Accurate and timely observations of individual-scale transpiration are critical for predicting ecosystem responses to climate change. Existing remote sensing methods for measuring transpiration lack the spatial resolution needed to resolve individual plants, and their sources of uncertainty are not well-constrained. We present two novel approaches for independently quantifying fine-scale transpiration using thermal imagery and a suite of environmental sensors mounted on an unmanned aerial vehicle (UAV) platform. The first is a surface energy balance (SEB) approach designed for fine-scale thermal imagery; the second uses profiles of air temperature (Ta) and humidity (hr) to calculate transpiration from the Bowen Ratio. Both approaches derive the energy equivalent of transpiration, latent heat flux (λE), solely using data acquired from the UAV. We compare the two approaches and their sources of uncertainty using data from several flights at a grassland eddy covariance site in 2021 and 2022 and using typical diurnal conditions to evaluate the uncertainty of λE estimates for each approach. The SEB approach generated independent, UAV-based estimates of λE within ∼20% of eddy covariance measurements and was most sensitive to surface temperature and resistance to heat transfer. λE calculated from the Bowen Ratio approach was ∼30% higher than tower values due to inaccuracies in Ta and hr, the main sources of uncertainty in this approach. The Bowen Ratio approach has a lower overall potential uncertainty, indicating its potential for improvement over the SEB approach. Our results are the first physically-based observations of transpiration derived solely from a UAV platform, with no ancillary data inputs.
{"title":"Estimating Fine-Scale Transpiration From UAV-Derived Thermal Imagery and Atmospheric Profiles","authors":"Bryn E. Morgan, Kelly K. Caylor","doi":"10.1029/2023wr035251","DOIUrl":"https://doi.org/10.1029/2023wr035251","url":null,"abstract":"Accurate and timely observations of individual-scale transpiration are critical for predicting ecosystem responses to climate change. Existing remote sensing methods for measuring transpiration lack the spatial resolution needed to resolve individual plants, and their sources of uncertainty are not well-constrained. We present two novel approaches for independently quantifying fine-scale transpiration using thermal imagery and a suite of environmental sensors mounted on an unmanned aerial vehicle (UAV) platform. The first is a surface energy balance (SEB) approach designed for fine-scale thermal imagery; the second uses profiles of air temperature (<i>T</i><sub><i>a</i></sub>) and humidity (<i>h</i><sub><i>r</i></sub>) to calculate transpiration from the Bowen Ratio. Both approaches derive the energy equivalent of transpiration, latent heat flux (<i>λE</i>), solely using data acquired from the UAV. We compare the two approaches and their sources of uncertainty using data from several flights at a grassland eddy covariance site in 2021 and 2022 and using typical diurnal conditions to evaluate the uncertainty of <i>λE</i> estimates for each approach. The SEB approach generated independent, UAV-based estimates of <i>λE</i> within ∼20% of eddy covariance measurements and was most sensitive to surface temperature and resistance to heat transfer. <i>λE</i> calculated from the Bowen Ratio approach was ∼30% higher than tower values due to inaccuracies in <i>T</i><sub><i>a</i></sub> and <i>h</i><sub><i>r</i></sub>, the main sources of uncertainty in this approach. The Bowen Ratio approach has a lower overall potential uncertainty, indicating its potential for improvement over the SEB approach. Our results are the first physically-based observations of transpiration derived solely from a UAV platform, with no ancillary data inputs.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 2","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398405","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}
N. K. LeRoux, S. K. Frey, D. R. Lapen, J. A. Guimond, B. L. Kurylyk
Climate change will increase sea levels, driving saltwater into coastal aquifers and impacting coastal communities and land use viability. Coastal aquifers are also impacted by tides that control groundwater-ocean interactions and maintain an “upper saline plume” (USP) of brackish groundwater. Coastal dikes are designed to limit the surface impacts of high-amplitude tides, but, due to ongoing sea-level rise (SLR), low-lying dikelands and underlying aquifers are becoming increasingly vulnerable to flooding from high tides and storm surges. This study combines field observations with numerical modeling to investigate ocean-aquifer mixing and future saltwater intrusion dynamics in a mega-tidal (tidal range >8 m) dikeland along the Bay of Fundy in Atlantic Canada. Field data revealed strong connectivity between the ocean and coastal aquifer, as evidenced by pronounced tidal oscillations in deeper groundwater heads and an order of magnitude intra-tidal change in subsurface electrical resistivity. Numerical model results indicate that SLR and surges will force the migration of the USP landward, amplifying salinization of freshwater resources. Simulated storm surges can overtop the dike, contaminating agricultural soils. The presence of dikes decreased salinization under low surge scenarios, but increased salinization under larger overtopping scenarios due to landward ponding of seawater behind the dike. Mega-tidal conditions maintain a large USP and impact aquifer freshening rates. Results highlight the vulnerability of terrestrial soil landscapes and freshwater resources to climate change and suggest that the subsurface impacts of dike management decisions should be considered in addition to protection measures associated with surface saltwater intrusion processes.
