LAUSR

The Amazon region of Brazil is a vitally important region for water and carbon cycling both for the region and the globe. This region is experiencing the impacts of global climate change as well as local land cover changes. Here, we investigated water and carbon estimates and related remotely sensed variables from both MODIS satellite and the Noah-MP land surface model for 3 years (2015–2017) in the state of Mato Grosso, Brazil. Land surface temperature agrees well between MODIS and the model, while the leaf area index (LAI) is higher in the model simulations. The monthly evapotranspiration (ET) from MODIS (MOD16A2) and gross primary productivity (GPP, MOD17A2) were lower than, but well correlated with, the model simulations. A noticeable exception was in the Broadleaf Forest class, which accounts for approximately 50% of the land cover in the state, where the modeled LAI was out of phase with the satellite observations, resulting in significantly poorer performance in the water and carbon fluxes for that land cover class. In addition, we investigated the sensitivity of the ET and GPP to precipitation forcing. The modeled ET relationships show correlations of approximately 0.6 for all classes (Broadleaf Forest being the exception, 0.24), while the MODIS shows reduced values averaging about 0.5 (Broadleaf Forest = 0.03). The slopes of the relationships illustrated the same sensitivity between MODIS and Noah-MP with the exception of Grasslands and Open Shrublands. The GPP relationships with precipitation show lower correlations across all land cover types for both MODIS and Noah-MP, with the slopes being significantly different for the Open Shrublands and Broadleaf Forest classes. In each of these classes, the Noah-MP simulations resulted in increased sensitivity to precipitation than was observed in the MODIS products. We highlight that this comparison is essential for increasing our understanding of how these different sources estimate water and carbon cycling and can be utilized for assessing the impacts of climate and land cover change in the region.