Spatial Scales Research Articles

Accuracy evaluation and comparison of GSMaP series for retrieving precipitation on the eastern edge of the Qinghai-Tibet Plateau

Study RegionMin River Basin on the eastern Qinghai-Tibet Plateau. Study FocusPrecipitation is critical for hydrological processes, making accurate data essential for water management and flood forecasting. Satellite precipitation products offer valuable high-resolution spatiotemporal information, with the GSMaP series being widely used. However, comprehensive evaluations of different versions are limited. This study assesses the accuracy of Gauge and MVK products (versions 06, 07, and 08) across spatial and temporal scales and evaluates their performance in detecting precipitation events of varying intensities. New Hydrological Insights for the Study Region(1) GSMaP versions 06 and 07 exhibit higher detection rates for precipitation events, with POD values exceeding 0.8, while version 08 has a lower false alarm rate, with FAR values below 0.15. (2) GSMaP products are more successful in capturing precipitation events during the rainy season than the dry season. (3) With increasing elevation, the Gauge product consistently maintains a high hit rate and reduced false alarm rate, whereas the MVK product's hit rates improve. (4) For different rainfall intensities, GSMaP products more accurately detect moderate and heavy rain events, with the Gauge product outperforming the MVK product in terms of accuracy. These insights enhance the understanding of GSMaP product performance on eastern edge of the Qinghai-Tibet Plateau, aiding in improved water management practices.

Introduction to perspectives on developing geospatial expertise symposium

ABSTRACT Geospatial expertise draws on and recognizes a vast array of seemingly unrelated interconnections allowing the geographer to find solutions to otherwise incomprehensible problems. Geospatial thinking involves both time and space, acknowledges cause-and-effect relationships of geographic phenomena at multiple scales, and recognizes the impact of spatial and temporal scale on processes. The complexity of geographic thinking necessitates an equally complex set of learning experiences that collectively are characterized by the professional geographer. Due to the inherent complexity of geospatial expertise, its nuanced relationship to spatial thinking, and a lack of research on how individuals attain geospatial expertise, we organized an interdisciplinary group of experts to present their perspectives on developing geospatial expertise in this special edition. In this introduction, we review geospatial expertise and distinguish it from spatial thinking, review research on developing geospatial expertise, and end with recommendations for future research. Through these reviews and recommendations, this introduction provides background context for the articles in this special edition and elucidates their connections. Given the complexity of geospatial expertise, it is only through connecting researchers and educators across multiple disciplines that the nature of one of the most integrative professional disciplines – that of the geographer – can be fully understood.

The relationships between structure and function of plant communities in the desert steppe

BackgroundAssessing the relationships between spatial and temporal structures and functions of plant communities is an effective way to understand the changing dynamics of plant communities in specific environments. In this study, we investigated the response of structural and functional stabilities of plant communities to stocking rate in the desert steppe over a 16-year grazing period as the research background.MethodsWe used classical statistical methods to investigate the quantitative characteristics of plant communities over time (2014–2019) and space (2017–2019) at four stocking rates (control, CK, 0 sheep·ha–1·month–1; light grazing, LG, 0.15 sheep·ha–1·month–1; moderate grazing, MG, 0.30 sheep·ha–1·month–1; heavy grazing, HG, 0.45 sheep·ha–1·month–1) in the Stipa breviflora desert steppe of Inner Mongolia. We then examined the relationship between structural and functional stability of plant communities.ResultsOn the spatial scale, the structural stability of plant community was the highest in the LG treatment and the lowest in the MG treatment. The functional stability of plant community was the highest in the MG treatment and the lowest in the HG treatment. On the temporal scale, the structural stability of plant community was the highest in the MG treatment and the lowest in the LG treatment. The functional stability of plant community was the highest in the LG treatment and the lowest in the HG treatment. Affected by the stocking rate, the structural stability of plant community fluctuated more widely on the spatial scale and its functional stability varied more widely on the temporal scale. Nonetheless, the functional stability of the plant community is more responsive to the stocking rate.ConclusionsOur findings suggest that influenced by the disturbance of stocking rate, the structural stability of plant community is more significant than the functional stability in the desert grassland ecosystem, which lays a solid foundation for the study of ecosystem stability.

Terrestrial land use signals on groundwater fauna beyond current protection buffers.

