Search Results (1,154)

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR. Full article

In the Wadi Cheliff basin (Algeria), a 48-year (1971–2018) time series of annual maximum daily rainfall was studied to identify and quantify trends observed at 150 rain gauges. Initial trends in annual maximum daily rainfall were determined using the Mann–Kendall test, with a significance level of 95%. The slope or increase/decrease in the annual maximum daily precipitation was assessed using the Theil–Sen estimator. A running trend analysis was then performed to quantify the effects of different time windows on trend detection. Finally, to assess the different spatial distribution of annual maximum daily precipitation during the observation period, spatial analysis was performed using a geo-statistical approach for the whole observation period and at different decades. The results showed a predominant negative trend in annual maximum daily rainfall (about 11% of rain gauges at a 95% significance level), mainly affecting the north-eastern area of the catchment. The spatial distribution of annual maximum daily rainfall showed high rainfall variability in the period of 1970–1980, with a decrease in the decades of 1980–1990 and 2010–2017 when the maximum values were more evenly distributed across the region. Full article

This study utilized annual precipitation data collected from 76 meteorological stations located on the Loess Plateau and its adjacent regions. It employed empirical orthogonal function (EOF) analysis, the Mann–Kendall trend test (M-K), and continuous wavelet transform (CWT) to investigate the spatial distribution patterns, temporal trends, and periodicity of annual precipitation from 1961 to 2016. The results showed the following: (1) The long-term averages of annual rainfall on the Loess Plateau exhibited a general decline from the southeast to the northwest, with certain areas demonstrating a trend of reduction radiating outward from the central region. This precipitation regime was fundamentally governed by the interplay between geographic coordinates and topo-graphical characteristics. Nevertheless, this spatial distribution pattern is expected to undergo changes in the future. (2) Annual precipitation in the southern and eastern parts decreased significantly, while the western part reported the greatest increase, and thus the spatial variability of precipitation will decrease in the future. (3) Annual precipitation on the Loess Plateau generally has a period of about 4 years. The wavelet coherence analysis reveals that El Niño events, occurring over a brief 4-year interval, correlate with diminished precipitation patterns across the eastern and southern sectors of the Loess Plateau, consequently attenuating the precipitation’s spatial variability throughout the entire geographical domain. Therefore, in the future, when El Niño occurs, it is necessary to prevent droughts in the eastern and southern regions of the Loess Plateau. Full article

The southeastern hilly region of China is ecologically significant but highly vulnerable to climate change and human activities. This study developed a Modified Remote Sensing Ecological Index (MRSEI) using satellite imagery and Human Footprint data to assess ecological quality across 14 cities surrounding the Wuyi Mountains. We applied Sen’s slope analysis, the Mann–Kendall test, and spatial autocorrelation to evaluate spatiotemporal ecological changes from 2000 to 2020, and used partial correlation analysis to explore the drivers of these changes. The main findings are as follows: (1) Ecological quality generally improved over the study period, with significant year-to-year fluctuations. The eastern region, characterized by higher altitudes, consistently exhibited better ecological quality than the western region. The area of low-quality ecological zones significantly decreased, while Ji’an, Ganzhou, Heyuan, and Meizhou saw the most notable improvements. In contrast, urban areas experienced a marked decline in ecological quality. (2) The region is undergoing warming and wetting trends. Increased precipitation, especially in the western and northern regions, improved ecological quality, except in urban areas, where it heightened flood risks. Rising temperatures had mixed effects: they enhanced ecological quality in high-altitude areas (~516 m) but negatively impacted low-altitude regions (~262 m) due to intensified heat stress. (3) Although industrial restructuring reduced environmental pressure, rapid population growth and urban expansion created new ecological challenges. This study provides an innovative method for the ecological monitoring of hilly regions, effectively integrating human activity and climatic factors into ecological assessments. The findings offer valuable insights for sustainable development and ecological management in similar sensitive regions. Full article

