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Journal of Resources and Ecology >

2024 , Vol. 15 >Issue 2: 431 - 438

DOI: https://doi.org/10.5814/j.issn.1674-764x.2024.02.017

Natural Disaster Assessment

Assessment of Landslide in Singati Sub-watershed of the Sunkoshi River Basin, Nepal

  • BASNET Bhuwan , 1 ,
  • JOSHI Rajeev , 2, * ,
  • SHARMA Ram Prasad 1 ,
  • SUBEDI Rajan 1
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  • 1. Institute of Forestry, Tribhuvan University, Pokhara Campus, Pokhara, Hariyokharka 33700, Nepal
  • 2. College of Natural Resource Management, Faculty of Forestry, Agriculture and Forestry University, Katari, Udayapur 56310, Nepal
*JOSHI Rajeev, E-mail:

BASNET Bhuwan, E-mail:

Received date: 2022-11-17

  Accepted date: 2023-04-20

  Online published: 2024-03-14

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Abstract

Landslides are the major problems of mountainous areas of Nepal basically due to its fragile geomorphology, intense rainfall, improper land management practices and rapid construction of hilly roads without applying necessary stabilization measures. There are huge loss of life and property, decline in ecosystem productivity and functionality and sedimentation on downstream areas due to frequent landslides. Multiple spatial factors like land use land cover, topography, road, drainage, geological setting and climatic factors like rainfall are found more associated with landslide occurrence and determine landslide susceptibility of the area. Understanding of landslide phenomenon and associated factors are crucial for adopting appropriate prevention, control and rehabilitation measures. This study was carried out in Singati sub-watershed of Sunkoshi River Basin with objectives to understand landslide hazards, associated factors and activities performed to minimize possible hazards with best strategy of minimizing hazards. Landslide assessments were made through identification and digitization of landslides using temporal Google image and field investigation. Where landslide hazard assessment and preparation of factor map was done by using GIS software and field observed data were analyzed and dimension of landslides were calculated. Along with, People perceptions on major effects of landslides on livelihood assets, were explore from associated household's questionnaire survey (n=60) using purposive sampling. And most causative factor of landslide on the perception of local people was calculated using nonparametric test (Friedman). From the field survey, a total of 46 landslides were surveys in different location of the study area. The total area of the landslide was found to be 4.77 km2. From the comparison matrix analysis using Friedman test, the study identified the main responsible factor for the occurrence of landslide are rural road, construction, slope factor rainfall and land use. The road factor up to 200 m area and 200-500 m distance from road covered the highest percentage of landslide hazard. Southeastern aspect with the slope class 30°-45° had the highest susceptibility to landslide. Dense forest was found to be more effected by landslide in comparison to other land use due to the construction of road in sloppy area inside the forest. Comparison the effects of landslide before and after the road construction about 80 percent of the respondent had the positive response on it.

Key words： landslide hazard; landslide assessment; landslide factor analysis

Cite this article

BASNET Bhuwan , JOSHI Rajeev , SHARMA Ram Prasad , SUBEDI Rajan . Assessment of Landslide in Singati Sub-watershed of the Sunkoshi River Basin, Nepal[J]. Journal of Resources and Ecology, 2024 , 15(2) : 431 -438 . DOI: 10.5814/j.issn.1674-764x.2024.02.017

1 Introduction

Nepal is the country highly susceptible to landslides and stand in highly risk position for human casualties due to landslide and flood also it causes devastating economic as well as human losses every year (Nepal et al., 2019). Landslide scale deforestation unplanned land use systems and the construction of physical infrastructure, such as roads, canals and dams in the hazardous mountainous region, have contributed to landslides, debris flows, soil erosion and floods. The term landslide refers to a variety of processes that result in the downward and outward movement of slope-forming materials, including rock, soil, artificial fill, or a combination of these. The materials may move by falling, toppling, sliding, spreading, or flowing (Bhatt et al., 2013). The triggering factors that cause landslide episodes are numerous and complexly interdependent, comprising intensive rainfalls, earthquakes, rapid stream erosion, geomorphological processes and human activities (deforestation of slopes, road construction, uncontrolled irrigation, quarries and mines, etc.) (Psomiadis et al., 2020).
Landslides play an important role in the evolution of landforms and represent a serious hazard in many areas of the World. Most of the terrain in the mountainous areas has been generally subjected to slope failure under the influence of a variety of causal factors and triggered by events such as earthquake and rainfall (Adhikari and Tian, 2021). Landslides becomes a problem when they interfere with human lives, activities and properties. Landslides are destructive natural hazards that frequently lead to loss of human life and property, as well as causing severe damage to natural resources (Intarawichian and Dasananda, 2010). Landslide causes serious threat to the settlement, roadways, waterways and building potential site that effects on livelihood, transportation, natural resource management, tourism, biodiversity and ecosystem (Dahal, 2017). Hilly region of Nepal has been facing a serious problem of environmental hazard notably, soil erosion, destructive landslide, flood, rapid sedimentation, excessive surface run-off and other catastrophic events (Bijukchhen et al., 2013). It also found that many local bodies have been using excavator owned by people’s representatives in road construction project. Now a days there is unhealthy competition to connect every household and village with road network. The roads are constructed but not repaired or maintained on regular basis that causes disaster. The impact of landslide can be extensive, including loss of life, destruction of infrastructure, damage to land and loss of natural resources (Heyojoo and Sharma, 2013). The main objective of the study is to access landslides and its associated factors in Singati Sub watershed of Sunkoshi River Basin and analysis of multiple spatial factors responsible for landslide hazards.

