资源环境科技发展态势分析平台

Earth's land cover (LC) has significant influence on land-atmospheric processes and affects the climate at multiple scales. There are multiple global LC (GLC) data sets which are yet to be evaluated for uncertainties and their propagation into the simulation of land surface fluxes (LSFs) in land surface/climate modeling. The present study assesses the uncertainties in seven GLC products with reference to a regional data set for the simulation of LSFs in India using a macro-scale land surface model. There is considerable overestimation of the extent of croplands in most of the GLCs. The uncertainties in LCs exert significant bias in the simulation of the LSFs of actual evapotranspiration (ETa), latent heat (LE), and sensible heat (H) fluxes. Uncertainty propagation in LSFs is proportional to the bias in cropping intensity under rainfed condition. The high underrepresentation of cropland area in the UMd data set results in highest bias in LSFs whereas the least cropland bias in Globland30 leads to least bias. Irrigation has higher potential to alter the LSFs than uncertainties related to LC especially in regions with large area under irrigation like India. The changes in LSFs are higher in arid/semiarid regions with medium irrigation intensity than in subhumid regions with high irrigation intensity. This has significant implications for the country's future irrigation expansion plans in the arid/semiarid regions. The study also emphasizes the need for focused efforts to quantify the uncertainties from varying irrigation intensities in the next generation CMIP6 experiments.