1. INTRODUCTION
Although science and technology continue to progress, the world is still
severely affected by natural disasters. Numerous studies have explored
the mechanism of natural disasters (Shuin, Hotta, Suzuki, & Ogawa,
2012; Oku & Nakakita, 2013; Zanandrea, Michel, Kobiyama, & Cardozo,
2019; Nam, Kim, Kang, & Kim, 2019); however, factors affecting
disasters are complex. In addition to the environmental condition of the
catchment area, anthropogenic activity is a prominent factor. Taiwan, an
island surrounded by ocean, is no exception; moreover, Taiwan faces the
threat of natural disasters because of its geographical location. The
climate is affected by the monsoon throughout the year, mainly the
northeast monsoon prevailing from mid-October to April of the following
year and the southwest monsoon prevailing from mid-June to September.
Specifically, July to September is the typhoon season (Kuo, Lee, & Lu,
2016), and during this period, three to four typhoons striking Taiwan is
common. Rainfall during the passage of typhoons is characterized by its
long duration and high intensity; such rainfall patterns often cause
severe disasters (Yang et al., 2011; Milliman, Lee, Huang, & Kao,
2017).
To mitigate disasters during the passage of typhoons, experiential and
physical models have been developed by several studies and can be used
to analyze the processes of disaster occurrence (Chen, Jhong, Wu, &
Chen, 2013; Yang et al., 2015). Specifically, experiential models can be
used to obtain data in a short time; however, this type of model
requires a large amount of observational data, and thus is
time-consuming. Physical models are usually developed based on a fine
theoretical foundation; however, the parameters are usually overly
complex. Therefore, both model types have advantages and disadvantages.
After the United States (US) government passed the Flood Control Act of
1936, numerous American hydrologists vigorously developed
rainfall-runoff models. Finally, in 1956, the US Soil Conservation
Service (now Natural Resources Conservation Service) developed the curve
number (CN) method, which has been widely applied to simulate or predict
runoff from rainfall (Huang, Gallichand, Wang, & Goulet, 2006; Ali,
Khan, Aslam, & Khan, 2011; Deshmukh, Chaube, Hailu, Gudeta, & Kassa,
2013; Lal, Mishra, & Pandey, 2015; Ozdemir & Elbaşı, 2015).
Sediment yield (SY) information is essential for environmental and flood
control; thus, catchment survey projects usually require it as a
reference for catchment planning and design. In the 1960s, a model for
assessing SY was developed in response to the need for environmental
management (Wischmeier & Smith, 1965). The model effectively simplified
the calculation of catchment areas by using natural empirical
parameters, and it can be applied to estimate SY during rainstorms or
throughout the year. In recent decades, numerous models have been
developed to simulate SY, including the universal soil loss equation,
revised universal soil loss equation, and modified universal soil loss
equation (Chandramohan, Venkatesh, & Balchand, 2015; Furl, Sharif, &
Jeong, 2015; Zerihun, Mohammedyasin, Sewnet, Adem, & Lakew, 2018). For
the conservation of soil and water resources in catchment areas, runoff
and SY are crucial information; thus, studies have used the CN method to
estimate the SY in catchment areas (Mishra, Tyagi, Singh, & Singh,
2006; Gajbhiye, Mishra, & Pandey, 2014). In addition to providing
runoff information, the CN method can be used to estimate SY.
Although hydrological and SY models have been well established, they
require high-quality input data, which are difficult to acquire from
long-term monitored catchment areas. Therefore, this study sought to
estimate the SY of a catchment area by using big data and the CN method.
In addition, the conservation of soil and water resources in a catchment
area is essential; thus, this study proposed a hierarchical sediment
risk management method based on the concept of subdivisions management
and established a management mechanism to serve as a reference for
future disaster management.