BUSCADOR RECOLECTA

Se ha realizado un control de calidad de datos diarios de precipitación, temperatura máxima, temperatura mínima, humedad relativa, velocidad del viento, presión atmosférica e insolación del banco nacional de datos de AEMET, para el periodo 1950-2015. Los controles realizados pertenecen a tres grupos: i) control de errores de codificación; ii) control de valores propios; y iii) comparación con vecinos. Entre los errores de codificación destacan las series de días consecutivos con un mismo valor, la repetición de cadenas de valores entre estaciones distintas, y la aparición de cadenas idénticas dentro de una misma serie. El control de valores propios consiste en detectar los valores que están fuera del umbral de la variable o que suponen un extremo sospechoso. En el control de vecinos se realiza una comparación de los datos con los registrados en estaciones vecinas para marcar posibles anomalías. Mientras que algunos controles implicarían la eliminación directa del dato, otros únicamente marcan el dato como sospechoso y requieren de un control posterior para determinar si el dato tiene que ser eliminado. Al finalizar el proceso un dato puede estar en 4 estados: dato inexistente, dato original, dato sospechoso, dato eliminado. En la aplicación de estos controles se han detectado un número elevado de errores de codificación, con afectaciones ligeramente por encima del 0.5% de la serie en temperatura y viento, mientras que los porcentajes son menores en el resto. Para la comparación con vecinos se ha testado un control con percentiles móviles a 365 días, obteniendo resultados poco satisfactorios., A quality control of maximum temperature, minimum temperature, relative humidity, wind speed, atmospheric pressure and sunshine duration data from the national database of AEMET, for the period 1950-2015 has been done. Controls can be grouped into three groups: i) codification controls; ii) local values controls; and iii) neighbor comparison. Among codification controls we can find the detection of consecutive days with repeated values, the repetition of values between distinct weather stations. The local values controls are focused on the outliers detection. Neighbor control is focused on the comparison of values against nearby data in order to detect anomalous values. Some controls are programmed to suppress automatically detected errors, while others only mark the detected error as a suspicious value. At the end of the process, a value can be in 4 distinct states: Non-available value, original value, suspicious value or suppressed value., Este trabajo ha sido posible gracias a la financiación del proyecto CGL2014-52135-C03-01. El trabajo de Miquel Tomas-Burguera ha sido posible gracias a una beca predoctoral FPU del Ministerio de Educación, Cultura y deporte.

25 Pags.- 10 Figs.- 5 Tabls. Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-19-0244.s1.

© 2019 American Meteorological Society. This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/)., Observational datasets of climatic variables are frequently composed of fragmentary time series covering different time spans and plagued with data gaps. Most statistical methods and environmental models, however, require serially complete data, so gap filling is a routine procedure. However, very often this preliminary stage is undertaken with no consideration of the potentially adverse effects that it can have on further analyses. In addition to numerical effects and trade-offs that are inherent to any imputation method, observational climatic datasets often exhibit temporal changes in the number of available records, which result in further spurious effects if the gap-filling process is sensitive to it. We examined the effect of data reconstruction in a large dataset of monthly temperature records spanning over several decades, during which substantial changes occurred in terms of data availability. We made a thorough analysis in terms of goodness of fit (mean error) and bias in the first two moments (mean and variance), in the extreme quantiles, and in long-term trend magnitude and significance. We show that gap filling may result in biases in the mean and the variance of the reconstructed series, and also in the magnitude and significance of temporal trends. Introduction of a two-step bias correction in the gap-filling process solved some of these problems, although it did not allow us to produce completely unbiased trend estimates. Using only one (the best) neighbor and performing a one-step bias correction, being a simpler approach, closely rivaled this method, although it had similar problems with trend estimates. A trade-off must be assumed between goodness of fit (error minimization) and variance bias., This work has been supported by the research projects CGL2014-52135-C3-1-R, CGL2014-52135-C3-3-R, CGL2017-83866-C3-1-R, CGL2017-83866-C3-3-R, and PCIN-2017-020, financed by the Spanish Commission of Science and Technology and EU ERDF, and INNOMED financed by the ERA-NET WaterWorks 2015 cofunded call of the European Commission, which is an integral part of the 2016 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI) as a result of a joint collaborative effort with the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE JPI), and funded by Agencia Estatal de Investigación of Spain, Research Promotion Foundation of Cyprus, Agence Nationale de la Recherche and Office national de l’eau et des milieux aquatiques of France, Ministry for Education, University and Research of Italy, Center of International Projects of Moldova, and Foundation for Science and Technology of Portugal with cofunding by the European Union. R.S.N. is funded by postdoctoral Grant FJCI-2017-31595 of the Juan de la Cierva Programe, funded by the Spanish Ministry of Science, Innovation and Universities, and EU ERDF., Peer reviewed

