Climate Change and Temporal and Spatial Evolution of the Multifractal Universal Parameters in Ebro River Basin

Multifractal techniques are applied to the study of rainfall daily time series over 14 stations from Ebro river Basin over the second half of XX century. The aim is to determine how climate change affects the evolution of the probability distribution of daily precipitation, through the values of universal multifractal parameters: C1; ; H y s for different periods of time. These will offer direct explanations of the shape of the distribution, especially about the extreme events: C1 is the mean intermittency codimension. When C1 increases the precipitation becoming less continuous and more sporadic in time. Therefore there is an increase of extremes. ; is a mesure of multifratility so an increase of it corresponds to a larger variation of the range of precipitation intensity, and thus also of extremes. H is the degree of non-conservation of the field, which measures the scale dependency of the average field. s; the maximal probable singularity that can be observed on a unique sample. It’s directly related to the ratio of the range and the mean of field. From the data collected, we perceive a decline in average rainfall, from 1980. But how is affected the global parameters in this situation?. Results vary according to different rainfall stations

Frequency and Intensity of drought events over Ebro River basin.

Lately, several researchers have pointed out that climate change is expected to increase temperatures and lower rainfall in Mediterranean regions, simultaneously increasing the intensity of extreme rainfall events. These changes could have consequences regarding rainfall regime, erosion, sediment transport and water quality, soil management, and new designs in diversion ditches. Climate change is expected to result in increasingly unpredictable and variable rainfall, in amount and timing, changing seasonal patterns and increasing the frequency of extreme weather events. Consequently, the evolution of frequency and intensity of drought periods is of most important as in agro-ecosystems many processes will be affected by them. Realising the complex and important consequences of an increasing frequency of extreme droughts at the Ebro River basin, our aim is to study the evolution of drought events at this site statistically, with emphasis on the occurrence and intensity of them. For this purpose, fourteen meteorological stations were selected based on the length of the rainfall series and the climatic classification to obtain a representative untreated dataset from the river basin. Daily rainfall series from 1957 to 2002 were obtained from each meteorological station and no-rain period frequency as the consecutive numbers of days were extracted. Based on this data, we study changes in the probability distribution in several sub-periods. Moreover we used the Standardized Precipitation Index (SPI) for identification of drought events in a year scale and then we use this index to fit log-linear models to the contingency tables between the SPI index and the sub-periods, this adjusted is carried out with the help of ANOVA inference

Change of extreme rainfall indexes at Ebro River Basin

The purpose of this work is to provide a description of the heavy rainfall phenomenon on statistical tools from a Spanish region. We want to quantify the effect of the climate change to verify the rapidity of its evolution across the variation of the probability distributions. Our conclusions have special interest for the agrarian insurances, which may make estimates of costs more realistically. In this work, the analysis mainly focuses on: The distribution of consecutive days without rain for each gauge stations and season. We estimate density Kernel functions and Generalized Pareto Distribution (GPD) for a network of station from the Ebro River basin until a threshold value u. We can establish a relation between distributional parameters and regional characteristics. Moreover we analyze especially the tail of the probability distribution. These tails are governed by law of power means that the number of events n can be expressed as the power of another quantity x : n(x) = x? . ? can be estimated as the slope of log-log plot the number of events and the size. The most convenient way to analyze n(x) is using the empirical probability distribution. Pr(X mayor que x) ? x-?. The distribution of rainfall over percentile of order 0.95 from wet days at the seasonal scale and in a yearly scale with the same treatment of tails than in the previous section

Universal Multifractal description applied to precipitation pattern in the Ebro River Basin

