Land Surface Modeling

Improving the accuracy of land surface models (LSMs) is crucial for reducing uncertainties in climate change projections. Parameter data assimilation (DA), which fine-tunes model parameters to better match observed data, is key to enhancing LSM performance. However, the complexity of LSMs poses challenges for global optimization. Advances in computational power, novel data sets, and machine learning (ML) offer promising solutions to improve LSMs. ML can streamline the DA process, handling large data sets and reducing computational demands. This article discusses the progress made in LSM parameter estimation and the challenges faced by the community. We then discuss how ML can help address these challenges and outline future priorities. International collaboration, fostered by initiatives like the Analysis, Integration and Modeling of the Earth System Land DA Working Group and the International Land Model Forum, is essential for accelerating progress, facilitating knowledge exchange, and developing standardized methods for more accurate climate modeling.

In this study we show how a surrogate model can accurately predict the spatial structure of Land Surface Model’s (LSM) hydrological output fields. For a target level of complexity, the optimal LSM tile configuration was determined using a multi-objective Pareto efficiency analysis. We found that approximately 100 tiles can effectively reproduce a quasi-fully distributed LSM setup with 1% of the computational cost. 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.

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. Its largest benefit of SMAP-HB is the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products.

Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decades, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between tiling schemes and river networks in the HydroBlocks land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.

Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.

HydroBlocks A field-scale resolving land surface model over continental extents HydroBlocks is a hyper-resolution field-scale resolving land surface model that accounts for the water, energy, and carbon balance to solve land surface processes at high spatial and temporal resolutions.