Regional Scale Research Articles

Planning for Deer-Hunting Management at the Local and Regional Scales: Reconciling Economic, Social and Ecological Functions

Game species with home ranges exceeding the area of the management units may entail conflicts over hunting rights and cause damage to crops and forest stands in surrounding areas. This is currently the case in the Mendro Mountain Range (Portugal), inhabited by free-ranging red (Cervus elaphus) and fallow deer (Dama dama) populations. This study’s primary goal was to uncover the processes underlying these tensions and identify solutions to overcome them, thus reconciling the economic, social, and ecological functions of hunting. We analyzed data from three different sources of information regarding the surveyed management units: biophysical and anthropical spatial data collected using a GIS; typology, whether fenced, area and game bag results, data provided by a public institute; crop and forest damage locations reported by game managers. Approximately half of the surveyed open management units reported damage. We found no relationship between damage and game bag results, regardless of the typology and habitat quality index. To address this disconnection between the negative and positive values associated with deer locally, we proposed habitat management solutions. It is of chief importance to keep valuable crops apart from deer’s refuge cover, such as bushy areas, to minimize damage in management units where deer hunting is a subsidiary activity. Conversely, in management units where deer hunting is of significant economic importance, the food and refuge cover should be closely interspersed to increase the management unit’s carrying capacity. To improve the efficacy of measures such as this at a regional scale, as in the Mendro Mountain Range, we recommend implementing a so-called Global Management Plan. In Portuguese law, this governance instrument applies to the entire biologic unit where the deer populations occur, thus implying arrangements between the involved stakeholders and multiple other concerned institutions.

Higher crop rotational diversity in more simplified agricultural landscapes in Northeastern Germany

Abstract Context Both crop rotational diversity and landscape diversity are important for ensuring resilient agricultural production and supporting biodiversity and ecosystem services in agricultural landscapes. However, the relationship between crop rotational diversity and landscape diversity is largely understudied. Objectives We aim to assess how crop rotational diversity is spatially organised in relation to soil, climate, and landscape diversity at a regional scale in Brandenburg, Germany. Methods We used crop rotational richness, Shannon’s diversity and evenness indices per field per decade (i.e., crop rotational diversity) as a proxy for agricultural diversity and land use and land cover types and habitat types as proxies for landscape diversity. Soil and climate characteristics and geographical positions were used to identify potential drivers of the diversity facets. All spatial information was aggregated at 10 × 10 km resolution, and statistical associations were explored with interpretable machine learning methods. Results Crop rotational diversity was associated negatively with landscape diversity metrics and positively with soil quality and the proportion of agricultural land use area, even after accounting for the other variables. Conclusion Our study indicates a spatial trade-off between crop and landscape diversity (competition for space), and crop rotations are more diverse in more simplified landscapes that are used for agriculture with good quality of soil conditions. The respective strategies and targets should be tailored to the corresponding local and regional conditions for maintaining or enhancing both crop and landscape diversity jointly to gain their synergistic positive impacts on agricultural production and ecosystem management.

Contributions of atmospheric forcing and ocean preconditioning in the 2016 Antarctic sea ice extent drop

The 2016 Antarctic sea ice extent (SIE) drop was a rapid decrease that led to persistent low sea ice conditions. The event was triggered by atmospheric anomalies, but the potential preconditioning role of the ocean is unsettled. Here, we use sensitivity experiments with a fully-coupled regional climate model to elucidate the impact of the ocean conditions on the drop and on the persistence of the negative SIE anomalies during 2017. In particular, we re-initialize the model in January 2016 using different ocean and sea ice conditions, keeping lateral boundary forcings in the atmosphere and ocean unchanged. We find that the state of the Southern Ocean in early 2016 does not determine whether the drop occurs or not, but indeed has an impact on its amplitude and regional characteristics. Our results also indicate that the ocean initialization affects the sea ice recovery after the drop in the short term (one year), especially in the Weddell sector. The ocean’s influence appears not to be linked to the ocean surface and sea-ice initialization, but rather to the sub-surface conditions (between 50 m and 150 m) and heat exchange fluctuations at the regional scale, while the atmospheric forcing triggering the drop is driven by the large-scale circulation.

