Brief Bio

I am a Research Engineer / Scientist in the Department of Civil, Construction, and Environmental Engineering at the University of Alabama, working with Prof. Mukesh Kumar. I work at the intersection of ecohydrology, Earth system science, and data science. By combining eddy-covariance (EC) observations of water&carbon fluxes, satellite remote sensing (including next-generation high-resolution thermal/SAR/optical missions e.g., Hydrosat's VanZyl constellation, NISAR, etc.), physical land surface models, and physics-informed machine learning (for improved model parameterization and structural improvements of process-based models), I work to improve how we observe and predict evapotranspiration and its individual components (direct soil/canopy evaporation and plant transpiration), soil moisture (surface and root-zone), and eco-hydrological extremes such as plant mortality, droughts, and floods.

Positions / Employment

Education

Research at a Glance

Click to enlarge.

I combine ground-based observations (e.g., ecosystem-scale water & carbon fluxes from EC flux towers; plant-scale water use from SAPFLUXNET, PSInet, etc.) with satellite remote sensing across the thermal, optical, and LiDAR domains (ECOSTRESS, MODIS, Landsat, GEDI, and next-generation missions such as Hydrosat's VanZyl constellation and NISAR) to validate and constrain land surface models (ELM, CLM, ECOSYS) and physics-informed machine learning for improved parameterization and structure of land surface models.

The goal is to better observe and predict the evapotranspiration (along with its primary components viz. biotic plant transpiration & abiotic soil evaporation), root-zone soil moisture, and stomatal conductance and to turn those improvements into tools that matter for people: drought detection, flood forecasting, numerical weather prediction, and early warning of eco-hydrological extremes.

Research Interests

• Land-atmosphere interactions and the terrestrial water, energy, and carbon cycles
• Evapotranspiration and its partitioning (biotic transpiration vs. abiotic evaporation); soil moisture dynamics
• Remote sensing of the hydrosphere and biosphere (thermal, optical, SAR, LiDAR)
• Land surface and Earth system modeling (ELM, CLM, ECOSYS, H08, VIC)
• Physics-informed machine learning for model improvement and process discovery
• Hydrological extremes (droughts, flash droughts, and floods), and stakeholder-relevant early-warning tools

Selected Research

A few studies that capture the direction of my work; the full list is below.

Publications

FA first author  ·  ^* equal contribution  ·  see also Google Scholar.

• 16FA Wagle, P.^*, Raghav, P.^*, Kumar, M., Scanlon, T. M., Northup, B. K., Moffet, C., Gunter, S., & Xiao, X. (2026). Partitioning Evapotranspiration in C3-C4 Mixed Tallgrass Prairies: An Inter-comparison of Five Methods and Evaluation of Seasonal Parameterization. Agricultural Water Management. DOI
• 15FA Raghav, P., & Kumar, M. (2026). PULSE: A novel potential underlying water-use-efficiency-based method for latent heat and surface energy imbalance correction. Water Resources Research. DOI
• 14FA Raghav, P., & Kumar, M. (2026). Dynamic parameterization of the Priestley–Taylor coefficient for more accurate potential evapotranspiration estimation in ungauged regions. Environmental Research Letters. DOI
• 13Massoud, E. C., Collier, N., Wang, Y., Mao, J., Harpold, A., Kannenberg, S. A., Koren, G., Kumar, M., Raghav, P., Ray, P., Shi, M., Tao, J., Vasu, S. P., Wang, H., Zhu, Q., & Hoffman, F. M. (2026). Benchmarking soil moisture and its relationship to ecohydrologic variables in Earth system models. Geoscientific Model Development, 19, 3427. DOI
• 12 Salvi, K.^*, Raghav, P.^*, Kumar, M., Magliocca, N., & Bisht, G. (2026). Discrepancy in the sign of temperature trends in reanalysis datasets. Environmental Research Letters. DOI
• 11Thakur, H., Raghav, P., & Kumar, M. (2026). How well does the Evaporative Stress Index from ECOSTRESS capture site-based stresses? Canadian Journal of Remote Sensing, 52(1). DOI
• 10Thakur, H., Raghav, P., & Kumar, M. (2025). Surface Flux Equilibrium theory–derived evapotranspiration estimate outperforms ECOSTRESS, MODIS, and SSEBop products. Geophysical Research Letters, 52(10), e2025GL114822. DOI
• 9Ghaseminejad, A., Bhat, N., Raghav, P., & Kumar, M. (2025). Influence of interannual leaf phenology dynamics on evapotranspiration predictions. Journal of Hydrometeorology, 26, 259–272. DOI
• 8FA Raghav, P., & Kumar, M. (2024). Are the ecosystem-level evaporative stress indices representative of evaporative stress of vegetation? Agricultural and Forest Meteorology, 357, 110195. DOI
• 7FA Raghav, P., Kumar, M., & Liu, Y. (2024). Structural constraints in current stomatal conductance models preclude accurate prediction of evapotranspiration. Water Resources Research, 60(8), e2024WR037652. DOI
• 6Rathore, L. S., Hanasaki, N., Kumar, M., Mekonnen, M., & Raghav, P. (2023). Water scarcity challenges across urban regions with expanding irrigation. Environmental Research Letters, 19(1), 014065. DOI
• 5FA Raghav, P., & Eldho, T. I. (2023). Investigations on the hydrological impacts of climate change on a river basin using macroscale model H08. Journal of Earth System Science, 132(2), 87. DOI
• 4FA Wagle, P.^*, Raghav, P.^*, Kumar, M., & Gunter, A. G. (2022). Influence of water-use efficiency parameterizations on flux-variance-similarity-based partitioning of evapotranspiration. Agricultural and Forest Meteorology, 328, 109254. DOI
• 3FA Raghav, P., Wagle, P., Kumar, M., Banerjee, T., & Neel, J. P. S. (2022). Vegetation index–based partitioning of evapotranspiration is deficient in grazed systems. Water Resources Research, 58(8), e2022WR032067. DOI
• 2FA Raghav, P., & Kumar, M. (2021). Retrieving gap-free daily root-zone soil moisture using Surface Flux Equilibrium theory. Environmental Research Letters, 16(10), 104007. DOI
• 1FA Raghav, P., Borkotoky, S. S., Joseph, J., Chattopadhyay, R., Sahai, A. K., & Ghosh, S. (2020). Revamping extended-range forecast of the Indian Summer Monsoon. Climate Dynamics, 55, 3397–3411. DOI

