Search

With the development of remote sensing technology, satellite-based soil moisture estimates become more and more available, and the potential of using satellite soil moisture products in landslide hazard assessment has been widely recognized. However, to our knowledge, there is a lack of studies exploring the performance difference of various satellite soil moisture products for such an application. Therefore, this study aims to compare several state-of-the-art satellite soil moisture products on their potentials in landslide applications. The selected products include the ESA CCI soil moisture dataset, the SMAP Level-3 (L3), enhanced Level-3 (L3), Level-4 (L4) surface, and Level-4 (L4) root zone soil moisture datasets. Specifically, the completeness of different datasets is calculated to assess their applicability in practical applications. To investigate the relationship between the soil moisture and the commonly used rainfall information in landslide predictions, the correlation study of the satellite soil moisture with the antecedent cumulated rainfall is also carried out. In addition, to explore whether the satellite soil moisture can provide valuable information for landslide hazard assessment, infiltration events are identified based on the time series of satellite soil moisture, and the significance of event characteristics (such as event duration, soil moisture change, etc.) in landslide occurrence is then investigated with Bayesian analysis. This study is carried out in a landslide-prone area, the Emilia-Romagna region in northern Italy. Results show that the SMAP L4 product does not have any missing values, beneficial to the continuous monitoring of landslides. As for the correlation relationship between soil moisture and antecedent cumulated rainfall, the SMAP L4 product also has more rational spatial distribution of the Pearson correlation coefficients compared with other datasets, which can be better explained by the distribution of slope and TWI (topographic wetness index). Bayesian analysis on the infiltration events shows that our prior knowledge of the probability of landslide occurrence is better improved by using the ‘SMAP L4 root zone soil moisture’-derived infiltration events, indicating its greater potential to be used for landslide hazard assessment in the study region.

Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering residents' travel behavior. First, a flood simulation was conducted using the LISFLOOD-FP model to predict the spatiotemporal distribution of flooding. Second, an agent-based model was used to simulate residents' movements during the urban flooding period. Finally, to study the evolution and patterns of urban flooding exposure, the exposure of population, roads, and buildings to urban flooding was simulated using Lishui, China, as a case study. The results showed that water depth was the major factor affecting total urban exposure in Lishui. Urban exposure to fluvial flooding was concentrated along the river, while exposure to pluvial flooding was dispersed throughout the area (independent from the river). Additionally, the population distribution on weekends was more variable than on weekdays and was more sensitive to floods. In addition, residents' response behavior (based on their subjective consciousness) may result in increased overall exposure. This study presents the first fully formulated method for dynamic urban flood exposure simulation at a high spatiotemporal resolution. The quantitative results of this study can provide fundamental information for urban flood disaster vulnerability assessment, socioeconomic loss assessment, urban disaster risk management, and emergency response plan establishment.