Search Results (240)

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

Arid and semi-arid regions serve as crucial ecological barriers in China, making the spatiotemporal evolution of their ecological environmental quality (EEQ) scientifically significant. This study developed a Modified Remote Sensing Ecological Index (MRSEI) by innovatively integrating the Comprehensive Salinity Indicator (CSI) into the Remote Sensing Ecological Index (RSEI) and applied it to systematically evaluate the spatiotemporal evolution of EEQ (2014–2023) in Yinchuan City, a typical arid region of northwest China along the upper Yellow River. The study revealed the spatiotemporal evolution patterns through the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test, and adopted the Light Gradient Boosting Machine (LightGBM) combined with the Shapley Additive Explanation (SHAP) to quantify the contributions of ten natural and anthropogenic driving factors. The results suggest that (1) the MRSEI outperformed the RSEI, showing 0.41% higher entropy and 5.63% greater contrast, better characterizing the arid region’s heterogeneity. (2) The EEQ showed marked spatial heterogeneity. High-quality areas are concentrated in the Helan Mountains and the integrated urban/rural development demonstration zone, while the core functional zone of the provincial capital, the Helan Mountains ecological corridor, and the eastern eco-economic pilot zone showed lower EEQ. (3) A total of 87.92% of the area (7609.23 km²) remained stable with no significant changes. Notably, degraded areas (934.52 km², 10.80%) exceeded improved zones (111.04 km², 1.28%), demonstrating an overall ecological deterioration trend. (4) This study applied LightGBM with SHAP to analyze the driving factors of EEQ. The results demonstrated that Land Use/Land Cover (LULC) was the predominant driver, contributing 41.52%, followed by the Digital Elevation Model (DEM, 18.26%) and Net Primary Productivity (NPP, 12.63%). This study offers a novel framework for arid ecological monitoring, supporting evidence-based conservation and sustainable development in the Yellow River Basin. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

In agro-pastoral transitional zones, monitoring vegetation fraction coverage (FVC) and understanding its relationship with land use and climate change are crucial for comprehending how complex land-use/land-cover change (LUCC) improves ecological restoration and land management. This study focuses on the agro-pastoral transitional zone of Inner Mongolia, aiming to analyze vegetation cover changes from 2000 to 2020 using the Mann–Kendall (MK) significance test, Theil–Sen median trend analysis, and coefficient of variation (CV) analysis. Additionally, the study explores the impacts of LUCC, precipitation, and temperature on vegetation cover using methods such as geo-detector, pixel-based statistical analysis, and univariate linear regression. Based on the PLUS land-use prediction model and linear regression results, vegetation cover was simulated under different land-use scenarios for the future. The main findings are as follows: first, from 2000 to 2020, the spatial distribution of vegetation cover in the study area showed a distinct pattern of higher vegetation cover in the east compared to the west, with significant spatiotemporal heterogeneity. Although the overall vegetation cover slightly increased, there were notable differences in the trend across regions, with some areas experiencing a decrease in FVC. Second, LUCC is the most significant explanatory factor for vegetation cover changes, and the interactions between LUCC and other factors have a particularly notable impact on vegetation cover. Third, scenario simulations based on the PLUS model indicate that, by 2040, vegetation cover will perform optimally under the farmland protection and sustainable development scenarios. Particularly under the farmland protection scenario, the conversion of cropland, forestland, and grassland is notably suppressed. In contrast, the unmanaged natural development scenario will lead to a decline in vegetation cover. The results of this study show that vegetation cover in the agro-pastoral transitional zone of Inner Mongolia exhibits substantial fluctuations due to land-use change. Future ecological restoration policies should incorporate land-use optimization to promote vegetation recovery and address ecological degradation. Full article

