Low resolution of input data and equifinality in model calibration can lead to inaccuracy and insufficient reflection of spatial differences, thereby increasing model errors. However, the impact of input data accuracy, catchment threshold area, and calibration algorithm on model uncertainty reduction has not yet been well understood. The sequential uncertainty fitting version 2 (SUFI‐2) that is linked with the Soil and Water Assessment Tool (SWAT) in the package called SWAT Calibration Uncertainty Programs (SWAT‐CUP) was introduced to quantify the effects of different input data resolutions on parameter sensitivity and model uncertainty in the Jinghe River watershed, and the effects of different sub‐basin delineations and other two calibration algorithms on model uncertainty were also comparatively analysed. (i) USLE_C, EPCO, ALPHA_BNK, and CN2 are the most sensitive parameters among all SWAT projects. When the change of digital elevation model (DEM) resolution is small, the sensitivity of parameters does not change obviously. When the DEM resolution changes significantly, BIOMIX, LAT_SED, USLE_K, and CH_N1 become highly sensitive parameters by replacing OV_N, SMTMP, SURLAG, and USLE_P. However, the change in land use resolution has little impact on parameter sensitivity, with only a slight change in the sensitivity ranking of specific parameters. (ii) Model uncertainty responded to changes in the resolution of DEM more than land use. Most of the runoff simulations had smaller uncertainties (P factor, R factor, percentage of bias [PBIAS]) than sediment. High resolution DEM data reduced model uncertainty, but the models with 2000 m DEM resolution also achieved small uncertainty. Small catchment threshold area leads to high uncertainty of the model, and large catchment threshold areas decrease the model uncertainty. The model has relatively good simulation effects in runoff and sediment when the catchment threshold area was 2000 km2. (iii) The SWAT model has different simulation deviations and uncertainties in different calibration algorithms, the SUFI‐2 and generalized likelihood uncertainty estimation (GLUE) algorithms show better applicability than particle swarm optimization (PSO). The NSE indicators of the three algorithms are in the following order: SUFI‐2 > GLUE > PSO for runoff, and GLUE > SUFI‐2 > PSO for sediment. This study helps us understand the cause, knowledge of which moves from the particular to the general by the comprehension of essence, power, and nature in reducing model uncertainty.

中文翻译：

---

输入数据精度、流域阈值区域和校准算法对模型不确定性降低的影响


输入数据的低分辨率和模型校准的等价性会导致空间差异的不准确和不充分反映，从而增加模型误差。然而，输入数据精度、流域阈值面积和校准算法对模型不确定性降低的影响尚未得到很好的理解。引入了与 SWAT 校准不确定性程序 (SWAT-CUP) 包中的土壤和水评估工具 (SWAT) 链接的顺序不确定性拟合版本 2 (SUFI-2)，以量化不同输入数据分辨率对参数的影响比较分析了泾河流域的敏感性和模型不确定性，以及不同子流域划分和其他两种校准算法对模型不确定性的影响。 (i) USLE_C、EPCO、ALPHA_BNK 和 CN2 是所有 SWAT 项目中最敏感的参数。当数字高程模型（DEM）分辨率变化较小时，参数的灵敏度变化不明显。当DEM分辨率发生较大变化时，BIOMIX、LAT_SED、USLE_K和CH_N1通过替换OV_N、SMTP、SURLAG和USLE_P而成为高度敏感的参数。但土地利用分辨率的变化对参数敏感性影响不大，仅对特定参数的敏感性排序略有变化。 (ii) 模型不确定性对 DEM 分辨率变化的响应大于对土地利用的响应。大多数径流模拟的不确定性（P 因子、R 因子、偏差百分比 [PBIAS]）比沉积物更小。高分辨率DEM数据降低了模型的不确定性，但具有2000 m DEM分辨率的模型也实现了较小的不确定性。 流域阈值面积小导致模型的不确定性较高，流域阈值面积大则模型的不确定性降低。当流域阈值面积为2000 km2时，模型对径流和泥沙的模拟效果较好。 (iii) SWAT模型在不同的标定算法中具有不同的模拟偏差和不确定性，SUFI-2和广义似然不确定性估计(GLU​​E)算法比粒子群优化(PSO)表现出更好的适用性。三种算法的NSE指标顺序如下：对于径流而言SUFI-2>GLUE>PSO，对于沉积物而言GLUE>SUFI-2>PSO。这项研究有助于我们通过理解本质、力量和本质来理解减少模型不确定性的原因、知识从特殊到一般。