Search Results (368)

The study aims to analyze the long-term trends of climate variables in Erzurum, Türkiye. Trend analyses were conducted on the maximum, minimum, and average temperature and total precipitation series for a 96-year period covering the period 1929–2024. Annual, seasonal, and monthly time series of the variables were illustrated along with their linear trends. Statistical analysis was conducted using the Mann–Kendall test and Sen’s innovative trend analysis methods. The Mann–Kendall test Z-statistic results were evaluated at the 90%, 95%, and 99% significance levels. Maximum temperature series show increasing trends in all months except November, December, January, and February and on the annual scale at the α = 0.01 significance level. Minimum temperature series show decreasing trends for all time periods except March and April. The average temperature variable shows no trend in the annual, summer, and winter series, increasing in spring, March, and April (α = 0.05) and decreasing in November (α = 0.1). Trend analysis of the precipitation series indicates a decreasing trend in winter snowfall, as well as in March and June precipitation. Sen’s methodology, in addition to trend indicators, offers a layered assessment opportunity for any time series, with subcategorization based on the magnitude of the data values. According to annual average values, the diurnal temperature range was determined as 11.3 °C in 1929 and 13.5 °C in 2024. Important findings have been obtained for determining sustainable resource management strategies through the monitoring of climate variables. Full article

The fact that 2023 and subsequently 2024 were the hottest years in history makes it even more important to monitor temperature changes over time. In this study, trends in the mean, maximum, and minimum temperature data of 81 provinces in Türkiye were examined using three traditional methods (Mann–Kendall, Linear Regression Analysis and Sen’s slope), one innovative method (ITA), and the QuarTrend (QT) method proposed in this study, which uses quartiles of the data series. The objectives of this research are (1) to determine and evaluate the long-term temperature trends in Türkiye (1960–2022) and (2) to comparatively evaluate the trend results of the proposed QT method, traditional trend detection methods, and ITA. In the study, a statistically significant (p < 0.05) increasing trend was found in the mean (0.027 °C/year), maximum (0.031 °C/year), and minimum (0.038 °C/year) annual temperatures of Türkiye. While traditional trend tests detected similar trends with ITA and QT for mean temperatures; ITA and QT detected more trends than traditional methods for maximum and minimum temperatures. The results have direct implications for the impacts of climate change in the study region. The results have the potential to support the development of climate-resilient and adaptive policies for effective water resource planning and management to sustain the environment and agricultural productivity in Türkiye. Full article

The impacts of climate change are globally evident and cause significant damage to ecosystems and human activities. These impacts intensify social and economic inequality in Southern Africa (SA), where agriculture is vital for livelihoods and economic development. This study aimed to assess long-term trends in climate elements and parameters relevant to drought regimes in SA to identify drought hotspots and relate them to socioeconomic indicators. The methods include the Theil–Sen slope estimator and the Mann–Kendall statistical significance test. The study analysed ERA5 data for the 1971–2020 to compute the Standardised Precipitation Index (SPI) and Standardised Precipitation Evapotranspiration Index (SPEI) drought indices and descriptors. Results of the trend analysis reveal (i) the existence in almost the entire SA of statistically significant trends of increasing temperature and potential evapotranspiration and decreasing precipitation; (ii) increasing drought risk hotspots in the SPI and SPEI across all timescales, in the north central rainforest region, south and southeast of SA, while decreasing in the northwest coast, central west region, and in the northeast more recently; and (iii) hotspots in the drought descriptors within the same regions, but of a smaller size. Our findings pinpoint drought hotspots in regions with moderate-to-high population density and agricultural systems that involve species vital for food security and of considerable socioeconomic and commercial importance, emphasising the significance of our results for managers and decision-makers. Full article

