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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.

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.

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.

Regional-scale urban residential densification provides an opportunity to tackle multiple challenges of sustain-ability in cities. But framework for detailed large-scale analysis of densification potentials and their integration with natural capital to assess the housing capacity is lacking. Using a combination of Machine Learning Random Forests algorithm and exploratory data analysis (EDA), we propose density scenarios and housing-capacity es-timates for the potential residential lands in the Oxford-Cambridge Arc region (whose current population of 3.7 million is expected to increase up to 4.7 million in 2035) in the UK. A detailed analysis was done for Oxfordshire, assuming different densities in urban and rural areas and protecting lands with high-value natural capital from development. For a 30,000 dwellings-per-year scenario, the land allocated in Local Plans could cover housing growth in the four districts but not in Oxford City itself (which accounts for 48% of the demand); only 19% of the need would be covered in low but 59% in high housing density scenarios. Our study suggests a decision-support method for quantifying how the impact of housing growth on natural capital can be significantly reduced using more compact development patterns, protection of land with high-value natural capital, and use of low-biodiversity brownfield sites where available.

Energy Poverty (EP) is a situation in which households experience inadequate levels of energy services such as space heating and cooling. EP is a complex multidimensional phenomenon and one of the most significant societal challenges in the European Union (EU), currently affecting millions of people, resulting in negative consequences for the health, quality of life, and well-being of the population. Studies and metrics to identify and measure energy poverty usually focus on a country level. Although several analyses have been developed at higher resolution spatial scales, some territorial contexts, such as the EU Islands, are still overlooked. The present work aims to assess energy poverty vulnerability among EU Island regions by focusing on available metrics and indicators frequently referred to in literature to measure this state of condition. Altogether, 13 energy poverty indicators were analysed for 19 NUTS2 regions, covering 11 member states. The method used to evaluate energy poverty in the EU islands involves the development of three types of analysis carried out for each indicator, which could potentially lead to awareness about energy poverty vulnerability. Firstly, it was provided an overview with an EU Map concerning each indicator's results, aiming to detect the main regional hotspots. Secondly, the evolution of the results for each region with a three-year time horizon was explored. Finally, the variation between regions for each indicator was calculated to determine whether the performance on the island NUTS2 regions is lower, higher, or equal when compared to the average value of their matching overall country data. Results reveal a wide-ranging distinction across the EU island regions, which can be explained by the differences in geography, climate, income levels, and local-policy action. Considering the EU and several member states' goals on tackling energy poverty, as well as improving energy efficiency, such analysis may contribute with some valuable insights for the development of regionally tailored policy to eradicate energy poverty in the EU islands, towards an improvement in the population wellbeing and lower expenditure on health care. A Pobreza Energética é um fenómeno multidimensional bastante complexo e um dos maiores desafios inerentes à União Europeia (UE) que, atualmente, afeta milhões de cidadãos e, que resulta em consequências bastante negativas ao nível da saúde, qualidade de vida e bem-estar da população. Os consumidores em situação de pobreza energética experienciam níveis inadequados dos principais serviços energéticos como arrefecimento e aquecimento. A maioria dos estudos e índices desenvolvidos para monitorizar e identificar pobreza energética foca-se, normalmente, apenas ao nível dos países. Apesar de várias análises terem já sido desenvolvidas com resoluções espaciais mais complexas, alguns territórios como as ilhas constituintes da União Europeia continuam ainda por explorar face a este fenómeno. O presente estudo tem como principal objetivo a análise de Pobreza Energética existente nas Ilhas pertencentes à UE, através do foco em métricas e indicadores frequentemente referidos na literatura para identificar este estado de condição. No total, foram analisados 13 indicadores de pobreza energética relativos a 19 regiões NUTS2, correspondentes a 11 estados-membros distintos. O método utilizado para avaliar pobreza energética nas Ilhas da UE consiste no desenvolvimento de três tipos de análise diferentes para cada indicador, que poderão conduzir a uma tomada de consciência acerca da condição de vulnerabilidade a pobreza energética existente nestes territórios. Primeiramente, foi fornecida uma perspetiva ao redor da União Europeia através de mapas para cada indicador, onde foi possível detetar os principais hotspots. Em segundo lugar, foi explorada a evolução dos resultados para os indicadores relativos a cada região com um horizonte temporal de três anos. Por fim, foi calculada a variação entre as diferentes regiões, com vista a determinar se a performance nas Ilhas (NUTS2) é menor, maior ou igual quando comparando com o valor médio dos seus países associados, para cada indicador. Os resultados revelam uma ampla distinção nas diferentes ilhas da UE, que pode ser explicada em função das disparidades existentes ao nível da geografia, clima, rendimentos e políticas locais. Tendo em conta os objetivos da UE em acabar com a pobreza energética, assim como melhorar a eficiência energética, tal análise poderá contribuir perspetiva bastante valiosas para o desenvolvimento de políticas regionais à medida. Desta forma, através da erradicação de pobreza energética nas ilhas da UE, será possível atingir uma melhoria do bem-estar da população e uma diminuição das despesas associadas aos cuidados de saúde.

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.

International audience; 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 hydrologicalhydrogeological 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•m −2), groundwater fluxes (48.1 W•m −2) and surface runoff (22.7 W•m −2).

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.

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.

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.

The aim of this paper is to achieve a quantitative assessment of rockfall protection forest efficiency at regional scale, considering site specific forest, morphological and lithological parameters. At first, a semi-automatic GIS-based method, integrated with a multi-scenario 3D-rockfall model realized by using the simulation code HY-STONE, was used to map protection forests of Regione Lombardia (central Italian Alps). For each different forest type, a rockfall protective efficiency was assessed by using empirical (energy line angle) and modelling (HY-STONE) approaches. The empirical approach shows an increase of the energy line angle value from about 36° for the bare slopes to over 40° in forested slopes, with a value ranging from 37° to 44° for different forests types. The modelling approach is based on a new efficiency index EEI ranging from 0 (minimum efficiency, equal to no forest condition) to 1 (maximum efficiency): the efficiency of different forest types ranges from 0.08 to 0.98 by using average values of the controlling parameters. To modulate the efficiency in each single forest at regional scale, a set of parametric simulations was performed to evaluate the effects of controlling parameters. The parametric simulations show that block volume, slope gradient, DBH (diameter at breast height) and forest density are the most important parameters at controlling the efficiency. These parameters were used within a multiple linear regression function to associate a protection efficiency to each specific protection forest in the regional map. This allows to discriminate quantitatively the individual forests according to their actual efficiency. Most of the protection forest area (46%) shows an efficiency greater than 0.50 and only the 24% of the total covered area shows a value lower than 0.25.

