Combined statistical and dynamic modeling for real time forecasting of rain induced landslides in Matara district, Sri Lanka - a case study

Abstract

Among the natural hazards, landslides are attracting more and more attention due to its increasing effect on economic and human losses. While hazard zonation mapping plays a vital role in identifying the vulnerable zones for future land-use planning activities, designing of early warning systems and adequate mitigation measures in landslide-prone areas, lack of real time early warnings significantly increases the damages world wide. Landslides associated with intense rain during monsoon and inter-monsoon seasons are the most pressing natural disaster in the Central Highland of Sri Lanka. About 13,000 km2 (20% area of the country) within ten administrative districts are considered to be prone to landslides and almost 42% of the total population of the country is living in these districts. According to the available records, major landslides occurred during past three decades until 2008 have caused a loss of more than 775 human lives making over 90,000 people homeless. Most significantly, Galle, Matara, and Hambantota districts which had not been considered earlier as landslide prone regions were severely affected by the catastrophic event occurred on 17th May 2003 killing more than 150 people in a single day. Every year huge economic and human losses are recorded and damages are on the rise throughout the island. This is mainly because people live everywhere at their own risk and use even the marginal lands for housing, farming, and infrastructure and development activities without an adequate attention to the problem as a result of higher demand of lands with rising population. Thus, as a measure to save lives and property it is incumbent upon to develop real time prediction models for such regions to manage future events successfully. Theoretically, slope instability hazard zonation is defined as the mapping of areas with an equal probability of occurrence of landslides in a given area within a specific period of time. However, since the determination of landslide probability is extremely difficult due to scarcity of necessary data, susceptibility assessment to identify the critical locations and establishment of triggering thresholds to predict the timing of the events can be considered as a realistic approach in landslide hazard zonation. In the present study, five susceptibility maps were prepared using 13 landslide causative factors and existing landslide data in an area of 263 km2 within Matara district of Sri lanka. Two of the commonly applied bivariate methods such as Information Value method and Weights of Evidence (WOE) modeling and, multivariate Logistic Regression (LR) modeling were utilized for the analysis. Among comparison of them, the model delivered by the Information Value method was chosen as the best representative model. Subsequently, by assigning different percentage of factor weightings according to the expert judgment and testing the success with trial and error procedure, this model was further improved and the study area was reclassified into three susceptibility zones, high, medium and low. In the final expert weighted landside susceptibility map, the zone corresponding to high susceptibility class constitutes 14.78% of the total study area predicting 65.09% of the existing landslides. A 50.69% of the study area is designated to be low susceptible with corresponding 6.03% of the existing landslides. The remaining area is classified into medium susceptibility class. Rainfall is commonly known as one of the principal landslide triggers. Thus the concept of hydrological triggering thresholds can be utilized for the prediction of timing of rain induced landslides. Under the present study, a hydrological slope stability model was used and dynamic slope stability conditions according to a given rainfall event during the month of May 2003 were calculated. Deterministically calculated factor of safety map for a selected basin on 17th May 2003 was validated with the actual landslide event. Due to the simplistic assumptions used in the model equations and the uncertainties associated with the spatially variable input data, only 21% of the actual landslide area was accurately predicted by the model. However, almost 62% of the unstable pixels are located within an area of 100 m buffer from the rupture zone of the existing landslides showing evidence of instabilities within the region of near proximity to those failures. Hence the model is accepted as a reasonable approach to identify the slope stability conditions according to daily or antecedent rainfall for preliminary predictions. Subsequently, this information combined with the best fit susceptibility model collectively with expertise about the terrain conditions can be more appropriately used for better landuse planning activities, prediction of landslide events and more importantly for the development of real time early warning systems.

Unter den Naturgefahren richtet sich das Augenmerk wegen der damit verbundenen wirtschaftlichen Schäden, aber auch Verlusten an Menschenleben zunehmend insbesondere auf die Hangrutschungen. Zwar spielt die Erfassung und Darstellung der Gefahrenzonen in fachspezifischen Karten eine grundlegende Rolle, um bei der Raumplanung und künftigen Infrastrukturmaßnahmen entsprechend auf die gefährdeten Gebiete hinzuweisen, ebenso die Entwicklung von Frühwarnsystemen sowie geeigneter Vorsorgemaßnahmen in den rutschgefährdeten Landesteilen, vor allem das Fehlen von Vorwarnungen möglichst in Echtzeit führt jedoch weltweit zu einem signifikanten Anstieg der Schäden.

Hangrutschungen in Verbindung mit intensiven Regenfällen während, aber auch außerhalb der Monsunzeit stellen die wichtigste Naturgefahr im zentralen Hochland von Sri Lanka dar. Ungefähr 13 000 km2, das entspricht 20% der Landesfläche, werden als rutschgefährdet eingeschätzt. Davon sind insgesamt zehn Distrikte betroffen, in denen 42% der gesamten Landesbevölkerung lebt. Ausgehend von den verfügbaren Aufzeichnungen kamen in den vergangenen 30 Jahren mehr als 775 Menschen bei größeren Hangrutschungen um ihr Leben, und über 90 000 Menschen verloren ihren gesamten Besitz. Hierbei ist vor allem auch das katastrophale Ereignis vom 17.05.2003 zu nennen, das sich insbesondere in den Distrikten Galle, Matara und Hambantota ereignete, die bis dahin überhaupt nicht als rutschgefährdet galten. An diesem einzigen Tag wurden damals 150 Menschen getötet und 855 Häuser völlig zerstört. Ursache für die auf ganz Sri Lanka seit Jahren ständig zunehmenden Schäden und Verluste ist die wegen der steigenden Bevölkerungsdichte und dem damit verbundenen Flächenverbrauch nahezu unkontrollierte Ausdehnung der besiedelten, aber auch landwirtschaftlich genutzten Gebiete. Bei deren Erschließung wurde das Problem der Hangrutschungen zumindest bislang nicht in angemessener Weise berücksichtigt. Als eine von mehreren Maßnahmen erscheint es daher auch als angebracht, für solche Gebiete geeignete Frühwarnmodelle zu entwickeln, um so wenigstens in Zukunft besser auf Rutschereignisse vorbereitet zu sein und letztlich Verluste an Menschenleben und Wirtschaftsgütern zu minimieren. Mit der vorliegenden Studie wird ein Beitrag dazu geleistet, bereits vorhandene statische und dynamische Modelle auf ihre Möglichkeiten zu überprüfen und diese zusammen zu bringen, um damit dem Ziel, einer Echtzeit-Vorhersage von durch Niederschläge ausgelöste Hangrutschungen im Distrikt Matara auf Sri Lanka näher zu kommen.