Linearity of the Climate Response to Increasingly Strong Tropical Volcanic Eruptions in a Large Ensemble Framework

Abstract Large explosive volcanic eruptions cause short-term climatic impacts on both regional and global scales. Their impact on tropical climate variability, in particular El Niño–Southern Oscillation (ENSO), is still uncertain, as is their combined and separate effect on tropical and global precipitation. Here, we investigate the relationship between large-scale temperature and precipitation and tropical volcanic eruption strength, using 100-member MPI-ESM ensembles for idealized equatorial symmetric Northern Hemisphere summer eruptions of different sulfur emission strengths. Our results show that for idealized tropical eruptions, global and hemispheric mean near-surface temperature and precipitation anomalies are negative and linearly scalable for sulfur emissions between 10 and 40 Tg S. We identify 20 Tg S emission as a threshold where the global ensemble-mean near-surface temperature and precipitation signals exceed the range of internal variability, even though some ensemble members emerge from variability for lower eruption strengths. Seasonal and ensemble mean patterns of near-surface temperature and precipitation anomalies are highly correlated across eruption strengths, in particular for larger emission strengths in the tropics, and strongly modulated by ENSO. There is a tendency to shift toward a warm ENSO phase for the first postvolcanic year as the emission strength increases. Volcanic cooling emerges on a hemisphere-wide scale, while the precipitation response is more localized, and emergence is mainly confined to the tropics and subtropics. Significance Statement The purpose of this study is to investigate at which strength the climate responses of volcanic forcing can be distinguished from the internal climate variability and whether the responses will linearly increase as the emission strengths become stronger. We ran 100-member MPI-ESM ensembles of idealized equatorial volcanic eruptions of different sulfur emission strengths and find that seasonal and ensemble mean patterns of near-surface temperature and precipitation anomalies are distinguishable and linearly scalable for sulfur emissions from 10 to 40 Tg S if their forcing patterns are similar. The identification of volcanic fingerprints is important for seasonal to decadal forecasts in the case of potential future eruptions and could help to prepare society for the regional climatic consequences of such an event.

Crayfish communities converge over succession in beaver pond metacommunities

Abstract Metacommunity theory predicts that the relative importance of regional and local processes structuring communities will change over ecological succession. Determining effects of these processes on taxonomic and evolutionary diversity in spatially structured freshwater habitats of different successional stages may greatly improve understanding of the maintenance of diversity across temporal and spatial scales. In this study, we evaluated crayfish diversity at local and regional scales in pond metacommunities undergoing secondary succession from beaver (Castor canadensis) disturbance. Following theoretical predictions from metacommunity ecology of the increasing importance of local processes over succession, we hypothesised a decline in crayfish local and β diversity over succession from stronger local structuring as the older ponds may provide less suitable habitat than streams. Crayfish species and phylogenetic diversity were evaluated in beaver pond metacommunities and reference headwater streams located in three catchment regions. DNA sequences from the mitochondrial cytochrome oxidase I gene were used to assign crayfish to species for community and phylogenetic diversity tests. Local and β diversity were contrasted across beaver ponds ranging in age from 24 to 70 years and as a function of metacommunity processes. Counter to predictions, local species diversity among streams and the successional stages of ponds categorised by age class (24–39 years; 42–57 years; 60–70 years) did not differ, but community and phylogenetic convergence occurred in the oldest pond ecosystems. Crayfish community composition differed between the youngest and oldest ponds, resulting from higher abundance in the youngest ponds and community convergence in the oldest ponds. The association between community composition and the environment was strongest in streams and decoupled with pond age. In contrast, the correlation between intraspecific haplotype composition and the environment increased over succession. Among the three metacommunities, the regional crayfish species diversity arose from a combination of the temporal and environmental drivers from beaver‐constructed ecosystems and dispersal limitation within catchments. This study represents the first investigation of the taxonomic and phylogenetic diversity response to the successional stages of beaver pond metacommunities. The detection of differential crayfish composition and haplotype sorting to pond age suggests a role for local structuring and further indicates that future studies should acknowledge succession in shaping species diversity at local and regional scales. Dispersal limitation within catchment regions probably contributes to the evolution of crayfish species diversity in metacommunities and the overall maintenance of biodiversity. The results support a transition in community and freshwater ecology from a recent emphasis on spatial processes towards the integration of temporal drivers to better identify regulators of taxonomic and phylogenetic diversity across scales.