The relationships between crop yield and its selected related impact factors has often been explored using ordinary least squares regression (OLSR). However, this model is non-spatial and non-robust. This study first used stepwise regression to identify the main factors affecting winter wheat yield from twelve potential related factors in Yucheng County, China. Next, robust geographically weighted regression (RGWR) was used to explore the spatially non-stationary relationships between wheat yield and its main impact factors. Then, its modeling effect was compared with that of GWR and OLSR. Last, robust geostatistical analysis was conducted for spatial soil management measures in low-yield areas. Results showed that: (i) three main impact factors on wheat yield were identified by stepwise regression, namely soil organic matter, soil total phosphorus, and pH; (ii) the spatially non-stationary effects of the main impact factors on wheat yield were revealed by RGWR but were ignored by OLSR; (iii) RGWR obtained the best modeling effect (RI = 52.31%); (iv) robust geostatistics obtains a better spatial prediction effect and the low-yield areas are mainly located in the northeast and the middle east of the study area. Therefore, the integrated yield-based methodology effectively improves soil nutrient management at a regional scale.

About half of the Danish agricultural land is drained artificially. Those drains, mostly in the form of tile drains, have a significant effect on the hydrological cycle. Consequently, the drainage system must also be represented in hydrological models that are used to simulate, for example, the transport and retention of chemicals. However, representation of drainage in large-scale hydrological models is challenging due to scale issues, lacking data on the distribution of drain infrastructure, and lacking drain flow observations. This calls for more indirect methods to inform such models. Here, we investigate the hypothesis that drain flow leaves a signal in streamflow signatures, as it represents a distinct streamflow generation process. Streamflow signatures are indices characterizing hydrological behaviour based on the hydrograph. Using machine learning regressors, we show that there is a correlation between signatures of simulated streamflow and simulated drain fraction. Based on these insights, signatures relevant to drain flow are incorporated in hydrological model calibration. A distributed coupled groundwater–surface water model of the Norsminde catchment, Denmark (145 km2) is set up. Calibration scenarios are defined with different objective functions; either using conventional stream flow metrics only, or a combination with hydrological signatures. We then evaluate the results from the different scenarios in terms of how well the models reproduce observed drain flow and spatial drainage patterns. Overall, the simulation of drain in the models is satisfactory. However, it remains challenging to find a direct link between signatures and an improvement in representation of drainage. This is likely attributable to model structural issues and lacking flexibility in model parameterization.

Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer's accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region., Agencia Estatal de Investigación, Universidad de Alcalá, Comunidad de Madrid, Departamento de Educación del Gobierno Vasco, Ministerio de Ciencia e Innovación

Persistent Scatterer Interferometry (PSI) is a consolidated tool for detecting and monitoring ground surface displacements. The availability of satellite data with free access policy and high monitoring capabilities (in terms of resolution and acquisition frequency) is increasing. Moreover, the first continental displacement map of Europe will be freely available in quarter one 2022 by the latest Copernicus Service, the European Ground Motion Service (EGMS). The EGMS will provide ground displacement maps, updated every year, delivering valuable information to a wide range of users, such as public or governmental institutions, industry, academia, and citizens. This vast amount of information needs semi-automatic tools and methodologies to derive user-oriented products that can be easily used by land use and urban planning decision-makers, who are often unfamiliar with PSI. This work proposes a semi-automatic procedure to identify damage prone areas in urban environments from wide-area PSI displacement maps. The proposed method identifies the most significant Active Deformation Areas (ADAs) to calculate three products based on the displacement intensity gradient: the Gradient Intensity Map, the Gradient Vectors and Time Series, and the Potential Damage Map. These products allow identifying buildings and urban structures exposed to potential damage, which could be followed by a more detailed building-based vulnerability and risk assessment. The methodology has been applied to an area of the province of Granada (Andalucía, Spain) but it can be applied to any other urban environment where PSI displacement maps are available. To demonstrate the advantages and limitations of the proposed method, results are discussed in five coastal resorts (Cerro Gordo, Punta de la Mona, Marina del Este, Alfa Mar, and Monte de los Almendros), strongly affected by slope movements. The methodology allowed to derive 175 ADAs from about 200,000 measurement points. About 15% of the resulting area has been found to correspond to high or very high gradient intensity class, and 192 out of 633 buildings have been identified to be prone to moderate or high potential damage. A damage prediction test has been realized through Receiver Operating Characteristic (ROC) analysis, based on a damage inventory map derived from field surveys. The results demonstrate the effectiveness of the methodology to localize damaged or potentially damaged buildings, substantially reducing the time of analysis. © 2022

Persistent Scatterer Interferometry (PSI) is a consolidated tool for detecting and monitoring ground surface displacements. The availability of satellite data with free access policy and high monitoring capabilities (in terms of resolution and acquisition frequency) is increasing. Moreover, the first continental displacement map of Europe will be freely available in quarter one 2022 by the latest Copernicus Service, the European Ground Motion Service (EGMS). The EGMS will provide ground displacement maps, updated every year, delivering valuable information to a wide range of users, such as public or governmental institutions, industry, academia, and citizens. This vast amount of information needs semi-automatic tools and methodologies to derive user-oriented products that can be easily used by land use and urban planning decision-makers, who are often unfamiliar with PSI. This work proposes a semi-automatic procedure to identify damage prone areas in urban environments from wide-area PSI displacement maps. The proposed method identifies the most significant Active Deformation Areas (ADAs) to calculate three products based on the displacement intensity gradient: the Gradient Intensity Map, the Gradient Vectors and Time Series, and the Potential Damage Map. These products allow identifying buildings and urban structures exposed to potential damage, which could be followed by a more detailed building-based vulnerability and risk assessment. The methodology has been applied to an area of the province of Granada (Andalucía, Spain) but it can be applied to any other urban environment where PSI displacement maps are available. To demonstrate the advantages and limitations of the proposed method, results are discussed in five coastal resorts (Cerro Gordo, Punta de la Mona, Marina del Este, Alfa Mar, and Monte de los Almendros), strongly affected by slope movements. The methodology allowed to derive 175 ADAs from about 200,000 measurement points. About 15% of the resulting area has been found to correspond to high or very high gradient intensity class, and 192 out of 633 buildings have been identified to be prone to moderate or high potential damage. A damage prediction test has been realized through Receiver Operating Characteristic (ROC) analysis, based on a damage inventory map derived from field surveys. The results demonstrate the effectiveness of the methodology to localize damaged or potentially damaged buildings, substantially reducing the time of analysis. © 2022

