Models of climate change impacts could be missing significant risks to hydrologic and water infrastructure systems through a shared feature: the idea that temperatures rise monotonically. By contrast, temperature overshoot pathways describe non-monotonic warming trajectories, in which global temperatures first exceed a given target before declining to that target. Risks from overshoot pathways are qualitatively different from risks associated with monotonic warming trajectories, and are likely underestimated in current research and policy. Models suggest overshoot may be almost unavoidable if the more stringent Paris Agreement target limiting warming to 1.5°C over preindustrial levels is to be met by 2100. While overshoot has been relatively widely described in the climate literature, the impacts of overshoot on individual system characteristics have not. We suggest that failure to consider disparities between monotonic and overshoot warming impacts on hydrology and water resources presents particular risks due to divergent adaptation needs. Processes with decadal hysteresis are especially vulnerable. These include glacial contributions to streamflow; hydrologic consequences of vegetation change; altered groundwater; higher water use for fossil fuel combustion and carbon dioxide removal; and water infrastructure and policy that depends on climate conditions. We argue that risks of overshoot cannot be fully captured in current integrated assessment models and that overshoot needs to be specifically evaluated to adequately characterize risk in the water system. We consider how current modeling tools could be adapted to evaluate overshoot consequences, but also recognize that decisions must be made even without perfect knowledge.
{"title":"Temperature Overshoot Would Have Lasting Impacts on Hydrology and Water Resources","authors":"Adrienne Marshall, Emily Grubert, Sara Warix","doi":"10.1029/2024wr037950","DOIUrl":"https://doi.org/10.1029/2024wr037950","url":null,"abstract":"Models of climate change impacts could be missing significant risks to hydrologic and water infrastructure systems through a shared feature: the idea that temperatures rise monotonically. By contrast, temperature overshoot pathways describe non-monotonic warming trajectories, in which global temperatures first exceed a given target before declining to that target. Risks from overshoot pathways are qualitatively different from risks associated with monotonic warming trajectories, and are likely underestimated in current research and policy. Models suggest overshoot may be almost unavoidable if the more stringent Paris Agreement target limiting warming to 1.5°C over preindustrial levels is to be met by 2100. While overshoot has been relatively widely described in the climate literature, the impacts of overshoot on individual system characteristics have not. We suggest that failure to consider disparities between monotonic and overshoot warming impacts on hydrology and water resources presents particular risks due to divergent adaptation needs. Processes with decadal hysteresis are especially vulnerable. These include glacial contributions to streamflow; hydrologic consequences of vegetation change; altered groundwater; higher water use for fossil fuel combustion and carbon dioxide removal; and water infrastructure and policy that depends on climate conditions. We argue that risks of overshoot cannot be fully captured in current integrated assessment models and that overshoot needs to be specifically evaluated to adequately characterize risk in the water system. We consider how current modeling tools could be adapted to evaluate overshoot consequences, but also recognize that decisions must be made even without perfect knowledge.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"82 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924751","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}
Carly H. Hansen, Bilal Iftikhar, Rachel M. Pilla, Natalie A. Griffiths, Paul G. Matson, Henriette I. Jager
Ebullitive methane (CH4) emissions in lentic ecosystems tend to concentrate at river-lake interfaces and within shallow littoral zones. However, inconsistent definitions of the littoral zone and static representations of the lake or reservoir surface area contribute to major uncertainties in greenhouse gas (GHG) emissions estimates, particularly in reservoirs with large water-level fluctuations. This study examines temporal variation in littoral and total surface areas of US reservoirs and demonstrates how different methods and data sources lead to discrepencies in reservoir GHG emissions at large scales and over time. We also explore variability in remotely sensed water occurrence according to maximum surface area, reservoir purposes, and hydrologic regions. Notably, the largest relative variability in surface area is exhibited by small reservoirs with a maximum surface area <1 km2 and non-hydroelectric reservoirs. Additionally, we use a case study of measured CH4 emissions from the southeastern United States (Douglas Reservoir) to illustrate the effects of varying surface area on reservoir-wide GHG estimates. Upscaled CH4 emissions in Douglas Reservoir differed by nearly two-fold depending on the source of total surface area data and whether estimates accounted for seasonal fluctuations in surface area. During seasonal drawdown in Douglas Reservoir, relative littoral area varies non-linearly; periods of lower pool elevation (and thus larger relative littoral area) likely contribute disproportionately high CH4 emission rates compared to the commonly sampled summer season when water levels are at full-pool elevation. Improved GHG monitoring and upscaling techniques require accounting for temporal variability in reservoir surface extent and littoral area.