14 Pags.- 6 Tabls.- 9 Figs.- Appendix (supplementary data)., The standard approach for computing reference crop evapotranspiration (ETo) is the FAO-56 Penman-Monteith (FAO-PM) method, which requires data on air temperature, radiation, air humidity and wind speed. Unlike air temperature the other variables are less frequently available, hindering the application of FAO-PM. A lot of efforts exist to find the best method to estimate FAO-PM ETo when some variables are not available. The FAO-56 manual recommends to estimate the missing variables based on those currently observed (PM-R), or use the less demanding Hargreaves and Samani method (HS). Additionally, if the missing variables are measured at nearby stations, spatial interpolation can be used to estimate the missing data previous to applying FAO-PM (PM-IC). This paper focuses on the comparison, at the monthly time scale, of the performance of these methods to in the Iberian Peninsula. By using 53 weather stations with all data to calculate FAO-PM, data scarcity scenarios are simulated and the mentioned methods are tested (PM-R, HS, PM-IC)PM-IC yielded consistently the best results according to a number of tests. It yielded the lowest mean absolute error (MAE) at 7.56 mm/month, while PM-R yielded values of 10.15 mm/month and HS 9.36 mm/month and biased results. PM-IC was also best at reproducing the long-term variability and trends in ETo. A good and unbiased estimation of monthly ETo time series are required for irrigation planning and crop design., This work was supported by the research projects CGL2011-24185, CGL2011-29263-C02-02, CGL2014-52135-C3-1-R and CGL2014-517221-REDT financed by the Spanish Commission of Science and Technology and FEDER and “LIFE12 ENV/ES/000536-Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)” financed by the EU LIFE program. The first author was supported by the predoctoral FPU program 2013 (Ministerio de Educación, Cultura y Deporte)., Peer reviewed

13 páginas.- Ilustraciones.- This is an Open Access article distributed under the Creative Commons Attribution 3.0 License., There is an ongoing debate on whether the observed decadal variations in surface solar radiation, known as "dimming" and "brightening" periods, are a large-scale or solely local phenomenon. We investigated this issue using long-term sunshine duration records from China, which experienced a rapid increase in urbanization during the past decades. Over the period 1960-2013, 172 pairs of urban and nearby rural stations were analyzed. Urban and rural sunshine duration trends show similar spatial patterns during a dimming phase (1960-1989) and a subsequent period during which trends were leveling off (1990-2013). This indicates that rather than local effects, the trends in sunshine duration are on more of a national or regional scale in China. Nevertheless, in the dimming phase, the declining rate of sunshine duration in rural areas is around two-thirds of that in urban areas. The ratio of rural to urban dimming generally increases from a minimum of 0.39 to a maximum of 0.87 with increasing indices of urbanization calculated based on the year 2013. It reaches a maximum when the urbanization level exceeds 50 %, the urban population exceeds 20 million, or the population density becomes higher than 250 person km(-2). After the transition into the leveling-off period, sunshine duration trends are no longer significantly affected by urbanization. Meanwhile, the number of laws and regulations related to air pollution and investment in pollution treatment have been increasing in China., The paper is funded by the National Key Basic Research Program of China (2016YFA0602701) and the National Natural Science Foundation of China (grant no. 41501036). The co-author Arturo Sanchez-Lorenzo is supported by a postdoctoral fellowship JCI-2012-12508 and projects CGL2014-55976-R and CGL2014-52135-C3-1-R financed by the Spanish Ministry of Economy and Competitiveness. Dimming and brightening research at ETH Zurich has been supported by the Swiss National Science Foundation (grant nos. 200021 135395 and 200020 159938) and ETH research grant CH2-0111-1. The authors would also like to thank Yonghui Yang, Dandan Ren, and Qiuli Hu for their strong support of this research., Peer reviewed

