Search Results (4,019)

The generation of biomass residues and biowaste is both a challenge to the environment and to resource management worldwide but is also a significant renewable resource for energy and material recovery. Biomass residues are utilised along multiple pathways such as direct use for energy production, anaerobic digestion and biogas production, biofuel generation, composting, soil amendment, and conversion into value-added products at a global scale. On the other hand, many lignocellulosic and other biomass residues are characterized by low bulk density, heterogeneous characteristics, and seasonal availability, thus complicating collection, handling, storage, and transportation. Densification into solid fuel pellets is a viable solution for transforming these low-density residues into a compact, handleable, and transportable form with increased energy density. Although biomass residues have global potential, past pelletisation studies in Punjab, Pakistan, have generally considered only a few of two to three seasonal feedstocks that do not cover the uncertainty of potential feedstock availability in several seasons of the year, alongside the judgment of suitable biomass input. This study was, therefore, designed to explore nine agro-residues with different seasons in Punjab, e.g., tree residues, grass clippings, animal waste, crop residues, and herb biomass, for robust conversion into solid fuel pellets. Gate-to-gate Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methods were used to evaluate the production system of the pellets from biomass residues. A functional unit of 1 tonne of biomass solid fuel pellets was defined, and the resulting environmental impacts were modelled using GaBi software and the ReCiPe 2016 methodology. Process performance and production hotspots were assessed by attributing the environmental impacts that occurred at individual pellet-production stages to the overall impacts, while LCC was used to evaluate economic costs, both internal and external, from pellet production. The results of hotspot analysis indicated that flash/pneumatic drying represented the most important contributor for all investigated impact categories, followed by the pellet-milling and packaging stages. The total estimated cost of the pellets was USD 72.6 t⁻¹, with an internal cost of USD 51.7 t⁻¹ and an environmental cost of USD 20.9 t⁻¹. Two other energy scenarios provided evidence through which to understand the potential for reductions in GHG emissions linked to the production of pellets. These findings suggest that differing and seasonally accessible biomass residues in Punjab can be converted into solid biofuels to concurrently aid sustainable waste management, energy security, climate-change amelioration, and rural economic growth. This study is novel because it compares nine heterogeneous biomass residues in a seasonal common-pellet production framework, using an integrated environmental and economic approach to provide stakeholders with practical evidence they can use to select the biomass feedstock that is most environmentally and economically preferable. These results help achieve the Sustainable Development Goals 7, 11, 12, and 13. Full article

Wetlands regulate hydrological processes, maintain biodiversity, and moderate local climate. However, long-term evidence on hydroclimatic variability and anthropogenic pressure in West African coastal wetlands remains limited. This study assesses rainfall, land surface temperature (LST), and land use/land cover (LULC) change in the Keta Lagoon Complex (KLC) and Muni-Pomadze Ramsar Site (MPRS) from 2000 to 2024. Mann–Kendall tests and Sen’s slope estimators were applied to annual and seasonal rainfall, while Landsat observations were used to evaluate seasonal LST and classify LULC. The indicators were compared across sites and through time to assess their correspondence without attributing causality. Annual rainfall trends were weak and not statistically significant, although minor rainy-season rainfall increased significantly at KLC (p = 0.018). MPRS showed greater thermal variability, with some major rainy season LST values exceeding 44 °C. Built-up cover increased by 9.89 percentage points at KLC and 8.34 percentage points at MPRS, while natural vegetation at MPRS declined from 27.56% to 7.25%. These concurrent patterns indicate stronger landscape modification and lower apparent buffering capacity at MPRS. The findings support site-specific controls on land conversion, restoration of vegetation buffers, improved waste management, and protection of hydrological connectivity. Because the study is comparative rather than causal, the results identify co-variation among indicators but do not quantify the relative contributions of climatic and anthropogenic drivers. Full article