Terrestrial and aquatic ecosystems are tightly linked, with direct implications for applied resource management and conservation. It is well known that human land use change and intensification of terrestrial systems can have large impacts on surface freshwater ecosystems. Contrastingly, the study and understanding of such land use impacts on groundwater communities is lagging behind. Both the impact strength of land use on groundwater communities and the spatial extents at which such interlinkages are operating are largely unknown, despite our reliance on groundwater for drinking water extraction as a key ecosystem service. Here, we analyzed groundwater amphipod occurrence from several hundred shallow groundwater aquifers used for drinking water extraction across a region of varying agricultural intensity and human population density in Switzerland. Despite drinking water extraction sites being generally built at locations with expected minimal aboveground impacts on water quality, we found a direct correlation between land use type and intensity within the surrounding catchment area and the locally measured nitrate concentrations, which is a direct proxy for drinking water quality. Furthermore, groundwater amphipods were more likely to be found at sites with higher forest coverage than at sites with higher crop and intensive pasture coverages, clearly indicating a tight connection between aboveground land use and groundwater biodiversity. Our results indicate that land use type effects on groundwater communities are most relevant and pronounced to spatial scales of about 400-1000 m around the groundwater sampling site. Importantly, the here identified spatial scale is 1.2- to 3-fold exceeding the average extent of currently defined groundwater protection zones. We postulate that incorporating an ecosystem perspective into groundwater management strategies is needed for effective protection of groundwater quality and biodiversity.

Estimating plant β-diversity using airborne and spaceborne imaging spectroscopy

ABSTRACT The increasing threats to grassland ecosystems from land-use/land-cover change, disturbances, and invasive species underscore the importance of monitoring grassland plant diversity. While most remote sensing studies have mainly focused on quantifying α-diversity (diversity within communities), less attention has been given to remotely estimating β-diversity (diversity between communities) in naturally-assembled grasslands. In this study, we used remote sensing to map plant β-diversity in a naturally-assembled grassland managed using prescribed fire and grazing in a North American tallgrass prairie ecosystem within the US Southern Great Plains. We aimed to assess the impact of time since fire on the relationship between in situ plant β-diversity and remotely–sensed β-diversity, also known as spectral β-diversity. We collected in situ observations at 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m plots, alongside airborne and spaceborne DESIS imaging spectroscopy data with spatial resolutions of 1 m and 30 m, respectively. To assess how management practices influenced β-diversity, we grouped our in situ observations into three categories based on time since fire, including recently-burned (burn age <1 year), transitional (burn age of 1–2 years), and unburned (burn age >2 years). Our findings showed that the association between in situ and spectral β-diversity was strongly influenced by time since fire. Excluding plots with high soil cover (≥50%) improved our remote estimation of β-diversity, despite soil exposure effects. Our study demonstrated that remote sensing of β-diversity in grassland ecosystems is influenced by spatial scale, time since fire, and soil exposure, all of which can be addressed with scale-appropriate imaging spectrometry. This study also highlighted the capability of spaceborne imaging spectrometers to estimate β-diversity of grassland ecosystems across large spatial domains.

Exploring the Effect of Road Network Structure on Inter-Regional Accessibility in a Diverse Road Network

Abstract. Accessibility analyses quantify the level of access to certain areas or opportunities, such as employment and healthcare facilities. Since public data is often aggregated at the level of regions, such as administrative units, it is useful to quantify accessibility between regions. Many factors influence inter-regional accessibility, most notably the accessibility metric used, and the way in which regions are chosen. This paper investigates the effects of road network structure on accessibility, using a previously developed inter-regional accessibility model that bases its accessibility metric on travel distance via the road network. This paper considers an area within the City of Tshwane municipality in South Africa. We investigate the effects of road structure in two ways. Firstly, regions are chosen based on the road network structure, which is done by extending a previously developed road network clustering algorithm for this novel use. Different spatial scales of regionalisation are considered, and the accessibility between these regions is compared to the accessibility between administrative units within the study area. Secondly, the effect of road network homogeneity on accessibility is investigated, where homogeneity corresponds to a uniform concentration of roads across a region. The results show that although road network homogeneity does not significantly correlate with accessibility, the way in which regions are chosen and their spatial scale has a strong effect on the results of the accessibility model. Our novel method of obtaining regions thus provides fresh insights into road-based accessibility within the City of Tshwane.

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development.

