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Santiago Beguería and Sergio M. Vicente-Serrano

Abstract

The occurrence of rainfalls of high magnitude constitutes a primary natural hazard in many parts of the world, and the elaboration of maps showing the hazard of extreme rainfalls has great theoretical and practical interest. In this work a procedure based on extreme value analysis and spatial interpolation techniques is described. The result is a probability model in which the distribution parameters vary smoothly in space. This methodology is applied to the middle Ebro Valley (Spain), a climatically complex area with great contrasts because of the relief and exposure to different air masses. The database consists of 43 daily precipitation series from 1950 to 2000. Because rainfall tends to occur highly clustered in time in the area, a declustering process was applied to the data, and the series of daily cluster maxima were used hereinafter. The mean excess plot and error minimizing were used to find an optimum threshold value to retain the highest records (peaks-over-threshold approach), and a Poisson–generalized Pareto model was fitted to the resulting series. The at-site parameter estimates (location, scale, and shape) were regressed upon a set of location and relief variables, enabling the construction of a spatially explicit probability model. The advantages of this method to obtain maps of extreme precipitation hazard are discussed in depth.

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Sergio M. Vicente-Serrano, Santiago Beguería, and Juan I. López-Moreno

Abstract

The authors propose a new climatic drought index: the standardized precipitation evapotranspiration index (SPEI). The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. The procedure to calculate the index is detailed and involves a climatic water balance, the accumulation of deficit/surplus at different time scales, and adjustment to a log-logistic probability distribution. Mathematically, the SPEI is similar to the standardized precipitation index (SPI), but it includes the role of temperature. Because the SPEI is based on a water balance, it can be compared to the self-calibrated Palmer drought severity index (sc-PDSI). Time series of the three indices were compared for a set of observatories with different climate characteristics, located in different parts of the world. Under global warming conditions, only the sc-PDSI and SPEI identified an increase in drought severity associated with higher water demand as a result of evapotranspiration. Relative to the sc-PDSI, the SPEI has the advantage of being multiscalar, which is crucial for drought analysis and monitoring.

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Santiago Beguería, Sergio M. Vicente-Serrano, and Marta Angulo-Martínez

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J. Ignacio López-Moreno and Sergio M. Vicente-Serrano

Abstract

In this study, droughts are analyzed using the standardized precipitation index (SPI) at different time scales for all of Europe over the period 1901–2000. The SPI is calculated at different time scales (1–12 months), as are the average values that correspond to negative and positive phases of the North Atlantic Oscillation (NAO). The responses of droughts to the phases of the NAO vary spatially, but the response also depends on the month of the year and the time scale of the analysis. During the positive/negative phases, negative/positive SPI values are generally recorded in southern Europe, with the opposite pattern recorded in northern Europe. In certain regions, significant differences in the SPI are also recorded during spring, summer, and even autumn. In several regions, the magnitude of the average SPI anomalies is noticeably different for the positive and negative phases of the NAO, indicating the asymmetric response of droughts to the NAO. The unstable response of drought occurrence is also demonstrated, at different time scales, to positive and negative phases of the NAO throughout the twentieth century. During the second half of the twentieth century, there is a strengthening of the influence of the positive phases of the NAO on droughts. In contrast, the negative phases show a weaker influence on the SPI during the second half of the twentieth century. This pattern is related to changes in the wintertime sea level pressure fields associated with positive and negative phases of the NAO.

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Luis Gimeno, Raquel Nieto, Ricardo M. Trigo, Sergio M. Vicente-Serrano, and Juan Ignacio López-Moreno

Abstract

This study investigated the main sources of moisture in the atmosphere over the Iberian Peninsula (IP) at annual and seasonal scales using FLEXPART, a powerful new 3D Lagrangian diagnosis method that identifies the humidity contributions to the moisture budget of a region. This method can identify moisture sources at lower cost and with greater accuracy than standard isotopic content methods. The results are based on back-tracking analysis of all air masses residing over the IP in the 5-yr period from 2000 to 2004. The results show that the two most important moisture source regions affecting the IP are in a tropical–subtropical North Atlantic corridor that extends from the Gulf of Mexico to the IP, and the IP itself and the surrounding Mediterranean. The importance of these two source areas varies throughout the year, and also with respect to different climatic regions inside the IP. The former source region is the dominant moisture source for the entire IP during winter and in western regions throughout the year, whereas the latter source region dominates the moisture supply to the IP in summer and in the eastern Mediterranean region of the IP throughout the year. The results also demonstrate that winter precipitation in the IP is influenced by both atmospheric instability that forces air masses to rise, and the supply of moisture from the tropical–subtropical North Atlantic corridor on a daily scale and a seasonal basis. Thus, a combination of high (low) moisture supply from the North Atlantic corridor and high (low) atmospheric instability appears to be responsible for the most recent wet (dry) winter in the IP.

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Santiago Beguería, Miquel Tomas-Burguera, Roberto Serrano-Notivoli, Dhais Peña-Angulo, Sergio M. Vicente-Serrano, and José-Carlos González-Hidalgo

Abstract

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.

