A Study on the Geo-Environmental and Social Impacts of Koottickal and Kokkayar Landslides

doi:10.21203/rs.3.rs-3245693/v1

Download PDF

Research Article

A Study on the Geo-Environmental and Social Impacts of Koottickal and Kokkayar Landslides

https://doi.org/10.21203/rs.3.rs-3245693/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Many countries allocate significant resources and labour to mitigate natural hazards, particularly landslides, due to their potential for casualties, property damage, and loss of life worldwide. Over the years, geotechnical and environmental investigations have been conducted to gain a better understanding of landslides. Heavy rain on October 16th, 2021, triggered a devastating landslide in Kerala. Thirteen people were killed, and significant damage was caused by the landslides in Koottickal (Kottayam) and Kokkayar (Idukki). Two separate landslides were reported in Plappalli and Kavali, destroying three shops and three houses. In addition, two footbridges and five bridges were structurally damaged. The study explores the geo-environmental and social characteristics of this catastrophic event due to the heavy rainfall and infiltration around highly fragile and vulnerable geographical surroundings.

An environmental Study of the Koottickal and Kokkayar landslide examined the adverse effects on the environment, focusing on the sediment transport that destroyed flora and contamination of water resources that resulted in water pollution. As a result of analysing primary and secondary data, the effects of the landslide have been identified to some extent. Social impacts of the Koottickal landslide have been discussed considering the future development of Koottickal and Kokkayar village as a tourist destination, maintaining the diversity of flora and fauna, and conserving the water source, the perennial river Manimala, which keeps the village a fertile land. Geotechnical and environmental characteristics of the soil are examined in the study. It aims to contribute to a better understanding of landslide dynamics, help formulate effective mitigation strategies, and promote sustainable development practices in landslide-prone areas.

Many countries face significant challenges due to natural hazards like floods and landslides, and extensive resources and technologies are being utilised to address them (Fauzi et al. 2011; Gao et al. 2017; Mukamana et al. 2020; Ahmed et al. 2021). Among these hazards, rainfall-related landslides are particularly catastrophic in hilly terrains (Tsaparas et al. 2002; Gao et al. 2017; Fustos et al. 2020). Developing countries are the hardest hit by such disasters, resulting in significant loss of life and damage worldwide (Alexander 1986; Agrawal et al. 2013; Pollock and Wartman 2020). Inadequate knowledge about these hazards negatively impacts socio-economic development in these regions. Mathematical and numerical models have been developed to identify and assess areas susceptible to rainfall-induced landslides (Liu et al. 2006; Ceccatelli et al. 2017). Various factors, including rainfall, melting snow, temperature changes, tremors and vibrations on earth, volcanic activity, and anthropoid activity, can affect the stability of slopes (Geertsema et al. 2009; Høeg 2013; Setyawan et al. 2021; Komadja et al. 2021). Geotechnical and numerical studies have been conducted on landslides, and it is evident that unanticipated settlements and unplanned hill cutting at the crest and toe of the hill are responsible for most casualties. An immense amount of money and resources is being utilised around the globe to deal with natural hazards, especially landslides. Mountainous areas often suffer economic losses due to landslides since they damage communication links, destroy fertile soil, and kill people. In developing countries like India, landslides are among the significant disasters (Pande 2006; Haigh and Rawat 2012; Achu et al. 2021; Hao et al. 2022; Ajin et al. 2022). On October 14th, 2021, the low-pressure system developed in the east-central Arabian Sea and drastically changed the weather conditions in Kerala. During heavy to extremely heavy rain, Kerala experienced flash floods and landslides in at least six southern districts.

Geotechnical investigation and environmental studies play a crucial role in understanding landslides and their impacts. The studies reviewed in the literature have contributed valuable insights into landslides' mechanics, triggers, impacts, and mitigation measures. Further research can provide the necessary knowledge for practical landslide risk assessment, mitigation, and management. The current study aims to understand the causes and effects of landslides and social and environmental impact in the Plapally and Kavali regions of Koottickal and Kokkayar Panchayats. Based on the available data, geotechnical and environmental, the present study aims to identify the primary causes of landslides in the Kootickal region. Insitu features that were assumed to be the cause of landslides, the pattern of sediment deposited in the flooded area and its effects on water sources were also examined in the study. The study relied on primary and secondary data from various agencies of both the pre-and post-flood times.

2.1 Study Area

On October 16th 2021, a series of landslides occurred in the top eastern ranges of the Kottayam and Idukki districts. Heavy rainfall due to a low-pressure area in the Arabian Sea was identified as the leading cause of the landslides, which resulted in the worst rain-related damage ever recorded in Kottayam and Idukki. These places are commercial centres known for their rubber, pepper, and tea trade, among other agricultural and plantation products. The districts produce a substantial portion of the state's food and rubber as cash crops. Animal husbandry operations are common to the socio-economic development of rural regions, particularly in terms of providing work and money to the poorest members of society. The area lacks a seacoast but has plenty of lakes and rivers, which serve as the foundation for interior fishing.

Koottikkal is a town in the Kottayam district of Kerala state, at the foot of the Western Ghats Mountain ranges. It is located on the eastern boundary of the Kottayam District, 55 kilometres from Kottayam and roughly 5 kilometres from Mundakayam, on NH 220 (Kottayam–Kumily Road). In the immediate vicinity, there are streams and waterfalls. Nearby are the Muthukora Hills, which rise to 2,500 feet (760 metres) above sea level. The region lies between 9°35'13.93''N Latitude and 76°53'6.21''E Longitude. Figure 1 illustrates the landslide occurred areas in Kootickal and Kokkayar.

Kokkayar is a village in the Azhutha block of Peerumedu taluk in Idukki district, Kerala, India. It is surrounded by Koottickal and Kokkayar and Elappara Panchayaths to the north, Peerumedu and Elappara Panchayaths to the east, Peruvanthanam to the south, and Koottickal and Kokkayar and Mundakkayam Panchayaths to the west. The village is separated from Koottickal and Kokkayar by the Manimala River. It is positioned at approximately 9°34'31.59" N latitude and 76°53'52.93" E longitude.

