“Satark”: Landslide Prediction System over Western Ghats of India

Abstract

Mountains on the west coast of India are known as the Western Ghat (WG). The WG region has a landslide (LS) susceptibility index of four and is prone to LSs in the monsoon season due to rainfall activity. The LS study focuses on the area between 15.5–20.5° N, 72.5–77.0° E in the Maharashtra state. A catalog of 115 LS events in the study area has been prepared by collecting LS data for 17 years (2000–2016) from various sources. The area from the windward to the leeward side of the WG mountains is divided into three regions: (1) the windward region (72.5–73.4° E) (90 km width), (2) the immediate lee side (ILS) (73.40–74.20° E) (80 km width), and (3) distant lee side (DLS) (74.2–77.0° E) (280 km width). The Center for Citizen Science (CCS), Pune, India, developed the LS-predicting model “Satark” using data from satellites, the India Meteorological Department weather forecasts, radar products, synoptic conditions, and atmospheric sounding data from the Wyoming site for inferring conditions for a hydraulic jump on the WG. The model validation for the 5 years (2017–2021) showed a reasonably good Heidke skill score of 0.44. The model showed 76.5% success in LS prediction 1 day in advance. It is the first attempt of this kind in the Indian region.

1. Introduction

Landslide (LS) is a general term that includes: (1) downhill movement of pebbles, stones of small to bigger sizes from hillslopes, (2) rockslide, (3) rock-fall, and (4) debris flow. Landslides (LSs) occur at many places in the Western Ghat (WG) mountain regions of Maharashtra state in India (Figure 1) in the summer monsoon (June–September) season. The word “Ghat” is in local Indian languages and has three meanings. These are (1) approaching the road to the river with stony steps, (2) roads in mountain areas, and (3) mountains. When it is used in the sense of a road in the mountain area, the name of the city is prefixed with it, indicating the road connecting to that city. The word “Ghat” is used as an adjective for mountains on the west coast of India, referred to as “Western Ghat (WG)”.

In the past, information about LS events remained confined to the local areas and hence has missed the attention of the people at large. On 30 July 2014, an LS occurred in the village of Malin (19.16° N, 73.68° E) in the Ambegaon taluka of the Pune district in Maharashtra, India [1,2,3]. The author of [4] reported in his blog that it was the second-worst LS of the year till that date. Malin is about 150 km from Pune (18.5° N, 73.8° E) city. The Malin LS wiped out the whole village except a primary school with a cement concrete structure. The LS at Malin occurred early in the morning while residents were in a deep sleep. A total of 151 people were killed. In addition to the reported deaths, a total of 160 people were believed to be buried under the debris. Accumulated rainfall of 215 mm was recorded over the area in the 7 days prior to the incident. The incident was widely reported in the press and electronic media.

In the WG mountain region, LS events are found to occur even with one-day very extreme rainfall activity. As per the India Meteorological Department’s (IMD) definition, the term “extreme” is used if the rainfall in a 24-h period is more than 200 mm. There is no category in the IMD classification for one-day rainfall of more than 200 mm. Here, we define the “high rainfall” category if the 1-day rainfall is 300 mm and more or accumulated in a 7-day period. A large number of deaths and losses occurred in the past in high rainfall events. The two events worth quoting in this category are (1) the Ghatkopar (19.08° N, 72.91° E) LS event on 12 July 2000. Ghatkopar is a suburb in the thickly populated city of Mumbai. Mumbai is the capital city of the state of Maharashtra. The 24-h rainfall was 350 mm. A total of 78 people died. (2) The Jui (18.0° N, 73.4° E) LS event on 25 June 2005. Jui is a village in Mahad district on the west coast of Maharashtra. The 16-h rainfall was 476.02 mm. In this LS event, three villages in the surrounding area were completely buried. The number of reported human deaths was 48. These are two representative examples of disastrous LS events which occurred due to high rainfalls before the Malin 30 July 2014 LS event. However, such LS events were overlooked by the citizens, media, and government authorities and did not attract serious attention from the geological and meteorological community in India. Therefore, studies regarding LSs over the WG region are almost non-existent before the Malin event.

The Malin LS event drew people’s attention from different sections of society, including print and electronic media, geologists, and meteorologists. The reasons are improvement in communication systems and increased awareness about the severe losses of human lives and materials. After the Malin incident, the Geological Survey of India (GSI) initiated an action to identify LS-prone sites in the WG [5]. The Malin LS event was an eye-opener for taking a serious note about the impacts of LS events. It raised concerns about the safety of the people residing in such LS-prone areas. In recent times, in the monsoon season, the frequency of extreme events has increased due to climate change impact [6]. Further, vulnerable LS-prone areas have increased many times in the WG region due to increased anthropogenic activities, such as deforestation, improper land use planning, and road and building constructions on hillslopes. These have exposed people residing in such vulnerable areas to more losses and deaths in recent times. As a consequence, demand arose in all the sections of society for identifying LS-prone areas and, if possible, an early warning system of LS occurrences over the WG region. The early warning system is considered to be a proactive measure in reducing the death toll by evacuating the people staying in vulnerable areas.

The factors which drive LSs are grouped into two categories. These are (1) preparatory variables and (2) dynamic triggering. The preparatory variables include topography, tectonics, the geological history of the region, land use, anthropogenic activities such as deforestation, terracing of the hills for agriculture, and construction activity. The construction activity is a continuous process required to develop the roads, railway tracks, and dams in the mountainous areas in the WG region. The dynamic triggering variables include heavy rainfall, earthquake, and coastal erosion [7]. The scale of damages by an earthquake is very large, and an LS is a small part of the total damages by the earthquake. The earthquake phenomenon shows no regularity and is therefore impossible to predict well in advance. The LS events occur due to coastal erosion during the landfall of a cyclonic storm on the coast. The cyclonic systems form over the Indian region in pre-monsoon (April–May), sometimes in the early part of June, and post-monsoon (October–November) seasons. Therefore, in non-seismic and non-cyclonic conditions, rainfall activity in the monsoon season is the most common trigger responsible for LS occurrences in a large number and at many locations in the WG region.

The author of [8] speculated the relationship between rainfall and the triggering of a shallow LS. Interestingly, in the present study region, apart from the term high rainfall, we define the term “medium rainfall” as the accumulated rainfall of the order of 50–100 mm in a week’s period. LSs occur in high as well as in medium rainfall conditions. The mechanisms that drive an LS and operate in these two rainfall regimes are: (a) in high rainfall cases, the rainwater percolates in the soil layer covering rock. After soil saturation by rainwater, some rainwater drains out through the channels in the rocks and soil cover. In some cases, if the channels on the surface are open, then rainwater drains out easily, forming waterfalls. Percolated rainwater to the bottom of the soil layer forms a thin layer of mud between the rock’s surface and soil resting on the rock. This decreases the frictional force between the two. The frictional force keeps the layer of deep soil in stable conditions on the surface of the rock. The shear strength at a point on the slope depends upon (1) the cohesive force of the material and (2) the total stress on the hillslope, which is proportional to the weight of pore-water [7]. When the total weight of a volume of saturated soil exceeds the shear strength of rock, a sliding force is generated, overcoming the geological resisting force to trigger an LS. Another factor contributing the slope stability is the vegetation cover. High rainfall areas are favourable for the thick and widespread vegetation cover. The deep soil layer supports thick and widespread vegetation cover comprising tall trees whose roots go deep inside the soil layer. The thick vegetation and roots of big trees hold the soil together in stable conditions against the sliding force. At Malin, the soil thickness was 10 m. An important factor that was responsible for the LS in the Malin case was the terracing of the hillslope for agriculture activity. The soil and rocky material after terracing were dumped on the slopes. This choked the natural drainage channels and caused obstructions to the free flow of the rainwater. All the rainwater percolated in the soil 10 m deep. This destabilized the slope, resulting in the LS. The (b) LS occurrence in medium rainfall regime: As per the IMD classification, India is divided into 36 meteorological subdivisions (Figure 2). The meteorological subdivisions of Madhya Maharashtra (MM) and Marathwada (MDA) lie on the leeward side of the WG mountains (Figure 1). Monsoon seasonal rainfalls in these subdivisions are low of the order of 500–600 mm. These areas have low vegetation cover and have vegetation of shallow shrub types. The soil layer is thin, and therefore the roots of shallow shrubs extend only in the upper layers of the soil. The rainfalls of the order of 50–100 mm occur in the weekly period under favorable synoptic conditions in these subdivisions. As the soil layer is thin, very little rainwater is absorbed into the soil. Most of the rainwater flows downward on the surface of the mountain slope. The material on the slopes is loose and unconsolidated. The downward moving rainwater on the slope carries these materials with it, resulting in shallow LSs. However, these types of shallow LSs are not disastrous. If the rocky and soil materials fall on roads, they cause temporary road blockages.