Research, part of a Special Feature on High Nature Value Farming Systems in Europe 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 info:eu-repo/semantics/publishedVersion

Seismic safety assessments at the large-scale are of paramount importance for densely urbanised areas with non-negligible seismic hazard. The topic is gaining increasing interest, particularly in nations with a longstanding-built environment. Methods based on non-linear history analyses of simplified building models are increasingly relevant and popular. This paper proposes a simplified model for moment resisting frame reinforced concrete buildings, and shows its efficiency in performing non-linear history analyses on a case study building. The proposed model has three degrees of freedom per storey, two translational and one rotational around the vertical axis. The building stiffness matrix and forces are derived from the column elements. Each column is modelled with two force-inter storey drift springs, one in each horizontal direction. These springs reflect the behaviour of the columns and account also for the flexibility and strength of the connected beams through calibration of their backbone curves. A genetic algorithm is used for the calibration of the parameters for each spring. Despite the approach simplicity, the proposed modelling scheme accounts for the effects associated with irregularities both in plan and in elevation. For this reason, it represents a major improvement with respect to similar approaches for large-scale vulnerability assessments, particularly for torsionally-flexible buildings. The proposed modelling approach is applied to a three-storey plan-irregular moment resisting frame building. A refined model is used as benchmark to show how the proposed approach can reproduce the main non-linear characteristics of the case study building subjected to a suite of ground motions.

The new European Ground Motion Service (EGMS) opens a new prospect in the study of the ground deformation phenomena influencing structures and infrastructures, at regional scale, exploiting the huge archives of Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 satellites. The research is currently oriented toward developing new methodologies to exploit this great volume of data, the management of which is difficult and onerous in terms of time. A new methodology for the monitoring of the deformations of urban settlements, based on the application of the ADAfinder tool to EGMS measure points, is proposed in this work. It targets the semi-automatic extraction of active deformation areas (ADA), given in the form of maps, with the goal to identify the buildings affected by displacements above a given threshold among all the buildings included in the investigated area. This allows a smart selection of the buildings needing insights about their condition through on-site monitoring or inspections, providing real support for the management of the urban areas. The proposed methodology is applied to two different case study areas in the city of Barcelona (Spain): the Eixample, in the heart of the city, and the Zona Franca, an industrial area near to the harbor.

International audience; Hightlights:• Predictions of physiological process depends on climate model, species and region.• Predictions were improved after correction for the three models considered.• Processes simulated exhibited large variability at the plot scale.• This variability faded out at larger scales, owing to an aggregation effect.• Process predictions were more variable during the driest years.Abstract: Climate change affects various aspects of ecosystem functioning, especially photosynthesis, respiration and carbon storage. We need accurate modelling approaches (impact models) to simulate forest functioning and vitality in a warmer world so that forest models can estimate multiple changes in ecosystem service provisions (e. g., productivity and carbon storage) and test management strategies to promote forest resilience.Here, we aimed to quantify the bias in these models, addressing three questions: (1) Do the predictions of impact models vary when forcing them with different climate models, and how do the predictions differ under climate model vs. observational climate forcings? (2) Does the climate impact simulation variability caused by climate forcings fade out at large spatial scales? (3) How does using simulated climate data affect process-based model predictions in stressful drought events? To answer these questions, we present historical results for 1960-2010 from the CASTANEA ecophysiological forest model and use the data from three climate models. Our analysis focuses on monospecific stands of European beech (Fagus sylvatica), temperate deciduous oaks (Quercus robur and Q. petraea), Scots pine (Pinus sylvestris) and spruce (Picea abies) in French forests.We show that prediction of photosynthesis, respiration and wood growth highly depends on the climate model used and species and region considered. Predictions were improved after a monthly mean bias or monthly quantile mapping correction for the three models considered. The processes simulated by the impact model exhibited large variability under different climate forcings at the plot scale (i.e., a few hectares). This variability faded out at larger scales (i.e., an ecological region, 100 km(2)), owing to an aggregation effect. Moreover, process predictions obtained under different climate forcings were more variable during the driest years. These results highlight the necessity of quantifying the bias correction effect on process predictions before predicting flux dynamics with a process-based model.

International audience; Leptospirosis is a neglected waterborne zoonosis of growing concern in tropical and low-income regions. Endemic in Southeast Asia, its distribution and environmental factors such as climate controlling its dynamics remain poorly documented. In this paper, we investigate for the first time the current and future leptospirosis burden at a local scale in mainland Southeast Asia. We adjusted machine-learning models on incidence reports from the Thai surveillance system to identify environmental determinants of leptospirosis. The explanatory variables tested in our models included climate, topographic, land cover and soil variables. The model performing the best in cross-validation was used to estimate the current incidence regionally in Thailand, Myanmar, Cambodia, Vietnam and Laos. It then allowed to predict the spatial distribution of leptospirosis future burden from 2021 to 2100 based on an ensemble of CMIP6 climate model projections and 4 Shared Socio-economics Pathways ranging from the most optimistic to the no-climate policy outcomes (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Leptospirosis incidence was best estimated by 10 environmental variables: four landscape-, four rainfall-, two temperature-related variables. Of all tested scenario, the worst-case scenario of climate change (SSP5-8.5) surprisingly appeared as the best-case scenario for the future of leptospirosis since it would induce a significant global decline in disease incidence in Southeast Asia mainly driven by the increasing temperatures. These global patterns are however contrasted regionally with some regions showing increased incidence in the future. Our work highlights climate and the environment as major drivers of leptospirosis incidence in Southeast Asia. Applying our model to regions where leptospirosis is not routinely monitored suggests an overlooked burden in the region. As our model focuses on leptospirosis responses to environmental drivers only, some other factors, such as poverty, lifestyle or behavioral changes, could further influence these estimated future patterns.

