Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia |
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| Institution: | 1. Institute of Desert Meteorology, China Meteorological Administration, Key Laboratory of Tree-ring Physical and Chemical Research of China Meteorological Administration, Key Laboratory of Tree-ring Ecology of Xinjiang Uygur Autonomous Region, Urumqi 830002, China;2. Key Laboratory of Western China''s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China;1. Departamento de Biología Animal, Parasitología, Ecología, Edafología y Química Agrícola, Área de Ecología, Facultad de Biología, Universidad de Salamanca, Campus Miguel de Unamuno s/n, 37007 Salamanca, Spain;2. Departamento de Ingeniería del Medio Agronómico y Forestal, Área de Ingeniería Agroforestal, Centro Universitario de Plasencia, Universidad de Extremadura, Avenida Virgen del Puerto 2, 10600 Plasencia, Spain;3. Instituto Pirenaico de Ecología (IPE-CSIC), Avda. Montañana 1005, 50059 Zaragoza, Spain;1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China;2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, PR China;3. School of Geography, Geology and the Environment, Centre for Landscape and Climate Research, University of Leicester, Leicester LE1 7RH, UK;4. Shaanxi Provincial Meteorological Bureau, Xi’an 710014, PR China;1. Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany;2. Center for Development Research (ZEF), Walter-Flex-Straße 3, 53113 Bonn, Germany;3. Institute of Agricultural and Nutritional Sciences, University of Halle-Wittenberg, Betty-Heimann-Str. 5, D-06120 Halle (Saale), Germany;4. Thünen-Institute of Biodiversity, Bundesallee 65, Federal Research Institute for Rural Areas, Forestry and Fisheries, D-38116 Braunschweig, Germany;5. Leibniz Centre for Agricultural Landscape Research, Institute of Landscape Systems Analysis, D-15374 Müncheberg, Germany;6. Department of Crop Sciences, University of Göttingen, Von-Siebold-Strasse 8, 37075 Göttingen, Germany;1. Reservoir Engineering Research Institute, Palo Alto, CA, USA;2. Yale University, New Haven, USA |
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| Abstract: | Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model. |
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| Keywords: | Soil moisture Model intercomparison JULES CABLE Data assimilation Evolutionary algorithms |
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