This study investigates flood risk trends using rainfall data collected from 13 districts of Diyarbakır Province, Turkey, with a focus on supporting sustainability-oriented climate adaptation. Both annual and seasonal precipitation variations were examined, with particular emphasis on the role of maximum daily rainfall in driving flood potential. In addition, the analysis integrates extreme precipitation patterns with regional hazard characteristics to provide a more comprehensive flood risk assessment framework. Non-parametric statistical methods, including the Mann–Kendall trend test and Spearman’s Rho correlation, were applied to detect trends in annual and seasonal datasets. Flood magnitudes were estimated using the Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) approaches. The dataset covers varying periods between 2009 and 2023, depending on station availability. The results show a statistically significant increase in both annual and winter precipitation at Bismil, and a significant winter increase at Çermik. Other stations displayed upward trends that were not statistically significant. Çüngüş, Lice, and Kulp were identified as particularly susceptible to extreme rainfall. Although the relatively short observation period poses a limitation, consistent patterns of intensified precipitation were detected. Previous studies in Turkey have demonstrated that such events often cause severe infrastructure damage and displacement of vulnerable communities. The findings of this study provide practical insights for national and regional authorities, including the Disaster and Emergency Management Authority (AFAD), the General Directorate of State Hydraulic Works (DSİ), and the Ministry of Environment, Urbanization, and Climate Change, to strengthen sustainable climate adaptation planning and disaster risk reduction strategies. Overall, this research highlights the importance of integrating extreme precipitation analysis into sustainable flood management, resilient infrastructure development, and long-term sustainability policies, thereby reinforcing the connection between hydrological risk assessment and sustainability science. Full article

In response to the sustainability challenges of mining, restrictive policies aimed at improving ecological quality have been enacted in various countries and regions. The purpose of this study is to examine the environmental changes in the Ningdong mining area, located on the Loess Plateau, over the past 25 years, due to many factors, such as coal mining, using the area as a case study. In this study, Landsat satellite images from 2000 to 2024 were used to derive the remote sensing ecological index (RSEI), while the RSEI results were comprehensively analyzed using the Sen+Mann-Kendall method with Geodetector, respectively. Simultaneously, this study utilized land use datasets to calculate the ecological grade (EG) index. The EG index was then analyzed in conjunction with the RSEI. The results show that in the time dimension, the ecological quality of the Ningdong mining area shows a non-monotonic trend of decreasing and then increasing during the 25-year period; The RSEI average reached its lowest value of 0.279 in 2011 and its highest value of 0.511 in 2022. In 2024, the RSEI was 0.428; The coupling matrix between the EG and RSEI indicates that the ecological environment within the mining area has improved. Through ecological factor-driven analysis, we found that the ecological environment quality in the study area is stably controlled by natural topography (slope) and climate (precipitation) factors, while also being disturbed by human activities. This experimental section demonstrates that ecological and environmental evolution is a complex process driven by the nonlinear synergistic interaction of natural and anthropogenic factors. The results of the study are of practical significance and provide scientific guidance for the development of coal mining and ecological environmental protection policies in other mining regions around the world. Full article

Seasonal design floods and operational water levels are critical for high-efficient water resource utilization. In this study, statistical and rational analyses methods were applied to divide the flood season based on seasonal rainfall patterns. The Mann–Kendall test and Theil–Sen analysis were used to detect trend changes in the observed flow series. Both stationary and nonstationary flood frequency analysis methods were conducted to estimate seasonal design floods. The Three Gorges Reservoir (TGR) in the Yangtze River, China, was selected as the case study. Results show that the TGR flood season could be divided into four periods: the reservoir drawdown period (1 May–20 June), the Meiyu flood period (21 June–31 July), the transition period (1 August–10 September), and the Autumn Rain refill period (11 September–31 October). Trend analyses indicate that the flow series at the TGR dam site exhibited a decreasing trend in recent decades. Upstream reservoir regulation has significantly reduced inflow discharges of TGR, and the nonstationary seasonal 1000-year design floods in the transition period are decreased by about 20%, and the flood control water level could rise from 145 m to 157 m, which can generate 2.288 billion kW h more hydropower (16.57% increase) while maintaining unchanged flood prevention standards. This study provides valuable insights into the TGR operational water level in the flood season and highlights the necessity of considering the regulation impact of upstream reservoirs for design floods and reservoir operational water levels. Full article

