Climate change increasingly affects the sustainability and reliability of urban water and wastewater infrastructure. This study analyzes the relationship between climatic variables and the frequency of failures in water and sewage networks in northeastern Poland, using operational data from the Mrągowo system (2020–2023) and meteorological records from 1966 to 2023. Statistical analyses and trend assessments were employed to identify climate-related failure patterns and infrastructure vulnerabilities. Climatic parameters—including temperature extremes, precipitation, snow cover, and sunshine duration—were analyzed in relation to infrastructure reliability. The results indicate rising temperatures, reduced snowfall, and altered precipitation regimes. Although extreme cold corresponded with increased sewage network failures, no significant association was found for high temperatures. Precipitation and snow cover showed weak correlations, except during heavy rainfall events. The study highlights the need to integrate climate resilience into water infrastructure management through preventive maintenance, smart monitoring, and nature-based solutions. Findings contribute to sustainable urban development strategies by demonstrating how climate variability directly affects service reliability. By identifying climate-sensitive failure thresholds, the study supports sustainable infrastructure management by enabling risk-informed adaptation strategies that reduce service disruptions, resource losses, and environmental impacts. This case study offers methodological insights and empirical evidence that may support the assessment of climate-related vulnerability of water and wastewater infrastructure in similar urban contexts.

ABSTRACT Evaporation is a pivotal process in the hydrological cycle, particularly in water‐scarce regions under climate change pressures. This study presents an integrated framework combining permutation entropy (PE), GeoDetector and wavelet coherence (WTC) to analyse evaporation variability across Iran's diverse climatic zones. Daily meteorological observations (1995–2024) were aggregated to monthly values for preprocessing, from which long‐term station‐wise climatic summaries were derived for GeoDetector analysis, while WTC was applied to the monthly series and PE was computed using the original daily evaporation data. For WTC and GeoDetector analyses, these daily observations were aggregated into monthly values, whereas PE was computed from the original daily evaporation time series to preserve high‐frequency variability. GeoDetector results indicate that minimum temperature (26%), mean temperature (21%) and sunshine duration (12%) are the strongest contributors to evaporation variability, followed by humidity (16%), while precipitation (5%) and wind speed (4%) have smaller effects. The combined interaction of temperature‐related variables further amplifies their influence (up to 47% explanatory power). WTC analysis reveals significant coherence between evaporation and both temperature and sunshine duration at 6–24 month and annual scales, whereas wind speed and humidity show weaker or intermittent associations. These findings provide quantitative evidence of the climatic controls on evaporation and demonstrate the effectiveness of entropy‐based methods in capturing nonlinear and multi‐scale variability. Such insights are essential for improving hydrological models, predicting water availability and guiding adaptive water‐resource management under climate change.

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain.

A nationwide study on the occurrence, driving factors and exposure assessment of typical pesticides in groundwater in China.

AI-driven framework for high-precision seamless sunshine duration estimation using Himawari-8 satellite and ground observations

This study examined the effect of the South Asia High Pressure (SAH) system and the Western Pacific Subtropical High Pressure (WPSH) system on surface meteorology and ozone (O3) pollution over Sichuan Basin in China during spring. Analyzing 2015-2020 O3 data (>100μg m-³ defines regional persistent pollution, ORPP) with matched meteorological data reveals: (1) Greater SAH Route Angle (SAH_RA) deviation prolongs ORPP duration; (2) Higher WPSH intensity (P index) suppresses O3 pollution; (3) Large ridge index (R index) fluctuations and SAH-WPSH index differences exacerbate pollution. SAH_RA/WPSH index differences correlate strongly with temperature, sunshine duration, and diurnal temperature range. Specifically, R index fluctuations increase average sunshine hours and daily temperature range while reducing humidity, creating O3-favorable conditions. Conversely, elevated P index raises surface pressure and modifies thermal structure, inhibiting pollution. The high-altitude circulation system regulates ground meteorological conditions (temperature/sunshine/humidity) through the synergistic effect of "path deviation - intensity variation - ridge line fluctuation", thereby controlling O3 pollution. This study provides key meteorological criteria for the precise prevention and control of air pollution in river basins.

