Cover Data Research Articles

Positive impact of urbanization on vegetation growth has been continuously strengthening in arid regions of China

The ecological environment is fragile in arid regions, and the direct and indirect impacts of continuing urbanization on vegetation growth in cities still need to be studied in depth. In this study, we focused on four provincial capital cities (Urumqi, Lanzhou, Yinchuan, and Hohhot) in arid regions of China. We used continuous 30 m land cover and vegetation greenness (VG) data from 1990 to 2021 to extract the impact of urbanization on vegetation growth by separating the impact of natural conditions. Our results showed that the study area’s urban area (UA) had expanded rapidly at a rate of 39.2 km2/a, increasing by 3.39 times between 1990 and 2021. While urban expansion occurred rapidly, the overall VG of the study area also increased (slope = 2.16 × 10−3), with the enhancement of VG increasing gradually from west to east. The VG and its trend in the UA were significantly higher than those in the natural vegetation area (NA). In addition, the duration of the urban vegetation growth season was longer than that of the NA, which also confirmed the positive impact of urbanization on the vegetation growth period. Furthermore, we found that the positive impact of urbanization on vegetation had continuously strengthened over time. In 1990 and 2021, the VG areas had 83% and 87% above the ‘zero-impact line’ in the UA, respectively, indicating a very significant impact of urbanization on vegetation growth in arid regions with fragile natural conditions. Our study identified the long-term dynamic trends in urbanization and VG in arid regions and clarified the non-linear relationship between the two concurrent growth factors. This has significant implications for correctly understanding the impact of urbanization on vegetation in arid regions and can provide a scientific reference for the ecological construction of urban environments in the region.

Analysis of the spatiotemporal changes in global land cover from 2001 to 2020

Land cover changes have far-reaching effects on climate change and the evolution of ecological environments, making them important components of global change research. Based on MODIS land cover data (MCD12Q1), statistical parameters such as land cover dynamic degree, transition probability, and transition matrix were employed to analyse the spatiotemporal characteristics of global land cover changes from 2001 to 2020. The results indicated that land cover exhibits a latitudinal distribution in mid-latitude regions and a longitudinal distribution in high and low-latitude regions. During the study period, the areas of evergreen needleleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, closed shrublands, and permanent wetlands exhibited fluctuating changes. Evergreen broadleaf forests and barren lands have decreased annually, whereas mixed forests, woody savannas, grasslands, urban and built-up areas, and cropland/natural vegetation mosaics have increased annually. In terms of the dynamic degree, the most significant changes occurred in deciduous broadleaf forests, closed shrublands, permanent wetlands, and cropland/natural vegetation mosaics. Additionally, there were significant variations in the types and regions of land cover changes and transitions. Evergreen needleleaf forests transitioned into grassland types, and the conversion between grassland types was the main transition from mid- to high- latitude regions. In tropical areas, evergreen broadleaf forests, open shrublands, and croplands have shifted towards grassland types as primary transitions. These findings provide significant implications for the in-depth exploration of land-atmosphere interactions, climate change, and numerical models.

Disaggregation of the Copernicus Land Use/Land Cover (LULC) and Population Density Data to Fit Mesoscale Flood Risk Assessment Requirements in Partially Urbanized Catchments in Croatia

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment.

Assessing Endokarst Potential in the Northern Sector of Santo António Plateau (Estremadura Limestone Massif, Central Portugal)