{"title":"Temporal Variability in Reservoir Surface Area Is an Important Source of Uncertainty in GHG Emission Estimates","authors":"Carly H. Hansen, Bilal Iftikhar, Rachel M. Pilla, Natalie A. Griffiths, Paul G. Matson, Henriette I. Jager","doi":"10.1029/2024wr037726","DOIUrl":"https://doi.org/10.1029/2024wr037726","url":null,"abstract":"Ebullitive methane (CH<sub>4</sub>) emissions in lentic ecosystems tend to concentrate at river-lake interfaces and within shallow littoral zones. However, inconsistent definitions of the littoral zone and static representations of the lake or reservoir surface area contribute to major uncertainties in greenhouse gas (GHG) emissions estimates, particularly in reservoirs with large water-level fluctuations. This study examines temporal variation in littoral and total surface areas of US reservoirs and demonstrates how different methods and data sources lead to discrepencies in reservoir GHG emissions at large scales and over time. We also explore variability in remotely sensed water occurrence according to maximum surface area, reservoir purposes, and hydrologic regions. Notably, the largest relative variability in surface area is exhibited by small reservoirs with a maximum surface area <1 km<sup>2</sup> and non-hydroelectric reservoirs. Additionally, we use a case study of measured CH<sub>4</sub> emissions from the southeastern United States (Douglas Reservoir) to illustrate the effects of varying surface area on reservoir-wide GHG estimates. Upscaled CH<sub>4</sub> emissions in Douglas Reservoir differed by nearly two-fold depending on the source of total surface area data and whether estimates accounted for seasonal fluctuations in surface area. During seasonal drawdown in Douglas Reservoir, relative littoral area varies non-linearly; periods of lower pool elevation (and thus larger relative littoral area) likely contribute disproportionately high CH<sub>4</sub> emission rates compared to the commonly sampled summer season when water levels are at full-pool elevation. Improved GHG monitoring and upscaling techniques require accounting for temporal variability in reservoir surface extent and littoral area.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924746","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}
Dilute species transport in generalized Newtonian fluids (GNFs) is typically described using explanatory empirical approaches assuming a traditional Fickian form, which is an approach that lacks predictive ability for systems and conditions not specifically investigated. Dilute species transport was investigated for a wide range of Cross and Carreau fluids flowing through a set of monodisperse and polydisperse sphere pack porous media. Both microscale and macroscale simulations were performed to demonstrate that GNF fluid flow can be predicted based upon Newtonian characterization of the media and rheological characterization of the fluid. Dilute species transport was shown to have a Fickian limit with dispersivity dependent on the porous media, fluid properties, and the flow rate in a nonlinear fashion. Dimensionless analysis and symbolic regression was used to deduce an explanatory and predictive function to describe dispersivity in terms of relevant system properties, enabling prediction of dilute species transport for GNFs flowing through porous media that does not require any non-Newtonian experiments or parameter estimation.
