Excessively low stream flows harm ecosystems and societies, so two key goals of low-flow hydrology are to understand their drivers and to predict their severity and frequency. We show that linear regressions can accomplish both goals across diverse catchments. We analyze 230 unregulated moderate to high relief catchments across rainfall-dominated, hybrid, snowmelt-dominated, and glacial regimes in British Columbia, Canada, with drainage areas spanning 5 orders of magnitude from 0.5 to 55,000 km2. Summer low flows are decreasing in rainfall-dominated and hybrid catchments but have been stable in catchments that remain snowmelt or glacial-dominated. However, we find that since 1950 approximately one third of snowmelt-dominated catchments have transitioned to a hybrid rain-snow regime. The declines in rainfall-dominated and hybrid catchments are dominantly driven by summer precipitation and temperature, and only weakly influenced by winter storage. We apply this understanding to create regression models that predict the minimum summer flow using monthly temperature and precipitation data. These models outperform distributed process-based models for every common goodness-of-fit metric; the performance improvement is mostly a result of abandoning the requirement to simulate all parts of the annual hydrograph. Using these regression models we reconstruct streamflow droughts and low flow anomalies from 1901 to 2022. We reproduce recent drying trends in rainfall-dominated and hybrid catchments, but also show that present conditions are comparable to those seen one hundred years ago. However, anomalously low flows last century were caused by large precipitation deficits while current declines are driven by rising summer temperatures despite near-normal precipitation.
{"title":"Rising Temperatures Drive Lower Summer Minimum Flows Across Hydrologically Diverse Catchments in British Columbia","authors":"S. W. Ruzzante, T. Gleeson","doi":"10.1029/2024wr038057","DOIUrl":"https://doi.org/10.1029/2024wr038057","url":null,"abstract":"Excessively low stream flows harm ecosystems and societies, so two key goals of low-flow hydrology are to understand their drivers and to predict their severity and frequency. We show that linear regressions can accomplish both goals across diverse catchments. We analyze 230 unregulated moderate to high relief catchments across rainfall-dominated, hybrid, snowmelt-dominated, and glacial regimes in British Columbia, Canada, with drainage areas spanning 5 orders of magnitude from 0.5 to 55,000 km<sup>2</sup>. Summer low flows are decreasing in rainfall-dominated and hybrid catchments but have been stable in catchments that remain snowmelt or glacial-dominated. However, we find that since 1950 approximately one third of snowmelt-dominated catchments have transitioned to a hybrid rain-snow regime. The declines in rainfall-dominated and hybrid catchments are dominantly driven by summer precipitation and temperature, and only weakly influenced by winter storage. We apply this understanding to create regression models that predict the minimum summer flow using monthly temperature and precipitation data. These models outperform distributed process-based models for every common goodness-of-fit metric; the performance improvement is mostly a result of abandoning the requirement to simulate all parts of the annual hydrograph. Using these regression models we reconstruct streamflow droughts and low flow anomalies from 1901 to 2022. We reproduce recent drying trends in rainfall-dominated and hybrid catchments, but also show that present conditions are comparable to those seen one hundred years ago. However, anomalously low flows last century were caused by large precipitation deficits while current declines are driven by rising summer temperatures despite near-normal precipitation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486037","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}
Fupeng Li, Anne Springer, Jürgen Kusche, Benjamin D. Gutknecht, Yorck Ewerdwalbesloh
Hydrological Models face limitations in simulating the water cycle due to deficiencies in process representation and such problems also weaken their forecasting skills. Here, we use Machine Learning (ML) to forecast the Gravity Recovery and Climate Experiment (GRACE) derived total water storage anomaly (TWSA) up to 1 year ahead over Europe with near real-time meteorological observations as predictors. Subsequently, we assimilate the forecasted and GRACE TWSA into the Community Land Model (CLM) to enhance its performance in both reanalysis and forecast. As found in five hindcast experiments, ML forecasted TWSA for the following year fits quite well to the actual GRACE observations over Europe, with an average correlation of 0.91, 0.92, and 0.94 in the Iberian peninsula, Danube, and Volga basins. Validation by observations and reanalysis data suggests that assimilating forecasted TWSA can improve CLM's capacity to forecast not only hydrological states but also hydrological droughts. Additionally, ML forecasted TWSA is a viable alternative to GRACE data in terms of enhancing hydrological forecasting on seasonal to annual scales through Data assimilation (DA). We also highlight the contribution of GRACE DA for generating a CLM based TWSA reanalysis that overcomes deficiencies of purely model-based TWSA. This study suggests that seasonal drought or water resource forecasting services might not only consider to integrate GRACE TWSA but would also benefit from constraining models with ML-forecasted TWSA. At shorter timescales, such forecasts could also be useful in the quick-look analysis of near real-time TWSA processing as is suggested for upcoming satellite gravity missions.