Accurately predicting evaporation duct height (EDH) is a crucial technology for enabling over-the-horizon communication and radar detection at sea. To address the issues of overfitting in neural network training and the low efficiency of manual hyperparameter tuning in conventional evaporation duct height (EDH) prediction, this study proposes the application of Bayesian optimization (BO)-based deep learning techniques to EDH forecasting. Specifically, we developed a novel BO–LSTM hybrid model to enhance the predictive accuracy of EDH. First, based on the CFSv2 reanalysis data from 2011 to 2020, we employed the NPS model to calculate the hourly evaporation duct height (EDH) over the Yongshu Reef region in the South China Sea. Then, the Mann–Kendall (M–K) method and the Augmented Dickey–Fuller (ADF) test were employed to analyze the overall trend and stationarity of the EDH time series in the Yongshu Reef area. The results indicate a significant declining trend in EDH in recent years, and the time series is stationary. This suggests that the data can enhance the convergence speed and prediction stability of neural network models. Finally, the BO–LSTM model was utilized for 24 h short-term forecasting of the EDH time series. The results demonstrate that BO–LSTM can effectively predict EDH values for the next 24 h, with the prediction accuracy gradually decreasing as the forecast horizon extends. Specifically, the 1 h forecast achieves a root mean square error (RMSE) of 0.592 m, a mean absolute error (MAE) of 0.407 m, and a model goodness-of-fit (R²) of 0.961. In contrast, the 24 h forecast shows an RMSE of 2.393 m, MAE of 1.808 m, and R² of only 0.362. A comparative analysis between BO–LSTM and LSTM reveals that BO–LSTM exhibits marginally superior accuracy over LSTM for 1–15 h forecasts, with its performance advantage becoming increasingly pronounced for longer forecast horizons. This confirms that the Bayesian optimization-based hyperparameter tuning method significantly enhances model prediction accuracy. Full article

The farming-pastoral ecotone (FPE) of northern China serves as a critical ecological transition zone, in which poplar plantations significantly contribute to afforestation for large-scale ecological restoration projects. Due to concerns about sustainability, precise monitoring of the spatiotemporal dynamics of poplar plantations is needed, but systematic research is lacking. This study investigated the spatiotemporal dynamics of poplar plantation area and growth status from 1989 to 2022, taking the Anguli Nao watershed, a typical region in the FPE of northern China, as an example. Firstly, by utilizing satellite images and the random forest classification algorithm, the poplar plantation areas were well extracted, with a high accuracy over 93% and extremely strong consistency as demonstrated by a Kappa coefficient larger than 0.88. Significant changes in poplar plantation areas existed from 1989 to 2022, with an overall increasing trend (1989: 130.3 km², 2002: 275.9 km², 2013: 256.0 km², and 2022: 289.2 km²). Furthermore, the accuracy of our extraction method significantly outperformed six widely used global land cover products, all of which failed to capture the distribution of poplar plantations (producer’s accuracy < 0.21; Kappa coefficient < 0.18). In addition, the analysis of vegetation growth status revealed large-scale degradation from 2002 to 2013, with a degradation ratio of 24.4% that further increased to 31.1% by 2022, satisfying the significance test via Theisl–Sen trend analysis and the Mann–Kendall test. This study points out the uncertainty of existing land cover products and risk of poplar plantations in the FPE of northern China and provides instructive reference for similar research. Full article

The increasing droughts and climate change effects and their frequencies worldwide are a critical threat, especially to regions facing water scarcity and wars. Therefore, comprehensive drought evaluation and trend analysis are crucial for water resources management, climate change, and drought mitigation plans. Classical drought evaluation methods predominantly rely on in situ observations, often limited or unavailable in many regions, particularly in developing countries such as Palestine. This study investigates the temporal and spatial characteristics and trends of drought across Palestine between 1940 and 2025. To the best of our knowledge, for the first time in the literature, bias-corrected ERA5 precipitation data are employed alongside ground-based observations to assess drought using the Standardized Precipitation Index (SPI) at multiple timescales (1-, 6-, and 12-month). Trend detection was performed through conventional statistical approaches, including the Mann–Kendall test, Spearman’s Rho, and Sen’s slope (SS), as well as the Frequency-Innovative Trend Analysis (F-ITA) method. Furthermore, the performance of the original and bias-corrected ERA5 precipitation datasets was evaluated against observational data using statistical metrics. The main findings indicated that the bias correction significantly improves the accuracy of the ERA5 precipitation data. Also, droughts in SPI-1 and SPI-6 ranged from 4 to 5 months, the minimum at which a drought can be classified. In addition, the average drought duration at a 12-month timescale ranged between 14 and 16 months. At short (SPI-1) and medium (SPI-6) timescales, no significant trends were found, whereas at the long timescale (SPI-12) all stations showed a significant decreasing SPI trend, such as −5.611 in Jenin, reflecting intensifying drought conditions. For F-ITA, the frequencies of extreme drought classification increased from 0.4% in the first period to 2.18% in the second period. The findings of this research have important implications for drought management, water policy planning, and climate adaptation in Palestine. Full article

