Search Results (7,252)

With the accelerating pace of global warming, the imperative of selecting robust, long-term drought monitoring tools is becoming increasingly pronounced. In this study, we computed the Standardized Precipitation Evapotranspiration Index (SPEI) at both 3-month and 12-month temporal scales, utilizing observational data from 102 stations across Xinjiang and gridded observations spanning China. Our objective encompassed an assessment of the efficacy of three widely employed global meteorological estimation datasets (GMEs) in the context of drought monitoring across Xinjiang over the period of 1960–2020. Moreover, we conducted an in-depth examination into the origins of discrepancies observed within these GMEs. The findings of our analysis revealed a notable discrepancy in performance among the three GMEs, with CRU and ERA5 exhibiting significantly superior performance compared to NCEP-NCAR. Specifically, CRU (CC = 0.78, RMSE = 0.39 in northern Xinjiang) performed excellently in capturing regional wet–dry fluctuations and effectively monitoring the occurrence of droughts in northern Xinjiang. ERA5 (CC = 0.46, RMSE = 0.67 in southern Xinjiang) demonstrates a stronger capability to reflect the drought dynamics in the southern Xinjiang. Furthermore, the adequacy of these datasets in delineating the spatial distribution and severity of major drought events varied across different years of drought occurrence. While CRU and ERA5 displayed relatively accurate simulations of significant drought events in northern Xinjiang, all three GMEs exhibited substantial uncertainty when characterizing drought occurrences in southern Xinjiang. All three GMEs exhibited significant overestimation of the SPEI before 1990, and notable underestimation of this value thereafter, in Xinjiang. Discrepancies in potential evapotranspiration (PET) predominantly drove the disparities observed in CRU and ERA5, whereas both precipitation and PET influenced the discrepancies in NCEP-NCAR. The primary cause of PET differences stemmed from the reanalysis data’s inability to accurately simulate surface wind speed trends. Moreover, while reanalysis data effectively captured temperature, precipitation, and PET trends, numerical errors remained non-negligible. These findings offer crucial insights for dataset selection in drought research and drought risk management and provide foundational support for the refinement and enhancement of global meteorological estimation datasets. Full article

This study aims to explore the impacts of climate change on potato planting in Gansu Province so as to be able to adjust potato planting pattern scientifically and rationally. (1) Air precipitation and temperature time-series datasets were obtained from 87 meteorological stations in the study area over the past 50 years. The backpropagation neural network was employed to interpolate irregular and missing data in the time-series data. The altitude, the precipitation from June to July, the average temperature in July and the accumulated temperature above 10 °C were selected as the agricultural zoning indicators for the regionalization of potato planting. (2) The linear propensity rate method, cumulative anomaly method, ArcGIS technology and the Mann–Kendall mutation test were employed to examine the spatial–temporal variation in and mutation testing of the three zoning indicators. (3) The experimental results demonstrated that the amount of precipitation from June to July was registered at 139.94 mm, indicating a slight humidifying trend characterized by an annual increase rate of approximately 1.81 mm/10 a. Furthermore, a significant abrupt change was observed in 1998. The average temperature in July was registered at 20.53 °C, which showed an increasing trend at a rate of 0.55 °C/10 a, marked by a sudden shift in 1998. Lastly, the accumulated temperature above 10 °C was registered at 2917.05 °C, manifesting a significant warming trend at a rate of 161.96 °C/10 a, without any abrupt changes. For spatial distribution, the precipitation from June to July showed a decreasing spatial distribution pattern from south to north and from east to west, while its tendency rate showed a gradually decreasing trend from north to south and from east to west. The average temperature in July showed a decreasing spatial pattern from northeast to southwest, while its tendency rate showed a decreasing trend from west to east and from north to south. The accumulated temperature above 10 °C showed a spatial pattern of high accumulated temperatures in the northwestern and southeastern regions and low accumulated temperatures in the remaining regions, while its tendency rate showed a decreasing trend from west to east and from north to south. (4) The impacts of climate change on potato planting in Gansu Province were mainly manifested as a decrease of 0.30 × 10⁶ hm² in the cultivated land area in the most suitable region for potato planting post-1998, while the suitable area diminished by 0.96 × 10⁶ hm², the sub-suitable area expanded by 0.47 × 10⁶ hm², and the plantable area increased by 0.79 × 10⁶ hm². However, the unsuitable area experienced a reduction of 0.30 × 10⁴ hm². The findings of this study can provide a scientific foundation for optimizing and adjusting the potato planting structure, considering the backdrop of climate change. Moreover, they contribute to regional decision-making, thereby promoting sustainable agricultural development as well as enhancing both the yield and quality of potato in Gansu Province. Full article