Climate change affects various aspects of the functioning of ecosystem, especially photosynthesis, respiration and carbon storage. We need accurate modelling approaches (impact models) to simulate the functioning, vitality and provision of ecosystem services of forests in a warmer world. These impact models require climate data as forcings, which are often produced by climate models comparing more or less well with observational climate data. The bias percentage of the climate forcings propagates throughout the modeling chain from the climate model to the impact model.In this study, we aimed to quantify these bias percentage, addressing three questions: (1) Do the impact model predictions vary when forcing it with different climate models, and how do the predictions under climate model vs. observational climate forcing differ? (2) Does the variability in the impact climate simulations caused by climate forcings fade out at large spatial scale? (3) How the fact of using simulated climatic data affects the process-based model predictions in the case of stressful events?To answer these questions, we present results obtained over the historical period (e.g. 1970-2010) with the CASTANEA ecophysiological forest model and use the data from three climate models. Our analysis focuses on French forests, studying European beech (Fagus sylvatica), temperate deciduous oaks (Quercus roburandQ. petraea), Scots pine (Pinus sylvestris) and spruce (Picea abies) monospecific stands.We show that prediction of photosynthesis, respiration and wood growth highly depends on the climate model used, whether debiased or not, and also on species and region considered. Overall, we observed an improvement of prediction after a monthly mean bias or monthly quantile mapping correction for three model considered, but not with the same success. Then we highlighted a large variability in the processes simulated by the impact model under different climate forcings when considering the plot (i.e. scale of a few hectares) scale. This variability fades out at larger scale (e.g. the scale of an ecological region, i.e. 100 km2), owing to an aggregation effect. Moreover, process predictions obtained under different climate forcings are more variable during driest years. These results highlight the necessity to quantify bias and uncertainties in climate forcings before predicting fluxes dynamics with process-based model.

Vegetation restoration often has a significant effect on the supply of an ecosystem service (ES). Assessment of this effect is crucial for informed decision-making in sustainable ecosystem management. In this respect, this study analyses three regulating, two provisioning, and a single cultural ES over a 30-year period (1985 to 2015, with 15 years pre-restoration and 15 years post-restoration) in the Loess Plateau, China, using data from a combination of modelling and statistical yearbooks. On applying a suite of standard statistical tools, results indicate: (1) regional scale restoration promotes the increase of vegetation cover as the coverage increased faster between 2000 and 2015 than between 1985 and 2000; (2) vegetation restoration changes the temporal trend of regulating ESs, and enhances the supply of provisioning and cultural ESs; (3) the 40 municipalities of the Loess Plateau can be divided into four ES categories where areas with poor ES delivery account for about 30% of the Loess Plateau; (4) vegetation restoration changes the interaction among ESs, resulting in synergistic relationships between provisioning and regulating ESs; (5) precipitation has a significant impact on regulating ESs, while population density is critical for provisioning and cultural ESs. This study demonstrates that ESs, their interactions and their groupings can change across both time and space following the implementation of a vegetation restoration programme, which makes understanding ES dynamics complicated. Recommendations are provided for improved and coherent ecosystem management.

The WOFOST cropping systems model has been applied operationally over the last 25 years as part of the MARS crop yield forecasting system. In this paper we provide an updated description of the model and reflect on the lessons learned over the last 25 years. The latter includes issues like system performance, model sensitivity, spatial model setup, parameterization and calibration approaches as well as software implementation and version management. Particularly for spatial model calibrations we provide experience and guidelines on how to execute calibrations and how to evaluate WOFOST model simulation results, particularly under conditions of limited field data availability. As an open source model WOFOST has been a success with at least 10 different implementations of the same concept. An overview is provided for those implementations which are managed by MARS or Wageningen groups. However, the proliferation of WOFOST implementations has also led to questions on the reproducibility of results from different implementations as is demonstrated with an example from MARS. In order to certify that the different WOFOST implementations and versions available can reproduce basic sets of inputs and outputs we make available a large set of test cases as appendix to this publication. Finally, new methodological extensions have been added to WOFOST in simulating the impact of nutrients limitations, extreme events and climate variability. Also, a difference is made in the operational and scientific versions of WOFOST with different licensing models and possible revenue generation. Capitalizing both on academic development as well as model testing in real-world situations will help to enable new applications of the WOFOST model in precision agriculture and smart farming.

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.

International audience; Analysis of trade-offs and synergies between ecosystem services (ES) and their underlying drivers is a main issue in ES research. The analysis is complex and requires innovative analytical approaches. To address this complexity, we used an original approach that combines a multivariate regression tree (MRT), data analysis, and spatial mapping. We applied this approach to the main cropping region in France (mainly the Paris basin of production) using an existing dataset (i.e. soil, climate, crop sequences and management) from the French National Ecosystem Assessment to determine relationships between agricultural production, two services to farmersnitrogen provision to crops and water provision to cropsand three services to societyblue water provision, water quality regulation, and climate regulation. To support land managers and decision-makers, we also analyzed the extent to which manageable soil properties and agricultural practices (crop rotation and management) are major drivers of trade-offs or synergies. We demonstrated that water quality regulation, nitrogen provision to crops, and climate regulation have synergistic relationships in production situations in the northeastern region of the study area due to the types of crop rotation, frequency of cover crops in the crop rotation, the soil pH, and the soil available water capacity. We also identified that cover crops, while promoting these three ES, can drive a trade-off between two key water-related services: water provision to crops and blue water provision (i.e. between a service to farmers and one to society). By capturing non-linear relationships and threshold effects, our MRT-based approach overcomes the main limitations of classic statistical approaches. The approach is also spatially explicit and simple and intuitive to interpret, especially for non-scientists; our results thus provide researchers and ecosystem managers (e.g. agricultural policy makers) with key information to design ecosystem management strategies that promote a balanced bundle of ES.

