Probabilistic rainfall thresholds for debris flows occurred after the Wenchuan earthquake using a Bayesian technique

Jiang, Zhuoyan; Fan, Xuanmei; Subramanian, Srikrishnan Siva; Yang, Fan; Tang, Ran; Xu, Qiang; Huang, Runqiu

doi:10.1016/j.enggeo.2020.105965

Cited by 30 publications

(12 citation statements)

References 59 publications

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“…The nonunique distribution characteristics of geohazards led to the partial dependence of EI in 2008 generating a significant peak and trough in XI and IX intensity areas, respectively. After the Wenchuan earthquake, the activity difference between the main faults decreased, and the loose deposits left by earthquakes in the mountains triggered geohazards that were mainly caused by precipitation [ 115 ]. Therefore, in 2009–2012, the ranking of EI decreased significantly, and relative importance in different intensity areas also decreased significantly.…”

Section: Discussionmentioning

confidence: 99%

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Zhao

et al. 2022

IJERPH

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Multitemporal geohazard susceptibility analysis can not only provide reliable results but can also help identify the differences in the mechanisms of different elements under different temporal and spatial backgrounds, so as to better accurately prevent and control geohazards. Here, we studied the 12 counties (cities) that were severely affected by the Wenchuan earthquake of 12 May 2008. Our study was divided into four time periods: 2008, 2009–2012, 2013, and 2014–2017. Common geohazards in the study area, such as landslides, collapses and debris flows, were taken into account. We constructed a geohazard susceptibility index evaluation system that included topography, geology, land cover, meteorology, hydrology, and human activities. Then we used a random forest model to study the changes in geohazard susceptibility during the Wenchuan earthquake, the following ten years, and its driving mechanisms. We had four main findings. (1) The susceptibility of geohazards from 2008 to 2017 gradually increased and their spatial distribution was significantly correlated with the main faults and rivers. (2) The Yingxiu-Beichuan Fault, the western section of the Jiangyou-Dujiangyan Fault, and the Minjiang and Fujiang rivers were highly susceptible to geohazards, and changes in geohazard susceptibility mainly occurred along the Pingwu-Qingchuan Fault, the eastern section of the Jiangyou-Dujiangyan Fault, and the riparian areas of the Mianyuan River, Zagunao River, Tongkou River, Baicao River, and other secondary rivers. (3) The relative contribution of topographic factors to geohazards in the four different periods was stable, geological factors slowly decreased, and meteorological and hydrological factors increased. In addition, the impact of land cover in 2008 was more significant than during other periods, and the impact of human activities had an upward trend from 2008 to 2017. (4) Elevation and slope had significant topographical effects, coupled with the geological environmental effects of engineering rock groups and faults, and river-derived effects, which resulted in a spatial aggregation of geohazard susceptibility. We attributed the dynamic changes in the areas that were highly susceptible to geohazards around the faults and rivers to the changes in the intensity of earthquakes and precipitation in different periods.

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Section: Discussionmentioning

confidence: 99%

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Zhao

et al. 2022

IJERPH

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“…The inter-event time (IET) was defined as a measure of the minimum time interval between two consecutive rainfall pulses (Adams et al, 1986). Although the IET strongly affects the start and end times of an event (Bel et al, 2017), there are no standard criteria for rainfall episode separation (Jiang et al, 2021). Peres et al (2018) noted that the IET depends on whether the rainfall during an IET is smaller than the mean daily potential evapotranspiration (MDPE).…”

Section: ◆ Historical Debris Flow and Rainfall Datamentioning

confidence: 99%

“…AEP has an important effect on the rainfall threshold for triggering debris flow. Debris flow prediction can be improved by quantifying this effect (Chen et al, 2018;Zhao et al, 2019;Hirschberg et al, 2021;Marino et al, 2020;Jiang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…rainfall (Kim et al, 1991;Glade et al, 2000;Dahal and Hasegawa, 2008;Giannecchini et al, 2012) or a combination of daily rainfall intensity and rainfall duration (Hasnawir and Kubota, 2008;Khan et al, 2012;Zhao et al, 2019;Kim et al, 2020;Yang et al, 2020) to investigate the effects of AEP on the rainfall threshold. Jiang et al (2021) investigated the probabilistic rainfall thresholds for debris flows after the Wenchuan earthquake and found that antecedent precipitation plays an important role in long-duration rainfall-induced debris flows. Zhao et al (2019) introduced the simulated antecedent soil moisture into a probabilistic threshold and found that it exhibited better prediction performance than the daily rainfall intensity and rainfall duration (ED) threshold.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative effects of antecedent effective rainfall on ID threshold for debris flow

