Land Surface Models Evaluation for Two Different Land-Cover Types: Cropland and Forestopen access
- Authors
- Kim, Daeun; Kang, Seokkoo; Choi, Minha
- Issue Date
- Feb-2016
- Publisher
- CHINESE GEOSCIENCE UNION
- Keywords
- Water and energy fluxes; Common Land Model; Noah Land Surface Model
- Citation
- TERRESTRIAL ATMOSPHERIC AND OCEANIC SCIENCES, v.27, no.1, pp.153 - 167
- Indexed
- SCIE
SCOPUS
- Journal Title
- TERRESTRIAL ATMOSPHERIC AND OCEANIC SCIENCES
- Volume
- 27
- Number
- 1
- Start Page
- 153
- End Page
- 167
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/5135
- DOI
- 10.3319/TAO.2015.09.14.02(Hy)
- ISSN
- 1017-0839
- Abstract
- Land Surface Model (LSM) is an important tool used to understand the complicated hydro-meteorological flux interaction systems between the land surface and atmosphere in hydrological cycles. Over the past few decades, LSMs have further developed to more accurately estimate weather and climate hydrological processes. Common Land Model (CLM) and Noah Land Surface Model (Noah LSM) are used in this paper to estimate the hydro-meteorological fluxes for model applicability assessment at two different flux tower sites in Korea during the summer monsoon season. The estimated fluxes such as net radiation (R-N), sensible heat flux (H), latent heat flux (LE), ground heat flux (G), and soil temperature (T-s) were compared with the observed data from flux towers. The simulated R-N from both models corresponded well with the in situ data. The root mean-square error (RMSE) values were 39 - 44 W m(-2) for the CLM and 45 - 50 W m(-2) for the Noah LSM while the H and LE showed relatively larger discrepancies with each observation. The estimated T-s from the CLM corresponded comparatively well with the observed soil temperature. The CLM estimations generally showed better statistical results than those from the Noah LSM, even though the estimated hydro-meteorological fluxes from both models corresponded reasonably with the observations. A sensitivity test indicated that differences according to different locations between the estimations from models and observations were caused by field conditions including the land-cover type and soil texture. In addition the estimated RN, H, LE, and G were more sensitive than the estimated T-s in both models.
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