About

Eric Booth, PhD, is an Associate Research Scientist at the University of Wisconsin - Madison, affiliated with the Departments of Plant & Agroecosystem Sciences and Civil & Environmental Engineering, as well as the Wisconsin Energy Institute. He holds a BS in Environmental Engineering from UW-Madison (2004), an MS in Hydrologic Science from UC-Davis (2006), and a PhD in Limnology from UW-Madison (2011). His research broadly focuses on the intersection of water, land, climate, and human systems, employing a transdisciplinary approach that emphasizes collaboration across diverse academic and non-academic disciplines. His disciplinary expertise centers on water-related topics including hydroecology, climate and land-use change impacts, urban stormwater management, wetland and stream restoration, water quality, groundwater hydrology, fluvial geomorphology, environmental history, agroecology, remote sensing, and computer modeling. Dr. Booth's research methods integrate biophysical field monitoring using ground-, satellite-, and drone-based technologies, biophysical modeling, decision-support tool development, and social science techniques such as surveys and interviews. His current and recent projects address issues such as stormwater infiltration and flood reduction in forested hillslopes of the Driftless Area, flood resilience and stream restoration in the Coon Creek and West Fork Kickapoo Watersheds, agroecological transformation to perennial grassland agriculture, sustaining food, energy, and water security in agricultural landscapes of the Upper Mississippi River Basin, nutrient runoff reduction into the Great Lakes, water quality impacts of the Renewable Fuels Standard, groundwater pumping effects on calcareous fens in Wisconsin, and riparian management and restoration in the Kickapoo watershed. Previously, Dr. Booth contributed to a National Science Foundation-funded project under the Water Sustainability and Climate Program that explored future scenarios for water, food, and ecosystems in the Yahara River watershed under potential climate, land-use, urbanization, and agricultural changes. This work combined narrative storylines with computer modeling to quantitatively assess human well-being measures such as water quality, supply, flooding, and agricultural production, fostering community-wide dialogue on long-term regional decision-making.

Research topics

• Ecology
• Environmental science
• Engineering
• Waste management
• Agronomy
• Agroforestry
• Business
• Psychology
• Agricultural economics
• Environmental planning
• Agricultural engineering
• Physics
• Natural resource economics
• Mathematics
• Biology
• Economics
• Geography
• Environmental resource management

Selected publications

• Comment on essd-2025-445

  2026-03-02

  peer-reviewOpen access1st authorCorresponding

  <strong class="journal-contentHeaderColor">Abstract.</strong> Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) &ndash; the Harmonized Land Nutrient Irrigation Dataset (HLNID) &ndash; that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938&ndash;2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arc-seconds. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting.

  PublisherDOI

• Targeted grassland restoration increases potential for reduced nutrient export in the Upper Mississippi River Basin

  Environmental Research Letters · 2026-02-26

  articleOpen access

  Abstract Agriculture remains a leading cause of poor water quality in the Upper Mississippi River Basin (UMRB), despite ongoing nutrient reduction strategies and incentivized conservation programs. Converting agricultural land to grasslands offers a potential pathway toward achieving nutrient reduction goals for this region. We evaluated whether conversion of agriculture to grassland four decades ago could have lowered contemporary nutrient loads at the basin outlet. Using an integrated modeling framework, we assessed how the extent and spatial configuration (random vs targeted) of land conversion affected nitrogen and phosphorus loading and identified the levels of conversion needed to meet U.S. Environmental Protection Agency goals for nutrient loading. Model results indicate a 45% nitrogen load reduction could have been achieved in 20 years after implementation in 1981 with a targeted 53% conversion of agricultural land, whereas a 45% phosphorus load reduction would require highly extensive conversion of agricultural land and 40+ years, or additional nutrient-reduction practices alongside transformative land-cover change. These simulations demonstrate that earlier and more extensive grassland restoration may substantially reduce nutrient loads, with the targeted approach outperforming random conversion. While a targeted approach offers greater water quality gains and potential for greater return on investment for funded programs, a random approach may be more feasible across large watersheds due to lower social-ecological coordination demands. Nonetheless, achieving UMRB water quality goals by mid-century will require immediate, transformative changes in land use and nutrient management, and support from targeted government policies.

  PublisherDOI

• Reply on RC1

  2026-04-21

  peer-reviewOpen access1st authorCorresponding

  <strong class="journal-contentHeaderColor">Abstract.</strong> Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) &ndash; the Harmonized Land Nutrient Irrigation Dataset (HLNID) &ndash; that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938&ndash;2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arc-seconds. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting.

  PublisherDOI

• Comment on essd-2025-445

  2026-02-15

  peer-reviewOpen access1st authorCorresponding

  <strong class="journal-contentHeaderColor">Abstract.</strong> Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) &ndash; the Harmonized Land Nutrient Irrigation Dataset (HLNID) &ndash; that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938&ndash;2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arc-seconds. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting.

  PublisherDOI

• Reply on RC2

  2026-04-21

  peer-reviewOpen access1st authorCorresponding

  <strong class="journal-contentHeaderColor">Abstract.</strong> Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) &ndash; the Harmonized Land Nutrient Irrigation Dataset (HLNID) &ndash; that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938&ndash;2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arc-seconds. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting.