{"title":"Reanalysis and Forecasting of Total Water Storage and Hydrological States by Combining Machine Learning With CLM Model Simulations and GRACE Data Assimilation","authors":"Fupeng Li, Anne Springer, Jürgen Kusche, Benjamin D. Gutknecht, Yorck Ewerdwalbesloh","doi":"10.1029/2024wr037926","DOIUrl":"https://doi.org/10.1029/2024wr037926","url":null,"abstract":"Hydrological Models face limitations in simulating the water cycle due to deficiencies in process representation and such problems also weaken their forecasting skills. Here, we use Machine Learning (ML) to forecast the Gravity Recovery and Climate Experiment (GRACE) derived total water storage anomaly (TWSA) up to 1 year ahead over Europe with near real-time meteorological observations as predictors. Subsequently, we assimilate the forecasted and GRACE TWSA into the Community Land Model (CLM) to enhance its performance in both reanalysis and forecast. As found in five hindcast experiments, ML forecasted TWSA for the following year fits quite well to the actual GRACE observations over Europe, with an average correlation of 0.91, 0.92, and 0.94 in the Iberian peninsula, Danube, and Volga basins. Validation by observations and reanalysis data suggests that assimilating forecasted TWSA can improve CLM's capacity to forecast not only hydrological states but also hydrological droughts. Additionally, ML forecasted TWSA is a viable alternative to GRACE data in terms of enhancing hydrological forecasting on seasonal to annual scales through Data assimilation (DA). We also highlight the contribution of GRACE DA for generating a CLM based TWSA reanalysis that overcomes deficiencies of purely model-based TWSA. This study suggests that seasonal drought or water resource forecasting services might not only consider to integrate GRACE TWSA but would also benefit from constraining models with ML-forecasted TWSA. At shorter timescales, such forecasts could also be useful in the quick-look analysis of near real-time TWSA processing as is suggested for upcoming satellite gravity missions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"14 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486034","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}
Hrishikesh Kumar, Tajdarul Hassan Syed, Falk Amelung, Sara Mirzaee, A. S. Venkatesh, Ritesh Agrawal
While most recent assessments of groundwater resources disclose drastic overexploitation in the Northwestern parts of India, for the first time, we reveal that effective regulatory measures have resulted in substantial recovery of heavily stressed aquifer systems in India's capital (Delhi). We use advanced InSAR techniques to derive high-quality vertical displacement time series for October 2014–October 2023. Our results reveal a halting of subsidence since mid-2016 in the Dwarka area and subsequent rebound of the aquifer system by 5–10 cm at an uplift rate reaching ∼2 cm/year. Even the subsidence zone located north of Gurgaon, which subsided by more than 1 m during the study period, exhibits exponential decay of subsidence. A significant reduction in the magnitude of subsidence in the central (from 15 to 7 cm/year) and southern parts (from 7 to 2 cm/year) is observed during 2019−October 2023 as compared to November 2014−18. In contrast, the subsidence rate in Faridabad, located outside the administrative boundary of Delhi, increased by 2 cm/year from August 2017 onwards. Our analysis suggests a gain in groundwater storage (0.002–0.007 km3/year) and the onset of pore pressure saturation due to groundwater level recovery in the Dwarka area. The decay of subsidence in the subsidence zone near Gurgaon suggests reduced groundwater extraction/enhanced recharge. The recovery of groundwater levels by more than 1.5 m over the entire Delhi is evident from 2018 onwards despite decreasing rainfall trend and is attributed to improved groundwater management.
