Annual Precipitation Research Articles

Impact of climate changes on agroresources of Ukrainian Polissia based on geospatial data

Aim. To determine the patterns of the climate change impact on the transformation of agricultural production and the ecosystem of Ukrainian Polissia in terms of time and space. Methods. Satellite and statistical data for the last 40 years (1982–2022) were used. The mean temperature for the vegetation period was defined by the sum of radiation temperatures of the terrestrial surface, calculated using the data of the infrared range (10.3–11.3; 11.4–12.4 μm) of the high-precision AVHRR radiometer of artificial meteorological Earth's satellites, NOAA, and the precipitation dynamics – using the data of ERA5 ECMWF/Copernicus Climate Change Service. The impact of climate change on the vegetation state and phenological parameters, including the beginning, end, and duration of the vegetation season, was determined by the normalized difference vegetation index (NDVI), obtained using the data of the infrared (0.72–1.1 μm) and red (0.58–0.68 μm) ranges of the AVHRR radiometer on the website of STAR NESDIS NOAA. Crop yields and the dynamics of sown areas were determined by the data from the State Statistics Service of Ukraine. The information about the dynamics of the forest cover was obtained using the Global Forest Watch satellite data. To evaluate the impact of climate change on Ukraine's forests, the investigation was conducted on burnt forest areas, using the MCD64A1 6.1 index, developed on the basis of the MODIS satellite data. Results. According to the satellite data, the radiation temperature of the terrestrial surface during the growing period increased by 2.2 ºС on average in the territory of Ukrainian Polissia over the last 40 years. There is a regional difference in the temperature regime observed in the direction from west to east. In the territory of western Polissia, the increase in the temperature of the terrestrial surface in recent decades was within the range of 1.2–1.6, and in the central and eastern parts – within 2.3–2.9 ºС. Due to the warming, the duration of the growing period was extended by 21–35 days, mostly because of earlier spring onset. There is a descending trend in the average annual precipitation amount down to 20–30 mm, which is especially notable for central and eastern Polissia. The warming was the reason for the introduction of the crops, new for this region, into the structure of sown areas, especially corn and sunflower, which had a generally positive effect on the vegetation state by the NDVI index, which increased from 0.30 in 1982–1992 to 0.36 in 2012–2022 on average. The crop yield increased accordingly, and according to the statistical data for the recent years, amounted to: corn – 7.0–9.5 t/ha, winter wheat – 4.5–5.0, and sunflower – 1.5–2.0 t/ha, which is close to the level of their yield on chornozem. At the same time, due to climate change and the transformation of agricultural activity, there has been a higher risk of deterioration in the ecological state of typical Polissia landscapes, droughts, and soil degradation. According to the results of the analysis of twenty-two years of the dynamics in forest fires, the largest areas of burnt forests were registered in 2012 (694.30 sq.km), 2015 (1,078.81 sq.km), and 2020 (776.27 sq.km), which demonstrated the increase in fires during the recent decade along with the tendency towards longer fire hazard period. Conclusions. The increase in the temperature of the terrestrial surface of the Ukrainian Polissia during the growing period and the lengthening of the growing period created the conditions for corn and sunflower to be introduced into the arable areas, which had a positive impact on the vegetation state index, NDVI. As a result of these transformations both in the sown area of these crops and their yield, Polissia is becoming a new grain-oil belt of Ukraine. Concurrently, there are higher risks associated with maintaining high performance of agroecosystems and the increase in the risks for soil degradation processes, the deterioration of the ecological state of wetlands and forests, as well as drying-out of small rivers and lakes. Balancing the high yield of modern agroecosystems and safe nature management requires systemic measures in adapting the agricultural activity and nature management to new climatic conditions, which requires implementing the management of soil, water, and bio-resources, achieving optimal parameters of fertility for mineral and peaty-swampy soils. Reconstructing current land reclamation systems to optimize water regimes for the agricultural lands and protect typical Polissia ecosystem is needed.

