Keywords

1 Introduction

Rainfall-induced landslides are a common occurrence in almost all parts of the world, causing billions of US dollars in damage annually, and thousands of deaths globally. The Philippines is among those countries most prone to this type of hazard, ranking fifth in the world in terms of number of landslide (wet) disaster occurrences from 1970 to 2023 (EMDAT, CRED/UCLouvain 2023) (Fig. 1). The reason for this lies in the climate, geographic location and topography of the country. Climate in the Philippines is generally tropical maritime, characterized by high temperature and high amounts of rainfall. Some regions, particularly those falling within the equatorial climate zone, are hot and wet year-round. Rainfall is governed by the Southwest Monsoon in the boreal summer months, and by the Northeast Monsoon in the boreal winter, with contribution from tropical cyclones (Bagtasa 2017; Matsumoto et al. 2020). The mean annual rainfall varies from about 960 to 4000 mm annually, with the easternmost parts receiving the greatest amount of rain (Olaguera et al. 2022; PAGASA 2023).

Fig. 1
A horizontal bar graph depicts the global occurrences of rainfall disasters from 1970 to 2023. From top to bottom, the values are as follows. China, 73. Indonesia, 60. Columbia, 41. India, 41. Philippines, 30. Peru, 22. Nepal, 20. Japan, 15. Papua New Guinea, 15. Ecuador, 12.

The Philippines ranks fifth in terms of global occurrences of rainfall-induced landslide disaster from 1970 to 2023 (Data Source: EM-DAT, CRED/UCLouvain 2023)

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Situated in the Northwestern Pacific basin, a great portion of the rain in the Philippines, particularly along the northwestern coast of Luzon, is due to both direct and indirect influence of typhoons (Bagtasa 2017; Cinco et al. 2016). The basin is the most active tropical cyclone (TC) basin on the planet, accounting for about 30% of all TC activities (Fudeyasu et al. 2014). Worldwide, the Philippines ranks second, next only to China, in terms of TC landfalls since 1970 (NOAA’s Atlantic Oceanographic and Meteorological Laboratory 2021). About 20 TCs enter the Philippine Area of Responsibility per year, with approximately nine making landfall (Cinco et al. 2016).

Moreover, convective rainfall is also common due to the country’s mountainous terrain, interspersed with narrow coastal plains (WBG and ADB 2021; Olaguera et al. 2022). This mountainous terrain is characterized by steep slopes and are, therefore, susceptible to all types of mass movement.

Like in all parts of the world, the strong warming trend over the past few decades has affected the rainfall pattern in the Philippines. For effective disaster management, it is important to determine this change in rainfall pattern and how such a change is, in turn, affecting hydrometeorological hazard patterns. In line with this, the present study was conducted for Davao Oriental as a case study, with the aim of assessing how the rainfall is anticipated to change in the future and how such a change will affect the landslide distribution in this province. Davao Oriental was chosen as the study area due to data availability for validation of results. Nonetheless, the methodology presented in this study can be applied to other parts of the Philippines, and other regions as well.

2 Study Area

Davao Oriental is located on the island of Mindanao, lying at the southeasternmost tip of the Philippines. It covers a total area of 5680 km2, and comprises 10 municipalities and one city. Mati City serves as the province’s capital. Davao Oriental is bounded by the Philippine Sea to the east and Davao Gulf to the west (Fig. 2).

Fig. 2
A map of the United States. The areas are surrounded by the Philippine Sea and the Davao Gulf. Boston, Cateel, Baganga, Caraga, Manay, Banaybanay, Lupon, Tarragona, Mati city, San Isidro, and Governor Generoso. The inset map on the right depicts Davao Oriental.

Location of Davao Oriental and the municipalities comprising the province [Sources: Global Administrative Areas (GADM), U.S. Geological Survey and General Bathymetric Chart of the Oceans (GEBCO)]

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Rainfall in the province is more or less distributed evenly throughout the year, and is influenced by TCs, low pressure systems, cold surges, cold surge shearline, cold surge vortices, and local convective systems (Olaguera et al. 2022). The province also experiences its fair share of TCs. Figure 3a shows the TC tracks from 1971 to 2015 that may have directly or indirectly affected Davao Oriental (i.e., TCs falling within a 250-km buffer zone that is centered in the province). The decade from 1991–2000 has the most numbers of TCs, mostly tropical depressions (Fig. 3b). The decade from 1981–1990 was characterized by three destructive typhoons of Category 4. Figure 3c indicates that most TCs occurred in November and December, which were also the months when two Category 5 TCs passed through this region: Typhoon Mike (Ruping) in November 1990, and Typhoon Bopha (Pablo) in December 2012 (Narisma et al. 2017). A recent analysis of the rainfall associated with TCs (i.e., within a 1100-km radius from the TC center) from 1951–2014 indicates less than 20% contribution of TCs to rainfall over Davao Oriental (Bagtasa 2017); however, this study did not consider the contribution of tropical depressions or low-pressure systems.

