# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 2 Background and Methodology
### Hydrological Characteristics Alteration
#### 2.2.1 Jinsha River  
According to the hydrologic data statistics during 1956-2020, the muti-annual average discharge of Jinsha River was 4483 m\({}^{3}\)/s, the maximum annual average runoff was 6205 m\({}^{3}\)/s (occurring in 1988), and the minimum one was 3190 m\({}^{3}\)/s (occurring in 2011). The total tendency of the annual average runoff was not sharp but fluctuated, and the maximum variable amplitude was 1700 m\({}^{3}\)/s, which indicated that the climate of the upper basin of the Yangtze River had not changed much. Just like other monsoon climate watersheds, the average month was seriously different in a year; the flood period was from June to October, during which the total runoff was 65.1% of water volumes in a whole year. Accordingly, only 14.6% of water volumes in a whole year were distributed in the dry period from December to April of the next year. In light of the significance of dry discharge for ship navigation, urban water consumption, and ecological maintenance, the trend in dry discharge variation was further analyzed using the Theil-Sen median trend method combined with the Mann-Kendall [30; 31] test. As a robust trend calculation method, the Theil-Sen median trend method is frequently employed alongside the MK test to identify trend variations in long-term sequential data [32; 33]. Through trend test analysis of the monthly average flow of the Jinsha River in February and March from 1956 to 2020, the Theil-Sen median value was calculated as 10.53, with a normalized test statistic Z of 4.48, exceeding the 95% confidence level of 1.96. This indicates a clear increasing trend in the monthly average discharge during the dry period. However, the time series variation process can be divided into three stages, and this paper adopts the M-K mutation detection method, as shown in Figure 2a. The UF and UB are calculated sequence test statistics. Since 2000, the sequential test statistic UF has consistently been positive, indicating that the dry discharge has continued to increase. The point at which the UF and UB curves intersect within the confidence interval represents the computed breakpoint. The curves of Mann-Kendall test statistic UF and UB intersected in 2014, although this intersection was not within the confidence zone but only approached it. The Pettitt change point test and cumulative anomaly method were employed to further analyze the aforementioned catastrophic events. The Pettitt change point test is based on non-parametric analysis and can identify a single change point in a sequence. However, it is limited to detecting only one change point and is often used in combination with other methods. The cumulative anomaly method entails plotting a cumulative series displaying the differences between each value in a time series and the series' mean value. By scrutinizing the inflection points on the resultant cumulative anomaly curve, this method identifies breakpoints or change points. The results demonstrated that significant inflection points occurred in 1998 and 2012. Consequently, the construction of the Ertan, Xiluodu, Xiangjiaba, and Jinping 1 st dams took place in 1998 and 2012, respectively. It is evident that the dam construction in the Jinsha River had a notable impact on alterations in dry  
Figure 2: Variation trend and catastrophic analysis of monthly average discharged change of Jinsha River in the dry. (**a**) Runoff situation at different periods. (**b**) The MK test and cumulative anomaly values. discharge. As a result, catastrophic alterations were observed with the construction of Ertan in 2000, and Xiluodu and Jinping 1 st in 2014. During the first stage, spanning from 1955 to 2000 the monthly average discharge amounted to 1343 m\({}^{3}\)/s. Following the completion of the Ertan Reservoir, the average discharge increased to 1718 m\({}^{3}\)/s during the second stage from 2001 to 2014. With the completion of the Xiluodu, Xiangjiaba, and Jinping 1 st Dams in 2014, the average discharge further increased to 2379 m\({}^{3}\)/s, as shown in Figure 2b.