令和6年度砂防学会研究発表会概要集

R3-7

Effects of Mean Annual Rainfall on the Rainfall Thresholds for Occurring Landslides

University of Tsukuba 〇WAKS Ariyakumara and Taro Uchida

1. Introduction
Rainfall thresholds for occurring landslides are playing a significant role to forecast the possible occurrence of landslides inmany early warning systems against rain induced landslides thereby protecting the lives and properties of vulnerable
communities (e.g., Osanai et al., 2010). Generally, rainfall threshold for rain induced landslides represents the lower bound ofrainfall condition that caused in landslides (Reichenbach et al., 1998). The determination of rainfall thresholds may be influenced
by site-specific conditions such as lithology, soil conditions, topography, land use, land cover, season, and mean annual rainfall(MAR) (e.g., Peruccacci et al., 2017; Jia et al., 2020; Lee et al., 2022). Clarifying and quantifying of the effects of thosecontrolling factors on the rainfall thresholds are crucial to enhance the predicting power of the landslide early warning systems.
Recently, there has been a considerable increase in the number of studies on rainfall thresholds for rain-induced landslides(Gonzalez et al., 2024). However, the role of site-specific conditions has not been fully examined through intercomparison.
Therefore, this study aims to clarify the role of MAR on the rainfall thresholds for occurring landslides by comparing previously
defined rainfall thresholds for targeting different MARs available in the literature.
2. Methodology
Intensity-Duration (I-D) and total Event rainfall - Duration (E-D) type rainfall thresholds are most common and widely usedamong different types of rainfall thresholds (Segoni et al., 2018; He et al., 2020). So, in this study we focused on these two types.
I-D type rainfall thresholds can be generalised as a power law equation as 𝐼 = 𝛼𝐷 −𝛽 (eq. 1) where, D is the responsible rainfall
duration (h), and I is the average rainfall intensity for the relevant rainfall period (mmh -1), α and β (gradient) are parameters ofthe power law. Furthermore, I = E / D (eq. 2) where, E is cumulative (total) event rainfall in millimetres (mm). Hence, the aboveeq. 1 can be re written as 𝐸 = 𝛼𝐷𝛾 (eq. 3) where, 𝛾 = -β+1 and it’s the generalised power law equation for the E-D type rainfall
thresholds. Therefore, it is possible to convert given E-D type rainfall threshold to its I-D version.
In this study we collected 21 I-D type and 30 E-D type rainfall threshold equations through a literature survey. Some of therainfall thresholds covered the entire world and large regions with varying mean annual rainfall (MAR). Therefore, we selectedrainfall thresholds with a relatively narrow target area and for which MAR can be identified. First, we tried to gather threshold
equations which author directly show the information about the targeted MAR category for those thresholds in the original article
and next we collected some rainfall thresholds which able to find targeted MAR category of those thresholds from the outside
data sources (ex: other research articles, MAR data bases and maps). Finally, we selected 22 rainfall thresholds and categorised
in to two categories according to their targeted MARs as low mean annual rainfall (applicable for MAR < 1300 mm), and highmean annual rainfall (applicable for MAR > 1300 mm). We analysed the variation of β values and 10 h threshold rainfall
intensities (I10) of the selected rainfall thresholds according to their targeted MAR categories.
3. Results
Most of the instances the I-D thresholds belong to low MAR category are lower than the I-D thresholds belong to high MAR
category and β values (gradients) belong to low MAR category are higher than the β values belong to high MAR category (Figs.
1 and 2). So, I-D thresholds defined for targeting low MARs are commonly steep than the I-D thresholds defined for targetinghigh MARs (Fig. 1). Generally, I10 of low MAR category are lower than the I10 of high MAR category (Fig. 3).

