Urbanization has an important effect on ecosystem services (ESs) and identifying the relationship between urbanization and ESs can provide a decision-making reference for regional ecological protection and management. Taking the areas along the Yellow River of Henan Province (AYRHP) as a research area, a coupling system of ESs and urbanization is established in this study to reveal the coupling relationship between the two. ESs are estimated by using Carnegie–Ames–Stanford approach, revision universal soil loss equation, and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. The urbanization level is evaluated from three dimensions, namely, population, economy, and land. The coupling coordination relationship between various ESs and urbanization in AYRHP is quantified from 2000 to 2018 on the county scale based on the coupling coordination degree (CCD) model. The lead–lag relationship between ESs and urbanization is identified by using the relative development degree model, and ecological management zoning is conducted. Results show that in the study period, net primary production (NPP), soil conservation, and food production are increased, whereas water yield is decreased. In the study period, population, economy, and land urbanization level are increasing, and the comprehensive urbanization level is increased by 51.63%. The total CCD between NPP, food production, and water yield and comprehensive urbanization is basic or moderate coordination, whereas that between soil conservation and comprehensive urbanization is moderate maladjustment. In the research period, the coupling coordination between NPP and food production and comprehensive urbanization is increasing; that between water yield and comprehensive urbanization is fluctuated; and that between soil conservation and comprehensive urbanization is decreasing. The result of the research into the relative development degree in 2018 showed that food production, water yield, and soil conservation lag behind the urbanization level in most regions and counties along the Yellow River of Henan Province. On the basis of the lead–lag relationship between different ESs and urbanization level, the AYRHP are divided into ecological reconstruction area, ecological and agricultural improvement area, and ecological conservation area. CCD and relative development degree models can be used to evaluate the coordination relationship between ESs and urbanization, which provides scientific support for regional ES management.

With the rapid development of the social economy, human activities have had a severe impact on the environment. The global climate issue caused by CO₂ emissions has attracted the attention of various countries around the world, and reducing CO₂ emissions is urgent. This article simulates the changes in carbon storage in Anhui Province from 2030 to 2070 based on SSP1-2.5, SSP2-4.5, and SSP5-5.8 scenarios. First, based on the land use data of Anhui Province in 2010, the PLUS model was used to simulate the land use data of 2015, and the accuracy of the simulation results was verified against real data. Then, the land use data of Anhui Province were simulated in the future period from 2030 to 2070 under different SSP scenarios. Finally, based on the InVEST model, the spatiotemporal changes in future carbon storage were calculated. The research showed that, during the period of 2030 to 2070, the spatial distribution of carbon storage in Anhui Province under three scenario simulations generally showed a distribution pattern of high carbon storage in the north and south, and low carbon storage in the central region. Under the SSP1-2.6 scenario, Anhui Province’s carbon storage decreased by 0.33 million tons, a decrease of 0.029%. Under the SSP2-4.5 scenario, carbon storage increased by 0.25 million tons, an increase of 0.021%. Under the SSP5-8.5 scenario, carbon storage decreased by 1.54 million tons, a decrease of 0.133%. The reasons for the changes in carbon storage were related to the areas of arable land, forest land, and grassland. This study can provide a reference for future low-carbon land use planning.

The Yarlung Zangbo River (YZR) is the largest river on the Qinghai Tibet Plateau, and changes in its meteorology, hydrology and vegetation will have a significant impact on the ecological environment of the basin. In order to deepen our understanding of the relationship of climate–vegetation–hydrological processes in YZR, the purpose of this study is to explore how vegetation growth in the YZR affects its runoff changes. We first identified the abrupt year of discharge in the YZR using a heuristic segmentation algorithm and cumulative anomaly mutation test approach. After that, the functional equation for NDVI and the Budyko parameter (n) was computed. Finally, the NDVI was introduced into the Budyko equation to evaluate the impact of vegetation changes on the streamflow in the YZR. Results showed that: (1) NDVI and discharge in the YZR both presented an increasing trend, and the mutation year of annual runoff in Nuxia station occurred in 1997. (2) n had a significant negative correlation with NDVI in the YZR (p < 0.01). (3) The contributions of Pr, ET₀, NDVI, and n on streamflow change in the S2 period (1998–2015) were 5.26%, 1.14%, 43.04%, and 50.06%. The results of this study can provide scientific guidance and support for the evaluation of the effects of ecological restoration measures, as well as the management and planning of water resources in the YZR.

