Search Results (1,007)

Climate change has accelerated the retreat of Andean glaciers, with significant recent losses in the tropical Andes. This study evaluates the extinction of the Carihuairazo volcano glacier (Ecuador), quantifying its area from 1312.5 m² in September 2023 to 101.2 m² in January 2024, its thickness (from 2.5 m to 0.71 m), and its volume (from 2638.85 m³ to 457.18 m³), before its complete deglaciation in February 2024; this rapid melting and its small size classify it as a glacierette. Multivariate analyses (PCA and biclustering) were performed to correlate climatic variables (temperature, solar radiation, precipitation, relative humidity, vapor pressure, and wind) with glacier surface and thickness. The PCA explained 70.26% of the total variance, with Axis 1 (28.01%) associated with extreme thermal conditions (temperatures up to 8.18 °C and radiation up to 16.14 kJ m⁻² day⁻¹), which probably drove its disappearance. Likewise, Axis 2 (21.56%) was related to favorable hydric conditions (precipitation between 39 and 94 mm) during the initial phase of glacier monitoring. Biclustering identified three groups of variables: Group 1 (temperature, solar radiation, and vapor pressure) contributed most to deglaciation; Group 2 (precipitation, humidity) apparently benefited initial stability; and Group 3 (wind) played a secondary role. These results, validated through in situ measurements, provide scientific evidence of the disappearance of the Carihuairazo volcano glacier by February 2024. They also corroborate earlier projections that anticipated its extinction by the middle of this decade. The early disappearance of this glacier highlights the vulnerability of small tropical Andean glaciers and underscores the urgent need for water security strategies focused on management, adaptation, and resilience. Full article

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. Full article

Anoplophora glabripennis (Asian longhorned beetle, ALB) (Motschulsky, 1854) is a local forest pest in China. Although the suitable area for this pest has some research history, it does not accurately predict the future distribution area of ALB. Accurate prediction of its suitable area can help control the harm caused by ALB more effectively. In this study, we applied the maximum entropy model to predict the suitable area for ALB. Moreover, the prediction results revealed that ALB is distributed mainly in northern, eastern, central, southern, southwestern, and northwestern China, and its high-fit areas are located mainly in northern, northwestern, and southwestern China. The average minimum temperature in September, precipitation seasonality (coefficient of variation), the average maximum temperature in April, and average precipitation in October had the greatest influence on ALB. The greatest distribution probabilities were observed at the September average minimum temperature of 16 °C, the precipitation seasonality (coefficient of variation) of 130%, the April average maximum temperature of 14 °C, and the October average precipitation of 30 mm. Furthermore, with climate change, the non-suitability area for the ALB will show a decreasing trend in the future. The intermediate suitability area will increase, while the low and high suitability areas will first increase and then decrease. Taken together, the potentially suitable areas for ALB in China include the Beijing–Tianjin–Hebei region and the Shanghai region in North China and East China, providing a deeper understanding of ALB control. Full article

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

As global climate change intensifies, extreme climate events are becoming more frequent, presenting significant challenges to socioeconomic systems and ecosystems. Northeast China, a region highly sensitive to climate change, has been profoundly impacted by compound drought and heat extremes (CDHEs), affecting agriculture, society, and the economy. To evaluate the characteristics and evolution of summer CDHEs in this region, this study analyzed observational data from 81 meteorological stations (1961–2020) and developed a Standardized Temperature–Precipitation Index (STPI) using the Copula joint probability method. The STPI’s effectiveness in characterizing compound drought and heat conditions was validated against historical records. Using the constructed STPI, this study conducted a comprehensive analysis of the spatiotemporal distribution of CDHEs. The Theil–Sen median trend analysis, Mann–Kendall trend tests, and the frequency of CDHEs were employed to examine drought and heatwave patterns and their influence on compound events. The findings demonstrated an increase in the severity of compound drought and heat events over time. Although the STPI exhibited a slight interannual decline, its values remained above −2.0, indicating the continued intensification of these events in the study area. Most of the stations showed a non-significant decline in the Standardized Precipitation Index and a significant rise in the Standardized Temperature Index, indicating that rising temperatures primarily drive the increasing severity of compound drought and heat events. The 1990s marked a turning point with a significant increase in the frequency, severity, and spatial extent of these events. Full article