{"title":"Mega-Tidal and Surface Flooding Controls on Coastal Groundwater and Saltwater Intrusion Within Agricultural Dikelands","authors":"N. K. LeRoux, S. K. Frey, D. R. Lapen, J. A. Guimond, B. L. Kurylyk","doi":"10.1029/2023wr035054","DOIUrl":"https://doi.org/10.1029/2023wr035054","url":null,"abstract":"Climate change will increase sea levels, driving saltwater into coastal aquifers and impacting coastal communities and land use viability. Coastal aquifers are also impacted by tides that control groundwater-ocean interactions and maintain an “upper saline plume” (USP) of brackish groundwater. Coastal dikes are designed to limit the surface impacts of high-amplitude tides, but, due to ongoing sea-level rise (SLR), low-lying dikelands and underlying aquifers are becoming increasingly vulnerable to flooding from high tides and storm surges. This study combines field observations with numerical modeling to investigate ocean-aquifer mixing and future saltwater intrusion dynamics in a mega-tidal (tidal range >8 m) dikeland along the Bay of Fundy in Atlantic Canada. Field data revealed strong connectivity between the ocean and coastal aquifer, as evidenced by pronounced tidal oscillations in deeper groundwater heads and an order of magnitude intra-tidal change in subsurface electrical resistivity. Numerical model results indicate that SLR and surges will force the migration of the USP landward, amplifying salinization of freshwater resources. Simulated storm surges can overtop the dike, contaminating agricultural soils. The presence of dikes decreased salinization under low surge scenarios, but increased salinization under larger overtopping scenarios due to landward ponding of seawater behind the dike. Mega-tidal conditions maintain a large USP and impact aquifer freshening rates. Results highlight the vulnerability of terrestrial soil landscapes and freshwater resources to climate change and suggest that the subsurface impacts of dike management decisions should be considered in addition to protection measures associated with surface saltwater intrusion processes.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398444","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}
Eutrophication is one of the largest threats to aquatic ecosystems and chlorophyll a measurements are relevant indicators of trophic state and algal abundance. Many studies have modeled chlorophyll a in rivers but model development and testing has largely occurred at individual sites which hampers creating generalized models capable of making broad-scale predictions. To address this gap, we compiled a large data set of chlorophyll a concentrations matched to other water quality, meteorological, and reach characteristic data for a diverse set of 82 streams and rivers across the United States. We used this data set and extreme gradient boosting, a tree-based machine learning algorithm, to predict daily chlorophyll a concentrations. Furthermore, we tested several practical considerations of broad-scale models, such as making predictions at sites not included in model training or the utility of in situ water quality data versus universally available remotely estimated model inputs. Predictions were very strongly correlated to observations when compared against a randomly withheld subset of days; however, the model had lower accuracy when applied to completely novel sites withheld from model training. Turbidity and total nitrogen were the two most important variables for predicting chlorophyll a. Although in situ variables improved modeled estimates and were identified as more important during model interpretation, using only remote inputs still resulted in highly correlated predictions with small bias. Testing a model across many sites allowed for identification of common variables relevant to chlorophyll a and highlighted several challenges for applying data-driven models to new sites or at larger spatial scales.
