Pub Date : 2024-01-31DOI: 10.3389/frwa.2024.1306044
Kristin B. Raub, Stephen E. Flynn, Kristine F. Stepenuck, Ciaran Hedderman
As climate change has worsened, so too has the risk weather-driven natural disasters pose to critical infrastructure, such as vital food, energy, and water systems. While both the concepts of a food-energy-water (FEW) nexus and resilience emphasize the interdependence of complex systems, academic studies have largely neglected a potential synthesis between the two. When applied in tandem, we believe the FEW nexus and resilience can be mutually reinforcing. Nexus approaches can enhance cross-sectoral evaluation and decision making in resilience planning, and resilience-oriented approaches can better situate the FEW nexus within a broader social, ecological, and governance context. From the small body of existing academic literature considering these concepts in tandem, we have identified a promising foundation for relevant future research that targets three key challenges: coordination, scale, and heterogeneity. Responding to these challenges, in turn, can lead to actions for constructing more resilient infrastructure systems that meet vital human needs in the midst of increasingly frequent floods and other extreme weather events.
{"title":"Integrating resilience and nexus approaches in managing flood risk","authors":"Kristin B. Raub, Stephen E. Flynn, Kristine F. Stepenuck, Ciaran Hedderman","doi":"10.3389/frwa.2024.1306044","DOIUrl":"https://doi.org/10.3389/frwa.2024.1306044","url":null,"abstract":"As climate change has worsened, so too has the risk weather-driven natural disasters pose to critical infrastructure, such as vital food, energy, and water systems. While both the concepts of a food-energy-water (FEW) nexus and resilience emphasize the interdependence of complex systems, academic studies have largely neglected a potential synthesis between the two. When applied in tandem, we believe the FEW nexus and resilience can be mutually reinforcing. Nexus approaches can enhance cross-sectoral evaluation and decision making in resilience planning, and resilience-oriented approaches can better situate the FEW nexus within a broader social, ecological, and governance context. From the small body of existing academic literature considering these concepts in tandem, we have identified a promising foundation for relevant future research that targets three key challenges: coordination, scale, and heterogeneity. Responding to these challenges, in turn, can lead to actions for constructing more resilient infrastructure systems that meet vital human needs in the midst of increasingly frequent floods and other extreme weather events.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":"404 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140473020","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 : 2024-01-26DOI: 10.3389/frwa.2024.1332888
Andrew DelSanto, Richard N. Palmer, Konstantinos Andreadis
In the northeast U.S., resource managers commonly apply 7-day, 10-year (7Q10) low flow estimates for protecting aquatic species in streams. In this paper, the efficacy of process-based hydrologic models is evaluated for estimating 7Q10s compared to the United States Geological Survey's (USGS) widely applied web-application StreamStats, which uses traditional statistical regression equations for estimating extreme flows. To generate the process-based estimates, the USGS's National Hydrologic Modeling (NHM-PRMS) framework (which relies on traditional rainfall-runoff modeling) is applied with 36 years of forcings from the Daymet climate dataset to a representative sample of ninety-four unimpaired gages in the Northeast and Mid-Atlantic U.S. The rainfall-runoff models are calibrated to the measured streamflow at each gage using the recommended NHM-PRMS calibration procedure and evaluated using Kling-Gupta Efficiency (KGE) for daily streamflow estimation. To evaluate the 7Q10 estimates made by the rainfall-runoff models compared to StreamStats, a multitude of error metrics are applied, including median relative bias (cfs/cfs), Root Mean Square Error (RMSE) (cfs), Relative RMSE (RRMSE) (cfs/cfs), and Unit-Area RMSE (UA-RMSE) (cfs/mi2). The calibrated rainfall-runoff models display both improved daily streamflow estimation (median KGE improving from 0.30 to 0.52) and 7Q10 estimation (smaller median relative bias, RMSE, RRMSE, and UA-RMSE, especially for basins larger than 100 mi2). The success of calibration is extended to ungaged locations using the machine learning algorithm Fuzzy C-Means (FCM) clustering, finding that traditional K-Means clustering (FCM clustering with no fuzzification factor) is the preferred method for model regionalization based on (1) Silhouette Analysis, (2) daily streamflow KGE, and (3) 7Q10 error metrics. The optimal rainfall-runoff models created with clustering show improvement for daily streamflow estimation (a median KGE of 0.48, only slightly below that of the calibrated models at 0.52); however, these models display similar error metrics for 7Q10 estimation compared to the uncalibrated models, neither of which provide improved error compared to the statistical estimates. Results suggest that the rainfall-runoff models calibrated to measured streamflow data provide the best 7Q10 estimation in terms of all error metrics except median relative bias, but for all models applicable to ungaged locations, the statistical estimates from StreamStats display the lowest error metrics in every category.
