Xiafei Guan, Kailun Hu, Xin Chen, Junliang Gao, Huabin Shi
Extreme precipitation is increasing the risk of dam breaks and formation occurring debris dams. Accurate prediction of dam-break wave propagation is critical to disaster emergency management. Intense bed-load transport by dam-break floods can result in a dramatic change of topography, which in turn may affect flood propagation. However, only a very few studies have investigated the thin intense bed-load layer under dam-break floods. In this paper, a meshless two-phase mathematical model is utilized to examine the water velocity, sediment velocity and volumetric fraction, and bed-load transport flux as well as energy dissipation in bed-load layer. The model is applied to simulate two- and three-dimensional laboratory experiments of dam-break wave over erodible beds. For the two-dimensional experiment, the relative root mean square errors in computed water surface are all below 3.60% and those in profiles of bed-load layer and static bed are mostly below 13.40%. For the three-dimensional case, the relative error in computed highest water level is lower than 5.9%. Sediment stream-wise velocity in bed-load layer follows a power-law vertical distribution while sediment volumetric fraction decreases linearly upwards. Accordingly, a formulation of the vertical distribution of bed-load transport flux, contradictory to the parabolic law in existing studies, is proposed. Most of the water mechanical energy transferred to the sediment is dissipated due to the shear stress in the intense bed-load layer while only a limit part is kept by the sediment grains. Energy dissipation due to sediment shear stress dominates the consumption of total mechanical energy in the two-phase system.
{"title":"Investigating the Intense Sediment Load by Dam-Break Floods Using a Meshless Two-Phase Mathematical Model","authors":"Xiafei Guan, Kailun Hu, Xin Chen, Junliang Gao, Huabin Shi","doi":"10.1029/2023wr035399","DOIUrl":"https://doi.org/10.1029/2023wr035399","url":null,"abstract":"Extreme precipitation is increasing the risk of dam breaks and formation occurring debris dams. Accurate prediction of dam-break wave propagation is critical to disaster emergency management. Intense bed-load transport by dam-break floods can result in a dramatic change of topography, which in turn may affect flood propagation. However, only a very few studies have investigated the thin intense bed-load layer under dam-break floods. In this paper, a meshless two-phase mathematical model is utilized to examine the water velocity, sediment velocity and volumetric fraction, and bed-load transport flux as well as energy dissipation in bed-load layer. The model is applied to simulate two- and three-dimensional laboratory experiments of dam-break wave over erodible beds. For the two-dimensional experiment, the relative root mean square errors in computed water surface are all below 3.60% and those in profiles of bed-load layer and static bed are mostly below 13.40%. For the three-dimensional case, the relative error in computed highest water level is lower than 5.9%. Sediment stream-wise velocity in bed-load layer follows a power-law vertical distribution while sediment volumetric fraction decreases linearly upwards. Accordingly, a formulation of the vertical distribution of bed-load transport flux, contradictory to the parabolic law in existing studies, is proposed. Most of the water mechanical energy transferred to the sediment is dissipated due to the shear stress in the intense bed-load layer while only a limit part is kept by the sediment grains. Energy dissipation due to sediment shear stress dominates the consumption of total mechanical energy in the two-phase system.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141578109","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}
Tianjiao Pu, Cynthia Gerlein-Safdi, Ying Xiong, Mengze Li, Eric A. Kort, A. Anthony Bloom
The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single-satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N–37.4°S). The sole reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS's indication of water existence. Berkeley-RWAWC provides monthly, low-latency inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water data sets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo-Gangetic Plain. The comparisons reveal Berkeley-RWAWC's enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley-RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics.
{"title":"Berkeley-RWAWC: A New CYGNSS-Based Watermask Unveils Unique Observations of Seasonal Dynamics in the Tropics","authors":"Tianjiao Pu, Cynthia Gerlein-Safdi, Ying Xiong, Mengze Li, Eric A. Kort, A. Anthony Bloom","doi":"10.1029/2024wr037060","DOIUrl":"https://doi.org/10.1029/2024wr037060","url":null,"abstract":"The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single-satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N–37.4°S). The sole reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS's indication of water existence. Berkeley-RWAWC provides monthly, low-latency inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water data sets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo-Gangetic Plain. The comparisons reveal Berkeley-RWAWC's enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley-RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141578091","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}
Ayman Alzraiee, Richard Niswonger, Carol Luukkonen, Josh Larsen, Donald Martin, Deidre Herbert, Cheryl Buchwald, Cheryl Dieter, Lisa Miller, Jana Stewart, Natalie Houston, Scott Paulinski, Kristen Valseth
Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per-capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm-season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.