2 Materials and methods

2.1 Study area

Singati watershed in one of the main sub-watersheds of Sunkoshi River Basin. Singati sub-watershed lies in Dolakha District of Bagmati Province, Nepal (Fig. 1). The total area of this sub-watershed is 273.87 km2. Singti sub-watershed have the mean temperature range from 5 ℃ in winter to 16 ℃ in summer (Larson, 2012). The rainfall pattern is monsoonal, with 85% of the total rainfall occurring during June to September. The average annual precipitation of this area is 2091 mm and altitude varies from 438 m to 3450 m. The mainstream occurs in Singati sub-watershed are Kalinchok Khola, Pegu Khola, Sagu Khola. The streams are highly energetic in the upper reaches of the hill slope where the gradient is high (>70°) and these streams can carry anything on its course. This watershed covers the six numbers of ward of Bigu Rural Municipality and five wards of Kalinchok Municipality.
Fig. 1 Map of the study area

2.2 Methodology

In this study, ArcGIS 10.3 version and GPS (Garmin), were used for the entire analysis. DEM and LULC map of Nepal was used to prepare the slop map, Aspect map which is used for the analysis. Desk study was convoyed by having with different literatures. Based on these literatures, the main factors responsible in Landslide’s occurrence were slope factor, rainfall factor, stream factor, road construction, earthquake, rock type, aspect, geological information like presence of fault of thrust. Survey was conducted to locate existing active landslides in the study area with the help of local people as well as rural municipality representatives. After locating the existing landslides, boundary survey of the landslides along with a simple key informant interview and morphometric study of the landslides was carried out. Each landslide was visited, and the boundary was surveyed using GPS. Also, morphometric data of each of them was collected using the field data sheet. The morphometric data of the landslides that includes location of the landslides, soil structure, vegetation cover, type of land use around the landslide, slope, road construction, river, and water ways, altitude and aspect. Landslide causes and local adaptation practices were explored through household interview in ward no. 6 and 3 of Kalinchok Rural Municipality and six numbers of ward in Bigu Rural Municipality. All together 60 household survey using purposive sampling was used to captured desired information. Selection of community had been done nearest to the landslide effected region. The data collected was linked with GIS to prepare the digital landslide distribution map (Figs. 2, 3, 4, 5 and 6).
Fig. 2 Distance to stream factor map
Fig. 5 Aspect factor map

2.3 Landslide factor map preparation

For the investigation of the triggering factors which were responsible of landslide occurrence in the study area, the different triggering factor maps were prepared using the GIS Software. The different factor maps include rivers and streams factor map (Fig. 2), slope factor map (Fig. 3), rural road factor map (Fig. 4), aspect factor map (Fig. 5) and land use factor map (Fig. 6) were prepared for the analysis.

2.4 Perception of local people on landslide triggering factor and its effects

Qualitative and quantitative analysis methods were applied to analyze the data in this research. All the information was collected in the form of semi-structured forms, diaries and photographs. Data collected were checked, refined and scrutinized as per the objectives. Finally, data were analyzed using Microsoft Excel program and later exported to Statistical Package for Social Sciences (SPSS) for further analysis. All the qualitative and quantitative results were presented graphically in the form of tables and figures. A non-parametric Friedman test was performed from the perception of local people to rank the different factors of landslides. Data gathered through qualitative methods were analyzed in descriptive way using simple analysis of frequency and proportions. The Spearman’s correlation coefficient was used to analyze the correlation between risk perception of landslides.

