Landslide Hazard Mapping in Son La Province, Vietnam Under Climate Change Scenarios for 2025 &amp; 2050

doi:10.21203/rs.3.rs-3105747/v1

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Landslide Hazard Mapping in Son La Province, Vietnam Under Climate Change Scenarios for 2025 & 2050

https://doi.org/10.21203/rs.3.rs-3105747/v1

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In recent years, landslides in mountainous provinces are increasing. Whenever the rainy season comes, landslides occur, causing casualties to people and damages to the infrastructure. Son La, a mountainous province in the North of Vietnam, has suffered significant damage from landslides.

Therefore, the purpose of this research is to introduce an application of the analytic hierarchy process (AHP) method with the integration of representative concentration pathways (RCP) scenario 4.5 to study landslides in Son La province, Vietnam. With the integration of the digital elevation model (DEM) and collected data into ArcGIS software, we obtained different factor maps: Slope map, Aspect map, Cleavage density map, River density map, Rainfall map, Lithology map, Fault density map, Vegetation cover map, Distance to roads. These were the important input parameters for the AHP method. The data collected from 2009 to the present and the research documents of the authors based on the field investigations in 2018, 2019, 2020, and 2021 were the basis for us to complete this article. The landslide risk maps with the integration of maximum daily rainfall from the climate change scenario for 2025 and 2050 were produced. The results revealed that the landslide risk in Son La province increases with time. The results from this research will assist local authorities in land use planning, disaster control, and achieving sustainable development goals.

Landslide

landslide hazard index

Son La

AHP

Vietnam

There have been many authors in the world and Vietnam interested in the research of landslides (Ayalew et al. 2004; Binh, D.V et al. 2022; Chung, C.F et al. 1994; Crozier, M.J. 1986; Cruden, D.M et al. 1996; Dietrich, W.E et al. 1995; Guzzetti, F et al. 1999; Guzzetti, F et al. 2005; Hutchinson, J.N. 1988; Hutchinson, J.N. 1995). Vietnam is one of the countries most affected by climate change. In recent years, under the impact of climate change, the frequency and intensity of landslides have increased, causing great loss of life, property, and infrastructure, along with adverse impacts on the environment.

Research and development of landslide susceptibility maps in Vietnam has been conducted for a relatively long time. Especially, a series of landslide susceptibility maps at the scales of 1:50,000 and 1:10,000 have been developed from the government project The investigation, and study of landslides in the northern mountainous provinces of Vietnam, implemented by the Institute of Geosciences and Mineral Resources since 2012 (VIGMR, 2021). In the context of current climate change, it is necessary to develop landslide risk maps that are trend-oriented with the identified climate change scenarios to help local authorities come up with optimal plans for disaster prevention and control.

In 2009, the Ministry of Natural Resources and Environment (MONRE) for the first time announced the climate change and sea level rise scenarios for Vietnam (CCS) so that ministries, industrial sectors, and local authorities can promptly implement the National Target Program to response to climate change and integrate climate change issues into socio-economic development strategies. Following that, the updated versions of the CCS were continuously published by MONRE in 2011, 2016 and 2021.

The detailed climate change scenario in 2016 is built based on hydrometeorological data and sea level of Vietnam updated to 2016 (MONRE, 2016). Then UNDP in 2018 supported the Institute of Meteorology, Hydrology and Climate Change to update a number of meteorological model parameters for the assessment of vulnerability and risk of landslides and flash floods to the irrigation system under the impact of climate change scenarios (IMHEN, 2018). In which, the maximum daily rainfall models with high resolution have been built according to the climate change scenarios RCP 4.5 and RCP 8.5 for the years 2018, 2025, 2050 and 2100.

Son La province is located in the North of Vietnam, covers an area of 14,125 km². Location of Son La province limited by coordinates from 20039' - 22002' N to 103011' - 105002' E (Figure 1). Landslides are causing economic losses to Son La province. In 2020, Son La province suffered damage from natural disasters up to 219 billion VND (Nguyen, 2020). Therefore, the study of landslides for sustainable development is very necessary.

