Rainfall and soil moisture conditions for the triggering of torrential flows at the Rebaixader catchment (Central Pyrenees)

2020 ◽  
Author(s):  
Raül Oorthuis ◽  
Marcel Hürlimann ◽  
Clàudia Abancó ◽  
José Moya ◽  
Antonio Lloret ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Water Pressure ◽  
Early Warning Systems ◽  
Rainfall Data ◽  
Critical Rainfall ◽  
Moisture Conditions ◽  
Debris Floods ◽  
Central Pyrenees

<p>Torrential flows, like debris flows and debris floods, can mobilize large volumes at high velocities in mountainous regions. Therefore, they represent an important erosional process and a significant hazard towards infrastructures and people (sometimes catastrophic).</p><p>Monitoring-based analysis is a crucial task to improve the understanding of the mechanisms triggering torrential flows and its propagation, which are necessary to implement early warning systems. The monitoring of triggering conditions generally focusses on rainfall measurements and the characterization of the critical rainfall conditions. However, rainfall data do not provide a complete picture of the physical processes involved. Very few studies include soil moisture and/or pore water pressure measurements to define the hydrologic response at the natural slopes of the catchment. In that respect, this study analyses both rainfall and soil moisture data at a Mediterranean-influenced torrential basin located in Central Pyrenees (the Rebaixader site).</p><p>The Rebaixader site has a high torrential activity, with 11 debris flows and 24 debris floods detected since 2009. The temporal distribution of rainfall episodes and torrential flows shows a clear shift between the most frequent rainfall episodes (beginning of June) and torrential flows (mid-July). This suggests that soil moisture conditions, depending on antecedent rainfall and/or snowmelt, affect the triggering of torrential flows. Regarding critical rainfall conditions, a previously published rainfall threshold was updated including total rainfall duration and mean intensity of 2009-2019 rainfalls. On the other hand, measured volumetric water content (VWC) was analysed for triggering and non-triggering rainfall events. Preliminary results show lower VWC increment on wetter soils at the beginning of rainstorms that triggered torrential flows. This indicates that soil saturates with lower rainfall amount if the soil is initially wetter; which subsequently generates higher runoff rate and therefore a higher erosion and transport energy that may trigger torrential flows. In addition, a slight trend was observed when comparing rainfall intensity and soil moisture; generally larger rainfall intensity is necessary to trigger torrential flows when soil is drier.  </p><p>The analysis of VWC data was more complicated in contrast to the one of rainfall data, since the time series are shorter (2013-2019) and the physical interpretation is not straightforward. Therefore, additional data are necessary to confirm and define soil moisture thresholds triggering torrential flows.</p>

scholarly journals The role of antecedent soil moisture conditions on rainfall-triggered shallow landslides

2018 ◽  
Author(s):  
Maurizio Lazzari ◽  
Marco Piccarreta ◽  
Salvatore Manfreda
Keyword(s):  
Soil Moisture ◽  
Rainfall Intensity ◽  
Human Life ◽  
Early Warning Systems ◽  
Shallow Landslides ◽  
Antecedent Soil Moisture ◽  
Antecedent Moisture ◽  
Moisture Conditions ◽  
Antecedent Moisture Conditions

Abstract. Rainfall-triggered shallow landslides have caused losses of human life and millions of euros in damage to property in all parts of the world. The need to prevent such phenomena combined with the difficulty to describe the geo-physical processes over large scales led to the adoption of empirical rainfall thresholds derived from the observed relationship between rainfall intensity/duration and landslide occurrence. These thresholds are generally obtained neglecting the role of the antecedent moisture conditions that should be taken into consideration. In the present manuscript, we explored the role of antecedent soil moisture on the critical rainfall intensity–duration thresholds highlighting its critical impact. Therefore, traditional approaches that neglect such parameter may have a limited value in the early-warning systems. This study was carried out using a record of 326 landslides occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e. rainstorm intensity and duration), we also derived the antecedent moisture conditions using a parsimonious hydrological model.

