# Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}. The town is getting densely populated and expanded along the roads without proper planning. Multi-storied buildings are getting constructed along the streets and on steeper slopes.

#### 2.1. Geology

#### 2.2. Geohydrology

## 3. Landslides in Kalimpong

#### 3.1. Rainfall

#### 3.2. Drainage System

#### 3.3. Data for the Hydrological Simulation

^{2}and spatializing the data within this small area may cause overfitting problems. To overcome this limitation, the parameters used were calibrated using moisture content values collected from a different database, called the Modern Era Retrospective-Analysis for Research and Applications (MERRA) using Goddard Earth Observing System Data Assimilation System Version 5 (GEOS 5) [54]. The data provides hourly surface data with 30′ horizontal resolution. The data is also a reanalysis product that has been validated.

## 4. Methodology of Study

#### 4.1. Simulation of Soil Moisture

- (a)
- Penman–Monteith equation [57] for evapotranspiration;
- (b)
- Saint–Venant equations, two dimensional diffusion wave approximation [57] for overland flow;
- (c)
- Saint–Venant equations, one dimensional diffusion wave approximation [57] for channel flow;
- (d)
- Rutter equation [57] for canopy drip and interception;
- (e)
- Variably saturated flow equation (3D) [58] for subsurface flow;

#### 4.2. Rainfall Events and Empirical Thresholds

^{γ}

_{f}was calculated. By using a kernel density estimation [64], the probability density function of the distribution of δE was determined. The result fitted with a Gaussian function in the form

#### 4.3. Bayes’ Theorem

_{min}, T

_{5}, T

_{10}, T

_{20}and T

_{50}) six categories were defined. Hence, 30 cells (5 by 6) were available for the two-dimensional Bayesian analysis.

- N
_{A}= The total number of landslide events (If n number of landslides occur on the same day, it is considered as one landslide event) - N
_{R}= The total number of rainfall events during the study period - N
_{B,C}= The number of events in each cell condition - N
_{(B,C|A)}= The number of rainfall events that resulted in landslides while satisfying a cell condition

## 5. Results and Discussion

#### 5.1. Soil Moisture Estimation

^{2}value of 0.84. The calibrated parameters are listed in Table 2 below:

#### 5.2. Rainfall Thresholds

_{min}, T

_{5}, T

_{10}, T

_{20}, and T

_{50}). The defined thresholds and data points are plotted in Figure 5.

_{min}threshold is the threshold below which no landslides are expected to occur. The obtained results are found to be in good agreement with the ID thresholds defined for the area [44]. The variation in duration was found to be relatively less, from one to eight days while the event rainfall values varied the order of tens to hundreds. Out of the 61 events, 5% of the events are expected to fall below the T

_{5}line, 10% below the T

_{10}line, and so on. The T

_{50}line is the best fit line where 50% of the data points are expected to be below the line and 50% above the line. The equation of the minimum threshold has been obtained as E = 1.50D

^{0.65}and the best fit line is defined as E = 6.03D

^{0.65}. The values of α and γ of different thresholds are tabulated in Table 3.

#### 5.3. Probabilistic Thresholds

_{min}, T

_{min}–T

_{5}, T

_{5}–T

_{10}, T

_{10}–T

_{20}, T

_{20}–T

_{50}, and greater than T

_{50}. The classification of soil wetness values are 0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1.0. For certain cases, the probability of occurrence of landslide was obtained as zero where either $P\left(B,C|A\right)\text{}or\text{}P\left(A\right)$ is becoming zero. The probabilistic thresholds are plotted in Figure 6.

- (a)
- the severity of a rainfall event exceeds 50% and the soil wetness is between 0.4 and 0.6
- (b)
- the severity of a rainfall event between 20% and 50% and the soil wetness is between 0.6 and 0.8
- (c)
- the severity of a rainfall event between 5% and 10% and the soil wetness is between 0.6 and 0.8.

## 6. Validation

_{min}threshold is considered. While increasing the threshold level, the value of specificity is increased, but at the cost of many missed alarms, reducing the value of sensitivity in both cases. The term likelihood ratio is the ratio of sensitivity to the false positive rate and can be used to evaluate the model performance. The more the likelihood ratio, the better is the model. The likelihood ratio is also the highest for the minimum thresholds considered in both cases. The relative values of likelihood ratio for two-dimensional probabilistic model is higher than those of an empirical model in all the cases, assuring the better performance of the probabilistic model. The lesser values of sensitivity are expected as the total number of displacement events considered for validation is as less than seven and even single missed alarms can have a significant effect on the term.