{"title":"Dilute Species Transport During Generalized Newtonian Fluid Flow in Porous Medium Systems","authors":"Christopher A. Bowers, Cass T. Miller","doi":"10.1029/2024wr037658","DOIUrl":"https://doi.org/10.1029/2024wr037658","url":null,"abstract":"Dilute species transport in generalized Newtonian fluids (GNFs) is typically described using explanatory empirical approaches assuming a traditional Fickian form, which is an approach that lacks predictive ability for systems and conditions not specifically investigated. Dilute species transport was investigated for a wide range of Cross and Carreau fluids flowing through a set of monodisperse and polydisperse sphere pack porous media. Both microscale and macroscale simulations were performed to demonstrate that GNF fluid flow can be predicted based upon Newtonian characterization of the media and rheological characterization of the fluid. Dilute species transport was shown to have a Fickian limit with dispersivity dependent on the porous media, fluid properties, and the flow rate in a nonlinear fashion. Dimensionless analysis and symbolic regression was used to deduce an explanatory and predictive function to describe dispersivity in terms of relevant system properties, enabling prediction of dilute species transport for GNFs flowing through porous media that does not require any non-Newtonian experiments or parameter estimation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"72 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924747","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}
In the Dolomites, steep rocky cliffs are marked by numerous narrow gullies. When high-intensity short-duration precipitation occurs, these gullies concentrate and direct surface runoff to the screes at the foot of rock cliffs. Surface runoff mixes with loose sediments, creating a solid-liquid surge that, as it moves downhill, increases its volume entraining debris material and transforms into a granular debris flow. Given the ongoing challenge of modeling the relationship between intense rainfall, surface runoff, and debris flow initiation, we take advantage of data from three monitoring stations operating in distinct debris flow active catchments in our study area to make progress. These stations, strategically positioned close to debris flows initiation zones, record videos and different types of flow-stage data, helping us pinpoint the timing and form of incoming discharge hydrographs. Over a 15-year period of observation, we collected a comprehensive data set on runoff and mass movement in these catchments, offering valuable insights into their hydrological behavior and the initiation of granular debris flows. To compute infiltration excess runoff generation, we refined an already existing hydrological model and calibrated it using discharge measured at one of the monitoring stations. Testing this updated model against observations from two other larger debris flow sites showed that it can reproduce the initial phases of a debris flow, when sediment concentration rapidly rises. These findings suggest that a well-tuned hydrological model can predict the discharge from intense, short rainfall events that typically trigger debris flows, as well as the early stages of these phenomena.
{"title":"Rainfall-Runoff Modeling in Rocky Headwater Catchments for the Prediction of Debris Flow Occurrence","authors":"Martino Bernard, Matteo Barbini, Matteo Berti, Mauro Boreggio, Alessandro Simoni, Carlo Gregoretti","doi":"10.1029/2023wr036887","DOIUrl":"https://doi.org/10.1029/2023wr036887","url":null,"abstract":"In the Dolomites, steep rocky cliffs are marked by numerous narrow gullies. When high-intensity short-duration precipitation occurs, these gullies concentrate and direct surface runoff to the screes at the foot of rock cliffs. Surface runoff mixes with loose sediments, creating a solid-liquid surge that, as it moves downhill, increases its volume entraining debris material and transforms into a granular debris flow. Given the ongoing challenge of modeling the relationship between intense rainfall, surface runoff, and debris flow initiation, we take advantage of data from three monitoring stations operating in distinct debris flow active catchments in our study area to make progress. These stations, strategically positioned close to debris flows initiation zones, record videos and different types of flow-stage data, helping us pinpoint the timing and form of incoming discharge hydrographs. Over a 15-year period of observation, we collected a comprehensive data set on runoff and mass movement in these catchments, offering valuable insights into their hydrological behavior and the initiation of granular debris flows. To compute infiltration excess runoff generation, we refined an already existing hydrological model and calibrated it using discharge measured at one of the monitoring stations. Testing this updated model against observations from two other larger debris flow sites showed that it can reproduce the initial phases of a debris flow, when sediment concentration rapidly rises. These findings suggest that a well-tuned hydrological model can predict the discharge from intense, short rainfall events that typically trigger debris flows, as well as the early stages of these phenomena.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"6 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917579","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}
Honghua Cao, Feng Chen, Mao Hu, Tiyuan Hou, Xiaoen Zhao, Shijie Wang, Heli Zhang
As the largest city in northern China and the capital of China, the rapid increases in Beijing’s water consumption in recent years have made water resources provision an increasing problem. To rationally allocate water resources, it is important to obtain long-term runoff information in Beijing. In this study we develop a 236-year chronology of tree-ring widths based on cores from Pinus tabuliformis from four sampling sites. The resulting regression model reconstructs December–July runoff of the Yongding River in Beijing, with 49.5% of the variance explained, back to 1786 CE. Among the last 236 years, 1868, 1956, 1991, 1998, 2018, and 2021 were extremely high runoff years; and 1900, 1906, 1999, and 2000 were extremely low runoff years. Comparison of the runoff reconstruction results with climate grid data demonstrated a large magnitude of climate change in North China during the study period. Linkage analysis between the reconstructed runoff and large-scale water vapor indicated that the high runoff years occurred during negative phases of the Pacific Decadal Oscillation, which may be influenced by the East Asian Summer Monsoon. Projections indicate that the flow of the Yongding River will increase in the future. Supported by policies such as the Ecological Water Supply and South-to-North Water Diversion, regional vegetation productivity and Yongding River runoff have increased substantially since 2000. Vegetation growth interacts with runoff volume. It is unclear how long these increases will continue.