Urban heat islands (UHIs) are widely recognized as one of the clearest indicators of anthropogenic modification of the local climate system, resulting from the transformation of natural land surfaces into dense built environments characterized by altered thermal, radiative, and hydrological properties. The replacement of vegetated surfaces with impervious materials such as asphalt and concrete significantly modifies the surface energy balance, promoting heat storage during daytime and delayed release during nighttime, thereby enhancing urban–rural thermal contrasts. This study investigates the urban heat island (UHI) over the Berlin metropolitan region using a long-term high-resolution gridded dataset of daily minimum temperature from the EMO-1 (European Meteorological Observations gridded meteorological dataset with a spatial resolution of 1 arcmin × 1 arcmin) dataset, combined with Lamb weather types (LWTs) representing large-scale atmospheric circulation patterns. The analysis covers the period 1990–2023, enabling robust detection of long-term trends and variability. UHI intensity is quantified relative to a dynamic rural baseline defined by the 10th percentile temperature, allowing improved robustness compared to fixed rural references and reducing biases associated with spatial heterogeneity. Results demonstrate a persistent UHI centered over the urban core, with pronounced seasonal variability and statistically significant warming trends ranging from 0.004 to 0.018 °C yr⁻¹ across circulation regimes. Anticyclonic and Unclassified conditions dominate both the frequency and magnitude of UHI trends, confirming the dominant role of synoptic forcing controlling urban thermal dynamics. These findings underscore the necessity of integrating large-scale atmospheric circulation into UHI assessments and urban climate modeling. Full article

The Yangtze River Basin is a critical water supply region in China. To quantify the contributions of climate change and human activities to water resources across multiple temporal scales, this study focuses on actual evapotranspiration (AET), which directly influences water availability. Monthly runoff data from the Zhimenda Hydrological Station in the source region of the Yangtze River for the period 1982–2019 were analyzed using the Mann–Kendall (M-K) abrupt change test and the Bernaola–Galván (B-G) segmentation algorithm to identify the change point in runoff depth. The study period was subsequently divided into a baseline period and a post-change period. The ABCD hydrological model was employed to simulate monthly runoff variations during both periods, and the simulated results were used to calculate intra-annual (seasonal and monthly) AET. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was then applied to the AET series derived from the ABCD model to analyze temporal trends and intra-annual distribution characteristics. Finally, a multi-time scale Budyko framework was constructed to conduct attribution analysis based on AET data, quantifying the respective contributions of climate change and human activities to AET variation. The results indicate that: (1) The change point in the runoff series was detected in 2008. The Nash–Sutcliffe efficiency coefficients for both the baseline and post-change periods exceeded 0.88. (2) At the monthly scale, AET showed an increasing trend in January, February, July, September, November, and December. At the seasonal scale, AET exhibited a decreasing trend in spring and summer and an increasing trend in autumn and winter, though these trends were not statistically significant. Despite the varying directional trends observed across individual months and seasons, AET showed no statistically significant changes at either the seasonal or monthly intra-annual scale. (3) The intra-annual distribution of AET in the source region of the Yangtze River was highly consistent with precipitation patterns, showing significant synchronous variation. (4) Attribution analysis revealed that human activities played a dominant role in driving the observed trends in AET. Full article

Urbanization is a pervasive form of land-use change that alters surface energy, carbon, and water fluxes. Using the Simple Biosphere 2 (SiB2) land surface model, we quantify the effects of urban land transformation on land surface temperature (LST), gross primary productivity (GPP), and surface water discharge across Morocco. We find that urban effects on LST exhibit strong seasonality as urban areas are consistently warmer than surrounding rural land in winter, whereas summer conditions can produce urban heat sink effects, with rural surfaces becoming warmer than urban centers by up to 0.4 °C as a seasonal mean. We quantify the relationship between urbanization intensity and its local impact on LST, showing that it holds strongly throughout most of the year but weakens in summer, when background regional climate and topography play a greater role. Urbanization was found to reduce ecosystem productivity, with the largest urban induced GPP losses occurring in northern urban settings. Across the Climate Modeling Grid (CMG) cells meeting the study’s inclusion criterion of ≥5% impervious surface area (ISA), three counterfactual pre-urbanization scenarios were developed to quantify the range of GPP losses associated with urban land transformation, estimating potential losses of up to 120 Gg C/year when urbanization occurs on the most productive vegetative land covers. We also show that impervious surfaces systematically increase surface runoff, particularly during autumn and winter in northern Mediterranean cities, where elevated rainfall may amplify flash-flood susceptibility. These results indicate that urbanization exerts a measurable and policy-relevant influence on Morocco’s surface energy, water and carbon budgets, underscoring the need to account for it in future development planning. Full article

