Emergence of landslide phenomena can be due to the different factors like geology, geomorphology, hydrology, biology and human activities but main role in landslide initiation only is created by triggered factor. Intense rainfall, rapid snowmelt, water-level change, seismic loading and rapid erosion are the most important landslide triggering mechanisms. Rainfall has been known as the most common factor in occurring landslides. In general, infiltration of rainfall in soil causes to increase soil water pressure, reduce matric suction and increase the weight of soil mass and finally, soil strength is decreases and landslide occurs. Moreover, groundwater conditions responsible for slope failures are related to rainfall through infiltration, soil characteristics, antecedent moisture content, and rainfall history. Thus, the most important section of landslide modeling (empirical, statistical or physically-based) is hydrological part. In this study, it has been tried to present the obtained results and the applied idioms in most cited researches about hydrological modeling of landslides. With respect to the human and financial damages due to occurring landslides in our country, designing and applying a landslide warning system is necessary and doing study in this background is huge step for decreasing damages and sustainable managing of natural hazards. Authors hope to witness of creating the advance warning systems for landslides in our country by using obtained experiences around the world and motivations of internal researchers.
Materials and Methods
We investigated many researches about hydrological modeling of landslides. Then, we selected those researches that have been presented in some highly cited books and papers. Based on our literature review, the time period between 1975 (presenting rainfall threshold for initiation of landslide by Campbell) and 2008 (presenting a physically-based model (HSB-SM) by Telabi et al.) was considered for our studies.
Results and Discussion
The consideration that some storms produce failures and others do not, could induce scientific research to analyze the relationship between rainfall amount and landslide initiation. This is aimed at identifying the rainfall thresholds. A threshold is the minimum or maximum level of some quantity needed for a process to take place or a state to change. For rainfall-induced landslides a threshold may define the rainfall, soil moisture, or hydrological conditions that, when reached or exceeded, are likely to trigger landslides. Rainfall thresholds can be defined on physical (process based, conceptual) or empirical (historical, statistical) bases.
Empirical thresholds for the initiation of landslides can be loosely defined as global, regional, or local thresholds. Review of the literature reveals that no unique set of measurements exists to characterize the rainfall conditions that are likely (or not likely) to trigger slope failures. Based on the considered rainfall measurements, empirical rainfall thresholds can be grouped in three broad categories: (i) thresholds that use event rainfall measurements, (ii) thresholds that employ antecedent conditions, and (iii) thresholds that consider other conditions (for instance antecedent discharge, storms with different frequencies and relative parameters). First category can be further subdivided in four subcategories: (i) intensity-duration (ID) thresholds, (ii) thresholds based on the total event rainfall, (iii) rainfall event-duration (ED) thresholds, and (iv) rainfall event-intensity (EI) thresholds. Intensity-duration thresholds are the most common type of thresholds proposed in the literature.
Physically-based models attempt to extend spatially the slope stability models (e.g., the ‘‘infinite slope stability method’’) widely adopted in geotechnical engineering (Wu and Sidle, 1995; Iverson, 2000). To link rainfall pattern and its history to slope stability/instability conditions, physically-based models incorporate infiltration models (e.g., Green and Ampt, 1911; Philip, 1954; Salvucci and Entekabi, 1994). Various approaches have been proposed to predict the accumulation of the infiltrated water into the ground. Crosta and Frattini (2003) compared three infiltration models, including a steady-state model (Montgomery and Dietrich, 1994), a transient “piston-flow’’ model (Green and Ampt, 1911; Salvucci and Entekabi, 1994), and a transient diffusive model (Iverson, 2000), to predict the location and time of debris flows. In this paper, some physically-based models (ASWS, SHALSTAB, SINMAP, dSLAM, IDSSM) and recently developed model (HSB-SM) have been studied. The HSB-SM model is composed of three parts: a topography model conceptualizing three-dimensional soil mantled landscapes, a dynamic hydrology model for shallow subsurface flow and water table depth (HSB model) and an infinite slope stability method based on the Mohr-Coulomb failure law. The model (HSB-SM) is able to simulate rain-induced shallow landsliding on hillslopes with non-constant bedrock slope and non-parallel plan shape. Review of the literature reveals that physically based models perform best when attempting to predict shallow landslides (soil slides and debris flows).
Process-based models can determine the amount of precipitation needed to trigger slope failures, and the location and time of the expected landslides, making them of interest for landslide warning systems. However, limitations exist. physical models require detailed spatial information on the hydrological, lithological, morphological, and soil characteristics that control the initiation of landslides. This information is difficult to collect precisely over large areas, and is rarely available outside specifically equipped test fields. Process based models are calibrated using rainfall events for which precipitation measurements and the location and the time of slope failures are known. This information is not commonly available and is costly to obtain. Therefore, with respect to the availability of rainfall data in most regions of Iran, empirical-statistical models can be used for determination of a confidence relation between rainfall characteristics and landslide occurrence in each region. The inputs of these models are mainly the rainfall data with different time intervals which makes their applications to be simple. On the other hand, with respect to the applied researches in different sites of the world and also in Iran (by authors), the obtained results have appropriate accuracy that has been studied in this paper.