Search Results (2,743)

Climate change is increasing the frequency of heat stress events in dairy farming, adversely affecting milk production, milk composition, cow behavior, and animal health. However, many previous studies relied on distant weather-station data and low-frequency milk recording systems, limiting the assessment of short-term and delayed heat stress responses. Therefore, the aim of this study was to combine dense longitudinal data from automatic milking systems (AMS) with continuously recorded on-farm meteorological measurements to investigate the immediate and lagged effects of heat stress on Holstein dairy cows. The study included 386,587 AMS visit records from 790 cows on three commercial dairy farms in Germany, corresponding to up to 127,310 cow-day records collected between August 2022 and August 2025. Temperature–humidity index (THI) values were calculated based on dense on-farm temperature and relative humidity records and were evaluated for multiple lag periods prior to AMS recordings. Linear mixed models were applied to infer the effects of THI on production, physiological, behavioral, and milking process traits. Increasing THI was associated with reduced daily milk yield, altered milk fat and protein percentages, decreased AMS visit frequency, prolonged milking intervals, and increased milk temperature. For contemporaneous THI, an increase from THI 50 to THI 70 corresponded to model-estimated declines of −0.86 kg in daily milk yield, −0.20% in milk fat content and −0.06% in milk protein content, −0.12 daily AMS visits, and +1.14 °C in milk temperature. The strongest associations were generally observed for prompt and short-term lagged THI windows. In contrast, longer lag periods were associated with weaker and less distinct trait responses. Rather than merely confirming the established decline in milk yield under heat stress, the integrated and temporally resolved analysis revealed trait-specific response patterns across production, behavioral, physiological, health-related, and milking-process traits. In particular, milk temperature and voluntary AMS attendance showed pronounced associations with contemporaneous and short-term THI, demonstrating the value of combining AMS-derived phenotypes with high-resolution on-farm climate data for heat stress monitoring. Full article

Non-stationarity is increasingly recognized as a defining feature of contemporary hydroclimatic regimes, challenging the statistical assumptions that underpin inter-annual discharge deficits analysis and water-resources design. This study investigates how shifts in first- and second-order statistical moments (mean and variance, respectively) alter the perceived rarity and persistence of inter-annual drought events in low-memory, rapid-response mountain systems. I develop a stochastic Monte Carlo framework to explore changes in inter-annual discharge deficit frequency and multi-year drought clustering across successive climatic regimes, using the Northern Apennines (Italy) as a representative case study. The model is explicitly exploratory: it does not aim to reproduce observed discharge distributions, but to quantify how regime shifts in mean and variability propagate into tail exceedances and drought spells under stationarity-based metrics. Results show a pronounced amplification of annual hydrological drought exceedances and the emergence of persistent multi-year drought spells under contemporary conditions, which are strongly underestimated when historical baselines are assumed stationary. A comparison with long-term regional discharge trends—while acknowledging the distinct hydro-climatic response of high-memory versus low-memory basins—serves to contextualize the systemic nature of the observed drought amplification. The findings highlight the structural vulnerability of low-memory catchments to non-stationary forcing and underscore the limitations of traditional design thresholds for drought-risk assessment under the evolving climate. Full article

This study presents a systematic literature review of spatial planning frameworks for coastal hazard mitigation, conducted in accordance with the PRISMA 2020 guidelines. A structured search of two academic databases, Scopus and Web of Science, covering the period 2016 to 2026 identified 368 records, of which 27 peer-reviewed studies were included in the final synthesis. The review pursues four interrelated objectives: analyzing global publication trends in research on spatial planning for coastal hazard mitigation; identifying and describing coastal hazard typologies and their associated mitigation approaches; examining how spatial planning frameworks are integrated with hazard mitigation strategies; and synthesizing cross-cutting constructs that structure an integrative analytical model. Findings reveal a marked intensification of scholarly output over the final third of the review period, with Asia–Pacific and Europe as the most represented regions, while sub-Saharan Africa and small island developing states remain critically underrepresented. Coastal hazards are classified into four categories: slow-onset climate and hydro-geological processes, acute hydro-meteorological events, multi-risk systemic hazard interactions, and ecological and environmental degradation. Three analytically distinct integration families are identified: geospatial and technical modeling, NbS and ecosystem-planning integration, and participatory governance and institutional integration. From these, four cross-cutting constructs emerge inductively across the evidence base, namely spatial risk assessment and geospatial modeling, land-use governance and climate-proof planning, community-based resilience and participatory governance, and ecosystem-based adaptation and nature-based solutions, which together constitute an integrative analytical framework. The discussion demonstrates that governance capacity, rather than technical sophistication, is the primary moderator of implementation effectiveness, and that socio-spatial equity in hazard planning represents a cross-regional challenge irrespective of governance capacity level. Limitations include the geographic concentration of the evidence based in high-capacity planning contexts and the predominantly projected rather than implemented nature of effectiveness evidence. Full article

