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

Abstract

AbstractHistorical grassland aboveground plant productivity (ANPP) was simulated by the DayCent-UV ecosystem model across the midwestern and western conterminous United States. For this study we developed a novel method for informing the DayCent-UV model and validating its plant productivity estimates for grasslands of the midwestern and western conterminous USA by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model 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 ANPP modified version offered by the Analysis Rangeland Platform (RAP). This modified RAP ANPP calculation incorporated total annual precipitation, instead of mean annual temperature, as the control factor for the fraction of carbon allocated to roots. Strong temporal correlations were obtained between RAP and DayCent-UV, especially across the Great Plains. Good agreement was also found when the model results were compared with ANPP observations at the site and county level. The data produced by this study will serve as a valuable resource for validation or calibration of various models that aim to capture accurate productivity dynamics across diverse grassland ecosystems.Plain Language SummaryThis research used a computer model called DayCent-UV to simulate daily grassland growth across the central and western regions of the contiguous United States. To improve the agreement between the simulations and real-world conditions, we incorporated data from local field measurements and satellite imagery. This data helped determine the start and end dates of the growing season at each location. The simulated annual growth showed good agreement with satellite estimates from the Rangeland Analysis Platform (RAP), another computer application that monitors rangeland vegetation, and with local observations based on harvesting and weighing vegetation, particularly across the Great Plains. These results are valuable for validating and refining other computer models that aim to accurately simulate plant growth in grassland ecosystems; the predictions of these models are crucial for understanding the balance of carbon between plants, soils, and the atmosphere as the climate changes.Key PointsThe DayCent-UV model was used to simulate historical aboveground net primary productivity (ANPP) for different grassland ecosystems across the midwestern and western United States.MODIS MCD12Q2 was used to provide the phenology for the model.The Rangeland Analysis Platform (RAP) fraction of biomass production allocated to roots calculation was modified, resulting in a stronger agreement between its ANPP estimates and those from the DayCent-UV model.Site- and county-level ANPP observations were used to validate the model.