Towards an Optimal Representation of Sub‐Grid Heterogeneity in Land Surface Models

One of the persistent challenges of Land Surface Models (LSMs) is to determine a realistic yet efficient sub‐grid representation of heterogeneous landscapes. This is particularly important in emulating the fine‐scale and nonlinear interactions between water, energy, and biogeochemical fluxes at the land surface. In LSMs, landscape heterogeneity can be represented using sub‐grid tiling techniques, which partition macroscale grid cells (e.g., 1°) into smaller units or “tiles.” However, there is currently no formal procedure to define the number of tiles required to adequately represent the heterogeneity of hydrologic processes within a macroscale grid cell, and across spatial scales. To address these challenges, a new approach is presented to diagnose sub‐grid process heterogeneity formally and to infer an optimal number of tiles per macroscale grid cell. The approach is demonstrated using the HydroBlocks modeling framework coupled to Noah‐MP LSM implemented over a 1.0‐degree domain in Western Colorado in the United States. Our results show that (a) a surrogate model can accurately infer the spatial structure of the LSM's time‐averaged hydrological fields, with over 95% overall R2 performance in the validation stage; (b) the optimal configurations for a target level of complexity can be determined using a multi‐objective Pareto efficiency analysis, which includes the simultaneous representation of the multi‐scale heterogeneity of several processes; (c) the use of ∼100 tiles effectively reproduces a quasi‐fully distributed LSM setup (i.e., 83,000 tiles) with approximately 1% of the computational expense. This method provides a path forward to efficiently determine the optimal tile configurations for LSMs while simultaneously considering the spatial heterogeneity and spatial accuracy of hydrologic processes.