{"title":"Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates","authors":"Andrew DelSanto, Richard N. Palmer, Konstantinos Andreadis","doi":"10.3389/frwa.2024.1332888","DOIUrl":"https://doi.org/10.3389/frwa.2024.1332888","url":null,"abstract":"In the northeast U.S., resource managers commonly apply 7-day, 10-year (7Q10) low flow estimates for protecting aquatic species in streams. In this paper, the efficacy of process-based hydrologic models is evaluated for estimating 7Q10s compared to the United States Geological Survey's (USGS) widely applied web-application StreamStats, which uses traditional statistical regression equations for estimating extreme flows. To generate the process-based estimates, the USGS's National Hydrologic Modeling (NHM-PRMS) framework (which relies on traditional rainfall-runoff modeling) is applied with 36 years of forcings from the Daymet climate dataset to a representative sample of ninety-four unimpaired gages in the Northeast and Mid-Atlantic U.S. The rainfall-runoff models are calibrated to the measured streamflow at each gage using the recommended NHM-PRMS calibration procedure and evaluated using Kling-Gupta Efficiency (KGE) for daily streamflow estimation. To evaluate the 7Q10 estimates made by the rainfall-runoff models compared to StreamStats, a multitude of error metrics are applied, including median relative bias (cfs/cfs), Root Mean Square Error (RMSE) (cfs), Relative RMSE (RRMSE) (cfs/cfs), and Unit-Area RMSE (UA-RMSE) (cfs/mi2). The calibrated rainfall-runoff models display both improved daily streamflow estimation (median KGE improving from 0.30 to 0.52) and 7Q10 estimation (smaller median relative bias, RMSE, RRMSE, and UA-RMSE, especially for basins larger than 100 mi2). The success of calibration is extended to ungaged locations using the machine learning algorithm Fuzzy C-Means (FCM) clustering, finding that traditional K-Means clustering (FCM clustering with no fuzzification factor) is the preferred method for model regionalization based on (1) Silhouette Analysis, (2) daily streamflow KGE, and (3) 7Q10 error metrics. The optimal rainfall-runoff models created with clustering show improvement for daily streamflow estimation (a median KGE of 0.48, only slightly below that of the calibrated models at 0.52); however, these models display similar error metrics for 7Q10 estimation compared to the uncalibrated models, neither of which provide improved error compared to the statistical estimates. Results suggest that the rainfall-runoff models calibrated to measured streamflow data provide the best 7Q10 estimation in terms of all error metrics except median relative bias, but for all models applicable to ungaged locations, the statistical estimates from StreamStats display the lowest error metrics in every category.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":"41 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139595044","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 : 2024-01-23DOI: 10.3389/frwa.2024.1289400
S. Hemmerling, Allison Haertling, Wanyun Shao, Diana Di Leonardo, Audrey Grismore, Alyssa Dausman
In Louisiana's Capital Area Groundwater Conservation District (CAGWCD), extensive groundwater withdrawals from the Southern Hills Aquifer System have begun to accelerate the infiltration of saltwater into the aquifer's freshwater sands. This accelerated saltwater intrusion has the potential to reduce the amount of groundwater available for public consumption and other industrial and agricultural uses throughout the region. In response to this threat, the Capital Area Ground Water Conservation Commission has begun development of a long-term strategic plan to achieve and maintain sustainable and resilient groundwater withdrawals from the aquifer system. The development of the strategic plan includes an assessment of public attitudes regarding groundwater and groundwater management in the CAGWCD. This paper presents the results of mixed methods public participatory research to evaluate current and historical views and attitudes around groundwater quality, quantity, and cost in the CAGWCD. The mixed methods approach used in this research employed a sequential explanatory design model consisting of two phases. The first phase involved the implementation of an internet-based survey, followed by a qualitative phase aimed at explaining and enhancing the quantitative results. The qualitative phase employed a combination of one-on-one interviews and focus groups. The research found that the primary governance obstacle that decision-makers may face in managing groundwater is a broad lack of public awareness of groundwater and groundwater issues in the CAGWCD. Despite the criticality of over-pumping and saltwater intrusion into the aquifer system, survey research and subsequent interviews and focus groups have shown that the public is largely unaware of these issues. This research also found a general lack of trust in both industry and government to manage groundwater issues and highlighted the need for groundwater management efforts to be led by unbiased, trusted institutions.