3 Result

3.1 Landslide distribution

The total of 46 landslides were investigated in different location of the study area. About 12 new landslides were recorded from the field survey. The total area of the overall landslide is 4.77 km2 (Tables 1 to 5). The final landslide distribution map was prepared by combining the landslide digitized from Google earth pro and the GPS point collected from field survey.

3.2 Analysis of landslide factor

3.2.1 Distance to stream

The total area and numbers of landslide is higher in 0-100 m distance class which has the intensity (landslide area/ no. of landslides) of 0.11 due to saturation of materials and pore water pressure. In this class total area of landslide coverage is 2.85 km2. Followed by 100-300 m class has the landslide area of 1.08 km2 and number of landslides occurred in this class are 12 with the intensity of 0.09 (Table 1).
Table 1 Landslide details based on distance from stream
Distance from stream (m) Land area (km2) Percentage (%) Landslide area (km2) Intensity Number of landslides
0-100 89.21 42.41 2.85 0.11 24
100-300 50.86 24.17 1.08 0.09 12
300-600 41.51 19.73 0.59 0.09 6
>600 28.77 13.67 0.25 0.06 4
Total 210.35 100 4.77 0.37 46

3.2.2 Slope factor

The slope class had been categorized into five classes and the landslide occurrence in 30° to 45° in higher number with an intensity of 0.12, which has the landslide area 2.39 km2 followed by the class greater than 45° the landslide area is 1.27 km2 with an intensity of 0.15 which is higher than 30° to 45° class. The class 0° to 10° has smaller landslides area of 0.13 km2 (Table 2).
Table 2 Landslide details based on slope class
Slope class (°) Land area (km2) Percentage (%) Landslide area (km2) Intensity Number of landslides
0-10 19.27 9.18 0.13 0.13 1
10-20 60.04 28.61 0.31 0.07 4
20-30 60.96 29.05 0.67 0.04 14
30-45 42.01 20.02 2.39 0.12 19
> 45 27.49 13.10 1.27 0.15 8
Total 209.79 100 4.77 0.51 46

3.2.3 Distance to road factor

Above table shows that total number of landslide occurrence is found in the class 0-200 m distance from the roadside which has an area of 2.2 km2 having 20 number of landslides followed by the class 200-500 m, the total numbers of landslides observed are 13 and have the area 1.22 km2 (Table 3).
Table 3 Landslide details based on the distance from road
Distance to road class (m) Land area (km2) Percentage (%) Landslide
area (km2)		 Number of
landslides
0-200 14.56 6.92 2.2 20
200-500 29.64 14.09 1.22 13
500-1000 37.87 18.00 0.73 7
1000-1500 48.76 23.18 0.51 4
1500-2000 79.52 37.80 0.11 2
Total 210.35 100 4.77 46

3.2.4 Aspect factor

From the above table we know that the number of landslides is higher in southwest aspect which has the area of 2.48 km2 and the intensity is 0.14 which was followed by southeast aspect the landslide area is 1.14 km2 and intensity is 0.11. Both these aspects face more solar energy thus becoming less saturated with moisture while least percentage of landslide was observed at north-facing slope (Table 4).
Table 4 Landslide details based on Aspect
Aspect
class		 Landslide
intensity		 Landslide area
(km2)		 Number of
landslides
Northeast 0.06 0.52 8
Southeast 0.11 1.14 10
Southwest 0.14 2.48 17
Northwest 0.67 0.63 9
Total 0.98 4.77 46

3.2.5 Land use/land cover factor

From the above table it was found that the landslide affects more in forest area which has the total landslide area of 1.77 km2 and followed by grassland and barren land (1.57 km2) and shrub land (0.86 km2) (Table 5). Due to the haphazard construction of road inside the forest in sloppy area it leads to landslide. Also, in the agriculture field mostly in paddy field unplanned structures of terrace and irrigation system trigger the landslide. One of the factors for the occurrence of landslide inside the forest area is due to the devastating earthquake in 2015 and its epicenter on that region.
Table 5 Land use system and landslide details
Land use Class area (km2) Landslide area (km2) Intensity Number of landslide
Agriculture 30.83 0.57 0.03 17
Forest 156.39 1.77 0.14 12
Shrub land 2.59 0.86 0.12 7
Grassland and barren land 20.03 1.57 0.15 10
Total 209.84 4.77 0.44 46