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Panagoulia, D.; Zarris, D.; Maggina, K. An Assessment of the Interaction Between Storm Events and Sediment Transport. In Proceedings of the 5th International Synposium on Ecohydraulics, Aguatic Habitats: Analysis & Restoration, Madrid, Spain, 12–17 September 2004. [Google Scholar]
- Guzzetti, F.; Peruccacci, S.; Rossi, M.; Stark, C.P. Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorol. Atmos. Phys.
**2007**, 98, 239–267. [Google Scholar] [CrossRef] - Crozier, M.J. Deciphering the effect of climate change on landslide activity: A review. Geomorphology
**2010**, 124, 260–267. [Google Scholar] [CrossRef] - Lin, Q.; Wang, Y. Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016. Landslides
**2018**, 15, 2357–2372. [Google Scholar] [CrossRef] - Kirschbaum, D.; Stanley, T.; Zhou, Y. Spatial and temporal analysis of a global landslide catalog. Geomorphology
**2015**, 249, 4–15. [Google Scholar] [CrossRef] - Bordoni, M.; Corradini, B.; Lucchelli, L.; Valentino, R.; Bittelli, M.; Vivaldi, V.; Meisina, C. Empirical and physically based thresholds for the occurrence of shallow landslides in a prone area of northern italian apennines. Water
**2019**, 11, 2653. [Google Scholar] [CrossRef] [Green Version] - Caine, N. The rainfall intensity-duration control of shallow landslides and debris flows: An update. Geogr. Ann. Ser. A Phys. Geogr.
**1980**, 62, 23–27. [Google Scholar] - Guzzetti, F.; Peruccacci, S.; Rossi, M.; Stark, C.P. The rainfall intensity-duration control of shallow landslides and debris flows: An update. Landslides
**2008**, 5, 3–17. [Google Scholar] [CrossRef] - Melillo, M.; Brunetti, M.T.; Peruccacci, S.; Gariano, S.L.; Guzzetti, F. An Algorithm for the objective reconstruction of rainfall events responsible for landslides. Landslide
**2014**, 12, 311–320. [Google Scholar] [CrossRef] - Abraham, M.T.; Pothuraju, D.; Satyam, N. Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach. Water
**2019**, 11, 2113. [Google Scholar] [CrossRef] [Green Version] - Fusco, F.; De Vita, P.; Mirus, B.B.; Baum, R.L.; Allocca, V.; Tufano, R.; Clemente, E.D.; Calcaterra, D. Physically based estimation of rainfall thresholds triggering shallow landslides in volcanic slopes of Southern Italy. Water
**2019**, 11, 1915. [Google Scholar] [CrossRef] [Green Version] - Baum, R.L.; Savage, W.Z.; Godt, J.W. TRIGRS—A Fortran Program for Transient Rainfall Infiltration and Grid-Based Regional Slope Stability Analysis, Version 2.0; Open File Report 2008–1159; US Geological Survey: Reston, VA, USA, 2008; 75p.
- Dikshit, A.; Satyam, N.; Pradhan, B. Estimation of rainfall—induced landslides using the trigrs model. Earth Syst. Environ.
**2019**, 3, 575–584. [Google Scholar] [CrossRef] - Dikshit, A.; Satyam, D.N. Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India. Innov. Infrastruct. Solut.
**2018**, 3, 24. [Google Scholar] [CrossRef] - Innes, J.L. Debris flows. Prog. Phys. Geog.
**1983**, 7, 469–501. [Google Scholar] [CrossRef] - Aleotti, P. A warning system for rainfall-induced shallow failures. Eng. Geol.
**2004**, 73, 247–265. [Google Scholar] [CrossRef] - Segoni, S.; Piciullo, L.; Gariano, S.L. A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides
**2018**, 15, 1483–1501. [Google Scholar] [CrossRef] - Lagomarsino, D.; Segoni, S.; Rosi, A.; Rossi, G.; Battistini, A.; Catani, F.; Casagli, N. Quantitative comparison between two different methodologies to define rainfall thresholds for landslide forecasting. Nat. Hazards Earth Syst. Sci.
