Reservoirs are essential structures to provide reliable water supply, hydropower, and flood control. Climate change could be a significant factor that increases the sediment yield leading to rapid reduction of the reservoir’s storage capacity and design life. Previous studies of reservoir sedimentation-related impact of climate change often coupled a hydrological model with the raw outputs of general circulation model (GCM)/regional circulation model (RCM), which shows bias when comparing with observations data. This study aims to integrate the soil and water assessment tool (SWAT) model with 14 bias-corrected GCM/RCM models under two emissions scenarios, representative concentration pathway (RCP) 4.5 and 8.5, applied to Pleikrong reservoir to estimate its sedimentation in the long term period. The results show the reduction in reservoir storage capacity due to sedimentation ranges from 25% to 62% by 2050, depending on the defferent climate change models. The reservoir reduced storage volume’s rate in considering the impact of climate change is much faster than in the case of no climate change. The outcomes of this study will be helpful for a sustainable and climate-resilient plan of sediment management for the Pleikrong reservoir.

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We present a first evaluation of a Programming Model for real-time streaming applications on high performance embedded multi-and many-core systems. Realistic streaming applications are highly dependent on the execution context (usually of physical world), past learned strategies, and often real-time constraints. The proposed Programming Model encompasses both realtime requirements, determinism of execution and context dependency. It is an extension of the well-known Cyclo-Static Dataflow (CSDF), for its desirable properties (determinism and composability), with two new important data-flow filters: Select-duplicate, and Transaction which retain the main properties of CSDF graphs and also provide useful features to implement real-time computational embedded applications. We evaluate the relevance of our programming model thanks to several real-life case-studies and demonstrate that our approach overcomes a range of limitations that use to be associated with CSDF models.

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