Changes in meteorology, hydrology, and vegetation will have significant impacts on the ecological environment of a basin, and the middle-upper reach of Huaihe River (MUHR) is one of the key regions for vegetation restoration in China. However, less studies have quantitatively accounted for the contribution of vegetation changes to land surface discharge in the MUHR. To quantitatively evaluate the influence of vegetation changes on land surface discharge in the MUHR, the Bernaola–Galavan (B–G) segmentation algorithm was utilized to recognize the mutation year of the Normalized Difference Vegetation Index (NDVI) time sequence data. Next, the functional relationship between the underlying surface parameter and the NDVI was quantitatively analyzed, and an adjusted Budyko formula was constructed. Finally, the effects of vegetation changes, climate factors, and mankind activities on the surface discharge in the MUHR were computed using the adjusted Budyko formula and elastic coefficient method. The results showed the following: (1) the surface runoff and precipitation from 1982 to 2015 in the MUHR presented a falling trend, yet the NDVI and potential evaporation presented an upward trend; (2) 2004 was the mutation year of the NDVI time series data, and the underlying surface parameter showed a significant linear regression relationship with the NDVI (p < 0.05); (3) the vegetation variation played a major role in the runoff variation during the changing period (2005–2015) in the MUHR. Precipitation, potential evaporation, and human activities accounted for −0.32%, −15.11%, and 18.24% of the surface runoff variation, respectively.

Predicting future land-use patterns and carbon storage is essential for understanding regional terrestrial ecosystems, as regional land-use change plays a crucial role in ecosystem carbon storage variations. Using the Patch-generating Land Use Simulation Model (PLUS), we simulated the 2020 land-use types in the Yellow River Basin (YRB) based on the 2010 data. Subsequently, we predicted YRB land-use types for 2030 to 2100. Finally, based on these simulated land-use patterns, we calculated the carbon storage in the YRB from 1980 to 2100 using the InVEST model. The results showed that: (1) From 1980 to 2020, the InVEST model showed that carbon storage in the Yellow River Basin (YRB) exhibited an increasing trend of 12.10%. Rapid carbon storage increases can be observed in 2000–2020 (16.9 million tons). The largest carbon storage was found in Grassland (2487.24 million tons), which accounts for 51.03% of the total carbon storage in YRB. (2) During 2030 to 2100, the grassland area showed a decrease trend in SSP1-2.6 (−12.22%). The forest area showed an increase trend in SSP1-2.6 (3.49%). (3) Among the different scenarios, SSP1-2.6 (103.99 million tons) and current scenarios (23.07 million tons) showed the largest carbon storage gains from 2030 to 2100, primarily attributed to the cultivated land and forest, despite a major loss from grassland. SSP2-4.5 showed a carbon storage loss of 23.48 million tons, while a slight gain of 6.49 million tons was observed under SSP5-8.5. (4) Carbon storage losses were primarily observed in the grassland-dominated northern regions of the YRB. In contrast, the southernmost and eastern regions showed an increasing trend. This research provides essential scientific support for optimizing land-use structure and enhancing land management strategies across the YRB basin.