Spatial Variability Research Articles

El Niño and positive Indian Ocean Dipole conditions simultaneously reduce the production of multiple cereals across India

Abstract Natural climate phenomena like El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) influence the Indian monsoon and thereby the region’s agricultural systems. Understanding their influence can provide seasonal predictability of agricultural production metrics to inform decision-making and mitigate potential food security challenges. Here, we analyze the effects of ENSO and IOD on four agricultural production metrics (production, harvested area, irrigated area, and yields) for rice, maize, sorghum, pearl millet, and finger millet across India from 1968 to 2015. El Niños and positive-IODs are associated with simultaneous reductions in the production and yields of multiple crops. Impacts vary considerably by crop and geography. Maize and pearl millet experience large declines in both production and yields when compared to other grains in districts located in the northwest and southern peninsular regions. Associated with warmer and drier conditions during El Niño, >70% of all crop districts experience lower production and yields. Impacts of positive-IODs exhibit relatively more spatial variability. La Niña and negative-IODs are associated with simultaneous increases in all production metrics across the crops, particularly benefiting traditional grains. Variations in impacts of ENSO and IOD on different cereals depend on where they are grown and differences in their sensitivity to climate conditions. We compare production metrics for each crop relative to rice in overlapping rainfed districts to isolate the influence of climate conditions. Maize production and yields experience larger reductions relative to rice, while pearl millet production and yields also experience reductions relative to rice during El Niños and positive-IODs. However, sorghum experiences enhanced production and harvested areas, and finger millet experiences enhanced production and yields. These findings suggest that transitioning from maize and rice to these traditional cereals could lower interannual production variability associated with natural climate variations.

Spatial Analysis of Air Pollutants in an Industrial City Using GIS-Based Techniques: A Case Study of Pavlodar, Kazakhstan

The given research employs high-resolution air quality monitoring and contemporary statistical methods to address gaps in understanding the urban air pollution in Pavlodar, a city with a significant industrial presence and promising touristic potential. Using mobile air quality sensors for detailed spatial data collection, the research aims to quantify concentrations of particulate matter (PM2.5, PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground-level ozone (O3); assess their distribution; and identify key influencing factors. In this study, we employed Geographic Information Systems (GISs) for spatial analysis, integrating multi-level B-spline interpolation to model spatial variability. Correlation analysis and structural equation modeling were utilized to explore the relationships between variables, while regression analysis was conducted to quantify these relationships. These techniques were crucial for accurately mapping and interpreting spatial patterns and their underlying factors. The study identifies PM2.5 and NO2 as the primary contributors to air pollution in Pavlodar, with NO2 exceeding the 24 h threshold in 87.38% of locations and PM2.5 showing the highest individual air quality index (AQI) in 75.7% of cases. Correlation analysis reveals a positive association between PM2.5 and AQI and a negative correlation between NO2 and AQI, likely due to the dominant influence of PM2.5 in AQI calculations. Structural equation modeling (SEM) further underscores PM2.5 as the most significant impactor on AQI, while NO2 shows no significant direct impact. Humidity is positively correlated with AQI, though this relationship is context-specific to seasonal patterns observed in May. The sectoral analysis of landscape indices reveals weak correlations between the green space ratio (GSR) and air quality, indicating that while vegetation reduces pollutants, its impact is minimal due to urban planting density. The road ratio (RR) lacks sufficient statistical evidence to draw conclusions about its effect on air quality, possibly due to the methodology used. Spatial variability in pollutant concentrations is evident, with increasing PM2.5, PM10, and AQI towards the east-northeast, likely influenced by industrial activities and prevailing wind patterns. In contrast, NO2 pollution does not show a clear geographic pattern, indicating vehicular emissions as its primary source. Spatial interpolation highlights pollution hotspots near industrial zones, posing health risks to vulnerable populations. While the city’s overall AQI is considered “moderate”, the study highlights the necessity of implementing measures to improve air quality in Pavlodar. This will not only enhance the city’s attractiveness to tourists but also support its sustainable development as an industrial center.

