Acta Geographica Sinica ›› 2016, Vol. 71 ›› Issue (11): 1886-1897.doi: 10.11821/dlxb201611002

• Climate and Hydrology • Previous Articles     Next Articles

Evaluation and uncertainty analysis of a two-source evapotranspiration model

Genan WU1,2, Zhongmin HU1,2, Shenggong LI1,2, Han ZHENG1,2, Xianjin ZHU1, Xiaomin SUN1,2, Guirui YU1,2, Jingbao LI3   

  1. 1. Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research,CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. College of Resources and Environmental Science, Hunan Normal University, Changsha 410081, China
  • Received:2016-04-07 Revised:2016-07-18 Online:2016-11-25 Published:2016-11-25
  • Supported by:
    Natural Sciences Foundation of China, No.41301043;Youth Innovation Promotion Association, CAS, No.2015037;Funding of Talented Young Scientists of IGSNRR, CAS, No.2013RC203


Evapotranspiration (ET) is one of the core processes of water cycle in ecosystem and ET modeling is a hotspot and frontier in the field of the global climate changes. It is therefore important to provide spatiotemporal information of ET across diverse ecosystems in order to predict the response of ecosystem carbon and water cycles to changes in global climate and land use. The SWH model incorporates the Ball-Berry stomatal conductance model and a light use efficiency-based gross primary productivity (GPP) model into the Shuttleworth-Wallace model, which can simulate both ET and GPP. The newly developed SWH model presents a satisfactory prediction ability of simulating ET in a forest and a grassland ecosystem, respectively. However, the SWH model still lacks comprehensive evaluation and uncertainty analysis at regional scale. In this study, we (1) tested the model's performances on estimating ET and GPP at seasonal and annual time scales; (2) quantified the uncertainties of the model parameters and driving variables, including Normalized Difference Vegetation Index, NDVI and meteorological data; (3) quantified the sensitivity of model outputs to the parameters and driving variables; (4) quantified and separated the uncertainties of ET simulation from the parameters and driving variables. Results showed that the SWH model performed well for ET simulation at regional scale as indicated by high coefficient of determination (R2 = 0.75) of linear regression of modeled against measured ET. Among the key parameters in the SWH model, two parameters related to estimating canopy stomatal conductance (g0 and a1) make great contribution to the model uncertainty. Among the forcing variables, NDVI is most critical in estimating GPP, which contributes much to uncertainty in ET simulation. In comparison, the climatic forcing variables contributes less to uncertainty in ET simulation owing to the high accuracy of the climate data (such as radiation and air temperature) or model’s low sensitivities to some variables (such as precipitation).

Key words: SWH model, Shuttleworth-Wallace model, evapotranspiration, uncertainty analysis