Anticipatory action requires models that can accurately and reliably predict the impact of natural hazards. However, impact forecasts are often underestimated when consecutive hazards are not considered. In the Bicol region in the Philippines, typhoons trigger 90% of landslides, causing a lot of fatalities and damage to infrastructure and agriculture. The lack of information on past landslide events has hampered the construction of landslide forecasting models. Currently, a machine learning (ML) impact-based forecasting (IBF) model for typhoons is operational in the Philippines. The model was developed by 510, an initiative of the Netherlands Red Cross. The model predicts impact due to the high wind speeds associated with typhoons and includes the possible impact due to landslides only via a static landslide susceptibility map. Hence, this study focused on extending the 510 typhoon model via hybrid modeling into a multi-hazard forecasting model for both typhoons and landslides to improve the forecast by considering impact from typhoon-induced landslides. The implementation of the hybrid multi-hazard impact-based forecasting model was tested on two typhoon events in the Bicol region.

A hydrometeorological landslide IBF model was successfully created, even with the limited data on landslide occurrences and rainfall available. The newly established regional event duration threshold for Bicol was applied on the case study events with an increased impact boundary of 300 km compared to the typhoon impact boundary of 100 km. The results of the hybrid multi-hazard model showed an improved impact forecast -compared to the model considering solely static input of landslides, which underestimated impact- in both location extent of the impact forecast and in accuracy: the True Positives doubled, whereas the False Negatives reduced by half. The separate landslide IBF model as an extension of the existing ML typhoon model provided additional benefits as these models can be decoupled to optimize the performance and reliability of both. This study resulted in the prototype of an impact-based multi-hazard model for typhoons and landslides for the Philippines and demonstrated the importance of considering impact from consecutive hazards.

Regional climate model (RCMs) simulations are used in hydrological (climate-change) impact assessment studies. However, RCMs exhibit noticeable deviation from observation, and can show large variation in ensemble projections (biases). The objective of this study is twofold, first to assess the robustness of two high-skill bias-correction methods; empirical quantile mapping (QM), and scaled distribution mapping (SDM) in improving the hydrological modelling of the Rhine River. The second is to assess the potential impacts of climate change on low flows at Lobith based on RCP8.5 scenario. The two correction methods are applied to correct the systematic bias from five climate datasets from the Coordinated Downscaling Experiment in Europe (EURO-CORDEX) covering the Rhine domain, using high resolution gridded datasets (1 km2) spanning from 1979 to 2019.
The bias-corrected simulations from the hydrological model provided more accurate discharge estimates than the wet biased simulations, with an average error of less than 100 m³/s at Lobith. The correction methods are also capable of correcting unprecedented temperature and precipitation values, making them useful in climate assessment studies in the Rhine river. However, it appears that the accuracy of the bias correction depends on the parent GCM, performance of the raw RCM and the skill of the hydrological model in estimating discharges at the point of interest. In addition to that, the drizzling effect could not be reduced using these methods.
Noticeable climate change impacts at Lobith are found using the bias-corrected projections. These projections suggest that low flows are going to be more frequent and longer in the coming 38 years. Unprecedented discharges (< 700 m³/s) are projected to occur at least 50 times between 2020 - 2060. This is coupled by a decrease in the long-term mean annual flow by 100 m³/s and a slight shift in the seasonality of low flows (2 weeks shift).
The general hydrograph at Lobith is set to change due to climate change for the period (2020 – 2060), with relatively higher discharges from early June to end of August followed by relatively lower discharges in the last four months of the year. Water levels are projected to decline in average by 20 cm (early June to the end of August) and increase in average by 30 cm (end of August till to end of December). The study recommends the need of combining bias correction, the feedbacks in the climate system (land use changes) and climate adaptation strategies to study these effects further.

Due to global warming, temperatures are expected to rise and, with it, the increase in moisture holding capacity of the atmosphere. Changes in precipitation extremes with temperature are governed by the Clausius-Clapeyron relationship (CC), which states that precipitation increases 7% per degree of warming. While global models and observations are consistent with CC, research at hourly observations have showed deviations in the CC relationship, showing steeper increase up to 14% per degree (super-CC) and <7% per degree for larger temperatures (sub-CC). Studies have shown that scaling rates can be influenced by several factors, such as the length of observations, rainfall percentiles or the timescale of rainfall, among others. Apart from methodological limitations, research on scaling rates is not readily available in all parts of the world, with scarce studies carried out in the African continent. In this context, there is a need for better understanding the relationship between precipitation and temperature as well as the implications for future extremes. To address these challenges, this research investigated 3 main topics. Firstly, existing methodologies were compared and analysed for 353 rain gauges in Germany. Using as extreme rainfall indicators wet percentiles (80th-99th quantile), 3 methods were tested and statistically compared to estimate scaling rates with dew point temperature (DPT): (i) binning methods, (ii) quantile regression and (iii) change-point models at an hourly timescale. Secondly, the presence of super-CC was evaluated for storm events instead of fixed hourly intervals. For that, data from Germany at 10-min resolution was used. The last step involved the analysis of scaling rates for a dense network of newly available rain stations in 4 countries in sub-Saharan Africa: Ghana,Kenya, Tanzania and Uganda. The results from measuring the scaling of extreme precipitation with DPT shows that two regimes arise for Germany: for DPT below 13ºC- 15ºC the scaling rates are close to the CC rate across all quantiles. For warmer DPT of 15ºC or more, scaling rates above the CC are observed, with increasing slope for the highest quantiles. The study of storm events shows that, for mean intensity and peak rainfall intensity, there is a strong sensitivity to DPT, with higher DPT associated with higher rainfall. The analysis of storm duration indicates that shorter events are more frequent for larger temperatures than for shorter ones. The study in sub-Saharan countries shows consistent negative scaling rates with dew point temperature. In addition, for large surface air temperatures (SAT), there is a decrease in relative humidity, suggesting that there is a general lack of moisture availability at the higher SAT ranges. To better understand the physical processes that influence these results, the use of alternative predictors that describe the atmospheric moisture are proposed. In a future warming world, current literature indicates that the greatest rainfall increases will be more visible for short-duration storms rather than increases in annual mean precipitation. As such, within this project, a novel analysis on scaling rates for storm properties shows the potential of these approach to obtain detailed information on the storm properties and their relationship with temperature, which could be used in storm simulations and projects characterising current or future climate.