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Marco Turco, Sonia Jerez, Markus G. Donat, Andrea Toreti, Sergio M. Vicente-Serrano, and Francisco J. Doblas-Reyes

Abstract

Accurate and timely drought information is essential to move from postcrisis to preimpact drought-risk management. A number of drought datasets are already available. They cover the last three decades and provide data in near–real time (using different sources), but they are all “deterministic” (i.e., single realization), and input and output data partly differ between them. Here we first evaluate the quality of long-term and continuous climate data for timely meteorological drought monitoring considering the standardized precipitation index. Then, by applying an ensemble approach, mimicking weather/climate prediction studies, we develop Drought Probabilistic (DROP), a new global land gridded dataset, in which an ensemble of observation-based datasets is used to obtain the best near-real-time estimate together with its associated uncertainty. This approach makes the most of the available information and brings it to the end users. The high-quality and probabilistic information provided by DROP is useful for monitoring applications, and may help to develop global policy decisions on adaptation priorities in alleviating drought impacts, especially in countries where meteorological monitoring is still challenging.

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Ricardo García-Herrera, Jose M. Garrido-Perez, David Barriopedro, Carlos Ordóñez, Sergio M. Vicente-Serrano, Raquel Nieto, Luis Gimeno, Rogert Sorí, and Pascal Yiou

Abstract

We have analyzed the record-breaking drought that affected western and central Europe from July 2016 to June 2017. It caused widespread impacts on water supplies, agriculture, and hydroelectric power production, and was associated with forest fires in Iberia. Unlike common continental-scale droughts, this event displayed a highly unusual spatial pattern affecting both northern and southern European regions. Drought conditions were observed over 90% of central-western Europe, hitting record-breaking values (with respect to 1979–2017) in 25% of the area. Therefore, the event can be considered as the most severe European drought at the continental scale since at least 1979. The main dynamical forcing of the drought was the consecutive occurrence of blocking and subtropical ridges, sometimes displaced from their typical locations. This led to latitudinal shifts of the jet stream and record-breaking positive geopotential height anomalies over most of the continent. The reduction in moisture transport from the Atlantic was relevant in the northern part of the region, where decreased precipitation and increased sunshine duration were the main contributors to the drought. On the other hand, thermodynamic processes, mostly associated with high temperatures and the resulting increase in atmospheric evaporative demand, were more important in the south. Finally, using flow circulation analogs we show that this drought was more severe than it would have been in the early past.

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Sergio M. Vicente-Serrano, Santiago Beguería, Jorge Lorenzo-Lacruz, Jesús Julio Camarero, Juan I. López-Moreno, Cesar Azorin-Molina, Jesús Revuelto, Enrique Morán-Tejeda, and Arturo Sanchez-Lorenzo

Abstract

In this study, the authors provide a global assessment of the performance of different drought indices for monitoring drought impacts on several hydrological, agricultural, and ecological response variables. For this purpose, they compare the performance of several drought indices [the standardized precipitation index (SPI); four versions of the Palmer drought severity index (PDSI); and the standardized precipitation evapotranspiration index (SPEI)] to predict changes in streamflow, soil moisture, forest growth, and crop yield. The authors found a superior capability of the SPEI and the SPI drought indices, which are calculated on different time scales than the Palmer indices to capture the drought impacts on the aforementioned hydrological, agricultural, and ecological variables. They detected small differences in the comparative performance of the SPI and the SPEI indices, but the SPEI was the drought index that best captured the responses of the assessed variables to drought in summer, the season in which more drought-related impacts are recorded and in which drought monitoring is critical. Hence, the SPEI shows improved capability to identify drought impacts as compared with the SPI. In conclusion, it seems reasonable to recommend the use of the SPEI if the responses of the variables of interest to drought are not known a priori.

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Cesar Azorin-Molina, Sergio M. Vicente-Serrano, Tim R. McVicar, Sonia Jerez, Arturo Sanchez-Lorenzo, Juan-I. López-Moreno, Jesus Revuelto, Ricardo M. Trigo, Joan A. Lopez-Bustins, and Fátima Espírito-Santo

Abstract

Near-surface wind speed trends recorded at 67 land-based stations across Spain and Portugal for 1961–2011, also focusing on the 1979–2008 subperiod, were analyzed. Wind speed series were subjected to quality control, reconstruction, and homogenization using a novel procedure that incorporated the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)-simulated series as reference. The resultant series show a slight downward trend for both 1961–2011 (−0.016 m s−1 decade−1) and 1979–2008 (−0.010 m s−1 decade−1). However, differences between seasons with declining values in winter and spring, and increasing trends in summer and autumn, were observed. Even though wind stilling affected 77.8% of the stations in winter and 66.7% in spring, only roughly 40% of the declining trends were statistically significant at the p < 0.10 level. On the contrary, increasing trends appeared in 51.9% of the stations in summer and 57.4% in autumn, with also around 40% of the positive trends statistically significant at the p < 0.10 level. In this article, the authors also investigated (i) the possible impact of three atmospheric indices on the observed trends and (ii) the role played by the urbanization growth in the observed decline. An accurate homogenization and assessment of the long-term trends of wind speed is crucial for many fields such as wind energy (e.g., power generation) and agriculture–hydrology (e.g., evaporative demand).

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