Original Research ArticleFloods in mountainous river basins are generally highly destructive, usually causing enormous losses of lives and property. It is important and necessary to develop an effective flood forecast method to prevent people from suffering flood disasters. This paper proposed a general framework for a service-oriented architecture (SOA) for ensemble flood forecast based on numerical weather prediction (NWP), taking advantage of state-of-the-art technologies, e.g., high-accuracy NWP, high-capacity cloud computing, and an interactive web service. With the predicted rainfall data derived from the NWP, which are automatically downloaded, hydrological models will be driven to run on the cloud. Judging from the simulation results and flood control requirements offered by users, warning information about possible floods will be generated for potential sufferers and then sent to them as soon as possible if needed. Moreover, by using web service in a social network, users can also acquire such information on the clients and make decisions about whether to prepare for possible floods. Along with the real-time updates of the NWP, simulation results will be refreshed in a timely manner, and the latest warning information will always be available to users. From the sample demonstrations, it is concluded that the SOA is a feasible way to develop an effective ensemble flood forecast method. After being put into practice, it would be valuable for preventing or reducing the losses caused by floods in mountainous river basins. © 2015 Elsevier B.V.postprin

SummaryLarge-scale problems that demand high precision have remarkably increased the computational time of numerical simulation models. Therefore, the parallelization of models has been widely implemented in recent years. However, computing time remains a major challenge when a large model is calibrated using optimization techniques. To overcome this difficulty, we proposed a double-layer parallel system for hydrological model calibration using high-performance computing (HPC) systems. The lower-layer parallelism is achieved using a hydrological model, the Digital Yellow River Integrated Model, which was parallelized by decomposing river basins. The upper-layer parallelism is achieved by simultaneous hydrological simulations with different parameter combinations in the same generation of the genetic algorithm and is implemented using the job scheduling functions of an HPC system. The proposed system was applied to the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River, to calibrate the model effectively by making full use of the computing resources in the HPC system and to investigate the model’s behavior under various parameter combinations. This approach is applicable to most of the existing hydrology models for many applications

Over the past century, vegetation change has been reported at global, national, and regional scales, accompanied by significant climate change and intensified human activities. Among the regions is the rangeland of the Three-River Headwaters Region (TRHR) in China. However, which factor dominates in causing vegetation change in this region is still under considerable debate, and how would the grasslands adapt to the changing environment is largely unknown. To address these issues, we attribute growing season vegetation activity to climate change and human activities, investigate the interactions among different driving variables, and explore the dynamic relationship between vegetation activity and the driving variables. We perform Mann–Kendall trend analysis, Pearson correlation analysis, and partial correlation analysis. The results indicate that the dominant factor for vegetation growth, during the period 1995–2014, was temperature for the southeastern and southern parts of the TRHR, precipitation for the western part, and solar radiation for the northeastern part. The regulation effects of temperature on precipitation and cloud cover contributed to vegetation growth, while grazing activity and population activity offset the positive contribution of climate change. The dynamic relationship between vegetation activity and the driving variables reflected the acclimatization and adaption processes of vegetation, which needs further investigation.