###### Abstract
(2009) [18] developed an eco-hydroological model (BEPS-Terrainlab, BTL) by integrating the BEPS model with a spatially distributed hydrological model called TerrainLab. BEPS is a two-leaf process-based model, with daily canopy-level photosynthesis simulated by up-scaling [PERSON]'s leaf-level biochemical model [57]. Over the past two decades, the BEPS model has been adopted as an effective tool to simulate the carbon and water fluxes between terrestrial ecosystems and the atmosphere in China [58; 59; 60], North America [61], Canada [62; 63], East Asia [64], Europe [65], and the globe [66; 67]. TerrainLab was designed to obtain accurate estimations of the water table and soil moisture content in mountainous areas through the use of a subsurface saturated flow mechanism. The TerrainLab model not only considers topographic effects on the spatial distributions of climatic variables, but also describes the movement of soil water by assuming that each pixel is connected to its surrounding eight pixels. The improvement of BTL in simulating carbon and water fluxes over BEPS was proved in a mountainous watershed [18]. Generally, GPP estimation at a site scale is always limited by its spatial representation, whereas a watershed-scale GPP estimation could provide spatial-continuous information on vegetation photosynthesis. The GPP estimates from the above three mountain GPP models (i.e., MTL-LUE, MTG, and BTL) have been proven to match better with tower-based GPP than those from the original models (i.e., TL-LUE, TG, and BEPS) at the site scale [22; 23]. However, the discrepancies among the three mountain GPP models, and their abilities to characterize spatial variation in GPP at the watershed scale, are not yet known. BTL considers the combined spatial effect of various environmental indicators and ecological processes on GPP estimation, but its complicated model structure requires a large number of input data and ecological parameters. MTL-LUE has a relatively simple model structure and only considers the process of radiation conversion, and it also requires meteorological data as input. Although the understanding of the vegetation photosynthesis process in MTG is not as detailed as that in BTL and MTL-LUE, MTG has the advantage of extensive remotely sensed information (i.e., estimating GPP without ground data). Therefore, MTL-LUE, MTG, and BTL have different scopes of application. The MTL-LUE, MTG, and BTL models are developed from different model structures, and use different modeling strategies to describe the complex effect of topography on the vegetation photosynthesis process. The main aims of this study are to: (1) investigate the improvement in the MTL-LUE, MTG, and BTL models over the original models (i.e., TL-LUE, TG, and BEPS) at the watershed scale, (2) compare the GPP estimates from MTL-LUE, MTG, and BTL models at the watershed scale, and (3) analyze and compare the responses of the three mountain GPP models to surface topography (i.e., elevation, slope, aspect, and SVF). This study was carried out in a mountainous watershed, and the outcomes can serve as an example, and the first application, for watershed-scale carbon modeling over mountainous areas.