India's flood risk assessment and mapping with multi-criteria decision analysis and GIS integration

ABSTRACT India's diverse geography poses significant flood risks, addressed in this study through the geographic information system and multi-criteria decision analysis. This comprehensive flood risk assessment considers seven parameters: mean annual precipitation, elevation, slope, drainage density (DD), land use and land cover, proximity to roads, and distance to rivers. The findings indicate that flood vulnerability is primarily influenced by rainfall, elevation, and slope, with DD, land use, and proximity to roads and rivers also playing crucial roles. Experts weighed these factors to create a thorough flood risk map using the normalized rank index and normalized weight index, categorizing areas into five risk levels: very high, high, moderate, low, and very low. The study reveals that 3.40% of the area is at very high risk, 32.65% at high risk, 39.72% at moderate risk, 20.97% at low risk, and 3.25% at very low risk. These results highlight how human and natural factors interact to influence flood risk, with vulnerable areas characterized by low elevations, steep slopes, high drainage densities, and proximity to rivers or roads. The findings provide valuable insights for policymakers, scientists, and local authorities to develop strategies to mitigate flood losses across India's varied landscapes.

Developing a Method to Estimate Above-Ground Carbon Stock of Forest Tree Species Pinus densata Using Remote Sensing and Climatic Data

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R2 and RMSE of the model based on source III were better than the R2 and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R2 by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work.

Assessing the Moisture Resilience of Wood Frame Wall Assemblies

Resilience has been used as a building performance metric that measures the building’s capability of absorption, response, and recovery from one or a series of disruptive events, e.g., extreme weather events or power outage events. With respect to resilience, in relation to the moisture performance of the building envelope (moisture resilience), this aspect has not yet been thoroughly explored nor defined. Given the expected increase in annual precipitation in certain regions of Canada as induced by climate change effects occurring both currently and in the future, the moisture resilience of building envelops will require immediate attention given that wall assemblies of buildings are predicted to be subjected to excessive moisture loads in the coming years. In this study, the moisture resilience of wood frame wall assemblies to mould growth was described from three aspects: (i) absorption—the ability of the wall to maintain a low level of relative humidity on the OSB; (ii) response—the fluctuation of the relative humidity on the OSB; and (iii) recovery—the rate at which the relative humidity recovers to an acceptable level. The metrics used to demonstrate the relative impact of these factors on moisture performance were also developed. The results have revealed a robust correlation between moisture performance and the relative influence of various newly defined aspects of moisture resilience.

Research on National Short-Term Precipitation Forecast Method Based on Machine Learning

Abstract. Climate change is closely related to human lives. With the development of information technology, meteorological data gradually show the characteristics of big data. The purpose of this study is to compare the application effects of different machine learning models in short-term precipitation prediction, to improve the accuracy and efficiency of prediction. Based on the annual average precipitation data of prefecture-level cities in China from 1990 to 2022, this paper uses Random Forest, eXtreme Gradient Boosting and Neural Networks to construct prediction models, and comprehensively evaluate them. In this study, the data is preprocessed to ensure the data quality of the input model. Next, the data is fed into three machine learning algorithm models, Random Forest, eXtreme Gradient Boosting and Neural Networks. Finally, the prediction performance of each model is evaluated by various indexes. In this paper, it is found that the Random Forest model has the best performance, and its prediction accuracy is higher than the other two models, which has great application potential in the field of short-term precipitation prediction. This study shows that a reasonable selection of machine learning methods and optimization of model parameters can effectively improve the accuracy of short-term precipitation prediction. This paper provides some empirical evidence for precipitation prediction, which will help to make more effective decisions in dealing with extreme weather events and climate change challenges in the future.