{"title":"Tree-Ring Insights Into Past and Future Streamflow Variations in Beijing, Northern China","authors":"Honghua Cao, Feng Chen, Mao Hu, Tiyuan Hou, Xiaoen Zhao, Shijie Wang, Heli Zhang","doi":"10.1029/2024wr038084","DOIUrl":"https://doi.org/10.1029/2024wr038084","url":null,"abstract":"As the largest city in northern China and the capital of China, the rapid increases in Beijing’s water consumption in recent years have made water resources provision an increasing problem. To rationally allocate water resources, it is important to obtain long-term runoff information in Beijing. In this study we develop a 236-year chronology of tree-ring widths based on cores from <i>Pinus tabuliformis</i> from four sampling sites. The resulting regression model reconstructs December–July runoff of the Yongding River in Beijing, with 49.5% of the variance explained, back to 1786 CE. Among the last 236 years, 1868, 1956, 1991, 1998, 2018, and 2021 were extremely high runoff years; and 1900, 1906, 1999, and 2000 were extremely low runoff years. Comparison of the runoff reconstruction results with climate grid data demonstrated a large magnitude of climate change in North China during the study period. Linkage analysis between the reconstructed runoff and large-scale water vapor indicated that the high runoff years occurred during negative phases of the Pacific Decadal Oscillation, which may be influenced by the East Asian Summer Monsoon. Projections indicate that the flow of the Yongding River will increase in the future. Supported by policies such as the Ecological Water Supply and South-to-North Water Diversion, regional vegetation productivity and Yongding River runoff have increased substantially since 2000. Vegetation growth interacts with runoff volume. It is unclear how long these increases will continue.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"132 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917701","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}
Alexander Young, John D. Albertson, Giovanni Moretti, Stefano Orlandini
Emergency response to flood plain inundations requires real-time forecasts of flow depth, velocity, and arrival time. Detailed and rapid flood inundation forecasts can be obtained from numerical solution of 2D unsteady flow equations based on high-resolution topographic data and geomorphologically informed unstructured meshes. However, flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data remain uncertain. In the present study, flow resistance parameters representing the effects of roughness, vegetation, and buildings are determined hydraulically in real-time using flow depth observations. A detailed numerical reproduction of a real flood has been largely corroborated by observations and subsequently used as a surrogate of the ground truth target. In synthetic numerical experiments, flow depth observations are obtained from a network of in-situ flow depth sensors assigned to hydraulically relevant locations in the flood plain. Starting from a generic resistance parameter set, the capability of a tandem 2D surface flow model and Bayesian optimization technique to achieve convergence to the target resistance parameter set is tested. Convergence to the target resistance parameter set was obtained with 50 or fewer tandem flow + optimization iterations for each forecasting cycle in which the difference between simulated and observed flow depths is minimized. The flood arrival time errors across a 52 <span data-altimg="/cms/asset/38542f77-d5ab-40b0-a37b-8f79f83ce532/wrcr27640-math-0001.png"></span><mjx-container ctxtmenu_counter="294" ctxtmenu_oldtabindex="1" jax="CHTML" role="application" sre-explorer- style="font-size: 103%; position: relative;" tabindex="0"><mjx-math aria-hidden="true" location="graphic/wrcr27640-math-0001.png"><mjx-semantics><mjx-mrow><mjx-msup data-semantic-children="0,1" data-semantic- data-semantic-role="unknown" data-semantic-speech="km Superscript 2" data-semantic-type="superscript"><mjx-mtext data-semantic-annotation="clearspeak:unit" data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="unknown" data-semantic-type="text"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext><mjx-script style="vertical-align: 0.421em;"><mjx-mn data-semantic-annotation="clearspeak:simple" data-semantic-font="normal" data-semantic- data-semantic-parent="2" data-semantic-role="integer" data-semantic-type="number" size="s"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display="inline" unselectable="on"><math altimg="urn:x-wiley:00431397:media:wrcr27640:wrcr27640-math-0001" display="inline" location="graphic/wrcr27640-math-0001.png" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup data-semantic-="" data-semantic-children="0,1" data-semantic-role="unknown" data-semantic-speech="km Superscript 2" data-semantic-type="superscript"><mtext data-semantic-="" data-semantic-annotation="clearspea
