T he monitoring of related hourly and accumulated rainfall index requires that critical thresholds of accumulated 72 hours rainfall are updated frequently according with the factors and local conditions (natural and anthropic) of each specific risk area. The importance of empirical methods is fundamental to confirm the relationship between rainfall intensity and accumulated rainfall with the mass movement events, in order to establish the critical threshold values. The present work performs an evaluation of the record data of mass movement events occurred in Sao Paulo State North coast region for a 4 - year period (2014 to 2018) considering different mass movement characteristics (slope type, magnitude and impact level). Some rainfall values were obtained to show that within these parameters an event related to natural and anthropic features was triggered. A database was created, sorting source of information and municipalities monitored, to implement the correlation between the mass movement events and the rainfall values. To elaborate the event’s map, reliable record data of localization of the mass movement events was selected, as well as the nearest possible raingauges of CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters); also the exact event triggering time, selection by the slope type, the magnitude and the impact level of the mass movement event. The rainfall values of these raingauges allowed the calculation of the accumulated rainfall index for 1, 3, 6, 24, 48, 72 and 96 hours, with the adoption of the 72 hours index for this work. The correlation graphics are divided by the slope type, the magnitude and the impact level of the mass movement event. Different critical thresholds appear, classifying such event by the influence level of triggering factors, natural and/or anthropic.
Accordingly with the United Nations (1993) the mass movements are one of the natural phenomena that cause the most financial impact and deaths in the world. The mass movements assume catastrophic proportions in urban areas causing structural damages and human losses. In Brazil rainfall is the principal natural factor that causes floods, riverside collapses, intensification of erosional processes and evidently, they decisively contribute to mass movement triggering. As a result of specifically geological risk scenarios natural processes may occur in different scales becoming disasters and catastrophes with huge material and social impact [
The main operability of PPDC involves the monitoring of the related rainfall values (hourly and accumulated 72 hours rainfall index), as well as weather forecast, field inspection and emergency situations [
Therefore, the aim of this study is to present the interaction between the accumulated 6 h × 72 h rainfall correlated with the triggering time of the mass movement events in connection of the critical pluviometric thresholds [
Paulo State north coast between Serra do Mar Mountains and the South Atlantic Ocean. This area covers 13 municipalities―Peruibe, Itanhaem, Mongagua, Praia Grande, Sao Vicente, Cubatao, Santos, Guaruja and Bertioga (Baixada Santistaregion) and SaoSebastiao, Ilhabela, Caraguatatuba and Ubatuba (Litoral Norteregion). The mass movement susceptibility varies of each COMDEC (Regional Civil Defense Municipality Units) attendance area of all these 13 municipalities, being a reflex of the physical factors and conditions (lithological and pedological substrate, relief, slope position and declivity, vegetal covering, pluviometric precipitation patterns, etc), as well as the anthropic conditions of the risk area (environmental degradation, slope cut, dumped landfills and embankments on top of the talus, pipe leakages and other).
For development of this study mass movement events data has been acquired from official registers [
Other data sources that could be used―COMDEC (Regional Civil Defense Municipality Units), Meteorological Research Institute (IPMet) of the State University of Sao Paulo (UNESP) and of the newly created database of the Natural Disaster Alerting System (SIADEN) of CEMADEN, but these data sources will be included and analyzed at the next stage of the study. The correlations in this work were elaborated with SIDEC mass movement events data, essentially (
