Will vegetation restoration affect the supply-demand relationship of water yield in an arid and semi-arid watershed?

Natural processes, combined with human activities, determine the inherent quality of regional water supply and demand. However, the interaction between artificial vegetation restoration and water supply-demand dynamics remains insufficiently understood, particularly in arid and semi-arid regions. This study focuses on the Jinghe River Basin (JRB) in the central Loess Plateau, aiming to investigate the changes in supply and demand of ecosystem water yield services and analyze factors affecting the water supply-demand relationship during the vegetation restoration, using the InVEST model, scenario analysis, and the Geodetector.

A study on ecohydrological mutual feedback relationship of the Shangdong River basin based on hydrological connectivity.

Investigating eco-hydrology in desert grasslands is pivotal to comprehend the dynamic evolution patterns of vegetation. Nonetheless, a research void persists in understanding the eco-hydrological mutual feedback mechanisms associated with hydrological connectivity and the corresponding health index evaluation of a small watershed. This study is centered on the Shangdong River watershed in Inner Mongolia and uses SWAT (Soil and Water Assessment Tool) to simulate hydrological processes.

Factor analysis of hydrologic services in water-controlled grassland ecosystems by InVEST model and geodetector.

Water conservation is highly important for a successful desert grassland ecosystem, but there was no comprehensive view on how to assess influencing factors in managing and addressing water yield and water conservation in desert steppe. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which is specifically used for the assessment of ecosystem services, was combined with geographic detectors to identify the priority areas for water conservation function and analyze the driving factors of water conservation in the Tabu River Basin, Inner Mongolia Autonomous Region, China, using different meteorological data sources. (i) The InVEST model has the advantage of modeling water yield and water conservation at spatial scales by fusion downscaling data.

A spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination in sub-watersheds.

Best management practices (BMPs) have been extensively employed in effective watershed management for non-point source pollution. The weights of objective functions and the restrictive conditions of combined BMPs are the vital requirements for BMPs allocation. Therefore, it is more beneficial to explore that a spatial optimal allocation method considering multi-attribute decision making and multiple BMPs random combination.