{"title":"Real-Time Flood Inundation Modeling With Flow Resistance Parameter Learning","authors":"Alexander Young, John D. Albertson, Giovanni Moretti, Stefano Orlandini","doi":"10.1029/2024wr038424","DOIUrl":"https://doi.org/10.1029/2024wr038424","url":null,"abstract":"Emergency response to flood plain inundations requires real-time forecasts of flow depth, velocity, and arrival time. Detailed and rapid flood inundation forecasts can be obtained from numerical solution of 2D unsteady flow equations based on high-resolution topographic data and geomorphologically informed unstructured meshes. However, flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data remain uncertain. In the present study, flow resistance parameters representing the effects of roughness, vegetation, and buildings are determined hydraulically in real-time using flow depth observations. A detailed numerical reproduction of a real flood has been largely corroborated by observations and subsequently used as a surrogate of the ground truth target. In synthetic numerical experiments, flow depth observations are obtained from a network of in-situ flow depth sensors assigned to hydraulically relevant locations in the flood plain. Starting from a generic resistance parameter set, the capability of a tandem 2D surface flow model and Bayesian optimization technique to achieve convergence to the target resistance parameter set is tested. Convergence to the target resistance parameter set was obtained with 50 or fewer tandem flow + optimization iterations for each forecasting cycle in which the difference between simulated and observed flow depths is minimized. The flood arrival time errors across a 52 <span data-altimg=\"/cms/asset/38542f77-d5ab-40b0-a37b-8f79f83ce532/wrcr27640-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"294\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/wrcr27640-math-0001.png\"><mjx-semantics><mjx-mrow><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-role=\"unknown\" data-semantic-speech=\"km Superscript 2\" data-semantic-type=\"superscript\"><mjx-mtext data-semantic-annotation=\"clearspeak:unit\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"text\"><mjx-c></mjx-c><mjx-c></mjx-c></mjx-mtext><mjx-script style=\"vertical-align: 0.421em;\"><mjx-mn data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"integer\" data-semantic-type=\"number\" size=\"s\"><mjx-c></mjx-c></mjx-mn></mjx-script></mjx-msup></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:00431397:media:wrcr27640:wrcr27640-math-0001\" display=\"inline\" location=\"graphic/wrcr27640-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-role=\"unknown\" data-semantic-speech=\"km Superscript 2\" data-semantic-type=\"superscript\"><mtext data-semantic-=\"\" data-semantic-annotation=\"clearspea","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"66 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917588","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}
Climate models simulate extreme precipitation under nonstationarity due to continuous climate change. However, systematic errors in local-scale climate projections are often corrected using stationary or quasi-stationary methods without explicit and continuous nonstationarity treatment, like quantile mapping (QM), detrended QM, and quantile delta mapping. To bridge this gap, we introduce nonstationary QM (NS-QM) and its simplified version for consistent nonstationarity patterns (CNS-QM). Besides, correction approaches for extremes often rely on limited extreme-event records. To leverage ordinary-event information while focusing on extremes, we propose integrating the simplified Metastatistical extreme value (SMEV) distribution into NS-QM and CNS-QM (NS-QM-SMEV and CNS-QM-SMEV). We demonstrate the superiority of NS- and CNS-QM-SMEV over existing methods through a simulation study and show several real-world applications using high-resolution-regional and coarse-resolution-global climate models. NS-QM and CNS-QM reflect nonstationarity more realistically but may encounter challenges due to data limitations like estimation errors and uncertainty, particularly for the most extreme events. These issues, shared by existing approaches, are effectively mitigated using the SMEV distribution. NS- and CNS-QM-SMEV offer lower estimation error, approximate unbiasedness, reduced uncertainty, and improved representation of the entire distribution, especially for samples of ∼70 years, and greater superiority with larger samples. We show existing methods may perform competitively for short samples but exhibit substantial biases in quantile-quantile matching due to bypassing nonstationarity modeling. NS- and CNS-QM-SMEV avoid these biases, adhering better to their theoretical functioning. Thus, NS- and CNS-QM-SMEV enhance the correction of extremes under nonstationarity. Yet, properly identifying nonstationarity patterns is crucial for reliable implementations.