An evaluation of evapotranspiration products over the Tibetan Plateau

Abstract In situ observations from 13 sites located over the Tibetan Plateau (TP) are used to evaluate evapotranspiration (ET) products, including remote sensing-based, land surface modeled, and reanalysis products. It is found that the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the simulations by the Community Land Model - Dynamic Global Vegetation Model (CLM-BGCDV) are the top-rank products measured by the Percentage bias, Root Mean Square Error, and Correlation Coefficient against in-situ observations. The evaluated data are then used to examine the consistency in spatial and temporal variability of summer ET and its controlling factors on the TP and the Three-River Sources Region (TRSR). All products show consistently that precipitation in central semiarid part of TP is the dominant factor influencing summer ET, while air temperature plays a certain role in the southeastern and eastern TP. Uncertainties exist in western TP, possibly due to the lack of observations or gaps in the satellite data. Some suggestions for improving ET product development based on models and satellite retrievals, particularly for the cold and complex surface of the TP are also given.

High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects

Accurate spatial distribution of carbon dioxide (CO2) emissions is essential information needed to peaking emissions and achieving carbon neutral in China. The aim of this study was to map CO2 emissions with high spatial resolution at provincial scale and then explore the scale effect on mapping results. As an example, the spatiotemporal pattern and factors influencing CO2 emissions were examined in Guizhou Province in Western China. With the proposed method, a reasonable spatial distribution of CO2 emissions with high spatial resolution was obtained, which had relatively accurate information on spatial details. The optimal resolution of CO2 emissions at the provincial scale under high spatial resolution was approximately 90 m and 1260 m. More detailed grid data can better reflect the spatial variability of CO2 emissions. Emissions of CO2 were spatially heterogeneous in Guizhou, with high emissions in centers of big cities that gradually spread and decreased from city centers. From 2009 to 2019, the spatial distribution of CO2 emissions developed from agglomeration to dispersion. Areas of high carbon emissions decreased, those of medium carbon emissions increased, and many areas changed from no emissions to carbon emissions. Industrial land had the highest emissions, followed by commercial and transportation lands. Over 10 years, changes occurred in the relation between interregional economic level of Guizhou and CO2 emissions, with the relation changing from linear into an inverted U-shaped relation. The effect of industrial structure on CO2 emissions decreased, and the linear increase between CO2 emissions and the urban scale became more evident. The results of this study will contribute to accurate monitoring and management of carbon emissions in Guizhou, as well as provide support to formulate policies related to controls on carbon emissions in different regions.

Using a Nested Sampling Design Across Spatial Scales to Gain Insights Into Distribution Patterns of Fishes, Mussels and Macroinvertebrates in a Riverine System

ABSTRACTAimThe goal of our study was to use a nested sampling design to compare the distribution of different groups of organisms with different dispersal modes and examine their responses in community composition to environmental heterogeneity at different spatial scales (local pool vs. riffle habitat, sections within a river and between rivers).LocationUpper Colorado River basin, Texas, USA.TaxonFishes, mussels and macroinvertebrates.MethodsWe sampled 100 sites using a nested sampling design across five rivers (four tributaries and the mainstem), with four river sections and five sampling sites per section, each consisting of a pool and riffle habitat. Collected data on species abundances, local and landscape environmental variables and spatial variables were analysed using a combination of dissimilarity, redundancy and variation partitioning analysis.ResultsAt the river scale, environmental heterogeneity explained 28% and 34% of the variation in mussel and fish communities, respectively, and 15% for macroinvertebrates. Community changes between sections in the tributaries were highest for fishes, but similarly high for fishes and macroinvertebrates in the mainstem. Significant patterns for mussels were only detected in rivers with higher abundances. No significant differences in dissimilarity were found between mesohabitats, but a small significant effect of mesohabitat was detected for mussels with RDA after removing the river effect.Main ConclusionsAlthough the depauperate mussel communities made it more difficult to draw any conclusions, there were some indications that their distribution was driven by environmental heterogeneity across scales. Section and river scales were relevant for fish, whereas macroinvertebrates showed a weaker response to environmental heterogeneity at these scales. The combination of approaches detected more ecologically meaningful patterns than one analysis alone would have. This study highlights the complexity of riverine community dynamics and underscores the need for a multiscale approach to identify their distribution patterns.