• ·Raghav, P., Liu, Y., Kumar, M., & Bisht, G. (2026). Multilayer canopy model outperforms big-leaf model for evapotranspiration predictions under high water and heat stress conditions. Journal of Advances in Modeling Earth Systems.
• ·Raghav, P., & Kumar, M. (2026). Padé approximation of the Clausius–Clapeyron relation: an explicit and computationally efficient alternative to the Penman–Monteith equation. Geophysical Research Letters.
• ·Thakur, H., Raghav, P., & Kumar, M. (2026). Benchmark data of root-zone soil moisture over the conterminous USA based on vegetation-specific rooting depths. Vadose Zone Journal.
• ·Wolkeba, F. T., Raghav, P., Kumar, M., & Thakur, H. (2026). CALFRET: a high-performing, calibration-free 1-km evapotranspiration product over the conterminous USA. Water Resources Research. Preprint
• ·Alam, M. S., Koriche, S. A., Johnson, R., Neisary, S., Raghav, P., Tebbey, B., Khattak, A. J., Ogden, F. L., Kumar, M., & Burian, S. (2026). Understanding National Water Model performance for hydrologic extremes using basin attributes and hydrologic signatures. Journal of the American Water Resources Association.

• ·Raghav, P., & Kumar, M. (2026). Large underestimation of flash drought events in reanalysis data. Drafted for Nature Communications.
• ·Raghav, P., & Kumar, M. (2026). Evapotranspiration dynamics and partitioning in soybean under different irrigation practices. In prep. for Agricultural and Forest Meteorology.
• ·Raghav, P.^*, Tang, ACI.^*, & Forbrich, I. (2026). Evapotranspiration partitioning in tidal wetlands across hydrological, salinity, and vegetation gradients. In preparation.
• ·Moreno, H., Massoud, E., Yaari, A., Huang, J., Koren, G., Raghav, P., et al. (2026). How is artificial intelligence enhancing soil moisture estimation and prediction? In preparation.
• ·Desai, A., Wanner, L., Levy, P., Zhang, W., Durden, D., Metzger, S., Raghav, P., et al. (2026). Evaluating mechanistic corrections for surface energy imbalance in eddy covariance. In preparation.
• ·Kooperman, G. J., Ray, P., Hsu, W.-C., Puthiyamadam Vasu, S., Zhang, L., Ahmed, M. H., Chen, A., Fang, Y., Kibria, M. G., Kim, Y., Koren, G., Li, C., Li, L., Lin, T.-S., Liu, L., Mao, J., Massoud, E., Nikolopoulos, E., Ogunmokun, F., Raghav, P.., Saha, S. K., Shi, M., Sridhar, V., & Tewari, M. (2026). The Roles of Soil Moisture-Convection Interactions in the Tropics: A Review. Reviews of Geophysic.
• ·Wang et al. (2026). Development of a boreal soil moisture dataset using updated quality control algorithms and deep learning.

