Heterogeneity Index: Quantifying the Variability and Co-variability in Land-Surface Heterogeneity at the Global Scale

Abstract:

An understanding of land surface heterogeneity has been deemed important for modeling earth system processes and developing a mechanistic understanding of soil hydrology. Recognizing this need, national agencies like NRCS have developed qualitative classifications of the land-surface like Major Land Resource Areas (MLRAs) that are further subdivided into Common Resource Areas (CRAs). However, these indices are mainly aimed at agricultural planning and lack quantified information that can be very useful in monitoring changes in heterogeneity in a changing land-use land cover scenario. In this study, we have developed a Heterogeneity Index (H-index) that quantifies the soil, topography and seasonal vegetation based heterogeneity at a global scale. Our study follows the scaling nomograph which was introduced to incorporate the scale and site-specific dependence of soil moisture on geophysical heterogeneity and antecedent wetness conditions at the regional scale (Gaur and Mohanty, 2019). The index has is based on an eigenvalue decomposition of sand%, leaf area index (LAI) and flow accumulation and hence, quantifies the variability and co-variability of land-surface heterogeneity overall. The index has been modified for global scale analysis. The major changes incorporated include normalization of LAI and flow accumulation values at the global scale to ensure equal representation of soil, vegetation and topography in the index and changing resolution of all land surface heterogeneity based factors (sand%, LAI and flow accumulation) to 500m for computing the H-index at 40 km. The index has been found to respond to changes in vegetation cover and can be used to assess the change in land-surface heterogeneity over time. The algorithm for index generation has been kept modular and efficient such that it can also input partially available new datasets like region specific elevation from regional LIDAR surveys or improvements in global or regional soil texture maps. The formulation of the index has the potential to be incorporated in earth system models as well. In this work, we will present the computed H-index for the 2002- 2020 period and provide a comparison of the index with available classification measures like the MLRA and CRA.