The author of [9] introduced the term “rainfall-intensity-duration (RID)” for triggering an LS. Since then, RID has been considered a useful parameter for predicting LS occurrence. The author of [10] identified RID for debris flow in the central Santa Cruz Mountains, California. The authors of [7] developed a real-time warning system for issuing warnings of LSs for the San Francisco Bay region using the RID method based on empirical and analytical relationships between rainfall and LS occurrence. They reported that the threshold of antecedent rainfall was 250 to 400 mm for the occurrence of an LS in the San Francisco Bay region.

The author of [11] studied 73 global LS events to determine the relationship between the RID and LSs. The authors of [12] developed RID thresholds for the Puerto Rico region. The authors of [13] estimated the RID threshold for the North Island of New Zealand. The authors of [14] used the RID method for predicting an LS in areas of the Himalayas of Nepal. The author of [15] used RID thresholds for LS occurrences in the Garhwal Himalayas region of India. The authors of [16] used the RID method for LS predictions over central and southern Europe.

The authors of [17] prepared a global LS susceptibility map based on indices derived from six parameters, viz. (1) slope, (2) soil type, (3) soil texture, (4) elevation, (5) land cover, and (6) drainage density. Their map shows that the WG region has a high susceptibility index. The authors of [18] prepared the global LS atlas (GLC) using three years (2003, 2007, and 2008) of LS data. They classified all the LS events on scales ranging from 1 to 5. Scale 1 indicates the types of LS that occur at single locations and on small slopes, and scale 5 indicates the types of LS that occur at steep slopes and multiple sites causing numerous fatalities. As per [18]’s study, India, among other Asian countries, has a large number of LS occurrences and fatalities for all the years considered. India lies in the top-ranked countries based on a number of LS-related fatalities. Further, they reported that the catalogue could be further improved by integrating LS inventory studies at the regional levels.

However, studies regarding LSs over the WG region are few, except for [1,2,4,19,20,21]. The lack of studies may be attributed to (1) LS events occurring mostly at isolated remote locations that go unreported and occur only in monsoon season. This has developed a lack of seriousness toward such incidents in Indian society. (2) LS events are mostly scaled 1, meaning occurring over small slopes and at isolated sites. (3) Media reporting tends to be biased toward LSs with human casualties [22]. (4) In India, studies related to LS events do not come under the roof of one department of government agencies as they occur due to combined meteorological and geological forcings. Therefore, neither the IMD nor India’s geological department has a dedicated group or unit to systematically gather and archive LS data and predict LS activity. In India, a good number of LS studies are carried out in the geography and geological departments of universities, however, these are scattered. The studies regarding predictions of LS events are lacking in India.

From the past data, it is noticed that LSs in the WG area occur in monsoon season during persistent high rainfall episodes for a week’s period. The thresholds of rainfalls are different for different locations. With the establishment of monsoon circulation over India, persistent rainfall for the period of a week and more occur due to conducive synoptic systems.

After the launch of the Tropical Rainfall Measurement Mission (TRMM) satellite in the year 1997, the rainfall data over the tropical region became available to researchers. TRMM Multi-satellite Precipitation Analysis (TMPA) rainfall data at 0.250 × 0.250 3-h intervals have shown promise in predicting LS events using satellite rainfall data. The authors of [23,24,25,26] developed an algorithm for LS predictions using LS susceptibility and satellite-derived rainfalls. The authors of [27] created an online decision support system for forecasting potential LS activity in near real time, termed “Landslide Hazard Assessment for Situational Awareness”.

The fatalities and damages in the Malin landslide incident prompted the necessity of identifying the hotspots of LSs over the WG region and developing a real-time forecasting system to help the disaster management authority of the state government. LS forecasts on specific LS locations are much more useful to the disaster mitigating authority for taking precautionary actions. The preventive actions include pre-warning, waking residents living in vulnerable areas regarding the forthcoming disaster, and mass evacuation from the danger spots.

Internationally, interest in quantifying LS risk has been developed since the activity initiated by the International Association of Engineering Geology (IAEG) Commission on LSs to compile a list of worldwide LS events for the UNESCO annual summary of information on natural disasters in 1971 [22]. Asia records the highest number of LSs, and a substantial number in the Himalayan region [22], and the next region is the WG in India. Considering the societal requirement, and as per [18]’s recommendation that LS studies on the regional scale are required, an inventory of LSs over the WG region is prepared using past data for the period of 2000–2016. A prediction system for LS occurrences in the WG region of Maharashtra was developed using the data of LSs and rainfalls for the period from 2000 to 2016. The system has been in operation since June 2017 under the name “Satark”. Satark is a word in the local language “Marathi” meaning “vigilant”. The LS forecasts are uploaded in real-time on the internet site www.satarkindia.wordpress.com, accessed on 1 April 2022, for societal and governmental use. The initial results were presented at the National Space Science Symposium held in 2015 [28].

The first objective of the paper is to present LS inventory over the WG region. This data may find a place in [18] Global Landslide Catalog (GLC). The second objective of the paper is to present the real-time LS forecasting system “Satark” developed by CCS. To the best of our knowledge, this is the first effort of its kind in the Indian region.

The paper is divided into five sections. The geological conditions and rainfall distributions are important features of LS events. Section 2 describes these features of the WG region. Section 3 describes the materials and methods used in the study. Section 4 provides an inventory of the LS events that occurred over the region. It also presents the “Satark” LS prediction system. Conclusions are given in Section 5.

2. Geology and Rainfall Features of the WG Region

2.1. Geology of the WG Region

The WG mountains are also locally known as “Sahyadri”. The WG mountains run north to south, parallel to the western coast of the Indian peninsula through six states consisting of Gujrat, Maharashtra, Goa, Karnataka, Kerala, Tamilnadu, and ending at the southern tip of India. The part of the WG in Maharashtra state is known as the Konkan. The central part of the WG lies in Karnataka state and is known as the Kanara, and the southern part lies in Kerala state and is known as the Malabar Coast.

Figure 3 shows the topography of the study area. The contours of the heights of the mountain peaks are given in meters. The WG mountains have mean heights of 800–1000 m above mean sea level (amsl) parallel to the west coast of India. The mountain range is at a distance of approximately 50 km from the coast. The narrow strip along the coast where the surface heights are low is the Konkan and Goa meteorological subdivision (shown by 23 in Figure 2).

Four subdivisions lie in the state of Maharashtra. These are: Konkan, MM, MDA, and Vidarbha. The MM, MDA, and Vidarbha subdivisions of Maharashtra state lie on the lee side of the WG (Figure 1 and Figure 2 shown by 24, 25, and 26). The mean sea elevation of lee side subdivisions is around 600 m amsl. This plateau region is part of a larger plateau of peninsular India referred to the as “Deccan Plateau”. The WG separates the plateau from a narrow coastal strip along the Arabian Sea (AS). The WG mountains rise abruptly from the coastal strip as an escarpment of variable height. The WG mountain range is approximately 1600 km in length and covers an area of 160,000 km². The slope of the mountains gradually increases from north to south. At some locations, mountain ranges radiate from the WG and spread in the southeast direction. Small hillocks are in the coastal city of Mumbai (19.07° N, 72.87° E, Figure 1).

