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[Objective] Soil and water conservation is important in protecting and improving the ecological environment, which plays an important role for the sustainable development of ecosystems. [Methods] While the study of the soil and water conservation effectiveness in regional scale provide an effective theoretical basis for soil and water conservation. [Results] We supposed an estimation method for the regional-scale soil and water conservation rates by remote sensing based on the RUSLE (revised universal soil loss equation), and estimated its the spatial pattern in Shanxi Province.The results showed that the soil erosion in Shanxi Province is dominated by moderate erosion, with an area of about 49 545.68 km2, and the soil and water conservation rates is 68.38% in 2022, and the soil and water conservation rates accounted for the largest proportion in high level. The soil and water conservation rates of natural vegetation is higher than that of cropland, among which, the rates of forest is the highest at 84.37%. The soil and water conservation rates of the natural vegetation and cropland accounted for the largest proportion in high level with 48.11% and 46.46%, respectively. The rates is greatly affected by slopes, and showed a trend of significant decrease followed by gentle fluctuation. Compared with previous researches, the estimated spatial pattern of soil erosion in Shanxi Province were consistent with the existing data. And the estimated soil and water conservation rates was significantly correlated with the published targets. [Conclusion] The method of estimating soil and water conservation rates at the regional scale by remote sensing can provide an important theoretical and methodological basis for evaluating the effectiveness and impact of soil and water conservation ecological projects and other national large-scale ecological projects.

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Heathlands are extensive systems often dominated by slow‐growing and long‐lived woody plants. These systems require longer‐term studies to capture if and how they are changing over time. In 2020, we resurveyed species richness and cover of vascular plant communities in 139 heathlands along the coastline of northern Fennoscandia, first surveyed during 1965–1975. The first survey included six heathland types, each with dominance – a cover of 25% or more – of the dwarf shrubs Calluna vulgaris, Kalmia procumbens, Betula nana, Vaccinium myrtillus and Empetrum nigrum. The two latter heathland types made up 29% and 48%, respectively, of all heathlands. In addition to the dominant dwarf shrubs giving their names to the heathland types, a few other species qualified as dominant. In the resurvey, all the heathland types had E. nigrum as the single dominant species, except for the heathland formerly dominated by B. nana. Most other species had low cover both at the time of the original survey and the resurvey. Also, the heathland types were species poor at the time of the original survey, with an average of eight vascular plant species per 4 m2 and were found equally species poor in the resurvey. Species richness differed between heathland types only at the time of the original survey, and the ratio of species exchange between the two surveys was negatively related to the original cover of E. nigrum. Here we provide a half‐century perspective on vegetation change, during which several heathland types in northern Fennoscandia have changed to Empetrum heathlands, reducing the diversity of heathland types across the Boreal to Arctic landscape. As a native plant, E. nigrum cannot be considered invasive, but its allelopathic capacity has likely already modified these heathland ecosystems and will continue to do so, reducing ecosystem multifunctionality across the region. [ABSTRACT FROM AUTHOR]

Understanding how habitat attributes (e.g., patch area and sizes, connectivity) control recruitment and how this is modified by processes operating at larger spatial scales is fundamental to understanding population sustainability and developing successful long‐term restoration strategies for marine foundation species—including for globally threatened reef‐forming oysters. In two experiments, we assessed the recruitment and energy reserves of oyster recruits onto remnant reefs of the oyster Saccostrea glomerata in estuaries spanning 550 km of coastline in southeastern Australia. In the first experiment, we determined whether recruitment of oysters to settlement plates in three estuaries was correlated with reef attributes within patches (distances to patch edges and surface elevation), whole‐patch attributes (shape and size of patches), and landscape attributes (connectivity). We also determined whether environmental factors (e.g., sedimentation and water temperature) explained the differences among recruitment plates. We also tested whether differences in energy reserves of recruits could explain the differences between two of the estuaries (one high‐ and one low‐sedimentation estuary). In the second experiment, across six estuaries (three with nominally high and three with nominally low sedimentation rates), we tested the hypothesis that, at the estuary scale, recruitment and survival were negatively correlated to sedimentation. Overall, total oyster recruitment varied mostly at the scale of estuaries rather than with reef attributes and was negatively correlated with sedimentation. Percentage recruit survival was, however, similar among estuaries, although energy reserves and condition of recruits were lower at a high‐ compared to a low‐sediment estuary. Within each estuary, total oyster recruitment increased with patch area and decreased with increasing tidal height. Our results showed that differences among estuaries have the largest influence on oyster recruitment and recruit health and this may be explained by environmental processes operating at the same scale. While survival was high across all estuaries, growth and reproduction of oysters on remnant reefs may be affected by sublethal effects on the health of recruits in high‐sediment estuaries. Thus, restoration programs should consider lethal and sublethal effects of whole‐estuary environmental processes when selecting sites and include environmental mitigation actions to maximize recruitment success. [ABSTRACT FROM AUTHOR]

Ephemeral gully (EG) erosion is an important type of water erosion. Understanding the spatial distribution of EGs and other influencing factors at a regional scale is crucial for developing effective soil and water management strategies. Unfortunately, this area has not been sufficiently studied. The present study visually interpreted the EGs based on Google Earth images in 137 small watersheds uniformly distributed in the Loess Plateau, compared them with measured results, and analyzed the factors influencing EG formation and density using GeoDetector. The results showed that visually interpreting EGs from Google Earth images was suitable for EG regional studies. Out of the 137 small watersheds, 33.6% had EG occurrence with an average density of 3.41 km/km2. Rainfall (R) and slope gradient (S) were the primary factors influencing the formation of EGs, while the area proportion of sloping farmland (APSF) and soil erodibility (K) were the main factors affecting EG density. The interaction of dual factors had a greater influence compared to single factors, with the interaction between S and Normalized Difference Vegetation Index (NDVI) having the greatest impact on EG formation and the interaction between K and NDVI on EG density. Although natural forces significantly influence whether EGs can form in a specific area, human activities greatly affect the density of the gullies that develop. This underscores the importance of proper land management in controlling gully erosion. These findings could provide theoretical support for EG prediction models and a scientific basis for soil and water loss control strategies at the regional scale. [ABSTRACT FROM AUTHOR]

Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user's expertise, limits their applicability. Furthermore, the neglect of SAR's potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples. [ABSTRACT FROM AUTHOR]

AbstractThe peak roof drift ratio is one of the most important engineering parameters to describe the expected seismic damage in a building. A predictive model of the drift ratio was developed using a machine learning approach (Gaussian process regression model) on a dataset of approximately 11,800 records from 34 monitored buildings in Japan. Four predictors for ground motion and three predictors for building vulnerability are used in the machine-learning modelling. The residual analysis shows a reduction of 50% compared to the state of the art. The Gaussian process regression model is applied in a second analysis on an original dataset of approximately 4,500 records for 127 monitored buildings in Italy. A satisfactory comparison emerges by comparing the drift ratio prediction map with the observed damage pattern produced by satellite imagery for a test site in central Italy after the 2009 earthquake. The drift ratio map plays an important role in the simulation of an earthquake scenario at regional scale, which is needed by Civil Protection for emergency planning and management activities.

