Characterizing the scaling structure of topography is essential for linking observable land-surface morphology to the processes governing landscape evolution. Conventional geomorphometric approaches often describe terrain using self-affine models parameterized by a single roughness exponent, implicitly assuming statistical homogeneity across scales. Such formulations, however, may not adequately capture landscapes shaped by the coupled action of tectonic deformation, climatic forcing, and nonlinear erosional dynamics. In this study, we evaluate the scale dependence of surface roughness using high-resolution digital elevation data from the Iranian Plateau, a morphotectonically diverse region representative of actively evolving continental domains in West Asia. Fractal analysis based on box-counting yields a dimension of Df≈2.20, while spectral analysis of elevation fields provides a Hurst-type roughness exponent of α≈0.48. The lack of agreement with the canonical self-affine relation Df=3−α indicates that the terrain cannot be characterized by a single scaling descriptor. To investigate this departure, we compute higher-order statistical measures of elevation variability, including generalized structure functions, multiscale height–height correlations, and scale-dependent curvature metrics. These analyses reveal that the scaling exponents vary systematically with statistical moment, demonstrating clear multiscaling and supporting a multifractal description of the surface. The results suggest that topographic organization in tectonically active settings reflects heterogeneous process interactions operating over a hierarchy of spatial scales, rather than uniform rough-surface behavior. This work underscores the limitations of single-exponent geomorphic parameterizations and highlights the value of multifractal diagnostics for quantitative terrain analysis, comparative morphotectonic studies, and improved representation of landscape complexity in Earth-surface models.
