Trends in the Mediterranean rainfall

Summary The trends appearing in the annual rainfall of the 14 selected coastal and island stations of the Mediterranean were invetigated by running 30-year averages. The periods used as well as the standard deviation, the average variability and the coefficient of variation of the annual rainfall are given for each of the 14 stations. It was found that in the majority of the stations upward and downward trends in the annual rainfall appeared but in a few only stations these trends coincide in the same intervals. A relative similarity appeared in the stations of Marseille-Trieste, Malta-Tunis, Gibraltar-Rome, Nicosia-Limassol and Beyrut-Alexandria. By examination of the three more important maxima and minima in the course of rainfall it was observed that many of them coincide simultaneously at about the same time in the different stations and also that these coincidences occurred near the maximum or minimum of sunspots.     

Cloud tracking using satellite data for predicting the probability of heavy rainfall events in the Mediterranean area

The use of cloud tracking techniques and storm identification procedures is proposed in this paper with the aim of predicting the evolution of cloud entities associated with the highest rainfall probability within a given meteorological scenario. Suitable algorithms for this kind of analysis are based on the processing of digital images in the thermal infrared (IR) band from geostationary satellites: a selection of such algorithms is described in some detail together with a few real case applications. Three heavy rainfall events have been selected for this purpose with reference to the extreme meteorological situation observed during Fall 1992 and 1993 over the Mediterranean area. A window from 30 to 60 °N and from 20 °W to 30 °E has been identified for the analysis of data from the radiometer on board the ESA Meteosat platform. In conclusion, the suitability of cloud tracking techniques for predicting the probability of heavy rainfall events is discussed provided that the former are associated with proper modeling of small scale rainfall distribution.     

Stochastic multi-site generation of daily rainfall occurrence in south Florida

This paper presents a stochastic model to generate daily rainfall occurrences at multiple gauging stations in south Florida. The model developed in this study is a space–time model that takes into account the spatial as well as temporal dependences of daily rainfall occurrence based on a chain-dependent process. In the model, a Markovian method was used to represent the temporal dependence of daily rainfall occurrence and a direct acyclic graph (DAG) method was introduced to encode the spatial dependence of daily rainfall occurrences among gauging stations. The DAG method provides an optimal sequence of generation by maximizing the spatial dependence index of daily rainfall occurrences over the region. The proposed space–time model shows more promising performance in generating rainfall occurrences in time and space than the conventional Markov type model. The space–time model well represents the temporal as well as the spatial dependence of daily rainfall occurrences, which can reduce the complexity in the generation of daily rainfall amounts.     

Simulation and spatio-temporal disaggregation of multi-site rainfall data for urban drainage applications

Abstract

For urban drainage and urban flood modelling applications, fine spatial and temporal rainfall resolution is required. Simulation methods are developed to overcome the problem of data limitations. Although temporal resolution higher than 10–20 minutes is not well suited for detailed rainfall—runoff modelling for urban drainage networks, in the absence of monitored data, longer time intervals can be used for master planning or similar purposes. A methodology is presented for temporal disaggregation and spatial distribution of hourly rainfall fields, tested on observations for a 10-year period at 16 raingauges in the urban catchment of Dalmuir (UK). Daily rainfall time series are simulated with a generalized linear model (GLM). Next, using a single-site disaggregation model, the daily data of the central gauge in the catchment are downscaled to an hourly time scale. This hourly pattern is then applied linearly in space to disaggregate the daily data into hourly rainfall at all sites. Finally, the spatial rainfall field is obtained using inverse distance weighting (IDW) to interpolate the data over the whole catchment. Results are satisfactory: at individual sites within the region the simulated data preserve properties that match the observed statistics to an acceptable level for practical purposes.     

Evaluating spatio-temporal representations in daily rainfall sequences from three stochastic multi-site weather generation approaches

Many hydrological and agricultural studies require simulations of weather variables reflecting observed spatial and temporal dependence at multiple point locations. This paper assesses three multi-site daily rainfall generators for their ability to model different spatio-temporal rainfall attributes over the study area. The approaches considered consist of a multi-site modified Markov model (MMM), a reordering method for reconstructing space–time variability, and a nonparametric k-nearest neighbour (KNN) model. Our results indicate that all the approaches reproduce adequately the observed spatio-temporal pattern of the multi-site daily rainfall. However, different techniques used to signify longer time scale observed temporal and spatial dependences in the simulated sequences, reproduce these characteristics with varying successes. While each approach comes with its own advantages and disadvantages, the MMM has an overall advantage in offering a mechanism for modelling varying orders of serial dependence at each point location, while still maintaining the observed spatial dependence with sufficient accuracy. The reordering method is simple and intuitive and produces good results. However, it is primarily driven by the reshuffling of the simulated values across realisations and therefore may not be suited in applications where data length is limited or in situations where the simulation process is governed by exogenous conditioning variables. For example, in downscaling studies where KNN and MMM can be used with confidence.     

