The research team, led by Franck Hervé Akaffou and colleagues, set out to solve a persistent problem in climate impact studies: single models tend to capture only part of the complex, lagged relationships between weather and river behavior. Rain that falls in a basin today may take weeks or months to reach a reservoir, and temperature drives evaporation losses that further complicate the picture. To handle these dynamics, the team built a five-step framework that begins with a broad pool of fifteen machine learning algorithms and progressively narrows and combines them into stronger predictive systems.

Central to the approach is what the authors call a multi-lag integration of precipitation and temperature. Rather than feeding a model only the current month’s weather, the framework incorporates rainfall and temperature from preceding periods, allowing the algorithms to learn how antecedent conditions propagate through a watershed into reservoir inflow. This matters enormously in West African basins, where seasonal monsoon dynamics and long hydrological memory mean that a wet season’s signature can appear in dam inflows long after the rains have ended.

The selection process was deliberately competitive. All fifteen candidate models were first evaluated on their ability to reproduce historical inflow and generation patterns, and only the top performers advanced to the next stage. Through iterative ensemble stacking, in which the outputs of strong models are themselves combined and retrained, and through the systematic elimination of weak learners that added noise rather than signal, the team refined its predictions layer by layer. The result was a marked improvement in accuracy and efficiency across the modeling chain, with the coefficient of determination and the Nash-Sutcliffe efficiency, two standard measures of hydrological model skill, both exceeding 0.6 for all inflow simulations and for the energy simulations at the Bagre, Nangbeto, and Taabo dams.

To train and validate the models, the researchers drew on the CHIRPS and CHIRTS datasets, which provide high-resolution satellite-based and station-calibrated records of precipitation and temperature from 1983 to 2014. This three-decade historical window gave the algorithms a rich sample of West Africa’s climate variability, including major droughts and wet years, against which to learn the relationships between climate and dam performance. Once calibrated, the models were turned toward the future using projections from twelve bias-adjusted CMIP6 climate models, the latest generation of global climate simulations, together with their ensemble mean.

The future scenarios spanned three emissions pathways: SSP1-2.6, a low-emissions world consistent with ambitious mitigation; SSP2-4.5, an intermediate pathway; and SSP5-8.5, a high-emissions trajectory with continued reliance on fossil fuels. Projections were made for two future periods, a near future from 2036 to 2067 and a far future from 2068 to 2099, allowing the team to distinguish changes likely to emerge within the working lifetime of today’s dams from those that may arrive by century’s end.

The climate projections themselves are stark. Under the highest emissions scenario, temperatures across the study basins could rise by as much as 4.5 degrees Celsius, a level of warming that would sharply increase evaporation from reservoir surfaces and alter the intensity and timing of monsoon rainfall. Precipitation changes, by contrast, are spatially heterogeneous: some basins are projected to become wetter while others dry out, and the direction and magnitude of change vary across scenarios and time horizons. This patchiness is precisely why basin-by-basin machine learning assessments are valuable, since a single regional average would obscure the divergent fates of individual dams.

For reservoir inflows, the projections reveal a region divided. Buyo faces the steepest declines, with inflows projected to fall by up to 24 percent, while Nangbeto could see reductions of up to 13 percent. Reduced inflow means less water passing through turbines, and the knock-on effects for electricity generation are severe: hydropower output at Nangbeto may decline by up to 19 percent, and at Taabo the projected drop reaches a remarkable 58 percent, a loss that would ripple through national grids that rely on these plants for a substantial share of their supply. Yet the picture is not uniformly grim. Manantali and Taabo are projected to experience inflow increases, and Bagre may actually see energy gains of up to 42 percent under the high-emissions scenario, illustrating how climate change can create winners as well as losers even within a single region.

These contrasting outcomes carry important implications for how West African countries plan their energy futures. A dam projected to gain generation capacity may be able to shoulder more of the regional load, but planners cannot simply reallocate risk from one basin to another, because the projections are scenario-dependent and the high-emissions pathway that produces the largest gains at Bagre also produces the most dangerous warming elsewhere. The authors argue that the findings underscore an urgent need for adaptive management strategies: strengthening the resilience of hydropower systems themselves, diversifying national and regional energy portfolios, and integrating additional renewable sources such as solar, which is abundant across the Sahel and largely uncorrelated with hydrological variability.

For hydropower managers and policymakers, the message is that proactive measures must begin now, well before the mid-century projections materialize. Reservoir operating rules calibrated to a stable twentieth-century climate may become maladaptive as inflow patterns shift, and the long lead times for infrastructure investment mean that decisions made this decade will determine whether the region’s power systems bend or break under climate stress. By demonstrating that machine learning ensembles can skillfully link climate projections to dam-level outcomes, the study offers a practical template for the kind of granular, basin-specific risk assessment that energy security in a changing climate will increasingly demand.

Subject of Research: Climate change impacts on reservoir inflow and hydropower generation in West African dam basins assessed with ensemble machine learning

Article Title: Integrating machine learning to assess climate risks on reservoir’s inflow and hydropower generation across West African basins

Article References: Akaffou, F. H., Obahoundje, S., Diedhiou, A., Kouassi, K. L., Diallo, D., Amoussou, E., Yamegueu, D., & Ofosu, E. A. (2026). Integrating machine learning to assess climate risks on reservoir’s inflow and hydropower generation across West African basins. PLOS Climate, 5(9), e0000986. https://doi.org/10.1371/journal.pclm.0000986

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0000986

Keywords: hydropower, machine learning, climate change, West Africa, CMIP6, reservoir inflow, ensemble modeling, SSP scenarios, CHIRPS, energy security, adaptive management, PLOS Climate

The study, published in npj Sustainable Agriculture, tackles a long-standing weakness in drought science. Most drought assessments rely on indices built from precipitation or temperature, which describe the climatic drivers of drought but say little about the actual water available to plant roots. Agricultural drought, by contrast, is fundamentally a soil moisture phenomenon: it occurs when water held in the root zone falls below the levels crops need to sustain growth and yield. By working directly with bias-corrected soil moisture outputs from seventeen climate models in the Coupled Model Intercomparison Project Phase 6, or CMIP6, the team shifted the analytical focus from the atmosphere to the ground itself, capturing the variable that matters most to farmers.

Methodologically, the framework is built on careful statistical ground. The researchers used the Global Land Data Assimilation System, specifically the GLDAS-Noah v2.1 dataset produced by NASA, as their reference. This product integrates observational forcing, reanalysis data and satellite assimilation to deliver a globally consistent, gap-free simulation of soil moisture at a resolution of 0.25 degrees, making it a reliable benchmark against which climate model outputs can be judged. Because satellite-only soil moisture products suffer from cloud interference, retrieval uncertainties and sensor limitations, the physically based GLDAS record offers the temporal continuity that a global drought assessment demands. The team aggregated soil layers in both the reference data and the model outputs to represent total root-zone storage, ensuring that the two datasets described comparable conditions relevant to crops.

Raw climate model output is notoriously prone to systematic bias, arising from simplified assumptions, imperfect parameterisations and structural errors in how physical processes are represented. To address this, the researchers applied a linear scaling bias correction, adjusting each model’s monthly soil moisture values by the ratio of the GLDAS reference mean to the model mean over the 2000 to 2014 baseline period. Crucially, this approach corrects the systematic mean bias while preserving the temporal structure of each simulation, so the historical trends and relative anomalies simulated by the models remain intact. When the seventeen models were evaluated across a suite of performance metrics, including bias, correlation, the index of agreement, the Kling-Gupta efficiency and root mean square error, the bias-corrected multi-model ensemble mean outperformed every individual model, and it was therefore selected for the drought projections.

With corrected soil moisture in hand, the team computed the Standardised Soil Moisture Index, or SSMI, using a non-parametric formulation based on the empirical Gringorten plotting position rather than a fitted parametric distribution. This choice matters in a warming world. Parametric distributions assume that the statistical properties of soil moisture remain stationary, yet under climate change those properties evolve continuously, and a fixed distribution fitted to historical data can distort future drought estimates. The non-parametric approach makes no such assumption, ranking each month’s soil moisture against the full empirical record and converting those probabilities into a standardised index. Negative SSMI values flag dry conditions, classified from abnormally dry down to exceptionally dry, while positive values indicate wet conditions. A Kolmogorov-Smirnov test confirmed that the empirical distribution adequately described both the reference and the corrected ensemble data, lending statistical confidence to the classification.