Snow cover in the Himalayas plays a vital role in regulating elevation-dependent climate processes and sustaining downstream hydrology. However, its altitude-specific dynamics and implications for snow mass balance remain underexplored. Using the MOD09A1 dataset (2004–2024), this study conducts a pixel-based, elevation-stratified analysis with advanced spectral filtering and gap-filling techniques to enhance snow cover detection in complex terrain. The mean SCA was ~2.10 × 10⁵ km², with sub-regional contributions from WH: 8.59 × 10⁴ km², CH: 9.55 × 10⁴ km², and EH: 2.99 × 10⁴ km², indicating distinct spatiotemporal variability. Correlation analysis revealed that SCA in WH and CH is mainly precipitation-driven (r = +0.70 and r = +0.91), whereas EH is temperature-dominant (r = −0.65), reflecting strong climatic control. Altitudinal and zonal snow cover changes were assessed using Equilibrium Line Altitude–AAR and AABR methods for mass balance estimation. Regional trends showed a positive mass balance of 0.0389 at 4105 m in WH, with increasing SCA around 4516.12 ± 531.94 m; CH exhibited a negative balance (−0.0268 at 4989 m), with declines at higher altitudes; and EH demonstrated a negative balance (−0.015 at 4378 m), with notable SCA reduction. Mann–Kendall and Kendall Tau tests validated these trends, highlighting spatially heterogeneous snow-cover dynamics and their implications for Himalayan snow-mass balance. Full article

Nowadays, climate change is one of the significant threats humanity faces. Many researchers have documented its effects on water availability and vulnerable systems. This study examines the long-term precipitation record (1965–2019) from the Tulcea station, located just 4 km from the Danube Delta Biosphere Reserve (DDBR), to evaluate the impact of climate change on precipitation variability, which can significantly affect biodiversity in this protected area. We integrated change point detection (CPD), stationarity tests, trend analysis, and series decomposition to characterize shifts and patterns in the time series. The Lee & Heghinian test detected a change point (CP) in all data series, whereas the Hubert segmentation methods and Cumulative Sum Method (CUSUM) found fewer series that present at least a CP. The Mann–Kendall (MK) trend test and Innovative Trend Analysis (ITA) indicated an increasing trend in the annual, monthly, and October precipitation series. The Seasonal-Trend decomposition using Loess STL decomposition found the highest seasonality indices in June and July. The Ensemble Empirical Mode Decomposition (EEMD) emphasizes a substantial difference in the seasonal cycle. The results indicate a high variability in the precipitation pattern, with periods of high precipitation followed by dry periods. Full article

