Pub Date : 2023-09-14DOI: 10.3389/frwa.2023.1174570
Allen G. Hunt, Behzad Ghanbarian, Boris Faybishenko
Predicting the temporal and spatial evolution of the river network is part of the Earth's critical zone investigations, which has become an important endeavor. However, modeling integration of the river network and critical zone over millions of years is rare. We address the problem of how to predict integrated river length development as a function of time within a framework of addressing the critical zone depth as a function of time. In case of groundwater-river interaction, we find a non-linear spatio-temporal scaling relationship between time, t , and total river length L , given by t ≈ L p with power p being near 1.2. The basis of our model is the presumption that groundwater flow paths are relevant to river integration. As river integration may proceed over disconnected basins with irregular relief, the relevant optimal subsurface flow paths are proposed to be defined within a 3D network, with optimal path exponent 1.43. Because the 2D model of the river length has already been shown to relate to a power of the Euclidean distance across a drainage basin with the predicted universal optimal path exponent from percolation theory, D opt = 1.21, the optimal groundwater paths should relate to the surface river length with an exponent equaling the ratio 1.43/1.21 = 1.18. To define a predictive relationship for the river length, we need to use specific length and time scales. We assume that the fundamental specific length scale is a characteristic particle size (which is commonly used to define the pore scale flow network), and the fundamental time scale is the ratio of the particle size to the regional groundwater flow rate. In this paper, we consider cases of predicting spatio-temporal scaling of drainage organization in the southwestern USA–the Amargosa, Mojave, Gila (and its tributaries) and the Rio Grande, and Pecos Rivers. For the Mojave and Gila Rivers, theoretical results for time scales of river integration since ca. 10 Ma are quite predictive, though the predicted time scales exceed observation for the Rio Grande and Pecos.
{"title":"A model of temporal and spatial river network evolution with climatic inputs","authors":"Allen G. Hunt, Behzad Ghanbarian, Boris Faybishenko","doi":"10.3389/frwa.2023.1174570","DOIUrl":"https://doi.org/10.3389/frwa.2023.1174570","url":null,"abstract":"Predicting the temporal and spatial evolution of the river network is part of the Earth's critical zone investigations, which has become an important endeavor. However, modeling integration of the river network and critical zone over millions of years is rare. We address the problem of how to predict integrated river length development as a function of time within a framework of addressing the critical zone depth as a function of time. In case of groundwater-river interaction, we find a non-linear spatio-temporal scaling relationship between time, t , and total river length L , given by t ≈ L p with power p being near 1.2. The basis of our model is the presumption that groundwater flow paths are relevant to river integration. As river integration may proceed over disconnected basins with irregular relief, the relevant optimal subsurface flow paths are proposed to be defined within a 3D network, with optimal path exponent 1.43. Because the 2D model of the river length has already been shown to relate to a power of the Euclidean distance across a drainage basin with the predicted universal optimal path exponent from percolation theory, D opt = 1.21, the optimal groundwater paths should relate to the surface river length with an exponent equaling the ratio 1.43/1.21 = 1.18. To define a predictive relationship for the river length, we need to use specific length and time scales. We assume that the fundamental specific length scale is a characteristic particle size (which is commonly used to define the pore scale flow network), and the fundamental time scale is the ratio of the particle size to the regional groundwater flow rate. In this paper, we consider cases of predicting spatio-temporal scaling of drainage organization in the southwestern USA–the Amargosa, Mojave, Gila (and its tributaries) and the Rio Grande, and Pecos Rivers. For the Mojave and Gila Rivers, theoretical results for time scales of river integration since ca. 10 Ma are quite predictive, though the predicted time scales exceed observation for the Rio Grande and Pecos.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.3389/frwa.2023.1241451
Tarik Bouramtane, Marc Leblanc, Ilias Kacimi, Hamza Ouatiki, Abdelghani Boudhar
The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R 2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.
