Search Results (184)

This study analyzes spatiotemporal aridity dynamics in Central Kazakhstan (1960–2022) using a monthly Aridity Index (AI = P/PET), where P is precipitation and PET is potential evapotranspiration, Mann–Kendall trend analysis, and climate zone classification. Results reveal a northeast–southwest aridity gradient, with Aridity Index ranging from 0.11 to 0.14 in southern deserts to 0.43 in the Kazakh Uplands. Between 1960–1990 and 1991–2022, southern regions experienced intensified aridity, with Aridity Index declining from 0.12–0.15 to 0.10–0.14, while northern mountainous areas became more humid, where Aridity Index increased from 0.40–0.44 to 0.41–0.46. Seasonal analysis reveals divergent patterns, with winter showing improved moisture conditions (52.4% reduction in arid lands), contrasting sharply with aridification in spring and summer. Summer emerges as the most extreme season, with hyper-arid zones (8%) along with expanding arid territories (69%), while autumn shows intermediate conditions with notable dry sub-humid areas (5%) in northwestern regions. Statistical analysis confirms these observations, with northern areas showing positive Aridity Index trends (+0.007/10 years) against southwestern declines (−0.003/10 years). Key drivers include rising temperatures (with recent degradation) and variable precipitation (long-term drying followed by winter and spring), and PET fluctuations linked to temperature. Since 1991, arid zones have expanded from 40% to 47% of the region, with semi-arid lands transitioning to arid, with a northward shift of the boundary. These changes are strongly seasonal, highlighting the vulnerability of Central Kazakhstan to climate-driven aridification. Full article

This study focuses on developing a climate-flood model to investigate and interpret the relationship and impact of climate on runoff/flooding at a sub-regional scale using multiple linear regression (MLR) with 30 years of hydro-climatic data for the Cross River Basin, Nigeria. Data were obtained from Nigerian Meteorological Agency (NIMET) for the following climatic parameters: annual average rainfall, maximum and minimum temperatures, humidity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure. These hydro-meteorological data were analysed and used as parameters input to the climate-flood model. Results from multiple regression analyses were used to develop climate-flood models for all the gauge stations in the basin. The findings suggest that at 95% confidence, the climate-flood model was effective in forecasting the annual runoff at all the stations. The findings also identified the climatic parameters that were responsible for 100% of the runoff variability in Calabar (R² = 1.000), 100% the runoff in Uyo (R² = 1.000), 98.8% of the runoff in Ogoja (R² = 0.988), and 99.9% of the runoff in Eket (R² = 0.999). Based on the model, rainfall depth is the only climate parameter that significantly predicts runoff at 95% confidence intervals in Calabar, while in Ogoja, rainfall depth, temperature, and evaporation significantly predict runoff. In Eket, rainfall depth, relative humidity, solar radiation, and soil temperatures are significant predictors of runoff. The model also reveals that rainfall depth and evaporation are significant predictors of runoff in Uyo. The outcome of the study suggests that climate change has impacted runoff and flooding within the Cross River Basin. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

The equator’s unique combination of high humidity and temperature renders optical satellite imagery highly susceptible to persistent cloud cover. In contrast, synthetic aperture radar (SAR) offers a robust alternative due to its ability to penetrate clouds with microwave imaging. This study addresses the challenges of cloud-induced data gaps and cross-sensor geometric biases by proposing an advanced optical and SAR image-matching framework specifically designed for cloud-prone equatorial regions. We use a prompt-driven visual segmentation model with automatic prompt point generation to produce cloud masks that guide cross-modal feature-matching and joint adjustment of optical and SAR data. This process results in a comprehensive digital orthophoto map (DOM) with high geometric consistency, retaining the fine spatial detail of optical data and the all-weather reliability of SAR. We validate our approach across four equatorial regions using five satellite platforms with varying spatial resolutions and revisit intervals. Even in areas with more than 50 percent cloud cover, our method maintains sub-pixel edging accuracy under manual check points and delivers comprehensive DOM products, establishing a reliable foundation for downstream environmental monitoring and ecosystem analysis. Full article