Simple Summary The Cyprus grass snake (Natrix natrix cypriaca) is a critically endangered subspecies, endemic to the island of Cyprus. The species can be found in areas mainly related to water (lakes, dams and rivers), that are protected under the Natura 2000 network. Recently volunteers reported sightings of the species outside of its currently known distribution. To address those reports and investigate the possible presence of the species outside its distribution, a rapid survey approach was designed and implemented. The survey provided evidence on several sightings outside of the species’ distribution, identifying new localities for the first time in 25 years and highlighting the importance of sparse springs within systems of ephemeral streams for the species population. This exciting discovery brings new opportunities and responsibilities regarding the conservation of the species. We are proposing targeted conservation actions and stress the importance of expanding the research on this critically endangered subspecies, based on current findings. Abstract The critically endangered Cyprus grass snake (Natrix natrix cypriaca) has been studied for the past 25 years. Although the need for a complete survey on the distribution of its population, outside the strict boundaries of the known mountainous localities, had been stressed, such an effort has not been conducted to date. In this study, we used a rapid survey approach to investigate possible sightings of the species upstream of the known distribution in the Troodos Mountains. We are presenting evidence from 13 sightings of the species that expand the previously known distribution in Cyprus by nearly 70%. This is the first time that new localities for the critically endangered N. n. cypriaca have been reported since the rediscovery of the species in 1992 and the extensive work that followed. Almost all new localities were discovered outside of the currently known species distribution, while individuals were found, for the first time, within the Natura 2000 site of Dasos Machaira (CY2000004) with indications of healthy populations in the area. New localities were recorded within watersheds of the Pediaios and Gialias Rivers, the first sightings outside of Serrachis watershed, indicating an even broader distribution of the species in the Troodos region. The importance of sparse springs within systems of ephemeral streams is highlighted as an exceptional niche for the population in the region. We are discussing the importance of our findings for the conservation of the species and propose targeted conservation actions that will highly improve connectivity of the populations in the region. We underline the necessity of expanding the research on this critically endangered subspecies, based on current findings, so as to fully comprehend its ecology and distribution range in the region.

Accurately and reliably estimating total terrestrial gross primary production (GPP) on a large scale is of great significance for monitoring the carbon cycle process. The Sentinel-3 satellite provides the OLCI FAPAR and OTCI products, which possess a higher spatial and temporal resolution than MODIS products. However, few studies have focused on using LUE models and VI-driven models based on the Sentinel-3 satellites to estimate GPP on a large scale. The purpose of this study is to evaluate the performance of Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data in estimating GPP at site and regional scale. Firstly, we integrated OLCI FAPAR and meteorology reanalysis data into the MODIS GPP algorithm and eddy covariance light use efficiency (EC-LUE) model (GPPMODIS-GPP and GPPEC-LUE, respectively). Then, we combined OTCI and meteorology reanalysis data with the greenness and radiation (GR) model and vegetation index (VI) model (GPPGR and GPPVI, respectively). Lastly, GPPMODIS-GPP, GPPEC-LUE, GPPGR, and GPPVI were evaluated against the eddy covariance flux data (GPPEC) at the site scale and MODIS GPP products (GPPMOD17) at the regional scale. The results showed that, at the site scale, GPPMODIS-GPP and GPPEC-LUE agreed well with GPPEC for the US-Ton site, with R2 = 0.73 and 0.74, respectively. The performance of GPPGR and GPPVI varied across different biome types. Strong correlations were obtained across deciduous broadleaf forests, mixed forests, grasslands, and croplands. At the same time, there are overestimations and underestimations in croplands, evergreen needleleaf forests and deciduous broadleaf forests. At the regional scale, the annual mean and maximum daily GPPMODIS-GPP and GPPEC-LUE agreed well with GPPMOD17 in 2017 and 2018, with R2 > 0.75. Overall, the above findings demonstrate the feasibility of using Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data through LUE and VI-driven models to estimate GPP, and fill in the gaps for the large-scale evaluation of GPP via Sentinel-3 satellites.

Irrigation schedule is essential for improving crop production and allocating water resources in agricultural regions that heavily rely on irrigation. This study designs a framework based on the AquaCrop model to optimize the irrigation schedule of winter wheat under dry, normal and wet hydrologic scenarios over a large region in China. The model parameters were calibrated for one cultivar using observed data from three locations in the Fenwei Plain, northern China, and were shown to slightly vary spatially across this region. Regional weather data at high spatio-temporal resolution were generated by interpolation and were combined with regional soil data on a 2 × 2 km grid to drive the model. The irrigation schedule for the study area was optimized by combining a multi-objective algorithm with the exponential efficacy coefficient method. The optimization objectives included crop yield, water use efficiency (WUE), irrigation WUE and economic irrigation benefit. The results showed that the optimized irrigation schedule performed better than the current irrigation schedule applied by the farmers under studied hydrologic scenarios, resulting in increased crop yield, WUE, irrigation WUE and irrigation economic benefit by 1.1–9.7% and decreased irrigation amount by 4.2–5.7%, depending on regions within the study area. The framework developed in this study reallocated irrigation water amounts between regions, thereby improving water allocation to achieve optimal crop yield, water use and economic benefit for the Fenwei Plain. The results can also serve as a guide for local farmers and irrigation district managers.

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.

International audience; Aim: To test the hypothesis that spatial variations in climate and land use explain the distribution and diversity of reptiles’ ecological traits in a biodiversity hotspot of the Mediterranean basin.Location: French Mediterranean region.Taxon: Reptiles (25 species).Methods: We defined 288 species assemblages from 27,858 opportunistic occurrence records within 10 × 10 km square grid cells. Using a RLQ analysis, we investigated the distribution of nine ecological traits describing reptiles’ reproductive, dietary and behavioural strategies along environmental gradients formed by 10 climatic, topographic and land‐use descriptors. After homogenizing survey effort through rarefaction, we used generalized additive models to assess variations in species richness and several measures of functional diversity and composition along these gradients.Results: Although ecological traits were not structured by environmental gradients at a species level, species richness and all measures of functional diversity varied nonlinearly with climate, elevation and secondarily land use. Species richness and body size peaked at intermediate altitudes, indicating a climatic transition zone between Mediterranean and medio‐European herpetofaunas. Conversely, functional diversity increased from Mediterranean plains, dominated by urbanization and agriculture, to higher elevations. These changes were associated with shifts towards less productive, more diurnal and more forest‐related species.Main conclusions: Species‐scale patterns are insufficient to assess regional variations in ecological traits in space. Our results support the hypothesis that climatic limitations in species’ distributions, rather than environmental filtering, explain the taxonomic and ecological diversity of reptiles at a regional scale. Although secondary to climate, land abandonment has a strong structuring effect which may contribute to homogenize the composition of reptile assemblages.

