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![Figure 6. Front view of the Mequinenza dam. [Source: CHE].](publication/370359422/figure/fig5/AS:11431281154207392@1682700594215/Front-view-of-the-Mequinenza-dam-Source-CHE_Q320.jpg)
The computational simulation of rivers is a useful tool that can be applied in a wide range of situations from providing real time alerts to the design of future mitigation plans. However, for all the applications, there are two important requirements when modeling river behavior: accuracy and reasonable computational times. This target has led to...
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... to incorporate that information to the global DTM, geo-referenced historical maps prior to the construction of the dam, also available on the IGN website (http://centrodedescargas. cnig.es/CentroDescargas/locale?request_locale=en, accessed on 1 March 2023) were used. Using their contour lines, new cross-sections are obtained formed by groups of five points with coordinates (x, y, z), as shown in the example in Figure 8, which, after interpolation, produces a DTM with the appropriate reservoir bed elevations. Figure 9 shows an image of the historical map of part of the reservoir, comparing it with the current state. ...
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... example of the result is shown in Figure 10. Using the presented strategy, a channel in the reservoir region similar to the one shown in Figure 11 is obtained, containing the information of the interpolated sections shown in Figure 8. By doing this, a new DTM fo the bottom of the reservoir is obtained and can be added to the global DTM. ...
Citations
... These events highlight the recurring nature and severity of large-scale floods, underscoring the urgent need for effective flood management and preparedness measures [7,8]. Natural disasters such as typhoons, heavy rains, and floods have caused severe damage to Japan's infrastructure, including its river embankment systems, resulting in levee failures and widespread flooding [9,10]. The rising risk of river embankment failure due to increased storm rainfall has become a significant concern for safeguarding communities and infrastructure from flooding [11]. ...
This study investigated erosion during infiltration and overflow events and considered different grain sizes and hydraulic conductivity properties; four experimental cases were conducted under saturated conditions. The importance of understanding flow regimes during overflow experiments including their distinct flow characteristics, shear stresses, and erosion mechanisms in assessing the potential for levee failure are discussed. The failure mechanism of levee slopes during infiltration experiments involves progressive collapse due to piping followed by increased liquefaction and loss of shear stress, with the failure progression dependent on the permeability of the foundation material and shear strength. The infiltration experiments illustrate that the rate of failure varied based on the permeability of the foundation material. In the case of IO-E7-F5, where the levee had No. 7 sand in the embankment and No. 5 sand in the foundation (lower permeability), the failure was slower and limited. It took around 90 min for 65% of the downstream slope to fail, allowing more time for response measures. On the other hand, in the case of IO-E8-F4, with No. 8 sand in the embankment and No. 4 sand in the foundation (higher hydraulic conductivity), the failure was rapid and extensive. The whole downstream slope failed within just 18 min, and the collapse extended to 75% of the levee crest. These findings emphasize the need for proactive measures to strengthen vulnerable sections of levees and reduce the risk of extensive failure.
Extensions to the Roe and HLL method have been previously formulated in order to solve the Shallow Water equations in the presence of source terms. These were named the Augmented Roe (ARoe) method and the HLLS method, respectively. This paper continues developing these formulations by examining how entropy corrections can be appropriately fitted in for the ARoe method and how the HLLS method can be formulated more generally. This is done in two ways. Firstly, this paper extends the reasoning of Harten and Hyman required by the ARoe method to include the source term contributions and thus arrives to a more complete formulation of the entropy fix, which will be compared with the approximation presented in previous works through numerical experiments. Secondly, it is shown how a relaxation of the criteria used when choosing waves in the HLLS method yields better solutions to problems where the HLLS would previously fail. In summary, this paper seeks to offer a comprehensible review of the ARoe and HLLS methods while improving its performance in cases with transcritical rarefactions for the inhomogeneous Shallow Water Equations in one dimension.
