Spatial extreme model for rainfall intensity: application to the estimation of IDF curves in the Basque Country

Intensity-duration-frequency (IDF) curves are commonly used in engineering practice for the hydraulic design of flood protection infrastructures and flood risk management. IDF curves are simple functions between the rainfall intensity, the timescale at which the rainfall process is studied, and the return period. This work proposes and tests a new methodological framework for the spatial analysis of extreme rainfall intensities at different time scales, taking advantage of two different precipitation datasets: local observational and gridded products. On the one hand, the proposed method overcomes or reduces known issues related to observational datasets (missing data and short temporal coverage, outliers, systematic biases and inhomogeneities, etc.). On the other hand, it allows incorporating appropriately terrain dependencies on the spatial distribution of the extreme precipitation regime. Finally, it allows to estimate the IDF curves at regional level in contrast with the local approach only based on rain gauges commonly used in practice. The method has been tested to compute IDF curves all over the Basque Country, contrasting results with respect to local analyses. Results show the method robustness against outliers, missing data, systematic biases and short length time series. Moreover, since GEV-parameters from daily gridded dataset are used as covariates, the proposed approach allows coherent spatial interpolation/extrapolation of IDF curves properly accounting for the influence of orographic factors. In addition, due to the current coexistence of local observations and gridded datasets at regional (e.g. The Alps), national (e.g. Spain, France, etc.) or international scale (e.g. E-OBS for Europe or Daymet for the United States of America), the proposed methodology has a wide range of applicability in order to fulfill the known gaps of the observational datasets and reduce the uncertainty related with analysis and characterization of the extreme precipitation regime

Future trends of snowfall days in northern Spain from ENSEMBLES regional climate projections

In a previous study Pons et al. (Clim Res 54(3):197-207, 2010. doi:10.3354/cr01117g) reported a significant decreasing trend of snowfall occurrence in the Northern Iberian Peninsula since the mid 70s. The study was based on observations of annual snowfall frequency (measured as the annual number of snowfall days NSD) from a network of 33 stations ranging from 60 to 1350 m. In the present work we analyze the skill of Regional Climate Models (RCMs) to reproduce this trend for the period 1961-2000 (using both reanalysis- and historical GCM-driven boundary conditions) and the trend and the associated uncertainty of the regional future projections obtained under the A1B scenario for the first half of the twenty-first century. In particular, we consider the regional simulation dataset from the EU-funded ENSEMBLES project, consisting of thirteen state-of-the-art RCMs run at 25 km resolution over Europe. While ERA40 severely underestimates both the mean NSD and its observed trend (-2.2 days/decade), the corresponding RCM simulations driven by the reanalysis appropriately capture the interannual variability and trends of the observed NSD (trends ranging from -3.4 to -0.7, -2.1 days/decade for the ensemble mean). The results driven by the GCM historical runs are quite variable, with trends ranging from -8.5 to 0.2 days/decade (-1.5 days/decade for the ensemble mean), and the greatest uncertainty by far being associated with the particular GCM used. Finally, the trends for the future 2011-2050 A1B runs are more consistent and significant, ranging in this case from -3.7 to -0.5 days/decade (-2.0 days/decade for the ensemble mean), indicating a future significant decreasing trend. These trends are mainly determined by the increasing temperatures, as indicated by the interannual correlation between temperature and NSD (-0.63 in the observations), which is preserved in both ERA40- and GCM-driven simulations.This research has received funding from the European Union’s Seventh Framework Programme under Grant Agreements 606799 (INTACT Project). The RCM simulations used in this study were obtained from the European Union-funded FP6 Integrated Project ENSEMBLES (Contract No. 505539)

Sensitivity of species climate envelope models to baseline climatology and effect on RCM-BASED future projections

