降水量作为人工增水效果分析常用指标，受地理、经济、技术等影响，往往可研究分析的代表性数据站点数量较少，一定程度上对区域性效果检验精度造成影响。为此，基于1973—2018年的5—9月巴音布鲁克气象站逐日气象资料和开都河上游大山口水文站逐月径流量资料，利用Budyko模型构建径流模拟方程，并运用序列试验检验、不成对秩和检验以及t检验等统计方法，以降水量、径流量等作为指标，探讨该区域暖季不同统计指标对人工增水作业效果检验的差异性。结果表明：（1） 基于Budyko模型得出的径流量与降水量不但相关性极高（R²=0.9971，P<0.001），而且速率与趋势变化一致，表明模拟径流量不仅能准确反映降水量变化趋势，还能代表降水对径流的影响量。（2） 将实测径流量、模拟径流量和降水量作为统计变量，利用不对称秩和检验及t检验，分析得出人工增水作业后，降水量与径流量增加显著（P<0.02）。（3） 降水量作为统计指标检验功效最好，人工增水作业开展后只需增值11.59%就能显著检验出效果，而模拟径流量相比实测径流量的检验功效值低3.72%，说明检验功效有提升。（4） 选取统计显著性水平90%的置信区间，得出作业期（1994—2018年）比历史期（1973—1993年）的暖季月均降水量绝对增加值为5.38 mm，相对增率为12.05%；模拟径流量绝对增加值为4.53 m³·s^-1，相对增率为14.7%；实测径流量绝对增加值为28.48 m³·s^-1，相对增率为18.48%，表明巴音布鲁克山区暖季人工增水作业效果显著。

Although precipitation serves as a common statistical indicator, its utility is limited by the availability of representative station data, which is affected by geographical, economic, technical, and other effects. Such limitations can introduce inaccuracies in the assessment of the effectiveness of artificial precipitation enhancement. To address these challenges, this study focused on daily meteorological data from the Bayanbulak meteorological station and monthly runoff data from the Dashankou hydrological station in Xinjiang, China, from May to September in 1973—2018. This study involved building a runoff simulation equation using the Budyko model. To objectively and quantitatively analyze the impact of artificial precipitation enhancement in the Bayanbulak mountain area during warm seasons, sequence tests, unpaired rank sum tests, and t-tests were used. The results show the following: (1) The use of the Budyko model for building simulated runoff not only synchronized with the changing trends and growth rates of precipitation but also demonstrated a highly significant correlation (R²=0.9971, P<0.001). This shows that the simulated runoff not only captured the overall precipitation trends but also quantified the impact of precipitation on runoff. (2) Utilizing an unpaired rank sum test and t-test, it was found that both precipitation and runoff significantly increased (P<0.02) after artificial precipitation enhancement when considering measured runoff, simulated runoff, and precipitation as statistical variables. (3) The best statistical indicator for assessing the impact of artificial precipitation enhancement was precipitation, accounting for only 11.59% of the added value to statistically test the effect. Compared with the measured runoff, the test efficiency value of the simulated runoff decreased by 3.72%, indicating that the test efficiency was improved. (4) With a selected significance level of a 90% confidence interval, the absolute increase in precipitation was 5.38 mm, representing a relative increase rate of 12.05%. The absolute increase in the simulated runoff was 4.53 m³·s^-1, indicating a relative increase rate of 14.70%. The absolute increase in the measured runoff was 28.48 m³·s^-1, corresponding to a relative increase rate of 18.48%. This indicates the important impact of artificial precipitation enhancement during the warm seasons in the operation period (1994—2018) compared with the historical period (1973—1993) in the Bayanbulak mountain area.