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.

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Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

10.5194/egusphere-egu21-669 ◽

2021 ◽

Author(s):

Samuele Segoni ◽

Minu Treesa Abraham ◽

Neelima Satyam ◽

Ascanio Rosi ◽

Biswajeet Pradhan

Keyword(s):

Sigma Model ◽

Confusion Matrix ◽

Regional Scale ◽

Geographical Location ◽

Daily Rainfall ◽

Rain Gauge ◽

Integrated System ◽

Rainfall Thresholds ◽

Skill Scores ◽

Landslide Forecasting

SIGMA (Sistema Integrato Gestione Monitoraggio Allerta &#8211; integrated system for management, monitoring and alerting) is a landslide forecasting model at regional scale which is operational in Emilia Romagna (Italy) for more than 20 years. It was conceived to be operated with a sparse rain gauge network with coarse (daily) temporal resolution and to account for both shallow landslides (typically triggered by short and intense rainstorms) and deep seated landslides (typically triggered by long and less intense rainfalls). SIGMA model is based on the statistical distribution of cumulative rainfall values (calculated over varying time windows), and rainfall thresholds are defined as the multiples of standard deviation of the same, to identify anomalous rainfalls with the potential of triggering landslides.In this study, SIGMA model is applied for the first time in a geographical location outside of Italy, i.e. Kalimpong town in India. The SIGMA algorithm is customized using the historical rainfall and landslide data of Kalimpong from 2010 to 2015 and has been validated using the data from 2016 to 2017. The model was validated by building a confusion matrix and calculating statistical skill scores, which were compared with those of the state-of-the-art intensity-duration rainfall thresholds derived for the region.Results of the comparison clearly show that SIGMA performs much better than the other models in forecasting landslides: all instances of the validation confusion matrix are improved, and all skill scores are higher than I-D thresholds, with an efficiency of 92% and a likelihood ratio of 11.28. We explain this outcome mainly with technical characteristics of the site: when only daily rainfall measurements from a spare gauge network are available, SIGMA outperforms other approaches based on peak measurements, like intensity &#8211; duration thresholds, which cannot be captured adequately by daily measurements. SIGMA model thus showed a good potential to be used as a part of the local Landslide Early Warning System (LEWS).

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Regional rainfall thresholds for landslide occurrence using a centenary database

10.5194/nhess-2017-362 ◽

2017 ◽

Author(s):

Teresa Vaz ◽

José Luís Zêzere ◽

Susana Pereira ◽

Sérgio C. Oliveira ◽

Ricardo A. C. Garcia ◽

...

Keyword(s):

Daily Rainfall ◽

Early Warning Systems ◽

Rain Gauge ◽

Warning Systems ◽

Rainfall Thresholds ◽

Period Analysis ◽

Landslide Occurrence ◽

Landslide Event ◽

Landslide Early Warning ◽

Regional Rainfall

Abstract. This work proposes a comprehensive methodology to assess rainfall thresholds for landslide initiation, using a centenary landslide database associated with a single centenary daily rainfall dataset. The methodology is applied to the Lisbon region and include the rainfall return period analysis that was used to identify the critical rainfall combination (quantity-duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds is assessed and validated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, such thresholds may be used with acceptable confidence up to 50 km distance from the rain gauge. The obtained rainfall thresholds using linear and potential regression have a good performance in ROC metrics. However, the intermediate thresholds based on the probability of landslide events, established in the zone between the lower limit threshold and the upper limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

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Spatial and temporal dynamics of monsoon-induced landslides in Nepal in 2020

10.5194/egusphere-egu21-14000 ◽

2021 ◽

Author(s):

Kaushal Raj Gnyawali ◽

Dwayne D. Tannant ◽

Yogesh Bhattarai ◽

Rijan Jayana ◽

Rocky Talchabhadel

Keyword(s):

