About

Binayak P. Mohanty is a Regents Professor and COALS Chair in Hydrologic Engineering and Sciences at Texas A&M University. He is affiliated with the Biological and Agricultural Engineering department as well as the Ecosystem Science and Management program, focusing on Water Management and Hydrologic Science. His work is connected to the Vadose Zone Research Group, which is part of the Texas A&M College of Agriculture and Life Sciences and Texas A&M AgriLife Research. The group conducts research on water stores and fluxes, critical for understanding and modeling water resources and their sustainability, particularly in Texas and the Southern USA. The Texas Water Observatory, a distributed network of field observatories in the Brazos River corridor, is one of the advanced observational platforms associated with this research, monitoring groundwater, soil water, surface water, and atmospheric water at multiple locations across space and time using near-real-time sensors.

Research topics

• Artificial Intelligence
• Machine Learning
• Computer Science
• Mathematics
• Soil science
• Geography
• Meteorology
• Environmental science
• Geology
• Geotechnical engineering

Selected publications

• The Role of Soil Properties in Modulating the Impact of Groundwater-induced Drainage Dynamics on Soil Moisture Availability

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Irrigation Water Productivity and Climate Impact Indices: The Multiple Dimensions of Climate-Smart Irrigation

  2025-07-31

  preprintOpen access

  • Irrigation is framed as a multi-objective optimization balancing crop productivity and soil emissions (climate impact) goals. • We derive a dual-index framework to assess irrigation productivity and climate impacts across field studies of varying crops and emissions. • The framework identifies Pareto-optimal irrigation practices and shows the tradeoff between productivity and climate impact goals.

  PublisherOA PDFDOI

• Evaluating Various Energy Balance Aggregation Schemes in Cotton Using Unoccupied Aerial Systems (UASs)-Based Latent Heat Flux Estimates

  Remote Sensing · 2025-10-29

  articleOpen access

  Daily evapotranspiration (ET) estimated from an unoccupied aerial system (UAS) could help improve irrigation practices, but its spatial resolution needs to be upscaled to coarser pixel resolutions before applying surface energy balance models. The purpose of this study was to evaluate the impact of various energy balance-based aggregation schemes on generating spatially distributed latent heat flux (LE), and, in comparison, to existing occupied aircraft and satellite remote sensing platforms. In 2017, UAS multispectral and thermal imagery, along with ground truth data, were collected at various cotton growth stages. These data sources were combined to model LE using a Two-Source Energy Balance Priestley–Taylor (TSEB-PT) model. Several UAS aggregation schemes were tested, including the mode of aggregation (i.e., input image and output flux) as well as the averaging scheme (i.e., simple aggregation vs. Box–Cox). Results indicate that output flux aggregation with Box–Cox averaging produced the lowest relative upscaling pixel-scale variability in LE and the lowest absolute prediction errors (relative to eddy covariance flux tower measurements). Output flux aggregation with simple averaging was also more accurate in reproducing LE from occupied aircraft and satellite imagery. Although results are limited to a single site, UAS LE estimates were reliably aggregated to coarser pixel resolutions, which made for faster image processing for operational applications.

  PublisherDOI

• Classifying the potential for soil organic carbon gain under regenerative agriculture

  Environmental Research Letters · 2025-03-19 · 4 citations

  articleOpen access

  Abstract Regenerative agriculture is pivotal for mitigating climate change, with no-tillage practices on cropland being generally effective at raising soil organic carbon (SOC). Yet, our understanding of the compound impact of soil and environmental factors on SOC gain potential after transitioning to no-till practices is still developing. Using imbalanced machine learning classification, here we quantify key thresholds to hierarchically classify SOC gain potential by switching from conventional tillage to long-term no-tillage with residue retention. Our findings reveal that antecedent SOC level exerts the primary influence, with a reduced gain potential for antecedent SOC exceeding 50 tonnes per hectare. Wet climate (Dryness Index <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>&lt;</mml:mo> </mml:mrow> </mml:math> 1.5) and low productivity (net annual primary productivity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>&lt;</mml:mo> </mml:mrow> </mml:math> 5.5 tonnes per hectare) could further lessen the effectiveness of SOC sequestration. These key thresholds identify vast areas across Africa, Australia, South Asia, Southern Europe, and parts of North and South America as high-potential croplands for carbon sequestration and offer guidelines for assessing the reliability of regenerative agriculture in local and regional contexts.

