This paper develops a methodology for optimizing the hydro unit commitment (HUC) for the Three Gorges Project (TGP) in China. The TGP is the world's largest and most complex hydropower system in operation. The objective is to minimize the total operational cost. The decision variables are the startup or shutdown of each of the available units in the system and the power releases from the online units. The mathematical formulation must take into account the head variation over the operation periods as the net head changes from hour to hour and affects power generation. Additionally, the formulation must consider the operation of 32 heterogeneous generating units and the nonlinear power generation function of each unit. A three-dimensional interpolation technique is used to accurately represent the nonlinear power generation function of each individual unit, taking into account the time-varying head as well as the non-smooth limitations for power output and power release. With the aid of integer variables that represent the on/off and operation partition statuses of a unit, the developed HUC model for the TGP conforms to a standard mixed integer linear programming (MILP) formulation. We demonstrate the performance and utility of the model by analyzing the results from several scenarios for the TGP.

This paper introduces the development of a dynamic parallel algorithm for conducting hydrological model simulations. This new algorithm consists of a river network decomposition method and an enhanced master-slave paradigm. The decomposition method is used to divide a basin river network into a large number of subbasins, and the enhanced master-slave paradigm is adopted to realize the function of this new dynamic basin decomposition method through using the Message-Passing Interface (MPI) and C++ language. This new algorithm aims to balance computation load and then to achieve a higher speedup and efficiency of parallel computing in hydrological simulation for the river basins which are delineated by high-resolution drainage networks. This paper uses a modified binary-tree codification method developed by Li etal. (2010) to code drainage networks, and the basin width function to estimate the possible maximum parallel speedup and the associated efficiency. As a case study, with a hydrological model, the Digital Yellow River Model, this new dynamic parallel algorithm is applied to the Chabagou basin in northern China. The application results reveal that the new algorithm is efficient in the dynamic dispatching of simulation tasks to computing processes, and that the parallel speedup and efficiency are comparable with the estimations made by using the basin width function. © 2011 Elsevier Ltd.postprin

The analysis of spatio-temporal signals plays an important role in various fields including sociology, climatology, and environmental studies, etcetera. Due to the abrupt breakdown of the sensors in the sensor network, always there are missing entries in the observed spatio-temporal signals. In this paper, we study the problem of recovering spatio-temporal signals from partially known entries. Based on both the global and local correlated property of spatio-temporal signals, we propose a Low Rank and Differential Smoothness-based recovery method (LRDS), which novelly introduces the differential smooth prior of time-varying graph signals to the field of spatio-temporal signal analysis. The performance of the proposed method is analyzed theoretically. Considering the case where a priori information about the signal's global pattern is available, we propose Prior LRDS (PLRDS) to further improve the reconstruction accuracy. Such improvement is also verified by synthetic experiments. Besides, experiments on several real-world datasets demonstrate the improvement on recovery accuracy of the proposed LRDS over the state-of-the-art spatio-temporal signal recovery methods.

As a result of climate change/variation and its aggravation by human activities over the past several decades, the hydrological conditions in the middle Yellow River in China have dramatically changed, which has led to a sharp decrease of streamflow and the drying up of certain tributaries. This paper simulated and analysed the impact of sediment-trapping dams (STDs, a type of large-sized check dam used to prevent sediment from entering the Yellow River main stem) on hydrological processes, and the study area was located in the 3246 km² Huangfuchuan River basin. Changes in the hydrological processes were analysed, and periods of natural and disturbed states were defined. Subsequently, the number and distribution of the STDs were determined based on data collected from statistical reports and identified from remote sensing images, and the topological relationships between the STDs and high-resolution river reaches were established. A hydrological model, the digital Yellow River integrated model, was used to simulate the STD impact on the hydrological processes, and the maximum STD impact was evaluated through a comparison between the simulation results with and without the STDs, which revealed that the interception effect of the STDs contributed to the decrease of the streamflow by approximately 39%. This paper also analysed the relationship between the spatial distribution of the STDs and rainfall in the Huangfuchuan River basin and revealed that future soil and water conservation measures should focus on areas with a higher average annual rainfall and higher number of rainstorm hours. © 2015 The Authors Hydrological Processes Published by John Wiley & Sons Ltd.