Trends and amount changes of temperature and precipitation under future projections in high–low groups and intra-period for the Eastern Black Sea, the Wettest Basin in Türkiye

AbstractThis study investigates the possible effects of climate change on temperature and precipitation variables in the Eastern Black Sea Basin, Türkiye’s wettest and flood-prone region. The outputs of three GCMs under historical, RCP4.5, and RCP8.5 scenarios were downscaled to regional scale using the multivariate adaptive regression splines method. The future monthly temperature and precipitation for 12 stations in the basin were projected for three periods: the 2030s (2021–2050), 2060s (2051–2080), and 2090s (2081–2100). In addition to relative changes, high and low groups and intra-period trends were analyzed for the first time using innovative methods. For the pessimistic scenario, an increase of 3.5 °C in the interior and 3.0 °C in the coastal areas of the basin is projected. For the optimistic scenario, these values are expected to be 2.5 and 2.0 °C, respectively. A decrease in precipitation is projected for the interior region, and a significant increase is expected for the eastern and coastal areas of the basin, especially in spring. This result indicates that floods will occur frequently coastal areas of the basin in the coming periods. Also, although the monotonic trends of temperatures during periods are higher than precipitation in interior regions, these regions may have more uncertainty as their trends are in different directions of low and high groups of different scenarios and GCMs and contribute to all trends, especially precipitation.

What Rivers Did: a Study of if and how Rivers Shaped Later Prehistoric Lives in Britain and Beyond

Countering the passive representation of rivers in many previous accounts of later prehistory – as static vessels for spectacular deposits, highways for transport and communication, and backdrops for settlement and farming – this paper asks if and how rivers actively shaped prehistoric lives. Rivers have long been hailed as conduits for prehistoric materials and ideas. However, positive archaeological correlates of the processes involved are notoriously difficult to identify and have rarely been scrutinised in detail. Using the example of Late Bronze and Early Iron Age pottery in the east of England (1150–350 bc), we examine in detail how prehistoric pottery-making traditions cohered around river valleys over an extended time period and were thus, to a certain extent, generated by rivers. Drawing on wider evidence for the flow of people and things in this region we build a broader multi-dimensional account of how people, objects, and practices moved in a period of diverse lifeways in which the makeup of human mobility is not well understood. In doing so, we hope to tether abstract arguments about the active role of rivers and other non-human elements in shaping past lives and to approach the often missing ‘middle ground’ – small-scale movements at local and regional scales – in existing archaeological discussions about mobility.

Modeling the Potential Distribution Patterns of the Invasive Plant Species Phytolacca americana in China in Response to Climate Change

Phytolacca americana, introduced to China in the 20th century for its medicinal properties, has posed a significant ecological and agricultural challenge. Its prolific fruit production, high reproductive coefficient, adaptability, and toxic roots and fruits have led to the formation of monoculture communities, reducing native species diversity and posing threats to agriculture, human and animal health, and local ecosystems. Understanding its potential distribution patterns at a regional scale and its response to climate change is essential for effective monitoring, management, and control. In this study, we utilized the Maxent model to simulate potential habitat areas of P. americana across three timeframes (current, 2050s, and 2070s) under three climate change scenarios (SSP126, SSP245, and SSP585). Leveraging data from 556 P. americana sites across China, we employed ROC curves to assess the prediction accuracy. Our findings highlight key environmental factors influencing P. americana’s geographical distribution, including the driest month’s precipitation, the coldest month’s minimum temperature, the wettest month’s precipitation, isothermality, and temperature annual range. Under current climate conditions, P. americana potentially inhabits 280.26 × 104 km2 in China, with a concentration in 27 provinces and cities within the Yangtze River basin and its southern regions. While future climate change scenarios do not drastically alter the total suitable area, the proportions of high and low-suitability areas decrease over time, shifting towards moderate suitability. Specifically, in the SSP126 scenario, the centroid of the predicted suitable area shifts northeastward and then southwestward. In contrast, in the SSP245 and SSP585 scenarios, the centroid shifts northward.