2.1.1 Topography: Located on NH 220 (Kottayam-Kumily Road), the site sits 2,000 feet (610 m) above sea level. Overall, both Koottickal and Kokkayar exhibit hilly landscapes with undulating terrains, typical of the Western Ghats region in the Idukki district. The 4000 square feet area in this place falls under the ecologically fragile zones of Western Ghats. Images captured after the landslide.

2.1.2 Geomorphology: The district is naturally divided into highlands, midlands, and lowlands, featuring highland and midland areas home to forest trees. Beneath these trees lies forest soil, followed by weathered crystalline rock fragments and weathered bedrock that is less than 5m thick and laterite. The laterite has a vesicular structure and is primarily composed of hydrated iron and aluminium oxides. Despite the district's tropical climate, which is typically moderately pleasant with no distinct seasons, these places experience average annual temperatures ranging from 20°C to 35°C due to high to moderate humidity. Both places have a humid tropical climate with an oppressive hot season and plenty of rainfall during monsoon. Pre-monsoon rains in this area are accompanied by lightning and thunder and occur during the warmer months of March, April, and May.

2.1.3 Rainfall: According to the data, it rained heavily during the landslide occurrence in the Kottayam and Idukki districts collected from Mundakayam RH Station. The rainfall in September was 415.6 mm, and in November, 821.8 mm. In comparison, it was 1270 mm in October 2021. From the Hyetograph of the Kanjirappally rain gauge, which is the nearest to the landslide-affected area, the prominent 266 mm of rainfall occurred only on October 16th 2021, the landslide day (Ajin et al., 2022).

2.2 Data Collection

Data collection methods and methods for assessing landslide hazards are employed when landslide effects are severe, and assessment of losses has to be carried out at a higher confidence level. In this section, the GPS data collection and analysis process was conducted to evaluate landslides' impact.

2.2.1 Field Investigation: Following the landslide event, extensive field investigations were conducted to understand the situation. A calibrated hand-held GPS device was used during the investigations in order to collect accurate field data. During the field visits, socio-economic conditions related to the landslide were analysed by studying human settlements and topography.

2.2.2 Sampling and Primary Data Collection: A structured questionnaire was administered in the affected areas as part of the primary data collection process. The questionnaire covered various topics: income sources, social capital, households, living conditions, land use, production, and past landslides.

2.2.3 Secondary Data Collection and Analysis: Besides primary data, secondary information and maps were essential in assessing the socio-economic status before the landslide. In order to ensure accuracy and relevance, socio-economic data were obtained from recently updated databases. A socio-economic assessment of the study area was conducted using the collected information and maps. Data from a Geographic Information System (GIS) and socio-economic sources were integrated with the landslide impact assessment. A laboratory test was also conducted on soil samples collected from sites affected by the landslide as part of the experimental program. A landslide slope adjacent to the failed slope and a stable slope nearby were collected for disturbed samples.

2.2.4 Landslide Inventory and Affected Area Mapping

To study landslide damage, the boundaries of the affected areas must be delineated and mapped. The boundary mapping was carried out by incorporating the GPS locations of the affected area owned by the households where severe damages were observed. Google Earth data and images where Landslide impacts were visualised using these mapping efforts and understood spatially. Figure 2 shows the photographs of the landslide site.

In order to understand the socio-economic and geographic impacts of the landslide, field investigations, primary and secondary data collection, and mapping techniques were integrated. The field data collected, the GPS data, geotechnical data and details of the affected households were cross-checked with Koottickal and Kokkayar Panchayat's updated database, the Mining and Geology Department reports and mass media reports.

Plappally and Kavali in Koottickal and Kokkayar Panchayat, there was a severe impact by two landslides that destroyed three houses and a tea shop. In addition to 13 deaths, the disaster damaged five bridges and two footbridges. The disasters in Kottayam and Idukki districts resulted in the death of several people, including three members of the same family. Around 20 people were reported missing as a result of rain-related accidents.

The high-intensity heavy rainfall that occurred and was affected by landslides are environmentally sensitive fragile areas of the Western Ghats. Based on the studies conducted, the biodiversity board had cautioned the government against allowing quarrying activity till a comprehensive environmental impact assessment study was conducted to prevent further serious ecological degradation. The landslide that struck hills in Kavali and Plappally of Koottickal and Kokkayar Panchayat is only five kilometres from the quarries. It is apparent from this disaster that unregulated quarrying in ecologically sensitive areas has negative impacts and is susceptible to landslides due to high precipitation, earthquake and other geomorphological factors. Figure 3 shows the long strip of land affected by landslides in the Kootickal Panchayath.

The geotechnical investigation and detailed analysis of the slope stability study of the affected area were conducted using PLAXIS 2D software. The input parameters for the numerical modelling using PLAXIS 2D software were obtained from the soil study conducted in the laboratory. These parameters allowed for the simulation and analysis of the landslide behaviour and stability under different conditions. A comprehensive analysis of the slope stability was conducted by comparing the obtained factor of safety values with field data. This analysis provides insights into the safety margin of the slopes and their susceptibility to failure.

4.1 Laboratory tests

The experimental program includes the collection of soil samples from landslide-affected sites and laboratory studies. Soil samples were taken from slopes adjacent to both the failed and stable landslides. The collected samples underwent laboratory tests to determine their engineering and index properties, including grain size distribution curves. The preliminary tests involved determining soil density using a core cutter, calculating moisture content, determining specific gravity, conducting grain size distribution tests using sieve analysis, and performing direct shear tests. The direct shear test on the disturbed samples provided the data to calculate total shear strength parameters such as cohesion intercept and angle of internal friction. The laboratory index property determination tests were conducted to classify the soils. These experimental results are taken as the input parameters for numerical modelling.

