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

The 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.

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.

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.

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.

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.

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.

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.

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.

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.

The effects of temperature and precipitation on tree growth of given species may vary across its distribution range. To determine how temperature and precipitation influence tree radial growth, we conducted a dendrochronology-based meta-analysis using a network of 113 tree-ring width chronologies of Pinus massoniana trees from the geographical range of the species in southern China. Our results revealed that the difference in the sensitivity of P. massoniana growth to temperature and precipitation was not observed in the entire tree species distribution and each sub-region (all P > 0.05), indicating that there are similarities with respect to the effects of temperature and precipitation on P. massoniana growth among different regions. January–March temperature (T1–3), June–August temperature (T6–8), and July–September precipitation (P7–9) were the key climate signals influencing P. massoniana growth. T1–3 and P7–9 generally enhanced tree growth, whereas T6–8 lead to a decrease in tree growth. Sites with significant negative correlations between tree-ring width index (RWI) and T1–3 showed a northward shift (P 0.05), indicating that high summer temperatures are widespread climate stressors for this species. The growth-climate relationship was determined by combinations of both temperature and precipitation condition of the sampling sites. Temperature seasonality, temperature annual range, and mean temperature of driest quarter of the sampling sites were major contributors to this relationship. These findings have crucial implications for predicting tree growth, introducing the selection of appropriate provenance for afforestation, as well as for climate reconstruction at the regional scale.

Land use and land cover (LULC), as well as their geographical and temporal variations, affect landslide occurrence and the related risk, in ways that are difficult to determine. Here, we propose a method for the regional analysis of variations in landslide frequency and distribution in response to observed and projected LULC changes. The method is quantitative and reproducible. We test it in Calabria, southern Italy, where a catalogue of 7037 landslides occurred between 1921 and 2010 is available. First, we defined empirical relationships linking the observed LULC variations to landslide occurrence. We found that, in the period 1921-1965, the majority of the landslides occurred in forests and grassland areas, while, in the period 1966-2010, the landslide density became higher in grassland areas, lower in arable lands and remained about constant in the forests. We consider this an evidence of the positive effect of agricultural practices and land management in reducing landslide occurrence. We noticed that both the landslide occurrence and the distribution of the vulnerable elements changed in the observation period. Then, we calculated the projected variations (to 2050) in landslide occurrence related to 32 scenarios of LULC changes. Our projections reveal a modest increase in landslide occurrence in all scenarios, in the range from 0.9 to 3.2%, with an average increase of 2%. Considering all scenarios, we expect an increase in the number of landslides due to LULC variations in 291 municipalities in Calabria (71%), with 4 municipalities where the increase is expected to exceed 50%. We maintain that the observed and the projected variations in the occurrence of landslides in Calabria are related to changes in natural (i.e. the number and distribution of the triggering events) and environmental and societal (i.e. the number and the distribution of the exposed elements) components.

A simple and efficient finite-difference scheme is developed to calculate seismic wave propagation in a partial spherical shell model of a three-dimensionally (3-D) heterogeneous global Earth structure for modeling on regional or sub-global scales where the effects of the Earth’s spherical geometry cannot be ignored. This scheme solves the elastodynamic equation in the quasi-Cartesian coordinate form similar to the local Cartesian one, instead of the spherical polar coordinate form, with a staggered-grid finite-difference method in time domain (FDTD) that is one of the most popular numerical methods in seismic-motion simulations for local-scale models. The proposed scheme may be a local-friendly approach for modeling on a sub-global scale to link regional-scale and local-scale simulations. It can be easily implemented using an available 3-D Cartesian FDTD local-scale modeling code by changing a very small part of the code. We implement the scheme in an existing Cartesian FDTD code and demonstrate the accuracy and validity of the present scheme and the feasibility to apply it to real large simulations through numerical examples. Graphical abstract .

In natural farmland ecosystems, cadmium (Cd) pollution in rice has attracted increasing attention because of its high toxicity, relative mobility, and high water solubility. This study aims to develop a spectral index for detecting Cd stress in rice on a regional scale. Three experimental sites are selected in Zhuzhou City, Hunan Province. The hyperspectral data, chlorophyll (Chl) content, leaf area index, average leaf angle, Cd concentration in soil, and Sentinel-2A images from 2017 and 2018 are collected. A new spectral index sensitive to Cd stress in rice is established based on the global sensitivity analysis of the radiative transfer model PROSPECT + SAIL (commonly called PROSAIL) model with the auxiliary of the field-measured data. The heavy metal Cd stress-sensitive spectral index (HCSI) is devised as an indicator of the degree of Cd stress in rice. Results indicate that (1) the HCSI developed based on Chl is a good indicator of rice damage caused by Cd stress, that is, low values of HCSI occur in rice subject to relatively high pollution, (2) compared with common spectral indices, such as red-edge position and red-edge Chl index, HCSI is more sensitive to Chl content with higher Pearson correlation coefficients with respect to Chl content, ranging from 0.85 to 0.95, (3) HCSI is successfully applied in Sentinel-2A images from the two different years of monitoring rice Cd stress on a regional scale. Cd stress levels in rice stabilized, and the largest area percentage of each pollution levels of Cd decreased in the following order: No pollution (i.e., 40%), low pollution (i.e., 35%), and high pollution (i.e., 25%). This study indicates that a combination of simulation data from the PROSAIL model and measured data appears to be a promising method for establishing a sensitivity spectral index to heavy metal stress, which can accurately detect regional Cd stress in crops.

