Infiltration losses may be significant and warrant proper incorporation into mathematical models for river floods in arid and semi-arid areas, rainfall-induced surface runoffs in watersheds and swashes on beaches. Here, a depth-averaged twodimensional hydrodynamic model is presented for such processes based on the cellcentred finite volume method on unstructured meshes, with the full Green-Ampt equation evaluating the infiltration rate. A local time stepping strategy is employed along with thread parallelization with Open Multi-processing and high-performance computing to reduce model run time and therefore facilitate applications for largescale processes. The numerical solutions generally agree with the experimental and field-measured data for typical cases with significant infiltration losses. The case study shows that neglecting infiltration leads to an overestimated discharge hydrograph, which cannot be compensated by means of varied bed resistance as estimated by Manning roughness, and the infiltration parameters play disparate roles in modifying shallow flows compared with Manning roughness. In addition, infiltration affects bed shear stress, which in turn modifies the critical bed sediment size that could be initiated for incipient motion by the flow and therefore needs to be properly accounted for when sediment transport and morphological evolution are to be resolved.
K E Y W O R D Shydrodynamic model, infiltration loss, river flood, surface runoff, swash
Climate modeling data are usually multidimensional arrays of floating-point numbers. These arrays typically have two or three spatial dimensions and one temporal dimension, describing the evolvement of climate variables in a time span. With the advances of high performance computing, the volume of climate data is expanding exponentially, bringing tough challenges for climate data archiving and sharing. In this paper, we propose a lossless compression algorithm for the time-spatial climate floating-point arrays. Our compression algorithm can eliminate more data redundancy efficiently through adaptive prediction, XOR-differencing, and multi-way compression. In addition, static regions, which are very common in climate data, can be identified and compressed more efficiently. Moreover, to utilize the multi-cores on modern computers, we proposed a method to parallelize our compression algorithm. Evaluations demonstrate that single thread version of our compression method can achieve the best balance in compression ratios, deflating throughputs and inflating throughputs. And the parallel version can achieve 800 MB/s deflating throughputs and over 2600 MB/s inflating throughputs on a 16-core server.
Flash flooding is one of the most severe natural hazards and commonly occurs in mountainous and hilly areas. Due to the rapid onset of flash floods, early warnings are critical for disaster mitigation and adaptation. In this paper, a flash flood warning scheme is proposed based on hydrodynamic modelling and critical rainfall. Hydrodynamic modelling considers different rainfall and initial soil moisture conditions. The critical rainfall is calculated from the critical hazard, which is based on the flood flow depth and velocity. After the critical rainfall is calculated for each cell in the catchment, a critical rainfall database is built for flash flood warning. Finally, a case study is presented to show the operating procedure of the new flash flood warning scheme.
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