Search Results (189)

As a pivotal ecological–economic nexus in China, the Yangtze River Basin (YRB)’s spatiotemporal evolution of habitat quality (HQ) profoundly influences regional sustainable development. This study establishes a tripartite analytical framework integrating remote sensing big data, socioeconomic datasets, and ecological modeling. By coupling the InVEST and PLUS models with Theil–Sen median trend analysis and Mann–Kendall tests, we systematically assessed HQ spatial heterogeneity across the basin during 2000–2020 and projected trends under 2030 scenarios (natural development (S1), cropland protection (S2), and ecological conservation (S3)). Key findings reveal that basin-wide HQ remained stable (0.599–0.606) but exhibited marked spatial disparities, demonstrating a “high-middle reach (0.636–0.649), low upper/lower reach” pattern. Urbanized downstream areas recorded the minimum HQ (0.478–0.515), primarily due to landscape fragmentation from peri-urban expansion and transportation infrastructure. Trend analysis showed that coefficient of variation (CV) values ranged from 0.350 to 2.72 (mean = 0.768), indicating relative stability but significant spatial variability. While 76.98% of areas showed no significant HQ changes, 15.83% experienced declines (3.56% with significant degradation, p < 0.05) concentrated in urban agglomerations (e.g., the Wuhan Metropolitan Area, the Yangtze River Delta). Only 7.18% exhibited an HQ improvement, predominantly in snowmelt-affected Qinghai–Tibet Plateau regions, with merely 0.95% showing a significant enhancement. Multi-scenario projections align with Theil–Sen trends, predicting HQ declines across all scenarios. S3 curbs decline to 0.33% (HQ = 0.597), outperforming S1 (1.07%) and S2 (1.15%). Nevertheless, downstream areas remain high-risk (S3 HQ = 0.476). This study elucidated compound drivers of urbanization, agricultural encroachment, and climate change, proposing a synergistic “zoning regulation–corridor restoration–cross-regional compensation” pathway. These findings provide scientific support for balancing ecological protection and high-quality development in the Yangtze Economic Belt, while offering systematic solutions for the sustainable governance of global mega-basins. Full article

Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km² in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less). Full article

Big head croaker (Collichthys lucidus) is a dominant fish species in the Yangtze River estuary, with significant economic and ecological value in the local ecosystem. In this study, the dietary composition of big head croaker in the Yangtze River estuary from 2022 to 2023 was determined using stomach content analysis. Statistical methods such as cluster analysis and canonical correspondence analysis were also applied to study the ontogenetic variation in the feeding habits of big head croaker and their relationships with environmental factors. The results indicated that big head croaker in the Yangtze River estuary fed primarily on 15 prey groups and 33 prey species. Copepods were the dominant prey group, followed by mysids, shrimp, and fish. The dominant prey species included Acanthomysis longirostris, Neomysis awatschensis, and Calanus sinicus. Compared with historical studies, the proportion of large prey such as fish and crustaceans in the diet of big head croaker has increased since the implementation of the “10-Year Fishing Ban” on the Yangtze River, which reflects the improved aquatic habitat for organisms in the Yangtze River estuary to some extent. The feeding habits of big head croaker exhibited clear ontogenetic and seasonal variations. The empty stomach rate gradually decreased as the body size of big head croaker increased and their main prey shifted from small individuals such as Acetes chinensis and A. longirostris to larger individual fishes and Brachyura. In addition, big head croaker primarily fed on N. awatschensis in spring, A. longirostris in summer and autumn, and Acrocalanus gibber in winter. Canonical correspondence analysis indicated that salinity and length were the factors most strongly correlated with the feeding habits of big head croaker, followed by latitude and longitude. Full article