As a parallel to the "biodiversity hotspot" concept used in conservation biology, "geodiversity hotspots" can be defined as geographic areas that harbor very high levels of geodiversity while being threatened by human activities. Identifying geodiversity hotspots may offer a powerful way to set geoconservation priorities, but numerical methods integrating both geodiversity values and threats are still lacking. Here we propose for the first time an integrated approach using GIS and geoprocessing to map geodiversity hotspots at a regional scale, with a cartographic application to the Ceará State (Northeastern Brazil). The method is based on the quantification and mapping of two numerical indices: a geodiversity index (GI) and a threat index (TI). On one hand, the GI is calculated as the sum of four sub-indexes representing the main components of geodiversity, i.e., geological diversity (rocks, minerals, fossils), geomorphodiversity (topography and landforms), pedodiversity (soils and palaeosoils) and hydrodiversity (surface and underground waters). On the other hand, the TI is calculated as the sum of three sub-indexes including the level of environmental protection, the degree of land degradation and the type of land use. Mapping and delineation of geodiversity hotspots are automatically obtained from a combination of GI and TI, i.e., in areas where higher geodiversity indexes coincide with higher threat indexes. In the study area, results show the spatial delimitation of five geodiversity hotspots, including the Araripe basin (to the South), partly recognized as a UNESCO Global Geopark since 2006, and the Fortaleza metropolitan region (to the North), both faced with severe threats to geodiversity. In addition to a tool for geoconservation, geodiversity hotspots could also provide useful support for biodiversity research and action programs, given the structural and functional links between geodiversity and biodiversity.

Como aferir e monitorizar desigualdades sociais na sociedade portuguesa contemporânea, nomeadamente à escala regional? Os debates científicos contemporâneos apelam para que as problemáticas das desigualdades e do desenvolvimento se aproximem entre si. Este artigo pretende ser um contributo para a compreensão das relações entre estas problemáticas, considerando as desigualdades sociais do país, as suas assimetrias regionais e as desvantagens específicas dos territórios de baixa densidade. Para o efeito, analisamos como demografia, a educação, o emprego, as classes sociais e a saúde estão associadas a desigualdades sociais entre as regiões do território nacional. Os resultados da pesquisa apontam para a persistência, na sociedade portuguesa, de processos de desenvolvimento desiguais à escala regional e no que diz respeito aos territórios de baixa densidade. ABSTRACT: How to reveal and monitor social inequalities in contemporary Portuguese society, particularly on a regional scale? Contemporary scientific debates call for the problematics of inequality and development to come close to each other. This article intends to contributes to the understanding of the relations between these scientific issues, considering the social inequalities of the country and the specific disadvantages of the low-density territories. To this end, we analyse how demography, education, employment, social classes and health are associated to social inequalities between the regions of the national territory. The empirical results show that in the Portuguese society there is a persistence of unequal development processes at the regional scale and in regard to the low density territories. info:eu-repo/semantics/publishedVersion

Este artículo contiene 10 páginas, 6 figuras, 2 tablas., Understanding the complexity of social-ecological systems is fundamental for achieving sustainability. Historically, humans have benefited from the ecosystem services offered by nature at the same time that natural systems have increasingly changed because of anthropogenic activities. The lack of methods to unveil and understand such associations might hinder the integrated management of coastal marine areas. In our study, we applied a methodological framework used in terrestrial systems to identify and spatially locate the coastal marine social-ecological systems (CMSESs) on the southern Mediterranean Spanish coast. These CMSESs represent areas with similar human-nature associations that result from sharing similar socioeconomic and marine environmental characteristics. We applied several multivariate analyses to identify and characterize these CMSESs. We found the presence of twelve CMSESs that suggest a co-evolution of the social-ecological associations in these areas. Our results highlight the need for integrated coastal planning and management that consider the specific characteristics and conservation challenges of each CMSES. Our study provides evidence that a successful methodological framework to identify and characterize social-ecological systems can be applied in coastal areas and contribute to integrated management for the sustainability of these fragile systems., Financial support was provided by the Project MARINERES, CGL2013-49122-C3-1-R of the Spanish Ministry of Economy and Competitiveness.

The increase of coastal erosion due to intense tropical storms and unsustainable urban development in Vietnam demands vulnerability assessments at different research scales. This study proposes (1) a new approach to classify coastlines and (2) suitable criteria to evaluate coastal vulnerability index (CVI) at national and regional/local scales. At the national scale, the Vietnamese coastline was separated into 72 cells from 8 coast types based on natural features, whereas the Center region of Vietnam was separated into 495 cells from 41 coast types based on both natural and socio-economic features. The assessments were carried out by using 17 criteria related to local land use/cover, socio-economic, and natural datasets. Some simplified variables for CVI calculation at the national scale were replaced by quantitative variables at regional/local scales, particularly geomorphology and socio-economic variables. As a result, more than 20% of Vietnam&rsquo, s coastline has high CVI values, significantly more than 350 km of the coasts in the center part. The coastal landscapes with residential and tourism lands close to the beaches without protection forests have been strongly affected by storms&rsquo, erosion. The new approach is cost-effective in data use and processing and is ideal for identifying and evaluating the CVI index at different scales.