There were evaluated 32 mass movement event registers, but some of them (8) cannot be used due to absence of important data, such as correct date and time of the event. For example, the lack of event time or confusion on the report tab page between triggering time and Civil Defense actioning/attending time, and the lack of correct event location (wrong coordinates or/and incomplete addresses). These generate a lot of uncertainties on the organization of the mass movement database [
Is necessary to geo-reference the coordinates (latitude and longitude) to provide the correct location of each mass movement event data, as well as to identify the
N | MUNICIPALITY | LOCATION1 | TYPE | MAGNITUDE2 | IMPACTS3 | DATE/TIME4 | |
---|---|---|---|---|---|---|---|
BAIXADA SANTISTA region | |||||||
92 | Guaruja | 7,347,705 | 372,006 | Natural slope/dumped landfill | 500 m3 | 44 remov. residents/ 11 interd. housing | 26/03/14 - 16.00 h |
118 | Mongagua | 7,335,069 | 336,162 | Slope cut | 4 m3 | 8 remov. residents/ 3 interd. housing | 28/02/16 - 22.40 h |
119 | Mongagua | 7,335,343 | 336,331 | Natural/slope cut/ compacted landfill | 5 m3 | 13 remov. residents/ 3 interd. housing | 29/02/16 - 00.30 h |
120 | Mongagua | 7,335,318 | 336,319 | Natural/slope cut | 10 m3 | 4 remov. residents/ 4 interd. housing | 29/02/16 - 06.00 h |
146 | Santos | 7,351,980 | 364,754 | Slope cut/dumped landfill | 90 m3 | 12 remov. residents/ 3 interd. housing | 23/01/15 - 01.40 h |
147 | Santos | 7,349,107 | 361,915 | Slope cut/dumped landfill | 600 m3 | 64 remov. residents/ 16 interd. housing | 23/01/15 - 00.50 h |
148 | Santos | 7,349,230 | 361,593 | Natural slope | 960 m3 | 0 remov. residents/ 0 interd. housing | 23/01/15 - 02.30 h |
163 | Sao Vicente | 7,348,211 | 358,756 | Compacted landfill | no data | 0 remov. residents/ 0 interd. housing | 21/01/17 - 17.40 h |
LITORAL NORTE region | |||||||
48 | Caraguatatuba | 7,389,376 | 462,078 | Slope cut | 100 m3 | 28 remov. residents/ 7 interd. housing | 31/01/15 - 20.00 h |
49 | Caraguatatuba | 7,386,697 | 460,046 | Slope cut | 41 m3 | 8 remov. residents/ 2 interd. housing | 23/03/16 - 22.00 h |
50 | Caraguatatuba | 7,389,634 | 459,494 | slope cut | 15 m3 | 28 remov. residents/ 7 interd. housing | 15/03/17 - 05.00 h |
51 | Caraguatatuba | 7,389,181 | 460,017 | Natural slope/debris flow | no data | 60 remov. residents/ 15 interd. housing | 15/03/17 - 05.30 h |
151 | Sao Sebastiao | 7,366,229 | 457,076 | Natural/slope cut | 100 m3 | 12 remov. residents/ 3 interd. housing | 23/12/14 - 16.40 h |
153 | Sao Sebastiao | 7,366,081 | 457,654 | Natural/slope cut | 90 m3 | 20 remov. residents/ 5 interd. housing | 15/01/16 - 03.20 h |
155 | Sao Sebastiao | 7,372,365 | 426,521 | Natural slope | 3000 m3 | 148 remov. residents/ 37 interd. housing | 29/02/16 - 04.50 h |
157 | Sao Sebastiao | 7,370,362 | 433,404 | Natural slope | 320 m3 | 24 remov. residents/ 6 interd. housing | 29/02/16 - 05.10 h |
158 | Sao Sebastiao | 7,370,014 | 432,415 | Natural/slope cut | 160 m3 | 0 remov. residents/ 1 interd. housing | 29/02/16 - 05.10 h |
159 | Sao Sebastiao | 7,371,245 | 433,533 | Natural slope/debris flow | 800 m3 | 20 remov. residents/ 5 interd. housing | 29/02/16 - 05.10 h |
160 | Sao Sebastiao | 7,370,747 | 437,769 | Natural/slope cut | 900 m3 | 26 remov. residents/ 7 interd. housing | 29/02/16 - 05.30 h |
176 | Ubatuba | 7,404,719 | 491,165 | Slope cut | 250 m3 | 6 remov. residents/ 3 interd. housing | 23/02/18 - 01.00 h |
178 | Ubatuba | 7,404,657 | 491,033 | Slope cut | 80 m3 | 8 remov. residents/ 4 interd. housing | 23/02/18 - 01.00 h |
179 | Ubatuba | 7,401,486 | 491,541 | Natural/slope cut | 400 m3 | 15 remov. residents/ 5 interd. housing | 23/02/18 - 05.00 h |
---|---|---|---|---|---|---|---|
180 | Ubatuba | 7,402,055 | 491,413 | Natural slope | 50 m3 | 8 remov. residents/ 2 interd. housing | 23/02/18 - 05.00 h |
187 | Sao Sebastiao | 7,366,179 | 457,060 | Natural/slope cut | no data | 28 remov. residents/ 7 interd. housing | 15/02/18 - 03.40 h |
TOTAL | 24 MASS MOVEMENT EVENTS | 584 removed residents/156 interdited housing |
1UTM Location of Fuse 23 South. 2Estimated Volume of Mobilized Mass Material. 3Interim and/or Permanent Measures and Actions. 4GMT Revised Date/Time.