{"title":"“You turn the tap on, the water's there, and you just think everything's fine”: a mixed methods approach to understanding public perceptions of groundwater management in Baton Rouge, Louisiana, USA","authors":"S. Hemmerling, Allison Haertling, Wanyun Shao, Diana Di Leonardo, Audrey Grismore, Alyssa Dausman","doi":"10.3389/frwa.2024.1289400","DOIUrl":"https://doi.org/10.3389/frwa.2024.1289400","url":null,"abstract":"In Louisiana's Capital Area Groundwater Conservation District (CAGWCD), extensive groundwater withdrawals from the Southern Hills Aquifer System have begun to accelerate the infiltration of saltwater into the aquifer's freshwater sands. This accelerated saltwater intrusion has the potential to reduce the amount of groundwater available for public consumption and other industrial and agricultural uses throughout the region. In response to this threat, the Capital Area Ground Water Conservation Commission has begun development of a long-term strategic plan to achieve and maintain sustainable and resilient groundwater withdrawals from the aquifer system. The development of the strategic plan includes an assessment of public attitudes regarding groundwater and groundwater management in the CAGWCD. This paper presents the results of mixed methods public participatory research to evaluate current and historical views and attitudes around groundwater quality, quantity, and cost in the CAGWCD. The mixed methods approach used in this research employed a sequential explanatory design model consisting of two phases. The first phase involved the implementation of an internet-based survey, followed by a qualitative phase aimed at explaining and enhancing the quantitative results. The qualitative phase employed a combination of one-on-one interviews and focus groups. The research found that the primary governance obstacle that decision-makers may face in managing groundwater is a broad lack of public awareness of groundwater and groundwater issues in the CAGWCD. Despite the criticality of over-pumping and saltwater intrusion into the aquifer system, survey research and subsequent interviews and focus groups have shown that the public is largely unaware of these issues. This research also found a general lack of trust in both industry and government to manage groundwater issues and highlighted the need for groundwater management efforts to be led by unbiased, trusted institutions.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":"117 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605416","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 : 2024-01-17DOI: 10.3389/frwa.2023.1332968
Pascal Bodmer, R. Vroom, Tatiana Stepina, P. D. del Giorgio, S. Kosten
Freshwater ecosystems, including lakes, wetlands, and running waters, are estimated to contribute over half the natural emissions of methane (CH4) globally, yet large uncertainties remain in the inland water CH4 budget. These are related to the highly heterogeneous nature and the complex regulation of the CH4 emission pathways, which involve diffusion, ebullition, and plant-associated transport. The latter, in particular, represents a major source of uncertainty in our understanding of inland water CH4 dynamics. Many freshwater ecosystems harbor habitats colonized by submerged and emergent plants, which transport highly variable amounts of CH4 to the atmosphere but whose presence may also profoundly influence local CH4 dynamics. Yet, CH4 dynamics of vegetated habitats and their potential contribution to emission budgets of inland waters remain understudied and poorly quantified. Here we present a synthesis of literature pertaining CH4 dynamics in vegetated habitats, and we (i) provide an overview of the different ways the presence of aquatic vegetation can influence CH4 dynamics (i.e., production, oxidation, and transport) in freshwater ecosystems, (ii) summarize the methods applied to study CH4 fluxes from vegetated habitats, and (iii) summarize the existing data on CH4 fluxes associated to different types of aquatic vegetation and vegetated habitats in inland waters. Finally, we discuss the implications of CH4 fluxes associated with aquatic vegetated habitats for current estimates of aquatic CH4 emissions at the global scale. The fluxes associated to different plant types and from vegetated areas varied widely, ranging from−8.6 to over 2835.8 mg CH4 m−2 d−1, but were on average high relative to fluxes in non-vegetated habitats. We conclude that, based on average vegetation coverage and average flux intensities of plant-associated fluxes, the exclusion of these habitats in lake CH4 balances may lead to a major underestimation of global lake CH4 emissions. This synthesis highlights the need to incorporate vegetated habitats into CH4 emission budgets from natural freshwater ecosystems and further identifies understudied research aspects and relevant future research directions.