Temporal Dynamics ◽

Monsoon Season ◽

Warning System ◽

Early Morning ◽

Online News ◽

Temporal Database ◽

Rainfall Thresholds ◽

Landslide Occurrence ◽

Late Night ◽

Hourly Rainfall

In the monsoon season, landslides are major disasters in Nepal, causing loss of life and economic impacts. The landslides triggered in the 2020 monsoon (June &#8211; September) in Nepal caused more than 300 fatalities and affected about 800 families. A spatial and temporal database of landslides in this region does not exist, which has hindered an understanding of landslide dynamics and the development of a regional early warning system (EWS). In this study, we prepare a time-stamped (hourly) geo-referenced database of the landslides triggered by the 2020 monsoon in Nepal and investigate their dynamic trends. We track landslides from online news for each day during the monsoon to map their location and time. The database contains 332 mapped landslides, out of which accurate time stamps are available for 126 landslides. The spatial pattern shows a large concentration of landslides in central Nepal (districts of Parbat, Kaski, Myagdi, Baglung, Gulmi, and Syangja). The temporal pattern reveals that landslides in this region occur mostly during late night or early morning. We estimate hourly rainfall thresholds for landslide occurrence from the Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall product. The database and analysis provide a basis for estimating regional rainfall thresholds for Nepal and the design of an EWS.

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Combining landslide susceptibility maps and rainfall thresholds using a matrix approach

Revista de Geomorfologie ◽

10.21094/rg.2017.003 ◽

2017 ◽

Vol 19 (1) ◽

pp. 58-74

Author(s):

THIEBES Benni ◽

BAI Shibiao ◽

XI Yanan ◽

GLADE Thomas ◽

BELL Rainer

Keyword(s):

Landslide Susceptibility ◽

Regional Scale ◽

Daily Rainfall ◽

Rainfall Threshold ◽

Landslide Susceptibility Mapping ◽

Matrix Approach ◽

Hazard Maps ◽

Rainfall Thresholds ◽

Rainfall Model ◽

Simple Matrix

On the regional scale, investigations on future landslide can broadly be distinguished in spatial or temporal analyses, i.e. landslide susceptibility or hazard maps, and landslide triggering rainfall thresholds. Even though both approaches have its uses e.g. in spatial planning, risk management and early warning, they also have limitations. Susceptibility and hazard maps do not contain information on when landslides will be triggered, while rainfall thresholds give no detailed indication on where a landslide might take place. The combination of spatial and temporal landslide research remains a complex issue and no ready-to-use methodology for combined spatiotemporal landslide analyses is presently available. In our study, we present a simple matrix approach to combine spatial and temporal landslide probabilities and highlight its application for a case study in the Wudu region, China. Landslide susceptibility mapping is based on a previous study involving logistic regression; the analysis of rainfall threshold was carried out applying the daily rainfall model. A 4x4 matrix was used to combine and reclassify the spatial and temporal landslide information. The results are then plotted on a map to highlight the susceptibility for rainfall events with varying likelihood of triggering landslides.

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Regional rainfall thresholds for landslide occurrence using a centenary database

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-1037-2018 ◽

2018 ◽

Vol 18 (4) ◽

pp. 1037-1054 ◽

Cited By ~ 14

Author(s):

Teresa Vaz ◽

José Luís Zêzere ◽

Susana Pereira ◽

Sérgio Cruz Oliveira ◽

Ricardo A. C. Garcia ◽

...

Keyword(s):

Daily Rainfall ◽

Early Warning Systems ◽

Rain Gauge ◽

Warning Systems ◽

Rainfall Thresholds ◽

Data Set ◽

Landslide Occurrence ◽

Comprehensive Method ◽

Landslide Event ◽

Landslide Early Warning

Abstract. This work proposes a comprehensive method to assess rainfall thresholds for landslide initiation using a centenary landslide database associated with a single centenary daily rainfall data set. The method is applied to the Lisbon region and includes the rainfall return period analysis that was used to identify the critical rainfall combination (cumulated rainfall duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds are assessed and calibrated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, these thresholds may be used with acceptable confidence up to 50 km from the rain gauge. The rainfall thresholds obtained using linear and potential regression perform well in ROC metrics. However, the intermediate thresholds based on the probability of landslide events established in the zone between the lower-limit threshold and the upper-limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

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A procedure for identifying rainfall thresholds for the occurrence of landslides

10.5194/egusphere-egu21-1792 ◽

2021 ◽

Author(s):

Michele Calvello ◽

Gaetano Pecoraro

Keyword(s):