Today, outdoor air quality occupies an important place into civic dialogue because of the many impacts of atmospheric pollution on health. Different legislations and strategies have thus been implemented across the world to improve outdoor air quality. Analysing and comparing the different strategies can help adopting the best practices in favour of air quality. In France, in particular, the national air quality policy is reflected at a regional scale through legislative documents called Atmosphere Protection Plans (PPAs). The analysis of such a document thus represents a powerful way to assess a regional air quality policy. Here, Normandie’s PPA has been analysed through a literature review, working meetings as well as qualitative interviews. The study highlighted that Normandie’s air quality policy had to answer a complex legislative ecosystem of international, national and local documents. The analysis also underlined that the questions of the perimeter of action of the policy and the targeted pollutants were as central as the measures implemented to improve air quality. The working meetings and the qualitative interviews led to consensus that Normandie’s air quality policy should target 3 atmospheric pollutants: nitrogen dioxide, particulate matter 10 and particulate matter 2.5. If road traffic has been identified as the major emitting source of the first, particulate matter emitting sources were pointed as varied and multi-sectoral. Finally, some measures of Normandie’s air quality policy appeared to be particularly efficient and advanced, such as agricultural measures. Yet, many difficulties were encountered when implementing several measures of the policy. The main difficulties were the lack of financial resources, the lack of project leader and the lack of technical expertise. More than a basic analysis of an air quality public policy, these results can also be perceived as a sharing of experience on how a region addresses air quality issues. Because a work of collection of best practices and best strategies to improve air quality is more than ever needed, further research can build on this study to elaborate a relevant air quality policy. Luftkvaliteten visar den antropogena nedbrytningen av det naturliga tillståndet för luft. Detta ämne har spelat en allt viktigare roll i den offentliga debatten under de senaste decennierna, på grund av de olika effekterna som luftföroreningar har på hälsan. I denna masteruppsats har Normandies politik för luftkvalitet analyserats. I Frankrike regleras nationell luftkvalitetspolitik i regional skala genom lagstiftningsdokument som kallas Atmosphere Protection Plan (PPA). Analysen av detta dokument gör det möjligt att bedöma den regionala luftkvalitetspolitiken. Således har hela studien baserats på analysen av Normandies PPA. Här har Normandies PPA analyserats genom litteraturöversikt, arbetsmöten och kvalitativa intervjuer. Först och främst belyste litteraturöversikten att Normandies PPA måste ta hänsyn till ett komplext lagstiftningsekosystem, allt från internationell till lokal skala. I synnerhet är Normandies PPA-huvudmål att uppnå de tröskelvärden som definieras av de europeiska direktiven. I ett andra steg tillät en granskning av PPA från sex franska regioner att jämföra Normandies luftkvalitetspolitik med andra regioners politik. Under denna fas har såväl strukturer dokumenterats som de presenterade åtgärderna utvärderats med avseende på strukturerna och åtgärderna för de andra PPA. I synnerhet har återkommande kategorier och underavsnitt identifierats i de sex PPA. Dessutom tycktes motiveringen av applikationomkrets vara central, liksom de riktade atmosfäriska föroreningarna. På samma sätt tillät jämförelsen av åtgärderna för de sex PPA att identifiera de relevanta och banbrytande åtgärderna i Normandies PPA, som tillhör hamnsektorn och de sektorsövergripande åtgärderna. Omvänt verkade åtgärderna av Normandies PPA vara otillfredsställande inom industri- och transportsektorn. En mer fördjupad analys genomfördes genom arbetsmöten och kvalitativa intervjuer. Flera frågor har bedömts, till exempel applikationomkrets och PPAs struktur. I synnerhet har man dragit slutsatsen att minskningen av applikationomkretsen som Normandie avsåg var relevant och förhindrade PPAs komplikationer. Dessutom påpekade deltagarna att om Normandies PPA-struktur var ganska tydlig, saknades fortfarande viktiga underavsnitt i Normandies PPA och de bör läggas till. Arbetsmötena liksom de kvalitativa intervjuerna gjorde det också möjligt att ta upp frågan om de riktade föroreningarna och deras källor. Alla deltagare och intervjuade gick med på att rikta in NO2, PM10 och PM2.5 i Normandies PPA. Integrationen av SO2 och O3 har dock diskuterats mer, den första har endast släppts ut i skadlig mängd under industriella olyckor, och den andra var för svår att hantera eftersom den är en sekundär förorening. När det gäller utsläppskällorna visade studien att en hög atmosfärisk koncentration av NO2 främst berodde på vägtrafik. Utsläppskällorna PM10 och PM2.5 identifierades som mer varierade och flersektoriella. Slutligen tillät de kvalitativa intervjuerna att avgöra vilka åtgärder av Normandies PPA som har slutförts eller inte, och att identifiera varför. Åtgärder inom jordbrukssektorn nämndes som de mest avancerade åtgärderna i Normandies PPA. Omvänt har två åtgärder övergivits helt, en inom hamnsektorn, den andra inom transportsektorn. Några skäl som förklarade varför alla åtgärder inte har genomförts var bristen på ekonomiska resurser, bristen på projektledare och bristen på teknisk expertis. Mer allmänt verkar det vara avgörande att involvera de olika intressenterna från varje utsläppssektor och sätta dem runt ett bord för att skapa effektiva åtgärder till förmån för luftkvaliteten.

Agraïments: This study was supported by the National Natural Science Foundation of China (41571022, 41625001), the Beijing Natural Science Foundation (Grant 8151002). The application of phosphorus (P) fertilizer to agricultural soils increased by 3.2 % annually from 2002 to 2010. We quantified in detail the P inputs and outputs of cropland and pasture, and the P fluxes through human and livestock consumers of agricultural products, at global, regional, and national scales from 2002 to 2010. Globally, half of the total P input (21.3 Tg P yr−1) into agricultural systems accumulated in agricultural soils during this period, with the rest lost to bodies of water through complex flows. Global P accumulation in agricultural soil increased from 2002 to 2010, despite decreases in 2008 and 2009, and the P accumulation occurred primarily in cropland. Despite the global increase of soil P, 32 % of the world's cropland and 43 % of the pasture had soil P deficits. Increasing soil P deficits were found for African cropland, versus increasing P accumulation in Eastern Asia. European and North American pasture had a soil P deficit because continuous removal of biomass P by grazing exceeded P inputs. International trade played a significant role in P redistribution among countries through the flows of P in fertilizer and food among countries. Based on country-scale budgets and trends we propose policy options to potentially mitigate regional P imbalances in agricultural soils, particularly by optimizing the use of phosphate fertilizer and recycling of waste P. The trend of increasing consumption of livestock products will require more P inputs to the agricultural system, implying a low P-use efficiency aggravating the P stocks scarcity in the future. The global and regional phosphorus budgets and their PUEs in agricultural systems is publicly available at https://doi.pangaea.de/10.1594/PANGAEA.875296.

