The maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) are three recently developed models to estimate evapotranspiration. Although the connection between ETRHEQ and SFE is evident, no attempts have been made to investigate the congruence, distinctions, or potential complementarity between the two models and MaxEnt. Our mathematical analysis demonstrates that minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt, under the assumption of the same eddy diffusivity of heat and water vapor and with a specific expression for the ratio between the thermal inertia terms for H and LE. The connection between ETRHEQ, SFE, and MaxEnt is independent of Monin-Obukhov similarity theory (MOST)’s extremum solution, and MOST's extreme solution can be viewed as equivalent to introducing a constant correction factor to account for atmospheric stability. While ETRHEQ and MaxEnt can be united within a single hydrometeorological framework, they diverge in their approaches to modeling evapotranspiration, particularly in how they address the roles of vegetation and land surface heterogeneity. More importantly, the unified framework suggests that turbulence fluxes within the atmospheric boundary layer adhere to the principles of maximum information entropy production. The way in which dissipation, along with its associated entropy production, is established using information entropy theory deviates from traditional thermodynamic entropy formulations. Exploring the connection between thermodynamic and information entropy and developing proper formulations of dissipation for energy fluxes presents an appealing avenue for prospective research.
{"title":"Toward a Unified Understanding of Estimating Evapotranspiration: The Linkage Between Three Effective Parsimonious Models","authors":"Yi Wang, Richard M. Petrone, Mazda Kompanizare","doi":"10.1029/2023wr036910","DOIUrl":"https://doi.org/10.1029/2023wr036910","url":null,"abstract":"The maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) are three recently developed models to estimate evapotranspiration. Although the connection between ETRHEQ and SFE is evident, no attempts have been made to investigate the congruence, distinctions, or potential complementarity between the two models and MaxEnt. Our mathematical analysis demonstrates that minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt, under the assumption of the same eddy diffusivity of heat and water vapor and with a specific expression for the ratio between the thermal inertia terms for H and LE. The connection between ETRHEQ, SFE, and MaxEnt is independent of Monin-Obukhov similarity theory (MOST)’s extremum solution, and MOST's extreme solution can be viewed as equivalent to introducing a constant correction factor to account for atmospheric stability. While ETRHEQ and MaxEnt can be united within a single hydrometeorological framework, they diverge in their approaches to modeling evapotranspiration, particularly in how they address the roles of vegetation and land surface heterogeneity. More importantly, the unified framework suggests that turbulence fluxes within the atmospheric boundary layer adhere to the principles of maximum information entropy production. The way in which dissipation, along with its associated entropy production, is established using information entropy theory deviates from traditional thermodynamic entropy formulations. Exploring the connection between thermodynamic and information entropy and developing proper formulations of dissipation for energy fluxes presents an appealing avenue for prospective research.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abbey L. Marcotte, Juul Limpens, João Pedro Nunes, Ben C. Howard, Alexander G. Hurley, Kieran Khamis, Stefan Krause, Danny Croghan, Angeliki Kourmouli, Samantha Leader, Tanu Singh, Cathelijne R. Stoof, Sami Ullah, Nicholas Kettridge
Intact peatlands provide hydrological ecosystem services, such as regulating water regimes and immobilizing pollutants within catchments. Climate change impacts including drought and wildfire may impair their functioning, potentially impacting ecosystem service delivery. Here we investigate stream water quality changes following the combined impacts of a summer drought and wildfire in a peat-dominated catchment in the UK during 2018. The study catchment stores legacy pollutants (i.e., metals) due to past industrial activity, thus making it particularly susceptible to pollutant release during wildfires. We quantified changes in water chemistry during five storm events over a 9-month period following the wildfire. Concentration-discharge (C-Q) relationships for nine solutes were analyzed to explore changes in activation and connectivity of solute source zones. Hysteresis and flushing indices of C-Q responses further described solute dynamics during storm events. We found that most nutrient and base cation concentrations in the stream discharge were highest in the immediate post-fire storm events and decreased during subsequent autumn and spring storms. Metal concentrations increased during autumn and spring storms, indicating delayed mobilization from within-peat or distal headwater sources. Our findings suggest that seasonal re-wetting and hydrologic connectivity following disturbance influenced solute source zone activation and transport in the study catchment. Water quality responses associated with wildfire and drought were primarily observed in the months following the wildfire, suggesting mobilization of pollutants peaks shortly after fire. Our results contribute to a critical understanding of the future of water quality risks in temperate peatland catchments subject to disturbances exacerbated by climate change.