Xanthium spinosum (X. spinosum) is a highly invasive weed native to South America and distributed in 17 provinces (municipalities) of China. It has severely negative influences on ecosystems, agriculture, and husbandry. However, few studies have reported on the impact of human activity and climate change on the future distribution and centroid shift of X. spinosum. This study aimed to investigate the potential geological distribution of X. spinosum in China, as well as the distribution pattern, centroid shift, and key environmental factors influencing its distribution, under four future climate scenarios (SSP1-26, SSP2-45, SSP3-70, and SSP5-85) based on the biomod2-integrated model. The results indicated that the suitable habitats for X. spinosum would expand in the future, mainly in Inner Mongolia, Northeast China, and the plateau regions (e.g., Xinjiang and Xizang). Under future climate scenarios, the centroid would shift toward the northwest or northeast part of China, with the SSP2-45-2050s scenario showing the maximum shift distance (161.990 km). Additionally, the key environmental variables influencing the distribution of X. spinosum, including human impact index, bio5, bio7, and bio12, were determined, revealing that most of them were related to human activities, temperature, and precipitation. This study enhances the understanding of the influence of human activity and climate change on the geographic range of X. spinosum. It provides references for early warning and management in the control of X. spinosum. Full article

Climate change and human activities are reshaping the structure and function of terrestrial ecosystems, particularly in vulnerable regions such as agro-pastoral ecotones. However, the extent to which climate change impacts vegetation growth in these areas remains poorly understood, largely due to the modifying effects of human-induced land cover changes on vegetation sensitivity to climatic variations. This study utilizes satellite-derived vegetation indices, land cover datasets, and climate data to investigate the influence of both land cover and climate changes on vegetation growth in the agro-pastoral ecotone of northern China (APENC) from 2001 to 2022. The results reveal that the sensitivity of vegetation productivity, as indicated by the kernel Normalized Difference Vegetation Index (kNDVI), varies depending on the land cover type to climate change in the APENC. Moreover, ridge regression modeling shows that pre-season climate conditions (i.e., pre-season precipitation and temperature) have a stronger positive impact on growing-season vegetation productivity than growing season precipitation and temperature, while the effect of vapor pressure deficit (VPD) is negative. Notably, the kNDVI exhibits significant positive sensitivity (p < 0.05) to precipitation in 34.12% of the region and significant negative sensitivity (p < 0.05) to VPD in 38.80%. The ridge regression model explained 89.10% of the total variation (R² = 0.891). These findings not only emphasize the critical role of both historical and contemporary climate conditions in shaping vegetation growth but also provide valuable insights into how to adjust agricultural and animal husbandry management strategies to improve regional climate adaptation based on climate information from previous seasons in fragile regions. Full article