**2015**, 15, 2413–2423. [Google Scholar] [CrossRef] [Green Version] - Campbell, R.H. Debris flows originating from soil slips during rainstorms in Southern California. Q. J. Eng. Geol.
**1974**, 7, 339–349. [Google Scholar] [CrossRef] - Crosta, G.B.; Frattini, P. Rainfall thresholds for soil slip and debris flow triggering. In Proceedings of the 2nd EGS Plinius Conference on Mediterranean Storms, Siena, Italy, 16–18 October 2000; pp. 463–487. [Google Scholar]
- Song, S.; Wang, W. Impacts of antecedent soil moisture on the rainfall-runoff transformation process based on high-resolution observations in soil tank experiments. Water
**2019**, 11, 296. [Google Scholar] [CrossRef] [Green Version] - Glade, T.; Crozier, M.; Smith, P. Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical Antecedent Daily Rainfall Model. Pure Appl. Geophys.
**2000**, 157, 1059–1079. [Google Scholar] [CrossRef] - Ponziani, F.; Pandolfo, C.; Stelluti, M.; Berni, N.; Brocca, L.; Moramarco, T. Assessment of rainfall thresholds and soil moisture modeling for operational hydrogeological risk prevention in the Umbria region (central Italy). Landslides
**2012**, 9, 229–237. [Google Scholar] [CrossRef] - Zhao, B.; Dai, Q.; Han, D.; Dai, H.; Mao, J.; Zhuo, L.; Rong, G. Estimation of soil moisture using modified antecedent precipitation index with application in landslide predictions. Landslides
**2019**, 16, 2381–2393. [Google Scholar] [CrossRef] - Segoni, S.; Rosi, A.; Lagomarsino, D.; Fanti, R.; Casagli, N. Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system. Nat. Hazards Earth Syst. Sci.
**2018**, 18, 807–812. [Google Scholar] [CrossRef] [Green Version] - Segoni, S.; Rosi, A.; Fanti, R.; Gallucci, A.; Monni, A.; Casagli, N. A regional-scale landslide warning system based on 20 years of operational experience. Water
**2018**, 10, 1297. [Google Scholar] [CrossRef] [Green Version] - Zhao, B.; Dai, Q.; Han, D.; Dai, H.; Mao, J.; Zhuo, L. Probabilistic thresholds for landslides warning by integrating soil moisture conditions with rainfall thresholds. J. Hydrol.
**2019**, 574, 276–287. [Google Scholar] [CrossRef] - Dikshit, A.; Satyam, D.N.; Towhata, I. Early warning system using tilt sensors in Chibo, Kalimpong, Darjeeling Himalayas, India. Nat. Hazards
**2018**, 94, 727–741. [Google Scholar] [CrossRef] - Raffelli, G.; Previati, M.; Canone, D.; Gisolo, D.; Bevilacqua, I.; Capello, G.; Biddoccu, M.; Cavallo, E.; Deiana, R.; Cassiani, G.; et al. Local-and plot-scale measurements of soil moisture: Time and spatially resolved field techniques in plain, hill and mountain sites. Water
**2017**, 9, 706. [Google Scholar] [CrossRef] - Panagoulia, D.; Dimou, G. Sensitivities of groundwater-streamflow interaction to global climate change. Hydrol. Sci. J.
**1996**, 41, 781–796. [Google Scholar] [CrossRef] [Green Version] - Parkin, G. SHETRAN Water Flow Component, Equations and Algorithms. Ph.D. Thesis, Newcastle University, Newcastle upon Tyne, UK, 1995. [Google Scholar]
- Birkinshaw, S.J.; Ewen, J. Nitrogen transformation component for SHETRAN catchment nitrate transport modelling. J. Hydrol.
**2000**, 230, 1–17. [Google Scholar] [CrossRef] - Bathurst, J.C.; Moretti, G.; Burton, A.; Bathurst, J.C.; Moretti, G.; Scenario, A.B. Scenario modelling of basin-scale, shallow landslide sediment yield, Valsassina, Italian Southern Alps. Nat. Hazards Earth Syst. Sci.
**2005**, 5, 189–202. [Google Scholar] [CrossRef] [Green Version] - Beven, K.; Freer, J. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J. Hydrol.