Spatio-temporal assessment of multi-scalar meteorological and hydrological droughts over Bundelkhand, India

Drought, a recurrent natural hazard, significantly impacts agricultural productivity, water resources, and socio-economic development, especially in semi-arid regions like Bundelkhand in India. Here, frequent droughts challenge the livelihoods of its predominantly agrarian population. These challenges, compounded by poverty, land degradation, and resource conflicts, have profound implications for local communities and the attainment of Sustainable Development Goals (SDGs). Understanding the spatiotemporal conditions of drought is crucial for effective planning and management. This study evaluates meteorological and hydrological drought attributes at a high resolution within the region, using the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI) across a range of temporal scales. Findings indicate moderate droughts are more probable than severe or extreme droughts, with SPI-12 showing up to 20% probability over some grids, implying drought occurrence once in five years. Trend analysis reveals decreasing SPI trends in certain northern districts during monsoon months, suggesting more frequent droughts in vulnerable areas. The study also finds higher drought probabilities with longer SPI durations, highlighting significant temporal and spatial variability. Additionally, the applicability of SDI using GloFAS discharge data is evaluated, offering an alternative in the absence of gauge data. The combined SPI and SDI analysis underscores higher and more extensive drought probabilities for longer periods, emphasizing the need for multi-temporal scale assessment for effective drought management. This study enhances understanding of drought characteristics in the region, aiding policymaking for drought management and supporting evidence-based strategies to achieve SDGs, particularly those related to water security, climate resilience, and sustainable development.

Estimating Ground-Level NO2 Concentrations Using Machine Learning Exclusively with Remote Sensing and ERA5 Data: The Mexico City Case Study

This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a pre-trained machine learing model. Our findings indicate that the model captures the spatial and temporal variability of NO2 concentrations across the urban landscape. Key meteorological parameters, such as temperature and wind speed, were identified as significant factors influencing NO2 levels. The model’s adaptability was further tested by incorporating additional variables, such as atmospheric boundary layer height. In order to compare the model’s performance to alternative ML models, we estimated the ground-level NO2 using the state-of-the-art TimeGPT. The results demonstrate that our baseline model has the best performance with a mean normalised root mean square error of 84.47%. This research underscores the potential of combining satellite observations with ML for scalable air quality monitoring, particularly in low- and middle-income countries with limited ground-based infrastructure. The study provides critical insights for air quality management and policy-making, aiming to mitigate the adverse health and environmental impacts of NO2 pollution.

A national-scale sampled temperate fuel moisture database

Fuel moisture content (FMC) is important for the ignitability, behaviour and severity of wildfires. Understanding the drivers of FMC and its spatial and temporal variability can help us develop fuel moisture models and inform assessments of wildfire behaviour and danger. Here we present the first United Kingdom (UK) national-scale temperate FMC dataset of 8,057 samples of eighteen different fuel constituents collected across 58 sampling sites between 2021–2023. We sampled fuels across emerging fire-prone ecosystems in the UK across three studies: (1) UK-wide longer-term sampling characterising the spatio-temporal drivers of FMC; (2) landscape-scale measurement through the North Yorkshire Moors to investigate landscape-driven variability in FMC; (3) plot-scale intensive sampling in the West Midlands to quantify diurnal patterns and among-sampler variability in fuel measurements. This database addresses a global fuel moisture measurement gap within traditionally non-fire prone regions. The database will advance our understanding of temperate fuel moisture dynamics and forms a fundamental contribution towards the development of a fire danger rating system for traditionally non-fire prone regions such as the UK.

Prevalence, spatial distribution and determinants of complete childhood pneumococcal conjugate vaccination in Ethiopia: spatial and multilevel analyses