Predicting extreme rainfall is crucial for supporting planning in the agricultural sector, infrastructure development, and disaster mitigation in the city of Bogor. However, the asymmetric distribution of daily rainfall and the presence of outliers make linear regression methods less suitable. Quantile regression offers an alternative that captures the influence of explanatory variables across different parts of the data distribution, particularly in the extreme regions. This study compares the Simplex and Nelder-Mead methods for estimating quantile regression parameters on extreme rainfall data in Bogor. Daily rainfall data were obtained from the West Java BMKG Climate Station for the period from May 2024 to April 2025, comprising 365 observations, with four explanatory variables: average temperature, average humidity, sunshine duration, and average wind speed. Modeling was conducted at the 0.75, 0.85, and 0.95 quantiles to represent extreme rainfall. The results show that the Simplex method outperformed Nelder-Mead, as indicated by lower Pinball Loss and Mean Absolute Error (MAE) values at most quantiles. Humidity and average wind speed had a significantly positive effect on extreme rainfall intensity, while average temperature had a negative effect. Sunshine duration showed less consistent effects. Overall, the Simplex method is recommended for quantile regression optimization in extreme rainfall data due to its greater stability and accuracy in generating model parameters. However, this study is limited by the number of explanatory variables and the relatively short observation period. Incorporating additional variables such as air pressure, ENSO index, or topographical data, along with extending the observation period, could improve model accuracy and generalizability in future research.

This study investigates the impact of weather conditions on solar energy production by comparing the performance of different time series forecasting models. Production data obtained from the İkitelli Solar Power Plant in Istanbul, together with simultaneous meteorological variables such as sunshine duration, cloudiness, humidity, and temperature, were analyzed. Three forecasting models—ARIMA, LSTM, and FB-Prophet—were implemented to evaluate their predictive performance. The accuracy of the models was assessed using widely accepted statistical metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results show that the ARIMA model achieved the highest accuracy in short-term forecasting with the lowest error rates, demonstrating its effectiveness in handling stationary time series. The LSTM model, a deep learning approach, proved successful in capturing long-term dependencies, offering a robust alternative despite requiring larger datasets for optimal performance. The FB-Prophet model stood out for its ability to account for seasonality and trends but exhibited lower accuracy in short-term fluctuations compared to ARIMA and LSTM. Additionally, the analysis revealed that solar energy production is strongly correlated with weather conditions. In particular, an increase in sunshine duration positively influenced electricity generation, while greater cloud cover led to significant reductions in production levels. These findings highlight the importance of incorporating meteorological data into forecasting models to enhance the accuracy and reliability of renewable energy predictions. Furthermore, the study emphasizes that selecting the appropriate forecasting model according to data characteristics is critical for effective energy management. The outcomes provide methodological insights that may contribute to the optimization of solar energy projects and the integration of renewable energy into sustainable energy strategies.

Accurately estimating reference crop evapotranspiration (ET0) is essential for agricultural water-resource management, yet the traditional Penman–Monteith (PM) method requires multiple meteorological variables and is difficult to apply in data-sparse regions. To explore more data-efficient alternatives, this study systematically evaluates several machine-learning (ML) models capable of capturing nonlinear relationships, using daily observations from 698 meteorological stations across China. In addition, we incorporate SHapley Additive exPlanation (SHAP), a game-theory-based interpretability approach, to quantify the contribution of input variables at both national and regional scales. The results show that the Random Forest (RF) model performs best (coefficient of determination, R2 = 0.957; mean absolute percentage error, MAPE = 9.214%), significantly outperforming multiple linear regression and approaching the accuracy of the PM method. SHAP analysis indicates that maximum temperature, sunshine duration, and month are the most influential factors nationwide. Geographic variables contribute less overall but become important in specific regions, such as Southwest China. The study also reveals pronounced spatial heterogeneity in the drivers of ET0, highlighting the necessity of regionalized interpretations. Furthermore, sensor-reduction experiments demonstrate that reasonable estimation accuracy can be maintained even without radiation or wind-speed observations, offering guidance for low-cost monitoring scenarios. Overall, this study provides transparent model comparisons for ML-based ET0 estimation, uncovers regional differences in controlling factors, and offers practical insights for designing meteorological monitoring strategies in data-limited environments.

