Landslides are one of the most widespread natural hazards, causing thousands of casualties every year and significant economic damage worldwide. In mountainous regions of Thailand, damages and fatalities due to landslides have increased over the last decades due to intensified human interventions.

This research aims to contribute to the development of a near real-time, joint early warning system for flash floods and landslides in Khao Yai National Park, Thailand. Spatial and temporal landslide occurrences in Khao Yai National Park were assessed. To extend the available landslide inventory, the detection of historical landslides was automated on the Google Earth Engine platform using remotely sensed relative changes in NDVI. Predisposing factors of landslide occurrence were determined using the frequency ratio method. A susceptibility map was then derived by linking the detected landslides to the predisposing factors: slope angle, aspect, land use, lithology, and distance to road. Validation with the landslide inventory shows excellent classification performance.

The temporal probability was assessed using a physically-based multi-hazard model. The hydrological model includes the driving hydrological processes for each grid cell. The streamflow model output was calibrated using observed nested river discharge events. The model performance was tested under spatially distributed precipitation forcing of varying quality. The results show good performance in predicting streamflow and landslide occurrence based on modeled antecedent conditions when using high-quality weather radar information as forcing.

This research demonstrates the effectiveness of integrating fully distributed physically-based modeling with high-resolution spatial rainfall observations from weather radar. The findings are expected to enhance the development of a multi-hazard early warning system and improve disaster risk management in northeastern Thailand.

In a Ghana project, field measurements at the Black Volta aimed to enhance hydrological predictions for the Bui power dam. Validating devices and exploring floodplain contributions, such as with GNSS reflectometry and cameras, provides crucial insights for dam management and model improvement.

In this multidisciplinary project, conducted at the Black Volta in western Ghana, the focus is on addressing challenges related to data scarcity in hydrological predictions, particularly for the Bui power dam. The dam has faced issues during flood events, leading to spillages and subsequent damages. To enhance predictions, field measurements were carried out, employing innovative methods for remote areas within the TEMBO Africa project. The project aimed to validate devices measuring river parameters and improve the understanding of floodplain contributions to river discharge. Key methods included GNSS reflectometry for water level measurement and a camera-based approach for discharge determination. The GNSS-reflectometry device was successfully tested, validated, and installed at the Black Volta, automating water level measurements. The camera-based method demonstrated success on smaller streams but faced challenges on wider rivers. Despite this, it was installed at the Black Volta for daily discharge measurements, promising a reduction in rating curve uncertainty when combined with water level measurements. Field observations were utilized to enhance an existing hydraulic model, refining the floodplain's representation. The study also delved into determining the floodplain's roughness coefficient, involving manual measurements and attempts at automation using remote sensing techniques. The roughness coefficients were implemented into the Delft3D model, showing contributions of floodplains to river discharge. Despite challenges, including equipment installation timing and location disruptions due to bridge construction, the project provides valuable insights for improving hydrological models and preventing dam spillages in the future.