The general circulation models are used for climate predictions and weather forecasting, resolving governing prognostic equations at resolutions at which the convection is typically unresolved. As convection is responsible for important feedback in the climate system and is associated with severe weather events, the climate and weather models must estimate the convection as a function of the resolved mean state of the atmosphere. This procedure is called convective parameterization, and the specific approach in which this task is achieved depends on the specific parameterization scheme employed by the general circulation model. However, almost all of the modern parameterization schemes are based on the same theoretical framework. Although state-of-the-art parameterization schemes still struggle to model convective transport and fractions of convective clouds accurately, limited attempts to change the fundamental theoretical bases have been made in recent years. The aim of this article is to discuss the theoretical bases under which the plume model is introduced in the mass-flux convective parameterization schemes and how these bases have changed in recent years as an attempt to change some of the current problems of the mass-flux parameterizations. This review examines the evolution of the plume model underlying mass–flux convection parameterizations. The discussion focuses on five key topics: (i) the interpretation of convective plumes as representations of cloud ensembles, (ii) entrainment and detrainment assumptions, (iii) prognostic formulations and convective memory, (iv) unified formulations for shallow and deep convection, and (v) challenges associated with convection in the gray zone. The review argues that many recent developments can be interpreted as relaxations of assumptions originally associated with the steady-state plume framework, and discusses the implications of these relaxations for the physical interpretation of mass–flux parameterizations. Full article

Accurate precipitation data are critical for hydrological and climatic studies in Guangdong Province, where complex terrain and frequent extreme rainfall pose substantial challenges. However, the performance of gridded precipitation products is still not well understood. This study evaluates nine products, including gauge-based (CHM_PRE, CN05.1, GMCP, NOAA CPC), satellite-based (IMERG-E, IMERG-F, TMPA RT, TMPA 3B42), and ERA5 reanalysis against NCDC observations from 2001 to 2019 using metrics including trend significance, correlation (R), root mean square error (RMSE), categorical statistics (POD, FAR, ETS), and relative bias across rainfall intensities. The results indicate that, based on validation against NCDC observations, CHM_PRE performs the best across all temporal scales, capturing significant increasing trends (p < 0.05) and achieving the highest consistency with observations at the annual (R = 0.99), monthly (R = 0.99), and daily (R = 0.89) scales. Using CHM_PRE as the reference, CN05.1 shows the highest spatial consistency with it, especially for extreme events. NOAA CPC exhibits the best performance in monthly event detection (ETS = 0.42; BIAS ≈ 1). Satellite products show acceptable performance at the monthly scale but exhibit intensity-dependent biases and high daily variability, with pronounced “light rain overestimation and heavy rain underestimation.” ERA5 shows limitations, particularly in its severe underestimation of extreme precipitation. CHM_PRE is thus identified as the most suitable dataset for Guangdong based on its agreement with NCDC observations. With CHM_PRE as the reference, CN05.1 provides a reliable alternative for spatial analyses; NOAA CPC performs the best in monthly event detection. Satellite products suit monthly use but require daily-scale caution; ERA5 shows a relatively poor performance. Full article