{"title":"Predicting Daily River Chlorophyll Concentrations at a Continental Scale","authors":"Philip Savoy, Judson W. Harvey","doi":"10.1029/2022wr034215","DOIUrl":"https://doi.org/10.1029/2022wr034215","url":null,"abstract":"Eutrophication is one of the largest threats to aquatic ecosystems and chlorophyll <i>a</i> measurements are relevant indicators of trophic state and algal abundance. Many studies have modeled chlorophyll <i>a</i> in rivers but model development and testing has largely occurred at individual sites which hampers creating generalized models capable of making broad-scale predictions. To address this gap, we compiled a large data set of chlorophyll <i>a</i> concentrations matched to other water quality, meteorological, and reach characteristic data for a diverse set of 82 streams and rivers across the United States. We used this data set and extreme gradient boosting, a tree-based machine learning algorithm, to predict daily chlorophyll <i>a</i> concentrations. Furthermore, we tested several practical considerations of broad-scale models, such as making predictions at sites not included in model training or the utility of in situ water quality data versus universally available remotely estimated model inputs. Predictions were very strongly correlated to observations when compared against a randomly withheld subset of days; however, the model had lower accuracy when applied to completely novel sites withheld from model training. Turbidity and total nitrogen were the two most important variables for predicting chlorophyll <i>a</i>. Although in situ variables improved modeled estimates and were identified as more important during model interpretation, using only remote inputs still resulted in highly correlated predictions with small bias. Testing a model across many sites allowed for identification of common variables relevant to chlorophyll <i>a</i> and highlighted several challenges for applying data-driven models to new sites or at larger spatial scales.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"58 12","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398445","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}
Alfonso Senatore, Giuseppina A. Corrente, Eugenio L. Argento, Jessica Castagna, Massimo Micieli, Giuseppe Mendicino, Amerigo Beneduci, Gianluca Botter
This study investigates the spatial and temporal dynamics of Dissolved Organic carbon (DOC) concentration in a Mediterranean headwater catchment (Turbolo River catchment, southern Italy) equipped with two multi-parameter sondes providing more than two-year (May 2019–November 2021) continuous high-frequency measurements of several DOC-related parameters. The sondes were installed in two nested sections, a quasi-pristine upstream sub-catchment and a downstream outlet with anthropogenic water quality disturbances. DOC estimates were achieved by correcting the fluorescent dissolved organic matter—fDOM—values through an original procedure not requiring extensive laboratory measurements. Then, DOC dynamics at the seasonal and storm event scales were analyzed. At the seasonal scale, results confirmed the climate control on DOC production, with increasing background concentrations in hot and dry summer months. The hydrological regulation proved crucial for DOC mobilization and export, with the top 10th percentile of discharge associated with up to 79% of the total DOC yield. The analysis at the storm scale using flushing and hysteresis indices highlighted substantial differences between the two catchments. In the steeper upstream catchment, the limited capability of preserving hydraulic connection over time with DOC sources determined the prevalence of transport as the limiting factor to DOC export. In the downstream catchment, transport- and source-limited processes were observed almost equally. The correlation between the hysteretic behavior and antecedent precipitation was not linear since the process reverted to transport-limited for high accumulated rainfall values. Exploiting high-resolution measurements, the study provided insights into DOC export dynamics in nested headwater catchments at multiple time scales.
{"title":"Seasonal and Storm Event-Based Dynamics of Dissolved Organic Carbon (DOC) Concentration in a Mediterranean Headwater Catchment","authors":"Alfonso Senatore, Giuseppina A. Corrente, Eugenio L. Argento, Jessica Castagna, Massimo Micieli, Giuseppe Mendicino, Amerigo Beneduci, Gianluca Botter","doi":"10.1029/2022wr034397","DOIUrl":"https://doi.org/10.1029/2022wr034397","url":null,"abstract":"This study investigates the spatial and temporal dynamics of Dissolved Organic carbon (DOC) concentration in a Mediterranean headwater catchment (Turbolo River catchment, southern Italy) equipped with two multi-parameter sondes providing more than two-year (May 2019–November 2021) continuous high-frequency measurements of several DOC-related parameters. The sondes were installed in two nested sections, a quasi-pristine upstream sub-catchment and a downstream outlet with anthropogenic water quality disturbances. DOC estimates were achieved by correcting the fluorescent dissolved organic matter—<i>f</i>DOM—values through an original procedure not requiring extensive laboratory measurements. Then, DOC dynamics at the seasonal and storm event scales were analyzed. At the seasonal scale, results confirmed the climate control on DOC production, with increasing background concentrations in hot and dry summer months. The hydrological regulation proved crucial for DOC mobilization and export, with the top 10th percentile of discharge associated with up to 79% of the total DOC yield. The analysis at the storm scale using flushing and hysteresis indices highlighted substantial differences between the two catchments. In the steeper upstream catchment, the limited capability of preserving hydraulic connection over time with DOC sources determined the prevalence of transport as the limiting factor to DOC export. In the downstream catchment, transport- and source-limited processes were observed almost equally. The correlation between the hysteretic behavior and antecedent precipitation was not linear since the process reverted to transport-limited for high accumulated rainfall values. Exploiting high-resolution measurements, the study provided insights into DOC export dynamics in nested headwater catchments at multiple time scales.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 5","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398402","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}