• ·Raghav, P., & Eldho, T. I. (2021). Assessing the impacts of climate change on crop yield in the Upper Godavari River sub-basin using the H08 hydrological model. In Climate Change Impacts on Water Resources, 193–205. Springer. DOI

Awards & Honors

• Travel Grant, AGU Energy Balance Chapman Conference, Boulder, CO (2025)
• Outstanding Dissertation Award, Dept. of Civil, Construction & Environmental Engineering, Univ. of Alabama (2025)
• Top rank, M.Tech Water Resources Engineering, IIT Bombay (2018)
• Ministry of Human Resource Development (MHRD), India scholarship (2016–2018)

Grants & Proposals

• VIEW-2 (Virtual Institute for Earth's Water): FLARE: field-scale retrieval of plant water use and ecosystem resilience from space (EOI submitted, 2026)
• U.S. DOE (BER, DE-FOA-0003600): ML-enhanced energy & water partitioning in the E3SM Land Model over the Southeastern U.S. (under review, 2026)
• USDA ARS AI Innovation Fund: High-resolution root-zone soil moisture over CONUS via SFE-informed ML (under review, 2026)
• AGU Chapman Travel Grant: WUE-based correction of eddy-covariance latent heat fluxes (approved, 2025)

Talks, Presentations & Posters

^* presenting author / convener

• Advancing measurements and modeling of invisible components of the water budget - AGU Fall Meeting, New Orleans (2025)
• Flux partitioning in wetland systems - AmeriFlux Annual Meeting (2025)
• How representative are ecosystem-level evaporative stress indices of vegetation stress? - AGU Fall Meeting, Washington, D.C. (2024)
• Evapotranspiration predictions: gaps, challenges, and the way forward - University of Toledo (2024)
• Retrieving gap-free daily root-zone soil moisture using SFE theory - The Ohio State University (2024)

• Raghav, P.^*, & Kumar, M. (2025). Evaluating the accuracy of Evaporative Stress Index–based flash drought detection in global reanalysis data. AGU Fall Meeting (H21B-04), New Orleans, LA.
• Raghav, P., & Kumar, M.^* (2025). Leveraging FLUXNET data for improved estimation of potential evapotranspiration using the Priestley–Taylor method. AGU Fall Meeting (B12C-07), New Orleans, LA.
• Salvi, K., Raghav, P.^*, & Kumar, M. (2025). Evaluation of temperature trends simulated by reanalysis data at observation sites in the continental United States. AGU Fall Meeting (BH11M-1043), New Orleans, LA.
• Kumar, M.^*, Raghav, P., Liu, Y., & Bisht, G. (2025). The complexity paradox: when advanced evapotranspiration models underperform (invited). AGU Fall Meeting (H52A-02), New Orleans, LA.
• Nishan, B.^*, Wagle, P., Raghav, P., & Shayeghi, A. (2025). Advancing measurements and modeling of invisible components of the water budget (oral). AGU Fall Meeting (H13M), New Orleans, LA.
• Tang, A.^*, Raghav, P.^*, & Forbrich, I. (2025). Partitioning evapotranspiration in coastal wetlands using flux variance similarity. AGU Fall Meeting (H42D-06), New Orleans, LA.
• Wolkeba, F.^*, Kumar, M., Raghav, P., & Thakur, H. (2025). A high-performing 1-km daily gridded evapotranspiration dataset for CONUS based on Surface Flux Equilibrium theory. AGU Fall Meeting (H41D-07), New Orleans, LA.
• Raghav, P.^*, & Kumar, M. (2025). Evaluating the influence of model parameters, structure, input data, and site characteristics on energy-partitioning errors in land surface models. AGU Chapman Conference on the Energy Balance Closure Problem, Boulder, CO.
• Raghav, P.^*, Liu, Y., & Kumar, M. (2024). Enhancing water and carbon flux predictions: a comparative study of multi-layer canopy and big-leaf models under varying water stress and light conditions. AGU Fall Meeting, Washington, D.C.
• Raghav, P.^*, & Kumar, M. (2024). How representative are ecosystem-level evaporative stress indices of vegetation evaporative stress? AGU Fall Meeting, Washington, D.C.
• Raghav, P.^*, & Kumar, M. (2024). The accuracy of evapotranspiration prediction is impeded by structural limitations in current stomatal conductance models. Ecological Society of America Annual Meeting, Long Beach, CA.
• Raghav, P.^*, & Kumar, M. (2023). A physics-informed machine learning approach to study the global dynamics of the transpiration-to-evapotranspiration ratio. AGU Fall Meeting (B43E-2592), San Francisco, CA.
• Raghav, P.^*, & Kumar, M. (2023). Evapotranspiration partitioning in evaporation-dominant settings. AGU Fall Meeting (H21Q-1573), San Francisco, CA.
• Ghaseminejad, A., Raghav, P., & Kumar, M.^* (2023). Is the representation of interannual leaf phenology dynamics needed for accurate estimation of evapotranspiration? AGU Fall Meeting (B43E-2604), San Francisco, CA.
• Wagle, P.^*, Raghav, P., Kumar, M., Scanlon, T. M., Northup, B. K., Moffet, C., Gunter, S., & Xiao, X. (2023). A comparison of FVS, MREA, and CEC methods to partition evapotranspiration in tallgrass prairies. AGU Fall Meeting (H13D-06), San Francisco, CA.
• Raghav, P.^*, & Kumar, M. (2022). Assessing evapotranspiration partitioning obtained using land surface models across varied ecosystems. AGU Fall Meeting (B12E-1121), Chicago, IL.
• Ghaseminejad, A.^*, Bhat, M., Raghav, P., & Kumar, M. (2022). Machine-learning-driven temporal downscaling of groundwater recharge estimates to obtain event-based response. AGU Fall Meeting (H35H-1215), Chicago, IL.
• Kumar, M.^*, Rathore, L. S., Hanasaki, N., Mekonnen, M., & Raghav, P. (2022). Rain-fed to irrigation-fed transition of cropped agriculture may enhance urban–rural water conflict. AGU Fall Meeting (GC35D-05), Chicago, IL.
• Wagle, P.^*, Raghav, P., & Kumar, M. (2022). Sensitivity of water-use-efficiency parameterizations on flux-variance-similarity-based partitioning of evapotranspiration. AGU Fall Meeting (B12E-1123), Chicago, IL.
• Raghav, P.^*, & Kumar, M. (2021). Are canonical relations to partition evapotranspiration valid for managed systems? AGU Fall Meeting (B12B-02), New Orleans, LA.
• Raghav, P., & Kumar, M.^* (2021). Obtaining daily root-zone soil moisture using a calibration-free approach. AGU Fall Meeting (H55C-0777), New Orleans, LA.
• Raghav, P., & Eldho, T. I.^* (2019). Investigations on the impacts of future climate change on the hydrology of a river basin in India using a macroscale hydrological model. 11th World Congress on Water Resources and Environment (EWRA), Madrid, Spain.
• Raghav, P.^* (2019). Assessing the impacts of climate change on crop yield in the Upper Godavari River basin using the H08 hydrological model. ISIMIP Cross-Sectoral Workshop, Paris, France.