The main types of rocks are basalt, laterite, and limestone. Geologic evidence indicates that the WG mountains were formed during the break-up of the supercontinent of Gondwana some 150 million years ago. These mountains were formed by the subduction of the AS basin and tilting of the peninsula in east and northeast during the Himalayan uplift.

The study area lies between 15.5–20.5° N and 72.0–77.0° E. The study is focused on LSs in Maharashtra and Goa states. In this region, there are four areas that are vulnerable to LS occurrences. These are:

(1)

A disastrous LS-prone area in Konkan and the immediate lee side of the WG mountains: Because of weathering and high monsoon rainfalls, more than 2000 mm, over millions of years, the thin outer layers of rocks in the WG mountains in Konkan subdivision and the immediate lee side of the WG mountains on ILS are crushed and converted into a deep layer of soil. The mountains are covered with a deep layer of soil. As a consequence, thick and widespread vegetation developed over these regions in the form of medium-tall height trees. Due to anthropogenic activity and removal of vegetation, during high rainfall episodes, the deep layer of soil slides down, resulting in a disastrous LS impacting villages at the foothills. LSs at Malin and Jui discussed above are examples of the disastrous category in this region.

(2)

Shallow LS-prone area: On the leeward side of the WG in the subdivisions of MM and MDA, the soil depth is shallow. Monsoon rainfall is low, of the order of 500–600 mm. The vegetation types are grassy and short thorny bushes. Loose unconsolidated material consisting of boulders of small to large sizes are formed due to erosion of the exposed rocky materials to solar radiation. The roads have been constructed to join the coastal city Mumbai in the Konkan subdivision to Pune city (18.5° N, 73.8° E) in the MM subdivision, through the WG mountains along the hill bottoms. In the monsoon season, during persistent rainfall episodes, these materials slide down and fall on roads in the form of a shallow LS due to the flow of water over the slopes. The fall of the boulders on the roads causes traffic blockages, damage to vehicles plying, and to people working at the base of hillslopes. These LSs are of shallow types impacting only a small area.

(3)

A disastrous LS in Mumbai: Mumbai is a coastal city in the Konkan subdivision having a number of hillocks. Mean monsoon rainfall is 2000 mm. Mumbai city has a number of hillocks. Many people live in houses built on the slopes and at the bottom of hillocks. Due to vibrations of construction activity, and the anthropogenic activity of removing vegetation, the rocks on the hillocks have become brittle and are crushed into fragments of stones of small to large sizes. In the monsoon season, during high rainfall episodes, the fragments of rocks are detached from rocks. These slide down on hillslopes and hit the roofs of the hutments. The human settlements on the slope and at foothills in Mumbai city are vulnerable to the risk of LSs in monsoon season. As many people have built houses on the slopes and at the foothills of the hillocks, the death toll becomes significant in LS events. The disastrous LS event discussed above, at Ghatkopar, a suburb of Mumbai, is an example of this type of LS.

(4)

Disastrous LS events on the Konkan railway track: In recent times (in the last 50–60 years), LS-prone areas have developed along the railway track connecting Mumbai to Kerala through the WG mountains called “Konkan Railway”. Many tunnels were dug to lay the railway track by blasting explosives. The rocks have become brittle due to the impact of the explosives. This loose material is vulnerable to sliding down during high rainfall episodes. The Konkan railway track has been turned into a susceptive area for occurrences of LS activity. LS activity causes disruption of rail and road traffic.

For the detailed analysis, the study area is divided into six subareas in the subdivisions of Konkan, MM, and MDA. The WG mountains do not extend up to the Vidarbha region; therefore, LS activity is low in the Vidarbha subarea, so it is not considered in the present study.

2.2. Rainfall

Ninety percent of annual rainfalls in Konkan, MM, and MDA subdivisions occur in monsoon season (June–September) only. Mean rainfalls over Konkan, MM, and MDA are 2389.9, 579.4, and 695.1 mm, respectively [29]. The low rainfalls over MM and MD are due to the fact that these subdivisions lie on the lee side of the WG. These subdivisions are also termed rain-shadow subdivisions. On the windward side, rainfalls increase with height, attaining the highest values at the mountain tops [30]. The stationary gentle forced ascent of moisture-laden south-westerly monsoon winds over the windward side of the WG is conducive to the formation of clouds and rainfall on the windward side and on the mountain tops.

Mean monthly rainfalls (mm) in the four monsoon months, June, July, August, and September, in these three subdivisions, are Konkan: 685.0, 879.0, 537.3, and 323.1; MM: 126.7, 182.2, 131.0, and 142.4; and MDA: 139.7, 184.2, 169.7, and 195.8, respectively [29]. In all these subdivisions, rainfalls are higher in the month of July. June is the month of the onset of monsoon season and September is the month of the withdrawal of monsoon season. Episodes of high rainfall activity also occur in the months of June and September, which are conducive for LS occurrences.

Indian monsoon circulation has certain features which are seen on daily weather charts. These are termed as stationary features. The stationary features comprise the monsoon trough (MT), surface high-pressure area at Mascarene (20.0° S, 57.0° E) island, called “Mascarene high”, westerly low-level jet at 850 hPa level (LLJ), high-pressure area at 200 hPa level over the Tibet (29.6° N, 91.1° E) region, called “Tibetan high”, and tropical easterly jet at 150 hPa level (TEZ). These stationary components maintain the mean monsoon circulation [31]. The MT is an elongated low-pressure area that extends from heat, low over Pakistan to the head Bay of Bengal (BoB). Southward migration of the MT results inactive/vigorous rainfall activity over major parts of India [30].

The major rain-producing systems over India during the summer monsoon season are the monsoon trough (MT) and the low-pressure systems (LPS) [30]. A trough of low pressure is developed along the west coast of India due to the interaction of monsoon westerly winds and the WG orography [32]. This is termed “West coast off-shore Trough” or simply as “West coast Trough (WCT)” (Figure 1). The term LPS is the general term that includes monsoon low (L), depression (D), deep depression (DD), cyclonic storm (C), and severe cyclonic storm (SCS). As per the IMD definition, surface pressure area is termed as “L” if the wind speed around it is less than or equal to 17 knots (nautical miles per hour), a “D” if the wind speed is 18–33 knots, “DD” if the wind speed is 34–49 knots, “C” if the wind speed is 50–64 knots, and “SCS” if the wind speed is more than 64 knots. Lows and depressions produce rainfall over wide areas of the country. Cyclonic circulation (Cycir) is a synoptic scale monsoon circulation feature identified by the cyclonic wind field, in the lower, middle, or upper troposphere. The vertical extent of cycir is limited to a few layers. Cycirs provide upward vertical velocities to monsoon air mass for cloud development and, in turn, for rainfall activity.

The authors of [33] showed that the probability of getting intense rainfall along the west coast of India is maximum between 14–16° N and near 19° N. Thus, the study area lies in the high probability zone of intense rainfall activity as per [33]’s study. The boundary layer over the west coast region is warm and moist throughout the day. This sustains the convection for a longer time, which results in high rainfall. The flow is convectively unstable, so gentle lift by orography is sufficient to release the instability. The authors of [34] showed that the rainfall over the Konkan area is initially triggered over coastal terrain as deep convective echoes. When these echoes age and weaken, they transform into stratiform systems which last for longer times. These stratiform systems travel eastwards towards the leeside of the WG to produce continuous moderate rainfalls over the MM and MDA regions.

Winds are westerly in the surface layer. These strengthen with height and establish into a strong wind regime at the 850 hPa level, termed as low-level jet (LLJ). Winds weaken above this height and become easterlies above the 500 hPa level. The strong vertical shear exists in the horizontal winds [30]. The vertical wind shear plays an important role in (1) the interaction among the clouds to grow from small to large sizes [35], (2) the organization of tropical convection [36], and (3) the development of stratiform precipitation system from the convective system [37].

