Lhasa River basin is situated in the southern part of the Qinghai-Tibet Plateau, which is the most important region of economic and social development in Tibet. In order to efficiently utilize water resources in the basin and ease the shortage of regional electric power supply, Zhikong Reservoir was built in the upstream reach of the Lhasa River in 2006. Impoundment of this reservoir evidently affected the morphology and stability of the downstream braided channel below the dam. Yet, little is known about the complex responses of the downstream braided channel to the Zhikong Dam. Landsat images in the 2000–2016 period, together with daily discharges and field observations in the 2017–2018 period, were used to investigate the morphological response of the braided channel to the Zhikong Dam. The downstream Lhasa River below the Zhikong Dam was divided into four reaches (i.e., RS1, RS2, RS3 and RS4) based on the confluence of three downstream tributaries. Results showed that the number and area of central bars in the braided reach closest to Zhikong Dam (RS1) were increased because of main channel incision and water level drop. This increasing trend attenuated along the downstream channel of this reach. Braiding number index of multithread channels in RS1 obviously increased by 3 in one section and reduced by 2 in two sections, while changed in all sections randomly with no pronounced trend along the RS2 to RS3 and RS4 reaches. The average bar area in two focus reaches, RS1_B1 and RS2_B2, 6.0 km and 36.8 km far away to the Zhikong Dam, respectively, followed opposite trends with the former increasing and the later reducing. Furthermore, the mean dissection, landscape dissection and fragmentation shape indices in RS1, showed an increasing trend from 2001 to 2016, indicating the shape of irregular central bars varied greatly because clean water release of Zhikong Dam eroded the downstream braided channel.
Precipitation forecasting is an important guide to the prevention and control of regional droughts and floods, the rational use of water resources and the ecological protection. The precipitation process is extremely complex and is influenced by the intersection of many variables, with significant randomness, uncertainty and non-linearity. Based on the advantages that Complementary ensemble empirical modal decomposition (CEEMD) can effectively overcome modal aliasing, white noise interference, and the ability of Long Short-Term Memory (LSTM) networks to handle problems such as gradient disappearance. A CEEMD-LSTM coupled long & short-term memory network model was developed and adopted for monthly precipitation prediction of Zhengzhou city. The performance shows that the CEEMD-LSTM model has a mean absolute error of 0.056, a root mean square error of 0.153, a mean relative error of 2.73% and a Nash efficiency coefficient of 0.95, which is better than the CEEMD- Back Propagation (BP) neural network model, the LSTM model and the BP model in terms of prediction accuracies. This demonstrates its powerful non-linear and complex process learning capability in hydrological factor simulation for regional precipitation prediction.
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