Press

Improving Eddy Tower Evapotranspiration Estimates

Eos (AGU) · Research Spotlight · by Rebecca Dzombak · May 2026

Featured coverage of our PULSE method for correcting eddy-covariance evapotranspiration measurements for surface energy imbalance using potential water-use efficiency (Raghav & Kumar, 2026, Water Resources Research).

Image via Eos

Software & Data

• Getting started with Hydrosat satellite data - STAC access, LST, NDVI & red-edge indices, cloud masking, and geospatial analysis. GitHub · 2026
• Evaluating multilayer vs. big-leaf canopy models for evapotranspiration under water and heat stress. Zenodo · Jan 2026
• Dynamic parameterization of the Priestley–Taylor coefficient for potential ET in ungauged regions. Zenodo · Nov 2025
• PULSE - correcting eddy-covariance latent heat fluxes for energy imbalance via potential water-use efficiency. Zenodo · Oct 2025
• Water flux partitioning using flux-variance-similarity theory in wetland settings. GitHub · Oct 2025
• Guide to downloading and processing remote sensing data in R. GitHub · Apr 2025
• Information-theory-based model performance diagnostics. GitHub · Aug 2024
• Ecosystem-scale transpiration from the SAPFLUXNET database. GitHub · Apr 2024
• Water flux partitioning using high-frequency eddy-covariance data. GitHub · Zenodo · Aug 2023
• Physical and machine-learning models of stomatal conductance for ET prediction. Zenodo · Mar 2023
• Water flux partitioning data in disturbed (grazed) wheat systems. USDA Ag Data Commons · Aug 2022

Professional Service & Teaching

Reviewer: Water Resources Research · JGR-Atmospheres · JGR-Biogeosciences · Remote Sensing of Environment · Journal of Hydrology · Journal of Water and Climate Change · Agricultural and Forest Meteorology.
Community: American Geophysical Union · Ecological Society of America · Alabama Water Institute. AGU student judging; AGU Chapman & AmeriFlux breakout facilitator / co-organizer.
Teaching: Process Hydrology (CEE 491/591), Univ. of Alabama (2023-2024); graduate-student mentoring.

Contact

Office 1033, Cyber Hall, 248 Kirkbride Ln, Tuscaloosa, AL 35401
ppushpendra@ua.edu |praghav444@gmail.com · +1 313-742-3251