Xinchun Yang, Wei You, Siyuan Tian, Zhongshan Jiang, Xiangyu Wan
The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized global terrestrial water storage anomalies (TWSA) measurements. However, the 11-month data gap between the two GRACE missions disrupts the measurement continuity and limits its further applications. Previous attempts to fill this data gap require further improvement in terms of method robustness and product quality. Here, we propose a novel two-step linear model using precipitation, temperature data, and hydrological model-simulated TWSA as predictors to fill the 11-month data gap between the two GRACE missions and generate six global gridded GRACE-like TWSA products from April 2002 to July 2021. These products are evaluated at grid scale globally and also basin scale for the world's largest 72 river basins. Results indicate that our GRACE-like data show great consistency with the GRACE/GRACE-FO observations. While most basins exhibit consistent performance across the six GRACE-like TWSA products, certain areas with lower signal-to-noise ratios show significant variability. Furthermore, we assess the performance of our GRACE-like data during the data gap using one previous reconstruction, a hydrological model simulation, and the Swarm satellite measurement. The results confirm that our GRACE-like data exhibit equivalent performance within and outside the data gap. This study introduces a more simple and robust method for predicting the missing data between the two GRACE missions and provides readily applicable continuous GRACE-like TWSA products for hydrologic applications.
{"title":"A Two-Step Linear Model to Fill the Data Gap Between GRACE and GRACE-FO Terrestrial Water Storage Anomalies","authors":"Xinchun Yang, Wei You, Siyuan Tian, Zhongshan Jiang, Xiangyu Wan","doi":"10.1029/2022wr034139","DOIUrl":"https://doi.org/10.1029/2022wr034139","url":null,"abstract":"The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized global terrestrial water storage anomalies (TWSA) measurements. However, the 11-month data gap between the two GRACE missions disrupts the measurement continuity and limits its further applications. Previous attempts to fill this data gap require further improvement in terms of method robustness and product quality. Here, we propose a novel two-step linear model using precipitation, temperature data, and hydrological model-simulated TWSA as predictors to fill the 11-month data gap between the two GRACE missions and generate six global gridded GRACE-like TWSA products from April 2002 to July 2021. These products are evaluated at grid scale globally and also basin scale for the world's largest 72 river basins. Results indicate that our GRACE-like data show great consistency with the GRACE/GRACE-FO observations. While most basins exhibit consistent performance across the six GRACE-like TWSA products, certain areas with lower signal-to-noise ratios show significant variability. Furthermore, we assess the performance of our GRACE-like data during the data gap using one previous reconstruction, a hydrological model simulation, and the Swarm satellite measurement. The results confirm that our GRACE-like data exhibit equivalent performance within and outside the data gap. This study introduces a more simple and robust method for predicting the missing data between the two GRACE missions and provides readily applicable continuous GRACE-like TWSA products for hydrologic applications.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"48 7","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92290973","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}
Shan Zheng, Chenge An, Hualin Wang, Lingyun Li, Fei Wang, Marwan A. Hassan
Rivers disrupted by sediment cutoff often experience degradation, but the migration of the erosion center, defined as the location with the greatest degradation rates, has not been thoroughly understood. This paper focuses on the streamwise migration of the erosion center along the ∼400-km-long Yichang to Chenglingji reach (YCR) downstream of the Three Gorges Dam (TGD), China. We analyzed channel morphological adjustment based on water, sediment and channel geometry data collected during 2002–2020. Based on the location and time for the occurrence of relatively large channel degradation, a clustering algorithm was used to identify the location of the erosion center. Characteristics and morphodynamic controls of the erosion centers were studied based on the migration of incisional and coarsening waves simulated by a one-dimensional morphodynamic model for nonuniform sediment. Results show that the erosion center migrated downstream along the Yichang-Zhicheng reach with gravel-sand bed during 2002–2012, the migration rate was rapid after the dam closure then decreased with time. After ∼2012, large cascade dams started to operate along the upper Yangtze River, sediment load further decreased and degradation accelerated at the YCR. Correspondingly, the erosion center migrated to the sand-bedded upper Jingjiang reach with faster rates. The erosion center migrated for a total of over 200 km with an average rate of ∼14 km/yr during 2002–2020. The underlying gravel layer was exposed due to degradation, which enhanced bed coarsening and resulted in the propagation of the erosion center downstream of the TGD.