Fig. 3
Three graphs. a. The tropical cyclone tracks are highlighted in the rectangular box. b, c. The stacked bar graph plot of the number of tropical cyclones depicts the categories T D, T S, c a t 1 to 5 with counts 57, 19, 5, 3, 0, 2, and 2 respectively.

(a) Tropical cyclone tracks (blue lines) within 250 km of the Davao Oriental boundary (enclosed in dashed box) from 1971 to 2015; (b) Decadal Total Number of Tropical Cyclones for Davao Oriental (1971–2015). Note that there are only five years for 2011 to 2015, (c) Monthly Total Number of Tropical Cyclones in Davao Oriental (1971–2015) (© 2017 The Oscar M. Lopez Center for Climate Change Adaptation and Disaster Risk Management Foundation, Inc., In: Narisma et al. 2017, used with permission)

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Geologically, Davao Oriental is mainly composed of Late Jurassic to Cretaceous basement rocks consisting of meta-greenstones, greenschists and ophiolitic sequences, and Early Miocene clastic rocks (Buena et al. 2019). There are two main tectonic features in the region: (1) the Philippine trench offshore to the east, which marks the subduction of the Philippine Sea Plate beneath the Philippine Mobile Belt, and (2) the southern extension of the Philippine Fault Zone, which is a 1200 km-long strike-slip fault that transects the entire country from north to south, and splits into a number of branches in the study area (Fig. 4). Davao Oriental is therefore prone not only to rainfall-induced landslides but to earthquake-triggered landslides as well.

Fig. 4
A map of the Philippine Trench and fault zone. P F Z is highlighted in the rectangular box. The strike-slip fault cuts across the entire country from north to south and splits into a number of branches in the study area.

Main tectonic features in the study area (bounded by rectangle); (1) Philippine Trench (2) PFZ—Philippine Fault Zone. Modified from PHIVOLCS (2018)

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3 Methodology

3.1 Base Landslide Susceptibility Map

This study used as a base map the official government landslide susceptibility map of Davao Oriental published by the Mines and Geosciences Bureau of the Department of Environment and Natural Resources of the Philippines (DENR-MGB 2022) (Fig. 5). Mainly derived using a field-based, qualitative approach, the map was generated considering the following parameters: slope gradient, weathering/soil characteristics, rock mass strength, ground stability, and human initiated effects (Table 1). The map therefore depicts the likelihood of a landslide occurring in an area based on the local terrain (Soeters and Van Westen 1996; Reichenbach et al. 2018). Ranking into different landslide susceptibility levels (very high, high, moderate and low) was based on the criteria in Table 1. Triggering mechanisms, such as rainfall, were not considered. Thus, in order to get an overview of how the terrain conditions may interact with rainfall at present and in the future, historical and projected rainfall data were incorporated in the present study.

Fig. 5
A map of Davao Oriental labels Boston, Cateel, Baganga, Caraga, Manay, Tarragona, Lupon, Anaybanay, San Isidro, Mati City, and Governor Generoso, from top to bottom, respectively. The inset map of Mindanao highlights the Davao Oriental.

The landslide susceptibility map of DENR-MGB (2022) used as the base map for this study (used with permission)

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Table 1 DENR-MGB Landslide susceptibility ranking (Garas 2013)
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3.2 Historical and Projected Rainfall

As extreme rainfall, from short cloudbursts to prolonged events that last for several days to weeks, is the most significant trigger of landslides (Kirschbaum et al. 2020), extreme rainfall was considered in this study. In particular, the index R95pTOT was used, which is the total amount of annual precipitation that falls on “very wet days”, or when daily rainfall RR exceeds the 95th percentile threshold of the base period (1986–2005), i.e., RR > 95p (Karl et al. 1999; Peterson et al. 2001).

Expressed mathematically,

$$ R95{p}_j TOT={\sum}_{w=1}^W{RR}_{wj}\qquad \quad {RR}_{wj}>{RR}_{wn}95 $$

where RRwj is the precipitation amount on a wet day w (i.e., RR ≥ 1.0 mm in period j), RRwn95 is the 95th percentile of precipitation on wet days in the historical (or projection) period, and W is the number of wet days in the period.