Selecting Suitable Sites for Wind Energy Harvesting in Iraq using GIS Techniques

Wind energy harnesses the kinetic force of the wind through turbines to produce electricity. As a critical renewable energy source, it presents a sustainable alternative to fossil fuels. The availability of wind energy is geographically contingent. This paper aims to pinpoint optimal sites for wind energy development in Iraq and to secure future energy needs from renewable sources to achieve this, a multi-criteria analysis utilizing Geographic Information Systems (GIS) was employed to determine the prime locations for wind energy extraction. Vital climatic data incorporated into this analysis included a RASTER file of the study area's annual wind speed, temperature, precipitation, soil moisture, and NDVI (Normalized Difference Vegetation Index) roads, urban centers, terrain gradients, and land utilization patterns were instrumental in constructing a GIS-based model. This model harnessed the weighted overlay tool within ArcGIS 10.7.1's Spatial Analysis Tools to classify and integrate various raster datasets. Findings reveal that the most favorable locations for wind energy exploitation are in Iraq's southeastern sectors. These zones are not only marked by vigorous winds averaging speeds over 6.2 meters per second but also benefit from their proximity to power grids, accessible roads, and gentle slopes, making them prime candidates for wind farm installation. The research also notes that regions with a high suitability index for wind energy development constitute 5.3% of the total examined area. This is a robust indicator of Iraq's capacity for wind energy utilization. Meanwhile, 79.7% of the territory received a moderate suitability rating, 9.5% was categorized as poorly suitable, and 5.4% was considered unsuitable for wind energy projects. These insights underscore the significant potential for harvesting wind energy in Iraq and guide strategic planning for its implementation.

Spatiotemporal changes and driving factors of ecological environmental quality in the Yongding-Luan River Basin based on RSEI

The ecological environmental quality (EEQ) of the Yongding-Luan River Basin (YLRB) is pivotal to the ecological security of the Beijing-Tianjin-Hebei (JJJ) region's core area. Evaluating the EEQ and analyzing its changes are essential for regional ecological management. However, long-term ecological changes in the YLRB remain uncovered. In this study, we constructed a seamless Remote Sensing Ecological Index (RSEI) for the YLRB from 1986 to 2022 using time-series Landsat imagery on the Google Earth Engine (GEE) platform. The Sen + Mann-Kendall method was employed to analyze the spatiotemporal trends of EEQ, and the Geodetector was used to quantitatively assess the driving factors and their interactions. The results show that: 1) The mean RSEI of the YLRB increased from 0.486 in 1986 to 0.532 in 2022, marking a 9.5% rise and indicating a fluctuating upward trend. 2) The EEQ of the YLRB experienced three distinct phases: improvement, deterioration, and re-improvement. Improvements were predominantly in the western YLRB, while deterioration was mainly in the northern Xilinguole region and the southern urban expansion areas of Beijing, Langfang, Tianjin, and Tangshan. 3) The driving factor detection indicates that land use type and annual average precipitation are the primary driving factors of RSEI change in the YLRB. Furthermore, their interaction results in a significant effect on RSEI, with a maximum of 0.691. These findings align with the historical urban expansion in the YLRB and the environmental policies implemented by the Chinese government. The ecological evolution and driving factors identified in this study offer a scientific basis for regional ecological decision-making and management.

Prediction of the potentially suitable areas of Eucommia ulmoides Oliver in China under climate change based on optimized Biomod2 and MaxEnt models

Eucommia ulmoides Oliver is a medicinal plant of significant economic importance. Its cortex has been employed for centuries to alleviate various conditions such as lumbar pain, knee pain, and osteoporosis. Additionally, E. ulmoides possesses substantial industrial value. With the growing demand for this medicinal herb, ensuring its sustainable supply has become imperative. Climate change has caused habitat restrictions or migration of medicinal plants. Therefore, predicting the impact of climate change on the distribution of E. ulmoides is crucial for its conservation and sustainable use. This study evaluated the potential distribution of E. ulmoides across China under various climate change scenarios since the last interglacial period by modeling suitable areas based on 257 distribution records and 19 major environmental factors related to E. ulmoides. The model selection process initially identified the MaxEnt model as the most suitable. The optimized MaxEnt model, with RM = 2.0 and FC = LQHPT settings, generated the most precise predictions. Results indicate that the minimum temperature of the coldest month, annual mean temperature, and annual precipitation significantly affect the distribution of E. ulmoides. Under current environmental conditions, highly suitable areas for E. ulmoides are found in Southwest and Southeast China, with a total suitable habitat area of 23.12 × 104 km2. However, the range of suitable habitat has shifted due to global warming’s negative impact. Under different climate scenarios, suitable areas for E. ulmoides have either increased or decreased, with expansions primarily in high-latitude regions. Future climate scenarios predict shifts in the centroid of suitable E. ulmoides habitat towards Yichang City in Hubei Province. The findings of this study support the development, artificial cultivation, and conservation of E. ulmoides resources.

