Abstract:In China, 9Ð5% of the landmass is karst terrain and of that 47,000 km 2 is located in semiarid regions. In these regions the karst aquifers feed many large karst springs within basins of thousands of square kilometres. Spring discharges reflect the fluctuation of ground water level and variability of ground water storage in the basins. However, karst aquifers are highly heterogeneous and monitoring data are sparse in these regions. Therefore, for sustainable utilization and conservation of karst ground water it is necessary to simulate the spring flows to acquire better understanding of karst hydrological processes. The purpose of this study is to develop a parsimonious model that accurately simulates spring discharges using an artificial neural network (ANN) model. The karst spring aquifer was treated as a non-linear input/output system to simulate the response of karst spring flow to precipitation and applied the model to the Niangziguan Springs, located in the east of Shanxi Province, China and a representative of karst springs in a semiarid area. Moreover, the ANN model was compared with a previous time-lag linear model and it was found that the ANN model performed better.

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Pluvial flooding is a common type of natural hazard caused by rainfall events with high intensity and short duration, which may lead to substantial property damages, transportation interruptions, and casualties. Modern cities are susceptible to pluvial flooding due to dense population and advanced economic development. To facilitate the development of better flood control and risk mitigation strategies, this study presents a new quantitative flood susceptibility analysis framework to estimate the potential flood extents and scale. The framework is based on the multi-criteria decision-making methods within a platform of the geographic information system (GIS). A composite urban flood risk index (FRI) is derived from various flood conditioning factors. The FRI consists of flood vulnerability index, hazard factors, and resilience capacity indicators. The flood-susceptible map is generated using the GIS spatial analysis tools and the analytic hierarchy process method. Zhengzhou city, China, is selected as the case study area. The result map shows that the highly susceptible areas are mainly located in Jinshui District, accounting for 64% of the total area of the risk zone. To further validate this framework, a flood inventory map is produced by mapping 74 test locations identified through survey data in this area, followed by plotting a receiver operating characteristic (ROC) curve. The ROC shows an area under the curve of 74.27%, which validates the proposed framework. Compared with other methods, the proposed framework is particularly suitable for application in data-scarce cases.

Confronting the frequent flood disasters triggered by torrential downpour, the vulnerability of urban rainstorm flood disasters was analyzed with one highly popular area of research in mind: big data. Web crawler technology was used to extract text information related to floods from Internet and popular social media platforms. Combining these text data with traditional statistical data, a flood disaster vulnerability assessment model based on Analytic Hierarchy Process (AHP) was established to evaluate rainstorm and flood disaster vulnerability, and the spatial distribution characteristics of vulnerability to pluvial flooding were analyzed based on Geographic Information System (GIS). The established model was applied in Zhengzhou, a city that often suffers from heavy rainstorms. The results show that the areas located near downtown Zhengzhou were more vulnerable to rainstorm and flooding than others, and most of the city could be at moderate and high vulnerability. Finally, the waterlogging spots extracted from various sources were used to evaluate the performance of the proposed model. The results show that most of waterlogging spots were located in very-high and high risk zones, while less waterlogging spots were found in districts with low vulnerability, which demonstrates the discriminative power of the established model based on big data sources. This study overcomes limited data in flood disaster vulnerability assessment methods and provides a basis for flood control and management in cities.

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