Assessing the impact of digital elevation model resolution on hypsometric analysis in large river Basins (India): a non-parametric statistical approach

Hypsometric or area-altitude analysis unveils the geomorphic evolution of a river basin and its interactions with tectonics, lithology, and climate. The results of hypsometric analysis i.e., hypsometric curve (HC) along with the hypsometric parameters (HP) namely, hypsometric integral (HI), skewness (SK), kurtosis (KU), density skewness (DSK), and density kurtosis (DKU) can also be further analyzed to evaluate the geological erosional status of a river basin. However, the spatial scale’s significance in such analyses is often underestimated, assuming consistency across various resolutions of Digital Elevation Models (DEMs). To address this oversight, this study compares HP values obtained from DEMs of different sources and resolutions—Shuttle Radar Topographic Mission (30 m), Global Multi-resolution Terrain Elevation Data 2010 (225 m), and Global 30 Arc-Second Elevation Data Set (1000 m). A total of 108 sub-watersheds spanning four large Indian River basins (Mahanadi, Godavari, Krishna, and Cauvery) underwent analysis using both parametric and non-parametric statistical tests, namely Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests, to assess the distribution of hypsometric parameters. Subsequently, the null hypothesis regarding the robustness of HP across varying DEM resolutions was tested using non-parametric statistical tests such as the Mann-Whitney U test, Kruskal-Wallis H test, and Friedman test. The study’s findings challenge the prevalent notion of robustness, revealing that DEM resolution significantly impacts the results of hypsometric analysis. The Friedman test, in particular, highlighted significant differences in the full distribution of hypsometric parameters across different DEM resolutions, suggesting that variations in DEM resolution can alter the shape and variability of these parameters, not just their central tendencies. Higher-resolution DEMs, such as SRTM (30 m), provided more accurate representations of landscape features, especially in capturing subtle variations that influence erosion rates, sediment transport, and geomorphic classification.