Foot-and-mouth disease (FMD), bluetongue (BT), and Peste des Petits Ruminants (PPR) are major emerging and re-emerging viral infections affecting ruminants. These diseases can threaten livestock health, food security, and economic stability in low- and middle-income countries, including Algeria. However, their dynamics remain mostly unknown, limiting the implementation of effective preventive and control measures. We analyzed outbreak data reported by Algerian veterinary authorities and the WAHIS database from 2014 to 2022 for FMD; from 2006 to 2020 for BT; and from 2011 to 2022 for PPR to investigate their spatiotemporal patterns and environmental drivers. Over these periods, Algeria reported 1142 FMD outbreaks (10,409 cases; 0.16/1000 incidence), 167 BT outbreaks (602 cases; 0.018/1000), and 222 PPR outbreaks (3597 cases; 0.096/1000). Small ruminants were the most affected across all diseases, although cattle bore the highest burden of FMD. BT primarily impacted sheep, and PPR showed a higher incidence in goats. Disease peaks occurred in 2014 for FMD, 2008 for BT, and 2019 for PPR. Spatial analyses revealed distinct ecological hotspots: sub-humid and semi-arid zones for FMD and BT, and semi-arid/Saharan regions for PPR. These patterns may be influenced by species susceptibility, animal movement, trade, and climatic factors such as temperature and rainfall. The absence of consistent temporal trends and the persistence of outbreaks suggest multiple drivers, including insufficient vaccination coverage, under-reporting, viral evolution, and environmental persistence. Our findings underscore the importance of targeted species- and region-specific control strategies, including improved surveillance, cross-border coordination, and climate-informed risk mapping. Strengthening One Health frameworks will be essential to mitigate the re-emergence and spread of these diseases. Full article

Protostrongylid nematodes (small lungworms) are very common in Mediterranean sheep and have long been recorded in North Africa. Here, the following four species are found: Muellerius capillaris, Neostrongylus linearis, Cystocaulus ocreatus, and Protostrongylus rufescens. Previous risk factors studies for protostrongylids have been conducted in a single farm and therefore have limitations. Sixty-one meat sheep farms in north-eastern Algeria were surveyed for protostrongylid infection and anthelmintic treatment in late autumn/early winter. The climates of the nine regions ranged from subhumid to arid for humidity and from mild to cool for winter temperature. The highest infection, estimated by the number of larvae per gram of faeces (LPG), was found in subhumid and semi-arid climates. The Rembi breed was more infected than the Ouled Djellal or their crosses. LPG decreased with increasing number of treatments. The latter was also associated with an increased percentage of M. capillaris and a decrease in species diversity. The anthelmintic regimen (ivermectin, levamisole, and albendazole) directly targets gastrointestinal nematodes and indirectly protostrongylids. The use of effective drugs targeting protostrongylids at key moments may provide more effective control. Full article

Thornthwaite’s water balance approach serves as a fundamental tool for assessing hydrological dynamics, particularly in regions vulnerable to aridity and water stress. This study evaluates the performance of gridded datasets in estimating Thornthwaite’s water balance attributes in Greece, leveraging climatic averages of the period 1960–1997. Ground station data from 91 meteorological sites and gridded data from the Climate Research Unit (CRU) of the University of East Anglia were utilized to assess key water balance components. The results indicate that while gridded datasets offer an alternative for regions with limited ground data, local calibration is required due to notable discrepancies. More specifically, it was found that gridded data tended to underestimate precipitation, with estimates approximately 25% lower compared to ground station data. The potential evapotranspiration (PET) estimates using gridded data were more accurate, with underestimation on the order of 10%. Moreover, the gridded data produced overestimations for all of the water balance key components including soil moisture (St), monthly changes in soil moisture (ΔSt), and actual evapotranspiration (AE) compared to the ground station data. The water surplus (S) estimates showed a significant dispersion of values when using the gridded data, particularly in regions characterized by more arid conditions. In addition, the application of gridded data led to a great increase in the aridity index (AI) values, altering the desertification classification of sites from semi-arid to sub-humid or humid categories. These findings underscore the importance of careful consideration when utilizing gridded datasets for hydrological and bioclimatic assessments, particularly in Mediterranean climate regions characterized by a complex topography and temporal climatic variability. Full article