The area from the windward to the leeward side of the WG mountains is divided into three regions. The three regions are delineated as: (1) windward region (72.5–73.4°E) (90 km width), (2) immediate lee side (ILS) of the WG mountains (73.40–74.20°E) (80 km width), and (3) distant lee side (DLS) of WG mountains (74.2–77.0°E) (280 km width) [38]. The air experiences vertical lift referred to as “hydraulic jump (HJ)” after crossing the WG mountains on the ILS of the WG [38]. HJ is conducive to convection and the formation of convective clouds on ILS of the WG mountains. Mean rainfall in monsoon season on ILS side is about 2000 mm and on the DLH side is about 500–600 mm. The ILS region is part of the MM subdivision. Thus, the subdivision MM is comprised of two dissimilar rainfall regimes, viz. a high rainfall regime on the west side (ILS side of the WG mountains) and a low rainfall regime on the east side (DLS of the WG mountains).

3. Materials and Methods

For the model development, archived TMPA rainfall data with spatial resolution 0.250 × 0.250 degrees (250 × 250 m) have been used to compute cumulative rainfalls for each location at the LS-prone sites. The IMD publishes the Indian Daily Weather Report (IDWR), which provides descriptions of synoptic systems present on every day. The IDWR data for the period 2000–2016 have been used to get large scale synoptic systems prevailing prior to LS events. The IMD issues medium-range (10-day period) and short-range (3-day period) rainfall forecasts over the Indian region using numerical models (www.imd.gov.in, accessed on 1 April 2022). These have been used in real time to anticipate the occurrences of persistent high rainfall activity over the study area. An “S” band doppler radar is in operation by the IMD at Mumbai (19.1° N, 72.8° E Figure 1). There is another radar at Panjim (15.5° N, 73.8° E) in the state of Goa, on the Indian west coast region, south of Mumbai, at a distance of approximately 600 km. Both these radars cover most of the study area. The radar products’, viz. spatial and vertical reflectivities, surface rainfall intensities (SRI) are used to get spatial and temporal distributions of convection and rainfalls at a high temporal resolution (every 11 min) over the study area and to supplement the rainfalls estimated from the satellite and inferred empirically from synoptic-LPS systems.

Persistent high rainfalls on the ILS region are due to convection generated by the HJ phenomenon [38]. The HJ is estimated using the Froude (Fr) number. Fr = U/(NH). It is the ratio of zonal wind speed (U) to the product of Brunt Vaisala frequency (N) and the height of the mountain (H) [38]. N is estimated using standard formula N = $\sqrt{\frac{g d\theta }{\theta dz}}$. Daily values of potential temperature θ and gradient of potential temperature dθ/dz were computed using Mumbai radiosonde data available on the Wyoming site. The mean height of the Western Ghat mountains have been taken as 1000 m to estimate the potential temperature at the mean height of the Western Ghat mountains. If Fr ≥1, then wind flows over the mountain and experiences HJ. If Fr < 1, the winds go around the mountain. The height of mountain H at each LS site on the ILS area varies between 800–1200 m above mean sea level. The value of N is of the order of 0.01 s⁻¹. The wind speed required to obtain Fr > 1 for these representative values of N and H is 20 ms⁻¹. Daily Fr values were estimated using daily radiosonde data for the period 2000–2016 of stations Mumbai and Goa from the Wyoming site (http://weather.uwyo.edu/upperair/sounding.html, accessed on 1 April 2022) (from 1 June 2000 to 30 September 2016) for the model development and (from 1 June 2017 to 2021) for the period of 2017–2021 for real-time LS predictions.

The Center for Citizen Science (CCS) is a non-governmental organization established in Pune, India, involving citizens in scientific data collection to foster scientific research to address societal problems. The CCS installed 10 ordinary rain gauges in villages in the areas near to LS-prone sites in Raigad–Ratnagiri subareas in the WG region in 2021 in the monsoon season. Daily rainfall data in the monsoon season of 2021 from these rain gauges are ingested into the LS prediction system “Satark” developed by the CCS in the 2021 monsoon season. The LS predictions are made available to the public and media through the website www.satarkindia.wordpress.com, accessed on 1 April 2022.

LS Prediction Model

It is seen that the maximum frequency of LS events is on the ILS of the WG mountains. High rainfalls over this region occur due to the hydraulic jump (HJ) phenomenon. Occurrence of HJ depends on the Froude number (Fr). Values of Fr are considered for estimating the probabilities of LS occurrences along with the IMD medium-, short-range weather forecasts, radar reflectivity data, and synoptic weather conditions.

The cumulative rainfalls curves are taken as reference curves for initiating predictions of LSs for those locations. The forecast system operates in three stages.

Stage 1 Watch: If the IMD forecast charts show presence of transient systems, southerly position of MT, an increase in rainfall over the LS-prone areas for the ensuing 7 days, and if Fr > 1, then the “watch” stage is invoked. The people in the area are sensitized to keep watch on the rainfall intensity and duration. The rainfall at LS locations from TRMM is monitored continuously.

Stage 2 Alert: If the IMD synoptic charts show presence and westward movements of transient systems and arrival of the systems near to the north of the Maharashtra state, southward displacement of MT, Fr > 1, and cumulative rainfalls for 3 days are higher than the reference cumulative rainfalls, then the “Alert” stage is invoked. The people in area are informed to be ready for moving out from their houses in LS areas.

Stage 3 Warning: If the high rainfalls persist for the next 3 days and cumulative rainfalls at LS locations are more than the reference values, then the prediction of LS is continued. Then, LS forecasts are given for the next day. The people in the areas are suggested to move to safe shelter locations with essential material to survive for a few days till the debris/stony materials fallen due to an LS are removed.

The real-time LS forecasts have been in operation in monsoon season for 5 years since 2017 and the forecasts are uploaded on the site (www.satarkindia.wordpress.com, accessed on 1 April 2022) for societal and state government disaster management call. The disaster management department of the government of Maharashtra has started taking note of LS predictions since 2018.

4. Results

4.1. Inventory of the Landslides

Spatial and Temporal Distribution

A catalogue of landslide events provides spatial distribution of the LS. It forms the basis for estimation of human and economic losses and is useful as a basis for developing real-time prediction system. Such an inventory is not complete because all the events do not get recorded, therefore, there are limitations in the completeness of such an inventory [17]. Inventory on the local scale has important usage as it provides information on the potential LS threats locally.

The LS data are collected from various sources, which include press reports, media reports, local contacts, government machinery, etc. The authors of [39] introduced a metric “confidence limit” for the identification of location of the landslides. All the LS sites need to have exact latitudes/longitudes for high confidence in the locations. All the LS sites presented in the study are with the exact latitude/longitudes, indicating high confidence.

The locations of LS events are referred to by the cities, towns, or villages near to that location. Figure 4 shows the locations (cities, towns, villages) of the LS which occurred in the past in the state of Maharashtra. The study area is divided in to 6 subareas and named after the cities prominent in those areas (Figure 3). The subareas are numbered as per their locations in north–south direction. The subarea spans 1 degree east–west, south–north from the prominent city in that subarea.

Table 1. Locations of LS-prone sites in the Aurangabad region. The words windward, ILS and DLS indicate the locations of LS sites on the windward, immediate leeward and distant leeward sites, of the WG mountains, respectively.

No.	Site and Location in Bracket	Latitude N	Longitude E
1	Ajanta caves (DLS)	20°33′6.84″	75°4′13.26″
2	Ellora caves (DLS)	20°1′32.88″	75°10′40.8″

,

Table 2. Locations of LS-prone sites in the Nashik subregion. The words windward, ILS and DLS indicate the locations of LS sites on the windward, immediate leeward and distant leeward sites, of the WG mountains, respectively.

