# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 1 Introduction  
Among the most devastating natural disasters, flooding is particularly deadly in the high-frequency tropical cyclone zones of South and East Asia, where it can cause catastrophic damage and affect over 20,000 lives each year worldwide [1]. According to the Emergency Events Database (EM-DAT), natural catastrophes affect about 100,000 people annually in Yemen. Floods have caused significant economic and crop losses [2]. Furthermore, many world climate models predict that the high precipitation will potentially increase future flood severity and frequency in Yemen [3]. In October 2008, prolonged heavy rains in the Wadi Hadramout of Yemen caused disastrous flooding. With high population growth, largely unregulated urbanization, and a lack of environmental controls, Yemen has become increasingly prone to natural hazards [3]. The total damage caused by the flood was estimated to be $1638 million, and over 70 individuals were killed, 25,000 people were displaced, and over 2800 homes were destroyed; at least 340 dwellings were destroyed in Tarim, Al-Kotn, and Shibam, all of which were located in Hadramaut [4]. Whenever a river's discharge exceeds its network's capability, the river overflows its floodplain, causing flooding [5]. Flash floods, which occur primarily in river systems less than 200 km\({}^{2}\), are unique among such natural dangers because of their ferocity [6]. Due to continuous global climate change, flash flood dangers have increased in frequency and size in recent decades. Climate change has changed the current rainfall pattern, causing heavy rains in short periods and flooding as the rainfall surpasses the soil permeability capacity [7]. Deforestation precipitates in riverbeds, human settlements encroaching on riverbeds, dam construction, and unsustainable urbanization are just reasons why large-scale human-environmental interventions cause devastating flash floods [8]. Nevertheless, forecasting flash floods remains a challenging undertaking because of the complicated causes of this event [9]. For this reason, a high-precision model must be developed that could predict and map the possibility of flash floods. Thus, local governments and decision-makers could control disaster risks and minimize the effects of climate change. The first and most important step in flood modeling and risk assessment is flooding susceptibility mapping. Flood-prone locations can be detected via flood susceptibility mapping, and appropriate structural and non-structural solutions can be implemented to mitigate flood-related losses [10]. Flood susceptibility mapping (FSM) refers to determining the area vulnerable to flooding based on chosen risk variables [11]. Nevertheless, various elements contribute to the flood, including elevation, aspect, rainfall, drainage conditions, geology, geomorphology, land use, and cover [12]. Furthermore, all of the parameters may or may not be compatible with a specific model; additionally, all factors may or may not contribute to flooding susceptibility models [13]. FSM is an essential step in preventing and managing future flooding [10]. On the other hand, flooding is characterized by complicated conditions that make accurate forecasting impossible. FSM has been the subject of numerous research using a variety of approaches. During the last several decades, geographic information systems (GIS) and remote sensing (RS) have demonstrated their efficacy in managing massive hydrological datasets in order to develop increasingly realistic flood susceptibility maps [14]. Inferred RS approaches provide fast and reliable options for obtaining flood event spatial data, even in physically unapproachable areas [14]. It was reported that the use of synthetic aperture radar (SAR) is an optimized choice. Because radar flash has increased penetrating capacity, it resolves the cloud cover problem. However, its use in developing countries, in particular, has been restricted by its high cost and limited range [15].