Karst is a peculiar natural landscape arising from high rock solubility and well-developed underground solutional channel porosity. It is unique for its surface relief (exokarst) and subsurface drainage, including cave systems (endokarst). In Portugal, karst areas mainly consist of marginal or low-density territories with great fragility and vulnerability and great geo-environmental richness that merits better policies and practices regarding their geo-conservation. Endokarst potential assessments can provide decision-makers and local authorities insight into present and future territorial management and planning. In this context, the main objective of this study was to produce a cartographic model to identify areas with a greater probability of containing karstic caves—i.e., a greater endokarst potential—in the northern sector of the Santo António Plateau (Estremadura Limestone Massif, Central Portugal). Geological, topographic, hydrogeological, and land cover data were collected, processed, and integrated into a spatial database using a Geographic Information System. The locations of known cave entrances in the study area were also identified from local public institutions and speleological team records. Subsequently, four conditioning factors were extracted from the data: lithostratigraphic units, fracture density, relief energy, and land cover. Using a multi-criteria decision-making analysis, each previously chosen conditioning factor and its respective classes were weighted using an analytic hierarchy process. The locations of known cave entrances served to evaluate the cartographic model built, with results showing an agreement of 81.9%. This prototype of the endokarst potential map for the study area may be used for strategic and operational environmental planning (at least on a local scale) to assist decision-makers, competent authorities, and local speleological teams. Its application may promote a more accurate and thoughtful definition of areas to be investigated, substantially reducing the time and costs associated with field prospecting.

Heat transport from atmosphere through the subsurface to drinking‐water supply pipes

AbstractDrinking‐water quality in supply pipe networks can be negatively affected by high temperatures during hot summer months due to detrimental bacteria encountering ideal conditions for growth. Thus, water suppliers are interested in estimating the temperature in their distribution networks. We investigate both experimentally and by numerical simulation the heat and water transport from ground surface into the subsurface, (i.e., above drinking‐water pipes). We consider the meteorological forcing functions by a sophisticated approach to model the boundary conditions for the heat balance at the soil–atmosphere interface. From August to December 2020, soil temperatures and soil moisture were measured dependent on soil type, land‐use cover, and weather data at a pilot site, constructed specifically for this purpose at the University of Stuttgart with polyethylene and cast‐iron pipes installed under typical in situ conditions. We included this interface condition at the atmosphere–subsurface boundary into an integrated non‐isothermal, variably saturated (Richards') the numerical simulator DuMux 3. This allowed, after calibration, to match measured soil temperatures with ±2°C accuracy. The land‐use cover influenced the soil temperature in 1.5 m more than the soil material used for back‐filling the trench above the pipe.

XAI SHAP 기반 토지 피복 구성에 따른 지표면 온도의 영향 분석

Recently, with the development of artificial intelligence architecture and the rapid improvement of hardware performance, various artificial intelligence models are attracting attention and their usefulness are increasing. However, unlike the rate of development of performance, the development of model's explanatory ability is slow to progress. The increasingly complex decision branching points of AI models are increasing exponentially, which hinders researchers' ease of model interpretation. Explainable Artificial Intelligence, XAI has emerged to solve the above complexity, and helps to improve interpretability and reliability by decomposing the black box of the model to a level that researchers can understand. This study estimates the surface temperature of a specific spatial unit using land cover data of a cell unit in Korea, Seoul. The limitations of the existing statistical model for high-dimensional data were examined, and the estimation results were compared using a machine learning model. Models used include Lasso regression(Least absolute linkage and selection operator), random forest regression, and XGBoost (eXtreme Gradient Boosting) regression. Finally, based on the fitted result of the XGBoost regression model, XAI SHAP was carried out.

The Caddisfly Collective: Methods of assessing Trichoptera diversity on a continental scale with community scientists

Amidst a global biodiversity crisis, collecting data at large spatial scales can illuminate patterns. Community science can be an avenue to reduce costs, broaden the scope of sampling, and, most importantly, connect with members of the public who are interested in and impacted by long-term ecological change. In 2021, we formulated a community science project – The Caddisfly Collective. Our goal was to study the regional influences on the responses of stream caddisfly (Trichoptera) communities to urbanization in the United States and Canada. Community scientists helped us achieve this goal by collecting caddisflies across a wider geographic scale than we could have reached on our own. To build The Caddisfly Collective, we recruited participants through social media and other online forums. We mailed collecting kits with a USB-powered ultraviolet LED light, a collecting container, bottles of preservative, data sheets, and collection labels to each participant; participants mailed back specimens and completed data sheets. There was a 79.7% rate of follow-through from sign-up to collection. During the project, 63 participants set up light-traps near urban and non-urban streams in seven different North American geographic regions, collecting adult caddisflies at 141 sites across the United States and Canada. Most sites were in the Midwest region, while the fewest sites were in the Far North region. Urban areas, classified by land cover data, comprised ~29% of total sites. We hope the details of our project can help other interested scientists implement similar projects in the future, especially focused on ecologically important caddisfly communities.