scholarly journals Modeling Antecedent Soil Moisture to Constrain Rainfall Thresholds for Shallow Landslides Occurrence

2020 ◽  
Author(s):  
Maurizio Lazzari ◽  
Marco Piccarreta ◽  
Ram L. Ray ◽  
Salvatore Manfreda
Keyword(s):  
Soil Moisture ◽  
Rainfall Intensity ◽  
Shallow Landslide ◽  
Rainfall Thresholds ◽  
Antecedent Soil Moisture ◽  
Correct Definition ◽  
Triggering Conditions ◽  
Definition Of ◽  
Moisture Conditions ◽  
Traditional Approaches

Rainfall-triggered shallow landslide events have caused losses of human lives and millions of euros in damage to property in all parts of the world. The need to prevent such hazards combined with the difficulty of describing the geomorphological processes over regional scales led to the adoption of empirical rainfall thresholds derived from records of rainfall events triggering landslides. These rainfall intensity thresholds are generally computed, assuming that all events are not influenced by antecedent soil moisture conditions. Nevertheless, it is expected that antecedent soil moisture conditions may provide critical support for the correct definition of the triggering conditions. Therefore, we explored the role of antecedent soil moisture on critical rainfall intensity-duration thresholds to evaluate the possibility of modifying or improving traditional approaches. The study was carried out using 326 landslide events that occurred in the last 18 years in the Basilicata region (southern Italy). Besides the ordinary data (i.e., rainstorm intensity and duration), we also derived the antecedent soil moisture conditions using a parsimonious hydrological model. These data have been used to derive the rainfall intensity thresholds conditional on the antecedent saturation of soil quantifying the impact of such parameters on rainfall thresholds.

scholarly journals The Influence of Rainfall Amounts Not Exceeding Critical Rainfall on Landslide Occurrence in Japan

2021 ◽  
Author(s):  
Soichi Kaihara ◽  
Noriko Tadakuma ◽  
Hitoshi Saito ◽  
Hiroaki Nakaya
Keyword(s):  
Early Warning Systems ◽  
Rainfall Data ◽  
Warning Systems ◽  
Negative Data ◽  
True Negative ◽  
Rainfall Events ◽  
Landslide Occurrence ◽  
The Past ◽  
Critical Rainfall ◽  
Landslide Early Warning

Abstract Critical rainfall events are used in landslide early-warning systems to predict the occurrence and severity of disasters. In this study, past landslide disasters in Japan were identified for which the critical rainfall set for each 1-km grid was exceeded using historical landslide records, radar-based rainfall data over a 1-km grid, and standard rainfall data collected over the past 17 years. It was determined that nearly equal numbers of rainfall events were identified with higher and lower rainfall amounts than the critical rainfall. The probability that a series of rainfall events would cause a landslide was approximately 1.15% when the critical rainfall was exceeded and 0.09% otherwise, a difference of approximately 10 times. It was also found that even if critical rainfall was not exceeded, in the case of debris flow and slope failures, there was rainfall that exceeded the standard rainfall one or two days before. In the case of landslides, there was rainfall that exceeded the critical rainfall one or two weeks before, and if the critical rainfall was exceeded in another rainfall event, a landslide could occur. The operational evaluation of Japanese LEWSs has a recall value of 0.486 as the accuracy of occurrence prediction, which was related to the fact that almost half of the rainfall events occurred in nonexceedance of the reference rainfall. The specificity was 0.935, known as the accuracy of nonoccurrence prediction, which was also greatly influenced by the TN (true negative) data of nonexceeding rainfall events, which accounted for most of the data.

scholarly journals Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

2021 ◽  
Vol 21 (9) ◽  
pp. 2773-2789
Author(s):  
Jacob Hirschberg ◽  
Alexandre Badoux ◽  
Brian W. McArdell ◽  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Multiple Scales ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Early Warning Systems ◽  
Antecedent Rainfall ◽  
Data Set

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

Hydro-meteorological thresholds based on synthetic dataset for improved prediction of rainfall-induced shallow landslides.

