Post event flooding maps are currently extracted from synthetic-aperture radar (SAR) and/or optical satellite images or developing using hydraulic model simulations. Several sources of uncertainties impact the accuracy of such flood maps constructed from each method, especially in urban areas. An integrated approach that combines satellite imagines of flooded areas, hydraulic models, and markers from social media that should reduce these uncertainties and allow a more accurate reconstruction of flooded urban areas, is presented in this paper. The flooding associated with Hurricane Harvey in Houston, TX was chosen as a case study. Model validations demonstrate the effectiveness of our integrated approach in reconstructing an accurate flooding map, as well as the temporal and spatial patterns of flooding. Using the experience from this case study we discuss the possibility to use satellite data, instead of groundbased rainfall gauge measurements as precipitation inputs to the hydraulic model; and possible error sources in simulating flooding in urban areas using the hydraulic model.

This chapter attempts to describe the C and C++ programming languages in general and also how they apply to developing software for Internet applications. The reader will be taken through a brief history of the languages, as well as its most common strengths and weaknesses. Although the scope of the two languages makes it impossible to cover them completely in this context, the languages are discussed in sufficient detail so that, with the proper tools, the reader can develop and test simple C or C++ programs. Working example programs are included. Finally, attention is given to how the language is used to develop Internet applications, and how it competes with other languages such as Java and common scripting languages such as Perl and PHP.

To an extent, large-scale circulation situations and moisture transport are responsible for extreme precipitation occurrence. The aim of our study is to investigate the possible modifications of circulation patterns (CPs) in driving extreme precipitation over the central-eastern China (CEC). The selforganizing map (SOM) and event synchronization methods are used to link the extreme precipitation events with CPs. Results show that 23% of rain gauges have a significant change point (at the 90% confidence level) in annual extreme precipitation from 1960 to 2015. Based on the identified change points, we classified the data into two periods, that is, 1960-1989 and 1990-2015. Overall, CPs characterized by obvious positive anomalies of 500 hPa geopotential height over the Eastern Eurasia continent and negative values over the surrounding oceans are highly synchronized with extreme precipitation events. During 1990-2015, the predominant CPs are more related to the extreme precipitation with enhanced event synchronization. We found that the CP changes produce an increase in extreme precipitation frequency from 1960-1989 to 1990-2015.

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.