No.	Site and Location in Bracket	Latitude N	Longitude E
1	Saptshrungi (ILS)	20°18′0″	73°54′0″
2	Ambe Ghat (DLS)	18°6′0″	75°18′0″
3	Savil Ghat (ILS)	17°54′0″	74°18′0″
4	Bhuwan Ghat (ILS)	17°48′0″	74°12′0″
5	Pimplegaon Joga (ILS)	19°18′0″	73°54′0″
6	Ambit Ghat (DLS)	18°6′0″	75°18′0″
7	Rajur Ghat (ILS)	19°12′0″	73°48′0″
8	Pelhar (ILS)	17°42′0″	74°6′0″
9	Taloda (ILS)	21°33′46.08″	74°12′48.6″

,

Table 3. Locations of LS-prone sites in the Mumbai-Pune region. The words windward, ILS and DLS indicate the locations of LS sites on the windward, immediate leeward and distant leeward sites, of the WG mountains, respectively.

No.	Site and Location in Bracket	Latitude N	Longitude E
1	Kalyan (ILS)	19°12′0″	73°6′0″
2	Jummapatti (ILS)	19°1′22.8′	73°19′3″
3	Antop Hill (ILS)	19°0′0″	72°54′0″
4	Malin (ILS)	19°12′0″	73°42′0″
5	Karla (ILS)	18°48′0″	73°30′0″
6	Velhe (ILS)	18°12′0″	73°36′0″
7	Katraj Pune (ILS)	18°27′10.44″	73°51′54.72″
8	Sinhgad (ILS)	18°21′58.68″	73°45′21.24″
9	Malshej Ghat (ILS)	19°20′26.16″	73°46′28.56″
10	Matheran (windward)	18°59′19.32″	73°16′16.32″
11	Urse khind (ILS)	18°44′15.36″	73°40′28.92″
12	Adoshi Tunnel (ILS)	18°49′50.16′	73°17′4.56″
13	Lonawale (windward)	18°45′20.52″	73°24′32.76″
14	Khopoli (ILS)	18°47′38.04″	73°20′4.56″
15	Fosandi (ILS)	19°5′41.28″	740.74′ 80.0″
16	Ghatkopar (windward)	19°4′44.4″	72°54′28.8″
17	Chembur (windward)	19°3′7.92″	72°54′1.8″
18	Gibert Hill (windward)	9°6′48.96″	72°52′10.92″
19	Panjarpol (windward)	19°2′33.72″	72°54′36.72″
20	Lavasa (windward)	18°24′34.92″	73°30′23.76″
21	Male (windward)	18°8′44.96″	73°50′34.73′
22	Ghatghar (windward)	19°17′41.28″	73°42′23.76″
23	Tikona (windward)	18°37′54.48″	73°30′46.08″
24	Varandha Ghat(ILS)	18°8′44.88″	73°50′34.8″

,

Table 4. Locations of LS-prone sites in the Raigad-Satara region. The words windward, ILS and DLS indicate the locations of LS sites on the windward, immediate leeward and distant leeward sites, of the WG mountains, respectively.

No.	Site and Location in Bracket	Latitude N	Longitude E
1	Sukeli Khind (windward)	18°28′11.72″	73°11′40.80″
2	Rohan (windward)	18°4′37.13″	73°20′50.03″
3	Jui (windward)	18°4′17.23″	73°20′57.26″
4	Dasgaon (windward)	18°6′0.64″	73°21′18.47″
5	Poladpur (ILS)	17°59′4.18″	73°27′48.27″
6	Cholai (ILS)	17°58′17.56″	73°27′46.75″
7	Wazarwadi (ILS)	17°59′51.91″	73°29′39.94″
8	Kashedi Ghat (ILS)	17°54′19.18″	73°26′4.83″
9	Morbe Ghat (windward)	18°12′53.24″	73°14′55.77″
10	Raigad Fort (ILS)	18°14′4.81″	73°26′45.23″
11	Chirekhind (ILS)	17°56′5.91″	73°33′52.98″
12	Ambenali Ghat (ILS)	17°55′57.63″	73°33′4.98″
13	Mahabaleshwar (ILS)	17°55′45.69″	73°39′1.31″
14	Ruighar Ghat (ILS)	17°55′24.48″	73°46′48.18″
15	Pratapgad (ILS)	17°56′8.50″	73°34′43.08″
16	Kudpan (ILS)	17°52′58.16″	73°32′23.39″
17	Chikhali (ILS)	17°52′4.47″	73°41′6.95″
18	Medha Ghat (ILS)	17°52′12.12″	73°45′2.10″
19	Sahyadrinagar (ILS)	17°45′25.36″	73°49′58.15″
20	Kosumbi (ILS)	17°45′34.03″	73°48′36.65″
21	Kolghar (ILS)	17°44′35.81″	73°47′15.57″
22	Andhari (ILS)	17°43′39.31″	73°47′24.60″
23	Pasarani Ghat (ILS)	17°58′6.38″	73°51′35.00″
24	Shirgaon Ghat (ILS)	17°54′2.52″	74° 0′1.77″
25	Mandhardevi Ghat (ILS)	18° 2′50.61″	73°51′35.12″
26	Tapola (ILS)	17°45′52.03″	73° 44′ 28.32″
27	Yavateshwar Ghat (ILS)	17°41′17.27″	73° 57′ 2.52″
28	Pogarwadi (ILS)	17°38′22.75″	73°56′38.93″
29	Revade Ghat (ILS)	17°35′42.78″	73° 10′ 55.92″
30	Lamaj and nearby villages (ILS)	17°45′35.64″	73°39′34.85″
31	Chirekhind (ILS)	16°10′53.8356″	73°44′52.0296″
32	Medha Ghat (ILS)	17°47′39.48″	73°49′59.16″
33	Chiplun (windward)	17°31′54.84″	73°24′54.36′

,

Table 5. Landslide-prone sites: Ratnagiri Kolhapur region. Th e words windward, ILS and DLS indicate the locations of LS sites on the windward, immediate leeward and distant leeward sites, of the WG mountains, respectively.

Sr. No.	Site and Location in Bracket	Latitude	Longitude
1	Madangad (ILS)	17°59′0.63″ N	73°14′59.86″ E
2	Kelwat Ghat (ILS)	17°57′36.54″ N	73°16′45.11″ E
3	Chinchali Ghat ILS	18° 1′52.37″ N	73°17′11.99″ E
4	Sarang (ILS)	17°48′33.12″ N	73°10′15.61″ E
5	Dapoli (winward)	17°45′31.99″ N	73°11′11.55″ E
6	Harnai Road (windward)	17°48′23.57″ N	73° 6′26.88″ E
7	Dabhol (windward)	17°35′18.45″ N	73°10′32.20″ E
8	Khed (windward)	17°43′10.39″ N	73°23′48.53″ E
9	Bhoste Ghat (windward)	17°42′9.86″ N	73°24′25.92″ E
10	Raghuveer Ghat (ILS)	17°41′59.12″ N	73°35′43.88″ E
11	Tulashi Bk (windward)	17°52′10.33″ N	73°23′23.96″ E
12	Gowalkot (ILS)	17°32′44.65″ N	73°29′15.71″ E
13	Chinchghari (ILS)	17°29′38.11″ N	73°34′5.09″ E
14	Kumbharli Ghat (ILS)	17°23′29.98″ N	73°40′33.16″ E
15	Kumbharkhani (ILS)	17°13′9.43″ N	73°30′41.19″ E
16	Sangmeshwar (ILS)	73°27′33.68″ E	73°33′18.22″ E
17	Manjare (ILS)	17°11′27.57″ N	73°26′8.82″ E
18	Pangri Ghat (ILS)	17°3′28.71″ N	73°28′2.26″ E
19	Kurdhunda (ILS)	17°9′21.12″ N	73°29′51.21″ E
20	Kondye (ILS)	17°11′20.79″ N	73°27′33.68″ E
21	Kolambe (ILS)	17°7′29.00″ N	73°30′10.65″ E
22	Pethkilla (windward)	16°59′41.36″ N	73°16′43.49″ E
23	Pomendi Ghat (windward)	16°58′42.03″ N	73°22′5.33″ E
24	Ratnagiri (windward)	16°59′28.47″ N	73°18′41.50″ E
25	Amba Ghat (ILS)	16°59′50.58″ N	73°46′28.47″ E
26	Wakurde Yelapur Khind (windword)	17°3′34.96″ N	74°1′16.91″ E
27	Aini (windward)	16°21′53.33″ N	74°2′52.40″ E
28	Karul Ghat (ILS)	16°30′42.02″ N	73°48′6.83″ E
29	Mahad (windward)	18°4′59.49″ N	73°25′20.40″ E
30	Varkude Yelapa (ILS)	16°51′8.64″ N	74°34′53.4″ E

and

Table 6. Landslide-prone sites: Sindhudurg-Goa subarea. The words windward, ILS and DLS indicate the locations of LS sites on the windward, immediate leeward and distant leeward sides, of the WG mountains, respectively.

