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

Nejc Bezak,Matjaž Mikoš,Mateja Jemec Auflič

doi:10.3390/w13141977

Abstract

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.

Highlights

Landslides are one of the most common natural hazards in hilly and mountainous regions around the globe, and there exist numerous varieties of landslides types [1,2,3], posing a serious risk to populations and infrastructure in landslide prone areas [4,5]
This study provides a preliminary investigation of the added value of the volumetric soil moisture (VSM) data obtained from the Uncertainties in Ensembles of Regional Reanalyses (UERRA) reanalysis data from the perspective of landslides prediction
Based on the presented results, it can be concluded that the VSM information did not provide much benefit from the perspective of landslide prediction

Summary

Introduction

Landslides are one of the most common natural hazards in hilly and mountainous regions around the globe, and there exist numerous varieties of landslides types [1,2,3], posing a serious risk to populations and infrastructure in landslide prone areas [4,5]. A review of recent literature on rainfall thresholds for landslide occurrence was prepared by Segoni et al [12]. They introduced spatial scales for rainfall threshold definition and used slope, local and basin, over regional and national to global spatial scale. This study indicated that ERA5 soil moisture data can be regarded as a proxy of slope wetness conditions [16]. It was tested on a relatively small region. A recent study indicated that global rainfall products still have issues in detection of precipitation spatiotemporal patterns [18]. Products with a better potential generated by regional climate models were detected in some other parts of Asia [19] or in Europe [20]

Objectives

Results

Conclusion