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Malaria is a large health threat on the African continent. The transmission of the parasite is highly dependent on precipitation and temperature levels. Relatively low temperatures in the highlands of Uganda can be inhibiting the current spread of malaria. Future climate change may therefore cause a shift in the climatic suitability for malaria transmission in this region. This study uses downscaled climate projections of temperature and precipitation values in Uganda from 2015 to 2099 generated by the MPI-ESM1-2-LR climate model. This data is used as input for a simplified version of the Liverpool Malaria Model. The model mathematically simulates several processes of the malaria transmission dynamics on a daily basis for every grid cell of the input data. The output consists of the daily basic reproductive rate (R0 ). The rate serves as a measure for the climatic suitability for and intensity of malaria transmission. Error margins for the climate projections are estimated through an ensemble created on a spatial subset of Uganda and are combined with the input data. The malaria model uncertainty is assessed through a sensitivity analysis using a one-at-a-time (OAT) method. Results show an increase in areas where, on average, malaria will spread (R0 > 1). This is most notable under climate change scenarios SSP3-7.0 (up to 24.1% of the area) and SSP 5-8.5 (up to 34.7% of the area). Several high-altitude locations in the west and east of Uganda show higher R0 values (R0 ≥ 2.5) starting from 2050. This is mainly caused by high levels of projected precipitation in the aforementioned scenarios. These locations also display the most notable increase in malaria transmission season length from 80 up to 120 days per year. The moment in time the season occurs appears stable. The error analysis warrants caution regarding the interpretation of these results, showing high errors for precipitation at high-altitude locations. Furthermore, the sensitivity analysis indicates a model

Global hydrological models (GHMs) enable global estimation of freshwater availability, but their uncertainties and limitations hinder precise predictions. Multi-model combination (MMC) is a promising solution that combines the outputs of numerous hydrological models to create an ensembled output that surpasses the individual hydrological models. Moreover, the use of Machine Learning (ML) as a hybrid post-processing strategy is growing in popularity. However, there is a need to combine these two methods and investigate their performance in streamflow predictions. In this study, we demonstrate that using Random Forest (RF) as a nonlinear MMC approach significantly enhances streamflow forecasts when multiple global hydrological models' outputs are combined. In streamflow forecasting, the RF-MMC method outperforms individual models and linear MMC approaches, demonstrating its potential. In addition, incorporating catchment attributes improved the generalizability of the RF-MMC method when tested on a river basin that was not in the training set. Significant potential exists for the application of RF-MMC to generate accurate streamflow forecasts, thereby providing valuable support for water resource management, flood mitigation, and decision-making processes. Future research can investigate additional machine learning algorithms and incorporate additional variables to improve the predictive ability and generalizability of MMC strategies in hydrological modelling.

Hydrological models are important tools for making streamflow predic- tions and studying the effects of climate change on water resources around the world. This study aims to improve a global hybrid hydrological stream- flow framework based on PCR-GLOBWB and Random Forest (RF), with the addition of satellite products. The selected satellite products are liq- uid water equivalent from GRACE (Gravity Recovery and Climate Ex- periment), snow cover fraction from JASMES (JAXA Satellite Monitoring for Environmental Studies) and soil moisture from ESA CCI SM (Euro- pean Space Agency Climate Change Initiative Soil Moisture). These prod- ucts are selected because of their relevance for streamflow predictions. Five global and seven Local (Australia, Canada and the United States) model configurations were used for the RF model. The differences be- tween configurations are based on the inclusion of state variables from PCR-GLOBWB, catchment attributes, satellite products, lagged variables for meteorological input and satellite products of 4 and 12 months and exclusion of state variables from PCR-GLOBWB related to satellite prod- ucts. The results showed that the global and local configurations did not improve compared to the benchmark model (based on 51 predictors from PCR-GLOBWB) and the addition of lag was not significantly effective ei- ther. The configuration with only satellite and meteorological input and satellite products did, however show good performance with only six pre- dictors. After adding static variables to the previous configuration there was equal performance to configurations with state variables from PCR- GLOBWB. These result mean that good performance can be achieved with- out the need for a hydrological model and with a limited number of vari- ables.

Cost distance tool are embedded in various geographic information systems (GIS) giving insights into spatial relationships. Most GIS software use a serial cost distance algorithm. Cost distance calculations have a strong sequential nature due to the order we access cells in the raster. This limits the development of a parallel algorithm, hindering the usage of multiple processes for faster computation. This paper proposes a parallelization framework, accounting for the sequential nature while running in parallel. By dynamically distributing partitions to different processes, we ensure full workload distribution and minimising idle times for processes. Relative strong and weak scaling efficiencies drop below 80% when run with more than 3 and 2 workers respectively. We notice that this is partly caused by the fact that the size of the input data and the amount of work a worker has to perform, do not scale linearly. When scaling to more workers, we expect to run into a performance bottleneck caused by input output operations of the root node. Recommendations are made for future research to limit the amount of input output operations by statically assigning partitions to workers.

This study assesses how current and future urban heat stress in the Netherlands can possibly be reduced by increasing the vegetation cover. Climate change is expected to increase the global atmospheric temperature, causing the probability of being exposed to heat stress to increase (McCarthy et al., 2010). In urban areas, factors such as the urban heat island (UHI) cause unfavourable conditions for the energy balance of the human body, which leads to the occurrence of heat stress (Oleson et al., 2015). Increasing the vegetation cover is believed to reduce heat stress in the public outdoor space (Lindberg et al., 2016). The aim of this study is to quantify the effect of vegetation on heat stress in urban areas of the Netherlands, in the current and future climate. By doing this, knowledge is gained of how climate change will influence heat stress in urban regions of the Netherlands, and how heat stress can be reduced by increasing the vegetation. The study aims to assess how increasing the vegetation cover affects heat stress in urban areas, by analysing various factors that influence heat stress and the effect vegetation has on these factors. Furthermore, an empirical regression model is used to calculate the physiological equivalent temperature (PET) in Rotterdam, the Netherlands, under current and future climatic conditions. Multiple numerical simulations are carried out for different tree-scenarios involving varying numbers of trees in the study area to determine the heat stress in Rotterdam. The results of the literature study show that vegetation can considerably reduce heat stress by affecting shade and evapotranspiration rates, but its impact on wind can also slightly increase heat stress. Earlier research states that the type and arrangement of vegetation in an area have a strong impact on how effectively vegetation can lessen heat stress (Dimoudi & Nikolopoulou, 2003). The results of the model runs reveal that Rotterdam currently experiences strong to extre