{"title":"Improved Correction of Extreme Precipitation Through Explicit and Continuous Nonstationarity Treatment and the Metastatistical Approach","authors":"Cuauhtémoc Tonatiuh Vidrio-Sahagún, Jianxun He, Alain Pietroniro","doi":"10.1029/2024wr037721","DOIUrl":"https://doi.org/10.1029/2024wr037721","url":null,"abstract":"Climate models simulate extreme precipitation under nonstationarity due to continuous climate change. However, systematic errors in local-scale climate projections are often corrected using stationary or quasi-stationary methods without explicit and continuous nonstationarity treatment, like quantile mapping (QM), detrended QM, and quantile delta mapping. To bridge this gap, we introduce nonstationary QM (NS-QM) and its simplified version for consistent nonstationarity patterns (CNS-QM). Besides, correction approaches for extremes often rely on limited extreme-event records. To leverage ordinary-event information while focusing on extremes, we propose integrating the simplified Metastatistical extreme value (SMEV) distribution into NS-QM and CNS-QM (NS-QM-SMEV and CNS-QM-SMEV). We demonstrate the superiority of NS- and CNS-QM-SMEV over existing methods through a simulation study and show several real-world applications using high-resolution-regional and coarse-resolution-global climate models. NS-QM and CNS-QM reflect nonstationarity more realistically but may encounter challenges due to data limitations like estimation errors and uncertainty, particularly for the most extreme events. These issues, shared by existing approaches, are effectively mitigated using the SMEV distribution. NS- and CNS-QM-SMEV offer lower estimation error, approximate unbiasedness, reduced uncertainty, and improved representation of the entire distribution, especially for samples of ∼70 years, and greater superiority with larger samples. We show existing methods may perform competitively for short samples but exhibit substantial biases in quantile-quantile matching due to bypassing nonstationarity modeling. NS- and CNS-QM-SMEV avoid these biases, adhering better to their theoretical functioning. Thus, NS- and CNS-QM-SMEV enhance the correction of extremes under nonstationarity. Yet, properly identifying nonstationarity patterns is crucial for reliable implementations.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"2 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917840","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}
Natasha Harvey, Sean P. Burns, Keith N. Musselman, Holly Barnard, Peter D. Blanken
The interception of snow by the canopy is an important process in the water and energy balance in cold-region coniferous forests. Direct measurements of canopy snow interception are difficult at scales larger than individual trees, requiring indirect methods such as eddy covariance, time-lapse photography, or modeling. At the Niwot Ridge Subalpine Forest AmeriFlux site in the Colorado Front Range, USA, we compared methods that estimate or simulate the presence of snow interception. Timelapse photography images were analyzed using thresholding analysis and used to train a Convolutional Neural Network (CNN) model to estimate canopy snow presence. Interception was also estimated from eddy covariance measurements above and below the canopy, as well as from model simulations. These methods were applied over January 2019, with binarized results compared to a “ground truth” of human labeled images to calculate the Balanced Accuracy Score. The highest accuracy was achieved by the CNN predictions. Based on the Balanced Accuracy Scores, select methods were extended to estimate the presence of canopy snow for the 2018/2019 winter. All methods provided insight into the process of interception in a subalpine forest but presented challenges, including differing flux footprints of the above- and below-canopy eddy covariance measurements and the inability of red-green-blue imagery to monitor snow interception at night, during sunrise, and during sunset.