This study will develop a landslide hazard map of Son La province according to the RCP 4.5 climate change scenario forecast to 2025 and 2050. The method of building landslide hazard map is the combined method 9 factors causing landslides, including: (i) slope, (ii) petrology, (iii) rainfall distribution, (iv) vegetation cover, (v) fault density, (vi) density river degree, (vii) depth of separation, (viii) aspect, (ix) distance to the road. The climate change scenario is integrated into the landslide risk map development through the maximum daily rainfall coefficient according to the scenario RCP 4.5 forecast to 2025 and 2050.

2.1. Data and research methods

2.1.1. Data sources

Data source for the article:

• Geological, geomorphological and hydrogeological data from the Center for Geological Information and Archives (Department of Geology and Minerals of Vietnam) (DGMVN .2005; Dien T.N, et al. 2013)

• Data from landslide projects related to Son La province (Thinh et al. 2001; Yem et al. 2006)

• Documentation on hydrometeorology (IMHEN. 2016).

• Documentation on vegetation (VNIAP .2016; https://www.gso.gov.vn/)

• Landslide inventory document (http://canhbaotruotlo.vn/hientrang; WebGIS on landslides of the Project: “Investigation, assessment and warning zoning of landslides in mountainous Vietnam”. Institute of Geosciences and Mineral Resources (VIGMR).

• Research papers of the authors from 1996 to present

2.1.2. Research Methods

There are many methods to study landslide risk. Some case studies (Carrara et al. 1991; Crozier, 1986; Cruden et al. 1996; Dietrich et al. 1995; Dikau et al. 1996; Guzzetti et al. 1999; Guzzetti et al. 2005; Hansen. 1984; Hutchimson et al. 1988; Hutchimson. 1995; Rib et al. 1978; Soeters et al. 1996; Turner et al. 1996; Varnes. 1978; Varnes. 1984). In this paper, the method of analysis of hierarchical processes (AHP) is selected to study landslides in Son La province. Analytical Hierarchy was developed by Saaty (1977); Saaty (2000). The AHP Method is incorporated into GIS-based fit criteria, and decisions are made using weights for relative pairwise comparisons without inconsistencies in the process decision. The AHP is based on three principles: analysis, comparative evaluation and synthesis of priorities (Malczewski. 1999). Concepts and techniques in AHP include complexity hierarchies, pairwise comparisons, eigenvector calculations for weighting, and relevancy considerations.

In this research, the following processing steps for the AHP method was introduced in Kayastha et al. 2013: (i) break down the dicision problem into component factors; (ii) create an order of components factors from Table 1 and assigning numeric value for each component factor; (iii) compare each pair-wise component factor and evaluate the importannce. (iv) construct a comparison matrix; (v) computation of the normalized principal eigenvector, which gives the weight of each factor; (vi) checking the consistency of the comparison using the consistency index (CI).

Saaty (1980) applied Equation (1), to determine (RI) in Table 2. In which the CR < 0.1 will be accepted.

Where:

LSI: Landslide hazard index.
W_j: The weighted value of the parameter j
w_ij: The weight value of class i belongs to the jth factor causing the landslide
n: Number of factors causing landslides in the study area

Where the Wj values and the wj ratio values are determined based on the pairwise comparisons matrix.

Accordingly, the input parameters for us to study landslides in Son La province include 09 factors (j=9): Slope; Lithology; Rainfall distribution; Vegetation cover; Fault density; River density; Deep cleavage density; Aspect; Distance to roads.

The input parameters are processed and displayed as raster maps in GIS with a resolution of 30 x 30m for the 01-pixel image. Map of landslide hazard zoning in Son La province is based on different LSI values. The process is displayed in figure 2.

2.2. Input Parameters

The input parameters for AHP processing are 9 layers of maps of other landslide triggers. Four map layers, including slope, aspect, river density, and deep cleavage density were generated from the DEM (figure 3) has a resolution of 30 x30m, This data is downloaded from NASA’ website (NASA, 2015) (https://neo.sci.gsfc.nasa.gov).