In recent decades, understanding how climate variability and human activities drive long-term changes in river discharge and sediment load has become a crucial field of research in fluvial geomorphology, particularly for South Asia’s densely populated and environmentally sensitive regions. This study analyses spatio-temporal trends in water discharge (Q_d) and sediment load (Q_s) in the Krishna basin, Peninsular India’s second-largest catchment. Using nearly 50 years of daily discharge, sediment concentration, and rainfall data from the Central Water Commission (CWC) and India Meteorological Department (IMD), we applied Mann–Kendall, Pettitt tests, and double mass equations to detect long-term trends, abrupt changes, and the relative influence of climate and anthropogenic effects. Results showed a notable decline in the annual discharge, with 15 of 20 stations showing decreasing trends, especially along the Bhima, Ghataprabha, and lower Krishna rivers. The annual stream flow data showed a 53% decline in the mean Q_d from 26.01 × 10⁹ m³ year⁻¹ before 2000 to 12.32 × 10⁹ m³ year⁻¹ after 2000 at the terminal station. Eight of ten gauging stations showed a significant decrease (p-value < 0.05) in their annual sediment load, with a 76% reduction across the Krishna basin after its changepoint in 1983. The Pettitt test identified a statistically significant downward shift in discharge at seven stations. Double mass plots indicate that anthropogenic factors, such as large-scale reservoirs and water diversion, are the main contributors, accounting for 82.7% of sediment decline, with climatic factors contributing 17.1%. The combined trend analysis and double mass plots confirm these findings, underscoring the need for further study of human impacts on the basin’s hydro-geomorphology. This study offers a clear and robust approach to quantifying the relative effects of climate and human activities, providing a versatile framework that can enhance understanding in similar studies worldwide. Full article

The Qinghai–Tibet Plateau (QTP) is crucial for global climate regulation and ecological equilibrium. However, the phenomenon of global climate warming has increased the frequency of extreme weather events on the QTP, exerting substantial effects on both regional and global ecological systems. This study utilized long-term series NDVI and extreme climate indices to comprehensively evaluate the impact of extreme climatic changes on diverse vegetation types within the QTP. A variety of analytical methodologies, including trend analysis, a Mann–Kendall test, correlation analysis, and random forest importance ranking, were employed in this study. These methodologies were applied to investigate the distribution patterns and variation trends of diverse vegetation types and extreme climate indices. This comprehensive approach facilitated a detailed analysis of the responses of different vegetation types to interannual variability under extreme climatic conditions and enabled the assessment of the impact of extreme climate indices on these vegetation types. The findings have the following implications: (1) Except for forests, the annual NDVI for overall vegetation, meadows, steppes, deserts, and alpine vegetation in the QTP exhibits a significant upward trend (p < 0.01). Notably, meadows and deserts demonstrate the highest growth rates at 0.007/10y, whereas the annual NDVI of forests is not statistically significant (p > 0.05). Substantial increases in vegetation were predominantly detected in the central and northeastern regions of the QTP, while significant decreases were mostly observed in the southeastern and western regions. The area exhibiting significant vegetation increase (38.71%) considerably surpasses that of the area with a significant decrease (14.24%). (2) There was a statistically significant reduction (p < 0.05) in the number of days associated with extreme cold temperature indices, including CSDI, DTR, FD, ID, TN10p, and TX10p. In contrast, indices related to extremely warm temperatures, such as GSL, WSDI, SU25, TN90p, TNn, TNx, TX90p, and TXx, exhibited a statistically significant increase (p < 0.01). The pronounced rise in minimum temperatures, reflected by fewer cold days, has notably contributed to climate warming. Although extreme precipitation events have become less frequent, their intensity has increased. Notable spatial variations in extreme precipitation were observed, although no consistent changing pattern emerged. (3) The annual NDVI for non-forest vegetation types showed a significant negative correlation with most extreme cold temperature indices and a significant positive correlation with extreme warm temperature indices. A significant positive correlation (p < 0.05) between annual NDVI and extreme precipitation indices is found only in steppe and desert ecosystems, with no such correlation observed in other vegetation types. Both correlation analysis and random forest methodologies underscore the impact of extreme climate indices on vegetation variations, with the random forest model exhibiting superior capability in capturing nonlinear relationships. In conclusion, global climate change is projected to result in a heightened frequency of extreme warm events. Although these conditions might temporarily enhance vegetation growth, they are also associated with numerous detrimental impacts. Therefore, it is imperative to enhance awareness and take proactive measures for early warning and prevention. Full article