Dams are increasingly disrupting natural river systems, yet studies investigating their impact on microbial communities at regional scale are limited. Given the indispensable role of bacterioplankton in aquatic ecosystems, 16S rRNA gene sequencing was performed to explore how these communities respond to dam-influenced environmental changes at the regional scale in the Shaying River Basin. Our findings revealed that cascade dams create distinct environments, shaping bacterioplankton communities near the dams differently from those in natural rivers. In the upstream of the cascade dams, water quality was superior, while bacterioplankton community structure was simple with weak community interactions. In the midstream, nutrient and heavy metal content were increased, making bacterioplankton structures more susceptible to environmental changes. In the downstream of the cascade dams, water quality had a significant impact on the community and the bacterioplankton structures were highly complex. Additionally, environmental variables significantly influenced bacterioplankton functional groups. However, the response to these factors, as well as the interplay between functional and taxonomic diversity, varied markedly depending on the specific region of the cascade dams. We here delved into the effects of cascade dams on the taxonomic diversity and functional groups of bacterioplankton to provide a theoretical basis for segmentally regulating these dams. [ABSTRACT FROM AUTHOR]

Geochemical tracers have the potential to provide valuable insights for constructing conceptual models of groundwater flow, especially in complex geological contexts. Nevertheless, the reliability of tracer interpretation hinges on its integration into a robust geological framework. In our research, we concentrated on delineating the groundwater flow dynamics in the Innisfil Creek watershed, located in Ontario, Canada. We amalgamated extensive hydrogeological data derived from a comprehensive 3D geological model with the analysis of 61 groundwater samples, encompassing major ions, stable water isotopes, tritium, and radiocarbon. By seamlessly incorporating regional physiographic characteristics, flow pathways, and confinement attributes, we bolstered the efficiency of these tracers, resulting in several notable findings. Firstly, we identified prominent recharge and discharge zones within the watershed. Secondly, we observed the coexistence of relatively shallow and fast-flowing paths with deeper, slower-flowing channels, responsible for transporting groundwater from ancient glacial events. Thirdly, we determined that cation exchange stands as the predominant mechanism governing the geochemical evolution of contemporary water as it migrates toward confined aquifers situated at the base of the Quaternary sequence. Fourthly, we provided evidence of the mixing of modern, low-mineralized water originating from unconfined aquifer units with deep, highly mineralized water within soil–bedrock interface aquifers. These findings not only contribute significantly to the development a conceptual flow model for the sustainable management of groundwater in the Innisfil watershed, but also offer practical insights that hold relevance for analogous geological complexities encountered in other regions. [ABSTRACT FROM AUTHOR]

Grazing exclusion (GE) and rest grazing (RG) are important management systems for the restoration of degraded grassland ecosystems. In order to evaluate the effects and mechanisms of different grazing management systems on the scale dependency of species diversity, plant community indices and soil variables were determined in 32 plots in the Qilian Mountains in Gansu Province. The results show that diversity effects and their regulating mechanisms had space-scale dependence under different grazing management systems. The species richness and species diversity indices of RG grassland were significantly higher than those of GE at the regional scale. Species richness for RG and GE plots was 128 and 98, respectively, and the Shannon–Wiener and Simpson indices for RG and GE plots were 2.26 and 2.16, and 0.85 and 0.83, respectively. Additionally, three grazing management systems had a significant effect on species richness in mountain meadows, but different management systems had significantly different effects on species diversity indices in mountain meadows and temperate steppes. Meanwhile, soil variables only influenced species diversity at the regional scale. Most of the plant and soil variables at each scale had positive effects on species diversity. However, a negative correlation was seen between biodiversity and species coverage, mean plant height, soil porosity (SP) and bulk density (BD) under the two contrasting grazing management systems. In conclusion, choosing RG at the regional scale and selecting grazing management systems according to different grassland types at the local scale can help to restore degraded grassland vegetation. [ABSTRACT FROM AUTHOR]

Mobility as a service (MaaS) is a promising lever to achieve a more sustainable, co-modal, and flexible urban system for daily mobility. MaaS is crucial to addressing the challenges imposed by hyper-urbanization, social changes, saturation of public soil with private motor vehicles, and local and global environmental issues. The aim of this paper is to analyse important characteristics of MaaS, namely public governance, regional scale, and high level of development. A total of 13 MaaS initiatives implemented in some European cities were compared using multi-criteria analysis. The results show that, although the MaaS implementation process is faster for private initiatives, public governance is shown to be the most appropriate entity to develop stable MaaS services, as it can enable the achievement of a higher level of integration, coupled with a high rate of service adoption, as well as reduce market entry barriers for service providers while also guaranteeing fair competition, and consequently fostering a higher level of service provided to users. The regional coverage of MaaS is crucial to ensure service provision to a wider population, living in both metropolitan and rural areas. These important features are included in the BIPforMaaS project run by the Italian region of Piedmont, described in detail in this paper. [ABSTRACT FROM AUTHOR]

Ephemeral gully (EG) erosion is an important type of water erosion. Understanding the spatial distribution of EGs and other influencing factors at a regional scale is crucial for developing effective soil and water management strategies. Unfortunately, this area has not been sufficiently studied. The present study visually interpreted the EGs based on Google Earth images in 137 small watersheds uniformly distributed in the Loess Plateau, compared them with measured results, and analyzed the factors influencing EG formation and density using GeoDetector. The results showed that visually interpreting EGs from Google Earth images was suitable for EG regional studies. Out of the 137 small watersheds, 33.6% had EG occurrence with an average density of 3.41 km/km2. Rainfall (R) and slope gradient (S) were the primary factors influencing the formation of EGs, while the area proportion of sloping farmland (APSF) and soil erodibility (K) were the main factors affecting EG density. The interaction of dual factors had a greater influence compared to single factors, with the interaction between S and Normalized Difference Vegetation Index (NDVI) having the greatest impact on EG formation and the interaction between K and NDVI on EG density. Although natural forces significantly influence whether EGs can form in a specific area, human activities greatly affect the density of the gullies that develop. This underscores the importance of proper land management in controlling gully erosion. These findings could provide theoretical support for EG prediction models and a scientific basis for soil and water loss control strategies at the regional scale.

The acquisition of digital regional-scale information and ecological environmental data has high requirements for structural texture, spatial resolution, and multiple parameter categories, which is challenging to achieve using satellite remote sensing. Considering the convenient, facilitative, and flexible characteristics of UAV (unmanned air vehicle) remote sensing technology, this study selects a campus as a typical research area and uses the Pegasus D2000 equipped with a D-MSPC2000 multi-spectral camera and a CAM3000 aerial camera to acquire oblique images and multi-spectral data. Using professional software, including Context Capture, ENVI, and ArcGIS, a 3D(three-dimensional) campus model, a digital orthophotomap, andmultispectral remote sensing map drawing are realized, and the geometric accuracy of typical feature selection is evaluated. Based on a quantitative remote sensing model, the campus ecological environment assessment is performed from the perspectives of vegetation and water body. The results presented in this study could be of great significance to the scientific management and sustainable development of regional natural resources. [ABSTRACT FROM AUTHOR]

As a component of the MAP project, the study of the flora in Northeast Asia (comprising Japan, South Korea, North Korea, Northeast China, and Mongolia) convincingly underscores the indispensability of precise and comprehensive diversity data for flora research. Due to variations in the description of flora across different countries in Northeast Asia, it is essential to update our understanding of the region's overall flora using the latest high-quality diversity data. This study employed the most recently published authoritative data from various countries to conduct a statistical analysis of 225 families, 1782 genera, and 10,514 native vascular species and infraspecific taxa in Northeast Asia. Furthermore, species distribution data were incorporated to delineate three gradients in the overall distribution pattern of plant diversity in Northeast Asia. Specifically, Japan (excluding Hokkaido) emerged as the most prolific hotspot for species, followed by the Korean Peninsula and the coastal areas of Northeast China as the second richest hotspots. Conversely, Hokkaido, inland Northeast China, and Mongolia constituted species barren spots. The formation of the diversity gradients is primarily attributed to the effects of latitude and continental gradients, with altitude and topographic factors within the gradients modulating the distribution of species. [ABSTRACT FROM AUTHOR]