{"title":"The Migration of the Erosion Center Downstream of the Three Gorges Dam, China, and the Role Played by Underlying Gravel Layer","authors":"Shan Zheng, Chenge An, Hualin Wang, Lingyun Li, Fei Wang, Marwan A. Hassan","doi":"10.1029/2022wr034152","DOIUrl":"https://doi.org/10.1029/2022wr034152","url":null,"abstract":"Rivers disrupted by sediment cutoff often experience degradation, but the migration of the erosion center, defined as the location with the greatest degradation rates, has not been thoroughly understood. This paper focuses on the streamwise migration of the erosion center along the ∼400-km-long Yichang to Chenglingji reach (YCR) downstream of the Three Gorges Dam (TGD), China. We analyzed channel morphological adjustment based on water, sediment and channel geometry data collected during 2002–2020. Based on the location and time for the occurrence of relatively large channel degradation, a clustering algorithm was used to identify the location of the erosion center. Characteristics and morphodynamic controls of the erosion centers were studied based on the migration of incisional and coarsening waves simulated by a one-dimensional morphodynamic model for nonuniform sediment. Results show that the erosion center migrated downstream along the Yichang-Zhicheng reach with gravel-sand bed during 2002–2012, the migration rate was rapid after the dam closure then decreased with time. After ∼2012, large cascade dams started to operate along the upper Yangtze River, sediment load further decreased and degradation accelerated at the YCR. Correspondingly, the erosion center migrated to the sand-bedded upper Jingjiang reach with faster rates. The erosion center migrated for a total of over 200 km with an average rate of ∼14 km/yr during 2002–2020. The underlying gravel layer was exposed due to degradation, which enhanced bed coarsening and resulted in the propagation of the erosion center downstream of the TGD.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"52 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92290998","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}
C. M. Stephens, L. E. Band, F. M. Johnson, L. A. Marshall, B. E. Medlyn, M. G. De Kauwe, A. M. Ukkola
Much attention has been given to the disproportionate streamflow deficits (relative to rainfall deficits) experienced by many catchments during the Millennium Drought (1998–2009) in southeastern Australia, along with lack of post-drought streamflow recovery in some cases. However, mechanisms behind the coupled hydrologic and ecosystem dynamics are poorly understood. We applied a process-based ecohydrologic model (RHESSys) in a Melbourne water supply catchment to examine changes in ecohydrologic behavior during and after the drought. Our simulations suggested that average transpiration (green water) was maintained under drought despite a substantial (12%) decrease in average rainfall, meaning that the entire rainfall deficit translated to reduced streamflow (blue water). Altered spatial patterns of vegetation behavior across the terrain helped the ecosystem maintain this unexpectedly high green water use. Decreased transpiration upland was compensated by increases in the riparian zone, which was less water limited and therefore able to meet higher water demand during drought. In the post-drought period, we found greater transpiration and reduced subsurface water storage relative to pre-drought, suggesting a longer-term persistence in altered water partitioning. The post-drought outcome was attributed to a combination of warmer climate and the persisting effects of the drought on nutrient availability. Given the importance of shifting ecohydrologic patterns across space, our results raise concerns for applying lumped conceptual hydrologic models under nonstationary or extreme conditions. Additionally, the processes we identified have important implications for water supply in Australia's second largest city under projected drying.
