Connection Between Root Zone Soil Moisture and Surface Energy Flux Partitioning Using Modeling, Observations, and Data Assimilation for a Temperate Grassland Site in Germany

• Published in: Journal of geophysical research. Biogeosciences 2018-09, Vol.123 (9), p.2839-2862
• Main Authors: Shrestha, P., Kurtz, W., Vogel, G., Schulz, J.‐P., Sulis, M., Hendricks Franssen, H.‐J., Kollet, S., Simmer, C.
• Format: Article
• Language: English
• Subjects: Atmospheric models; Bias; Biological assimilation; Boundary conditions; Bowen ratio; Computer simulation; Data; Data assimilation; data assimilation with joint state‐parameter update; Data collection; Energy; Energy transfer; Fluxes; grassland; Grasslands; Modelling; Numerical weather forecasting; Parameter modification; Partitioning; Photosynthesis; Root zone; root zone soil moisture; Soil; Soil improvement; Soil moisture; Surface energy; surface energy flux partitioning; Surface properties; TERRA‐ML and CLM; uncertainty of photosynthetic parameters; Weather forecasting
• ISSN: 2169-8953; 2169-8961
• DOI: 10.1029/2016JG003753

Land surface models (LSMs) with different degrees of complexity are in use as lower boundary conditions for atmospheric models with the simpler LSMs preferentially used in numerical weather forecasting. This study evaluates the second‐generation TERRA Multi‐Layer and the third‐generation Community Land Model (CLM) to better understand the connection between root zone soil moisture and surface energy fluxes, which is important for predictions. Both LSMs were compared in multiyear, observation‐driven simulations at the Falkenberg grassland site (Germany), and their results were compared to observations. With their default settings for the site, both LSMs tend to overestimate the Bowen ratio, while CLM additionally exhibited a wet bias and a too low soil moisture variance. With modified photosynthetic parameters in CLM, the Bowen ratio improved considerably, but the soil moisture bias and its too low variance remained. Joint data assimilation with soil parameter update significantly improved the soil moisture variance but degraded the Bowen ratio. We could identify the default shallow root fraction distribution to be responsible for the overestimated Bowen ratio, which could be largely reduced by increasing the root fractions in deeper layers. This study demonstrates how observations and data assimilation with joint state‐parameter updating can be used to improve the realism of third‐generation LSMs and thus our understanding of the connection between root zone soil moisture and surface energy flux partitioning. Key Points Connection between root zone soil moisture and surface energy flux partitioning Evaluation of two different‐generation land surface models over the Falkenberg grassland site Model biases in third‐generation land surface models identified via data assimilation with joint state parameter update