Urban subsurface exploration improved by denoising of virtual shot gathers from Distributed Acoustic Sensing ambient noise

Summary Ambient noise tomography on the basis of Distributed Acoustic Sensing (DAS) deployed on existing telecommunication networks provides an opportunity to image the urban subsurface at regional scales and high-resolution. This capability has important implications in the assessment of the urban subsurface’s potential for sustainable and safe utilization, such as geothermal development. However, extracting coherent seismic signals from the DAS ambient wavefield in urban environments at low cost remains a challenge. One obstacle is the presence of complex sources of noise in urban environments, which may not be homogeneously distributed. Consequently, long recordings are required for the calculation of high-quality virtual shot gathers, which necessitates significant time and computational cost. In this paper, we present the analysis of 15 days of DAS data recorded on a pre-existing fiber optic cable (dark fibers), running along an 11 km long major road in urban Berlin (Germany), hosting heavy traffic including vehicles and trains. To retrieve virtual shot gathers, we apply interferometric analysis based on the cross-correlation approach where we exclude low-quality virtual shot gathers to increase the signal-to-noise ratio of the stacked gathers. Moreover, we modify the conventional ambient noise interferometry workflow by incorporating a coherence-based enhancement approach designed for wavefield data recorded with large-N arrays. We then conduct Multichannel Analysis of Surface Waves (MASW) to retrieve 1D velocity models for two exemplary fiber subsegments, and compare the results of the conventional and modified workflows. The resulting 1D velocity models correspond well with available lithology information. The modified workflow yields improved dispersion spectra, particularly in the low-frequency band (< 1 Hz) of the signal. This leads to an increased investigation depth along with lower uncertainties in the inversion result. Additionally, these improved results were achieved using significantly less data than required using conventional approaches, thus opening the opportunity for shortening required acquisition times and accordingly lowering costs.

A global database on coral recovery following marine heatwaves

Coral reefs support the world’s most diverse marine ecosystem and provide invaluable goods and services for millions of people worldwide. They are however experiencing frequent and intensive marine heatwaves that are causing coral bleaching and mortality. Coarse-grained climate models predict that few coral reefs will survive the 3 °C sea-surface temperature rise in the coming century. Yet, field studies show localized pockets of coral survival and recovery even under high-temperature conditions. Quantifying recovery from marine heatwaves is central to making accurate predictions of coral-reef trajectories into the near future. Here we introduce the world’s most comprehensive database on coral recovery following marine heatwaves and other disturbances, called Heatwaves and Coral-Recovery Database (HeatCRD) encompassing 29,205 data records spanning 44 years from 12,266 sites, 83 countries, and 160 data sources. These data provide essential information to coral-reef scientists and managers to best guide coral-reef conservation efforts at both local and regional scales.

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

High-resolution freshwater dissolved calcium and pH data layers for Canada and the United States

Freshwater ecosystems are biologically important habitats that provide many ecosystem services. Calcium concentration and pH are two key variables that are linked to multiple chemical processes in these environments, influence the biology of organisms from diverse taxa, and can be important factors affecting the distribution of native and non-native species. However, it can be challenging to obtain high-resolution data for these variables at regional and national scales. To address this data gap, water quality data for lakes and rivers in Canada and the continental USA were compiled and used to generate high-resolution (10 × 10 km) interpolated raster layers, after comparing multiple spatial interpolation approaches. This is the first time that such data have been made available at this scale and resolution, providing a valuable resource for research, including projects evaluating risks from environmental change, pollution, and invasive species. This will aid the development of conservation and management strategies for these vital habitats.

Infrastructure Impact Assessment through Multi-Hazard Analysis at Different Scales: The 26 November 2022 Flood Event on the Island of Ischia and Debris Management

A multi-hazard analysis (seismic, landslide, flood) is conducted to verify the impact on the road network. The ENEA CIPCast platform is an innovative Decision Support System (DSS) that is used to implement the analyses using GIS. Using analytical and geoprocessing tools, the hazards were assessed and mapped. The overlapping of different geospatial layers allowed the implementation of a specific hazard map for the road network. Multi-hazard values were obtained using an appropriate matrix of single values, which were classified, and then summarized into four classes of values. The analyses were conducted at the regional (Campania region), provincial (Metropolitan City of Naples), and local scales (island of Ischia and municipality of Casamicciola Terme). In particular, the landslide event that struck Ischia island on 26 November 2022 and the municipality of Casamicciola Terme was considered as a case study to determine the impact on the road network, infrastructures, buildings, and jeopardizing inter-municipal connections. The results are mainly visualized through map processing and statistical summaries of the data. The management of the landslide debris, which can contain a multitude of fractions (waste, biomass and vegetation, sludge, soil, and rocks transported downstream by water), was also explored. This is a frontier issue for which international manuals and guidelines, as well as national and emergency acts, have been examined. A specific protocol for the sustainable management of the debris generated by floods and landslides is needed, and discussed in the present paper, to overcome emergencies after catastrophic events.