Sr. No.	Site and Location in Bracket	Latitude	Longitude
1	Gaganbawada Ghat (ILS)	16°33′27.61″ N	73°49′32.31″ E
2	Bhuibawada Ghat (ILS)	16°33′48.70″ N	73°47′55.66″ E
3	Karul Ghat (ILS)	16°30′42.02″ N	73°48′6.83″ E
4	Vaibhavwadi (ILS)	16°29′47.01″ N	73°44′45.39″ E
5	Phonda Ghat (ILS)	16°21′30.13″ N	73°50′49.36″ E
6	Malsure (ILS)	16°10′19.03″ N	73°30′6.37″ E
7	Bilwas (ILS)	16° 7′13.31″ N	73°31′15.42″ E
8	Devali (ILS)	16° 1′39.58″ N	73°30′0.63″ E
9	Kasal Karlewadi Amrad Ghat (ILS)	16°11′32.17″ N	73°43′15.68″ E
10	Sawantwadi (ILS)	15°54′12.18″ N	73°48′57.22″ E
11	Insuli Ghat (ILS)	15°52′21.26″ N	73°50′12.58″ E
12	Pedane (ILS)	15°42′57.65″ N	73°47′33.82″ E
13	Pargad Namkhol Road (DLS)	15°49′28.14″ N	74° 2′56.40″ E
14	Amboli Ghat (ILS)	15°57′1.13″ N	73°59′50.66″ E
15	Chorla Ghat (DLS)	15°38′59.31″ N	74° 7′6.69″ E
16	Chandgad (ILS)	15°56′44.14″ N	74°10′31.17″ E
17	Malwan (ILS)	16°3′47.16″ N	73°28′15.96″ E

give locations of LSs in these subareas. The locations of the LS sites in the subareas are indicated by the words “windward” “immediate lee side (ILS)” and “distant lee side (DLS)“of the WG mountains. The subareas are:

(1) Aurangabad (19.8° N, 75.3° E) (Table 1). This subarea is in the MDA subdivision. There are two LS-prone sites in this region. These are: (1) Ajanta caves and (2) Ellora caves. Both these sites lie in the DLS of the WG mountains. The latitudes/longitudes of these LS-prone sites in this subarea are given in the Table 1.

(2) Nashik (19.9° N, 73.9° E) (Table 2). This subarea lies in the MM subdivision. There are nine LS-prone sites in this region. These sites are: (1) Saptshrungi, (2) Ambe Ghat, (3) Savil Ghat, (4) Bhuwan Ghat, (5) Pimplegaon Joga, (6) Ambit Ghat, (7) Rajur Ghat, (8) Pelhar, and (9) Taloda. The latitudes/longitudes of these LS-prone sites in this subarea are given in the Table 2. There are seven sites on the ILS and two on the DLS of the WG mountains.

(3) Mumbai-Pune (Table 3). Mumbai lies in the Konkan meteorological subdivision and Pune lies in the MM subdivision. Though the two cities are in two different meteorological subdivisions, they are considered to form a single subarea as these two cities are connected by the road and railway through the WG mountains. The road and railway tracks are adjacent to hillslopes. When LS events occur on hillslopes, the material/debris fall on the roads and railway tracks connecting the two cities. There are 24 LS-prone sites in this region. These are: (1) Kalyan, (2) Ghatkopar, (3) Gilbert hill, (4) Chembur, and (5) Antop hill. These sites are in the suburban areas of Mumbai city and on the windward side of the WG mountains. (6) Jummapatti, (7) Malshej Ghat, (8) Matheran, (9) Urse khind, (10) Adoshi tunnel, (11) Lonawle, (12) Khopoli (13) Karla, and (14) Lavasa. These LS sites are on the road connecting the two cities and on the windward of the WG mountains. (15) Malin, (16) Velhe, (17) Katraj, (18) Sinhgad, (19) Fosandi, (20) Panjarpol, (21) Male, (22) Ghatghar, (23) Tikona, and (24) Varandha Ghat. These sites are on the ILS of the WG mountains. There are 14 sites on the windward side and 10 sites on the ILS of the WG mountains. The latitudes/longitudes of these LS-prone sites in this subarea are given in the Table 3.

(4) Raigad (18.5° N 73.2° E) and Satara (17.6° N, 74.0° E) (Table 4). Raigad lies in the Konkan subdivision and Satara lies in the MM subdivision. These two cities lie on the west and east side of the WG mountains near to each other. There are 33 LS-prone sites in this region. These are: (1) Sukeli Khind, (2) Rohan, (3) Jui, (4) Dasgaon, (5) Poladpur, (6) Cholai, (7) Wazarwadi, (8) Kashedi Ghat, (9) Morbe Ghat, (10) Raigad Fort, (11) Chirekhind, (12) Ambenali Ghat, (13) Mahabaleshwar, (14) Righar Ghat, (15) Pratapgad, (16) Kudpan, (17) Kosumbi, (18) Chikhali, (19) Medha Ghat, (20) Sahyadrinagar, (21) Kolghar, (22) Andhari, (23) Pasarani Ghat, (24) Shirgaon Ghat, (25) Mandhardevi Ghat, (26) Tapola, (27) Yavateshwar Ghat, (28) Pogarwadi, (29) Revade Ghat, (30) Lamaj, (31) Chire khind, (32) Medha Ghat, and (33) Chiplun. There are 6 sites on the windward side and 27 sites on the ILS of the WG mountains. The latitudes/longitudes of these LS-prone sites in this subarea are given in Table 4.

(5) Ratnagiri (16.9° N, 73.3° E) and Kolhapur (16.7° N, 74.2° E) (Table 5). Ratnagiri lies in the Konkan subdivision and Kolhapur lies in the MM subdivision. Geographically, these two stations have similar weather characteristics, hence, these are clubbed together as a one subarea. There are 30 LS-prone sites in this subarea. These are: (1) Madangad, (2) Kelwat Ghat, (3) Chichali Ghat, (4) Sarang, (5) Dapoli, (6) Harnai road, (7) Dabhol, (8) Khed, (9) Bhoste Ghat, (10) Raghuveer Ghat, (11) Tulshai, (12) Gowalkot, (13) Chinchghari, (14) Kumbharli Ghat, (15) Kumbharkhani, (16) Sangmeshwar, (17) Manjare, (18) Pangri Ghat, (19) Kurdhunda, (20) Kondye, (21) Kolambe, (22) Peth killa, (23) Pomendi Ghat, (24) Ratnagiri, (25) Amba Ghat, (26) Wakurde Yelapur khind, (27) Aini, (28) Karul Ghat, (29) Mahad, and (30) Varkude Yelapa. There are 12 sites on the windward side and 18 sites on the ILS of the WG mountains. The latitudes/longitudes of these LS-prone sites in this subarea are given in Table 5).

(6) Sindhudurg (16.3°N, 73.5°E) and Goa (15.4° N, 73.8° E) (Table 6). This subarea lies in the Konkan and Goa meteorological subdivision along the west coast of India. There are 17 LS-prone sites in this subarea. These are: (1) Gaganbawda Ghat, (2) Bhuibawada Ghat, (3) Karul, (4) Vaibhavwadi Phonda Ghat, (5) Malusare, (6) Bijwas, (7) Devli, (8) Kasal Karle wadi, (9) Amrad Ghat, (10) Sawantwadi, (11) Insuli Ghat, (12) Pedne, (13) Pargad Namkhol, (14) Amboli Ghat (15) Chorla Ghat, (16) Chandgad, and (17) Malwan. There are 15 LS-prone sites on the ILS and 2 sites on the DLS of the WG mountains. The latitudes/longitudes of these LS-prone sites in this subarea are given in Table 6.