Moroccan vegetation faces significant pressure particularly from human activities and climate change, while most ecosystems lack detailed assessments. Phytoecological studies and species assessments are implemented using vegetation sampling, analysis of climate data, geological substrate maps, and the Digital Elevation Model (DEM). The study area hosts 565 plant species distributed into 74 families, with Asteraceae being the most abundant family, representing 17.7%. In addition, the correspondence analysis test demonstrates that species are grouped into six distinct blocks. Block 1 comprises a set of Quercus ilex forests. Block 2 encompasses Juniperus phoenicea lands and transition zones between Quercus ilex and Juniperus phoenicea. Block 3 represents Pinus halepensis forests and pine occurrences within Quercus ilex and Juniperus phoenicea stands. Block 4 indicates the emergence of xerophytic species alongside the aforementioned species; it forms the upper limits of Blocks 1, 2, and 3. Block 5 corresponds to formations dominated by Juniperus thurifera in association with xerophytes. Block 6 groups together a set of xerophytic species characteristic of high mountain environments. Additionally, Quercus ilex colonizes the subhumid zones and prefers limestone substrates, Juniperus phoenicea and Tetraclinis articulata, and Pinus halepensis occupies the hot part of the semi-arid in limestone, clays, and conglomerates, while the Juniperus thurifera and xerophytes inhabit the cold parts and limestone substrates. The thermo-Mediterranean vegetation level occupies low altitudes, dominated by Tetraclinis articulata, Juniperus phoenicea, and Olea europaea. The meso-Mediterranean level extends to intermediate altitudes, dominated by Quercus ilex and Juniperus phoenicea. While the supra-Mediterranean level is dominated by Quercus ilex, Arbutus unedo, and Cistus creticus. The mountain Mediterranean level, located in the high mountains, is dominated by Juniperus thurifera associated with xerophytes. Finally, the oro-Mediterranean level, found at extreme altitudes, is dominated by xerophytes. Some species within this region are endemic, rare, and threatened. Consequently, the implementation of effective conservation and protection policies is recommended. Full article

Water quality affects soils by promoting their degradation by the accumulation of salts that will lead to salinization and sodification. However, the magnitude of these processes varies with soil attributes. Saturated hydraulic conductivity (K_sat) is the rate at which water passes through saturated soil, which is fundamental to determining water movement through the soil profile. The K_sat may differ from soil to soil according to the sodium adsorption ratio (SAR), water electrical conductivity (EC_w), soil texture, and clay mineralogical assemblage. In this study, an experiment with vertical columns and constant-load permeameters was conducted to evaluate changes in soil K_sat with waters comprising five EC_w values (128, 718, 1709, 2865, and 4671 µS cm⁻¹) and five SAR values [0, 5, 12, 20, and 30 (mmol_c L⁻¹)^0.5] in combination. Horizons from nine northeastern Brazilian soils (ranging from tropical to semiarid) were selected according to their texture and clay mineralogical composition. The data obtained were fit with multiple regression equations for K_sat as a function of EC_w and SAR. This study also determined the null SAR at each EC_w level, using K_sat = 0 on each equation, to predict the SAR needed to achieve zero drainage on each soil for each EC_w level and the threshold electrolyte concentration (C_TH) that would lead to a 20% reduction of maximum K_sat. Neither the EC_w nor SAR of the applied waters affected the K_sat of soils with a mineralogical assemblage of oxides and kaolinite such as Ferralsol, Nitisol, and Lixisol, with an average K_sat of 2.75, 6.06, and 3.33 cm h⁻¹, respectively. In smectite- and illite-rich soils, the K_sat increased with higher EC_w levels and decreased with higher SAR levels, especially comparing the soil’s estimated K_sat for water with low EC_w and high SAR in combination (EC_w of 128 µS cm⁻¹ and SAR 30) and water with high EC_w and low SAR in combination (EC_w of 4671 µS cm⁻¹ and SAR 0) such as Regosol (4.95 to 10.94 cm h⁻¹); Vertisol (0.28 to 2.04 cm h⁻¹); Planosol (0 to 0.29 cm h⁻¹); Luvisol (0.46 to 2.12 cm h⁻¹); Cambisol (0 to 0.23 cm h⁻¹); and Fluvisol (1.87 to 3.34 cm h⁻¹). The C_TH was easily reached in soils with high concentrations of highly active clays such as smectites. In sandy soils, the target C_TH was only reached under extremely high SAR values, indicating a greater resistance of these soils to salinization/sodification. Due to their mineralogical assemblage, soils from tropical sub-humid/hot and semiarid climates were more affected by treatments than soils from tropical humid/hot climates, indicating serious risks of physical and chemical degradation. The results showed the importance of monitoring water quality for irrigation, mainly in less weathered, more clayey soils, with high clay activity to minimize the rate of salt accumulation in soils of the Brazilian semiarid region. Our study also proved that clay mineralogy had more influence on the K_sat than clay concentration, mainly in soils irrigated with saline and sodic waters, and that soils with highly active smectite are more prone to degradation than soils with high concentrations of kaolinite. Full article

