Understanding ecosystem services(ESs)and their interactions will help to formulate effective and sustainable land use management plans, and clarifying the balance and synergy between watershed ecosystem services can provide a basis for the regulation of the ecological environment in different regions of the watershed and the maximization of overall ecological benefits. This paper takes the Yellow River Basin as the research object and uses the Ecosystem Services and Trade Offs (InVEST)model to evaluate the water yield (WY), soil conservation (SC), carbon storage (CS) and habitat quality (HQ) of the Yellow River Basin. The paper adopts the Carnegie-Ames-Stanford Approach (CASA)model to evaluate the net primary productivity (NPP), draws the spatial distribution map of the five ecosystems, analyzes the trade-off and synergy between the five ecosystems using correlation and binary spatial correlation, and expresses it in space. In addition, it adopts self-organizing mapping (SOM) method to identify ecosystem service clusters. The results show that: (1) ES is generally higher in the upper reaches of the Yellow River, and lower in the middle reaches. (2) WY and NPP, HQ, CS and WY are trade-off relationships, and other ecosystem services are synergistic relationships. Trade-offs and synergy show obvious spatial heterogeneity. (3) The ecosystem services of the Yellow River Basin, driven by different factors, can be divided into three areas, namely WY and SC service leading functional areas, HQ and CS service leading functional areas, and NPP service leading functional areas. Finally, it discusses the driving factors of the spatial heterogeneity of the balance of the ecosystem service functions of the Yellow River Basin and the suggestions of land use management in the basin.

Climate changes, especially increased temperatures, and precipitation changes, have significant impacts on vegetation phenology. However, the response of vegetation phenology to the extreme climate in the Loess Plateau in Northwest China remains poorly quantified. The research described here analyzed the spatial change in vegetation phenology and the response of vegetation phenology to climate change in the Loess Plateau from 2001 to 2018, using data from seven extreme climate indices based on the ridge regression method. The results showed that extreme climate indexes, TNn (yearly minimum value of the daily minimum temperature), TXx (yearly maximum value of the daily maximum temperature), and RX5day (yearly maximum consecutive five-day precipitation) progressively increased from 2001 to 2018 in the Loess Plateau region, but decrease trend was found in DRT (diurnal temperature range). The start of the growing season (SOS) of vegetation gradually advanced with precipitation from northwest to southeast, and the rate was +0.38 d/a. The overall vegetation end of the growing season (EOS) was delayed, and the trend was −2.83 d/a. The sensitivity of the different vegetation phenology to different extreme weather indices showed obvious spatial differences, the sensitivity coefficient of SOS being mainly positive in the region, whereas the sensitivity coefficient of EOS was negative generally. More sensitivity was found in the EOS to extreme climate indexes than in the SOS. Forest, shrubland and grassland have similar responses to DRT and TNn; namely, both SOS and EOS are advanced with the increase in DRT and delayed with the increase in TNn (the sensitivity coefficient is quite different) but have different responses to RX5day and TXx. These results reveal that extreme climate events have a greater impact on vegetation EOS than on vegetation SOS, with these effects varying with vegetation types. This research can provide a scientific basis for formulating a scientific basis for regional vegetation restoration strategies and disaster prediction on the Loess Plateau.

Grassland is the most important land use/cover type in the Yellow River basin. Studying its ecosystem services and the trade-off and synergistic relationships between its various functions is of great significance to high-quality development and the protection of the ecological environment in the Yellow River basin. This paper evaluates the five typical functions of grassland in the Yellow River basin quantitatively, including water yield, carbon storage, soil conservation, habitat quality, and NPP by adopting the InVEST model and the CASA model. It analyzes changes in the trade-offs and the synergistic relationships between the five ecosystem functions from 1990 to 2018 by adopting the correlation coefficient method. The paper also analyzes and explores the spatial heterogeneity of the trade-offs and synergistic relationships by adopting the bivariate spatial autocorrelation method. The results show that from 1990 to 2018, the average water yield depth, carbon storage, and NPP of the grassland in the Yellow River basin tended to increase; soil conservation and habitat quality showed a decreasing trend; and the spatial distribution of the five functions were clearly in line with zonal law. The five ecological functions were synergistic; the synergistic relationship between water yield and the other functions was relatively weak, and there was a strong synergistic relationship between the other four functions. The trade-offs and synergistic relationships between the five functions demonstrated significant spatial heterogeneity in space. This research provides a scientific basis for determining the optimal utilization and sustainable development of grassland resources.

The coordinated development of territorial spatial functions is the main manifestation of the sustainable utilization of territorial resources. Identifying spatial functions and their coordinated relationship has become an important guarantee for regional coordinated development, and is of great significance to the construction of a sustainable land development and protection pattern. This study constructs a classification and function scoring system for Production-Living-Ecological Spaces (PLES) by using land-use data from Gansu Province in 2000 and 2020, and revises the spatial functions of the land to supplement the deficiencies of existing related researches by combining socio-economic data (GDP, population density) and ecosystem service value data. In the aforementioned works, the Mechanical Equilibrium Model in physics is referenced to explore the coordinated state among territorial spatial functions. The results show that the high-value areas of production function are mainly distributed in the eastern and central areas of the Loess Plateau, with a strip-like distribution in the Hexi Corridor. The high-value areas of living function are consistent with that in the center of the city. The distribution of high-value areas of ecological function is in line with the topographic distribution pattern of mountainous areas in Gansu Province. The distribution pattern of the coordinated state of land space function in Gansu Province is relatively stable from 2000 to 2020. Simultaneously, the functional dominant area and the promotion area are identified according to the deviation of the coordinated degree of production-living-ecological function. After clarifying the functional characteristics of each county, the author proposes a differentiated regulation strategy of territorial spatial function.

Studying the impact of regional or seasonal drought on vegetation water-use efficiency (WUE) can identify an effective theoretical basis by which vegetation can cope with future climate change. Based on remote sensing data and climate grid data, in this study, we calculated the ecosystem WUE and standardized precipitation evapotranspiration index (SPEI), analyzed the temporal and spatial divergence of seasonal drought and WUE, and explored the relationship between WUE and seasonal drought in the Loess Plateau. The results indicate that from 2001 to 2019, the humidity in spring and summer on the Loess Plateau shows an increasing trend, and the aridity in fall also shows an increasing trend. Averaged over four seasons, WUE presents distribution characteristics of “high in the southeast and low in the northwest”, with the highest WUE in summer. However, the geological distribution of the sensitivity of WUE to seasonal drought was significantly different. Spring drought increased WUE, whereas summer drought led to a decrease in WUE. When fall drought was less severe, the WUE increased; WUE response to winter SPEI was negative, but the sensitivity did not change with variation of drought degree. The sensitivity of WUE to the magnitude of seasonal drought was affected by regional dry and wet conditions. A clear seasonal divergence was found in four climate regions, along with increased drought intensity, and the sensitivity of WUE to drought magnitude in arid areas was generally higher than that in semi-arid, semi-humid areas, or humid areas. With this study, we deeply explored how ecosystems deal with the water supply strategy of seasonal drought, which is of great significance in the understanding of the coupling relationship between the carbon–water cycle and climate change.