Quantifying the impacts of climate change and extreme climatic events on crop yields is essential for safeguarding global food security. The rapid growth of data availability and advances in computational capacity have established machine learning (ML) as a critical tool for unraveling the complex, nonlinear relationships between climatic factors and agricultural productivity. This systematic review synthesizes evidence from 137 peer-reviewed studies published between 2015 and 2025 that applied ML models to assess the effects of both long-term climate trends and discrete extreme events on crop yields worldwide. Bibliometric and thematic analyses reveal a rapidly evolving field, with over 85% of studies published since 2020, and a strong concentration on staple cereals—wheat, maize, and rice—in major agricultural regions including China, the United States, and India. Random Forest (RF) was the most commonly used algorithm; ensemble and deep-learning models achieved high predictive accuracy within well-resourced study contexts. Temperature and precipitation extremes emerged as the most frequently examined stressors, with distinct methodological patterns: studies focusing on climate change trends predominantly employed RF and LSTM models, whereas those investigating extreme events increasingly adopted hybrid approaches that integrate ML with process-based crop models. This review highlights the transformative potential of ML while identifying persistent challenges, such as geographical imbalances in research coverage, the need for enhanced interpretability in extreme event attribution, and the critical importance of modeling compound extremes. Future research should prioritize the development of explainable, causally informed, and transferable ML frameworks to support equitable climate adaptation strategies in global agriculture. Full article

Peatlands, critical global carbon sinks, are facing increasing threats from climate change-driven heatwaves and droughts. These threats can cause a midday depression in carbon uptake through photosynthetic inhibition. Using high-temporal-resolution solar-induced chlorophyll fluorescence (SIF; ~30 s) and chamber-based CO₂ flux measurements, we investigated the coupling between SIF and gross primary production (GPP) during extreme events (air temperature > 25 °C and vapour pressure deficit > 15 hPa) in a northern peatland. Our results show that SIF tracks GPP closely under non-stress conditions (daily R² = 0.86–0.96). However, during combined heat and drought stress, midday correlations collapsed (Case A: R² = 0.04 on 27 June; Case B: R² = 0.15 and 0.01 on 29 and 30 June, respectively), indicating severe decoupling. Importantly, we discovered legacy effects from multi-day heat exposure: on 26 June, vegetation with prior cumulative stress (Case A) showed weak morning coupling (R² = 0.07), while vegetation without prior stress history (Case B) maintained strong coupling (R² = 0.93). This suggests that cumulative stress alters baseline physiology and can exacerbate midday mismatches; therefore, not just current condition controls photosynthetic regulation. These findings highlight limitations of SIF-based GPP estimation at sub-daily timescales during stress, particularly in heterogeneous peatland systems where canopy composition and physiological responses could vary among plant functional types. Full article

Drought is a natural hazard characterized by gradual onset and prolonged precipitation deficit. With climate change intensifying precipitation variability, accurate drought assessment is critical for effective water resource management and mitigation. Focusing on Maryland in the Mid-Atlantic region of the United States, this study computes and analyzes drought indices to assess both near (2021–2060) and late (2061–2100) drought conditions, in the context of climate variability. We employed three distinct objectives to enhance drought assessment and monitoring capabilities under projected climate scenarios: (1) calculation of the Standardized Precipitation Index (SPI) reflecting meteorological conditions using precipitation data from seven GCMs across three SSPs for two future periods (2021–2060 and 2061–2100); (2) integration of both precipitation and temperature projections in the Palmer Drought Severity Index (PDSI) (implemented here as a simplified PDSI based on a standardized Z-index) to reflect combined hydrological and thermal influences (i.e., Hydrological indices); and (3) a Low Precipitation Duration–Frequency Analysis (LPDF) as indicator of both meteorological and hydrological conditions to quantify and compare the frequency and severity of low precipitation events across different SSPs. These objectives were achieved by fitting a gamma distribution for SPI and an Extreme Value Type I distribution for LPDF, and applying Z-index (i.e., long-term moisture abnormalities) and weighting factors representing the ratio of precipitation to evapotranspiration. Results reveal notable variability in SPI values, with a general trend toward increased extreme wet conditions, especially under high emission scenarios (i.e., SSP585) in the latter half of the century (2061–2100). Meanwhile, PDSI analysis indicated a subtle shift toward drier conditions despite increases in precipitation, particularly under SSP126 and SSP245, suggesting that temperature rises may offset precipitation gains. In addition, LPDF values indicated a reduced frequency of prolonged low-precipitation events under SSP585 compared to SSP126 and SSP245; this reflects higher total precipitation and should not be interpreted as resilience to drought, since the concurrent rise in temperature-driven evaporative demand can still intensify hydrological and agricultural drought stress. These results highlight the importance of incorporating climatic variables in drought assessments to understand future meteorological and hydrological scenarios under climate change projections. The study can help water resource managers and illustrates how such integrations can enhance our understanding of future drought scenarios under different climate change projections. Full article