{"title":"Enhanced Hydrologic Connectivity and Solute Dynamics Following Wildfire and Drought in a Contaminated Temperate Peatland Catchment","authors":"Abbey L. Marcotte, Juul Limpens, João Pedro Nunes, Ben C. Howard, Alexander G. Hurley, Kieran Khamis, Stefan Krause, Danny Croghan, Angeliki Kourmouli, Samantha Leader, Tanu Singh, Cathelijne R. Stoof, Sami Ullah, Nicholas Kettridge","doi":"10.1029/2023wr036412","DOIUrl":"https://doi.org/10.1029/2023wr036412","url":null,"abstract":"Intact peatlands provide hydrological ecosystem services, such as regulating water regimes and immobilizing pollutants within catchments. Climate change impacts including drought and wildfire may impair their functioning, potentially impacting ecosystem service delivery. Here we investigate stream water quality changes following the combined impacts of a summer drought and wildfire in a peat-dominated catchment in the UK during 2018. The study catchment stores legacy pollutants (i.e., metals) due to past industrial activity, thus making it particularly susceptible to pollutant release during wildfires. We quantified changes in water chemistry during five storm events over a 9-month period following the wildfire. Concentration-discharge (C-Q) relationships for nine solutes were analyzed to explore changes in activation and connectivity of solute source zones. Hysteresis and flushing indices of C-Q responses further described solute dynamics during storm events. We found that most nutrient and base cation concentrations in the stream discharge were highest in the immediate post-fire storm events and decreased during subsequent autumn and spring storms. Metal concentrations increased during autumn and spring storms, indicating delayed mobilization from within-peat or distal headwater sources. Our findings suggest that seasonal re-wetting and hydrologic connectivity following disturbance influenced solute source zone activation and transport in the study catchment. Water quality responses associated with wildfire and drought were primarily observed in the months following the wildfire, suggesting mobilization of pollutants peaks shortly after fire. Our results contribute to a critical understanding of the future of water quality risks in temperate peatland catchments subject to disturbances exacerbated by climate change.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hui Wu, Zhijun Jin, Su Jiang, Hewei Tang, Joseph P. Morris, Jinjiang Zhang, Bo Zhang
A major challenge in the inversion of subsurface parameters is the ill-posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field-scale EGS model. Principal component analysis was used to map the aperture field to a low-dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble-based inversion method, and the inferred aperture field was used to predict thermal performance. With an over-simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long-term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.
{"title":"Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems","authors":"Hui Wu, Zhijun Jin, Su Jiang, Hewei Tang, Joseph P. Morris, Jinjiang Zhang, Bo Zhang","doi":"10.1029/2023wr036146","DOIUrl":"https://doi.org/10.1029/2023wr036146","url":null,"abstract":"A major challenge in the inversion of subsurface parameters is the ill-posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field-scale EGS model. Principal component analysis was used to map the aperture field to a low-dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble-based inversion method, and the inferred aperture field was used to predict thermal performance. With an over-simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long-term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jacob Alexander Gochenour, Alex J. Rinehart, Andrew J. Luhmann, Ronni Grapenthin, Susan L. Bilek
Tiltmeters have the potential to resolve ground deformation due to changes in hydraulic head induced by conduit pressurization. Conduit pressure variations cause groundwater to be stored or released from storage within the surrounding rock matrix. We modeled this process and infer whether the resulting deformation is measurable with tiltmeters and what behavior to expect by fully coupling porous media flow and solid mechanics in a poroelastic, 2D finite element model. Parameter sets globally representative of Paleozoic, Mesozoic, and Cenozoic confined and unconfined aquifers are considered. Our analysis focuses on the impact of the parameterization on pore pressure, vertical displacement, and tilt. We find that the spatial distribution of the poroelastic signal depends on the hydraulic diffusivity, and its magnitude depends on the mechanical and coupling parameters. Additional analysis of the impacts of conduit radius and depth suggests that tilt polarity could be an indicator of conduit location and relative conduit size. We calibrated the model to data observations acquired at the Santa Fe River Sink-Rise system in north-central Florida, US. We find that an overlying clay-rich layer may act to partially confine the aquifer. Although the observed tilt signal is present in radial and transverse components and polarity reversals occur, we were able to recover the magnitude and general trend of the tilt response.
