Simulation of soil nitrogen storage of the typical steppe with the DNDC model: A case study in Inner Mongolia, China

Soil nutrient depletion is one of the characteristics of steppe degradation. Soil nitrogen (N) storage is an indicator of ecosystem productivity, and its simulation is necessary to monitor steppe degradation and for recovery measures. The study presents a simulation framework of soil N storage by integrating a denitrification-decomposition (DNDC) ecosystem model-based simulation and multi-source remote sensing data-based inversion. The DNDC model is a key player in the framework, whereas remote sensing prepares the input parameters and verification data. To run a DNDC model spatially, climate, soil, and vegetation databases were built, and land use, slop, grazing, and mowing parameters were formulated by remote sensing inversion. A soil N storage prediction model was established with the maximum of normalized difference vegetation index (NDVI) to provide comparable results with the simulation of soil N storage with the DNDC model. The results indicate that soil N storage declined from east to west throughout the study area. From 1990 to 2011. no change in the spatial distribution of soil N storage was determined, and the spatial heterogeneity of soil N storage decreased with its increase in the low-N area and decrease in the high-N area. A significant correlation (P < 0.01) was determined between soil N storage data detected by remote sensing inversion and that simulated with DNDC, and both estimation results of soil N storage matched well. Soil N storage simulated with the DNDC model was more sensitive to soil organic carbon (SOC), bulk density, pH and N fixation index than other parameters, and using the most sensitive factor (MSF) method, the range of annual mean soil N storage was determined to be between 2339.61 and 5484.61 kg ha(-1). The variation in regional soil N storage in a typical steppe in Inner Mongolia, China can therefore be simulated using the DNDC model with support from remote sensing. (C) 2014 Elsevier Ltd. All rights reserved.