{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140042"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140042","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Modeling the Food-Water Nexus: A Spatio-temporal Accounting of Agricultural Land and Water Use in the United States","abstract":"Agriculture dominates both land and water use in the United States, and plays a pivotal role in both national food security and global agricultural trade. Yet, this critical system faces growing pressures from groundwater depletion, climate change, and competing demands for water across sectors. Addressing these challenges requires an integrated understanding of how croplands and water use have evolved through time, and how more efficient management can support agricultural production without expanding water withdrawals. This dissertation developed a unified, high-resolution framework linking agricultural land use, crop water consumption, and on-farm management to evaluate opportunities for sustainable intensification within the U.S. food–water nexus. This dissertation first created HarvestGRID, a gridded dataset of irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. Existing datasets often face a trade-off between spatial detail and temporal coverage. Remote sensing provides fine spatial resolution but limited historical depth, while administrative records extend further back in time but have coarse spatial resolution and contain missing values. HarvestGRID bridges this divide by combining USDA survey and census records with satellite-based land-use products to create spatially explicit and temporally consistent maps of harvested area. The dataset captured long-term agricultural shifts, revealing that while the national extent of irrigated cropland has remained relatively stable, irrigation has declined in water-scarce western states and expanded in more humid eastern regions, reflecting adaptive responses to changing water availability. Building on this spatial foundation, this dissertation then created MIrAg-US (Modeled Irrigated Agriculture of the United States), which provided the first multi-decadal, monthly record of crop water consumption for the same 30 crops using process-based crop growth models. U.S. irrigated croplands consumed an average of approximately 154 cubic kilometers of water annually, with about 70 percent derived from blue water sources (irrigation). Alfalfa and corn together accounted for nearly 40 percent of this total, underscoring the dominance of a few key crops in national water demand. Modelled estimates from MIrAg-US were rigorously evaluated against multiple independent data sources, including government water-use records, previously published model estimates, and remotely sensed evapotranspiration datasets. The comparisons demonstrated generally strong agreement, although alignment varied by region and crop type, reflecting both differences in modeling frameworks and input data. Finally, we utilized the modelling framework developed in previous chapters and evaluated the potential for increasing U.S. food production through improved on-farm water management, explicitly accounting for the rebound effect i.e. the reinvestment of saved water into expanded cultivation. Using AquaCrop-OS simulations, we quantified the water savings achievable through the adoption of high-efficiency irrigation technologies (sprinkler and drip) and organic mulching across 13 major irrigated crops, and modeled the reallocation of this saved water within the same watershed. Nationally, these practices could save up to 27.4 billion cubic meters of irrigation water annually (30% of current total applied irrigation), and reallocation of this water could expand irrigated croplands by as much as 6.2 million hectares, primarily by converting rainfed cropland. This expansion would increase national crop production by approximately 21 million metric tons per year (an 8.9% gain), valued at $4.7 billion annually. Together, these studies create a cohesive empirical and modeling foundation for understanding agricultural water sustainability in the United States. Beyond documenting past change, this work establishes a pathway that links crop modeling and human decision-making to guide data-driven strategies for managing water and food systems under a changing climate.","abstract_html":"Agriculture dominates both land and water use in the United States, and plays a pivotal role in both national food security and global agricultural trade. Yet, this critical system faces growing pressures from groundwater depletion, climate change, and competing demands for water across sectors. Addressing these challenges requires an integrated understanding of how croplands and water use have evolved through time, and how more efficient management can support agricultural production without expanding water withdrawals. This dissertation developed a unified, high-resolution framework linking agricultural land use, crop water consumption, and on-farm management to evaluate opportunities for sustainable intensification within the U.S. food–water nexus. This dissertation first created HarvestGRID, a gridded dataset of irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. Existing datasets often face a trade-off between spatial detail and temporal coverage. Remote sensing provides fine spatial resolution but limited historical depth, while administrative records extend further back in time but have coarse spatial resolution and contain missing values. HarvestGRID bridges this divide by combining USDA survey and census records with satellite-based land-use products to create spatially explicit and temporally consistent maps of harvested area. The dataset captured long-term agricultural shifts, revealing that while the national extent of irrigated cropland