{"title":"Poroelastic Response to Karst Conduit Pressurization: A Finite Element Modeling Exercise Toward the Use of Tiltmeters in Karst Aquifer Monitoring Applications","authors":"Jacob Alexander Gochenour, Alex J. Rinehart, Andrew J. Luhmann, Ronni Grapenthin, Susan L. Bilek","doi":"10.1029/2022wr034293","DOIUrl":"https://doi.org/10.1029/2022wr034293","url":null,"abstract":"Tiltmeters have the potential to resolve ground deformation due to changes in hydraulic head induced by conduit pressurization. Conduit pressure variations cause groundwater to be stored or released from storage within the surrounding rock matrix. We modeled this process and infer whether the resulting deformation is measurable with tiltmeters and what behavior to expect by fully coupling porous media flow and solid mechanics in a poroelastic, 2D finite element model. Parameter sets globally representative of Paleozoic, Mesozoic, and Cenozoic confined and unconfined aquifers are considered. Our analysis focuses on the impact of the parameterization on pore pressure, vertical displacement, and tilt. We find that the spatial distribution of the poroelastic signal depends on the hydraulic diffusivity, and its magnitude depends on the mechanical and coupling parameters. Additional analysis of the impacts of conduit radius and depth suggests that tilt polarity could be an indicator of conduit location and relative conduit size. We calibrated the model to data observations acquired at the Santa Fe River Sink-Rise system in north-central Florida, US. We find that an overlying clay-rich layer may act to partially confine the aquifer. Although the observed tilt signal is present in radial and transverse components and polarity reversals occur, we were able to recover the magnitude and general trend of the tilt response.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a new approach to understand the causes of groundwater drought events with interpretable deep learning (DL) models. As prerequisites, accurate long short-term memory (LSTM) models for simulating groundwater are built for 16 regions representing three types of spatial scales in the southeastern United States, and standardized groundwater index is applied to identify 233 groundwater drought events. Two interpretation methods, expected gradients (EG) and additive decomposition (AD), are adopted to decipher the DL-captured patterns and inner workings of LSTM networks. The EG results show that: (a) temperature-related features were the primary drivers of large-scale groundwater droughts, with their importance increasing from 56.1% to 63.1% as the drought events approached from 6 months to 15 days. Conversely, precipitation-related features were found to be the dominant factors in the formation of groundwater drought in small-scale catchments, with the overall importance ranging from 59.8% to 53.3%; (b) Seasonal variations in the importance of temperature-related factors are inversely related between large and small spatial scales, being more significant in summer for larger regions and in winter for catchments; and (c) temperature-related factors exhibited an overall “trigger effect” on causing groundwater drought events in the studying areas. The AD method unveiled how the LSTM network behaved differently in retaining and discarding information when emulating different groundwater droughts. In summary, this study provides a new perspective for the causes of groundwater drought events and highlights the potential and prospect of interpretable DL in enhancing our understanding of hydrological processes.