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

Assessment of Fine-Scale Urban Heat Health Risk and Its Potential Driving Factors Based on Local Climate Zones in Shenzhen, China

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore the potential driving factors. In this study, we estimated the potential HHRs based on the “hazard–exposure–vulnerability” framework by using multisource data, including the modified thermal–humidity index (MTHI), population density, and land cover. Then, the variations in the HHRs among different local climate zones (LCZs) at the fine spatial scale were analyzed in detail. Finally, we compared the different contributions of the LCZs and types of land cover to the HHRs and their three components by using multiple linear regression models. The results indicate that the spatial pattern of the HHRs was different from those of the individual components, and high-hazard regions do not mean high HHRs. There were huge variations in the HHRs among the different LCZs. The built-up LCZs typically had much higher HHRs than the natural ones, with compact LCZs facing the most severe risk. LCZ 6 (open low-rise buildings) had a relatively low HHR and should be paid more attention in future urban planning. Compared to the LCZs, the land covers better explained the variations in the HHR. In contrast, the LCZs better predicted the land surface temperatures. However, both the LCZs and land covers made only slight contributions to the heat exposure and vulnerability. Furthermore, the manmade buildings and impervious surface areas contributed much more to the HHR than the natural land covers. Therefore, the arrangement of the warming LCZs and land cover types is worthy of further investigation from the perspective of HHR mitigation.

Analysis of the spatial characteristics and driving forces of underground consumer service space in Chinese megacities based on multi-source data

Underground consumer service spaces (UCSS) offer new solutions for urban residents’ daily needs, but existing studies on their distribution and driving forces are often fragmented and overshadowed by research on other underground spaces, lacking targeted analysis. This study examines UCSS in the central urban areas of seven representative Chinese megacities. Using spatial analysis methods like kernel density estimation, multi-distance spatial clustering, and geographical detectors, the spatial characteristics and driving forces of UCSS are analyzed alongside aboveground consumer service spaces (ACSS). Results show that both ACSS and UCSS exhibit multi-centered, concentric spatial patterns, though UCSS demonstrates higher spatial aggregation. Unlike other underground public spaces (UPS), UCSS relies more on service industry agglomeration and market factors, while other UPS are more influenced by surrounding development intensity. UCSS follows the core principles of central place theory but deviates from the market-driven patterns typical of ACSS. Socioeconomic conditions and transportation infrastructure form the foundational basis for UCSS distribution, while service industry agglomeration, market dependence, and land development intensity exert more direct influence. The commercial atmosphere and existing underground space development play critical roles in UCSS distribution. Two key spatial scales for understanding UCSS distribution are the strong influence zones of shopping malls and metro stations, and high-density urban areas.

The comparison between single-point method and footprint-integrated validation method of the remote-sensing retrieval of evapotranspiration: a case study at Daman site

ABSTRACT In single-site validations, site-pixel, synthetic pixel and footprint-integrated methods are commonly used to validate evapotranspiration (ET) retrieval at the pixel scale. To reveal the reliability of these methods, this study analysed the performance of the three methods for ET retrieval validation based on eddy-covariance observations at the Daman site which is numerously used for validation in Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The analysis at different spatial scales in different seasons showed that the validation results of the site-pixel method were more similar that of the footprint-integrated method than the synthetic pixel method. However, the validation results of the site-pixel method showed significant discrepancies from those of the footprint-integrated method, especially in summer at the 480 M scale which is near to the spatial resolution of MODIS with a large difference of more than 125.65 W M−2. This error is due to aggregation and high spatial heterogeneity. The use of synthetic pixel can avoid this error. Normally, the error probably appears when the wind direction is 270°–360°, usually in summer, autumn and winter. In addition, the different aggregation methods had a slight effect on the ET estimation results. This study is helpful in determining the reliability of different single-point validation methods and provides a reliable way to select a validation method for single-site validation of remote sensing retrieval of ET.

Modelling the spatial variability and uncertainty for under-vaccination and zero-dose children in fragile settings

Universal access to childhood vaccination is important to child health and sustainable development. Here we identify, at a fine spatial scale, under-immunized children and zero-dose children. Using Chad, as an example, the most recent nationally representative household survey that included recommended vaccine antigens was assembled. Age-disaggregated population (12–23 months) and vaccination coverage were modelled at a fine spatial resolution scale (1km × 1 km) using a Bayesian geostatistical framework adjusting for a set of parsimonious covariates. There was a variation at fine spatial scale in the population 12–23 months a national mean of 18.6% (CrI 15.8%–22.6%) with the highest proportion in the South-East district of Laremanaye 20.0% (14.8–25.0). Modelled coverage at birth was 49.0% (31.2%–75.3%) for BCG, 44.8% (27.1–74.3) for DTP1, 24.7% (12.5–46.3) for DTP3 and 47.0% (30.6–71.0) for measles (MCV1). Combining coverage estimates with the modelled population at a fine spatial scale yielded 312,723 (Lower estimate 156055–409266) zero-dose children based on DTP1. Improving routine immunization will require investment in the health system as part of enhancing primary health care. The uncertainties in our estimates highlight areas that require further investigation and higher quality data to gain a better understanding of vaccination coverage.