IntroductionPneumococcal disease is a serious global public health concern. The primary causative agent of severe illnesses such as pneumonia, meningitis, acute otitis media, and bacteremia is the pneumococcus bacterium. The pneumococcal conjugate vaccine is a key strategy to reduce the burden of pneumococcal disease. Understanding the spatial distribution of complete childhood pneumococcal conjugate vaccine utilization and its associated factors is crucial for designing strategies to improve vaccination implementation. Therefore, this study aimed to determine the spatial distribution of complete childhood pneumococcal conjugate vaccination coverage and identify its determinants in Ethiopia.MethodA spatial and multilevel analysis was conducted using data from the 2019 Ethiopian Mini Demographic and Health Survey. The analysis included a total of 2,055 weighted children. The association between the outcome variable and the explanatory variables was determined by calculating adjusted odds ratios at a 95% confidence interval. Explanatory variables were considered significantly associated with the outcome if the p-value was less than 0.05.ResultThe prevalence of complete childhood pneumococcal conjugate vaccination in Ethiopia was 53.94% (95% CI: 51.77, 56.08). Higher complete childhood pneumococcal vaccination coverage was observed in the Addis Ababa, Tigray, Amhara, Benishangul-Gumuz, and Oromia regions, while lower coverage was seen in the Afar, Somali, and SNNPR regions of Ethiopia. Factors significantly associated with complete childhood pneumococcal conjugate vaccination included maternal age, antenatal care visits, place of delivery, region, community women’s literacy level, community poverty level, and community antenatal care utilization.ConclusionThe distribution of complete childhood pneumococcal conjugate vaccination exhibited spatial variability across Ethiopia. Approximately half of children aged twelve to thirty-five months received the full dose of the childhood pneumococcal conjugate vaccine in the country. Several factors were identified as statistically significant determinants of complete childhood pneumococcal conjugate vaccination, including maternal age, antenatal care visits, place of delivery, region, community women’s literacy level, community poverty level, and community ANC utilization. Therefore, policies and strategies aimed at combating pneumococcal disease should consider these determinants and address areas with low vaccination coverage.

Drivers of the taxonomic and functional structuring of aquatic and terrestrial floodplain bird communities

ContextThere has been a limited amount of research which comparatively examines the local and landscape scale ecological determinants of the community structure of both riparian and aquatic bird communities in floodplain ecosystems.ObjectivesHere, we quantified the contribution of local habitat structure, land cover and spatial configuration of the sampling sites to the taxonomical and functional structuring of aquatic and terrestrial bird communities in a relatively intact floodplain of the river Danube, Hungary.MethodsWe used the relative abundance of species and foraging guilds as response variables in partial redundancy analyses to determine the relative importance of each variable group.ResultsLocal-scale characteristics of the water bodies proved to be less influential than land cover and spatial variables both for aquatic and terrestrial birds and both for taxonomic and foraging guild structures. Purely spatial variables were important determinants, besides purely environmental and the shared proportion of variation explained by environmental and spatial variables. The predictability of community structuring generally increased towards the lowest land cover measurement scales (i.e., 500, 250 or 125 m radius buffers). Different land cover types contributed at each scale, and their importance depended on aquatic vs terrestrial communities.ConclusionsThese results indicate the relatively strong response of floodplain bird communities to land cover and spatial configuration. They also suggest that dispersal dynamics and mass-effect mechanisms are critically important for understanding the structuring of floodplain bird communities, and should therefore be considered by conservation management strategies.

Patterns of non‐migratory fish metacommunity from Neotropical floodplains: Local mechanisms, regional scale and hydrological dynamics

AbstractThe spatial distribution of biological communities may be regulated by environmental and spatial processes, and the intensity of these processes depend on the species characteristics, spatial heterogeneity and spatial extent. We investigated the relationship between the non‐migratory fish metacommunity with environmental and spatial variables using the variance partitioning procedure. We used data from lakes of four Neotropical floodplains: Amazon, Araguaia, Pantanal and Paraná. We hypothesised that the processes that drive the spatial distribution of non‐migratory fish in floodplain systems depend on connectivity driven by flood pulse. We predict that spatial variables are less important to explaining the fish community structures during rainy periods when there is no dispersal limitation. In contrast, environmental variables influence these structures during rainy and dry periods. During dry periods, metacommunities depend on species' dispersal abilities, but during rainy and dry periods, spatial distribution depends on the environmental filters selected by each species. For most of the environmental variables, it was possible to observe more similarity in the rainy period. Our results showed different patterns of fish communities for the lakes of the four floodplains systems, which led us to reject our hypothesis. Non‐migratory fish remain in their natal environment and perform only small lateral movements. In this scenario, we found a little influence from spatial variables in both periods and a great contribution of environmental variables for Amazon (16%) and Pantanal (32%) floodplains in structuring fishes' distribution in the rainy period.