Brazil presents a distinctive convergence of continental-scale climatic diversity, extensive urbanization, large-scale biomass burning, rapid land-use change, persistent air-quality monitoring gaps, and deep social inequalities, producing highly heterogeneous and compound environmental health risks. In this context, treating air pollution and climate change as parallel environmental crises obscures their structural interconnections through shared emission sources, mutually reinforcing exposure pathways, and overlapping health and social consequences. In this narrative review, we critically synthesize scientific and institutional lines of evidence and argue that air pollution and climate risks can be more effectively addressed in Brazil through a single strategic agenda for science, public health, and governance. We first discuss why these challenges cannot be managed in isolation, emphasizing the effects of heat, drought, stagnation events, biomass burning, and extreme weather on pollutant formation, dispersion, and health burden. We then examine Brazil as a critical case where recent regulatory advances coexist with structural limitations in monitoring, data integration, and territorial coverage. Based on this diagnosis, we propose an integrated national agenda organized around five mutually reinforcing priorities: monitoring through hybrid networks; predictive science through climate-informed modeling and early warning; public health through the convergence of epidemiology, toxicology, and mechanistic research; equity-oriented research and action through the explicit incorporation of vulnerability, inequality, and climate justice; and policy appraisal through the assessment of disease burden, economic costs, mitigation co-benefits, and trade-offs. We further discuss the governance mechanisms needed to connect these priorities and translate evidence into coordinated action and adaptive public policies. We also argue that the Amazon should be approached not as an isolated ecological exception but as a central component of a broader Brazilian and Global South discussion on environmental health, land-use change, and climate justice. In this scenario, Brazil has the scientific capacity and regulatory momentum to become a reference in the integrated management of air pollution and climate risks, but this will depend on replacing fragmented approaches with a coordinated framework capable of linking exposure, mechanism, burden, inequality, and action. Full article

Against the backdrop of global climate change, drought–flood abrupt alternation (DFAA) has become a major compound climate extreme threatening ecosystem stability and sustainable watershed management. This study investigated the spatiotemporal characteristics and ecological responses of DFAA in the Jialing River Basin, China, using meteorological and hydrological observations from 1971 to 2020. DFAA events were identified using the Standardized Weighted Average Precipitation Index (SWAP) and run theory, and their spatiotemporal heterogeneity was characterized using spatial autocorrelation analysis. The Long-duration DFAA Index (LDFAI) was derived using the WEP-L distributed hydrological model. Ecological responses during 2000–2020 were evaluated by integrating the Remote Sensing Ecological Index (RSEI), grey relational analysis, and a Copula-based conditional probability model. The results showed that drought-to-flood events exhibited stronger spatial clustering than flood-to-drought events. Ecosystem responses showed significant lag effects, averaging 6.9 months for spring–summer events and 5 months for summer–autumn events, with greater sensitivity during the summer–autumn period. Under DTF events, the probability of maintaining relatively high ecological quality was significantly higher than under FTD events, whereas FTD events were associated with a higher probability of ecological degradation. Under compound scenarios, consecutive same-type events were more conducive to ecosystem stability, while alternating sequences of different event types significantly amplified negative ecological stress and represented high-risk scenarios for ecological degradation. These findings provide scientific support for adaptive watershed management, ecological restoration, and climate change adaptation in drought–flood-prone regions. Full article