The projections, spanning 2015 to 2050 under two Shared Socioeconomic Pathways, reveal a clear intensification of drought characteristics toward mid-century. SSP2-4.5 represents a middle-of-the-road future in which emissions stabilise and radiative forcing reaches 4.5 watts per square metre by 2100, while SSP5-8.5 describes a high-emissions world of rapid, carbon-intensive growth pushing forcing to 8.5 watts per square metre. Under both scenarios, the number of land pixels classified in the severe to exceptional drought categories increases over time, and mean SSMI values decline steadily, with the deterioration most pronounced under SSP5-8.5. Continental analyses show strong and consistent drying signals in South America and Europe, gradual downward trajectories in Asia and North America, and pronounced interannual swings in Australia, reflecting that continent’s acute climate sensitivity and its potential for both extreme wet spells and prolonged dry ones.

One of the study’s more counterintuitive findings concerns drought frequency. Globally, the average number of drought events actually declines slightly under the high-emissions scenario, from roughly 9.1 events to 8.6 over the projection period, yet total drought duration remains essentially unchanged at around 68 months. The explanation is that individual droughts last longer under SSP5-8.5, so fewer distinct events fit into the same window. Rather than many short dry spells, the future favours fewer but more protracted episodes of soil moisture deficit, a shift that is arguably more dangerous for agriculture, since persistent multi-season deficits deplete reservoirs, exhaust soil water reserves and push crops past critical physiological thresholds in ways that brief droughts do not.

To translate these hazard projections into agricultural consequences, the team developed a Drought Exposure Index that combines normalised drought frequency, total duration and mean intensity through a geometric mean, then overlays the result on global cropland data from the Land Cover Climate Research Data Package. The index ranges from zero to one, with higher values signalling greater exposure. The results expose stark regional disparities: the highest exposure values cluster over major croplands in South Asia, southern Africa, South America, southern Europe and parts of the United States. Under SSP2-4.5 the global mean index reaches 0.71, but under SSP5-8.5 it climbs to 0.78 by 2050, a substantial rise in exposure attributable to the emissions pathway alone. Asia, which holds 34 percent of the world’s agricultural area, followed by South America with 22 percent, face the largest absolute areas of drought-affected cropland, with the affected share trending upward in every continent through 2050.

The implications reach well beyond hydrology. Soil moisture droughts of the kind projected here directly threaten food security, and the authors frame their findings against the Sustainable Development Goals on Zero Hunger, Climate Action and Life on Land. Recent history illustrates the stakes: the Millennium Drought that gripped Australia from 1997 to 2010, the simultaneous droughts of 2010 in the Amazon, southwestern China and Russia, the Texas-Mexico and East African droughts of 2011, and the United States summer droughts of 2012 and 2016 all devastated harvests, economies and livelihoods. The new projections suggest that events of this character will become longer and more widespread, particularly in the very regions that feed growing populations.

The study also charts a path forward. Climate-resilient farming practices such as precision irrigation, conservation tillage and drought-resistant crop varieties can improve soil moisture retention, while agroforestry and regenerative agriculture strengthen soil structure and water-holding capacity. On the water side, managed aquifer recharge, rainwater harvesting and smart irrigation systems can stretch supplies through dry periods, and transboundary cooperation becomes essential where rivers cross borders. The authors argue that drought risk must be embedded in national adaptation plans, supported by improved monitoring networks, early warning systems and climate-informed decision tools, and that advances in remote sensing, data assimilation and machine learning will sharpen future assessments. They are candid about the limitations that remain, including the simplicity of linear bias correction, uncertainties in land cover change and the difficulty of separating human influences from natural variability. Even so, by pinpointing where soil moisture droughts and croplands will collide hardest, the research gives policymakers a map of where every adaptation dollar will matter most.

Subject of Research: Global projections of soil moisture droughts and agricultural drought exposure under climate change scenarios

Article Title: Projecting global soil moisture droughts under climate change: characteristics, agricultural exposure, and adaptation insights

Article References: Moradian, S., Gharbia, S., Sonny, F., Torabi Haghighi, A., & Olbert, A. I. (2026). Projecting global soil moisture droughts under climate change: characteristics, agricultural exposure, and adaptation insights. npj Sustainable Agriculture, 4(1), Article 80. https://doi.org/10.1038/s44264-026-00144-x

Image Credits: AI Generated

DOI: 10.1038/s44264-026-00144-x

Keywords: soil moisture, agricultural drought, CMIP6, climate change, SSP scenarios, Standardised Soil Moisture Index, GLDAS, bias correction, drought exposure, food security, climate adaptation, water management

The findings carry weight well beyond the two bat species themselves. Frugivorous phyllostomid bats are keystone agents of forest regeneration, moving seeds across fragmented landscapes, pollinating chiropterophilous plants, redistributing nutrients through guano deposits, and maintaining genetic connectivity among plant populations. If V. caraccioli contracts across the Amazon, the study’s authors warn, the disruption could ripple through seed-dispersal networks and compromise the natural regeneration of one of the planet’s most biodiverse ecosystems. Both species are currently listed as Least Concern by the IUCN, but the researchers argue that this label, based largely on broad geographic ranges, may mask vulnerabilities that only become visible when climate data are brought into the picture.

The genus Vampyrodes has long been a taxonomic puzzle. Originally proposed as a subgenus in 1889 and later treated as a single species with two subspecies, it was only in 2011 that phylogenetic analysis of mitochondrial cytochrome b gene sequences, combined with a comprehensive review of cranial morphology, confirmed that V. major and V. caraccioli are distinct species. V. major occupies Central America from southern Mexico into Colombia, while V. caraccioli ranges across northern and eastern South America, dominating in the Amazon basin, the Andean foothills, and tropical southern Brazil. Because these bats depend on intact forest for both food and roosting sites, they have traditionally been treated as bioindicators of well-preserved forest, although recent records from restoring secondary vegetation hint that their tolerance may be broader than once assumed.

To build their models, the researchers compiled 190 verified occurrence records, 77 for V. major and 113 for V. caraccioli, drawn from the Global Biodiversity Information Facility and the published literature, deliberately excluding citizen-science observations because the cryptic morphology of these bats raises the risk of misidentification in non-vouchered records. The team filtered the data to remove duplicates and non-georeferenced points, then thinned the records spatially to a minimum separation of ten kilometers to reduce autocorrelation. Fifteen bioclimatic variables from the WorldClim 2.1 database were screened, with four excluded due to geographic discontinuities, and a Spearman correlation threshold of 0.75 narrowed the predictor set to six variables capturing annual mean temperature, temperature seasonality, temperature annual range, warmest-quarter temperature, annual precipitation, and wettest-month precipitation.

The modeling itself used the Maxent algorithm, implemented through the Wallace platform in R, with 120 candidate parameter combinations tested per species. The final model for V. major relied on linear features with a regularization multiplier of one, while V. caraccioli required a more complex linear-quadratic-hinge configuration with a multiplier of three. Model selection followed established statistical criteria, requiring omission rates below five percent and minimizing the corrected Akaike information criterion. Variable importance outputs revealed telling differences: temperature annual range dominated the V. major model at 67.9 percent permutation importance, whereas V. caraccioli’s suitability hinged primarily on annual precipitation at 37.2 percent, followed by temperature annual range and annual mean temperature. In other words, the Central American species is chiefly constrained by thermal variability, while its Amazonian relative is governed by moisture availability.

Niche comparisons in multivariate environmental space showed that the two species share broad climatic ground but are not identical. The first two principal components explained 75.7 percent of environmental variance, and the observed overlap was moderate, with Schoener’s D at 0.398 and the Hellinger-based I at 0.575. Randomization tests found no evidence that similarity exceeded what background environments would predict, and equivalency could not be strictly rejected, but the niche centroids were measurably displaced and the non-overlapping components were asymmetric. The authors interpret this pattern as partial niche conservatism: a shared evolutionary climatic background, consistent with the genus’s recent crown age of roughly two million years, superimposed with early differentiation along axes of temperature seasonality and dry-season moisture. A companion study published in 2026 reached a similar conclusion, linking seasonal climatic gradients to morphological divergence between the two species.

The future projections are where the story turns stark. The team ran an ensemble of five CMIP6 general circulation models under two Shared Socioeconomic Pathways for the period 2041 to 2060: SSP1-2.6, a sustainability-oriented scenario limiting warming to below two degrees, and SSP5-8.5, a fossil-fuel-intensive pathway with substantially higher warming. For V. major, roughly 98 percent of currently suitable area remained stable under both scenarios, with gains and losses each staying below two percent. V. caraccioli told a different story: suitable area shrank by about 44 percent under the low-emission scenario and 51 percent under the high-emission scenario, with the losses concentrated across the Amazon basin in Brazil, Colombia, Venezuela, Guyana, and Suriname. Multivariate environmental similarity surfaces confirmed that most projections fell within the range of training conditions, lending credibility to the forecasts, though the authors caution that extrapolation risk is not uniform across Amazonia.