**2001**, 249, 11–29. [Google Scholar] [CrossRef] - De Hipt, F.O.; Diekkrüger, B.; Steup, G.; Yira, Y.; Hoffmann, T.; Rode, M. Applying SHETRAN in a tropical west African catchment (Dano, Burkina Faso)-calibration, validation, uncertainty assessment. Water
**2017**, 9, 101. [Google Scholar] [CrossRef] [Green Version] - Teja, T.S.; Dikshit, A.; Satyam, N. Determination of Rainfall Thresholds for Landslide Prediction Using an Algorithm-Based Approach: Case Study in the Darjeeling Himalayas, India. Geosciences
**2019**, 9, 302. [Google Scholar] [CrossRef] [Green Version] - Dikshit, A.; Sarkar, R.; Satyam, N. Probabilistic approach toward Darjeeling Himalayas landslides-A case study. Cogent Eng.
**2018**, 5, 1–11. [Google Scholar] [CrossRef] - Kalimpong District Webpage. Available online: https://kalimpongdistrict.in/ (accessed on 3 December 2019).
- Chatterjee, R. Landslide Hazard Zonation Mapping of Kalimpong; VDM Verlag Dr. Müller: Saarbrücken, Germany, 2010. [Google Scholar]
- Chakraborty, I.; Ghosh, S.; Bhattacharya, D.; Bora, A. Earthquake Induced Landslides in the Sikkim-Darjeeling Himalayas—An Aftermath of the 18th September 2011 Sikkim Earthquake. Available online: http://www.sikenvis.nic.in/Database/Sikkimearthquake_4089.aspx (accessed on 28 July 2019).
- Mukherjee, A.; Mitra, A. Geotechnical Study of Mass Movements Along the Kalimpong Approach Road in the Eastern Himalayas. Indian J. Geol.
**2001**, 73, 271–279. [Google Scholar] - Save The Hills Blog. Available online: http://savethehills.blogspot.com/ (accessed on 3 December 2019).
- CartoDEM. Available online: https://bhuvan-app3.nrsc.gov.in/data/download/index.php (accessed on 20 August 2019).
- Dikshit, A.; Satyam, N. Rainfall Thresholds for the prediction of Landslides using Empirical Methods in Kalimpong, Darjeeling, India. In Proceedings of the JTC1 Workshop on Advances in Landslide Understanding, Barcelona, Spain, 24–26 May 2017. [Google Scholar]
- McGeary, D.; Charles, C.P.; Diane, H.C. Physical Geology: Earth Revealed; McGraw-Hill: New York, NY, USA, 2001. [Google Scholar]
- Kim, S.W.; Chun, K.W.; Otsuki, K.; Shinohara, Y.; Kim, M.I.l.; Kim, M.S.; Lee, D.K.; Seo, J.I.l.; Choi, B.K. Heavy rain types for triggering shallow landslides in South Korea. J. Fac. Agric. Kyushu Univ.
**2015**, 60, 243–249. [Google Scholar] - Zhang, R.; Santos, C.A.G.; Moreira, M.; Freire, P.K.M.M.; Corte-Real, J. Automatic calibration of the SHETRAN hydrological modelling system using MSCE. Water Resour. Manag.
**2013**, 27, 4053–4068. [Google Scholar] [CrossRef] [Green Version] - Zhang, K.; Wang, S.; Bao, H.; Zhao, X. Characteristics and influencing factors of rainfall-induced landslide and debris flow hazards in Shaanxi Province, China. Nat. Hazards Earth Syst. Sci.
**2019**, 19, 93–105. [Google Scholar] [CrossRef] [Green Version] - Marc, O.; Stumpf, A.; Malet, J.P.; Gosset, M.; Uchida, T.; Chiang, S.H. Initial insights from a global database of rainfall-induced landslide inventories: The weak influence of slope and strong influence of total storm rainfall. Earth Surf. Dyn.
**2018**, 6, 903–922. [Google Scholar] [CrossRef] [Green Version] - De Vita, P.; Reichenbach, P.; Bathurst, J.C.; Borga, M.; Crozier, G.M.; Glade, T.; Guzzetti, F.; Hansen, A.; Wasowski, J. Rainfall-triggered landslides: A reference list. Environ. Geol.
**1998**, 35, 219–233. [Google Scholar] [CrossRef] - Lazzari, M.; Piccarreta, M. Landslide disasters triggered by extreme rainfall events: The case of montescaglioso (Basilicata, Southern Italy). Geosciences
**2018**, 8, 377. [Google Scholar] [CrossRef] [Green Version] - CartoDEM: A National Digital Elevation Model from Cartosat-1 Stereo Data. Available online: https://www.nrsc.gov.in/ (accessed on 7 January 2020).
- Copernicus Climate Change Service. Available online: https://cds.climate.copernicus.eu/ (accessed on 30 December 2019).