5.1 Heavy Rainfall

When rainfall totals exceed 205 mm, it is considered exceptionally heavy rain. Cumulonimbus clouds were responsible for the heavy rain. The rise in temperature in the Arabian Sea was recorded at 29°C at the time of the landslide, which is higher than the maximum of 26 to 27°C. Hefty rainfall indicates 200 mm of rain in a day or sometimes 200 mm in just 1–2 hours.

5.2 Cloudbursts

Indian Meteorological Department (IMD) defines cloudburst as an event when an area receives 100 mm of rain in an hour due to cumulonimbus clouds. From October 7 to October 13, three days before the landslide occurrence, the area received more than 50mm of rainfall in two hours. This heavy rainfall was termed 'mini-cloud bursts' to highlight their intensity.

5.3 What triggered the Flood?

The major rivers in the district are the Meenachil River, the Muvattupuzha River and the Manimala River. The Manimala River flows through Kanjirappally and Changanacherry taluks, rising at an altitude of + 1156 m above MSL in Thattamala; the river gains shape only from Elamkadu Estate. Manimala River originates in the Muthuvara Hills in the Kottayam district near Peerumedu in the Idukki District of Kerala. The river passes through the districts of Kottayam, Pathanamthitta and finally joins the Pamba River at Muttar in Alappuzha,Keral,India District. It has a length of 92 km and a drainage area of 802.90 km². The river has a basin area of 847 km². The average annual rainfall is around 3300 mm, the average annual stream flow is 1560 mm3, and the flow rate is 0.178 m³/hr.

The records of the India Meteorological Department showed Kanjirappally station limit had received 266 mm rainfall from October 16 to 17. The Peermade station limits close to this place recorded a rainfall of 292 mm. The storm flow is calculated using the rational method using the equation Q = 10CRiA. Where Q is runoff or stormwater flow, C is the coefficient of runoff, A is the catchment area in hectares, R_i is the intensity of rainfall in mm per hour, and the intensity of rainfall is recorded as 266 mm at Kanjirapally station limit covering a catchment area of 4293.07 hectares of Koottickal and Kokkayar Panchayath and 1.5805 hectares in Poonji area. The study area contributing to the flood water runoff in Manimala river is assessed from the GPS coordinates and worked out to 0.1935 hectares. Now assuming C as 0.7, the storm flow is estimated as 360.297 m3/hr against the normal flow of 0.178 m³/hr. Due to continuous downpour for 24 hours the immense storm flow that entered the river resulted in the flooding of the downstream sides and heavy losses to the flora and fauna. The images in Fig. 4 depict various views of the area affected by the landslide.

5.4 Impact of Landslide on quality of water

The areas concentrated for the study are Plappally, Poonji and Kavali, and the major water source is the Manimala River. Due to landslides, the river basin is about 70 per cent full due to excessive accumulation of sand, silt and boulders in the river. The river is shallow, and the natural flow is partially disrupted. The water sample collected from the Manimala River was tested to determine the physical characteristics, pH, turbidity and Dissolved Oxygen to identify the contamination level.

5.5 Socio-economic Investigation

Landslides contributed to vegetation loss in two ways: uphill areas, which suffered landslides and downhill areas, which suffered debris/sediment deposition in the aftermath of the landslide. Based on the assessment of soil, top fertile soil was lost, which severely affected agricultural production. The forest area has been degraded due to landslides and slips. Debris flow wiped out vegetation and wildlife in its path. This has also affected the soil seed bank of this region and the soil microbiota, favouring germination and establishment of seedlings. Debris flow has uprooted several large trees, causing the closed canopies' opening to induce a gap effect.

Primary Data on wealth and human life losses was collected through a structured questionnaire survey. The survey focused on gathering information on household income, living standards, land use type, crop production before and after landslides and the type and extent of losses. Table 1 shows the consolidated responses from the questionnaire survey.

Table 1

Consolidated response from the questionnaire survey
Criteria	Response
Criteria	Poonji	Plapalli	Kavali
Income Before landslide	Agriculture and daily labour in estate	Agriculture	Agriculture
Income after landslide	Unemployment	Unemployment or daily wage labour	Unemployment
Type of household	Single-storeyed, private households, rented houses	Single-storeyed, private households, rented houses	Single-storeyed, private households, rented houses
Source of water supply	Well (water is available at a depth of about 8m.)	Well (water is available at a depth of about 4.8 m)	Well (water is available at a depth of about 11.8m)
Occupants	Elderly People	Migrated to nearby rented houses
Destruction	7 houses and 11 people lost	1 house, 1 shop and 4 people lost	1 house and 6 people lost

5.6 Secondary data collection and analysis

The evaluation of the socio-economic status before the landslide primarily relies on secondary information and maps. Socio-economic data are obtained from a recently updated database. The collected information and maps are utilised to assess the socio-economic conditions. In this study, the socio-economic and GIS data are integrated simultaneously to assess the landslide impact comprehensively. The data were collected from the updated databases of Koottickal and Kokkayar Panchayat, the Mining and Geology Department in Trivandrum, and mass media sources. Landslide losses from the current study are shown in Table 2.

Table 2

Assessed losses due to landslides from the current study
Place	No. of houses loss	No. of death	No. of people relocated
Poonji	7	11	100
Plapally	1	4	Not Available
Kavali	1	6	Not Available

5.7 Landslide inventory and affected area mapping

To examine the extent of damage, it is crucial to demarcate and map the boundaries of the landslide. Google Earth data and images have been employed to create household maps, incorporating various thematic layers like transportation networks and water streams in the area. Figure 5,6,7 shows the mapped area of Koottickal and Kokkayar, Kokkayar and Plappaly, respectively, before the landslide.

The total approximate area of the affected land area was calculated by integrating the coordinates from GPS data in the Google map and observed area as shown in Table 3.