2.2.1. Slope map

The slope map of Son La province was generated from DEM, using the SLOPE module available in ArcGIS 10.0 software (ArcGIS Manual, 2015). Based on the natural topographical characteristics of the province, the Son La slope map is classified into 6 layers: 0-3^o, 3-8^o, 8-15^o, 15-25^o, 25-45^o and >45^o (Figure 4a).

2.2.2. Aspect map

The aspect map of the study area was extracted from the DEM, based on the Aspect module available in ArcGIS 10.0 software (ArcGIS Manual, 2015) (Figure 4b).

2.2.3. River density map

The river density map layer is extracted from the digital elevation model (DEM), using the flow density method. Therefore, the river density map layer represents the density of rivers and streams per unit area (km/km2), in which the higher the density value, the better the ability to conduct water along the tributaries over a certain area. the better. Based on the natural topography of Son La province, the river density map of Son La province is classified into 4 layers ( Figure 4c)

2.2.4. Deep cleavage density map

The deeply dissected density map layer is extracted from the DEM, representing the difference in terrain elevation per unit area (m/km²). Therefore, the larger the value of the depth of cleavage density, the larger the topographic elevation difference in that area. Based on the natural topography of Son La province, the deep dissection density map of Son La is classified into 5 layers (Figure 4d).

2.2.5. Vegetation cover map

The vegetation cover map of Son La province was provided by the Vietnam National Institute of Agricultural Planning (VNIAP, 2016). The map was built as a shapefile by ArcGIS software (Figure 4e).

2.2.6. Fault density map

The fault density is defined as the total length of faults over an area (km/km²). The fault density map of Son La province was classified into five classes (km/km²) (Figure 4f).

2.2.7. Lithology

In the area of Son La province, there are 44 stratigraphic units and 15 magmatic complexes which belong to 7 structural zones: Tu Le, Phan Si Pang, Song Da, Nam Co, Song Ma, Sam Nua, and Dien Bien (Dien et al. 2013). According to the origin and degree of weathering, we divided the rocks of Son La province into the 9 following groups (figure 4g):

Quaternary sedimentary rock group (Q): including silt, clay, sand, gravel, etc. The total area is 177 km² which accounts for about 1.22% of Son La province.
Group of terrigenous deposit and metamorphic rocks rich in alumosilicate: including shale, claystone, siltstone, sandstone, grit, congromerate etc. The total area is 1867 km², which accounts for about 13.25% of Son La province.
Group of extrusive acid-intermediate rocks and their tuffs: including ryolite, porphyritic ryolite, trachyte, porphyritic trachyte, felsite, rhyolite tuff, dacitic tuff. The total area is 4105 km², which accounts for for about 28.28% of Son La province.
Group of extrusive mafic rocks and their tuffs: including basalt, porphyritic basalt, basaltic komatiite, basaltic tuff, etc. The total area is 1006 km², which accounts for about 7.14% of Son La province.
Group of felsic-ulramafic intrusive: including gabbro, gabbroolivine, gabbrodiabase, dunite, peridotite, serpentinite, serpentinite- apoharzburgit. The total area is 56 km2, which accounts for about 0.4% of Son La province.
Group of metamorphic rocks and terrigenous deposits rich in Quartz: including granite, syenite, grano-syenite, biotite granite, granodiorite, granite 2 mica, plagiogranite, gabrrodiorite, diorite, ect. The total area is 1119 km², which accounts for about 8.0% of Son La province.
Group of metamorphic rocks rich in alumosilicate: including quartz-sericite schist, quartz mica schist, silicate schist, biotite gneiss, granitic gneiss, biotic-amphibole. The total area is 1739 km², which accounts for about 12.33% of Son La province.
Group of terrigenous deposits rocks and extrusive rich in alumosilicate: including quartzite, quartz sandstone, silicate schist. The total area is 1180 km², which accounts for about 8.32% of Son La province.
Carbonate rock group: including limestone, dolomite, marble. The total area is 2876 km², which accounts for about 21.06% of Son La province.

2.2.8. Human factors

Some human activities that increase the likelihood of landslides are also taken into account. It's road construction. We chose the road factor because it strongly influences the landslide process. the "buffer calculation" algorithm in ArcGIS software helped us build the Map of distance to roads, see figure 4h.