High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets.

Objective&#xD;Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film (PTF) based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the RF-shielding caused by traditional MR-conditional nets.&#xD;&#xD;Approach&#xD;We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3T) and three human subjects at 7 Tesla (7T) with and without the Inknet2 to assess structural and functional MRI image quality.&#xD;&#xD;Main results&#xD;Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present.&#xD;&#xD;Significance&#xD;Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3T and 7T is achievable with an EEG net made with conductive ink and polymer thick film technology.&#xD.

Unravelling the EU’s promotional integration strategy in the MENA region: a dialectical analysis of the Agadir and GAFTA Agreements

Given the rhetoric of the EU on the necessity of economic integration for the MENA region, this article sheds light on the ‘puzzling’ fragmentation in its regional integration strategy through the cases of the GAFTA and Agadir Agreements. It problematises mainstream analyses and employs a dialectical approach based on the philosophy of internal relations, aiming to surpass existing critical literature that has only partially illuminated facets of the EU’s strategy. The article situates the EU strategy within the dynamics of neoliberal capitalism (and its interconnected tensions) and considers its spatial and temporal scales. It argues that the complex interplay of developments at the EU scale, global competition scale and MENA scale across three distinct time frames (1995–2003, 2004–11, 2012–21) has shaped the EU’s fragmented strategy. On the EU scale, while Brussels presents itself as a leading proponent of neoliberal ideology for global market openness, it strategically elevates the Agadir Agreement at the expense of the GAFTA to meet its economic needs. The global competitive dynamics, however, have constrained this preference, as the EU has been contested by the US in the Agadir Agreement area and China and, to a lesser extent, Russia in other GAFTA member states. Lastly, the agency of MENA states has been noticeable, since the EU encounters difficulties in influencing GAFTA’s regulatory framework and dealing with the diverse interests of state elites in the region. We conclude that the dialectical method of the article holds promise for analysing the regional integration strategies of other global powers.

Spatial heterogeneity and interaction effect of urban blue and green spaces on housing prices

Rapid urbanization presents policymakers and planners with the challenge of balancing public open spaces design with the conservation and improvement of natural resources. A comprehensive understanding of the land economic value of urban blue-green spaces (UBGS) holds immense significance for urban sustainable development, urban spatial justice and the promotion of human well-being. In this study, the MGWR model is employed to discuss the heterogeneous effects of UBGS on housing prices in Hangzhou. Additionally, the interaction effect between blue space and green space was examined at the district level, and the specific locations and spatial patterns were identified. The results show that (1) different types, features and accessibility of UBGS have different degrees and spatial scale of effect on housing prices, and will be affected by other attributes of UBGS; (2) in 30.92% of the main urban area of Hangzhou, the effect of blue spaces and green spaces on housing prices exhibits an interactive effect. The spatial patterns are divided into blue-green positive synergistic, antagonistic and negative synergistic regions; (3) green space has positive and negative effects on housing prices, while blue space has positive effects on housing prices at the regional level. The existence of water bodies can promote the positive effect of green spaces on housing prices or alleviate the negative effect. The results indicate that planners must transcend the singular focus on blue or green space planning and instead consider both in an integrated manner. This outcome can provide valuable references for UBGS planning.

Review of ecological valuation and equivalency analysis methods for assessing temperate nearshore submerged aquatic vegetation.

Nearshore seagrass, kelp, and other macroalgae beds (submerged aquatic vegetation [SAV]) are productive and important ecosystems. Mitigating anthropogenic impacts on these habitats requires tools to quantify their ecological value and the debits and credits of impact and mitigation. To summarize and clarify the state of SAV habitat quantification and available tools, we searched peer-reviewed literature and other agency documents for methods that either assigned ecological value to or calculated equivalencies between impact and mitigation in SAV. Out of 47 tools, there were 11 equivalency methods, 7 of which included a valuation component. The remaining valuation methods were most commonly designed for seagrasses and rocky intertidal macroalgae rather than canopy-forming kelps. Tools were often designed to address specific resource policies and associated habitat evaluation. Frequent categories of tools and methods included those associated with habitat equivalency analyses and those that scored habitats relative to reference or ideal conditions, including models designed for habitat suitability indices and the European Union's Water and Marine Framework Directives. Over 29 tool input metrics spanned 3 spatial scales of SAV: individual shoots or stipes, bed or site, and landscape or region. The most common metric used for both seagrasses and macroalgae was cover. Seagrass tools also often employed density measures, and some categories used measures of tissue content (e.g., carbon, nitrogen). Macroalgal tools for rocky intertidal habitats frequently included species richness or incorporated indicator species to assess habitat. We provide a flowchart for decision-makers to identify representative tools that may apply to their specific management needs.