Table 3

Landslide affected the extent of the area
Place	The approximate area affected (sq. m)
Poonji	15,805
Plapally	3,594
Kavali	908

6.1 Slope failure of the landslides

Google Earth applications from the internet are utilised to derive landslide maps, providing valuable insights into the spatial distribution of slope failures. The shape of the slope failure was determined by plotting the horizontal distance along the X-axis and the height along the Y-axis. The distance and heights were obtained from the elevation profile generated on Google Earth by plotting the latitudes and longitudes. The exposure of bedrock in the scar and the deposition of coarse debris downslope in the landslide-affected area can be identified through satellite images and manually captured images. Moreover, landslides disrupt the vegetation cover and appear as highly reflective areas amidst the surrounding light-absorbing surroundings. Contrasts between vegetated and non-vegetated zones fade as erosion scars are colonised by second growth, thus showing a visible distinction between recent and sub-recent events.

Hand-held GPS was used to obtain the latitudes and longitudes of the affected area from Plapally, Kavali and Poonji. The coordinates for 'after landslide' were taken on site. The profile for 'before landslide' was obtained by taking the coordinates of the unaffected area just beside the affected area. The profile of the slope failure was obtained by plotting the Horizontal distance along X-Axis and Height along Y-Axis. The distance and heights were obtained from the Elevation profile which was generated on Google Earth by mapping the latitudes and longitudes. The elevation profile was obtained to study the extent of sediment transport that occurred in the area and the impact of the slope of the area. The data for Plapally, Poonji and Kavali are shown in Table 4,5,6. Figure 8,9,10 shows the elevation difference of Plapally, Poonji and Kavali, respectively, before and after the landslide. The profile details of Plapally, Poonji and Kavali are shown in Table 7.

Table 4

Data of Plapally
Horizontal distance	Height before landslide	Height after landslide
0	342	331
2.5	341	330
5	339	329
7.5	338	328
10	337	328
12.5	336	327
15	335	326

Table 5

Data of Poonji
Horizontal Distance	Height before Landslide	Height after Landslide
0	135	130
10	133	128
20	131	127
30	128	123
40	127	113
50	124	109
60	119	103
70	115	96
80	110	96

Table 6

Data of Kavali
Horizontal Distance	Height before Landslide	Height after Landslide
0	245	247
3	244	247
4	244	245
5	244	245
6	244	244
7	244	244

Table 7

Profile Details
Area	Criteria	Before	After
Plappally	Elev. Gain/loss	0.66m, -7m	0.35m, -5m
Plappally	Avg. slope	24º 36' 40.11"	18º 10' 4.23"
Poonji	Elev. Gain/loss	0.66m, -25m	0.35m, -34m
Poonji	Avg. slope	17º 4' 57.21"	21º 26' 50.6"
Kavali	Elev. Gain/loss	0m, -1m	0.62m, -3m
Kavali	Avg. slope	3º 41’ 13.16”	21º 41’ 13.16”

Landslides are less frequent on gentle slopes due to lower shear stresses. For slopes ranging from 25° to 35°, the weighted contrast value is 1.427, while for slopes exceeding 45°, the value is 1.899. These values indicate a high probability of landslide occurrence (Prasannakumar and Vijith 2012). In the current study, the slopes of each region before and after the landslide, as per the observations, are given in Table 8.

Table 8

Changes in Slope Angle Before and After Landslide in Different Areas
Description of the area	Slope before Landslide	Slope after Landslide
Plapally	24º 36' 40.11"	18º 10' 4.23"
Poonji	17º 4' 57.21"	21º 26' 50.6"
Kavali	3º 41' 13.16"	21º 41' 13.16"

Based on the research analysis, the slopes are near to weighted contrast values, from 250–350, indicating the probability of landslide occurrence.

6.2 Laboratory Study Findings

Different types of sand with little silt content were observed during the site investigation. A relatively low amount of cohesion was observed at the remoulded state of these mountains and valleys soil, which consisted primarily of reddish-to-reddish brown sandy silt. The field density, specific gravity and water content of the soil collected from the field are shown in Table 9. The soil classified as per IS soil classification is Poorly graded sand, and the grain size distribution is shown in Table 10.

Table 9

Bulk Density, Specific Gravity and Water Content
Location		Bulk Density(kN/m³)	Water Content (%)	Specific Gravity
POONJI	1	19.44	20.75	2.3
	2	19.79	23.14	2.22
	3	19.02	16	2
KAVALI	1	19.29	13.51	2.88
	2	18.24	13.33	2.5
	3	20.35	18	2.5
PLAPPALLY	1	19.92	20	2.7
	2	18.11	19.17	2.5
	3	19.71	19.33	2.66

Table 10

Grain Size Distribution
Location		D₁₀	D₃₀	D₆₀	C_u	C_c	IS Soil classification
Poonji	1	0.235	0.790	2.90	12.34	0.915	SP
	2	0.200	0.550	1.60	8	0.94	SP
	3	0.490	1	1.8	3.67	1.133	SP
Kavali	1	0.170	0.520	1.90	11.17	0.837	SP
	2	0.170	0.560	1.9	11.17	0.97	SP
	3	0.170	0.570	2.80	16.47	0.68	SP
Plappally	1	0.2	0.27	0.86	4.3	0.42	SP
	2	0.170	0.410	1.9	11.17	0.52	SP
	3	0.52	1.19	2.5	4.8	1.089	SP

6.3 Sediment transport and features in the affected area

In mountain belts, the evolution of hillslopes is primarily influenced by mass movements, such as landslides, which involve bedrock. In these areas, the rock uplift rate surpasses the weathering-limited mass wasting, leading to the dominance of mass movements. Land mapping was done in this study to find out the affected area. Graphs showing the differences in Elevation before and after the landslide have been plotted to analyse the change in topography. Other causes that contributed to the landslide have also been considered in the study. Figure 11 shows the flooded area during the landslide.

