The technical core of the study rests on two satellite-derived indices that have become workhorses of environmental remote sensing. Surface water presence was measured using the Modified Normalized Difference Water Index, or MNDWI, computed from Landsat 8 imagery processed through Google Earth Engine. The MNDWI, first proposed by Hongxing Xu in 2006, improves on the earlier Normalized Difference Water Index by comparing green and shortwave-infrared reflectance, a combination that suppresses built-up land and makes open water stand out more cleanly. Vegetation health, meanwhile, was captured with the Normalized Difference Vegetation Index, or NDVI, drawn from the AVHRR GIMMS-3G+ dataset, a long-running record of global greenness maintained at ORNL DAAC. Across the 471 districts, the average normalized MNDWI value was 0.40 and the average NDVI value was 0.53, figures the authors interpret as indicating moderate surface water availability and vegetation cover for the country as a whole.

Climatic variability entered the analysis through anomalies rather than raw values, a choice that matters for interpretation. Temperature and precipitation anomalies were constructed from gridded datasets produced by the Climate Research Unit at the University of East Anglia, specifically the CRU TS4.08 precipitation product, and by the NOAA Physical Sciences Laboratory, which supplied temperature and relative humidity fields. An anomaly measures how far a given month departs from its long-term norm, so the analysis is not asking whether a district is hot or wet in an absolute sense, but whether it was hotter or wetter than usual. That framing isolates the signal of climatic variability from the static geography of climate, which is precisely the signal that water managers worry about as warming intensifies.

To estimate the associations, the team employed a random effects panel model, a standard econometric framework for data in which many units, here districts, are observed repeatedly over time. The panel was unbalanced, meaning not every district was observed in every month due to data availability constraints, but the estimation used all available district-time observations. The authors also went beyond simple main effects, interacting temperature anomalies with the long-run mean and coefficient of variation of temperature to probe how baseline climate heterogeneity moderates the relationships. This moderating analysis is what elevates the study beyond a national average, because it allows the effect of an extra degree or an extra millimeter of rain to differ between a cool Himalayan district and a parched stretch of the Deccan.

The headline finding concerns temperature, and it is unambiguous. Temperature anomalies exert a statistically significant negative effect on both surface water and vegetation. A one-standard-deviation increase in temperature anomalies reduces MNDWI by 0.9 percent of its standard deviation and NDVI by a striking 4.14 percent of its standard deviation. In other words, vegetation greenness is far more sensitive to anomalous heat than open water extent is, at least as measured by these indices. The mechanism is physically intuitive: higher temperatures drive up evaporative demand and evapotranspiration, drying soils and stressing plants even when rainfall is unchanged, while open water bodies shrink more slowly because they are buffered by volume, inflows, and groundwater exchange. The disproportionate hit to NDVI suggests that India’s ecosystems and rain-fed agriculture are absorbing thermal stress before the loss becomes visible in standing water.

Precipitation tells a more complicated story. The study found an inverted-U-shaped relationship between precipitation anomalies and MNDWI, meaning that surface water presence rises as rainfall departs upward from the norm, but only up to a point, after which additional anomalous rainfall no longer translates into more detectable surface water. This curvature is consistent with the hydrology of extreme wetness: once soils saturate and channels fill, further rain runs off rapidly or causes flooding that the monthly index does not register as persistent standing water. The direct effect of precipitation anomalies on NDVI, by contrast, was less consistent, echoing a long-standing puzzle in the vegetation-climate literature, where the greenness response to rainfall depends on soil moisture memory, land cover, and the time scale at which plants integrate water availability.

Perhaps the most policy-relevant results come from the moderating analysis, which shows that the same climatic shock lands differently depending on a district’s baseline endowment. The positive impact of additional rainfall on vegetation is stronger in drier regions, where plants are water-limited and every extra millimeter counts. Conversely, districts that already enjoy higher baseline surface water availability display a weaker vegetation response to added rain, presumably because vegetation there is limited by something other than water, such as nutrients, temperature, or land use. The asymmetry has a hard edge: the districts that benefit most from good rains are the ones least equipped to store them, while wetter districts gain comparatively little. Adaptation spending, the findings imply, should not be distributed uniformly across the map.

The study situates itself within a substantial body of remote sensing work on Indian water resources, and its district-level panel design distinguishes it from much of that literature. Earlier studies have used NDWI and MNDWI to document the shrinkage of individual water bodies, from Vembanad Lake in southwest India to wetlands in Assam and urban water bodies in Bangalore, and geospatial assessments have tracked water resources in semi-arid southern India. National-scale analyses have reported emerging trends in freshwater availability and total water storage, and separate research has documented alarming groundwater depletion in northern India, with warming temperatures shown to exacerbate depletion rates. What has been missing is a systematic, monthly, all-India statistical treatment linking climate anomalies to both water and vegetation simultaneously, which is the gap this study fills.

The authors are careful about what their design can and cannot claim. The random effects model estimates statistical associations, not fully identified causal effects, and the moderating results follow from interaction specifications that capture baseline climate heterogeneity rather than from a natural experiment. The panel spans only nine years, a window that may not capture slower hydrological shifts or the full frequency of extreme events. The authors note that future research should integrate groundwater dynamics, longer remote sensing time series, and extreme event indicators to better capture the adverse effects of climate variability, a list that acknowledges the close coupling between the surface water they measure and the aquifers beneath it, which millions of Indians draw on invisibly.

Even with those caveats, the practical implications are immediate. The finding that vegetation is roughly four times more sensitive to heat anomalies than surface water extent, in standardized terms, argues for drought-resilient cropping, soil moisture conservation, and irrigation efficiency as first-order climate adaptations, not afterthoughts. The inverted-U precipitation response cautions against assuming that wetter anomaly years solve water problems, since excess rain may simply leave the system. And the moderating results make a concrete case for region-specific water management: dry districts need rainwater harvesting and storage infrastructure that lets them hold onto the rainfall that helps them most, while already water-rich districts need strategies tuned to non-water constraints. As India’s monsoon grows more erratic under continued warming, studies of this kind turn satellite pixels into a planning instrument, showing precisely where each degree of heat and each millimeter of rain will matter most.

Subject of Research: Impacts of temperature and precipitation anomalies on district-level surface water availability and vegetation health in India

Article Title: Climate change and surface water resources: a district-level study of MNDWI, NDVI, and climate anomalies in India

Article References: Raj, R., Anjali, K., & Sahoo, B. K. (2026). Climate change and surface water resources: a district-level study of MNDWI, NDVI, and climate anomalies in India. Environmental Monitoring and Assessment, 198(10), Article 1118. https://doi.org/10.1007/s10661-026-15894-x

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15894-x

Keywords: climate change, surface water, MNDWI, NDVI, India, remote sensing, temperature anomalies, precipitation anomalies, panel data, vegetation health, water security, Landsat 8