The middle reaches of the Yellow River (MRYR) are a key area for carrying out China’s vegetation restoration project. However, the impact of vegetation variation on runoff in the MRYR is still unclear. For quantitatively evaluating the contribution rate of vegetation variation to runoff in the MRYR, this paper quantified the relationship between Normalized Difference Vegetation Index (NDVI) and Budyko parameters (w). Then, we used multiple linear regression to quantitatively calculate the contribution rate of different factors on vegetation variation. Finally, an adjusted Budyko formula was constructed to quantitatively calculate the influence of vegetation variation on runoff. The results showed that there is a linear relationship between NDVI and Budyko parameters (w) (p < 0.05); the fitting parameter and constant term were 12.327 and −0.992, respectively. Vegetation change accounted for 33.37% in the MRYR. The contribution of climatic and non-climatic factors on vegetation change is about 1:99. The contribution of precipitation, potential evaporation, anthropogenic activities on the runoff variation in the MRYR are 23.07%, 13.85% and 29.71%, respectively.

The occurrence of water scarcity and extreme hydrological events is becoming increasingly frequent worldwide. The river runoff process, as an important link in the water cycle of a basin, is an essential content of the study of hydrological processes in the basin. Vegetation is an influencing factor closely related to hydrological processes in a watershed. This article quantitatively analyzes the impact of vegetation restoration and climate variation on runoff in the Luan River Basin from 1982 to 2018. Firstly, trend analysis was conducted on runoff depth, precipitation, the Normalized Vegetation Index (NDVI), and reference evaporation. Then, Mann–Kendall mutation analysis and Pettitt mutation analysis were used to identify the year of runoff mutation. Finally, a Budyko model was constructed to quantitatively analyze the impact of vegetation and climate change on the runoff of the Luan River. The results showed that: (1) The runoff reduction rate of the Luan River was 1.2437 mm/a. The precipitation was increasing at a rate of 0.6977 mm/a. The reference evaporation rate decreased at a rate of 0.0977 mm/a. The annual growth rate of the Budyko parameter (n) was 0.0283. The annual growth rate of NDVI was 0.0022. (2) The coefficient of determination in the functional equation (a = 14.74, b = −3.4105) for NDVI and Budyko parameter (n) was 0.3297 (p < 0.01). (3) The contributions of Pr, ET₀, NDVI, and (n) to reduced flow were −17.49%, 1.46%, 35.25%, and 80.78%, respectively. The increase in vegetation would lead to a decrease in runoff. This study can clarify the impact of vegetation restoration on water resource security in the Luan River Basin.

Many studies quantify the impact of climate change and human activities on runoff changes on an annual scale, but few studies have examined this on multiple time scales. This paper quantifies the contribution of different factors to the variability of Jinsha River runoff at multiple time scales (annual, seasonal and monthly). First, the trend analysis of Jinsha River runoff is carried out, and the Mann–Kendall mutation test was then applied to the runoff data for mutation analysis. According to the mutation year, the research period is divided into the base period and the mutation period. By constructing an ABCD hydrological model simulation and monthly scale Budyko model, the contribution rate of human and climate factors to the multitime-scale runoff of Jinsha River is calculated. The results showed that: (1) The sudden year of change in the Jinsha River runoff is 1978, and the Nash coefficients of the ABCD hydrological model in the base period and sudden change period were 0.85 and 0.86, respectively. (2) Climate factors were the dominant factor affecting annual runoff changes (98.62%), while human factors were the secondary factor affecting annual runoff changes (1.38%). (3) The contribution rates of climate factors in spring, summer, autumn, and winter to runoff were 91.68%, 74.08%, 95.30%, and 96.15%, respectively. The contribution rates of human factors in spring, summer, autumn, and winter to runoff were 8.32%, 25.92%, 4.70%, and 3.85%, respectively. (4) The contribution rates of climate factors to runoff in May, June, and July were 95.14%, 102.15%, and 87.79%, respectively. The contribution rates of human factors to runoff in May, June, and July were 4.86%, −2.15%, and 12.21%, respectively.