{"title":"Explaining the Mechanism of Multiscale Groundwater Drought Events: A New Perspective From Interpretable Deep Learning Model","authors":"Hejiang Cai, Haiyun Shi, Zhaoqiang Zhou, Suning Liu, Vladan Babovic","doi":"10.1029/2023wr035139","DOIUrl":"https://doi.org/10.1029/2023wr035139","url":null,"abstract":"This study presents a new approach to understand the causes of groundwater drought events with interpretable deep learning (DL) models. As prerequisites, accurate long short-term memory (LSTM) models for simulating groundwater are built for 16 regions representing three types of spatial scales in the southeastern United States, and standardized groundwater index is applied to identify 233 groundwater drought events. Two interpretation methods, expected gradients (EG) and additive decomposition (AD), are adopted to decipher the DL-captured patterns and inner workings of LSTM networks. The EG results show that: (a) temperature-related features were the primary drivers of large-scale groundwater droughts, with their importance increasing from 56.1% to 63.1% as the drought events approached from 6 months to 15 days. Conversely, precipitation-related features were found to be the dominant factors in the formation of groundwater drought in small-scale catchments, with the overall importance ranging from 59.8% to 53.3%; (b) Seasonal variations in the importance of temperature-related factors are inversely related between large and small spatial scales, being more significant in summer for larger regions and in winter for catchments; and (c) temperature-related factors exhibited an overall “trigger effect” on causing groundwater drought events in the studying areas. The AD method unveiled how the LSTM network behaved differently in retaining and discarding information when emulating different groundwater droughts. In summary, this study provides a new perspective for the causes of groundwater drought events and highlights the potential and prospect of interpretable DL in enhancing our understanding of hydrological processes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuo Zheng, Zizhan Zhang, Bridget R. Scanlon, Haoming Yan, Alexander Y. Sun, Ashraf Rateb, Yan Li
Assessing spatiotemporal water storage variability in the Great Lakes Watershed (GLW) is critical given its transboundary status impacting both Canada and the United States. Here, we apply a novel inversion strategy to global positioning system (GPS) vertical movements to achieve high spatial resolution total water storage (TWS) variations in GLW through improved processing. The steps are composed of removing load changes driven by the lake water fluctuation by forward modeling, isolating the Great Lakes grids to solve the ill-conditioned problem in inversion, and inverting the GPS residual series to estimate TWS variations on land (TWSGPS). The results show that the regional dense continuous GPS observation network can successfully resolve TWS on land at monthly timescales with 30–45 km spatial resolution. We also could effectively capture fine-scale TWS features than GRACE/GFO mascon products. GRACE/GFO satellites largely underestimate seasonal and long-term TWS spatial fluctuations, but their temporal patterns coincide with those from GPS. The average annual amplitude of TWSGPS on land reaches 82.0 mm, greatly exceeding estimates from GRACE/GFO (∼48.0 mm) and composite hydrological model outputs (∼62.0 mm). The seasonal groundwater fluctuations inferred from GPS have peak-to-peak amplitudes of ∼40 km3 with the maximum around September. This coincides with that from GRACE/GFO. However, the magnitudes and phases of groundwater storage from GPS vary markedly among the subbasins in GLW, and the different snow and soil moisture amounts measured in each subbasin cause discrepancies among these GPS estimates. This study shows the value of GPS data in spatially downscaling GRACE/GFO data and providing high-resolution output at spatiotemporal scales with low latency.