  PublisherOA PDFDOI

• Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

  Journal of Advances in Modeling Earth Systems · 2025-03-01 · 3 citations

  articleOpen access

  Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) ( R 2 &gt; 0.6, RMSE &lt;390 g C m −2 ) relative to net ecosystem exchange of CO 2 (NEE) ( R 2 &gt; 0.4, RMSE &lt;180 g C m −2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks.

  PublisherOA PDFDOI

• Preface to Ecohydrology of Inland and Coastal Waters in Honour of Ignacio Rodriguez‐Iturbe

  Ecohydrology · 2025-07-01

  article1st authorCorresponding
  PublisherDOI

• Balancing Productivity and Climate Impact: A Framework to Assess Climate‐Smart Irrigation

  Earth s Future · 2025-11-01 · 1 citations

  articleOpen access

  Abstract Changes in rainfall and temperature regimes increasingly threaten global crop productivity, particularly in water‐limited regions. Climate‐smart agriculture aims to improve yields while minimizing its climate impact, such as from soil greenhouse gas (GHG) emissions driven by microbial activity. From an irrigation perspective, this underscores the need to assess irrigation practices beyond the traditional objectives of maximizing yield and water use efficiency by also considering their climate impact from soil GHG emissions. To address this gap, we frame climate‐smart irrigation as a multi‐objective optimization problem and derive a dual‐index framework for evaluating irrigation practices across productivity, water consumption, and climate impact dimensions. The Marginal Irrigation Water Productivity (MIWP) index quantifies additional yield per unit of irrigation water, while the Marginal Irrigation Climate Impact (MICI) index measures the associated changes in soil GHG emissions. We apply this dual‐index framework to wheat and rice field irrigation studies with varying soil GHG compositions, showing its ability to assess irrigation across different crop systems. Crop model simulations further demonstrate how different irrigation practices are mapped within the MIWP‐MICI space, where Pareto‐optimal solutions highlight trade‐offs between productivity and climate impact goals. Our approach provides a consistent, quantitative basis for comparing irrigation across multiple dimensions of climate‐smart irrigation.

  PublisherOA PDFDOI

• An Optimal Transport Framework for Water‐Energy Coupling in Soil‐Vegetation‐Atmosphere Continuum

  Water Resources Research · 2025-12-01 · 1 citations

  articleOpen accessSenior author

  Abstract The coupling between soil moisture (SM) and evapotranspiration (ET) governs key dynamics of Earth's climate and biosphere productivity. Yet, prevailing statistical models fall short of capturing the physics of water–energy exchange across diverse hydroclimates. In this study, we introduce an optimal transport framework based on the hypothesis that hydroclimates regulate SM–ET coupling near a quasi‐optimum state. This state is characterized by least action principle, defined by dynamic convolution between the water potential gradient () driving land‐to‐atmosphere moisture flux and the time weighted mass flux (referred as the SM‐ET coupling metric, ). Global validation of this framework using decadal (2010–2019) SM and ET remote sensing data reveals widespread convergence toward the least action state across hydroclimatic zones, supporting the notion of emergent climatic regulation in SM–ET coupling. As a corollary to the proposed hypothesis, we estimate two emergent properties of the SM–ET coupling: active root zone depth supporting ET, and the characteristic transit timescales over which SM is lost to atmosphere. Our root depth estimates show strong correspondence with in situ measurements (correlation &gt;0.86) across biomes, underscoring the framework's physical realism. Notably, dynamic transit times are also validated against isotope measurements and findings suggest that SM perturbations often cycle back into the atmosphere within 3–7 days, calling into question traditional metrics of bulk residence time, that often overestimates the actual turnover. Overall, this framework provides a physically grounded way to study water–energy interactions across diverse environments.