Calculations of the conversion factor from organic carbon to organic matter for aerosol mass balance

Aerosol mass balance studies based on filter samples require a conversion factor to derive organic matter (OM) concentrations from organic carbon (OC) measurements from thermo-optical methods. This factor provides indirect insights on the molecular structure of OM needed in chemical transport models. Site- and season-specific ratios of OC to OM (fOM:OC) were calculated using data from five rural background sites in France between 2012 and 2021 by relating the unidentified chemical fraction in PM2.5 samples to thermo-optical OC concentrations. Further, multiple linear formulations were used to evaluate the impact of possible artefacts on the determination of fOM:OC. The resulting fOM:OC was then compared to other estimates derived from online aerosol mass spectrometry data, showing good agreement. The spatial and temporal variability in fOM:OC is discussed considering factors such as seasonality, meteorological conditions and the atmospheric oxidative potential. Linear-mixed effect models were formulated to quantitatively determine the drivers which influence the fOM:OC at the French rural background sites. Both ozone and relative humidity were variables with statistically significant effects on fOM:OC, indicating that differences in the contributions from both photooxidation and water content, explain the variability in fOM:OC observed at the French rural background sites. Site-specific fOM:OC yielded more accurate PM2.5 mass closure and are therefore recommended in mass-balance exercises. Accurate fOM:OC are critical to maintain consistency in OM time series, especially in cases where filter-based time series may be replaced by state-of-the-art online instrumentation.

Tailor-made ammonia nitrogen risk management with machine learning models for aquatic environments in the Mainland of China

Efficient management of pollutant risks in water bodies is crucial for public health and aquatic ecosystem sustainability. However, the toxicities of pollutants, such as ammonia nitrogen (NH3-N), are often affected by multiple water quality factors, including the pH and water temperature. Extensive spatial and temporal variability in these factors hinders tailor-made management of risk. This study used high-frequency monitoring data collected over 1 year to evaluate the long-term NH3-N risk in China’s aquatic ecosystems. High accuracy and interpretability were achieved by decomposing NH3-N risk into the contributions of key influencing factors using random forest models and Shapley Additive Explanations. Two distinct types of NH3-N risk hotspots were identified across 18 cities: 15 cities with high NH3-N concentrations and 3 cities with low environmental carrying capacity due to high pH levels or elevated water temperatures. For the former, rapid NH3-N abatement measures are necessary to bring NH3-N concentrations back below the environmental capacity. For the latter, it is recommended that NH3-N related industries are relocated to regions with high environmental capacities because fragile environments are not suitable for such industries. Importantly, this study investigated methods for attributing pollutant risks in the context of non-linear influencing factors, and the risk of NH3-N was predicted to increase by 6.1 % by the end of 2100 in the context of increasing temperatures under the SSP 2–4.5 scenario. The methodology is also adaptable and suitable for integration into global ecosystem risk management efforts to balance development and aquatic ecological sustainability.

Spatiotemporal Changes of Glaciers in the Yigong Zangbo River Basin over the Period of the 1970s to 2023 and Their Driving Factors

The glaciers in southeastern Tibet Plateau (SETP) influenced by oceanic climate are sensitive to global warming, and there remains a notable deficiency in accurate multitemporal change analyses of these glaciers. We conduct glacier inventories in the Yigong Zangbo River Basin (YZRB) in SETP for the years 1988, 2015, and 2023 utilizing Landsat and Sentinel-2 imagery, and analyze the glacier spatiotemporal variation incorporating the existing glacier inventory data. Since the 1970s until 2023, the glaciers significantly retreated at a rate of 0.76 ± 0.11%·a−1, with the area decreasing from 2583.09 ± 88.80 km2 to 1635.89 ± 71.74 km2, and the ice volume reducing from 221.7017 ± 7.9618 km3 to 152.7429 ± 6.1747 km3. The most significant retreat occurred in glaciers smaller than 1 km2. Additionally, glaciers on southern aspects retreated slower than the northern counterparts. The glaciers in the western YZRB witnessed a significantly greater shrinkage rate than those in the eastern section, with the most pronounced changes occurring in Aso Longbu River Basin. Furthermore, severe glacier mass deficits were observed from 2000 to 2019, averaging a loss rate of 0.57 ± 0.06 m w.e. a−1. The continuous rise in air temperature has primarily induced a general widespread glacier change in the YZRB. However, diverse topography led to spatial variability in glacier changes with discrepancies as large as several times. The features of individual glaciers, such as glacier size, debris cover, and the development of ice-contact glacial lakes enhanced the local complexity of glacier change and elusive response behaviors to climate warming led by the different topographic conditions.