Temporal Resolution ◽

Regional Scale ◽

Economic Losses ◽

Landslide Risk ◽

Rainfall Thresholds ◽

Landslide Occurrence ◽

Precipitation Measurement ◽

Territorial Unit ◽

Precipitation Estimates ◽

Global Precipitation

Rainfall-induced landslides are widespread phenomena that cause casualties and economic losses every year. In Italy, intense or prolonged rainfall is the primary trigger of landslides. The identification of the rainfall conditions responsible for the initiation of landslides is a crucial issue and may contribute to reduce landslide risk at regional scale. In the literature, the most widely used criteria for the identification of rainfall conditions initiating slope failures are based on rainfall intensity-duration (I-D) or cumulative rainfall-duration (E-D) charts. In this study, a novel E-D procedure for the objective reconstruction of the rainfall conditions responsible for landslide occurrence is proposed. Rainfall measurements are derived from the satellite-based NASA Global Precipitation Measurement (GPM) database, which contains gridded precipitation estimates, with a half-hour temporal resolution and a 0.10-degree spatial resolution. Firstly, precipitation measurements are aggregated at hourly temporal resolution and the mean rainfall values over each territorial unit is calculated. Then, rainfall measurements are aggregated in order to obtain a sequence of rainfall events. Finally, for each rainfall event all the possible rainfall combinations are differentiated in two groups depending on whether they triggered or did not trigger landslides. The proposed procedure has been tested in a study area including six weather warning zones defined for hydrogeological risk management in Italy in the period between January 2010 and December 2018. Data on landslide occurrences are derived from the FraneItalia catalog (https://franeitalia.wordpress.com), a landslide inventory based on information retrieved from online Italian news. In the study area, the FraneItalia database reports 513 landslide events in the period 2010-2018. This procedure shall be a contribution toward objectively defining rainfall conditions responsible for landslides in different geographic areas, thus reducing the subjectivity inherent in the often-adopted heuristic treatment of rainfall and landslide data when defining rainfall thresholds for landslide occurrences.

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Integrating landslide susceptibility maps into warning models at regional scale in Italy

10.5194/egusphere-egu21-2005 ◽

2021 ◽

Author(s):

Gaetano Pecoraro ◽

Michele Calvello

Keyword(s):

Spatial Resolution ◽

Land Use Planning ◽

Landslide Susceptibility ◽

Spatial Information ◽

Regional Scale ◽

Susceptibility Map ◽

Rainfall Thresholds ◽

Landslide Occurrence ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

The importance of susceptibility maps in the initial phase of landslide hazard and risk assessment is widely recognized in the literature, since they provide to stakeholders a general overview of the location of landslide prone areas. Usually, the use of these maps is limited to support land use planning. However, many researchers have recently recognized that susceptibility maps may also be used to improve the performance and spatial resolution of landslide warning at regional scale and provide a better updating of hazard assessment over time. Indeed, landslides prediction may be difficult at regional scale only considering rainfall condition, due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geology, and lithology). As a result, a critical issue of models solely based on rainfall thresholds may be the issuing of warnings in areas that are not prone to landslide occurrence, resulting in an excessive number of false positives. In this work, we propose a methodology aimed at combining a susceptibility map and a set of rainfall thresholds by using a matrix approach to refine the performance of an early warning model at regional scale. The main aim is the combination of rainfall thresholds (typically used to accomplish a dynamic temporal forecasting with good temporal resolution but very coarse spatial resolution), with landslide susceptibility maps (providing static spatial information about the probability of landslide occurrence with a finer resolution). The methodology presented herein could allow a better prediction of &#8220;where&#8221; and &#8220;when&#8221; landslides may occur, thus: i) allowing to define a time-dependent level of hazard associated to their possible occurrence, and ii) markedly refining the spatial resolution of warning models employed at regional scale, given that areas susceptible to landslides typically represent only a fraction of territorial warning zones.