Water supplies in the Ebro River Basin are increasingly stressed, especially during the summer season. The year-to-year fluctuations in rainfall over this area exert vital influence on the regional hydrology, agriculture and several related industries in the region. Repeated anomalous rainfall in recent decades has had a devastating impact on this region, both socially and economically. We characterised the change in the rainfall variability pattern in the Ebro River Basin using universal multifractal (UM) analysis, which estimates the concentration of the data around the precipitation average (C1, codimension average), the degree of multiscaling behaviour in time (α index) and the maximum probable singularity in the rainfall distribution (γs ). Fourteen meteorological stations were selected based on the length of rainfall series and the climatic classification to obtain a representative untreated data set from the river basin. Daily rainfall series from 1957 to 2002 were obtained from each meteorological station and subdivided (1957–1980 and 1981–2002) to analyse the difference between the 2 periods. The general scenario observed in this study, through the UM parameters, can be summarised as follows: the range of variation of precipitation amounts was spatially more homogenous in 1980–2002 than in 1957–1979; at the same time, there is higher frequency of dry periods at different scales in 1980–2002; and in almost all of the stations, the range of precipitation over the years has been decreasing at a lower rate than the rainfall average. We then analysed the evolution of the UM parameters from 1957 to 2002. Continuous variations in C1 and α were found for 2 of the stations, indicating that a precipitation regime change has begun in the last few decades and should be considered in the agricultural development of the region

Spatial and temporal precipitation patterns of the Ebro River Basin, Spain

The Ebro River Basin, with around 85 000 km2 and located in NE Spain, is characterized by the high spatial heterogeneity of its geology, topography, climatology and land use. Rainfall is one of the most important climatic variables studied owing to its non-homogenous behaviour in event and intensity, which creates drought, water runoff and soil erosion with negative environmental and social consequences. In this work we characterized the rainfall variability pattern in the Ebro River Basin using universal multifractal (UM) analysis, which estimates the concentration of the data around the precipitation average (C1, codimension average), the degree of multiscaling behaviour in time (? index) and the maximum probable singularity in the rainfall distribution ( s). A spatial and temporal analysis of the UM parameters is applied to study the possible changes. With this porpoise, 60 daily rainfall series were selected from 132 synthetic series generated by Luna and Balairón (AEMet). These daily rainfall series present a length of 60 years, from 1950 to 2009. Each one of them was subdivided (1950?1970 and 1980?2009) to analyse the difference between the two periods. The range of variation of precipitation amounts and the frequency of dry events between both periods are discussed, as well as the evolution of the UM parameters through the years

Detection of pore space in CT soil images using artificial neural networks

Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, Fuzzy C-means, and Self Organising Maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An Artificial Neural Network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an Artificial Neural Network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively

Risk of nitrate pollution in agricultural systems

Se propone una metodología que nos permita evaluar un óptimo manejo de la fertirrigación integrando aspectos agronómicos y medioambientales

Impact of nitrogen uptake on field water balance in fertirrigated melon.

Agronomic management in Ciudad Real, a province in central Spain, is characteristic of semi-arid cropped areas whose water supplies have high nitrate (NO3?) content due to environmental degradation. This situation is aggravated by the existence of a restrictive subsurface layer of ?caliche? or hardpan at a depth of 0.60 m. Under these circumstances, fertirrigation rates, including nitrogen (N) fertilizer schedules, must be carefully calibrated to optimize melon yields while minimizing the N pollution and water supply. Such optimization was sought by fertilizing with different doses of N and irrigating at 100% of the ETc (crop evapotranspiration), adjusted for this crop and area. The N content in the four fertilizer doses used was: 0, 55, 82 and 109 kg N ha?1. Due to the NO3? content in the irrigation water, however, the actual N content was 30 kg ha?1 higher in all four treatments repeated in two different years. The results showed correlation between melon plant N uptake and drainage (Dr), which in turn affects the amount of N leached, as well as correlation between Dr and LAI (leaf area index) for each treatment. A fertilizer factor (?) was estimated through two methods, from difference in Dr and in LAI ratio with respect to the maximum N dose, to correct ETc based on N doses. The difference was found in the adjusted evapotranspiration in both years using the corresponding ? achieved 42?49 mm at vegetative period, depending on the method, and it was not significant at senescent period. Finally, a growth curve between N uptake and plant dry weight (DW) for each treatment was defined to confirm that the observed higher plant vigour, showing higher LAI and reduced Dr, was due mainly to higher N doses

Methodology to assess economic and environmental impacts of nitrogen in fertirrigation systems.

To determine the risk of nitrate pollution in agricultural systems have identified several indexes and efficiencies that may lead an effective N fertilizer management for obtain the maximum yield with minimum environmental impact and healt