{"title":"The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco","authors":"Tarik Bouramtane, Marc Leblanc, Ilias Kacimi, Hamza Ouatiki, Abdelghani Boudhar","doi":"10.3389/frwa.2023.1241451","DOIUrl":"https://doi.org/10.3389/frwa.2023.1241451","url":null,"abstract":"The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R 2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-06DOI: 10.3389/frwa.2023.1200440
J. V. D. Berge, Luca Scheunpflug, J. Vos, R. Boelens
In several cities and regions in Spain there has been a fight against privatization of water supply in the past decade. Some cities have decided to re-municipalise water supply and debates about implementing the human right to water and sanitation have been held in many parts of Spain, following the success of the Right2Water European Citizens' Initiative. This paper examines how the European “Right2Water” movement influenced struggles for access to and control over water in Spain from a political ecology perspective. It explores how “Right2Water” fuelled the debate on privatization and remunicipalization of water services and what heritage it has left in Spain. We unfold relationships with and between water movements in Spain—like the Red Agua Publica—and relationships with other networks—like the indignados movement and subsequently how water protests converged with austerity protests. In different places these struggles took different shapes. By deploying five case studies (Madrid, Valladolid, Terrassa, Barcelona, and Andalucía), we look at how the human right to water and sanitation framework served as a tool for social and water justice movements. Struggles for water justice in Spain are ongoing and we seek to identify the temporarily outcomes of these struggles, and whether power balances in Spain's water services provision have shifted in the past decade.
{"title":"Social movements in defense of public water services: the case of Spain","authors":"J. V. D. Berge, Luca Scheunpflug, J. Vos, R. Boelens","doi":"10.3389/frwa.2023.1200440","DOIUrl":"https://doi.org/10.3389/frwa.2023.1200440","url":null,"abstract":"In several cities and regions in Spain there has been a fight against privatization of water supply in the past decade. Some cities have decided to re-municipalise water supply and debates about implementing the human right to water and sanitation have been held in many parts of Spain, following the success of the Right2Water European Citizens' Initiative. This paper examines how the European “Right2Water” movement influenced struggles for access to and control over water in Spain from a political ecology perspective. It explores how “Right2Water” fuelled the debate on privatization and remunicipalization of water services and what heritage it has left in Spain. We unfold relationships with and between water movements in Spain—like the Red Agua Publica—and relationships with other networks—like the indignados movement and subsequently how water protests converged with austerity protests. In different places these struggles took different shapes. By deploying five case studies (Madrid, Valladolid, Terrassa, Barcelona, and Andalucía), we look at how the human right to water and sanitation framework served as a tool for social and water justice movements. Struggles for water justice in Spain are ongoing and we seek to identify the temporarily outcomes of these struggles, and whether power balances in Spain's water services provision have shifted in the past decade.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48301785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.3389/frwa.2023.1245207
H. Paltan, Fátima L. Benítez, Manuel Narvaez, Cristina Mateus, Carlos F. Mena
The Galapagos Islands, a hotspot of ecological richness, face challenging climatic and development conditions which undermine regional water security. Yet, the way by which these conditions may change in the future is highly uncertain. In this study, we applied for the first time an uncertainty-based approach in the Galapagos Islands to understand thresholds and potential scenarios of risks to water security of agricultural catchments in the Galapagos Island. We applied a water systems model to the agricultural catchments as well as climate and land use scenarios to estimate physical changes in water availability and implications for drought and extreme flow thresholds. Our results highlight the key role of baseflow and its important sensitivity to precipitation changes in determining water security states in the Islands. In fact, a decrease in just about 25% of total water flows, from historical conditions, in the Islands would drive drought conditions resembling those of the emergency state of 2016. We also note that under a land use scenario which promotes sustainable practices, the robustness of the Islands to climate variations increases. Our study then provides the basis for an application of uncertainty-based approaches to enhance resilience of the agricultural water systems as well as other systems. Our results also emphasize the need to design flexible and comprehensive policies in the water, agricultural and interconnected sectors which consider the interlinkages of climate with other social and economic variables.