{"title":"Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks","authors":"Ayman Alzraiee, Richard Niswonger, Carol Luukkonen, Josh Larsen, Donald Martin, Deidre Herbert, Cheryl Buchwald, Cheryl Dieter, Lisa Miller, Jana Stewart, Natalie Houston, Scott Paulinski, Kristen Valseth","doi":"10.1029/2023wr036632","DOIUrl":"https://doi.org/10.1029/2023wr036632","url":null,"abstract":"Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per-capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm-season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141577997","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}
Jieling Yin, Xin Li, Bernie A. Engel, Jiayi Ding, Xin Xing, Shikun K. Sun, Yubao B. Wang
Incorporating water footprints and virtual water into crop redistribution provides a new approach for efficient water resources utilization and synergistic development of water surplus and scarce regions. In this work, the absolute and comparative advantage of the production-based blue and gray water footprint (PWFblue and PWFgray), the calorie-based blue water footprint (CWFblue) and the net benefit-based blue water footprint (NBWFblue) were used as coefficients to establish a bi-level crop redistribution model. The mode considers upper-level decision makers interested in maximizing food security and ecological security and lower-level decision makers interested in water use efficiency, water use benefits and net benefits. The model was applied in the Hetao Irrigation District (HID), China. The results showed that after optimization, the PWFblue, CWFblue, NBWFblue, and gray water footprint (GWF) of the HID were reduced by 23.32%, 5.60%, 17.40%, and 6.67%, respectively. National benefits were improved, especially when considering synergistic optimization, although the net benefits of HID was affected. The calorie supply increased by 9.6 × 109 kcal, the GWF decreased by 8.29 × 106 m3, and water use efficiency and benefits were improved in China. In contrast, the calorie supply and the net benefits of the HID decreased, while the GWF increased. Moreover, multiple stakeholders were involved in crop redistribution and required national synergies. The bi-level model proved more suitable than the multi-objective model. The model proposed in this work considers synergies outside the region in crop redistribution within the region, and can provide new insight for water and soil resources management in arid and semi-arid regions.
{"title":"Inter-Regional Food-Water-Income Synergy Through Bi-Level Crop Redistribution Model Coupled With Virtual Water: A Case Study of China’s Hetao Irrigation District","authors":"Jieling Yin, Xin Li, Bernie A. Engel, Jiayi Ding, Xin Xing, Shikun K. Sun, Yubao B. Wang","doi":"10.1029/2023wr036572","DOIUrl":"https://doi.org/10.1029/2023wr036572","url":null,"abstract":"Incorporating water footprints and virtual water into crop redistribution provides a new approach for efficient water resources utilization and synergistic development of water surplus and scarce regions. In this work, the absolute and comparative advantage of the production-based blue and gray water footprint (<i>PWF</i><sub><i>blue</i></sub> and <i>PWF</i><sub><i>gr</i><i>a</i><i>y</i></sub>), the calorie-based blue water footprint (<i>CWF</i><sub><i>blue</i></sub>) and the net benefit-based blue water footprint (<i>NBWF</i><sub><i>blue</i></sub>) were used as coefficients to establish a bi-level crop redistribution model. The mode considers upper-level decision makers interested in maximizing food security and ecological security and lower-level decision makers interested in water use efficiency, water use benefits and net benefits. The model was applied in the Hetao Irrigation District (HID), China. The results showed that after optimization, the <i>PWF</i><sub><i>blue</i></sub>, <i>CWF</i><sub><i>blue</i></sub>, <i>NBWF</i><sub><i>blue</i></sub>, and gray water footprint (GWF) of the HID were reduced by 23.32%, 5.60%, 17.40%, and 6.67%, respectively. National benefits were improved, especially when considering synergistic optimization, although the net benefits of HID was affected. The calorie supply increased by 9.6 × 10<sup>9</sup> kcal, the GWF decreased by 8.29 × 10<sup>6</sup> m<sup>3</sup>, and water use efficiency and benefits were improved in China. In contrast, the calorie supply and the net benefits of the HID decreased, while the GWF increased. Moreover, multiple stakeholders were involved in crop redistribution and required national synergies. The bi-level model proved more suitable than the multi-objective model. The model proposed in this work considers synergies outside the region in crop redistribution within the region, and can provide new insight for water and soil resources management in arid and semi-arid regions.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141578090","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}