{"title":"Methane dynamics in vegetated habitats in inland waters: quantification, regulation, and global significance","authors":"Pascal Bodmer, R. Vroom, Tatiana Stepina, P. D. del Giorgio, S. Kosten","doi":"10.3389/frwa.2023.1332968","DOIUrl":"https://doi.org/10.3389/frwa.2023.1332968","url":null,"abstract":"Freshwater ecosystems, including lakes, wetlands, and running waters, are estimated to contribute over half the natural emissions of methane (CH4) globally, yet large uncertainties remain in the inland water CH4 budget. These are related to the highly heterogeneous nature and the complex regulation of the CH4 emission pathways, which involve diffusion, ebullition, and plant-associated transport. The latter, in particular, represents a major source of uncertainty in our understanding of inland water CH4 dynamics. Many freshwater ecosystems harbor habitats colonized by submerged and emergent plants, which transport highly variable amounts of CH4 to the atmosphere but whose presence may also profoundly influence local CH4 dynamics. Yet, CH4 dynamics of vegetated habitats and their potential contribution to emission budgets of inland waters remain understudied and poorly quantified. Here we present a synthesis of literature pertaining CH4 dynamics in vegetated habitats, and we (i) provide an overview of the different ways the presence of aquatic vegetation can influence CH4 dynamics (i.e., production, oxidation, and transport) in freshwater ecosystems, (ii) summarize the methods applied to study CH4 fluxes from vegetated habitats, and (iii) summarize the existing data on CH4 fluxes associated to different types of aquatic vegetation and vegetated habitats in inland waters. Finally, we discuss the implications of CH4 fluxes associated with aquatic vegetated habitats for current estimates of aquatic CH4 emissions at the global scale. The fluxes associated to different plant types and from vegetated areas varied widely, ranging from−8.6 to over 2835.8 mg CH4 m−2 d−1, but were on average high relative to fluxes in non-vegetated habitats. We conclude that, based on average vegetation coverage and average flux intensities of plant-associated fluxes, the exclusion of these habitats in lake CH4 balances may lead to a major underestimation of global lake CH4 emissions. This synthesis highlights the need to incorporate vegetated habitats into CH4 emission budgets from natural freshwater ecosystems and further identifies understudied research aspects and relevant future research directions.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":" 852","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139617325","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 : 2024-01-17DOI: 10.3389/frwa.2023.1332678
Jing Deng, Anaïs Couasnon, Ruben Dahm, Markus Hrachowitz, Klaas-Jan van Heeringen, Hans Korving, Albrecht Weerts, Riccardo Taormina
This study focuses on exploring the potential of using Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily scale with lead times up to 46 days ahead. A novel LSTM-based model architecture is designed to leverage both historical observation and forecasted meteorological data to carry out multi-step discharge time series forecasting. The feature and target selection for this deep learning (DL) model involves evaluating the use of different spatial resolutions for meteorological forcing (basin-averaged or subbasin-averaged), the impact of incorporating past discharge observations, and the use of different target variables (discharge Q or time-differenced discharge dQ). Then, the model is trained using the ERA5 dataset as meteorological forcing, and employed for operational forecast with ECMWF seasonal forecast (SEAS5) data. The forecast results are compared to a benchmark process-based model, wflow_sbm. This study also explores the flexibility of the DL model by fine-tuning the pretrained model with limited SEAS5 dataset. Key findings from feature and target selection include: (1) opting for subbasin-averaged meteorological variables significantly improves model performance compared to a basin-averaged approach. (2) Utilizing dQ as the target variable greatly boosts short-term forecast accuracy compared to using Q, with a mean absolute error (MAE) of 25 m3 s−1 and mean absolute percentage error (MAPE) of 0.02 for the first lead time, ensuring reliability and accuracy at the onset of the forecast horizon. (3) While incorporating historical discharge improves the forecasting of Q, its impact on predicting dQ is less pronounced for short lead times. In the operational forecast with SEAS5, compared to the wflow_sbm model, the DL model exhibits skill in forecasting low flows as evidenced by Continuous Ranked Probability Skill Score (CRPSS) median values of all lead times above zero, and better accuracy in forecasting drought events within short lead times. The wflow_sbm model shows higher accuracy for longer lead times. In the exploration of fine-tuning approach, the fine-tuned model generates marginal short-term enhancements in forecasting low-flow events over a non-fine-tuned model. Overall, this study contributes to advancing the field of low-flow forecasting using deep learning approach.