Blockings are large-scale indicators of climate change whose associated extreme weather can affect both adjacent and remote regions, making the estimation of their long-term trends and shifts a valuable prognostic tool. This study assesses linear and nonlinear trends and shifts in the seasonal and annual series of Southern Hemisphere (SH) blockings and examines their links to climate indices (CIs) and the general circulation. Blocking intensity and occurrence were derived from 500 hPa fields of the ERA5 and NCEP reanalyses over 1948–2022. Trends were located at relatively few longitudes and rarely coincided between the two datasets. In the annual series, both reanalysis products showed a strengthening of blockings at 140° E (linear rates of 3.40 and 5.20 m decade⁻¹ for ERA5 and NCEP, respectively), while several seasonal trends were confined to the Pacific sector. Shifts in blocking occurrence were more pronounced than those in intensity, the most notable being the 1976/77 climate shift—synchronous with a shift in the PDO—which raised blocking frequency at multiple Pacific longitudes, with increases of up to ≈23 blockings per year at 180° E. The results imply a strengthening and more frequent blockings in the near future, particularly at 70° W over southern South America, where more intense and frequent extreme weather could disrupt supply chains and production. Full article

The giant honeybee, Apis dorsata, is an important pollinator native to tropical and subtropical Asia. Understanding its potential response to climate change is important for pollinator conservation, ecological risk assessment, and biosecurity planning. This study used an optimized MaxEnt ecological niche modeling framework to predict the current and future global habitat suitability of A. dorsata. The model was developed using 1060 occurrence records and seven non-collinear bioclimatic variables and projected under three global climate models (IPSL-CM6A-LR, BCC-CSM2-MR, and MPI-ESM1-2-HR) and three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) for 2041–2060, centered on 2050. The optimized model used linear, quadratic, and hinge features (LQH) with a regularization multiplier of 0.5 and demonstrated good predictive performance under 5-fold spatial cross-validation (mean AUC = 0.905 ± 0.007; TSS = 0.746 ± 0.010). The results indicate that the potential distribution of A. dorsata is primarily associated with the combined effects of seasonal temperature and moisture availability. Current projections identified high climatic suitability across South and Southeast Asia, while also revealing potentially suitable environments in parts of Africa, the Americas, and northern Australia. Future projections suggest that suitable climatic conditions will largely persist through 2050, with habitat gains generally exceeding losses and increasing under higher climate-forcing scenarios. Continued monitoring and proactive biosecurity are essential to address habitat loss within the native range and prevent establishment in newly suitable regions. This study highlights the potential redistribution of A. dorsata under future climate change. Full article

Conventional evaluations of rainfall prediction models rely on average accuracy, often masking the conditions, locations and causes of model failure and reduced reliability. This study proposes a paradigm shift from conventional average-accuracy benchmarking toward failure-aware forecast-error diagnosis in the Bengawan Solo River Basin, a tropical monsoon river basin in Indonesia with moderate topographic gradients (grid elevations span ≈ 300–650 m). Methodologically, forecasts from previously published models are treated as fixed inputs and their errors are modelled as the response variable, so the analysis diagnoses when and where models fail rather than retraining them. By treating forecast errors as response variables, rather than as random residuals, this study analyses 345,180 model–grid records–month records from ten individual models (RF, XGB, LGBM, SVR, MLP, LSTM, GRU, TCN, CNN, Transformer) and one best ensemble model (Ensemble_Q, a stacking of RF, XGB, SVR, MLP, LGBM, LSTM, GRU, TCN, CNN, Transformer) against observed CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation, seasonal phase, ENSO and IOD regimes (El Niño–Southern Oscillation and Indian Ocean Dipole, respectively), the MJO index (Madden–Julian Oscillation) as an additional analysis, and elevation as a topographic control, using log-error models, high-error logistic regression, interaction tests, and block bootstrap validation (N = 1000), false discovery rate, and spatial statistics. Results indicate that prediction errors are not random but are systematically controlled: the Transition II phase increases log-error by 245% (pooled log-error model) and raises the odds of a high-error event roughly 40-fold relative to the dry season; La Niña conditions amplify errors by 41% and the odds of a high-error event by 3.3 times (though this ENSO signal is largely entangled with co-occurring Negative-IOD months), and every 100 m increase in elevation increases errors by 26%, with errors forming distinct spatial clusters (Moran’s I = 0.78; p = 0.001). Ensemble_Q outperforms the baseline on an aggregate basis (mean absolute error, MAE = 54.10 mm) but still experiences error amplification under these conditions, while spatial deep-learning architectures (TCN, CNN, Transformer) prove most vulnerable to elevation gradients. All major patterns persisted across variations in thresholds, model subsets, ENSO definitions, multiplicity corrections, and bootstrapping. These findings confirm that superior mean accuracy does not guarantee operational reliability, and that conditional failure diagnosis is an essential complement to benchmarking rainfall predictions in tropical monsoon regions. Full article