Burundi continues to face persistent electricity shortages due to limited generation capacity and strong dependence on climate-sensitive hydropower resources. This study aims to quantify wind and solar energy potential in western Burundi, assess their seasonal complementarity, and evaluate the feasibility of PV–wind hybrid systems for rural electrification. Long-term datasets from the Burundi Geographical Institute (2013–2020 for wind; 1977–2017 for sunshine duration) were analyzed using statistical and empirical models. Wind resources were assessed through monthly distributions, directional frequency, and wind power density (WPD), while solar potential was estimated using the Ångström–Prescott model, suitable for data-scarce contexts. Results indicate mean annual solar irradiance of ~5.3 kWh/m²/day (~1,930 kWh/m²/year) with modest seasonal variation (±10%), supporting reliable year-round PV generation. Wind exhibits strong seasonality, peaking at ~160 W/m² (Class 3) between June and October but averaging ~60 W/m² annually (Class 2), limiting standalone potential. These findings demonstrate how the complementary nature of wind and solar regimes can strengthen rural mini-grids and reduce vulnerability to hydrological variability. The study provides new evidence to guide renewable-energy planning and support progress toward universal energy access in Burundi.

This paper presents the results of forecasting electricity consumption in a humid zone, taking Togo as a case study. Consumption data were analyzed with the following input variables: Temperature (T), Relative Humidity (R), Precipitation (P), Wind Speed (W), and Sunshine Duration (S). XGBoost, ANFIS, and RNN are explored as modeling methods, with performance evaluated using R², MAE, MSE, and RMSE. A correlation analysis was conducted among all variables. The findings reveal correlations of 83% between relative humidity and precipitation; 73% between power consumption and precipitation; and 67% between power consumption and relative humidity. In contrast, only 8% correlation is observed between power consumption and temperature, and 4% between wind speed and sunshine duration. With respect to modeling, ANFIS metrics are found to be unsatisfactory. Its best performance yields R² = 41.3498% under the TRPWS configuration. XGBoost provides moderate results, with R² = 51.39% for the TRPWS configuration, representing its most acceptable model. By comparison, RNN delivers superior outcomes, with the majority of R² values exceeding 71%. The lowest performance, obtained with the PWS configuration, records RMSE = 909.7192, MAE = 567.9969, MSE = 827,589.1092, and R² = 71.45%. The highest performance, obtained with the TRPWS configuration, yields RMSE = 846.1036, MAE = 490.9964, MSE = 715,891.3167, and R² = 75.31%. Furthermore, residual analysis confirms that the distribution of errors aligns well with the Gaussian normal law. It is therefore concluded that RNN is well-suited for predicting electricity consumption in humid zones using the considered meteorological variables.

Acute myocardial infarction (AMI) remains a major health concern in cold regions, where extreme meteorological conditions and air pollution can jointly elevate cardiovascular risk. However, the nonlinear and delayed impacts of diverse environmental stressors on different AMI subtypes remain insufficiently understood. We conducted a retrospective time-series study including daily ST-elevation myocardial infarction (STEMI, ST refers to the ST segment on electrocardiograms) and non-ST-elevation myocardial infarction (NSTEMI) cases recorded in Heilongjiang Province, China from 2014-2023. Environmental exposures included key meteorological indicators (mean air temperature, surface temperature, snow depth, atmospheric pressure, sunshine duration, and ultraviolet intensity) and major air pollutants (PM[Formula: see text], PM[Formula: see text], NO[Formula: see text], SO[Formula: see text]). Extreme cold and heat were defined primarily using mean air temperature thresholds based on regional climatic characteristics (<-20[Formula: see text]C and >30[Formula: see text]C), while the full spectrum of non-extreme weather conditions was also assessed. Distributed Lag Nonlinear Models (DLNMs) were used to characterize nonlinear and lag-response relationships. STEMI risk significantly increased under stress, with acute effects observed at extremely low (<-20[Formula: see text]C) and high (>30[Formula: see text]C) mean air temperatures. NSTEMI showed a more gradual response, particularly to sustained heat exposure. Short-term PM[Formula: see text] exposure had a stronger effect on STEMI, whereas NO[Formula: see text] was more strongly associated with delayed NSTEMI risk. Atmospheric pressure between 990-1010 hPa elevated risk, while greater snow depth appeared inverse association. Non-linear patterns were also noted for sunshine duration and ultraviolet intensity. In cold-climate regions, both meteorological extremes and air pollution substantially affect AMI, but with distinct temporal and exposure-response characteristics between STEMI and NSTEMI. These findings highlight the need for subtype-specific environmental early warning systems and climate-adaptive cardiovascular prevention strategies.