20 Pags.- 1 Tabl.- 7 Figs., Drought is one of the key natural hazards impacting net primary production and tree growth in forest ecosystems. Nonetheless, tree species show different responses to drought events, which make it difficult to adopt fixed tools for monitoring drought impacts under contrasting environmental and climatic conditions. In this study, we assess the response of forest growth and a satellite proxy of the net primary production (NPP) to drought in peninsular Spain and the Balearic Islands, a region characterized by complex climatological, topographical, and environmental characteristics. Herein, we employed three different indicators based on in situ measurements and satellite image-derived vegetation information (i.e., tree-ring width, maximum annual greenness, and an indicator of NPP). We used seven different climate drought indices to assess drought impacts on the tree variables analyzed. The selected drought indices include four versions of the Palmer Drought Severity Index (PDSI, Palmer Hydrological Drought Index (PHDI), Z-index, and Palmer Modified Drought Index (PMDI)) and three multi-scalar indices (Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and Standardized Precipitation Drought Index (SPDI)). Our results suggest that—irrespective of drought index and tree species—tree-ring width shows a stronger response to interannual variability of drought, compared to the greenness and the NPP. In comparison to other drought indices (e.g., PDSI), and our results demonstrate that multi-scalar drought indices (e.g., SPI, SPEI) are more advantageous in monitoring drought impacts on tree-ring growth, maximum greenness, and NPP. This finding suggests that multi-scalar indices are more appropriate for monitoring and modelling forest drought in peninsular Spain and the Balearic Islands., This study was financially supported by the Spanish Ministry of Economy projects: CGL2015-69186-C2-1-R (Fundiver), CGL2015-69985-R (CLIMED), CGL2013-48843-C2-1-R (CoMo-ReAdapt), AGL2014-53822-C2-1-R (SATIVA), XIRONO (BFU2010-21451), CGL2014-52135-C03-01, PCIN-2015-220, and CGL2016-81706-REDT (Ecometas Network), 1560/2015 (ECOHIPRO). The study was also funded by IMDROFLOOD (Water Works 2014, EC) and INDECIS (European Research Areas for Climate Services) projects. This work also benefited from funding from Xunta de Galicia (PGIDIT06PXIB502262PR, GRC GI-1809, ROCLIGAL-10MDS291009PR), INIA (RTA200600117), and Interreg V-A POCTEFA (CANOPEE, 2014-2020-FEDER funds) projects. Marina Peña-Gallardo was granted by the Spanish Ministry of Economy and Competitiveness. Raúl Sánchez-Salguero and Antonio Gazol were supported by postdoctoral grants (IJCI-2015-25845 and MINECO-FPDI 2013-16600; FEDER funds)., Peer reviewed

This paper evaluates the response of streamflow in a Mediterranean medium-scaled basin under land-use and climate change scenarios and its plausible implication on the management of Boadella–Darnius reservoir (NE Spain). Land cover and climate change scenarios supposed over the next several decades were used to simulate reservoir inflow using the Regional Hydro-Ecologic Simulation System (RHESsys) and to analyze the future impacts on water management (2021–2050). Results reveal a clear decrease in dam inflow (−34%) since the dam was operational from 1971 to 2013. The simulations obtained with RHESsys show a similar decrease (−31%) from 2021 to 2050. Considering the ecological minimum flow outlined by water authorities and the projected decrease in reservoir’s inflows, different water management strategies are needed to mitigate the effects of the expected climate change., This research was funded by LIFE Programme| EU LIFE (LIFE12ENV/ES/000536). We would like to thank the Spanish Meteorological State Agency (AEMET), the Meteorologica Service of Catalonia (MeteoCat-SMC), and the Agència Catalana del Aigua (ACA) for providing the climatic and streamflow databases used in this study. This work has been supported by research projects: “LIFE12 ENV/ES/000536-Demonstration and validation of innovative methodology for regional climate change adaptation in the Mediterranean area (LIFE MEDACC)”, financed by the LIFE programme of the European Commission; PCIN-2015-220 and CGL2014-52135-C03-01, financed by the Spanish Commission of Science and Technology and FEDER; IMDROFLOOD, financed by the Water Works 2014, INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate and also funded by FORMAS (Sweden), DLR(Germany), BMWFW(Austria), IFD (Denmark), MINECO(Spain), and ANR (France), with co-funding by the European Union (Grant 690462)., Peer reviewed

In this dataset, artificial weekly periods are created dividing each month into four periods (days: 1-8; 9-15; 16-22; 23-end). There are 4 files in Netcdf format: 1) ETo.nc containing weekly reference evapotranspiration estimations; 2) ETo_var.nc containing uncertainty estimation of weekly reference evapotranspiration estimations; 3) ETo_Ae.nc containing estimations of the aerodynamic component of weekly reference evapotranspiration and 4) ETo_Ra.nc containing estimations of the radiative component of weekly reference evapotranspiration., SPanish reference evapotranspiration (SPETo) is a weekly gridded reference evapotranspiration dataset for Continental Spain and Balearic Islands, at 1.1 km of spatial resolution, covering the 1961-2014 period. Reference evapotranspiration was calculated using Penman-Monteith FAO-56 method using gridded data of maximum temperature, minimum temperature, dewpoint temperature, wind speed and sunshine duration., This work was supported by research projects CGL2014-52135-C03-01, CGL2014-52135-C03-02 and CGL2014-52135-C03-03 financed by Ministerio de Economía y Competitividad (España), There are 4 files in Netcdf format: 1) ETo.nc containing weekly reference evapotranspiration estimations; 2) ETo_var.nc containing uncertainty estimation of weekly reference evapotranspiration estimations; 3) ETo_Ae.nc containing estimations of the aerodynamic component of weekly reference evapotranspiration and 4) ETo_Ra.nc containing estimations of the radiative component of weekly reference evapotranspiration., No