{"title":"Changes in Blue/Green Water Partitioning Under Severe Drought","authors":"C. M. Stephens, L. E. Band, F. M. Johnson, L. A. Marshall, B. E. Medlyn, M. G. De Kauwe, A. M. Ukkola","doi":"10.1029/2022wr033449","DOIUrl":"https://doi.org/10.1029/2022wr033449","url":null,"abstract":"Much attention has been given to the disproportionate streamflow deficits (relative to rainfall deficits) experienced by many catchments during the Millennium Drought (1998–2009) in southeastern Australia, along with lack of post-drought streamflow recovery in some cases. However, mechanisms behind the coupled hydrologic and ecosystem dynamics are poorly understood. We applied a process-based ecohydrologic model (RHESSys) in a Melbourne water supply catchment to examine changes in ecohydrologic behavior during and after the drought. Our simulations suggested that average transpiration (green water) was maintained under drought despite a substantial (12%) decrease in average rainfall, meaning that the entire rainfall deficit translated to reduced streamflow (blue water). Altered spatial patterns of vegetation behavior across the terrain helped the ecosystem maintain this unexpectedly high green water use. Decreased transpiration upland was compensated by increases in the riparian zone, which was less water limited and therefore able to meet higher water demand during drought. In the post-drought period, we found greater transpiration and reduced subsurface water storage relative to pre-drought, suggesting a longer-term persistence in altered water partitioning. The post-drought outcome was attributed to a combination of warmer climate and the persisting effects of the drought on nutrient availability. Given the importance of shifting ecohydrologic patterns across space, our results raise concerns for applying lumped conceptual hydrologic models under nonstationary or extreme conditions. Additionally, the processes we identified have important implications for water supply in Australia's second largest city under projected drying.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 6","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398401","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 San Joaquin Valley, California has experienced dramatic subsidence over the past 100 years, but the regions with the most subsidence have shifted dramatically over this time period, from west (Kettleman City/Los Banos) to south (Tulare/Pixley/Corcoran). To date, no study has done an in-depth analysis of the mechanisms driving this shift in subsidence. We analyze head records, utilizing a novel approach that assimilates change in head data from multiple overlapping time periods, to produce an 80-year record of change in head over both the historical and modern regions of greatest subsidence. We then calibrate a deformation model to fit both historical (measured with leveling surveys) and modern (measured with Interferometric Synthetic Aperture Radar, or InSAR) data sets. We find that the stress history of the Kettleman City/Los Banos region with historically high subsidence plays a large role in reducing modern subsidence in that region, while declining heads in both regions are likely to result in major subsidence over the next several decades. This study highlights the need for active groundwater management to mitigate ongoing and future subsidence. One key data set needed in this effort is accurate long-term head histories to reconstruct the stress history of aquifers for accurate deformation modeling.
{"title":"Aquifer Stress History Contributes to Historic Shift in Subsidence in the San Joaquin Valley, California","authors":"Ryan Smith","doi":"10.1029/2023wr035804","DOIUrl":"https://doi.org/10.1029/2023wr035804","url":null,"abstract":"The San Joaquin Valley, California has experienced dramatic subsidence over the past 100 years, but the regions with the most subsidence have shifted dramatically over this time period, from west (Kettleman City/Los Banos) to south (Tulare/Pixley/Corcoran). To date, no study has done an in-depth analysis of the mechanisms driving this shift in subsidence. We analyze head records, utilizing a novel approach that assimilates change in head data from multiple overlapping time periods, to produce an 80-year record of change in head over both the historical and modern regions of greatest subsidence. We then calibrate a deformation model to fit both historical (measured with leveling surveys) and modern (measured with Interferometric Synthetic Aperture Radar, or InSAR) data sets. We find that the stress history of the Kettleman City/Los Banos region with historically high subsidence plays a large role in reducing modern subsidence in that region, while declining heads in both regions are likely to result in major subsidence over the next several decades. This study highlights the need for active groundwater management to mitigate ongoing and future subsidence. One key data set needed in this effort is accurate long-term head histories to reconstruct the stress history of aquifers for accurate deformation modeling.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"59 7","pages":""},"PeriodicalIF":5.4,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91398400","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}
Aline Meyer Oliveira, H. J. (Ilja) van Meerveld, Marc Vis, Jan Seibert
Abstract For many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat‐based surface water extent observations can inform the calibration of a lumped bucket‐type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April–October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely‐available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher‐resolution imagery will be helpful for model calibration for more sites, especially when time series length increases.