2.2.9. Rainfall map

The climate change scenario is integrated into the model of establishment of the landslide risk map through the maximum daily rainfall scenarios for 2025 and 2050 extracted from RCP 4.5. The predicted maximum daily rainfall maps of Son La province in 2025 and 2050 are shown in figures 4i and 4k respectively.

3.1. Landslide status map Son La province

Based on the available sources combined with filed investigations, we have identified 1894 landslide sites (figure 5)

Applying analytic hierarchy process methods in assessing the sensitive level of factors that causes landslides in Son La province, We define weight values Wj to estimate the landslide based on the eigenvector value in the pairwise comparison matrix shown in table 3.

The same comparison of couples has been carried out the information layers in each factor that causes landslides. These matrices for comparing couples are the basis for determining the weights for different information layers according to the values Eigenvector in each matrix (table 4).

From the data of Tables 3 and Tables 4, we can see that:

- For slope: The slope increases. The greater the gravity of the rock mass is, the higher the risk of landslides will be. The compared couples’ results showed that weight values or Wij values increased according to slope groups from 0.027, 0.040, 0.091, 0.159, 0.276 to 0.407, meaning from <3^o “flat” to > 45^o “hanging cliffs”

Maximum daily rainfall: The higher the maximum daily rainfall, the more prone it is to landslide occurrence. The weight value of groups of maximum daily rainfall increases from 0.122, 0.230 to 0.648.

Human activities affecting the landslide process represented by distance to the road also indicate that the closer it is to the roadway, the higher the risk of landslides will be. The double comparison process determined the weights for distance groups from <100m, 100-200m to > 200m, respectively 0.557, 0.320, and 0.123.

Landslide weights for groups of river density, deep cleavage density, fault density also increase with increasing river density, deep cleavage density and fault density (eigenvector value of Table 4).

- For vegetation cover: Vacant land is the most advantageous factor for the landslide process. The weight of vacant land in the comparison matrix is the highest value of 0.264. Other factors, from high to low, include Agricultural and other lands (0.187); Plantation forest (0.070); Bamboo forest (0.046); Rocky mountain (0.024) etc. (table 4).

For slope direction, weight increases from flat (0.021) to Northwest (0.266) (table 4).

- For rock groups: Based on origin research, degree of weathering combined with interpolation of analytic hierarchy process (AHP) method, we have determined the weight of rock groups. The weighted value increases from Carbonate rock group (0.016) to Group of terrigenous deposit and metamorphic rocks rich in alumosilicate (0.232) (table 4).

Tables 3 and 4 show that the consistency coefficients CR are both less than 0.1, that demonstrates the evaluation follows a reasonable and consistent rule. For each map, landslide factors, field Wij are generated and assigned to a corresponding attribute column as described in Table 4. Based on Wj and Wij values calculated in Tables 3 and 4, using the "Weighted sum" function in ArcGIS software, according to equation (3), the landslide hazard index (LSI) will be calculated. From there, landslide hazard index maps of Son La province in 2025 and 2050 were produced (Figure 6a & Figure 6b respectively).

3.2. Landslide risk maps in Son La province in 2025 and 2050

Based on the input source (see 3. Results), thanks to the comprehensive integration in ArcGIS, the maps of landslide risk with different scenarios were produced. We selected climate change maximum daily rainfall scenarios for years 2025 and 2050. The landslide risk in Son La province is classified into five classes from very low to very high (Figure 7a, Figure 7b).

In 2025, only 10% of the area of Son La province is in zones with high or very high landslide risk. These zones are mainly located in Quynh Nhai, Muong La, Moc Chau, and Van Ho provinces. The moderate zone, equally distributed in all districts, accounts for 29.14% of the area of the province. The zones with low and very low landslide risk are largely distributed in four districts, including Mai Son, Yen Chau, Song Ma, and Son La city. The results for 2050 show that landslides risk increases with time. In 2050, the zones with high or very high landslide risk cover more than 16% of the area of Son La province. Quynh Nhai, Muong La, and Van Ho and the districts that are predicted to have outstandingly high landslide risk in 2050. In 2050, the moderate, low and very low zones respectively account for 28.16%, 33.86%, and 21.88% of the total area of Son La province. The detailed distribution of landslide risk zones extracted from the maps are displayed in figure 7.