Background: Domingo de Ramos, or Palm Sunday, is a traditional Christian religious event where devotees use ramos, which are bouquets currently elaborated from palm leaves and other natural elements. In various countries, it is assumed this use of biodiversity leads to the depletion of the species involved. However, other important aspects must be considered, including the role of the people who produce and sell these ramos, the associated symbolism that has been overlooked, as well as commercial aspects that have barely been documented. This ethnobotanical study evaluates the regional-scale cultural, biological and socioeconomic aspects associated with Domingo de Ramos in central Mexico from an emic perspective. Methods: Ethnographic and commercial information was obtained through interviews with ramos sellers in 28 municipalities in the state of Hidalgo, Mexico. We specifically sought sociodemographic data regarding the interviewees, as well as information pertaining to the ramos themselves and the palms. These aspects were explored with all of the sellers. The free list method was used to describe the uses and key elements associated with the ramos. Results: Although the ramos are used for religious purposes, they have eight different uses in the daily life of the sellers, the main one being "protection." They serve to protect families, crops and animals, as well as against several diseases. Likewise, they are considered valuable for diminishing strong storms. This belief in the protection conferred by the ramos preserves pre-Hispanic concepts and is combined with their use in blessing corresponding to Western beliefs. Ramos are made from 35 introduced and native plant species and comprise a base (made of palm, wheat or sotol), a "reliquia" (palm, rosemary, chamomile and laurel) and natural or artificial flowers. The ramos sellers are mostly adult women of indigenous origin and heads of family. Conclusions: This study of Domingo de Ramos, carried out at a regional scale, highlights a syncretism that is reflected in both the symbolic importance of ramos palm and in the species used, as well as socioeconomic aspects that had not previously been identified in the study area and reflect the occurrence of complex relationships in non-timber forest products that remain little addressed. [ABSTRACT FROM AUTHOR]

The aim of this study was to delimit potential rockfall propagation zones based on simulated 2 m resolution rockfall trajectories using Rockyfor3D for block volume scenarios ranging from 0.05–30 m3, with explicit inclusion of the barrier effect of standing trees, for an area of approx. 7200 km2 in Switzerland and Liechtenstein. For the determination of the start cells, as well as the slope surface characteristics, we used the terrain morphology derived from a 1 m resolution digital terrain model, as well as the topographic landscape model geodataset of swisstopo and information from geological maps. The forest structure was defined by individual trees with their coordinates, diameters, and tree type (coniferous or broadleaved). These were generated from detected individual trees combined with generated trees on the basis of statistical relationships between the detected trees, remote sensing-based forest structure type definitions, and stem numbers from field inventory data. From the simulated rockfall propagation zones we delimited rockfall hazard indication zones (HIZ), as called by the practitioners (because they serve as a basis for the Swiss hazard index map), on the basis of the simulated reach probability rasters. As validation, 1554 mapped past rockfall events were used. The results of the more than 89 billion simulated trajectories showed that 94% of the mapped silent witnesses could be reproduced by the simulations and at least 82% are included in the delimited HIZ. [ABSTRACT FROM AUTHOR]

While it is well accepted that climate change and growing water needs affect long-term sustainable water resources management, performing accurate simulations of water cycle and energy balance dynamics at regional scale remains a challenging task.Traditional Soil-Vegetation-Atmosphere-Transfer (SVAT) models are used for numerical surface water and energy simulations. These models, by conception, do not account for the groundwater lower boundary that permits a full hydrosystem representation. Conversely, while addressing important features such as subsurface heterogeneity and river–aquifer exchanges, groundwater models often integrate overly simplified upper boundary conditions ignoring soil heating and the impacts of vegetation processes on radiation fluxes and root-zone uptakes. In this paper, one of the first attempts to jointly model water and energy fluxes with a special focus on both surface and groundwater at the regional scale is proposed on the Seine hydrosystem (78,650 km$^{2}$), which overlays one of the main multi-aquifer systems of Europe.This study couples the SVAT model ORCHIDEE and the process-based hydrological–hydrogeological model CaWaQS, which describes water fluxes, via a one-way coupling approach from ORCHIDEE toward CaWaQS based on the blueprint published by [de Marsily et al., 1978]. An original transport library based on the resolution of the diffusion/advection transport equation was developed in order to simulate heat transfer in both 1D-river networks and pseudo-3D aquifer systems. In addition, an analytical solution is used to simulate heat transport through aquitards and streambeds. Simulated ORCHIDEE surface water and energy fluxes feed fast surface runoff and slow recharge respectively and then is used as CaWaQS forcings to compute river discharges, hydraulic heads and temperature dynamics through space and time, within each of the hydrosystem compartments. The tool makes it possible to establish a fully consistent water and energy budget over a period of 17 years. It also simulates temperature evolution in each aquifer and evaluates that river thermal regulation mostly relies by order of importance on short wave radiations (109.3 W${\cdot }$m$^{-2}$), groundwater fluxes (48.1 W${\cdot }$m$^{-2}$) and surface runoff (22.7 W${\cdot }$m$^{-2}$).

The direct effects of relative humidity and soil on aboveground biomass (AGB) versus the indirect effects mediated by stand structural diversity remain unclear in forest ecosystems across large-scale elevation gradients. Forest inventory data containing 15,260 individual trees and 104 tree species from 200 forest plots were collected. The result shows that the relative humidity, elevation, and Coefficient of Variation of Diameter at breast height (CVD) significantly influence AGB in the Tropical Thorn Forest (TTF). Regarding elevation, CVD was positive and significant, and relative humidity and SR negatively impacted AGB in sub-tropical broad-leaved forests (STBLF). In moist temperate mixed forests (MTMF), soil phosphorus and CVD have a significant positive impact, while relative humidity, elevation, and SR negatively influence AGB. Elevation and CVD have positive, while SR and soil phosphorus have a negative and insignificant effect on AGB in Dry Temperate Conifer Forests (DTCF). Soil phosphorus and relative humidity positively affected AGB (β = 0.021), while elevation, CVD, and SR negatively affect AGB in dry temperate, pure pine forests (DTPPF). Relative humidity and soil phosphorus have a positive direct effect on AGB in multi-species forests. The current study suggests that AGB primarily depends on relative humidity, soil phosphorus, and elevation in different forest types. [ABSTRACT FROM AUTHOR]

Background and Aims The hart's tongue fern (HTF) complex is a monophyletic group composed of five geographically segregated members with divergent abundance patterns across its broad geographic range. We postulated hierarchical systems of environmental controls in which climatic and land-use change drive abundance patterns at the global scale, while various ecological conditions function as finer scale determinants that further increase geographic disparities at regional to local scales. Methods After quantifying the abundance patterns of the HTF complex, we estimated their correlations with global climate and land-use dynamics. Regional determinants were assessed using boosted regression tree models with 18 potential ecological variables. Moreover, we investigated long-term population trends in the USA to understand the interplay of climate change and anthropogenic activities on a temporal scale. Key Results Latitudinal climate shifts drove latitudinal abundance gradients, and regionally different levels of land-use change resulted in global geographic disparities in population abundance. At a regional scale, population isolation, which accounts for rescue effects, played an important role, particularly in Europe and East Asia where several hot spots occurred. Furthermore, the variables most strongly influencing abundance patterns greatly differed by region: precipitation seasonality in Europe; spatial heterogeneity of temperature and precipitation in East Asia; and magnitudes of past climate change, temperature seasonality and edaphic conditions in North America. In the USA, protected populations showed increasing trends compared with unprotected populations at the same latitude, highlighting the critical role of habitat protection in conservation measures. Conclusions Geographic disparities in the abundance patterns of the HTF complex were determined by hierarchical systems of environmental controls, wherein climatic and land-use dynamics act globally but are modulated by various regional and local determinants operating at increasingly finer scales. We highlighted that fern conservation must be tailored to particular geographic contexts and environmental conditions by incorporating a better understanding of the dynamics acting at different spatiotemporal scales. [ABSTRACT FROM AUTHOR]