The spatial distribution of LS-prone sites shows 31 on the windward side, 80 are on the ILS and 6 on the DLS of the WG mountains. Percentagewise, the LS spatial distribution shows 26.5% on the windward side, 68.4% on the ILS, and 5.1% on the DLS of the WG mountains.

4.2. LS Prediction Model “Satark”

4.2.1. Statistics of LS Events

Fifty-four LS events during the period 2000–2016 (

Table 7. Rainfall-triggered landslides over the WG region from 2000 to 2016. Date (date/month/year), location, latitude/longitude of the LS location, rainfall, and major losses.

No.	Date	Location	Lat N, Long E	Rainfall	Major Losses
1	12/7/2000	Ghatkopar, Mumbai	19.08 N, 72.91 E	350 mm/24 h	78 people died
2	23/6/2003	Vaibhavwadi, Sindhudurga District	16.50 N, 73.74 E	346.7 mm/7 days	23 People died
3	25/6/2005	Jui, Mahad	18.03 N, 73.36 E	476.0 mm/168 h	3 villages buried, 48 people died
4	31/5/2006	Ratnagiri	17.25 N, 73.37 E	210 mm/day	4 people died
5	3/09/2012	Taloda District, Nandurbar	21.56 N, 74.21 E	381 mm/7 days	8 people died
6	25/09/2012	Chembur, Mumbai	19.05 N, 72.9 E	Not available	Not available
7	17/06/2013	Ratnagiri	17.25 N, 73.37 E	256.2 mm/7 days	No injuries
8	10/07/2013	Antop Hill, Mumbai	19.03 N 72.87 E	Not available	5 people buried
9	22/08/2013	Panjrapol, Mumbai	19.04 N 72.91 E	Not available	Not available
10	30/07/2014	Malin, Pune district	19.00 N, 73.93 E	215 mm/7 days	160 people and 1 village buried
11	31/07/2014	Chembur, Mumbai	19.03 N 72.89 E	Not available	1 child died
12	12/06/2015	Gilbert Hill, Andheri West, Mumbai	19.12 N 72.84 E	Not available	Not available
13	2/06/2015	Kalyan-Nagar Road	19.09 N, 74.74 E	340 mm/7 days	Not available
14	23/06/2015	Ekvira Devi Temple, Karla	18.47 N 73.28 E	128 mm/7 days	5 deaths
15	23/06/2015	New Katraj Highway	18.45 N 73.87 E	39 mm/7 days	Highway block
16	23/06/2015	Malshej Ghat	19.34 N 73.77 E	287 mm/7 days	Highway block
17	19/07/2015	Adoshi Tunnel, Lonavla	18.44 N 72.28 E	462 mm/7 days	Highway block
18	18/09/2015	Saptashringi Gad, Nashik	20.39 N 73.90 E	136 mm/7 days	Road block
19	4/10/2015	Ellora Caves, Aurangabad	20.02 N, 75.17 E		No loss
20	1/07/2016	Ambenali Ghat	17.97 N 73.03 E	343.2 mm/7 days	Road block
21	2/07/2016	Sukeli Khind	18.31 N 73.51 E	480 mm/7 days	Rockfall, traffic Jam
22	2/07/2016	Yawateshwar Ghat	17.68 N, 73.95 E	29.2 mm/7 days	No loss
23	3/07/2016	Malshej Ghat	19.34 N 73.77 E	70 mm/7 days	Road block
24	3/07/2016	Matheran	18.99 N 73.27 E	576 mm/7 days	Unknown
25	3/07/2016	Urse Khind	18.73 N, 73.67 E	184 mm/7 days	Intense rockfall
26	4/07/2016	Vakurde Yalapur	14.96 N, 74.71 E	618 mm/7 days	Mudflow
27	4/07/2016	Tapola- Mahableshwar	17.92 N 73.66 E	736 mm/7 days	Unknown
28	7/07/2016	Medha- Marli Ghat	17.79 N, 73.83 E	300 mm/7 days	Unknown
29	9/07/2016	Malshej Ghat	19.34 N 73.77 E	352 mm/7 days	1 death
30	9/07/2016	Sawantwadi	15.90 N 73.82 E	454 mm/7 days	Unknown
31	9/07/2016	Phosandi	16.99 N, 73.31 E	195 mm/7 days	Unknown
32	11/07/2016	Karul Ghat	16.373 N, 73.79 E	396 mm/7 days	Unknown
33	1/08/2016	Amboli Ghat	15.94 N 73.99 E	346 mm/7 days	Unknown
34	1/08/2016	Poladpur	17.98 N 73.47 E	283 mm/7 days	Debris on truck
35	1/08/2016	Chiplun- Chinchgari	17.53 N 73.52 E	324 mm/7 days	Unknow
36	1/08/2016	Swarde	16.81 N, 74.35 E	346 mm/7 days	Unknown
37	1/08/2016	Rajur Ghat	19.24 N, 73.80 E	50 mm/7 days	Trees on roads with mud
38	1/08/2016	Kelwat Mandangad	17.99 N 73.26 E	387 mm/7 days	Unknown
39	1/08/2016	Ambenali Ghat	17.97 N 73.03 E	285.4 mm/7 days	Road block
40	1/08/2016	Sukedi khind	16.88 N, 73.81 E	351.8 mm/7 days	Unknown
41	1/08/2016	Chir khind	18.87 N, 73.05 E	319 mm/7 days	Unknown
42	1/08/2016	Mandherdevi, Bhor	17.68 N 73.99 E	30.2 mm/7 days	No loss
43	2/08/2016	Saptashringi, Nashik	20.39 N 73.90 E	10 mm/7 days	Loose soil, rocks on road
44	2/08/2016	Chorla Ghat	15.64 N, 74.11 E	469 mm/7 days	Unknown
45	4/08/2016	Karlewadi	18.40 N, 74.68 E	248 mm/7 days	Rocks on houses
46	4/08/2016	Malshej Ghat	19.34 N 73.77 E	298.1 mm/7 days	Unknown
47	4/08/2016	Lavasa, Pune	18.40 N 73.51 E	542 mm/7 days	Unknown
48	4/08/2016	Varandha Ghat, Bhor	18.11 N 73.66 E	249 mm/7 days	Unknown
49	6/08/2016	Kelghar, Satara	17.69 N 74.06 E	713 mm/7 days	Unknown
50	6/08/2016	Ambenali Ghat	17.97 N 73.03 E	>500 mm/7 days	Road block
51	6/08/2016	Kashedi Ghat	17.90 N 73.43 E	600 mm/7 days	Unknown
52	7/08/2016	Gaganbawda Ghat	16.54 N 73.83 E	543 mm/7 days	Unknown
53	7/08/2016	Bhuiwada	19.00 N 72.85 E	358 mm/7 days	Unknown
54	7/08/2016	Karul Ghat	16.37 N, 73.79 E	419 mm/7 days	Unknown

) are selected for the detailed discussions and the model development. The data of the earliest LS event in the list is on 12 July 2000. The period 2000–2014 had 11 events. The year 2015 had eight events. The year 2016 recorded 35 events. The large number of events in 2016 may be attributed to increased awareness about LSs, and therefore improved reporting of the events in the media. The monthly distribution of LS events shows: 1 May, 8 June, 18 July, 23 August, 3 September, and 1 October. An LS occurred at a single location in 69% of cases. LSs at multiple sites occurred in 31% of cases.

Temporal distribution shows July and August account for the highest frequency of 74% of the occurrence of LS events. June has 15% LS events. September accounts for 10% LS cases. The frequency of LS events in the month of September is 10%. May and October together account for a total of 1% of LS events.

Table 8. Number of days and number of events on each day.

