Unit stream power,minimum energy dissipation rate,and river engineering

Unit stream power is the most important and dominant parameter for the determination of transport rate of sand,gravel,and hyper-concentrated sediment with wash load.Minimum energy dissipation rate theory,or its simplified minimum unit stream power and minimum stream power theories,can provide engineers the needed theoretical basis for river morphology and river engineering studies.The Generalized Sediment Transport model for Alluvial River Simulation computer mode series have been developed based on the above theories.The computer model series have been successfully applied in many countries.Examples will be used to illustrate the applications of the computer models to solving a wide range of river morphology and river engineering problems.

Recent changing patterns of the Changjiang(Yangtze River) Estuary caused by human activities

To evaluate the controlling factors for coastline change of the Changjiang（Yangtze River） Estuary since 1974,we extracted the mean high tide line from multi-temporal remote sensing images that span from 1974 to 2014 at 2-year intervals.We chose 42 scenes to constrain the changing pattern of the Changjiang Estuary coastline,and implemented GIS technology to analyze the area change of the Changjiang（Yangtze） Subaerial Delta.Runoff,sediment discharge and coastal engineering were withal considered in the analysis of the coastline changes.The coastline has transgressed seaward since 1974,and a part of it presents inter-annual variations.The area of the Changjiang Subaerial Delta increased by 871 km2,with a net accretion rate of 21.8 km2/a.Based on the change of sediment discharge due to the major projects in the Changjiang River Basin,we divided the changing pattern of the coastline into three stages：the slow accretion stage（1974–1986）,the moderate accretion stage（1987–2002）,and the rapid accretion stage（2003–2014）.Liner regression analysis illustrated that there is a significantly positive correlation between the area changes and sediment discharge in the Chongming Eastern Shoal and Jiuduansha.This suggested that sediment load has a fundamental effect on the evolution of the Changjiang Estuary.Construction of Deep Waterway in the North Passage of the Changjiang River（1998–2010） led to a rapid accretion in the Hengsha Eastern Shoal and Jiuduansha by influencing the hydrodynamics in North Passage.Coastal engineering such as reclamation and harbor construction can also change the morphology of the Changjiang Estuary.We defined a contribution rate of area change to assess the impact of reclamation on the evolution of Changjiang Estuary.It turned out that more than 45.3% of area increment of the Changjiang Estuary was attributed to reclamation.

Conditions of Incipient Motion of Pebbles Based on Shape and the Equivalent Grain Size

Incipient motion of pebbles is an unsteady and random process,the main influence factors are pebble shape,non-uniformity,location in bed and flow condition.By virtue of the concept of the equivalent grain size,it is shown that d_ηfollows a normal distribution following a regression analysis of measured data.Consid- ering the influence of pebble shape and the relative exposure degree,formula of calculating the incipient veloc- ity for pebbles is deduced based on the equivalent grain size of pebble and the...

Evaluation of suspended load transport rate using transport formulas and arti- ficial neural network models (Case study: Chelchay Catchment)

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelehay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network （ANN） structures. Although the Chang formula with higher correlation coefficient （r = 0.69） and lower Root Mean Square Error （RMSE = 0.013） is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models inclu- ding 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.