{"title":"High Spatial Resolution in Total Water Storage Variations Inferred From GPS: Case Study in the Great Lakes Watershed, US","authors":"Shuo Zheng, Zizhan Zhang, Bridget R. Scanlon, Haoming Yan, Alexander Y. Sun, Ashraf Rateb, Yan Li","doi":"10.1029/2023wr035213","DOIUrl":"https://doi.org/10.1029/2023wr035213","url":null,"abstract":"Assessing spatiotemporal water storage variability in the Great Lakes Watershed (GLW) is critical given its transboundary status impacting both Canada and the United States. Here, we apply a novel inversion strategy to global positioning system (GPS) vertical movements to achieve high spatial resolution total water storage (TWS) variations in GLW through improved processing. The steps are composed of removing load changes driven by the lake water fluctuation by forward modeling, isolating the Great Lakes grids to solve the ill-conditioned problem in inversion, and inverting the GPS residual series to estimate TWS variations on land (TWS<sub>GPS</sub>). The results show that the regional dense continuous GPS observation network can successfully resolve TWS on land at monthly timescales with 30–45 km spatial resolution. We also could effectively capture fine-scale TWS features than GRACE/GFO mascon products. GRACE/GFO satellites largely underestimate seasonal and long-term TWS spatial fluctuations, but their temporal patterns coincide with those from GPS. The average annual amplitude of TWS<sub>GPS</sub> on land reaches 82.0 mm, greatly exceeding estimates from GRACE/GFO (∼48.0 mm) and composite hydrological model outputs (∼62.0 mm). The seasonal groundwater fluctuations inferred from GPS have peak-to-peak amplitudes of ∼40 km<sup>3</sup> with the maximum around September. This coincides with that from GRACE/GFO. However, the magnitudes and phases of groundwater storage from GPS vary markedly among the subbasins in GLW, and the different snow and soil moisture amounts measured in each subbasin cause discrepancies among these GPS estimates. This study shows the value of GPS data in spatially downscaling GRACE/GFO data and providing high-resolution output at spatiotemporal scales with low latency.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The mass discharge emanating from dense non-aqueous phase liquid (DNAPL) source zones (SZs) is often used as a key metric for risk assessment. To predict the temporal evolution of mass discharge, upscaled models have been developed to approximate the relationship between the depletion of SZ and the mass discharge. A significant challenge stems from the choice of the SZ parameterization, so that a limited number of domain-averaged SZ metrics can suffice as an input and accurately predict the complex mass-discharge behavior. Moreover, existing deterministic upscaled models cannot quantify prediction uncertainty stemming from modeling parameterization. To address these challenges, we propose a method based on a Bayesian Neural Network (BNN) which learns the non-linear relationship between SZ metrics and mass discharge from multiphase-modeling training data. The proposed BNN-based upscaled model allows uncertainty quantification since it treats trainable parameters as distributions, and does not require a manual parameterization of the SZ a-priori. Instead, the BNN model chooses three physically meaningful SZ quantities related to mass discharge as input features. Then, we use the expected gradients method to identify the feature importance for mass-discharge prediction. We evaluated the proposed model on laboratory-scale DNAPL dissolution experiments. The results show that the BNN model accurately reproduces the multistage mass-discharge profiles with fewer parameters than existing upscaled models. Feature importance analysis shows that all chosen features are important and sufficient to reproduce complex mass discharge. This model provides accurate mass-discharge predictions and uncertainty estimation, therefore holds a great potential for probabilistic risk assessments and decision-making.
{"title":"Modeling Upscaled Mass Discharge From Complex DNAPL Source Zones Using a Bayesian Neural Network: Prediction Accuracy, Uncertainty Quantification and Source Zone Feature Importance","authors":"Xueyuan Kang, Amalia Kokkinaki, Xiaoqing Shi, Jonghyun Lee, Zhilin Guo, Lingling Ni, Jichun Wu","doi":"10.1029/2023wr036864","DOIUrl":"https://doi.org/10.1029/2023wr036864","url":null,"abstract":"The mass discharge emanating from dense non-aqueous phase liquid (DNAPL) source zones (SZs) is often used as a key metric for risk assessment. To predict the temporal evolution of mass discharge, upscaled models have been developed to approximate the relationship between the depletion of SZ and the mass discharge. A significant challenge stems from the choice of the SZ parameterization, so that a limited number of domain-averaged SZ metrics can suffice as an input and accurately predict the complex mass-discharge behavior. Moreover, existing deterministic upscaled models cannot quantify prediction uncertainty stemming from modeling parameterization. To address these challenges, we propose a method based on a Bayesian Neural Network (BNN) which learns the non-linear relationship between SZ metrics and mass discharge from multiphase-modeling training data. The proposed BNN-based upscaled model allows uncertainty quantification since it treats trainable parameters as distributions, and does not require a manual parameterization of the SZ a-priori. Instead, the BNN model chooses three physically meaningful SZ quantities related to mass discharge as input features. Then, we use the expected gradients method to identify the feature importance for mass-discharge prediction. We evaluated the proposed model on laboratory-scale DNAPL dissolution experiments. The results show that the BNN model accurately reproduces the multistage mass-discharge profiles with fewer parameters than existing upscaled models. Feature importance analysis shows that all chosen features are important and sufficient to reproduce complex mass discharge. This model provides accurate mass-discharge predictions and uncertainty estimation, therefore holds a great potential for probabilistic risk assessments and decision-making.