Climate Envelope Models (CEMs) are predictive tools widely used in ecological research to estimate the distribution of species by combining observations of their occurrence/abundance with bioclimatic indicators. In this contribution, we show that the resulting projections are highly sensitive to the quality of the baseline climate data, an aspect often overlooked in model criticism. Using distributional data of European beech in northern Spain (Cantabria region), we analyse the discrepancies in model performance and future projections using three public high-resolution climate datasets: WorldClim (WC), the University of Barcelona Atlas (UAB) and a new regional climate grid developed by Cantabria University (UC). We considered the future climate scenarios from several regional climate models (RCMs) of the EU-funded project ENSEMBLES. We demonstrate that the quality of the baseline climate used to derive the present and future bioclimatic indices has a great impact on the stability of the estimated CEMs, although commonly used performance metrics (AUC, Cohen’s kappa) failed to detect this in the cross-validation experiments. WC models lead to unreliable future projections, whereas UAB models performed better but were outperformed by UC, demonstrating the paramount importance of reliable climate input data

Assessing the predictability of fire occurrence and area burned across phytoclimatic regions in Spain

Most fire protection agencies throughout the world have developed forest fire risk forecast systems, usually building upon existing fire danger indices and meteorological forecast data. In this context, the daily predictability of wildfires is of utmost importance in order to allow the fire protection agencies to issue timely fire hazard alerts. In this study, we address the predictability of daily fire occurrence using the components of the Canadian Fire Weather Index (FWI) System and related variables calculated from the latest ECMWF (European Centre for Medium Range Weather Forecasts) reanalysis, ERA-Interim. We develop daily fire occurrence models in peninsular Spain for the period 1990–2008 and, considering different minimum burned area thresholds for fire definition, assess their ability to reproduce the inter-annual fire frequency variability. We based the analysis on a phytoclimatic classification aiming the stratification of the territory into homogeneous units in terms of climatic and fuel type characteristics, allowing to test model performance under different climate/fuel conditions. We then extend the analysis in order to assess the predictability of monthly burned areas. The sensitivity of the models to the level of spatial aggregation of the data is also evaluated. Additionally, we investigate the gain in model performance with the inclusion of socioeconomic and land use/land cover (LULC) covariates in model formulation. Fire occurrence models have attained good performance in most of the phytoclimatic zones considered, being able to faithfully reproduce the inter-annual variability of fire frequency. Total area burned has exhibited some dependence on the meteorological drivers, although model performance was poor in most cases. We identified temperature and some FWI system components as the most important explanatory variables, highlighting the adequacy of the FWI system for fire occurrence prediction in the study area. The results were improved when using aggregated data across regions compared to when data were sampled at the grid-box level. The inclusion of socioeconomic and LULC covariates contributed marginally to the improvement of the models, and in most cases attained no relevant contribution to total explained variance – excepting northern Spain, where anthropogenic factors are known to be the major driver of fires. Models of monthly fire counts performed better in the case of fires larger than 0.1 ha, and for the rest of the thresholds (1, 10 and 100 ha) the daily occurrence models improved the predicted inter-annual variability, indicating the added value of daily models. Fire frequency predictions may provide a preferable basis for past fire history reconstruction, long-term monitoring and the assessment of future climate impacts on fire regimes across regions, posing several advantages over burned area as a response variable. Our results leave the door open to the development a more complex modelling framework based on daily data from numerical climate model outputs based on the FWI system.The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project)

Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim

Offline Land-Surface Model (LSM) simulations are useful for studying the continental hydrological cycle. Because of the nonlinearities in the models, the results are very sensitive to the quality of the meteorological forcing; thus, high-quality gridded datasets of screen-level meteorological variables are needed. Precipitation datasets are particularly difficult to produce due to the inherent spatial and temporal heterogeneity of that variable. They do, however, have a large impact on the simulations, and it is thus necessary to carefully evaluate their quality in great detail. This paper reports the quality of two high-resolution precipitation datasets for Spain at the daily time scale: the new SAFRAN-based dataset and Spain02. SAFRAN is a meteorological analysis system that was designed to force LSMs and has recently been extended to the entirety of Spain for a long period of time (1979/80-2013/14). Spain02 is a daily precipitation dataset for Spain and was created mainly to validate Regional Climate Models. In addition, ERA-Interim is included in the comparison to show the differences between local high-resolution and global low-resolution products. The study compares the different precipitation analyses with rain gauge data and assesses their temporal and spatial similarities to the observations. The validation of SAFRAN with independent data shows that this is a robust product. SAFRAN and Spain02 have very similar scores, although the later slightly surpasses the former. The scores are robust with altitude and throughout the year, save perhaps in summer, when a diminished skill is observed. As expected, SAFRAN and Spain02 perform better than ERA-Interim, which has difficulty capturing the effects of the relief on precipitation due to its low resolution. However, ERA-Interim reproduces spells remarkably well, in contrast to the low skill shown by the high-resolution products. The high-resolution gridded products overestimate the number of precipitation days, which is a problem that affects SAFRAN more than Spain02 and is likely caused by the interpolation method. Both SAFRAN and Spain02 underestimate high precipitation events, but SAFRAN does so more than Spain02. The overestimation of low precipitation events and the underestimation of intense episodes will probably have hydrological consequences once the data are used to force a land surface or hydrological model.We are grateful to the French National Centre for Meteorological Research (CNRM UMR3539, Météo-France CNRS) for allowing us to use the code of the SAFRAN analysis system for our studies, the Spanish State Meteorological Agency (AEMET) for sharing their very valuable observational data with us and the European Centre for Medium-Range Weather Forecasts (ECMWF) for making their ERA-Interim product openly available. This is a contribution to the FP7 eartH2Observe project (http://www.earth2observer.eu), which received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement no. 603608. This work has been funded by the Spanish Economy and Competitiveness Ministry and the European Regional Development Fund through grant CGL2013-47261-R. This work has been supported by the Metropolitan Area of Barcelona Project (no. 308321; flood evolution in the metropolitan area of Barcelona from a holistic perspective: past, present and future) and the Spanish Project HOPE (CGL2014-52571-R) supported by the Ministry of Economy and Competitiveness. This work is a contribution to the HyMeX program (Hydrological cycle in the Mediterranean EXperiment; http://www.hymex.org)

A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe

Quantitative estimate of observational uncertainty is an essential ingredient to correctly interpret changes in climatic and environmental variables such as wildfires. In this work we compare four state-of-the-art satellite fire products with the gridded, ground-based EFFIS dataset for Mediterranean Europe and analyse their statistical differences. The data are compared for spatial and temporal similarities at different aggregations to identify a spatial scale at which most of the observations provide equivalent results. The results of the analysis indicate that the datasets show high temporal correlation with each other (0.5/0.6) when aggregating the data at resolution of at least 1.0° or at NUTS3 level. However, burned area estimates vary widely between datasets. Filtering out satellite fires located on urban and crop land cover classes greatly improves the agreement with EFFIS data. Finally, in spite of the differences found in the area estimates, the spatial pattern is similar for all the datasets, with spatial correlation increasing as the resolution decreases. Also, the general reasonable agreement between satellite products builds confidence in using these datasets and in particular the most-recent developed dataset, FireCCI51, shows the best agreement with EFFIS overall. As a result, the main conclusion of the study is that users should carefully consider the limitations of the satellite fire estimates currently available, as their uncertainties cannot be neglected in the overall uncertainty estimate/cascade that should accompany global or regional change studies and that removing fires on human-dominated land areas is key to analyze forest fires estimation from satellite products.The authors thank EFFIS (European Forest Fire Information System of the European Commission Joint Research Centre, http://effis.jrc.ec.europa.eu) for providing access to fire series EFFIS. M.T. and E.T. have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 740073 (CLIM4CROP project) and grant agreement No. 748750 (SPFireSD project), respectively. The work of A.P. has been supported by the European Union's Horizon 2020 ECOPOTENTIAL project (grant agreement No. 641762)

Uso de la aplicación WhatsApp y la competencia de educación para el trabajo, en los estudiantes del segundo grado de la institución educativa secundaria José Gálvez Puno - 2020

