Search Results (3,468)

Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation. Full article

The extensive use of pesticides has led to the contamination of natural resources, sometimes causing significant and irreversible damage to the environment and human health. Even though the use of many pesticides is banned, these compounds are still being found in rivers worldwide. In this review, 205 documents have been selected to provide an overview of pesticide contamination in rivers over the last 10 years (2014–2024). After these documents were examined, information of 47 river systems was organized according to the types of pesticides most frequently detected, including organochloride, organophosphorus, and pyrethroid compounds. A total of 156 compounds were classified, showing that 46% of these rivers contain organochlorine compounds, while 40% exhibit organophosphorus pesticides. Aldrin, hexachlorocyclohexane, and endosulfan were the predominant organochlorine pesticides with concentration values between 0.4 and 37 × 10⁵ ng L⁻¹. Chlorpyrifos, malathion, and diazinon were the main organophosphorus pesticides with concentrations between 1 and 11 × 10⁵ ng L⁻¹. Comparing the pesticide concentrations with standard guidelines, we found that the Ganga River in India (90 ng L⁻¹), the Owan and Okura Rivers in Nigeria (210 and 9 × 10³ ng L⁻¹), and the Dong Nai River in Vietnam (68 ng L⁻¹) exceed the permissible levels of aldrin (30 ng L⁻¹). Full article

Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution (PD) shifts under climate change. By employing LMRDs, we analyse changes in PDs and their parameters over time, identifying key environmental predictors such as lagged precipitation for September 5-day low-flows. Our findings indicate a significant relationship between total August precipitation L-moment ratios (LMRs) and September 5-day low-flow LMRs (${\tau }_2$-Precipitation and ${\tau }_2$-Discharge: R² = 0.675, p-values < 0.001; ${\tau }_3$-Precipitation and ${\tau }_3$-Discharge: R² = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming $\alpha$ = 0.05 and a 31-year RWLM), which we later refine and use for prediction within our MLR algorithm. The methodology, applied to the Goat River near Creston, British Columbia, aids in understanding the implications of climate change on water resources, particularly for the yaqan nuʔkiy First Nation. We find that future low-flows under climate change will be outside the Natural Range of Variability (NROV) simulated from historical records (assuming a constant PD). This study provides insights that may help in adaptive water management strategies necessary to help preserve Indigenous cultural rights and practices and to help sustain fish and fish habitat into the future. Full article

The North China Plain is among the regions most afflicted by ground fissure disasters in China. Recent urbanization has accelerated ground fissure activity in the three counties of the northern North China Plain, posing significant threats to both the natural environment and socioeconomic sustainability. Despite the increased attention, a lack of comprehensive understanding persists due to delayed recognition and limited research. This study conducted field visits and geological surveys across 43 villages and 80 sites to elucidate the spatial distribution patterns of ground fissures in the aforementioned counties. By integrating these findings with regional geological data, we formulated a causative model to explain ground fissure formation. Our analysis reveals a concentration of ground fissures near the Niuxi and Rongxi faults, with the former exhibiting the most extensive distribution. The primary manifestations of ground fissures include linear cracks and patch-shaped collapse pits, predominantly oriented in east-west and north-south directions, indicating tensile failure with minimal vertical displacement. Various factors contribute to ground fissure development, including fault activity, ancient river channel distribution, bedrock undulations, rainfall, and ground settlement. Fault activity establishes a concealed fracture system in shallow geotechnical layers, laying the groundwork for ground fissure formation. Additionally, the distribution of ancient river channels and bedrock undulations modifies regional stress fields, further facilitating ground fissure emergence. Rainfall and differential ground settlement serve as triggering mechanisms, exposing ground fissures at the surface. This research offers new insights into the causes of ground fissures in the northern North China Plain, providing crucial scientific evidence for sustaining both the natural environment and the socio-economic stability of the region. Full article