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vegetation plays a crucial role in river hydrodynamic processes, and the accurate prediction of canopy drag force is essential for effective river management and ecosystem protection. The interactions within the vegetation canopies must be quantified to understand their impact on drag force. Through a series of flume experiments, we conducted an investigation into the canopy interaction mechanism of rigid emergent aquatic vegetation, particularly focusing on the blockage and sheltering effects. Our experimental design includes various combinations of lateral and longitudinal spacing, as well as special single-row and single-column arrangements. This allowed us to provide a more precise understanding of how lateral and longitudinal spacing affect the blockage and sheltering effects. Furthermore, we introduced a unified reference velocity that combines two effects, based on which we have established a widely applicable drag model that can predict drag under various density conditions. Lastly, we proposed a critical characteristic value for quantifying drag. This value is instrumental in revealing the ultimate performance of drag under different spacing arrangements. The findings provide a reliable approach for predicting drag in rigid emergent vegetation canopies, significantly enhancing our understanding of vegetation's influence on hydrodynamic processes and offering a practical tool for river management and ecosystem protection.
{"title":"Drag in Vegetation Canopy: Considering Sheltering and Blockage Effects","authors":"Yuyan Liu, Ping Wang","doi":"10.1029/2023wr036521","DOIUrl":"https://doi.org/10.1029/2023wr036521","url":null,"abstract":"Vegetation plays a crucial role in river hydrodynamic processes, and the accurate prediction of canopy drag force is essential for effective river management and ecosystem protection. The interactions within the vegetation canopies must be quantified to understand their impact on drag force. Through a series of flume experiments, we conducted an investigation into the canopy interaction mechanism of rigid emergent aquatic vegetation, particularly focusing on the blockage and sheltering effects. Our experimental design includes various combinations of lateral and longitudinal spacing, as well as special single-row and single-column arrangements. This allowed us to provide a more precise understanding of how lateral and longitudinal spacing affect the blockage and sheltering effects. Furthermore, we introduced a unified reference velocity that combines two effects, based on which we have established a widely applicable drag model that can predict drag under various density conditions. Lastly, we proposed a critical characteristic value for quantifying drag. This value is instrumental in revealing the ultimate performance of drag under different spacing arrangements. The findings provide a reliable approach for predicting drag in rigid emergent vegetation canopies, significantly enhancing our understanding of vegetation's influence on hydrodynamic processes and offering a practical tool for river management and ecosystem protection.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pasquale Perrini, Luis Cea, Francesco Chiaravalloti, Salvatore Gabriele, Salvatore Manfreda, Mauro Fiorentino, Andrea Gioia, Vito Iacobellis
Catchment-scale hydrological models encountered dichotomies with the numerical hydrodynamic models when describing surface routing process. We propose a new modeling framework, the so-called “Runoff-On-Grid” approach, for embedding distributed process-based hydrological modeling into shallow water models, as an alternative to the traditional Fully Hydrodynamic Approach (also known as Rain-On-Grid). Antecedent Soil Moisture, subsurface dynamics, and other topsoil hydrological processes are implicitly integrated in the governing hydrodynamic equations via the proposed methodology. The resulting hydrological-hydrodynamic coupling, based on the DREAM distributed hydrological model and the Iber+ shallow water model, enhances the capabilities of both reference models. Through introducing non-negligible runoff generation sources, the Runoff-On-Grid approach extends the surface hydrodynamic modeling to medium-sized vegetated and/or (semi)humid catchments, bypassing the limitations of the widespread hydrological losses' empirical formulations. Employed in an event-based analysis within a High-Performance Computing framework, the DREAM-Iber model provides an efficient and reliable reconstruction of the November 2020 flood that occurred in Crotone (Italy), envisaging consequences of similar future scenarios. We show that the proposed modeling technique, nested within emerging environmental technologies and robust on-site data, details the flood hazard inducing processes merging physical hydrology with advanced hydrodynamics.