ResumenUna de las principales amenazas para los sistemas costeros y las zonas bajas de todo el mundo es el incremento de los eventos de inundación debido a los efectos del cambio climático, fundamentalmente por el aumento del nivel medio del mar. Se presenta una metodología de análisis del impacto de inundación costera a escala regional —O (100km)—, que combina bases de datos históricas y proyecciones de dinámicas marinas de alta resolución. La exposición se representa sobre un modelo digital de terreno de alta resolución mejorado a partir de la inclusión de las defensas costeras. La sobreelevación del nivel debida a la rotura del oleaje se calcula mediante la aplicación de una formulación semiempírica calibrada con datos de campo, y se adecua al interior de los puertos. Dicha sobreelevación alimenta un modelo hidrodinámico bidimensional eficiente que permite obtener la inundación en tierra. A través de escenarios que combinan proyecciones locales de aumento del nivel medio del mar, combinadas con eventos extremos, la metodología propuesta se ha aplicado en la costa del Principado de Asturias (España).AbstractOne of the main threats to coastal systems and low-lying areas around the world is increasing flooding due to the effects of climate change, mainly due to sea-level rise. A regional-scale —O (100km)— methodology to analyze the impact of coastal flooding is herein presented. The procedure combines high-resolution historical databases and projections of marine dynamics. Exposure is well represented over a high-resolution digital terrain model deeply improved including coastal defences. An empirical parameterization of the run-up that results from wave breaking is calibrated with field data, and further improved to evaluate flooding in ports. Such run-up feeds an efficient two-dimensional hydrodynamic model that allows the characterization of the inundation inland. Using scenarios that combine regional projections of sea-level rise with extreme events, the proposed methodology has been applied to the coast of Asturias (North of Spain).

This paper presents the comparison of three global soil moisture products (ASCAT, AMSR and SMOS) versus a land surface model over a region representative of several Mediterranean landscapes located in the Northeast of the Iberian Peninsula. Our approach has been for agricultural and water management applications at the regional and local scale. Despite being a rather small area, we were able to observe different signal behaviours corresponding to major land cover classes in Mediterranean areas i.e.: dryland and irrigated crops, forests and natural vegetation (grass-shrubs). The area also allowed assessing the impact of topography. The first result of the study is that the results are very dependent on the normalizations used to make the data comparable, thus their impact must be carefully analysed. In this study, we applied two different normalisation methods (called ZV35 and ZV) and different moving average windows (1, 10 and 30 days) in order to enhance seasonal effects. Using no smoothing window, ASCAT is the soil moisture product that correlates best with the LSM over all cover classes, whatever the method. Using smoothing window, AMSR-E tends to outperform other soil moisture products with the ZV method. The ZV35 method is not able to identify a small heavily irrigated area. The reason for these different results is that ZV35, tends to eliminate the monthly scale soil moisture memory and therefore becomes more sensitive to precipitation and less sensitive to the monthly evolution of superficial soil moisture. The comparison shows in general good agreement for all soil moisture products with the LSM on the temporal series simulated over flat, non irrigated areas which are not close to the sea. SMOS has difficulties in areas close to the sea and in areas with steep relief and the current version of the L2 Operational Algorithm (V5.51) depicts few values in forested areas. ASCAT, in its turn, shows some limitations over agricultural and natural vegetation where it shows an increase of soil moisture from June to October probably due to increase of penetration depth in dry soil moisture conditions. AMSR-E LPRM shows a clear vegetation cycle over all the land cover classes. From all the remote sensing products, SMOS is the only one able to see irrigation and the only that does not show clear vegetation or roughness effects. In this study, we were able to assess the impact of higher resolution soil moisture products to map irrigated areas.

This study proposes and evaluates a relatively new concept for fire occurrence zoning based on documented historical fire records. The proposed method creates continuous kernel density surfaces based on wildland fire ignition observations. Kernels have the advantage of directly producing density estimates that are not influenced by grid size or localization effects. Within this scheme, kernel density surfaces have been created and reclassified to construct fire occurrence zones at local to global scales in the Mediterranean Basin. Specifically, fire occurrence zones were created for the European scale (European Mediterranean Basin), national scale (Greece), regional scale (Peloponnese, Greece) and local scale (Chalkidiki, Greece). To evaluate fire occurrence zones, we compared the observed with the expected distribution of the number of fires within these zones using a Monte Carlo randomization test, finding that these numbers were statistically different in all cases. The deviations observed from the expected distributions towards the high occurrence zone indicated their successful assessment and value. In this paper, we further discuss their potential role and use for multi-scale fire management and policy in a European context.

The natural abundance of δ15N in soils is a simple tool for understanding ecosystem nitrogen cycling. However, the main drivers for soil δ15N in different natural land use types are not clear enough. To investigate the main drivers for soil δ15N in different land uses, we measured δ15N in the top 10 cm of the soil along a precipitation gradient with various land uses on the Loess Plateau of China, which covers a total area of 620,000 km2 and is one of the most erodible area in the world, to determine effects of climatic, soil and vegetation factors on soil δ15N and the relationships between them using the partial least squares regression (PLSR) method. Soil δ15N ranged from 1‰ to 9‰ on the Loess Plateau with an average of 5.8‰ in forest lands, 5.6‰ in shrub lands, and 4.8‰ in grasslands. Calculated variable influence on projection by PLSR showed that both soil properties (including soil organic carbon content, total nitrogen content and soil texture) and mean annual precipitation influenced soil δ15N strongly. In addition, site elevation also influenced soil δ15N strongly in forest lands. The dominant controls of soil δ15N varied among three land use types on the Chinese Loess Plateau, and PLSR would be a reasonable tool to predict soil δ15N.