Los detalles de la metodología y los resultados se pueden encontrar en: Domínguez-Castro, F., Vicente-Serrano, S.M., Tomás-Burguera, M., Peña-Gallardo M., Beguería S., El Kenawy A., Luna, Y., Morata, A.
High-spatial-resolution probability maps of drought duration and magnitude across Spain.
https://doi.org/10.5194/nhess-2018-289
Acceso mediante licencia: http://opendatacommons.org/licenses/odbl/1-0/, This dataset includes 1.1 km spatial resolution of the Generalized Pareto parameters (X0, alpha, Kappa) of drought duration and magnitude for SPI and SPEI at 1, 3, 6 and 12 months timescales and the maximum drought duration and magnitude risk maps for 50 and 100 years for SPI and SPEI at 1, 3, 6 and 12 months timescales.

This dataset contains two zip files “parameters” and “risk_maps”. Parameters zip file contains 16 netCDF3 (8 for drought magnitude and 8 for drought duration) each one provides the Generalized Pareto parameters (1:X0, 2:alpha, 3:Kappa) for one index (SPI or SPEI) and one timescale (1, 3, 6 and 12 months). Risk_maps zip file contains 8 netCDF3 (4 for maximum drought magnitude and 4 for maximum drought duration) each one provides the risk maps for 4 time scales (1, 3, 6, 12 months) of one index (SPI or SPEI) and one time period (50 or 100 years). Each netCDF file contains 1115 longitudes and 834 latitudes., This work was supported by the following research projects: CGL2014-52135-C03-01 and PCIN-2015-220 financed by the Spanish Commission of Science and Technology and FEDER, 1560/2015; Herramientas de monitorización de la vegetación mediante modelización ecohidrológica en parques continentales financed by the Red de Parques Nacionales. IMDROFLOOD financed by Water Works 2014; a co-funded call of the European Commission and INDECIS, Peer reviewed

This paper provides early instrumental data recovered for 20 countries of Latin-America and the Caribbean (Argentina, Bahamas, Belize, Brazil, British Guiana, Chile, Colombia, Costa Rica, Cuba, Ecuador, France (Martinique and Guadalupe), Guatemala, Jamaica, Mexico, Nicaragua, Panama, Peru, Puerto Rico, El Salvador and Suriname) during the 18th and 19th centuries. The main meteorological variables retrieved were air temperature, atmospheric pressure, and precipitation, but other variables, such as humidity, wind direction, and state of the sky were retrieved when possible. In total, more than 300,000 early instrumental data were rescued (96% with daily resolution). Especial effort was made to document all the available metadata in order to allow further post-processing. The compilation is far from being exhaustive, but the dataset will contribute to a better understanding of climate variability in the region, and to enlarging the period of overlap between instrumental data and natural/documentary proxies., This work was partially supported by the PROMETEO research projects financed by the Secretariat of Higher Learning, Science, Technology and Innovation (Ecuador Government), AYA2014-57556-P and CGL2014-52135-C3-1-R financed by the Spanish Ministry of Economy and Competitiveness, IB16127 and grant GR15137 financed by the Economy and Infrastructure Counselling of the Junta of Extremadura (the latter co-financed by the European Regional Development Fund), and IMDROFLOOD financed by the Water Works 2014 co-funded call of the European Commission., Peer reviewed

