Pub Date : 2023-11-02DOI: 10.3389/frwa.2023.1315909
Ran Holtzman, Bjornar Sandnes, Marcel Moura, Matteo Icardi, Ramon Planet
{"title":"Editorial: Nonequilibrium multiphase and reactive flows in porous and granular materials","authors":"Ran Holtzman, Bjornar Sandnes, Marcel Moura, Matteo Icardi, Ramon Planet","doi":"10.3389/frwa.2023.1315909","DOIUrl":"https://doi.org/10.3389/frwa.2023.1315909","url":null,"abstract":"","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139290382","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 : 2023-11-02DOI: 10.3389/frwa.2023.1226612
Christina Tague, W. Tyler Brandt
Exponentially growing publication rates are increasingly problematic for interdisciplinary fields like Critical Zone (CZ) science. How does one “keep up” across different, but related fields with unique hypotheses, field techniques, and models? By surveying CZ academics in the Western US, a region with substantial CZ research, we document the challenge. While conventional knowledge synthesis products-particularly review papers clearly support knowledge transfer, they are static and limited in scope. More informal paths for knowledge transfer, including social networking at conferences and academic mentorship, are useful but are unstructured and problematic for young scientists or others who may not have access to these resources. While new machine-learning tools, including ChatGPT, offer new ways forward for knowledge synthesis, we argue that they do not necessarily solve the problem of information overload in CZ Science. Instead, we argue that what we need is a community driven, machine aided knowledge tool that evolves and connects, but preserves the richness of detail found in peer-reviewed papers. The platform would be designed by CZ scientists, machine-aided and built on the strengths of people-driven synthesis. By involving the scientist in the design of this tool, it will better reflect the practice of CZ science-including hypothesis generation, testing across different time and space scales and in different time periods and locations, and, importantly, the use and evaluation of multiple, often sophisticated methods including fieldwork, remote sensing, and modeling. We seek a platform design that increases the findability and accessibility of current working knowledge while communicating the CZ science practice.
{"title":"Critical zone science in the Western US—Too much information?","authors":"Christina Tague, W. Tyler Brandt","doi":"10.3389/frwa.2023.1226612","DOIUrl":"https://doi.org/10.3389/frwa.2023.1226612","url":null,"abstract":"Exponentially growing publication rates are increasingly problematic for interdisciplinary fields like Critical Zone (CZ) science. How does one “keep up” across different, but related fields with unique hypotheses, field techniques, and models? By surveying CZ academics in the Western US, a region with substantial CZ research, we document the challenge. While conventional knowledge synthesis products-particularly review papers clearly support knowledge transfer, they are static and limited in scope. More informal paths for knowledge transfer, including social networking at conferences and academic mentorship, are useful but are unstructured and problematic for young scientists or others who may not have access to these resources. While new machine-learning tools, including ChatGPT, offer new ways forward for knowledge synthesis, we argue that they do not necessarily solve the problem of information overload in CZ Science. Instead, we argue that what we need is a community driven, machine aided knowledge tool that evolves and connects, but preserves the richness of detail found in peer-reviewed papers. The platform would be designed by CZ scientists, machine-aided and built on the strengths of people-driven synthesis. By involving the scientist in the design of this tool, it will better reflect the practice of CZ science-including hypothesis generation, testing across different time and space scales and in different time periods and locations, and, importantly, the use and evaluation of multiple, often sophisticated methods including fieldwork, remote sensing, and modeling. We seek a platform design that increases the findability and accessibility of current working knowledge while communicating the CZ science practice.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934785","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 : 2023-10-31DOI: 10.3389/frwa.2023.1278205
Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, Stephen Boyles
Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.
