Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the Umbria region (central Italy)

Abstract

Rainfall thresholds represent the main tool for the Italian Civil Protection System for early warning of the threat of landslides. However, it is well-known that soil moisture conditions at the onset of a storm event also play a critical role in triggering slope failures, especially in the case of shallow landslides. This study attempts to define soil moisture (estimated by using a soil water balance model) and rainfall thresholds that can be employed for hydrogeological risk prevention by the Civil Protection Decentrate Functional Centre (CFD) located in the Umbria Region (central Italy). Two different analyses were carried out by determining rainfall and soil moisture conditions prior to widespread landslide events that occurred in the Umbria Region and that are reported in the AVI (Italian Vulnerable Areas) inventory for the period 1991-2001. Specifically, a "local" analysis that considered the major landslide events of the AVI inventory and an "areal" analysis subdividing the Umbria Region in ten sub-areas were carried out. Comparison with rainfall thresholds used by the Umbria Region CFD was also carried out to evaluate the reliability of the current procedures employed for landslide warning. The main result of the analysis is the quantification of the decreasing linear trend between the maximum cumulated rainfall values over 24, 36 and 48h and the soil moisture conditions prior to landslide events. This trend provides a guideline to dynamically adjust the operational rainfall thresholds used for warning. Moreover, the areal analysis, which was aimed to test the operational use of the combined soil moisture-rainfall thresholds showed, particularly for low values of rainfall, the key role of soil moisture conditions for the triggering of landslides. On the basis of these results, the Umbria Region CFD is implementing a procedure aimed to the near real-time estimation of soil moisture conditions based on the soil water balance model developed ad hoc for the region. In fact, it was evident that a better assessment of the initial soil moisture conditions would support and improve the hydrogeological risk assessment.

Statistical modelling of rainfall-induced shallow landsliding using static predictors and numerical weather predictions: preliminary results

“…Not surprisingly, for this event the maximum soil moisture in the first 10 cm below ground is classified as important in almost 75 % of the runs with an average rank of 3. This result confirms the findings of recent studies investigating the strong linkage between soil moisture and landslide occurrence (Ray et al, 2010). In particular, Ponziani et al (2012) in analysing the role of antecedent soil moisture conditions at the regional scale, assessed the soil moisture content as being as important as rainfall amounts for the triggering of landslides.…”

“…This result confirms the findings of recent studies investigating the strong linkage between soil moisture and landslide occurrence (Ray et al, 2010). In particular, Ponziani et al (2012) in analysing the role of antecedent soil moisture conditions at the regional scale, assessed the soil moisture content as being as important as rainfall amounts for the triggering of landslides. In addition, this finding suggests the possible use of NWP soil moisture contents for the initialization of hydrological states in modelling, instead of using NWP rainfall data as the main driver of soil moisture patterns (Schmidt et al, 2008).…”

“…Despite the fact that the RUSLE equation is commonly used to predict soil erosion on a cell-bycell basis, recently a high correlation has been found between (R)USLE-based soil erosion map and landslide locations (Pradhan et al, 2012). …”

The National Landslide Database of Great Britain: Acquisition, communication and the role of social media

“…For example, being able to model soil moisture (and pore pressures) more directly would be a great advantage (e.g. Ponziani et al, 2012), but this requires further research to achieve a better parameterisation of the UK landscape. The antecedent effective rainfall threshold analysis is a reflection of historical patterns and is therefore based on the assumption that there is continuity in the modes of failure, precipitation-pore pressure pathways and thresholds that trigger these events.…”

Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides

“…So far, most of the EWS dealing with rainfall-induced landslides are based on rainfall measurements, sometimes supported by weather forecasts (e.g., Keefer et al, 1987;Ponziani et al, 2012), but are rarely integrated into monitoring of certain soil variables (e.g., Ortigao and Justi, 2004;Chleborad et al, 2008;Baum and Godt, 2010). Rainfall is often interpreted merely statistically, with an empirical quantification of rainfall thresholds for landslide initiation (e.g., Sirangelo and Versace, 1996;Sirangelo and Braca, 2004;Guzzetti et al, 2007Guzzetti et al, , 2008Capparelli and Tiranti, 2010;Tiranti and Rabuffetti, 2010;Martelloni et al, 2012;Segoni et al, 2014;Tiranti et al, 2014;Piciullo et al, 2017).…”