- MERRA Date. Available online: http://www.cgd.ucar.edu/cas/catalog/reanalysis/merra/index.html (accessed on 30 December 2019).
- Birkinshaw, S.J. Physically-based modelling of double-peak discharge responses at Slapton Wood catchment. Hydrol. Process.
**2008**, 22, 1419–1430. [Google Scholar] [CrossRef] - Norouzi Banis, Y.; Bathurst, J.C.; Walling, D.E. Use of caesium-137 data to evaluate SHETRAN simulated long-term erosion patterns in arable lands. Hydrol. Process.
**2004**, 18, 1795–1809. [Google Scholar] [CrossRef] - Abbott, M.B.; Bathurst, J.C.; Cunge, J.A.; O’Connell, P.E.; Rasmussen, J. An introduction to the European Hydrological System—Systeme Hydrologique Europeen “SHE”, 2: Structure of a physically-based, distributed modelling system. J. Hydrol.
**1986**, 87, 45–59. [Google Scholar] [CrossRef] - Parkin, G. A Three-Dimensional Variably-Saturated Subsurface Modelling System for River Basins. Ph.D. Thesis, Newcastle University, Newcastle upon Tyne, UK, 1996. [Google Scholar]
- Nash, J.E.; Sutcliffe, I. V River Flow Forecasting Through Conceptual Models Part I—A Discussion of Priciples. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - Garambois, P.A.; Roux, H.; Larnier, K.; Labat, D.; Dartus, D. Caractérisation de comportements de bassins versants et sélection de pluies pour la calibration de modèles hydrologiques dans le cas de crues éclair: Bassins de l’est des Pyrénées. Hydrol. Sci. J.
**2015**, 60, 424–447. [Google Scholar] [CrossRef] [Green Version] - Berti, M.; Martina, M.L.V.; Franceschini, S.; Pignone, S.; Simoni, A.; Pizziolo, M. Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach. J. Geophys. Res. Earth Surf.
**2012**, 117, 1–20. [Google Scholar] [CrossRef] [Green Version] - Peruccacci, S.; Brunetti, M.T.; Luciani, S.; Vennari, C.; Guzzetti, F. Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy. Geomorphology
**2012**, 139–140, 79–90. [Google Scholar] [CrossRef] - Brunetti, M.T.; Peruccacci, S.; Rossi, M.; Luciani, S.; Valigi, D.; Guzzetti, F. Rainfall thresholds for the possible occurrence of landslides in Italy. Nat. Hazards Earth Syst. Sci.
**2010**, 10, 447–458. [Google Scholar] [CrossRef] - Silverman, B.W. Density Estimation for Statistics and Data Analysis; Chapman and Hall: London, UK, 1986. [Google Scholar]
- Lyons, L. Bayes and Frequentism: A particle physicist’s perspective. Contemp. Phys.
**2013**, 54, 1–16. [Google Scholar] [CrossRef] [Green Version] - Ippisch, O.; Vogel, H.J.; Bastian, P. Validity limits for the van Genuchten-Mualem model and implications for parameter estimation and numerical simulation. Adv. Water Resour.
**2006**, 29, 1780–1789. [Google Scholar] [CrossRef] - Schaap, M.G.; Leij, F.J.; Genuchten, M.T. Van ROSETTA: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J. Hydrol.
**2001**, 251, 163–176. [Google Scholar] [CrossRef] - Jensen, D.; Hargreaves, G.; Temesgen, B.; Allen, R. Com-putation of ETo under nonideal conditions. J. Irrig. Drain. Eng.
**1997**, 123, 394–400. [Google Scholar] [CrossRef] - Ho-Hagemann, H.T.M.; Hagemann, S.; Rockel, B. On the role of soil moisture in the generation of heavy rainfall during the Oder flood event in July 1997. Tellus Ser. A Dyn. Meteorol. Oceanogr.
**2015**, 6, 28611. [Google Scholar] [CrossRef] [Green Version] - Dikshit, A.; Satyam, N. Probabilistic rainfall thresholds in Chibo, India: Estimation and validation using monitoring system. J. Mt. Sci.
**2019**, 16, 870–883. [Google Scholar] [CrossRef]