{"title":"InSAR Reveals Recovery of Stressed Aquifer Systems in Parts of Delhi, India: Evidence for Improved Groundwater Management","authors":"Hrishikesh Kumar, Tajdarul Hassan Syed, Falk Amelung, Sara Mirzaee, A. S. Venkatesh, Ritesh Agrawal","doi":"10.1029/2024wr037704","DOIUrl":"https://doi.org/10.1029/2024wr037704","url":null,"abstract":"While most recent assessments of groundwater resources disclose drastic overexploitation in the Northwestern parts of India, for the first time, we reveal that effective regulatory measures have resulted in substantial recovery of heavily stressed aquifer systems in India's capital (Delhi). We use advanced InSAR techniques to derive high-quality vertical displacement time series for October 2014–October 2023. Our results reveal a halting of subsidence since mid-2016 in the Dwarka area and subsequent rebound of the aquifer system by 5–10 cm at an uplift rate reaching ∼2 cm/year. Even the subsidence zone located north of Gurgaon, which subsided by more than 1 m during the study period, exhibits exponential decay of subsidence. A significant reduction in the magnitude of subsidence in the central (from 15 to 7 cm/year) and southern parts (from 7 to 2 cm/year) is observed during 2019−October 2023 as compared to November 2014−18. In contrast, the subsidence rate in Faridabad, located outside the administrative boundary of Delhi, increased by 2 cm/year from August 2017 onwards. Our analysis suggests a gain in groundwater storage (0.002–0.007 km<sup>3</sup>/year) and the onset of pore pressure saturation due to groundwater level recovery in the Dwarka area. The decay of subsidence in the subsidence zone near Gurgaon suggests reduced groundwater extraction/enhanced recharge. The recovery of groundwater levels by more than 1.5 m over the entire Delhi is evident from 2018 onwards despite decreasing rainfall trend and is attributed to improved groundwater management.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"85 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486069","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}
Contradictory views are still existing on the dominating drivers and the underlying mechanisms for the overall increasing evapotranspiration (ET) in China, a region has undergone substantial vegetation and climate changes. Particularly, some studies conclude that climate change is the dominating factor, while other researchers believe that it is the vegetation change. To fill this knowledge gap, here we developed a physical-based ET model by combining the modified Penman–Monteith model and a newly developed canopy resistance model, which effectively links ET and its potential drivers, with the mean correlation and relative RMSE between the observed and modeled canopy resistance being 0.83 ± 0.09 and 3.4 ± 1.6%, respectively. The reliability of the model was also demonstrated by comparing the derived sensitivity of canopy resistance to air CO2 concentration (mean of 0.14 ± 0.03% ppm−1) and the observations (∼0.15% ppm−1). Based on this model and a scenario analysis approach, we demonstrated that vegetation change, air temperature, air CO2 concentration and soil moisture were the dominating factors of ET variabilities during 1982–2014, which dominated ET changes at 36.0 ± 16.3%, 16.5 ± 4.5%, 20.2 ± 11.6 and 18.2 ± 10.9% of the land grids, and averagely contributed 0.72 ± 0.32, 0.28 ± 0.15, −0.51 ± 0.15 and 0.13 ± 0.78 mm yr−2, respectively. These indicated that vegetation change was the most important factor for the increasing ET over China during the past several decades. These findings and the model are helpful for assessing the ecohydrological cycles in a changing environment.