The dynamics of flood event characteristics, such as the runoff coefficient and the recession time constant, differ in time and space, due to differences in climate, geology, and runoff generation mechanisms. This study examines the variability of event runoff characteristics and relates them to climatic and hydro-geological characteristics available at the regional scale. The main focus is to examine the role of rainfall patterns (i.e., event precipitation volume, precipitation intensity, and antecedent precipitation) and runoff regime (i.e., initial flow before runoff event and event duration) characteristics on the seasonal dynamics of runoff response. The analysis is performed in four small Austrian catchments representing different hydro-geological settings obtained by field mapping. The results are based on an analysis of 982 runoff events identified from hourly measurements of streamflow and precipitation in the period 2002 to 2013. The results show that larger event runoff coefficients and flow peaks are estimated in catchments with high mean annual precipitation than in drier catchments. In contrast to some previous studies, the results show only poor relation between antecedent precipitation (as an index of catchment wetness) and event runoff response. The initial flow is found to be the main factor influencing the magnitude of runoff coefficient and event peaks in all analyzed catchments and geological settings. The recession time constant tends to be inversely related to the maximum event precipitation intensity, with an exception for one catchment (Wimitzbach), which is characterized by the largest proportion of deep interflow contribution to runoff. The analysis of the runoff response by different event types indicates that runoff coefficients and recession time constants are the largest for snowmelt runoff events.

Landslide susceptibility mapping is of critical importance to identify landslide-prone areas to reduce future landslides, causalities, and infrastructural damages. This paper presents landslide susceptibility maps at a regional scale for the Chittagong Hilly Areas (CHA), Bangladesh. The frequency ratio (FR) was integrated with the analytical hierarchy process (AHP) (FR_AHP) and logistic regression (LR) (FR_LR). A landslide inventory of 730 landslide locations and 13 landslide predisposing factors including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), land use/land cover, rainfall, distance from drainage network, distance from fault lines, lithology, and normalized difference vegetation index (NDVI) were used. Landslide locations were randomly split into training (80%) and validation (20%) sites to support the susceptibility analysis. A safe zone was determined based on a slope threshold for logistic regression using the exploratory data analysis. The same number of non-landslide locations were randomly selected from the safe zone to train the model (FR_LR). Success and prediction rate curves and statistical indices, including overall accuracy, were used to assess model performance. The success rate curves show that FR_LR showed the highest area under the curve (AUC) (79.46%), followed by the FR_AHP (77.15%). Statistical indices also showed that the FR_LR model gave the best performance as the overall accuracy was 0.86 for training and 0.82 for validation datasets. The prediction rate curve shows similar results. The correlation analysis shows that the landslide susceptibility maps produced by FR and FR_AHP are highly correlated (0.95). In contrast, the correlation between the maps produced by FR and FR_LR was relatively lower (0.85). It indicates that the three models are highly convergent with each other. This study’s integrated methods would be helpful for regional-scale landslide susceptibility mapping, and the landslide susceptibility maps produced would be useful for regional planning and disaster management of the CHA, Bangladesh.

The building and public works sector is, in France as in Europe, a major consumer of raw materials for both the manufacture of products and the construction of buildings and structures. This sector has a direct impact on the natural and built environment. This effect is even more pronounced in the case of isolated territories, such as islands. The latter have their own constraints (geographical location, production of the local grid mix) and particularities: very small territory, massive importation of goods in all fields, such as food, automobile, building, and others). In this study, we focus on the building branch of the construction industry, which covers housing (single-family houses and apartment blocks). The study is based on the analysis of about twenty single-family houses built in metropolitan France and Reunion Island. The construction standards for these two regions comply with European standards (CE) and French regulations. However, in the case of Reunion Island, a tropical island, it applies in particular to the Thermal, Acoustic, and Ventilation Regulations for New Buildings in Overseas Departments and Regions (RTAA DROM). The approach that is used for the environmental assessment of single-family homes is the Life Cycle Assessment (LCA), from cradle to grave. The results initially showed that there is an additional environmental cost in the construction sector between France and Reunion Island. This is initially due to the choice of origin of materials and products, which can greatly contribute to the impacts of construction. Secondly, to the use of the countries&rsquo, electricity mix, which also contributes, in part, to the impact of the construction of these single-family homes during the assembly and transformation of the products. Finally, this additional cost also differs according to the transport used (sea, air, rail, road). For the Global Warming Potential (GWP) indicator, in our study we note that the additional environmental cost is 37% higher in Reunion Island. This figure explains the additional impact of the 218 kg-CO2eq/m2 of built-up area built for Reunion Island. This study is one of the first analyses demonstrating the additional environmental cost that exists between mainland France and overseas France. Thus, the results demonstrate the importance of creating a specialized and regionalized database for the case of remote islands. Thus, this database would allow for professionals to have a precise environmental assessment, not on a national but on a regional scale. This document also provides a framework and guideline for policy decision-making in the overseas islands.

Research Highlights: Understanding of the spatial variation of root exudation on a regional scale can help understand the response of plant physiological activities to environmental changes. Background and Objectives: Although root exudation has become an important topic in belowground ecology, its relationship with root traits and environmental factors is poorly understood. Our objective was to explore how root traits and environmental factors influence root exudation. Materials and Methods: We used a multi-factorial design consisting of three tree species spanning across sites located at three latitudes to assess root exudation dynamics, which was measured using a syringe-basis incubation system. Results: The strongest and clearest effect observed in our study was a decrease in root exudation rates of Korean pine (Pinus koraiensis Sieb. et Zucc.) and larch (Larix gmelinii (Rupr.) Kuze.) at sites located in higher latitudes. Root exudation rates were positively related to mean annual temperature, mean annual precipitation, and negatively related to soil total organic carbon. Conclusions: Root exudation in coniferous species decreased at sites located in higher latitudes. Despite differences in root exudation rate among sites located at different latitudes and species with suitable variation in root morphological traits and environmental factors, we could not identify consistent influencing factors on root exudation rates.