An analysis of the spatial and temporal variability of daily precipitation concentration (CI) in Spain was made based on a high‐resolution (5 × 5 km) daily gridded precipitation data set for the 1950–2012 period. For each grid point in the Iberian Peninsula (IP) and Balearic and Canary Islands, the average annual CI was computed, as well as its coefficient of variation and the 5th and 95th percentiles. Annual values were also computed, and the time series of the index were used to assess temporal trends over the whole period. The spatial distribution of the CI showed a strong relationship with the orographic barriers near the coastlines. The Canary Islands showed the highest values of CI, along with the eastern Mediterranean facade of the IP. The highest inter‐annual variations of the CI occurred in the southern IP and in the southern Canary Islands. The trends of CI were, overall, positive and significant, which indicates an increase of daily precipitation concentration over the study period and an increasing environmental risks scenario where erosivity, torrentiality, and floods may become more frequent., This study was supported by research projects CGL2015-69985-R and CGL2014-52135 C3-1-R, and financed by the Spanish Ministerio de Economía y Competitividad (MINECO) and EU FEDER-ERDF funds. RSN, MDL, MAS, and LAL were supported by the Government of Aragón through the ‘Programme of research groups’ (group H38, ‘Clima, Agua, Cambio Global y Sistemas Naturales’), as well as SB (group ‘E68, Geomorfología y Cambio Global’). The authors would also like to thank the Climatology Group (2014SGR300, Catalan Government), the Convenio de Desempeño UTA-MINEDUC, and the FONDECYT Project 11160059 of the Chilean Government., Peer Reviewed

We analysed recent evolution and meteorological drivers of the atmospheric evaporative demand (AED) in the Canary Islands for the period 1961-2013. We employed long and high-quality time series of meteorological variables to analyse current AED changes in this region and found that AED has increased during the investigated period. Overall, the annual ETo, which was estimated by means of the FAO-56 Penman-Monteith equation, increased significantly by 18.2 mm decade-1 on average, with a stronger trend in summer (6.7 mm decade-1). In this study we analysed the contribution of (i) the aerodynamic (related to the water vapour that a parcel of air can store) and (ii) radiative (related to the available energy to evaporate a quantity of water) components to the decadal variability and trends of ETo. More than 90 % of the observed ETo variability at the seasonal and annual scales can be associated with the variability in the aerodynamic component. The variable that recorded more significant changes in the Canary Islands was relative humidity, and among the different meteorological factors used to calculate ETo, relative humidity was the main driver of the observed ETo trends. The observed trend could have negative consequences in a number of water-depending sectors if it continues in the future.

A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

Daily precipitation datasets are usually large, bulky and hard to handle, but they are of key importance in many environmental studies. We developed a tool to create custom datasets from observed daily precipitation records. Reference values (RV) are computed for each day and location using multivariate logistic regression with altitude, latitude and longitude as covariates. The operations were compiled in an Open Source R package called reddPrec. The reddPrec package consists of a set of functions used to: i) apply a comprehensive quality control over original daily precipitation datasets, flagging suspect data based on five predefined criteria; ii) fill missing values in original data series by estimating precipitation values using the 10 nearest observations for each day; and iii) create new series and gridded datasets in locations where no data were recorded.

An analysis of the spatial and temporal variability of daily precipitation concentration (CI) in Spain was made based on a high-resolution (5 × 5 km) daily gridded precipitation data set for the 1950–2012 period. For each grid point in the Iberian Peninsula (IP) and Balearic and Canary Islands, the average annual CI was computed, as well as its coefficient of variation and the 5th and 95th percentiles. Annual values were also computed, and the time series of the index were used to assess temporal trends over the whole period. The spatial distribution of the CI showed a strong relationship with the orographic barriers near the coastlines. The Canary Islands showed the highest values of CI, along with the eastern Mediterranean facade of the IP. The highest inter-annual variations of the CI occurred in the southern IP and in the southern Canary Islands. The trends of CI were, overall, positive and significant, which indicates an increase of daily precipitation concentration over the study period and an increasing environmental risks scenario where erosivity, torrentiality, and floods may become more frequent.

Rainfall erosivity is an important parameter in many erosion models, and the EI30 defined by the Universal Soil Loss Equation is one of the best known erosivity indices. One issue with this and other erosivity indices is that they require continuous breakpoint, or high frequency time interval, precipitation data. These data are rare, in comparison to more common medium-frequency data, such as daily precipitation data commonly recorded by many national and regional weather services. Devising methods for computing estimates of rainfall erosivity from daily precipitation data that are comparable to those obtained by using high-frequency data is, therefore, highly desired. Here we present a method for producing such estimates, based on optimal regression tools such as the Gamma Generalised Linear Model and universal kriging. Unlike other methods, this approach produces unbiased and very close to observed EI30, especially when these are aggregated at the annual level. We illustrate the method with a case study comprising more than 1500 high-frequency precipitation records across Spain. Although the original records have a short span (the mean length is around 10 years), computation of spatially-distributed upscaling parameters offers the possibility to compute high-resolution climatologies of the EI30 index based on currently available, long-span, daily precipitation databases.