The intensifying effects of climate change have led to increased flooding, even in desert regions, resulting in significant socio-economic and ecological impacts. This study analyzes the causes and consequences of flooding in the Zhem River basin using data from ground stations, including Kazhydromet, and satellite platforms such as USGS FEWS NET and Copernicus. Spatial analyses conducted in ArcGIS utilized classified raster data to map the dynamics of flooding, snow cover, vegetation, and soil conditions. This enabled a geoecological analysis of flood damage on the vital components of the local landscape. Results show that flooding in the Zhem River basin was driven by heavy winter precipitation, rapid snowmelt, and a sharp rise in spring temperatures. The flood damaged Kulsary city and also harmed the region’s soil, vegetation, and wildlife. In July 2024, the flooded sail area tripled compared to the same period in 2023. Additionally, the area of barren land or temporary water bodies (pools) formed three months after the water receded also tripled, increasing from 84.9 km² to 275.7 km². This study highlights the critical need for continued research on the long-term environmental effects of flooding and the development of adaptive management strategies for sustainable regional development. Full article

The assessment of ecological resilience in arid regions is crucial for understanding and mitigating the impacts of climate change and human activities, ensuring the sustainable management of these vulnerable ecosystems. Taking the Economic Belt on the Northern Slope of the Tianshan Mountains (EBNSTM) as the research area, a multi-dimensional evaluation model coupling vulnerability, health, and connectivity was used to explore the spatiotemporal variation and driving forces of ecological resilience. Firstly, a sub-item evaluation of ecological resilience was conducted from three aspects, including ecological vulnerability evaluation based on the CRITIC and AHP models, ecological health evaluation based on the InVEST model, and landscape connectivity evaluation based on the MSPA method. Then, the sequence polygon method was utilized to conduct a comprehensive multi-dimensional assessment of ecological resilience based on the aforementioned three evaluation results. Finally, the geographical detector model was utilized to identify the driving factors behind the spatial heterogeneity of ecological resilience. The results show the following: (1) From 2000 to 2020, the overall ecological resilience showed an upward trend and significant spatial heterogeneity. The overall distribution pattern exhibited a spatial feature of south higher, north lower, where the southern region displayed a clear high-high clustering characteristic, exerting a positive and radiating influence on surrounding areas. (2) The main driving factors of the spatial heterogeneity are DEM, precipitation, NPP, GDP, and PM2.5. And among different factors, the dual-factor enhancement effect is greater than the nonlinear enhancement of a single factor. (3) Human activities are important influencing factor, and the impact of urban expansion and economic growth on ecological resilience is becoming increasingly significant. Therefore, in the process of economic development, full consideration should be given to the self-repairing and adaptive capabilities of the ecosystem. Full article

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below −65 °C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day⁻¹ and 48 mm h⁻¹, combined with the region’s soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters. Full article

Cropland is a critical component of food security. Under the multiple contexts of climate change, urbanization, and industrialization, China’s cropland faces unprecedented challenges. Understanding the spatiotemporal dynamics of cropland non-agriculturalization (CLNA) and quantifying the contributions of its driving factors are vital for effective cropland management and the optimal allocation of land resources. This study investigated the spatiotemporal dynamics and driving mechanisms of CLNA in Shaanxi Province (SP), a major grain-producing region in China, from 2001 to 2020, using geospatial statistical analysis and machine learning techniques. The results showed that, between 2001 and 2020, approximately 17,200.8 km² of cropland (8.4% of the total area) was converted to non-cropland, with a pronounced spatial clustering pattern. XGBoost-SHAP attribution analysis revealed that among the 15 selected driving factors, precipitation, road network density, rural population, population density, grain yield, registered population, and slope length exerted the most significant influence on CLNA in SP. Notably, the interaction effects between these factors contributed more substantially than the individual factors. These findings highlight the pronounced regional disparities in CLNA across SP, driven by a complex interplay of multiple factors, underscoring the urgent need to implement water-saving agricultural practices and optimize rural land-use planning to maintain the dynamic balance of cropland and ensure food security in the region. Full article