The results show that the distribution of the risk zones on the landslide risk maps is consistent with the distribution of the input factors, especially slope and rainfall. In both maps, the distribution area of areas with very high risk increases gradually with the increase of slope’s value, such as in Thuan Chau, Muong La, Quynh Nhai and Bac Yen provinces. Landslide risk also increase significantly with rainfall. Moc Chau and Van Ho provinces were predicted to have high rainfall in both 2025 and 2050 from RCP 4.5 scenarios, which is consistent with the large distribution of zones with high and very risk in these provinces. In 2050, Quynh Nhai and Muong La were also predicted to have higher rainfall, which makes zones with moderate, high, and very high risk cover about half of the area of these provinces.

The analytic hierarchy process method has been successful in many countries in the world, including Vietnam. The advantage of this method is the Comprehensive integration in ArcGIS and the ability to process many map layers with large capacity, high pixel resolution. Data sources are selected for precedence through comparing couples.

The results showed high dependent of landslide risk and some natural factors such as topography and rainfall. Specifically, the risk of landslide occurence increase with the increase in slope steepness and rainfall amount.

With the integration of climate change data from RCP 4.5 scenarios for 2025 and 2050, our results also revealed that landslide risk increases with time. In 2025, the distribution area of 5 zoning levels namely very low, low, moderate, high and very high are respectively 3437.84 km², 5149.55 km², 4115.42 km², 1172.55 km², and 248.14 km². In 2050, the areas of these zones are respectively 3090.44 km², 4780.31 km², 3976.69 km², 1765.53 km², and 510.53 km².

The results of developing a landslide risk map in Son La province on the basis of the maximum daily rainfall scenarios of climate change from 2025 & 2050 will contribute positively to the management and serve as a basis science to make decisions on exploitation, use and sustainable development of land resources of Son La province.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Competing interests

The authors declare that they have no competing interests.

Funding

The authors thank the researchers of the Vietnam Institute of Geosciences and Mineral Resources. Moreover, this work was supported by the Research on scientific basis to develop a set of criteria and to identify the areas highly-susceptible to landslides, debris flows, flash floods in the mountainous and hilly regions of Vietnam. We also like to thank the anonymous reviewers for their valuable remarks that greatly contribute to the quality of the paper.

Authors' contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Le Canh Tuan, Nguyen Quoc Khanh, Nguyen Khac Hoang Giang. The first draft of the manuscript was written by Le Canh Tuan, and all authors commented on previous versions of the manuscript. Supervision, conceptualization, writing–review and editing by Le Canh Tuan. All authors read and approved the final manuscript.