To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (p < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development. Full article

The present study offers a comprehensive evaluation of the monthly rainfall and temperature patterns across nine stations and fifty-nine points in North-Eastern Nigeria using NASA’s Prediction of Worldwide Energy Resources data, spanning four decades (1981–2021). By employing the Mann–Kendall (MK) test and inverse distance weighting (IDW) interpolation, the researchers effectively detected and visualized trends in climate variables. The MK test results indicate contrasting rainfall trends, with notable decreases in Akko, Billiri, Maiduguri, Numan, and Yola, and increases in Gombe, Abadam, Biu, and Mubi. The trends in the maximum temperature were found to be statistically significant across all stations, showing a consistent increase, whereas the minimum temperature trends exhibited a slight but insignificant decrease. The application of the Theil–Sen slope estimator quantified these trends, providing nuanced insights into the magnitudes of changes in climate variables. The IDW results further corroborate the general trend of decreasing rainfall (z = −0.442), modest increases in the maximum temperature (z = 0.046), and a marginal decline in the minimum temperature (z = −0.005). This study makes an important contribution by advocating for the proactive dissemination of climate information. Given the evident climate shifts, particularly the increasing temperatures and fluctuating rainfall patterns, timely access to such information is crucial to enhancing climate resilience in the region. The rigorous statistical methods applied and the detailed spatial analysis strengthen the validity of these findings, making this study a valuable resource for both researchers and policymakers aiming to address climate variability in North-Eastern Nigeria. These research results may also be useful for understanding the climate variabilities in different parts of the world. Full article

In the context of water scarcity, understanding the mechanisms influencing and altering agricultural water consumption can offer valuable insights into the scientific management of limited water resources. Using Henan Province as a case study, this research applies the Mann–Kendall test method, the spatial Markov transfer chain model, the optimal parameter geo-detector model, and the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the evolution characteristics of agricultural water consumption in Henan Province and its key influencing factors. The findings revealed the following: (1) Agricultural water consumption has shown a significant decline from 1999 to 2022. (2) According to observations, the stability of agricultural water consumption exceeds the spillover effect, and cross-border grade transfer is challenging. Moreover, this phenomenon is influenced by the neighboring regions. (3) The key influencing factors of added agricultural value are the sown area of food crops, total sown area, irrigated area, and average annual air temperature. (4) Among the decomposition effects on agricultural water consumption, the contribution of each decomposition effect to changes in agricultural water consumption and the role of spatial distribution exhibit notable differences. Overall, these findings provide theoretical references for the efficient use of agricultural water resources and sustainable development in the region. Full article