Biomonitoring with bryophytes in managed forested areas. Three examples from the southern Italian Apennines

Three sites in the southern Italian Apennines were selected to assess correlation between forest structure and bryophyte flora. In two of the sites, the Index of Air Purity (IAP)–based on cover data of epiphytic bryophytes–was evaluated. The results show that bryophyte populations–and consequently IAP–are affected by forest structure and development, and that studies including different sites require a precise assessment of silvicultural characteristics to allow comparisons. Indicator values of mosses and liverworts were also taken into consideration in characterizing ecologically the three sites.

Impact of Updating Vegetation Information on Land Surface Model Performance

AbstractVegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modeled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover data sets in these models are often outdated. In this study, we update land surface data in the European Centre for Medium‐range Weather Forecast (ECMWF) land surface modeling system (ECLand) using Earth observation‐based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation‐based data sets. Updating the vegetation information does not always yield better model performances because the model's parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation‐related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth‐observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.

Threat assessment and conservation challenges for the herpetofaunal diversity of Dampa Tiger Reserve, Mizoram, India

Herpetofauna is an important group of vertebrates with key functions in ecosystem sustenance. Nonetheless, with ever-increasing anthropogenic activities and lack of evidence-based studies, about 80% of the herpetofauna diversity of southern Asian region is threatened. Our study reports 80 herpetofauna species distributed across different habitat types in Dampa Tiger Reserve (DTR), Mizoram. We revise the amphibian list of DTR throughthe addition of seven species and establish the identity of cryptic species such as Microhyla ornata which is actually two distinct species,i.e., M. mukhlesuri and M. mymensinghensis. Through the questionnaire survey, it was found that 90% of the respondents depended on varied forms of forest resources. Herpetofaunal species account for 30% of the faunal resources with Varanus bengalensis, Ophiophagus hannah, and Python bivittatus being the most consumed reptile species. All chelonians and some amphibians like Duttaphrynus melanostictus, Pterorana khare, Hoplobatrachus tigerinus, Hoplobatrachus litoralis, Hydrophylax leptoglossa, Minervarya asmati, Polypedates teraiensis, and Sylvirana lacrima were also found to be consumed and used for their presumed medicinal values. In addition to hunting, road-kills, use of chemical pesticides, and habitat alteration were recorded to be the prominent threats in the region. The land use and land cover (LULC) data shows a steady recovery of dense forest and a better forest fire scenario as over 90% of the DTR core region falls in the ‘low severity’ and ‘unburned’ category. With continuous alteration in forested areas, the present study will not only provide a fundamental baseline for the conservation of herpetofauna and better management of protected areas but also stimulate future herpetological-based research.

HISDAC-ES: historical settlement data compilation for Spain (1900–2020)