Akgün, A., and Bulut, F. (2007) GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environmental Geology, 51: 1377–1387 pp.
Ayalew, L., Yamagishi, H., Ugawa, N. (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata prefecture, Japan. Landslides, 1: 73-81pp.
Barredo, J.I., Benavides, A., Hervas, J., Van Westen C.J. (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. International Journal of Applied Earth Observation and Geoinformation, 2(1): 9-23pp.
Binh. D, V., Igor K. Fomenko., Trung K. Nguyen., Thi Hong L. Vi., Oleg V. Zerkal., Hong D. Vu. (2022) Application of gis-based bivariate statistical methods for landslide potential assessment in Sapa, Vietnam. Bulletin of the Tomsk Polytechnic University-Geo assets Egineering, Volume 333 No 4 (2022); DOI https://doi.org/10.18799/24131830/2022/4/3473.
Carrara, A., Cardinali, M., Detti, R., Guzzetti, F., Pasqui, V., Reichenbach, P. (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Processes and Landforms, 16: 427-445.
Chung, C.F., and Leclerc, Y. (1994) A quanititative technique for zoning landslide hazard. International Association for Mathematical Geoloogy Annual Conference, Mont Tremblant, Québec, 87-93 pp.
Crozier, M.J. (1986) Landslides: causes, consequences and environment. Croom Helm, London, 252 pp.
Cruden, D.M., and Varnes, D.J. (1996) Landslide types and processes. In: Turner, A.K., and Schuster, R.L. (eds), Landslides investigation and mitigation, special report 247. Transportation Research Board, National Academy Press, Washington D.C, 36–75 pp.
DGMVN (Department of Geology and Minerals of Vietnam). (2005) Geology- Minerals sheet Muong Kha - Son La, scale 1:200.000.
Dietrich, W.E., Reiss, R., Hsu, M.L., and Montgomery, D.R. (1995) A process-based model for colluvial soil depth and shallow landsliding using digital elevation data, Hydrological Process 9: 383-400 pp.
Dien, T.N., Tai, V.D., Tuan, V. V., Tien, B. C., Phu, D. V., Phuong, T.Q. (2013) Report on survey results on the current state of soil and rock in Son La province. Northern Vietnam Geological mapping Division. Department of Geology and Minerals of Vietnam.
Dikau, R., Brunsden, D., Schrott, L., and Ibsen, M.L. (1996) Landslide Recognition. Wiley, Chichester. 251 pp.
Guzzetti, F., Carrara, A., Cardinali, M., Reichenbach, P. (1999) ‘Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy’, Geomorphology, 31(1-4): 181-216pp.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., Ardizzone, F. (2005) ‘Landslide hazard assessment in the Staffora basin, Northern Italian Apennines’, Geomorphology, 72: 272-299 pp.
Hansen, A. (1984) Landslide hazard analysis. In: Brunsden, D., and Prior, D.B. (eds), Slope Instability, John Wiley and Sons, New York, 523-602pp.
https://www.gso.gov.vn/
Hutchinson, J.N. (1988) ‘General report: morphological and geotechnical parameters of landslides in relation to geology and hydrology’, 5th International Symposium on Landslides, Balkema, Rotterdam, 1: 3-35 pp.
Hutchinson, J.N. Keynote paper. (1995) Landslide hazard assessment. In Landslides, Proceeding of VI International Symp. On Landslides, February, Christchurch, New Zealand. A.A. Balkema, Rotterdam, The Netherlands, 1805-1841pp.
IMHEN (Vietnam Institute of Meteorology, Hydrology and Climate Change). (2016) Report Database of historical climate information and future projections of precipitation changes for periods up to 2025 and 2050.
IMHEN (Institute of Meteorology, Hydrology and Climate Change). (2018) Update on some meteorological and hydrological parameters according to Vietnam's climate change scenarios.
Kayastha, P., Dhital, M.R. and De Smedt, F. (2013) ‘Application of the Analytical Hierarchy Process (AHP) for Landslide Susceptibility Mapping: A Case Study from the Tinau Watershed, West Nepal’, Computers & Geosciences, 52, 398-408 pp.doi:https://doi.org/10.1016/j.cageo.2012.11.003
Malczewski, J. (1999) GIS and multicriteria decision analysis. John Wiley and Sons, New York. 392 pp.
MONRE (Ministry of Natural Resources and Environment). (2016) Climate change and sea level rise scenarios for Vietnam. Viet Nam publishing house of Natural Resources, Environment and Catography.
MONRE (Ministry of Natural Resources and Environment). ( 2021) Climate change and sea level rise scenarios for Vietnam.
Nguyen, N. (2020) Son La: damage more than 219 billion VND due to disaster in 2020. URL: https://baotainguyenmoitruong.vn/son-la-thiet-hai-hon-219-ty-dong-do-thien-tai-nam-2020.
Rib, H.T., and Liang, T. (1978) Recognition and identification. In: Schuster R.L. & Krizek R.J. (eds), Landslide analysis and control. Washington Transportation Research Board, Special Report 176, NAS, Washington, 34-80 pp.
Saaty, T.L. (1977) ‘A scaling method for priorities in hierarchical structures’, Journal of Mathematical Psychology, 15: 234-281 pp.
Saaty, T.L. (2000) ‘The fundamentals of decision making and priority theory with the analytic hierarchy process’. Vol VI. 2nd Ed., RWS Publications, Pitsburg. 478 pp.
Soeters, R., and Van Westen, C.J. (1996) Slope instability recognition analysis and zonation. In: Turner, K.T., Schuster, R.L. (eds), Landslide: investigation and mitigation. Spec Rep 47. Transportation Research Board, National Research Council, Washington, DC, 129–177 pp.
Turner, A.K., and Schuster, R.L. (1996) Landslides: investigation and mitigation. Transport Research Board Spec. Rep. 247. Washington, DC, National Academy of Sciences, 36-75 pp.
Thinh, Đ. V., Loi, V. T.H., Ngoi, C. V., Dong. N.P., Nguyen, V. V., Thay, B. V. (2001) Investigation of geological hazards in the Northwest region. Geological Archives and Information Center. Hanoi.
Varnes, D.J. (1978) Slope movements, types and processes. In: Schuster, R.L., Krizek, R.J. (eds), Landslide analysis and control, National Academy Sciences, Washington DC, 11-33 pp.
Varnes, D.J.(1984) International Association of Engineering Geology Commission on Landslides and Other Mass Movements on Slopes: Landslide hazard zonation: a review of principles and practice, UNESCO, Paris. 63 pp.
VNIAP (Vietnam National Institute of Agricultural Planning). (2016) Map of vegetation cover at scale 1:100,000 in northern Vietnam. Archives of the Institute of Agricultural Planning, Hanoi.
VIGMR (Institute of Geosciences and Mineral Resources). (2021) Project "Investigation, assessment and warning zonation for landslides in the mountainous regions of Vietnam". Institute of Geosciences and Mineral Resources, MONRE.
Voogd, H. (1983) Multi-criteria evaluation for urban and regional planning. Pion, London. 367 pp.
Website of General Statistics Office of Vietnam. (1983) URL:https: //www.gso.gov.vn/
Yalcin, A. (2007) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey). Catena, 72(1): 1-12.
Yem, N. T., Thom, B. V., Hung, D. D., Tuan, D. A., Phuong, N.T., Tuc, N.D., Hung, N.V., Hung, P.V., Chinh, V.V. (2006) Land cracking in Vietnam's territory and solutions to prevent and mitigate damage. Vietnam Academy of Science and Technology.

