## 1 Introduction  
Carbon storage (CS) in terrestrial ecosystems is a critical component of the regional carbon cycle and has a significant impact on atmospheric carbon dioxide (CO\({}_{2}\)) levels [1, 2, 3]. The importance of its protection and enhancement is reflected in the Paris Agreement, the REDD+ program, and the Chinese government's \"Double Carbon Target\". However,ecosystem CS is significantly affected by natural environment changes and human activities, especially land use/land cover change (LUCC) [4; 5], in which land use (LU) change is particularly critical to the carbon cycle. However, current quantitative studies on the effects of LUCC on CS are inadequate, especially in terms of ecosystem size, pattern and uncertainty [6; 7]. Therefore, a comprehensive survey of spatial and temporal scales is needed to accurately capture the dynamics of CS within terrestrial ecosystems. Combined climate and land impacts may generate positive (net emissions) or negative (net sequestration) biosphere feedbacks in the direction and magnitude which will hinder or facilitate the achievement of regional GHG reduction targets [8]. Therefore, understanding the characteristics of regional CS changes through the lens of LU types is crucial, including the shift in CS engendered by the LU structure, the potential of CS under various scenarios and the effect of land resource management on CS. In the study of CS in terrestrial ecosystems, the InVEST model is an equivalent factor model to evaluate CS based on LU types and changes [9]. Compared with traditional CS estimation methods such as CASA [10; 11] and FORCCHN [12], the InVEST model possesses the benefits of less data requirements and higher operational efficiency [13], and its accuracy has been fully verified on a regional scale [14]. However, this model cannot directly predict the response of CS in specific scenarios or complex dynamic landscapes, and has limitations in dynamically evaluating the effects of LU change on CS change. However, integrating an LU dynamic simulation model can serve to productively address this limitation. The GeoSOS-FLUS model is an integrated geospatial simulation and multi-scenario optimization design model developed by [PERSON] et al. [15] built on neural networks. This model effectively simulates the complex nonlinear interactions involved in the transformation of various LU types and demonstrates a high degree of simulation accuracy. Therefore, the combination of the GeoSOS-FLUS model and InVEST model not only overcomes the limitations of a single model, but also effectively predicts the future change trend of terrestrial CS, providing strong decision support for regional sustainable development. Climate change generally affects semi-arid regions, and their carbon sequestration capacity and ecosystem stability are relatively weak. Large-scale drought conditions severely reduce the total carbon content of the ecosystem [16]. With the rise in drought stress, the photosynthetic rate and productivity of forest and grassland gradually decline, and the plant population density and coverage also decrease, leading to the decline in the CS of vegetation and soil [8]. Therefore, the dynamic characteristics of LU types and CS in semi-arid watershed areas deserve more attention. The semi-arid transitional zone between the eastern margin of the Qinghai-Tibet Plateau (QTP) and the Loess Plateau (LP) is particularly noteworthy. This region's zone of sharp deformation exhibits unique characteristics along environmental gradients on spatial and temporal scales [17]. The complex terrain can produce significant gradient effects in terms of temperature and precipitation. Affected by significant climate differences, different topography, and geomorphic diversity, vegetation cover presents an obvious vertical distribution, which intensifies the spatial heterogeneity of CS. However, research on how variations in topographic gradients impact CS remains relatively limited [18].