Figure 1: categorisation of the I-D thresholds according to theirtargeted MAR category. Sources: (01) Jibson (1989); (02) Saito etal., (2010); (03) Brunnetti et al., (2010); (04)-(07) Peruccacci et al.,(2012); (08) Ma et al., (2015); (09)-(12) Peruccacci et al., (2017);(13)-(14) Nawagamuwa and Perera (2017); (15) Melillo et al.,(2018); (16)-(18) Jordanova et al., (2020); (19) Maturidi et al.,(2021); (20)-(22) Lee et al., (2022). Targeted areas of the I-D
thresholds: (01)-(02) Japan; (03) Abruzzo, Central Italy; (04)-(07)Abruzzo, Marche, and Umbria, central Italy; (08) Zhejiang
province of China; (09)-(12) Italy; (13) Sri Lanka; (14) Badulla
District, Sri Lanka; (15) Liguria region of Italy; (16)-(18) Slovenia;(19) Peninsular Malaysia; (20)-(22) South Korea.

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N=14

N=8

N=8Figure 2: Variation of the β values of the I-D thresholds
according to their targeted MAR category. N is theamount of data for each category.
Figure 3: Variation of the I10 of the I-D thresholds
according to their targeted MAR category. N is theamount of data for each category.
category.

4. Discussion

Previous studies have evaluated the impact of MAR by analysing rainfall thresholds using a uniform method in a limited area,such as one country and/or focused on one type of landslide (e.g., Wilson, 2000; Peruccacci et al., 2017; Jordanova et al., 2020).
In contrast, this study evaluates the impact of MAR by analysing rainfall thresholds, which were defined for targeting different
types of landslides using data of different quality and quantity from various regions with different topography, geology and soon. However, the observed effect of MAR in this study is still consistent with previous research, suggesting a significant impactof MAR on rainfall thresholds to induce sediment disasters.
The heterogeneity of the other controlling factors addition to the MAR of our studied threshold collection is also high. It may be
influenced to the results also therefore, more studies are recommended to confirm the results furthermore.
5. Conclusion
The MAR considerably effects to decide the rainfall threshold for occurring landslides in a particular region. The findings of thisstudy suggest the fact that where MAR is low it requires less amount of rainfall to trigger the landslides than where MAR is high.
However, this scenario can be affected by the involvement of the other controlling factors which are responsible to decide therainfall thresholds for occurring landslides.
6. References
Brunetti, M. T. et al. (2010). Natural Hazards and Earth System Sciences, 10(3), 447-458. / Gonzalez, F. C. et al. (2024). Heliyon,
10(1). / He, S. et al. (2020). Water, 12(2), 494. / Jia, G. et al. (2020). Landslides, 17(2), 283-299. / Jibson, R.W. (1989). GeologicalSociety of America special paper, 236, 29-55. / Jordanova, G. et al. (2020). Water, 12(5), 1449. / Lee, J. U. et al. (2022). Water,
14(13), 2051. / Ma, T. et al. (2015). Geomorphology, 245, 193-206. / Maturidi, A. M. A. M. et al. (2021). KSCE Journal of Civil
Engineering, 25, 4552-4566. / Melillo, M. et al. (2018). Environmental Modelling & Software, 105, 230-243. / Nawagamuwa,
U. P., & Perera, L. P. (2017). In Advancing Culture of Living with Landslides: Volume 5 Landslides in Different
Environments (pp. 267-272). Springer International Publishing. / Osanai, N. et al (2010). Landslides, 7, 325-338. / Peruccacci,
S. et al. (2012). Geomorphology, 139, 79-90. / Peruccacci, S. et al. (2017). Geomorphology, 290, 39-57. / Reichenbach, P. et al.
(1998). Environmental Geology, 35(2), 146-159. / Saito, H. et al. (2010). Geomorphology, 118(1-2), 167-175. / Segoni, S. et al.
(2018). Landslides, 15(8), 1483-1501. / Wilson, R. C. (2000). In Proc. 1st Plinius Conf. on Mediterranean Storms, edited by:
Claps, P. and Siccardi, F., Maratea (pp. 415-424).

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