Yaru Zhang

The water yield module of the InVEST model was used to estimate the water yield and its temporal and spatial variation characteristics in Shaanxi Province from 2000 to 2020. Moreover, the influences of future precipitation changes and land use changes on water yield in Shaanxi Province were discussed in the 2030s and 2050s. The results showed that: (1) from 2000 to 2020, the multi-year average water yields in northern Shaanxi, Guanzhong and southern Shaanxi were 33.23 × 10⁸ m³, 73.75 × 10⁸ m³, and 280.63 × 10⁸ m³, respectively; (2) the spatial pattern of water yield depth displayed a characteristic of gradually increasing from north to south; (3) under the precipitation change scenario, the water yield under different emission scenarios wa s in the order of RCP (Representative Concentration Pathways) 8.5 > RCP2.6 > RCP4.5; under the land use change scenario, the water yield depth of Shaanxi Province as a whole and in the three regions in the 2030s and 2050s showed a decline. The research results can provide scientific support for water ecological security, water resources, and regional high-quality sustainable development in Shaanxi Province.

In the context of the continuous change in global climate, the frequency and intensity of drought and heatwaves are increasing. This study took the extreme drought event in southwest China in 2009/2010 as a case study. Based on the sunlight-induced chlorophyll fluorescence (SIF), we explored the effects of high-temperature weather on the photosynthetic efficiency, the vegetation responses to drought in two ecosystems, and the differences in influencing factors. The results showed a disproportionate change between the vegetation productivity represented by SIF and the greenness symbolized by the leaf area index (LAI). The response of photosynthetic efficiency to drought was significantly inequitable between the grassland and cropland. The geodetector showed that grassland ecosystems with more superficial canopy structures were more susceptible to high temperature. The correlation between the Photosynthesis efficiency index (PEI) and temperature (T) and vapor pressure deficit (VPD) of the grassland ecosystem was above 0.6. This study suggests that drought exacerbates the disproportionate change between vegetation productivity and greenness, and grasslands are more vulnerable to drought. The result is helpful for ecosystem management.

With the increasing trend of global warming, drought events frequently occur, which have an impact on human life and the environment. In this study, an extreme drought event in Southwest China in 2009/2010 was used as an example to explore the potential of using satellite observations of sun-induced chlorophyll fluorescence (SIF) for drought monitoring. The results indicated that the SIF observations show more proper responses to drought than EVI, which underestimated the losses by approximately 50%. The SIF reduction in this drought event (19% in March 2010 and 11% in May 2010) was more obvious than that of the enhanced vegetation index (EVI) (4% and 5%). The drought severity index (DSI) overestimates the drought during most dry months. SIF can be a reliable tool for monitoring drought in a timely and accurate manner. In addition, the significant correlation coefficient with SIF and ET (reaching 0.8 at the beginning and end of the drought stage), indicates the ability of SIF to reveal the interaction of carbon and water during drought, which provides us with ideas for future research on the terrestrial carbon–water cycle.

The spatiotemporal variation and driving force of the Normalized Difference Vegetation Index (NDVI) are helpful to ecological environment protection and natural resource management. Using the Sen and Mann–Kendall methods, Hurt index, and the Geodetector, this study investigated the temporal and spatial changes and driving forces of NDVI during 1982–2015. The results showed that (1) From 1982 to 2015, the high vegetation coverage was mainly distributed in the Qinling Mountains and the Daba Mountains, while the low vegetation coverage was in high altitude areas in the west, low altitude in the east, and the Hanjiang River valley. (2) NDVI in the Qinba Mountains increased continuously accounting for 81.1%, with 68% showing slow growth. In the future, only 37.8% of the vegetation will have significant change. The area of vegetation increase will be greater than the area of decrease. (3) NDVI increased firstly and then decreased with the increase of altitude, reaching the maximum value at 1100 m. NDVI showed a trend of fluctuating growth. It reached the maximum value of 0.86 in 2015. (4) Through the Geodetector, the main factors affecting NDVI were natural factors mainly including rainfall, soil type, and digital elevation model (DEM), while human activities, including population density, had little influence on NDVI. Natural environment factors and human activities together had a greater impact on the spatial distribution of NDVI. This study could provide help for the sustainable development of the natural environment in the Qinba Mountains.