{"title":"Water security and agricultural systems in the Galapagos Islands: vulnerabilities under uncertain future climate and land use pathways","authors":"H. Paltan, Fátima L. Benítez, Manuel Narvaez, Cristina Mateus, Carlos F. Mena","doi":"10.3389/frwa.2023.1245207","DOIUrl":"https://doi.org/10.3389/frwa.2023.1245207","url":null,"abstract":"The Galapagos Islands, a hotspot of ecological richness, face challenging climatic and development conditions which undermine regional water security. Yet, the way by which these conditions may change in the future is highly uncertain. In this study, we applied for the first time an uncertainty-based approach in the Galapagos Islands to understand thresholds and potential scenarios of risks to water security of agricultural catchments in the Galapagos Island. We applied a water systems model to the agricultural catchments as well as climate and land use scenarios to estimate physical changes in water availability and implications for drought and extreme flow thresholds. Our results highlight the key role of baseflow and its important sensitivity to precipitation changes in determining water security states in the Islands. In fact, a decrease in just about 25% of total water flows, from historical conditions, in the Islands would drive drought conditions resembling those of the emergency state of 2016. We also note that under a land use scenario which promotes sustainable practices, the robustness of the Islands to climate variations increases. Our study then provides the basis for an application of uncertainty-based approaches to enhance resilience of the agricultural water systems as well as other systems. Our results also emphasize the need to design flexible and comprehensive policies in the water, agricultural and interconnected sectors which consider the interlinkages of climate with other social and economic variables.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43127691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-31DOI: 10.3389/frwa.2023.1274748
Ning Sun, N. Cristea
{"title":"Editorial: Advances in observations and modeling of snow, forest-snow processes and snow hydrology","authors":"Ning Sun, N. Cristea","doi":"10.3389/frwa.2023.1274748","DOIUrl":"https://doi.org/10.3389/frwa.2023.1274748","url":null,"abstract":"","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42106026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.3389/frwa.2023.1275554
Ankit Agarwal, Alban Kuriqi, John C. Matthews
{"title":"Editorial: Emerging talents in water science","authors":"Ankit Agarwal, Alban Kuriqi, John C. Matthews","doi":"10.3389/frwa.2023.1275554","DOIUrl":"https://doi.org/10.3389/frwa.2023.1275554","url":null,"abstract":"","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43562819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-22DOI: 10.3389/frwa.2023.1258088
J. Wilbur, S. Cavill, J. Willetts
{"title":"Editorial: World Water Day 2022: importance of WASH, equal access opportunities, and WASH resilience - A social-inclusion perspective","authors":"J. Wilbur, S. Cavill, J. Willetts","doi":"10.3389/frwa.2023.1258088","DOIUrl":"https://doi.org/10.3389/frwa.2023.1258088","url":null,"abstract":"","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43753453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-22DOI: 10.3389/frwa.2023.1237592
Mohammad Zeynoddin, S. Gumiere, H. Bonakdari
Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models.
