Using model-data fusion to understand cold regions hydrological and biophysical processes in a changing climate
Mousong Wu#*1, Wexin Zhang*2, Youhua Ran*3
1. International Institute for Earth System Science, Nanjing University, 20023, Nanjing, China, mousongwu@nju.edu.cn
2. Department of Physical Geography and Ecosystem Science, Lund University, SE 22362, Lund, Sweden, wenxing.zhang@nateko.lu.se
3. Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, China, ranyh@lzb.ac.cn
* PYRN Member # session organizer and corresponding author
Session description (250 word maximum in English):
Climate change has posed large hydrological and biogeophysical impacts on cold regions, for instance, causing changes in components of the hydrological cycle, regimes for soil thawing and freezing, and biophysical properties of the land surface. Persistent efforts of conducting field observations and numerical modeling have been made to increase our knowledge about the interaction between climatic, vegetation and soil. However, large uncertainties still exist in both observational data sets and model estimates due to measurement errors, scaling issues in both time and space, parameter estimates, underrepresentation of processes and mechanisms, and particularly, less efficient integration between models and observations.
To better refine our understanding on how climate impacts hydrological cycle, biophysical properties of land surface and soil physical processes in cold regions, we propose a session to welcome studies which can demonstrate approaches to merge observational data with models for improving model performance and characterizing model’s uncertainties, which is also termed “model-data fusion”. With recent advances in both observational technologies (Space missions and flux measurement network) and numerical techniques for modeling (Top-down inversion models and bottom-up process-based models), model-data fusion has become a widely used approach to study impacts of climate change on eco-hydrological processes in cold regions, and the model quality can be improved with a high degree of confidence. In this session, we are going to bridge the gap between modeling and observations in understanding hydrological and biogeophysical processes in cold regions and to provide a synthesis of current research applications with an approach of model-data fusion.
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