2020 ◽  
Author(s):  
Pasquale Marino ◽  
Roberto Greco ◽  
David James Peres ◽  
Thom A. Bogaard
Keyword(s):  
Soil Moisture ◽  
Rainfall Intensity ◽  
Root Zone ◽  
Water Pressure ◽  
Shallow Landslides ◽  
Root Penetration ◽  
Landslide Prediction ◽  
Antecedent Soil Moisture ◽  
Near Surface ◽  
Physically Based

<p>Prediction of rainfall-induced landslides is often entrusted to the definition of empirical thresholds (usually expressed in terms of rainfall intensity and duration), linking the precipitation to the triggering of landslides. However, rainfall intensity-duration thresholds do not exploit the knowledge of the hydrological processes developing in the slope, so they tend to generate false and missed alarms. Rainfall-induced shallow landslides usually occur in initially unsaturated soil covers following an increase of pore water pressure, due to the increase of soil moisture, caused by large and persistent rainfall. Clearly, it should be possible to use soil moisture for landslide prediction. Recently, Bogaard & Greco (2018) proposed the cause-trigger conceptual framework to develop hydro-meteorological thresholds that combine the antecedent causal factors and the actual trigger connected with landslide initiation. In fact, in some regions where rainfall-induced shallow landslides are particularly dangerous and pose a serious risk to people and infrastructures, the antecedent saturation is the predisposing factor, while the actual landslide triggering is associated with the hydrologic response to the recent and incoming precipitation. In fact, numerous studies already tried to introduce, directly or with models, the effects of antecedent soil moisture content in the empirical thresholds for improving landslide forecasting. Soil moisture can be measured locally, by a range of on-site measurement techniques, or remotely, from satellites or airborne. On-site measurements have proved promising in improving the performance of thresholds for landslide early warning. On-site data are accurate but sparse, so there is an increasing interest on the possible use of remotely sensed data. And in fact, recent research has shown that they can provide useful information for landslide prediction at regional scale, despite their coarse resolution and inherent uncertainty.</p><p>However, while remote sensing techniques provide near-surface (5cm depth) soil moisture estimate, the depth involved in shallow landslide is typically 1-2m below the surface. This depth, overlapping with the root penetration zone, is influenced by antecedent precipitation, soil texture, vegetation and, so, it is very difficult to find a clear relationship with near-surface soil moisture. Many studies have been conducted to provide root-zone soil moisture through physically-based approaches and data driven methods, data assimilation schemes, and satellite information.</p><p>In this study, the question if soil moisture information derived from current or future satellite products can improve landslide hazard prediction, and to what extent, is investigated. Hereto, real-world landslide and hydrology information, from two sites of Southern Italy characterized by frequent shallow landslides (Peloritani mountains, in Sicily, and Partenio mountains, in Campania), is analyzed. To get datasets long enough to carry out statistical analyses, synthetic time series of rainfall and soil cover response have been generated, with the application of a stochastic rainfall model and a physically based infiltration model, for both the sites. Near-surface and root-zone soil moisture have been tested, accounting also for effects of uncertainty and of coarse spatial and temporal resolution of measurements. The obtained results show that, in all cases, soil moisture information allows building hydro-meteorological thresholds for landslide prediction, significantly outperforming the currently adopted purely meteorological thresholds.</p><p> </p><p> </p>

Multi-parametric observations of debris-flow initiation at the headwaters of the Gadria catchment (eastern Italian Alps)

2020 ◽  
Author(s):  
Velio Coviello ◽  
Matteo Berti ◽  
Lorenzo Marchi ◽  
Francesco Comiti ◽  
Giulia Marchetti ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Debris Flow ◽  
Early Warning ◽  
Debris Flows ◽  
Lead Time ◽  
Early Warning Systems ◽  
Force Balance ◽  
Italian Alps ◽  
Alarm Systems ◽  
Debris Flow Initiation