Soybean (Glycine max) is a protein-rich legume crop that plays an important role in achieving food security. The aim of this study was to isolate soybean-nodulating rhizobia from Côte d’Ivoire soils and evaluate their potential as efficient strains in order to develop local bioinoculants. For this objective, 38 composite soil samples were collected from Côte d’Ivoire’s five major climatic zones. These soils were used as substrate to trap the nodulating rhizobia using the promiscuous soybean variety R2-231. A total of 110 bacterial strains were isolated and subsequently identified. The analysis of ITS (rDNA16S-23S), glnII and recA sequences revealed a relatively low genetic diversity of these native rhizobia. Moreover, the ITS phylogeny showed that these were scattered into two Bradyrhizobium clades dominated by the B. elkanii supergroup, with ca. 75% of all isolates. Concatenated glnII-recA sequence phylogeny confirmed that the isolates belong in the majority to ‘B. brasilense’, together with B. vignae and some putative genospecies of Bradyrhizobium that needs further elucidation. The core gene phylogeny was found to be incongruent with nodC and nifH phylogenies, probably due to lateral gene transfer influence on the symbiotic genes. The diversity and composition of the Bradyrhizobium species varied significantly among different sampling sites, and the key explanatory variables identified were carbon (C), magnesium (Mg), nitrogen (N), pH, and annual precipitation. Based on both shoot biomass and leaf relative chlorophyll content, three isolates consistently showed a higher symbiotic effectiveness than the exotic inoculant strain Bradyrhizobium IRAT-FA3, demonstrating their potential to serve as indigenous elite strains as bioinoculants. Full article

Field trials were carried out over two tobacco cropping seasons (2020 and 2021) to assess the effectiveness of soil (PRE-T) and post-transplant (POST-T (OT)) herbicides in a tobacco crop, depending on rainfall and the type of soil. The effectiveness of PRE-T and POST-T (OT) herbicides alternated according to the presence of weeds, treatments, the region, and years. Unpredictable meteorological conditions throughout the two study years likely influenced the control of weeds. An unusually moist May in 2020 with a precipitation of 29 mm in the first WA PRE-T before the emergence of weeds generated the leaching of the PRE-T herbicide from the surface of the soil, which was likely the most probable reason for the reduced effectiveness of PRE-T-applied herbicides (less than 77%) in comparison to the POST-T (OT) application treatment in 2020 in the Prilep region. Conversely, the restricted rainfall after PRE-T and POST-T (OT) application may have caused the unsatisfactory efficacy of both PRE-T and POST-T (OT) herbicide treatments in the Titov Veles region in 2021 (less than 78 and 80%, respectively) in comparison with 2020. Excessive rain immediately after PRE-T and POST-T (OT) application resulted in the injury of tobacco plants in the Prilep region in 2020 and 2021, which was between 8 and 25%, and 7 and 22%, respectively, after seven DAHAs across both treatments. The injuries caused by pendimethalin and metolachlor were more serious. The yields of tobacco after both PRE-T and POST-T treatment in each region typically reflect the overall effectiveness of weed control and the extent of tobacco crop injury. Full article

Reliable rainfall thresholds are critical for effective early warning and mitigating the risks of rainfall-induced landslides. Traditional statistical models have limitations in multi-variable modeling, while machine learning models face interpretability challenges. Explainable machine learning methods can address these challenges, but they are rarely applied to rainfall threshold modeling. In this study, we compared the performance of an empirical statistical model and machine learning models for predicting rainfall-induced landslides in Italy. Based on the optimal model, we visualized refined rainfall thresholds at three probability levels and employed SHAP (Shapley Additive Explanations) to enhance model explainability by quantifying the contribution of each input variable to the predictions. The results demonstrated that the XGBoost model achieved a good performance (AUC = 0.917 ± 0.026) with well-balanced sensitivity (0.792 ± 0.075) and specificity (0.812 ± 0.033) in landslide susceptibility modeling. Hydrological factors, particularly total rainfall, were identified as the dominant triggering mechanisms, with SHAP analysis confirming their substantially greater contribution compared to environmental factors in rainfall threshold modeling. The developed visualized threshold maps revealed distinct spatial variations in landslide-triggering rainfall thresholds across Italy, characterized by lower thresholds in gentle slope areas with moderate annual precipitation and higher thresholds in steep slope and mid-to-low-elevation regions, while these regional differences decreased under high-probability scenarios. This study offered a modeling approach for regional rainfall threshold assessment by integrating multi-variable modeling with explainable methods, contributing to the development of landslide early warning systems. Full article