Background: Residential radon exposure is a leading risk factor for lung cancer, and climate change may exacerbate this risk by increasing radon entry into homes. In North Carolina (NC), disparities in lung cancer outcomes and low radon awareness disproportionately affect racially and ethnically diverse and low-income populations. However, little is known about how use of conventional address-based sampling (ABS) compares with community-engaged recruitment strategies for enrolling historically underrepresented populations into such research. Community-engaged approaches are used to improve participation in environmental health research, yet few studies compare their effectiveness with traditional approaches in NC. This manuscript compares recruitment outcomes between ABS and community-engaged approaches within the CLOVER study to address this gap. Materials and Methods: The Climate Impact on Lung Cancer via Exposure to Radon (CLOVER) study is a cross-sectional study examining climate-impacted radon exposure and lung cancer risk in NC. Participants were recruited using either ABS through a commercial address database or targeted community-engaged approaches implemented through partnerships with community organizations and culturally responsive, in-person outreach events. After providing informed consent, participants completed a household survey and a 7-day home radon test. Recruitment outcomes—consent, survey completion, and radon test return—were stratified by race and ethnicity and compared across strategies. Results: Of 236 consented participants, community-engaged recruitment enrolled a higher proportion of Non-Hispanic (NH) Black, Hispanic/Latino, and American Indian participants than ABS (57.3% vs. 41.9%). Community-engaged recruitment also had a higher consent rate than ABS (10.2% vs. 1.3%). Of the 145 participants who completed the survey, ABS participants completed surveys at a higher rate than community-engaged participants (73.3% vs. 54.7%), while of the 86 who returned the radon test kits, community-engaged participants did so at twice the rate of ABS participants (44.7% vs. 22.1%). Conclusions: Community-engaged recruitment enrolled a more racially and ethnically diverse sample and achieved higher consent and return rates than ABS, though ABS participants completed surveys at a higher rate. These findings highlight the importance of community partnerships and in-person recruitment strategies to improve participation of historically underrepresented populations in environmental health research and support equitable approaches to radon mitigation and lung cancer prevention. Full article

Against the backdrop of climate change and compounded by human activities, increasing water scarcity has triggered a series of drought disasters, which have already severely impacted both ecological environments and socioeconomic production. The SWAT model, recognized for its strong portability and superior spatial heterogeneity, has gained widespread acceptance in fields such as hydrology and environmental science, and is extensively applied in hydrological simulation studies across large-scale river basins. Hydrological models of the study area can be constructed in the SWAT model to simulate changes in hydrological variables by conducting spatial discretization, parameter specification, and boundary condition definition. Standardized drought index can effectively reflect the spatiotemporal variations in drought disasters, holding significant importance for clarifying and predicting drought characteristics. This study took the Luanhe River Basin as the research area, constructed a watershed hydrological model based on SWAT, and projected changes in the basin’s hydrological processes for the period 2030–2060. Based on the model’s projected data, we calculated drought indices and extracted drought events for the basin. The results indicate the following: (1) During the simulation period, only 30% of the years in the Luanhe River basin had annual runoff above the long-term average, with a range of 228.18 mm. The range of mean annual runoff across sub-basins was 173.32 mm. Drought and uneven water resource allocation over both spatial and temporal scales coexisted, and this issue is expected to intensify under future climate warming and drying. (2) The mid-reaches of the Luanhe River are more prone to drought compared to the upper reaches for its higher water demand. However, due to a stronger capacity for ecological restoration, droughts there are mostly of low intensity in the mid-reaches. In contrast, the upper reaches experience more periods classified as severe or extreme drought, and the drought events encountered are generally more intense than those in the mid-reaches. (3) The method proposed in this study can screen extreme drought events based on outliers in the characteristic values of drought events. Taking the simulation from this study as an illustration, anomalies in drought event characteristic values suggest a potential basin-scale, prolonged extreme drought event in the Luanhe River Basin from June 2038 to July 2042. Proactive drought prevention policies should be formulated for this period. The findings of this study provide guiding significance and practical value for drought assessment, risk management, and policy application in the Luanhe River Basin. This study methodologically combines hydrological model predictions with drought event responses, providing a novel method for predicting basin-scale drought conditions and issuing early warnings for extreme drought events. Full article

