover, the details of sampling procedure for BMA prediction distribution are clarified. On the other hand, we present a fair comparison of the default Expectation-Maximization, M-H, and differential evolution adaptive Metropolis (DREAM) algorithms in estimating BMA parameters based on a numerical experiment. Results reinforce the findings obtained from Huang and Merwade (2023a) https://doi.org/10.1029/2023wr034947 and further indicate that the proposed M-H algorithm is better than the DREAM algorithm in terms of sampling efficiency and prediction accuracy. Accordingly, we raise concerns on the use of DREAM algorithm in BMA analysis and suggest conducting peer reviews on the MODELAVG toolbox.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"67 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696645","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}
Qianqiu Longyang, Seohye Choi, Hyrum Tennant, Devon Hill, Nathaniel Ashmead, Bethany T. Neilson, Dennis L. Newell, James P. McNamara, Tianfang Xu
In many regions globally, snowmelt-recharged mountainous karst aquifers serve as crucial sources for municipal and agricultural water supplies. In these watersheds, complex interplay of meteorological, topographical, and hydrogeological factors leads to intricate recharge-discharge pathways. This study introduces a spatially distributed deep learning precipitation-runoff model that combines Convolutional Long Short-Term Memory (ConvLSTM) with a spatial attention mechanism. The effectiveness of the deep learning model was evaluated using data from the Logan River watershed and subwatersheds, a characteristically karst-dominated hydrological system in northern Utah. Compared to the ConvLSTM baseline, the inclusion of a spatial attention mechanism improved performance for simulating discharge at the watershed outlet. Analysis of attention weights in the trained model unveiled distinct areas contributing the most to discharge under snowmelt and recession conditions. Furthermore, fine-tuning the model at subwatershed scales provided insights into cross-subwatershed subsurface connectivity. These findings align with results obtained from detailed hydrogeochemical tracer studies. Results highlight the potential of the proposed deep learning approach to unravel the complexities of karst aquifer systems, offering valuable insights for water resource management under future climate conditions. Furthermore, results suggest that the proposed explainable, spatially distributed, deep learning approach to hydrologic modeling holds promise for non-karstic watersheds.
{"title":"An Attention-Based Explainable Deep Learning Approach to Spatially Distributed Hydrologic Modeling of a Snow Dominated Mountainous Karst Watershed","authors":"Qianqiu Longyang, Seohye Choi, Hyrum Tennant, Devon Hill, Nathaniel Ashmead, Bethany T. Neilson, Dennis L. Newell, James P. McNamara, Tianfang Xu","doi":"10.1029/2024wr037878","DOIUrl":"https://doi.org/10.1029/2024wr037878","url":null,"abstract":"In many regions globally, snowmelt-recharged mountainous karst aquifers serve as crucial sources for municipal and agricultural water supplies. In these watersheds, complex interplay of meteorological, topographical, and hydrogeological factors leads to intricate recharge-discharge pathways. This study introduces a spatially distributed deep learning precipitation-runoff model that combines Convolutional Long Short-Term Memory (ConvLSTM) with a spatial attention mechanism. The effectiveness of the deep learning model was evaluated using data from the Logan River watershed and subwatersheds, a characteristically karst-dominated hydrological system in northern Utah. Compared to the ConvLSTM baseline, the inclusion of a spatial attention mechanism improved performance for simulating discharge at the watershed outlet. Analysis of attention weights in the trained model unveiled distinct areas contributing the most to discharge under snowmelt and recession conditions. Furthermore, fine-tuning the model at subwatershed scales provided insights into cross-subwatershed subsurface connectivity. These findings align with results obtained from detailed hydrogeochemical tracer studies. Results highlight the potential of the proposed deep learning approach to unravel the complexities of karst aquifer systems, offering valuable insights for water resource management under future climate conditions. Furthermore, results suggest that the proposed explainable, spatially distributed, deep learning approach to hydrologic modeling holds promise for non-karstic watersheds.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"8 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696642","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}
Flood risk analyses often focus on a single flooding source, typically storm surge or rainfall-driven flooding, depending on the predominant threat. However, hurricanes frequently cause compound flooding through significant storm surges accompanied by heavy rainfall. This study employs a hydrodynamic model based on Delft3D-Flexible Mesh that couples flow, waves, and rainfall-driven flow to simulate five historical tropical cyclones in Virginia's southeast coastal region. These storms produced varying intensities of storm surge and rainfall in the study area. Model simulations, incorporating rainfall through a rain-on-grid approach, account for the dynamic interaction between storm tides, and pluvial flow and enable the definition of flood zones as hydrologic, transitional, and coastal zones. This compound flooding model was validated with water level data from in-water and overland gauges. The results indicate that the magnitude of the coastal zone correlates strongly with the extent of the surge-inundated area (SIA) obtained from simulations that only considered storm surges. The extent of the transitional zone correlates strongly with the product of SIA and total rainfall. As an additional measure for flood hazards besides water depth, we calculated flow momentum flux at different flood zones to assess potential damage from hydrodynamic loads on structures, vehicles, and pedestrians. A strong correlation was found between the magnitude of the surge and momentum flux. Furthermore, high rainfall rates and winds can cause a significant increase in momentum flux locally. Understanding flood zones and their flow dynamics helps to identify effective flood risk management strategies that address the dominant flood driver.
