Search Results (237)

Land use change is a critical factor influencing regional carbon emissions, and understanding its spatiotemporal variability is essential for supporting science-based emission-reduction strategies. In this study, we constructed an improved measurement framework by integrating high-resolution land use data, gridded anthropogenic carbon emission data, multi-source remote sensing indicators, and socioeconomic variables to quantify land use carbon emissions (LUCEs) in the Chengdu–Chongqing Urban Agglomeration (CCUA) from 2000 to 2022. We analyzed the temporal trends and spatial clustering of carbon emissions using the Mann–Kendall (MK) trend test and global/local Moran’s I statistics, and further explored the driving mechanisms through the Geodetector (GD) model, including both single-factor explanatory power and two-factor interaction effects. The results show that total LUCEs in the CCEC increased continuously during the study period, with significant spatial clustering characterized by high–high emission hotspots in the core areas of Chengdu and Chongqing and low–low clusters in western mountainous regions. Socioeconomic factors played a dominant role in shaping emission patterns, with construction land proportion, nighttime light intensity, and population density identified as the strongest drivers. Interaction detection revealed nonlinear enhancement effects among key socioeconomic variables, indicating an increasing spatial lock-in of human activities on carbon emissions. These findings provide scientific evidence for optimizing land use structure and formulating region-specific low-carbon development policies in rapidly urbanizing megaregions. Full article

Understanding long-term variations in temperature and precipitation is essential for interpreting regional hydroclimatic behavior and detecting potential shifts in water availability. This study analyzes annual and seasonal temperature–precipitation trends in the Yeşilırmak Basin, Türkiye, using data from seven meteorological stations over a 38-year period (1975–2012). The Randomness Test, Mann–Kendall (MK), and Innovative Trend Analysis (ITA) were applied to detect trends. In addition, a climograph was constructed to characterize seasonal climatic patterns. The climograph for Tokat and Dökmetepe stations shows May precipitation to be 40–50% higher than in winter, while August precipitation is nearly 89% lower than in May. Temperatures rise by approximately 20 °C from January to July, reflecting continental climatic characteristics influenced by the semi-arid transition between northern and central Türkiye. Results indicate statistically significant warming trends at confidence levels above 90%, particularly during summer and autumn, with autumn temperatures increasing by approximately 0.03–0.05 °C per year (Z = 2.3–2.5) at most stations. Precipitation exhibited moderate increases at certain stations, while overall patterns remained steady. While MK and ITA yielded largely consistent results, ITA proved advantageous in weak or borderline cases by detecting structural patterns across value zones. Across all seasonal and annual analyses, ITA identified additional trends in approximately 83% of the cases where MK detected no significant change, corresponding to 25 out of 30 seasonal comparisons. Moreover, in over 92% of statistically significant MK results, ITA outcomes were fully consistent, reinforcing its robustness. Full article

Understanding the persistence and stability of hydroclimatic trends is essential for climate adaptation and sustainable water resource management, particularly in Mediterranean regions characterized by irregular precipitation regimes. This study examines long-term rainfall variability (1974–2021) at six meteorological stations along the southern coasts of Türkiye using three complementary non-parametric techniques: the Mann-Kendall (MK) test, the Wilcoxon Signed-Rank Test (WT), and the Innovative Trend Analysis (ITA). The three tests were applied with their respective methodological extensions to enhance sensitivity and better capture trend stability. Results show that while most stations exhibit generally stable rainfall regimes, period- and location-specific variations with non-monotonic or oscillatory tendencies are present, revealing patterns that standard trend tests often fail to detect. The WT method was more responsive to short-term fluctuations, whereas ITA and its three-dimensional version (3D-ITA) provided valuable insights into trend persistence and stability. Overall, the findings highlight that trend stability assessment enables the distinction between transient climate variability and sustained hydroclimatic change, offering a stronger scientific basis for adaptive water management and regional sustainability planning under climate uncertainty. Full article

Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; NDVI = normalized difference vegetation index) and 1-km gridded meteorological data to analyze the spatiotemporal variations of surface albedo across China during 2001–2020 at a gridded scale. Temporal trends were quantified with the Theil–Sen slope and the Mann–Kendall test, and the seasonal contributions of NDVI, air temperature, and precipitation were assessed with a geographically and temporally weighted regression (GTWR) model. China’s mean annual shortwave albedo was 0.186 and showed a significant decline. Attribution indicates NDVI is the dominant driver (~48% of total change), followed by temperature (~27%) and precipitation (~25%). Seasonally, NDVI explains ~43.94–52.02% of the variation, ~26.81–28.07% of the temperature, and ~21.17–28.57% of the precipitation. Clear spatial patterns emerge. In high-latitude and high-elevation snow-dominated regions, albedo tends to decrease with warmer conditions and increase with greater precipitation. In much of eastern China, albedo is generally positively associated with temperature and negatively with precipitation. NDVI—reflecting vegetation greenness and canopy structure—captures the effects of vegetation greening, canopy densification, and land-cover change that reduce surface reflectivity by enhancing shortwave absorption. Temperature and precipitation affect albedo primarily by regulating vegetation growth. This study goes beyond correlation mapping by combining robust trend detection (Theil–Sen + MK) with GTWR to resolve seasonally varying, non-stationary controls on albedo at 1-km over 20 years. By explicitly separating snow-covered and snow-free conditions, we quantify how NDVI, temperature, and precipitation contributions shift across climate zones and seasons, providing a reproducible, national-scale attribution that can inform ecosystem restoration and land-surface radiative management. Full article

Understanding long-term rainfall variability is essential for addressing Saudi Arabia’s growing challenges of water scarcity, climate resilience, and sustainable resource management in its arid to hyper-arid environment. This study analyzes the spatiotemporal variations and long-term rainfall trends across the 13 administrative regions of the Kingdom of Saudi Arabia (KSA) using four decades of observed data (1982–2021) from the National Center for Meteorology (NCM). The non-parametric Mann–Kendall (M–K) test and Sen’s slope estimator were applied to detect and quantify rainfall trends. Results reveal that 10 of the 13 regions show statistically significant negative trends, excluding the Eastern, Mecca, and Tabuk regions, with declines ranging from −4 to −16 mm/yr. The most pronounced decreases occurred in Hail, Al-Qassim, Riyadh, Medina, and Asir, while Mecca and Tabuk exhibited weak positive signals during the last decade, likely linked to Red Sea Trough dynamics. Seasonal analysis indicates the largest declines during winter and spring, crucial periods for groundwater recharge and agriculture, whereas summer rainfall remains localized in the southwestern highlands with a slight decreasing trend. Overall, rainfall variability in Saudi Arabia reflects both long-term drying and short-term oscillations. The findings provide a robust rainfall baseline to support water security, climate adaptation, and sustainable management strategies in one of the world’s driest regions. Full article

Vegetation phenology (VP) is a crucial biological indicator for monitoring terrestrial ecosystems and global climate change. However, VP monitoring using traditional remote sensing vegetation indices has significant limitations in precise analysis. Furthermore, most studies have overlooked the distinction between stable and short-term VP in relation to climate change and have failed to clearly identify the seasonal variation in the impact of climatic factors on stable VP (SVP). This study compared the accuracy of solar-induced chlorophyll fluorescence (SIF) and three traditional vegetation indices (e.g., Normalized Difference Vegetation Index) for estimating SVP in China, using ground-based data for validation. Additionally, this study employs Sen’s slope, the Mann–Kendall (MK) test, and the Hurst index to reveal the spatiotemporal evolution of the Start of Season (SOS), End of Season (EOS), and Length of Growing Season (LOS) over the past two decades. Partial correlation analysis and random forest importance evaluation are used to accurately identify the key climatic drivers of SVP across different climate zones and to assess the seasonal contributions of climate to SVP. The results indicate that (1) phenological metrics derived from SIF data showed the strongest correlation coefficients with ground-based observations, with all correlation coefficients (R) exceeding 0.69 and an average of 0.75. (2) The spatial distribution of SVP in China has revealed three primary spatial patterns: the Tibetan Plateau, and regions north and south of the Qinling–Huaihe Line. From arid, cold-to-warm, and humid regions, the rate of SOS advancement gradually increases; EOS transitions from earlier to nearly unchanged; and the rate of LOS delay increases accordingly. (3) The spring climate primarily drives the advancement of SOS across China, contributing up to 70%, with temperatures generally having a negative effect on SOS (r = −0.53, p < 0.05). In contrast, EOS is regulated and more complex, with the vapor pressure deficit exerting a dual ‘limitation–promotion’ effect in autumn (r = −0.39, p < 0.05) and summer (r = 0.77, p < 0.05). This study contributes to a deeper scientific understanding of the interannual variability in SVP under seasonal climate change. 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