{"title":"A network-based analysis of critical resource accessibility during floods","authors":"Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, Stephen Boyles","doi":"10.3389/frwa.2023.1278205","DOIUrl":"https://doi.org/10.3389/frwa.2023.1278205","url":null,"abstract":"Numerous government and non-governmental agencies are increasing their efforts to better quantify the disproportionate effects of climate risk on vulnerable populations with the goal of creating more resilient communities. Sociodemographic based indices have been the primary source of vulnerability information the past few decades. However, using these indices fails to capture other facets of vulnerability, such as the ability to access critical resources (e.g., grocery stores, hospitals, pharmacies, etc.). Furthermore, methods to estimate resource accessibility as storms occur (i.e., in near-real time) are not readily available to local stakeholders. We address this gap by creating a model built on strictly open-source data to solve the user equilibrium traffic assignment problem to calculate how an individual's access to critical resources changes during and immediately after a flood event. Redundancy, reliability, and recoverability metrics at the household and network scales reveal the inequitable distribution of the flood's impact. In our case-study for Austin, Texas we found that the most vulnerable households are the least resilient to the impacts of floods and experience the most volatile shifts in metric values. Concurrently, the least vulnerable quarter of the population often carries the smallest burdens. We show that small and moderate inequalities become large inequities when accounting for more vulnerable communities' lower ability to cope with the loss of accessibility, with the most vulnerable quarter of the population carrying four times as much of the burden as the least vulnerable quarter. The near-real time and open-source model we developed can benefit emergency planning stakeholders by helping identify households that require specific resources during and immediately after hazard events.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863006","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 : 2023-10-27DOI: 10.3389/frwa.2023.1291305
Justice Lin, Chhayly Sreng, Emma Oare, Feras A. Batarseh
Flood events have the potential to impact every aspect of life, economic loss and casualties can quickly be coupled with damages to agricultural land, infrastructure, and water quality. Creating flood susceptibility maps is an effective manner that equips communities with valuable information to help them prepare for and cope with the impacts of potential floods. Flood indexing and forecasting are nonetheless complex because multiple external parameters influence flooding. Accordingly, this study explores the potential of utilizing artificial intelligence (AI) techniques, including clustering and neural networks, to develop a flooding susceptibility index (namely, NeuralFlood) that considers multiple factors that are not generally considered otherwise. By comparing four different sub-indices, we aim to create a comprehensive index that captures unique characteristics not found in existing methods. The use of clustering algorithms, model tuning, and multiple neural layers produced insightful outcomes for county-level data. Overall, the four sub-indices' models yielded accurate results for lower classes (accuracy of 0.87), but higher classes had reduced true positive rates (overall average accuracy of 0.68 for all classes). Our findings aid decision-makers in effectively allocating resources and identifying high-risk areas for mitigation.
{"title":"NeuralFlood: an AI-driven flood susceptibility index","authors":"Justice Lin, Chhayly Sreng, Emma Oare, Feras A. Batarseh","doi":"10.3389/frwa.2023.1291305","DOIUrl":"https://doi.org/10.3389/frwa.2023.1291305","url":null,"abstract":"Flood events have the potential to impact every aspect of life, economic loss and casualties can quickly be coupled with damages to agricultural land, infrastructure, and water quality. Creating flood susceptibility maps is an effective manner that equips communities with valuable information to help them prepare for and cope with the impacts of potential floods. Flood indexing and forecasting are nonetheless complex because multiple external parameters influence flooding. Accordingly, this study explores the potential of utilizing artificial intelligence (AI) techniques, including clustering and neural networks, to develop a flooding susceptibility index (namely, NeuralFlood) that considers multiple factors that are not generally considered otherwise. By comparing four different sub-indices, we aim to create a comprehensive index that captures unique characteristics not found in existing methods. The use of clustering algorithms, model tuning, and multiple neural layers produced insightful outcomes for county-level data. Overall, the four sub-indices' models yielded accurate results for lower classes (accuracy of 0.87), but higher classes had reduced true positive rates (overall average accuracy of 0.68 for all classes). Our findings aid decision-makers in effectively allocating resources and identifying high-risk areas for mitigation.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136318160","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 : 2023-10-25DOI: 10.3389/frwa.2023.1255495
Yasmin Mbarki, Silvio José Gumiere, Paul Celicourt, Jhemson Brédy
Agricultural soil compaction adversely affects crop water use and yield performance and should be avoided or remediated through appropriate soil management strategies. The investigation of the impact of different levels of soil compaction on its hydrodynamic properties remains a crucial step in improving water use and crop yields. We examined five compaction levels of silty sand soil sampled from a potato field in the agricultural regions of northern Quebec (Canada). Soil hydraulic characteristics (saturated and unsaturated hydraulic conductivity, soil water retention capacity) were measured using the constant head method, the HYPROP device, and a WP4C dew point potentiometer. The sixteen hydraulic models integrated into the HYPROP software were fitted to the soil water retention curve (SWRC) data for the studied compaction levels. Statistical parameters such as the mean bias error, mean absolute error, correlation coefficient, and root mean square error were used to measure the performance of the models. The results show that saturated and unsaturated conductivity decreases with increasing soil compaction. The lowest saturated hydraulic conductivity (Ks) value is observed for the highest level of soil compaction, reflecting a solid medium with less pore space and connectivity. Among the hydraulic models, the Peters-Durner-Iden (PDI) variant of van Genuchten's unconstrained bimodal model (VGm-b-PDI) outperformed all other models for SWRC simulation of different soil compaction levels and was, accordingly, selected as the optimal model. This model was implemented in HYDRUS-1D to estimate the amount of irrigation for different compaction levels. We simulated irrigation scenarios with the dual-porosity model. The results indicated that soil compaction can strongly influence soil hydraulic properties and water differently. However, the amount of irrigation for the potato crop was optimal at a moderate level of soil compaction. Overall, combined HYPROP and HYDRUS 1D can provide helpful information on the soil hydraulics properties dynamics and a rigorous simulation for irrigation planning and management in potato fields.