{"title":"Operational low-flow forecasting using LSTMs","authors":"Jing Deng, Anaïs Couasnon, Ruben Dahm, Markus Hrachowitz, Klaas-Jan van Heeringen, Hans Korving, Albrecht Weerts, Riccardo Taormina","doi":"10.3389/frwa.2023.1332678","DOIUrl":"https://doi.org/10.3389/frwa.2023.1332678","url":null,"abstract":"This study focuses on exploring the potential of using Long Short-Term Memory networks (LSTMs) for low-flow forecasting for the Rhine River at Lobith on a daily scale with lead times up to 46 days ahead. A novel LSTM-based model architecture is designed to leverage both historical observation and forecasted meteorological data to carry out multi-step discharge time series forecasting. The feature and target selection for this deep learning (DL) model involves evaluating the use of different spatial resolutions for meteorological forcing (basin-averaged or subbasin-averaged), the impact of incorporating past discharge observations, and the use of different target variables (discharge Q or time-differenced discharge dQ). Then, the model is trained using the ERA5 dataset as meteorological forcing, and employed for operational forecast with ECMWF seasonal forecast (SEAS5) data. The forecast results are compared to a benchmark process-based model, wflow_sbm. This study also explores the flexibility of the DL model by fine-tuning the pretrained model with limited SEAS5 dataset. Key findings from feature and target selection include: (1) opting for subbasin-averaged meteorological variables significantly improves model performance compared to a basin-averaged approach. (2) Utilizing dQ as the target variable greatly boosts short-term forecast accuracy compared to using Q, with a mean absolute error (MAE) of 25 m3 s−1 and mean absolute percentage error (MAPE) of 0.02 for the first lead time, ensuring reliability and accuracy at the onset of the forecast horizon. (3) While incorporating historical discharge improves the forecasting of Q, its impact on predicting dQ is less pronounced for short lead times. In the operational forecast with SEAS5, compared to the wflow_sbm model, the DL model exhibits skill in forecasting low flows as evidenced by Continuous Ranked Probability Skill Score (CRPSS) median values of all lead times above zero, and better accuracy in forecasting drought events within short lead times. The wflow_sbm model shows higher accuracy for longer lead times. In the exploration of fine-tuning approach, the fine-tuned model generates marginal short-term enhancements in forecasting low-flow events over a non-fine-tuned model. Overall, this study contributes to advancing the field of low-flow forecasting using deep learning approach.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":"43 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527035","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 : 2024-01-11DOI: 10.3389/frwa.2023.1295712
María Custodio, Anthony Fow, H. de la Cruz, Fernán Chanamé, Javier Huarcaya
High Andean rivers are fragile ecosystems in the face of various threats, including heavy metal contamination. The objective of this study was to evaluate the potential ecological risk of heavy metals in surface sediment of lotic systems in the central region of Peru. Composite samples of surface sediments were collected from the Chía and Miraflores rivers and the concentrations of heavy metals were determined. The ecological risk analysis was carried out based on the contamination indexes and confirmed by the modified degree of contamination (mCd). The concentration of heavy metals in the sediment of the Chía river was in the following descending order: Fe > Mn > Zn > V > Pb > Cr > Ni > Cu > Mo > Hg, y en el río Miraflores fue: Fe > Mn > Zn > Ni > V > Cr > Cu > Pb > Hg > Mo. The mean concentration of Cu, Cr, Fe, Mn, Mo, Ni, Pb, and V in the sediment samples in both rivers did not exceed the threshold values of the continental crust concentration, nor the interim sediment quality guidelines of the Canadian Council of Ministers of the Environment. However, the mean concentration of Hg exceeded the guideline values in the Miraflores river and the likely effect (0.7 mg.kg−1) adverse effects. The values of the enrichment factor (EF), contamination factor (CF), geoaccumulation index (Igeo), and pollution load index (PLI) indicated low contamination in the sediments of the rivers studied, being confirmed by the modified degree of contamination (mCd). Finally, the risk assessment showed that heavy metals in the sediments presented a low potential ecological risk.