洪水风险分析通常侧重于单一洪水来源，通常是风暴潮或降雨导致的洪水，具体取决于主要威胁。然而，飓风经常通过伴随暴雨的巨大风暴潮造成复合洪水。本研究采用基于 Delft3D-Flexible Mesh 的流体力学模型，将流、波浪和降雨驱动的流结合在一起，模拟了弗吉尼亚东南沿海地区历史上的五次热带气旋。这些风暴在研究区域产生了不同强度的风暴潮和降雨。模型模拟通过栅上降雨方法将降雨纳入其中，考虑了风暴潮和冲积流之间的动态互 动，并将洪水区定义为水文区、过渡区和沿岸区。这一复合洪水模型通过水内和水上测量仪的水位数据进行了验证。结果表明，沿岸带的大小与只考虑风暴潮的模拟所得到的浪涌淹没区（SIA）的范围密切相关。过渡带的范围与 SIA 和总降雨量的乘积密切相关。除水深外，我们还计算了不同淹没区的水流动量通量，以评估水动力负荷对建筑物、车辆和行人可能造成的损害，作为洪水危害的额外衡量标准。结果发现，洪峰的规模与动量通量之间存在很强的相关性。此外，高降雨率和大风也会导致局部动量通量显著增加。了解洪泛区及其水流动力学有助于确定有效的洪水风险管理策略，以应对主要的洪水驱动因素。
{"title":"Compound Flooding Hazards Due To Storm Surge and Pluvial Flow in a Low-Gradient Coastal Region","authors":"Sunghoon Han, Navid Tahvildari","doi":"10.1029/2023wr037014","DOIUrl":"https://doi.org/10.1029/2023wr037014","url":null,"abstract":"Flood risk analyses often focus on a single flooding source, typically storm surge or rainfall-driven flooding, depending on the predominant threat. However, hurricanes frequently cause compound flooding through significant storm surges accompanied by heavy rainfall. This study employs a hydrodynamic model based on Delft3D-Flexible Mesh that couples flow, waves, and rainfall-driven flow to simulate five historical tropical cyclones in Virginia's southeast coastal region. These storms produced varying intensities of storm surge and rainfall in the study area. Model simulations, incorporating rainfall through a rain-on-grid approach, account for the dynamic interaction between storm tides, and pluvial flow and enable the definition of flood zones as hydrologic, transitional, and coastal zones. This compound flooding model was validated with water level data from in-water and overland gauges. The results indicate that the magnitude of the coastal zone correlates strongly with the extent of the surge-inundated area (SIA) obtained from simulations that only considered storm surges. The extent of the transitional zone correlates strongly with the product of SIA and total rainfall. As an additional measure for flood hazards besides water depth, we calculated flow momentum flux at different flood zones to assess potential damage from hydrodynamic loads on structures, vehicles, and pedestrians. A strong correlation was found between the magnitude of the surge and momentum flux. Furthermore, high rainfall rates and winds can cause a significant increase in momentum flux locally. Understanding flood zones and their flow dynamics helps to identify effective flood risk management strategies that address the dominant flood driver.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"20 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697030","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}
J. D. Abad, H. Chicchon, J. Chuctaya, A. Mendoza, H. Valverde, C. Oshiro, M. Montoya
The transition from the Andes to the Amazon lowland hosts a high biodiversity and currently is facing several anthropogenic activities, including hydropower infrastructure projects. Little is known about the geomorphology of the Andean gorges, rivers and the interaction with the fish diversity upstream and downstream of gorges. The Marañón River is a major river that connects the Andes to the Amazon lowland and it carries 40% of the sediment load arriving to Brazil. The Santiago River is the last tributary into the Marañón River before the last gorge (Manseriche). Current plans for hydropower reservoirs include the construction of several dams along the Marañón River, being the largest with a 4,500 MW capacity at the Manseriche Gorge (MG). This study seeks to characterize the baseline processes of the hydrogeomorphology and fish diversity. Results show that the Santiago River is under transitional morphodynamic regime while the Marañón River is a fully developed anabranching river. This study reveals a clear difference in fish species richness and abundance between the upstream and downstream regions of the MG, with some species only found in specific regions. The MG acts as a natural boundary condition for the hydrogeomorphology and fish diversity. If the hydropower dam at MG was built, the reservoir in the upstream reach will produce the Santiago River to disappear and sedimentation to occur, consequently modifying sediment transport boundary conditions for the lower Marañón River. Downstream of the potential dam incision will occur, reducing lateral connectivity, particularly at sites where unique species were found.