In this paper, we discuss the prediction of the delivery efficiency of magnetic carriers based on their properties and field parameters. We developed a theory describing the behavior of magnetic capsules in the capillaries of living systems. A partial differential equation for the spatial distribution of magnetic capsules has been obtained. We propose to characterize the interaction between the magnetic field and the capsules using a single vector, which we call “specific magnetic force”. To test our theory, we performed experiments on a model of a capillary bed and on a living organism with two types of magnetic capsules that differ in size and amount of magnetic material. The experimental results show that the distribution of the capsules in the field correlated with the theory, but there were fewer actually accumulated capsules than predicted by the theory. In the weaker fields, the difference was more significant than in stronger ones. We proposed an explanation for this phenomenon based on the assumption that a certain level of magnetic force is needed to keep the capsules close to the capillary wall. We also suggested a formula for the relationship between the probability of capsule precipitation and the magnetic force. We found the effective value of a specific magnetic force at which all the capsules attracted by the magnet reach the capillary wall. This value can be considered as the minimum level for the field at which it is, in principle, possible to achieve a significant magnetic control effect. We demonstrated that for each type of capsule, there is a specific radius of magnet for which the effective magnetic force is achieved at the largest possible distance from the magnet’s surface. For the capsules examined in this study, the maximum distance where the effective field can be achieved does not exceed 1.5 cm. The results of the study contribute to our understanding of the behavior of magnetic particles in the capillaries of living organisms when exposed to a magnetic field. Full article

Global climate change has increased the frequency of compound drought–heatwave events (CDHEs), seriously threatening tropical forest ecosystems. However, due to the complex structure of natural tropical forests, related research remains limited. To address this, we focused on rubber plantations on Hainan Island, which have simpler structures, to explore the impacts of CDHEs on their primary productivity. We used Pearson and Spearman correlation analyses to select the optimal combination of drought and heatwave indices. Then, we constructed a Compound Drought–Heatwave Index (CDHI) using Copula functions to describe the temporal patterns of CDHEs. Finally, we applied a Bayes–Copula conditional probability model to estimate the probability of GPP loss under CDHE conditions. The main findings are as follows: (1) The Standardized Precipitation Evapotranspiration Index (SPEI-3) and Standardized Temperature Index (STI-1) formed the best index combination. (2) The CDHI successfully identified typical CDHEs in 2001, 2003–2005, 2010, 2015–2016, and 2020. (3) Temporally, CDHEs significantly increased the probability of GPP loss in April and May (0.58 and 0.64, respectively), while the rainy season showed a reverse trend due to water buffering (lowest in October, at 0.19). (4) Spatially, the northwest region showed higher GPP loss probabilities, likely due to topographic uplift. This study reveals how tropical plantations respond to compound climate extremes and provides theoretical support for the monitoring and management of tropical ecosystems. Full article

In recent years, satellite products have played an increasingly significant role in monitoring and estimating global extreme weather events, owing to their advantages of an excellent spatiotemporal continuity and broad coverage. This study systematically evaluates the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for the GPM Late Run (IMERG-L) product for regional precipitation events based on the observations in Zhejiang Province from 2001 to 2020. In this study, seven typical precipitation indices with seven accuracy evaluation indexes are applied to analyze the performance of IMERG-L from multiple perspectives in terms of the precipitation intensity, frequency and spatial distribution dimensions. The results show that IMERG-L is capable of capturing the spatial distribution trends, especially in the frequency-based precipitation indices (CWD, R10mm and R20mm), which can depict the regional wetness and precipitation pattern. However, the product suffers from a systematic overestimation in capturing heavy precipitation and an extreme precipitation intensity, with a high false alarm rate and unstable accuracy, especially in heavy rainfall and above class events, where the Probability of Detection (POD) drops significantly, showing an obvious reduction in the recognition capability and risk of misclassification. Specifically, IMERG-L failed to reproduce the observed eastward-increasing trends in the annual maximum precipitation for both one-day (RX1day) and five-day (RX5day) durations, demonstrating its limitations in accurately capturing extreme precipitation patterns across Zhejiang Province. Overall, furthering the optimization and improvement of IMERG-L in reducing the intensity-dependent biases in heavy rainfall detection, increasing spatial inhomogeneity in trend representations and improving the false alarm suppression for extreme events are needed for the accurate monitoring and quantitative estimation of high-intensity extreme precipitation events. Full article