Accurate and continuous crop phenology information at a regional scale is important for agronomic management and yield estimation. However, detecting continuous crop phenology remains challenging due to the low sensitivity of remote sensing signals to certain phenological stages and the limited of availability remote sensing images. Therefore, this study developed a layered strategy to detect continuous crop phenology. First, a novel Phenology Separability Index (PSI) is established to select features from the Gaussian probability density distribution. PSI quantifies the capability of optical vegetation indexes (VIs), Synthetic Aperture Radar (SAR) signals, and meteorological factors to distinguish between various phenological stages. Then, the multi-temporal sample is established to enhance training sample representation and quantity. Finally, a random forest model is trained using features extracted from multi-temporal samples to improve detection accuracy. This model effectively reduces phenological stage confusion due to redundant features and limited samples. In addition, this study validated its extensibility by mapping crop phenology in the cities of Acheng, Zhaozhou, Lishu, and Buxin and assessed its uncertainty using the Sobol approach. Results indicated that growing degree day has the highest separability among meteorological factors, surpassing both SAR singles and optical VIs. Moreover, the proposed layered strategy was robust, explaining 96% of spatial variation in crop phenology at the regional scale. The accuracy of the layered strategy method (total RMSE = 8.74 days) surpassed that of the multi-temporal sample method (total RMSE = 15.76 days) and the traditional method with a single-temporal sample (total RMSE = 17.21 days). In addition, this study indicated that optical VIs are prone to confuse with the early or late phenological stage of corn, whereas SAR singles are highly sensitive to jointing date.

Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user’s expertise, limits their applicability. Furthermore, the neglect of SAR’s potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples.

In recent decades, the effect of human activities on freshwater cycle transgressed the catchment scale. In order to solve the global and regional water problems effectively, a new framework is needed to transform from the traditional hydrology which focuses on catchment processes to the global-scale hydrology which focuses on the earth system processes. Based on the recent progress of global hydrology, this paper summarizes the patterns of anthropogenic effect on global freshwater cycle, reviews the framework of Planetary Boundaries for Water (PBW) and the research progress, and analyzes the possible approaches of freshwater management from the catchment scale to the global scale. Directed by the Earth system science, PBW pushes forward water cycle research to the global scale from the local and catchment scale, and evaluates the safe operating space of global freshwater use, with the aim to maintain the functions of the Earth system. The framework provides an important merit to evaluate the global sustainability of water resources. Water management should not only balance off the relations of water resources, ecological environment and socio-economic development locally, but also the relations of water cycle, Earth system functions and global sustainability. PBW is an important supplement to the watershed or aquifer management, and puts forward a new framework for the global and regional water management. In order to meet the demand of water management under the new system of natural resources management in China, the study of PBW should focuses on the following aspects: to establish global hydrological models and simulation and prediction platform on the basis of better understanding of global water cycle; to advance the study of relations between PBW and other planetary boundaries such as land use, biodiversity, and atmospheric CO2; to improve the double evaluation approaches (resource and environment carrying capacity, territorial development suitability) for territorial spatial planning with the framework of PBW.

Slope has obvious scale-dependent characteristics and it changes with the change in DEM resolution, which brings uncertainty to the evaluation process of regional resource and environment. In this paper, one typical county in each of the six water erosion regions in China was selected as the sample area, respectively. Based on DEM data of ALOS DEM, ASTER GDEM and SRTM DEM with different spatial resolutions, slope characteristics, such as gradient, eigenvalue, frequency and cumulative frequency curves, were calculated by using the third-order inverse distance square weighted difference algorithm, to explore the ability of depicting terrain by these three DEM data. Based on the idea of geo-information map, the "surface-slope conversion atlas" under different resolutions was constructed to achieve the grading correction of slope extraction under low resolution. The results showed that: (1) with the resolution of DEM decreases, the slope information of each area tends to be more generalized and gradually concentrated. The slope frequency curve gradually changed from "tall and thin" to "short and fat", and the peak of the cumulative frequency curve moved to the low-slope area. Six sample areas showed different degrees of slope reduction. (2) In the process of slope grading correction, except for Maoxian, the proportion of low-resolution results converted to medium and low slope grades (0°–25°) is large. (3) The slope spectrum conversion method has a good correction effect on the errors generated by the slope extraction results of DEM90 and DEM30, and the correction rates reached 80% and 90%, respectively. A slope conversion atlas can effectively improve the expression ability of low-resolution DEM data on topography, which can provide a basis for regional resource and environment evaluation, and territorial space optimization. [ABSTRACT FROM AUTHOR]

While it is well accepted that climate change and growing water needs affect long-term sustainable water resourcesmanagement, performing accurate simulations of water cycle and energy balance dynamics at regional scale remains a challenging task. Traditional Soil-Vegetation-Atmosphere-Transfer (SVAT) models are used for numerical surface water and energy simulations. These models, by conception, do not account for the groundwater lower boundary that permits a full hydrosystem representation. Conversely, while addressing important features such as subsurface heterogeneity and river-aquifer exchanges, groundwater models often integrate overly simplified upper boundary conditions ignoring soil heating and the impacts of vegetation processes on radiation fluxes and root-zone uptakes. In this paper, one of the first attempts to jointly model water and energy fluxes with a special focus on both surface and groundwater at the regional scale is proposed on the Seine hydrosystem (78,650 km²), which overlays one of the main multi-aquifer systems of Europe. This study couples the SVAT model ORCHIDEE and the process-based hydrological-hydrogeological model CaWaQS, which describes water fluxes, via a one-way coupling approach from ORCHIDEE toward CaWaQS based on the blueprint published by de Marsily et al. [1978]. An original transport library based on the resolution of the diffusion/advection transport equation was developed in order to simulate heat transfer in both 1D-river networks and pseudo-3D aquifer systems. In addition, an analytical solution is used to simulate heat transport through aquitards and streambeds. Simulated ORCHIDEE surface water and energy fluxes feed fast surface runoff and slow recharge respectively and then is used as CaWaQS forcings to compute river discharges, hydraulic heads and temperature dynamics through space and time, within each of the hydrosystem compartments. The tool makes it possible to establish a fully consistent water and energy budget over a period of 17 years. It also simulates temperature evolution in each aquifer and evaluates that river thermal regulation mostly relies by order of importance on short wave radiations (109.3W·m-2), groundwater fluxes (48.1W·m-2) and surface runoff (22.7W·m-2). [ABSTRACT FROM AUTHOR]

