Performance of the ecosystem demography model (EDv2.2) in simulating gross primary production capacity and activity in a dryland study area

Dryland ecosystems play an important role in the global carbon cycle, including regulating the inter-annual global carbon sink. Dynamic global vegetation models (DGVMs) are essential tools that can help us better understand carbon cycling in different ecosystems. Currently, there is limited knowledge of the performance of these models in drylands partly due to characterizing the heterogeneity of the vegetation and hydrometeorological conditions. The aim of this study is to evaluate the performance of a DGVM for drylands to facilitate improved understanding of gross primary production (GPP) as one of the important components of the carbon cycle. We performed a sensitivity analysis and calibrated the Ecosystem Demography (EDv2.2) DGVM to simulate GPP in a dryland watershed (Reynolds Creek Experimental Watershed, Idaho) in the western US for the years 2000-2017. GPP capacity and activity were investigated by comparing model simulations with GPP estimated from eddy covariance data (available from 2015-2017) and remote sensing products (2000-2017). Our results show good performance of EDv2.2 at daily timesteps (RMSE approximate to 0.38 [kgC /m(2)/year]) between simulated and measured GPP in lower elevations of the watershed. Moreover, remote sensing analysis show that EDv2.2 captures the long-term trends in this ecosystem and performs relatively well in capturing phenometrics (start/ end of the season). The performance of the model degrades in more productive sites with greater GPP (located at higher elevations in the watershed). To improve model performance, future studies will need to introduce additional plant functional types for drylands such as our study area, and modify plant processes (e.g., plant hydraulics and phenology) in the model.