Benjamin R. K. Runkle Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Associate Professor
faculty
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Biography and Research Information
OverviewAI-generated summary
Benjamin R. K. Runkle is an Associate Professor at the University of Arkansas at Fayetteville. He holds a B.S.E. in Civil & Environmental Engineering from Princeton University and a Ph.D. in Civil & Environmental Engineering from UC-Berkeley. He also completed a postdoctoral fellowship at the University of Hamburg Institute of Soil Science.
Runkle's research focuses on ecosystem dynamics, particularly the emission of greenhouse gases like methane from various environments. His work investigates the environmental factors influencing these emissions, including seasonality and temperature sensitivity. He has contributed to the development of large-scale datasets, such as FLUXNET-CH4, which provides global methane flux data from freshwater wetlands. His publications explore methods for gap-filling flux data using machine learning and assess the potential of nature-based climate solutions.
His research has involved collaborations with several colleagues at the University of Arkansas at Fayetteville, including Beatriz Moreno‐García, Gerardo Celis, Will Richardson, and Dakota S. Dale. Runkle maintains an active lab website. He is recognized as a highly cited researcher, with 192 publications and over 3,000 citations, and an h-index of 31.
Metrics
- h-index: 32
- Publications: 196
- Citations: 3,092
Selected Publications
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from October 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from October 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from October 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
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Data for EMSL Project 61115 from November 2025 (2026)
Federal Grants 9 $8,675,914 total
Note: 9 grants are marked "Topic-matched" — attributed by description keywords rather than verified PI records.
Collaboration Network
Top Collaborators
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Impacts of alternate wetting and drying and delayed flood rice irrigation on growing season evapotranspiration
- Socio-Technical Changes for Sustainable Rice Production: Rice Husk Amendment, Conservation Irrigation, and System Changes
- Rice Inundation Assessment Using Polarimetric UAVSAR Data
- The effect of water management and ratoon rice cropping on methane emissions and yield in Arkansas
Showing 5 of 18 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- Paddy rice methane emissions across Monsoon Asia
Showing 5 of 11 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- An Ecosystem-Scale Flux Measurement Strategy to Assess Natural Climate Solutions
Showing 5 of 11 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- Paddy rice methane emissions across Monsoon Asia
Showing 5 of 10 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
- Paddy rice methane emissions across Monsoon Asia
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
Showing 5 of 10 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Paddy rice methane emissions across Monsoon Asia
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
Showing 5 of 9 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Paddy rice methane emissions across Monsoon Asia
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
Showing 5 of 9 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
Showing 5 of 9 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Paddy rice methane emissions across Monsoon Asia
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
Showing 5 of 8 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
Showing 5 of 8 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
Showing 5 of 8 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
Showing 5 of 8 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions
- Paddy rice methane emissions across Monsoon Asia
Showing 5 of 8 shared publications
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Socio-Technical Changes for Sustainable Rice Production: Rice Husk Amendment, Conservation Irrigation, and System Changes
- Rice Inundation Assessment Using Polarimetric UAVSAR Data
- Environmental sustainability assessment of rice management practices using decision support tools
- The effect of water management and ratoon rice cropping on methane emissions and yield in Arkansas
Showing 5 of 8 shared publications
- FLUXNET-CH <sub>4</sub> : a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- Paddy rice methane emissions across Monsoon Asia
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
- FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands (Appendix B and Figure 3)
Showing 5 of 7 shared publications
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