The protected-area analysis added a sobering dimension. Under current conditions, about 25.5 percent of V. caraccioli’s climatically suitable habitat, roughly 91,217 square kilometers, falls within protected areas, mostly in the Amazon. By mid-century, that figure collapses: protected suitable area drops to about 48,517 square kilometers under SSP1-2.6 and 39,795 square kilometers under SSP5-8.5, meaning more than 46 percent of the species’ current protected habitat could lose its climatic suitability. V. major, by contrast, holds steady, with around 20.5 percent of its suitable area within protected zones across all scenarios. The message is clear: existing reserves, however crucial, may be insufficient to safeguard V. caraccioli if climate and land-use changes continue unabated, and static protected-area networks cannot track dynamically shifting climates.

The authors are careful to note the limitations of their approach. Macroclimatic niche models capture regional suitability but miss fine-scale determinants such as roost microclimates, cave and cavity buffering, riparian corridors, and topography-driven microrefugia that can decouple local conditions from broad climate layers. Occurrence records for V. caraccioli also remain sparse across large portions of Amazonia, meaning the projected contractions could be conservative if suitable climates exist in poorly sampled subregions. Bats’ strong attachment to home ranges and roosts may further limit their ability to track shifting suitability through dispersal, since long-distance migration is restricted to a subset of species. The team recommends pairing multi-season fieldwork on roost and foraging ecology with population genomics to identify locally adapted, climate-resilient populations, an approach shown in other systems to reduce predicted range losses.

The conservation prescriptions that emerge from the study are deliberately species-specific. For V. caraccioli, the priorities are strengthening protected-area networks, restoring degraded habitats, and designing ecological corridors that connect humid forest zones across the Amazon, allowing bats to move toward microclimatic refugia while preserving the seed-dispersal services on which forest regeneration depends. For V. major, climatic stability should not be mistaken for invulnerability: ongoing deforestation and agricultural expansion across Central America continue to fragment habitats and erode connectivity, so corridor networks and intact-forest preservation remain urgent there as well. Because suitable areas and potential refugia span national borders, the authors argue that coordinated transboundary planning will be essential. In a warming world where the Amazon has just recorded unprecedented drought and warmth, the fate of these unassuming seed dispersers may serve as an early warning for the mutualistic webs that hold tropical forests together.

Subject of Research: Climate-driven habitat suitability and conservation of Vampyrodes fruit bats in the Neotropics

Article Title: Habitat characterization and climate-driven niche shifts of Vampyrodes bats reveal contrasting futures for V. major and V. caraccioli

Article References: Hernández-Rodríguez, S., Martínez-Borrego, D., Jácome-Flores, M., & Cruz, D. D. (2026). Habitat characterization and climate-driven niche shifts of Vampyrodes bats reveal contrasting futures for V. major and V. caraccioli. Web Ecology, 26(2), 157-173. https://doi.org/10.5194/we-26-157-2026

Image Credits: AI Generated

DOI: 10.5194/we-26-157-2026

Keywords: Vampyrodes, fruit bats, ecological niche modeling, climate change, seed dispersal, Amazon basin, Maxent, protected areas, Neotropics, habitat loss, SSP scenarios, conservation corridors

The bamboo borer is a powderpost beetle in the family Bostrichidae, and it is widely regarded as one of the most destructive post-harvest pests of bamboo worldwide. Unlike pests that attack living plants in the field, D. minutus infests both seasoned and unseasoned bamboo after harvest, boring into culms and feeding on the starch-rich tissues inside. This concealed feeding habit reduces the structural integrity of the material and renders it unsuitable for commercial use, undermining storage life, utilization potential, and market value. Because the beetle spends most of its life cycle hidden inside the culm, early detection is notoriously difficult, and infestations often spread silently during storage and transportation. The consequences ripple through bamboo-based economies, particularly in tropical and subtropical developing countries where bamboo supports rural livelihoods, handicraft industries, construction, and paper production.

The biology of the species makes it especially sensitive to climate. D. minutus thrives under warm, humid conditions, with optimal development occurring between roughly 25 and 35 degrees Celsius, temperatures that accelerate its life cycle and allow multiple overlapping generations each year. Temperature and relative humidity strongly influence its population dynamics, which means that shifts in global climate patterns could profoundly reshape where the beetle can survive and reproduce. Rising temperatures, altered precipitation regimes, and changing seasonality are expected to affect insect physiology, phenology, and distribution across the board, and climate-sensitive species such as the bamboo borer are likely candidates for significant range shifts.

To quantify that risk, the researchers assembled occurrence records for the species from the Global Biodiversity Information Facility. The initial dataset contained 483 records, of which 297 carried usable geographic coordinates. Rigorous cleaning followed: records lacking locality information, those with coordinate errors, and 17 points located in marine environments were removed, along with 172 duplicates sharing identical coordinates. Spatial thinning with a 5-kilometer distance threshold was then applied to reduce sampling bias and spatial autocorrelation arising from uneven survey effort. The final dataset comprised 88 georeferenced presence records spanning latitudes from 42.88 degrees south to 59.12 degrees north, covering the Afrotropical, Indomalayan, Palearctic, and Oceanian biogeographic realms.

Environmental predictors came from the WorldClim version 2.1 database at a resolution of 2.5 arc-minutes, roughly 5 kilometers. The team began with the 19 standard bioclimatic variables plus elevation, then screened for multicollinearity by calculating pairwise Pearson correlations at the species’ occurrence localities. Variables with correlation coefficients above 0.80 in absolute value were pruned, leaving six predictors: mean temperature of the warmest quarter, annual precipitation, precipitation of the warmest quarter, temperature seasonality, temperature annual range, and elevation. Elevation was retained because it indirectly shapes local temperature, humidity, and microclimatic conditions that govern insect survival, and it captures topographic heterogeneity that climate variables alone may miss.

The MaxEnt model itself was implemented in R using the maxnet package, with 10,000 background points sampled from the accessible study extent and complementary log-log output producing suitability scores between 0 and 1. Hinge, linear, and product features were permitted, appropriate for moderate sample sizes, and the regularization multiplier was tuned rather than left at its default to balance model complexity against predictive accuracy. Performance was assessed with five-fold cross-validation, and the results were striking: the model achieved a mean area under the receiver operating characteristic curve, or AUC, of 0.891, indicating excellent discriminatory ability. Omission rates at the 10th percentile training threshold closely matched observed rates on test data, further supporting the model’s calibration and reliability.

Under current climate conditions, the model predicts that only 5.36 percent of the global terrestrial area is climatically suitable for the bamboo borer, concentrated in South and Southeast Asia, sub-Saharan Africa, Central America and the Caribbean, northern Australia, and coastal lowlands of East Asia. The global distribution of suitability was strongly right-skewed, with a median value of just 0.001, meaning that highly favorable habitat occupies a geographically restricted footprint. Only 4.46 percent of terrestrial grid cells exceeded a suitability score of 0.5, and a mere 2.22 percent exceeded 0.7. Boreal forests, temperate continental interiors, hyper-arid deserts, and high montane systems received near-zero scores, reflecting the beetle’s incompatibility with cold winters, extreme thermal seasonality, and moisture deficits.

The variable importance analysis revealed a nuanced ecological picture. Annual precipitation was the top contributor at 26.8 percent, with suitability peaking at roughly 800 to 1,000 millimeters of rainfall per year and declining beyond about 2,000 millimeters, suggesting the beetle is adapted to seasonally dry to sub-humid environments rather than persistently wet tropics. Elevation contributed 24.1 percent, with suitability highest below about 200 meters and declining steadily with altitude. Temperature seasonality and temperature annual range together contributed nearly 30 percent, indicating that climatic stability is critical for population persistence. Notably, mean temperature of the warmest quarter, although fourth in percent contribution, showed the highest permutation importance at 31.1 percent, with suitability peaking between 20 and 25 degrees Celsius, a result consistent with the thermophilic physiology of bostrichid beetles and other stored-product insects.