This study examines the trends and interannual variability of extreme precipitation in Antarctica, using six decades (1963–2023) of daily precipitation data from Russia’s Novolazarevskaya Station in East Antarctica. The results reveal declining trends in both the annual number of extreme precipitation days and the total amount of extreme precipitation, as well as a decreasing ratio of extreme to total annual precipitation. These trends are linked to changes in northward water vapor flux and enhanced downward atmospheric motion. The synoptic pattern driving extreme precipitation events is characterized by a dipole of negative and positive height anomalies to the west and east of the station, respectively, which directs southward water vapor flux into the region. Interannual variability in extreme precipitation days shows a significant correlation with the Niño 3.4 index during the austral winter semester (May–October). This relationship, weak before 1992, strengthened significantly afterward due to shifting wave patterns induced by tropical Pacific sea surface temperature anomalies. These findings shed light on how large-scale atmospheric circulation and tropical-extratropical teleconnections shape Antarctic precipitation patterns, with potential implications for ice sheet stability and regional climate variability. Full article

The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹ (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg⁻¹·H₂O) > Cropland WUE (0.972 gC·kg⁻¹·H₂O) > Grassland WUE (0.805 gC·kg⁻¹·H₂O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10⁻² gC·kg⁻¹·H₂O·a⁻¹) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg⁻¹·H₂O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation. Full article

The geostationary meteorological satellite Fengyun-4A (FY-4A) has rapidly advanced, generating abundant high spatiotemporal resolution atmospheric precipitable water vapor (PWV) products. However, remote sensing satellites are vulnerable to weather conditions, and these latest operational PWV products still require systematic validation. This study presents a comprehensive evaluation of FY-4A PWV products by separately using PWV data retrieved from radiosondes (RS) and the Global Navigation Satellite System (GNSS) from 2019 to 2022 in China and the surrounding regions. The overall results indicate a significant consistency between FY-4A PWV and RS PWV as well as GNSS PWV, with mean biases of 7.21 mm and −8.85 mm, and root mean square errors (RMSEs) of 7.03 mm and 3.76 mm, respectively. In terms of spatial variability, the significant differences in mean bias and RMSE were 6.50 mm and 2.60 mm between FY-4A PWV and RS PWV in the northern and southern subregions, respectively, and 5.36 mm and 1.73 mm between FY-4A PWV and GNSS PWV in the northwestern and southern subregions, respectively. The RMSE of FY-4A PWV generally increases with decreasing latitude, and the bias is predominantly negative, indicating an underestimation of water vapor. Regarding temporal differences, both the monthly and daily biases and RMSEs of FY-4A PWV are significantly higher in summer than in winter, with daily precision metrics in summer displaying pronounced peaks and irregular fluctuations. The classic seasonal, regional adjustment model effectively reduced FY-4A PWV deviations across all regions, especially in the NWC subregion with low water vapor distribution. In summary, the accuracy metrics of FY-4A PWV show distinct spatiotemporal variations compared to RS PWV and GNSS PWV, and these variations should be considered to fully realize the potential of multi-source water vapor applications. Full article

The Soil and Water Assessment Tool (SWAT) model is extensively used globally for hydrological and water quality assessments but encounters challenges in karst regions due to their complex surface and groundwater hydrological environments. This study aims to refine the delineation of hydrological response units within the SWAT model by combining geomorphological classification and to enhance the model with an epikarst zone hydrological process module, exploring the accuracy improvement of SWAT model simulations in karst regions of Southwest China. Compared with the simulation results of the original SWAT model, we simulated runoff and nutrient concentrations in the Mudong watershed from January 2017 to December 2021 using the improved SWAT model. The simulation results indicated that the modified SWAT model responded more rapidly to precipitation events, particularly in bare karst landform, aligning more closely with the actual hydrological processes in Southwest China’s karst regions. In terms of the predictive accuracy for monthly loads of total nitrogen (TN) and total phosphorus (TP), the coefficient of determination (R²) value of the modified model increased by 10.3% and 9.7%, respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) increased by 11.3% and 9.9%, respectively. The modified SWAT model improves prediction accuracy in karst areas and holds significant practical value for guiding non-point source pollution control in agricultural watersheds. Full article