3.3 Perception of local people on landslide triggering factor

In this study, Friedman (Non-parametric) test is applied to detect the main causing factor of landslide in the study area. The study area is facing the landslide problems severely. The major causing factors of landslide in this area are Rural Road construction, Distance to river, slope, aspect, LULC and Rainfall. More than 72% respondents were perceived that rural road construction was major causes, so respondents kept in 1st rank, followed by slope (55%), heavy rainfall (47%), distance to stream (61%) and LULC (94%) in 2nd, 3rd and 4th rank respectively. The average rank of different causes of landslide by different opinion of respondents differ significantly (χ2 = 98.822, df = 4, P<0.05) (Table 6). People perceived different rank of factors differently as shown in following table.
Table 6 People’s perception on landslide triggering factor
Factors Rank (%) Mean rank Friedman n value df P-value
1 2 3 4 5
Rural road construction 72.22 19.44 2.78 5.56 0 4.58 98.822
4

<0.05

Slope 16.66 55.56 19.44 8.38 0 3.81
Heavy rainfall 5.56 22.22 47.22 25 0 3.80
Distance to stream 2.77 2.77 27.78 61.12 5.56 2.36
Land use/land cover 2.78 02 2.78 0 94.44 1.17

3.4 Landslide impact on productivity

Local people perceived that there were different causes responsible for reducing crop productivity which are differently significant at P<0.05. The different major causes of reducing the productivity are ranked by them differently. The increasing numbers of landslide problem was deposited soil materials in agriculture field and respondent agreed the soil deposition (mean rank=4.19) was the most significant causes of reducing productivity. Similarly, topsoil removal of sloppy land (mean rank=3.56), destruction of irrigation cannel (mean rank=2.92), quality seed (mean rank=2.33) and insect and pest attack (mean=2.00) were the major causes of landslides to reduce the productivity (Table 7).
Table 7 Ranking landslide’s problem on productivity
Rank Causes Mean rank Friedman n value df P-value
1 Soil deposition in agriculture field 4.19 48.517 4 <0.001
2 Topsoil removal 3.56
3 Destruction the irrigation cannel 2.92
4 Quality seed 2.33
5 Insect and pests attack 2

3.5 Risk perception towards landslide

For the risk perception about landslide questions were asked to respondents and the result was drawn on their perception. Asking to respondents above seven items about risk perception is analyzed Spearman’s Correlation between them as in Table 8. The likelihood of occurring the landslide is positively significant with threaten of life, quality of life, financial loss and dread but negatively correlated without significant relation with able to control from huge loss from landslide. It means smaller is the landslide occurring the threaten of life is not serious, quality of life not so seriously affect, less financial loss and no dread from landslide how ever it is easy to control from huge loss. Likewise, there is inter-correlation between each of above items that reflects perception on risk of landslide in different aspects (Table 8).
Table 8 Inter correlation between the seven-risk perception items.
Items 1 2 3 4 5 6 7
1. Likelihood 0.108 -0.006 -0.389* 0.250** 0.193 0.217
2. Know mitigation action 0.333** 0.088 0.027 0.025 -0.057
3. Able to control huge loss 0.15 0.013 0.133 0.05
4. Threaten life quality 0.680** 0.550** 0.227
5. Affect the life quality 0.729** 0.155
6. Financial loss 0.032
7. Dread

Note: *P<0.05, **P<0.01. The numbers are Spearman’s correlation coefficient and asterisks indicate significance.

4 Discussion

In this study, five factors i.e., stream, road, slope, aspect, lithology and land-use were found to be responsible for the landslide occurrence. Rural road construction increases the potential of landslide by toe cutting of the slope, erosion of the slope and increasing the degree of saturation of materials of slope due to rainfall. Also another potential factor is river or stream. Devkota et al. (2013) have demonstrated that the more landslides are in the distance between 200-250 m from the road. Yalcin (2008) again demonstrated the similar type of result in which they have indicated that most of the landslide density was observed within the class interval of 0-50 m and as the distance increases, the landslide density also significantly decreases. Dai and Lee (2002) also have demonstrated that the more landslides are in roadside along the riverbanks in hilly region. Here in my findings landslides closer to roadside i.e., 0-200 m shows the highest number of landslides and all above mentioned findings supports my finding as well.
We believe that mostly landslides occur nearest to the stream but in recent years construction of road by using excavator leads to higher number of landslides near the road area. Regmi et al. (2014) demonstrated that the landslides are more occurring closer to the river and streams where there is the construction of road. We believe that as the degree in slope increases, the landslide density also increases but, in this study, it was found that the steep inclination does not necessarily means high occurrence of landslides. The study carried out by Vuillez et al. (2018) had also demonstrated the similar type of results. In this study most of the landslide density was found in slope category of 30°-45° which supports the findings of Tian et al. (2020).
Local people of the watershed area were highly dependent on agriculture, total 36% of households were still dependent on rainwater for raising agricultural crops. Destruction of irrigation source by landslide also leads to the decline in productivity rate. Crop productivity of the study area was in decreasing trend by decrease in land holding size of the farmers. It is primarily caused by stream bank cutting and soil deposition in the farmland.