The future projections are where the study delivers its most consequential findings. Using the MIROC6 global climate model under four Shared Socioeconomic Pathways, from the low-emission SSP126 to the high-emission SSP585, the researchers projected habitat suitability for three future periods: 2041 to 2060, 2061 to 2080, and 2081 to 2100. Across all scenarios and periods, suitable area expanded to between 9.07 and 10.39 percent of global land area, a substantial increase over the current 5.36 percent. The single largest expansion occurred during 2041 to 2060 under SSP370, which produced 1,293,897 suitable cells, or 10.39 percent of terrestrial area. Interestingly, the low-emission SSP126 pathway maintained the most stable suitable area throughout the century, holding between 10.27 and 10.38 percent, while the higher-emission scenarios showed strong early-century gains followed by gradual late-century declines, with SSP585 falling to 9.07 percent by 2081 to 2100.

This pattern suggests that moderate warming may enhance suitability by relaxing climatic constraints in currently marginal regions, whereas more extreme warming could push some tropical areas beyond the beetle’s optimal temperature and moisture envelope. The spatial projections point to new risk zones in southern Europe, the Mediterranean basin, temperate East Asia, and the southern United States, regions where bamboo industries and trade networks could suddenly face a threat they have never contended with. The authors acknowledge limitations, including the absence of globally consistent bamboo distribution layers and land-use data, and the use of a 1970 to 2000 climate baseline that may not fully capture recent shifts. Even so, the message is clear: climate change is likely to facilitate the global spread of D. minutus throughout the twenty-first century, and the researchers argue that strengthened quarantine measures, early-warning systems, and long-term monitoring programs are urgently needed to protect bamboo resources and the economies that depend on them before the beetle arrives.

Subject of Research: Projected global habitat expansion of the bamboo borer Dinoderus minutus under CMIP6 climate change scenarios using MaxEnt species distribution modeling

Article Title: Climate driven habitat expansion of the bamboo borer under CMIP6 climate change scenarios using MaxEnt modeling

Article References: Climate driven habitat expansion of the bamboo borer under CMIP6 climate change scenarios using MaxEnt modeling. (n.d.). https://doi.org/10.1007/s44372-026-00845-0

Image Credits: AI Generated

DOI: 10.1007/s44372-026-00845-0

Keywords: bamboo borer, Dinoderus minutus, MaxEnt, CMIP6, climate change, habitat suitability, species distribution modeling, invasive pests, SSP scenarios, post-harvest pest, biosecurity, WorldClim

The study arrives at a moment when the vulnerability of global supply chains to extreme weather has moved from theoretical concern to lived experience. As the authors note in their introduction, supply chains have become increasingly interconnected, and that connectivity cuts both ways: it enables efficiency and cost reduction, but it also means that a disruption in one segment can propagate across industries, shaking economic stability far from the point of origin. For firms that depend on specific regions for critical inputs, climate change transforms a distant flood from someone else’s problem into a direct threat to production lines, delivery schedules, and ultimately the pace of the energy transition itself.

To quantify that threat, the research team turned to a global flood model, running its outputs under two contrasting climate scenarios drawn from the Shared Socioeconomic Pathway framework: SSP126, which represents a future of relatively strong climate mitigation, and SSP585, a high-emissions pathway with substantially greater warming. By overlaying modeled flood hazards onto the locations of lithium hydroxide production facilities, the researchers could estimate which plants sit in harm’s way, how deep floodwaters might reach at each site, and what the resulting damage could cost. This approach, combining flood depth-damage functions developed by the European Commission’s Joint Research Centre with detailed facility-level data, allowed the team to move beyond coarse regional averages and assess risk at the resolution that actually matters for a factory floor.

The geographic findings are unambiguous. Two Chinese provinces, Jiangxi and Sichuan, stand out as hotspots of flood exposure for lithium hydroxide production. Jiangxi, in southeastern China, has become a hub of lithium processing built around the region’s lepidolite deposits, while Sichuan, in the southwest, hosts significant lithium resources and processing capacity in mountainous terrain where river systems can swell dramatically during monsoon seasons. The study’s flood modeling under both climate scenarios identifies significant exposure in these key production regions, meaning that the raw material backbone of the world’s electric vehicle battery industry is disproportionately located in areas where extreme precipitation and riverine flooding are projected to intensify.

What makes the analysis particularly consequential is its finding that flood risk is highly concentrated in just a few sites. In supply chain terms, this is the definition of a single point of failure: if one or two of the most exposed facilities were knocked offline by a major flood, the shortfall could not be easily absorbed by the rest of the network. The identification of Ganfeng Lithium as the site with the greatest potential losses, up to 1.2 billion yuan, underscores how much value is packed into individual production complexes. For battery manufacturers, automakers, and the investors financing the energy transition, the implication is that climate risk in the lithium supply chain is not a diffuse background hazard but a specific, nameable, and potentially insurable exposure.

Equally important is the study’s methodological warning about how flood risk is measured. The researchers found that flood depth distributions are highly uneven across sites, which means that aggregate indicators, such as the share of a province’s production lying within a flood zone, can seriously mislead. Two facilities in the same region may face radically different water depths under the same storm, and therefore radically different damage. Depth matters because flood damage does not scale linearly: shallow flooding may cause modest cleanup costs, while deeper inundation can destroy electrical systems, corrode equipment, and halt production for months. The authors argue that site-level analysis is essential, and that reliance on aggregate indicators would obscure exactly the concentrations of risk that matter most for resilience planning.

The choice of lithium hydroxide as the focal chemical is itself telling. Lithium hydroxide is the preferred lithium compound for high-nickel cathode chemistries, such as nickel-cobalt-aluminum and high-nickel nickel-cobalt-manganese formulations, which dominate in many long-range electric vehicles. Converting spodumene ore or lithium-rich brines into battery-grade hydroxide is an energy- and capital-intensive process, and the global capacity for it is geographically skewed toward China, which processes the majority of the world’s lithium. That concentration means that climate hazards in a few Chinese provinces ripple through battery plants in Korea, gigafactories in Europe, and assembly lines in North America. The study’s use of the BACI bilateral trade database from the United Nations, covering trade flows for 200 countries across 5,000 product categories, reflects the researchers’ effort to trace these international dependencies rigorously.

The broader context of the research is the scientific consensus, articulated most recently in the Intergovernmental Panel on Climate Change’s Sixth Assessment Report, that extreme precipitation and flooding are intensifying in many regions as the atmosphere warms. A warmer atmosphere holds more moisture, and the statistical distribution of rainfall shifts toward more intense downpours. For industrial facilities sited along rivers or in floodplains, often for historical reasons of water access and transport, this means that the design storms of the twentieth century are no longer reliable guides to twenty-first-century risk. The study’s dual-scenario approach captures this uncertainty: under SSP585, with high emissions and greater warming, flood hazards grow more severe, while SSP126 offers a lower but still significant baseline of exposure, demonstrating that some level of climate risk to lithium hydroxide production is already locked in.

What can be done? The authors’ prescriptions are direct: geographic diversification and flexible supply chain models. Diversification means spreading lithium hydroxide production across more regions and more firms, so that no single flood event can cripple supply. Flexibility means designing supply chains that can reroute flows, substitute inputs, and draw down strategic inventories when a disruption strikes. Both strategies carry costs, and the study is candid that firms have historically prioritized efficiency over resilience, concentrating production where it is cheapest. But as the authors argue, in turbulent times, resilience is not a luxury; it is a condition of survival for firms whose entire business depends on uninterrupted flows of critical materials.

The findings resonate well beyond lithium. The methodological template, combining global flood models under multiple climate scenarios with facility-level exposure mapping and depth-damage estimation, can be applied to any critical input concentrated in hazard-prone regions, from semiconductor fabs to pharmaceutical plants to grain terminals. As climate change accelerates, the study suggests, the invisible architecture of global trade will need to be re-examined not just for cost and carbon, but for water. For the electric vehicle industry in particular, the message is sobering: the clean energy transition depends on a chemical supply chain whose most important nodes are sitting in the path of rising floodwaters, and the time to diversify, relocate, or reinforce them is now, before the next catastrophic monsoon season tests the system’s limits.