冠层截流积雪是寒区针叶林水能平衡的重要过程。在比单个树木更大的尺度上，直接测量冠层积雪拦截是困难的，需要间接方法，如涡动相关、延时摄影或建模。在美国Colorado Front Range的Niwot Ridge亚高山森林AmeriFlux站点，我们比较了估算或模拟雪拦截存在的方法。使用阈值分析对延时摄影图像进行分析，并用于训练卷积神经网络（CNN）模型来估计冠层积雪的存在。截流也通过冠层上方和下方的涡动相关测量以及模式模拟进行了估计。这些方法于2019年1月应用，将二值化结果与人类标记图像的“基本事实”进行比较，以计算平衡精度分数。准确度最高的是CNN的预测。基于平衡精度分数，将选择的方法扩展到估计2018/2019冬季冠层积雪的存在。所有方法都提供了对亚高山森林拦截过程的深入了解，但也存在挑战，包括冠层上方和冠层下方涡动相关测量的通量足迹不同，以及红绿蓝图像无法监测夜间、日出和日落期间的积雪拦截。
{"title":"Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods","authors":"Natasha Harvey, Sean P. Burns, Keith N. Musselman, Holly Barnard, Peter D. Blanken","doi":"10.1029/2023wr036996","DOIUrl":"https://doi.org/10.1029/2023wr036996","url":null,"abstract":"The interception of snow by the canopy is an important process in the water and energy balance in cold-region coniferous forests. Direct measurements of canopy snow interception are difficult at scales larger than individual trees, requiring indirect methods such as eddy covariance, time-lapse photography, or modeling. At the Niwot Ridge Subalpine Forest AmeriFlux site in the Colorado Front Range, USA, we compared methods that estimate or simulate the presence of snow interception. Timelapse photography images were analyzed using thresholding analysis and used to train a Convolutional Neural Network (CNN) model to estimate canopy snow presence. Interception was also estimated from eddy covariance measurements above and below the canopy, as well as from model simulations. These methods were applied over January 2019, with binarized results compared to a “ground truth” of human labeled images to calculate the Balanced Accuracy Score. The highest accuracy was achieved by the CNN predictions. Based on the Balanced Accuracy Scores, select methods were extended to estimate the presence of canopy snow for the 2018/2019 winter. All methods provided insight into the process of interception in a subalpine forest but presented challenges, including differing flux footprints of the above- and below-canopy eddy covariance measurements and the inability of red-green-blue imagery to monitor snow interception at night, during sunrise, and during sunset.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"41 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917580","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 dimensionality of parameters and objectives has been increasing due to the accelerating development of models and monitoring network, which brings potential challenges for calibration. In this study, two common philosophies for multi-objective optimisation in hydrology (the use of aggregated scalar criterion or vector functions) were revisited with different sampling strategies: (a) random sampling, (b) DiffeRential Evolution Adaptive Metropolis (DREAM as an example of an aggregated scalar function), and (c) Non-Dominated Sorting Genetic Algorithm II (NSGA-II as Pareto-based multi-objective optimisation). By testing the ability of algorithms to simultaneously capture soil moisture and soil water isotopes at three depths under four vegetation covers, we found random sampling performed poorly in matching observations due to its inability to explore high-dimensional parameter space. DREAM, in contrast, could provide efficient parameter convergence with informal likelihood functions, but the choice of formal likelihood function is difficult due to the lack of knowledge about model residuals, leading to poor performance. NSGA-II is effective and efficient after aggregating objectives to ≤4, but failed when calibrating against all 24 objectives. Overall, both philosophies and all three approaches are challenged by increasing dimensionality, and it generally requires a degree of trial-and-error before achieving a successful calibration. This suggests the potential to explore a more flexible way to describe model residuals (e.g., by defining limits of acceptability). Alternatively, improvements could be made by using an ensemble of models to represent the system (instead of “best” model) given the average of a calibrated ensemble usually performed better than any individual model.