Huancui Hu, L. Ruby Leung, Francina Dominguez, David Gochis, Xingyuan Chen, Stephen Good, Aubrey Dugger, Laurel Larsen, Michael Barlage
Most current land models approximate terrestrial hydrological processes as one-dimensional vertical flow, neglecting lateral water movement from ridges to valleys. Such lateral flow is fundamental at catchment scales and becomes crucial for finer-scale land models. To test the effect of incorporating lateral flow toward three-dimensional representations of hydrological processes in the next generation land models, we integrate a water tracer model into the WRF-Hydro framework to track water movement from precipitation to discharge and evapotranspiration. This hydrologic-tracer integrated system allows us to identify the key mechanisms by which lateral flow affects the flow paths and transit times in WRF-Hydro. By comparing modeling experiments with and without lateral routing in two contrasting catchments, we determine the impacts of lateral flow on the transit times of precipitation event-water. Results show that with limited hydrologic connectivity, lateral flow extends the transit times by reducing (increasing) event-water drainage loss (accumulation) in ridges (valleys) and allowing reinfiltration of infiltration-excess flow, which is missing in most land models. On the contrary with high hydrologic connectivity, lateral flow can effectively accelerate the water release to streams and reduce the transit time. However, the transit times are substantially underestimated by the model compared with isotope-derived estimates, indicating model limitations in representing flow paths and transit times. This study provides some insights on the fundamental differences in terrestrial hydrology simulated by land models with and without lateral flow representation.
{"title":"Integrating a Water Tracer Model Into WRF-Hydro for Characterizing the Effect of Lateral Flow in Hydrologic Simulations","authors":"Huancui Hu, L. Ruby Leung, Francina Dominguez, David Gochis, Xingyuan Chen, Stephen Good, Aubrey Dugger, Laurel Larsen, Michael Barlage","doi":"10.1029/2023wr034938","DOIUrl":"https://doi.org/10.1029/2023wr034938","url":null,"abstract":"Most current land models approximate terrestrial hydrological processes as one-dimensional vertical flow, neglecting lateral water movement from ridges to valleys. Such lateral flow is fundamental at catchment scales and becomes crucial for finer-scale land models. To test the effect of incorporating lateral flow toward three-dimensional representations of hydrological processes in the next generation land models, we integrate a water tracer model into the WRF-Hydro framework to track water movement from precipitation to discharge and evapotranspiration. This hydrologic-tracer integrated system allows us to identify the key mechanisms by which lateral flow affects the flow paths and transit times in WRF-Hydro. By comparing modeling experiments with and without lateral routing in two contrasting catchments, we determine the impacts of lateral flow on the transit times of precipitation event-water. Results show that with limited hydrologic connectivity, lateral flow extends the transit times by reducing (increasing) event-water drainage loss (accumulation) in ridges (valleys) and allowing reinfiltration of infiltration-excess flow, which is missing in most land models. On the contrary with high hydrologic connectivity, lateral flow can effectively accelerate the water release to streams and reduce the transit time. However, the transit times are substantially underestimated by the model compared with isotope-derived estimates, indicating model limitations in representing flow paths and transit times. This study provides some insights on the fundamental differences in terrestrial hydrology simulated by land models with and without lateral flow representation.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141578093","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}
Elizabeth M. Prior, Nathan Michaelson, Jonathan A. Czuba, Thomas J. Pingel, Valerie A. Thomas, W. Cully Hession
Topography and the computational mesh grid are fundamental inputs to all two-dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter-scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC-RAS model with varying mesh grid resolutions (1 and 2 m) and drone-based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel-floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.