面对重金属污染等各种威胁，安第斯高原河流的生态系统十分脆弱。本研究的目的是评估秘鲁中部地区地块系统表层沉积物中重金属的潜在生态风险。研究人员从奇亚河和米拉弗洛雷斯河采集了表层沉积物的复合样本，并测定了重金属的浓度。根据污染指数进行了生态风险分析，并通过修正污染度（mCd）进行了确认。奇亚河沉积物中的重金属浓度降序如下Fe > Mn > Zn > V > Pb > Cr > Ni > Cu > Mo > Hg：Fe > Mn > Zn > Ni > V > Cr > Cu > Pb > Hg > Mo。两条河流的沉积物样本中铜、铬、铁、锰、钼、镍、铅和钒的平均浓度都没有超过大陆地壳浓度的临界值，也没有超过加拿大环境部长理事会的临时沉积物质量准则。不过，汞的平均浓度超过了米拉弗洛雷斯河的指导值和可能产生的不利影响（0.7 毫克/千克-1）。富集因子 (EF)、污染因子 (CF)、地质累积指数 (Igeo) 和污染负荷指数 (PLI) 的数值表明，所研究河流的沉积物污染程度较低，修正的污染程度 (mCd) 也证实了这一点。最后，风险评估表明，沉积物中的重金属对生态环境的潜在风险较低。
{"title":"Potential ecological risk from heavy metals in surface sediment of lotic systems in central region Peru","authors":"María Custodio, Anthony Fow, H. de la Cruz, Fernán Chanamé, Javier Huarcaya","doi":"10.3389/frwa.2023.1295712","DOIUrl":"https://doi.org/10.3389/frwa.2023.1295712","url":null,"abstract":"High Andean rivers are fragile ecosystems in the face of various threats, including heavy metal contamination. The objective of this study was to evaluate the potential ecological risk of heavy metals in surface sediment of lotic systems in the central region of Peru. Composite samples of surface sediments were collected from the Chía and Miraflores rivers and the concentrations of heavy metals were determined. The ecological risk analysis was carried out based on the contamination indexes and confirmed by the modified degree of contamination (mCd). The concentration of heavy metals in the sediment of the Chía river was in the following descending order: Fe > Mn > Zn > V > Pb > Cr > Ni > Cu > Mo > Hg, y en el río Miraflores fue: Fe > Mn > Zn > Ni > V > Cr > Cu > Pb > Hg > Mo. The mean concentration of Cu, Cr, Fe, Mn, Mo, Ni, Pb, and V in the sediment samples in both rivers did not exceed the threshold values of the continental crust concentration, nor the interim sediment quality guidelines of the Canadian Council of Ministers of the Environment. However, the mean concentration of Hg exceeded the guideline values in the Miraflores river and the likely effect (0.7 mg.kg−1) adverse effects. The values of the enrichment factor (EF), contamination factor (CF), geoaccumulation index (Igeo), and pollution load index (PLI) indicated low contamination in the sediments of the rivers studied, being confirmed by the modified degree of contamination (mCd). Finally, the risk assessment showed that heavy metals in the sediments presented a low potential ecological risk.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139625335","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 : 2024-01-05DOI: 10.3389/frwa.2023.1296434
Colin A. Richardson, R. Beighley
Surface water flooding represents a significant hazard for many infrastructure systems. For example, residential, commercial, and industrial properties, water and wastewater treatment facilities, private drinking water wells, stormwater systems, or transportation networks are often impacted (i.e., in terms of damage or functionality) by flooding events. For large scale events, knowing where to prioritize recovery resources can be challenging. To help communities throughout North Carolina manage flood disaster responses, near real-time state-wide rapid flood mapping methods are needed. In this study, Height Above Nearest Drainage (HAND) concepts are combined with National Water Model river discharges to enable rapid flood mapping throughout North Carolina. The modeling system is calibrated using USGS stage-discharge relationships and FEMA 100-year flood maps. The calibration process ultimately provides spatially distributed channel roughness values to best match the available datasets. Results show that the flood mapping system, when calibrated, provides reasonable estimates of both river stage (or corresponding water surface elevations) and surface water extents. Comparing HAND to FEMA hazard maps both in Wake County and state-wide shows an agreement of 80.1% and 76.3%, respectively. For the non-agreement locations, flood extents tend to be overestimated as compared to underestimated, which is preferred in the context of identifying potentially impacted infrastructure systems. Future research will focus on developing transfer relationships to estimate channel roughness values for locations that lack the data needed for calibration.