This article explores the impacts of climate change on the rural and natural landscapes in the region of Eastern Macedonia and Thrace, northeastern Greece. The spatial distributions of the bioclimatic de Martonne Index and the phytoclimatic Emberger Index were calculated at a very high resolution (~500 m) for present conditions (1970–2000), two future time periods (2030–2060; 2070–2100), and two greenhouse gas concentration scenarios (RCP4.5; RCP8.5). The results show significant bioclimatic changes, especially in the Rhodope Mountain range and along almost the whole length of the Greek–Bulgarian border, where forests of high ecosystem value are located, together with the rural areas along the Evros river valley, as well as in the coastal zone of the Aegean Sea. The article describes the processes of bioclimatic changes that can significantly modify the study area’s landscapes. The study area reveals a shift toward xerothermic environments over time, with significant bioclimatic changes projected under the extreme RCP8.5 scenario. By 2100, de Martonne projections indicate that around 40% of agricultural areas in the eastern, southern, and western regions will face Mediterranean and semi-humid conditions, requiring supplemental irrigation for sustainability. The Emberger Index predicts that approximately 42% of natural and agricultural landscapes will experience sub-humid conditions with mild or cool winters. In comparison, 5% will face drier humid/sub-humid, warm winter conditions. These foreseen futures propose initial interpretations for key landscape conservation, natural capital, and ecosystem services management. Full article

In this study, a study area was chosen in China to analyze the lagged response relationship between normalized difference vegetation index (NDVI) and extreme precipitation/drought from 1982 to 2015. A logistical function was applied to explain the increase in NDVI with mean annual precipitation in nine sub-regions, and the inflection point of precipitation was found to be very close to the threshold value for separating arid or humid regions. NDVI had a strong positive correlation with drought and extreme precipitation in the arid regions, while in humid regions, it presented a strong correlation with drought during 2000–2015; however, a weak correlation with drought was found before the 21st century. In this study, we quantified the time-lagged response of vegetation to drought (LTRD) and extreme precipitation (LTREP). Then, we defined four gradients (${\displaystyle \frac{\partial LTRD}{\partial P}}$, ${\displaystyle \frac{\partial LTRD}{\partial T}}$, ${\displaystyle \frac{\partial LTREP}{\partial P}}$, and ${\displaystyle \frac{\partial LTREP}{\partial T}}$) to quantify the precipitation and temperature gradients with the lag-time response to drought or extreme precipitation, respectively. Decreasing gradients were observed for humid regions with ${\displaystyle \frac{\partial LTRD}{\partial P}}$ = −0.19 month·100 mm⁻¹ for “wetting” and ${\displaystyle \frac{\partial LTRD}{\partial T}}$ = −0.13 month·K⁻¹ for “warming”, while increasing gradients were found in the same regions with ${\displaystyle \frac{\partial LTREP}{\partial P}}$ = +0.18 month·100 mm⁻¹ for “wetting” and ${\displaystyle \frac{\partial LTREP}{\partial T}}$ = +0.14 month·K⁻¹ for “warming”. These results suggest that the lagging responses of vegetation to extreme precipitation and droughts exhibit opposing regional patterns across China. Full article