International audience; Digital soil mapping is becoming a powerful tool to increase the spatial detail of soil information over large areas, which is essential to address agronomic and environmental issues. When it exists, information about soil is often sparse or available at a coarser resolution than required. The spatial distribution of soil at the regional scale is usually represented as a set of polygons defining Soil Map Units (SMUs), each including several Soil Type Units (STUs), which are not spatially delineated but semantically described in a database. Delineation of STUs within SMUs, i.e. spatial disaggregation of SMU, should improve the precision of soil information derived from legacy and ancillary data. The aim of this study was to predict STUs by spatially disaggregating SMUs through a decision-tree approach that considered expert knowledge about soil-landscape relationships embedded in soil databases. In a 27,376 km(2) study area in north-western France (Brittany), 434 SMUs were delineated at 1:250,000 scale, and 320 STUs, their relative area in the SMUs, and their geomorphological and geological contexts were described. A calibration dataset of points was established using stratified random sampling,(n = 352,188). To retrieve soil-landscape relationships, expert rules for soil distribution defined by soil surveyors and based on topography, parent material and waterlogging index were considered in order to allocate an STU to 83% of the calibration dataset. The calibration dataset and covariates (i.e. pedological, geological and terrain attributes; land use; airborne gamma-ray spectrometry) were then used to: build and extrapolate the decision tree using the C.5 algorithm in DSMART software. Several iterations were performed, providing a probability of occurrence of each possible STU within the study area. External validation was perforined by comparing predictions of the disaggregation procedure to available soil maps at scales of 1:25,000 or 1:50,000 and observed profiles. Overall accuracies ranged from 41 to 72%, depending on the validation method.(per pixel vs. 3 x 3 windows of pixels, per STU vs. STU grouped by semantic proximity (n = 204)). Introducing expert rules based on soil-landscape relationships to allocate STUs to calibration samples enabled production of a soil"map with clear spatial structures, yielding expected spatial patterns of soil organisation. Future work notably concerns estimating soil properties at multiple depths deriving from STU predictions, according to the GlobalSoilMap project.

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to conduct a 33-year regional NPP study in southwest China, at a 1-km scale. A multi-sensor fusion framework was applied to obtain a new normalized difference vegetation index (NDVI) time series from 1982 to 2014, combining the respective advantages of the different remote sensing datasets. As another key parameter for NPP modeling, the total solar radiation was calculated by the improved Yang hybrid model (YHM), using meteorological station data. The verification described in this paper proved the feasibility of all the applied data processes, and a greatly improved accuracy was obtained for the NPP calculated with the final processed NDVI. The spatio-temporal analysis results indicated that 68.07% of the study area showed an increasing NPP trend over the past three decades. Significant heterogeneity was found in the correlation between NPP and precipitation at a monthly scale, specifically, the negative correlation in the growing season and the positive correlation in the dry season. The lagged positive correlation in the growing season and no lag in the dry season indicated the important impact of precipitation on NPP., 20 pages, 11 figures

Digital Soil Mapping (DSM) products are simplified representations of more complex and partially unknown patterns of soil variations. Therefore, any prediction of a soil property that can be derived from these products has an irreducible uncertainty that needs to be mapped. The objective of this study was to compare the most current DSM method – Regression Kriging (RK) – with a new approach derived from RandomForest – Quantile Regression Forest (QRF) – in regard to their ability of predicting the uncertainties of GlobalSoilMap soil property grids. The comparison was performed for three soil properties, pH, organic carbon and clay content at 5–15 cm depth in a 27,236 km2 Mediterranean French region with sparse sets of measured soil profiles (1/13.5 km2) and for a set of environmental covariates characterizing the relief, climate, geology and land use of the region. Apart from classical performance indicators, comparisons involved accuracy plots and the visual examinations of the uncertainty maps provided by the two methods. The results obtained for the three soil properties showed that QRF provided more accurate and more interpretable predicted patterns of uncertainty than RK did, while having similar performances in predicting soil properties. The use of QRF in operational DSM is therefore recommended, especially when spatial sampling of soil observations are too sparse for applying RK.

An intercomparison of climate change impacts projected by nine regional-scale hydrological models for 12 large river basins on all continents was performed, and sources of uncertainty were quantified in the framework of the ISIMIP project. The models ECOMAG, HBV, HYMOD, HYPE, mHM, SWAT, SWIM, VIC and WaterGAP3 were applied in the following basins: Rhine and Tagus in Europe, Niger and Blue Nile in Africa, Ganges, Lena, Upper Yellow and Upper Yangtze in Asia, Upper Mississippi, MacKenzie and Upper Amazon in America, and Darling in Australia. The model calibration and validation was done using WATCH climate data for the period 1971–2000. The results, evaluated with 14 criteria, are mostly satisfactory, except for the low flow. Climate change impacts were analyzed using projections from five global climate models under four representative concentration pathways. Trends in the period 2070–2099 in relation to the reference period 1975–2004 were evaluated for three variables: the long-term mean annual flow and high and low flow percentiles Q10 and Q90, as well as for flows in three months high- and low-flow periods denoted as HF and LF. For three river basins: the Lena, MacKenzie and Tagus strong trends in all five variables were found (except for Q10 in the MacKenzie); trends with moderate certainty for three to five variables were confirmed for the Rhine, Ganges and Upper Mississippi; and increases in HF and LF were found for the Upper Amazon, Upper Yangtze and Upper Yellow. The analysis of projected streamflow seasonality demonstrated increasing streamflow volumes during the high-flow period in four basins influenced by monsoonal precipitation (Ganges, Upper Amazon, Upper Yangtze and Upper Yellow), an amplification of the snowmelt flood peaks in the Lena and MacKenzie, and a substantial decrease of discharge in the Tagus (all months). The overall average fractions of uncertainty for the annual mean flow projections in the multi-model ensemble applied for all basins were 57% for GCMs, 27% for RCPs, and 16% for hydrological models. Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesis

According to the fifth report of the Intergovernmental Panel on Climate Change, an increase in the frequency and the intensity of extreme rainfall is expected in the Mediterranean area. Among different impacts, this increase might result in a variation in the frequency and the spatial distribution of rainfall-induced landslides, and in an increase in the size of the population exposed to landslide risk. We propose a method for the regional-scale evaluation of future variations in the occurrence of rainfall-induced landslides, in response to changes in rainfall regimes. We exploit information on the occurrence of 603 rainfall-induced landslides in Calabria, southern Italy, in the period 1981–2010, and daily rainfall data recorded in the same period in the region. Furthermore, we use high-resolution climate projections based on RCP4.5 and RCP8.5 scenarios. In particular, we consider the mean variations between a 30-year future period (2036–2065) and the reference period 1981–2010 in three variables assumed as proxy for landslide activity: annual rainfall, seasonal cumulated rainfall, and annual maxima of daily rainfall. Based on reliable correlations between landslide occurrence and weather variables estimated in the reference period, we assess future variations in rainfall-induced landslide occurrence for all the municipalities of Calabria. A + 45.7% and + 21.2% average regional variation in rainfall-induced landslide occurrence is expected in the region for the period 2036–2065, under the RCP4.5 and RCP8.5 scenario, respectively. We also investigate the future variations in the impact of rainfall-induced landslides on the population of Calabria. We find a + 80.2% and + 54.5% increase in the impact on the population for the period 2036–2065, under the RCP4.5 and RCP8.5 scenario, respectively. The proposed method is quantitative and reproducible, thus it can be applied in similar regions, where adequate landslide and rainfall information is available.

We explore and compare the capabilities and limitations of different optical sensors (Sentinel-2/ESA, Landsat 7/8/USGS, Ven μ s/CNES-ISA, Pléiades/AirbusD&S and Planet Labs images) for mapping the surface speeds of mountain glaciers on a regional scale. We present here our automated workflow designed to download data from institutional or commercial servers, prepare images, launch the feature tracking algorithm, calibrate glacier surface speeds, and our post-processing treatment to obtain filtered and time-averaged velocity maps. We applied our methodology to three regions: (1) the European alps; (2) the Peruvian Cordillera Blanca; and (3) the Southern Alps of New Zealand for years 2017 and 2018 and quantified ice velocity for every possible repeat cycle from few days up to 400 days. For these regions, we demonstrate the ability of our processing chain to derive precise time-averaged ice flow maps. The statistical analysis of the results provided by each individual repeat cycles shows that velocity mapping from Sentinel-2 is about twice more precise than that from Landsat 7/8. If Sentinel-2 captures more details than Landsat, some of the smallest glaciers ( μ s imagery, we finally highlight the advantages of high-resolution sensors to map glacier surface speed with finer details in space and time.

High-frequency rainfall data is needed in different practical hydrologic applications, such as the construction of the intensity-duration-frequency curves (IDF). This paper presents an investigation of trends (station-wise and regional) for several rainfall durations that were constructed based on the 5 min rainfall data. Moreover, changes in IDF results for two 22-year sub-samples were also analyzed. Additionally, changes in extreme events clustering at the regional scale were also analyzed. Ten rainfall stations (44 years of data 1975&ndash, 2018) located in Slovenia (central EU, approx. 20,000 km2) were used in this study. Results indicate that no clear pattern in the detected trends can be found based on the analyzed stations. However, all the statistically significant trends at the significance level of 0.05 for the 5 min rainfall data were negative. Moreover, regional trends for this duration were also statistically significant. The changes in the design rainfall events between two equal sub-samples (1975&ndash, 1996, 1997&ndash, 2018) were between &minus, 30% and 60%. The investigation of changes in extreme rainfall event clustering indicated that extreme 5, 30, and 60 min events could more frequently occur a few days earlier in spring or summer compared to the past period. On the other hand, longer duration events (i.e., 360 and 720 min) tend to more frequently occur a few days later in autumn compared to the past. In most cases, changes are not statistically significant.

International audience; The Languedoc-Roussillon coastline is a large unit stretching out over 200 km of sandy low coast in a wave dominated environment. The nearshore is characterized by a quasi continuous system of double sandbar that displays a wide range of typology. The interannual sandbar dynamic was investigated using 2D bathymetric profiles and 3D LiDAR imagery. This study has allowed determining the sandbar systems affected by the Net Offshore Migration (NOM). At a regional scale, conditions necessary to NOM development depend on the sandbar morphologies (crescentic or straight), the wave energy and the associated coastal orientation (low or high energy, sheltered area), the coastal structures (harbour and coastal defences) and the nearshore sedimentary budget. The areas where the NOM occurs show cycle dynamic differences. This regional inter-site comparison highlights that nearshore morphology and bar parameters seem to influence the nearshore bar behaviour. Interaction between the nearshore slope, width of the bar zone and the migration rate control the NOM duration. The sandbar volume and the regional wave climate influence also the migration rate of the system. On the Languedoc-Roussillon coast, the sediment grain size does not appear to influence the seaward bar migration.

The net radiation flux available at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The only cost-effective way to capture its spatial and temporal variability at regional and global scales is remote sensing. However, the accuracy of net radiation derived from remote sensing data has been evaluated up to now over a limited number of in situ measurements and ecosystems. This study aims at evaluating estimates and uncertainties on net radiation derived from Landsat-7 images depending on reliability of the input surface variables albedo, emissivity and surface temperature. The later includes the reliability of remote sensing information (spectral reflectances and top of canopy brightness temperature) and shortwave and longwave incoming radiations. Primary information describing the surface is derived from remote sensing observations. Surface albedo is estimated from spectral reflectances using a narrow-to-broadband conversion method. Land surface temperature is retrieved from top of canopy brightness temperature by accounting for land surface emissivity and reflection of atmospheric radiation; and emissivity is estimated using a relationship with a vegetation index and a spectral database of soil and plant canopy properties in the study area. The net radiation uncertainty is assessed using comparison with ground measurements over the Crau-Camargue and lower Rhone valley regions in France. We found Root Mean Square Errors between retrievals and field measurements of 0.25-033 (14-19%) for albedo, similar to 1.7 K for surface temperature and similar to 20 W.m(-2) (5%) for net radiation. Results show a substantial underestimation of Landsat-7 albedo (up to 0.024), particularly for estimates retrieved using the middle infrared, which could be due to different sources: the calibration of field sensors, the correction of radiometric signals from Landsat-7 or the differences in spectral bands with the sensors for which the models where originally derived, or the atmospheric corrections. We report a global uncertainty in net radiation of 40-100 W.m(-2) equally distributed over the shortwave and longwave radiation, which varies spatially and temporally depending on the land use and the time of year. In situ measurements of incoming shortwave and longwave radiation contribute the most to uncertainty in net radiation (10-40 W.m(-2) and 20-30 W.m(-2), respectively), followed by uncertainties in albedo (