Spatial variability of temperature inside atoll lagoons assessed with Landsat-8 satellite imagery

Sea Surface Temperature (SST) maps are necessary for managing marine resources in a climate change context, but are lacking for most of the 598 world's atolls. We assessed the feasibility of using the Landsat-8 (L8) satellite to infer SST maps for four French Polynesia atolls of aquaculture interest in Tuamotu Archipelago, namely Takaroa, Raroia, Tatakoto, and Reao. Specifically, we (1) used sensors to measure in situ the range of spatial temperature differences recorded in these four atoll lagoons; (2) calibrated and assessed the performances of SST algorithms to estimate lagoon temperature from L8 signals; (3) generated temperature maps for the lagoons and compared spatial patterns of temperature obtained from these maps with patterns highlighted by in situ sensors. Good agreements between satellite and in situ temperature data were obtained, with better results achieved when using an atoll-by-atoll optimization (average bias = −0.26 °C; RMSE = 0.55 °C). However, we also show that the range of temperature inside atoll lagoons is low, and of the same order of magnitude than RMSE achieved with SST algorithms. Because of the L8 overpass time (∼9 a.m.) and the revisit time (16 days), L8 SST could not capture the entire range of spatial differences measured in situ in the four lagoons, but could capture spatial gradients and fronts better than with few in situ sensors. Considering the achieved accuracies and the actual temperature differences at the four study sites, we discuss the usefulness of L8 derived SST maps to assist fishery and aquaculture management in atoll lagoons, as well as the possible generalization to other lagoons.

Incorporating spatial variability in surface runoff modeling with new DEM-based distributed approaches

Incorporating spatial variability in surface runoff modeling with new DEM-based distributed approaches

Evaluating the impact of urban traffic patterns on air pollution emissions in Dublin: a regression model using google project air view data and traffic data

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

Spatiotemporal patterns of greenhouse gas fluxes in the subtropical wetland ecosystem of Indian Himalayan foothill.

The study characterized the temporal and spatial variability in greenhouse gas (GHG) fluxes (CO2, CH4, and N2O) between December 2020 and November 2021 and their regulating drivers in the subtropical wetland of the Indian Himalayan foothill. Five distinct habitats (M1-sloppy surface at swamp forest, M2-plain surface at swamp forest, M3-swamp surface with small grasses, M4-marshy land with dense macrophytes, and M5-marshy land with sparse macrophytes) were studied. We conducted in situ measurements of GHG fluxes, microclimate (AT, ST, and SMC(v/v)), and soil properties (pH, EC, N, P, K, and SOC) in triplicates in all the habitat types. Across the habitats, CO2, CH4, and N2O fluxes ranged from 125 to 536mgm-2h-1, 0.32 to 28.4mgm-2h-1, and 0.16 to 3.14mgm-2h-1, respectively. The habitats (M3 and M5) exhibited higher GHG fluxes than the others. The CH4 flux followed the summer > autumn > spring > winter hierarchy. However, CO2 and N2O fluxes followed the summer > spring > autumn > winter. CO2 fluxes were primarily governed by ST and SOC. However, CH4 and N2O fluxes were mainly regulated by ST and SMC(v/v) across the habitats. In the case of N2O fluxes, soil P and EC also played a crucial role across the habitats. AT was a universal driver controlling all GHG fluxes across the habitats. The results emphasize that long-term GHG flux monitoring in sub-tropical Himalayan Wetlands has become imperative to accurately predict the near-future GHG fluxes and their changing nature with the ongoing climate change.

