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 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.

Definition of 3D rainfall thresholds for operative LEWS

2021 ◽  
Author(s):  
Ascanio Rosi ◽  
Antonio Monni ◽  
Angela Gallucci ◽  
Nicola Casagli
Keyword(s):  
Soil Moisture ◽  
False Positives ◽  
Time Interval ◽  
Antecedent Rainfall ◽  
Rainfall Thresholds ◽  
Antecedent Soil Moisture ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Definition Of ◽  
Landslide Triggering

<p>Rainfall induced landslide is one of the most common hazards worldwide and it is responsible every year of huge losses, both economic and social. <br>Because of the high impact of this kind of natural hazard, the forecasting of the meteorological condition associated with the initiation of landslide has become paramount in the recent years and several papers addressing this issue have been published.<br>When working over large areas, the definition of rainfall thresholds is the most used approach, since it requires few data that can be easily retrieved: landslide triggering date and location and rainfall recording associated to landslide events.<br>The intensity-duration threshold is the most used approach and it showed over the time its potential to be implemented in an operative landslide early warning system (LEWS), but literature papers showed that this approach is affected by a main drawback, i.e., the high number of false positives (events that are not capable of triggering landslides are classified as landslide triggering events).<br>To overcome this problem several authors tried to combine these thresholds with other parameters and recently one of the most promising approach is the use of the antecedent soil moisture condition, but this parameter is note very easily available for large areas and it is difficult to retrieve it in real time, so as it can be used in a LEWS.<br>In our work we used antecedent rainfall to simulate the progressive saturation of the soil and then the soil moisture condition associated with the initiation of landslides.<br>In a given area the total rainfall recorded by each rain gauge over a defined period of time prior the landslide is considered and used to define a parameter named MeAR (Mean Antecedent Rainfall), which represent the mean rainfall of the area over a given time interval, as recorded by all the active rain gauges.<br>The MeAR parameter has been coupled with classical I-D thresholds to define 3D thresholds, where the conditions associated with the initiation of a landslide are defined by a portion of a 3D space, instead of a portion of a 2D plane. This approach has been tested in Emilia-Romagna region (Italy) and it resulted the possibility of reducing false positives from 30% up to 80% on different areas.</p>

Exploring the potential of soil moisture reanalysis data for improving the identification of regional landslide triggering thresholds

2021 ◽  
Author(s):  
Nunziarita Palazzolo ◽  
David J. Peres ◽  
Enrico Creaco ◽  
Antonino Cancelliere
Keyword(s):  
Soil Moisture ◽  
Rainfall Event ◽  
Rainfall Thresholds ◽  
Rainfall Duration ◽  
Soil Moisture Data ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
Different Depths ◽  
Moisture Conditions ◽  
Landslide Triggering

<p>Landslide triggering thresholds provide the rainfall conditions that are likely to trigger landslides, therefore their derivation is key for prediction purposes. Different variables can be considered for the identification of thresholds, which commonly are in the form of a power-law relationship linking rainfall event duration and intensity or cumulated event rainfall. The assessment of such rainfall thresholds generally neglects initial soil moisture conditions at each rainfall event, which are indeed a predisposing factor that can be crucial for the proper definition of the triggering scenario. Thus, more studies are needed to understand whether and the extent to which the integration of the initial soil moisture conditions with rainfall thresholds could improve the conventional precipitation-based approach. Although soil moisture data availability has hindered such type of studies, yet now this information is increasingly becoming available at the large scale, for instance as an output of meteorological reanalysis initiatives. In particular, in this study, we focus on the use of the ERA5-Land reanalysis soil moisture dataset. Climate reanalysis combines past observations with models in order to generate consistent time series and the ERA5-Land data actually provides the volume of water in soil layer at different depths and at global scale. Era5-Land project is, indeed, a global dataset at 9 km horizontal resolution in which atmospheric data are at an hourly scale from 1981 to present. Volumetric soil water data are available at four depths ranging from the surface level to 289 cm, namely 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm. After collecting the rainfall and soil moisture data at the desired spatio-temporal resolution, together with the target data discriminating landslide and no-landslide events, we develop automatic triggering/non-triggering classifiers and test their performances via confusion matrix statistics. In particular, we compare the performances associated with the following set of precursors: a) event rainfall duration and depth (traditional approach), b) initial soil moisture at several soil depths, and c) event rainfall duration and depth and initial soil moisture at different depths. The approach is applied to the Oltrepò Pavese region (northern Italy), for which the historical observed landslides have been provided by the IFFI project (Italian landslides inventory). Results show that soil moisture may allow an improvement in the performances of the classifier, but that the quality of the landslide inventory is crucial.</p>