**Figure 2.**Drainage map of Kalimpong, overlaid on a digital elevation model [43].

**Figure 6.**Histogram of conditional probability of occurrence of landslides based on rainfall severity and antecedent soil moisture.

**Table 1.**The monthly rainfall data (mm) during monsoon season in Kalimpong town (2010–2017) [42].

Month | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |

June | 316.8 | 337.0 | 354.9 | 248.0 | 396.4 | 568.0 | 327.2 | 153.7 |

July | 665.4 | 678.0 | 433.1 | 424.6 | 371.2 | 534.4 | 869.8 | 811.5 |

August | 425.3 | 525.6 | 250.8 | 401.0 | 571.8 | 242.3 | 262.6 | 432.1 |

September | 268.2 | 384.1 | 467.9 | 113.0 | 265.4 | 331.2 | 366.8 | 287.6 |

Parameters | Calibrated Value |
---|---|

Canopy storage | 5 mm |

AE/PE at field capacity | 1 |

Maximum Rooting Depth | 1.6 m |

Saturated water content | 0.40 |

Strickler overland flow coefficient | 0.50 m^{1/3}s^{−1} |

Saturated hydraulic conductivity | 1.14 m/day |

Leaf Area Index | 1 |

Residual water content | 0.08 |

vanGenuchten-n | 1.17 |

vanGenuchten-alpha | 0.03 cm^{−1} |

Exceedance Probability (%) | α | γ |
---|---|---|

50 | 6.03 | 0.65 |

20 | 4.08 | 0.65 |

10 | 3.31 | 0.65 |

5 | 2.38 | 0.65 |

min | 1.50 | 0.65 |

Statistical Attributes | TP | FP | FN | TN | Sensitivity = TP/(TP + FN) | Specificity = TN/(FP + TN) | False Positive Rate = FP/(FP + TN) | Likelihood Ratio = Sensitivity/(1 − Specificity) | |
---|---|---|---|---|---|---|---|---|---|

Empirical Thresholds | Tmin | 5 | 67 | 2 | 291 | 0.7143 | 0.8128 | 0.1872 | 3.8166 |

T5 | 2 | 47 | 5 | 311 | 0.2857 | 0.8687 | 0.1313 | 2.1763 | |

T10 | 1 | 37 | 6 | 321 | 0.1429 | 0.8966 | 0.1034 | 1.3822 | |

T20 | 1 | 27 | 6 | 331 | 0.1429 | 0.9246 | 0.0754 | 1.8942 | |

T50 | 1 | 15 | 6 | 343 | 0.1429 | 0.9581 | 0.0419 | 3.4095 | |

Probabilistic Thresholds | P > 0.1 | 6 | 41 | 1 | 317 | 0.8571 | 0.8855 | 0.1145 | 7.4843 |

P > 0.2 | 2 | 27 | 5 | 331 | 0.2857 | 0.9246 | 0.0754 | 3.7884 | |

P > 0.4 | 2 | 26 | 5 | 332 | 0.2857 | 0.9274 | 0.0726 | 3.9341 | |

P > 0.6 | 2 | 25 | 5 | 333 | 0.2857 | 0.9302 | 0.0698 | 4.0914 | |

P > 0.8 | 2 | 17 | 5 | 341 | 0.2857 | 0.9525 | 0.0475 | 6.0168 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Abraham, M.T.; Satyam, N.; Pradhan, B.; Alamri, A.M.
Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas. *Water* **2020**, *12*, 804.
https://doi.org/10.3390/w12030804

**AMA Style**

Abraham MT, Satyam N, Pradhan B, Alamri AM.
Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas. *Water*. 2020; 12(3):804.
https://doi.org/10.3390/w12030804

**Chicago/Turabian Style**

Abraham, Minu Treesa, Neelima Satyam, Biswajeet Pradhan, and Abdullah M. Alamri.
2020. "Forecasting of Landslides Using Rainfall Severity and Soil Wetness: A Probabilistic Approach for Darjeeling Himalayas" *Water* 12, no. 3: 804.
https://doi.org/10.3390/w12030804