has remained relatively stable, irrigation has declined in water-scarce western states and expanded in more humid eastern regions, reflecting adaptive responses to changing water availability. Building on this spatial foundation, this dissertation then created MIrAg-US (Modeled Irrigated Agriculture of the United States), which provided the first multi-decadal, monthly record of crop water consumption for the same 30 crops using process-based crop growth models. U.S. irrigated croplands consumed an average of approximately 154 cubic kilometers of water annually, with about 70 percent derived from blue water sources (irrigation). Alfalfa and corn together accounted for nearly 40 percent of this total, underscoring the dominance of a few key crops in national water demand. Modelled estimates from MIrAg-US were rigorously evaluated against multiple independent data sources, including government water-use records, previously published model estimates, and remotely sensed evapotranspiration datasets. The comparisons demonstrated generally strong agreement, although alignment varied by region and crop type, reflecting both differences in modeling frameworks and input data. Finally, we utilized the modelling framework developed in previous chapters and evaluated the potential for increasing U.S. food production through improved on-farm water management, explicitly accounting for the rebound effect i.e. the reinvestment of saved water into expanded cultivation. Using AquaCrop-OS simulations, we quantified the water savings achievable through the adoption of high-efficiency irrigation technologies (sprinkler and drip) and organic mulching across 13 major irrigated crops, and modeled the reallocation of this saved water within the same watershed. Nationally, these practices could save up to 27.4 billion cubic meters of irrigation water annually (30% of current total applied irrigation), and reallocation of this water could expand irrigated croplands by as much as 6.2 million hectares, primarily by converting rainfed cropland. This expansion would increase national crop production by approximately 21 million metric tons per year (an 8.9% gain), valued at $4.7 billion annually. Together, these studies create a cohesive empirical and modeling foundation for understanding agricultural water sustainability in the United States. Beyond documenting past change, this work establishes a pathway that links crop modeling and human decision-making to guide data-driven strategies for managing water and food systems under a changing climate.","abstract_has_math":false,"creators":["Lamsal, Gambhir"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Civil Engineering","degree_department":"Civil and Environmental Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Marston, Landon T."],"committee_members":["Sridhar, Venkataramana Rao","Rippy, Megan A.","Easton, Zachary"],"year":2025,"date_issued":"2025-12-18","date_published":"2025-12-18","updated_at":"2026-07-22T22:20:03Z","subjects":["crop water consumption","crop modeling"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45141"],"render_values":[{"text":"vt_gsexam:45141","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140042","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Marston, Landon T."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Sridhar, Venkataramana Rao","Rippy, Megan A.","Easton, Zachary"]},{"key":"dc:contributor.department","label":"Department","values":["Civil and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Lamsal, Gambhir"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-19T09:01:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-19T09:01:23Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-18"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["crop water consumption","crop modeling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45141"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140042"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Agriculture dominates both land and water use in the United States, and plays a pivotal role in both national food security and global agricultural trade. Yet, this critical system faces growing pressures from groundwater depletion, climate change, and competing demands for water across sectors. Addressing these challenges requires an integrated understanding of how croplands and water use have evolved through time, and how more efficient management can support agricultural production without expanding water withdrawals. This dissertation developed a unified, high-resolution framework linking agricultural land use, crop water consumption, and on-farm management to evaluate opportunities for sustainable intensification within the U.S. food–water nexus. This dissertation first created HarvestGRID, a gridded dataset of irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. Existing datasets often face a trade-off between spatial detail and temporal coverage. Remote sensing provides fine spatial resolution but limited historical depth, while administrative records extend further back in time but have coarse spatial resolution and contain missing values. HarvestGRID bridges this divide by combining USDA survey and census records with satellite-based land-use products to create spatially explicit and temporally consistent maps of harvested area. The dataset captured long-term agricultural shifts, revealing that while the national extent of irrigated cropland has remained relatively stable, irrigation has declined in water-scarce western states and expanded in more humid eastern regions, reflecting adaptive responses to changing water availability. Building on this spatial foundation, this dissertation then created MIrAg-US (Modeled Irrigated Agriculture of the United States), which provided the first