Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC-CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200-5900 m), and mainly consumed in the mid-low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low-coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid-coverage grassland (MG; 15.6%), bare land (12.5%), high-coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water.

To explore the spatio-temporal dynamics and mechanisms underlying vegetation cover in Haryana State, India, and implications thereof, we obtained MODIS EVI imagery together with CHIRPS rainfall and MODIS LST at annual, seasonal and monthly scales for the period spanning 2000 to 2022. Additionally, MODIS Potential Evapotranspiration (PET), Ground Water Storage (GWS), Soil Moisture (SM) and nighttime light datasets were compiled to explore their spatial relationships with vegetation and other selected environmental parameters. Non-parametric statistics were applied to estimate the magnitude of trends, along with correlation and residual trend analysis to quantify the relative influence of Climate Change (CC) and Human Activities (HA) on vegetation dynamics using Google Earth Engine algorithms. The study reveals regional contrasts in trends that are evidently related to elevation. An annual increasing trend in rainfall (21.3 mm/decade, p < 0.05), together with augmented vegetation cover and slightly cooler (-0.07 degrees C/decade) LST is revealed in the high-elevation areas. Meanwhile, LST in the plain regions exhibit a warming trend (0.02 degrees C/decade) and decreased in vegetation and rainfall, accompanied by substantial reductions in GWS and SM related to increased PET. Linear regression demonstrates a strongly significant relationship between rainfall and EVI (R-2 = 0.92), although a negative relationship is apparent between LST and vegetation (R-2 = -0.83). Additionally, increased LST in the lowelevation parts of the study area impacted PET (R-2 = 0.87), which triggered EVI loss (R2 = 0.93). Moreover, increased HA resulted in losses of 25.5 mm GSW and 1.5 mm SM annually. The relative contributions of CC and HA are shown to vary with elevation. At higher elevations, CC and HA contribute respectively 85% and 15% to the increase in EVI. However, at lower elevations, reduced EVI is largely (79%) due to human activities. This needs to be considered in managing the future of vulnerable socio-ecological systems in the state of Haryana.