This study addresses the challenges related to the distribution of elderly care facilities in the Yangtze River Delta (YRD) region, which is experiencing a rapidly aging population. With over 176 million people aged 65 and above in China as of 2019 and the elderly population in the YRD continuing to grow, the study analyzes the spatial distribution, evolution, and supply–demand balance of elderly care facilities. Using GIS technologies, multi-source data analysis, and spatial autocorrelation techniques, the research identifies key regional patterns. Shanghai exhibits a clear hierarchical distribution of facilities, Jiangsu shows a “south strong, north weak” trend, while Zhejiang and Anhui demonstrate the opposite. The study also highlights a shift towards smaller, community-based care facilities, reflecting the growing demand for more localized services. It uncovers significant spatial mismatches and low coordination between supply and demand, particularly in rural and urban fringe areas, indicating the need for better regional coordination and more balanced resource distribution. To address these challenges, the study recommends (a) establishing cross-regional elderly care resource-sharing mechanisms; (b) promoting the development of small, community-based facilities; (c) integrating urban and rural services; and (d) leveraging technology for smart elderly care, including the use of big data and AI to optimize service delivery. These strategies aim to improve the equity and accessibility of elderly care services, ensuring that underserved areas receive better support. The findings provide a comprehensive framework for elderly care policies, offering valuable insights for other rapidly urbanizing regions and countries facing similar demographic challenges. Full article

Microplastic pollution levels and potential sources of contamination in North Central Appalachia are evaluated to fill a major knowledge gap regarding microplastics in freshwater systems, which lead to negative consequences for the sustainability of healthy freshwaters. Fifty-five northern hogsucker fish were sampled from nine sites throughout seven freshwater streams in the region. Microplastic particles were extracted from the gastrointestinal (GI) tracts via 10% KOH digestion and identified visually. A total of 2185 particles were identified, ranging between 8 and 274 particles/individual and an average of 39.73 particles/individual. The most particles were found in fish within the Cheat watershed, particularly at the Big Sandy Creek downstream site, followed by tributaries of the Monongahela and Ohio Rivers. The most identified particle type was fiber (96.61%). There was a positive relationship between the total length of fish and number of particles. Agricultural land use and E. coli abundance were both positively correlated with microplastic abundance. Agricultural land use and sewage input both appear to be important drivers of microplastic pollution in these streams, although we cannot rule out the influence of atmospheric deposition. These results point to widespread levels of microplastic contamination in freshwater ecosystems in North Central Appalachia. Full article

This review, “The Development of a River Quality Prediction Model That Is Based on the Water Quality Index using Machine Learning: A Review”, discusses and evaluates research articles and attempts to incorporate ML algorithms into the water quality index (WQI) to improve the prediction of river water quality. This original study confirms how new methodologies like LSTM, CNNs, and random forest perform better than previous methods, as they offer real-time predictions, operational cost saving, and opportunities for handling big data. This review finds that, in addition to good case studies and real-life applications, there is a need to expand in the following areas: impacts of climate change, ways of enhancing data representation, and concerns to do with ethics as well as data privacy. Furthermore, this review outlines issues, such as data scarcity, model explainability, and computational overhead in real-world ML applications, as well as strategies to preemptively address these issues in order to improve the versatility of data-driven models in various domains. Moving to the analysis of the review specifically to discuss the propositions, the identified key points focus on the use of complex approaches and interdisciplinarity and the involvement of stakeholders. Due to the added specificity and depth in a number of comparisons and specific technical and policy discussions, this sweeping review offers a broad view of how to proceed in enhancing the usefulness of the predictive technologies that will be central to environmental forecasting. Full article

Tailings generated by mining account for the largest world-wide waste from industrial activities. As an element, copper is relatively uncommon, with low concentrations in sediments and waters, yet is very elevated around mining operations. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901 and 1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. About 10 MMT formed a beach that has migrated 7 km from the original Gay pile to the Traverse River Seawall. Another 11 MMT are moving underwater along the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Here we use remote sensing techniques to document geospatial environmental impacts and initial phases of remediation. Aerial photos, multiple ALS (crewed aeroplane) LiDAR/MSS surveys, and recent UAS (uncrewed aircraft system) overflights aid comprehensive mapping efforts. Because natural beach quartz and basalt stamp sands are silicates of similar size and density, percentage stamp sand determinations utilise microscopic procedures. Studies show that stamp sand beaches contrast greatly with natural sand beaches in physical, chemical, and biological characteristics. Dispersed stamp sand particles retain copper, and release toxic levels of dissolved concentrations. Moreover, copper leaching is elevated by exposure to high DOC and low pH waters, characteristic of riparian environments. Lab and field toxicity experiments, plus benthic sampling, all confirm serious impacts of tailings on aquatic organisms, supporting stamp sand removal. Not only should mining companies end coastal discharges, we advocate that they should adopt the UNEP “Global Tailings Management Standard for the Mining Industry”. Full article