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

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article

Global warming impacts agricultural production and food security, particularly in high-latitude regions with high temperature sensitivity. As a major grain-producing area in China and one of the fastest-warming regions globally, Northeast China (NEC) has received considerable research attention. However, the existing literature lacks sufficient exploration of the spatiotemporal heterogeneity in climate change impacts. Based on data on rice, corn, and soybean yields, as well as temperature, rainfall, and sunshine duration in NEC from 1993 to 2022, this study employs Sen’s slope estimation, the Mann–Kendall (MK) test, spatial autocorrelation analysis, and the Geographically and Temporally Weighted Regression (GTWR) model to analyze the spatiotemporal evolution of grain yields and their responses to climate change. The results show that ① 1993–2022 witnessed an overall rise in grain yields per unit area in NEC, with Liaoning growing fastest. Rice yields increased regionally; corn yields rose in Liaoning and Jilin, while soybean yields increased only in Liaoning. During the growing season, rainfall trended upward with fluctuations, temperatures rose steadily, and sunshine duration declined in Heilongjiang. ② Except for corn and soybeans in the early period, other crops exhibited significant yield spatial agglomeration. High–high agglomeration areas first expanded, then shrank, eventually shifting northward to the region of Jilin Province. ③ Climatic factors show marked spatiotemporal heterogeneity in impacts: positive effect areas of rainfall and temperature expanded northward; sunshine duration’s influence weakened, but its negative effect areas spread. ④ Differences in crop responses are closely linked to their physiological characteristics, regional climate evolution, and agricultural adaptation measures. This study provides a scientific basis for formulating region-specific agricultural adaptation strategies to address climate change in NEC. Full article

Understanding long-term precipitation variability is essential for assessing the climate’s impact on sensitive ecosystems, particularly in regions of high environmental value, such as the Danube Delta Biosphere Reserve (DDBR). This study examines the temporal dynamics of monthly precipitation in the Danube Delta, Romania, spanning the period from 1965 to 2019. Three approaches were used to analyze climatic variability: Change Point detection (CPD) to identify shifts in precipitation regimes, the De Martonne Index (I_M) to assess aridity trends, and the Standardized Precipitation Index (SPI) to evaluate drought conditions across annual and monthly scales. Using robust monthly precipitation and temperature datasets from the Sulina meteorological station, CPD analysis revealed statistically significant structural breaks in precipitation trends, suggesting periods of altered climate behavior likely associated with broader regional or global climate changes. I_M values indicated mostly hyper-aridity and aridity at monthly and annual scales, respectively. No monotonic trend was found in this index during the analyzed segments, as emphasized by the Mann–Kendall (MK) test. SPI values provided further evidence of variability in the precipitation regime, highlighting a transition toward more extreme hydrological conditions in the region. The combined use of these indices offers a comprehensive view of the evolution of climatic conditions in the Danube Delta. The findings underscore the growing vulnerability of this unique wetland ecosystem to climatic variability, supporting the need for adaptive water management strategies in the face of anticipated future changes. Full article

