Landslides are one of the most disastrous natural hazards that frequently occur in Indonesia. In 2017, Balai Sabo developed an Indonesia Landslide Early Warning System (ILEWS) by utilizing a single rainfall threshold for an entire nation, leading to inaccuracy in landslide predictions. The study aimed to improve the accuracy of the system by updating the rainfall threshold. We analyzed 420 landslide events in Java with the 1-day and 3-day effective antecedent rainfall for each landslide event. Rainfall data were obtained from the Global Precipitation Measurement (GPM), which is also used in the ILEWS. We propose four methods to derive the thresholds: the first is the existing threshold applied in the Balai Sabo ILEWS, the second and third use the average and minimum values of rainfall that trigger landslides, respectively, and the fourth uses the minimum value of rainfall that induces major landslides. We used receiver operating characteristic (ROC) analysis to evaluate the predictability of the rainfall thresholds. The fourth method showed the best results compared with the others, and this method provided a good prediction of landslide events with a low error value. The chosen threshold was then applied in the Balai Sabo-ILEWS.

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

Remote sensing has shown its potential to assess soil properties and is a fast and non-destructive method for monitoring soil surface changes. In this paper, we monitor soil aggregate breakdown under natural conditions. From November 2014 to February 2015, images and weather data were collected on a daily basis from five soils susceptible to detachment (Silty Loam with various organic matter content, Loam and Sandy Loam). Three techniques that vary in image processing complexity and user interaction were tested for the ability of monitoring aggregate breakdown. Considering that the soil surface roughness causes shadow cast, the blue/red band ratio is utilized to observe the soil aggregate changes. Dealing with images with high spatial resolution, image texture entropy, which reflects the process of soil aggregate breakdown, is used. In addition, the Huang thresholding technique, which allows estimation of the image area occupied by soil aggregate, is performed. Our results show that all three techniques indicate soil aggregate breakdown over time. The shadow ratio shows a gradual change over time with no details related to weather conditions. Both the entropy and the Huang thresholding technique show variations of soil aggregate breakdown responding to weather conditions. Using data obtained with a regular camera, we found that freezing–thawing cycles are the cause of soil aggregate breakdown.

An accurate flood detection method is essential for obtaining areas of irrigated rice fields affected by flooding. This paper aims to distinguish between rice fields with flooding and rice fields with agronomic inundation using MODerate resolution Imaging Spectroradiometer (MODIS) 8 day 500 m spatial resolution (MOD09A1) imageries over irrigated rice fields with complex cropping patterns in West Java. Over the past decade, Enhanced Vegetation Index (EVI) ≤ 0.1 derived from moderate resolution remote sensing imageries has been used for detecting flooding in irrigated rice fields. Without additional farming information, this paper argues that EVI ≤ 0.1 cannot estimate flood areas correctly, given the existence of both hazardous flooding and non-hazardous agronomic inundation in irrigated rice fields. Adding a threshold of 40-day duration representing land preparation and transplanting activities enables EVI ≤ 0.1 to distinguish between agronomic inundation and flooding in irrigated rice fields. The difference in the Start of Season (SOS) between the wet planting season 2013/2014 and long-term average (2000–2015) shows that the Overall Accuracy (OA) and F1 scores are 75.96% and 81.74%, respectively. The confusion matrix using the respondents’ reports shows OA of 80.5% and Kappa of 60.16%. The quality of flood maps is partly influenced by environmental processes, human decisions, and mixed pixels.

Small Island Developing States (SIDS) are acknowledged as particularly vulnerable to extreme climate events; however, the realities for transport infrastructure and bridges are still poorly studied. Assessing bridges in this context can be challenging due to data scarcity, a lack of local standards, and uncertainty due to climate change. While bridges are designed to connect transport networks, they also carry energy, water, and communication networks, making them critical cascading failure points worthy of special attention in terms of risk assessment and resilience measures. We explore what resilience actually means for the design and management of SIDS bridge infrastructure by applying a post disaster forensics and systems approach that is not reliant on complex methods or large amounts of data. To demonstrate the practicality of our approach, we apply it to the island of Dominica, which is regularly impacted by both tropical storms and hurricanes. Our results document the extreme conditions for infrastructure and nearby settlements and the complex interrelated physical processes that occur during these events. We reflect on the implications for design approaches for bridges under these conditions and detail specific recommendations on how the resilience of existing and new bridges can be enhanced through practical measures that are achievable, even within the constraints experienced by those managing bridge infrastructure in SIDS contexts. This work adds to the growing number of studies exploring forensic disaster investigation and systems thinking, but is the first to explore bridge resilience in SIDS.

Floods are frequent hydro-meteorological hazards which cause losses in many parts of the world. In hilly and mountainous environments, floods often contain sediments which are derived from mass movements and soil erosion. The deposited sediments cause significant direct damage, and indirect costs of clean-up and sediment removal. The quantification of these sediment-related costs is still a major challenge and few multi-hazard risk studies take this into account. This research is an attempt to quantify sediment deposition caused by extreme weather events in tropical regions. The research was carried out on the heavily forested volcanic island of Dominica, which was impacted by Hurricane Maria in September 2017. The intense rainfall caused soil erosion, landslides, debris flows, and flash floods resulting in a massive amount of sediments being deposited in the river channels and alluvial fan, where most settlements are located. The overall damages and losses were approximately USD 1.3 billion, USD 92 million of which relates to the cost for removing sediments. The deposition height and extent were determined by calculating the difference in elevation using pre- and post-event Unmanned Aerial Vehicle (UAV) data and additional Light Detection and Raging (LiDAR) data. This provided deposition volumes of approximately 41 and 21 (10³ m³) for the two study sites. For verification, the maximum flood level was simulated using trend interpolation of the flood margins and the Digital Terrain Model (DTM) was subtracted from it to obtain flooding depth, which indicates the maximum deposition height. The sediment deposition height was also measured in the field for a number of points for verification. The methods were applied in two sites and the results were compared. We investigated the strengths and weaknesses of direct sediment observations, and analyzed the uncertainty of sediment volume estimates by DTM/DSM differencing. The study concludes that the use of pre- and post-event UAV data in heavily vegetated tropical areas leads to a high level of uncertainty in the estimated volume of sediments.