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias, NW Spain)

Landslides ◽  
2017 ◽  
Vol 15 (2) ◽  
pp. 273-282 ◽  
Author(s):  
Pablo Valenzuela ◽  
María José Domínguez-Cuesta ◽  
Manuel Antonio Mora García ◽  
Montserrat Jiménez-Sánchez
Keyword(s):  
Soil Moisture ◽  
Nw Spain ◽  
Rainfall Thresholds ◽  
Moisture Conditions

scholarly journals A case study on the use of appropriate surrogates for antecedent moisture conditions (AMCs)

2010 ◽  
Vol 7 (3) ◽  
pp. 3329-3363 ◽  
Author(s):  
G. A. Ali ◽  
A. G. Roy
Keyword(s):  
Soil Moisture ◽  
Point Scale ◽  
Nonlinear Regression Models ◽  
Terrain Attributes ◽  
Antecedent Moisture ◽  
Triggering Conditions ◽  
Linear And Nonlinear ◽  
Active Processes ◽  
Moisture Conditions ◽  
Antecedent Moisture Conditions

Abstract. While a large number of non-linear hillslope and catchment rainfall-runoff responses have been attributed to the temporal variability in antecedent moisture conditions (AMCs), two problems emerge: 1) the difficulty of measuring AMCs, and 2) the absence of explicit guidelines for the choice of surrogates or proxies for AMCs. This paper aims at determining whether or not multiple surrogates for AMCs should be used in order not to bias our understanding of a system hydrological behaviour. We worked in a small forested catchment, the Hermine, where soil moisture has been measured at 121 different locations at four depths on 16 occasions. Without making any assumption on active processes, we used various linear and nonlinear regression models to evaluate the point-scale temporal relations between actual soil moisture contents and selected meteorological-based surrogates for AMCs. We then mapped the nature of the "best fit" model to identify 1) spatial clusters of soil moisture monitoring sites whose hydrological behaviour was similar, and 2) potential topographic influences on these behaviours. Two conclusions stood out. Firstly, it was shown that the sole reference to AMCs indices traditionally used in catchment hydrology, namely antecedent rainfall amounts summed over periods of seven or ten days, would have led to an incomplete understanding of the Hermine catchment dynamics. Secondly, the relationships between point-scale soil moisture content and surrogates for AMCs were not spatially homogeneous, thus revealing a mosaic of linear and nonlinear catchment "active" and "contributing" sources whose location was often controlled by surface terrain attributes or the topography of a soil-confining layer interface. These results represent a step forward in developing a hydrological conceptual model for the Hermine catchment as they indicate depth-specific processes and spatially-variable triggering conditions. Further investigations are, however, necessary in order to derive general guidelines for the choice of the best surrogates for AMCs in a catchment.

scholarly journals Probabilistic thresholds for landslides warning by integrating soil moisture conditions with rainfall thresholds

2019 ◽  
Vol 574 ◽  
pp. 276-287 ◽  
Author(s):  
Binru Zhao ◽  
Qiang Dai ◽  
Dawei Han ◽  
Huichao Dai ◽  
Jingqiao Mao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Rainfall Thresholds ◽  
Moisture Conditions ◽  
Probabilistic Thresholds

Multidimensional context for extreme analysis of daily streamflow, rainfall and accumulated rainfall across USA

2020 ◽  
Author(s):  
Maria Nezi ◽  
Ioannis Tsoukalas ◽  
Charalampos Ntigkakis ◽  
Andreas Efstratiadis
Keyword(s):  
Soil Moisture ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Annual Rainfall ◽  
Event Analysis ◽  
Antecedent Soil Moisture ◽  
Daily Streamflow ◽  
Accumulated Rainfall ◽  
Moisture Conditions ◽  
Parameter Values