Subject of Research: Climate-induced flood risks to lithium hydroxide production and the electric vehicle battery supply chain in China

Article Title: Flood risks to the supply chain of electric vehicle batteries under climate change: a case study of lithium hydroxide production

Article References: Yang, Y., Xie, Q., Hu, X., Pant, R., & Huang, J. (2026). Flood risks to the supply chain of electric vehicle batteries under climate change: a case study of lithium hydroxide production. Journal of Industrial Ecology, 30(4), 1597-1608. https://doi.org/10.1007/s44498-026-00107-y

Image Credits: AI Generated

DOI: 10.1007/s44498-026-00107-y

Keywords: climate change, flooding, lithium hydroxide, electric vehicle batteries, supply chain, China, Jiangxi, Sichuan, Ganfeng Lithium, flood risk modeling, SSP scenarios, supply chain resilience

The team built their projections using LuccME, the Land Use and Cover Change Modelling Environment, an open-source framework developed by Brazil’s National Institute for Space Research. The model integrates three interlocking components: a demand module that specifies how much of each land class must change at each time step, a potential module that estimates where change is most likely to occur, and an allocation module that distributes those changes across the landscape. What distinguishes LuccME from many conventional land-change models is its use of Spatial Lag Regression in the potential component, which explicitly accounts for spatial autocorrelation. In practical terms, the model recognizes that land change is contagious: the fate of any given cell depends not only on its own attributes but also on the condition of its neighbors, capturing the clustering and spatial continuity that characterize real landscapes.

To ground the simulation in observed reality, the researchers drew on MapBiomas land use and cover data, reclassifying the detailed legend into five broad categories: Agriculture, Forest, Pasture, Grassland, and Other Uses. Twenty-one explanatory variables were compiled for the 2000 to 2016 period, ranging from agricultural land aptitude, livestock counts, and sugarcane mills to the Gini index of inequality, conservation areas, permanent protection status, railroads, state highways, and population density. All variables were homogenized onto a cellular grid at a resolution of 100 square kilometers using the TerraView GIS environment, creating a common spatiotemporal framework in which vector and raster data could be combined. The period 2010 to 2015 served for calibration, 2016 to 2019 for validation, and 2020 to 2050 for scenario projection.

The scenarios themselves were regionalized from the Shared Socioeconomic Pathways, the standard narrative framework used in international climate research. SSP1-1.9 represents a sustainability-oriented future built on strict environmental enforcement, reduced deforestation, ecosystem restoration, and protection of conservation units and indigenous territories. SSP2-4.5 assumes an intermediate trajectory in which some positive trends of the past decade continue. SSP3-7.0 depicts a strong-inequality world marked by weakened socio-environmental governance and intensified resource exploitation. Each narrative was translated into quantitative land-use demands, with annual change calculated as the difference between the 2050 target area and the 2010 initial area divided across the forty-year simulation horizon, then fed into the allocation algorithm, which distributes transitions according to suitability and inter-class competition.

Model performance proved robust. For the validation year 2019, the overall Spatial Adjustment Index reached 89.48 percent, indicating strong agreement between simulated and observed spatial patterns, while omission and commission errors remained low at 2.59 percent and 2.16 percent respectively. Forest showed the highest spatial adjustment at 97.13 percent, followed by Pasture at 94.48 percent and Agriculture at 88.75 percent. Grassland proved hardest to reproduce, at 78.13 percent, with higher errors concentrated in the central portion of the basin. The regression models underpinning each class also demonstrated strong explanatory power, with coefficients of determination of 0.8378 for Pasture, 0.8081 for Grassland, 0.7957 for Agriculture, and 0.7824 for Other Uses, while Forest’s more moderate fit of 0.5574 suggested that additional, unmeasured drivers influence its dynamics.

Those driver analyses revealed a landscape governed by an intricate interplay of economics, infrastructure, and regulation. Agricultural expansion was favored by good land aptitude, permanent protection designations, and proximity to railroads, but constrained by conservation areas, priority areas, settlements, and distance from state highways. Grassland occurrence correlated strongly with the Gini index, unsuitable areas, and restricted areas, pointing to a striking association between socioeconomic inequality and land degradation trajectories. Pasture responded positively to agricultural production value and restricted aptitude but negatively to conservation areas and inequality. Forest, meanwhile, was positively associated with regular land areas and agricultural production value but negatively affected by proximity to sugarcane mills and priority-area designations. High standard deviations for variables such as permanent protection, state highways, and average annual precipitation underscored pronounced spatial heterogeneity, meaning the same factor can push land change in different directions in different parts of the basin.

The 2050 projections themselves tell a story of divergent futures. Agriculture expands under all three scenarios, from a 2010 baseline of roughly 80,989 square kilometers to 96,668 square kilometers under the sustainability pathway, an increase of 19.4 percent, and up to 118,248 square kilometers under the strong-inequality pathway, a 46.0 percent surge. Grassland grows by as much as 45.0 percent under SSP3-7.0 and 29.6 percent under SSP2-4.5, while Pasture climbs from 5.1 percent under SSP1-1.9 to 36.6 percent under SSP3-7.0. Forest, the basin’s ecological backbone, declines everywhere: by 15.1 percent under the sustainability scenario, 25.2 percent under the intermediate pathway, and a devastating 41.2 percent under the strong-inequality scenario. Other Uses remain comparatively stable, shifting only slightly under SSP1-1.9 but rising 19.3 percent under SSP3-7.0.

Crucially, the spatial patterns of these changes matter as much as their magnitude. Forest loss concentrates in areas adjacent to expanding agricultural land, tracing the advance fronts where natural vegetation is progressively converted. Pasture expansion clusters in regions with established human occupation and high land-use intensity, and the most dramatic transformations occur in transition zones between natural and anthropogenic landscapes. In the upper basin, where native Cerrado vegetation still dominates alongside agriculture, the projections suggest continued erosion of one of the world’s most biodiverse tropical savannas. The basin’s climate, spanning three Köppen types with annual rainfall between 600 and 1,200 millimeters and strong spatial and temporal variability, adds another layer of fragility, since altered vegetation cover directly affects the hydrological cycle that feeds a river discharging approximately 94 cubic kilometers per year.

The implications extend well beyond ecology. Land use and land cover change alters streamflow regimes, reduces water availability, degrades ecosystem services, and increases pressure on hydrological systems already stressed by drought and competing demands from agriculture, cities, and energy generation. The authors emphasize that the projected maps should be read as plausible scenarios rather than deterministic predictions, since uncertainties arise from demand assumptions, variable selection, parameterization based on historical patterns, and the inherent difficulty of allocating change where multiple transitions share similar suitability. LuccME itself has limitations: it cannot freely combine continuous and discrete components, it often relies on accumulated historical patterns rather than observed class-to-class transitions, and it cannot fully capture non-stationary drivers and socioeconomic feedbacks.

Yet the study’s central message is ultimately one of agency. The smallest forest losses occur precisely under the scenario that assumes strong environmental regulation, restoration policy, and land-use control, demonstrating that governance choices materially shape the basin’s future. The researchers argue that limiting agricultural expansion alone will not suffice; complementary strategies, including improved land-use efficiency, more effective territorial regulation, ecosystem restoration, and sustainable economic alternatives, are essential to blunt the adverse effects of projected change. By providing a spatially explicit map of where pressure will intensify, the modeling framework offers water managers and policymakers a practical tool for prioritizing conservation and restoration, anticipating shifts in watershed conditions, and integrating land-use governance with long-term water resource planning across a river basin on which much of Brazil depends.

Subject of Research: Spatially explicit modeling of future land use and land cover change in the São Francisco River Basin, Brazil

Article Title: Spatially explicit modeling of future land use and land cover dynamics in the São Francisco River Basin, Brazil

Article References: Vasco, G., Montenegro, S. M. G. L., Miranda, R. D. Q., Viana, J. F. D. S., Bressiani, D., Mendiondo, E. M., Bezerra, G., Galvíncio, J. D., Santos, C. A. G., & da Silva, R. M. (2026). Spatially explicit modeling of future land use and land cover dynamics in the São Francisco River Basin, Brazil. Earth Science Informatics, 19(11), Article 201. https://doi.org/10.1007/s12145-026-02250-3

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02250-3

Keywords: land use change, land cover, São Francisco River Basin, LuccME, spatial modeling, deforestation, Brazil, SSP scenarios, water resources, geospatial analysis, Cerrado, watershed management

The team simulated river flow in the basin from 2020 to 2100 under two Shared Socioeconomic Pathways: SSP1-2.6, a low-emission scenario consistent with strong climate mitigation, and SSP5-8.5, a high-emission trajectory in which fossil fuel use continues largely unchecked. Atmospheric forcing came from a five-model ensemble of CMIP6 global climate models at daily resolution, covering variables from precipitation and temperature to humidity, wind speed and radiation. The researchers compared two future windows, a near future spanning 2020 to 2055 and a far future from 2065 to 2100, against a historical baseline running from 1979 to 2014, with floods defined as streamflow exceeding the 95th percentile.