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale. Full article

Black locust (Robinia pseudoacacia L.), one of the major afforestation species adopted in vegetation restoration, is notable for its rapid root growth and drought resistance. It plays a vital role in improving the natural environment and soil fertility, contributing significantly to soil and water conservation and biodiversity protection. However, compared with natural forests, due to the low diversity, simple structure and poor stability, planted forests including Robinia pseudoacacia L. are more sensitive to the changing climate, especially in the aspects of growth trend and adaptive range. Studying the ecological characteristics and geographical boundaries of Robinia pseudoacacia L. is therefore important to explore the adaptation of suitable niches to climate change. Here, based on 162 effective distribution records in China and 22 environmental variables, the potential distribution of suitable niches for Robinia pseudoacacia L. plantations in past, present and future climates was simulated by using a Maximum Entropy (MaxEnt) model. The results showed that the accuracy of the MaxEnt model was excellent and the area under the curve (AUC) value reached 0.937. Key environmental factors constraining the distribution and suitable intervals were identified, and the geographical distribution and area changes of Robinia pseudoacacia L. plantations in future climate scenarios were also predicted. The results showed that the current suitable niches for Robinia pseudoacacia L. plantations covered 9.2 × 10⁵ km², mainly distributed in the Loess Plateau, Huai River Basin, Sichuan Basin, eastern part of the Yunnan–Guizhou Plateau, Shandong Peninsula, and Liaodong Peninsula. The main environmental variables constraining the distribution included the mean temperature of the driest quarter, precipitation of driest the quarter, temperature seasonality and altitude. Among them, the temperature of the driest quarter was the most important factor. Over the past 90 years, the suitable niches in the Sichuan Basin and Yunnan–Guizhou Plateau have not changed significantly, while the suitable niches north of the Qinling Mountains have expanded northward by 2° and the eastern area of Liaoning Province has expanded northward by 1.2°. In future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. are expected to expand significantly in both the periods 2041–2060 and 2061–2080, with a notable increase in highly suitable niches, widely distributed in southern China. A warning was issued for the native vegetation in the above-mentioned areas. This work will be beneficial for developing reasonable afforestation strategies and understanding the adaptability of planted forests to climate change. Full article

The Henan area of the middle and lower reaches of the Yellow River is situated within the third sedimentary loess area, positioned as the southeasternmost segment within the transitional belt connecting the Loess Plateau with the North China Plain. Addressing concerns related to loess collapse, landslides, and subgrade settlement across various regions attributable to the collapsible nature of Malan loess in western Henan, this study undertook collapsibility testing of undisturbed Malan loess in the province. The different mechanisms of loess collapsibility in different regions were explained from the microstructure by using the indoor immersion-compression test double-line method, scanning electron microscope (SEM), and particles and cracks analysis system (PCAS). The relationship between quantitative factors of microstructure and collapsibility of loess was analyzed by linear regression analysis. The findings indicate that under identical overburden pressure and immersion conditions, the collapsibility of Malan loess in western Henan diminishes progressively from west to east. Microstructural tests were conducted on various loess specimens using scanning electron microscopy, revealing that the distribution of loess particles is notably concentrated in the Xingyang and Gongyi areas, leading to a reduction in pore area compared to the Shanzhou and Mianchi areas. While the Mianchi and Shanzhou areas exhibit a loose arrangement of loess particles, those in Xingyang and Gongyi are comparatively denser. Analysis of microstructural images through the particles and cracks analysis system elucidated that the pore arrangement in the Gongyi and Xingyang areas is more stable than in the Mianchi and Shanzhou areas. Additionally, there is a gradual concentration in particle distribution, accompanied by an increase in agglomeration degree. According to the analysis and comparison of microstructure and quantitative parameters of four groups of loess samples before and after collapsibility, it is revealed that the change mechanism underlying loess collapsibility in various regions of western Henan primarily stems from the external factors influencing the microstructural alterations within the loess. The microstructural determinants contributing to collapsibility changes in different regions encompass three principal aspects: Firstly, modifications in the grain morphology of the Malan loess skeleton in western Henan are notable. Secondly, variations in the internal pore characteristics of loess microstructure are observed. Thirdly, disparities exist in the interconnections between soil particles. The findings of this research hold significant worth for improving construction safety and geological hazard prevention within the Loess region of western Henan. Full article

