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Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been widely used in studies of climate changes related to scenarios at global and regional scales. However, CMIP5 outputs cannot be used directly in analysis of climate changes due to coarse spatial resolution. Here, we proposed a new statistical downscaling method for the downscaling practice of the CMIP5 outputs, i.e. Bias-corrected and station-based Non-linear Regression Downscaling method based on Randomly-Moving Points (BNRD). And up to now, there are only two global downscaled CMIP5 precipitation datasets, i.e. NASA daily downscaled CMIP5 precipitation product and BCSD-based (Bias Correction Spatial Disaggregation) monthly downscaled CMIP5 precipitation product available online, which are both based on BCSD downscaling method. Hence, we evaluated downscaling performance of BNRD by comparing it with the downscaled CMIP5 outputs using the BCSD method in this current study. The results indicate that: (1) during the period for development of the model (1964–2005), the error between downscaled CMIP5 precipitation and GPCC ranges between −50 mm–50 mm at monthly scale. When compared to BCSD-downscaled CMIP5 precipitation, BNRD-downscaled CMIP5 precipitation well reduces errors and avoids underestimation and overestimation of GPCC by BCSD-downscaled CMIP5 precipitation; (2) during period for verification of the downscaling models (2006–2013), the maximum (182 mm), minimum (15 mm) and average (68 mm) RMSEs between BNRD-downscaled CMIP5 precipitation and GPCC are all lower than those between BCSD-downscaled CMIP5 precipitation and GPCC at continental scales. Besides, from the average precipitation viewpoint, BNRD-downscaled CMIP5 precipitation is in higher correlation (around 0.75) with GPCC than BCSD-downscaled CMIP5 precipitation under RCP4.5 and RCP8.5 scenarios at continental scales; (3) BNRD resolved the negative relation to GPCC in the areas near equator, including north part of the South America, southern Africa, northern Australia. In all, BNRD downscaling method developed in this study performs better in describing GPCC changes in both space and time when compared to BCSD and can be used for downscaling practice of CMIP5 and even potentially CMIP6 precipitation outputs over the globe.

Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada’s (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Quebec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.

A novel approach is presented to spatially disaggregate coarse soil moisture (SM) by only using remotely sensed vegetation index. The approach is based on the conditional relationship of vegetation with time-aggregated SM, allowing the coarse-scale SM to be disaggregated to the spatial resolution of the vegetation product. The method was applied to satellite-derived SM over January 2010–December 2011, using the high-resolution normalized difference vegetation index (NDVI). The results were evaluated against ground measurements during the two-year period over the contiguous United States and Spain, and also compared with an existing disaggregation method that also requires land surface temperature observations. It is shown that the proposed approach can provide fine-resolution SM with reasonable spatial variability.

Bias correction and spatial disaggregation (BCSD) is widely used in coupling general circulation models (GCMs) and hydrological models. However, there are some disadvantages in BCSD, such as only one GCM being selected, correcting biases through quantile-mapping (QM), and downscaling through interpolation. Then a combined approach of canonical correlation analysis filtering, multi-model ensemble, and extreme learning machine (ELM) regressions (CEE) was advanced. The performance of CEE and BCSD was evaluated with Manas River Basin as a study area. Results show it is unreasonable to correct biases through QM as it implies that the climate remains unchanged. Multi-model ensemble provides additional information, which is beneficial for regressions. CEE performs better than BCSD in temperature and precipitation rate downscaling. In CEE, the residual in temperature forecasting can be lower than 0.05 times temperature range and that in precipitation rate can be 0.33 times precipitation rate range. The performance of CEE in temperature downscaling in plains is better than mountainous areas, but for precipitation rate downscaling, it is better in mountainous areas. Increasing rate of temperature in the basin is 0.0254 K/decade, 0.1837 K/decade, and 0.5039 K/decade, and that of precipitation rate is 0.0028 mm/(day × decade), 0.0036 mm/(day × decade), and 0.0022 mm/(day × decade) in RCP2.6, RCP4.5, and RCP8.5, respectively.