rain gauges within the radius of the pluviometric influence. It is very important detail before to start the acquisition and processing of the rainfall triggering values [
The classification of landslide and mud/debris flow events allows making a more detailed evaluation with regard to the proportions and the triggering causes in order to define the critical thresholds of each mass movement event locality:
・ Typology of the events―were prioritized only translational/rotational landslides, natural/induced, mud/debris flows. The correlation graphic (
・ Magnitude―it was estimated considering the total volume of the mobilized material (m3) in mass movement volumescale (
・ Impact and damages―a classification was adopted by 4 (four) groups of affected houses (
Rainfall data was obtained directly from the information platform of the CEMADEN monitoring automatic pluviometric network. To select all specific rainfall data, a 3 kilometers effective distance was adopted between the mass movement event location and the closest rain gauge in operation (
N | MUNICIPALITY | STATION1 | DISTANCE2 | ACCUMULATED RAINFALL INDEX3 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
1 h | 3 h | 6 h | 24 h | 48 h | 72 h | 96 h | ||||
BAIXADA SANTISTA region | ||||||||||
92 | Guaruja | 351870119A | 1.014 | 49.3 | 63.5 | 63.5 | 64.9 | 64.9 | 65.3 | 111.4 |
118 | Mongagua | 353110003A | 2.755 | 74.6 | 99.4 | 99.4 | 99.4 | 99.4 | 100.2 | 100.2 |
119 | Mongagua | 353110003A | 3.050 | 36.0 | 144.0 | 165.3 | 165.3 | 165.3 | 166.1 | 166.1 |
120 | Mongagua | 353110003A | 3.022 | 1.2 | 2.6 | 92.5 | 228.5 | 228.5 | 229.3 | 229.3 |
146 | Santos | 354850013A | 1.272 | 26.7 | 72.2 | 97.5 | 119.1 | 171.7 | 171.7 | 171.7 |
---|---|---|---|---|---|---|---|---|---|---|
147 | Santos | 355100903A | 0.599 | 18.1 | 55.6 | 74.0 | 106.1 | 130.5 | 130.5 | 130.5 |
148 | Santos | 355100903A | 1.237 | 33.2 | 93.9 | 133.6 | 145.0 | 190.5 | 190.5 | 190.5 |
163 | Sao Vicente | 355100904A | 0.931 | 36.3 | 76.8 | 76.8 | 85.7 | 101.5 | 117.6 | 138.3 |
LITORAL NORTE region | ||||||||||
48 | Caraguatatuba | 351050017A | 0.447 | 3.5 | 10.0 | 20.8 | 77.2 | 77.2 | 77.2 | 77.2 |
49 | Caraguatatuba | 351050007A | 2.800 | 21.4 | 66.8 | 66.8 | 66.8 | 66.8 | 87.3 | 87.3 |
50 | Caraguatatuba | 351050010A | 1.802 | 55.1 | 55.9 | 56.1 | 68.8 | 80.0 | 80.0 | 80.0 |
51 | Caraguatatuba | 351050010A | 1.082 | 66.3 | 87.3 | 87.5 | 99.8 | 111.4 | 111.4 | 111.4 |
151 | Sao Sebastiao | 355070409A | 0.778 | 32.1 | 33.7 | 33.7 | 48.0 | 60.9 | 60.9 | 60.9 |
153 | Sao Sebastiao | 355070409A | 0.378 | 11.4 | 23.6 | 39.2 | 43.5 | 43.7 | 52.2 | 63.7 |
155 | Sao Sebastiao | 355070411A | 0.135 | 82.3 | 86.3 | 205.9 | 258.5 | 258.5 | 264.2 | 264.2 |
157 | Sao Sebastiao | 355070406A | 0.86 | 84.6 | 88.2 | 127.3 | 177.5 | 177.5 | 180.5 | 180.5 |
158 | Sao Sebastiao | 355070406A | 1.128 | 84.6 | 88.2 | 127.3 | 177.5 | 177.5 | 180.5 | 180.5 |
159 | Sao Sebastiao | 355070406A | 0.828 | 84.6 | 88.2 | 127.3 | 177.5 | 177.5 | 180.5 | 180.5 |
160 | Sao Sebastiao | 355070419A | 1.655 | 88.7 | 89.7 | 126.1 | 173.6 | 173.8 | 174.2 | 174.2 |
176 | Ubatuba | 355540606A | 1.877 | 27.0 | 31.5 | 71.3 | 73.3 | 79.0 | 79.2 | 79.8 |
178 | Ubatuba | 355540606A | 2.019 | 27.0 | 31.5 | 71.3 | 73.3 | 79.0 | 79.2 | 79.8 |
179 | Ubatuba | 355540623A | 2.602 | 7.5 | 109.0 | 215.1 | 230.7 | 238.7 | 238.9 | 241.1 |