{"title":"River Geomorphology and Fish Diversity Around the Manseriche Gorge, the Last Andean Crossing Is in Peril","authors":"J. D. Abad, H. Chicchon, J. Chuctaya, A. Mendoza, H. Valverde, C. Oshiro, M. Montoya","doi":"10.1029/2024wr037322","DOIUrl":"https://doi.org/10.1029/2024wr037322","url":null,"abstract":"The transition from the Andes to the Amazon lowland hosts a high biodiversity and currently is facing several anthropogenic activities, including hydropower infrastructure projects. Little is known about the geomorphology of the Andean gorges, rivers and the interaction with the fish diversity upstream and downstream of gorges. The Marañón River is a major river that connects the Andes to the Amazon lowland and it carries 40% of the sediment load arriving to Brazil. The Santiago River is the last tributary into the Marañón River before the last gorge (Manseriche). Current plans for hydropower reservoirs include the construction of several dams along the Marañón River, being the largest with a 4,500 MW capacity at the Manseriche Gorge (MG). This study seeks to characterize the baseline processes of the hydrogeomorphology and fish diversity. Results show that the Santiago River is under transitional morphodynamic regime while the Marañón River is a fully developed anabranching river. This study reveals a clear difference in fish species richness and abundance between the upstream and downstream regions of the MG, with some species only found in specific regions. The MG acts as a natural boundary condition for the hydrogeomorphology and fish diversity. If the hydropower dam at MG was built, the reservoir in the upstream reach will produce the Santiago River to disappear and sedimentation to occur, consequently modifying sediment transport boundary conditions for the lower Marañón River. Downstream of the potential dam incision will occur, reducing lateral connectivity, particularly at sites where unique species were found.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"80 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713160","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}
Detecting and quantifying the global teleconnections with flash droughts (FDs) and understanding their causal relationships is crucial to improve their predictability. This study employs causal effect networks (CENs) to explore the global predictability sources of subseasonal soil moisture FDs in three regions of the United States (US): upper Mississippi, South Atlantic Gulf (SAG), and upper and lower Colorado river basins. We analyzed the causal relationships of FD events with global 2-m air temperature, sea surface temperature, water deficit (precipitation minus evaporation), and geopotential height at 500 hPa at the weekly timescale over the warm season (April to September) from 1982 to 2018. CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high-pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. Moreover, a strong link from South America suggests that atmospheric circulation forcings could affect the SAG through the low-level atmospheric flow, reducing inland moisture transport, and leading to a precipitation deficit. Machine learning utilizing the identified causal regions and factors can well predict major FD events up to 4 weeks in advance, providing useful insights for improved subseasonal forecasting and early warnings.