{"title":"Study of the effect of the compaction level on the hydrodynamic properties of loamy sand soil in an agricultural context","authors":"Yasmin Mbarki, Silvio José Gumiere, Paul Celicourt, Jhemson Brédy","doi":"10.3389/frwa.2023.1255495","DOIUrl":"https://doi.org/10.3389/frwa.2023.1255495","url":null,"abstract":"Agricultural soil compaction adversely affects crop water use and yield performance and should be avoided or remediated through appropriate soil management strategies. The investigation of the impact of different levels of soil compaction on its hydrodynamic properties remains a crucial step in improving water use and crop yields. We examined five compaction levels of silty sand soil sampled from a potato field in the agricultural regions of northern Quebec (Canada). Soil hydraulic characteristics (saturated and unsaturated hydraulic conductivity, soil water retention capacity) were measured using the constant head method, the HYPROP device, and a WP4C dew point potentiometer. The sixteen hydraulic models integrated into the HYPROP software were fitted to the soil water retention curve (SWRC) data for the studied compaction levels. Statistical parameters such as the mean bias error, mean absolute error, correlation coefficient, and root mean square error were used to measure the performance of the models. The results show that saturated and unsaturated conductivity decreases with increasing soil compaction. The lowest saturated hydraulic conductivity (Ks) value is observed for the highest level of soil compaction, reflecting a solid medium with less pore space and connectivity. Among the hydraulic models, the Peters-Durner-Iden (PDI) variant of van Genuchten's unconstrained bimodal model (VGm-b-PDI) outperformed all other models for SWRC simulation of different soil compaction levels and was, accordingly, selected as the optimal model. This model was implemented in HYDRUS-1D to estimate the amount of irrigation for different compaction levels. We simulated irrigation scenarios with the dual-porosity model. The results indicated that soil compaction can strongly influence soil hydraulic properties and water differently. However, the amount of irrigation for the potato crop was optimal at a moderate level of soil compaction. Overall, combined HYPROP and HYDRUS 1D can provide helpful information on the soil hydraulics properties dynamics and a rigorous simulation for irrigation planning and management in potato fields.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113537","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 : 2023-10-25DOI: 10.3389/frwa.2023.1279838
Sara R. Warix, Sarah E. Godsey, Gerald Flerchinger, Scott Havens, Kathleen A. Lohse, H. Carrie Bottenberg, Xiaosheng Chu, Rebecca L. Hale, Mark Seyfried
Geologic, geomorphic, and climatic factors have been hypothesized to influence where streams dry, but hydrologists struggle to explain the temporal drivers of drying. Few hydrologists have isolated the role that vegetation plays in controlling the timing and location of stream drying in headwater streams. We present a distributed, fine-scale water balance through the seasonal recession and onset of stream drying by combining spatiotemporal observations and modeling of flow presence/absence, evapotranspiration, and groundwater inputs. Surface flow presence/absence was collected at fine spatial (~80 m) and temporal (15-min) scales at 25 locations in a headwater stream in southwestern Idaho, USA. Evapotranspiration losses were modeled at the same locations using the Simultaneous Heat and Water (SHAW) model. Groundwater inputs were estimated at four of the locations using a mixing model approach. In addition, we compared high-frequency, fine-resolution riparian normalized vegetation difference index (NDVI) with stream flow status. We found that the stream wetted and dried on a daily basis before seasonally drying, and daily drying occurred when evapotranspiration outputs exceeded groundwater inputs, typically during the hours of peak evapotranspiration. Riparian NDVI decreased when the stream dried, with a ~2-week lag between stream drying and response. Stream diel drying cycles reflect the groundwater and evapotranspiration balance, and riparian NDVI may improve stream drying predictions for groundwater-supported headwater streams.