International audience; Large-scale studies at the regional, national and global scales are increasingly needed to assess nutrient flows in agroecoystems and to identify their drivers. In this study, we aimed to quantify the extent to which regional phosphorus (P) flows and soil P budget depend on agricultural production systems. Phosphorus was taken as a case study due to its sorbing properties in soil and to the many questions concerning its future availability. The issue was studied from two different stages: (1) for the 21 administrative regions of France; and (2) by selecting four contrasting French regions—the Centre region (a crop farming region), Brittany (an animal farming region) and Lorraine and Aquitaine (mixed farming regions). Phosphorus flows and soil P budgets were quantified on a yearly basis for the 21 regions from 1990 to 2006, whereas a 5-year average was calculated for the years 2002–2006 to compare the four selected regions. At the beginning of the study (1990), the calculated P budget was positive for all regions averaging 17.5 kg P ha−1 year−1 which declined over years to 4.4 kg P ha−1 year−1 in 2006, but huge differences between the 21 regions were observed, confirming the heterogeneity of the regional P flows. Agricultural production systems strongly influenced the P flows through feed, fodder and animal excretion, while P fertiliser consumption was only partially influenced. As a consequence, both the regional soil P budgets and the magnitude of soil P inflows and outflows were strongly dependent on the regional agricultural production systems. A balanced soil P budget could be obtained in crop farming regions, but it strongly relied on P fertiliser use. The animal farming regions continued to accumulate P in the soil, and further use of P fertilisers in these regions is questionable. Finally, there was no simple rule between the mixed farming systems and the soil P budget. This study offers some initial elements for understanding the drivers of mineral P fertiliser use, i.e. the factors governing P fertiliser use by the farmers. It may also contribute to the redesign of regional farming systems oriented towards more effective use of nutrients at the global scale.

International audience; Assessing the collapse susceptibility of abandoned cavities at a regional scale is associated with large uncertainties that are mainly related to the very nature of the phenomena, but also to the difficulty in collecting exhaustive information at such a scale on often "forgotten" structures. In this context, the expert's role is essential, because he is able to synthesize the information resulting from the inventory and from the commonly imprecise, if not vague, criteria on the basis of his experience and his knowledge of the regional geological and historical and economic context. In this article, we propose mathematical tools for representing and processing this information in order to give flexibility to this step and manage the uncertainty inherent in the expert's information. The first tool, based on the weight of evidence theory, is for managing the uncertainty due to the heterogeneous spatial distribution of the data, whereas the second tool, based on the fuzzy set theory, is for managing the imprecision and incompleteness of available data, which hinder the definition of the class boundaries of the quantitative decision criteria. Based on an appropriate representation of the uncertainty sources (related to the input data and to the expert diagnostic), we then propose a methodology that integrates the uncertainty in the final output of the collapse susceptibility assessment and provides a confidence indicator useful within the decision-making process. The proposed methodology is applied to the Arras territory in the North of France, where abandoned chalk pits (dating back to the Roman ages) and war saps (i.e. covered trenches) located in the vicinity of the First World War front lines both raise difficulties for urban planning.

International audience; This paper presents the assimilation of satellite NO2 observations into a chemistry-transport model (CTM). The NO2 columns from Ozone Monitoring Instrument (OMI) aboard NASA Aura satellite are used during November-December 2005. These satellite observations are assimilated in an air quality model from Polyphemus, in order to better forecast NO2 in Europe. The optimal-interpolation method is applied to produce analyzed columns, and these analyzed columns are mapped to model concentrations assuming that the model vertical profile is perfect. Good consistency is seen in the comparisons of model simulations, satellite data and ground observations before assimilation. The model results with and without assimilation are then compared with ground observations for evaluating the assimilation effects. It is found that the assimilation can improve the NO2 forecasts by reducing their discrepancies against ground observations, indicating a better NO2 forecast obtained with OMI observations. Such improvements are seen in a cold season rather than in a warm one probably due to the longer lifetime of NOx and the initial condition changes having more impacts in winter. This also suggests that the assimilation of the short-lived species like NO2 is a complicate problem.

International audience; The aim of this paper is to present preliminary results of aerosol modeling over Greater Paris. We briefly describe the parameterizations and the numerical methods related to the general dynamic equation for aerosols: a size resolved aerosol model (SIREAM) has been hosted by the chemistry transport model Polair3D inside the Polyphemus system. Then the results of a simulation over Greater Paris are presented with preliminary comparisons to measured data for aerosols (PM10 and PM2.5) and gases (ozone, NO2) for six months of 2001 (from April to the end of September). A sensitivity analysis is also performed in order to assess the robustness of the model configuration.

