Terrestrial linkages between carbon and water cycle interactions in India

Abstract

In a warming climate, elevated atmospheric CO2 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.