SOC storage (SOCS) plays a vital role in global climate change. Understanding the spatial pattern and features of soil organic carbon (SOC) and its influencing factors is important for increasing SOC fixation. However, few studies exist on the organic carbon reserves of farmland on a regional scale. This study revealed the SOCD and SOCS values and distribution using Hubei Province as a sampling region. The results demonstrated that the spatial distributions of farmland system carbon storage and density were uneven, and the spatial heterogeneity was related to geography, cultivated area, and soil type. The SOCD ranged from 0.559 to 10.613 kg/m2, with an average of 3.3710 ± 0.0337 kg/m2, and the soil carbon reserve of the farmland system was ~17.81 Tg. The SOCD varied with topography and soil type: in mountainous cultivated land, it was generally higher than that in hilly land and in the plains. However, the plain cultivated areas contained the highest carbon reserves. Within the farmland system, paddy soil, the dominant soil type, exhibited higher SOCD and larger SOC storage capacity. Soil types with the same physicochemical properties exhibited different organic carbon storage capacity in different geomorphic and regional environments. Specifically, paddy soil was found to have higher SOCD and SOCS than the other soil types, and its soil carbon storage capacity was high; the SOC reserves of wheat-rice tillage were the largest among the main tillage methods. Boosting the soil carbon sink requires fundamental improvement in soil properties by improving soil texture, using conservation tillage to increase soil organic matter, and reducing unnecessary human interference. [ABSTRACT FROM AUTHOR]

Few commercial-scale carbon capture and storage (CCS) projects are currently operating in the world, with almost all in the USA and China. Despite a high number of CCS pilot-scale projects achieved in Europe, only two commercial-scale projects are operating today. The goal of this study is to present a case study in France to select a promising location to deploy a notable CCS pilot-scale project based on a multicriteria regional-scale approach. The methodology applied in this case study describes and assesses different aspects involved in CCS technology at the regional scale, and then an evaluation of economic key performance indicators (KPI) of CCS is carried out. The assessment at the regional scale gives an overview of where CCS could be applied, when CCS could be deployed and how to launch CCS considering the needs and concerns of stakeholders in the region. Technical aspects were mapped, such as the location of irreducible CO2 sources and long-lasting emissions and the location of storage resources and existing potential transport infrastructures. We identified the waste-to-energy and chemical sectors as the main CO2 sources in the region. An economic analysis of a hypothetical scenario of CCS deployment was elaborated considering three of the higher emitters in the region. A CCS scenario in the Paris Basin region with a deployment between 2027 and 2050 indicates a low CO2 cost per ton avoided between 43 EUR/t and 70 EUR/t for a cumulated total of 25 Mt and 16 Mt, respectively, of CO2 captured and stored for 26 years, including 7.7 Mt of CO2 from biomass (potential negative emissions). Storage maturity and availability of the resource are the most uncertain parameters of the scenario, although they are the key elements to push investment in capture facilities and transport. Geological storage pilot projects are mandatory to prove storage resource and should be located in strategic locations close to potential CO2 sources in case of confirmation of proven resources. Well-perceived pilot-scale projects are the first step to start engaging in deciding and investing in commercial-scale CCS projects.

The Orbiting Carbon Observatory 2/3 (OCO-2/3) employs a full-physics inversion algorithm and estimates the column-averaged dry air mole fraction of carbon dioxide (XCO 2). This research focuses on the spatiotemporal coverage and associated uncertainties of OCO-2/3's XCO 2 measurements within the Chinese region. We undertook an extensive statistical examination of the daily, weekly, 16-day, and monthly spatiotemporal coverage of XCO 2 across China, employing data from both OCO-2 and OCO-3 and their amalgamated datasets. Additionally, we used a posterior error analysis method to rigorously evaluate the uncertainties induced by various input parameters in the XCO 2 inversion model across different regions in China. Our findings reveal that OCO-2's spatial coverage spans 2.3–53.8% in China at a spatial resolution of 1° × 1°, and OCO-3's spatial coverage spans 3.3 and 47.4% across these time scales in China. Notably, the combined coverage rates of both satellites are significantly higher, ranging from 5.4 to 72.6%. Our analysis also reveals a gradual decrease in XCO 2 coverage and inversion rates from north to south and west to east across China. A significant portion of the uncertainties in OCO-2/3 XCO 2 measurements, over 90%, were attributed to primary error sources such as instrument, aerosol, and CO 2 prior profile. Aerosol-induced errors show regional variations, with higher levels observed in Southeast China. Compared to TCCON data, the accuracy of XCO 2 measurements in the two Chinese sites was lower than the global average. Our research identifies the spatiotemporal distribution and uncertainty decomposition of carbon satellites at a regional scale in China, providing an essential reference for future research. [Display omitted] • OCO-2&3 boosted China's XCO 2 coverage by 5.4%, 29.3%, 50.8%, and 72.6% for daily, weekly, 16-day, and monthly scales. • OCO-2/3 XCO 2 coverage and efficiency decline regionally, decreasing from north to south and west to east. • Key OCO-2/3 error sources: instrument, aerosol, and CO 2 prior profile contribute to over 90% of XCO 2 errors. • Aerosol errors, 20% of XCO 2 uncertainties, are higher in Southeast China, with distinct regional variations. [ABSTRACT FROM AUTHOR]

Geochemical tracers have the potential to provide valuable insights for constructing conceptual models of groundwater flow, especially in complex geological contexts. Nevertheless, the reliability of tracer interpretation hinges on its integration into a robust geological framework. In our research, we concentrated on delineating the groundwater flow dynamics in the Innisfil Creek watershed, located in Ontario, Canada. We amalgamated extensive hydrogeological data derived from a comprehensive 3D geological model with the analysis of 61 groundwater samples, encompassing major ions, stable water isotopes, tritium, and radiocarbon. By seamlessly incorporating regional physiographic characteristics, flow pathways, and confinement attributes, we bolstered the efficiency of these tracers, resulting in several notable findings. Firstly, we identified prominent recharge and discharge zones within the watershed. Secondly, we observed the coexistence of relatively shallow and fast-flowing paths with deeper, slower-flowing channels, responsible for transporting groundwater from ancient glacial events. Thirdly, we determined that cation exchange stands as the predominant mechanism governing the geochemical evolution of contemporary water as it migrates toward confined aquifers situated at the base of the Quaternary sequence. Fourthly, we provided evidence of the mixing of modern, low-mineralized water originating from unconfined aquifer units with deep, highly mineralized water within soil–bedrock interface aquifers. These findings not only contribute significantly to the development a conceptual flow model for the sustainable management of groundwater in the Innisfil watershed, but also offer practical insights that hold relevance for analogous geological complexities encountered in other regions.

Grazing exclusion (GE) and rest grazing (RG) are important management systems for the restoration of degraded grassland ecosystems. In order to evaluate the effects and mechanisms of different grazing management systems on the scale dependency of species diversity, plant community indices and soil variables were determined in 32 plots in the Qilian Mountains in Gansu Province. The results show that diversity effects and their regulating mechanisms had space-scale dependence under different grazing management systems. The species richness and species diversity indices of RG grassland were significantly higher than those of GE at the regional scale. Species richness for RG and GE plots was 128 and 98, respectively, and the Shannon–Wiener and Simpson indices for RG and GE plots were 2.26 and 2.16, and 0.85 and 0.83, respectively. Additionally, three grazing management systems had a significant effect on species richness in mountain meadows, but different management systems had significantly different effects on species diversity indices in mountain meadows and temperate steppes. Meanwhile, soil variables only influenced species diversity at the regional scale. Most of the plant and soil variables at each scale had positive effects on species diversity. However, a negative correlation was seen between biodiversity and species coverage, mean plant height, soil porosity (SP) and bulk density (BD) under the two contrasting grazing management systems. In conclusion, choosing RG at the regional scale and selecting grazing management systems according to different grassland types at the local scale can help to restore degraded grassland vegetation.