{"title":"Increasing Evapotranspiration in China: Quantifying the Roles of CO2 Fertilization, Climate and Vegetation Changes","authors":"Meixian Liu, Kairong Lin, Xinjun Tu","doi":"10.1029/2024wr038148","DOIUrl":"https://doi.org/10.1029/2024wr038148","url":null,"abstract":"Contradictory views are still existing on the dominating drivers and the underlying mechanisms for the overall increasing evapotranspiration (ET) in China, a region has undergone substantial vegetation and climate changes. Particularly, some studies conclude that climate change is the dominating factor, while other researchers believe that it is the vegetation change. To fill this knowledge gap, here we developed a physical-based ET model by combining the modified Penman–Monteith model and a newly developed canopy resistance model, which effectively links ET and its potential drivers, with the mean correlation and relative RMSE between the observed and modeled canopy resistance being 0.83 ± 0.09 and 3.4 ± 1.6%, respectively. The reliability of the model was also demonstrated by comparing the derived sensitivity of canopy resistance to air CO<sub>2</sub> concentration (mean of 0.14 ± 0.03% ppm<sup>−1</sup>) and the observations (∼0.15% ppm<sup>−1</sup>). Based on this model and a scenario analysis approach, we demonstrated that vegetation change, air temperature, air CO<sub>2</sub> concentration and soil moisture were the dominating factors of ET variabilities during 1982–2014, which dominated ET changes at 36.0 ± 16.3%, 16.5 ± 4.5%, 20.2 ± 11.6 and 18.2 ± 10.9% of the land grids, and averagely contributed 0.72 ± 0.32, 0.28 ± 0.15, −0.51 ± 0.15 and 0.13 ± 0.78 mm yr<sup>−2</sup>, respectively. These indicated that vegetation change was the most important factor for the increasing ET over China during the past several decades. These findings and the model are helpful for assessing the ecohydrological cycles in a changing environment.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"40 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477839","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}
Guoliang Ma, Yang Xiao, Jian Chu, Zhen-Yu Yin, Bo Zhou, Hanlong Liu
The microstructure of microbially induced carbonate precipitation (MICP) stabilized soils is typically used to explain the macro-scale properties of the soils. However, the microstructure is usually inferred from scanning electron microscopy results after breakage, as directly observing the processes inside the pores is challenging. Microfluidics technique provides the solution for visually observing the in situ precipitation process at pore scales. This work endeavors to visually observe and quantitatively analyze the pore scale precipitation process of MICP in characteristic pore structures with the help of the microfluidics technique. Pore structure is one of the most important factors affecting the flow field in pore networks, which might further affect the transport of reactive components and the distribution of precipitates in pores. Therefore, two groups of simplified pore networks were designed to investigate the influence of pore structure. The current work gives an implication of how pore structure and flow rate influence the MICP process and precipitation efficiency at the pore scale. The results also highlight the importance of the diffusion of reactants, and the dissolution and scouring of crystals on the distribution of precipitates at pore scale.
{"title":"Pore-Scale Investigation of MICP in Simplified Pore Structures Through Microfluidic Tests","authors":"Guoliang Ma, Yang Xiao, Jian Chu, Zhen-Yu Yin, Bo Zhou, Hanlong Liu","doi":"10.1029/2024wr037807","DOIUrl":"https://doi.org/10.1029/2024wr037807","url":null,"abstract":"The microstructure of microbially induced carbonate precipitation (MICP) stabilized soils is typically used to explain the macro-scale properties of the soils. However, the microstructure is usually inferred from scanning electron microscopy results after breakage, as directly observing the processes inside the pores is challenging. Microfluidics technique provides the solution for visually observing the in situ precipitation process at pore scales. This work endeavors to visually observe and quantitatively analyze the pore scale precipitation process of MICP in characteristic pore structures with the help of the microfluidics technique. Pore structure is one of the most important factors affecting the flow field in pore networks, which might further affect the transport of reactive components and the distribution of precipitates in pores. Therefore, two groups of simplified pore networks were designed to investigate the influence of pore structure. The current work gives an implication of how pore structure and flow rate influence the MICP process and precipitation efficiency at the pore scale. The results also highlight the importance of the diffusion of reactants, and the dissolution and scouring of crystals on the distribution of precipitates at pore scale.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"24 4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477852","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}
Allison M. Herreid, Brent J. Dalzell, Kade Flynn, John Baker
Many agricultural landscapes have undergone significant modifications to drain farmland and improve crop productivity. Subsurface field drainage, ditching and channelization of streams limit opportunities for biogeochemical processing of carbon and nutrients within the channel network. In this study, we used spatially rich water quality data collected from two contrasting regions of an agricultural watershed in south-central Minnesota, USA to assess how watershed features, such as channelization, tile drainage, and presence of lakes or wetlands, influence biogeochemical processing of nitrate (NO3−) and dissolved organic carbon (DOC). In the channelized upstream region, land use is predominantly agricultural (>92%) with subsurface tile drainage commonly discharging directly to the stream channel. Further downstream, the channel is more natural with increasing lakes and wetlands, including riparian wetlands. We used the concept of reach leverage to interpret biogeochemical behavior (i.e., source vs. sink) in each region of the watershed. Results indicate variability in biogeochemical behavior between the distinct watershed regions, suggesting that channel characteristics and the presence of lentic waters play a role in regulating biogeochemical processing. The upstream, channelized region acts primarily as a conservative transporter or small source of both NO3− and DOC across sampling dates. In contrast, the lentic-influenced region exhibited shifts between source and sink behavior over time, especially for NO3−, influenced by factors such as hydrologic connectivity and discharge. These findings highlight the value of collecting spatially resolved data to enhance our understanding of biogeochemical processing which may be useful to inform effective management and conservation strategies.