The choice of model matters. TerM, developed at the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences and Lomonosov Moscow State University, simulates the exchange of water and energy between the land surface and the atmosphere and forms the land component of the INMCM Earth System Model. Before running future scenarios, the team calibrated the model’s runoff parameters against observed discharge at a gauge on the Limpopo River, optimising soil infiltration capacity, which governs how rainfall is partitioned between surface runoff and infiltration, and maximum groundwater discharge, which controls baseflow during dry periods. The calibration prioritised a near-zero bias in total water volume, a deliberate trade-off that sacrifices some day-to-day timing accuracy in favour of eliminating systematic volume errors that would otherwise distort century-long water balance assessments.

The projections paint a picture of a basin caught between intensifying atmospheric demand and erratic rainfall. Incoming shortwave radiation is projected to rise across the basin, by around 3 watts per square metre per month in the near future and by 4 to 6 watts per square metre in the far-future interior of Gauteng and Limpopo Provinces, likely reflecting declining cloud cover and the eastward expansion of semi-arid conditions from the Kalahari. Temperatures climb along an east-to-west gradient, from 1.6 to 2.5 degrees Celsius in the near future to more than 3.7 degrees Celsius in the far future, with some stations projected to warm by 5.5 degrees Celsius. Relative humidity falls in step, and soil water content declines across all scenarios, from modest near-future deficits to losses exceeding 7 millimetres per month in far-future coastal and southern zones.

Underlying these shifts is a familiar piece of thermodynamics: the Clausius-Clapeyron relationship, which dictates that warmer air holds more water vapour. As temperatures rise, evaporative demand intensifies even during wet periods, so gains in rainfall are largely offset by losses to evaporation. The study projects that terrestrial water storage will decline across the basin by up to 4 millimetres per month, with the steepest deficits concentrated near the river mouth in Mozambique, where surface water evaporation is expected to increase by 0.5 to 0.8 millimetres per month. Groundwater runoff, after a brief near-future increase of up to 0.5 millimetres per month, falls substantially in the far future, with declines reaching 2.5 millimetres per month along the coast. The result is a basin transitioning toward water limitation, where the atmosphere’s thirst outpaces natural replenishment.

The streamflow projections are where the study’s most striking finding emerges. While the long-term annual trend in streamflow is downward and statistically insignificant between 2020 and 2100, individual years tell a very different story. Under the high-emission scenario, peak monthly flows at the river mouth frequently exceed 4,000 cubic metres per second, compared with historical baseline peaks of about 2,500 cubic metres per second, an increase of more than 50 percent. Extreme years cluster around 2030, 2041, 2052, 2073 and 2091. At interior gauges, discharge is projected to exceed 900 cubic metres per second during high-emission years such as 2068 and 2079. Yet the same simulations show synchronised collapses between 2092 and 2094, and again around 2034, when flow at all interior stations falls below 150 cubic metres per second. The authors describe this as hydro-climatic whiplash: a regime in which unprecedented floods and severe droughts alternate with little respite.

The seasonal rhythm of the river is also expected to shift. Peak flows will continue to arrive during the austral summer, concentrated in January and February, when convective rainfall, tropical cyclones landfalling from the Mozambique Channel and the El Niño-Southern Oscillation all conspire to deliver the basin’s heaviest downpours. The 95th-percentile analysis suggests February high flows at the river mouth could reach 4,800 cubic metres per second in the far future under high emissions, with increases of up to 2,250 cubic metres per second relative to the baseline. But the shoulder seasons contract. Pronounced decreases are projected during spring, from September to November, with September rainfall and evaporation under SSP5-8.5 falling by more than 26 percent in the far future. Low-flow months such as August, September and October are projected to see the river dwindle toward zero flow at all monitoring stations, effectively lengthening the dry season.

The urgency of these projections is underscored by recent history. The basin has long been a flood hotspot, recording 48 significant floods between 1980 and 2012, and historical disasters in 1955, 1967, 1972, 1975, 1977, 1981 and 2000 were repeatedly linked to tropical cyclone rainfall pushing the river past bank-full capacity. The pattern has continued into the present decade: flooding along the Phalala River in January 2025 inundated 352 houses and more than 100 educational facilities in Limpopo Province, and in early January 2026 the Nsami and Dap Naude dams exceeded their design capacities by more than 120 percent, causing losses of roughly 4 billion rand, about 247 million US dollars, and the deaths of 17 people. The study’s finding that summer flood peaks will intensify suggests such events may become more frequent and severe, while the projected spring declines threaten the rain-fed agriculture on which much of the basin’s population depends.

Statistically, the picture is nuanced. Using the Modified Mann-Kendall test, which corrects for the serial correlation that plagues hydrological time series in semi-arid climates, the researchers found that long-term downward trends in streamflow, rainfall, evaporation and runoff remain statistically insignificant through most of the century, particularly under the low-emission scenario. But under high emissions, the far future brings significance: models including GFDL and MPI project highly significant streamflow declines, and station A7H008 records the largest projected decrease of 7.87 cubic metres per second per year. The authors interpret this shift from insignificance to significance as a potential tipping point in the basin’s water balance toward the century’s end, with high interannual variability masking a steadily tightening water budget until the trend breaks through the statistical noise.

The study is not without acknowledged limitations. The coarse 0.5-degree resolution of the global ISIMIP forcing data limits the model’s ability to capture station-scale dynamics, yielding a Nash-Sutcliffe efficiency of only about 0.1 at the calibration gauge, compared with 0.60 when the model is driven by high-resolution ERA5 reanalysis. The researchers argue that for centennial-scale projections of monthly means and percentile trends, preserving mass balance matters more than reproducing daily variance, and they express high confidence in the direction and relative magnitude of the projected shifts. What the work delivers, for the first time in the African context, is a physically grounded, process-based account of how one of southern Africa’s most vulnerable transboundary basins will absorb the shock of a warming climate. The authors hope the evidence base will strengthen climate adaptation policies, from flood mitigation infrastructure to water resource planning, before the projected whiplash between flood and drought becomes the basin’s new normal.

Subject of Research: Projected hydrological changes and riverflow extremes in the Limpopo River Basin under CMIP6 climate scenarios

Article Title: Projections of hydrological changes and riverflow extremes using TerM land surface model in the Limpopo River Basin, South Africa

Article References: Projections of hydrological changes and riverflow extremes using TerM land surface model in the Limpopo River Basin, South Africa. (n.d.). https://doi.org/10.1007/s00704-026-06579-z

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06579-z

Keywords: Limpopo River Basin, TerM land surface model, CMIP6, ISIMIP, streamflow projections, flooding, drought, climate change, SSP scenarios, hydrological extremes, South Africa, water resources

The science behind the threat begins with chemistry. Steel bars embedded in concrete are protected by a passive oxide film sustained by the high alkalinity of the pore solution. Carbon dioxide from the atmosphere diffuses into the concrete and reacts with the cement matrix, a process called carbonation that lowers the pH and dissolves that protective layer. Once the carbonation front, which advances from the surface inward, reaches the steel, depassivation occurs and active corrosion begins. The corrosion products occupy far more volume than the original steel, generating tensile stresses that crack the concrete cover, degrade the bond between steel and concrete, and ultimately erode the structure’s stiffness and ductility.

How fast this front moves depends on a delicate interplay of material and environmental factors: the concrete’s water-to-cement ratio, its porosity and compressive strength, the ambient temperature, the relative humidity, and the concentration of carbon dioxide in the air. The new research, published in Case Studies in Construction Materials by Chiara Pinheiro Teodoro, Rogério Carrazedo and Emilio Bastidas-Arteaga, adopts a climate-dependent carbonation model in which carbonation depth accelerates with rising temperature and elevated carbon dioxide, but slows when relative humidity exceeds roughly 65 percent. Paradoxically, the most dangerous humidity range sits between about 25 and 65 percent, where pores are moist enough for carbon dioxide to react but dry enough for gas to diffuse rapidly.

That sensitivity is precisely what makes climate change so consequential. Under most future Shared Socioeconomic Pathways, both temperature and atmospheric carbon dioxide are projected to rise throughout Brazil, while relative humidity declines across much of the country, with the strongest reductions expected in the Amazon region. The team used a dataset spanning 1961 to 2100 at a spatial resolution of 0.2 degrees, covering five emission scenarios from the ambitious SSP1-1.9 to the fossil-fueled SSP5-8.5. In many locations, the projected drops in humidity fall squarely into the critical range that accelerates carbonation, compounding the effects of warming and rising carbon dioxide.