6.4 Impact of landslide on water resources

The water sample collected from the Manimala River was tested for the physical characteristics of water, and observations are given below.

pH- The test was conducted using a pH meter. The water sample showed a pH of 5.67, less than the neutral value of 7, or it was in the acidic range.
Turbidity- The turbidity of the water sample was measured using a turbidity meter which measured a value of 15 NTU. The turbidity of fair-quality water usually lies between 1 to 5 NTU. The high turbidity is due to excessive sediment contamination.
The pH of the Manimala River and Meenachil River, which is mostly near normal, has become acidic due to the decaying of vegetative and animal bodies and soil minerals deposited during landslides. It may purify by its own utilising its natural assimilative capacity in due course of time.

6.5 Numerical analysis using PLAXIS 2D

All the slopes were modelled and analysed using the software Plaxis 2D. The analysis shows that the safety factor of all nine landslide slopes is less than 1. 015. According to the literature, a FOS less than 1.4 is questionable for dams and slopes, and a FOS less than 1.0 is unsafe. The obtained FOS states that some of the slopes are supposed to fail, and some slopes are prone to failure. The FOS of slopes of more than one might get unstable due to heavy rainfall. The FOS is minimum for the slopes because of the absence of clay matter. Hence due to the absence of a cohesion parameter, the significant shear strength is provided only from the friction angle.

Using the numerical analysis, the failure patterns are identified for each case. When slopes intersect, the failure surface intersects at the toe. This type of failure is called a toe failure. Sandy soils with moderate to high friction angles, little cohesion, and steep slopes are most likely to encounter such a failure. Hill slopes greater than the internal angle of friction are prone to failure, according to the literature. In this study, the Ф values of the existing slopes are smaller than the slope angles, which also contributes to the instability of the slope. Table 11 shows the factor of safety and failure types of different slopes in various locations.

Table 11

The factor of safety and failure types of different slopes.
Location	Slope	Factor of Safety	Failure Type
Poonji 1	42.176°	0.991	Toe Failure
Poonji 2	42.176°	0.962	Toe Failure
Poonji 3	42.176°	1.015	Toe Failure
Plappally 1	41.6°	0.964	Toe Failure
Plappally 2	41.6°	0.967	Toe Failure
Plappally 3	41.6°	0.949	Toe Failure
Kavali 1	44.27°	1.014	Toe Failure
Kavali 2	44.27°	0.925	Toe Failure
Kavali 3	44.27°	0.917	Toe Failure

Based on a review of the referenced documentation, site visits, soil characterisation, and numerical modelling, the primary causes of the landslides include excessive rainfall and soft soil deposits on the slopes. The critical outcomes can be summarised as follows:

All the existing hill slopes had slope angles greater than 40°, and from the numerical analysis, it was found that most of the slopes were susceptible to failure. Therefore, the angle of slopes must be lowered, or soil retaining measures must be adopted to increase slope stability and safety.
The results of the FEM analysis were found to be similar to the field observations. Different types of failure occur depending on the type of soil and the angle of the slope.
Addressing the failure mechanism and soil field investigation, adequate measures should be taken to prevent landslides. Appropriate water drainage systems, retaining systems for the protection of hill slopes and valleys, geo fabric slope protection and erosion control systems, natural and plant-based efficient biological protection systems, etc., should be considered.

This study can help identify the hill slopes that are prone to landslides. Further investigation can be conducted with the help of subsoil investigation, which can provide detailed information about different soil layers. A landslide susceptibility map needs to be developed using an integrated sensing system based on remote sensing using satellite, unmanned aerial vehicle (UAV), and the Internet of Things (IoT) technology. An acoustic emission landslide early warning system can also be developed. These technologies will be able to predict landslide-prone zones based on rainfall intensities. Thus, appropriate measures can be taken to minimise damages.

The questionnaire survey shows that very few households continued their lives in the affected land after the landslide as they had no other better option. There are no agriculture jobs, and many of the people are elderly people, not able to migrate to other locations to set up their life. The loss sustained by each family is intangible. The tangible loss is the direct loss by way of land cost, cost of building, cost of other belongings, loss of income from agriculture and the exgratia on the life of the family members. Since a direct method to analyse the economic value is unavailable, prediction models are commonly used for estimation. Parameters such as land size and number of years of cultivation are typically utilised to estimate these values. The model developed using the primary data collected from the affected villages by Perera et al. (2018) is followed in this study. The mathematical expression is

Y = α + β₁X₁+ β₂X₂

where y = economic value of the land; α, β₁and β₂ = regression coefficients; α = 4.61; β₁ = 0.007; β₂ = 0.003 are values applicable for multiple crop pattern and medium type of plantations which is similar to the project area. X₁ = land area in sq. m; X₂ = number of years in cultivation. Table 12 shows the economic valuation of land in Poonji, Plapally, and Kavali Areas.

Table 12

Economic Valuation of Land
	X₁ (sq. m)	X₂ (years)	Y	Total economic value (Rs)
Poonji	15,805	30	115.335/m²	18,22,869.675
Plapally	3,594	30	29.858/m²	1,07,369.368
Kavali	908	30	11.056/m²	10,447.448

As a result of the landslide, the owners lost their income from the agricultural or the value of the crop, and the loss sustained is the difference in cost of the economic value calculated based on the model and the minimum land value of the property as per the Gazette notification SRO No. 420/2023 dated 25/03/2023 of Government of Kerala (Revised norms). Economic valuation and losses of land affected in the Koottickal, Kokkayar, Plapally, Kavali, and Poonji Areas are shown in Table 13.

Table 13

Assessment of loss of economic value
Place	Area (sq. m)	The fair value of the land affected (Rs.)	Total economic value (Rs.)	Value lost (Rs.)
Koottickal and Kokkayar Panchayath - Plapally and Kavali areas	3594 + 908 (4502)	495,220	1,17,816.816	3,77,403.184
Kokkayar Panchaayth - Poonji area	15,805	69,54,200	18,22,869.675	51,31,330.33

The determination of direct losses from the landslide includes the damages to agricultural land, the crop value they could earn, income on cultivation and daily earnings of the households. Additionally, all replacement costs associated with landslide-related damage were taken into account during the estimation of losses in accordance with the guidelines of the Ministry of Home Affairs, Government of India, vide order "Revised list of items & norms of assistance from State Disaster Response Fund (SDRF)/ National Disaster Response Fund (NDRF) (Period 2015-20, MHA Letter No. 32 − 7/2014-NDM-I Dated 8th April 2015) (Revised norms).