Rainfall data were obtained from a subset of the climate projections from the Southeast Asia Regional Climate Downscaling (SEACLID)/Coordinated Regional Climate Downscaling Experiment Southeast Asia (CORDEX-SEA) project, described in Tangang et al. (2020). Five Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM) were used, namely the CNRM-CM5, MPI-ESM-MR, EC-Earth, CSIRO-Mk3.6.0, and HADGEM2-ES. Kamworapan and Surussavadee (2019) provide details on these GCMs. Used for understanding and predicting climate behavior, GCMs are complex mathematical representations of the major climate system components (atmosphere, land surface, ocean, and sea ice) and their interactions. Climate projections from GCMs have coarse resolution and require downscaling at finer scales to be suitable for local analyses. In this study, the GCMs were dynamically downscaled to 25 km resolution using the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model version 4.3 (RegCM4.3) (Giorgi and Anyah 2012). The model results have biases when compared to observational datasets. Thus, bias-adjustments were performed against the 0.25-degree resolution Fifth Generation European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis (ERA5) dataset (Hersbach et al. 2018) using a Quantile Delta Mapping technique described in Cannon et al. (2015).

The ensemble mean was taken to prepare the 20-year mean of the annual extreme rainfall total during the historical (1986–2005) and future projection (2046–2065) periods, under two scenarios: RCP4.5 and RCP8.5. Used by the Intergovernmental Panel on Climate Change (IPCC) in its fifth assessment report, RCP stands for Representative Concentration Pathways, which predicts the concentration of greenhouse gases (GHG) in the atmosphere as a result of human activities. The RCP4.5 refers to a moderate scenario in which GHG emissions peak around 2040 and then decline. On the other hand, RCP8.5 is the highest emissions scenario in which emissions continue to rise throughout the twenty-first century (IPCC 2014).

Using the ArcGIS platform, the aggregated rainfall data were then rendered in maps, where areas are categorized into classes based on the relative amount of rainfall received.

3.3 Modified Landslide Susceptibility Map

As a final step, the rainfall maps were combined with the base landslide susceptibility map to generate the modified landslides susceptibility maps. To do this, the values of the rainfall classes and the landslide susceptibility levels in the respective maps were first reclassified into 1 to 4 using the Reclassify tool of ArcGIS, with 1 corresponding to low and 4 corresponding to very high. Then, using the Raster Calculator tool, the reclassified rainfall and landslide susceptibility values were multiplied with each other on a per pixel basis. This step serves as the cross-tabulation of the two variables being combined, which were assumed to have equal weights. Finally, the resulting values from the cross tabulation were classified into different susceptibility levels following the pre-assigned categories in the matrix in Fig. 6.

Fig. 6
A 4 by 4 matrix of rainfall versus landslide susceptibility. The modified landslide susceptibility areas are classified into very high, high, moderate, and low susceptibility. The three cells at the top right have very high susceptibility, while the three bottom left cells have very low susceptibility.

Scheme for the raster multiplication process and susceptibility level generation

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4 Results and Discussion

Figure 7a–c show the distribution of historical and projected extreme annual rainfall in the study area. Historical total annual extreme rainfall ranges from 244 mm to 477 mm. A clear north-south divide is noticeable where the historical extreme rainfall was higher over the northern than over the southern part of the province. The highest amounts were recorded mainly in Baganga and Cateel, and in parts of Caraga and Boston. This result is consistent with the fact that TCs in the Philippines, with which extreme rainfall is usually associated, generally originate in the Pacific Ocean and follow a northwesterly direction over Mindanao, usually affecting only the northern part of the region, as is also evident from Fig. 3a.

Fig. 7
3 maps of Davao Oriental mark the municipality and city boundaries and bathymetric contours. The rainfall is measured as greater than 420, 361 to 420, 301 to 360, and less than 301 millimeters. The inset map of Mindanao highlights the Davao Oriental.

Annual extreme rainfall in Davao Oriental (a) Historical (1986–2005), (b) Projected under RCP4.5 (2046–2065), and (c) Projected under RCP8.5 (2046–2065). A decrease in areas affected by very high extreme rainfall is expected by the mid-twenty-first century under both scenarios (see also Table 2), mainly in Baganga, Cateel and Caraga

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The same general rainfall distribution pattern as in the historical period is observed for the projected period. However, drier conditions are expected by the mid-twenty-first century under both the RCP4.5 and RCP8.5 scenarios, with lesser areas experiencing very high extreme rainfall (Table 2), mainly in Baganga, Cateel and Caraga. Relative to the baseline extreme rainfall total range of 244–477 mm, the decrease in extreme rainfall is greater under RCP4.5, with annual extreme rainfall total range of 221–462 mm, compared to 223–474 mm under RCP8.5.

Table 2 Areas affected by different rainfall classes
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The resulting modified rainfall-induced landslide susceptibility (RILS) maps are shown in Fig. 8a–c. Comparing the modified historical RILS map (Fig. 8a) with the base RILS map (Fig. 5), it is seen that incorporating rainfall data into the latter map allows further delineation of very highly susceptible areas (Fig. 8a, Table 3). Specifically, the very highly susceptible areas increased by more than 45-fold. For disaster risk management, this modification is deemed very important as it allows better prioritization and more efficient allocation of resources.