Abstract. Multi-temporal measurements quantifying the changes to the Earth's surface are critical for understanding many natural, anthropogenic, and social processes. Researchers typically use remotely sensed Earth observation data to quantify and characterize such changes in land use and land cover (LULC). However, such data sources are limited in their availability prior to the 1980s. While an observational window of 40 to 50 years is sufficient to study most recent LULC changes, processes such as urbanization, land development, and the evolution of urban and coupled nature–human systems often operate over longer time periods covering several decades or even centuries. Thus, to quantify and better understand such processes, alternative historical–geospatial data sources are required that extend farther back in time. However, such data are rare, and processing is labor-intensive, often involving manual work. To overcome the resulting lack in quantitative knowledge of urban systems and the built environment prior to the 1980s, we leverage cadastral data with rich thematic property attribution, such as building usage and construction year. We scraped, harmonized, and processed over 12 000 000 building footprints including construction years to create a multi-faceted series of gridded surfaces, describing the evolution of human settlements in Spain from 1900 to 2020, at 100 m spatial and 5-year temporal resolution. These surfaces include measures of building density, built-up intensity, and built-up land use. We evaluated our data against a variety of data sources including remotely sensed human settlement data and land cover data, model-based historical land use depictions, and historical maps and historical aerial imagery and find high levels of agreement. This new data product, the Historical Settlement Data Compilation for Spain (HISDAC-ES), is publicly available (https://doi.org/10.6084/m9.figshare.22009643, Uhl et al., 2023a) and represents a rich source for quantitative, long-term analyses of the built environment and related processes over large spatial and temporal extents and at fine resolutions.

Mapping Fractional Vegetation Cover Using Unoccupied Aerial Vehicle Imagery to Guide Conservation of a Rare Riparian Shrub Ecosystem in Southern California

The use of unoccupied aerial vehicles (UAVs) for vegetation monitoring is widespread in agriculture and forestry but far less so in ecological restoration where it has tremendous unrealized potential. We tested the ability of multispectral data and a derived vegetation index to classify shrub, herbaceous vegetation, and bare soil cover in a rare alluvial floodplain vegetation community in semiarid Southern California, where shrub cover is manipulated in restoration efforts aimed to provide open habitats required by several threatened and endangered species. Three classifiers and three levels of spatial aggregation were compared for their ability to provide accurate shrub cover estimates at a scale commensurate with the needs of conservation managers. We used object-based image analysis (OBIA) and compared maximum likelihood (ML), support vector machine (SVM), and random forest (RF) classifiers applied to high-spatial resolution (0.14 m) data from a four-band Parrot Sequoia+ multispectral sensor. The SVM and RF classifiers yielded similarly high classification accuracy evaluated using the training data (overall accuracy of 96.4% and 97.6%, respectively), much higher than ML (88%). Aggregating shrub cover data to 25 and 50 m resolutions yielded more accurate and well-calibrated cover estimates (mean absolute error 12% and 11%, respectively, for RF) than 10 m aggregation (MAE 19% for RF). Shrub cover estimated using RF and SVM was able to meet the restoration monitoring needs to distinguish the three phases of shrub habitat characterized by their cover (10–30%, 30–75%, >75%) that differ in habitat quality and restoration prescriptions.

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

An integrated framework for examining groundwater vulnerability in the Mekong River Delta region.

The Mekong River provides water, food security, and many other valuable benefits to the more than 60 million Southeast Asian residents living within its basin. However, the Mekong River Basin is increasingly stressed by changes in climate, land cover, and infrastructure. These changes can affect water quantity and quality and exacerbate related hazards such as land subsidence and saltwater intrusion, resulting in multiple compounding risks for neighboring communities. In this study, we demonstrate the connection between climate change, groundwater availability, and social vulnerability by linking the results of a numerical groundwater model to land cover and socioeconomic data at the Cambodia-Vietnam border in the Mekong River Delta region. We simulated changes in groundwater availability across 20 years and identified areas of potential water stress based on domestic and agriculture-related freshwater demands. We then assessed adaptive capacity to understand how communities may be able to respond to this stress to better understand the growing risk of groundwater scarcity driven by climate change and overextraction. This study offers a novel approach for assessing risk of groundwater scarcity by linking the effects of climate change to the socioeconomic context in which they occur. Increasing our understanding of how changes in groundwater availability may affect local populations can help water managers better plan for the future, leading to more resilient communities.