	Number of Days	Number of Events on a Day	Total Events
1	16	1	16
2	6	2	12
3	4	3	12
4	4	1	4
5	1	10	10
Total			54

shows the number of days and the number of LS events per day. It is seen from the table that a single LS event occurred on a maximum number of 16 days. On only one day, i.e., on 1 August 2016, a maximum of 10 LS events occurred. There are 6 days on which 2 LS events occurred. Multiple LS events occurred on a single day in the months of July and August. LS events in June are associated with the high rainfall activity during the onset/post-onset phase of monsoon over this region. Of the total events, 50% occurred in the mountain road areas. Roads were blocked due to LSs causing traffic congestion. 6 LS events occurred in the Mumbai hillock areas. The construction activity in these hillock areas is the cause of destabilization of the hillslopes, which resulted in an LS by heavy rainfall. The houses built on the hillocks in Mumbai collapsed due to landslides causing about 80 deaths.

4.2.2. Synoptic Systems and Rainfalls Associated with the Landslide Events

Synoptic systems causing high rainfalls prior to LS events have been examined. The 54 events occurred in a total of 27 days. The locations of these transient systems on the LS day are given subdivision-wise in Table 7. It is seen from the table that in 20 days, low-pressure systems were present over parts of central India over subdivisions of Odisha and Madhya Pradesh. On only two occasions, LSs occurred during the monsoon depression period and only once in deep depression and cyclone periods. Interestingly, the west coast trough was present on 21 days of LS events. On 14 days, cycir occurred, and on 15 days, HJ occurred. Thus, a low-pressure system over central India, west coast trough, cycir over central India, and HJ are important synoptic features found during LS events over the WG mountains.

4.2.3. Cumulative Rainfall Distribution

The link between rainfall and LSs operates through the saturation of land mass in the hillslopes to a sufficient depth. The structure of soil depth, slope, hardness/brittleness of the rock, and impact of the construction activity varies from location to location. Therefore, the amount of rainfall required to trigger an LS varies from site to site. The synoptic scale systems have a characteristic period of 7 days. The rainfall activity due to synoptic scale systems persists for a period of a week. Hence, cumulative rainfalls from the prior 8 days (minus day “−8”) and to the event day (day 0) have been considered in the present study for estimating the threshold for LS occurrence.

Figure 5a shows daily rainfalls 8 days prior to LS occurrence and cumulative rainfalls at 14 selected stations in the monsoon seasons of 2000–2015 and Figure 5b is the similar figure for monsoon season 2016. The rainfalls variations at other stations in these periods are similar and are hence not shown. These stations are representative of all the LS events in the six subareas. The curves show a large spread. The rainfall begins with as low as 25 mm (Gilbert hill Mumbai) to 180 mm (at Malin) on −8th day. Rainfalls at all the stations are a low order of 30–40 mm on the −8th day of the LS event. This is a very crucial day for making decision for issuing the “watch” stage of the LS prediction. Presence of (a) synoptic systems, (b) WCT, (c) cycir, (d) HJ, (e) position of MT, and (f) the IMD medium-range, short-range forecasts are considered to continue the three stages of LS predictions from this first “watch” stage. On many days, low order rainfalls occur; however, if the above-mentioned systems are absent, rainfalls decrease on the following days. In such conditions, subsequent stages of LS predictions are not continued. It is seen that rainfalls persist and there is sudden increase in rainfalls on the −5th day of the LS event. On this day, the second stage of the LS prediction system “Alert” is issued. An increase in rainfalls occurs due to the arrival of the LPS system near to or north of the study area. Rainfalls persist from this day to −1 day of the LS event. Cumulative rainfalls vary from 50 mm to 300 mm and above. On this day, cumulative rainfalls are of the order of 300 mm and above and occur on the ILS and windward regions of the WG mountains. On this day, “warnings” of an LS are issued. Persistence and increase in rainfalls occur because of the intensification of the monsoon circulation due to combined impact of all the synoptic scale systems. LSs over these regions (ILS and windward regions of the WG mountains) of high cumulative rainfalls are due to the first mechanism discussed above. On −1 day of the LS event, cumulative rainfalls of the order of 50–100 mm are observed over the DLS region of the WG mountains. LS activities over this region of low cumulative rainfalls are due to the second mechanism discussed above.

4.2.4. Model Prediction Skill

The performance of the model is tested by estimating the Heidke skill score (HSS). The number of LS events in the years 2017, 2018, 2019, 2020, and 2021 were 30, 35, 30, 40, and 35, respectively. A total 170 LS events occurred; predictions were given for 130 LS events which were found to be correct. For 17 LS events, predictions were given but LSs did not occur. These cases fall in the category of a false alarm. The percentage of false alarms is very low, about 10 percent. This gives confidence to the government authorities to take proactive measures. In 40 LS events, predictions were not given, however, LSs occurred. These events occurred due to extremely heavy rainfalls one day prior to the LS event. In 53 cases, neither LS predictions were issued, for LS events occurred as there were no conducive LPS and HJs on those days. Table 7 gives the distribution of LS events in the categories predicted and observed for estimating the HSS. The HSS value calculated is 0.48, which is reasonably good. A good skill in predictions of an LS is due to (1) incorporation of information about medium- and short-range weather forecasts by the IMD, (2) utilising information of prevalent synoptic systems in monsoon season, (3) using radar reflectivity products, and (4) using the Fr number for anticipating the HJ phenomenon.

5. Conclusions

The inventory of LSs over the WG region has been prepared using various sources.

One hundred fifteen events could be traced from 2000 to 2016. The locations of these events with latitudes/longitudes have been reported. This data may find entry in the Global Landslide Catalogue for wider use. The landsides over the WG region in monsoon season are triggered by rainfall activity. The high rainfall activity is sustained in the presence of synoptic scale systems present over the Indian region. The stationary systems, viz. the low-level westerly jet, monsoon trough, and surface pressure distributions, provide the moisture advection over the WG region. The flow is inertially unstable. The slow ascent of the moisture-laden westerly winds provides additional upward motion and the release of instability for the growth of the clouds. The convection is sustained due to moist boundary layer. All these factors contribute towards sustained high rainfall activity over the WG region. This causes wetting of the deep soil layer conducive for the slope instability. Though overall geology is the same over the region, there are differences in the preparatory variables from location to location. Therefore, there is no unique threshold value for the region as a whole. Cumulative rainfall curves have been found for different locations for forecast purposes. The study area is of the WG mountains, from west–east, and is divided into three regions: (a) windward, (b) immediate lee side of the WG mountains (ILS), and (c) distant lee side (DLS) of the WG mountains. The cumulative rainfalls of the order of 300 m and above trigger landslides over the windward and immediate lee side of the WG mountains. The maximum number of LSs occurred on the ILS area of the WG mountains. These landslides are disastrous. The cumulative rainfalls of the order of 100 mm trigger landslides over the distant lee side of the WG mountains. These landslides are mostly shallow.

The forecast system operates in three stages. If forecast charts of the IMD show the formation of LPS, and associated increase in rainfall over the landslide-prone areas for the ensuing 7 days, and westerly winds at the 850 hPa level are higher than 20 m/s, then the “watch” stage is invoked. The rainfall at LS locations from the TRMM is monitored continuously. If cumulative rainfall for the next 3 days becomes higher than the reference value, then the “alert” stage is invoked. The people in the area are advised to keep watch of the rainfall intensity and duration. If the high rainfall persists for the next two–three days and the accumulated rainfall crosses the reference value, the “warning” stage is invoked. If the rainfall persists for one more day, then LSs predictions are given for the next day. The operational forecasts of LSs were given for five monsoon seasons 2017–2021. The forecasts skill HSS showed a value of 0.48, which is reasonably good. However, those landslide events which occurred due to extremely heavy rainfalls (one-day rainfall of the order of 300–400 mm) could not be predicted as there are no predictions of such rainfalls by the IMD or any other meteorological agency in the world. The study is important as it is the first attempt of this kind over the Indian region. The study suggests that automatic rain gauges should be installed at all the landslide-prone areas which will help in monitoring real-time rainfall intensity to give a landslide forecast some hours in advance. This will increase the forecast skill.