The Indian subcontinent suffers from a decline in the per capita of water resources in the course of recent decades due to exponential population growth. In India, the majority of the population is reliant on agribusiness which is in turn primarily dependent on water from monsoons. Regions that get lower precipitation than needed experience water deficits which impact agriculture. However, investigation of linkages between the trends in water balance components and land-cover distribution has not been performed. In our work, we focus on the water balance for the major river basins in India utilizing the following satellite and model-based datasets: Terrestrial Water Storage Anomaly (TWSA) from Gravity Recovery and Climate Experiment (GRACE), Precipitation (P) from Tropical Rainfall Measuring Mission (TRMM), Evapotranspiration (ET) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and Total Runoff (surface overflow and baseflow) (R) from the NASA Global Land Data Assimilation System (GLDAS). We assessed the seasonal spatio-temporal changes in the water balance from 2002 to 2019 and observed that ‘Agriculture’ and ‘Urban’ are the two most disrupted land-cover types displaying a monotonic increasing/decreasing trend in the components of water balance. We also observed from the SVD (Singular Value Decomposition) analysis that the inherent spatial variability between P-ET-R and TWSA do not correlate well. We also found that North-Eastern India and regions in Southern India along the west coast show large negative trends of P (-15 to −20 mm) and R (-10 to −15 mm) in the monsoon season, indicating that these regions suffer from drier monsoon seasons over the study period (2002–2019). From the land-cover linkages, it was found that 74% of monotonic trends observed in several of river basins were linked to ‘Agricultural’ land cover type and 19% were linked to ‘Urban’ land cover type. These linkages suggest that agricultural lands are more vulnerable to changes in the components of the water balance.

近几十年来，由于人口呈指数增长，印度次大陆的人均水资源量不断下降。在印度，大多数人口依赖农业综合企业，而农业企业又主要依赖季风水。降水量低于所需的地区会出现缺水现象，这会影响农业。然而，尚未对水平衡组成部分的趋势与土地覆盖分布之间的联系进行调查。在我们的工作中，我们利用以下卫星和基于模型的数据集关注印度主要河流流域的水平衡：来自重力恢复和气候实验 (GRACE) 的陆地储水异常 (TWSA)，来自热带的降水 (P)降雨量测量任务（TRMM），中分辨率成像光谱仪 (MODIS) 的蒸散量 (ET) 和 NASA 全球陆地数据同化系统 (GLDAS) 的总径流（地表溢流和基流）(R)。我们评估了 2002 年至 2019 年水平衡的季节性时空变化，并观察到“农业”和“城市”是两种最受干扰的土地覆盖类型，在水平衡的组成部分中显示出单调的增加/减少趋势。我们还从 SVD（奇异值分解）分析中观察到 P-ET-R 和 TWSA 之间的固有空间变异性没有很好的相关性。我们还发现印度东北部和印度南部西海岸地区在季风季节表现出较大的 P（-15 至 -20 毫米）和 R（-10 至 -15 毫米）负趋势，表明这些地区在研究期间（2002-2019 年）遭受干燥的季风季节。从土地覆盖联系中，发现在几个流域观察到的单调趋势中，74% 与“农业”土地覆盖类型相关，19% 与“城市”土地覆盖类型相关。这些联系表明农业用地更容易受到水平衡组成部分变化的影响。