To translate these climate signals into engineering risk, the researchers ran Monte Carlo simulations with Latin hypercube sampling, generating 100,000 samples for each combination of location, construction period, climate scenario, exposure condition and aggressiveness class. The limit state function compared the concrete cover depth against the predicted carbonation depth, treating cover, diffusivity, ageing and urban carbon dioxide factors as random variables with realistic variability. The results were striking: for structures built in 2000 under the NB 1:1978 code, the probability of depassivation exceeded 50 percent within 40 years at nearly all of the 500 sampled locations, whereas structures built in 2020 under the stricter NBR 6118:2014 mostly stayed below that threshold over the same service life.

The contrast reveals how much design codes matter. Brazil’s concrete standard has evolved from the permissive NB 1 rules of 1940 through 1978, which prescribed covers as thin as 1.5 centimeters indoors and set no water-to-cement limits, to the current NBR 6118:2023, which mandates covers up to 5 centimeters and water-to-cement ratios as low as 0.45 for the harshest environments. Structures designed under the older codes combine shallow cover with high diffusivity, leaving them doubly exposed as the climate warms. The study found that buildings erected before 2003 systematically show higher depassivation probabilities, and that the differences among future emission scenarios widen over time, meaning long-term durability decisions should never rest on a single climate projection.

Geography matters just as much as construction date. The machine learning analysis, which trained a Random Forest model on simulations from 500 representative locations, showed that inland regions with intermediate humidity and elevated temperatures are the most vulnerable. Manaus, with high temperatures and humidity often between 40 and 70 percent, shows markedly higher depassivation probabilities than Porto Alegre, where cooler temperatures and humidity above 65 percent suppress carbonation. Coastal cities such as Fortaleza and Recife benefit from persistently high humidity, although the authors caution that their results there capture only the carbonation contribution, since chloride-induced corrosion, often more severe near the sea, was outside the scope of the study.

The Random Forest model itself proved remarkably powerful, achieving a coefficient of determination near 0.999 on a random test split and an average of 0.941 in a stricter spatial validation that excluded entire geographic regions from training. It also trained in about 18 minutes, whereas the artificial neural network alternative required more than 24 hours with lower accuracy. This efficiency means engineers can now estimate corrosion initiation probability for virtually any location, construction year, exposure class and climate scenario in seconds, turning what was once a computationally prohibitive reliability analysis into a practical design tool.

The stakes are far from academic. A 2024 investigation following a bridge collapse in northern Brazil reported that at least 736 bridges in the country are in poor or critical condition, and field studies have repeatedly found that actual concrete covers in Brazilian buildings frequently fall short of design specifications, with one survey reporting nearly half of measured covers non-compliant. The new study’s national statistics show a median 50-year depassivation probability of about 2.8 percent for pre-2025 structures, dropping to 1.5 percent for those built under the stricter modern code, but with regional medians reaching 14.2 percent in the South and sharp variability across exposure classes.

The authors argue that Brazil’s current four-class exposure scheme is too coarse to capture the country’s enormous climatic diversity, and that future revisions of the durability provisions should become region-specific and climate-informed. Their framework, which could be extended to include chloride ingress and corrosion propagation, points toward a shift from prescriptive rules to performance-based design, in which cover depths and concrete quality are tailored to local climate futures rather than one-size-fits-all tables. As global emissions continue to climb, the invisible chemistry unfolding inside Brazil’s concrete may become one of the most expensive and overlooked consequences of a changing climate.

Subject of Research: Probabilistic assessment of carbonation-induced reinforcement depassivation in Brazilian reinforced concrete structures under climate change

Article Title: Probabilistic assessment of carbonation-induced depassivation in Brazilian reinforced concrete structures under climate change

Article References: Teodoro, C. P., Carrazedo, R., & Bastidas-Arteaga, E. (2026). Probabilistic assessment of carbonation-induced depassivation in Brazilian reinforced concrete structures under climate change. Case Studies in Construction Materials, 25, Article e06584. https://doi.org/10.1016/j.cscm.2026.e06584

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06584

Keywords: reinforced concrete, carbonation, corrosion, climate change, Brazil, depassivation, Monte Carlo simulation, machine learning, random forest, durability, design codes, SSP scenarios

The research team focused on the regions dominated by Coffea arabica Linnaeus, the species that underpins the livelihoods of millions of smallholder farmers across the Ethiopian highlands. To generate projections at a resolution useful for agricultural planning, the scientists applied empirical quantile mapping, a statistical bias-correction technique that adjusts the systematic errors of global climate models against observed station records and satellite-based reference datasets. The correction proved highly effective. For daily maximum and minimum temperature, the corrected ensemble achieved Nash-Sutcliffe efficiency scores of 0.92 and 0.75 respectively, with coefficients of determination of 0.96 and 0.82. For precipitation, the ensemble reached a Nash-Sutcliffe efficiency of 0.91 and an R-squared of 0.96, indicating that the downscaled simulations reproduced the historical climate of the coffee zones with remarkable fidelity before any future scenario was considered.

With the corrected ensemble validated, the researchers examined two future pathways drawn from the ScenarioMIP framework: SSP2-4.5, a moderate emissions scenario in which global society follows a middle-of-the-road trajectory, and SSP5-8.5, a high-emissions future with continued reliance on fossil fuels. The temperature signal under both scenarios was unambiguous. Maximum temperatures in the coffee regions are projected to rise by 1.01 degrees Celsius by the 2031 to 2060 period under SSP2-4.5 and by 1.37 degrees under SSP5-8.5, climbing further to 1.72 and 3.27 degrees respectively by 2071 to 2100. Minimum temperatures warm even faster, increasing by 1.26 and 1.88 degrees Celsius at mid-century and by 2.00 and 3.94 degrees by the end of the century under the two scenarios. Every one of these warming trends was statistically significant at the five percent level.

The asymmetry of this warming, with nighttime minimums rising faster than daytime maximums, carries consequences that go well beyond a simple increase in average heat. A shrinking diurnal temperature range means that coffee plants experience intensified heat stress during the night, a period when the crop would normally recover from daytime photosynthetic demands. Elevated nighttime temperatures accelerate respiration, causing the plant to burn through carbohydrates that would otherwise be allocated to bean filling, vegetative growth, and stress defense. Agronomists have long linked warm nights to reduced yield stability and degraded cup quality in Arabica, and the projections suggest that Ethiopian farmers will face exactly this pressure, particularly in the lower-elevation portions of the growing zones where baseline nighttime temperatures are already close to the physiological limits of the species.

Rainfall tells a more complicated story. On an annual basis, mean precipitation over the coffee regions is actually projected to increase, by 3.47 to 9.44 percent under SSP2-4.5 and by 18.01 to 23.74 percent under SSP5-8.5. Yet the study found these annual changes to be statistically insignificant, meaning the models cannot rule out that they arise from natural variability rather than a forced climate response. The seasonal breakdown, by contrast, exposes significant and troubling shifts. Rainfall during the June to September season, the primary rainy period known locally as Kiremt and abbreviated as JJAS, is projected to decrease significantly at mid-century under the moderate scenario. Meanwhile, rainfall during the March to May Belg season, which is critical for coffee flowering, shows a significant increase at mid-century that reverses into a significant decline by the late century under the high-emissions pathway.

This seasonal whiplash is precisely the kind of change that catches farming systems off guard. Coffee flowering in Ethiopia is triggered by the onset of the rains after a dry spell, and the synchrony of flowering determines the uniformity of the harvest. A wetter March-to-May period followed by a drier June-to-September season would disrupt the moisture supply during fruit development, the stage at which the coffee cherry demands the most water. The authors caution that the decrease in rainfall during the core rainy season points to increased intra-seasonal variability and potentially serious water stress, even as the annual totals climb. In other words, more water may arrive in the year as a whole, but it may arrive at the wrong times, in the wrong intensities, and with longer dry gaps in between, a pattern familiar from broader research on how the tropical water cycle responds to warming.

The study’s methodology reflects the current best practice in regional climate impact research. Rather than relying on a single global model, the team used a multi-model ensemble, which averages out the idiosyncratic errors of individual simulations and provides a more defensible estimate of the range of possible futures. Trend detection was carried out with the Mann-Kendall nonparametric test and Sen’s slope estimator, standard tools for identifying monotonic changes in noisy hydroclimatic records. The bias-correction approach drew on established quantile-mapping methods designed to preserve changes in both the mean and the extremes of the model distributions, and the baseline climate record was anchored by observations from the Ethiopian Meteorological Institute alongside the CHIRPS satellite rainfall product and the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts.

