Spatially Explicit Modeling of Coupled Water and Carbon Processes Using a Distributed Ecohydrological Model in the Upper Heihe Watershed, China

Jin, Chen, Sun, Zhang, Measho, Lin, Guo
2019 Water  
A fully coupled simulation of ecophysiological, hydrological and biochemical processes is significant for better understanding the individual and interactional impact of sophisticated land surface processes under future disturbances from nature and human beings. In this study, we spatially explicitly modelled evapotranspiration (ET) and photosynthesis (GPP) using a distributed hydrological model, Dynamic Land Model DLM-Ecohydro, over the Upper Heihe watershed for the years of 2013 and 2014.
more » ... r considering the lateral water movements, the model fairly captured the variations in ET (R2 = 0.82, RMSE = 1.66 mm/day for 2013; R2 = 0.83, RMSE = 1.53 mm/day for 2014) and GPP (R2 = 0.71, RMSE = 5.25 gC/m2/day for 2013; R2 = 0.81, RMSE = 3.38 gC/m2/day for 2014) compared with the measurements from the Arou monitoring station. Vegetation transpiration accounted for total ET of around 65% and 64% in 2013 and 2014, respectively. A large spatial variability was found in these two indicators (14.30–885.36 mm/year for annual ET and 0–2174 gC/m2/day for annual GPP) over the watershed. Soil texture and vegetation functional types were the major factors affecting ET and GPP spatial variability, respectively. The study manifested a coupled water–carbon mechanism through the strong linear relationship between the variations in ET and GPP and the control of hydrological processes on the carbon cycle at the watershed scale. Although the model had a reasonable performance during most parts of the growing seasons, the lack of a soil freezing–thawing scenario caused inevitable discrepancies for the simulation of soil water and heat transfer mechanisms, hence inaccurately estimating the biophysiological processes in the transition period of winter to spring, which should be further improved especially for alpine regions.
doi:10.3390/w11061242 fatcat:l52s7xvglzdcxjios3tvxookty