On the basis of daily discharges recorded in 140 water gauges located on 96 Polish rivers, the long-term changes of runoff and the number of days with low flows (ND_LF) in relation to selected meteorological variables were studied. The analyses were performed for the entire multi-annual period 1951–2020 and two sub-periods: 1951–1988 and 1988–2020 that are before and after climate change. The average values of these hydro-meteorological variables in the two sub-periods were then compared. It was found that after 1988, a statistically significant (p < 0.001) increase in the average air temperatures, ranging from 0.9 to over 1.3 °C, occurred. Similarly, statistically significant changes were determined for evaporation, which increased by about 10–25%. Precipitation did not show such changes—a statistically significant decrease in precipitation (by over 5%) was recorded only in the southern part of the Odra River basin, and in most stations, statistically insignificant increases were recorded. The most complex changes took place in river runoff. After 1988, in most gauges, a decrease in runoff by about 5–15% was detected; in some cases, these decreases were statistically significant. In the south-eastern part of the country, primarily in the catchments of the right tributaries of the Vistula River, an increase in runoff by about 5–10% was detected. However, only in the case of one gauge, these tendencies were statistically significant. Next, in order to determine spatial regularities in long-term changes in the ND_LF, the cluster analysis method was used, and the gauges were grouped according to the values of 70 annual ND_LF. This resulted in separating three relatively homogenous territorially groups of rivers, demonstrating a clear regional differentiation of ND_LF. It was concluded that separation of these three groups of rivers in terms of different long-term changes in ND_LF was mainly influenced by climatic conditions. Full article

Biological soil crusts are important components of dryland ecosystems, showing variations in appearance, morphology, and function across developmental stages. However, the methods for recording biocrust developmental stages have not been simplified and standardized. In this study, three developmental grades for both cyanobacterial crust and moss crust were defined based on visual indicators such as color, thickness, and moss height. A field survey was conducted across three precipitation regions in northern China, during which the developmental grades of cyanobacterial and moss crusts were visually recorded. Key biocrust developmental indicators, including shear strength, penetration resistance, coverage, chlorophyll a content, and bulk density were measured for each grade. The results showed that both cyanobacterial and moss crusts could be effectively classified into three developmental grades based on these indicators, with a 90% concordance between the measured indicators and the defined grading method. This finding validated that the method could accurately reflect biocrust developmental stages while simplifying field recordings. Developmental indicators in various grades of cyanobacterial and moss crusts showed a moderate (30% < CV < 100%) to strong (CV > 100%) variation, highlighting the importance of environmental heterogeneity at the regional scale. Moreover, the grading method proved effective across varying spatial scales, highlighting its broad applicability. However, its validation across the comprehensiveness of target objects and the geographical scope remains limited. Future research should focus on expanding the grading method to include lichen crust, refining it across diverse ecosystems, and exploring the integration of advanced technologies such as hyperspectral imaging and machine learning to automate and improve the classification process. This study provides a simple and effective grading method for visually recording the developmental stages of biological soil crusts, which is useful for ecological research and field applications. Full article

This study was conducted to determine potential groundwater storage areas in the semi-arid Oltu Basin in northeastern Turkey. The groundwater potential of the basin was analyzed by evaluating eight geographical factors: lithology, linear density, soil depth, land use, precipitation, geomorphology, slope, and drainage density. These factors were classified and weighted using remote sensing, geographical information systems (GIS), and the analytic hierarchy process (AHP). The obtained data were modeled using ArcGIS software, and a potential groundwater storage map of the Oltu Basin was created. The results show that there is a high groundwater potential in areas of the basin close to the stream bed, while the groundwater potential is low in mountainous and steeply sloped regions. The study provides significant findings for sustainable water resource management in the region and future water resources planning. Full article