The crucial roles of soil carbon (C) and nutrients and their stoichiometric characteristics in indicating the soil interior nutrient cycling and plant nutrient supply of forest ecosystems have been widely verified, whereas it has been less explored when considering the influencing factors regionally, especially for the widely cultivated plantation tree species. In the current study, the patterns of soil organic C (SOC), total nitrogen (TN), and total phosphorus (TP) stoichiometry in Chinese fir [Cunninghamia lanceolata (Lamb.) Hook] plantations across subtropical China were analyzed, and their influencing factors were also investigated. The results showed that the range of SOC: TN (C:N), SOC: TP (C:P), and TN: TP (N:P) ratios were 7.32–18.27, 20.15–230.48, and 2.11–15.05 with a mean value of 13.22, 83.50, and 6.05, respectively. Well-constrained correlations were found in SOC and TN, as well as in TN and TP. Soil TN and TP contents increased with increasing altitude, whereas soil C:N, C:P, and N:P ratios decreased. Soil TP content decreased, and the C:P ratio increased with increasing mean annual temperature (MAT) and annual total solar radiation (ATSR). Soil C:N, C:P, and N:P ratios increased with increased mean annual precipitation (MAP) and mean annual evaporation (MAE). Overall, our findings suggested that the soil nutrient supply is relatively adequate in Chinese fir plantations across subtropical China. Meanwhile, soil C, N, and P stoichiometric characteristics were affected by geographical and climatic variables to different degrees.

A high amount of CO2 causes numerous health effects, including headaches, restlessness, difficulty in breathing, increased heart rate, high blood pressure, asphyxia, and dizziness. This issue of increasing atmospheric CO2 can only be solved via above-ground and below-ground carbon sequestration (CS). This study was designed to determine the relationship between CS with the crown area (CA), diameter at breast height (DBH), height (H), species richness (SR), and elevation in different forest types of Pakistan with the following specific objectives: (1) to quantify the direct and indirect relationship of carbon sequestration with CA, DBH, H, and SR in various natural forest types and (2) to evaluate the effect of elevation on the trees functional traits and resultant CS. We used the linear structural equation model (SEM) for each conceptual model. Our results confirmed that the highest CS potential was recorded for dry temperate conifer forests (DTCF) i.e., 52.67%, followed by moist temperate mix forests (MTMF) and sub-tropical broad-leaved forests (STBLF). The SEM further described the carbon sequestration variation, i.e., 57, 32, 19, and 16% under the influence of CA (β = 0.90 and P-value < 0.001), H (β = 0.13 and p-value = 0.05), DBH (β = 0.07 and p-value = 0.005), and SR (β = −0.55 and p-value = 0.001), respectively. The individual direct effect of SR on carbon sequestration has been negative and significant. At the same time, the separate effect of CA, DBH, and H had a positive and significant effect on carbon sequestration. The remaining 20% of CS variations are indirectly influenced by elevation. This means that elevation affects carbon sequestration indirectly through CA, DBH, H, and SR, i.e., β = 0.133 and P-value < 0.166, followed by β = 0.531 and P-value < 0.001, β = 0.007 and P-value < 0.399, and β = −0.32 and P-value < 0.001, respectively. It is concluded that abiotic factors mainly determined carbon sequestration in forest ecosystems along with the elevation gradients in Pakistan. Quantifying the role of various forest types in carbon dioxide (CO2) reduction leads to improved air quality, which positively impacts human health. This is an imperative and novel study that links the dynamics of the biosphere and atmosphere.

Despite decades of investigation, there are few ecological studies on the Amazonian fish fauna that have looked both at the main factors shaping the fish assemblages at a regional scale and used environmental, spatial and historical approaches simultaneously. The current study aimed to measure the relative influence of environmental aspects, spatial distance and historical processes on fish genera assemblages in the upper Amazon basin (Peru and Ecuador). Climate, slope and elevation were used as environmental variables; watercourse was used to represent the spatial distance between sites and phylogenetic relationships were used as a proxy for historical processes. A partial Redundancy Analysis (pRDA) was used to compute the influence of each group of factors (environment, space and historical processes) on the fish genera assemblage. Our analysis indicated historical processes had the greatest influence on fish genera assemblages, followed by spatial distance and environment. Such patterns are most likely due to the extinction processes caused by environmental filters generated by the Andean orogeny and possible speciation in each sub-basin. The results suggest that the use of phylogeny, in addition to environment and space, provides a useful tool with which to assess the drivers responsible for fish assemblages at a regional scale. [ABSTRACT FROM AUTHOR]

The aim of this study was to delimit potential rockfall propagation zones based on simulated 2 m resolution rockfall trajectories using Rockyfor3D for block volume scenarios ranging from 0.05–30 m3, with explicit inclusion of the barrier effect of standing trees, for an area of approx. 7200 km2 in Switzerland and Liechtenstein. For the determination of the start cells, as well as the slope surface characteristics, we used the terrain morphology derived from a 1 m resolution digital terrain model, as well as the topographic landscape model geodataset of swisstopo and information from geological maps. The forest structure was defined by individual trees with their coordinates, diameters, and tree type (coniferous or broadleaved). These were generated from detected individual trees combined with generated trees on the basis of statistical relationships between the detected trees, remote sensing-based forest structure type definitions, and stem numbers from field inventory data. From the simulated rockfall propagation zones we delimited rockfall hazard indication zones (HIZ), as called by the practitioners (because they serve as a basis for the Swiss hazard index map), on the basis of the simulated reach probability rasters. As validation, 1554 mapped past rockfall events were used. The results of the more than 89 billion simulated trajectories showed that 94% of the mapped silent witnesses could be reproduced by the simulations and at least 82% are included in the delimited HIZ.

As a component of the MAP project, the study of the flora in Northeast Asia (comprising Japan, South Korea, North Korea, Northeast China, and Mongolia) convincingly underscores the indispensability of precise and comprehensive diversity data for flora research. Due to variations in the description of flora across different countries in Northeast Asia, it is essential to update our understanding of the region’s overall flora using the latest high-quality diversity data. This study employed the most recently published authoritative data from various countries to conduct a statistical analysis of 225 families, 1782 genera, and 10,514 native vascular species and infraspecific taxa in Northeast Asia. Furthermore, species distribution data were incorporated to delineate three gradients in the overall distribution pattern of plant diversity in Northeast Asia. Specifically, Japan (excluding Hokkaido) emerged as the most prolific hotspot for species, followed by the Korean Peninsula and the coastal areas of Northeast China as the second richest hotspots. Conversely, Hokkaido, inland Northeast China, and Mongolia constituted species barren spots. The formation of the diversity gradients is primarily attributed to the effects of latitude and continental gradients, with altitude and topographic factors within the gradients modulating the distribution of species.

