Review on applications of remote sensing in urban flood modeling
2018
Urban flooding is serious in China. More than 360 cities have suffered
rainstorm floods from 2007 to 2013. Floods in Beijing, Guangzhou,
Jinan, et al. have caused serious casualties. In recent years, sea-views
in cities have repeated here and there, and rainstorm induced flood
has gradually become a chronic illness in many large and medium-sized
cities in China. Simulation model for floods is a key technology for
urban flood control and land use planning, and play a crucial role
in the construction of urban water conservancy projects and infrastructures
such as urban drainage system and sponge city. With the continuous
and rapid development of the economics and society, urban flood risk
warning and management requires a higher demand on the efficiency
and elaboration of flood simulation. Obviously, the degree of elaboration
on flood simulation highly depends on the temporal and spatial resolution
of the basic input data. Rainfall data with smaller time intervals
will present more comprehensive rainfall characteristics and further
reflect the characteristics of flood processes. Meanwhile, higher
resolution terrain data can reflect subtle ground features of urban
surface such as buildings, curbs, bridges, etc. By summarizing and
analyzing the literature on the researches of hydrological modeling
with the utilization of remote sensing data, the application of remote
sensing data (including meteorological and hydrological data, and
surface information based on remote sensing) in flood simulation is
reviewed. Frequently used remote sensing data sources with relatively
high precision such as LiDAR (light imaging, detection, and ranging),
InSAR (interferometric synthetic aperture radar), oblique photography
and hyperspectral remote sensing as well as their applications are
presented. Advanced technologies and methods (airborne LiDAR and UAV
Oblique Photography) producing basic remote sensing information used
in urban flood modeling are presented. Finally, an example is given
to illustrate the extraction framework of the basic information of
urban flood simulation based on remote sensing, and the prospect to
apply remote sensing technology in urban flood simulation is also
presented. It is pointed out that flood simulation will be more effective
and refined with the support of massive multi-source data in the future.
At the same time, rich data sources also bring the following challenges
and opportunities: (1) Rapidly increasing amount of high-resolution
remote sensing data poses greater challenges to data storage and management,
while high-performance distributed storage technologies and data processing
technologies can provide solutions for this objective. (2) Massive
data and the relatively low processing and utilization capacity will
cause a huge waste of resources. Based on data analysis methods such
as machine learning and data assimilation, massive multi-source data
will be integrated to form a comprehensive database for urban flood
modeling, which can provide scientific and technological support for
modern urban construction and management. (3) Urban flood simulation
with high resolution is extremely computational intensive that the
time consumption will exponentially increase. However, this is unacceptable
for rapid and efficient urban flood forecasting and early warning.
High-performance computing technology such as GPU-based parallelization
on CUDA platform will greatly benefit for urban flood simulation.
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