180 | Ubatuba | 355540623A | 2.259 | 7.5 | 109.0 | 215.1 | 230.7 | 238.7 | 238.9 | 241.1 |
187 | Sao Sebastiao | 355070409A | 0.797 | 26.2 | 58.1 | 77.4 | 214.9 | 224.3 | 257.0 | 257.0 |
1Nearest Pluviometric Station (CEMADEN); 2Distance in km; 3Percipitation in mm.
Cold Front), the distance higher than 3 kilometers puts at risk the quality and relevance of the correlation results obtained.
Rainfall IndexIn Baixada Santista and Litoral Norte regions, rainfall values related to each individual mass movement event (32) were the basis for definition of the critical thresholds and their associated triggering rainfalls. The CEMADEN automatic pluviometric network sends the rainfall values acquired with 10 minutes frequency. Pluviometric indices were calculated according to the studies of Tatizana et al. [
In Figures 3-5 stands out that many mass movement events are triggered due to rainfall intensity or/and accumulated precipitation regime below the PPDC official critical threshold pattern [
Critical thresholds by the type of the slope and the talus rupture―the graphic suggests that the slope cut landslides could have lower triggering thresholds than the PPDC proposed (
Critical thresholds by magnitude―the graphic suggests that an accumulated rainfall above of 50 mm/72 h precipitation index could cause high magnitude landslides (
Critical thresholds by the impact―the graphic suggests that accumulated rainfall above of 50 mm/72 h of precipitation index could cause medium to high impact landslides (
Main critical threshold line―the graphic suggests that the principal critical threshold line is fixed in 100 mm (accumulated 72 h rainfall index) separating rainfall triggered mass movement events from induced events by anthropic factors (
Is important to highlight that between the first (50 mm) and the second (100
mm) critical threshold line there is clear evidence of interaction between the inducing and the natural triggering factor. However, when the critical threshold line increase, the level of data uncertainties reduce. The same happen when the critical threshold line decrease and inducing factor begins to prevail, the level of data uncertainties reduce.
The results, although preliminary, indicate some relevant trends on analysis, which point to reach a better understanding of the mass movement dynamics in the regions concerned. The COMDEC (Regional Civil Defense Municipality Units) data sources and nonevent rainfall data to be incorporated in the future will allow more consistency of the mass movement event analysis and could confirm these correlations.
This kind of data in Brazil, such as rainfall values as well as mass movement event data, is very sparse and hampers the robust analysis, making more difficult the critical threshold actualization with the necessary precision. That takes the critical threshold definition at even lower levels, which can create a disproportionately large amount of alerts, many of them without any associated mass movement event.
The authors declare no conflicts of interest regarding the publication of this paper.
Metodiev, D., de Andrade, M.R.M., Mendes, R.M., de Moraes, M.A.E., Konig, T., Bortolozo, C.A., Bernardes, T., Luiz, R.A.F. and Coelho, J.O.M. (2018) Correlation between Rainfall and Mass Movements in North Coast Region of Sao Paulo State, Brazil for 2014-2018. International Journal of Geosciences, 9, 669-679. https://doi.org/10.4236/ijg.2018.912040