Chen Hu, Jun Xia, Dunxian She, Zhaoxia Jing, Si Hong, Zhihong Song, Gangsheng Wang
The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression-based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least-squares regression is identified as optimal regression-based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression-based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.
{"title":"Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments","authors":"Chen Hu, Jun Xia, Dunxian She, Zhaoxia Jing, Si Hong, Zhihong Song, Gangsheng Wang","doi":"10.1029/2023wr035071","DOIUrl":"https://doi.org/10.1029/2023wr035071","url":null,"abstract":"The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression-based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least-squares regression is identified as optimal regression-based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression-based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561770","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}
Nafyad Serre Kawo, Jesse Korus, Yaser Kishawi, Erin Marie King Haacker, Aaron R. Mittelstet
Characterizing the 3D distribution of hydraulic properties in glacial sediments is challenging due to fine-scale heterogeneity and complexity. Borehole lithological data provide high vertical resolution but low horizontal resolution. Geophysical methods can fill gaps between boreholes, providing improved horizontal resolution but low vertical resolution. Machine learning can combine borehole and geophysical data to overcome these challenges. However, few studies have compared multiple machine learning methods for predicting hydrofacies in glacial aquifer systems. This study uses colocated airborne electromagnetic resistivity and borehole lithology data to train multiple machine learning models and predict the 3D distribution of hydrofacies in glacial deposits of eastern Nebraska, USA. Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model 3D probabilistic distributions of hydrofacies (sand and clay) at a grid size of 200 m × 200 m × 3 m. Comparison of the predicted 3D hydrofacies models shows that the probability distributions and the contrasts between hydrofacies vary. The classification metrics show that the Stacking Classifier model performed better than other machine learning models in predicting hydrofacies. Multi-Layer Perceptron and Stacking Classifier models show sharp vertical transitions between the low and high sand probability while other machine learning models show gradual transitions. K-means clustering was used to translate the Stacking Classifier model into a 4-class hydraulic conductivity model. This study shows that machine learning methods advance our understanding of glacial hydrogeology by improving the vertical and horizontal resolution of hydrofacies distribution and resolving aquifer-aquifer and stream-aquifer connections.
由于冰川沉积物的细尺度异质性和复杂性，确定其水力特性的三维分布具有挑战性。钻孔岩性数据可提供较高的垂直分辨率，但水平分辨率较低。地球物理方法可以填补钻孔之间的空白，提高水平分辨率，但垂直分辨率较低。机器学习可以结合钻孔和地球物理数据来克服这些挑战。然而，很少有研究比较多种机器学习方法来预测冰川含水层系统中的水成岩。本研究利用同位机载电磁电阻率和钻孔岩性数据来训练多个机器学习模型，并预测美国内布拉斯加州东部冰川沉积物中水成岩的三维分布。随机森林、梯度提升分类器、极端梯度提升、多层感知器和堆叠分类器被用于在 200 m × 200 m × 3 m 的网格大小上建立水成岩（砂和粘土）的三维概率分布模型。分类指标显示，堆叠分类器模型在预测水成层方面的表现优于其他机器学习模型。多层感知器和堆叠分类器模型在低砂和高砂概率之间显示出急剧的垂直过渡，而其他机器学习模型则显示出渐进的过渡。K-means 聚类用于将堆叠分类器模型转化为 4 级水力传导模型。这项研究表明，机器学习方法提高了水成岩分布的垂直和水平分辨率，并解决了含水层-含水层和溪流-含水层之间的联系问题，从而加深了我们对冰川水文地质学的理解。
{"title":"Three-Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning","authors":"Nafyad Serre Kawo, Jesse Korus, Yaser Kishawi, Erin Marie King Haacker, Aaron R. Mittelstet","doi":"10.1029/2023wr035910","DOIUrl":"https://doi.org/10.1029/2023wr035910","url":null,"abstract":"Characterizing the 3D distribution of hydraulic properties in glacial sediments is challenging due to fine-scale heterogeneity and complexity. Borehole lithological data provide high vertical resolution but low horizontal resolution. Geophysical methods can fill gaps between boreholes, providing improved horizontal resolution but low vertical resolution. Machine learning can combine borehole and geophysical data to overcome these challenges. However, few studies have compared multiple machine learning methods for predicting hydrofacies in glacial aquifer systems. This study uses colocated airborne electromagnetic resistivity and borehole lithology data to train multiple machine learning models and predict the 3D distribution of hydrofacies in glacial deposits of eastern Nebraska, USA. Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model 3D probabilistic distributions of hydrofacies (sand and clay) at a grid size of 200 m × 200 m × 3 m. Comparison of the predicted 3D hydrofacies models shows that the probability distributions and the contrasts between hydrofacies vary. The classification metrics show that the Stacking Classifier model performed better than other machine learning models in predicting hydrofacies. Multi-Layer Perceptron and Stacking Classifier models show sharp vertical transitions between the low and high sand probability while other machine learning models show gradual transitions. K-means clustering was used to translate the Stacking Classifier model into a 4-class hydraulic conductivity model. This study shows that machine learning methods advance our understanding of glacial hydrogeology by improving the vertical and horizontal resolution of hydrofacies distribution and resolving aquifer-aquifer and stream-aquifer connections.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141578092","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}