7.1 Assessed Loss on human life

The life of a person is valuable, but its cost cannot be estimated precisely. However, the lowest value that is accounted for the study purpose is the Ex-Gratia payment to families of deceased persons as per the National Disaster Relief Fund (Period 2015-20, MHA Letter No. 32 − 7/2014-NDM-I Dated 8th April 2015) of Government of India. The estimated Ex-Gratia Payments for Persons Affected in Koottickal, Kokkayar, and Kokkayar-Poonji Areas are given in Table 14.

Table 14

Loss of human life
Affected village	No. of person loss	Ex-Gratia payment	Total Amount
Koottickal and Kokkayar	10	4,00,000	40,00,000
Kokkayar– Poonji	11	4,00,000	44,00,000

7.2 Other direct Losses: Loss of house, utensils & other amenities and loss of agricultural crops

The cost of the housings fully washed away due to the landslide could not be assessed critically. In this study, the minimum value is taken as the NRDF assistance on the loss of building plus the cost estimated for housing projects: Life Mission Project of Government of India for a single-story building with 500 sqft area, a bare minimum facility for an average farmer to build his house is considered for assessment of other direct losses. Assessment of damages and ex-gratia payments for houses, agriculture, and families affected in Koottikkal, Plapalli, Kavali, Kokkayar, and Poonji Areas are shown in Fig. 15.

Table 15

The assessed value of Other direct Losses
Affected village	No. of houses lost/buildings	The minimum cost of damages as per Government norms (Rs)	Total Amount	Total Loss	Grand Total
Koottikkal – Plapalli & Kavali	1	NRDF aasistance (Shop vanished)	1,01,900	1,01,900	11,21,404
	2	NDRF assistance + cost of construction of the new building with all amenities as per Life Mission Housing Scheme, Government of India	101900 + 400000/family	10,03,800
	2	Clothing and utensils/household goods for families whose houses have been washed away	3800/family	7,600
	0.4502 hectare	Agriculture loss ha. for all types of perennial crops	18000/ha	8,104
Kokkayar- Poonji	7	NDRF assistance + cost of construction of the new building with all amenities as per Life Mission Housing Scheme, Government of India	101900 + 400000/family	35,13,300	35,68,349
	7	Clothing and utensils/household goods for families whose houses have been washed away	3800/family	26,600
	1.5805hectare	Agriculture loss ha. for all types of perennial crops	18000/ha	28,449
Grand Total – 46.90 Lakhs					46,89,753

The total economic losses by loss of life, housing, agricultural land, agriculture income etc., are estimated per the Government of India norms and worked out values in Table 16.

Table 16

Assessed Economic loss of the landslides
Village affected	Loss of economic value of the land	Loss on the death of family members	Loss of housing, amenities, and crops	Total loss of each village
Koottickal and Kokkayar Village – Plapalli & Kavali	3,77,403	40,00,000	11,21,404	54,98,807 ( 55 laks)
Kokkayar village – Poonji	51,31,330	44,00,000	35,68,349	130,99,676 (131 lakhs)
Total loss	55,08,733	84,00,000	46,89,753	186 lakhs

8.1 Geotechnical Engineering Practices and Monitoring:

Conduct a comprehensive geotechnical investigation in the landslide-prone areas of Kootickal to assess the stability of slopes and identify potential landslide risks.
Implement regular slope stability monitoring using geotechnical instruments such as inclinometers, piezometers, and ground-penetrating radar.
Establish an early warning system that utilises real-time monitoring data to detect changes in slope conditions and issue timely alerts to the local authorities and communities.
Conduct periodic geotechnical inspections and infrastructure maintenance in landslide-prone areas to ensure stability and resilience.

8.2 Land Use Planning and Regulations:

Ensure that high-risk areas for landslides are prohibited from construction or development through land use planning regulations.
Identify safe zones for human settlements and infrastructure development based on geotechnical assessments and hazard mapping.
Enhance slope stability and reduce erosion by implementing sustainable land management practices, like terracing and revegetation.
Using geospatial data and hazard maps raises awareness about landslide risks and prevents inappropriate development.

8.3 Emergency Response and Evacuation Planning:

A comprehensive emergency response plan that specifies early warning dissemination, evacuation procedures, and coordination of relevant authorities during landslides should be developed.
A designated evacuation centre should be established in a safe location with adequate equipment to provide the affected populations with temporary shelter, food, and medical care.
Regularly drill and train residents on evacuation procedures and enhance their preparedness for landslides.
Enhance the capacity of residents to respond to landslides by collaborating with local communities, NGOs, and government agencies.

8.4 Community Engagement and Public Awareness:

Through awareness campaigns, educate and raise awareness of landslide risks among the local population.
Engage residents in land use decisions, risk reduction measures, and emergency response plans by involving them in decision-making.
Encourage the formation of local landslide mitigation and preparedness committees and forums encouraging residents to interact with officials and experts.
Inform the public about landslide hazards, mitigation measures, and emergency procedures using various media, including brochures, posters, community meetings, and digital platforms.

By implementing these recommendations, Kootickal and Kokkayar can enhance their risk management strategies and improve the resilience of their communities against landslides. The active involvement of relevant stakeholders, including residents, local authorities, and experts, is crucial in implementing these measures effectively.

9.1 Afforestation and Vegetation Management:

Promote afforestation activities in landslide-prone areas to improve slope stability and reduce soil erosion.
Plant native tree species with deep root systems that can bind the soil and enhance its stability.
Implement proper vegetation management practices, including regular pruning and maintenance, to ensure the health and stability of the planted vegetation.

9.2 Soil Erosion Control Measures:

Implement erosion control measures such as contour ploughing, mulching, and terracing to minimise soil erosion and surface runoff.
Construct check dams, retaining walls, and bioengineering structures to slow down the flow of water and prevent erosion in vulnerable areas.
Geotextiles and erosion control blankets should be encouraged to prevent soil displacement and erosion.