Aims: The green chafer beetle is considered an occasional pest across different crop ecosystems in the Tarai region of Uttarakhand, India. The present study aimed to examine its population dynamics under natural field conditions on maize crop. Study Design: Maize variety PCM-4 was sown manually at a spacing of 60 × 30 cm in six replications following Randomised Block Design, each comprising a plot of 5 × 6 m. Ten maize plants were randomly selected for sampling. Beetle counts were recorded from the germination stage through to crop maturity. Place and Duration of Study: The investigation was carried out during the kharif season of 2024 at the Norman E. Borlaug Crop Research Centre, G.B. Pant University of Agriculture and Technology, Pantnagar. Methodology: Data of adult beetle population along with key abiotic factors such as maximum and minimum temperature, morning and evening relative humidity, sunshine duration, and rainfall were recorded and analysed using simple correlation and regression techniques. Results: Chafer beetle infestation on maize in Pantnagar occurred mainly between the 38th and 45th standard weeks during the kharif season of 2024. Pearson’s correlation analysis revealed significant negative correlations between beetle population and minimum temperature (r= –0.613*), morning relative humidity (r= –0.665**), and evening relative humidity (r= –0.747**). Sunshine hours exhibited a significant positive correlation with beetle abundance (r= 0.588*), indicating that brighter and longer sunshine periods favoured chafer beetle activity. Conclusion: The study concludes that chafer beetle infestation in maize at Pantnagar is sporadic, emerging during the tasseling and pollen-shedding period. Low minimum temperature, both maximum and minimum relative humidity, reduce beetle buildup, whereas sunshine hours play a major role in enhancing adult beetle abundance.

Climate change induces widespread effects on crop production, influencing multiple developmental stages and associated agronomic outcomes. Using long-term meteorological data from Jilin Province, Northeast China, this study examined temporal and spatial variations in climatic conditions through trend analysis, Mann–Kendall tests, and inverse distance weighting interpolation. A fuzzy comprehensive evaluation model was applied to classify maize cultivation suitability into four levels across major production areas, with Level I representing the most suitable regions, Level II highly suitable regions, Level III moderately suitable regions, and Level IV low suitable regions. Changes in suitable areas were analyzed before and after abrupt climatic shifts. From 1976 to 2020, Jilin Province experienced a significant rise in annual mean temperature, a marked decline in sunshine duration, and a slight increase in precipitation. The area of Level I suitability remained stable, while Level II expanded to approximately 1.3 times its original area. Conversely, Level III and IV areas decreased by 4.59% and 28.77%, respectively, compared with the pre-transition period. Spatially, the most suitable maize cultivation areas shifted from central to northern and eastern Jilin due to climatic warming. Although rising temperatures enhanced thermal conditions for maize production, reduced sunshine and variable precipitation constrained further expansion. These findings provide a scientific basis for optimizing maize variety selection and cropping structure in high-latitude regions, supporting yield improvement and sustainable development of the maize industry under a changing climate.

Harvest-disrupting rain events (HDREs) are prolonged cloudy–rainy spells during winter wheat maturity that impede harvesting and drying, induce pre-harvest sprouting and grain mould, and threaten food security in the Huang–Huai–Hai Plain (HHHP), China’s core winter wheat region. Using daily meteorological records (1960–2019), remote sensing-derived land-use data and topography, we develop a hazard–exposure–vulnerability framework to quantify HDRE risk and its drivers at 1 km resolution. Results show that HDRE risk has increased markedly over the past six decades, with the area of medium-to-high risk rising from 26.9% to 73.1%. The spatial pattern evolved from a “high-south–low-north” structure to a concentrated high-risk belt in the central–northern HHHP, and the risk centroid migrated from Fuyang (Anhui) to Heze (Shandong), with an overall displacement of 124.57 km toward the north–northwest. GeoDetector analysis reveals a shift from a “humidity–temperature dominated” mechanism to a “sunshine–humidity–precipitation co-driven” mechanism; sunshine duration remains the leading factor (q &gt; 0.8), and its interaction with relative humidity shows strong nonlinear enhancement (q = 0.91). High-risk hot spots coincide with low-lying plains and river valleys with dense winter wheat planting, indicating the joint amplification of meteorological conditions and underlying surface features. The results can support regional decision-making for harvest-season early warning, risk zoning, and disaster risk reduction in the HHHP.