Fish farming is increasingly important globally and nationally, playing a crucial role in fish production for human consumption. Monitoring microbiological and chemical contaminants from water discharge is essential to mitigate the risk of contaminating water and fish for human consumption. This study analyzes the physicochemical and E. coli parameters of water and tambacu fish muscles (Colossoma macropomum × Piaractus mesopotamicus) in Western Maranhão, Brazil. It also includes a qualitative characterization of zooplankton in the ponds. Samples were collected from tambacu ponds in a dam system fed by natural watercourses from the Tocantins River tributaries, located at the connection of the Brazilian savanna and Amazon biomes. The physicochemical and E. coli parameters of water did not meet national standards. The zooplankton community included Rotifera, Cladocera, Copepoda, and Protozoa representatives, with no prior studies on zooplankton in the region, making these findings unprecedented. The biological quality of freshwater is crucial in fish farming, as poor quality can lead to decreased productivity and fish mortality, raising significant food safety concerns. The water quality studied is related to the potential influence of untreated wastewater as a source of contamination, leaving the studied region still far from safe water reuse practices. The findings on chemical and E. coli contamination of fish farming waters concern human health and emphasize the need for appropriate regulations. Full article

River discharge is an essential input to hydrosystem projects. This paper aimed to modify the group method of data handling (GMDH) to create a new artificial intelligent forecast model (abbreviated as MGMDH) for predicting discharges at river cross-sections (CSs). The basic idea was to optimise the weights for selected hydrometric and meteorological predictors. One novelty of this study was that MGMDH could take the discharge observed from a neighbouring CS as a predictor when observations from the CS of interest had ceased. Another novelty was that MGMDH could include meteorological parameters as extra predictors. The model was validated using data from natural rivers. For given lead times, MGMDH automatically determined the best forecast equations, consistent with physical river hydraulics laws. This automation minimised computing time while improving accuracy. The model gave reliable forecasts, with a coefficient of determination greater than 0.978. For lead times close to the advection time from upstream to the CS of interest, the forecast had the highest reliability. MGMDH results compared well with some other machine learning models, like neural networks and the adaptive structure of the group method of data handling. It has potential applications for efficiently forecasting discharge and offers a tool to support flood management. Full article

The environmental vulnerability diagnosis of a river basin depends on a holistic analysis of its environmental aspects and degradation factors. Based on this diagnosis, the definition of priority areas where interventions for environmental recovery should be carried out is fundamental, since financial and natural resources are limited. In this study, we developed a methodology to assess these fragilities using an environmental vulnerability index (EVI) that combines physical and environmental indicators related to the natural sensitivity of ecosystems and their exposure to anthropogenic factors. The developed EVI was applied to the headwater region of the São Francisco River Basin (SFRB), Brazil. The proposed index was based on the AHP multicriteria analysis and was adapted to include four variables representative of the study area: Land Use Adequacy, Burned Area, Erosion Susceptibility, and quantitative water balance. The EVI analysis highlighted that the presence of easily erodible soils, associated with sloping areas and land use above their capacity, generate the most vulnerable areas in the headwaters of the SFRB. The highest EVI values are primarily linked to regions with shallow, easily erodible soils like Leptosols and Cambisols, found in steep areas predominantly used for pasture. In the SFBR, the greatest vulnerability was observed within a 5 km buffer around conservation units, covering approximately 32.4% of the total area. The results of this study indicate where resources should be applied for environmental preservation in the basin under study, directing the allocation of efforts to areas with lower resilience to maintain ecosystem services. Full article