Wenguang Shi, Quanrong Wang, Maria Klepikova, Hongbin Zhan
A fundamental assumption in numerous studies of heat transfer in porous media is local thermal equilibrium (LTE), which assumes that the temperature of the porous media at the fluid and solid interface is in instantaneous equilibrium. Although significant efforts have been made to quantify the occurrence and consequences of local thermal nonequilibrium (LTNE), where the temperatures of the fluid and adjacent solid phases differ, there is no simple expression for quantifying the occurrence and effects of local thermal disequilibrium. Using a numerical model combining LTE and LTNE models, we develop here two simple general criteria based on Darcian velocities (q) and particle sizes (dp) of porous media for determining when LTNE effects occur (denoted as g(dp, q)) and when they become significant (denoted as f(dp, q)). Results show that using an LTE model can result in an underestimation of effective thermal diffusivity and the unaffected Darcian velocities when g(dp, q) > 0. It is possible that using the LTE model can result in an underestimation of the effective thermal diffusivity by more than 200 times within Darcian velocities ranging from 0 to 60 m/d. In the case of g(dp, q) < 0, the use of the LTE model can result in an overestimation of effective thermal diffusivity and Darcian velocities. The performances of the newly developed general criteria are demonstrated using three typical data sets and corresponding numerical models. These data sets include new heat tracer tests conducted in the laboratory and the field, as well as temperature-time series collected in streambed sediments from a previous study by Shanafield et al. (2012, https://doi.org/10.5194/hessd-9-4305-2012). The potential LTNE effects should be considered when using heat as a tracer to characterize flow and heat transport in porous media in the presence of Darcian velocities less than 2 m/d and particle sizes larger than 10 mm.
{"title":"New Criteria to Estimate Local Thermal Nonequilibrium Conditions for Heat Transport in Porous Aquifers","authors":"Wenguang Shi, Quanrong Wang, Maria Klepikova, Hongbin Zhan","doi":"10.1029/2024wr037382","DOIUrl":"https://doi.org/10.1029/2024wr037382","url":null,"abstract":"A fundamental assumption in numerous studies of heat transfer in porous media is local thermal equilibrium (LTE), which assumes that the temperature of the porous media at the fluid and solid interface is in instantaneous equilibrium. Although significant efforts have been made to quantify the occurrence and consequences of local thermal nonequilibrium (LTNE), where the temperatures of the fluid and adjacent solid phases differ, there is no simple expression for quantifying the occurrence and effects of local thermal disequilibrium. Using a numerical model combining LTE and LTNE models, we develop here two simple general criteria based on Darcian velocities (<i>q</i>) and particle sizes (<i>d</i><sub><i>p</i></sub>) of porous media for determining when LTNE effects occur (denoted as <i>g</i>(<i>d</i><sub><i>p</i></sub>, <i>q</i>)) and when they become significant (denoted as <i>f</i>(<i>d</i><sub><i>p</i></sub>, <i>q</i>)). Results show that using an LTE model can result in an underestimation of effective thermal diffusivity and the unaffected Darcian velocities when <i>g</i>(<i>d</i><sub><i>p</i></sub>, <i>q</i>) > 0. It is possible that using the LTE model can result in an underestimation of the effective thermal diffusivity by more than 200 times within Darcian velocities ranging from 0 to 60 m/d. In the case of <i>g</i>(<i>d</i><sub><i>p</i></sub>, <i>q</i>) < 0, the use of the LTE model can result in an overestimation of effective thermal diffusivity and Darcian velocities. The performances of the newly developed general criteria are demonstrated using three typical data sets and corresponding numerical models. These data sets include new heat tracer tests conducted in the laboratory and the field, as well as temperature-time series collected in streambed sediments from a previous study by Shanafield et al. (2012, https://doi.org/10.5194/hessd-9-4305-2012). The potential LTNE effects should be considered when using heat as a tracer to characterize flow and heat transport in porous media in the presence of Darcian velocities less than 2 m/d and particle sizes larger than 10 mm.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546256","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}
Sandra Armengol, Hoori Ajami, Juan S. Acero Triana, James O'Sickman, Lucia Ortega
Mountain System Recharge processes are significant natural recharge pathways in many arid and semi-arid mountainous regions. However, Mountain System Recharge processes are often poorly understood and characterized in hydrologic models. Mountains are the primary water supply source to valley aquifers via lateral groundwater flow from the mountain block (Mountain Block Recharge) and focused recharge from mountain streams contributing to focused Mountain Front Recharge at the piedmont zone. Here, we present a multi-tool isogeochemical approach to characterize mountain flow paths and Mountain System Recharge in the northern Tulare Basin, California. We used groundwater chemistry data to delineate hydrochemical facies and explain the chemical evolution of groundwater from the Sierra Nevada to the Central Valley aquifer. Stable isotopes and radiogenic groundwater tracers validated Mountain System Recharge processes by differentiating focused from diffuse recharge, and estimating apparent groundwater age, respectively. Novel application of End-Member Mixing Analysis using conservative chemical components revealed three Mountain System Recharge end-members: (a) evaporated Ca-HCO3 water type associated with focused Mountain Front Recharge, (b) non-evaporated Ca-HCO3 and Na-HCO3 water types with short residence times associated with shallow Mountain Block Recharge, and (c) Na-HCO3 groundwater type with long residence time associated with deep Mountain Block Recharge. We quantified the contribution of each Mountain System Recharge process to the valley aquifer by calculating mixing ratios. Our results show that deep Mountain Block Recharge is a significant recharge component, representing 31%–53% of the valley groundwater. Greater hydraulic connectivity between the Sierra Nevada and Central Valley has significant implications for parameterizing groundwater flow models. Our framework is useful for understanding Mountain System Recharge processes in other snow-dominated mountain watersheds.
{"title":"Isogeochemical Characterization of Mountain System Recharge Processes in the Sierra Nevada, California","authors":"Sandra Armengol, Hoori Ajami, Juan S. Acero Triana, James O'Sickman, Lucia Ortega","doi":"10.1029/2023wr035719","DOIUrl":"https://doi.org/10.1029/2023wr035719","url":null,"abstract":"Mountain System Recharge processes are significant natural recharge pathways in many arid and semi-arid mountainous regions. However, Mountain System Recharge processes are often poorly understood and characterized in hydrologic models. Mountains are the primary water supply source to valley aquifers via lateral groundwater flow from the mountain block (Mountain Block Recharge) and focused recharge from mountain streams contributing to focused Mountain Front Recharge at the piedmont zone. Here, we present a multi-tool isogeochemical approach to characterize mountain flow paths and Mountain System Recharge in the northern Tulare Basin, California. We used groundwater chemistry data to delineate hydrochemical facies and explain the chemical evolution of groundwater from the Sierra Nevada to the Central Valley aquifer. Stable isotopes and radiogenic groundwater tracers validated Mountain System Recharge processes by differentiating focused from diffuse recharge, and estimating apparent groundwater age, respectively. Novel application of End-Member Mixing Analysis using conservative chemical components revealed three Mountain System Recharge end-members: (a) evaporated Ca-HCO<sub>3</sub> water type associated with focused Mountain Front Recharge, (b) non-evaporated Ca-HCO<sub>3</sub> and Na-HCO<sub>3</sub> water types with short residence times associated with shallow Mountain Block Recharge, and (c) Na-HCO<sub>3</sub> groundwater type with long residence time associated with deep Mountain Block Recharge. We quantified the contribution of each Mountain System Recharge process to the valley aquifer by calculating mixing ratios. Our results show that deep Mountain Block Recharge is a significant recharge component, representing 31%–53% of the valley groundwater. Greater hydraulic connectivity between the Sierra Nevada and Central Valley has significant implications for parameterizing groundwater flow models. Our framework is useful for understanding Mountain System Recharge processes in other snow-dominated mountain watersheds.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546010","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}
Geological formations with fractures are frequently encountered in various research fields. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and solute transport within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observable state variables. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non-Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as parameter estimator with deep learning (PEDL) that harnesses the capabilities of DL to capture nonlinear relationships and extract non-Gaussian features. To evaluate PEDL's performance, we conduct three case studies, comprising two numerical cases and one real-world case. In these cases, we systematically compare PEDL with three widely used DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL-based update (ESDL). Notably, in the problems characterized by highly non-Gaussian features, ESMDA and ILUES produce significantly divergent results. Conversely, employing the DL-based update, ESDL demonstrates improved performance. However, its estimation uncertainty remains high, potentially attributable to ESDL's updating mechanism. Comprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks.
在各种研究领域中，经常会遇到带有裂缝的地质构造。当需要精确预测裂缝中的液体流动和溶质传输时，准确描述这些裂缝介质的特征至关重要。由于直接测量断裂介质本身就存在挑战，因此通常采用数据同化（DA）技术，利用可观测的状态变量对介质属性进行反向估算。然而，由于断裂介质的强异质性和非高斯性所带来的巨大困难，降低了现有数据同化方法的有效性。在本研究中，我们提出了一种名为 "深度学习参数估计器"（PEDL）的新型数据分析方法，该方法利用了深度学习捕捉非线性关系和提取非高斯特征的能力。为了评估 PEDL 的性能，我们进行了三个案例研究，包括两个数值案例和一个真实世界案例。在这些案例中，我们系统地比较了 PEDL 和三种广泛使用的 DA 方法：具有多重 DA 的集合平滑器 (ESMDA)、迭代局部更新 ES (ILUES) 和基于 DL 更新的 ES (ESDL)。值得注意的是，在高度非高斯特征的问题中，ESMDA 和 ILUES 产生了明显不同的结果。相反，采用基于 DL 的更新后，ESDL 的性能有所提高。然而，ESDL的估计不确定性仍然很高，这可能与ESDL的更新机制有关。综合分析证实了 PEDL 在各种集合规模和 DL 模型架构下的有效性和适应性。此外，即使在精确参考模型和简化预测模型之间存在结构差异的情况下，PEDL 也能熟练识别断裂网络的主要特征。
{"title":"Effective Characterization of Fractured Media With PEDL: A Deep Learning-Based Data Assimilation Approach","authors":"Tongchao Nan, Jiangjiang Zhang, Yifan Xie, Chenglong Cao, Jichun Wu, Chunhui Lu","doi":"10.1029/2023wr036673","DOIUrl":"https://doi.org/10.1029/2023wr036673","url":null,"abstract":"Geological formations with fractures are frequently encountered in various research fields. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and solute transport within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observable state variables. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non-Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as parameter estimator with deep learning (PEDL) that harnesses the capabilities of DL to capture nonlinear relationships and extract non-Gaussian features. To evaluate PEDL's performance, we conduct three case studies, comprising two numerical cases and one real-world case. In these cases, we systematically compare PEDL with three widely used DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL-based update (ESDL). Notably, in the problems characterized by highly non-Gaussian features, ESMDA and ILUES produce significantly divergent results. Conversely, employing the DL-based update, ESDL demonstrates improved performance. However, its estimation uncertainty remains high, potentially attributable to ESDL's updating mechanism. Comprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561708","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}
Haoyang Qin, Qiuhua Liang, Huili Chen, Varuna De Silva
In recent years, flood risk in urban areas has been rapidly increasing due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Flood risk assessment should realistically take into account the complex interactions between human and natural systems to better inform risk management and improve resilience. In this study, we propose a novel Coupled Human And Natural Systems (CHANS) modeling framework to capture the intricate interactive human behaviors and flooding process at a high spatial resolution. The new CHANS modeling framework integrates a high-performance hydrodynamic model with an agent-based model to simulate the complex responses of individual households to the evolving flood conditions, leveraging the computing power of graphics processing units (GPUs) to achieve real-time simulation. The framework is applied to reproduce the 2015 Desmond flood in the 2,500 km2 Eden Catchment in England, demonstrating its ability to predict interactive flood-human dynamics and assess flood impact at the household-level. The study also further explores the effectiveness of different flood risk management strategies, including the provision of early warning and distribution of sandbags, in mitigating flood impact. The new CHANS model potentially provides a useful tool for understanding short-term human behaviors and their impact on flood risk during a flood event, which is important for the development of effective disaster risk management plans.
{"title":"A High-Performance Coupled Human And Natural Systems (CHANS) Model for Flood Risk Assessment and Reduction","authors":"Haoyang Qin, Qiuhua Liang, Huili Chen, Varuna De Silva","doi":"10.1029/2023wr036269","DOIUrl":"https://doi.org/10.1029/2023wr036269","url":null,"abstract":"In recent years, flood risk in urban areas has been rapidly increasing due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Flood risk assessment should realistically take into account the complex interactions between human and natural systems to better inform risk management and improve resilience. In this study, we propose a novel Coupled Human And Natural Systems (CHANS) modeling framework to capture the intricate interactive human behaviors and flooding process at a high spatial resolution. The new CHANS modeling framework integrates a high-performance hydrodynamic model with an agent-based model to simulate the complex responses of individual households to the evolving flood conditions, leveraging the computing power of graphics processing units (GPUs) to achieve real-time simulation. The framework is applied to reproduce the 2015 Desmond flood in the 2,500 km<sup>2</sup> Eden Catchment in England, demonstrating its ability to predict interactive flood-human dynamics and assess flood impact at the household-level. The study also further explores the effectiveness of different flood risk management strategies, including the provision of early warning and distribution of sandbags, in mitigating flood impact. The new CHANS model potentially provides a useful tool for understanding short-term human behaviors and their impact on flood risk during a flood event, which is important for the development of effective disaster risk management plans.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561675","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}
Inter-catchment groundwater flow (IGF) plays an essential role in streamflow generation and water quality in forested headwaters. Multiple factors are thought to contribute to IGF, including climate, topographical, and geological factors. However, studies have not clarified the relationships between IGF and catchment properties in the headwater catchments due to the lack of observational data at scales smaller than 100 ha. This study examined possible factors influencing IGF using random forest analysis based on annual water balance data from 152 forested catchments ranging from 0.09 to 9400 ha in Japan. The results showed that catchment scale had the greatest influence on IGF, and IGF tended to decrease with increasing catchment area at scales of less than 10 ha. The average IGF stabilized around zero in catchments greater than 10 ha. The averaged IGF trend with catchment scale indicated more outward groundwater flow in catchments smaller than 10 ha, but no relationship between IGF and catchment size in catchments larger than 10 ha. The variability in IGF decreased with catchment size and was lowest at 10–100 ha. The decrease in variability in catchments less than 100 ha was mainly due to river confluence and the increased variability in catchments larger than 100 ha indicated potential observation errors increase in catchments of this size.
{"title":"Scale-Dependent Inter-Catchment Groundwater Flow in Forested Catchments: Analysis of Multi-Catchment Water Balance Observations in Japan","authors":"Tomoki Oda, Kenta Iwasaki, Tomohiro Egusa, Tayoko Kubota, Sho Iwagami, Shin'ichi Iida, Hiroki Momiyama, Takanori Shimizu","doi":"10.1029/2024wr037161","DOIUrl":"https://doi.org/10.1029/2024wr037161","url":null,"abstract":"Inter-catchment groundwater flow (IGF) plays an essential role in streamflow generation and water quality in forested headwaters. Multiple factors are thought to contribute to IGF, including climate, topographical, and geological factors. However, studies have not clarified the relationships between IGF and catchment properties in the headwater catchments due to the lack of observational data at scales smaller than 100 ha. This study examined possible factors influencing IGF using random forest analysis based on annual water balance data from 152 forested catchments ranging from 0.09 to 9400 ha in Japan. The results showed that catchment scale had the greatest influence on IGF, and IGF tended to decrease with increasing catchment area at scales of less than 10 ha. The average IGF stabilized around zero in catchments greater than 10 ha. The averaged IGF trend with catchment scale indicated more outward groundwater flow in catchments smaller than 10 ha, but no relationship between IGF and catchment size in catchments larger than 10 ha. The variability in IGF decreased with catchment size and was lowest at 10–100 ha. The decrease in variability in catchments less than 100 ha was mainly due to river confluence and the increased variability in catchments larger than 100 ha indicated potential observation errors increase in catchments of this size.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141521611","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}
Toshiyuki Bandai, Teamrat A. Ghezzehei, Peishi Jiang, Patrick Kidger, Xingyuan Chen, Carl I. Steefel
The constitutive relations of the Richardson-Richards equation encode the macroscopic properties of soil water retention and conductivity. These soil hydraulic functions are commonly represented by models with a handful of parameters. The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free-form approach to learning the constitutive relations using physically constrained neural networks. We implemented the inverse modeling framework in a differentiable modeling framework, JAX, to ensure scalability and extensibility. For efficient gradient computations, we implemented implicit differentiation through a nonlinear solver for the Richardson-Richards equation. We tested the framework against synthetic noisy data and demonstrated its robustness against varying magnitudes of noise and degrees of freedom of the neural networks. We applied the framework to soil moisture data from an upward infiltration experiment and demonstrated that the neural network-based approach was better fitted to the experimental data than a parametric model and that the framework can learn the constitutive relations.
{"title":"Learning Constitutive Relations From Soil Moisture Data via Physically Constrained Neural Networks","authors":"Toshiyuki Bandai, Teamrat A. Ghezzehei, Peishi Jiang, Patrick Kidger, Xingyuan Chen, Carl I. Steefel","doi":"10.1029/2024wr037318","DOIUrl":"https://doi.org/10.1029/2024wr037318","url":null,"abstract":"The constitutive relations of the Richardson-Richards equation encode the macroscopic properties of soil water retention and conductivity. These soil hydraulic functions are commonly represented by models with a handful of parameters. The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free-form approach to learning the constitutive relations using physically constrained neural networks. We implemented the inverse modeling framework in a differentiable modeling framework, JAX, to ensure scalability and extensibility. For efficient gradient computations, we implemented implicit differentiation through a nonlinear solver for the Richardson-Richards equation. We tested the framework against synthetic noisy data and demonstrated its robustness against varying magnitudes of noise and degrees of freedom of the neural networks. We applied the framework to soil moisture data from an upward infiltration experiment and demonstrated that the neural network-based approach was better fitted to the experimental data than a parametric model and that the framework can learn the constitutive relations.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546009","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}
Yalin Ma, Yun Pan, Chong Zhang, Pat J.-F. Yeh, Li Xu, Zhiyong Huang, Huili Gong
Groundwater storage anomaly (GWSA) can be estimated either at the large scale from the Gravity Recovery and Climate Experiment (GRACE) or at the local scale based on in situ observed groundwater level (GWL) and aquifer storage parameters. Yet, the accuracy of GRACE-based estimate is affected by leakage errors, while that of local GWL-based estimate requires the reliable specific yield (Sy) data that are usually not available. Here, we developed a novel approach, the coordinated forward modeling (CoFM), based on the iterative forward modeling to improve GWSA estimation at the sub-regional scale smaller than the typical GRACE footprint. It is achieved by solving Sy through iterative comparisons between GRACE-based and observation-based GWSA at 0.5° grid scale, and then re-calculating GWSA using the updated Sy and observed GWL. The utility of CoFM is explored by using the hypothetical experiments and a real case study in the Piedmont Plain (PP, ∼54,000 km2) and East-central Plain (ECP, ∼86,000 km2) of North China Plain. Results show that CoFM can detect GWSA at 0.5° grid scale in the hypothetical experiments given the large spatial variability of GWL. While in the real case study, the CoFM distinguishes between the divergent unconfined GWSA trends (2005–2016) in PP (−41.80 ± 0.55 mm/yr) and ECP (−7.57 ± 0.60 mm/yr) caused by the differences in hydrogeological conditions and groundwater use. The improvement made by CoFM can be attributed to the use of the distributed GWL information to constrain GRACE leakage errors. This study highlights a practical important solution for improving sub-regional GWSA estimation through the joint use of large-scale GRACE data and local-scale well observations.