Table 1. Value comparison of objects in AHP

Level	Preference level	Explain
1	Equally	The contribution to the goal of the two factors is the same
3	Moderately	Experience and prediction for reasonable profit
		another factor
5	Strongly	Strong or preferred experience and predictions about a
		another factor
7	Very strongly	One factor is rated higher than the other, and
		Its important role is applied in practice
9	Especially strong	Appreciate one factor over another to determine
		the highest level of an influencing factor
2,4,6,8	Intermediate	Used for reasonable selection in
		weights 1, 3, 5, 7 and 9
Reciprocals	Opposite	Used to compare analysis in the opposite direction

Table 2. Random consistency index (RI)

n	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

Table 3. Matrix for comparing couples, eigenvector values of each factor group, and the consistency coefficient of the group comparison matrix of failure causing factors in Son La province

Factor groups	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	Eigenvector
[1] River density	1									0.017
[2] Deep cleavage density	2	1								0.024
[3] Distance to roads	4	3	1							0.045
[6] maximum daily rainfall	5	4	2	1						0.068
[5] Fault density	6	5	4	3	1					0.119
[6] Vegetation cover	7	7	6	4	2	1				0.175
[4] Lithology	8	8	7	5	3	2	1			0.254
[8] Slope	9	9	8	6	3	2	1	1		0.268
[9] Aspect	3	2	1/2	1/4	1/6	1/7	1/9	1/9	1	0.030
Consistency coefficient CR = 0.067

Table 4. Matrix for comparing couples, weight values (Wij/eigenvector), and consistent coefficients of groups of landslide triggering factors in Son La province.