Joint species distribution modeling with competition for space

AbstractJoint species distribution models (JSDM) are among the most important statistical tools in community ecology. However, existing JSDMs cannot model mutual exclusion between species. We tackle this deficiency in the context of modeling plant percentage cover data, where mutual exclusion arises from limited growing space and competition for light. We propose a hierarchical JSDM where latent Gaussian variable models describe species' niche preferences and Dirichlet‐Multinomial distribution models the observation process and competition between species. We also propose a decision theoretic model comparison and validation approach to assess the goodness of JSDMs in four different types of predictive tasks. We apply our models and methods to a case study on modeling vegetation cover in a boreal peatland. Our results show that ignoring the interspecific interactions and competition reduces models' predictive performance and leads to biased estimates for total percentage cover. Models' relative predictive performance also depends on the predictive task highlighting that model comparison and assessment should resemble the true predictive task. Our results also demonstrate that the proposed JSDM can be used to simultaneously infer interspecific correlations in niche preference as well as mutual competition for space and through that provide novel insight into ecological research.

Multi-scale habitat selection throughout the annual cycle of a long-distance avian migrant

Long-distance migrants are constrained by widely separated hospitable habitats in geographically isolated locations, making them vulnerable to environmental change, both through natural and anthropogenic causes. Knowledge about their resource selection decisions is imperative to understand the drivers of their declines. The distinct periods within an annual cycle, when individuals experience different environmental circumstances, are inextricably linked through carry-over effects which can have important consequences for the individual, and consequently the population. In this study, we employ precise archival GPS-tracking data of European Nightjars (Caprimulgus europaeus) and high-resolution global land cover data to examine habitat selection during the sedentary wintering and breeding periods, as well as during autumn and spring migration, using a correlational approach. We demonstrate how nightjars use general habitat characteristics, such as landscape diversity, for high-order habitat selection, while resource selection at a finer spatial scale is reliant on fine-scale variables related to a habitat’s suitability, such as surface area of grassland and shrubland. We show that nightjars favour spatially diverse landscapes, which allows them to minimize time spent searching for optimal habitats. The considerable variation in the drivers of habitat selection between and within seasons shows how anthropogenic land-use change can have an array of different impacts on migrants by influencing large- and fine-scale habitat selection. This study shows the advantages of an individual based GPS-tracking approach, combined with high spatial resolution remote sensing data, and highlights the need for full annual-cycle research on scale dependent habitat selection of long-distance avian migrants.

Significant decreasing trends in snow cover and duration in Northeast China during the past 40 years from 1980 to 2020

Snow cover in Northeast China (NC) is not only a vital variable in water availability but also a significant indicator of climate change. Limited by the short time span, incomplete spatial coverage, large uncertainty, and low spatial resolution of the current snow cover products, the long-term snow cover changes in NC remain unclear. To resolve this issue, a high-quality, long-term, and daily 500 m fractional snow cover (NCFSC) data were generated by integrating ERA5-land FSC data and several auxiliary data, with the help of a random forest (RF) regression model, during the period 1980–2020. The validation against in situ snow-depth observations from 2000 to 2018 indicated that the NCFSC effectively distinguished snow cover conditions (the overall accuracy (OA) was 0.88, producer’s accuracy (PA) was 0.78, user’s accuracy (UA) was 0.81, and Cohen’s kappa (CK) value was 0.71). The spatiotemporal comparison between the NCFSC and MODIS FSC data (MDFSC) during 2015–2018 showed that NCFSC was reliable and highly comparable, with bias, RMSE, and R values of −0.02, 0.14, and 0.73, respectively. Additionally, based on NCFSC, the spatiotemporal variations in snow cover in NC over the past 40 years were analyzed for the first time, and the results showed that the FSC had a significant decreasing trend (0.0017/year, p < 0.05). The FSC in farmland had the fastest decline rate, 0.0020/year (p < 0.05), and the snow cover areas decreased by approximately 20 % from 1.55×105 km2 in 1980 to 1.19×105 km2 in 2020. The snow onset date had a delayed trend (0.141 day/year, p > 0.05), the snow end date had a significant advanced trend (0.243 day/year, p < 0.05), and the snow duration days had a significant decreasing trend (0.384 day/year, p < 0.05) in NC. In summary, the NCFSC seamlessly depicts the spatiotemporal changes in snow cover in NC over the past 40 years, providing a scientific basis and decision-making support for hydrological studies and climate change.

