Abstract. Over the past decade, there has been appreciable progress towards modeling the water, energy, and carbon cycles at field scales (10–100 m) over continental to global extents in Earth system models (ESMs). One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via Hydrologic Response Units (HRUs), more commonly known as “tiles” in ESMs. In HydroBlocks, these HRUs are learned via a hierarchical clustering approach from available global high-resolution environmental data. However, until now there has yet to be a river routing approach that is able to leverage HydroBlocks' approach to modeling field-scale heterogeneity; bridging this gap will make it possible to more formally include riparian zone dynamics, irrigation from surface water, and interactive floodplains in the model. This paper introduces a novel dynamic river routing scheme in HydroBlocks that is intertwined with the modeled field-scale land surface heterogeneity. Each macroscale polygon (a generalization of the concept of macroscale grid cell) is assigned its own fine-scale river network that is derived from very high resolution (∼ 30 m) digital elevation models (DEMs); the inlet–outlet reaches of a domain's macroscale polygons are then linked to assemble a full domain's river network. The river dynamics are solved at the reach-level via the kinematic wave assumption of the Saint-Venant equations. Finally, a two-way coupling between each HRU and its corresponding fine-scale river reaches is established. To implement and test the novel approach, a 1.0∘ bounding box surrounding the Atmospheric Radiation and Measurement (ARM) Southern Great Plains (SGP) site in northern Oklahoma (United States) is used. The results show (1) the implementation of the two-way coupling between the land surface and the river network leads to appreciable differences in the simulated spatial heterogeneity of the surface energy balance, (2) a limited number of HRUs (∼ 300 per 0.25∘ cell) are required to approximate the fully distributed simulation adequately, and (3) the surface energy balance partitioning is sensitive to the river routing model parameters. The resulting routing scheme provides an effective and efficient path forward to enable a two-way coupling between the high-resolution river networks and state-of-the-art tiling schemes in ESMs.
Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb.
Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site‐specific studies, little is known about how SM varies locally (1–100‐m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long‐standing challenge in hydrology. We reveal the striking variability of local‐scale SM across the United States using SMAP‐HydroBlocks — a novel satellite‐based surface SM data set at 30‐m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local‐scale complexity yields a remarkable and unique multi‐scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1‐km resolution, with complete loss expected at the scale of current state‐of‐the‐art SM monitoring and modeling systems (1–25 km resolution).
Abstract. Over the past decade, there has been appreciable progress towards modeling the water, energy, and carbon cycles at field-scales (10–100 m) over continental to global extents. One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via sub-grid tiles, or Hydrologic Response Units (HRUs), learned via a hierarchical clustering approach from available global high-resolution environmental data. However, until now, there has yet to be a macroscale river routing approach that is able to leverage HydroBlocks' approach to sub-grid heterogeneity, thus limiting the added value of field-scale land surface modeling in Earth System Models (e.g., riparian zone dynamics, irrigation from surface water, and interactive floodplains). This paper introduces a novel dynamic river routing scheme in HydroBlocks that is intertwined with the modeled field-scale land surface heterogeneity. The primary features of the routing scheme include: 1) the fine-scale river network of each macroscale grid cell's is derived from very high resolution (
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.
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