5 Conclusions

From the field survey, a total of 46 landslides were surveys in different location of the study area. The total area of the landslide was found to be 4.77 km2. The distribution of landslides is largely governed by the combined effect of various geo-environmental conditions particularly proximity of road, slope, streams, rainfall and land-use. From the comparison matrix analysis, the study identified the main responsible factor for the occurrence of landslide are distance to road, slope factor, stream factor, lithology, aspect and land use. The study also revealed that the southeast aspect is more susceptible to landslide, which means that southeast aspect in the study area is more hazardous in comparison to other aspects. The distance to rural road had the prominent effect in the landslide occurrence in the study area as majority of landslide had found in the proximity of the road network. The road factor up to 200 m area covered the highest percentage of landslide hazard. Southeastern aspect with the slope class 30°-45° had the highest susceptibility to landslide. Dense forest was found to be more effected by landslide in comparison to other land use due to the construction of road in sloppy area inside the forest. Long duration and high intensity rainfall is the most triggering climatic factor for landslide occurrence. Comparison the effects of landslide before and after the road construction, about 80 percent of the respondent had the positive response on it.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Adhikari B R, Tian B. 2021. Spatiotemporal distribution of landslides in Nepal. Cham, Switzerland: Springer.

[2]
Bhatt B P, Awasthi K D, Heyojoo B P, et al. 2013. Using geographic information system and analytical hierarchy process in landslide hazard zonation. Applied Ecology and Environmental Sciences, 1(2): 14-22.

DOI

[3]
Bijukchhen S M, Kayastha P, Dhital M R. 2013. A comparative evaluation of heuristic and bivariate statistical modelling for landslides susceptibility mappings in Ghurmi-Dhad Khola, east Nepal. Arabian Journal of Geosciences, 6: 2727-2743.

DOI

[4]
Dahal R K. 2017. Landslide hazard mapping in GIS. Journal of Nepal Geological Society, 53: 63-91.

DOI

[5]
Dai F C, Lee C F. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3-4): 213-228.

DOI

[6]
Devkota K C, Regmi A D, Pourghasemi H R, et al. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards, 65(1): 135-165.

DOI

[7]
Heyojoo B P, Sharma R P. 2013. Rural road-induced sedimentation: Severity and local perception in the Phewa Watershed, Kaski District, western Nepal. Journal of Nepal Geological Society, 46: 171-178.

[8]
Intarawichian N, Dasananda S. 2010. Analytical hierarchy process for landslide susceptibility mapping in lower Mae Chaem Watershed, Northern Thailand. Suranaree Journal of Science & Technology, 17(3): 277-292.

[9]
Larson K P. 2012. The geology of the Tama Kosi and Rolwaling Valley region, East-Central Nepal. Geosphere, 8(2): 507-517.

DOI

[10]
Nepal N, Chen J, Chen H, et al. 2019. Assessment of landslide susceptibility along the Araniko Highway in Poiqu/Bhote Koshi/Sun Koshi Watershed, Nepal Himalaya. Progress in Disaster Science, 3: 100037. DOI: 10.1016/j.pdisas.2019.100037.

[11]
Psomiadis E, Papazachariou A, Soulis K X, et al. 2020. Landslide mapping and susceptibility assessment using geospatial analysis and earth observation data. Land, 9(5): 050133. DOI: 10.3390/LAND9050133.

[12]
Regmi A D, Yoshida K, Pourghasemi H R, et al. 2014. Landslide susceptibility mapping along Bhalubang-Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. Journal of Mountain Science, 11(5): 1266-1285.

DOI

[13]
Tian X F, Li Y, Tian Q Y. 2020. Landslides distribution at tributaries with different evolution stages in Jiangjia Gully, southwestern China. Natural Hazards and Earth System Sciences Discussions, (20): 1-26.

[14]
Vuillez C, Tonini M, Sudmeier-Rieux K, et al. 2018. Land use changes, landslides and roads in the Phewa Watershed, Western Nepal from 1979 to 2016. Applied Geography, 94(3): 30-40.

DOI

[15]
Yalcin A. 2008. GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations. Catena, 72: 1-12.

DOI

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