Understanding the historical peak situation and the rules for CO₂ emissions from buildings helps to formulate reasonable building mitigation strategies, accelerating the achievement of the Chinese government’s carbon peak goal. As developed regions, cities in the Guangdong–Hong Kong–Macao Great Bay Area (GBA) provide valuable reference cases. This study quantified the historical building CO₂ emissions of GBA cities and analyzed the contribution of driving factors using the Kaya identity and logarithmic mean Divisia index. Furthermore, we assessed the historical peak situation using the MK trend test method and discussed the reasons behind the inter-city difference in the peak situation shown by the environmental Kuznets curve. The results indicate that the building-related CO₂ emissions of the GBA will slowly increase to 96.90 Mt CO₂ by 2020 and that P&C buildings accounted for a larger proportion of emissions. Emission factors and population made the largest positive and negative contributions, respectively, to this total. At the city level, Guangzhou, Shenzhen, and Hong Kong ranked as the top three sources of building CO₂ emissions. Hong Kong peaked, Dongguan and Macao plateaued, and other cities maintained either slow or quick growth. CO₂ emissions unit area, per capita building CO₂ emissions, and building CO₂ emissions reached a peak in that order. This study provides a valuable reference for formulating a city-level path showing building CO₂ emissions peaks. Full article

The China Baijiu Golden Triangle (BGT) serves as the core production hub of China’s Baijiu industry, where the ecological environment plays a pivotal role in ensuring the industry’s sustainable development. However, urbanization, industrial expansion, and climate change pose potential threats to the region’s vegetation dynamics. Utilizing Landsat remote sensing data from 2002 to 2022, this study integrates Theil–Sen trend analysis, the Mann–Kendall (MK) test, coefficient of variation (CV) analysis, and the Geodetector model (GD model) to investigate the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its underlying driving mechanisms within the BGT. The findings reveal an overall upward trend in vegetation NDVI, with the annual mean NDVI increasing from 0.45 to 0.67, corresponding to a growth rate of 0.49%. Spatially, areas of high vegetation cover are predominantly located in mountainous forest zones with favorable ecological conditions, whereas regions of low vegetation cover are concentrated in zones of urban expansion. Precipitation and topographic factors (elevation and slope) emerge as the primary natural drivers of vegetation change, while land use change and the night-time light index stand out as the most influential human-induced factors. Further analysis uncovers a nonlinear interactive enhancement effect between natural and anthropogenic factors, with the interaction between the night-time light index and precipitation being particularly pronounced. This suggests that urbanization not only directly impacts vegetation but may also exert indirect effects on the ecosystem by altering regional hydrological and climatic processes. The results indicate that ecological protection policies in the BGT have yielded some success; however, vegetation fragmentation and ecological pressures stemming from urban expansion remain significant challenges. Moving forward, optimizing land use policies and promoting eco-friendly development models will be essential to achieving ecosystem stability and sustaining industrial growth. Full article

This study aims to explore the spatio-temporal changes in terrestrial water storage (TWS) in the five provinces of Northwest China and to assess the influences of various driving factors on the changes in TWS. Based on the Gravity Recovery and Climate Experiment (GRACE) satellite data of the five provinces from April 2002 to December 2022, combined with datasets of various driving factors (precipitation, evapotranspiration, runoff, and anthropogenic water use) from 1980 to 2022, a trend analysis was conducted using Sen’s slope method and Mann–Kendall (M-K) tests to characterize the spatial–temporal changes in TWS. The water balance method and quantification of contribution rates were used to analyze the spatio-temporal response of the change in TWS to driving factors and the contributions of driving factors thereto. The results showed that the eastern part of the Xinjiang Uygur Autonomous Region and the northern parts of Shaanxi Province and Ningxia Hui Autonomous Region belonged to the decreasing centers of TWS, while the northern part of the Qinghai–Tibet Plateau belonged to the enriching center of TWS, with a decreasing trend at a rate of 2.86 mm/yr. Precipitation contributed positively to the change in TWS and had a high spatio-temporal response, while the other driving factors (evapotranspiration, runoff, and anthropogenic water use) all contributed negatively to certain extents. The contribution rates of precipitation, evapotranspiration, runoff, and anthropogenic water use were 0.363, −0.265, −0.258 and −0.115, respectively. The results are helpful for the scientific planning and management of water resources in Northwest China. Full article