The consequence of climatic change and anthropogenic environmental modifications is a notable diminution in runoff across arid and semi-arid regions. For the sustainable management of regional water resources, it is crucial to comprehend the impacts of climatic and anthropogenic factors on runoff patterns. The Zuli River was designated as the study area for this study, and the Mann–Kendall test, double cumulative curve method, slope change ratio of cumulative quantity method, and elasticity coefficient method were employed to identify mutation points and to quantify the relative impacts of climatic variation and human activities on runoff. The results revealed a statistically insignificant downward trend in mean annual precipitation, a significant declining trend in runoff, and an evident increasing trend in potential evapotranspiration and temperature between the years 1957 and 2019. The analysis revealed that the point of sudden change in runoff at Huining station occurred in 1992, whereas the mutation point at Guo Chengyi station was identified in 1985 and that at Jingyuan station in 1995. The contribution of climate change to runoff was found to range from 28.7% to 58.5%, while the contribution of human activities to runoff ranged from 41.5% to 71.3%, based on different methodologies. Therefore, human activities were recognized as the main factor affecting the variations in runoff within the Zuli River Basin, while climate change acts as a secondary contributor. The results of the study hold considerable importance for enhancing the scientific understanding of hydrological processes within the basin and for guiding regional water administration strategies. Full article

Amid persistent global food security challenges, the efficient utilization of cultivated land resources has become increasingly critical, as optimizing Cultivated Land Utilization Efficiency (CLUE) is paramount to ensuring food supply. This study introduced a cultivated land utilization index (CLUI) based on Fractional Vegetation Cover (FVC) to assess the spatiotemporal variations in Henan Province’s CLUE. The Theil–Sen slope and the Mann–Kendall test were used to analyze the spatiotemporal variations of CLUE in Henan Province from 2000 to 2020. Additionally, we used a genetic algorithm optimized Artificial Neural Network (ANN) and a particle swarm optimization-based Random Forest (RF) model to assess the comprehensive in-fluence between topography, climate, and human activities on CLUE, in which incorporating Shapley Additive Explanations (SHAP) values. The results reveal the following: (1) From 2000 to 2020, the CLUE in Henan province showed an overall upward trend, with strong spatial heterogeneity across various regions: the central and eastern areas generally showed decline, the northern region remained stable with slight increases, the western region saw significant growth, while the southern area exhibited complex fluctuations. (2) Natural and economic factors had notable impacts on CLUE in Henan province. Among these factors, population and economic factors played a dominant role, whereas average temperature exerted an inhibitory effect on CLUE in most parts of the province. (3) The influenced factors on CLUE varied spatially, with human activity impacts being more concentrated, while topographical and climatic influences were relatively dispersed. These findings provide a scientific basis for land management and agricultural policy formulation in major grain-producing areas, offering valuable insights into enhancing regional CLUE and promoting sustainable agricultural development. Full article

This study evaluated the effectiveness of the NASA Earth Exchange Global Daily Downscaled models from CMIP6 experiments (hereafter; NEX-GDDP-CMIP6) in reproducing observed precipitation and temperature across East Africa (EA) from 1981 to 2014. Additionally, climate changes were estimated under various emission scenarios, namely low (SSP1-2.6), medium (SSP2-4.5), and high (SSP5-8.5) scenarios. Multiple robust statistics metrics, the Taylor diagram, and interannual variability skill (IVS) were employed to identify the best-performing models. Significant trends in future precipitation and temperature are evaluated using the Mann-Kendall and Sen’s slope estimator tests. The results highlighted IPSL-CM6A-LR, EC-Earth3, CanESM5, and INM-CM4-8 as the best-performing models for annual and March to May (MAM) precipitation and temperature respectively. By the end of this century, MAM precipitation and temperature are projected to increase by 40% and 4.5 °C, respectively, under SSP5-8.5. Conversely, a decrease in MAM precipitation and temperature of 5% and 0.8 °C was projected under SSP2-4.5 and SSP1-2.6, respectively. Long-term mean precipitation increased in all climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), with near-term MAM precipitation showing a 5% decrease in Rwanda, Burundi, Uganda, and some parts of Tanzania. Under the SSP5-8.5 scenario, temperature rise exceeded 2–6 °C in most regions across the area, with the fastest warming trend of over 6 °C observed in diverse areas. Thus, high greenhouse gas (GHG) emission scenarios can be very harmful to EA and further GHG control is needed. Full article