Projections of future bioclimatic indicators using bias-corrected CMIP6 models: a case study in a tropical monsoon region.

This study evaluates the potential impacts of climate change on Bangladesh by analyzing 19 bioclimatic indicators based on temperature and precipitation. Data from 18 bias-corrected CMIP6 global climate models (GCMs) were used, covering four Shared Socioeconomic Pathways (SSPs)-SSP126, SSP245, SSP370, and SSP585-across three future timeframes: near-term (2015-2044), mid-term (2045-2074), and long-term (2075-2100). Under the high-emission SSP585 scenario, average temperatures are projected to rise by up to 3.76°C, and annual precipitation could increase by 52.6%, reaching up to 3446.38mm by the end of the century. The maximum temperature (Bio5) could reach 32.91°C, while the minimum temperature (Bio6) might rise by 4.43°C, particularly during winter. Precipitation seasonality (Bio15) is projected to increase by as much as 7.9% in the northwest, indicating heightened variability between wet and dry seasons. The diurnal temperature range (Bio2) is expected to decrease by up to - 1.3°C, signifying reduced nighttime cooling, which could exacerbate heat stress. Significant reductions in temperature seasonality (Bio4) are forecast for the northeast, with notable declines in isothermality (Bio3) under SSP585, pointing to increased climatic extremes. These climatic shifts pose severe risks to agricultural productivity, water resource availability, and biodiversity, particularly in flood-prone regions. The findings highlight the need for urgent adaptation measures, including improved flood management systems, efficient water resource use, and climate-resilient agricultural practices. By providing robust region-specific projections, this study offers critical insights for policymakers and stakeholders to mitigate the adverse effects of climate change and safeguard environmental and economic sustainability in Bangladesh.

Development of a global thermal detection index to prioritize primate research with thermal drones.

Thermal Infrared (TIR) drones are emerging as effective tools for wildlife ecology monitoring and are increasingly employed in primate surveys. However, systematic methods for assessing primate detectability are lacking. We present a comprehensive approach utilizing a novel Thermal Detection Index (TDI) to evaluate the potential of TIR drones for primate monitoring. We developed TDIs for 389 primate species, considering activity patterns, locomotion types, body mass, densities, habitat utilization, and sleeping behaviors during diurnal and nocturnal surveys. Through the integration of TDIs with primates' distribution and climatic variables (average annual temperature, precipitation, and wind speed), we established a Global TDI Suitability Score aimed at pinpointing species and regions most compatible with TIR drone-based monitoring. Atelidae, Cercopithecidae, and Indridae showed the highest TDI values, suggesting their suitability for TIR-drone surveys. We identified optimal regions in Africa, Asia and Latin America for primate monitoring with TIR drones, driven by favorable ecological conditions, habitat types, and high TDI species diversity. However, local ecological factors and regulatory frameworks also influence drone survey feasibility, necessitating careful consideration prior to implementation. Overall, our study provides a valuable framework for prioritizing primate species and regions for TIR drone-based monitoring, facilitating targeted conservation efforts and advancing primate monitoring research.