{"title":"Evapotranspiration and groundwater inputs control the timing of diel cycling of stream drying during low-flow periods","authors":"Sara R. Warix, Sarah E. Godsey, Gerald Flerchinger, Scott Havens, Kathleen A. Lohse, H. Carrie Bottenberg, Xiaosheng Chu, Rebecca L. Hale, Mark Seyfried","doi":"10.3389/frwa.2023.1279838","DOIUrl":"https://doi.org/10.3389/frwa.2023.1279838","url":null,"abstract":"Geologic, geomorphic, and climatic factors have been hypothesized to influence where streams dry, but hydrologists struggle to explain the temporal drivers of drying. Few hydrologists have isolated the role that vegetation plays in controlling the timing and location of stream drying in headwater streams. We present a distributed, fine-scale water balance through the seasonal recession and onset of stream drying by combining spatiotemporal observations and modeling of flow presence/absence, evapotranspiration, and groundwater inputs. Surface flow presence/absence was collected at fine spatial (~80 m) and temporal (15-min) scales at 25 locations in a headwater stream in southwestern Idaho, USA. Evapotranspiration losses were modeled at the same locations using the Simultaneous Heat and Water (SHAW) model. Groundwater inputs were estimated at four of the locations using a mixing model approach. In addition, we compared high-frequency, fine-resolution riparian normalized vegetation difference index (NDVI) with stream flow status. We found that the stream wetted and dried on a daily basis before seasonally drying, and daily drying occurred when evapotranspiration outputs exceeded groundwater inputs, typically during the hours of peak evapotranspiration. Riparian NDVI decreased when the stream dried, with a ~2-week lag between stream drying and response. Stream diel drying cycles reflect the groundwater and evapotranspiration balance, and riparian NDVI may improve stream drying predictions for groundwater-supported headwater streams.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217357","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 : 2023-10-17DOI: 10.3389/frwa.2023.1271780
Scott M. Reed
The South Platte river system contains a mixture of natural streams, reservoirs, and pipeline projects that redirect water to front range communities in Colorado. At many timepoints, a simple persistence model is the best predictor for flow from pipelines and reservoirs but at other times, flows change based on snowmelt and inputs such as reservoir fill rates, local weather, and anticipated demand. Here we find that a convolutional Long Short-Term Memory (LSTM) network is well suited to modeling flow in parts of this basin that are strongly impacted by water projects as well as ones that are relatively free from direct human modifications. Furthermore, it is found that including an active learning component in which separate Convolutional Neural Networks (CNNs) are used to classify and then select the data that is then used for training a convolutional LSTM network is advantageous. Models specific for each gauge are created by transfer of parameter from a base model and these gauge-specific models are then fine-tuned based a curated subset of training data. The result is accurate predictions for both natural flow and human influenced flow using only past river flow, reservoir capacity, and historical temperature data. In 14 of the 16 gauges modeled, the error in the prediction is reduced when using the combination of on-the-fly classification by CNN followed by analysis by either a persistence or convolutional LSTM model. The methods designed here could be applied broadly to other basins and to other situations where multiple models are needed to fit data at different times and locations.
