Empirical Rainfall Thresholds for Landslide Occurrence in Serra do Mar, Angra dos Reis, Brazil

In the tropical environment such as Brazil, the frequency of rainfall-induced landslides is particularly high because of the rugged terrain, heavy rainfall, increasing urbanization, and the orographic effect of mountain ranges. Since such landslides repeatedly interfere with human activities and infrastructures, improved knowledge related to spatial and temporal prediction of the phenomenon is of interest for risk management. This study is an analysis of empirical rainfall thresholds, which aims to establish local and regional scale correlations between rainfall and the triggering of landslides in Angra dos Reis in the State of Rio de Janeiro. A statistical analysis combining quantile regression and binary logistic regression was performed on 1640 and 526 landslides triggered by daily rainfall over a 6-year period in the municipality and the urban center of Angra dos Reis, in order to establish probabilistic rainfall duration thresholds and assess the role of antecedent rainfall. The results show that the frequency of landslides is highly correlated with rainfall events, and surprisingly the thresholds in dry season are lower than those in wet season. The aspect of the slopes also seems to play an important role as demonstrated by the different thresholds between the southern and northern regions. Finally, the results presented in this study provide new insight into the spatial and temporal dynamics of landslides and rainfall conditions leading to their activation in this tropical and mountainous environment.

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

The frequency of unprecedented extreme precipitation events is increasing, and consequently, catastrophic debris flows occur in regions worldwide. Rapid velocity and long-runout distances of debris flow induce massive loss of life and damage to infrastructure. Despite extensive research, understanding the initiation mechanisms and defining early warning thresholds for extreme-precipitation-induced debris flows remain a challenge. Due to the nonavailability of extreme events in the past, statistical models cannot determine thresholds from historical datasets. Here, we develop a numerical model to analyze the initiation and runout of extreme-precipitation-induced runoff-generated debris flows and derive the Intensity-Duration (ID) rainfall threshold. We choose the catastrophic debris flow on 6 August 2020 in Pettimudi, Kerala, India, for our analysis. Our model satisfactorily predicts the accumulation thickness (7 m to 8 m) and occurrence time of debris flow compared to the benchmark. Results reveal that the debris flow was rapid, traveling with a maximum velocity of 9 m/s for more than 9 minutes. The ID rainfall threshold defined for the event suggests earlier thresholds are not valid for debris flow triggered by extreme precipitation. The methodology we develop in this study is helpful to derive ID rainfall thresholds for debris flows without historical data.

Reanalysis of Soil Moisture Used for Rainfall Thresholds for Rainfall-Induced Landslides: The Italian Case Study

Landslides are one of the most frequent natural disasters that can endanger human lives and property. Therefore, prediction of landslides is essential to reduce economic damage and save human lives. Numerous methods have been developed for the prediction of landslides triggering, ranging from simple methods that include empirical rainfall thresholds, to more complex ones that use sophisticated physically- or conceptually-based models. Reanalysis of soil moisture data could be one option to improve landslide forecasting accuracy. This study used the publicly available FraneItalia database hat contains almost 9000 landslide events that occurred in the 2010–2017 period in Italy. The Copernicus Uncertainties in Ensembles of Regional Reanalyses (UERRA) dataset was used to obtain precipitation and volumetric soil moisture data. The results of this study indicated that precipitation information is still a much better predictor of landslides triggering compared to the reanalyzed (i.e., not very detailed) soil moisture data. This conclusion is valid both for local (i.e., grid) and regional (i.e., catchment-based) scales. Additionally, at the regional scale, soil moisture data can only predict a few landslide events (i.e., on average around one) that are not otherwise predicted by the simple empirical rainfall threshold approach; however, this approach on average, predicted around 18 events (i.e., 55% of all events). Despite this, additional investigation is needed using other (more complete) landslide databases and other (more detailed) soil moisture products.

Brief communication: Rainfall thresholds based on Artificial neural networks can improve landslide early warning

In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy), show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow to easily add other variables, like peak rainfall intensity, with a further performance improvement (TSS = 0.64). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds.

Rainfall-Induced Landslides and Erosion Processes in the Road Network of the Jaén Province (Southern Spain)

Rainfall thresholds are one of the most widely applied methods for indirectly estimating landslide return periods, which are subsequently used in hazard analyses. In this study, the starting point is an incidence database of landslides and erosive processes affecting the road network of the province of Jaén (southern Spain), in which the positions and dates of civil repair works can be found. Meanwhile, the use of a daily rainfall database in a dense grid (1 km) allowed for the estimation of the rainfall series at each incidence point with high precision. Considering the news in the local media and applying spatial proximity, temporal proximity, and maximum return period criteria, rainfall events of various duration (1 to 90 days) could be associated approximately with each point. Then, the rainfall thresholds and their return periods were estimated. A linear equation was adjusted for the rainfall duration threshold (E = 6.408 D + 74.829), and a power-law curve was adjusted for the intensity–duration pair (I = 47.961 D−0.458). Non-significant differences were observed between the thresholds and the return periods for the lower and higher magnitude incidences, but the durations for the former were lower (1–13 days), compared to those of the latter (7–22 days). From the equations, rainfall events of different durations could be estimated for use in hazard analysis, as well as for the future development of warning systems.

Spatio-temporal analysis of short-term rainfall extremes in Croatia

<p><span>Extreme rainfall events of short duration and their possible intensification in the future may induce large implications in the existing engineering structures and water risk assessment. In Croatia, the sub-daily time scale (6 hours and 12 hours) is used to select an appropriate threshold value to define yellow, green and red alarming categories in the Meteoalarm rainfall warning system. The study aims i</span><span>) </span><span>to </span><span>provide the general climatology of short-rainfall extremes to analyse regional variability in precipitation extremes, and ii) to </span><span>update the alarming rainfall thresholds in Croatia and to upgrade the alarming system from regional (presently 8 regions) to the county level (21 counties). The short-term (from 5 minutes to one hour) and sub-daily (from 2 to 24 hours) precipitation amounts are analysed in terms of their spatial and temporal characteristics. The study comprises the ombrographic station network of 56 stations, evenly distributed over Croatia, with datasets covering the period 1961-2019. For each duration, the Generalized Extreme Value (GEV) distribution is fitted to the annual-maxima precipitation amounts, employing both the stationary and non-stationary model. Spatial distribution of the corresponding 2, 5, 10, 25, 50 and 100 years return values shows a clear distinction between the continental and maritime regions for each duration of rainfall with a pronounced maximum in the northern Adriatic (Kvarner) region. The implications on the current rainfall thresholds in the alarming system and new proposals will be discussed. Additionally, the </span><span>changes in characteristics of short-term precipitation extremes will be presented providing the climatological background for climate change adaptation activities and impact assessment in Croatia.</span></p>