Long-term distribution planning requires weather sequences that capture not only changes in temperature and precipitation, but also the dependence and persistence among heat, humidity, wind, solar radiation, and rainfall. This paper develops multivariate weather scenario generation for Shaanxi Province, China, using daily data from six NEX-GDDP-CMIP6 models for 1985–2014 and 2031–2060 under SSP2-4.5 and SSP5-8.5 at four locations. Two generators are compared: a vector autoregressive model with seven-day residual blocks, and a season-conditioned multivariate nearest-neighbor analog. The comparison leaves out complete five-year periods within 24 climate-model–location units and tests whether the future-minus-historical changes in 16 weather and compound-event indices are preserved. The two methods each obtain the lower overall loss in 12 units. The vector autoregressive method better preserves most marginal and correlation signals, whereas the nearest-neighbor method better preserves RX3day, hot–dry–low-wind days, dry-spell duration, and three-day compound stress. Both are retained to generate 960 30-year paths, which drive a fixed IEEE 33-node resilience example showing that generator differences propagate to demand, renewable availability, repair duration, and unserved energy. The resulting conditional scenario sets provide an auditable weather basis for climate-resilient and sustainable long-term planning of distribution systems, which is a prerequisite for a sustainable energy transition under a changing climate. Full article

Lumpy skin disease (LSD) is a rapidly expanding transboundary vector-borne disease threatening livestock systems across Asia, with increasing risk of introduction into previously unaffected regions such as South Korea. This study developed an integrated, spatially explicit framework to assess the plausibility, timing and source-region structure of LSD virus (LSDV) introduction via windborne dispersal of infected arthropod vectors. Maximum Entropy models based on climatic, environmental, and topographic variables were used to determine habitat suitability for four major vector families (Culex spp., Musca domestica, Stomoxys calcitrans, and Culicoides spp.). All four were run via the transport simulation; S. calcitrans had the best experimental transmission efficiency, whereas Culicoides spp. provides the strongest evidence for passive long-distance transport. Livestock density and vector suitability were used to calculate the risk of LSD incidence in the adjacent source regions. A fourth-order Runge–Kutta trajectory model driven by ERA5 reanalysis winds at 850 hPa (about 1458 m) was used to predict windborne dissemination. This model included degradation of mechanically transmitted virus as well as survival limitations based on temperature, humidity, and wind. The simulated trajectories arrived in South Korea within the retention period of mechanically transported LSDV, with a median transit time of 18 h (interquartile range 10.0–30.3 h), 61.4% arriving within 24 h, and 87.1% within 48 h. North Korea accounted for 76.5% of weighted arrivals, with a median transit time of 11 h. Transit durations varied significantly by source location. For all four vector species, the predicted infectious arrival mass peaked in September and decreased thirteen-fold by November. Infected farms were found in regions of greater anticipated exposure than non-infected controls when compared to the October 2023 Korean epidemic (58.1% vs. 34.3% in the two highest risk groups; median exposure 0.214 versus 0.045; p < 0.001), although randomly relocating the dispersal surface reproduced comparable agreement in 23.6% of permutations, indicating that outbreak locations provide only limited validation of a pathway-specific introduction model. Random permutation of the dispersion surface produced a comparable result in 23.6% of permutations, indicating limited robustness of the observed spatial relationship to spatial randomization. With pairwise geographical and temporal distances significantly associated (Mantel r = +0.136, p = 0.013), spatiotemporal analysis revealed that the 74 reported outbreaks originated from a maximum of 25 introduction events over a 13-day period, consistent with significant farm-to-farm dissemination after introduction. The seasonal window, source-region structure, and transit timeframes of windborne LSDV introduction into South Korea are described in this paper. Reported epidemic sites only partially validate a pathway-specific introduction model since they reflect both the point of introduction and subsequent local transmission. This evaluation is pathway-specific and does not evaluate overall incursion risk; windborne transport is one of numerous possible introduction paths to consider. Full article