  PublisherDOI

• Historical phosphorus flows and legacy accumulation and depletion in a midwestern agricultural watershed, 1987–2017

  Journal of Soil and Water Conservation · 2026-01-02

  article
  PublisherDOI

• A Process Based Modeling Approach to Assess Temperature Impacts of Riparian Vegetation Management in a Southwestern Wisconsin Trout Stream

  River Research and Applications · 2025-06-24

  articleOpen access

  ABSTRACT Rising water temperatures driven by climate change threaten culturally and economically important salmonid fisheries throughout the Upper Midwest. Unsuitable thermal regimes degrade the effectiveness of habitat restoration projects in the region, thus strategies for mitigating peak summer stream temperatures are of interest to state and non‐profit fisheries managers. Using a process‐based stream temperature model, this study explores the thermal impact of riparian tree planting and tree removal in a 179 km 2 watershed in the unglaciated Driftless Area of southwestern Wisconsin. By creating hypothetical riparian vegetation scenarios and systematically adding and removing woody vegetation from the banks, we explore the influence of shade and channel geometry on July stream temperatures with an emphasis on salmonid thermal suitability. We used this model to analyze an 18.5 km study reach to identify management areas that have the most potential to buffer downstream water temperatures throughout the summer with added shade. We developed a downstream thermal change (DTC) metric to measure the magnitude and downstream distance of temperature change following stream alterations. The magnitude of stream cooling was mediated by channel width in our scenarios, with more pronounced thermal changes in narrower stream reaches ( p &lt; 0.05). Modeled tree planting scenarios decreased the maximum July maximum weekly average temperature (MWAT) and July maximum weekly maximum temperature (MWMT) within the study reach by 0.52°C and 0.53°C, respectively. This study offers a workflow using free and open‐source modeling tools to determine the thermal impact of restoration and prioritize future management efforts in cold water stream ecosystems.

  PublisherOA PDFDOI

• Development of historical maps of land use-land cover, crop type, nutrients, and irrigation across CONUS (1938–2020) at different spatial resolutions

  Earth system science data · 2025-09-05 · 1 citations

  articleOpen access1st authorCorresponding

  Abstract. Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) – the Harmonized Land Nutrient Irrigation Dataset (HLNID) – that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938–2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arcsec. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting. The data presented in this work are available at https://doi.org/10.5061/dryad.r4xgxd2rh (Booth and Kucharik, 2026).

  PublisherOA PDFDOI

• Simulating Pasture Yield Under Alternative Environments and Grazing Management in Wisconsin, USA

  Agronomy · 2025-02-11 · 1 citations

  articleOpen access

  Pasture yield is crucial to the economic viability of grass-based livestock enterprises, yet the difficulty in predicting yields under various environmental and management conditions prevents effective planning. We used USDA-SSURGO data to create a random forest model that predicts pasture yield potential based on soil properties for the state of Wisconsin (USA). This model is highly accurate (RMSE = 0.11 tons/acre, or 4% of the average yield), predicting pasture yields in Wisconsin grasslands to range from 1.0 to5.3 tons/acre, with an average yield of 2.6 tons/acre. We then integrated this model with guidelines from a USDA-NRCS grazing planning tool to adjust pasture yield potential for different levels of grazing intensity. The adjustments were multiplied to the random forest model output and ranged from 0.65 for continuously grazed pasture to 1.2 for pastures rotated more than once per day. The model is available to use within an online decision support tool through an R-shiny interface and can be easily replicated for other states in the Midwest US. The tool is easy to use and can support farmer analysis of the costs and benefits of grass-based agriculture.

  PublisherOA PDFDOI

• Industrial Agriculture’s Influence on Recreation: The Need for a Relational Approach

  Journal of Park and Recreation Administration · 2025-03-02

  articleSenior author

  Recent reviews of leisure and recreation literature have asserted a need to con-sider the relationship between ecosystem function and recreation at multiple spatial scales. One way socio-ecological interactions across a regional landscape impact recreation are through the industrialization of agricultural practices. Little research has examined how the industrialization of agriculture, mainly through confined animal feeding and the intensification of monocultural row crop farming, has impacted recreational opportunities in locations such as the Upper Midwest of the United States. While beach closures and fish kills attributed to non-point source pollution are glaring examples, it is likely that the relationship between industrialized agriculture and recreational opportunities is not fully understood. It is increasingly evident that a fundamental transformation of socio-ecological systems will be necessary to adapt to our changing climate and community needs. Recreation and leisure studies could play a critical role in shaping the conversation and assessing the impacts of these socio-ecological transitions.

  PublisherDOI

Frequent coauthors

• Steven P. Loheide

  University of Wisconsin–Madison

  33 shared

• Christopher J. Kucharik

  University of Wisconsin–Madison

  32 shared

• Samuel C. Zipper

  United States Geological Survey

  26 shared

• Jiangxiao Qiu

  University of Fort Lauderdale

  25 shared

• Melissa Motew

  Indigo Information Services (United States)

  17 shared

• Stephen R. Carpenter

  University of Wisconsin–Madison

  13 shared

• Jenny Seifert

  North Central College

  8 shared

• Monica G. Turner

  University of Wisconsin–Madison

  7 shared

Labs

• Eric Booth LabPI

Education

• Ph.D., Hydrology

  University of Wisconsin-Madison

  2005

• M.S., Hydrology

  University of Wisconsin-Madison

  2001

• B.S., Environmental Engineering

  University of Wisconsin-Madison

  1999