multi-decadal, monthly record of crop water consumption for the same 30 crops using process-based crop growth models. U.S. irrigated croplands consumed an average of approximately 154 cubic kilometers of water annually, with about 70 percent derived from blue water sources (irrigation). Alfalfa and corn together accounted for nearly 40 percent of this total, underscoring the dominance of a few key crops in national water demand. Modelled estimates from MIrAg-US were rigorously evaluated against multiple independent data sources, including government water-use records, previously published model estimates, and remotely sensed evapotranspiration datasets. The comparisons demonstrated generally strong agreement, although alignment varied by region and crop type, reflecting both differences in modeling frameworks and input data. Finally, we utilized the modelling framework developed in previous chapters and evaluated the potential for increasing U.S. food production through improved on-farm water management, explicitly accounting for the rebound effect i.e. the reinvestment of saved water into expanded cultivation. Using AquaCrop-OS simulations, we quantified the water savings achievable through the adoption of high-efficiency irrigation technologies (sprinkler and drip) and organic mulching across 13 major irrigated crops, and modeled the reallocation of this saved water within the same watershed. Nationally, these practices could save up to 27.4 billion cubic meters of irrigation water annually (30% of current total applied irrigation), and reallocation of this water could expand irrigated croplands by as much as 6.2 million hectares, primarily by converting rainfed cropland. This expansion would increase national crop production by approximately 21 million metric tons per year (an 8.9% gain), valued at $4.7 billion annually. Together, these studies create a cohesive empirical and modeling foundation for understanding agricultural water sustainability in the United States. Beyond documenting past change, this work establishes a pathway that links crop modeling and human decision-making to guide data-driven strategies for managing water and food systems under a changing climate."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Agriculture uses more land and water than any other sector in the United States, and is pivotal for both national food security and global trade. Yet this system is under increasing pressure from shrinking groundwater supplies, climate change, and rising competition for water. To address these challenges, we need a better understanding of how croplands and water use have changed over time, and how improved farm practices can support food production without increasing water withdrawals. This dissertation develops an integrated, high-resolution framework that links where crops are grown, how much water they use, and how management decisions shape these patterns. First, it introduces HarvestGRID, a new dataset that maps irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. By combining government records with satellite data, HarvestGRID reveals long-term shifts in U.S. agriculture, including a decline in irrigation in the water-scarce West and expansion in more humid eastern regions. Next, it presents MIrAg-US, the first multi-decadal, monthly estimates of crop water consumption for these same crops using a process-based crop model. U.S. croplands used about 154 cubic kilometers of water each year, with corn and alfalfa accounting for nearly 40 percent of this demand. These estimates were compared with multiple independent data sources and showed strong overall agreement. Finally, the dissertation evaluates how improved on-farm practices—such as switching to efficient irrigation technologies and using organic mulches—could reduce water use and boost production. These strategies could save up to 27 billion cubic meters of water each year, allowing that water to be reused within the same watersheds to expand irrigation and boost national production by nearly 21 million metric tons annually (8.9% gain). Together, these studies provide a unified picture of how land, water, and management interact in U.S. agriculture. They show how more efficient practices can support higher food production while reducing pressure on limited water resources, offering practical insights for building a more sustainable and resilient food–water system."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Modeling the Food-Water Nexus: A Spatio-temporal Accounting of Agricultural Land and Water Use in the United States"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Marston, Landon T."],"dc:contributor.committeemember":["Sridhar, Venkataramana Rao","Rippy, Megan A.","Easton, Zachary"],"dc:contributor.department":["Civil and Environmental Engineering"],"dc:creator":["Lamsal, Gambhir"],"dc:date.accessioned":["2025-12-19T09:01:23Z"],"dc:date.available":["2025-12-19T09:01:23Z"],"dc:date.issued":["2025-12-18"],"dc:description.abstract":["Agriculture dominates both land and water use in the United States, and plays a pivotal role in both national food security and global agricultural trade. Yet, this critical system faces growing pressures from groundwater depletion, climate change, and competing demands for water across sectors. Addressing these challenges requires an integrated understanding of how croplands and water use have evolved through time, and how more efficient management can support agricultural production without expanding water withdrawals. This dissertation developed a unified, high-resolution framework linking agricultural land use, crop water consumption, and on-farm management to evaluate opportunities for sustainable intensification within the U.S. food–water nexus. This dissertation first created HarvestGRID, a gridded dataset of irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. Existing datasets often face a trade-off between spatial detail and temporal coverage. Remote sensing provides fine spatial resolution but limited historical depth, while administrative records extend further back in time but have coarse spatial resolution and contain missing values. HarvestGRID bridges this divide by combining USDA survey and census records with satellite-based land-use products to create spatially explicit and temporally consistent maps of harvested area. The dataset captured long-term agricultural shifts, revealing that while the national extent of irrigated cropland has remained relatively stable, irrigation has declined in water-scarce western states and expanded in more humid eastern regions, reflecting adaptive responses to changing water availability. Building on this spatial foundation, this dissertation then created MIrAg-US (Modeled Irrigated Agriculture of the United States), which provided the first multi-decadal, monthly record of crop water consumption for the same 30 crops using process-based crop growth models. U.S. irrigated croplands consumed an average of approximately 154 cubic kilometers of water annually, with about 70 percent derived from blue water sources (irrigation). Alfalfa and corn together accounted for nearly 40 percent of this total, underscoring the dominance of a few key crops in national water demand. Modelled estimates from MIrAg-US were rigorously evaluated against multiple independent data sources, including government water-use records, previously published model estimates, and remotely sensed evapotranspiration datasets. The comparisons demonstrated generally strong agreement, although alignment varied by region and crop type, reflecting both differences in modeling frameworks and input data. Finally, we utilized the modelling framework developed in previous chapters and evaluated the potential for increasing U.S. food production through improved on-farm water management, explicitly accounting for the rebound effect i.e. the reinvestment of saved water into expanded cultivation. Using AquaCrop-OS simulations, we quantified the water savings achievable through the adoption of high-efficiency irrigation technologies (sprinkler and drip) and organic mulching across 13 major irrigated crops, and modeled the reallocation of this saved water within the same watershed. Nationally, these practices could save up to 27.4 billion cubic meters of irrigation water annually (30% of current total applied irrigation), and reallocation of this water could expand irrigated croplands by as much as 6.2 million hectares, primarily by converting rainfed cropland. This expansion would increase national crop production by approximately 21 million metric tons per year (an 8.9% gain), valued at $4.7 billion annually. Together, these studies create a cohesive empirical and modeling foundation for understanding agricultural water sustainability in the United States. Beyond documenting past change, this work establishes a pathway that links crop modeling and human decision-making to guide data-driven strategies for managing water and food systems under a changing climate."],"dc:description.abstractgeneral":["Agriculture uses more land and water than any other sector in the United States, and is pivotal for both national food security and global trade. Yet this system is under increasing pressure from shrinking groundwater supplies, climate change, and rising competition for water. To address these challenges, we need a better understanding of how croplands and water use have changed over time, and how improved farm practices can support food production without increasing water withdrawals. This dissertation develops an integrated, high-resolution framework that links where crops are grown, how much water they use, and how management decisions shape these patterns. First, it introduces HarvestGRID, a new dataset that maps irrigated and rainfed harvested areas for 30 major crops from 1981 to 2019. By combining government records with satellite data, HarvestGRID reveals long-term shifts in U.S. agriculture, including a decline in irrigation in the water-scarce West and expansion in more humid eastern regions. Next, it presents MIrAg-US, the first multi-decadal, monthly estimates of crop water consumption for these same crops using a process-based crop model. U.S. croplands used about 154 cubic kilometers of water each year, with corn and alfalfa accounting for nearly 40 percent of this demand. These estimates were compared with multiple independent data sources and showed strong overall agreement. Finally, the dissertation evaluates how improved on-farm practices—such as switching to efficient irrigation technologies and using organic mulches—could reduce water use and boost production. These strategies could save up to 27 billion cubic meters of water each year, allowing that water to be reused within the same watersheds to expand irrigation and boost national production by nearly 21 million metric tons annually (8.9% gain). Together, these studies provide a unified picture of how land, water, and management interact in U.S. agriculture. They show how more efficient practices can support higher food production while reducing pressure on limited water resources, offering practical insights for building a more sustainable and resilient food–water system."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45141"],"dc:identifier.uri":["https://hdl.handle.net/10919/140042"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["crop water consumption","crop modeling"],"dc:title":["Modeling the Food-Water Nexus: A Spatio-temporal Accounting of Agricultural Land and Water Use in the United States"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:03Z"}