How to quickly and accurately obtain the basal crop coefficient is the key to estimating evapotranspiration in sparse vegetation. To enhance the accuracy of vineyard evapotranspiration estimation in the subhumid region of Northwest China, this study utilized the actual evapotranspiration (ET_c) measured by the Bowen ratio system as the reference standard. The reference crop evapotranspiration (ET_o) was calculated using the Penman formula, and the grape crop coefficient (K_c) was subsequently derived. The FAO-56 dual crop coefficient method was then employed to determine the soil evaporation coefficient (K_e) and the water stress coefficient (K_s), leading to the acquisition of the basal crop coefficient (K_cb). Concurrently, multispectral remote sensing images captured by unmanned aerial vehicle (UAV) were used to gather grape spectral data, from which the reflectance of multiple bands was extracted to compute four vegetation indices: the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Ratio Vegetation Index (RVI), and the Difference Vegetation Index (DVI). Relationship models between the grape basal crop coefficient (K_cb) and these vegetation indices were established using univariate linear regression, polynomial regression, and multiple linear regression. These models were then used to estimate vineyard evapotranspiration and validate the accuracy of the UAV multispectral remote sensing in estimating the grape K_cb. The results indicated that: (1) The growth stage, type of vegetation index, and modeling method were three significant factors influencing the fitting accuracies of the relationship models between the grape basal crop coefficient (K_cb) and vegetation indices. These model fitting accuracies had a notable impact on the estimation accuracies of evapotranspiration. (2) The application of UAV-based multispectral remote sensing to estimate the grape basal crop coefficient in the subhumid region of Northwest China was feasible. Compared to the K_cb values recommended by the FAO-56, the K_cb values derived from the UAV data improved the estimation accuracies of evapotranspiration by more than 11% in 2021 and 13% in 2022. Full article

Malaria remains a significant public health problem in India. Although temperature influences Anopheline mosquito feeding intervals, population density, and longevity, the reproductive potential of the Plasmodium parasite and rainfall influence the availability of larval habitats, and evidence to correlate the impact of climatic factors on the incidence of malaria is sparse. Understanding the influence of climatic factors on malaria transmission will help us predict the future spread and intensification of the disease. The present study aimed to determine the impact of temporal trend of climatic factors such as annual average maximum, minimum, mean temperature, and rainfall on the annual incidence of malaria cases in India for a period of 61 years from 1961 to 2021 and relative humidity for a period of 41 years from 1981 to 2021. Two different analyses were performed. In the first analysis, the annual incidence of malaria and meteorological parameters such as annual maximum, minimum, and mean temperature, annual rainfall, and relative humidity were plotted separately in the graph to see if the temporal trend of climatic factors had any coherence or influence over the annual incidence of malaria cases. In the second analysis, a scatter plot was used to determine the relationship of the incidence of malaria in response to associated climatic factors. The incidence of malaria per million population was also calculated. In the first analysis, the annual malaria cases showed a negative correlation of varying degrees with relative humidity, minimum, maximum, and mean temperature, except rainfall, which showed a positive correlation. In the second analysis, the scatter plot showed that the rainfall had a positive correlation with malaria cases, and the rest of the climatic factors, such as temperature and humidity, had negative correlations of varying degrees. Out of the total 61 years studied, in 29 years, malaria cases increased more than 1000 square root counts when the minimum temperature was at 18–19 °C; counts also increased over a period of 33 years when the maximum temperature was 30–31 °C, over 37 years when the mean temperature was 24–25 °C, over 20 years when the rainfall was in the range of 100–120, and over a period of 29 years when the relative humidity was at 55–65%. While the rainfall showed a strong positive correlation with the annual incidence of malaria cases, the temperature and relative humidity showed negative correlations of various degrees. The increasing temperature may push the boundaries of malaria towards higher altitude and northern sub-tropical areas from the southern peninsular region. Although scanty rainfall reduces the transmission, increases in the same would increase the malaria incidence in India. Full article