9.3 Stormwater Management:

Develop effective stormwater management systems to control the volume and velocity of water runoff, reducing the risk of saturation and slope instability.
Construct detention ponds and retention basins to capture and slowly release stormwater, preventing excessive water pressure on slopes.

9.4 Slope Stabilisation Measures:

Landslide-prone areas should implement slope stabilisation methods such as soil nailing, rock bolting, and geosynthetic slope reinforcement. Conduct slope grading and benching to reduce the slope angle and enhance stability.
Install drainage systems, including surface and subsurface drains, to control groundwater and reduce pore water pressure.

9.5 Retaining Structures:

Build retaining walls and slope stabilisation structures to support vulnerable slopes.
Maintain structural integrity and stability of the retaining structures using appropriate engineering techniques and materials.

9.6 Geotechnical Monitoring:

Develop a comprehensive geotechnical monitoring system to continuously monitor slope movements, groundwater levels, and other relevant parameters.

Deploy early warning techniques, such as inclinometers, piezometers, and geotechnical instrumentation, to detect slope instability early on.

9.7 Land Use Planning and Regulation:

Establish and enforce land use planning regulations restricting or prohibiting construction in high-risk landslide areas.
Perform detailed geotechnical assessments and hazard mapping to determine which areas can be developed and which need to be restricted.
Formulate land use policies and regulations based on environmental and geotechnical findings.

Ultimately, Kootickal and Kokkayar will need comprehensive landslide risk management plans that integrate environmental mitigation strategies and geotechnical measures. A sustainable long-term implementation plan requires collaboration among relevant stakeholders, including government agencies, local communities, and experts. Regular monitoring and evaluation of the implemented measures are also crucial to assess their effectiveness and make necessary adjustments.

An environmental investigation revealed that an exceptionally heavy rainfall of 266 mm occurred in the study area due to severe weather conditions. A cumulonimbus cloud caused a cloudburst that resulted in 100 mm of rain in one hour. The heavy rainfall and cloudbursts led to excessive infiltartion in the sandy soil triggered the landslide and the water levels of the Manimala River rised causing flooding in the downstream. Due to the excessive accumulation of sand, silt, and boulders caused by the landslide, the Manimala River became shallow and lost its natural flow. Several physical characteristics, pH, turbidity, and dissolved oxygen levels of water samples from the research area showed signs of contamination. A socio-economic study revealed that landslides affected agricultural production and degraded forest areas due to vegetation loss. The debris flow destroyed vegetation and wildlife in its path, resulting in livelihood and income losses. The economic assessment of the landslide losses showed that households experienced substantial financial losses of around 186 Lakhs due to damage of land, housing, and crops. The loss of 21 lives in the landslide is a tragic blow to their families.

Koottickal and Kokkayar landslides are studied to gain a better understanding of the factors that have contributed to the frequent occurence of landslide in the region. From the geotechnical investigation, lab tests and numerical analysis performed using PLAXIS 2D software, it is identified that slope angles greater than 40° are the primary cause of failure in the existing slopes. Some slopes in the analysis were already at risk of failure, while others were susceptible to failure under heavy rainfall. The study employed GIS data, questionnaire surveys, and secondary data to assess the magnitude of landslide-induced damages.For a better understanding, the spatial distribution of slope failures, Google Earth data and images were used to develop the inventory and map of the affected areas.

Ultimately, the landslide in the study area had severe consequences for the environment, the economy, and human life. In order to mitigate the adverse effects of such natural disasters on communities and the environment, the findings of this investigation underscore the importance of monitoring and managing such events. It is possible to reduce future impacts of such events with effective disaster preparedness, land-use planning, and early warning systems. Excessive rainfall and the presence of loose sand deposits on slopes were identified as the primary causes of landslides. As the area is under the threat of landslides, several recommendations have been alleviating their impacts. Geotechnical engineering practices, land use planning, emergency response and evacuation planning, public awareness, and community engagement are among these recommendations. The implementation of these measures requires collaboration among various stakeholders, including government agencies, local communities, and experts. Regular monitoring and evaluation of the implemented strategies are essential to assess their effectiveness and make necessary modifications. Overall, by adopting these recommendations and integrating environmental mitigation strategies with geotechnical measures, Kootickal and Kokkayar can improve the resilience of their communities against future landslide events.