<p>The complete understanding of the mechanisms controlling debris-flow initiation is still an open challenge in landslide research. Most debris-flow models assume that motion suddenly begins when a large force imbalance is imposed by slope instabilities or the substrate saturation that causes the collapse of the channel sediment cover. In the real world, the initiation of debris flows usually results from the perturbation of the static force balance that retains sediment masses in steep channels. These perturbations are primarily generated by the increasing runoff and by the progressive erosion of the deposits. Therefore, great part of regional early warning systems for debris flows are based on critical rainfall thresholds. However, these systems are affected by large spatial-temporal uncertainties due to the inadequate number and distribution of rain gauges. In addition, rainfall analysis alone does not explain the dynamics of sediment fluxes at the catchment scale: short-term variations in the sediment sources strongly influence the triggering of debris flows, even in catchments characterized by unlimited sediment supply.</p><p>In this work, we present multi-parametric observations of debris flows at the headwaters of the Gadria catchment (eastern Italian Alps). In 2018, we installed a monitoring network composed of geophones, three soil moisture probes, one tensiometer and two rain-triggered videocameras in a 30-m wide steep channel located at about 2200 m a.s.l. Most sensors lie on the lateral ridges of this channel, except for the tensiometer and the soil moisture probes that are installed in the channel bed at different depths. This network recorded four flow events in two years, two of which occurred at night. Specifically, the debris flows that occurred on 21 July 2018 and 26 July 2019 produced remarkable geomorphic changes in the monitored channel, with up to 1-m deep erosion. For all events, we measured peak values of soil water content that are far from saturation (<0.25 at -20 cm, <0.15 at -40 cm, <0.1 at -60 cm). We derived the time of occurrence and the duration of these events from the analysis of the seismic signals. Combining these pieces of information with data gathered at the monitoring station located about 2 km downstream, we could determine the flow kinematics along the main channel.</p><p>These results, although still preliminary, show the relevance of a multi-parametric detection of debris-flow initiation processes and may have valuable implications for risk management. Alarm systems for debris flows are becoming more and more attractive due the continuous development of compact and low-cost distributed sensor networks. The main challenge for operational alarm systems is the short lead-time, which is few tens of seconds for closing a transportation route or tens of minutes for evacuating settlements. Lead-time would significantly increase installing a detection system in the upper part of a catchment, where the debris flow initiates. The combination of hydro-meteorological monitoring in the source areas and seismic detection of channelized flows may be a reliable approach for developing an integrated early warning - alarm system.</p>

Towards the use of hydrometeorological thresholds for the regional-scale LEWS of Catalonia (NE Spain).

2021 ◽  
Author(s):  
Rosa M Palau ◽  
Marcel Hürlimann ◽  
Marc Berenguer ◽  
Daniel Sempere-Torres
Keyword(s):  
Soil Moisture ◽  
Risk Mitigation ◽  
Regional Scale ◽  
Early Warning Systems ◽  
Landslide Risk ◽  
Predisposing Factor ◽  
Landslide Early Warning ◽  
Triggering Conditions ◽  
Moisture Conditions ◽  
Ne Spain

<p>Risk mitigation for shallow slides and debris flows at a regional scale is a challenge. Landslide early warning systems (LEWS) are a helpful tool to anticipate the time and location of possible landslide events so that the authorities in charge of managing the landslide risk can plan their actions.</p><p>Traditionally, regional LEWS rely on rainfall information to asses if the landslide triggering conditions are met. However, in many cases, soil moisture is a predisposing factor that plays a major role in landslide initiation. Therefore, accounting for soil moisture conditions could improve the performance of LEWS.</p><p>Here we present the preliminary results defining hydrometeorological thresholds for the region of Catalonia (NE Spain). Such thresholds have been derived combining rainfall information from ground-based radar observations and the volumetric water content simulated by the LISFLOOD hydrological model. The information of recent and historical landslide events contained in a landslide inventory has been used to adjust the hydrometeorological thresholds.</p><p>The new hydrometeorological thresholds have been implemented into the regional-scale LEWS for the region of Catalonia.  Finally, the performance of the two versions of the LEWS (i.e. solely based on rainfall observations and adding soil moisture conditions) has been analysed for a recent rainfall event that triggered multiple landslides.</p>

scholarly journals Monitoring of Rainfall and Soil Moisture at the Rebaixader Catchment (Central Pyrenees)