International audience; A research procedure is proposed which aims to analyse the agricultural spatial dynamics during the last thirty years using two levels of organisation of farming activity: the agricultural production system and the cropping system. Based on methods of statistical mapping and data mining, this procedure involves modelling the diversity of production systems and cropping systems (crop successions and sequences of cultural practices for each crop) in the form of classes independently of their localisation within the basin. It identifies homogeneous regions made up of groups of contiguous agricultural districts which exhibit similar combinations of production systems, crop successions or cultural practices during a given period of time. The results show a major increase in arable farms since 1970 at the expense of dairy farms and mixed cropping/livestock. This trend however appeared to be greatly spatially differentiated according to the agricultural districts, since livestock remained important on the edges of the basin, whereas it practically disappeared in its centre. The crop successions practiced in the basin and the cultural practices used on them also appear to be spatially differentiated, although the link to the production systems is not always clear. Thus it appears pertinent to combine the analysis of the two levels of organisation of the agriculture (methods of land use described by the concept of cropping system, and also the production systems into which the cropping systems fit) in the context of an environmental problem.

International audience; To ensure that forest ecosystem services will be maintained in future climate conditions, the challenge of developing and implementing forest adaptation strategies has become a priority shared across Europe (European Commission 2010) and around the world (FAO 2012). A critical aspect of any adaptation (and mitigation) strategy is to ensure that management choices have sufficient flexibility and are consistent with sustainable forest management (SFM) principles that balance the forest’s social, ecological and economic outputs. Most current studies, however, still tend to consider forest adaptation independently of exogenous developments (Seppälä et al. 2009), or are based on forecasting and thus only extrapolate from current trends. Yet the futures of forest systems are also uncertain because change may come mainly from socio-economic, and not ecological, drivers (Schoene and Bernier 2012). Climate change will, moreover, have a highly uncertain effect on socio-ecological systems such as forests. A number of authors and international publications on the environment have posited that scenarios studies offer a framework for dealing with strong uncertainties and the complexity of socio-ecological systems: “Comparisons among a set of contrasting scenarios are used to understand the systemic interrelation and dynamics of complex socio-ecological systems and to define a range of possibilities and uncertainties in quantitative and qualitative terms” (Thompson et al. 2012). Likewise, SFM practices are beginning to underscore the importance of taking into account landscape-level views of the forest, and of the many exogenous drivers of regional change, when thinking about integrated land use (Schoene and Bernier 2012). Adaptation strategies must therefore move away from single technical solutions and must not rely on one-size-fits-all mechanisms. New modes of governance that enable rapidly accelerating social, economic and institutional changes are required (Seppälä et al. 2009). Recent emphasis has also been placed on the importance of implementing regionally based forest adaptation strategies that are accepted by local stakeholders (Spathelf et al. 2013). A scenario planning approach that engages stakeholders in scenario development could be a way to overcome such challenges by combining anticipatory learning of climate change impacts (Tschakert and Dietrich 2010) with land use planning. Thus, forest adaptation could be seen from a perspective of forest planning rather than of forest management. It could involve a more indirect form of management that is broader in scale in terms of time, space and organisational level (Mermet and Farcy 2011).The objective of this paper is to discuss how a foresight approach and scenario planning can offer an appropriate framework for dealing with climate change uncertainties at local level, and for elaborating a plurality of forest adaptation strategies derived from plausible sectoral, socio-ecological and regional changes. To test this assumption, the Landes de Gascogne Forest (LGF) 2050 case study (Mora et al. 2012) appears particularly interesting, due both to recent storm damage in 1999 and 2009 and because of the increasing, diverse demands in changing economic and social conditions for various forest ecosystem goods and services. The Landes region of south-western France is located at the intersection of Northern Europe and the Iberian Peninsula. It covers a surface area of about 15,000 km2. The major biophysical features of the area include temperate, oceanic climate conditions, podzolic sandy soils and shallow groundwater levels. The LGF, which was expanded in the nineteenth century to become the largest cultivated and privately owned (92 %) forest in Europe, is primarily composed of maritime pine (Pinus pinaster). The LGF covers two thirds of the Landes de Gascogne region, with agricultural and built-up land representing 18 and 7 %, respectively (Mora et al. 2012). The forest also has a significant economic impact through its forestry and forest-based industries. A unique aspect of the Landes forest-wood chain is the location of both timber processing stages within the region and its largely exclusive use of local maritime pine wood resources. However, the forestry and forest-based sector have faced difficulties since the Martin and Klaus storms in 1999 and 2009 that created approximately 70 million cubic metres of windthrow damage. The forestry sector is also experiencing structural challenges as synergies among industry subsectors weaken and biorefinery activities gain prominence. Bordered by the Atlantic Ocean to the west and the large urban centres of Bordeaux to the north and Bayonne to the south (Fig. 1), the Landes area has become an increasingly popular place to live and to work. Almost all of the 390 municipalities in the region are now experiencing population growth, urbanisation, the emergence of a broader base of economic activity and, more generally, a diversification of their forest-derived goods and services. The contribution of forest-related activities to the rural economy and to social well-being may change as a result of these trends (Slee et al. 2004).

International audience; Airborne laser scanning provides continuous coverage mapping of forest canopy height and thereby is a powerful tool to scale-up above-ground biomass (AGB) estimates from stand to landscape. A critical first step is the selection of the plot variables which can be related to light detection and ranging (LiDAR) statistics. A universal approach was previously proposed which combines local and regional estimates of basal area (BA) and wood density with LiDAR-derived canopy height to map carbon at a regional scale (Asner et al. in Oecologia 168:1147–1160, 2012). Here we explore the contribution of stem diameter distribution, specific wood density and height-diameter (H–D) allometry to forest stand AGB and propose an alternative model. By applying the new model to a large tropical forest data set we show that an appropriate choice of input variables is essential to minimize prediction error of stand AGB which will propagate at larger scale. Stem number (N) and average stem cross-sectional area should be used instead of BA when scaling from tree to plot. Stand quadratic mean diameter above the census threshold diameter size should be preferred over stand mean diameter as it reduces the prediction error of stand AGB by a factor of ten. Wood density should be weighted by stem volume per species instead of BA. LiDAR-derived statistics should prove useful for estimating local H-D allometries as well as mapping N and the mean quadratic diameter above 10 cm at the landscape level. Prior stratification into forest types is likely to improve both estimation procedures significantly and is considered the foremost current challenge

The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power — VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM 10 , NO x and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM 10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM 10 , CO and O 3 . For NO 2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM 10 , CO and O 3 .

