Climate Research Foundation FIC

Madrid, Spain

Climate Research Foundation FIC

Madrid, Spain

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Guardiola-Albert C.,Geological Survey of Spain | Rivero-Honegger C.,Complutense University of Madrid | Monjo R.,University of Castilla - La Mancha | Diez-Herrero A.,Geological Survey of Spain | And 3 more authors.
Journal of Hydroinformatics | Year: 2017

For the purposes of weather nowcasting, flood risk monitoring and water resources assessment, it is often difficult to achieve a reliable spatio-temporal representation of rainfall due to a low rain gauge network density. However, quantitative precipitation estimation (QPE) has acquired new prospects with the introduction of weather radars, thanks to their higher spatio-temporal resolution. Although a wide number of QPE algorithms are available for using C-band radar data, only a few studies have employed X-band radar. In this study the microscale rainfall variability in a small catchment is automatically measured using short-range X-band radar variograms and classifying precipitation into convective and stratiform types with a recently published index. The aim is to apply a straightforward geostatistical algorithm, named ordinary kriging of radar errors (OKRE), to integrate X-band radar and rain gauge measurements in a mountainous catchment (15 km2) in central Spain. As expected, convective events presented higher estimation errors due to their complex spatial and temporal variability. Despite this fact, errors are sufficiently small and results are reliable rainfall estimations. The two main contributions of this work are the adaptation of the OKRE method to small spatial scales and its application automatically differentiating between convective and stratiform events. © 2017 IWA Publishing.


Monjo R.,Climate Research Foundation FIC | Martin-Vide J.,University of Barcelona
International Journal of Climatology | Year: 2016

The temporal concentration of precipitation may be characterized using several methods. For climate-scale precipitation, concentration measures are usually performed by means of dimensionless indices such as the Gini index (GI) or the Theil index. For the purposes of the present paper, a set of 66 409 daily time series from around the world were analysed to estimate the climatic concentration of precipitation. To this end, some of the most widely used indices were tested, i.e. the Theil index, the Gini index, the concentration index, the classic n index and an ordered version of the n index. Results show a strong connection between several indices, mainly between the GI and the ordered n index. The high correlation of these indices (R = 0.98) reflects a theoretical connection between the shape and integration of the Lorenz curve. With regard to spatial distribution, the three main indices present the same relative areas of high and low concentration. The high temporal concentration of precipitation is generally linked to the rapid pace of physical processes such as convection in areas with a high degree of insolation and warm seas. The low temporal concentration of rainfall can be interpreted as a consequence of regular patterns (maritime flows or highly recurrent storms). A relationship between the number of rainy days and concentration indices was noted; however, their correlation depends on the region analysed. © 2016 Royal Meteorological Society


Monjo R.,Climate Research Foundation FIC | Gaitan E.,Climate Research Foundation FIC | Portoles J.,Climate Research Foundation FIC | Ribalaygua J.,Climate Research Foundation FIC | Torres L.,Climate Research Foundation FIC
International Journal of Climatology | Year: 2016

The Mediterranean coast of Spain often experiences intense rainfall, sometimes reaching remarkable amounts of more than 400 mm in one day. The aim of this work is to study possible changes of extreme precipitation in Spain for this century, simulated from several Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Eighteen climate projections (nine models under RCP4.5 and nine RCP8.5 scenarios) were downscaled using a two-step analogue/regression statistical method. We have selected 144 rain gauges as the rainiest of a network by using a threshold of 250 mm in one day for a return period of 100 years. Observed time-series have been extended using the ERA40 reanalysis and have subsequently been used to correct the climate projections according to a parametric quantile-quantile method. Five theoretical distributions (Gamma, Weibull, Classical Gumbel, Reversed Gumbel and Log-logistic) have been used to fit the empirical cumulative functions (entire curves, not only the upper tail) and to estimate the expected precipitation according to several return periods: 10, 20, 50 and 100 years. Results in the projected changes for 2051-2100 compared to 1951-2000 are similar (in terms of sign and value) for the four return periods. The analysed climate projections show that changes in extreme rainfall patterns will be generally less than the natural variability. However, possible changes are detected in some regions: decreases are expected in a few kilometres inland, but with a possible increase in the coastline of southern Valencia and northern Alicante, where the most extreme rainfall was recorded. These results should be interpreted with caution because of the limited number of climate projections; anyway, this work shows that the developed methodology is useful for studying extreme rainfall under several climate scenarios. © 2016 Royal Meteorological Society.

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