Multi-Source Data-Based Investigation of Spatiotemporal Heterogeneity and Driving Mechanisms of Coupling and Coordination in Human Settlements in Urban Agglomeration in the Middle Reaches of the Yangtze River

In the information age, the new wave of the information technology revolution has profoundly changed our mode of production and way of life. Pseudo human settlements (PHS), consisting of digits and information, have become increasingly important in human settlements (HS) systems, and become a strong support for the high-quality development of global HS. Against this background, clarifying the spatiotemporal heterogeneity and driving mechanisms of the coupling and coordination between the PHS and real human settlements (RHS) is of great significance to the high-quality development of HS and providing a reasonable explanation of today’s man–land relationship. Therefore, we developed a theoretical framework system for describing PHS–RHS coupling and coordination based on multi-source data such as internet socialization, public utility, and remote sensing images, etc. Taking the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR), which is the key region consolidating China’s “two horizontal and three vertical” urbanization strategy, as a case study area, we have comprehensively analyzed the spatiotemporal heterogeneity of the coupling and coordination of PHS and RHS and its driving mechanism in UAMRYR during the period of 2011–2021, by comprehensively applying the modified coupling coordination degree (CCD) and other models. The results show are as follows: (1) Temporal process—The CCD exhibited a reverse L-shaped increasing trend. The CCD class varied significantly, with the extremely uncoordinated and severely uncoordinated classes present at the beginning of the study period and disappearing toward the end of the study period, while the well coordinated and highly coordinated classes were absent at the beginning of the study period and appeared toward the end of the study period. (2) Spatial pattern—The CCD exhibited an equilateral triangle-shaped, core–margin spatial pattern and a characteristic of core polarization. Overall, the spatial distribution of the CCD exhibited a characteristic of “high in the central region, low in the eastern and western regions, and balanced in the south–north direction”. (3) Dynamic evolution—The CCD increased more rapidly in the north-eastern direction than in the south-western direction; the CCD exhibited north-eastward migration and dispersion, and the spatial variability decreased. (4) Driving mechanisms—The primary factors affecting the CCD varied significantly over time. The living system was dominant in the PHS, whereas the human system was dominant in the RHS. The PHS had a greater effect than the RHS on the CCD. The study broadens the research scope of human settlements geography, establishes a scientific foundation for advancing urban HS construction in the UAMRYR, and offers theoretical support for the high-quality development of cities in the UAMRYR.

Geomorphology and sediment mobility on sand banks: A study of Dogfish Bank, Hecate Strait, Northeast Pacific Ocean

AbstractMovement of sediment along shallow continental shelves is a natural process with wide‐ranging environmental and economic implications, making it of high importance to marine spatial planning efforts in the offshore. Development of marine renewable energy, for instance, requires detailed understanding of the morphodynamics of mobile bedforms to select foundation types and ensure safe installation of infrastructure in shallow shelf environments. This study evaluates geomorphology and sediment mobility of Dogfish Bank (< 20 mbsl) in the Hecate Strait offshore British Columbia, Canada, using hydroacoustic and airborne bathymetric data combined with seismic profiles and grain‐size information. These data reveal current‐swept features ranging from sediment‐depleted lag to sediment‐abundant sand ridges and dunes, with sand ribbons and furrows in‐between. Seismic reflection data show up to 15 m of surficial sand concentrated beneath north‐aligned sand ridges that dominate the bathymetry of northwest Hecate Strait. Sand ribbons (typically understood sediment‐limited features in shallow marine environments) are notably maintained over seabed with comparable sand thickness to adjacent dunes (i.e. sediment‐abundant features), suggesting local spatial variability in hydrodynamics and sediment characteristics (principally grain size) influence expression of mobile bedforms. Repeat mapping between 2008 and 2019 shows dunes and ribbons both migrate northwards, with largest seafloor changes along northeast‐facing lee sides of dunes, matching closely with published models of sediment mobility which suggest northward bedform migration is largely driven by storms. Median total migration distance is 164 m (northward) for dunes (time‐averaged rate of 14.9 m/year). Sand ribbons show less migration (median northward distance of 73 m) and migrate in a depth‐dependent manner. Because sand ribbons are typically flow‐parallel features, their lateral migration likely results from varying current directions and flow acceleration over shallower seabed. Sand ribbon migration should therefore a consideration in studies examining seabed change, particularly when they are formed over unconsolidated sediment.