**1. Introduction**

Under the combined influences of global climate change and rapid urban development, the occurred frequency of record-breaking rainfall events has increased significantly [1,2]. Floods caused by extreme rainfall events not only bring serious economic losses, but also cause huge casualties [3,4]. According to the data report of the World Resources Institute, the global economic loss caused by flood events was nearly 45.9 billion dollars; as well, 4500 people were killed, accounting for 40% of the global natural disaster deaths in 2019 [5]. The number of casualties caused by floods and the economy will continue to increase in the next decades [6,7]. Numerous studies have shown that record-breaking short-duration rainfall is an important factor causing the increasingly serious urban flood, while the lack of high temporal resolution rainfall records restricts the practices of hydrological engineering and urban flood analysis [8–10]. Zhu et al. [11] and Yu et al. [12] emphasized that hydrologic model-based flood analysis should carefully consider rainfall temporal resolution in the changing complex environment; they found that the simulated peak discharges can be significantly impacted by rainfall with different temporal resolution (e.g., 1-h and 24-h) at the same magnitude. However, most regions lack long-term and hightemporal resolution (sub-daily) rainfall records, especially for developing countries and newly built cities [13]. The available rainfall records show a decrease and non-stationary

**Citation:** Zhu, Z.; Yang, Y.; Cai, Y.; Yang, Z. Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records. *Remote Sens.* **2021**, *13*, 2204. https://doi.org/10.3390/rs13112204

Academic Editors: Yongqiang Zhang, Donghai Zheng and Dongryeol Ryu

Received: 13 May 2021 Accepted: 2 June 2021 Published: 4 June 2021

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trend in a changing environment [14,15]. In hydrological practice, however, the length-ofrecord limitations can limit the traditional methods for calculating the rainfall intensity– frequency–duration relationship.

In order to overcome the lack of rainfall records in urban flood analysis, many researchers have provided various coping methods (e.g., Li et al. [16], Kastridis et al. [17], Papaioannou et al. [18]), which can be categorized into five types. (i) Empirical probability statistics method. Traditional urban flood analysis is often based on frequency statistical methods and empirical assumptions, such as Gumbel, Pearson-III, maximum likelihood estimation, and other probability distribution models for parametric empirical statistical analysis [19]. However, the data time series is highly requisite based on the empirical value hypothesis [20]. Moreover, climate change and human activities lead to non-stationary changes of regional rainfall, making it difficult to ensure the accuracy and rationality of the estimation results [21]. (ii) Hydrologic model-based simulation. With the continuous improvement of hydrological models and hydrological theory, using a hydrological model to simulate urban flooding has become one of the most common methods. To some extent, the scope and application of hydrological data, theory, and tools are improved through the hydrological model. However, it needs detailed basic data to improve its accuracy [22,23]. (iii) Surrogate-data technique. Due to the lack of rainfall datasets, many studies use the rainfall data from adjacent stations to analyze regional flood frequency or calculate hydrological engineering. For example, Mohanty et al. [24] moved the rainfall data of three neighboring rain gauge stations to the study area, which was used for flood analysis. Although the surrogate-data technique can increase the rainfall sample size and make up for the lack of observation data, its accuracy is difficult to guarantee and its uncertainty is high [25]. (iv) Rainfall generator. Rainfall generators are often used to generate more diverse rainfall scenarios or higher spatial and temporal resolution rainfall data to enrich the regional rainfall sample size [26,27]. For example, the meteorological model (e.g., GCM) can simulate more rainfall events and other meteorological elements based on short-record data sets, but it needs strict meteorological data such as temperature and wind speed, and has the disadvantage of requiring complex calculations [26,28]. (v) Remote sensing analysis method. Combined with GIS technology, remote sensing data and the digital elevation model are often used to obtain regional hydrological characteristics and draw flood risk maps for flood analysis [29,30]. It can analyze the distribution of flood risk in a large area with coarse data, but it cannot fully consider the hydrological process [31].

It is undeniable that the above methods can solve the problem of data shortage in flood analysis to a certain extent, but there are still obvious disadvantages in different types of methods [32]. With the increase of high temporal resolution remote sensing rainfall data, there is a new way to do flood analysis in both natural and urban watersheds [33–35]. In recent years, it has become popular to comprehensively analyze floods by coupling remote sensing rainfall data and hydrological models, which solves the shortages of high spatialtemporal resolution rainfall data. For example, Shakti et al. [36] combined remote sensing rainfall data and a distributed hydrological model to analyze inundation. Komi et al. [37] have shown that using relatively rough spatial resolution remote sensing data as inputs to the distributed hydrological model can also roughly predict the flood range in Africa, where topographic and hydrological data are scarce. The coupling of high spatial-temporal resolution remote sensing rainfall data and a hydrological model is used to analyze the regional flood characteristics and widely used by more and more scholars [11,38].

On the other hand, urban flood analysis based on hydrological model mainly focuses on a single factor such as maximum rate, meaning many important indicators are often ignored [39,40]. Zhu et al. [40] emphasized that urban flood analysis should consider not only the maximum rate, but also the flood time, total inundation volume, and other factors. Hereby, urban flood analysis needs to address the high-dimension disaster problem. In order to reflect the characteristics of urban flooding, traditional methods such as the fuzzy comprehensive evaluation method, principal component analysis, and analytic hierarchy process (AHP) are often used for analyzing flood characteristics (e.g., Yang et al. [41];

Nandi eta al. [42]; Sarmah et al. [43]), but most of them have the shortcomings of humansubjective perceptions or being based on an ideal hypothesis [44]. In order to overcome these drawbacks, Zhu et al. [40] used the projection pursuit method to comprehensively analyze urban flood characteristics, and pointed out that this method can objectively evaluate urban flood characteristics.

As stated above, a lack of high-temporal rainfall records is a prominent limitation to flood analysis and hydrological engineering practices [14,45]. Rainfall remote sensing datasets with high temporal-spatial resolution and large coverage can overcome this limitation. This study seeks to propose a modeling framework for urban flood assessments based on short-record remotely sensed rainfall and hydrologic model in ungauged drainage basins. We do so by combining short (2008–2016), hourly remote sensing rainfall data and the RainyDay model to estimate the regional design rainfall under different frequencies. To be consistent with convention [46], the obtained design rainfall is transformed into the Chicago rainfall pattern and put into the SWMM hydrological model to simulate and analyze runoff processes and flood characteristics under different return periods. The projection pursuit method is used to comprehensively analyze flood characteristics based on the outputs of the SWMM hydrological model. It is worth mentioning that this study is not meant to demonstrate the superiority of the proposed framework compared with the traditional methods, but to explore the feasibility of analyzing small ungauged urban drainage basins based on short-term remote sensing rainfall data, and to provide an alternative framework for urban flood assessment.
