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Bivariate distribution of (a) river floods and wildfires, (b) river floods and wildfires, and (c) heatwaves and wildfires, across 50-year time periods representing the early industrial period (1861–1910), the present day (1956–2005), and the end of the century (2050–2099) under RCP2.6, 6.0, and 8.5. The marginal distributions of each extreme event (per scenario), based on the KDE method , are shown along the top and right axes of the plots. The contours (dotted lines) illustrate smooth estimates of the underlying distribution of co-occurring extremes. The 68th percentile contour, which envelops data within 1 standard deviation to either side of the mean, is used per scenario to show a generalized view of the distribution of the percentage of area affected by co-occurring extremes per year during the respective scenarios.
Source publication
Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, partic...
Citations
... One hazard changes environmental parameters, resulting in an increased probability of another hazard occurring 38 . For instance, heatwaves increase the probability of wildfires in East Africa 40 . ...
This perspective critically examines the challenges and opportunities of implementing people-centred Multi-Hazard Early Warning Systems (MHEWS) in the Global South. Despite global initiatives, such as the Early Warnings for All initiative, operational realities lag behind. By exploring the needs of the most vulnerable and how core concepts of multi-hazard thinking (e.g., hazard interrelationships and vulnerability dynamics) integrate into different pillars and cross-cutting components of an MHEWS, the perspective highlights a mismatch between current ambitions and realities on the ground. Drawing on extensive experience from Practical Action, we identify opportunities to move towards MHEWS through outlining potential entry points in research, policy, and practice. We emphasise a need for localised, inclusive strategies that genuinely address the needs of the most vulnerable populations and fully encompass the meaning of multi-hazards, including hazard interrelationships, the dynamics of risk components, and the complexity of multi-hazard impacts.
... CC BY 4.0 License. climate events (Muheki et al., 2024), and project future indices regarding water scarcity in the context of CC and societal changes (Yin et al., 2020). ...
Global hydrological models are essential tools for understanding water resources and assessing climate change impacts at planetary scales, supporting water management, flood risk assessment, and sustainable development initiatives worldwide. The Soil and Water Assessment Tool (SWAT+) has demonstrated robust performance across various environments and scales, from local to continental applications. However, despite its widespread use, a global implementation of SWAT+ is currently lacking due to computational demands and data management challenges, while existing global models often lack the detailed process representation and high spatial resolution needed for comprehensive hydrological analysis. A global SWAT+ model would offer unique advantages through its integrated simulation of water quantity, quality, and land management processes, while supporting multiple UN Sustainable Development Goals and enhancing research opportunities in global hydrology. This study aimed to develop a High-resolution Global SWAT+ Model and establish a reproducible framework for large-scale SWAT+ applications. We developed the Community SWAT (CoSWAT) modeling framework, an open-source solution that automates data retrieval, preprocessing, and model configuration using Python, while maxmising parallel processing for computational efficiency. The global model was then set up using the framework at 2 km resolution using ASTER DEM, ESA land use data, FAO soil data, and ISIMIP climate data, with performance evaluated against GRDC flow data and GLEAM evapotranspiration dataset. Results without calibration showed reasonable spatial patterns in evapotranspiration simulation with 78.54 % of sampled points showing differences within ±100 mm compared to GLEAM data, though river discharge performance was limited due to lack of reservoir implementation with 23.02 % of stations showing positive Kling-Gupta Efficiency values. The development of this first global SWAT+ model demonstrates the feasibility of high-resolution global hydrological modeling using SWAT+, while the CoSWAT framework provides a robust foundation for reproducible large-scale modeling. These advances enable more detailed analysis of global water resources and climate change impacts, though future work should focus on incorporating water management practices, improving process representation with calibration, and enhancing computational efficiency.
... Extreme climatic events, like droughts, are increasing in frequency and severity with anthropogenic climate change (Pörtner et al. 2023). Droughts pose a significant threat to rural communities and, in particular, pastoral communities throughout the Greater Horn of Africa (Megersa et al. 2014;Muheki et al. 2024). Droughts have long-term consequences not only for the environment and human health, but also for livelihoods, which trigger a cycle of health deterioration (WHO 2023). ...
... The Greater Horn of Africa, an area characterized by extreme heat and low rainfall, is particularly impacted by climate change, including both drought and flooding, with projections indicating that climate change will exacerbate existing vulnerabilities (Anyah and Qiu 2012;Muheki et al. 2024;Tierney, Ummenhofer, and Demenocal 2015). Between October 2020 and April 2023, the Greater Horn of Africa, including northern Kenya, experienced the most severe drought in 40 years. ...