Climatic factors, but not geographic distance, promote genetic structure and differentiation of Cleistogenes squarrosa (Trin.) Keng populations

Climate can shape plant genetic diversity and genetic structure, and genetic diversity and genetic structure can reflect the adaptation of plants to climate change. We used rbcl and trnL-trnF sequences to analyze the genetic diversity and genetic structure of C. squarrosa under the influence of different environmental factors in Inner Mongolia grassland. The results showed that the genetic diversity of this species was low. (The trnL-trnF sequences have higher genetic diversity than rbcl sequences.) C. squarrosa had low genetic diversity compared to other prairie plants, but had a more pronounced genetic structure. The haplotype network diagram of the combined sequences could be divided into two categories, and the results of the NJ, MP, and ML trees also showed that the haplotypes were divided into two branches. The results of genetic structure analysis showed that that the populations located in the desert steppe fall into exactly one cluster, and the populations located in the typical steppe fall into exactly another cluster. The neutrality tests were all negative and the mismatch distribution also showed a single peak across the population, suggesting that C. squarrosa had undergone population expansion and was well adapted to the local environment. The results of the mantel test showed that climate had a greater influence on the genetic distance of C. squarrosa, with annual precipitation having a higher influence than mean annual temperature. This study provided basic genetic information on the genetic structure of C. squarrosa and contributes to the study of genetic adaptation mechanisms in grassland plants.

Understanding the Global Distribution of Groundwater Sulfate and Assessing Population at Risk.

Besides sulfate-induced diarrhea, recent studies have emphasized that groundwater sulfate drives the release of arsenic in groundwater and accelerates water pipeline corrosion. Despite its impact on public health and urban infrastructure, sulfate has been overlooked in water supply research. Here, we used a random forest model to develop a 1 km global map depicting the probability of sulfate exceeding 250 mg/L in groundwater based on the World Health Organization's guidelines. The map was further applied to estimate the exposed population and identify contributors of sulfate exceedance in global hotspot regions. The results revealed that sulfate exceedance in groundwater was widespread in 156 countries. Approximately 194 million people use water with sulfate levels exceeding 250 mg/L. Among them, around 17 million people face groundwater sulfate concentrations surpassing 500 mg/L, with 82% of these individuals residing in ten specific countries. Contributing factor analysis in these countries indicates that annual precipitation and sedimentary rocks are the primary factors contributing to sulfate concentration prediction, while other natural and anthropogenic predictors exhibit region-specific impact patterns. This study uncovered a significant prevalence of elevated sulfate levels in groundwater, highlighting the need to integrate sulfate into water safety management practices.

Microbial diversity in the arid and semi-arid soils of Botswana.

To date, little research has been conducted on the landscape-scale distribution of soil microbial communities and the factors driving their community structures in the drylands of Africa. We investigated the influence of landscape-scale variables on microbial community structure and diversity across different ecological zones in Botswana. We used amplicon sequencing of bacterial 16S rRNA gene and fungal internal transcribed spacers (ITS) and a suite of environmental parameters to determine drivers of microbial community structure. Bacterial communities were dominated by Actinomycetota (21.1%), Pseudomonadota (15.9%), and Acidobacteriota (10.9%). The dominant fungal communities were Ascomycota (57.3%) and Basidiomycota (7.5%). Soil pH, mean annual precipitation, total organic carbon, and soil ions (calcium and magnesium) were the major predictors of microbial community diversity and structure. The co-occurrence patterns of bacterial and fungal communities were influenced by soil pH, with network-specific fungi-bacteria interactions observed. Potential keystone taxa were identified for communities in the different networks. Most of these interactions were between microbial families potentially involved in carbon cycling, suggesting functional redundancy in these soils. Our findings highlight the significance of soil pH in determining the landscape-scale structure of microbial communities in Botswana's dryland soils.

Influence of climate and landscape structure on soil erosion in China's Loess Plateau: Key factor identification and spatiotemporal variability.