The stakes for Ethiopia’s economy and cultural identity are considerable. Coffee is the country’s leading export commodity and a source of income for an estimated five million or more farming households, and the genetic diversity of wild Arabica in the Ethiopian forests represents an irreplaceable global resource. Previous research has already mapped substantial contractions in suitable Arabica habitat under warming scenarios, and studies from Tanzania and other East African producers have documented yield declines tied to rising temperatures and shifting rainfall. The new projections add a crucial layer of regional specificity, showing that the canonical coffee landscapes of southwest and western Ethiopia, including zones around Jimma, will not escape the thermal trajectory of the wider East African highlands. The combination of hotter nights, altered seasonal rainfall, and heightened variability threatens both the quantity and the quality of production in areas where alternative crops are limited.

The authors argue that their results make a compelling case for climate-resilient adaptation strategies tailored to the coffee zones. Among the options highlighted are the deployment of heat-tolerant coffee varieties, improved soil and water conservation measures that help capture and store the increasingly erratic rainfall, and shade-based agroforestry systems that buffer the microclimate of coffee plots against extreme temperatures. Shade trees, in particular, address the nighttime warming problem directly by moderating both daytime heating and nighttime radiative cooling, while also contributing organic matter and suppressing erosion on the steep highland slopes. The study, conducted under the Future Africa Research Leadership Fellowship funded by the Carnegie Corporation of New York, underscores that the window for proactive adaptation is open but narrowing. As the projections make clear, the highlands that gave the world Arabica coffee are warming on a schedule set by global emissions, and the farmers who tend those slopes will need every tool available to keep the crop, and the centuries of heritage behind it, viable through the rest of this century.

Subject of Research: Projected climate change impacts on Arabica coffee growing regions in Ethiopia using CMIP6 climate model ensembles

Article Title: Projected changes in precipitation and temperature for ethiopian coffee growing regions using CMIP6 multi-model ensembles

Article References: Belay, F., Garedew, W., Gandidzanwa, C., Oljira, A., Falola-Olasunkanmi, J. A., Agyekum, J., Nyengere, J., & Dibaba, W. T. (2026). Projected changes in precipitation and temperature for ethiopian coffee growing regions using CMIP6 multi-model ensembles. Theoretical and Applied Climatology, 157(10), Article 659. https://doi.org/10.1007/s00704-026-06601-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06601-4

Keywords: Ethiopia, Arabica coffee, CMIP6, climate projections, precipitation, temperature, bias correction, quantile mapping, SSP scenarios, agroforestry, climate adaptation, highlands

The research was carried out by Ayşe Özlem Tursun of Malatya Turgut Özal University, who combined experimental plant physiology with species distribution modeling, a pairing that remains surprisingly uncommon in climate-impact studies. Conventional distribution models treat a species as a passive follower of climate envelopes, predicting where conditions might be tolerable but saying little about whether the organism will perform better or worse once it arrives. By integrating experimentally derived physiological responses, Tursun’s framework attempts to capture how photosynthesis, growth and secondary metabolite production in lemon balm shift under the novel thermal and atmospheric regimes of the coming decades, and then translates those responses into a scenario-based sensitivity analysis of range change.

The modeling backbone of the study is the Maximum Entropy algorithm, or MaxEnt, one of the most widely used tools for mapping potential species distributions from presence-only records. Tursun assembled 117 spatially independent occurrence records for lemon balm, drawing on a formally documented download from the Global Biodiversity Information Facility, and paired them with bioclimatic variables from the WorldClim version 2.1 database. To keep the projections honest, model complexity was tuned using spatial block cross-validation, a technique that separates training and testing data geographically so the model cannot simply memorize local conditions. The resulting statistics were strong: a training area under the curve of 0.825, a test AUC of 0.878, and a True Skill Statistic of 0.620, values that indicate the model discriminates suitable from unsuitable habitat considerably better than chance.

Two climatic variables emerged as the decisive levers shaping where lemon balm can live. The minimum temperature of the coldest month, known among modelers as Bio6, contributed most by variable importance, while the annual mean temperature, Bio1, ranked highest by permutation importance. In plain terms, lemon balm’s Turkish distribution is governed above all by how cold winters get and how warm the year runs on average. That makes intuitive sense for a herb of Mediterranean and Irano-Turanian affinity, and it also explains the geography of the projected expansion: as winters soften and annual temperatures climb, the cold walls that currently fence the species into parts of western and southern Türkiye begin to dissolve.

The spatial pattern of the forecast is striking. Under all scenarios and both future time horizons, 2050 and 2070, the model projects progressive habitat expansion rather than contraction, with newly suitable territory emerging along the Black Sea coastline and across the northern Central Anatolian plateau. The expansion is pronouncedly poleward and upward in elevation, a signature consistent with the general expectation that species will track their thermal niches toward higher latitudes and altitudes. For a country whose medicinal and aromatic plant sector is economically and culturally significant, the map carries practical weight: it identifies where lemon balm cultivation might plausibly be established in the coming decades and where current strongholds could face shifting competitive and climatic conditions.

What lifts the study beyond a standard MaxEnt exercise is its treatment of carbon dioxide. Rising atmospheric CO2 does not merely warm the planet; it also fertilizes photosynthesis in many plant species, a physiological effect that pure correlative models routinely ignore. Drawing on earlier experimental work in which lemon balm was grown under different temperature and CO2 concentrations, Tursun ran a scenario-based sensitivity analysis to estimate how much of the projected expansion could be attributed to this fertilization effect. The answer was roughly 25.1 percentage points of the total expansion, a substantial share, though the analysis concluded that rising temperatures remain the dominant driver of the range shift. The result is a rare quantitative partitioning of the climatic and physiological components of a forecast range change.

Robustness was addressed through an ensemble assessment across three CMIP6 general circulation models. The coefficient of variation across the ensemble came out at just 5.6 percent, a low figure indicating that the expansion signal is not an artifact of one particular climate model’s quirks. The projections were run under two Shared Socioeconomic Pathways, SSP2-4.5 representing a moderate emissions trajectory and SSP5-8.5 representing a fossil-fuel-intensive future, and the direction of change held under both, with magnitude scaling with emissions. In a field where projections often swing wildly between climate models, that degree of agreement is notable and lends credibility to the headline number of a doubling of suitable area by mid-to-late century.

Yet the study’s own framing makes clear that a larger map is not an unalloyed good. Climate change poses what the author calls dual challenges to medicinal and aromatic plants: the contraction of suitable habitats on one hand, and the alteration of secondary metabolite biosynthesis under new thermal and atmospheric conditions on the other. For lemon balm, the commercial and medicinal value lies in its essential oils and phenolic compounds, and the scientific literature has long documented that environmental stressors, temperature and CO2 among them, can reshape the chemistry of such plants. A habitat that is climatically suitable may therefore produce raw material of different quality and potency than today’s harvests, a dimension that distribution maps alone cannot capture.

The methodological choices also deserve attention from practitioners. The occurrence data were spatially thinned to reduce sampling bias, the model was evaluated with metrics appropriate to presence-only modeling, and the threshold selection for converting continuous suitability scores into binary habitat maps followed established procedures for such data. The R script underlying the analysis was released as supplementary material, and the occurrence dataset carries a persistent citable DOI, making the workflow reproducible end to end. In an era when species distribution models increasingly inform conservation planning and agricultural policy, that transparency matters as much as the headline projection.

For Türkiye, the practical message is one of climate-smart agricultural planning. The identification of the Black Sea coast and northern Central Anatolia as zones of emerging suitability offers a forward-looking guide for growers, cooperatives and policymakers in the medicinal plant sector, suggesting where cultivation trials, land-use investments and conservation measures might be directed before the climate arrives. At the same time, the study is a reminder that even the winners of climate change live in a transformed world: a lemon balm field in 2070 will grow under different skies, breathe different air, and likely synthesize a different bouquet of compounds than its predecessors. The expansion of its habitat, dramatic as it is, is only the first chapter of the story.

Subject of Research: Projected habitat expansion of lemon balm in Türkiye under CMIP6 climate scenarios using integrated physiological and distribution modeling

Article Title: Integrating experimental physiological responses with species distribution modeling to forecast the future habitat expansion of Melissa officinalis L. in Türkiye under CMIP6 scenarios

Article References: Integrating experimental physiological responses with species distribution modeling to forecast the future habitat expansion of Melissa officinalis L. in Türkiye under CMIP6 scenarios. (n.d.). https://doi.org/10.1007/s00704-026-06613-0

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06613-0

Keywords: lemon balm, Melissa officinalis, species distribution modeling, MaxEnt, CMIP6, climate change, Türkiye, CO2 fertilization, medicinal plants, habitat expansion, bioclimatic variables, SSP scenarios