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management. Full article

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. Full article

Vietnam is a country highly vulnerable to climate change. Specifically, climate change has seriously impacted all aspects of Vietnam’s economic and social life, especially agricultural production. In this article, we focus on analyzing the current situation and the impacts of climate change on agricultural production and food security in Vietnam, especially in the Mekong River Delta (MRD) region. Vietnam’s climate change scenarios (RCP4.5 and RCP 8.5) have warned of serious increases in temperature, rainfall, and sea level rises for the MRD in coming times. This will lead to a risk of flooding in nearly 50% of the region’s area and will seriously affect agricultural production in many aspects such as soil quality degradation, scarcity of water resources, increased droughts and floods, reduced crop productivity, and so on. These impacts will reduce Vietnam’s food supply capacity, but do not compromise national food security from a short-term perspective. Faced with this situation, the Government of Vietnam has implemented many comprehensive measures to transform agriculture towards ecology, sustainability, and low carbon emissions, with the goal of green growth and neutral carbon emissions by 2050. In particular, the focus is on combining nature-based solutions with the application of modern science and technology in agricultural production, raising awareness and the response capacity of domestic people, with international cooperation in addressing climate change issues. Full article

Wetland ecosystems are experiencing rapid degradation due to human activities, particularly the diversion of natural flows for various purposes, leading to significant alterations in wetland hydrology and their ecological functions. However, understanding and quantifying these eco-hydrological changes, especially concerning inundation dynamics, presents a formidable challenge due to the lack of long-term, observation-based spatiotemporal inundation information. In this study, we classified wetland areas into ten equal-interval classes based on inundation probability derived from a dense, 30-year time series of Landsat-based inundation maps over an Australian dryland riparian wetland, Macquarie Marshes. These maps were then compared with three simplified vegetation patches in the area: river red gum forest, river red gum woodland, and shrubland. Our findings reveal a higher inundation probability over a small area covered by river red gum forest, exhibiting persistent inundation over time. In contrast, river red gum woodland and shrubland areas show fluctuating inundation patterns. When comparing percentage inundation with the Normalized Difference Vegetation Index (NDVI), we observed a notable agreement in peaks, with a lag time in NDVI response. A strong correlation between NDVI and the percentage of inundated area was found in the river red gum woodland patch. During dry, wet, and intermediate years, the shrubland patch consistently demonstrated similar inundation probabilities, while river red gum patches exhibited variable probabilities. During drying events, the shrubland patch dried faster, likely due to higher evaporation rates driven by exposure to solar radiation. However, long-term inundation probability exhibited agreement with the SAGA wetness index, highlighting the influence of topography on inundation probability. These findings provide crucial insights into the complex interactions between hydrological processes and vegetation dynamics in wetland ecosystems, underscoring the need for comprehensive monitoring and management strategies to mitigate degradation and preserve these vital ecosystems. Full article

Sandbars are an integral part of the alluvial river’s geophysical system due to these rivers’ wide sediment availability and varied transport capacity. The sandbars’ evolution and translation considerably influence the stability of the riverbank. However, while designing the riverbank protection structures (RBPS), the impact of such sandbars is often overlooked, as the evolution of such bars is quite uncertain in terms of location, amplitude, and translation. This study evaluates the localized impact of sandbars on bank protection structures in two types of alluvial rivers: meandering (Ganges) and braided (Brahmaputra-Jamuna), utilizing time series satellite images, hydraulic characteristics, and numerical modeling. We found that sandbar development initiates width adjustment in both meandering and braided rivers when the ratio of width to depth surpasses 90. In the case of meandering rivers, riverbank erosion mostly occurs as a result of the presence of alternate bars or point bars. Sandbars in a meandering river (Ganges) can lead to an approximate 18% increase in flow depth. The depth-averaged velocity is anticipated to rise by approximately 29%, and the tractive force may increase by a factor of 1.6. On the other hand, the braided river (the Brahmaputra-Jamuna) underwent significant bank erosion due to the presence of both free unit and hybrid types of bars. In such rivers, the depth of the flow may experience a notable increase of 18%, while the depth-averaged velocity undergoes an approximate increase of 50%, and the tractive force has the potential to grow by a factor of 5.3. Consequently, we recommend allowing the natural evolution of sandbars while preserving the riverbank (where needed only) through RBPS, considering these additional loads. Full article