International audience; The morphology of rocky coastlines is the expression of the interaction between external forcings and the intrinsic rock mass properties, leading to a specific erosional response. Among these rock mass properties the contribution of structural discontinuities is still poorly understood. Nevertheless, recent studies suggest that geological structures may have a first order influence in controlling the coast morphology. Understanding how the structural pattern interacts with erosion processes is fundamental to assess the evolution of these environments. We propose a regional comparison between the orientation of rocky shorelines and faults segments in six different litho-structural units in the Provence and Cote d'Azur area (southern France). Our analysis reveals that in many of these sectors the dominant fault trend (E-W) is also present in the distribution of coastlines orientations. In some particular areas, a complex geological inheritance (Maures) blurs this apparent relationship between faults and coastal morphology. We conclude that, in our study area, the level of complexity of the structural setting seems to significantly control the nature and shape of the coastline.

A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing., JRC.H.4-Monitoring Agricultural Resources

International audience; Numerous studies showed the impact of droughts events on the seasonal dynamic of satellite-based vegetation index but validation and relation to ground observations is still lacking. Focusing on the impacts of 2003 and 2011 droughts events, we defined and validated forest health indicators using extensive in-situ damage inventories carried out on all French forests. Analysis of vegetation indices dynamics (EVI, LAI) at the national scale was performed using MODIS and SPOT VEGETATION products (MOD13A2 V5 and CYCLOPES V3.1). Satellite-based indicators of (i) crown condition - which is itself a commonly used forest health indicator - and (ii) leaf phenology anomalies were calibrated and validated. In our study, average of CYCLOPES LAI during the growing season is a good proxi of crown condition (r2=0.79, p

We analysed, at a regional scale, the braiding pattern and the structure of the water channel habitats of 53 braided reaches located in the Rhone hydrographical district (France), in relation to three control factors: (i) spatial location along the network (i.e. slope, altitude), (ii) hydrological conditions and (iii) morphological conditions. This research is based on aerial orthophotos belonging to the French National Geographic Institute (IGN) taken between 2002 and 2006. We defined a regional typology of braided reaches based on the distribution of the variables listed above, and identified five hydro-geographical types of braided reaches. Following this regional classification, we compared the pattern of braided reaches by testing several parameters, which describe the physical characters of water channel habitats (such as braided index, channel sinuosity, or aquatic habitat diversity). We found that discharge appears to be less relevant than sediment supply or groundwater level in structuring the braided geometry and the channel habitat pattern in natural braided rivers at a regional scale. We discussed the importance of local factors, such as the position of the groundwater table and the width of the active channel, for explaining the intensity of the braided pattern. There are numerous cases where the discharge is very low but the braided index high. These findings support management and conservation recommendations of braided rivers for implementing the European Water Framework Directive (2000).

The spawning habitats of many large marine pelagic predators are poorly known. This lack of knowledge hampers conservation efforts that are aimed at identifying critical habitats for the spawning of these species. We hypothesized that phylogenetically related species show different adaptations and respond differently to environmental and geographical cues for when and where to spawn. We tested this hypothesis on a 5 yr data set (2001 to 2005) from the Balearic Islands region (Mediterranean Sea), targeting the early larval stage of 3 abundant species of tuna: bluefin tuna Thunnus thynnus, a large migratory oceanic species; albacore T. alalunga, a smaller species; and bullet tuna Auxis rochei, often found closer to coastal areas. The spatial distribution of spawning locations at the regional scale differed for the 3 tuna species. Bluefin tuna relied heavily on environmental signals, and therefore their spawning habitats can vary depending on the environment. In contrast, the spawning habitat of albacore and bullet tuna was driven mostly by geography and was less variable over contrasting environmental conditions. These regional adaptations for spawning habitats among the 3 tuna species may play a critical role in offspring survival and species interactions, and should be taken into account when establishing conservation practices, Sí

Despite remarkable technological advances in earth observation systems and radiative transfer modelling, enabling the retrieval of canopy biophysical variables from satellite imagery, the use of remote sensing for operational crop monitoring at a regional or global scale has remained more qualitative than quantitative. Perhaps the main reason lies in the fact the imagery that can be used operationally and economically over large areas with high temporal frequency have coarse spatial resolution. However, recent research has demonstrated that coherent crop specific biophysical variables such as green area index (GAI) can be retrieved from medium spatial resolution imagery such as MODIS even when the size of the fields is close to the size of the pixels (close to 250~m). Leveraging on these results, the present paper attempts to go beyond by retrieving GAI from a more fragmented landscape, over a much larger geographical area and covering a 10-year period. Results demonstrate the possibility to monitor the dynamic processes of growth and senescence of winter wheat and grasp the inter-annual seasonal variability of growing conditions encountered over a decade. Furthermore, the satellite-derived GAI is not only consistent with ground measurements at regional scale (RMSE=0.65), but even shows encouraging results at field level (RMSE=0.82 when pixel/field spatial adequacy is high). By showing the possibility of monitoring crop specific growth quantitatively over a complicated landscape, the only step necessary before implementing such approach in a real operational situation remains identifying early in the season where the target crop is., JRC.H.4-Monitoring Agricultural Resources