{"title":"Causal Discovery Analysis Reveals Global Sources of Predictability for Regional Flash Droughts","authors":"Sudhanshu Kumar, Di Tian","doi":"10.1029/2024wr038391","DOIUrl":"https://doi.org/10.1029/2024wr038391","url":null,"abstract":"Detecting and quantifying the global teleconnections with flash droughts (FDs) and understanding their causal relationships is crucial to improve their predictability. This study employs causal effect networks (CENs) to explore the global predictability sources of subseasonal soil moisture FDs in three regions of the United States (US): upper Mississippi, South Atlantic Gulf (SAG), and upper and lower Colorado river basins. We analyzed the causal relationships of FD events with global 2-m air temperature, sea surface temperature, water deficit (precipitation minus evaporation), and geopotential height at 500 hPa at the weekly timescale over the warm season (April to September) from 1982 to 2018. CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high-pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. Moreover, a strong link from South America suggests that atmospheric circulation forcings could affect the SAG through the low-level atmospheric flow, reducing inland moisture transport, and leading to a precipitation deficit. Machine learning utilizing the identified causal regions and factors can well predict major FD events up to 4 weeks in advance, providing useful insights for improved subseasonal forecasting and early warnings.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"198 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690871","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}
Fanzhang Zeng, Yu Zhang, Jeffrey S. Geurink, Kshitij Parajuli, Lili Yao, Dingbao Wang
An analytical model is developed for mean annual groundwater evapotranspiration (GWET) at the watershed scale based on a three-stage precipitation partitioning framework. The ratio of mean annual GWET to precipitation, defined as GWET ratio, is modeled as a function of climate aridity index (CAI), storage capacity index, the shape parameter ‘a’ for the spatial distribution of storage capacity, and the shape parameter ‘b’ for the spatial distribution of available water for GWET. In humid regions, GWET ratio tends to increase with increasing CAI due to the limited energy supply and shallower depth to water table (DWT) for a given storage capacity index. In contrast, in arid regions, the GWET ratio tends to decrease as the CAI increases because of the limited water availability and the presence of a deeper DWT for a given storage capacity index. In arid regions, the GWET ratio decreases as the parameter ‘a’ increases, mainly because of increased ET from a thicker unsaturated zone in environments with a deeper DWT. GWET ratio increases as parameter ‘b’ increases due to more watershed area with larger available water for GWET. The storage capacity index and shape parameters are estimated for 31 study watersheds in Tampa Bay Florida area based on the simulated GWET from an integrated hydrologic model and for 21 watersheds from literature. A possible correlation has been identified between the two shape parameters in the Tampa Bay watersheds. The analytical model for mean annual GWET can be further tested in other watersheds if data are available.
基于三阶段降水分区框架，建立了流域尺度的年平均地下水蒸散量（GWET）分析模型。年平均地下水蒸发蒸腾量与降水量之比（定义为 GWET 比率）是气候干旱指数（CAI）、储水能力指数、储水能力空间分布形状参数 "a "和 GWET 可用水量空间分布形状参数 "b "的函数。在湿润地区，由于能量供应有限，在给定储水量指数的情况下，地下水位深度（DWT）较浅，因此 GWET 比率往往随着 CAI 的增加而增加。与此相反，在干旱地区，GWET 比率往往随着 CAI 的增加而降低，这是因为在给定的蓄水能力指数下，水供应有限且地下水位深度较深。在干旱地区，GWET 比值随着参数 "a "的增加而降低，这主要是由于在 DWT 较深的环境中，较厚的非饱和带所产生的蒸散发增加。GWET 比率随着参数 "b "的增加而增加，这是因为流域面积越大，GWET 可用水量越大。根据综合水文模型模拟的 GWET 和文献资料估算了佛罗里达坦帕湾 31 个研究流域的蓄水能力指数和形状参数。发现坦帕湾流域的两个形状参数之间可能存在相关性。如果有数据，可在其他流域进一步测试年平均 GWET 分析模型。
{"title":"A Three-Stage Partitioning Framework for Modeling Mean Annual Groundwater Evapotranspiration","authors":"Fanzhang Zeng, Yu Zhang, Jeffrey S. Geurink, Kshitij Parajuli, Lili Yao, Dingbao Wang","doi":"10.1029/2024wr037248","DOIUrl":"https://doi.org/10.1029/2024wr037248","url":null,"abstract":"An analytical model is developed for mean annual groundwater evapotranspiration (GWET) at the watershed scale based on a three-stage precipitation partitioning framework. The ratio of mean annual GWET to precipitation, defined as GWET ratio, is modeled as a function of climate aridity index (CAI), storage capacity index, the shape parameter ‘a’ for the spatial distribution of storage capacity, and the shape parameter ‘b’ for the spatial distribution of available water for GWET. In humid regions, GWET ratio tends to increase with increasing CAI due to the limited energy supply and shallower depth to water table (DWT) for a given storage capacity index. In contrast, in arid regions, the GWET ratio tends to decrease as the CAI increases because of the limited water availability and the presence of a deeper DWT for a given storage capacity index. In arid regions, the GWET ratio decreases as the parameter ‘a’ increases, mainly because of increased ET from a thicker unsaturated zone in environments with a deeper DWT. GWET ratio increases as parameter ‘b’ increases due to more watershed area with larger available water for GWET. The storage capacity index and shape parameters are estimated for 31 study watersheds in Tampa Bay Florida area based on the simulated GWET from an integrated hydrologic model and for 21 watersheds from literature. A possible correlation has been identified between the two shape parameters in the Tampa Bay watersheds. The analytical model for mean annual GWET can be further tested in other watersheds if data are available.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"203 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690824","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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