Riverbed dynamics are natural processes that are strongly driven by human and climatic factors. In the last two centuries, the anthropogenic influence and impact of climate change on European rivers has resulted in significant degradation of riverbeds. This research paper aims to determine the historical evolution (1856–2021) and future trends of the Ialomița riverbed (Romania) under the influence of anthropogenic impact and climate change. The case study is a reach of 66 km between the confluences with the Ialomicioara and Pâscov rivers. The localisation in a contact zone between the Curvature Subcarpathians and the Târgoviște Plain, the active recent tectonic uplift of the area, and the intense anthropogenic intervention gives to this river reach favourable conditions for pronounced riverbed dynamics over time. To achieve the aim of the study, we developed a complex methodology which involves the use of Geographical Information System (GIS) techniques, hierarchical cluster analysis (HCA), the Mann–Kendall test (MK), and R programming. The results indicate that the evolution of the Ialomița River aligns with the general trends observed across Europe and within Romania, characterised by a reduction in riverbed geomorphological complexity and a general transition from a braided, multi-thread into a sinuous, single-thread fluvial style. The main processes consist of channel narrowing and incision alternating with intense meandering. However, specific temporal and spatial evolution patterns were identified, mainly influenced by the increasingly anthropogenic local influences and confirmed climate changes in the study area since the second half of the 20th century. Future evolutionary trends suggest that, in the absence of river restoration interventions, the Ialomița riverbed is expected to continue degrading on a short-term horizon, following both climatic and anthropogenic signals. The findings of this study may contribute to a better understanding of recent river behaviours and serve as a valuable tool for the management of the Ialomița River. Full article

Increasing drought conditions at the global level have created concerns about the decrease in water resources. This situation has made the correct planning of irrigation applications the most important situation. Irrigation management in future periods is possible with the correct determination of the reference evapotranspiration (ET₀) trend. In the current situation, the trend is usually determined using one or two methods. Failure to conduct a detailed trend analysis results in incorrect irrigation management. With the new approach presented in the research, all of the Mann–Kendall (MK), innovative trend analysis (ITA), Sen’s slope (SS) and Spearman’s rho (SR) tests were used, and the common results of the four tests, namely increase, decrease, and no trend, were taken into account. The ET₀ values calculated in different approaches were focused on temporal and spatial analysis for the future irrigation management of Türkiye with the Blaney–Criddle (BC), Turc (TR), and Coutagne (CT) methods. The future period forecast was made using four different trend analyses with geographical information system (GIS) based spatial applications using 12-month ET₀ data calculated from 59 years of data between 1965 and 2023. Statistical analysis was performed to reveal the relationship between ET₀ calculation methods. The findings showed that although there is a general increasing trend in ET₀ values in the region, this situation is more pronounced, especially in the provinces in the western and central regions. The research results improve the determination of plant water needs for future periods in terms of irrigation management. This new approach, which determines ET₀ trend analysis in the Black Sea region, can be used in regional, national, and international studies by supporting different calculations to be made in order to plan future water management correctly, to reduce the concern of decreasing water resources in drought conditions, and to obtain comprehensive data in order to provide appropriate irrigation. Full article

Rivers play a crucial role in the hydrological cycle and serve as essential freshwater resources for both human populations and ecosystems. Climate change significantly alters precipitation patterns and river discharge variability. However, the impact of precipitation patterns (rainfall and snowfall) and air temperature on river discharge in coastal zones remains inadequately understood. This study focused on Toyama Prefecture, located along the Sea of Japan, as a representative coastal area. We analyzed over 30 years of datasets, including air temperature, precipitation, snowfall, and river discharge, to assess the effects of climate change on river discharge. Trends in hydroclimatic datasets were assessed using the rescaled adjusted partial sums (RAPS) method and the Mann–Kendall (MK) non-parametric test. Furthermore, a correlation analysis and the Structural Equation Model (SEM) were applied to construct a relationship between precipitation, temperature, and river discharge. Our findings indicated a significant increase in air temperature at a rate of 0.2 °C per decade, with notable warming observed in late winter (January and February) and early spring (March). The average river fluxes for the Jinzu, Oyabe, Kurobe, Shou, and Joganji rivers were 182.52 m³/s, 60.37 m³/s, 41.40 m³/s, 38.33 m³/s, and 18.72 m³/s, respectively. The tipping point for snowfall decline occurred in 1992, marked by an obvious decrease in snowfall depth. The SEM showed that, although rainfall dominated the changes in river discharge (loading = 0.94), the transition from solid (snow) to liquid (rain) precipitation may alter the river discharge regime. The percentage of flood occurrence increased from 19% (1940–1992) to 41% (1993–2020). These changes highlight the urgent need to raise awareness about the impacts of climate change on river floods and freshwater resources in global coastal regions. Full article