{"title":"Revising Common Approaches for Calibration: Insights From a 1-D Tracer-Aided Hydrological Model With High-Dimensional Parameters and Objectives","authors":"Songjun Wu, Doerthe Tetzlaff, Chris Soulsby","doi":"10.1029/2024wr037656","DOIUrl":"https://doi.org/10.1029/2024wr037656","url":null,"abstract":"The dimensionality of parameters and objectives has been increasing due to the accelerating development of models and monitoring network, which brings potential challenges for calibration. In this study, two common philosophies for multi-objective optimisation in hydrology (the use of aggregated scalar criterion or vector functions) were revisited with different sampling strategies: (a) random sampling, (b) DiffeRential Evolution Adaptive Metropolis (DREAM as an example of an aggregated scalar function), and (c) Non-Dominated Sorting Genetic Algorithm II (NSGA-II as Pareto-based multi-objective optimisation). By testing the ability of algorithms to simultaneously capture soil moisture and soil water isotopes at three depths under four vegetation covers, we found random sampling performed poorly in matching observations due to its inability to explore high-dimensional parameter space. DREAM, in contrast, could provide efficient parameter convergence with informal likelihood functions, but the choice of formal likelihood function is difficult due to the lack of knowledge about model residuals, leading to poor performance. NSGA-II is effective and efficient after aggregating objectives to ≤4, but failed when calibrating against all 24 objectives. Overall, both philosophies and all three approaches are challenged by increasing dimensionality, and it generally requires a degree of trial-and-error before achieving a successful calibration. This suggests the potential to explore a more flexible way to describe model residuals (e.g., by defining limits of acceptability). Alternatively, improvements could be made by using an ensemble of models to represent the system (instead of “best” model) given the average of a calibrated ensemble usually performed better than any individual model.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"34 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911800","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}
Understanding how the net Solar radiation is partitioned into heat fluxes on land surface is fundamental to understand water, energy, and carbon cycles. Here we claim that, in forests under energy-limited environment, the proportion in the net radiation occupied by the sum of the sensible and latent heat fluxes rarely varies over time; the variability in the latent heat fraction is mostly compensated by that of the sensible heat flux. This mutual compensation is rooted in the energy conservation principle and also in accordance with the principle of Maximum Entropy Production (MEP). The ratio of inertia parameters corresponding to latent and sensible heat fluxes in the MEP-based model, is found approximately the reciprocal Bowen ratio. With this seesaw relationship, the formulation of the MEP-based model for the surface energy partitioning problem is simplified. The new formulation is tested for a wide range of flux tower sites with different biome, demonstrating promising results.
{"title":"Complementary Relationship Among Heat Flux Ratios and Maximum Entropy Production Principle in Humid Forests","authors":"Kwanghun Choi, Kyungrock Paik","doi":"10.1029/2024wr037746","DOIUrl":"https://doi.org/10.1029/2024wr037746","url":null,"abstract":"Understanding how the net Solar radiation is partitioned into heat fluxes on land surface is fundamental to understand water, energy, and carbon cycles. Here we claim that, in forests under energy-limited environment, the proportion in the net radiation occupied by the sum of the sensible and latent heat fluxes rarely varies over time; the variability in the latent heat fraction is mostly compensated by that of the sensible heat flux. This mutual compensation is rooted in the energy conservation principle and also in accordance with the principle of Maximum Entropy Production (MEP). The ratio of inertia parameters corresponding to latent and sensible heat fluxes in the MEP-based model, is found approximately the reciprocal Bowen ratio. With this seesaw relationship, the formulation of the MEP-based model for the surface energy partitioning problem is simplified. The new formulation is tested for a wide range of flux tower sites with different biome, demonstrating promising results.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"45 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905277","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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