Knowing the impact of land-use and land-cover (LULC) changes on the distribution of water yield (WY) is essential for water resource management. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, we investigated the spatial-temporal variations of WY from 1990 to 2015 in China’s northern semi-arid region of Beijing–Tianjin–Hebei (Jing-Jin-Ji). We quantified the combined effects of LULC dynamics and climatic variation on WY. Furthermore, we identified the relative contribution of main LULC types to WY. For our study region, the built-up area increased by 35.66% (5380 km²) during the study period. In the meantime, cropland, grassland, and wetland decreased continuously. The expansion of built-up area and decline of vegetated land led to an increase of 1047 million m³ (5.1%) in total WY. The impacts of LULC changes on WY were mainly determined by the biophysical characteristics of LULC composition. Vegetated land has relatively lower WY coefficients due to higher rates of evapotranspiration and water infiltration. Built-up areas and bare land have higher WY coefficients as a result of their impermeable surface. The spatial-temporal analysis of WY with specification of WY coefficients by LULC types can facilitate integrated land-use planning and water resource management.

In China, the regional development policy has been shifting from solely economic orientation to ecologically sound economic growth. Using the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region as a case study, we evaluated the temporal variations in ecosystem service values (ESVs) associated with land use changes from 1990 to 2015. We analyzed the dynamic relations between ESVs and the economy (indicated by the gross domestic product, GDP) by introducing the elasticity indicator (EI), which reflects the growth synchronism between the two, and the ecosystem service load (ESL), which reflects the ecological efficiency of economic growth. The results showed that the land use changes in Jing-Jin-Ji have been characterized by decreases in water areas, cropland, and grassland and increases in woodland and built-up areas. The ESVs of woodland and water areas contributed to 80% of the total ESV of the region, and the total ESV increased by 13.87% as a result of an area increase in woodland (26.87%). The average EI of Jing-Jin-Ji improved from 0.028 to 0.293 over the study period, indicating that the growth of ESVs was being balanced with the growth in the GDP. The average ESL decreased by 1.24, suggesting a significant improvement in ecological efficiency per unit GDP. Within the Jing-Jin-Ji region, large disparities in EI and ESL were shown to exist among Beijing, Tianjin, and Hebei owing to their differences in ecological resources, GDP compositions, and development levels. The study highlights the needs to reinforce woodland and water conservation, adjust economic structures, and balance the intraregional development to achieve the ecological-economic integrity of the region.

A comprehensive study on the dynamics of ecosystem services and their driving factors is the key prerequisite for enhancing local ecological sustainability. Based on relevant sets of big data, including spatial land data, soil data, DEM, climatic data and social-economic data, using InVEST model and multivariate logistic regression model, the study firstly assessed the spatiotemporal variation of ecosystem services for China’s Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1990 to 2015. The study then analyzed the natural and socioeconomic factors affecting the ecosystem services. The results show that large spatial and quantitative differences exist in the supply of multiple ecosystem services, and the changes of different types of ecosystem services are driven by different factors. For water yield, the areas of arable land, wetland and built-up land and precipitation are the most influential factors; The areas of arable land, precipitation, temperature, altitude, urbanization rate and amount of nutrient applied per unit area are determinants of changes in nutrient retention; The areas of grassland and forest, temperature, altitude, GDP per capita and urbanization rate affect the soil retention to great extent; for carbon storage, its key influential factors are the areas of different land use types and urbanization rate. The study can facilitate identification of where and how to enhance multiple ecosystem services.Keywords: dynamics of ecosystem services, driving factors, InVEST, multivariate logistic regression model