Limited surface observations of turbulent heat fluxes result in incomplete knowledge about the surface energy balance that drives the climate system. Here, we developed a novel, purely physics-based analytical method grounded on the thermodynamic principle of maximum power. The approach derives the turbulent heat flux only from the four inputs of incoming and outgoing radiations at the land surface. The proposed approach does not use any parameterization, unlike the existing surface energy balance models, and hence does not suffer from uncertainty due to the same. We validated our methodology with 102 eddy covariance observation stations around the globe with different land use land covers. Using the satellite observations from CERES at a spatial resolution of 1 degree, we have obtained spatially distributed global analytical estimates of Sensible (H), latent heat (LE), and land surface heat storage (delQs) fluxes for the first time. For a global observed land Net radiation of 84 W/m2 from the satellite, we found H, LE and delQs to be 42 W/m2, 40 W/m2 and 2 W/m2, respectively. The theoretical and precise estimates of all surface energy balance components will improve our understanding of surface warming for different land use land covers across the globe.

In a warming climate, elevated atmospheric CO ₂ concentrations are altering ecological and hydrological systems globally. As the second-largest contributor to global greening, driven largely by agricultural intensification and with the country’s rapid economic growth, India’s vegetation dynamics have significant implications for the global water and carbon cycles and their interactions. India, as a major contributor to global greening, offers key insights into the complex interactions among climate variability, land-use shifts, and anthropogenic activities. However, large uncertainties persist in India’s water cycle, particularly in understanding monsoon precipitation processes, sparsely monitored in situ evapotranspiration (ET), and soil moisture, which propagate into uncertainties in global model products and predictions over the region and fundamentally limit reliable projections of vegetation productivity and carbon dynamics. Vegetation, in turn, provides feedback to the water cycle through both biophysical (energy and water fluxes) and physiological (stomatal regulation) processes, emphasizing the need to quantify two-way land–atmosphere interactions. Understanding and reducing these inherent uncertainties are necessary for projecting the future of India’s ecosystems under ongoing global climate change. This review synthesizes current understanding of terrestrial carbon–water interactions in India, exploring regional carbon budgets, atmospheric controls on vegetation productivity, the profound effects of land management, ecosystem feedback via ET and land surface change. We highlight emerging state-of-the-art methods, such as causal inference and machine learning, which offer powerful tools for accurately diagnosing and predicting these coupled processes. Despite these advancements, significant limitations persist: long-term field observations remain sparse, plant physiological processes are underrepresented in current land surface models (LSMs), and human interventions like irrigation are often poorly parameterized. To address these critical gaps, we propose an integrated, multi-scale research framework that cohesively connects field data, remote sensing, and LSM. Strengthening this integrated understanding is essential for improving projections of ecosystem change and hydroclimatic extremes in India under projected global warming scenarios, supporting climate resilience and sustainable development.

At the local scale, energy exchange shapes microclimates and ecosystems crucial for human health and well-being. For urban areas, the effect, such as Urban Heat Island, is directly manifested in these surface energy fluxes with contrasting responses in values between urban and rural areas. Although progress has been achieved in modeling the land surface energy balance, challenges arise from complex, variable parameterizations linked to surface and climate characteristics, introducing uncertainties. In this work, we utilized the thermodynamic theory that considers the land-atmosphere as a radiative-convective system to analytically estimate total turbulent heat flux and land surface heat storage flux for 20 Urban sites and compared them with Eddy covariance observations. The heat fluxes are determined only from four primary parameters: incoming and outgoing longwave and shortwave radiations at the terrestrial surface. Using the monthly averages derived from the total turbulent flux estimates at the eddy covariance sites, we observed root-mean-square error (RMSE) of 29.16 ± 11.3 Wm−2, a mean bias error (MBE) of -7.09 ± 19.6 Wm−2 and R2 value of 0.82 ± 0.16. We further tested the analytical estimates with land use land cover of Urban sites. Our findings illustrate the distribution of land surface heat storage flux estimates following land use land cover characteristics. The analytical estimates of heat fluxes for urban areas offer several advantages, such as ease of implementation and inexpensive computation, facilitating the evaluation of urban land use feedback for informed urban planning.