实时土壤基质势测量用于确定马铃薯生产的水分有效性，目前用于精确灌溉。众所周知，基于土壤基质潜力(SMP)管理灌溉有助于提高水分利用效率和减少作物对环境的影响。然而，SMP监测带来了挑战，有时会导致收集的数据存在空白。本研究试图解决这些数据缺口在SMP时间序列。利用气象和野外测量，我们开发了一种滤波和imputation算法，通过在算法中实现三个突出的预测模型来估计缺失值。在2个多月的时间里，我们收集了加拿大quacemenbecc - saint - jean的psamribonka河以北的一个油田的每小时SMP值。我们的研究评估了各种数据输入组合，包括仅气象数据、SMP测量或两者的混合。结果表明，极限学习机(ELM)模型是最有效的。它优于k近邻(kNN)模型和进化优化逆距离方法(gaIDW)。ELM模型的相关系数为0.992，均方根误差为0.164 cm，平均绝对误差为0.122 cm, Nash-Sutcliffe效率为0.983。ELM模型需要至少五个输入才能在研究环境中获得最佳结果。这些可以是气象输入，如相对湿度、露水温度、土地输入，或两者的组合。结果与我们之前确定的最佳输入组合相差不到5%。为了减轻这些模型的计算需求，可以使用更快的基线模型进行初始输入滤波。使用这种方法，我们期望简单模型(如gaIDW和kNN)的输出变化不超过20%。然而，这种差异可以通过利用更复杂的模型来有效地管理。
{"title":"Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series","authors":"Mohammad Zeynoddin, S. Gumiere, H. Bonakdari","doi":"10.3389/frwa.2023.1237592","DOIUrl":"https://doi.org/10.3389/frwa.2023.1237592","url":null,"abstract":"Real-time soil matric potential measurements for determining potato production's water availability are currently used in precision irrigation. It is well known that managing irrigation based on soil matric potential (SMP) helps increase water use efficiency and reduce crop environmental impact. Yet, SMP monitoring presents challenges and sometimes leads to gaps in the collected data. This research sought to address these data gaps in the SMP time series. Using meteorological and field measurements, we developed a filtering and imputation algorithm by implementing three prominent predictive models in the algorithm to estimate missing values. Over 2 months, we gathered hourly SMP values from a field north of the Péribonka River in Lac-Saint-Jean, Québec, Canada. Our study evaluated various data input combinations, including only meteorological data, SMP measurements, or a mix of both. The Extreme Learning Machine (ELM) model proved the most effective among the tested models. It outperformed the k-Nearest Neighbors (kNN) model and the Evolutionary Optimized Inverse Distance Method (gaIDW). The ELM model, with five inputs comprising SMP measurements, achieved a correlation coefficient of 0.992, a root-mean-square error of 0.164 cm, a mean absolute error of 0.122 cm, and a Nash-Sutcliffe efficiency of 0.983. The ELM model requires at least five inputs to achieve the best results in the study context. These can be meteorological inputs like relative humidity, dew temperature, land inputs, or a combination of both. The results were within 5% of the best-performing input combination we identified earlier. To mitigate the computational demands of these models, a quicker baseline model can be used for initial input filtering. With this method, we expect the output from simpler models such as gaIDW and kNN to vary by no more than 20%. Nevertheless, this discrepancy can be efficiently managed by leveraging more sophisticated models.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45197210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-16DOI: 10.3389/frwa.2023.1167872
D. Meisenheimer, D. Wildenschild
There is a need to better understand the presence and transport of bubbles in multi-phase subsurface porous media so that these processes can be accurately described, and more efficient engineered solutions can be developed. To this end, constitutive relationships between geometric state variables (fluid-fluid curvature, Jnw; non-wetting phase volume, Vn; fluid-fluid interfacial area, anw; and Euler characteristic, χn) have become increasingly more common in efforts to uniquely predict the state of a two-fluid flow system. Both lattice Boltzmann simulations and fast X-ray microtomography (μCT) imaging experiments have shown that a geometric state function using the non-dimensionalized invariant properties of saturation, specific interfacial area, and Euler characteristic can uniquely predict the mean curvature of the system for both quasi- and non-equilibrium conditions, however, the presence of bubble evolution and the subsequent transport phenomena have not been explored. This study investigates whether the geometric state function remains unique with the inclusion of bubble generation and transport under quasi- and non-equilibrium two-fluid flow. The data presented here suggests that bubble formation and entrapment occur in a manner that cannot be predicted by the more traditional capillary pressure-saturation-interfacial area, Pc(Sw, anw), relationship, and further extensions to the constitutive relationship are needed to fully capture these mechanisms.