{"title":"Using Spatially Rich Data Sets to Assess the Influence of Channel Characteristics on Biogeochemical Behavior in Agricultural Watersheds","authors":"Allison M. Herreid, Brent J. Dalzell, Kade Flynn, John Baker","doi":"10.1029/2024wr038265","DOIUrl":"https://doi.org/10.1029/2024wr038265","url":null,"abstract":"Many agricultural landscapes have undergone significant modifications to drain farmland and improve crop productivity. Subsurface field drainage, ditching and channelization of streams limit opportunities for biogeochemical processing of carbon and nutrients within the channel network. In this study, we used spatially rich water quality data collected from two contrasting regions of an agricultural watershed in south-central Minnesota, USA to assess how watershed features, such as channelization, tile drainage, and presence of lakes or wetlands, influence biogeochemical processing of nitrate (NO<sub>3</sub><sup>−</sup>) and dissolved organic carbon (DOC). In the channelized upstream region, land use is predominantly agricultural (>92%) with subsurface tile drainage commonly discharging directly to the stream channel. Further downstream, the channel is more natural with increasing lakes and wetlands, including riparian wetlands. We used the concept of reach leverage to interpret biogeochemical behavior (i.e., source vs. sink) in each region of the watershed. Results indicate variability in biogeochemical behavior between the distinct watershed regions, suggesting that channel characteristics and the presence of lentic waters play a role in regulating biogeochemical processing. The upstream, channelized region acts primarily as a conservative transporter or small source of both NO<sub>3</sub><sup>−</sup> and DOC across sampling dates. In contrast, the lentic-influenced region exhibited shifts between source and sink behavior over time, especially for NO<sub>3</sub><sup>−</sup>, influenced by factors such as hydrologic connectivity and discharge. These findings highlight the value of collecting spatially resolved data to enhance our understanding of biogeochemical processing which may be useful to inform effective management and conservation strategies.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"209 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143486035","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}
River meanders are one of the most recurrent and varied patterns in fluvial systems. Multiple attempts have been made to detect and categorize patterns in meandering rivers to understand their shape and evolution. A novel data-driven approach was used to classify single-bend meanders. A data set containing approximately 10 million single-lobe meander bends was generated using the Kinoshita curve. A neural network autoencoder was trained over the curvature energy spectra of Kinoshita-generated meanders. Then, the trained network was tested on 7521 real meander bends extracted from satellite images, and the energy spectrum in the meander curvature was reconstructed accurately thanks to the autoencoder architecture. The meander spectrum reconstruction was clustered, and three main bend shapes were found associated with the meander data sets, namely symmetric, upstream-skewed, and downstream-skewed. The autoencoder-based classification framework allowed bend shape detection along rivers, finding the dominant pattern with implications on migration trends. The classification framework proposed in this study was used to analyze the morphological evolution of the Ucayali river over 32 years. The shift from prevalent downstream-skewed to prevalent upstream-skewed bends (or vice versa) after big cutoffs suggests a plausible transition from super-resonant dominated to sub-resonant dominated behavior (or the reverse). Overall, the method proposed opens the venue to data-driven classifications to understand and manage meandering rivers. Bend shape classification can thus inform restoration and flood control practices and contribute to predicting meander evolution from satellite images or sedimentary records.