Near-surface air temperature (Tair) is critical for addressing urban challenges in China, particularly in the context of rapid urbanization and climate change. While many studies estimate Tair at a national scale, they typically provide only daily data (e.g., maximum and minimum Tair), with few focusing on sub-daily urban Tair at high spatial resolution. In this study, we integrated MODIS-based land surface temperature (LST) data with 18 auxiliary data from 2013 to 2023 to develop a Tair estimation model for major Chinese cities, using random forest algorithms across four diurnal and seasonal conditions: warm daytime, warm nighttime, cold daytime, and cold nighttime. Four model schemes were constructed and compared by combining different auxiliary data (time-related and space-related) with LST. Cross-validation results were found to show that space-related and time-related variables significantly affected the model performance. When all auxiliary data were used, the model performed best, with an average RMSE of 1.6 °C (R² = 0.96). The best performance was observed on warm nights with an RMSE of 1.47 °C (R² = 0.97). The importance assessment indicated that LST was the most important variable across all conditions, followed by specific humidity, and convective available potential energy. Space-related variables were more important under cold conditions (or nighttime) compared with warm conditions (or daytime), while time-related variables exhibited the opposite trend and were key to improving model accuracy in summer. Finally, two samples of Tair patterns in Beijing and the Pearl River Delta region were effectively estimated. Our study offered a novel method for estimating sub-daily Tair patterns using open-source data and revealed the impacts of predictive variables on Tair estimation, which has important implications for urban thermal environment research. Full article

The accurate and sub-daily identification of agricultural low-temperature disasters (LTDs) facilitates the understanding of their dynamic evolution, the evaluation of the characteristics of disaster events, and informs effective strategies aimed at disaster prevention and mitigation. In order to ensure the timely, precise, and comprehensive capture of disaster processes, we have developed a dynamic evaluation framework for winter wheat spring LTD in the Huang-Huai-Hai (HHH) region, driven by meteorological data. This framework consists of two primary components: a disaster classification module and a dynamic simulation-assessment module. Through disaster mechanisms and comprehensive statistical analysis, we have established the input features and structural framework of the classification module using a decision tree algorithm. The dynamic simulation evaluation module is based on our newly developed index for the cumulative hourly intensity of low-temperature stress (CHI) and its grade indicators. This index integrates the interaction between cold stress (low-temperature intensity, cooling amplitude, and duration) and mitigating conditions (air humidity) during the evolution process of LTD. Based on CHI, we found that as the intensity of low temperatures and the amplitude of cooling rise, along with an extended duration of stress and a reduction in relative humidity, the severity of spring LTDs in winter wheat get worse. The overall validation accuracy of the evaluation framework is 92.6%. High validation accuracy indicates that our newly established framework demonstrates significant efficacy in identifying LTDs and assessing grade. Through the analysis of the characteristics of the disaster process, spring LTDs affecting winter wheat are mainly mild, with frost identified as the primary category of LTD. The duration of freeze injury typically exceeds 24 h, while the duration of frost damage and cold damage is less than 24 h. From 1980 to 2022 in the HHH region, the frequency of spring freeze injury and frost damage on winter wheat showed an overall decreasing trend, with a particularly significant decrease in frost damage occurrences. Conversely, cold damage occurrences are on the rise. In addition, the duration of individual disaster events for the three categories of spring LTDs is decreasing, while both the average intensity and extremity of these events show increasing trends. This study has important practical value for the sub-daily scale evaluation of the spring LTD affecting winter wheat in the HHH region and serves as an effective guide for agricultural disaster prevention and mitigation, as well as for the formulation of planting strategies. Full article