The Songhua River Basin (SRB) in Northeast China is a high-latitude basin experiencing significant snow cover changes under global warming. This study quantified spatiotemporal changes in snowmelt in the SRB (1961–2020). A specific focus was placed on the changes at event scale, including frequency, magnitude and duration, that have been underexplored in previous work. Correlations between snowmelt and key driving factors were assessed to identify the dominant controls governing the melt process. A significant elevation-dependent decreasing trend in annual snowmelt was found over the decades, with the decrease most pronounced at lower elevations. Relative to the baseline period (1961–1990), the snowmelt dates during 1991–2020 advanced, with the 25%, 50%, and 75% cumulative levels occurring 9, 6, and 2 days earlier, respectively. Seasonally, snowmelt increased significantly in early spring (February to March) but decreased notably in late spring (April to May). Snowmelt events exhibited reduced frequency, total volume, peak value, and mean rate, along with fewer extreme events. The strongest correlation across snowmelt event types was found with mean snow depth for complete depletion and with accumulated sunshine duration for incomplete depletion, while Rain-on-Snow Melt events were most closely associated with sunshine and temperature. This study can provide a crucial reference for sustainable water management and spring agricultural irrigation in the SRB.

BackgroundChina is one of the regions with high incidence of influenza, previous researches have primarily focused on the seasonal characteristics, spatio-temporal distribution, and associated influencing factors of influenza, while paying less attention to the public health significance of influenza-like illness (ILI) outbreaks. ILI is clinically defined as a syndrome characterized by fever accompanied by cough or sore throat. This case definition leads to distinct epidemiological characteristics, disease burden, and prevention strategies compared to laboratory-confirmed influenza. Currently, systematic epidemiological research on ILI outbreaks in Chinese mainland still has gaps. Therefore, a spatio-temporal modeling study was conducted to identify high-risk areas and potential risk factors for ILI outbreaks.MethodsThe study utilized data on ILI outbreaks from the Chinese National Influenza Center. Spatial autocorrelation analysis, median center and standard deviational ellipse analysis were performed using ArcGIS 10.7 software to identify high-risk areas and spatial-temporal evolution of ILI outbreaks. Space-time scanning analysis was conducted using SaTScan 10.1.2 software to determine spatio-temporal clusters of ILI outbreaks. A Bayesian hierarchical model was adopted to explore the socioeconomic and meteorological factors influencing ILI outbreaks from a spatial-temporal perspective.ResultsThe outbreaks of ILI showed a distinct seasonality, with those in northern regions predominantly occurring during winter, whereas southern regions experienced more outbreaks, mainly in winter and spring. High clustering of ILI outbreaks was primarily concentrated in province levels such as Guangdong, Guangxi, Shandong, Jiangsu, Anhui, Zhejiang, and Fujian. The Bayesian model revealed that higher temperatures (RR = 0.958, 95% CI: 0.945–0.972), longer sunshine duration (RR = 0.871, 95% CI: 0.801–0.947), and higher wind speeds (RR = 0.820, 95% CI: 0.748–0.899) served as protective factors against ILI outbreaks, whereas surface pressure (RR = 1.005, 95% CI: 1.000-1.011) showed a positive correlation. Furthermore, regions with a higher proportion of males (RR = 1.022, 95% CI: 1.006–1.039), a greater proportion of population aged 14 and below (RR = 1.116, 95% CI: 1.054–1.179), higher GDP per capita (RR = 1.923, 95% CI: 1.212–3.047), and a larger floating population (RR = 1.943, 95% CI: 1.507–2.499) was also associated with a higher risk of ILI outbreaks.ConclusionsThe study revealed distinct patterns and related influencing factors of ILI outbreaks in Chinese mainland from 2013 to 2022. Seasonality and spatial aggregation were its main characteristics. Temperature, sunshine duration, and wind speed were negatively correlated with the risk of ILI outbreaks, whereas surface pressure, the proportion of males, the proportion of population aged 14 and below, GDP per capita and floating population were positively correlated with the risk of ILI outbreaks. Relevant authorities should strengthen influenza surveillance in high-risk areas, optimize resource allocation, and enhance vaccination efforts to effectively prevent the exacerbation and spread of influenza outbreaks during peak seasons and in high-risk regions.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-25615-7.