2021 ◽  
Author(s):  
Raül Oorthuis ◽  
Marcel Hürlimann ◽  
Clàudia Abancó ◽  
José Moya ◽  
Luigi Carleo
Keyword(s):  
Soil Moisture ◽  
Debris Flows ◽  
Temporal Distribution ◽  
Rainfall Threshold ◽  
Water Dynamics ◽  
Monitoring Site ◽  
Maximum Increase ◽  
Data Set ◽  
Critical Rainfall ◽  
Definition Of

ABSTRACT The instrumental monitoring of torrential catchments is a fundamental research task and provides necessary information to improve our understanding of the mechanisms of debris flows. While most monitoring sites include meteorological sensors and analyze the critical rainfall conditions, very few contain soil moisture measurements. In our monitoring site, the Rebaixader catchment, 11 debris flows and 24 debris floods were detected during the last 9 years. Herein, the initiation mechanisms of these torrential flows were analyzed, focusing on the critical rainfall conditions and the soil water dynamics. Comparing the temporal distribution of both rainfall episodes and torrential flows, the Kernel density plots showed maximum values for rainfalls at the beginning of June, while the peak for torrential flows is on July 20. Thus, the antecedent rainfall, and especially the soil moisture conditions, may influence the triggering of torrential flows. In a second step, a new updated rainfall threshold was proposed that included total rainfall duration and mean intensity. The analysis of soil moisture data was more complicated, and no clear trends were observed in the data set. Therefore, additional data have to be recorded in order to quantitatively analyze the role of soil moisture on the triggering of flows and for the definition of thresholds. Some preliminary results show that the soil moisture at the beginning of a rainfall event affects the maximum increase of soil moisture, while a slight trend was visible comparing the initial soil moisture with the necessary rainfall amount to trigger a torrential flow.

scholarly journals Derivation of critical rainfall thresholds for shallow landslides as a tool for debris flow early warning systems

2013 ◽  
Vol 17 (10) ◽  
pp. 4095-4107 ◽  
Author(s):  
M. N. Papa ◽  
V. Medina ◽  
F. Ciervo ◽  
A. Bateman
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Early Warning Systems ◽  
Shallow Landslides ◽  
Slope Instability ◽  
Richards Equation ◽  
Warning Systems ◽  
Rainfall Characteristics ◽  
Infinite Slope Stability Model ◽  
Critical Rainfall

Abstract. Real-time assessment of debris-flow hazard is fundamental for developing warning systems that can mitigate risk. A convenient method to assess the possible occurrence of a debris flow is to compare measured and forecasted rainfalls to critical rainfall threshold (CRT) curves. Empirical derivation of the CRT from the analysis of past events' rainfall characteristics is not possible when the database of observed debris flows is poor or when the environment changes with time. For debris flows and mud flows triggered by shallow landslides or debris avalanches, the above limitations may be overcome through the methodology presented. In this work the CRT curves are derived from mathematical and numerical simulations, based on the infinite-slope stability model in which slope instability is governed by the increase in groundwater pressure due to rainfall. The effect of rainfall infiltration on landside occurrence is modelled through a reduced form of the Richards equation. The range of rainfall durations for which the method can be correctly employed is investigated and an equation is derived for the lower limit of the range. A large number of calculations are performed combining different values of rainfall characteristics (intensity and duration of event rainfall and intensity of antecedent rainfall). For each combination of rainfall characteristics, the percentage of the basin that is unstable is computed. The obtained database is opportunely elaborated to derive CRT curves. The methodology is implemented and tested in a small basin of the Amalfi Coast (South Italy). The comparison among the obtained CRT curves and the observed rainfall amounts, in a playback period, gives a good agreement. Simulations are performed with different degree of detail in the soil parameters characterization. The comparison shows that the lack of knowledge about the spatial variability of the parameters may greatly affect the results. This problem is partially mitigated by the use of a Monte Carlo approach.

Sign in / Sign up

Export Citation Format

Share Document