Fig. 8
3 maps of Davao Oriental highlight the municipality and city boundaries and bathymetric contours. The landslide susceptibility is measured as very high, high, moderate, low susceptibility, and low-lying flood-prone areas. The inset map of Mindanao highlights the Davao Oriental.

Modified rainfall-induced landslide susceptibility maps of Davao Oriental (a) Historical (1986–2005), (b) Projected under RCP4.5 (2046–2065), and (c) Projected under RCP8.5 (2046–2065). A decrease in landslide susceptibility is expected by the mid-twenty-first century (see also Table 3), mainly in Baganga, Cateel and Caraga, following a decrease in extreme rainfall in the area

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Table 3 Areas falling on each susceptibility category
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The slope failures/movements that were observed during a field survey in the province (Narisma et al. 2017) were plotted in the modified historical RILS map (Fig. 9). As can be seen, they mostly fall on highly to very highly susceptible areas, providing increased confidence in the validity of the modified RILS maps. Note, however, that the field survey was limited only to accessible areas and therefore, by no means an intensive landslide inventory of the study area.

Fig. 9
A map of Davao Oriental with 6 photos of landslide locations. It marks the municipality and city boundaries and bathymetric contours. The landslide susceptibility is measured as very high, high, moderate, low susceptibility, and low-lying flood-prone areas. The inset map of Mindanao highlights the Davao Oriental.

Observed slope failures/movements in the study area (Landslide photos © 2017 The Oscar M. Lopez Center for Climate Change Adaptation and Disaster Risk Management Foundation, Inc., In: Narisma et al. 2017, used with permission)

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During the inspection, it was observed that when it rains, the rainwater collects and seeps through the fissures in rocks/soils, runs downslope, and oftentimes collects at the foot of the slope. As is generally known, rainwater increases the weight of slope materials, reduces friction along potential failure planes, and increases pore water pressure that then reduces the strength of the slope mass. These may have contributed to the failure of the investigated slopes. The observed slope failures vary in type, including rotational slide, translational slide, debris flow, rock fall and creep.

It follows from the decreasing future trend of the extreme rainfall intensity that the RILS in the study area would also decrease during the projection period. The expected decrease is stronger under RCP4.5 than under RCP8.5 (Fig. 8; Table 3). Specifically, the very highly susceptible areas are projected to decrease by about 34% and 22% under RCP4.5 and RCP8.5, respectively, while the low susceptible areas are projected to increase by about 31% under both scenarios. Again, the changes are expected to be observed mainly in the municipalities of Baganga, Cateel and Caraga.

The same trend of decreasing landslide susceptibility is actually expected across all of Philippines, as a general drying trend is projected throughout the country, except in some areas in the north, mainly in Luzon Island (DOST-PAGASA, Manila Observatory and Ateneo de Manila University 2021; Narisma et al. 2017). Therefore, contrary to the expectation of future increase in the frequency and magnitude of landslides for most of the world’s regions, climate change is positively modifying the landslide hazard susceptibility in Davao Oriental, and the Philippines, in general.

It must be noted, however, that there remains high to very high landslide hazard for northern Davao Oriental well into the future, and that risk reduction work is still needed in this area. Moreover, while the projected decrease in rainfall and landslide susceptibility is a positive sign concerning landslide hazard management, such a drying trend may spawn other hazards, including drought and water shortage, which can have dire consequences on people’s health, livelihood and well-being. This underscores the importance of a multi-hazard assessment that takes into account the complex interactions and interrelationships between the different hazards that an area is subject to.

5 Conclusion and Outlook

One of the greatest concerns about the current warming of the planet is an increase in precipitation and, therefore, the consequent increase in hydro-meteorological hazards such as landslides. In this study, we show that for the study area in Davao Oriental, global warming is causing a general reduction in landslide hazard susceptibility as a consequence of the drying trend expected in the area in the mid-twenty-first century.

Going forward, we deem it necessary that studies like the present be conducted in the other parts of the country, focusing on areas where increased rainfall is expected. Nonetheless, regardless of whether the projection for a given area is of a drying or a wetting trend, it is important to determine the consequent change in the associated patterns of multiple hazards so as to allow better prioritization and more efficient allocation of resources for disaster risk management and reduction.

In this preliminary work, the landslide susceptibility was defined considering the relative amount of extreme rainfall within the study area. In future work, we intend to use empirically-derived rainfall threshold values to define the different susceptibility levels. In addition, the use of other extreme rainfall indices may be explored to better capture the relationship between rainfall and landslide occurrences.