Abstract. The lack of high-accuracy, fine-resolution meteorological datasets in China has hindered progress in climate, hydrological, and ecological studies. In this study, we present a 1 km daily dataset spanning 1961–2021 across China, which includes six key variables – average, maximum, and minimum temperature, atmospheric pressure, relative humidity, and sunshine duration – to provide a reliable foundation for advancing related research and applications. The dataset was generated using a novel hierarchical reconstruction framework that leveraged daily observations from 2345 meteorological stations and incorporated topographic attributes. This approach effectively decodes the nonlinear relationships between the meteorological variables and their spatial covariates, ensuring the generation of gridded daily fields that are both high-resolution and spatially continuous. Validation against 146 independent stations confirmed the high accuracy of the dataset. For average, maximum, and minimum temperatures, the errors are minimal (median root mean square errors (RMSEs): 1.16, 1.19, 1.29 °C; median mean errors (MEs): −0.04, −0.10, −0.01 °C), and the consistency with in-situ data is very high (median correlation coefficients (CCs): 0.99, 0.99, 0.99). Atmospheric pressure also shows very small errors (median RMSE: 2.65 hPa; median ME: −0.06 hPa) and strong correlation (median CC: 0.97). Relative humidity exhibits relatively lower accuracy (median RMSE: 6.33 %; median ME: −0.52 %; median CC: 0.90), but it still exceeds standard benchmarks. Sunshine duration maintains high precision (median RMSE: 1.48 h; median ME: 0.05 h; median CC: 0.93), indicating the robustness and reliability of the dataset. Further comparison reveals that in high-altitude and topographically complex regions, the reconstructed product demonstrates higher actual accuracy than suggested by station-to-grid validation, as spatial mismatches between stations and grid cells lead to systematic underestimation. Free access to the dataset is available at https://doi.org/10.11888/Atmos.tpdc.301341 or https://cstr.cn/18406.11.Atmos.tpdc.301341 (last access: 25 November 2025) (Zhao et al., 2024).

ABSTRACTEnclosure is one of the important methods used to restore grassland ecosystems. Locusts and grasshoppers are important components of grassland ecosystems and can accurately reflect changes in their habitats. However, currently, the response in abundance of grassland locusts and grasshoppers within short‐term enclosure mode remains unclear. In this study, a 3‐year short‐term enclosure experiment on a natural grassland in Ili Kazak Autonomous Prefecture, Xinjiang, China, aimed to explore the response patterns of grassland locusts under fences. According to the results of this study, there were significant differences in the diversity of locusts and grasshoppers in various grassland types after short‐term captivity. Canonical Correlation Analysis shows that temperature is the main factor affecting grassland locusts at different altitudes and latitudes; annual precipitation and relative humidity are the main factors affecting the distribution of dominant locusts and grasshoppers in different grassland types; the duration of sunshine and the highest daily temperature are the main factors affecting the dominance of locusts and grasshoppers at different altitudes and latitudes. As altitude increased from the temperate desert steppes to the mountain meadows, vegetation cover presented a “decreasing‐increasing” trend, and the number of locusts and grasshoppers per unit quadrat showed a “decreasing‐increasing‐decreasing” trend. As a result of increased vegetation cover and altitude and decreased latitude, the community structure of locusts and grasshoppers shifted from a decrease in terricoles species such as the Sphingonotus species to an increase in phytophilous species like Omocestus petraeus. In contrast to the results of a study performed in the same area 40 years ago, this survey showed that four species of locusts and grasshoppers, including Gomphocerus sibiricus, shifted toward higher altitudes, causing changes in the community structure of locusts and grasshoppers at higher altitudes; Stauroderus scalaris scalaris replaced Gomphocerus sibiricus and evolved into a high‐altitude dominant species; and Pararcyptera microptera microptera shifted toward low altitudes. Therefore, in order to more accurately assess the restoration status of grassland ecosystems under fences, it is necessary to consider the diversity changes and influencing factors of locusts and grasshoppers, which may vary depending on different grassland types.

Climate change is a major cause of morbidity and mortality in tropical and subtropical countries. In Indonesia, Papua is one of the provinces with a high malaria burden. However, there is limited empirical evidence on the relationship between climate and malaria in coastal areas. This study used an ecological-epidemiological study design with quantitative approach to analyze the relationships between climate variables and malaria incidence in coastal regions. The results showed that changes in climate variables significantly influence malaria incidence. Wind speed and sunshine duration were the most dominant and consistent factors influencing malaria incidence, in both linear and non-linear relationships. The GAM model provided the highest predictive performance compared to the GLM. The findings of this study imply that malaria early warning systems and climate change adaptation strategies require more flexible modeling approaches and the inclusion of non-conventional climate variables. This research also reinforces the need for the integration of climate and ecological data in public health policies, particularly in highly vulnerable coastal areas like papua.