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Rainfall thresholds for the forecasting of landslide occurrence at regional scale

Landslides ◽

10.1007/s10346-011-0308-2 ◽

2011 ◽

Vol 9 (4) ◽

pp. 485-495 ◽

Cited By ~ 149

Author(s):

G. Martelloni ◽

S. Segoni ◽

R. Fanti ◽

F. Catani

Keyword(s):

Regional Scale ◽

Rainfall Thresholds ◽

Landslide Occurrence

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Data limitations and potential of hourly and daily rainfall thresholds for shallow landslides

10.5194/nhess-2020-125 ◽

2020 ◽

Author(s):

Elena Leonarduzzi ◽

Peter Molnar

Keyword(s):

Daily Rainfall ◽

Predictive Performance ◽

Rainfall Threshold ◽

Mean Annual Precipitation ◽

Threshold Estimation ◽

Rainfall Thresholds ◽

Landslide Occurrence ◽

Data Limitations ◽

Hourly Rainfall ◽

The Impact

Abstract. Rainfall thresholds are a simple and widely used method to predict landslide occurrence. In this paper we provide a comprehensive data-driven assessment of the effects of rainfall temporal resolution (hourly versus daily) on landslide prediction performance in Switzerland, with sensitivity to two other important aspects which appear in many landslide studies – the normalisation of rainfall, which accounts for local climatology, and the inclusion of antecedent rainfall as a proxy of soil water state prior to landsliding. We use an extensive landslide inventory with over 3800 events and several daily and hourly, station and gridded rainfall datasets, to explore different scenarios of rainfall threshold estimation. Our results show that although hourly rainfall did show best predictive performance for landslides, daily data were not far behind, and the benefits of hourly resolutions can be masked by the higher uncertainties in threshold estimation connected to using short records. We tested the impact of several typical actions of users, like assigning the nearest raingauge to a landslide location and filling in unknown timing, and report their effects on predictive performance. We find that localisation of rainfall thresholds through normalisation compensates for the spatial heterogeneity in rainfall regimes and landslide erosion process rates and is a good alternative to regionalisation. On top of normalisation by mean annual precipitation or a high rainfall quantile, we recommend that non-triggering rainfall be included in rainfall threshold estimation if possible. Finally, we demonstrate that there is predictive skill in antecedent rain as a proxy of soil wetness state, despite the large heterogeneity of the study domain, although it may not be straightforward to build this into rainfall threshold curves.

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Examining the Temporal Dynamics of Anxiety and Depressive Symptoms During a Therapist-Supported, Smartphone-Based Intervention for Major Depression (Preprint)

10.2196/preprints.25280 ◽

2020 ◽

Author(s):

Santiago Allende ◽

Valerie Forman-Hoffman ◽

Philippe Goldin

Keyword(s):

Depressive Symptoms ◽

Temporal Relationship ◽

Temporal Dynamics ◽

Digital Health ◽

Quadratic Function ◽

Depression Symptoms ◽

Symptoms Of Depression ◽

Effect Model ◽

End Of Treatment ◽

Highly Correlated

UNSTRUCTURED Background: Anxiety and depression symptoms are highly correlated in adults with depression; however, little is known about their interaction and temporal dynamics of change during treatment. Thus, the primary aim of this study was to examine the temporal dynamics of anxiety and depressive symptoms during a 12-week therapist-supported, smartphone-delivered digital health intervention for symptoms of depression and anxiety, the Meru Health Program (MHP). Method: A total of 290 participants from the MHP were included in the present analyses (age Mean = 39.64, SD = 10.25 years; 79% female; 54% self-reported psychotropic medication use). A variance components model was used to examine whether (1) reporting greater anxiety during the current week relative to anxiety reported in other weeks would be associated with greater reporting of depressive symptoms during the current week, while a time-varying effect model was used to examine whether, (2) consistent with findings reported by Wright et al. (2014), the temporal relationship between anxiety and depressive symptoms during the intervention would be expressed as a quadratic function marked by a weak association at baseline, followed by an increase to a peak before demonstrating a negligible decrease until the end of treatment. Results: In support of hypothesis 1, we found that reporting greater anxiety symptoms during the current week relative to other weeks was associated with greater depressive symptoms during the current week. Contrary to hypothesis 2, the temporal relationship between anxiety and depressive symptoms evidenced a recurring pattern, with the association increasing during the initial weeks, decreasing during mid-treatment and sharply increasing toward the end of treatment. Conclusions: The present findings demonstrate that anxiety and depressive symptoms overlap and fluctuate in concert during a smartphone-based intervention for anxiety and depressive symptoms. The present findings may warrant more refined intervention strategies specifically tailored to co-occurring patterns of change in symptoms.

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