{"title":"A Curvature-Based Framework for Automated Classification of Meander Bends","authors":"Sergio Lopez Dubon, Alessandro Sgarabotto, Stefano Lanzoni","doi":"10.1029/2024wr037583","DOIUrl":"https://doi.org/10.1029/2024wr037583","url":null,"abstract":"River meanders are one of the most recurrent and varied patterns in fluvial systems. Multiple attempts have been made to detect and categorize patterns in meandering rivers to understand their shape and evolution. A novel data-driven approach was used to classify single-bend meanders. A data set containing approximately 10 million single-lobe meander bends was generated using the Kinoshita curve. A neural network autoencoder was trained over the curvature energy spectra of Kinoshita-generated meanders. Then, the trained network was tested on 7521 real meander bends extracted from satellite images, and the energy spectrum in the meander curvature was reconstructed accurately thanks to the autoencoder architecture. The meander spectrum reconstruction was clustered, and three main bend shapes were found associated with the meander data sets, namely symmetric, upstream-skewed, and downstream-skewed. The autoencoder-based classification framework allowed bend shape detection along rivers, finding the dominant pattern with implications on migration trends. The classification framework proposed in this study was used to analyze the morphological evolution of the Ucayali river over 32 years. The shift from prevalent downstream-skewed to prevalent upstream-skewed bends (or vice versa) after big cutoffs suggests a plausible transition from super-resonant dominated to sub-resonant dominated behavior (or the reverse). Overall, the method proposed opens the venue to data-driven classifications to understand and manage meandering rivers. Bend shape classification can thus inform restoration and flood control practices and contribute to predicting meander evolution from satellite images or sedimentary records.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"52 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477838","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}
Irrigated agriculture depends on surface water and groundwater, but we do not have a clear picture of how much water is consumed from these sources by different crops across the US over time. Current estimates of crop water consumption are insufficient in providing the spatial granularity and temporal depth required for comprehensive long-term analysis. To fill this data gap, we utilized crop growth models to quantify the monthly crop water consumption - distinguishing between rainwater, surface water, and groundwater - of the 30 most widely irrigated crops in the US from 1981 to 2019 at 2.5 arc min. These 30 crops represent approximately 95% of US irrigated cropland. We found that the average annual total crop water consumption for these 30 irrigated crops in the US was 154.2 km3, 70% of which was from irrigation. Corn and alfalfa accounted for approximately 16.7 and 24.8 km3 of average annual blue crop water consumption, respectively, which is nearly two-fifths of the blue crop water consumed in the US. Surface water consumption decreased by 41.2%, while groundwater consumption increased by 6.8%, resulting in a 17.3% decline in blue water consumption between 1981 and 2019. We find good agreement between our results and existing modeled evapotranspiration (ET) products, remotely sensed ET estimates (OpenET), and water use data from the US Geological Survey and US Department of Agriculture. Our data set and model can help assess the impact of irrigation practices and water scarcity on crop production and sustainability.
{"title":"Monthly Crop Water Consumption of Irrigated Crops in the United States From 1981 to 2019","authors":"Gambhir Lamsal, Landon T. Marston","doi":"10.1029/2024wr038334","DOIUrl":"https://doi.org/10.1029/2024wr038334","url":null,"abstract":"Irrigated agriculture depends on surface water and groundwater, but we do not have a clear picture of how much water is consumed from these sources by different crops across the US over time. Current estimates of crop water consumption are insufficient in providing the spatial granularity and temporal depth required for comprehensive long-term analysis. To fill this data gap, we utilized crop growth models to quantify the monthly crop water consumption - distinguishing between rainwater, surface water, and groundwater - of the 30 most widely irrigated crops in the US from 1981 to 2019 at 2.5 arc min. These 30 crops represent approximately 95% of US irrigated cropland. We found that the average annual total crop water consumption for these 30 irrigated crops in the US was 154.2 km<sup>3</sup>, 70% of which was from irrigation. Corn and alfalfa accounted for approximately 16.7 and 24.8 km<sup>3</sup> of average annual blue crop water consumption, respectively, which is nearly two-fifths of the blue crop water consumed in the US. Surface water consumption decreased by 41.2%, while groundwater consumption increased by 6.8%, resulting in a 17.3% decline in blue water consumption between 1981 and 2019. We find good agreement between our results and existing modeled evapotranspiration (ET) products, remotely sensed ET estimates (OpenET), and water use data from the US Geological Survey and US Department of Agriculture. Our data set and model can help assess the impact of irrigation practices and water scarcity on crop production and sustainability.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":"38 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470497","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}
Timothy M. Lahmers, Sujay V. Kumar, Shahryar K. Ahmad, Thomas Holmes, Augusto Getirana, Elijah Orland, Kim Locke, Nishan Kumar Biswas, Wanshu Nie, Justin Pflug, Kristen Whitney, Martha Anderson, Yun Yang
In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale of post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of the impacts of fire on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify the impacts of wildfire on runoff generation. We consider the disturbances in the vegetation and soil as the two main factors contributing to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We consider the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration for two case studies. These case studies demonstrate the general applicability of hydrophobicity formulation to serve as a guideline for exploring the range of hydrologic responses post-fire.
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