The aim of this study is improving currently applied methodology for spatial disaggregation, as well as mapping air emission inventories by taking into account the auxiliary spatial data on population density. District heating infrastructure occurring in more populated areas changes distinctly the spatial distribution of estimated air emission; however, it does not change the initial estimate. That means the total, disaggregated value is constant. Considered sources of domestic combustion are located in the central part of the Silesian Metropolis, in the southern part of Poland. A large part of this area is strongly urbanized and supplied with heat (hot water) from the district heating system. Data on population density help to determine the area within which the dwellers use heat energy and hot water supplied by the heating infrastructure, apart from heating with small domestic boilers and stoves. This causes the domestic combustion's emission impact within the distinguished area to be significantly lower in comparison to the official guidelines on air emission inventories. The important differences in spatial air emissions distributions calculated using a top-down approach are found for strongly urbanized areas supplied partly with heat and hot water from the district heating network. This fact should be taken into account when preparing detailed, high-resolution emission inventories for air regional and local quality modeling.The spatial issues connected with elaboration of the high-resolution emission inventories are presented for the example of the populated area of the Silesian Metropolis (Poland). Spatial distribution of the population density is used to determine the area supplied with heat and hot water from the district heating system. It changes distinctly the spatial distribution of the air emission from small residential combustion sources.

The spatial disaggregation problem is an interesting problem when investigating and processing geographically correlated data; it is a special case of the map overlay problem. The map overlay problem occurs when data that are presented in incompatible grids need to be combined or compared; the spatial disaggregation problem is a special case in which the cells of one grid partition the cells of the other grid. The main reason for spatial disaggregation is to increase the spatial resolution of the data. Traditionally, similar methods as for the map overlay problem are used; fairly straightforward assumptions on the geographical distribution of the data are made, but these assumptions do not match the real-world situation. The approach presented in this contribution uses additionally available data ( proxy data ) that provides information on the geographic distribution of the input data. As will be illustrated, having this proxy data still does not provide for a straightforward solution and incorporating it inquires some form of reasoning which will be achieved by means of a dynamically constructed fuzzy inference system.

International audience; Current level of morbidity generated by industrial air pollution requires effective tools combining pollutant rates and their risks on human health. Traditional Life Cycle Assessment (LCA) appears to be the most appropriate method for assessing impact of human toxicity by inhalation. However, its interest is limited due to lack of spatial-temporal specificities of emissions. To solve this problem, this research work shows the feasibility of a site-dependent dynamic LCA, obtained by a coupling between a traditional LCA, a method of temporal and spatial disaggregation of pollution rates and a model of numerical dispersion.

China pledges to reach a peak in CO2 emissions by 2030 and to make its best efforts to reach this peak earlier. Previous studies have paid much attention to the total amount of China's CO2 emissions, but usually only one dataset is used in each evaluation. The pledged national reduction target is administratively divided into provincial targets. Accurate interpretation of province-level carbon emissions is essential for making policies and achieving the reduction target. However, the spatiotemporal pattern of provincial emissions and the associated uncertainty are still poorly understood. Thus, an assessment of province-level CO2 emissions considering local statistical data and emission factors is urgently needed. Here, we collected and analyzed 7 published emission datasets to comprehensively evaluate the spatiotemporal distribution of provincial CO2 emissions. We found that the provincial emissions ranged from 20 to 649 Mt CO2 and that the standard deviations (SDs) ranged from 8 to 159 Mt. Furthermore, the emissions estimated from provincial-data-based inventories were more consistent than those from the spatial disaggregation of national energy statistics, with mean SDs of 26 and 65 Mt CO2 in 2012, respectively. Temporally, emissions in most provinces increased from 2000 to approximately 2012 and leveled off afterwards. The interannual variation in provincial CO2 emissions was captured by provincial-data-based inventories but generally missed by national-data-based inventories. When compared with referenced inventories, the discrepancy for provincial estimates could reach −57%–162% for national-data-based inventories but were less than 45% for provincial-data-based inventories. Using comprehensive data sets, the range presented here incorporated more factors and showed potential systematic biases. Our results indicate that it is more suitable to use provincial inventories when making policies for subnational CO2 reductions or when performing atmospheric CO2 simulations. To reduce uncertainties in provincial emission estimates, we suggest the use of local optimized coal emission factors and validations of inventories by direct measurement data and remote sensing results.

Area-to-point kriging (ATPK) is a geostatistical method for creating high-resolution raster maps using data of the variable of interest with a much lower resolution. The data set of areal means is often considerably smaller ($$<50 observations) than data sets conventionally dealt with in geostatistical analyses. In contemporary ATPK methods, uncertainty in the variogram parameters is not accounted for in the prediction; this issue can be overcome by applying ATPK in a Bayesian framework. Commonly in Bayesian statistics, posterior distributions of model parameters and posterior predictive distributions are approximated by Markov chain Monte Carlo sampling from the posterior, which can be computationally expensive. Therefore, a partly analytical solution is implemented in this paper, in order to (i) explore the impact of the prior distribution on predictions and prediction variances, (ii) investigate whether certain aspects of uncertainty can be disregarded, simplifying the necessary computations, and (iii) test the impact of various model misspecifications. Several approaches using simulated data, aggregated real-world point data, and a case study on aggregated crop yields in Burkina Faso are compared. The prior distribution is found to have minimal impact on the disaggregated predictions. In most cases with known short-range behaviour, an approach that disregards uncertainty in the variogram distance parameter gives a reasonable assessment of prediction uncertainty. However, some severe effects of model misspecification in terms of overly conservative or optimistic prediction uncertainties are found, highlighting the importance of model choice or integration into ATPK.

The skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

Emission inventories provide a description of the polluting activities that occur across a specific geographic domain, and are widely used as input for air quality modelling for the assessment of compliance with environmental legislation. The spatial scale to which these inventories are referred has an influence in the representativeness of the emission estimates, as these are underpinned by a number of considerations and data with different levels of granularity. This study proposes a comprehensive framework for the evaluation of emission inventories that allows identifying methodological issues by examining differences in performance to a chemical transport model (CTM) when such inventories are used as input. To demonstrate the approach, a comparison between the national and regional emissions inventories for the Autonomous Community of Madrid (ACM) was carried out (NEI and REI respectively). The analysis revealed discrepancies in compilation methodologies for the domestic sector (SNAP 02), industrial combustion (SNAP 03), road traffic (SNAP 07) and other mobile sources (SNAP 08); most of the differences were originally caused by taking into account different activity variables, fuel mixes, and spatial disaggregation and allocation proxies. The granularity of the base data (statistics, fuel consumption, facilities, etc.) proved to be an essential limiting factor, which means that whenever bottom-up approaches were followed, the description of emission sectors tended to be more accurate.

Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5ź×ź105 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75ź% of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning. Development of a scale independent model to disaggregate land use data.Generate high resolution maps for crops cultivated in EU-28.Validation of model results by comparison with detailed survey data.

In South Africa, the only soil resource available with full spatial coverage is the national resource inventory. Disaggregating this polygon-based inventory, is thus a logical step to create more detailed soil maps covering the entire country. The polygons are large in area encompassing complex soil-terrain patterns and research into disaggregation techniques has been limited. This study aimed to compare 10 algorithms, implemented through a modified DSMART (“Disaggregating and Harmonizing Soil Map Units Through Resampled Classification Trees”) model, in their ability to disaggregate two polygons into soil associations in two environmentally contrasting locations. One site had high relief and strong catenal sequences (eastern KwaZulu-Natal Province) and the other site had low relief and a strong geological control of soil types (northern Eastern Cape Province). The algorithms compared were based on previous studies which included k-nearest neighbour, nearest shrunken centroid, discriminatory analysis, multinomial logistics regression, linear and radial support vector machines, decision trees, stochastic gradient boosting, random forest, and neural networks. The method involves stratifying the polygons with landform elements, randomly sampling the landform elements, allocating the soil classes based on the resource inventory, and predicting soil associations across a stack of covariates. This was done in an iterative process, creating multiple realisations of the soil distribution. The performance of each algorithm was based on their kappa and uncertainties. It was found that in general, robust linear models which either utilise an embedded feature selection or regularise covariates, performed best. In the area with high relief and clear toposequences, nearest shrunken centroid was the top performing algorithm with a kappa of 0.42 and an average uncertainty of 0.22. In the area with relatively low relief and complex geology, the results were unsatisfactory. However, a regularised multinomial regression was the top performing algorithm, achieving a kappa of 0.17 and an average uncertainty of 0.84. The results of this study highlight the versatility of a technique to disaggregate South Africa's national resource inventory, where algorithms can be chosen on expert knowledge, model averaging can be performed, the top performing algorithm can be chosen, and algorithm parameters can be optimised.

Historical temperature and precipitation extremes and their potential future changes are quantified and evaluated throughout the landmass of China. A statistical model of climate extremes based on generalized extreme value (GEV) distribution is applied to both historical climate data and bias correction and spatial disaggregation (BCSD) downscaled Coupled Model Inter-comparison Project phase 5 (CMIP5) projections. The results indicate relatively moderate historical warm extreme conditions in China with regional means of maximum temperature 28.9, 29.4, and 29.8 °C for 10-, 20-, and 50-year return periods, respectively, whereas the corresponding regional means of minimum temperature are −20.1, −20.8, and −21.5 °C, manifesting a downward trend northwardly with relative larger regional variations in cold extremes. The historical precipitation extremes also decline gradually from south-southeast toward northwest with significant regional differences. As for the future, the warm extremes are expected to aggravate by roughly 1.66–4.92 °C projected by CMIP5, indicating larger increasing rate and spatial differences compared to cold extremes. The extreme precipitation is projected to increase 7.9–13.4 %, the dry regions would see a larger increasing rate compared to wet regions. The increasing radiative forcing concentration would trigger upward variations in both temperature and precipitation extreme magnitudes. Also, the warm extreme changes are more sensitive to the radiative forcing concentration than the cold extremes. The CMIP5 projections basically maintain a favorable inter-model consistency in temperature and rainfall extreme simulation for the future, but the inter-model difference of warm extremes is larger than cold extremes.

Soil surveys often contain multi-component map units comprising two or more soil classes, whose spatial distribution within the map unit is not represented. Digital Soil Mapping tools supported by information from soil surveys make it possible to predict where these classes are located. The aim of this study was to develop a methodology to increase the detail of conventional soil maps by means of spatial disaggregation of multi-component map units and to predict the spatial location of the derived soil classes. Three digital maps of terrain variables - slope, landforms, and topographic wetness index - were correlated with the soil map and 72 georeferenced profiles from the Porto Alegre soil survey. Explicit rules that expressed regional soil-landscape relationships were formulated based on the resulting combinations. These rules were used to select typical areas of occurrence of each soil class and to train a decision tree model to predict the occurrence of individualized soil classes. Validation of the soil map predictions was conducted by comparison with available soil profiles. The soil map produced showed high agreement (80.5 % accuracy) with the soil classes observed in the soil profiles; Ultisols and Lithic Udorthents were predicted with greater accuracy. The soil variables selected in this study were suitable to represent the soil-landscape relationships, suggesting potential use in future studies. This approach developed a more detailed soil map relevant to current demands for soil information and has potential to be replicated in other areas in which data availability is similar.

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.

Insurance catastrophe modeling of natural risks requires three key components of information: (1) a catalog of all possible events that can occur within the time period considered, (2) an exposure database consisting of the built parameters of insured structures or the population exposed to the hazard (usually in the form of a portfolio), and (3) the susceptibility of the structures or population at risk to the hazard. This chapter focuses on each of these components herewith highlighting recent trends in losses. To do this, CATDAT (Daniell et al., 2011) data are used for describing both economic as well as insured markets. Results indicate that normalized losses are in general decreasing most likely due to increasing hazard mitigation and decreasing vulnerability. Statistics as to the last 100 years (from 1900 to 2015) suggest that flood has been dominating historical losses. The statistical results have implications for future events. This article also considers how historic data are used in forming vulnerability functions along with how data might best be adjusted for changes in values, among others. Concepts such as replacement cost, hazard metrics, vulnerability functions, and spatial disaggregation of the average annual loss are then highlighted using an Australian earthquake model created by the authors.

International audience; Spatial disaggregation of soil map units involves downscaling existing information to produce new information at a finer scale than that of the original source. Currently, it is becoming a powerful tool to address the spatial distribution of soil information over large areas, where legacy soil polygon maps are the only source of soil information. Because of the high expense of additional resampling, few studies have sought to validate disaggregated soil maps using independent sampling. This study implemented spatial disaggregation approach to measure the quality of soil property predictions derived from disaggregated soil maps, using stratified simple random sampling of a study area of 6848 km2 (11 strata and 135 soil profiles). In a previous study, the existing legacy soil polygon map of Brittany (France) at 1:250,000 scale was spatially disaggregated at 50 m resolution using an algorithm called Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART), which uses soil-landscape expert rules of soil distribution in space. By fitting equal-area spline functions, soil properties were then estimated at six depth intervals according to GlobalSoilMap specifications. To validate disaggregated soil maps, two approaches were developed according to the soil attribute nature (continuous or categorical). For categorical soil properties (soil parent material, soil drainage class, soil type and soil depth class), the overall strict purity (the degree to which all classification criterion are respected) by the most probable STU (Soil Typological Unit) map was estimated at 34%, while the overall average purity reached 70%. The overall partial soil-type purity reached 60%, the overall partial parent material purity reached 78% and the overall partial soil drainage class as well as soil depth class purities reached 65% and 78%, respectively. Continuous soil properties (clay content, fine silt content, coarse silt content, total silt content, fine sand content, coarse sand content, coarse fragments, Cation Exchange Capacity (CEC) and pH) were validated at two soil depth intervals (5–15 and 30–60 cm) using 260 soil samples. In general, soil property predictions were unbiased except for coarse fragments and CEC in the 5–15 cm layer. Validation statistics (R2, RMSE, RRMSE and ME) were better for the 30–60 cm layer except for soil particle-size distribution. Thus, differences in prediction accuracies among strata (the validation support) denote areas where more soil data or better soil prediction models are needed to improve the disaggregation process.

This study utilizes a recent nonparametric disaggregation K-nearest neighbor (KNN) model, to resample monthly flows depending on annual flows at different sites. Both temporal and spatial approaches will be followed in this model while preserving the distributional statistics of the observed data. This model assumes that a set of aggregated annual streamflows at a key station is available and desired for disaggregation to a corresponding series of streamflow at key station (temporal disaggregation) as well as at tributary stations (spatial disaggregation). The model is applied to the annual streamflow data of particular stations in the Kızılırmak Basin, which is exposed to drought periods during the years (1970–1974 and 1994-1995). The aim of this study is to find the possibilities of using the nonparametric approaches as generators of monthly flows, with emphasis on the ability to reproduce the statistics related to drought and storage analysis for the selected stations in Turkey. The results show that the spatial disaggregation approach has the ability to reproduce the historical data better than the temporal approach for the tested sites and provides a variety of generated monthly sequence flows that can then be utilized to analyze the performance of the water resources planning system.

Over the last years, the capabilities of chemical transport models have been greatly improved and the need for more accurate emission data has increased as well. In the past, a number of emission models have been developed and present different functionalities and applications. The majority of these though cover very specific needs. This paper describes the development of a new emission model namely computer model for the construction of model-ready emission inventories (MOSESS) which is used to compile high-resolution emission inventories or improve existing ones, utilizing complex GIS techniques. The model aims in helping chemical modelers to obtain a better overview of their modeling application by having a comprehensive understanding of the emission input. MOSESS incorporates more than 70 different emission calculation methodologies, and it is capable of handling external emission databases (such as EMEP and EPER) from which emissions can be extracted. The temporal variation (annual/daily and diurnal processing), chemical speciation of NMVOCs and particles, vertical distribution and point source treatment, as well as the spatial disaggregation of emissions (utilizing numerous spatial proxies including high-resolution landuses) can help create model-ready emission inventories which can be used for contemporary modeling applications.

Extreme precipitation events damage infrastructure and property; thus, predicting future precipitation patterns in the context of climate change is important. In this study, precipitation projections from 36 downscaled General Circulation Models (GCMs) under two Representative Concentration Pathway (RCP) scenarios (RCP2.6 and RCP8.5) enabled examination of projected changes in future precipitation for the Clear Creek watershed in Houston, Texas, USA. Precipitation from 1950 to 2099 simulated with GCM were downscaled using the Bias-Correction Spatial Disaggregation method. Ten precipitation indices that represent precipitation amount, precipitation intensity, precipitation duration, and precipitation frequency evaluated how precipitation patterns will likely change. Results show that, at the annual scale, mean precipitation will significantly decrease based on RCP8.5, or remain relatively constant based on RCP2.6. Precipitation intensity and precipitation variability, however, will likely increase. Dry periods will lengthen significantly, whereas the length of wet spells will generally remain unchanged. At the monthly scale, the amount of precipitation, precipitation intensity, precipitation frequency and the length of wet spells will likely increase in September. In contrast, precipitation will likely decrease and dry spells will lengthen in April, May, August, November, and December. This finding illustrates that the intra-annual variability in precipitation will increase. The projected changes in precipitation under RCP8.5 are generally greater compared with RCP2.6. Differences between the scenarios are more pronounced towards the end of the century. Houston has recently experienced substantial precipitation variability, including severe drought and record-breaking precipitation from Hurricane Harvey in 2017. These events are consistent with the long-term GCM projections. Findings from this study can be applied to help manage water resources and enhance adaptability to climate change.

Agricultural policies have impacts on land use, the economy, and the environment and their analysis requires disaggregated data at the local level with geographical references. Thus, this study proposes a model for disaggregating agricultural data, which develops a supervised classification of satellite images by using a survey and empirical knowledge. To ensure the consistency with multiple sources of information, a minimum cross-entropy process was used. The proposed model was applied using two supervised classification algorithms and a more informative set of biophysical information. The results were validated and analyzed by considering various sources of information, showing that an entropy approach combined with supervised classifications may provide a reliable data disaggregation. Fundacao para a Ciencia e a Tecnologia [UID/ECO/04007/2013]; FEDER/COMPETE [POCI-01-0145-FEDER-007659] POCI-01-0145-FEDER-007659

International audience; The objective of this study is to improve evaluations of the future use of biomass sources in Sweden and France by representing the high spatial variations of the supply and cost of biomass sources in energy system models. The proposed methodology considers a high spatial disaggregation of biomass supply sources, as well as detailed biomass cost-supply curves, and is thereby able to account for spatial differences in heterogeneous land qualities, management strategies, and possible adaptation rates. Integrating the methodology into national or regional energy system models will enhance cost-effective evaluations of biomass sources as well as the development of the bioenergy sector. A TIMES energy system model was used to evaluate bioenergy production potential for France and Sweden based on domestic biomass sources and under the assumption that bioenergy production should not be at the expense of domestic food and forestry supply. Results show that by 2050, the biomass sources considered could provide as much as 250PJ of bioenergy in Sweden, and 1470PJ of bioenergy in France. Results further showed that 1st generation biofuels are likely to continue to play a substantial role in the biofuel mix. While 2nd generation biofuel production in Sweden is likely to be low, larger amounts of 2nd generation biofuels could be produced in France. However, the amount and type of 2nd generation biofuel produced in France was found to be highly dependent on the bioenergy demand level.

[EN] Availability of high resolution population distribution data, independent of the administrative units in which demographic statistics are collected, is a real necessity in many fields: risk evaluation due to earthquakes, flooding or fires, to name just a few, integration between socio-demographic and environmental or geographical information collected in different formats, policy design for the provision public services, such as health, education or public transport, or mobility studies in urban areas or metropolitan regions. Because of this, the literature has explored various methods of population downscaling, collected at communality or census tract level, into smaller areas; typically urban polygons from high resolution topographic maps or land use/land cover databases, or grid cells, allowing the elaboration of raster population layers. A common feature of all these methods is that they do not incorporate building height. In this way, downscaling methods don´t distinguish between the urban sprawl type of settlement, where most of the houses are detached or semi-detached, and compact cities with high buildings. This paper examines error reduction in downscaling census tract population into 1×1 km and 1 ha grids, when we add the third dimension, building height from LIDAR remote sensing data. Algorithms used are simple, and based on areal weighting with or without auxiliary land use/land cover information, since our focus is not in fine turning algorithms, but in measuring improvements due to the missing dimension: building height. Our results indicate that improvements are noticeable. They are comparable to the ones obtained when we move from binary dasymetric methods to more general models combining densities for different land use/land cover types. Hence, adding the third dimension to population downscaling algorithms seems worth pursuing. [ES] La disponibilidad de información sobre la distribución de la población a gran escala, e independiente de las unidades administrativas de recogida de la información, es una necesidad en muchos ámbitos: evaluación de riesgos de terremotos, inundaciones o incendios, integración de estadísticas demográficas con información medioambiental en formatos diversos, diseño de políticas de prestación de servicios públicos o estudios de accesibilidad y movilidad en las grandes ciudades. Por estas razones, la literatura ha explorado numerosos métodos de desagregación de la población, recogida a nivel municipal o de sección censal, en polígonos urbanos procedentes de los sistemas de información de coberturas y usos del suelo o celdas de una malla regular, permitiendo de esta forma la generación de capas raster de población, fáciles de combinar con información medioambiental y geográfica. Una característica común a todos estos métodos de desagregación es que la información auxiliar utilizada no incorpora la altura de las edificaciones. De esta forma los algoritmos de desagregación no distinguen entre asentamientos extensos, basados en edificaciones con pocas alturas, frente a asentamientos compactos, con predominio de edificios con muchas altu-ras. Este trabajo examina la reducción del error que se comente al desagregar la población por secciones censales a una malla regular, de 1×1 km y de 1 ha, cuando incorporamos la tercera dimensión, la altura de los edificios a partir de datos LIDAR, sobre una capa raster de densidad de edificación, y utilizamos algoritmos de desagregación muy simples, basados en distribuciones proporcionales por área o por volumen, con y sin información auxiliar de coberturas y usos del suelo. Los resultados indican que las mejoras, incluso con estos algoritmos sencillos, son notables; comparables a las que se producen al incorporar información sobre usos del suelo a métodos dasimétricos binarios. En consecuencia incorporar la tercera dimensión a los procesos de re-escalado espacial de la población resulta totalmente esencial. El autor agradece la ayuda del programa de Investigación FBBVA-Ivie, así como la del proyecto ECO2015-70632-R.

Currently three air quality modelling systems operate routinely with high-resolution over mainland Portugal for forecasting purposes, namely MM5-CHIMERE, MM5-EURAD, and CALIOPE. They each operate daily using different horizontal resolutions (10 km × 10 km, 5 km × 5 km, and 4 km × 4 km, respectively), specific physical and chemical parameterizations, and their own emission pre-processors (with a common EMEP emission database source but different spatial disaggregation methodologies). The operational BSC-DREAM8b model is coupled offline within the aforementioned air quality systems to provide the Saharan dust contribution to particulate matter. Bias-correction studies have demonstrated the benefit of using past observational data to reduce systematic model forecast errors. The present contribution aims to evaluate the application of two bias-correction techniques, the multiplicative ratio and the Kalman filter, in order to improve air quality forecasts for Portugal. Both techniques are applied to the three modelling systems over the full year of 2010. Raw and unbiased model results for the main atmospheric pollutants (O3, NO2, SO2, PM10, and PM2.5) are analysed and compared with data from 18 monitoring stations distributed within inland Portugal on an hourly basis. Statistical analysis shows that both bias-correction techniques improve the raw forecast skills (for all the modelling systems and pollutants). In the case of O3 max-8 h, correlation coefficients improve by 19–45%, from 0.56–0.81 (raw models) to 0.78–0.86 (corrected models). PM2.5 also presents significant improvements, for example correlation coefficients increase by more than 50% (with both techniques), reaching values between 0.50 and 0.64. The corrected primary pollutants NO2 and SO2 demonstrate significant relative improvements compared to O3, mostly because the original modelling system skills are lower for those species. Although the applied techniques have different mathematical formulations and complexity levels, there are comparable answers for all of the forecasting systems. Analysis performed over specific situations such as air quality episodes and cases of unvalidated or missing data reveals different behaviours of the bias-correction techniques under study. The results confirm the advantage of the application of bias-correction techniques for air quality forecasts. Both techniques can be applied routinely in operational forecast systems and they will be useful to provide accurate alerts about exceedances to the population. The authors acknowledge the CRUP by the support of the Integrated Action E 122-10 and Integrated Action PT2009-0029 from the Ministerio de Ciencia e Innovación. Thanks are extended to the Portuguese ‘Ministério da Ciência, da Tecnologia e do Ensino Superior’ for the financing of BIOGAIR (PTDC/AAC-AMB/103866/2008) project, for the PhD grant of Isabel Ribeiro (SFRH/BD/60370/2009) and the post doc grant of Alexandra Monteiro (SFRH/BPD/63796/2009). The Spanish Ministry of Science and Innovation is also thanked for the Formación de Personal Investigador (FPI) doctoral fellowship held by María Teresa Pay (CGL2006-08903). COST ES0602 is also acknowledged. The authors wish to thank Luca Delle Monache and Ronald B. Stull for providing the Kalman filter algorithm used in this study. The computation with CALIOPE system has been done at the MareNostrum supercomputer hosted by the Barcelona Supercomputing Center-Centro Nacional de Supercomputación. published

Traffic emission estimation in developing countries is a key-issue for air pollution management. In most cases, comprehensive bottom-up methodologies cannot be applied in mid-sized cities because of the resource cost related to their application. In this paper, a simplified emission estimation model (SEEM) is evaluated. The model is based on a top-down approach and gives annual global hot emission. Particular attention is paid to the quality of the input traffic data. The quality of results is assessed by application of the SEEM model in the Chilean Gran Concepcion urban area and by comparison with a bottom-up approach that has been led for the year 2000. The SEEM model estimates emissions with an accuracy of about 20 % and is related to important resource savings. The results of the SEEM model are then distributed in space with a disaggregation approach and using GIS techniques. The relevancy of the disaggregation approach is evaluated among several possibilities through statistical methods. A spatial disaggregation using principal roads density gives the best results in terms of emissions repartition and gives a globally accurate image of the distribution of hot emissions in a mid-sized city.

The objectives of this study were to investigate the influence of the nature of the deforestation process on an empirical spatial model of deforestation and to test a remote-sensing-based methodology to define the appropriate spatial entities for modelling deforestation, The study area is located in southern Cameroon, The study included the production and integration in a geographic information system (GIS) of a database of natural and cultural landscape variables, An empirical spatial model of deforestation was developed by relating a set of variables measuring landscape attributes to the frequency of occurrence of deforestation, The main conclusion of the study is that the definition of the spatial entities used to calibrate a spatial model of deforestation has a significant impact on the model design but not, in this case, on the predictive power of the model, The interpretation of the spatial pattern of forest/non-forest interfaces as detected by remote sensing proved to be efficient in identifying regions affected by different deforestation processes. (C) 1997 Elsevier Science Ltd.

Emission inventories are a fundamental input to atmospheric chemical transport models (CTMs). As the latter become increasingly demanding, modern inventories began to provide much more information (high spatial and temporal disaggregation, more chemical compounds etc). In this study we present a computational approach, an emission processing kernel that is used to compile a high spatially and temporally resolved emission inventory for the anthropogenic sources covering the Greater Istanbul Area (GIA) for the reference year 2007. The emission processor is used to produce emissions for a 92 x 57 km area covering the GIA with 2 km grid resolution. The emission inventory has high temporal resolution, covering monthly, weekly and diurnal processing and includes CO, NOx, SOx, NH3, and chemically speciated PM10, PM2.5 and NMVOCs emissions. PM10 and PM2.5 are chemically split into organic carbon, elemental carbon, sulfates, nitrates, ammonium and other particles while NMVOCs are chemically speciated into 23 chemical compounds. The compilation process includes the use of various activity information and statistical data that were gathered from local official authorities and experts, measurements, published studies for the region or extracted from pre-existing databases. The results indicate that the road transport sector is the main contributor to the emissions in the area, whereas residential combustion (for SOx) and solvent use (for NMVOCs) are also important source categories. Industrial combustion is found out to be the main SOx emitter. The temporal calculations show that monthly distributions follow the seasonal variation for most of the pollutants with higher emissions in winter time. Diurnal calculations show that the profile fits with the rush hours due to the highest contribution of traffic emissions. (C) Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 License.