To determine which interpolation technique is the most suitable for each case study is an essential task for a correct soil mapping, particularly in studies performed at a regional scale. So, our main goal was to identify the most accurate method for mapping 12 soil variables at three different depth intervals: 0–5, 5–10 and >10 cm. For doing that, we have compared nine interpolation methods (deterministic and geostatistical), drawing soil maps of the Spanish region of Extremadura (41,635 km2 in size) from more than 400 sampling sites in total (e.g., more than 500 for pH for the depth of 0–5 cm). We used the coefficient of determination (R2), the mean error (ME) and the root mean square error (RMSE) as statistical parameters to assess the accuracy of each interpolation method. The results indicated that the most accurate method varied depending on the property and depth of study. In soil properties such as clay, EBK (Empirical Bayesian Kriging) was the most accurate for 0–5 cm layer (R2 = 0.767 and RMSE = 3.318). However, for 5–10 cm in depth, it was the IDW (Inverse Distance Weighted) method with R2 and RMSE values of 0.689 and 5.131, respectively. In other properties such as pH, the CRS (Completely Regularized Spline) method was the best for 0–5 cm in depth (R2 = 0.834 and RMSE = 0.333), while EBK was the best for predicting values below 10 cm (R2 = 0.825 and RMSE = 0.399). According to our findings, we concluded that it is necessary to choose the most accurate interpolation method for a proper soil mapping. [ABSTRACT FROM AUTHOR]

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Few commercial-scale carbon capture and storage (CCS) projects are currently operating in the world, with almost all in the USA and China. Despite a high number of CCS pilot-scale projects achieved in Europe, only two commercial-scale projects are operating today. The goal of this study is to present a case study in France to select a promising location to deploy a notable CCS pilot-scale project based on a multicriteria regional-scale approach. The methodology applied in this case study describes and assesses different aspects involved in CCS technology at the regional scale, and then an evaluation of economic key performance indicators (KPI) of CCS is carried out. The assessment at the regional scale gives an overview of where CCS could be applied, when CCS could be deployed and how to launch CCS considering the needs and concerns of stakeholders in the region. Technical aspects were mapped, such as the location of irreducible CO2 sources and long-lasting emissions and the location of storage resources and existing potential transport infrastructures. We identified the waste-to-energy and chemical sectors as the main CO2 sources in the region. An economic analysis of a hypothetical scenario of CCS deployment was elaborated considering three of the higher emitters in the region. A CCS scenario in the Paris Basin region with a deployment between 2027 and 2050 indicates a low CO2 cost per ton avoided between 43 EUR/t and 70 EUR/t for a cumulated total of 25 Mt and 16 Mt, respectively, of CO2 captured and stored for 26 years, including 7.7 Mt of CO2 from biomass (potential negative emissions). Storage maturity and availability of the resource are the most uncertain parameters of the scenario, although they are the key elements to push investment in capture facilities and transport. Geological storage pilot projects are mandatory to prove storage resource and should be located in strategic locations close to potential CO2 sources in case of confirmation of proven resources. Well-perceived pilot-scale projects are the first step to start engaging in deciding and investing in commercial-scale CCS projects. [ABSTRACT FROM AUTHOR]

It has been long understood that land cover change from natural to impervious modifies the surface energy balance and hence the dynamical properties of the overlying atmosphere. The urban heat island is manifested in the formation of an urban boundary layer with distinct thermodynamic features that in turn govern transport processes of air pollutants. While many studies already demonstrated the benefits of urban canopy models (UCM) for atmospheric modelling, work on the impact on urban air chemistry is scarce. This study uses the state-of-the-art coupled chemistry-climate modelling system MECO(n) to assess the impact of the COSMO UCM TERRA_URB on the dynamics and gas phase chemistry in the boundary layer of the urban agglomeration Rhine-Main in Germany. Comparing the model results to ground observations and satellite and ground based remote sensing data, we found that the UCM experiment reduces the bias in temperature at the surface and throughout the boundary layer. This is true for ground level NO2 and ozone distribution as well. The application of MECO(n) for urban planning purposes is discussed by designing case studies representing two projected scenarios in future urban planning – densification of central urban areas and urban sprawl. Averaged over the core urban region and 10‑days during a heat wave period in July 2018, model results indicate a warming of 0.7 K in surface temperature and 0.2 K in air temperature per 10 % increase in impervious surface area fraction. Within this period, a 50 % total increase of imperviousness accounts for a 3 K and 1 K spatially averaged warming respectively. This change in thermodynamic features results in a decrease of surface NO2 concentration by 10–20 % through increased turbulent mixing in areas with highest impervious fraction and highest emissions. In the evening and nighttime however, increased densification in the urban centre results in amplified canyon blocking, which in turn results in average increase in near surface NO2 concentrations of about 10 %, compared to the status quo. This work intends to analyse regional scale features of surface-atmosphere interactions in an urban boundary layer and can be seen as preparatory work for higher resolution street scale models.

Existing local models based on multiple environmental variables clustering (LM-MEVC) treat the influences of environmental factors on leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) equally when grouping samples. In fact, the effects that environmental factors assert on LPC are different. So, environmental factors need to be treated differently so that the different effects can be taken into consideration when dividing samples into clusters or groups. According to this basic idea, a local model based on weighted environmental variables clustering (LM-WEVC) was developed. This approach consists of four steps. Firstly, the most important environmental variables that influence LPC were selected. Then, the weights of the selected environmental variables were determined. In the following, the selected environmental variables were weighted and used as clustering variables to group samples. Finally, within each cluster or group of samples, an estimation model was established. In order to verify its effectiveness in predicting LPC of rubber trees, the proposed method was applied to a case study in Hainan Island, China. Rubber tree (cultivar CATAS-7-33-97) leaf samples were collected from three different sampling periods. Spectral reflectance of the collected leaf samples was measured using an ASD spectroradiometer, FieldSpec 3. Leaf samples collected from the three different sampling periods were used separately to test LM-WEVC. Coefficient of determination (R2), root mean squared error (RMSE), and ratio of prediction deviation (RPD) were employed as evaluation criterion. Performance of LM-WEVC was compared with that of the existing LM-MEVC. Results indicated that for the three sampling periods, the prediction accuracies of LM-WEVC were always higher than those of LM-MEVC. The values of R2 and RPD for LM-WEVC were increased by 8.15%–36.68%, and by 11.33%–59.40% respectively, while values of RMSE were reduced by 9.09%–37.5%, compared with those for LM-MEVC. These results demonstrate that LM-WEVC was effective in estimating LPC of rubber trees, and also confirmed our hypothesis that environmental factors unequally influenced LPC of rubber trees.

【Background】 Along with drought, soil salinization is one of the most important abiotic stresses facing agricultural production in arid and semi-arid regions like Xinjiang in northwest China. Saline soil is estimated to have reached 11 million hm2 in Xinjiang, and is a major limiting factor in cotton industry in this region. Accurately measuring soil salinity is hence important to safeguard cotton production in Xinjiang. 【Objective】 The purpose of this paper is to present a new method to estimate salinity distribution at field and regional scales to help improve irrigation and cultivation management. 【Method】 The experiments were conducted at Alar reclamation area in southern Xinjiang. Electrical conductivity of 540 soil samples taken from 30 cotton fields with different salinization levels were measured in soil profile at 0~0.375, 0~0.750, and 0~1.000 m depth. Using the electromagnetic induction data, linear and nonlinear models were constructed to inversely calculate the electrical conductivity of soil at field and regional scales respectively. Stability and reliability of the models was verified against ground-true data using the sample-size-reduction method. 【Result】 At field scale, the coefficient of determination R2 associated with the multiple linear regression model (MLR), the partial least square regression (PLSR) model and the principal component regression model (PCR) varied from 0.88 to 0.95, while their associated R2 for regional scale was from 0.34 to 0.53. The R2 of the nonlinear model built on the random forest (RF), neural network (NN) and support vector machine (SVM) varied between 0.60 and 0.85, with the RF most accurate. The R2 of the RF model for calculating the electrical conductivity of the soil profile at 0~0.375, 0~0.750, 0~1.000 m was 0.80, 0.85 and 0.84, respectively. Compared with the linear model, the model for regional scale significantly improved the accuracy. The sample size in the RF model for regional scale was reduced from 540 to 240, while the accuracy remained almost unchanged, indicating the regional-scale model can reduce the number of soil profile without compromising modelling accuracy. 【Conclusion】 For constructing the inversion model for estimating soil-profile electrical conductivity at regional scale, the random forest method is most accurate.