On the relation of CMIP6 GCMs errors at RCM driving boundary condition zones and inner region for Central Europe region

AbstractGlobal climate models (GCMs) are essential for studying the climate system and climate change projections. Due to their coarse spatial resolution, downscaling is necessary on the regional scale. Regional climate models (RCMs) represent a standard solution for this issue. Nevertheless, the boundary conditions provided by GCMs unavoidably influence the outputs of RCMs. This study evaluates CMIP6 GCMs regarding the variables relevant to RCM boundary conditions. Particular focus is on the simulation of CNRM-ESM2-1, which is being used as a driving model for convection-permitting ALARO-Climate RCM, used as one source feeding new Czech climate change scenarios. The analysis is conducted over the boundaries and inside the RCM integration domain. Firstly, an evaluation of CFSR and ERA5 reanalyses against radiosondes is performed to choose an appropriate reference dataset for upper air variables. A high correlation between the two studied reanalysis and radiosondes was revealed, and it slightly decreases at the upper tropospheric levels. ERA5 is then chosen as the reference for the boundary analysis. Over the inner region, the simulated mean annual cycle of impact-relevant variables is validated against E-OBS. The CNRM-ESM2-1 performs well regarding near-surface variables over the Czech Republic, but it exhibits larger errors along the boundaries, especially for air temperature and specific humidity. The GCM performance in simulating the upper air atmospheric variables used as RCM boundary conditions relates rather weakly to the GCM performance in simulating the near-surface parameters in the inner region in terms of parameters relevant for impact studies.

Satellite-detected large CO2 release in southwestern North America during the 2020–2021 drought and associated wildfires

Abstract Southwestern North America (SWNA) continuously experienced megadroughts and large wildfires in 2020 and 2021. Here, we quantified their impact on the terrestrial carbon budget using net biome production (NBP) estimates from an ensemble of atmospheric inversions assimilating in-situ CO2 and Carbon Observatory 2 (OCO-2) satellite XCO2 retrievals (OCO-2 v10 MIP Extension), two satellite-based gross primary production (GPP) datasets, and two fire CO2 emission datasets. We found that the 2020–2021 drought and associated wildfires in SWNA led to a large CO2 loss, an ensemble mean of 95.07 TgC estimated by the satellite inversions using both nadir and glint XCO2 retrievals (LNLG) within the OCO-2 v10 MIP, greater than 80% of the annual total carbon sink. Moreover, the carbon loss in 2020 was mainly contributed by fire emissions while in 2021 mainly contributed by drought impacts on terrestrial carbon uptake. In addition, the satellite inversions indicated the huge carbon loss was mainly contributed by fire emissions from forests and grasslands along with carbon uptake reductions due to drought impacts on grasslands and shrublands. This study provides a process understanding of how some droughts and following wildfires affect the terrestrial carbon budget on a regional scale.

Using UNSEEN approach to attribute regional UK winter rainfall extremes

AbstractThree out of the five highest daily winter rainfall totals on record over Northern England have occurred from 2015 onwards. Heavy rainfall events in the winters of 2013–2014, 2015–2016 and 2019–2020 led to more than 2.8‐billion‐pounds of insurance losses from flooding in the UK. Has the frequency of these events been influenced by human‐induced climate change? Winter rainfall in the UK is extremely variable year‐to‐year, which makes the attribution of rainfall extremes particularly challenging. To tackle this problem, we introduce an UNprecedented Simulated Extreme Ensemble (UNSEEN) approach for the attribution of such extremes, thereby increasing the data available, and apply this approach to five recent flooding events on a regional scale. Using this method, for all five events we found a significant climate signal in the extreme regional rainfall totals immediately preceding the flooding. Results were fairly similar for each—with the events being found to become from 1.4 to 2.6 times more likely. An alternative attribution method that uses a different model with substantially less data did not find significant increases, reinforcing the need for very large amounts of data to detect significant changes in extreme rainfall against a noisy background of natural variability. We also examine how extreme rainfall is changing more broadly across English regions in winter, finding that 1‐in‐10 to 1‐in‐90‐year winter rainfall totals have changed significantly in Northern England. The high volume of data using UNSEEN has enabled us to examine the dynamics of these events, showing that daily extremes in winter are likely to have increased across all the circulation patterns responsible for high rainfall in English regions.

