Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil

Analysis of Debris Flow Triggering Conditions for Different Rainfall Patterns Based on Satellite Rainfall Products in Hengduan Mountain Region, China

Rainfall is the main factor that induces debris flow. Satellite rainfall products provide a new source of data in terms of debris flow-triggering conditions to overcome the lack of rainfall data coverage from ground-based rainfall gauges in large-scale mountainous regions. In this study, the applicability of four satellite rainfall products (CMORPH, GPM, MSWEP, and PERSIANN) in the Hengduan Mountain region (HMR) was evaluated with reference to ground observation data from 2000 to 2020. The critical rainfall and rainfall thresholds under different rainfall patterns and warning levels that trigger debris flows were analyzed according to the empirical cumulative distribution function (ECDF) and cumulative probability. The results showed that CMORPH (comprehensive indicator score (CI = 0.72) and GPM (CI = 0.70) performed better in the simulation of daily rainfall sequence consistency and extreme rainfall conditions in the study area. CMORPH also had the highest reconstruction rate for correctly capturing rainfall events that triggered debris flows, with a value of 89%. Approximately half of the rainfall patterns that cause debris flows are antecedent-effective-rainfall-dominated. Both intraday-rainfall-dominated and intraday-antecedent-rainfall-balanced patterns were below 30%. There were evident differences in the critical rainfall for different rainfall patterns under the same warning level. By comparing the results of previous studies on rainfall thresholds, it is believed that the results of this study confirm the application of satellite rainfall products; in addition, the calculated rainfall thresholds can provide a reference for the early warning of debris flows in the HMR. In general, this work is of great significance to the prediction and early warning of debris flow hazards.

Research on the Method of Rainfall Field Retrieval Based on the Combination of Earth–Space Links and Horizontal Microwave Links

High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored.

Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records

Analyzing flooding in urban areas is a great challenge due to the lack of long-term rainfall records. This study hereby seeks to propose a modeling framework for urban flood analysis in ungauged drainage basins. A platform called “RainyDay” combined with a nine-year record of hourly, 0.1° remotely sensed rainfall data are used to generate extreme rainfall events. These events are used as inputs to a hydrological model. The comprehensive characteristics of urban flooding are reflected through the projection pursuit method. We simulate runoff for different return periods for a typical urban drainage basin. The combination of RainyDay and short-record remotely sensed rainfall can reproduce recent observed rainfall frequencies, which are relatively close to the design rainfall calculated by the intensity-duration-frequency formula. More specifically, the design rainfall is closer at high (higher than 20-yr) return period or long duration (longer than 6 h). Contrasting with the flood-simulated results under different return periods, RainyDay-based estimates may underestimate the flood characteristics under low return period or short duration scenarios, but they can reflect the characteristics with increasing duration or return period. The proposed modeling framework provides an alternative way to estimate the ensemble spread of rainfall and flood estimates rather than a single estimate value.