Climate and landscape structure are widely recognized as the primary drivers of soil erosion; however, the spatiotemporal variability of their effects remains insufficiently understood, limiting our comprehension of the dynamic processes of soil erosion. To address this gap, this study analyzed soil erosion trends on the Loess Plateau from 2000 to 2018. extreme Gradient Boosting was used to identify key climatic and landscape structural factors, while a geographically and temporally weighted regression model was applied to assess the spatiotemporal variability of these influences. The results indicate a decreasing trend in soil erosion from 2000 to 2008, followed by a sharp increase from 2008 to 2018. Grassland edge density emerged as the most important factor, followed closely by grassland percentage and annual precipitation. Temporally, the positive effect of annual precipitation has been intensifying since 2010, contributing to increased erosion, while landscape structural factors progressively enhanced their hydrological regulatory roles, reflecting dynamic interactions with climate. Spatially, the direction of climatic influences remained generally stable, consistently promoting erosion, although by 2018, the effects of average annual temperature and annual sunshine duration reversed to suppress erosion in specific areas. In contrast, landscape structural influences exhibited greater spatial variability, often fluctuating or reversing depending on topography, human activity, and land use. This variability applied specifically and differentially to each metric of fragmentation and diversity, highlighting the critical importance of trade-offs in landscape management. The findings emphasize the complexity and dynamics of soil erosion in response to climate and landscape structure, suggesting implications for the development of spatially targeted soil erosion control strategies that accommodate the phases of temporal variation.

Impact of climate change on vegetation growth trends in a citrus farmland in the south-eastern Sicily

Abstract. The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.

Spatial heterogeneity analysis on distribution patterns and underlying determinants of historical rural settlements: a case study in Taihang Baxing

AbstractAs vital carriers of cultural heritage, rural areas are facing the threats of degradation and urbanization during rapid urbanization processes. In recent years, China has formulated and implemented a series of national policies, regulations, and heritage protection lists to safeguard and perpetuate the traditional patterns and historical features of rural heritage. However, these rural heritage listings exhibit significant macro-level distributional imbalances and generally lack consideration of regional characteristics. This study uses the Taihang Baxing (THBX) in China as a case study, initially employing historical local documents and maps as base data. It utilizes the stability in the evolution of Chinese characters to compare historical and contemporary village names, filtering out existing historical rural settlements (HRS) to construct a dataset. Further, a multi-scale geographically weighted regression (MGWR) model is applied to analysis the impacts of natural, economic, and social factors on the concentration of HRS (CHRS). The study reveals that the CHRS in the region is markedly characterized by clustering, with evident spatial heterogeneity in the impacts of various factors. Slope (SLP), total annual precipitation (TP), and potential crop yield (PCY) significantly influence the CHRS globally, while elevation (ELV), distance to ancient path (DAP) and surface runoff (DSR), gross domestic product (GDP), and road density (RD) have significant local impacts. This research offers a methodological framework for HRS data compilation and provides theoretical and methodological references for national and local levels of rural heritage protection, optimization of rural spatial patterns, prediction of rural evolution pathways, and implementation of China’s rural revitalization strategy.

Formation of single-dominant-species patches of Dicranopteris dichotoma primarily influenced by understory light intensity

IntroductionThe Dicranopteris dichotoma fern community plays vital roles in nutrient sequestration, succession regulation, and ecological threshold control. However, the mechanisms underlying the formation of the D. dichotoma–dominant community remain unclear.MethodsThis study established four different community types to investigate the effects of environmental factors on the formation of a D. dichotoma–dominant community.ResultsWe found that climate was the primary factor affecting the formation of patches dominated by D. dichotoma at the regional scale. Specifically, higher annual mean temperature and annual mean precipitation were associated with larger single-dominant-species patches of D. dichotoma. Understory light intensity was the major factor affecting the formation of the D. dichotoma community at the community scale. Light intensity ranging from 200 to 500 µmol·m⁻²·s⁻¹ was most conducive to the development of a large D. dichotoma community. Additionally, understory light intensity enhanced the importance value of D. dichotoma in the herb community by decreasing its biomass proportion of support modules and increasing its biomass proportion of photosynthetic and reproductive modules. Soil properties and D. dichotoma characteristics showed interactions with each other. Acidic red-yellow soil was most suitable for the formation of single-dominant-species patches of D. dichotoma, and the growth of D. dichotoma further decreased the soil pH. Soil total phosphorus content was identified as a limiting factor for formation of the D. dichotoma community.DiscussionIn summary, the formation of single-dominant-species patches of D. dichotoma is mainly influenced by a combination of climate, community, and soil.

