The 2008 Wenchuan earthquake lead to various complex multi-hazard chains that included seismically triggered landslide initiation, landslide runout, river damming, dam breaching, and flooding. The modeling of the interactions between such hazardous processes is challenging due to the complexity and uncertainty. Here we present an event-based physically based model that is able to simulate multi-hazard land surface process chains within a single unified simulation. The final model is used to simulate a multi-hazard chain event in the Hongchun watershed, where co-seismic landslides led to a landslide dam and, 2 years later, a debris flow that breached the landslide dam. While most aspects of the multi-hazard chain are predicted well, the correct prediction of slope failures remains the biggest challenge. Although the results should be treated carefully, the development of such a model provides a significant progress in the applicability of multi-hazard chain simulations.

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.

Mass movements such as debris flows and landslides differ in behaviour due to their material properties and internal forces. Models employ generalized multi-phase flow equations to adaptively describe these complex flow types. Such models commonly assume unstructured and fragmented flow, where internal cohesive strength is insignificant. In this work, existing work on two-phase mass movement equations are extended to include a full stress–strain relationship that allows for runout of (semi-)structured fluid–solid masses. The work provides both the three-dimensional equations and depth-averaged simplifications. The equations are implemented in a hybrid material point method (MPM), which allows for efficient simulation of stress–strain relationships on discrete smooth particles. Using this framework, the developed model is compared to several flume experiments of clay blocks impacting fixed obstacles. Here, both final deposit patterns and fractures compare well to simulations. Additionally, numerical tests are performed to showcase the range of dynamical behaviour produced by the model. Important processes such as fracturing, fragmentation and fluid release are captured by the model. While this provides an important step towards complete mass movement models, several new opportunities arise, such as application to fragmenting mass movements and block slides.

Numerical modeling is an important tool for prediction, analysis and understanding of the dynamics of land surface processes. To increase the usage and impact of such tools, it is crucial to decrease runtime by increasing computational efficiency. Dynamic processes such as water flow are typically described by higher-order differential equations. Solving these accurately requires numerical integration over time, where numerical errors depend on the time steps taken. Typically, flow simulation use the smallest required time steps in a model’s domain to simulate flow. In this paper, we analyze the usage of local time stepping, for catchment-scale simulation of land surface processes such as water flow, infiltration, slope stability and landslide runout. In such a scheme, temporal integration is cell specific, allowing for higher numerical efficiency. The implemented scheme works with fully free local time steps that are synchronized only for visualization. We implement this method in a monotonic upwind scheme for conservation laws (MUSCL). We investigate the influence on stability and the resulting changes in computation time and accuracy in a hydrology-coupled, catchment-scale flood simulation. Results show that local time stepping can be implemented in a total variation diminishing (TVD) numerical scheme that is second-order spatially accurate. Simulation results in both 1D dam-break scenarios and catchment-scale flash flood scenarios show insignificant changes in modeling result, while computation time reduces with over 50%. Finally, the method is successfully implemented in a multi-process lands surface model with hydrology, flooding, slope failure, and runout. The implementation of a local time stepping for computation of dynamic land surface processes could be implemented widely for increased computational efficiency without significant loss of accuracy.

Various stakeholders seek effective methods to communicate the potential impacts of tropical cyclone (TC) rainfall and subsequent flood hazards. While current methods, such as Intensity–Duration–Frequency curves, offer insights, they do not fully capture TC rainfall complexity and variability. This research introduces an innovative workflow utilizing GPM-IMERG satellite precipitation estimates to cluster TC rainfall spatial–temporal patterns, thereby illustrating their potential for flood hazard assessment by simulating associated flood responses. The methodology is tested using rainfall time series from a single TC as it traversed a 500 km diameter buffer zone around Dominica. Spatial partitional clustering with K-means identified the spatial clusters of rainfall time series with similar temporal patterns. The optimal value of K = 4 was most suitable for grouping the rainfall time series of the tested TC. Representative precipitation signals (RPSs) from the quantile analysis generalized the cluster temporal patterns. RPSs served as the rainfall input for the openLISEM, an event-based hydrological model simulating related flood characteristics. The tested TC exhibited three spatially distinct levels of rainfall magnitude, i.e., extreme, intermediate, and least intense, each resulting in different flood responses. Therefore, TC rainfall varies in space and time, affecting local flood hazards; flood assessments should incorporate variability to improve response and recovery.

The current status of technological advancement does not allow to generate complete flood simulations in real-time for large geographic areas. This hinders warning-systems, interactive planning tools and detailed forecasts and as a consequence the population cannot be quickly or reliably informed of where large masses of water will flow. Our novel method computes flood hazard maps over three orders of magnitude faster than current state-of-the-art methods. It applies physically-based principles of steady-state flow to evade full dynamic aspects of flood simulations. It directly estimates the relevant information for flood hazard, such as peak flow height, velocity and flood arrival time. Performance indicators show similar or exceeding accuracy compared to traditional flow models depending on the type of event and quality of the used elevation data. In our tests, computational costs are reduced on average by a factor 1500. As a result, the developed method provides new perspective for the field of flood hazards, flood risk reduction through new types of early-warning systems, and user-interactive hazard assessment systems. As climate change is expected to aggravate flood hazard, the presented method can bring necessary efficiency to flood simulation and thereby save lives and livelihoods.