{"title":"A Runoff-On-Grid Approach to Embed Hydrological Processes in Shallow Water Models","authors":"Pasquale Perrini, Luis Cea, Francesco Chiaravalloti, Salvatore Gabriele, Salvatore Manfreda, Mauro Fiorentino, Andrea Gioia, Vito Iacobellis","doi":"10.1029/2023wr036421","DOIUrl":"https://doi.org/10.1029/2023wr036421","url":null,"abstract":"Catchment-scale hydrological models encountered dichotomies with the numerical hydrodynamic models when describing surface routing process. We propose a new modeling framework, the so-called “Runoff-On-Grid” approach, for embedding distributed process-based hydrological modeling into shallow water models, as an alternative to the traditional Fully Hydrodynamic Approach (also known as Rain-On-Grid). Antecedent Soil Moisture, subsurface dynamics, and other topsoil hydrological processes are implicitly integrated in the governing hydrodynamic equations via the proposed methodology. The resulting hydrological-hydrodynamic coupling, based on the DREAM distributed hydrological model and the Iber+ shallow water model, enhances the capabilities of both reference models. Through introducing non-negligible runoff generation sources, the Runoff-On-Grid approach extends the surface hydrodynamic modeling to medium-sized vegetated and/or (semi)humid catchments, bypassing the limitations of the widespread hydrological losses' empirical formulations. Employed in an event-based analysis within a High-Performance Computing framework, the DREAM-Iber model provides an efficient and reliable reconstruction of the November 2020 flood that occurred in Crotone (Italy), envisaging consequences of similar future scenarios. We show that the proposed modeling technique, nested within emerging environmental technologies and robust on-site data, details the flood hazard inducing processes merging physical hydrology with advanced hydrodynamics.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jack Eggleston, Chris Mason, Dave Bjerklie, Mike Durand, Rob Dudley, Merritt Harlan
With growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite-based discharge measurements. Satellite-based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite-based discharge measurements because geomorphic river characteristics have strong control over the relationships between discharge and width, water surface elevation (or depth), slope, and velocity. A large ground-truth data set of 8,445 conventional hydraulic measurements, collected by acoustic Doppler current profilers at 503 stations in the United States, was developed and quality assured to examine correlation between river discharge and water surface width, depth, velocity, and cross-sectional area. A separate database of river surface slope and discharge time-series was developed from paired continuous monitoring stations to examine slope-discharge correlations. Results show that discharge correlates most strongly with velocity, cross-sectional area, depth, width, and slope, in that order. Uncertainty of satellite discharge estimates is affected by observed hydraulic variable and reach-specific variability in observed variable(s) characteristics including range of variability, georegistration accuracy, and stability over time of relationships between discharge and observed hydraulic variable.
{"title":"Siting Considerations for Satellite Observation of River Discharge","authors":"Jack Eggleston, Chris Mason, Dave Bjerklie, Mike Durand, Rob Dudley, Merritt Harlan","doi":"10.1029/2023wr034583","DOIUrl":"https://doi.org/10.1029/2023wr034583","url":null,"abstract":"With growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite-based discharge measurements. Satellite-based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite-based discharge measurements because geomorphic river characteristics have strong control over the relationships between discharge and width, water surface elevation (or depth), slope, and velocity. A large ground-truth data set of 8,445 conventional hydraulic measurements, collected by acoustic Doppler current profilers at 503 stations in the United States, was developed and quality assured to examine correlation between river discharge and water surface width, depth, velocity, and cross-sectional area. A separate database of river surface slope and discharge time-series was developed from paired continuous monitoring stations to examine slope-discharge correlations. Results show that discharge correlates most strongly with velocity, cross-sectional area, depth, width, and slope, in that order. Uncertainty of satellite discharge estimates is affected by observed hydraulic variable and reach-specific variability in observed variable(s) characteristics including range of variability, georegistration accuracy, and stability over time of relationships between discharge and observed hydraulic variable.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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