El estudio, tuvo como propósito determinar la relación que existe entre el uso de la aplicación WhatsApp y la competencia de Educación para el trabajo en los estudiantes del segundo año del nivel secundario del Área de Educación para el Trabajo de la Institución Educativa José Gálvez Puno - 2020. El método aplicado fue el no experimental y el diseño corresponde al descriptivo correlacional. La muestra aplicada para esta investigación ha sido de tipo probabilístico aleatorio simple constituido por 52 estudiantes. La técnica utilizada es la encuesta y rúbrica siendo el instrumento el cuestionario e informe de avance académico. Para el desarrollo de este estudio se tomará un cuestionario que mide la variable, uso de la aplicación del WhatsApp y rubrica para evaluar la competencia Gestiona Proyectos de Emprendimiento Económico o social en estudiantes del segundo año del nivel secundario del Área de Educación para el Trabajo de la Institución Educativa José Gálvez Puno-2020, lo cual fue sometido a una validez de contenido por juicio de expertos, para ver el contexto y la pertinencia de los factores medibles. El procesamiento de los datos se realizó con el programa estadístico IBM SPSS en el cual se vaciará los datos obtenidos en la aplicación de la misma. Posteriormente, se analizó e interpretaran los datos para la elaboración del informe final y la discusión de resultados

Assessment and Modeling of the Influence of Age, Gender, and Family History of Hearing Problems on the Probability of Suffering Hearing Loss in the Working Population

Hearing loss affects hundreds of millions of people all over the world, leading to several types of disabilities, ranging from purely physical to psychological and/or social aspects. A proper analysis to ascertain the main risk factors is essential in order to diagnose early and treat adequately. An exploratory analysis based on a heterogeneous sample of 1418 workers is presented in order to identify the main trigger factors for hearing loss. On the one hand, we recorded several medical and environmental parameters, and on the other, we created a model based on Bayesian networks in order to be able to infer the probability of hearing loss considering different scenarios. This paper focuses on three parameters: gender, age, and a family history of hearing problems. The results obtained allow us to infer or predict the best or worst auditory level for an individual under several different scenarios. The least relevant factor is the existence of a family history of deafness, followed by the gender factor, which slopes considerably toward better hearing for females, and most prominent of all, the age factor, given the large differences identified between the various age groups when the gender and family history of deafness variables remain constan

Bias Correction and Downscaling of future RCM Precipitation Projections using a MOS-Analog Technique

In this study we assess the suitability of a recently introduced analog-based Model Output Statistics (MOS) downscaling method (referred to as MOS-Analog, Turco_et_al_2011) for climate change studies, and compare the results with a quantile mapping bias correction method. To this aim, we focus on Spain and consider daily precipitation output from an ensemble of Regional Climate Models provided by the ENSEMBLES project. The reanalysis-driven RCM data provide the historical data (with day-to-day correspondence with observations induced by the forcing boundary conditions) to conduct the analog search of the control (20C3M) and future (A1B) GCM-driven RCM values. First, we show that the MOS-Analog method outperforms the raw RCM output in the control 20C3M scenario (period 1971-2000) for all considered regions and precipitation indices, although for the worst-performing models the method is less effective. Second, we show that the MOS-Analog method broadly preserves the original RCM climate change signal for different future periods (2011-2040, 2041-2070, 2071-2100), except for those indices related to extreme precipitation. This could be explained by the limitation of the analog method to extrapolate unobserved precipitation records. These results suggest that the MOS-Analog is a spatially consistent alternative to standard bias correction methods, although the limitation for extreme values should be taken with caution in cases where this aspect is relevant for the problem.This work was partially supported by the strategic action for energy and climate change by the Spanish R+D 2008–2011 Program ESTCENA (code 200800050084078), the project MULTI-SDM (CGL2015-66583- R, MINECO/FEDER), the Italian project of Interest NextData of the Italian Ministry for Education, University and Research, and by the European Science Foundation within the framework of COST ES1102 (Validating and integrating downscaling methods for climate change research). This paper has also been written under the framework of the International HYMEX project and the Spanish HOPE (CGL2014-52571-R) project. We also acknowledge the ENSEMBLES project (funded by the European Commission’s 6th Framework Programme through contract GOCE-CT-2003-505539) for the RCM data used in this work (http://ensemblesrt3.dmi.dk/). The authors thank AEMET and UC for the data provided for this work (Spain02 gridded precipitation data set, www.meteo.unican.es/es/datasets/spain02). Special thanks to the authors of the MeteoLab-Toolbox www.meteo.unican.es/software/meteolab) which helped us to postprocess the data and to validate the method. Finally, we thank the anonymous referees for their useful comments