Understanding the spatial variation of forest productivity and its driving factors on a large regional scale can help reveal the response mechanism of tree growth to climate change, and is an important prerequisite for efficient forest management and studying regional and global carbon cycles. Pinus massoniana Lamb. is a major planted tree species in southern China, playing an important role in the development of forestry due to its high economic and ecological benefits. Here, we establish a biomass database for P. massoniana, including stems, branches, leaves, roots, aboveground organs and total tree, by collecting the published literature, to increase our understanding of net primary productivity (NPP) geographical trends for each tree component and their influencing factors across the entire geographical distribution of the species in southern China. P. massoniana NPP ranges from 1.04 to 13.13 Mg&middot, ha&minus, 1&middot, year&minus, 1, with a mean value of 5.65 Mg&middot, 1. The NPP of both tree components (i.e., stem, branch, leaf, root, aboveground organs, and total tree) show no clear relationships with longitude and elevation, but an inverse relationship with latitude (p &lt, 0.01). Linear mixed-effects models (LMMs) are employed to analyze the effect of environmental factors and stand characteristics on P. massoniana NPP. LMM results reveal that the NPP of different tree components have different sensitivities to environmental and stand variables. Appropriate temperature and soil nutrients (particularly soil available phosphorus) are beneficial to biomass accumulation of this species. It is worth noting that the high temperature in July and August (HTWM) is a significant climate stressor across the species geographical distribution and is not restricted to marginal populations in the low latitude area. Temperature was a key environmental factor behind the inverse latitudinal trends of P. massoniana NPP, because it showed a higher sensitivity than other factors. In the context of climate warming and nitrogen (N) deposition, the inhibition effect caused by high temperatures and the lack or imbalance of soil nutrients, particularly soil phosphorus, should be paid more attention in the future. These findings advance our understanding about the factors influencing the productivity of each P. massoniana tree component across the full geographical distribution of the species, and are therefore valuable for forecasting climate-induced variation in forest productivity.

Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure, afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50&ndash, 360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 &times, 10&minus, 4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

Forest fire emissions have a great impact on local air quality and the global climate. However, the current and detailed regional forest fire emissions inventories remain poorly studied. Here we used Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate monthly emissions from forest fires at a spatial resolution of 500 m &times, 500 m in southwest China from 2013 to 2017. The spatial and seasonal variations of forest fire emissions were then analyzed at the provincial level. The results showed that the annual average emissions of CO2, CO, CH4, SO2, NH3, NOX, PM, black carbon, organic carbon, and non-methane volatile organic compounds from forest fires were 1423.19 &times, 103, 91.66 &times, 103, 4517.08, 881.07, 1545.04, 1268.28, 9838.91, 685.55, 7949.48, and 12,724.04 Mg, respectively. The forest fire emissions characteristics were consistent with the characteristics of forest fires, which show great spatial and temporal diversity. Higher pollutant emissions were concentrated in Yunnan and Tibet, with peak emissions occurring in spring and winter. Our work provides a better understanding of the spatiotemporal representation of regional forest fire emissions and basic data for forest fire management departments and related research on pollution and emissions controls. This method will also provide guidance for other areas to develop high-resolution regional forest fire emissions inventories.

This study deals with one of the regulating ecosystem services, nutrient regulation. In order to guarantee sustainable land management, it is of great relevance to gain spatial information on this ecosystem service. Unsustainable land management with regard to nutrient regulation may, for example, result in eutrophication which has been identified as a major threat for the environmental state of our water bodies. In the first step of research, the potential supplies and demands of/for nutrient regulation were assessed and mapped at two different spatial scales: The German federal state of Schleswig-Holstein (regional scale) and the Bornhöved Lakes District (local scale). The assessment was undertaken for nitrogen, as an exemplary nutrient. Subsequently, potential supply and demand, combined with the nitrate leaching potential and the groundwater nitrate concentration, were incorporated into a correlation analysis. The data was statistically analysed with varying pre-processing and spatial resolutions. The statistical analysis reveals that large scale data with low resolution leads to more uncertain results. Decreasing the spatial scale and increasing the resolution of the data through a spatially more explicit assessment, leads to more explicit results. It is striking that the study reveals a spatial mismatch between the potential supply and demand for the ecosystem service nutrient regulation, which denotes unsustainable land management in the study areas.

To allow for the assessment of regional-scale geographically non-contiguous production system derived environmental impacts, a combined method of Territorial Metabolism Life Cycle Assessment (TM-LCA) is proposed. by creating a two-pronged framework for the development of background system modelling, the TM-LCA method allows for process-based environmental impact modeling at a regional scale utilizing the concept of a production territory for the assessment of changes to durable production systems, such as energy infrastructure and agricultural systems. The TM-LCA framework creates the opportunity for direct assessment of environmental impacts, incorporation of system dynamics, and the use of multi-criteria decision analysis, which might be difficult or impossible to implement in other regional scale environmental impact assessment frameworks. (C) 2018 The Authors. Published by Elsevier B.V.

Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale.

In watershed mountain basins, affected in the last decades by strong rainfall events, the role of debris-flow and debris flood processes was investigated. Morphometric parameters have proven to be useful first-approximation indicators in discriminating those processes, especially in large areas of investigation. Computation of morphometric parameters in 19 watershed mountain basins of the western side valley of the Vallo di Diano intermontane basin (southern Italy) was carried out. This procedure was integrated by a semi-automatic elaboration of the potential susceptibility to debris flows, using Flow-R modelling. This software, providing an empirical model of the preliminary susceptibility assessment at a regional scale, was applied in many countries of the world. The implementation of Flow-R modelling requires a GIS application and some thematic base maps extracted using DEMs analysis. A 5-meter-resolution DEM has been used in order to produce the susceptibility maps of the whole study area, and the results are compared and discussed with the real debris flow/flood events that occurred in 1993, 2005, 2010, and 2017 in the studied area. The results have provided a good reliability of Flow-R modelling within small catchment mountain basins.

China has entered an era of rapid high-speed railway (HSR) development and the spatial structure of urban agglomerations will evolve in parallel with the development and evolution of the spatial structure of the HSR network. In this study, we explore how the spatial structure of an HSR network evolves at regional and local scales. Existing research into HSR network structures has mostly been carried out at a regional scale, and has therefore failed to reveal the spatial connections within a city. In this work, we progress the science by exploring it at a local scale. To describe the HSR network more accurately, we use the dwell time to simulate the passenger flow between stations and use the simulated passenger flow as the network weight. We use complex network analysis to investigate the evolution of the network’s spatial structure. Our results present the evolution of station locations, of community structure, and of the locations of connections between stations at a regional scale, and also show how HSR network development within core cities has impacted structures and connectivity at a local scale. These results help us to understand the spatial structure of urban agglomerations and cities, and provide evidence that can be used to optimize the structure of the HSR network within regions and cities.