This study analyzes hydrometeorological data (1950–2023) to examine the signatures of El Niño and La Niña events and assess their impact on rainfall distribution in the Piura Region, Peru. Using data from 23 stations, high-resolution gridded rainfall datasets (PISCO), and oceanic–atmospheric indices we investigated the frequency, intensity, and spatial variability of these events in the Piura River Basin (PRB). Return periods for very strong El Niño and La Niña events are 25 and 19 years, respectively, compared to 2 years for neutral conditions. Over the past 30 years, the recurrence of Coastal El Niño has significantly increased. This increased frequency contributes to the global rise in El Niño events, reducing the return period for very strong events from 5.2 to 3.4 years. This rise correlates with an increase in maximum daily precipitation across the basin centered in the middle PRB during El Niño years. Future rainfall projections, based on 20 CMIP6 GCMs under SSP2-4.5 and SSP5-8.5 scenarios, suggest continued intensification of rainfall events. These findings highlight the necessity of incorporating El Niño variability into infrastructure design, water resource management, and climate adaptation strategies to mitigate the impacts of these increasingly frequent and severe events in the PRB. Full article

With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. The challenge is to accurately characterize vegetation vulnerability under compound heat and drought stress using correlation-based methods. This article uses the Haihe River Basin, an ecologically sensitive area known for experiencing droughts nine out of ten years, as an example. Firstly, using daily precipitation and maximum temperature data from 38 meteorological stations in the basin from 1965 to 2019, methods such as univariate linear regression and the Mann–Kendall mutation test were employed to identify the temporal variation patterns of meteorological elements in the basin. Secondly, the Pearson correlation coefficient and other methods were applied to determine the most likely months for compound dry and hot events, and the joint distribution pattern and recurrence period of concurrent high temperature and intense drought events were explored. Finally, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to quantify the relationship of the response of watershed vegetation to different extreme events (high temperature, drought, and compound dry and hot climates). The results indicated that the basin’s precipitation keeps decreasing, evaporation rises, and the supply–demand conflict grows more severe. The correlation between the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) is strongest at the 3-month scale from June to August. Meanwhile, in most areas of the basin, the Standardized Normalized Difference Vegetation Index (sNDVI) is positively correlated with the SPI and negatively correlated with the STI. Compared to a single drought or high-temperature event, compound dry and hot climates further exacerbate the vegetation vulnerability of the Haihe River Basin. In compound dry and hot climates, the probability of vegetation loss in June, July, and August is as high as 0.45, 0.32, and 0.38, respectively. Moreover, vegetation vulnerability in the southern and northwestern mountainous areas of the basin is higher, and the ecological risk is severe. The research results contribute to an understanding of the vegetation’s response to extreme climate events, aiming to address terrestrial ecosystem risk management in response to climate change. Full article

With the intensification of climate change and anthropogenic impacts, the ecological environment in drylands faces serious challenges, underscoring the necessity for regionally adapted ecological quality evaluation. This study evaluates the suitability of the original Remote Sensing Ecological Index (oRSEI), modified RSEI (mRSEI), and adapted RSEI (aRSEI) in a typical dryland region of northern China. Spatio-temporal changes in ecological quality from 2000 to 2022 were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, and the Hurst exponent. Multiple regression residual analysis quantified the relative contributions of climate change and human activities to ecological quality changes. Results showed that (1) the aRSEI was the most suitable index for the study area; (2) observed changes exhibited significant spatial heterogeneity, with improvements generally in the inner areas of the Yellow River and declines in the outer areas; and (3) changes in ecological quality were primarily driven by climate change and human activities, with human activities dominating from 2000 to 2011 and the influence of climate change increasing from 2012 to 2022. This study compares the efficacy of RSEIs in evaluating dryland ecological quality, identifies spatio-temporal change patterns, and elucidates driving mechanisms, offering scientific evidence and policy recommendations for targeted conservation and restoration measures to address future changes in dryland regions. Full article