1. slope	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]	Eigen vector
[1] <3°	1													0.029
[2] 3-8°	2	1												0.040
[3] 8-15°	5	3	1											0.092
[4] 15-25°	7	5	3	1										0.159
[5] 25-45°	8	7	4	3	1									0.277
[6] >45°	9	9	5	4	2	1								0.407
Consistency coefficient CR = 0.072
2. Distance to the road
[1] < 100m	1													0.556
[2] 100- 200m	1/2	1												0.320
[3] >200m	1/4	1/3	1											0.124
Consistency coefficient CR = 0.020
3. Maximum daily rainfall
[1] < 150 mm/day	1													0.122
[2] 150 - 200 mm/day	2	1												0.230
[3] > 200 mm/day	5	3	1											0.648
Consistency coefficient CR =0.005
4. River density (km/km²)
[1] <0.3	1													0.043
[2] 0.3 – 0.6	4	1												0.124
[3] 0.6 – 0.9	7	3	1											0.271
[4] > 0.9	9	5	3	1										0.562
Consistency coefficient CR = 0.072
5. Deep cleavage density
[1] <100m/km2	1													0.051
[2] 100-200 m/km2	2	1												0.082
[3] 200-300 m/km2	4	3	1											0.150
[4] 300-400 m/km2	5	4	3	1										0.282
[5] >400 m/km2	6	4	5	2	1									0.435
Consistency coefficient CR = 0.081
6. Fault density (km/km²)
[1] < 0.1	1													0.051
[2] 0.1 – 0.3	2	1												0.082
[3] 0.3 – 0.5	4	3	1											0.150
[4] 0.5 – 0.7	5	4	3	1										0.282
[5] >0.7	6	4	5	2	1									0.435
Consistency coefficient CR = 0.081
7. Vegetation cover
[1] Rocky mountains without trees	1													0.015
[2] Residential	3	1												0.028
[3] Mixed bamboo, wood forest	5	3	1											0.076
[4] Rich evergreen broad-leaved	4	2	1/2	1										0.050
[5] Poor evergreen broad-leaved	7	5	3	3	1									0.126
[6] Recovery evergreen broad-leaved forest	4	2	1/2	1	1/3	1								0.057
[7] Medium evergreen broad-leaved	3	1	1/3	1/2	1/4	1/2	1							0.033
[8] Water surface	2	1	1/4	1/3	1/5	1/3	1/2	1						0.024
[9] Rocky mountain	2	1	1/4	1/3	1/5	1/3	1/2	1	1					0.024
[10] Bamboo forest	4	2	1/2	1	1/3	1	2	2	2	1				0.046
[11] Plantation forest	5	3	1	2	1/2	1/2	3	3	3	2	1			0.070
[12] Agricultural and other lands	8	6	4	5	2	5	5	6	6	6	3	1		0.187
[13] Vacant land	9	7	5	7	3	7	7	7	7	7	5	2	1	0.264
Consistency coefficient CR = 0.041
8. Slope direction
[1] Flat	1													0.021
[2] North	3	1												0.035
[3] Northeast	7	5	1											0.113
[4] East	7	5	1	1										0.113
[5] South East	7	5	1	1	1									0.113
[6] South	7	5	1	1	1	1								0.113
[7] Southwest	7	5	1	1	1	1	1							0.113
[8] West	7	5	1	1	1	1	1	1						0.113
[9] Northwest	5	3	3	3	3	3	3	3	1					0.266
Consistency coefficient CR =0.042
9. Rocks group
[1] Carbonate rock group	1													0.016
[2] Group of felsic-ulramafic intrusive	3	1	1											0.037
[3] Group of extrusive mafic rocks and their tuffs	3	1	1	1										0.037
[4] Group of extrusive acid-intermediate rocks and their tuffs	5	2	2	2	1									0.069
[5] Group of metamorphic rocks and terrigenous deposits rich in Quartz	5	2	2	2	1	1								0.069
[6] Group of terrigenous deposits rocks and extrusive rich in alumosilicate	7	5	4	4	2	2	1							0.135
[7] Group of metamorphic rocks rich in alumosilicate	7	5	4	4	2	2	1	1						0.135
[8] Group of terrigenous deposit and metamorphic rocks rich in alumosilicate	9	6	6	6	4	4	2	2	1					0.232
[9] Quaternary sedimentary rock group	9	6	6	6	4	4	2	2	1	1				0.232
Consistency coefficient CR = 0.099

No competing interests reported.

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Landslide Hazard Mapping in Son La Province, Vietnam Under Climate Change Scenarios for 2025 & 2050

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