The economically important species Phyllanthus emblica L. has received increasing attention, yet its climate-driven distributional shifts across China remain unexplored. Using 446 occurrence records and a parameter-optimized MaxEnt model (RM = 0.1, FC = LQ), we forecasted habitat changes under three SSP scenarios (126, 370, 585) across the 2050s, 2070s, and 2090s. Three temperature variables, namely temperature seasonality (Bio4), temperature annual range (Bio7), and mean temperature of the driest quarter (Bio9), collectively contributed 90.2% to the model, confirming that thermal conditions, particularly during dry seasons, dominate habitat suitability. Current suitable habitat covers 86.43 × 10⁴ km², largely confined to South China’s tropical and southern subtropical belts. Future projections indicate non-linear range responses, with a general northwestward centroid shift, although intermediate periods exhibit oscillatory northeastward and southwestward fluctuations. Under the high-emission SSP585 scenario, modest expansion occurs in high-elevation areas of southeastern Tibet, while contraction affects less than 1% of current suitable low-elevation zones. Based on these findings, we recommend designating long-term core conservation areas (southern Yunnan, southern Guangxi, and Hainan), conducting adaptive introduction trials in expansion zones (e.g., Panzhihua, Sichuan), and implementing planting controls in contraction zones (e.g., northern Guizhou). These insights provide a scientific basis for climate-adaptive management of P. emblica in China. Full article

Northwest China is characterized by severe water scarcity, and cloud water represents an important potential supplement to regional water availability. Cloud water path is a key physical parameter for quantitatively characterizing cloud water resources; understanding its variability and associated influencing factors is essential for the scientific assessment and sustainable utilization of regional cloud water resources. In this study, ERA5 and JRA-3Q reanalysis datasets (1960–2025) and satellite cloud products from MODIS and Cloud_cci (2003–2016) were used to evaluate the consistency of multi-source datasets in capturing cloud water path variations. Following the assessment of ERA5 applicability through multi-source comparisons, trend analysis, change-point detection, and ensemble empirical mode decomposition (EEMD) were applied to characterize the multi-timescale variability of cloud water path. Furthermore, the associations of cloud water path variability with climatic and environmental factors, including atmospheric circulation, aerosol optical depth (AOD), global mean surface temperature (GMST) anomaly, and the El Niño–Southern Oscillation (ENSO), were investigated. The results indicated these datasets generally agreed on the temporal variability of cloud water path, whereas differences were found in the absolute ice water path (IWP) and total cloud water path (CWP) values and estimated long-term trends. IWP contributed substantially to CWP variability across most timescales, while the long-term evolution of CWP reflected changes in both liquid water path (LWP) and IWP. LWP, IWP, and CWP showed gradual increasing tendencies, with no significant change points detected. EEMD analysis suggested variability components at approximately 3-year and 7–9-year timescales with relatively large variance contributions, although these components were not statistically significant. Cloud water path variability showed different relationships with climatic and environmental factors. The IWP IMF1 component, with an approximately 3-year timescale, exhibited a weak positive association with the mid-latitude westerly index, whereas no stable linear relationships were detected between cloud water path and summer monsoon or ENSO variability. IWP and CWP showed significant seasonal correlations with AOD, which may largely be attributable to their shared seasonal variations. After detrending, IWP exhibited a weak negative correlation with GMST anomaly. Full article

Extreme precipitation has caused catastrophic flooding across West Africa, worsened by climate change. Understanding which rainfall type (deep, stratiform, or shallow convective) drives flooding along the West African coast can improve disaster response. This study combines seven years (2015–2021) of Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) monthly rain-type retrievals with forty-two years (1984–2025) of in situ daily rainfall observations from SODEXAM at Abidjan. GPM DPR data were aggregated into monthly climatological maps to characterize each rain type’s spatial distribution and seasonal cycle, while SODEXAM daily records were analyzed for rainy-day frequency and depth to identify the flood-critical period. All three types of rainfall contribute to the region’s high seasonal totals, but deep convective rainfall dominates and peaks in May and June, contributing on average 60% of total monthly rainfall versus 35% for stratiform and 5% for shallow convective rainfall. The 42-year ground record showed elevated rainy-day frequency and rainfall depth between 29 May and 18 June, defining a flood-critical period. This period coincides with the climatological peak in GPM-derived deep convective rainfall, indicating that deep convective systems are closely associated with the recurrent severe flooding observed in Abidjan from late May to mid-June. Full article