{"title":"Incorporating bubble evolution and transport in constitutive relationships for quasi- and non-equilibrium two-phase flows in porous media","authors":"D. Meisenheimer, D. Wildenschild","doi":"10.3389/frwa.2023.1167872","DOIUrl":"https://doi.org/10.3389/frwa.2023.1167872","url":null,"abstract":"There is a need to better understand the presence and transport of bubbles in multi-phase subsurface porous media so that these processes can be accurately described, and more efficient engineered solutions can be developed. To this end, constitutive relationships between geometric state variables (fluid-fluid curvature, Jnw; non-wetting phase volume, Vn; fluid-fluid interfacial area, anw; and Euler characteristic, χn) have become increasingly more common in efforts to uniquely predict the state of a two-fluid flow system. Both lattice Boltzmann simulations and fast X-ray microtomography (μCT) imaging experiments have shown that a geometric state function using the non-dimensionalized invariant properties of saturation, specific interfacial area, and Euler characteristic can uniquely predict the mean curvature of the system for both quasi- and non-equilibrium conditions, however, the presence of bubble evolution and the subsequent transport phenomena have not been explored. This study investigates whether the geometric state function remains unique with the inclusion of bubble generation and transport under quasi- and non-equilibrium two-fluid flow. The data presented here suggests that bubble formation and entrapment occur in a manner that cannot be predicted by the more traditional capillary pressure-saturation-interfacial area, Pc(Sw, anw), relationship, and further extensions to the constitutive relationship are needed to fully capture these mechanisms.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45673386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-14DOI: 10.3389/frwa.2023.1011952
C. Castro, C. Carney, M. D. de Brito
Integrated water management (IWM) involves a range of policies, actions, and organizational processes that go beyond traditional hydrology to consider multifaceted aspects of complex water resource systems. Due to its transdisciplinary nature, IWM comprises input from diverse stakeholders, each with unique perceptions, values, and experiences. However, stakeholders from differing backgrounds may disagree on best practices and collective paths forward. As such, successful IWM must address key governance principles (e.g., information flow, collective decision-making, and power relations) across social and institutional scales. Here, we sought to demonstrate how network structure impacts shared decision-making within IWM.We explored a case study in Houston, Texas, USA, where decision-making stakeholders from various sectors and levels of governance engaged in a participatory modeling workshop to improve adoption of nature-based solutions (NBS) through IWM. The stakeholders used fuzzy cognitive mapping (FCM) to define an IWM model comprising multifaceted elements and their interrelationships, which influenced the adoption of NBS in Houston. We applied grounded theory and inductive reasoning to categorize tacit belief schemas regarding how stakeholders viewed themselves within the management system. We then used FCM-based modeling to explore how unique NBS policies would translate into more (or less) NBS adoption. Finally, we calculated specific network metrics (e.g., density, hierarchy, and centrality indices) to better understand the structure of human-water relations embedded within the IWM model. We compared the tacit assumptions about stakeholder roles in IWM against the quantitative degrees of influence and collectivism embedded within the stakeholder-defined model.Our findings revealed a mismatch between stakeholders' external belief statements about IWM and their internal assumptions through cognitive mapping and participatory modeling. The case study network was characterized by a limited degree of internal coordination (low density index), high democratic potential (low hierarchy index), and high-efficiency management opportunities (high centrality index), which transcended across socio-institutional scales. These findings contrasted with several of the belief schemas described by stakeholders during the group workshop. We describe how ongoing partnership with the stakeholders resulted in an opportunity for adaptive learning, where the NBS planning paradigm began to shift toward trans-scale collaboration aimed at high-leverage management opportunities. We emphasize how network analytics allowed us to better understand the extent to which key governance principles drove the behavior of the IWM model, which we leveraged to form deeper stakeholder partnerships by identifying hidden opportunities for governance transformation.
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