Climate change is becoming more pronounced and affecting all environmental components, leading to river flow changes. This study aimed to investigate the annual and seasonal discharge trends for six rivers in Bosnia and Herzegovina in Europe in the period from 1961 to 2020. The trends were analysed using a linear regression (LR) analysis, the Mann–Kendal test (MK), and an innovative trend analysis (ITA). The fewest significant trends were obtained by the LR analysis, followed by the MK test, and the most were obtained by the ITA method. The ITA method identified 76.67% significant negative trends and 13.33% significant positive trends in all data groups. It can be concluded that the discharges in the second part of the observed period (1991–2020) were significantly lower compared to the first part (1961–1990). A more detailed ITA of the flow by data group (low, medium, and high) was also carried out. The results showed the occurrence of increasingly large extremes. Therefore, in the second subperiod, the minimum values were smaller and the maximum values were larger than in the first subperiod. The occurrence of high water levels increases the possibility of floods, and a long dry period can cause problems with the generation of electricity from hydropower plants. For this reason, analysing discharge trends in the future is certainly a justified recommendation. Full article

This study examines the spatiotemporal characteristics of extreme precipitation indices in Iran. It analyzes data from 38 synoptic stations across the country, covering the period from 1990 to 2020, focusing on the 11 most common extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis employs the Mann–Kendall (M–K) trend test. The findings indicate that the indices PRCPTOT (annual total precipitation), R20 mm (very heavy precipitation days), R10 mm (heavy precipitation days), R25 mm (number of wet days), Rx1 day (maximum 1-day precipitation), Rx5 day (maximum 5-day precipitation), SDII (simple daily intensity index), R95p (very wet day precipitation), R99p (extremely wet day precipitation), and CWDs (consecutive wet days) showed the highest values in the northern and western regions of the country, particularly at stations like Ramsar, Hamedan, Ilam, Kermanshah, and Yasouj. Conversely, the eastern and southeastern parts of the country showed the lowest values for these indices. The Consecutive Dry Day (CDD) index exhibited the highest values at Zabol station (228 days) and Abadan station (193 days) in the southern region of the country. Generally, precipitation extremes in the western, northwestern, and Caspian Sea coasts showed an increasing trend, while the eastern, southeastern, and central parts of the country demonstrated a decreasing trend. The trend test results indicate significant mutations in all precipitation indices, except for SDII, with mutation points primarily occurring during the decade from 2000 to 2010. The magnitude of mutation for each index post-mutation is generally greater than before. This study provides valuable information for decision-makers in agriculture, food security, water, and the environment. It also serves as a resource for natural disaster prevention and mitigation. Full article

Zhejiang Province, a pivotal economically developed region within China’s Yangtze River Delta, requires systematic investigation of spatiotemporal vegetation dynamics and their drivers to formulate targeted ecological protection policies and optimize vegetation restoration strategies. Utilizing the Google Earth Engine (GEE) platform, this study applied the Kernel Normalized Difference Vegetation Index (kNDVI) to assess vegetation responses to climate variability and human activities in Zhejiang Province from 2000 to 2022. Analytical methods included simple linear regression, Theil Sen trend analysis (Sen), Mann Kendall test (MK), Hurst index, partial correlation analysis, and correlation analysis. The results show: (1) The kNDVI exhibited a significant upward trend (0.001/year), covering 61.5% of the province. The Hurst index analysis revealed that 69.1% of vegetation changes exhibited anti-sustainability characteristics, with future vegetation degradation areas (56.4%) projected to exceed improvement areas (28.1%). (2) Human activities (57.11%) contributed more to kNDVI changes than climate change (42.89%). (3) Against the backdrop of climate change, kNDVI demonstrated a positive partial correlation with temperature (coefficient: 0.44) but exhibited a negative correlation with precipitation (coefficient: −0.056), confirming temperature as the dominant climatic driver. Overall, vegetation dynamics in Zhejiang Province from 2000 to 2022 were jointly driven by climate change and human activities. Full article