Trends of Climate Extremes and Their Relationships with Tropical Ocean Temperatures in South America

South America has experienced significant changes in climate patterns over recent decades, particularly in terms of precipitation and temperature extremes. This study analyzes trends in climate extremes from 1979 to 2020 across South America, focusing on their relationships with sea surface temperature (SST) anomalies in the Pacific and Atlantic Oceans. The analysis uses precipitation and temperature indices, such as the number of heavy rainfall days (R10mm, R20mm, R30mm), total annual precipitation (PRCPTOT), hottest day (TXx), and heatwave duration (WSDI), to assess changes over time. The results show a widespread decline in total annual precipitation across the continent, although some regions, particularly in the northeast and southeast, experienced an increase in the intensity and frequency of extreme precipitation events. Extreme temperatures have also risen consistently across South America, with an increase in both the frequency and duration of heat extremes, indicating an ongoing warming trend. The study also highlights the significant role of SST anomalies in both the Pacific and Atlantic Oceans in driving these climate extremes. Strong correlations were found between Pacific SST anomalies (Niño 3.4 region) and extreme precipitation events in the northern and southern regions of South America. Similarly, Atlantic SST anomalies, especially in the Northern Atlantic (TNA), exhibited notable impacts on temperature extremes, particularly heatwaves. These findings underscore the complex interactions between SST anomalies and climate variability in South America, providing crucial insights into the dynamics of climate extremes in the region. Understanding these relationships is essential for developing effective adaptation and mitigation strategies in response to the increasing frequency and intensity of climate extremes.

The Future Migration Direction of Deer and Japanese Yew Is Consistent Under Climate Change

Climate change is becoming an important driver of biodiversity loss by altering the habitat, distribution and interspecific relationships of species. Japanese yew (Taxus cuspidata) is a first class protected plant in China, which has important ecological significance and occupies a certain position in the feeding habit of wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus). Due to human and animal damage, the number of Japanese yew has gradually decreased. Therefore, understanding the potential distribution of Japanese yew and the suitable areas for deer to browse on it under climate change will help to further protect these three species in Northeast China, especially migrate to more suitable areas in different scenarios in the future. From July 2021 to July 2024, we collected the information of species distribution and the variables associated with the species’ ecological limits in Muling National Nature Reserve to cross-reflect the current and future distribution and feeding area of the two species to assess each other’s impacts with Maximum entropy model (MaxEnt). The results showed that under the SSP2-4.5 and SSP5-8.5 scenarios, feeding pressure, driest quarter precipitation (BIO17) and seasonal temperature variation coefficient (BIO4) were the main variables affecting the distribution of Japanese yew, and the driest quarter precipitation (BIO17) and annual precipitation (BIO12) were the main variables affecting wapiti and Siberian roe deer foraging them. Under SSP2-4.5 and SSP5-8.5 scenarios, the suitable area of Japanese yew and the feeding area of the two species of deer gradually decreased from 2041 to 2100. Compared with wapiti, Siberian roe deer has a greater impact on the distribution range of Japanese yew, and the suitable feeding area is wider. It is expected that the potential centroid of Japanese yew, wapiti and Siberian roe deer will migrate to higher latitudes in the future. These findings provide a scientific basis for the reserve to develop relevant measures and plans and effectively protect the three species.

Global record-breaking recurrence rates indicate more widespread and intense surface air temperature and precipitation extremes.

We analyzed the evolution of extreme annual surface air temperature and rainfall on Earth, based on the recurrence rate of record-breaking events, and found the highest recurrence rates for record-high annual temperatures in the tropics, as opposed to the polar regions with the fastest warming. Both recurrence rates and the global surface area fraction with daily mean surface air temperatures exceeding 30° and 40°C provide further evidence for extremely hot years becoming more common and widespread. A similar analysis for precipitation highlighted some regions with more record-high annual total precipitation and others with record-low annual precipitation typically associated with drought. A multimodel ensemble of 306 runs with global climate models [Coupled Model Intercomparison Project phase 6 (CMIP) Shared Socioeconomic Pathways 2-45 (SSP2-45)] reproduced the statistics of record-breaking high temperatures, but there were some differences for the reanalysis precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates.