Search

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.

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

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.

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.

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.

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.

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.

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.

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.

Caves are excellent model systems to study the effects of abiotic factors on species distributions due to their selective conditions. Different ecological factors have been shown to affect species distribution depending on the scale of analysis, whether regional or local. The interplay between local and regional factors in explaining the spatial distribution of cave-dwelling organisms is poorly understood. Using the troglophilic subterranean spider Artema nephilit (Araneae: Pholcidae) as a model organism, we investigated whether similar environmental predictors drive the species distribution at these two spatial scales. At the local scale, we monitored the abundance of the spiders and measured relevant environmental features in 33 caves along the Jordan Rift Valley. We then extended the analysis to a regional scale, investigating the drivers of the distribution using species distribution models. We found that similar ecological factors determined the distribution at both local and regional scales for A. nephilit. At a local scale, the species was found to preferentially occupy the outermost, illuminated, and warmer sectors of caves. Similarly, mean annual temperature, annual temperature range, and solar radiation were the most important drivers of its regional distribution. By investigating these two spatial scales simultaneously, we showed that it was possible to achieve an in-depth understanding of the environmental conditions that governs subterranean species distribution.

The generation of projections under different climate scenarios and stand conditions provides a useful tool to explore the consequences of climate change on ecosystem dynamics and to define possible interventions to ensure their persistence over time. Over recent decades, climate change has been particularly severe in the Mediterranean basin, where a lack of natural regeneration is one of the main limitations threatening Mediterranean forests. In this context, in order to define the main drivers affecting this process, we evaluated the occurrence of regeneration in P. pinea and P. pinaster forests, taking into account the stand-structure as well as climate conditions. The regeneration data is taken from a network of 1703 plots located on the Northern Plateau of Spain and monitored over 15 years. Through survival analysis we modeled the probability of regeneration occurrence after a time t (years since regeneration fellings were carried out) at regional level. The model was then used to simulate regeneration success under two climatic scenarios. According to the modeling results, the ability of P. pinaster to regenerate was always lower than that of P. pinea, regardless of the scenario and stand conditions. The relationship between regeneration success and presence of adult trees varies depending on the species composition. Higher probabilities of regeneration occurrence were positively associated with mixed species composition in adult trees for both species, while P. pinaster exhibited significantly lower regeneration under monospecific P. pinea adult trees. Regeneration probability was negatively associated with increasing levels of grass cover on soils. The results also pointed to the existence of climate-mediated annual regeneration occurrence, reflecting the complex interaction which exists between environmental factors and the optimum conditions for natural regeneration. The findings obtained in the present study could help us to identify problematic areas with regard to natural regeneration so that appropriate management can be defined to favor the different species in future stands.

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; Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270 km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature (Tmax) ranging from 3.0 °C (upstream) to 1.3 °C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of Tmax simulated by the T-NET model by 0.62 °C on average between April and September. However, difference between the shading routines reaches up to 2 °C (monthly average) at the upstream-most station. Standard deviation of errors on Tmax is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale.

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.

This study presents a revised temporal scaling method based on a detection algorithm for the temporal stability of the evaporative fraction (EF) to estimate total daytime evapotranspiration (ET) at a regional scale. The study area is located in the Heihe River Basin, which is the second largest inland river basin in China. The remote sensing data and field observations used in this study were obtained from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. The half-hourly EF values (EFEC) calculated using meteorological observations from an eddy covariance (EC) system and an automatic meteorological station (AMS) represented the diurnal pattern of the EF across the majority of the study area. The remotely sensed instantaneous midday EF (EFASTER), which indicates the spatial distribution of the midday EF over the entire study area, was calculated from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. The temporal stability of EFEC was examined using a detection algorithm. Intervals with inconsistent EFEC values were distinguished from those with consistent EFEC values, the total daytime ET (from 9:00 to 19:00) within these interval types was integrated separately. Validation of the total daytime ET at the satellite pixel scale was conducted using measurements from17 EC towers. Using the detection algorithm for the temporal stability of the EF and dynamic adjustment, the revised temporal scaling method resulted in a root-mean-square error (RMSE) of 0.54 (mm·d−1), a mean relative error (MRE) of 7.26% and a correlation coefficient (Corr.) of 0.81, all of these values were superior to those of the two other methods (i.e., the constant EF and variable EF methods). The revised method easily extends to other areas and exhibits a superior performance in flat and regularly-irrigated farmlands at the regional scale.

Most land surface models require information on aerodynamic roughness length and its temporal and spatial variability. This research presents a practical approach for determining the aerodynamic roughness length at fine temporal and spatial resolution over the landscape by combining remote sensing and ground measurements. The basic framework of Raupach, with the bulk surface parameters redefined by Jasinski et al., has been applied to optical remote sensing data collected by the HJ-1A/1B satellites. In addition, a method for estimating vegetation height was introduced to derive the aerodynamic roughness length, which is preferred by users over the height-normalized form. Finally, mapping different vegetation classes was validated taking advantage of the data-dense field experiments conducted in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. Overall, the roughness model performed well against the measurements collected at most HiWATER flux tower sites. However, deviations still occurred at some sites, which have been further analyzed.

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

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

A numerical flow and transport model is developed to assess groundwater discharge and nutrient transport to the Ria Formosa coastal lagoon in southern Portugal. A total N load of 350 ton/year is estimated for the considered area, of which agriculture accounts for 73% of total N load, and domestic effluent and atmospheric deposition for the remaining 9% and 18% respectively. Model results suggest that nutrient recycling has led to the high concentrations observed in the Campina de Faro (M12) aquifer, but is still insufficient to account for observed values at the coastline. Furthermore results suggest that even for the best case mitigation scenario, good quality status will not be achieved by 2027, as mandated by the EU Water Framework Directive. info:eu-repo/semantics/publishedVersion

Rainfall interception loss of forest is an important component of water balance in a forested ecosystem. The Gash analytical model has been widely used to estimate the forest interception loss at field scale. In this study, we proposed a simple model to estimate rainfall interception loss of heterogeneous forest at regional scale with several reasonable assumptions using remote sensing observations. The model is a modified Gash analytical model using easily measured parameters of forest structure from satellite data and extends the original Gash model from point-scale to the regional scale. Preliminary results, using remote sensing data from Moderate Resolution Imaging Spectroradiometer (MODIS) products, field measured rainfall data, and meteorological data of the Automatic Weather Station (AWS) over a picea crassifolia forest in the upper reaches of the Heihe River Basin in northwestern China, showed reasonable accuracy in estimating rainfall interception loss at both the Dayekou experimental site (R2 = 0.91, RMSE = 0.34 mm∙d −1) and the Pailugou experimental site (R2 = 0.82, RMSE = 0.6 mm∙d −1), compared with ground measurements based on per unit area of forest. The interception loss map of the study area was shown to be strongly heterogeneous. The modified model has robust physics and is insensitive to the input parameters, according to the sensitivity analysis using numerical simulations. The modified model appears to be stable and easy to be applied for operational estimation of interception loss over large areas.

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.

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.

We investigate in this study (i) a redefinition of crop variety zonations at a spatial scale of 10x10 km, and (ii) the influence of recalibrated crop parameters on regional yield forecasting of winter wheat and grain maize in western Europe. The baseline zonation and initial crop parameter set was derived from the operational European crop growth monitoring system (CGMS) which involves the agrometeorological model WOFOST. Air temperature data from 325 weather stations over the 1992-2007 period were used to define new zonations in a 300 x 300 km test site. Two parameters which influenced mostly the phenological development stages (i.e. TSUM1 and TSUM2, the effective air temperature sums from emergence to anthesis, and from anthesis to maturity, respectively) were chosen and calibrated. The CGMS was finally run based on these new recalibrated parameters and simulated crop status indicators were compared with official statistics over the 2000-2007 period. Our results showed that the days of anthesis and maturity were simulated with coefficients of determination (R2) ranging from 0.22 to 0.87 for both crops over the study site. A qualitative assessment of maximum leaf area index and harvest index also revealed a more consistent spatial pattern than the initial zonation in the simulation results. Finally, recalibrated TSUM1 and TSUM2 led to improved relationships between official yield and simulated crop indicators (significant R2 in 17 out of 28 and in 14 out of 59 NUTS3 regions with respect to the best predictor for grain maize and winter wheat, respectively).

We evaluate a new approach to estimate regional evapotranspiration (ET) across a montane, Mediterranean climate gradient in the San Jacinto and Santa Rosa Mountains of Southern California. Spatially distributed evaporative fraction (EF) measurements were made monthly from October 2008 to September 2009 at 54 locations across an elevational gradient using a mobile measurement platform, called the Regional Evaporative Fraction Energy Balance (REFEB) method. We used these measurements and the Enhanced Vegetation Index (EVI) from MODerate resolution Imaging Spectroradiometer (MODIS) observations to derive EF at a regional scale. We converted EF to monthly ET using remote-sensing based observations of available energy. We compared the REFEB ET estimates, along with modified Priestly–Taylor (PT) ET estimates driven by MODIS data against four eddy covariance (EC) towers and eight gauged catchments. Both of the satellite-based ET estimates were highly correlated with tower ET observations (r 2 = 0.66 for REFEB and 0.95 for PT). The PT MODIS approach overestimated ET compared to precipitation estimates and stream gauge measurements, while REFEB ET was moderately lower than PT ET. The annual regional REFEB ET (193 mm) was 87 mm less than precipitation (280 mm). REFEB ET underestimated EC tower ET (regression slope = 0.78, p

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 (

Despite the fact that wind erosion seriously affects the sustainable use of land in a large part of the world, no validated wind erosion model that predicts windblown mass transport on a regional scale exists. Vegetation has the potential to decrease soil loss by wind erosion through the protection of the soil surface, through the reduction of wind speed and through the entrapment of saltating particles. Simulation of wind erosion process will differ at regional scale from field scale and as a result it will represent by different parameters. These parameters include vegetation cover, wind barriers, soil surface roughness and some other soil properties. The interaction of wind erosion with vegetation is the best example of a process that changes from plot to regional scale. The objective of this research was to make a starting point for a regional scale wind erosion model by establishing relations between the intensity of windblown mass transport and vegetation cover in a patchy landscape in Syria. Measurements on windblown mass transport were executed at 9 different land uses (and thus vegetation cover) in agricultural stabilization zones 4 and 5 in Khansser valley, Syria. At each plot 16 MWAC (Modified Wilson and Cooke) sediment catchers were installed and wind speed was recorded with five-minute intervals. The RWEQ (Revised Wind Erosion Equation) was translated into the dynamic modelling language of PCRaster and applied to determine the relations between vegetation density and pattern and intensity of mass transport. The results of this application showed that RWEQ in PCRaster (RiP) gave acceptable predictions for the uniform fields without incoming sediment sources. So, the predicted and observed results are in the same magnitude. However RiP needs more calibration to improve its prediction of the spatial variation of the windblown mass transport. With completing the calibration process, RiP will have the potential to form the basis of a regional scale wind erosion model

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.

Regional estimates of evapotranspiration (ET) are needed for environmental analysis and management purposes, yet can be difficult to obtain. Current methods for determining regional ET have spatial, temporal, methodological, and/or logistical limitations that affect their usefulness. To address these gaps, we developed a surface mobile measurement technique, the Regional Evaporative Fraction Energy Balance platform (REFEB), which measures evaporative fraction (EF) and water use efficiency (WUE) using a truck operating on a public right of way. REFEB can measure EF and WUE at 25 or more locations per day, which allows for rapid, dense, and spatially distributed sampling of fields across a region. We assessed the accuracy of the field measurements of EF and WUE with REFEB by comparing them to an Eddy covariance (EC) and Bowen ratio energy balance (BREB) tower. This site validation showed that REFEB has error and uncertainty similar to previous BREB approaches. We then used empirical relationships between field measurements and remote sensing vegetation indices to derive regional maps of EF. We combined these EF maps with satellite observations of net radiation to derive monthly and annual calculations of ET at a 250 m resolution during calendar year 2004 in the Imperial Valley, California, a major agricultural region that is dependent upon irrigation and which has a well constrained water budget. We then summed ET for the Imperial Valley and compared the result to a surface water budget based on irrigation and drainage measurements, which showed good annual and seasonal agreement. These results indicate REFEB produces accurate field measurements of EF and WUE, which can be scaled to estimate regional ET at time scales ranging from less than a week to annual sums.

The elaboration of conservation strategies at regional scale, dealing with the potential effects of climate change on the abundance and distribution of tree species, should be supported by models produced at the appropriate scale. We used a bioclimatic model aimed at analysing the large-scale effects of climate change on the abundance and distribution of tree species with respect to their chorological and ecological characteristics. Abundance data for 16 species, sampled in 912 plots, distributed on a 3x3 km grid were used. A climatic model provided high resolution current climatic surfaces and a climatic scenario for 2080 was obtained using the A1FI emission scenario of HadCM3 GCM. A deterministic Regression Tree Analysis (RTA) and Multiple Linear Regression (MLR) were applied in order to define the realised niche of the species in relation to the chosen environmental variables. The comparison between RMSE values showed that RTA always outperforms MLR, in terms of predicting species distribution. Zonal species were better predicted than rare species (extrazonal or with specific habitat requirements). Climate change is expected to determine a general increase of the average potential altitude. Only the Mediterranean species are likely to be favoured by the predicted climate change, while for the two other chorological types (Sub-Mediterranean and Eurosiberian) the response seems to be species-specific, depending on the ecological characteristic of each species: the more thermophilous and xerophilous species should benefit from the predicted drought in terms of area and mean abundance, while mesophilous species should suffer a strong reduction.

The assessment of uncertainties in satellite-derived global surface albedo products is a critical aspect for studying the climate, ecosystem change, hydrology or the Earth's radiant energy budget. However, it is challenged by the spatial scaling errors between satellite and field measurements. This study aims at evaluating the forthcoming MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V006) Bidirectional Reflectance Distribution Function (BRDF)/albedo productMCD43Dover aMediterranean agricultural area.Here,we present the results from the accuracy assessment of theMODIS blue-sky albedo. The analysis is based on collocated comparisonswith higher spatial resolution estimates from Formosat-2 that were first evaluated against local in situmeasurements. The intersensor comparison is achieved by taking into account the effective point spread function (PSF) for MODIS albedo, modeled as Gaussian functions in the North–South and East–West directions. The equivalent PSF is estimated by correlation analysis between MODIS albedo and Formosat-2 convolved albedo. Results show that it is 1.2 to 2.0 times larger in the East–West direction as compared to the North–South direction. We characterized the equivalent PSF by a full width at half maximum size of 1920 m in East–West, 1200 m in North–South. This provided a very good correlation between the products, showing absolute (relative) Root Mean Square Errors from 0.004 to 0.013 (2% to 7%), and almost no bias. By inspecting 1-km plots homogeneous in land cover type,we found poorer performances over rice and marshes (i.e., relative Root Mean Square Error of about 11% and 7%, and accuracy of 0.011 and −0.008, respectively), and higher accuracy over dry and irrigated pastures, as well as orchards (i.e., relative uncertainty b3.8% and accuracy b0.003). The study demonstrates that neglecting theMODIS PSFwhen comparing the Formosat-2 albedo against theMODIS one induces an additional uncertainty up to 0.02 (10%) in albedo. The consistency between fine and coarse spatial resolution albedo estimates indicates the ability of the daily MCD43D product to reproduce reasonably well the dynamics of albedoThe assessment of uncertainties in satellite-derived global surface albedo products is a critical aspect for studying the climate, ecosystem change, hydrology or the Earth's radiant energy budget. However, it is challenged by the spatial scaling errors between satellite and field measurements. This study aims at evaluating the forthcoming MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V006) Bidirectional Reflectance Distribution Function (BRDF)/albedo productMCD43Dover aMediterranean agricultural area.Here,we present the results from the accuracy assessment of theMODIS blue-sky albedo. The analysis is based on collocated comparisonswith higher spatial resolution estimates from Formosat-2 that were first evaluated against local in situmeasurements. The intersensor comparison is achieved by taking into account the effective point spread function (PSF) for MODIS albedo, modeled as Gaussian functions in the North–South and East–West directions. The equivalent PSF is estimated by correlation analysis between MODIS albedo and Formosat-2 convolved albedo. Results show that it is 1.2 to 2.0 times larger in the East–West direction as compared to the North–South direction. We characterized the equivalent PSF by a full width at half maximum size of 1920 m in East–West, 1200 m in North–South. This provided a very good correlation between the products, showing absolute (relative) Root Mean Square Errors from 0.004 to 0.013 (2% to 7%), and almost no bias. By inspecting 1-km plots homogeneous in land cover type,we found poorer performances over rice and marshes (i.e., relative Root Mean Square Error of about 11% and 7%, and accuracy of 0.011 and −0.008, respectively), and higher accuracy over dry and irrigated pastures, as well as orchards (i.e., relative uncertainty b3.8% and accuracy b0.003). The study demonstrates that neglecting theMODIS PSFwhen comparing the Formosat-2 albedo against theMODIS one induces an additional uncertainty up to 0.02 (10%) in albedo. The consistency between fine and coarse spatial resolution albedo estimates indicates the ability of the daily MCD43D product to reproduce reasonably well the dynamics of albedo

[EN] Synthetic nitrogen (N) fertilizer and field application of livestock manure are the major sources of ammonia (NH3) volatilization. This N loss may decrease crop productivity and subsequent deposition promotes environmental problems associated with soil acidification and eutrophication. Mitigation measures may have associated side effects such as decreased crop productivity (e.g. if N fertilizer application is reduced), or the release of other reactive N compounds (e.g. N2O emissions if manure is incorporated). Here, we present a novel methodology to provide an integrated assessment of the best strategies to abate NH3 from N applications to crops. Using scenario analyses, we assessed the potential of 11 mitigation measures to reduce NH3 volatilization while accounting for their side effects on crop productivity, N use efficiency (NUE) and N surplus (used as an indicator of potential N losses by denitrification/nitrification and NO3 − leaching/run-off). Spain, including its 48 provinces, was selected as a case study as it is the third major producer of agricultural goods in Europe, and also the European country with the highest increase in NH3 emissions from 1990 to 2011. Mitigation scenarios comprised of individual measures and combinations of strategies were evaluated at a country- and regional level. Compared to the reference situation of standard practices for the year 2008, implementation of the most effective region-specific mitigation strategy led to 63% NH3 mitigation at the country level. Implementation of a single strategy for all regions reduced NH3 by 57% at the highest. Strategies that involved combining mitigation measures produced the largest NH3 abatement in all cases, with an 80% reduction in some regions. Among the strategies analyzed, only suppression of urea application combined with manure incorporation and incorporation of N synthetic fertilizers other than urea showed a fully beneficial situation: yieldscaled NH3 emissions were reduced by 82%, N surplus was reduced by 9%, NUE was increased by 19% and yield was around 98% that of the reference situation. This study shows that the adoption of viable measures may provide an opportunity for countries like Spain to meet the international agreements on NH3 mitigation, while maintaining crop yields and increasing NUE, The authors are grateful to the Spanish Ministry of Science and Innovation and the Autonomous Community of Madrid for their economic support through the NEREA project (AGL2012-37815- C05-01, AGL2012-37815-C05-04) and GASPORC (AGL2011-30023-C03) projects. We wish to thank the FIRE (Federation Ile de France de Recherche en Environment, CNRS and UPMC) as well as the Agrisost Project (S2009/AGR-1630). Eduardo Aguilera gratefully acknowledges funding by the 895-2011-1020 project (Canadian Social Sciences and Humanities Research Council). This paper has been produced within the context of the REMEDIA network (http://redremedia.wordpress.com).

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

The traditional Vishu festival in the state of Kerala in South India is celebrated in April with extensive coordinated fireworks display. The influence of these celebrations on the immediate and long-term air quality and impact on the health and well being of the public needs research. The combustion clouds contain harmful fumes (sulfur dioxide, oxides of nitrogen) and particulate matter released at the surface. This study is focused on the influence of fireworks on the air quality at Kannur, India, during Vishu in April 2010 and 2011. Elevated concentrations of various air pollutants such as O 3, NO 2, NO and PM 10 were measured during the intense usage of fireworks. Surprisingly, the organic analysis of the Particulate Matter (PM) samples collected on Vishu day revealed the emission of a variety of hazardous organic compounds during the fireworks display. One of the unique observations in this work is the nighttime production of O 3 by the photodissociation of NO 2 from the flash of firecrackers. The concentration of O 3 was observed to increase two fold over the control days of observation during the same month. Moreover, the concentrations of NO 2, and PM 10 increased by 100%. The concentration of NO was reduced by four fold during the event. A scheme based on the organic combustion from fireworks and peroxyl radical mediation is proposed for the nighttime production of ozone. The diurnal profile of all pollutants except NO showed higher concentrations starting from the Vishu eve on April 14 to Vishu day on April 15 and this pattern repeated for years 2010 and 2011. The fireworks activities have been increasing every year and generation of pollutants at their increased levels for short duration can potentially cause adverse health impacts on a regional scale in a highly populated region. � 2011 Elsevier Ltd.

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

Digital soil mapping can be helpful in providing pedological information over wide areas where existing soil information is limited. The aim of this study was to predict soil properties at a regional scale by parametrizing soil-landscape models using a machine-learning method recently applied to soil science concerns: boosted classification and regression trees. To examine soil properties interdependence, a two-step approach was tested: first soil parent material (PM), including bedrock formations and superficial deposits, was predicted; then, predicted PM was included as a predictive variable to estimate natural soil drainage (SD). Others predictive variables included environmental data representing known soil-forming factors: terrain attributes (elevation, slope, profile and plan curvatures, sub-watershed hillslope length, hydrological distance from the nearest stream, aspect, relative elevation above the nearest stream and a Compound Index initially proposed by Beven and Kirkby (1979) and modified by Merot et al. (1995)), geological data, airborne gamma-ray spectrometry (K:Th ratio, deviation from mean K emissions of the related lithological unit) and landscape data (derived from remotely sensed data). The study area is located in Brittany (northwestern France) and covers 4645 km². The training dataset was constructed from existing detailed soil maps (scale 1:25,000) available for 11% of the study area. An additional set of 1148 punctual soil observations spread over the study area represented an independent validation dataset. Based on 20,000 randomly selected pixels from the training area, PM and SD were predicted with overall accuracies of 73 and 70% respectively. While calculated on punctual observations, correct agreement between prediction and observation decreased to 49% for PM and 52% for SD. Predicted PM was the most influential variable for SD prediction, illustrating the relevance of the two-step approach tested. Boosted classification tree appeared to be a particularly adequate and robust procedure for predicting soil properties. Probability of occurrence of the predicted PM was demonstrated to be a relevant indication of prediction quality, allowing distinction between well-predicted and poorly-predicted situations.

International audience; Keywords: Green area index Leaf area index Canopy radiative transfer Crop growth monitoring Regional scale MODIS Pixel purity Observation coverage Point spread function Information on vegetation status can be retrieved from satellite observations by modelling and inverting canopy radiative transfer. Agricultural monitoring and yield forecasting could greatly benefit from such techniques by coupling crop growth models with crop specific information through data assimilation. An indicator which would be particularly interesting to obtain from remote sensing is the total surface of photosynthetically active plant tissue, or green area index (GAI). Currently, the major limitation is that the imagery that can be used operationally and economically over large areas with high temporal frequency has a coarse spatial resolution. This paper demonstrates how it is possible to characterise the regional crop specific GAI range along with its temporal dynamic using MODIS imagery by controlling the degree at which the observation footprints of the coarse pixels fall within the crop-specific mask delineating the target. This control is done by modelling the instrument's point spread function and by filtering out less reliable GAI estimations in both the spatial and temporal dimensions using thresholds on 3 variables: pixel purity, observation coverage and view zenith angle. The difference in performance between MODIS and fine spatial resolution to estimate the median GAI of a given crop over a 40 × 40 km study region can be reduced to a RMSE of 0.053 m 2 /m 2. The consistency between fine and coarse spatial resolution GAI estimations suggests a possible instrument synergy whereby the high temporal resolution of MODIS provides the general GAI trajectory and while high spatial resolution can be used to estimate the local GAI spatial heterogeneity.

Because of its role in soil functioning, our ability to predict soil organic matter (SOM) dynamics, as influenced by natural and anthropogenic processes, is essential to mitigating soil degradation, ensuring food security, and improving the global environment. Numerous mathematical models have been developed to predict the response of SOM to agricultural practices at the soil-profile or small-plot scales. The same models, coupled with spatial databases, have been applied to larger spatial extents, especially in response to the demand for national inventories of soil C sequestration potential. Modeling SOM dynamics must also be developed at an intermediate integrative level to better investigate the relative importance of transfer and transformation processes in SOM dynamics in agricultural landscapes. Predictive models at the landscape scale will facilitate the assessment of the impact of SOM dynamics on the environment and provide management guidelines at the farm and watershed levels. We review the existing approaches and outline the various needs toward an integrated modeling of SOM at the landscape scale. Landscape-scale modeling involves specific land area representation and model requirements, which include: modeling SOM dynamics in the uncultivated elements of a landscape; simulating SOM distribution and dif f erential dynamics along the soil profile; modeling SOM vertical and lateral fluxes linked to erosion, dissolved organic matter fluxes, and litter transfer; and modeling the spatial distribution of organic matter input and management practices. Even though progress is being made toward all of these aspects, a fully integrated framework for SOM modeling at the landscape level has still to be developed. This will only be possible with the design of a flexible, three-dimensional, spatially explicit representation of the landscape system and with the integration of functional interactions and organic matter transfer functions into the classical SOM modeling frameworks.

Arable soils are a significant source of nitric oxide (NO), a precursor of tropospheric ozone, and thereby contribute to ozone pollution. However, their actual impact on ozone formation is strongly related to their spatial and temporal emission patterns, which warrant high-resolution estimates. Here, we combined an agro-ecosystem model and geo-referenced databases to map these sources over the 12 000 km2 administrative region surrounding Paris, France, with a kilometric level resolution. The six most frequent arable crop species were simulated, with emission rates ranging from 1.4 kg N–NO ha-1 yr-1 to 11.1 kg N–NO ha-1 yr-1. The overall emission factor for fertilizer-derived NO emissions was 1.7%, while background emissions contributed half of the total NO efflux. Emissions were strongly seasonal, being highest in spring due to fertilizer inputs. They were mostly sensitive to soil type, crops' growing season and fertilizer N rates. The use of an agro-ecosystem model at regional scale makes it possible to map the emissions of nitric oxide from arable soils at a resolution compatible with tropospheric ozone models.

The inability to monitor wetland drag coefficients at a regional scale is rooted in the difficulty to determine vegetation structure from remote sensing data. Based on the fact that the backscattering coefficient is sensitive to marsh vegetation structure, this paper presents a methodology to estimate the drag coefficient from a combination of SAR images, interaction models and ancillary data. We use as test case a severe fire event occurred in the Paraná River Delta (Argentina) at the beginning of 2008, when 10% of the herbaceous vegetation was burned up. A map of the reduction of the wetland drag coefficient is presented. Fil: Salvia, Maria Mercedes. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Franco, Mariano Andrés. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Grings, Francisco Matias. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Perna, Pablo Alejandro. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Martino, Roman. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física. Grupo de Medios Porosos; Argentina Fil: Karszenbaum, Haydee. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina Fil: Ferrazzoli, Paolo. Universita Tor Vergata; Italia

Soil loss on arable agricultural land is typically an order of magnitude higher than under undisturbed native vegetation. Although there have been several recent attempts to quantify these accelerated fluxes at the regional, continental and even global scale, all of these studies have focused on erosion by water and wind and no large scale assessment of the magnitude of tillage erosion has been made, despite growing recognition of its significance on agricultural land. Previous field scale simulations of tillage erosion severity have relied on use of high resolution topographic data to derive the measures of slope curvature needed to estimate tillage erosion rates. Here we present a method to derive the required measures of slope curvature from low resolution, but large scale, databases and use high resolution topographical datasets for several study areas in the UK to evaluate the reliability of the approach. On the basis of a tillage model and land-use databases, we estimate the mean gross tillage erosion rates for the part of Europe covered by the CORINE database (6.5% of global cropland) and we obtained an average of 3.3 Mg ha(-1) y(-1), which corresponds to a sediment flux of 0.35 Pg y(-1). Water erosion rates derived for the same area are of a similar magnitude. This redistribution of soil within agricultural fields substantially accelerates soil profile truncation and sediment burial in specific landscape positions and has a strong impact on medium-term soil profile evolution. it is, therefore, clear that tillage erosion must be accounted for in regional assessments of sediment fluxes and in analyses that employ these in the analysis of land management strategies and biogeochemical cycles. Copyright (C) 2009 John Wiley & Sons, Ltd.