Abstract Background Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale. Results The semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest. Conclusions The semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km2. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery. [ABSTRACT FROM AUTHOR]

Clustered urban development has caused increasing fragmentation and islanding of regional ecological spaces. Creating a green infrastructure network (GIN) is a practical method of ensuring regional ecological security. This study proposed a method of GIN identification at the regional scale based on the Nanjing Metropolitan Area as an example. In this method, green hubs were identified using morphological spatial pattern analysis and connectivity indexes, green corridors were simulated based on the least-cost path model, and key optimization nodes were identified using circuit theory. The results indicated that green hubs covered an area of 5042.07 km2, of which, 15.40% were cross-border, and the potential corridors were distributed in a network, with the key ecological nodes primarily narrowly situated. By comparing the hubs with the statutory green space protection area and the urban ecological control line, the identification results were more than 70% accurate, showing that the results were valid and reliable. This method not only made the identification of regional GIN more practical and replicable but also further identified key areas that need priority protection. This study provides a method for constructing regional GIN and serves as a strong guide for ecological and development planning of other urban clustered areas. [ABSTRACT FROM AUTHOR]

Farming systems under contrasting management practices can contribute differently to the delivery of bundles of ecosystem services (ES) in agricultural landscapes. Low intensity farming systems, such as High Nature Value farmlands, are expected to deliver a wider range of ES, whereas landscapes under more intensive management are expected to deliver mainly provisioning services. Understanding the management practices associated with desirable outcomes in terms of biodiversity and ES in agricultural landscapes is needed. Our research aimed to understand the links between the delivery of ES bundles associated with agricultural landscapes, and their socio-ecological drivers, using a region in northern Portugal as a case study. Based on publicly available data on ecosystems services and drivers, we analyzed ES associations, delineated ES bundles, and investigated their relationship with socioecological drivers. Overall, our results suggested spatial trade-offs between landscapes delivering provisioning services of high economic value, and landscapes delivering a more balanced set of multiple ES. Bundle analysis highlighted an association between higher landscape multifunctionality and higher values of landscape complexity, higher number of farmers, and farm sizes. Our results reflected the complexity of social and ecological factors operating at the landscape level, pinpointed landscapes with higher multifunctionality and disclosed the conditions underlying their occurrence. The results also highlighted the importance of low-intensity farming systems, namely those supporting High Nature Value farmlands, for the delivery of a wider range of ES at the landscape scale. [ABSTRACT FROM AUTHOR]

Accurate spatio-temporal quantifying of ecosystem services (ESs) supply and social demand and their coordination dynamics, and determining which factors affect such coordination and the spatial distribution patterns of these effects, is essential to guide targeted ecosystem management decisions. Unfortunately, quantitative data demonstrating the explicit spatial relationships between the biophysical supply and social demand for ESs, as well as identifying the spatial non-stationary response of such relationships to influencing factors, is currently inadequate. To this end, this study employed China’s Loess Plateau (LP) as an example to quantify ES supply and social demand in 2000 and 2015, during which the society and ecology in this region have undergone tremendous changes. Then, the coupling coordination degree model was used to explore the coordination relationships between ES supply and social demand. Also, we analyzed the spatial correlations between such coordination relationships and various natural factors, policy factors and socio-economic factors, using geographically weighted regression. The results showed that ES supply and social demand had great spatio-temporal variations, and both the ES supply and social demand increased from 2000 to 2015. Although the incoordination between ES supply and social demand was great, such incoordination did improve from 2000 to 2015 with positive transitions from the lower coordination grades to the higher coordination grades. The occurrence of coordination between ES supply and social demand was highly correlated with vegetation cover driven by the “Grain for Green” Program, followed by slope (a natural factor) and socio-economic factors. This study helps deepen our understanding of the incoordination between the ES supply and social demand of the LP and the spatially explicit drivers of such incoordination, and could support auxiliary decision-making for regional ecosystem management.

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification. Google Earth Engine (GEE), a cloud computing analysis platform based on global geospatial analysis, provides a new opportunity for rapid analysis of remote sensing data. In this study, high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China. The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020, and Sentinel-2 imagery was used to verify the results. The spectral indices, texture, and terrain features of the image were derived, and their contribution to the classification accuracy of winter wheat was evaluated by scoring. Then the top nine features were selected to form an optimal feature subset. Comparing the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier, the results show that the accuracy difference between the two feature classification schemes is small, but the classification effect of all feature sets is slightly better than the optimal feature subset. The overall classification accuracy of sample plots verification was 86%-95%, the Kappa coefficient was between 0.7 and 0.85, and the percentage error of the total area was 5.42%. The research demonstrates a reliable method for mapping a wide range of winter wheat planting area, and provides a good prospect for exploring the precise mapping of other crops, which is of great significance to crop monitoring and agricultural development. [ABSTRACT FROM AUTHOR]

The outstanding diversity of Amazonian forests is predicted to be the result of several processes. While tree lineages have dispersed repeatedly across the Amazon, interactions between plants and insects may be the principal mechanism structuring the communities at local scales.Using metabolomic and phylogenetic approaches, we investigated the patterns of historical assembly of plant communities across the Amazon based on the Neotropical genus of trees Inga (Leguminosae) at four, widely separated sites.Our results show a low degree of phylogenetic structure and a mixing of chemotypes across the whole Amazon basin, suggesting that although biogeography may play a role, the metacommunity for any local community in the Amazon is the entire basin. Yet, local communities are assembled by ecological processes, with the suite of Inga at a given site more divergent in chemical defences than expected by chanceSynthesis. To our knowledge, this is the first study to present metabolomic data for nearly 100 species in a diverse Neotropical plant clade across the whole Amazonia. Our results demonstrate a role for plant–herbivore interactions in shaping the clade's community assembly at a local scale, and suggest that the high alpha diversity in Amazonian tree communities must be due in part to the interactions of diverse tree lineages with their natural enemies providing a high number of niche dimensions. [ABSTRACT FROM AUTHOR]

The spatiotemporal features of carbon and nitrogen fluxes over China between 1979 and 2015 were simulated by the Atmosphere–Vegetation Interaction Model (AVIM). The carbon fluxes of gross primary production and net primary production captured the distribution pattern in China better than MODIS and TRENDY data. The results for nitrogen deposition and biological nitrogen fixation show the good performance of the AVIM simulation compared with the CMIP6 and CABLE data, with a deposition rate >4 g N m−2 yr−1 in south China. The variation in the gross primary production and net primary production can be up to 300 and 200 g C m−2 yr−1 in south and southeast China, respectively, and there is a discrepancy between the AVIM and the data from MODIS and TRENDY. This shows the difficulty in simulating the carbon flux in a monsoon climate region and the importance of coupling the nitrogen–carbon fluxes. The standard deviation of nitrogen deposition and biological nitrogen fixation is simulated well by the AVIM and there is a large range in nitrogen deposition of 0.8–1.2 g N m−2 yr−1 in south China. The climatological mean of the fluxes performs better than the variation in the standard deviation and anomaly and this variation in the carbon–nitrogen flux is the key to decreasing bias in future modeling studies.