With the operation of six cascade reservoirs, the flow regime and sediment discharge of the Lancang River have changed greatly. The changes of runoff and suspended load have attracted extensive attention. The hydrological data of Gajiu and Yunjinghong stations in Lancang River from 1964 to 2019 were analyzed by using wavelet analysis, double mass curve and abrupt change analysis. The temporal trends in runoff and suspended load were evaluated. Results revealed that the reduction of suspended load was much more profound than the change of runoff. There was a slight downward trend in annual runoff due to climate change. After the completion of Xiaowan and Nuozhadu reservoirs, the proportion of runoff in flood season decreased by 22.64 and 30.75%, respectively. Wavelet analysis was used to reveal the characteristics of runoff evolution. With the operation of reservoirs, suspended load appeared abrupt changes in 1993 and 2008. The amount of suspended load during 2009–2019 decreased by 95.47–98.78% compared with that before the reservoir construction. This paper presents the latest quantitative study on the temporal variation of runoff and suspended load since the completion of Xiaowan and Nuozhadu reservoirs, which is of great importance for guiding the operation of reservoirs and maximizing the value of the whole Lancang-Mekong River basin.

The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction.

The Lancang reservoirs play an essential role in the national economy and life, and the study of reservoir siltation is of great significance to ensure its sustainable service for the nation and people. In this paper, reservoir sedimentation is quantified in stages by empirical models and theoretical methods using reservoir information and sediment data to reveal the latest status of siltation in Lancang reservoirs. Results show that the storage capacity loss of Manwan and Jinghong reservoirs has reached 51.4% and 1.54% until 2019, which illustrates that the situation of siltation is serious. The theoretical trapping efficiency of Manwan reservoir was about 69% and the estimation result of Brune method performed best with a value of 67.5% among the empirical methods. Brune method was then modified with a correction coefficient and the revised Brune method can be used for the estimation of trapping efficiency in other reservoirs. Overall, this paper can present relatively accurate information for managers to understand the current state of siltation in Lancang reservoirs, and provide scientific guidance and data support for them to take measures to reduce sedimentation and ensure the sustainability of reservoirs.