{"title":"An active learning convolutional neural network for predicting river flow in a human impacted system","authors":"Scott M. Reed","doi":"10.3389/frwa.2023.1271780","DOIUrl":"https://doi.org/10.3389/frwa.2023.1271780","url":null,"abstract":"The South Platte river system contains a mixture of natural streams, reservoirs, and pipeline projects that redirect water to front range communities in Colorado. At many timepoints, a simple persistence model is the best predictor for flow from pipelines and reservoirs but at other times, flows change based on snowmelt and inputs such as reservoir fill rates, local weather, and anticipated demand. Here we find that a convolutional Long Short-Term Memory (LSTM) network is well suited to modeling flow in parts of this basin that are strongly impacted by water projects as well as ones that are relatively free from direct human modifications. Furthermore, it is found that including an active learning component in which separate Convolutional Neural Networks (CNNs) are used to classify and then select the data that is then used for training a convolutional LSTM network is advantageous. Models specific for each gauge are created by transfer of parameter from a base model and these gauge-specific models are then fine-tuned based a curated subset of training data. The result is accurate predictions for both natural flow and human influenced flow using only past river flow, reservoir capacity, and historical temperature data. In 14 of the 16 gauges modeled, the error in the prediction is reduced when using the combination of on-the-fly classification by CNN followed by analysis by either a persistence or convolutional LSTM model. The methods designed here could be applied broadly to other basins and to other situations where multiple models are needed to fit data at different times and locations.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135993486","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 : 2023-10-13DOI: 10.3389/frwa.2023.1256799
Renske J. E. Vroom, Sarian Kosten, Rafael M. Almeida, Raquel Mendonça, Ive S. Muzitano, Icaro Barbosa, Jonas Nasário, Ernandes S. Oliveira Junior, Alexander S. Flecker, Nathan Barros
An ever-increasing demand for protein-rich food sources combined with dwindling wild fish stocks has caused the aquaculture sector to boom in the last two decades. Although fishponds are potentially strong emitters of the greenhouse gas methane (CH 4 ), little is known about the magnitude, pathways, and drivers of these emissions. We measured diffusive CH 4 emissions at the margin and in the center of 52 freshwater fishponds in Brazil. In a subset of ponds ( n = 31) we additionally quantified ebullitive CH 4 fluxes and sampled water and sediment for biogeochemical analyses. Sediments ( n = 20) were incubated to quantify potential CH 4 production. Ebullitive CH 4 emissions ranged between 0 and 477 mg m −2 d −1 and contributed substantially (median 85%) to total CH 4 emissions, surpassing diffusive emissions in 81% of ponds. Diffusive CH 4 emissions were higher in the center (median 11.4 mg CH 4 m −2 d −1 ) than at the margin (median 6.1 mg CH 4 m −2 d −1 ) in 90% of ponds. Sediment CH 4 production ranged between 0 and 3.17 mg CH 4 g C −1 d −1 . We found no relation between sediment CH 4 production and in situ emissions. Our findings suggest that dominance of CH 4 ebullition over diffusion is widespread across aquaculture ponds. Management practices to minimize the carbon footprint of aquaculture production should focus on reducing sediment accumulation and CH 4 ebullition.
{"title":"Widespread dominance of methane ebullition over diffusion in freshwater aquaculture ponds","authors":"Renske J. E. Vroom, Sarian Kosten, Rafael M. Almeida, Raquel Mendonça, Ive S. Muzitano, Icaro Barbosa, Jonas Nasário, Ernandes S. Oliveira Junior, Alexander S. Flecker, Nathan Barros","doi":"10.3389/frwa.2023.1256799","DOIUrl":"https://doi.org/10.3389/frwa.2023.1256799","url":null,"abstract":"An ever-increasing demand for protein-rich food sources combined with dwindling wild fish stocks has caused the aquaculture sector to boom in the last two decades. Although fishponds are potentially strong emitters of the greenhouse gas methane (CH 4 ), little is known about the magnitude, pathways, and drivers of these emissions. We measured diffusive CH 4 emissions at the