{"title":"Optimizing Height Above Nearest Drainage parameters to enable rapid flood mapping in North Carolina","authors":"Colin A. Richardson, R. Beighley","doi":"10.3389/frwa.2023.1296434","DOIUrl":"https://doi.org/10.3389/frwa.2023.1296434","url":null,"abstract":"Surface water flooding represents a significant hazard for many infrastructure systems. For example, residential, commercial, and industrial properties, water and wastewater treatment facilities, private drinking water wells, stormwater systems, or transportation networks are often impacted (i.e., in terms of damage or functionality) by flooding events. For large scale events, knowing where to prioritize recovery resources can be challenging. To help communities throughout North Carolina manage flood disaster responses, near real-time state-wide rapid flood mapping methods are needed. In this study, Height Above Nearest Drainage (HAND) concepts are combined with National Water Model river discharges to enable rapid flood mapping throughout North Carolina. The modeling system is calibrated using USGS stage-discharge relationships and FEMA 100-year flood maps. The calibration process ultimately provides spatially distributed channel roughness values to best match the available datasets. Results show that the flood mapping system, when calibrated, provides reasonable estimates of both river stage (or corresponding water surface elevations) and surface water extents. Comparing HAND to FEMA hazard maps both in Wake County and state-wide shows an agreement of 80.1% and 76.3%, respectively. For the non-agreement locations, flood extents tend to be overestimated as compared to underestimated, which is preferred in the context of identifying potentially impacted infrastructure systems. Future research will focus on developing transfer relationships to estimate channel roughness values for locations that lack the data needed for calibration.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":"11 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381019","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 : 2024-01-04DOI: 10.3389/frwa.2023.1334022
Neil Manewell, John Doherty, Phil Hayes
Groundwater modelers frequently grapple with the challenge of integrating aquifer test interpretations into parameters used by regional models. This task is complicated by issues of upscaling, data assimilation, and the need to assign prior probability distributions to numerical model parameters in order to support model predictive uncertainty analysis. To address this, we introduce a new framework that bridges the significant scale differences between aquifer tests and regional models. This framework also accounts for loss of original datasets and the heterogeneous nature of geological media in which aquifer testing often takes place. Using a fine numerical grid, the aquifer test is reproduced in a way that allows stochastic representation of site hydraulic properties at an arbitrary level of complexity. Data space inversion is then used to endow regional model cells with upscaled, aquifer-test-constrained realizations of numerical model properties. An example application demonstrates that assimilation of historical pumping test interpretations in this manner can be done relatively quickly. Furthermore, the assimilation process has the potential to significantly influence the posterior means of decision-pertinent model predictions. However, for the examples that we discuss, posterior predictive uncertainties do not undergo significant reduction. These results highlight the need for further research.
{"title":"Translating pumping test data into groundwater model parameters: a workflow to reveal aquifer heterogeneities and implications in regional model parameterization","authors":"Neil Manewell, John Doherty, Phil Hayes","doi":"10.3389/frwa.2023.1334022","DOIUrl":"https://doi.org/10.3389/frwa.2023.1334022","url":null,"abstract":"Groundwater modelers frequently grapple with the challenge of integrating aquifer test interpretations into parameters used by regional models. This task is complicated by issues of upscaling, data assimilation, and the need to assign prior probability distributions to numerical model parameters in order to support model predictive uncertainty analysis. To address this, we introduce a new framework that bridges the significant scale differences between aquifer tests and regional models. This framework also accounts for loss of original datasets and the heterogeneous nature of geological media in which aquifer testing often takes place. Using a fine numerical grid, the aquifer test is reproduced in a way that allows stochastic representation of site hydraulic properties at an arbitrary level of complexity. Data space inversion is then used to endow regional model cells with upscaled, aquifer-test-constrained realizations of numerical model properties. An example application demonstrates that assimilation of historical pumping test interpretations in this manner can be done relatively quickly. Furthermore, the assimilation process has the potential to significantly influence the posterior means of decision-pertinent model predictions. However, for the examples that we discuss, posterior predictive uncertainties do not undergo significant reduction. These results highlight the need for further research.","PeriodicalId":504613,"journal":{"name":"Frontiers in Water","volume":"46 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385894","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}
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