地形和计算网格是所有二维（2D）流体动力学模型的基本输入，但它们的分辨率往往是根据数据可用性任意选择的。随着无人机技术的应用日益广泛，最终用户可以收集到厘米级分辨率的地形数据。这一进步带来了选择分辨率的责任。在本研究中，我们研究了网格和数字高程模型（DEM）分辨率的选择如何影响二维水动力建模结果，特别是水深、流速和淹没范围。我们对弗吉尼亚州布莱克斯堡 Stroubles 溪 1.5 公里河段的二维 HEC-RAS 模型模拟结果进行了成对比较，该模型采用了不同的网格分辨率（1 米和 2 米）和无人机激光雷达 DEM 分辨率（0.1、0.25、0.5、1 和 2 米）。该模型在洪泛区粗糙度变化达 ±4% 时重新运行，以确定 DEM 和网格变化与粗糙度等效变化之间的关系。我们发现，分辨率变化造成的模型差异相当于将洪泛区粗糙度的深度改变了 12%，速度改变了 44%。速度的最大差异集中在河道与洪泛平原的交界处，而深度的差异则出现在整个洪泛平原的横向，并且与激光雷达地面点密度无关。我们还发现，淹没边界取决于 DEM 的分辨率。我们的结果表明，建模人员应仔细考虑哪种分辨率最能代表地形，同时还能解析重要的河岸地形特征。
{"title":"Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models","authors":"Elizabeth M. Prior, Nathan Michaelson, Jonathan A. Czuba, Thomas J. Pingel, Valerie A. Thomas, W. Cully Hession","doi":"10.1029/2024wr037165","DOIUrl":"https://doi.org/10.1029/2024wr037165","url":null,"abstract":"Topography and the computational mesh grid are fundamental inputs to all two-dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter-scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC-RAS model with varying mesh grid resolutions (1 and 2 m) and drone-based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel-floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561773","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}
O. Dombrowski, C. Brogi, H.-J. Hendricks Franssen, V. Pisinaras, A. Panagopoulos, S. Swenson, H. Bogena
Irrigation strongly influences land-atmosphere processes from regional to global scale. Therefore, an accurate representation of irrigation is crucial to understand these interactions and address water resources issues. While irrigation schemes are increasingly integrated into land surface models, their evaluation and further development remains challenging due to data limitations. This study assessed the representation of field-scale irrigation using the Community Land Model version 5 (CLM5) through comparison of observed and simulated soil moisture, transpiration and crop yield. Irrigation was simulated by (a) adjusting the current irrigation routine and (b) by implementing a novel irrigation data stream that allows to directly use observed irrigation amounts and schedules. In a following step, the effect of different irrigation scenarios at the regional scale was simulated by using this novel data stream. At the plot scale, the novel irrigation data stream performed better in representing observed SM dynamics compared to the current irrigation routine. Nonetheless, simplifications in crop and irrigation representation and uncertainty in the relation between water stress and yield currently limit the ability of CLM5 for field-scale irrigation scheduling. Still, the simulations revealed valuable insights into model performance that can inform and improve the modeling beyond the field scale. At regional scale, the simulations identified irrigation priorities and potential water savings. Furthermore, application of LSMs such as CLM5 can help to study the effects of irrigation beyond water availability, for example, on energy fluxes and climate, thus providing a powerful tool to assess the broader implications of irrigation at larger scale.
{"title":"Land Surface Modeling as a Tool to Explore Sustainable Irrigation Practices in Mediterranean Fruit Orchards","authors":"O. Dombrowski, C. Brogi, H.-J. Hendricks Franssen, V. Pisinaras, A. Panagopoulos, S. Swenson, H. Bogena","doi":"10.1029/2023wr036139","DOIUrl":"https://doi.org/10.1029/2023wr036139","url":null,"abstract":"Irrigation strongly influences land-atmosphere processes from regional to global scale. Therefore, an accurate representation of irrigation is crucial to understand these interactions and address water resources issues. While irrigation schemes are increasingly integrated into land surface models, their evaluation and further development remains challenging due to data limitations. This study assessed the representation of field-scale irrigation using the Community Land Model version 5 (CLM5) through comparison of observed and simulated soil moisture, transpiration and crop yield. Irrigation was simulated by (a) adjusting the current irrigation routine and (b) by implementing a novel irrigation data stream that allows to directly use observed irrigation amounts and schedules. In a following step, the effect of different irrigation scenarios at the regional scale was simulated by using this novel data stream. At the plot scale, the novel irrigation data stream performed better in representing observed SM dynamics compared to the current irrigation routine. Nonetheless, simplifications in crop and irrigation representation and uncertainty in the relation between water stress and yield currently limit the ability of CLM5 for field-scale irrigation scheduling. Still, the simulations revealed valuable insights into model performance that can inform and improve the modeling beyond the field scale. At regional scale, the simulations identified irrigation priorities and potential water savings. Furthermore, application of LSMs such as CLM5 can help to study the effects of irrigation beyond water availability, for example, on energy fluxes and climate, thus providing a powerful tool to assess the broader implications of irrigation at larger scale.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561706","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}
Land surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single-value objective function. To address this problem, we propose a novel Generative Adversarial Network-based Parameter Optimization (GAN-PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network-based surrogate models to make the physical model differentiable, we employ GAN-PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN-PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN-PO excels in maintaining spatial consistency, outperforming the state-of-the-art differentiable parameter learning (dPL) method in terms of model spatial performance.