Background: The Yellow River irrigation area in Ningxia faces spring drought, resalting, severe water resource shortage, and significant water wastage in saline–alkali soils. Objective: To explore the effects of two different improvement measures on maize fresh biomass and the basic physical and chemical properties of saline soil under four irrigation gradients, aiming to provide a theoretical basis for water-saving irrigation in the Yellow River irrigation area of Ningxia while ensuring maize yield. Methods: The experiment designed four irrigation gradients, W1: local conventional water volume (240 mm), W2: 10% water-saving (216 mm), W3: 20% water-saving (192 mm), W4: 30% water-saving (168 mm), and two different soil improvement treatments, a combination treatment of desulfurization gypsum, ETS microbial agent, and biochar (JC), and a combination treatment of desulfurization gypsum, humic acid, and mulching (FS), with a blank control (CK), resulting in 12 treatments in total. Results: The results showed that compared with CK, both JC and FS treatments reduced soil pH, with JC treatment showing a more significant reduction in soil alkalinity than FS treatment. Both JC and FS treatments inhibited the rise in soil electrical conductivity (EC), with JC showing a significantly higher ability to suppress the rise in EC than FS treatment. Both FS and JC treatments improved soil water retention, but in May 2023 during the maize seedling stage, FS treatment had a stronger water retention ability than JC treatment; however, in July at the maize big jointing stage and in September at the maize maturity stage, JC treatment exhibited better water retention ability than FS treatment. Both JC and FS treatments increased maize fresh biomass under four water conditions, but under WI and W2 conditions, there was no significant difference in the ability of JC and FS treatments to increase maize fresh biomass. Under any irrigation condition, the ability of JC treatment to improve WUE is higher than that of FS treatment. Under W3 and W4 conditions, JC treatment significantly outperformed FS treatment in increasing maize fresh biomass yield. Additionally, under W3 irrigation conditions, using JC treatment not only achieved greater water-saving goals but also prevented crop yield reduction due to water-saving measures. This article can provide a theoretical basis for agricultural irrigation management, especially in the Ningxia Yellow River irrigation area of China. It can help ensure crop yields while protecting the ecological environment and promoting sustainable agricultural development. Full article

Waterfronts are some of the most well known public spaces that can catalyse urban changes, yet their benefits have not been systematically explored. This study investigates the potential benefits of waterfront regeneration for the subsequent development of the wider surrounding areas and whether these benefits encompass a broader range of influences. Taking an extensive linear catalyst, the Huangpu River waterfronts in Shanghai, as an example, the catalytic effect of each waterfront section was investigated, visualised, compared and discussed within and across different sections and catalytic influential aspects. A multi-method approach driven by multi-sourced big data was used in this study, and the analysis was carried out at two scales: the waterfront area (the catalyst area) and its surroundings of influence (the areas affected by the catalyst area). The research findings suggest that the landscape catalytic effect is more pronounced in the catalyst area itself than in the surrounding areas affected by the catalyst area. Such effects also vary across waterfront sections, and the western bank of the Huangpu River was more obviously influenced than the eastern bank. The possible reasons for these differences may be related to the area’s original function, development limitations and available resources. This study also provides evidence indicating that the relationship between the catalyst and the spatial, social and economic aspects of changes it induces is one of ongoing and mutually supportive interaction. The outcomes of this study include a framework composed of 14 indicators that can disclose the depth and progress of a catalytic effect facilitated by the landscape, as well as implications for the decision-making process in the urban regeneration agenda that centres around waterfronts. Full article

Analyzing and mining spatiotemporal processes refers to the extraction of geographic phenomena from spatiotemporal data and the analysis of available geographic knowledge and patterns. It finds applications in various fields such as natural disaster evolution, environmental pollution, and human behavior prediction. However, training spatiotemporal models based on big data is time-consuming, and the traditional physical models and static objects used in existing geographic data analysis software have limitations in mining efficiency and simulation accuracy for dynamic spatiotemporal processes. In this paper, we develop an intelligent spatiotemporal process analysis and mining software tool, called STPam, which integrates a plug-and-play artificial intelligence model by a service-oriented method, distributed deep learning framework, and multi-source big data adaptation. The floods in the middle reaches of the Yangtze River have perennially affected safety and property in surrounding cities and communities. Therefore, this article applies the software to simulate the flooding process in the basin in 2022. The experimental results correspond to the rare drought phenomenon in the basin, demonstrating the practicality of the STPam software. In summary, STPam aids researchers in visualizing and analyzing geospatial processes and also holds potential application value in assisting regional management authorities in making disaster prevention and mitigation decisions. Full article