The relict tree Cercidiphyllum japonicum, a Tertiary paleoendemic with significant ecological and timber value, prefers warm–cool humid climates and acidic soils. Using MaxEnt and ArcGIS, we modeled its distribution under current and future climate scenarios (SSP, Shared Socioeconomic Pathways). High-suitability areas (>0.6 probability) under current conditions are mainly concentrated in the Sichuan Basin and the Yellow–Yangtze transition zones. By 2050, projections show northwestward expansions (14.32–18.76% increase in area) and eastward movement toward Central China under both SSP1-2.6 and SSP5-8.5 scenarios. However, by 2090, habitat loss could exceed 22% under SSP5-8.5. The main environmental drivers of its distribution are minimum coldest-month temperature (bio6, 38.7%), annual precipitation (bio12, 29.1%), and temperature range (bio7, 18.5%). Precipitation seasonality and thermal extremes are expected to become more significant constraints in the future. Conservation strategies should focus on the following: (1) protecting refugia in the Daba–Wushan mountains, (2) facilitating assisted migration to northwestern high-latitude regions, and (3) preserving microclimates. This study offers a framework for evidence-based conservation of paleoendemic species under climate change. Full article

Globally, crop productive systems exhibit climatic adaptation, resulting in increased overall yields over the past century. Nevertheless, inter-annual fluctuations in production can lead to food price volatility, raising concerns about food security. Within this framework, short-term crop yield predictions informed by climate observations may significantly contribute to sustainable agricultural development. In this study, we discuss the criteria for historical monitoring and forecasting of the productive system response to climatic fluctuations, both ordinary and extreme. Here, forecasting is intended as an assessment of the conditional probability distribution of crop yield, given the observed value of a key climatic index in an appropriately chosen month of the year. Wheat production in the Teramo province (central Italy) is adopted as a case study to illustrate the approach. To characterize climatic conditions, this study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) as a key indicator impacting wheat yield. Validation has been carried out by means of Monte Carlo simulations, confirming the effectiveness of the method. The main findings of this study show that the model describing the yield–SPEI relationship has time-varying parameters and that the study of their variation trend allows for an estimate of their current values. These results are of interest from a methodological point of view, as these methods can be adapted to various crop products across different geographical regions, offering a tool to anticipate production figures. This offers effective tools for informed decision-making in support of both agricultural and economic sustainability, with the additional benefit of helping to mitigate price volatility. Full article

There are very few data linking recent climatic changes to changes in biological communities in the Russian Arctic, and no palaeoecological data are available from the Novaya Zemlya archipelago (NZ). We studied chironomid, cladoceran, and diatom communities from a 165-year-old sediment core from a lake on Southern Island, NZ. Sixteen diatom and four cladoceran species new to NZ were found in the lake. Significant changes occurred in biological communities; species turnover was highest for diatoms (2.533 SD), followed by chironomids (1.781 SD) and cladocerans (0.614 SD). Biological communities showed a correlation with meteorologically recorded climate parameters. For chironomids, the strongest relationships were found for T_June, T_July, and T_ann. Both planktonic proxies, diatoms, and cladocerans showed a relationship with summer and annual air temperature and precipitation. The largest shifts in communities can be linked to recent climatic events, including the onset of steady warming following the variable conditions at the end of the LIA (ca. 1905), the cooling associated with the highest precipitation on record between 1950 and 1970, and, probably, the anthropogenic influence specific to Novaya Zemlya at this time. The new data provide a valuable basis for future ecological studies in one of the least explored and remote Arctic regions. Full article

The Quality by Design (QbD) concept has been widely applied to the optimization of traditional Chinese medicine production processes recently. This work focused on optimizing the critical purification process of Scutellaria baicalensis extract used in the preparation of Zhusheyong Shuanghuanglian. Considering the impact of noise parameters and changes in herbal properties, an experimental design method was employed for optimization. Multiple batches of Scutellaria baicalensis decoction were prepared in this research, and quantitative models of Scutellaria baicalensis herbal properties, critical process parameters (CPPs), and process evaluation indicators were established. The R² of the quantitative models were all higher than 0.80. According to the model, the yield of baicalin was identified as a critical material property (CMA). The pH of first acid precipitation (X₁), first temperature holding time (X₂), pH of alkalization (X₃), ethanol amount (X₄), and end pH of ethanol washing (X₅) were CPPs. Considering the difficulty in controlling the end pH of the ethanol washing, it was considered to be a noise parameter. The Monte Carlo probability-based method was used to calculate the design space, determining the range of controllable parameters, which was successfully validated through experiments. Normal operation ranges for controllable parameters are recommended as follows: X₁ of 0.8–2.2, X₂ of 25–35 min, X₃ of 6.5–7.5, and X₄ of 0.8–1.2 g/g. Full article