Qiang Yu, Liguang Jiang, Raphael Schneider, Yi Zheng, Junguo Liu
Prediction in ungauged basins (PUB) is a concerning hydrological challenge, prompting the development of various regionalization methods to improve prediction accuracy. The long short-term memory (LSTM) model has gained popularity in rainfall-runoff prediction in recent years and has proven applicable in PUB. Prior research indicates that incorporating static attributes in the training of regional LSTM models could improve performance in PUB. However, the underlying reasons for this enhancement have received limited exploration. This study aims to explore the role of static attributes in the training of the regional LSTM model. It is assumed that the regional LSTM model can induce streamflow generation mechanisms with the incorporation of static attributes and apply certain streamflow generation mechanisms to ungauged catchments based on their attributes. To this end, a grouping-based training strategy is proposed, that is, training and validating regional LSTM models on catchments with similar streamflow generation mechanisms within predefined groups. The training strategies of regional LSTM models, either incorporated with static catchment attributes or based on classification, are conducted in 363 catchments. Results demonstrate a high level of consistency in the enhancement achieved by the two training strategies. Specifically, 192 and 216 catchments exhibit enhancement compared to traditionally trained models without inclusion of attributes, with 132 catchments showing improvement under both training strategies. Furthermore, the findings indicate consistent spatial patterns and attribute distributions of enhanced catchments, as well as the notable improvement in reproducing low flow-related hydrological signatures.
{"title":"Deciphering the Mechanism of Better Predictions of Regional LSTM Models in Ungauged Basins","authors":"Qiang Yu, Liguang Jiang, Raphael Schneider, Yi Zheng, Junguo Liu","doi":"10.1029/2023wr035876","DOIUrl":"https://doi.org/10.1029/2023wr035876","url":null,"abstract":"Prediction in ungauged basins (PUB) is a concerning hydrological challenge, prompting the development of various regionalization methods to improve prediction accuracy. The long short-term memory (LSTM) model has gained popularity in rainfall-runoff prediction in recent years and has proven applicable in PUB. Prior research indicates that incorporating static attributes in the training of regional LSTM models could improve performance in PUB. However, the underlying reasons for this enhancement have received limited exploration. This study aims to explore the role of static attributes in the training of the regional LSTM model. It is assumed that the regional LSTM model can induce streamflow generation mechanisms with the incorporation of static attributes and apply certain streamflow generation mechanisms to ungauged catchments based on their attributes. To this end, a grouping-based training strategy is proposed, that is, training and validating regional LSTM models on catchments with similar streamflow generation mechanisms within predefined groups. The training strategies of regional LSTM models, either incorporated with static catchment attributes or based on classification, are conducted in 363 catchments. Results demonstrate a high level of consistency in the enhancement achieved by the two training strategies. Specifically, 192 and 216 catchments exhibit enhancement compared to traditionally trained models without inclusion of attributes, with 132 catchments showing improvement under both training strategies. Furthermore, the findings indicate consistent spatial patterns and attribute distributions of enhanced catchments, as well as the notable improvement in reproducing low flow-related hydrological signatures.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141631744","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}
Arfan Arshad, Ali Mirchi, Saleh Taghvaeian, Amir AghaKouchak
GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse-resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high-resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub-regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub-region. Comparison with in-situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in-situ data, evidenced by higher Kling-Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub-regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub-regions, underscoring the complex interplay of natural-human activities in the basin. These findings inform place-based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities.
{"title":"Downscaled-GRACE Data Reveal Anthropogenic and Climate-Induced Water Storage Decline Across the Indus Basin","authors":"Arfan Arshad, Ali Mirchi, Saleh Taghvaeian, Amir AghaKouchak","doi":"10.1029/2023wr035882","DOIUrl":"https://doi.org/10.1029/2023wr035882","url":null,"abstract":"GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse-resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high-resolution (1 km<sup>2</sup>) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub-regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RF<sub>gw</sub>) model maintained good consistency with original GRACE data at the 25 km<sup>2</sup> grid scale. The downscaled data at 1 km<sup>2</sup> resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub-region. Comparison with in-situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in-situ data, evidenced by higher Kling-Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub-regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub-regions, underscoring the complex interplay of natural-human activities in the basin. These findings inform place-based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141631743","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}
Accurate streamflow predictions are essential for water resources management. Recent studies have examined the use of hybrid models that integrate machine learning models with process-based (PB) hydrologic models to improve streamflow predictions. Yet, there are many open questions regarding optimal hybrid model construction, especially in Mediterranean-climate watersheds that experience pronounced wet and dry seasons. In this study, we performed model benchmarking to (a) compare hybrid model performance to PB and machine learning models and (b) examine the sensitivity of hybrid model performance to PB model parameter calibration, structural complexity, and variable selection. Hybrid models were generated by post-processing process-based models using Long Short-Term Memory neural networks. Models were benchmarked within two northern California watersheds that are managed for both municipal water supplies and aquatic habitat. Though model performance varied substantially by watershed and error metric, calibrated hybrid models frequently outperformed both the machine learning model (for 72% of watershed-model-metric combinations) and the calibrated process-based models (for 79% of combinations). Furthermore, hybrid models were relatively insensitive to PB model calibration and structural complexity, but sensitive to PB model variable selection. Our results demonstrate that hybrid models can improve streamflow prediction in Mediterranean-climate watersheds. Additionally, hybrid model insensitivity to PB model parameter calibration and structural complexity suggests that uncalibrated or less complex PB models could be used in hybrid models without any loss of streamflow prediction accuracy, improving model construction efficiency. Moreover, hybrid model sensitivity to the selection of PB model variables suggests a strategy for diagnosing poorly performing PB model components.
{"title":"Streamflow Prediction at the Intersection of Physics and Machine Learning: A Case Study of Two Mediterranean-Climate Watersheds","authors":"S. Adera, D. Bellugi, A. Dhakal, L. Larsen","doi":"10.1029/2023wr035790","DOIUrl":"https://doi.org/10.1029/2023wr035790","url":null,"abstract":"Accurate streamflow predictions are essential for water resources management. Recent studies have examined the use of hybrid models that integrate machine learning models with process-based (PB) hydrologic models to improve streamflow predictions. Yet, there are many open questions regarding optimal hybrid model construction, especially in Mediterranean-climate watersheds that experience pronounced wet and dry seasons. In this study, we performed model benchmarking to (a) compare hybrid model performance to PB and machine learning models and (b) examine the sensitivity of hybrid model performance to PB model parameter calibration, structural complexity, and variable selection. Hybrid models were generated by post-processing process-based models using Long Short-Term Memory neural networks. Models were benchmarked within two northern California watersheds that are managed for both municipal water supplies and aquatic habitat. Though model performance varied substantially by watershed and error metric, calibrated hybrid models frequently outperformed both the machine learning model (for 72% of watershed-model-metric combinations) and the calibrated process-based models (for 79% of combinations). Furthermore, hybrid models were relatively insensitive to PB model calibration and structural complexity, but sensitive to PB model variable selection. Our results demonstrate that hybrid models can improve streamflow prediction in Mediterranean-climate watersheds. Additionally, hybrid model insensitivity to PB model parameter calibration and structural complexity suggests that uncalibrated or less complex PB models could be used in hybrid models without any loss of streamflow prediction accuracy, improving model construction efficiency. Moreover, hybrid model sensitivity to the selection of PB model variables suggests a strategy for diagnosing poorly performing PB model components.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618439","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}
Homa Salehabadi, David G. Tarboton, Kevin G. Wheeler, Rebecca Smith, Sarah Baker
Stochastic hydrology produces ensembles of time series that represent plausible future streamflow to simulate and test the operation of water resource systems. A premise of stochastic hydrology is that ensembles should be statistically representative of what may occur in the future. In the past, the application of this premise has involved producing ensembles that are statistically equivalent to the observed or historical streamflow sequence. This requires a number of metrics or statistics that can be used to test statistical similarity. However, with climate change, the past may no longer be representative of the future. Ensembles to test future systems operations should recognize non-stationarity and include time series representing expected changes. This poses challenges for their testing and validation. In this paper, we suggest an evidence-based analysis in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized and assessed using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree-based classification approach to categorize both ensembles and metrics. This approach provides a way to visualize and interpret differences between streamflow ensembles. The metrics presented, along with the classification, provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non-stationarity. This contributes to better planning in large river basins, such as the Colorado, facing water supply shortages.
{"title":"Quantifying and Classifying Streamflow Ensembles Using a Broad Range of Metrics for an Evidence-Based Analysis: Colorado River Case Study","authors":"Homa Salehabadi, David G. Tarboton, Kevin G. Wheeler, Rebecca Smith, Sarah Baker","doi":"10.1029/2024wr037225","DOIUrl":"https://doi.org/10.1029/2024wr037225","url":null,"abstract":"Stochastic hydrology produces ensembles of time series that represent plausible future streamflow to simulate and test the operation of water resource systems. A premise of stochastic hydrology is that ensembles should be statistically representative of what may occur in the future. In the past, the application of this premise has involved producing ensembles that are statistically equivalent to the observed or historical streamflow sequence. This requires a number of metrics or statistics that can be used to test statistical similarity. However, with climate change, the past may no longer be representative of the future. Ensembles to test future systems operations should recognize non-stationarity and include time series representing expected changes. This poses challenges for their testing and validation. In this paper, we suggest an evidence-based analysis in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized and assessed using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree-based classification approach to categorize both ensembles and metrics. This approach provides a way to visualize and interpret differences between streamflow ensembles. The metrics presented, along with the classification, provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non-stationarity. This contributes to better planning in large river basins, such as the Colorado, facing water supply shortages.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618438","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}
Selecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro-climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best-performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling-Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high-accuracy models among the 7,488 options, with Kling-Gupta-Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.
{"title":"Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble","authors":"Diana Spieler, Niels Schütze","doi":"10.1029/2023wr036199","DOIUrl":"https://doi.org/10.1029/2023wr036199","url":null,"abstract":"Selecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro-climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best-performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling-Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high-accuracy models among the 7,488 options, with Kling-Gupta-Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618441","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}
Xin-Lun Ji, Chao-Sheng Tang, Xiao-Hua Pan, Zhao-Lin Cai, Bo Liu, Dian-Long Wang
Drought is a serious global environmental issue that causes water resource scarcity and threatens agriculture and food supplements. This study aims to investigate the long-term performance of an eco-friendly technique-microbial induced carbonate precipitation (MICP) on drought mitigation at field and laboratory scales. Seven in-situ slopes treated with different MICP rounds and cementation solution concentrations were subjected to 16-month weathering. Tests were conducted to evaluate the evaporation characteristics, water retention capacity, and CaCO3 distribution. Laboratory soil samples were further prepared to provide evidence related to underlying weathering mechanisms. The results show that MICP has a time-dependent performance on drought mitigation. After MICP treatment, soil performs a remarkable evaporation suppression ability and the evaporation rate can decrease by 50%. This is attributed to the soluble salts which increase soil water retention capability and dense hard crust which inhibits water vapor migration into the atmosphere. However, the soluble salts and crust are sensitive to weathering thus leading to degradation of MICP. Suffering 16-month weathering, the MICP-induced CaCO3 decreases by more than 60%. The evaporation rate of soil increases with MICP rounds and cementation solution concentrations and can reach nearly two times of untreated soil. MICP-treated field soil exhibits weaker water retention capacity than untreated soil because MICP alters soil microstructure which expands macropores and decreases volume of micropores. Connected macropores act as favorable evaporation channels and accelerate evaporation. To ensure MICP long-term effects, periodical treatments are necessary. The most effective MICP treatment scheme is four to six treatment rounds and 1.0 M cementation solution.
{"title":"Long-Term Performance on Drought Mitigation of Soil Slope Through Bio-Approach of MICP: Evidence and Insight from Both Field and Laboratory Tests","authors":"Xin-Lun Ji, Chao-Sheng Tang, Xiao-Hua Pan, Zhao-Lin Cai, Bo Liu, Dian-Long Wang","doi":"10.1029/2024wr037486","DOIUrl":"https://doi.org/10.1029/2024wr037486","url":null,"abstract":"Drought is a serious global environmental issue that causes water resource scarcity and threatens agriculture and food supplements. This study aims to investigate the long-term performance of an eco-friendly technique-microbial induced carbonate precipitation (MICP) on drought mitigation at field and laboratory scales. Seven in-situ slopes treated with different MICP rounds and cementation solution concentrations were subjected to 16-month weathering. Tests were conducted to evaluate the evaporation characteristics, water retention capacity, and CaCO<sub>3</sub> distribution. Laboratory soil samples were further prepared to provide evidence related to underlying weathering mechanisms. The results show that MICP has a time-dependent performance on drought mitigation. After MICP treatment, soil performs a remarkable evaporation suppression ability and the evaporation rate can decrease by 50%. This is attributed to the soluble salts which increase soil water retention capability and dense hard crust which inhibits water vapor migration into the atmosphere. However, the soluble salts and crust are sensitive to weathering thus leading to degradation of MICP. Suffering 16-month weathering, the MICP-induced CaCO<sub>3</sub> decreases by more than 60%. The evaporation rate of soil increases with MICP rounds and cementation solution concentrations and can reach nearly two times of untreated soil. MICP-treated field soil exhibits weaker water retention capacity than untreated soil because MICP alters soil microstructure which expands macropores and decreases volume of micropores. Connected macropores act as favorable evaporation channels and accelerate evaporation. To ensure MICP long-term effects, periodical treatments are necessary. The most effective MICP treatment scheme is four to six treatment rounds and 1.0 M cementation solution.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618442","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 complexity and heterogeneity of pore structure present significant challenges in accurate permeability estimation. Commonly used empirical formulas neglect its microscopic and topological characteristics, thus lacking accuracy and adaptability. While machine learning (ML) and deep learning (DL) models demonstrate promising performance, but encounter challenges of data availability, computational cost, and model interpretability. The present study aims to develop a more robust and accurate permeability prediction model via knowledge extraction from ML model. We first establish an ML model between permeability and the geometry-topology characteristics of porous media using Extreme Gradient Boosting (XGBoost) algorithm. The data set used to fit ML model is prepared from 458 samples of different types of porous media. Using the SHapley Additive exPlanations (SHAP) value, the influence of each feature on permeability prediction is quantified. It is found that the closeness centrality (topology feature), tortuosity, porosity (macroscopic features) and throat diameter, throat length, pore diameter (pore network features) are vital for permeability prediction. Guided by partial dependence calculation, the unknown function relationship between permeability and the top six important features is established. The novel permeability prediction model incorporating topology feature improves the prediction accuracy and demonstrates strong applicability across diverse data sets. This new model presents an optimal balance between simplicity and performance, rendering it a compelling alternative for permeability prediction in porous media. The research provides a novel referable framework of knowledge extraction via ML to reveal the important features and establish the potential relationship that can be extended and applied in other research fields.
孔隙结构的复杂性和异质性给准确估算渗透率带来了巨大挑战。常用的经验公式忽略了其微观和拓扑特征，因此缺乏准确性和适应性。虽然机器学习（ML）和深度学习（DL）模型表现出良好的性能，但也遇到了数据可用性、计算成本和模型可解释性等方面的挑战。本研究旨在通过从 ML 模型中提取知识，开发一种更稳健、更准确的渗透率预测模型。我们首先利用极端梯度提升（XGBoost）算法建立了渗透率与多孔介质几何拓扑特征之间的 ML 模型。用于拟合 ML 模型的数据集来自 458 个不同类型的多孔介质样本。利用 SHapley Additive exPlanations（SHAP）值，量化了每个特征对渗透率预测的影响。研究发现，闭合中心性（拓扑特征）、迂回度、孔隙度（宏观特征）和喉管直径、喉管长度、孔隙直径（孔隙网络特征）对渗透率预测至关重要。在部分依存计算的指导下，建立了渗透率与前六个重要特征之间的未知函数关系。包含拓扑特征的新型渗透率预测模型提高了预测精度，并在各种数据集中显示出很强的适用性。这一新模型在简单性和性能之间实现了最佳平衡，使其成为多孔介质渗透性预测的一个令人信服的替代方案。该研究提供了一个通过 ML 提取知识的新颖可参考框架，以揭示重要特征并建立潜在关系，该框架可扩展并应用于其他研究领域。
{"title":"Knowledge Extraction via Machine Learning Guides a Topology-Based Permeability Prediction Model","authors":"Jia Zhang, Gang Ma, Zhibing Yang, Jiangzhou Mei, Daren Zhang, Wei Zhou, Xiaolin Chang","doi":"10.1029/2024wr037124","DOIUrl":"https://doi.org/10.1029/2024wr037124","url":null,"abstract":"The complexity and heterogeneity of pore structure present significant challenges in accurate permeability estimation. Commonly used empirical formulas neglect its microscopic and topological characteristics, thus lacking accuracy and adaptability. While machine learning (ML) and deep learning (DL) models demonstrate promising performance, but encounter challenges of data availability, computational cost, and model interpretability. The present study aims to develop a more robust and accurate permeability prediction model via knowledge extraction from ML model. We first establish an ML model between permeability and the geometry-topology characteristics of porous media using Extreme Gradient Boosting (XGBoost) algorithm. The data set used to fit ML model is prepared from 458 samples of different types of porous media. Using the SHapley Additive exPlanations (SHAP) value, the influence of each feature on permeability prediction is quantified. It is found that the closeness centrality (topology feature), tortuosity, porosity (macroscopic features) and throat diameter, throat length, pore diameter (pore network features) are vital for permeability prediction. Guided by partial dependence calculation, the unknown function relationship between permeability and the top six important features is established. The novel permeability prediction model incorporating topology feature improves the prediction accuracy and demonstrates strong applicability across diverse data sets. This new model presents an optimal balance between simplicity and performance, rendering it a compelling alternative for permeability prediction in porous media. The research provides a novel referable framework of knowledge extraction via ML to reveal the important features and establish the potential relationship that can be extended and applied in other research fields.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141618440","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}
Hyporheic zones are commonly regarded as resilient and enduring interfaces between groundwater and surface water in river corridors. In particular, bedform-induced advective pumping hyporheic exchange (bedform-induced exchange) is often perceived as a relatively persistent mechanism in natural river systems driving water, solutes, and energy exchanges between the channel and its surrounding streambed sediments. Numerous studies have been based on this presumption. To evaluate the persistence of hyporheic zones under varying hydrologic conditions, we use a multi-physics framework to model advective pumping bedform-induced hyporheic exchange in response to a series of seasonal- and event-scale groundwater table fluctuation scenarios, which lead to episodic river-aquifer disconnections and reconnections. Our results suggest that hyporheic exchange is not as ubiquitous as generally assumed. Instead, the bedform-induced hyporheic exchange is restricted to a narrow range of conditions characterized by minor river-groundwater head differences, is intermittent, and can be easily obliterated by minor losing groundwater conditions. These findings shed light on the fragility of bedform-induced hyporheic exchange and have important implications for biogeochemical transformations along river corridors.
{"title":"The Fragility of Bedform-Induced Hyporheic Zones: Exploring Impacts of Dynamic Groundwater Table Fluctuations","authors":"L. Wu, J. D. Gomez-Velez, L. Li, K. C. Carroll","doi":"10.1029/2023wr036706","DOIUrl":"https://doi.org/10.1029/2023wr036706","url":null,"abstract":"Hyporheic zones are commonly regarded as resilient and enduring interfaces between groundwater and surface water in river corridors. In particular, bedform-induced advective pumping hyporheic exchange (bedform-induced exchange) is often perceived as a relatively persistent mechanism in natural river systems driving water, solutes, and energy exchanges between the channel and its surrounding streambed sediments. Numerous studies have been based on this presumption. To evaluate the persistence of hyporheic zones under varying hydrologic conditions, we use a multi-physics framework to model advective pumping bedform-induced hyporheic exchange in response to a series of seasonal- and event-scale groundwater table fluctuation scenarios, which lead to episodic river-aquifer disconnections and reconnections. Our results suggest that hyporheic exchange is not as ubiquitous as generally assumed. Instead, the bedform-induced hyporheic exchange is restricted to a narrow range of conditions characterized by minor river-groundwater head differences, is intermittent, and can be easily obliterated by minor losing groundwater conditions. These findings shed light on the fragility of bedform-induced hyporheic exchange and have important implications for biogeochemical transformations along river corridors.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597381","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}
Sarah M. Elliott, Michelle I. Hornberger, Donald O. Rosenberry, Rebecca J. Frus, Richard M. Webb
Wildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver-Factor-Stressor-Effect (DFSE) conceptual framework to assess the current state of the science related to post-wildfire water-quality. We reviewed 64 peer-reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water-quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long-term monitoring, multi-factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta-analyses by exploring causal linkages between influential variables and overall effects in post-wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post-wildfire water quality.
{"title":"A Conceptual Framework to Assess Post-Wildfire Water Quality: State of the Science and Knowledge Gaps","authors":"Sarah M. Elliott, Michelle I. Hornberger, Donald O. Rosenberry, Rebecca J. Frus, Richard M. Webb","doi":"10.1029/2023wr036260","DOIUrl":"https://doi.org/10.1029/2023wr036260","url":null,"abstract":"Wildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver-Factor-Stressor-Effect (DFSE) conceptual framework to assess the current state of the science related to post-wildfire water-quality. We reviewed 64 peer-reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water-quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long-term monitoring, multi-factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta-analyses by exploring causal linkages between influential variables and overall effects in post-wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post-wildfire water quality.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141597380","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}
Sarah Worden, A. Anthony Bloom, John Worden, Paul Levine, Mingjie Shi, Rong Fu
Large spatio-temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water-balance (evapotranspiration minus precipitation, or ET − P) is typically measured at basin-scales, limited primarily by river-discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrained ET. We use observations of the isotopic composition of water vapor to quantify the spatio-temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time-varying ET − P over four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi-year record, seasonality, and interannual variability of ET − P from both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity-based estimates with our water vapor isotope-based ET − P to calculate time and space averaged ET and net river discharge within the Congo Basin. These quadrant-scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averaged ET/P > 70%), consistent with models and visible light-based ET estimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance of ET in modulating the Congo water cycle relative to other water sources.
刚果盆地的植被和降雨量存在巨大的时空梯度。然而，其水分平衡（蒸散量减去降水量，或 ET - P）通常是在流域尺度上测量的，主要受限于河流排水数据、陆地储水测量的空间分辨率以及对蒸散量的限制。我们利用对水汽同位素组成的观测，量化了 2003 年至 2018 年刚果盆地地表水净通量的时空变化。利用卫星重力和刚果河总排水量测量数据对这些数据进行流域尺度校准，然后用于估算刚果盆地四个象限的时变蒸散发--P，首次为该地区提供了此类估算数据。我们发现，同位素和基于重力/河流排水量估算的蒸散发的多年记录、季节性和年际变化是一致的。此外，我们利用基于降水和重力的估算值以及基于水蒸气同位素的蒸散发-磷估算值，计算出刚果盆地的时空平均蒸散发和河流净排水量。这些象限尺度的水汽通量估算结果表明：（a）刚果盆地的水汽循环量很大（时间和空间平均蒸散发/P >70%），与模型和基于可见光的蒸散发估算结果一致；（b）刚果西部的河流净流出量最大，因为那里河流较多，流速较高。我们的研究结果证实，相对于其他水源，蒸散发在调节刚果水循环方面具有重要作用。
{"title":"Congo Basin Water Balance and Terrestrial Fluxes Inferred From Satellite Observations of the Isotopic Composition of Water Vapor","authors":"Sarah Worden, A. Anthony Bloom, John Worden, Paul Levine, Mingjie Shi, Rong Fu","doi":"10.1029/2023wr035092","DOIUrl":"https://doi.org/10.1029/2023wr035092","url":null,"abstract":"Large spatio-temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water-balance (evapotranspiration minus precipitation, or <i>ET</i> − <i>P</i>) is typically measured at basin-scales, limited primarily by river-discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrained <i>ET</i>. We use observations of the isotopic composition of water vapor to quantify the spatio-temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time-varying <i>ET</i> − <i>P</i> over four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi-year record, seasonality, and interannual variability of <i>ET</i> − <i>P</i> from both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity-based estimates with our water vapor isotope-based <i>ET</i> − <i>P</i> to calculate time and space averaged <i>ET</i> and net river discharge within the Congo Basin. These quadrant-scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averaged <i>ET/P</i> > 70%), consistent with models and visible light-based <i>ET</i> estimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance of <i>ET</i> in modulating the Congo water cycle relative to other water sources.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141588793","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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