{"title":"Improved Estimates of Sub-Regional Groundwater Storage Anomaly Using Coordinated Forward Modeling","authors":"Yalin Ma, Yun Pan, Chong Zhang, Pat J.-F. Yeh, Li Xu, Zhiyong Huang, Huili Gong","doi":"10.1029/2023wr036105","DOIUrl":"https://doi.org/10.1029/2023wr036105","url":null,"abstract":"Groundwater storage anomaly (GWSA) can be estimated either at the large scale from the Gravity Recovery and Climate Experiment (GRACE) or at the local scale based on in situ observed groundwater level (GWL) and aquifer storage parameters. Yet, the accuracy of GRACE-based estimate is affected by leakage errors, while that of local GWL-based estimate requires the reliable specific yield (Sy) data that are usually not available. Here, we developed a novel approach, the coordinated forward modeling (CoFM), based on the iterative forward modeling to improve GWSA estimation at the sub-regional scale smaller than the typical GRACE footprint. It is achieved by solving Sy through iterative comparisons between GRACE-based and observation-based GWSA at 0.5° grid scale, and then re-calculating GWSA using the updated Sy and observed GWL. The utility of CoFM is explored by using the hypothetical experiments and a real case study in the Piedmont Plain (PP, ∼54,000 km<sup>2</sup>) and East-central Plain (ECP, ∼86,000 km<sup>2</sup>) of North China Plain. Results show that CoFM can detect GWSA at 0.5° grid scale in the hypothetical experiments given the large spatial variability of GWL. While in the real case study, the CoFM distinguishes between the divergent unconfined GWSA trends (2005–2016) in PP (−41.80 ± 0.55 mm/yr) and ECP (−7.57 ± 0.60 mm/yr) caused by the differences in hydrogeological conditions and groundwater use. The improvement made by CoFM can be attributed to the use of the distributed GWL information to constrain GRACE leakage errors. This study highlights a practical important solution for improving sub-regional GWSA estimation through the joint use of large-scale GRACE data and local-scale well observations.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489823","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}
Fa Du, Zongxing Li, Juan Gui, Baijuan Zhang, Jian Xue, Huiling Zhou
This study investigates the temporal processes of suprapermafrost groundwater (SPG)-supplied streamflow in alpine permafrost regions, aiming to fill the gap in understanding this process from a water-age perspective. Precipitation, streamflow, and SPG samples were collected from the Three-Rivers Headwaters Region (TRHR). We defined the physical meaning of Fyw (the young water fraction) of the SPG and calculated it for the first time. The results showed that in the TRHR, the SPG mean travel time (MTT) was 159 days, and approximately 46.4% of SPG was younger than 77 days, whereas the streamflow MTT was 342 days, and approximately 12.2% of the streamflow was younger than 97 days. The correlation analysis revealed that various climatic factors played dominant roles in the recharge time variations of the SPG-supplied streamflow within the TRHR. The SPG recharge rate did not significantly affect the streamflow Fyw; however, the thickness of the active layer ultimately controlled the SPG transit time distribution. Regression analysis further demonstrated the nonlinear impact of precipitation, average temperature, and average freezing days on SPG Fyw, which is closely related to seasonal freeze–thaw heat conduction and groundwater heat advection in the active layer. During the initial ablation period, the streamflow was primarily recharged by young SPG, resulting in a short-tail travel time distribution. Our findings provide valuable insights into runoff generation and concentration processes in permafrost regions and have important implications for water resource management.
{"title":"Mechanisms of Suprapermafrost Groundwater Recharge Streamflow in Alpine Permafrost Regions: Insights From Young Water Fraction Analysis","authors":"Fa Du, Zongxing Li, Juan Gui, Baijuan Zhang, Jian Xue, Huiling Zhou","doi":"10.1029/2024wr037329","DOIUrl":"https://doi.org/10.1029/2024wr037329","url":null,"abstract":"This study investigates the temporal processes of suprapermafrost groundwater (SPG)-supplied streamflow in alpine permafrost regions, aiming to fill the gap in understanding this process from a water-age perspective. Precipitation, streamflow, and SPG samples were collected from the Three-Rivers Headwaters Region (TRHR). We defined the physical meaning of <i>F</i><sub>yw</sub> (the young water fraction) of the SPG and calculated it for the first time. The results showed that in the TRHR, the SPG mean travel time (MTT) was 159 days, and approximately 46.4% of SPG was younger than 77 days, whereas the streamflow MTT was 342 days, and approximately 12.2% of the streamflow was younger than 97 days. The correlation analysis revealed that various climatic factors played dominant roles in the recharge time variations of the SPG-supplied streamflow within the TRHR. The SPG recharge rate did not significantly affect the streamflow <i>F</i><sub>yw</sub>; however, the thickness of the active layer ultimately controlled the SPG transit time distribution. Regression analysis further demonstrated the nonlinear impact of precipitation, average temperature, and average freezing days on SPG <i>F</i><sub>yw</sub>, which is closely related to seasonal freeze–thaw heat conduction and groundwater heat advection in the active layer. During the initial ablation period, the streamflow was primarily recharged by young SPG, resulting in a short-tail travel time distribution. Our findings provide valuable insights into runoff generation and concentration processes in permafrost regions and have important implications for water resource management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141489832","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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