  PublisherDOI

• Balancing Productivity and Climate Impact: Climate-Smart Potential of Irrigation Practices

  2025-03-15

  preprintOpen access

  Traditional agricultural practices have placed unsustainable pressures on soils, resulting in degraded soil health and losses in biodiversity and fertility. Modern agriculture faces the dual challenge of increasing productivity while building resilience to climate change, particularly in water-scarce regions where crop productivity is at risk. Recognizing the potential of agricultural soils as a nature-based climate solution, climate-smart agriculture (CSA) offers a transformative strategy by integrating conservation practices and efficient water management to enhance soil health and mitigate climate impacts. From an irrigation perspective, this necessitates a comprehensive framework to holistically evaluate practices, moving beyond traditional objectives of maximizing yield and water use efficiency. In this study, we develop a multi-objective optimization framework for climate-smart irrigation (CSI), whereby a dual-index system evaluates irrigation systems (e.g., drip, sprinkler) and strategies (e.g., stress-avoidance, deficit irrigation) across productivity and climate impact dimensions. We first demonstrate the application of this framework by analyzing field studies of different crops (such as wheat and rice), irrigation practices and soil greenhouse gas (GHG) emission compositions, showing how the new indices jointly identify optimal irrigation practices. Additionally, using an ensemble of crop model simulations for corn production using irrigation across major U.S. production regions under varying climate and soil conditions, we explore trade-offs between productivity and climate impact goals. Results reveal a spectrum of Pareto-optimal irrigation practices that balance these dual objectives. These insights underscore the importance of holistic approaches in CSI and are critical for providing actionable insights into nature-based climate solutions in agricultural ecosystems.

  PublisherDOI

• Rootzone Soil Moisture Dynamics Using Terrestrial Water‐Energy Coupling

  Geophysical Research Letters · 2024-09-28 · 15 citations

  articleOpen accessCorresponding

  Abstract A lack of high‐density rootzone soil moisture ( θ RZ ) observations limits the estimation of continental‐scale, space‐time contiguous θ RZ dynamics. We derive a proxy of daily θ RZ dynamics — active rootzone degree of saturation ( S RZ ) — by recursive low‐pass (LP) filtering of surface soil moisture ( θ S ) within a terrestrial water‐energy coupling (WEC) framework. We estimate the LP filter parameters and WEC thresholds for the piecewise‐linear coupling between S RZ and evaporative fraction (EF) at remote sensing and field scale over the Contiguous U.S. We use θ S from the Soil Moisture Active‐Passive (SMAP) satellite and 218 in‐situ stations, with EF from the Moderate Resolution Imaging Spectroradiometer. The estimated S RZ compares well against SMAP Level‐4 estimates and in‐situ θ RZ , at the corresponding scale. The instantaneous hydrologic state ( S RZ ) vis‐à‐vis the WEC thresholds is proposed as a rootzone soil moisture stress index (SMS RZ ) for near‐real‐time operational agricultural drought monitoring and agrees well with established drought metrics.

  PublisherOA PDFDOI

Recent grants

• Measurement and Modeling of Flow and Transport in Macroporous Soils

  NSF · $295k · 2001–2006

• Flow-Induced Redox Geochemistry Within Fractured/Macroporous and Layered Vadose Zone

  NSF · $477k · 2007–2013

• CMG: Multi-Scale Data Assimilation of Soil Moisture Under Heterogeneous Soil Hydraulics

  NSF · $341k · 2006–2010

• Measurement and Modeling of Flow and Transport in Macroporous Soils

  NSF · $104k · 2001–2002

• CMG RESEARCH: Predictive Modeling of Thermally Driven Hydrologic Fluxes for Land and Atmosphere Interactions Across Space and Time Scales

  NSF · $450k · 2009–2014

Frequent coauthors

• Narendra N. Das

  Michigan State University

  67 shared

• Rabindra Kumar Panda

  57 shared

• Gurjeet Singh

  54 shared

• Dara Entekhabi

  Massachusetts Institute of Technology

  52 shared

• Bimal K. Bhattacharya

  49 shared

• Nandita Gaur

  University of Georgia

  38 shared

• Vinit Sehgal

  32 shared

• Amor V.M. Ines

  19 shared

Labs

• Vadose Zone Research GroupPI

Education

• B.S., Agricultural Engineering and Technology

  Orissa University of Agriculture & Technology – India

  1985

• Other, Agricultural Engineering

  Asian Institute of Technology – Thailand

  1987

• Ph.D.

  Iowa State University