{"title":"Learning Distributed Parameters of Land Surface Hydrologic Models Using a Generative Adversarial Network","authors":"Ruochen Sun, Baoxiang Pan, Qingyun Duan","doi":"10.1029/2024wr037380","DOIUrl":"https://doi.org/10.1029/2024wr037380","url":null,"abstract":"Land surface hydrologic models adeptly capture crucial terrestrial processes with a high level of spatial detail. Typically, these models incorporate numerous uncertain, spatially varying parameters, the specification of which can profoundly impact the simulation capabilities. There is a longstanding tradition wherein parameter calibration has served as the conventional procedure to enhance model performance. However, calibrating distributed land surface hydrologic models presents a great challenge, often resulting in uneven spatial performance due to the compression of information inherent in model outputs and observations into a single-value objective function. To address this problem, we propose a novel Generative Adversarial Network-based Parameter Optimization (GAN-PO) method. By leveraging a deep neural network to discern model spatial biases, we train a generative network to produce spatially coherent parameter fields, minimizing distinctions between simulations and observations. By leveraging neural network-based surrogate models to make the physical model differentiable, we employ GAN-PO to calibrate the Variable Infiltration Capacity (VIC) model against evapotranspiration (ET) over China's Huaihe basin. The results show that GAN-PO can diminish errors in simulated ET derived from default parameters across nearly all grid cells within the study region, surpassing the conventional calibration approach based on the parameter regionalization technique. Ablation analysis indicates that relying solely on the traditional loss could lead to deteriorated model performance, underscoring the crucial role of the discriminator. Notably, due to the discriminator's explicit identification of model spatial biases, GAN-PO excels in maintaining spatial consistency, outperforming the state-of-the-art differentiable parameter learning (dPL) method in terms of model spatial performance.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561705","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}
Jhilam Sinha, Ashish Sharma, Lucy Marshall, Seokhyeon Kim
Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in-situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in-situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in-situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.
{"title":"Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach","authors":"Jhilam Sinha, Ashish Sharma, Lucy Marshall, Seokhyeon Kim","doi":"10.1029/2022wr034019","DOIUrl":"https://doi.org/10.1029/2022wr034019","url":null,"abstract":"Drying of soil impacts land energy and water balance, influences the sustainability of vegetation growth, and modulates hydrological extremes including floods. While satellite soil moisture data are widely used for a range of environmental applications, systematic differences from regional in-situ data prevent their optimal use as key physical signatures (such as soil moisture recession, also termed drydown) are represented differently. This study investigates differences in drydowns from the Soil Moisture Active Passive (SMAP) level 4 product with reference to in-situ observations. A bivariate filtering alternative is proposed to minimize the disparity noted by modeling the relationship between the rate of drying and initial soil wetness and representing the same as in-situ. Considerable improvements are observed in the resulting SMAP soil moisture filtered estimates. Although the algorithm assumes spatial stationarity, improvements exist across different soil properties and climatic conditions, providing a parsimonious alternative to better capture the dynamics of soil moisture loss.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561707","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}
Chen Hu, Jun Xia, Dunxian She, Zhaoxia Jing, Si Hong, Zhihong Song, Gangsheng Wang
The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression-based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least-squares regression is identified as optimal regression-based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression-based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.
{"title":"Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments","authors":"Chen Hu, Jun Xia, Dunxian She, Zhaoxia Jing, Si Hong, Zhihong Song, Gangsheng Wang","doi":"10.1029/2023wr035071","DOIUrl":"https://doi.org/10.1029/2023wr035071","url":null,"abstract":"The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression-based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least-squares regression is identified as optimal regression-based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression-based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results.","PeriodicalId":23799,"journal":{"name":"Water Resources Research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561770","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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