<p>Statistical analysis of rainfall and runoff extremes plays a crucial role in hydrological design and flood risk management. Usually this analysis is performed separately for the two processes of interest, thus ignoring their dependencies, which appear at multiple temporal scales. Actually, the generation of a flood strongly depends on soil moisture conditions, which in turn depends on past rainfall. Using daily rainfall and runoff data from about 400 catchments in USA, retrieved from the MOPEX repository, we investigate the statistical behavior of the corresponding annual rainfall and streamflow maxima, also accounting for the influence of antecedent soil moisture conditions. The latter are quantified by means of accumulated daily rainfall at various aggregation scales (i.e., from 5 up to 30 days) before each extreme rainfall and streamflow event. Analysis of maxima is employed by fitting the Generalized Extreme Value (GEV) distribution, using the L-moments method for extracting the associated parameters (shape, scale, location). Significant attention is paid for ensuring statistically consistent estimations of the shape parameter, which is empirically adjusted in order to minimize the influence of sample uncertainty. Finally, we seek for the possible correlations among the derived parameter values and hydroclimatic characteristics of the studied basins, and also depict their spatial distribution across USA.</p>

scholarly journals Effects of antecedent soil moisture on runoff modeling in small semiarid watersheds of southeastern Arizona

2011 ◽  
Vol 15 (10) ◽  
pp. 3171-3179 ◽  
Author(s):  
Y. Zhang ◽  
H. Wei ◽  
M. A. Nearing
Keyword(s):  
Soil Moisture ◽  
Standard Deviation ◽  
Soil Layer ◽  
Arid Environment ◽  
Storm Events ◽  
Runoff Generation ◽  
Antecedent Soil Moisture ◽  
Small Watersheds ◽  
Runoff Modeling ◽  
Moisture Conditions

Abstract. This study presents unique data on the effects of antecedent soil moisture on runoff generation in a semi-arid environment, with implications for process-based modeling of runoff. The data were collected from four small watersheds measured continuously from 2002 through 2010 in an environment where evapo-transpiration approaches 100% of the infiltrated water on the hillslopes. Storm events were generally intense and of short duration, and antecedent volumetric moisture conditions were dry, with an average in the upper 5 cm soil layer over the nine year period of 8% and a standard deviation of 3%. Sensitivity analysis of the model showed an average of 0.05 mm change in runoff for each 1% change in soil moisture, indicating an approximate 0.15 mm average variation in runoff accounted for by the 3% standard deviation of measured antecedent soil moisture. This compared to a standard deviation of 4.7 mm in the runoff depths for the measured events. Thus the low variability of soil moisture in this environment accounts for a relative lack of importance of storm antecedent soil moisture for modeling the runoff. Runoff characteristics simulated with a nine year average of antecedent soil moisture were statistically identical to those simulated with measured antecedent soil moisture, indicating that long term average antecedent soil moisture could be used as a substitute for measured antecedent soil moisture for runoff modeling of these watersheds. We also found no significant correlations between measured runoff ratio and antecedent soil moisture in any of the four watersheds.

scholarly journals On the Relationship of Soil Moisture and Extreme Temperatures in East China

2014 ◽  
Vol 18 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Lei Meng ◽  
Yanjun Shen
Keyword(s):  
Soil Moisture ◽  
Standardized Precipitation Index ◽  
Heat Fluxes ◽  
East China ◽  
Daily Minimum Temperature ◽  
Antecedent Soil Moisture ◽  
Surface Temperatures ◽  
Quantile Regression Analysis ◽  
Temperature Indices ◽  
Moisture Conditions

Abstract Soil moisture conditions affect energy partitioning between sensible and latent heat fluxes, resulting in a change in surface temperatures. In this study, the relationships between antecedent soil moisture conditions [as indicated by the 6-month standardized precipitation index (SPI)] and several temperature indices are statistically quantified using the quantile regression analysis across East China to investigate the influence of soil moisture on summer surface temperatures. These temperature indices include percentage of hot days (%HD), heat-wave duration (HWD), daily temperature range (DTR), and daily minimum temperature (Tmin). It was demonstrated that soil moisture had a significant impact on %HD and HWD at higher quantiles in all regions but the east, suggesting that drier soil moisture conditions tend to intensity summer hot extremes. It was also found that hot extremes (%HD and HWD at higher quantiles) had increased substantially from 1958 to 2010. Soil moisture also significantly affected the DTR in all regions but tended to have more impacts on the DTR in soil moisture-limited regimes than in energy-limited regimes. This study provides observational evidence of soil moisture influences on hot extremes in East China.

Sign in / Sign up

Export Citation Format

Share Document