Objective
Extreme climatic events, like droughts, are increasing in frequency and severity. Droughts disrupt community livelihoods and resources with serious implications for human biology. This study investigated how chronic stress, measured by fingernail cortisol concentration (FCC), and water insecurity status were predictive of C‐reactive protein (CRP), a biomarker of inflammation, during a historic drought among Daasanach seminomadic pastoralists.
Methods
Data were collected at the height of the 2022 drought from 128 Daasanach household heads aged 16–80 years in northern Kenya using household surveys, anthropometric measurements, and dried blood spots to assess CRP levels and fingernails to assess FCC. We employed mixed‐effects linear and logistic regression models to examine the relationships between log‐transformed FCC, high water insecurity status measured via the Household Water Insecurity Experiences (HWISE ≥ 24) scale, and serum‐equivalent CRP (log‐transformed and dichotomized at mild, low‐grade inflammation ≥ 1 mg/L) adjusted for covariates.
Results
The mean serum‐equivalent CRP was 4.1 mg/L and 56.3% of Daasanach adults had at least mild, low‐grade inflammation. Linear models indicated that ln(FCC) was positively associated with ln(CRP) ( β = 0.56, SE = 0.12; p < 0.001). Further, logistic models demonstrated that ln(FCC) (OR = 2.69, 95% CI: 1.84–3.95; p < 0.001) and high water insecurity (OR = 2.23, 95% CI: 1.34–3.72; p = 0.002) were both associated with greater odds of low‐grade inflammation.
Conclusion
This study provides evidence for how chronic stress and severe water insecurity may impact inflammation levels among pastoralists during drought. Since inflammation is central to cardiometabolic disease etiology, this is an additional reason to mitigate the negative health impacts of droughts and water insecurity exacerbated by climate change.
... The results indicate that, while the models generally demonstrated consistent performance for monthly means of daily precipitation and temperature, there is a notable weakness in capturing temperature variations, especially reflected in a lower-than-anticipated correlation coefficient for minimum temperature under the high-emission SSP5-8.5 scenario. This finding raises significant concerns about the models' reliability for projecting climate impacts, particularly in regions such as East Africa and Ethiopia, where temperature changes can have profound implications for agriculture, water resources, and food security (Bezu et al. 2020;Muheki et al. 2024). ...
Historical meteorological data is necessary for modeling climate scenarios, yet there remain significant challenges in archiving, extracting relevant data, organization and provision. Data rescue efforts play an important role in collecting relevant information, yet the real challenge remains in the implementation of accurate and efficient methods for extracting information from historical handwritten tabular records which are largely recorded in handwritten logbooks. The diverse tabular structures, layouts, and writing styles in these documents, along with the accessibility and varying quality of source preservation, make it impractical to rely on a single solution. In an effort to fill this gap, we propose a human-in-the-loop workflow with benchmarks of open- and closed-source methods for extracting and processing handwritten tabular data. We explain through the case of climatological data from the Congo region (1907–1960) how HIL workflows can be implemented and their trade-offs for implementing solely computationally steered models. In addition, we outline how generalized large Vision Language models have changed how HIL workflows can be implemented as to increase accuracy and precision not only with semi-automatic solutions for data provision but also prompt engineering and the necessary (historical) context to achieve optimal results.
Forest-Savannah transition zones are increasingly vulnerable due to their position between the savannah and forest ecosystems, and their ecological and biophysical characteristics. This study evaluates the spatiotemporal dynamics of land use and land cover (LULC), degradation, and wildfire links. Random Forest (RF) classifier was used in Google Earth Engine (GEE) with Landsat 7 and 8 imageries (2001, 2015, 2023), high-quality LULC maps were produced with spectral indices. The classifier demonstrated accuracies ranging from 83% to 96%, supported by F1-Score, Precision, and Recall metrics. Overall, Wooded Land and Gallery Forest increased by 35.29%, followed by Savanna Grassland and Farmland (16.2%). Conversely, Rock Outcrop declined by 23.6%, while Bare and Built-Up Areas, Dense Forest, Water Bodies, and Mosaic Forest-Savannah decreased by 13.7%, 6.9%, 4%, and 3.7%, respectively. Savannah and farmland areas were the most burned (30–36%), followed by 25 Wooded Land (21–24%) and Dense Forest (10–14%). Savannah areas also experienced the highest fire frequency (19 events). Fire occurrence was higher in areas with low vegetation cover than densely vegetated areas and conservation zones. Savannah and farmland remained the most degraded and fire-prone. An improved monitoring approach is needed for reduction and mitigation to avoid irreversible degradation caused by wildfire.