margin and in the center of 52 freshwater fishponds in Brazil. In a subset of ponds ( n = 31) we additionally quantified ebullitive CH 4 fluxes and sampled water and sediment for biogeochemical analyses. Sediments ( n = 20) were incubated to quantify potential CH 4 production. Ebullitive CH 4 emissions ranged between 0 and 477 mg m −2 d −1 and contributed substantially (median 85%) to total CH 4 emissions, surpassing diffusive emissions in 81% of ponds. Diffusive CH 4 emissions were higher in the center (median 11.4 mg CH 4 m −2 d −1 ) than at the margin (median 6.1 mg CH 4 m −2 d −1 ) in 90% of ponds. Sediment CH 4 production ranged between 0 and 3.17 mg CH 4 g C −1 d −1 . We found no relation between sediment CH 4 production and in situ emissions. Our findings suggest that dominance of CH 4 ebullition over diffusion is widespread across aquaculture ponds. Management practices to minimize the carbon footprint of aquaculture production should focus on reducing sediment accumulation and CH 4 ebullition.","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135854777","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 : 2023-10-09DOI: 10.3389/frwa.2023.1287538
Sohom Mandal, Abhishek Gaur, H. Shirkhani
{"title":"Editorial: Resiliency of urban systems to water-related disasters","authors":"Sohom Mandal, Abhishek Gaur, H. Shirkhani","doi":"10.3389/frwa.2023.1287538","DOIUrl":"https://doi.org/10.3389/frwa.2023.1287538","url":null,"abstract":"","PeriodicalId":33801,"journal":{"name":"Frontiers in Water","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139321593","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 : 2023-10-06DOI: 10.3389/frwa.2023.1268593
Aboubakar Gasirabo, Chen Xi, Alishir Kurban, Tie Liu, Hamad R. Baligira, Jeanine Umuhoza, Adeline Umugwaneza, Umwali Dufatanye Edovia
The Nile Nyabarongo, which is Rwanda's largest river, is facing stress from both human activities and climate change. These factors have a substantial contribution to the water processes, making it difficult to effectively manage water resources. To address this issue, it is important to find out the most accurate techniques for simulating hydrological processes. This study aimed to calibrate the SWAT model employing various algorithms such as GLUE, ParaSol, and SUFI-2 for the simulation of hydrology in the basin of the Nile Nyabarongo River. Different data sources, such as DEM, Landsat images, soil data, and daily meteorological data, were utilized to input information into the SWAT modeling process. To divide the basin area effectively, 25 sub-basins were created, with due consideration of soil characteristics and the diverse land cover. The outcomes point out that SUFI-2 outperformed the other algorithms for SWAT calibration, requiring fewer computing model runs and producing the best results. ParaSol established residing the least effective algorithm. After calibration with SUFI-2, the most sensitive parameters for modeling were revealed to be (1) the Effective Channel Hydraulic Conductivity (CH K2) measuring how well water can flow through a channel, with higher values indicating better conductivity, (2) Manning's n value (CH N2) representing the roughness or resistance to flow within the channel, with smaller values suggesting a smoother channel, (3) Surface Runoff Lag Time (SURLAG) quantifying the delay between rainfall and the occurrence of surface runoff, with shorter values indicating faster runoff response, (4) the Universal Soil-Loss Equation (USLE P) estimating the amount of soil loss. The average evapotranspiration within the basin was calculated to be 559.5 mma-1. These calibration results are important for decision-making and updating policies related to water balance management in the basin.
尼亚巴龙戈尼罗河是卢旺达最大的河流，正面临着人类活动和气候变化的双重压力。这些因素对水的过程有很大的影响，使有效管理水资源变得困难。为了解决这个问题，找到最准确的模拟水文过程的技术是很重要的。本研究旨在利用GLUE、ParaSol和SUFI-2等多种算法对SWAT模型进行校正，用于尼罗尼亚巴隆戈河流域的水文模拟。利用不同的数据源，如DEM、Landsat图像、土壤数据和日常气象数据，将信息输入SWAT建模过程。为了有效划分流域面积，在充分考虑土壤特征和土地覆被多样性的情况下，划分了25个子流域。结果表明，SUFI-2在SWAT标定方面优于其他算法，需要较少的计算模型运行并产生最佳结果。ParaSol建立了驻留效率最低的算法。在用SUFI-2进行校准后，发现建模最敏感的参数是(1)有效通道水力导电性(CH K2)，测量水在通道中的流动情况，值越大表明导电性越好;(2)曼宁n值(CH N2)代表通道内的粗糙度或流动阻力，值越小表明通道越光滑。(3)地表径流滞后时间(SURLAG)，用于量化降雨与地表径流发生之间的滞后时间，SURLAG值越短表明径流响应越快;(4)通用土壤流失方程(USLE P)，用于估算土壤流失量。流域内平均蒸散量为559.5 mm -1。这些校准结果对流域水平衡管理的决策和更新政策具有重要意义。
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