Lake Nasser represents Egypt’s principal strategic freshwater reservoir; however, its long-term phytoplankton dynamics at the basin scale remain insufficiently characterized. This study presents a detailed spatiotemporal investigation of chlorophyll-a (Chl-a) variability across the reservoir during the 2002–2020 period using exclusively MODIS-Aqua Level-3 satellite observations. As no concurrent in situ measurements were available for validation, the reported values are treated as satellite-derived Chl-a estimates rather than direct observations of phytoplankton biomass. A non-parametric statistical approach, incorporating the Mann–Kendall test, Theil–Sen slope estimator, coefficient of variation (CV), and relative anomaly analysis, was used to characterize pixel-level climatology and temporal trends. The findings demonstrate that the reservoir exhibits pronounced spatial heterogeneity and a well-defined bimodal seasonal pattern associated with the annual Blue Nile flood pulse during August–October and winter convective mixing processes between November and February. Although annual-scale analysis did not indicate a statistically significant monotonic trend, seasonal investigations revealed strongly contrasting temporal responses throughout the year. At the pixel level, significant increases dominated ten of the twelve calendar months, from September through June, with median slopes among significant pixels of +0.36 to +1.47 mg m⁻³ yr⁻¹. July and August reversed this pattern, combining significant declines along the northern and central main channel with the steepest increases in the record at the extreme southern inflow. The directional contrast was unaffected by control of the false discovery rate within each calendar month (q = 0.05), which retained 66% of the significant pixels. Summer conditions were additionally marked by substantial temporal variability (CV > 60%) and pronounced positive anomalies, reaching +274.51% during August. Notably, these anomalies were spatially concentrated within the central and southern sectors of the reservoir, rather than within the conventionally productive northern region. This spatial redistribution, together with the broad enrichment tendency indicated by the pixel-level trends, points to an intensification of the summer maximum within the southern inflow zone and to a possible emergence of localized eutrophication risk, a hypothesis that requires confirmation through concurrent nutrient, transparency, and dissolved-oxygen observations. Overall, the study provides valuable geospatial insights for the environmental management of Lake Nasser, emphasizing the importance of implementing targeted early-warning systems for harmful algal bloom monitoring. The results also demonstrate the critical role of satellite-based Earth observation technologies in supporting adaptive water resource management under evolving climatic and hydrological conditions. Full article

This paper applies the von Mises family of circular distributions to model seasonal timing of heavy rainfall extremes in Kimberley, South Africa. Unlike conventional modelling approaches that emphasize the modelling of mean rainfall, circular modelling captures phase-specific characteristics, such as the onset, peak clustering, and transitional shoulders of extreme rainfall. We consider three variants of the von Mises family of distributions, namely the standard von Mises, generalized von Mises, and sine-skewed von Mises, and then use the maximum likelihood estimation with box-constrained optimization to estimate parameters. The results show that the standard von Mises distribution consistently outperforms more complex alternatives, thus providing the most parsimonious and reliable representation of Kimberley’s summer rainfall regime. Furthermore, the results reveal a dominant seasonal peak in January–February, a recurrent onset in November, and a dry season in June–July, which confirms the episodic and clustered nature of rainfall in semi-arid regions. Phase-shift analyses further indicate that there is approximately a 19-day delay in the seasonal peaks between baseline and monitoring periods, alongside stronger clustering of extremes. Although formal circular tests did not detect statistically significant differences, the phase analysis highlights emerging tendencies toward a later and more compressed rainfall season. These findings have practical implications for flood preparedness, reservoir operations, and agricultural scheduling, where timing rather than totals drives risk and resilience. This study demonstrates the methodological importance of circular statistics in extreme rainfall phase analysis, thus bridging a critical gap in semi-arid climate studies. Full article