Redirigiendo al acceso original de articulo en 20 segundos...
Inicio  /  Water  /  Vol: 12 Par: 6 (2020)  /  Artículo
ARTÍCULO
TITULO

Spatial Dependence Modeling of Flood Risk Using Max-Stable Processes: The Example of Austria

Hansjörg Albrecher    
Dominik Kortschak and Franz Prettenthaler    

Resumen

We propose a new approach to model the dependence structure for aggregating the risk of flood damages from a local level to larger areas, which is based on the structure of the river network of a country and can be calibrated with publicly available data of river discharges. Building upon a suitable adaptation of max-stable processes for a flood-relevant geometry as recently introduced in the literature, this enables the assessment of flood risk without the need for a hydrological model, and can easily be adapted for different countries. We illustrate its use for the particular case of Austria. We first develop marginal flood models for individual municipalities by intertwining available HORA risk maps with the actual location of buildings. As a second alternative for the marginal modeling, we advocate an approach based on suitably normalized historical damage data of municipalities together with techniques from extreme value statistics. We implement and compare the two alternatives and apply the calibrated dependence structure to each of them, leading to estimates for average flood damage as well as its extreme quantiles on the municipality, state, and country level. This also allows us to quantify the diversification potential for flood risk on each of these levels, a topic of considerable importance in view of the natural and strong spatial dependence of this particular natural peril.

 Artículos similares

       
 
Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen    
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a... ver más

 
Chunwei Hu, Xianfeng Liu, Sheng Wu, Fei Yu, Yongkun Song and Jin Zhang    
Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic g... ver más
Revista: Applied Sciences

 
Djouhaina Brella, Lazhar Belkhiri, Ammar Tiri, Hichem Salhi, Fatma Elhadj Lakouas, Razki Nouibet, Adeltif Amrane, Ryma Merdoud and Lotfi Mouni    
In this study, we analyzed the quality and the potential noncarcinogenic health risk of nitrate in groundwater in the El Milia plain, Kebir Rhumel Basin, Algeria. Moran?s I and the ordinary kriging (OK) interpolation technique were used to examine the sp... ver más
Revista: Hydrology

 
Guoyan Xu, Yuwei Lu, Zixu Jing, Chunyan Wu and Qirui Zhang    
The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IEALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for a si... ver más
Revista: Applied Sciences

 
Yuying Chen, Yajie Li, Xiangfeng Gu, Qing Yuan, Nan Chen and Qi Jin    
Cultural tourism development potential (CTDP) is the future value and supporting force of the environmental value, economic and social efficiency, innovation ability and supporting system of cultural tourism. At present, there are few relevant studies on... ver más