Informing grassland ecosystem modeling with in-situ and remote sensing observations

We simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-UV ecosystem model. For this study we developed new methods for informing DayCent-UV of growing season length and validating its plant productivity estimates for grasslands by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model’s phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model results from each simulated grid cell were compared to a remote-sensing estimate of grassland plant productivity offered by the Rangeland Analysis Platform (RAP). We determined that a modified RAP ANPP calculation that incorporated total annual precipitation instead of mean annual temperature to estimate the fraction of total productivity allocated to roots improved temporal correlations between RAP and field measurements and between RAP and DayCent-UV, We found that RAP provides a valuable data set for evaluating grassland ANPP predictions from ecosystem and other types of models because it provides estimates of grassland plant productivity over large spatial regions and a long historical period and captures temporal variablilty in plant production. This work provides the foundation for using the DayCent-UV model to predict climate change impacts on grassland cecosystem dynamics in the contiguous US.