## 4 Discussion  
Quantification of uncertainty using the SUFI-2 algorithm showed that the largest share of uncertainty was related to the curve number, CN, as well as the soil properties for monthly streamflow and water yield projections during most of the annual cycle in the Upper Xijiang River Basin. This uncertainty mainly arose from the statistical downscaling, as well as the use of global LULC and soil data combined at a regional scale. These findings are in general agreement with most previous studies, which concluded that the LULC classification scheme and the validation methodology had the highest error contribution and implementation priority and showed that there are many inconsistencies between the definition of the map classes [64; 65; 66; 67]. [PERSON], [PERSON] [68] pointed out that the sensitivity of hydrological projections is based on the selection of global climate models (GCMs). The main objective of this study was to evaluate the best-suited representative land-use and climate change scenario for deducing the impacts on streamflow and water yield over the Upper Xijiang River Basin using the SWAT model. The most effective parameters for surface runoff calibration and validation, as categorized by [PERSON], [PERSON] [41] based on the behaviors of 64 watersheds, are CN2, AWC, ESCO, EPCO, OV_N, and SURLAG. The SWAT model was calibrated over the Pearl River Basin to evaluate the effects of temperature change on water discharge, where it was found that CN2, ESCO, RCHRG_DP, and SOL_AWC were the most sensitive parameters [69]. [PERSON], [PERSON] [70] highlighted the impacts of land-use and -cover change (LUCC) on the hydrological conditions (water discharge  
Figure 10: Percent change in water yield comparison under historical and future scenarios. and sediment load) of a tropical basin were investigated by using the Soil and Water Assessment Tool (SWAT), where the most sensitive parameters were found to be RCHRG_DP, CN2, SOL_K, and SOL_AWC. The curve number, CN2, depends on soil and LULC characteristics and was found to be the most sensitive parameter, followed by SOL_AWC, ESCO, and SOL_K, in a study over the Eastern Nile Basin [71]. The main limitation of the SWAT model is that the baseflow simulation cannot generate acceptable values, as indicated by several studies [72; 73]. In this study, we also concluded that parameters such as ground flow, tile flow, sublimation, and other related parameters are not well-simulated, resulting in sensitivity of the ESCO and ALPHA_BF parameters. Uncertainty analysis (UA) is one of the techniques used to parameterize a hydrologic model and to reduce the uncertainty range [74]. The performance indices R\({}^{2}\) and NSE were used to assess the goodness of fit between the observed and simulated values [72]. A hydrologic model can be assessed by multiple statistical and graphical performance indices, to compensate for the shortcomings of every performance indicator [75]. PBIAS, KGE, and RSR can be used to highlight poor model performance and integrate the benefits of an error index [58; 76; 77]. In this study, we achieved acceptable values for the five performance indicators reported in Table 10 (i.e., R\({}^{2}\), NSE, PBIAS, KGE, and RSR). The result of calibration and validation for both hydrological stations shows that the parameter indices are satisfactory; several studies have concluded that such values are acceptable in this range [2; 78; 79]. Land use and land cover (LULC) have been changing, either directly or indirectly, due to human activities. These changes affect the hydrological cycle and water balance components. In this study, LULC data from 1995, 2000, 2005, and 2015 developed by European Space Agency (ESA), USGS, and GLC IFORCE were evaluated to study the impacts on discharge and water yield. [PERSON], [PERSON] [80] estimated a significant decrease in land cover, decline in surface water and base flow, and effects on annual water yield and evapotranspiration (ET).