Zhang

Yang

Liu

et al. 2022

Preprint

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Abstract. Studies have shown that the antecedent effect precipitation (AEP) is closely related to rainfall intensity-duration (ID) threshold of debris flow. However, the quantitative relationship between the AEP and ID threshold is still undetermined. In this study, a hydrological process based numerical model (Dens-ID) that can derive the ID threshold curve is adopted to address this issue. Jiangjia Gully (JJG) in Dongchuan District of Yunnan Province was chosen as the study area, Dens-ID was used to derive a series of ID threshold curves corresponding to different AEP. Based on calculated data sets including AEP, ID curves, parameters of ID curve equation (α and β), and debris flow density, the influence of AEP on the ID threshold curve is deeply explored. We found that although solid materials and runoff are the two necessary conditions for the formation of debris flow, the specific roles played in which are different: the volume of loose solid sources provides a basal condition for debris flow and determines the scale of debris flow, while the runoff volume will have a sudden change during the rainfall process, which is a key factor promoting the formation of debris flow. In the condition of AEP ranging from 20 mm to 90 mm, AEP and α can be described by the equation α = −0.0078AEP2 + 0.68AEP + 6.43, and β shows a linear change law with AEP. The error of the two equations were evaluated using 45 historical rainfall data that triggered debris flows, which is equal to 37.85 % and 11.1 %. Due to the two functions, the ID threshold curve can regularly move in the I-D coordinate system rather than a conventional threshold curve stay the same regardless of AEP variation, it is beneficial to improve the prediction capacity of the ID threshold.

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“…Where α and β are constant fitting parameters, though the relationship is empirical, Berti et al (2020) provided a physical interpretation of the I-D relationship for runoff-generated debris flows and demonstrated the initiation processes. Recently, Jiang et al (2021) used the ID thresholds to define interevent-time (IET) of rainfall data for debris flow early warning through machine learning. Due to the practicality and global usage of this approach, in this study, we use numerical modeling to identify the intensity and duration of the debris flow that occurred on 6 August 2020.…”

Section: Numerical Modeling and Analysismentioning

confidence: 99%

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

Subramanian

Yunus

Jasin

et al. 2021

Preprint

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The frequency of unprecedented extreme precipitation events is increasing, and consequently, catastrophic debris flows occur in regions worldwide. Rapid velocity and long-runout distances of debris flow induce massive loss of life and damage to infrastructure. Despite extensive research, understanding the initiation mechanisms and defining early warning thresholds for extreme-precipitation-induced debris flows remain a challenge. Due to the nonavailability of extreme events in the past, statistical models cannot determine thresholds from historical datasets. Here, we develop a numerical model to analyze the initiation and runout of extreme-precipitation-induced runoff-generated debris flows and derive the Intensity-Duration (ID) rainfall threshold. We choose the catastrophic debris flow on 6 August 2020 in Pettimudi, Kerala, India, for our analysis. Our model satisfactorily predicts the accumulation thickness (7 m to 8 m) and occurrence time of debris flow compared to the benchmark. Results reveal that the debris flow was rapid, traveling with a maximum velocity of 9 m/s for more than 9 minutes. The ID rainfall threshold defined for the event suggests earlier thresholds are not valid for debris flow triggered by extreme precipitation. The methodology we develop in this study is helpful to derive ID rainfall thresholds for debris flows without historical data.

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Probabilistic rainfall thresholds for debris flows occurred after the Wenchuan earthquake using a Bayesian technique

Cited by 30 publications

References 59 publications

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Susceptibility Analysis of Geohazards in the Longmen Mountain Region after the Wenchuan Earthquake

Quantitative effects of antecedent effective rainfall on ID threshold for debris flow

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

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