A Branched Convolutional Neural Network for Forecasting the Occurrence of Hazes in Paris Using Meteorological Maps with Different Characteristic Spatial Scales

A Branched Convolutional Neural Network for Forecasting the Occurrence of Hazes in Paris Using Meteorological Maps with Different Characteristic Spatial Scales

Estimation of Regional Electricity Consumption Using National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite Night-Time Light Data with Gradient Boosting Regression Trees

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

PPCon 1.0: Biogeochemical-Argo profile prediction with 1D convolutional networks

Abstract. Effective observation of the ocean is vital for studying and assessing the state and evolution of the marine ecosystem and for evaluating the impact of human activities. However, obtaining comprehensive oceanic measurements across temporal and spatial scales and for different biogeochemical variables remains challenging. Autonomous oceanographic instruments, such as Biogeochemical (BGC)-Argo profiling floats, have helped expand our ability to obtain subsurface and deep-ocean measurements, but measuring biogeochemical variables, such as nutrient concentration, still remains more demanding and expensive than measuring physical variables. Therefore, developing methods to estimate marine biogeochemical variables from high-frequency measurements is very much needed. Current neural network (NN) models developed for this task are based on a multilayer perceptron (MLP) architecture, trained over point-wise pairs of input–output features. Although MLPs can produce smooth outputs if the inputs change smoothly, convolutional neural networks (CNNs) are inherently designed to handle profile data effectively. In this study, we present a novel one-dimensional (1D) CNN model to predict profiles leveraging the typical shape of vertical profiles of a variable as a prior constraint during training. In particular, the Predict Profiles Convolutional (PPCon) model predicts nitrate, chlorophyll, and backscattering (bbp700) starting from the date and geolocation and from temperature, salinity, and oxygen profiles. Its effectiveness is demonstrated using a robust BGC-Argo dataset collected in the Mediterranean Sea for training and validation. Results, which include quantitative metrics and visual representations, prove the capability of PPCon to produce smooth and accurate profile predictions improving upon previous MLP applications.

A multi-instrument study of UV bursts and associated surges in AR 12957

The relationship between UV bursts and solar surges is complex, with these events sometimes being observed together and sometimes being observed independently. Why this sporadic association exists is unknown; however, it likely relates to the physical conditions at the site of the energy release that drives these events. Here, we aim to better understand the relationship between UV bursts and solar surges through a multi-instrument analysis of several associated events that occurred around the trailing sunspot in AR $12957$. We used data from Solar Orbiter, the Solar Dynamics Observatory (SDO), and the Interface Region Imaging Spectrograph (IRIS) to achieve our aims. These data were sampled on 3 March 2022 between $09$:$30$:$30$ UT and $11$:$00$:$00$ UT, during which time a coordinated observing campaign associated with the Slow Solar Wind Connection Solar Orbiter Observing Plan (SOOP) took place. Numerous small-scale negative polarity magnetic magnetic features (MMFs) were observed to move quickly (potentially up to $3.3$ km s$^ $) away from a sunspot until they collided with a more stable positive polarity plage region around $7$ Mm away. Several UV bursts were identified in IRIS slit-jaw imager (SJI) $1400$ data co-spatial to where these opposite polarity fields interacted, with spatial scales ($2$ Mm$&lt;$) and lifetimes ($20&lt;$ minutes) larger than typical values for such events. Two surges were also observed to occur at these locations, with one being short ($5$ Mm) and hot (bright in the IRIS SJI images), whilst the other was a cooler (dark in coronal imaging channels), longer surge that appeared to fill an active region loop. Magnetic reconnection between the negative polarity MMFs around the sunspot and the positive polarity plage region appears to be the driver of these events. Both the speed of the MMFs and the locally open magnetic topology of the plage region could possibly have been important for forming the surges.

Climate-Driven vs Human-Driven Land Degradation? The Role of Urbanization and Agricultural Intensification in Italy, 1960–2030

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R2 coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R2 diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times.