Estimating the maximum vegetation coverage and productivity capacity supported by rainwater resources on the Loess Plateau

Abstract

Large-scale revegetation presents a new set of challenges by augmenting water consumption in arid regions, despite its positive impact on ecosystems. In water-stressed areas, where precipitation is the primary source of water, extensive afforestation may disrupt the balance between water supply and demand. Consequently, it is crucial to assess the maximum extent of vegetation coverage and productivity that can be sustained by rainwater resources. Our study characterizes the sustainability of revegetation by determining the upper limit of the leaf area index (LAI) supported by rainwater resources. The research focuses on the Loess Plateau (LP), a region known for both large-scale vegetation restoration and severe water shortages. The upper limit on LAI is computed based on evapotranspiration (ET) supported by rainwater resources, utilizing an optimized Shuttleworth-Wallace (S-W) model that incorporates dynamic vegetation and carbon dioxide components. Carbon sequestration capacity and efficiency are compared between the maximum and actual vegetation scenarios using an analytical water use efficiency (WUE) model. Both models exhibit good performance and align with empirical observations. Results indicate that under the maximum vegetation scenario, the LAI is 11.5% higher than the actual scenario when vegetation on the LP is restored to its maximum level. The average gross primary productivity under the maximum vegetation scenario surpasses the actual scenario by 25.0%, with a 17.9% increase in ecosystem WUE. It is important to note that the maximum scenario represents a theoretical upper limit based on ideal assumptions. The findings emphasize that enhancing rainwater utilization efficiency can unlock the potential for sustainable vegetation restoration, improving its efficiency. This study provides valuable guidance and theoretical support for planning vegetation restoration in water-scarce regions.