Competing interests

The authors affirm that they have no financial or personal affiliations with any individuals or organisations that could exert undue influence on our work. Additionally, we do not possess any professional or personal interests that could be perceived as influencing the position presented in the review, including any connections with products, services, or companies.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Achu AL, Joseph S, Aju CD, Mathai J (2021) Preliminary analysis of a catastrophic landslide event at Pettimudi, Kerala state. India Landslides 18:1459–1463. https://doi.org/10.1007/s10346-020-01598-x
Agrawal S, Gopalakrishnan T, Gorokhovich Y, Doocy S (2013) Risk factors for injuries in landslide- and flood-affected populations in Uganda. Prehosp Disaster Med 28:314–321. https://doi.org/10.1017/S1049023X13000356
Ahmed KS, Basharat M, Riaz MT, et al (2021) Geotechnical investigation and landslide susceptibility assessment along the Neelum road: a case study from Lesser Himalayas, Pakistan. Arab J Geosci 2021 1411 14:1–19. https://doi.org/10.1007/S12517-021-07396-6
Ajin RS, Nandakumar D, Rajaneesh A, et al (2022) The tale of three landslides in the Western Ghats, India: lessons to be learnt. Geoenvironmental Disasters 9:1–8. https://doi.org/10.1186/S40677-022-00218-1/FIGURES/8
Alexander D (1986) Landslide damage to buildings. Environ Geol Water Sci 1986 83 8:147–151. https://doi.org/10.1007/BF02509902
Ceccatelli M, Gigli G, Lombardi L, et al (2017) Numerical modeling and characterisation of a peculiar flow-like landslide. Geoenvironmental Disasters 4:1–15. https://doi.org/10.1186/S40677-017-0087-8/FIGURES/15
Fauzi A, Nazmi WM, Fauzi UJ (2011) SUBGRADE STABILIZATION ASSESSMENT OF KUANTAN CLAY USING LIME, PORTLAND CEMENT, FLY ASH, AND BOTTOM ASH. 500–506. https://doi.org/10.1142/9789814365161_0065
Fustos I, Abarca-del-Río R, Mardones M, et al (2020) Rainfall-induced landslide identification using numerical modelling: A southern Chile case. J South Am Earth Sci 101:102587. https://doi.org/10.1016/J.JSAMES.2020.102587
Gao L, Zhang LM, Cheung RWM (2017) Relationships between natural terrain landslide magnitudes and triggering rainfall based on a large landslide inventory in Hong Kong. Landslides 2017 154 15:727–740. https://doi.org/10.1007/S10346-017-0904-X
Geertsema M, Highland L, Vaugeouis L (2009) Environmental impact of landslides. Landslides - Disaster Risk Reduct 589–607. https://doi.org/10.1007/978-3-540-69970-5_31/COVER
Haigh M, Rawat JS (2012) Landslide disasters: Seeking causes. a case study from uttarakhand, india. Manag Mt Watersheds 218–253. https://doi.org/10.1007/978-94-007-2476-1_18/COVER
Hao L, van Westen C, Rajaneesh A, et al (2022) Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India, for land use change analysis. CATENA 216:. https://doi.org/10.1016/j.catena.2022.106363
Høeg K (2013) Slope stability. Encycl Earth Sci Ser 919–924. https://doi.org/10.1007/978-1-4020-4399-4_322/FIGURES/1934 https://Alappuzha,Keral,India.nic.in/en/about-district/rivers-lakes
Komadja GC, Pradhan SP, Oluwasegun AD, et al (2021) Geotechnical and geological investigation of slope stability of a section of road cut debris-slopes along NH-7, Uttarakhand, India. Results Eng 10:100227. https://doi.org/10.1016/J.RINENG.2021.100227
Liu WW, Song YC, Zhu JJ, Tang JT (2006) Mathematical model research on landslide monitoring through GPS. J Cent South Univ Technol 2006 134 13:456–460. https://doi.org/10.1007/S11771-006-0067-8
Mukamana L, Sengendo M, Okiria E (2020) Analysis of landslide-induced fatalities and injuries in Bangladesh: 2000-2018. http://www.editorialmanager.com/cogentsocsci 6:. https://doi.org/10.1080/23311886.2020.1737402
Pande RK (2006) Landslide problems in Uttaranchal, India: Issues and challenges. Disaster Prev Manag An Int J 15:247–255. https://doi.org/10.1108/09653560610659793
Perera, E.N.C., Jayawardana, D.T., Jayasinghe, P. et al. Direct impacts of landslides on socio-economic systems: a case study from Aranayake, Sri Lanka. Geoenviron Disasters 5, 11 (2018). https://doi.org/10.1186/s40677-018-0104-6
Pollock W, Wartman J (2020) Human Vulnerability to Landslides. GeoHealth 4:. https://doi.org/10.1029/2020GH000287
Prasannakumar V, Vijith H (2012) Evaluation and validation of landslide spatial susceptibility in the Western Ghats of Kerala, through GIS-based weights of evidence model and area under curve technique. J Geol Soc India 80:. https://doi.org/10.1007/s12594-012-0171-3
Revised list of items & norms of assistance from State Disaster Response Fund (SDRF)/ National Disaster Response Fund (NDRF)
Setyawan A, Alina A, Suprapto D, et al (2021) Analysis slope stability based on physical properties in Cepoko Village, Indonesia. http://www.editorialmanager.com/cogenteng 8:. https://doi.org/10.1080/23311916.2021.1940637
Tsaparas I, Rahardjo H, Toll DG, Leong EC (2002) Controlling parameters for rainfall-induced landslides. Comput Geotech 29:1–27. https://doi.org/10.1016/S0266-352X(01)00019-2

Download PDF

Version 1

posted

You are reading this latest preprint version

A Study on the Geo-Environmental and Social Impacts of Koottickal and Kokkayar Landslides

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1 Study Area

2.2 Data Collection

2.2.4 Landslide Inventory and Affected Area Mapping

3. An Overview of the Disaster

4. Geotechnical Investigation

4.1 Laboratory tests

5. Environmental Investigation

5.1 Heavy Rainfall

5.2 Cloudbursts

5.3 What triggered the Flood?

5.4 Impact of Landslide on quality of water

5.5 Socio-economic Investigation

5.6 Secondary data collection and analysis

5.7 Landslide inventory and affected area mapping

6. Results and Discussions

6.1 Slope failure of the landslides

6.2 Laboratory Study Findings

6.3 Sediment transport and features in the affected area

6.4 Impact of landslide on water resources

6.5 Numerical analysis using PLAXIS 2D

7. Model economic value and direct loss estimation

7.1 Assessed Loss on human life

7.2 Other direct Losses: Loss of house, utensils & other amenities and loss of agricultural crops

8. Recommendations for Risk Management in Kootickal

9. Mitigation Strategies

9.6 Geotechnical Monitoring:

10. Conclusions

Declarations

Competing interests

References

Status:

Version 1

Horizontal Distance	Height before Landslide	Height after Landslide
0	135	130
10	133	128
20	131	127
30	128	123
40	127	113
50	124	109
60	119	103
70	115	96
80	110	96

Horizontal Distance	Height before Landslide	Height after Landslide
0	135	130
10	133	128
20	131	127
30	128	123
40	127	113
50	124	109
60	119	103
70	115	96
80	110	96

Horizontal Distance	Height before Landslide	Height after Landslide
0	135	130
10	133	128
20	131	127
30	128	123
40	127	113
50	124	109
60	119	103
70	115	96
80	110	96