This study systematically evaluates the performance of GPM_IMERG and GSMaP_Gauge hourly precipitation products in typhoon rainfall over Mainland China. Using hourly gauge observations from the China Meteorological Administration (CMA)’s national basic meteorological stations as reference, 32 landfalling typhoons during 2021–2025 are analyzed. A multi-layered evaluation framework is established based on 50 km annular stratification from 0 to 500 km relative to typhoon centers, multiple statistical metrics, and dual thresholds for light rain and extreme precipitation. Results indicate systematic underestimation of typhoon rainfall by both products, with GSMaP_Gauge exhibiting more severe negative bias that intensifies nonlinearly with increasing rainfall intensity. Spatially, widespread overestimation occurs in North China, while underestimation dominates elsewhere, with large negative biases concentrated in high-observation regions. Monthly variations show predominantly negative deviations across most months, with GSMaP_Gauge demonstrating persistent negative anomalies except for sporadic positive outliers. Regarding precipitation detection capability, both products perform adequately for light rain, but their capability to capture extreme precipitation remains rather limited, as evidenced by sharply declining Critical Success Index (CSI) across all distance ranges and omission of over 60% extreme precipitation events. GPM_IMERG shows only sporadic high CSI values in the inner-core region during June and October. Error distributions exhibit significant spatiotemporal non-stationarity: errors attenuate markedly within 0–100 km of typhoon centers; seasonally, June and September show higher correlation coefficients but larger RMSE, whereas August presents lower correlation yet smaller errors; diurnally, the 0–50 km zone displays a “three-peak–two-valley” pattern with error maxima in the afternoon, early morning, and evening. In conclusion, both products estimate light typhoon precipitation with reasonable accuracy but still have considerable room for improvement in estimating heavy and extreme rainfall. Dynamic error models based on three-dimensional stratification of distance–season–diurnal phase, coupled with bias correction, are imperative before their application to hydrometeorological modeling, disaster investigation, and climate research. Full article

Climate change and aging infrastructure are undermining the resilience of urban flood management systems, reducing their reliability and increasing the likelihood of systemic failure that may culminate in regime shifts. This study develops a conceptual and practitioner-oriented screening framework based on a quantitative mathematical model to examine facility-level resilience and identify threshold conditions that may trigger regime transitions under external disturbances and varying pre-disturbance facility conditions. The framework adopts the composite sigmoid function (CSF) to capture nonlinear performance trajectories of infrastructure systems. Building on this model, this study extends its application by developing a parameterization scheme directly linked to four resilience dimensions: robustness, redundancy, rapidity, and resourcefulness (4Rs), which can be derived from field investigations or expert surveys. The normalized 4R scores are mapped to the CSF parameters, thereby converting static resilience assessment results into degradation and recovery curves. To search for threshold conditions, a parametric analysis was conducted by systematically varying the 4R values across their defined ranges. Rather than indicating a single universal threshold value, the results revealed critical threshold regions formed by specific combinations of the 4R dimensions. Lower robustness reduced the initial performance buffer, and low redundancy accelerated and extended performance degradation, while insufficient rapidity and resourcefulness delayed or limited recovery, increasing the likelihood of transition into an alternative degraded regime. For example, even when R1 and R2 were set to relatively high normalized values of 0.90, and R3 was set to its maximum value of 1.00, full recovery could not be achieved when R4 decreased below approximately 0.20. An illustrative application was conducted using preliminary 4R assessment results for flood-control facilities in three districts of Seoul, Korea. The model-derived trajectories were qualitatively compared with reported historical vulnerability patterns. While this comparison was intended as a contextual assessment rather than an event-specific empirical validation, our framework supports comparative, scenario-based screening of potentially vulnerable facilities for preliminary maintenance and investment prioritization. Full article

Urban flooding is shaped by urban morphology, but the inferred relationships can change with the spatial units used to represent flooding and urban form. Existing studies often report results for a selected spatial configuration, leaving unclear whether predictive performance and identified dominant predictors remain robust when analytical scale, grid placement, and spatially separated validation are varied. Using the 22 May 2020 Guangzhou storm as an event-specific case, this study evaluates the robustness of multi-scale morphology–flood associations to these spatial analytical choices. The analysis integrated 119 unique official waterlogging locations with 67 geocoded social-media locations. After removing four cross-source matches within 100 m, 182 unique observations were used to construct a kernel density response surface and examine 1–5 km analytical grids. Spatial autocorrelation, repeated nested geographic cross-validation of XGBoost, out-of-fold SHAP attribution, and accumulated local effects (ALEs) were used to quantify scale-dependent patterns. Global Moran’s I increased from 0.125 at 1 km to 0.524 at 4 km and decreased to 0.479 at 5 km (all permutation p < 0.001). Mean spatially validated R² ranged from 0.483 to 0.610, with the highest R² and lowest RMSE at 4 km, although residual spatial autocorrelation remained. Road density was the largest individual SHAP contributor at every scale (35.54–40.89%). ALE indicated broad positive associations for road density, building density, and impervious surface ratio and a negative association for elevation, without supporting universal sharp thresholds. These event-specific results show that analytical scale and grid placement should be reported explicitly when morphology-based evidence is used for flood screening and climate-resilient planning. Full article