Study of Urban Thermal Environment and Local Circulations of Guangdong‐Hong Kong‐Macao Greater Bay Area Using WRF and Local Climate Zones

AbstractThe Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA), a cluster of world‐class cities, is undergoing rapid urbanization. However, the heterogeneity of the urban thermal environment resulting from the diversity of urban forms is not yet fully understood. This paper assesses the heterogeneity of the urban heat island (UHI) effect in the GBA using the coupled Weather Research and Forecasting (WRF) model/multi‐layer urban canopy and building energy model (BEP/BEM), with high‐resolution local climate zone (LCZ) map as urban land use/land cover data. The average UHI intensity is found to peak at 1.8 ± 0.4°C in the evening, when the average UHI intensity of LCZ 2 can reach a maximum of 2.4 ± 0.58°C. Properly setting air‐conditioning temperatures can effectively prevent the enhancement of the UHI phenomenon at night by the anthropogenic heat (AH) released from air‐conditioning. The UHI‐induced local circulations and enhanced surface roughness inhibit the penetration of sea breezes inland, and surface wind speed decreases in all LCZs, with a maximum change of more than 0.8 m s−1. However, the increased thermal difference between land and sea leads to enhanced sea breezes offshore, especially in the Pearl River estuary. In addition, a series of sensitivity experiments have been conducted in this paper on initial and boundary conditions, building drag coefficients and urban fractions, which paves the way for further analyzing urban climate in GBA using WRF model and LCZs.

Assessment of recent changes (2011–2020) in glacier thickness and runoff variability in Gangotri glacier, India

ABSTRACT Recent evidence shows a noticeable reduction in the glacier mass in the Himalaya due to increasing temperatures, and therefore the downstream water availability is becoming uncertain. This study analyse the effect of changes in the Gangotri glaciers (i.e. thickness), including Raktavaran and Chaturangi regions, on the melt runoff (2011–2020) by coupling two models: Glacier Bed Topolology (GlabTop2) and Spatial Process in Hydrology (SPHY). This study performs the two-step calibration approach utilizing the observed discharge and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data. The R2 values for SPHY-modelled runoff (Q) and observed Q at Bhojwasa are found comparable (~0.73 on a daily scale) and the proportions of glacier Q and snow Q in total Q are computed as 22.11% and 66.91%, respectively. In comparison to Chaturangi and Raktavaran, Gangotri glaciers experienced a higher rate of change in thickness, and a reduction of ~9.40% in the mean glacier thickness is calculated during 2011–2020.

Coastal surface soil carbon stocks have distinctly increased under extensive ecological restoration in northern China

Coastal soil carbon stock is critical owing to the coexistence of terrestrial and marine carbon sinks and undergoes drastic changes under complex factors. Here we conduct surface soil organic carbon (SOC) stock mapping in northern China’s coastal areas in 2020 and 2010 based on large-scale field survey, remote sensing, and land cover data. Our results indicate that a 100 m resolution is the optimum mapping resolution for its good simulation accuracy and precise spatial details. The surface SOC stock and density in 2020 increased by 39.19% and 37.82%, respectively, compared with those in 2010 under extensive ecological restoration. The SOC densities of forests, grasslands, croplands, wetlands, and built-up areas increased by 72.58%, 74.25%, 41.39%, 4.58%, and 26.30% from 2010 to 2020, respectively. The study determines the optimum mapping resolution and denotes the positive effects of ecological restoration on coastal soil carbon.