Wave propagation speeds and source term influences in single and integral porosity shallow water equations

In urban flood modeling,so-called porosity shallow water equations(PSWEs),which conceptually account for unresolved structures, e.g.,buildings, are a promising approach to addressing high CPU times associated with state-of-the-art explicit numerical methods. The PSWE can be formulated with a single porosity term, referred to as the single porosity shallow water model(SP model), which accounts for both the reduced storage in the cell and the reduced conveyance, or with two porosity terms: one accounting for the reduced storage in the cell and another accounting for the reduced conveyance. The latter form is referred to as an integral or anisotropic porosity shallow water model(AP model). The aim of this study was to analyze the differences in wave propagation speeds of the SP model and the AP model and the implications of numerical model results. First, augmented Roe-type solutions were used to assess the influence of the source terms appearing in both models. It is shown that different source terms have different influences on the stability of the models. Second, four computational test cases were presented and the numerical models were compared. It is observed in the eigenvalue-based analysis as well as in the computational test cases that the models converge if the conveyance porosity in the AP model is close to the storage porosity. If the porosity values differ significantly, the AP model yields different wave propagation speeds and numerical fluxes from those of the BP model. In this study, the ratio between the conveyance and storage porosities was determined to be the most significant parameter.

Comparison of depth-averaged concentration and bed load flux sediment transport models of dam-break flow

This paper presents numerical simulations of dam-break flow over a movable bed. Two different mathematical models were compared: a fully coupled formulation of shallow water equations with erosion and deposition terms(a depth-averaged concentration flux model), and shallow water equations with a fully coupled Exner equation(a bed load flux model). Both models were discretized using the cell-centered finite volume method, and a second-order Godunov-type scheme was used to solve the equations. The numerical flux was calculated using a Harten, Lax, and van Leer approximate Riemann solver with the contact wave restored(HLLC). A novel slope source term treatment that considers the density change was introduced to the depth-averaged concentration flux model to obtain higher-order accuracy. A source term that accounts for the sediment flux was added to the bed load flux model to reflect the influence of sediment movement on the momentum of the water. In a onedimensional test case, a sensitivity study on different model parameters was carried out. For the depth-averaged concentration flux model,Manning's coefficient and sediment porosity values showed an almost linear relationship with the bottom change, and for the bed load flux model, the sediment porosity was identified as the most sensitive parameter. The capabilities and limitations of both model concepts are demonstrated in a benchmark experimental test case dealing with dam-break flow over variable bed topography.

Preface for special section on flood modeling and resilience

Climate change has led to increased frequency, intensity,and duration of extreme weather events, e.g., intense rainfall,heat waves, droughts, and storm surges, worsened byrapid population growth and urbanization at the global scale.Evidence can be found in the exceptional number of unprecedentedweather extremes and the resulting naturalhazards, especially flooding, as seen in the last few decades.For example, the UK has experienced numerous storms andsevere floods in the last decade, particularly in 2007, 2012,and 2015, with 2012 being recorded as the second wettest year in the UK and the wettest ever in England. These events have resulted in lives lost and tremendous economic damage. The UK is not alone. Similarly unusual weather events have been reported across the globe. In China,different types of flooding threaten 1/10 of the country's total area, millions of hectares of farmland, and over 100 large cities, making it one of the most vulnerable countries to flooding.

Impacts of climate change on water quantity, water salinity, food security, and socioeconomy in Egypt

Climate change might have direct impacts on water quantity in Egypt and lead to indirect effects on Mediterranean saltwater intrusion to groundwater,which exposes agriculture to vulnerability.This study investigated impacts of climate change on agriculture,with particular regard to food security and socioeconomy,and quantified the effectiveness of cropping pattern adaptation measures by integrating three mathematical models.The BlueM model was used for hydrological simulations of Nasser Lake under flooding scenarios to predict the water supply from the High Aswan Dam.The water and salinity balance(WB-SAL)model was adopted to estimate the water salinity in the Nile Delta.The simulated results from the BlueM and WB-SAL models were integrated with the agricultural simulation model for Egypt(ASME)to project cropping patterns,food security,and socioeconomy throughout the country.The results showed that future climate change will directly affect the total crop area;crop areas for 13 crop types;the self-sufficiency of wheat,rice,cereal,and maize supplies;and socioeconomic indicators.The proposed cropping pattern adaptation measures focus on fixing the crop areas of rice and orchards and providing half of the population with lentils,maize,onion,vegetables,milk,and meat.The adaptation measures have the potential to promote food security without causing deterioration of the socioeconomic situation.However,water availability has much more significant effects on food security and socioeconomy than cropping pattern adaptation measures do.Accordingly,the country should rationalize water use efficiency and increase water supply.©2021 Hohai University.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Concrete corrosion in wastewater systems:Prediction and sensitivity analysis using advanced extreme learning machine

The implementation of novel machine learning models can contribute remarkably to simulating the degradation of concrete due to environmental factors.This study considers the sulfuric acid corrosive factor in wastewater systems to simulate concrete mass loss using five machine learning models.The models include three different types of extreme learning machines,including the standard,online sequential,and kernel extreme learning machines,in addition to the artificial neural network,classification and regression tree model,and statistical multiple linear regression model.The reported values of concrete mass loss for six different types of concrete are the target values of the machine learning models.The input variability was assessed based on two scenarios prior to the application of the predictive models.For the first assessment,the machine learning models were developed using all the available cement and concrete mixture input variables;the second assessment was conducted based on the gamma test approach,which is a sensitivity analysis technique.Subsequently,the sensitivity analysis of the most effective parameters for concrete corrosion was tested using three different approaches.The adopted methodology attained optimistic and reliable modeling results.The online sequential extreme learning machine model demonstrated superior performance over the other investigated models in predicting the concrete mass loss of different types of concrete.