Narrow Fluvial Sand Bodies are primarily developed along the river center, with horizontal wells for injection and production in some Bohai waterflooded oilfields. This results in a rapid increase in water cut due to a single injection–production direction. Over time, dominant water breakthrough channels form between wells, and the remaining oil moves to deeper regions, which makes conventional profile control measures less effective. We developed a quantitative method based on integrated dynamic and static big data to identify these breakthrough channels and measure the flow intensity between injection and production wells. To address deep remaining oil mobilization, we performed micro-analysis and physical simulations with heterogeneous core models, which led to the development of a deep profile control system using emulsion polymer gel and self-assembling particle flooding. Experiments show that the combined technology can reduce oil saturation in low-permeability layers to 45.3% and improve recovery by 30.2% compared to water flooding. Field trials proved to be completely effective, with a cumulative oil increase of over 23,200 cubic meters and a 12% reduction in water cut per well. This deep profile control technology offers significant water cut reduction and enhanced oil recovery. It can provide technical support for efficient water control and profile management in similar reservoirs. Full article

In this study, the ANSYS 2020R1 software simulation is employed to examine the diffusion process of oil leakage and the underground water solute transport law in the Dashagou Yellow River crossing section of the oil pipeline. The simulation results demonstrate that under identical leakage pressure conditions, diesel fuel leakage in powdery, sandy soil is diminished, the emergency window is extended, and the corresponding leakage risk is reduced. In addition, the leakage rate of crude oil is slower than that of diesel oil. After 850 days of downward migration of approximately 190 m, the pollutant reaches quasi-static equilibrium in the big sand ditch. The results of the surface water oil spill analysis demonstrated that the oil film on the river surface migrated for 100 min after the spill, with a thickness that remained between 0.02 and 0.05 mm and a concentration that approached equilibrium. Full article

It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes. Full article

Rivers occupy less than 1% of the earth’s surface and yet they perform ecosystem service functions that are crucial to civilisation. Global monitoring of this asset is within reach thanks to the development of big data portals such as Google Earth Engine (GEE) but several challenges relating to output quality and processing efficiency remain. In this technical note, we present a new deep learning pipeline that uses attention-based deep learning to perform state-of-the-art semantic classification of fluvial landscapes with Sentinel-2 imagery accessed via GEE. We train, validate and test the network on a multi-seasonal and multi-annual dataset drawn from a study site that covers 89% of the Earth’s surface. F1-scores for independent test data not used in model training reach 92% for rivers and 96% for lakes. This is achieved without post-processing and significantly reduced computation times, thus making automated global monitoring of rivers achievable. Full article

Blue spaces, water-based open spaces, are becoming focal points for urban vitalization. While previous studies have explored waterfronts’ various effects, little research has focused on their influence on actual visitation and vitality. This study addresses this gap by analyzing the effect of riverfronts on mobility using de facto population data, which tracks citizen activity by location through mobile information. The study focuses on two major rivers in northwestern Seoul, covering nine major riverfront facilities. Population Vitality (PV) and Vitality Index (VI), two novel measures derived from the de facto population data, were calculated for 266 tracts and used as a new indicator of spatial activation. Explanatory variables include regional and riverfront factors, such as the density of facilities, riverfront spaces’ specifications, and vitalization patterns. The findings show that higher densities of park-green spaces and commercial activities significantly enhance vitality, aligning with previous research on open spaces. Compact riversides with higher densities of riverfront facilities also exhibit greater vitality. The VI has demonstrated feasibility as a dynamic metric for assessing spatial activation, effectively capturing temporal fluctuations. By utilizing population big data and novel indices, this study empirically demonstrates the magnetic effects of riverfronts, providing deeper insights into effective riverfront planning. Full article