Understanding the impact of drought on the water use efficiency (WUE) of grasslands is essential for comprehending the mechanisms of the carbon–water cycle in the context of global warming. Nevertheless, the cumulative and lagged effects of drought on WUE across different grassland types in China remain unclear. This study investigates the cumulative and lagged effects of drought on WUE across different grassland types in China from 1982 to 2018. We employed the Sen-MK trend test and correlation analysis to identify the primary factors influencing the temporal effects of drought on WUE. The results indicated that WUE in Chinese grasslands, across various grassland types, exhibited an upward trend over time, with the most rapid increase observed in meadow. Drought had both cumulative and lagged effects on WUE, with cumulative effects lasting an average of 5.2 months and lagged effects lasting 6.1 months. Specifically, the cumulative effects of drought on WUE lasted for 5.6 months for alpine and subalpine meadow, slope, and desert grassland, whereas the lagged effects lasted 9 months for alpine and subalpine plain grassland. Furthermore, the influence of drought on WUE in grasslands varied across different grassland types and intensified with increasing altitude. The trends observed in the cumulative and lagged impacts of drought on WUE across various aridity index (AI) zones were consistent with those for grasslands as a whole. Our findings underscore that the response of WUE to drought in grasslands and their distinct types is primarily characterized by lagged effects. This research provides an important reference value for enhancing the stability of grassland ecosystems. Full article

Variability identification and uncertainty characteristic analysis, under the impacts of climate change and human activities, is beneficial for accurately predicting the future evolution trend of hydrological variables. In this study, based on the evolution trend and characteristic analyses of historical precipitation and temperature sequences from monthly, annual, and interannual scales through the Linear Tendency Rate (LTR) index, as well as its variability point identification using the M–K trend test method, we further utilized three cloud characteristic parameters comprising the average Ex, entropy En, and hyper-entropy He of the Cloud Model (CM) method to quantitatively reveal the uncertainty features corresponding to the diverse cloud distribution of precipitation and temperature sample scatters. And then, through an application analysis of the proposed research framework in Anhui Province, China, the following can be summarized from the application results: (1) The annual precipitation of Anhui Province presented a remarkable decreasing trend from south to north and an annual increasing trend from 1960 to 2020, especially in the southern area, with the LTR index equaling 55.87 mm/10a, and the annual average temperature of the entire provincial area also presented an obvious increasing trend from 1960 to 2020, with LTR equaling about 0.226 °C/10a. (2) The uncertainty characteristic of the precipitation series was evidently intensified after the variability points in 2013 and 2014 in the southern and provincial areas, respectively, according to the derived values of entropy En and hyper-entropy He, which are basically to the contrary for the historical annual average temperature series in southern Anhui Province. (3) The obtained result was basically consistent with the practical statistics of historical hydrological and disaster data, indicating that the proposed research methodologies can be further applied in related variability diagnosis analyses of non-stationary hydrological variables. Full article

Forest fires are an important disturbance that affects ecosystem stability and pose a serious threat to the ecosystem. However, the recovery process of forest ecological quality (EQ) after a fire in plateau mountain areas is not well understood. This study utilizes the Google Earth Engine (GEE) and Landsat data to generate difference indices, including NDVI, NBR, EVI, NDMI, NDWI, SAVI, and BSI. After segmentation using the Simple Non-Iterative Clustering (SNIC) method, the data were input into a random forest (RF) model to accurately extract the burned area. A 2005–2020 remote sensing ecological index (RSEI) time series was constructed, and the recovery of post-fire forest EQ was evaluated through Theil–Sen slope estimation, Mann–Kendall (MK) trend test, stability analysis, and integration with topographic information systems. The study shows that (1) from 2006 to 2020, the post-fire forest EQ improved year by year, with an average annual increase rate of 0.014/a. The recovery process exhibited an overall trend of “decline initially-fluctuating increase-stabilization”, indicating that RSEI can be used to evaluate the post-fire forest EQ in complex plateau mountainous regions. (2) Between 2006 and 2020, the EQ of forests exhibited a significant increasing trend spatially, with 84.32% of the areas showing notable growth in RSEI, while 1.80% of the regions experienced a declining trend. (3) The coefficient of variation (CV) of RSEI in the study area was 0.16 during the period 2006–2020, indicating good overall stability in the process of post-fire forest EQ recovery. (4) Fire has a significant impact on the EQ of forests in low-altitude areas, steep slopes, and sun-facing slopes, and recovery is slow. This study offers scientific evidence for monitoring and assessing the recovery of post-fire forest EQ in plateau mountainous regions and can also inform ecological restoration and management efforts in similar areas. Full article