{"title":"Assessment of the Value of Remotely Sensed Surface Water Extent Data for the Calibration of a Lumped Hydrological Model","authors":"Aline Meyer Oliveira, H. J. (Ilja) van Meerveld, Marc Vis, Jan Seibert","doi":"10.1029/2023wr034875","DOIUrl":"https://doi.org/10.1029/2023wr034875","url":null,"abstract":"Abstract For many catchments, there is insufficient field data to calibrate the hydrological models that are needed to answer water resources management questions. One way to overcome this lack of data is to use remotely sensed data. In this study, we assess whether Landsat‐based surface water extent observations can inform the calibration of a lumped bucket‐type model for Brazilian catchments. We first performed synthetic experiments with daily, monthly, and limited monthly data (April–October), assuming a perfect monotonic relation between streamflow and stream width. The median relative performance was 0.35 for daily data and 0.17 for monthly data, where values above 0 imply an improvement in model performance compared to the lower benchmark. This indicates that the limited temporal resolution of remotely sensed data is not an impediment for model calibration. In a second step, we used real remotely sensed water extent data for calibration. For only 76 of the 671 sites the remotely sensed water extent was large and variable enough to be used for model calibration. For 30% of these sites, calibration with the actual remotely sensed water extent data led to a model fit that was better than the lower benchmark (i.e., relative performance >0). Model performance increased with river width and variation therein. This indicates that the coarse spatial resolution of the freely‐available, long time series of water extent used in this study hampered model calibration. We, therefore, expect that newer higher‐resolution imagery will be helpful for model calibration for more sites, especially when time series length increases.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"29 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135454861","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}
Abstract In recent years, the use of deep learning methods has rapidly increased in many research fields. Similarly, they have become a powerful tool within the climate scientific community. Deep learning methods have been successfully applied for different tasks, such as the identification of atmospheric patterns, weather extreme classification, or weather forecasting. However, due to the inherent complexity of atmospheric processes, the ability of deep learning models to simulate natural processes, particularly in the case of weather extremes, is still challenging. Therefore, a thorough evaluation of their performance and robustness in predicting precipitation fields is still needed, especially for extreme precipitation events, which can have devastating consequences in terms of infrastructure damage, economic losses, and even loss of life. In this study, we present a comprehensive evaluation of a set of deep learning architectures to simulate precipitation, including heavy precipitation events (>95th percentile) and extreme events (>99th percentile) over the European domain. Among the architectures analyzed here, the U‐Net network was found to be superior and outperformed the other networks in simulating precipitation events. In particular, we found that a simplified version of the original U‐Net with two encoder‐decoder levels generally achieved similar skill scores than deeper versions for predicting precipitation extremes, while significantly reducing the overall complexity and computing resources. We further assess how the model predicts through the attribution heatmaps from a layer‐wise relevance propagation explainability method.
{"title":"Intercomparison of deep learning architectures for the prediction of precipitation fields with a focus on extremes","authors":"Noelia Otero, Pascal Horton","doi":"10.1029/2023wr035088","DOIUrl":"https://doi.org/10.1029/2023wr035088","url":null,"abstract":"Abstract In recent years, the use of deep learning methods has rapidly increased in many research fields. Similarly, they have become a powerful tool within the climate scientific community. Deep learning methods have been successfully applied for different tasks, such as the identification of atmospheric patterns, weather extreme classification, or weather forecasting. However, due to the inherent complexity of atmospheric processes, the ability of deep learning models to simulate natural processes, particularly in the case of weather extremes, is still challenging. Therefore, a thorough evaluation of their performance and robustness in predicting precipitation fields is still needed, especially for extreme precipitation events, which can have devastating consequences in terms of infrastructure damage, economic losses, and even loss of life. In this study, we present a comprehensive evaluation of a set of deep learning architectures to simulate precipitation, including heavy precipitation events (>95th percentile) and extreme events (>99th percentile) over the European domain. Among the architectures analyzed here, the U‐Net network was found to be superior and outperformed the other networks in simulating precipitation events. In particular, we found that a simplified version of the original U‐Net with two encoder‐decoder levels generally achieved similar skill scores than deeper versions for predicting precipitation extremes, while significantly reducing the overall complexity and computing resources. We further assess how the model predicts through the attribution heatmaps from a layer‐wise relevance propagation explainability method.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"188 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136371661","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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