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Connection Between Root Zone Soil Moisture and Surface Energy Flux Partitioning Using Modeling, Observations, and Data Assimilation for a Temperate Grassland Site in Germany
Author(s) -
Shrestha P.,
Kurtz W.,
Vogel G.,
Schulz J.P.,
Sulis M.,
Hendricks Franssen H.J.,
Kollet S.,
Simmer C.
Publication year - 2018
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1029/2016jg003753
Subject(s) - grassland , environmental science , water content , data assimilation , flux (metallurgy) , soil science , moisture , temperate climate , atmospheric sciences , dns root zone , soil water , meteorology , geology , agronomy , chemistry , ecology , physics , organic chemistry , biology , geotechnical engineering
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.