Impact of herd mobility on brucellosis seroprevalence and spread risk resulting from cross-border transhumance.

Cross-border livestock mobility through transhumance is mainly practiced in West African countries for seasonal access to resources and market. Cross-border herds are involved in the dynamic of transboundary animal diseases among them brucellosis taken as model. Brucellosis is a zoonotic disease causing abortion. This study explores the seroprevalence of brucellosis according to mobility and infection spread between Mali and Côte d'Ivoire in the context of seasonal cross-border transhumance. From February to April 2021, a transversal serological survey of brucellosis was conducted on 521 cattle from 111 transhumant herds and 283 cattle from 59 sedentary herds, all from Mali. The global individual seroprevalence for Brucella spp. in transhumant and sedentary cattle from Mali was 8.2% (95% CI=6.0-10.5). At herd level, seroprevalence was 21.2% with a significant variation between transhumant (11.7%) and sedentary (39.0%) herds. For herds in transhumance, cattle seropositivity was associated with a previous infection suspected by herdsmen odds ratio (OR=4.4; 95% CI=1.1-18.1) and unknown abortion aetiology (OR=4.3; 95% CI=1.0-17.3). The departure region (coming from Sikasso) and previous brucellosis infection or unexplained abortion could be used to predict Brucella infection in transhumant herds with a probability of around 60%. The risk of brucellosis introduction in host regions was high despite the individual animal seroprevalence of 3.6% and a low sale rate in transhumant cattle. The findings suggest that testing transhumant during border control and survey of cattle markets and sales could improve risk control of the spread of disease at regional scale.

How can green finance promote the transformation of China's economic growth momentum? A perspective from internal structures of green total-factor productivity

Green financing is a crucial tool for promoting China's economic growth and helping to achieve carbon neutrality, but its implications on the internal dynamics of green economy remain uncertain. This study examines the impacts of green finance on China’s Green Total Factor Productivity (GTFP) and its internal factors, including technical innovation, industrial structure, balanced regional growth, scale, and resource allocation efficiency, by using a three-hierarchy meta-frontier Data Envelopment Analysis (DEA) and provincial panel data from 2008 to 2020. The findings show that: (1) regional green finance development may greatly boost the growth of GTFP, and the findings remain reliable after applying a series of robustness tests; (2) further research finds that green finance enhances GTFP by promoting technological innovation, facilitating the optimization of industrial structure, and improving resource allocation efficiency; and (3) there are significant spatial heterogeneities in this effect. The effect of green finance on GTFP is more pronounced in developed and market-oriented regions. This study helps to understand the pathways of green finance affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions.

Mapping Soil Textural Fractions at Regional Scale Based on Local Morphometric Variables Using a Hybrid Approach (Case Study: Khuzestan Province, Iran)

Mapping Soil Textural Fractions at Regional Scale Based on Local Morphometric Variables Using a Hybrid Approach (Case Study: Khuzestan Province, Iran)

Long‐Term Trends in the Distribution of Ocean Chlorophyll

AbstractThe concentration of chlorophyll‐a (CHL) is an important proxy for autotrophic biomass and primary production in the ocean. Quantifying trends and variability in CHL are essential to understanding how marine ecosystems are affected by climate change. Previous analyses have focused on assessing trends in CHL mean, but little is known about observed changes in CHL extremes and variance. Here we apply a quantile regression model to detect trends in CHL distribution over the period of 1997–2022 for several quantiles. We find that the magnitude of trends in upper quantiles of global CHL (>90th) are larger than those in lower quantiles (≤50th) and in the mean, suggesting a growing asymmetry in CHL distribution. On a regional scale, trends in different quantiles are statistically significant at high latitude, equatorial, and oligotrophic regions. Assessing changes in CHL distribution has potential to yield a more comprehensive understanding of climate change impacts on CHL.