Background: Rural fires represent a significant environmental and socioeconomic challenge in Mediterranean regions, particularly in Portugal, which experiences some of the highest fire incidences in Europe. Understanding the temporal evolution of fire occurrences and their relationship with climatic parameters is crucial for developing effective fire management strategies and adapting to climate change impacts. This study aims to analyze the evolution of rural fire occurrences in Guimarães, northern Portugal, during the period 1980–2020, and to investigate their relationship with climatic parameters, specifically temperature and precipitation patterns. Methods: We analyzed a comprehensive dataset of rural fire occurrences and burnt areas in the Guimarães municipality from 1980 to 2020, along with corresponding climatic data including mean annual temperature and total annual precipitation. Statistical analyses included descriptive statistics, Mann–Kendall trend analysis, Pearson and Spearman correlation analyses, and multiple linear regression modeling. The relationships between fire variables and climatic parameters were examined using both parametric and non-parametric approaches. Results: The analysis revealed significant temporal trends and climate–fire relationships. Mean annual temperature showed a statistically significant increasing trend (Mann–Kendall Z = 3.055, p = 0.002) with a Sen’s slope of 0.032 °C/year, representing approximately 1.3 °C warming over the 40-year period. Rural fire occurrences demonstrated a positive correlation with mean temperature (Pearson r = 0.459, p = 0.003; Spearman ρ = 0.453, p = 0.003), while total burnt area also showed significant positive correlation with temperature (Pearson r = 0.426, p = 0.005; Spearman ρ = 0.466, p = 0.002). Precipitation showed no significant correlation with fire variables. Multiple regression models explained 23.1% of the variance in fire occurrences and 18.3% of the variance in burnt area, with temperature being the primary climatic predictor. Conclusions: The study provides evidence of a significant warming trend in Guimarães over the past four decades, which is positively associated with increased rural fire activity. The consistent relationship between temperature and fire variables suggests that continued climate warming may lead to increased fire risk in the region. These findings have important implications for fire management strategies and climate adaptation planning in northern Portugal. Full article

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region. Full article

Global warming has intensified the hydrological cycle, resulting in more frequent extreme precipitation events and altered spatiotemporal precipitation patterns in urban areas, thereby increasing the risk of urban flooding and threatening socio-economic and ecological security. This study investigates the characteristics of summer extreme precipitation in the Poyang Lake City Group (PLCG) from 1971 to 2022, utilizing the China Daily Precipitation Dataset and NCEP/NCAR reanalysis data. Nine extreme precipitation indices were examined through linear trend analysis, Mann–Kendall tests, wavelet transforms, and correlation methods to quantify trends, periodicity, and atmospheric drivers. The key findings include: (1) All indices exhibited increasing trends, with RX1Day and R95p exhibiting significant rises (p < 0.05). PRCPTOT, R20, and SDII also increased, indicating heightened precipitation intensity and frequency. (2) R50, RX1Day, and SDII demonstrated east-high-to-west-low spatial gradients, whereas PRCPTOT and R20 peaked in the eastern and western PLCG. More than over 88% of stations recorded rising trends in PRCPTOT and R95p. (3) Abrupt changes occurred during 1993–2009 for PRCPTOT, R50, and SDII. Wavelet analysis revealed dominant periodicities of 26–39 years, linked to atmospheric oscillations. (4) Strong subtropical highs, moisture convergence, and negative OLR anomalies were closely associated with extreme precipitation. Warmer SSTs in the eastern equatorial Pacific amplified precipitation in preceding seasons. This study provides a scientific basis for flood prevention and climate adaptation in the PLCG and highlighting the region’s vulnerability to monsoonal shifts under global warming. Full article

Throughout history, humans have modified the environment, transforming natural biomes into agricultural areas. In the 1990s, economic policies accelerated the expansion of agricultural frontiers in Brazil, including the Triângulo Mineiro and Alto Paranaíba regions. This study analyzes rainfall variability from 1990 to 2021 and its relationship with land use. For this purpose, satellite data from MapBiomas, ERA5, and NASA POWER were processed using Google Earth Engine and QGIS. Statistical methods included the Spearman correlation and the Mann–Kendall trend test. The results revealed that average annual precipitation decreased from 1663.35 mm in 1991 to 1128.94 mm in 2022—a 32.14% reduction. Simultaneously, agricultural and urban areas increased by 365% and 237.59%, respectively. Spearman analysis showed negative correlations between precipitation and agriculture (ρ = −0.51) and urbanization (ρ = −0.51), and positive correlations with pasture (ρ = +0.52) and water bodies (ρ = +0.46). These trends suggest that land use intensification significantly affects regional rainfall patterns. Unlike studies focusing mainly on Amazon deforestation, this research emphasizes the Cerrado biome’s climatic vulnerability. The use of long-term, high-resolution remote sensing data allows a robust analysis of land use impacts. By highlighting a clear link between land transformation and precipitation decline, this study offers insights for policymaking aimed at balancing agricultural development and water resource preservation. This research underscores the importance of sustainable land management practices, such as agroecology, reforestation, and ecological corridors, for regional climate resilience. Full article