The lithosphere in the central-eastern Mediterranean area

The lithosphere beneath the central-eastern Mediterranean area has been investigated by the inversion of the regional dispersion relations derived from analysis of surface waves. It is possible to distinguish several types of crust with average S-wave velocities in the range 3.0–3.8 km/sec, and thicknesses varying from a minimum of about 30 km, which corresponds to the Apennines, Crete and Otranto Channel regions, to a maximum of about 51 km beneath the Ionian Sea, which can be considered as a submerged continent. Associated with these crustal features, large lateral variations have been detected in the lithosphere thickness, which varies from a minimum of about 30 km corresponding to the Tyrrhenian Sea and south of Crete to a maximum of about 130 km corresponding to south-eastern Alps and north-central Greece, while the sub-Moho S-wave velocity varies in the range 4.2–4.8 km/sec. The constraint furnished by our results to the geological-tectonic setting of the investigated area, characterized by the continent continent collision between Africa and Europe, is pointed out.Publication No. 405, P. F. Geodinamica, CNR, Roma, Italy.     

A comparison of cumulus parameterization schemes in a numerical weather prediction model for a monsoon rainfall event

In order to evaluate cumulus parameterization (CP) schemes for hydrological applications, the Pennsylvania State University–National Center for Atmospheric Research's fifth‐generation mesoscale model (MM5) was used to simulate a summer monsoon in east China. The performances of five CP schemes (Anthes–Kuo, Betts–Miller, Fritsch–Chappell, Kain–Fritsch, and Grell) were evaluated in terms of their ability to simulate amount of rainfall during the heavy, moderate, and light phases of the event. The Grell scheme was found to be the most robust, performing well at all rainfall intensity and spatial scales. The Betts–Miller scheme also performed well, particularly at larger scales, but its assumptions may make it inapplicable to non‐tropical environments and at smaller scales. The Kain–Fritsch scheme was the best at simulating moderate rainfall rates, and was found to be superior to the Fritsch–Chappell scheme on which it was based. The Anthes–Kuo scheme was found to underpredict precipitation consistently at the mesoscale. Simulation performance was found to improve when schemes that included downdrafts were used in conjunction with schemes that did not include downdrafts. Copyright © 2005 John Wiley & Sons, Ltd.     

A simple model for spatial rainfall fields

Spatial rainfall amounts accumulated over short to medium periods of time, say a few days, tend to have a probabilistic structure with very distinctive features. Some of these that are specially relevant for the purpose of spatial modeling are the presence of mixed sampling distributions, right skewed distributions conditional on rainfall occurrence, and a complex spatial association structure. The goal of this work is to construct a family for the bivariate distributions of spatial rainfall fields that incorporates these distinctive features. It is based on the separate modeling of spatial occurrence of rainfall and the spatial distribution of positive rainfalls. The main properties of the bivariate distributions are derived, and some properties of the random field realizations are illustrated through simulation. Some limitations of the proposed model are also discussed.     

Investigation of the fractal dimension of rainfall occurrence in a semi-arid Mediterranean climate

Abstract

The scale invariance of rainfall series in the Tunis area, Tunisia (semi-arid Mediterranean climate) is studied in a mono-fractal framework by applying the box counting method to four series of observations, each about 2.5 years in length, based on a time resolution of 5 min. In addition, a single series of daily rainfall records for the period 1873–2009 was analysed. Three self-similar structures were identified: micro-scale (5 min to 2 d) with fractal dimension 0.44, meso-scale (2 d to one week) and synoptic-scale (one week to eight months) with fractal dimension 0.9. Interpretation of these findings suggests that only the micro-scale and transition to saturation are consistent, while the high fractal dimension relating to the synoptic scale might be affected by the tendency to saturation. A sensitivity analysis of the estimated fractal dimension was performed using daily rainfall data by varying the series length, as well as the intensity threshold for the detection of rain.

Editor Z.W. Kundzewicz; Associate editor S. Grimaldi

Citation Ghanmi, H., Bargaoui, Z., and Mallet, C., 2013. Investigation of the fractal dimension of rainfall occurrence in a semi-arid Mediterranean climate. Hydrological Sciences Journal, 58 (3), 483–497.     

Surface wave and seismotectonic studies in the Mediterranean area

Pure and Applied Geophysics -     

Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.     

Doubly stochastic Poisson pulse model for fine-scale rainfall

Stochastic rainfall models are widely used in hydrological studies because they provide a framework not only for deriving information about the characteristics of rainfall but also for generating precipitation inputs to simulation models whenever data are not available. A stochastic point process model based on a class of doubly stochastic Poisson processes is proposed to analyse fine-scale point rainfall time series. In this model, rain cells arrive according to a doubly stochastic Poisson process whose arrival rate is determined by a finite-state Markov chain. Each rain cell has a random lifetime. During the lifetime of each rain cell, instantaneous random depths of rainfall bursts (pulses) occur according to a Poisson process. The covariance structure of the point process of pulse occurrences is studied. Moment properties of the time series of accumulated rainfall in discrete time intervals are derived to model 5-min rainfall data, over a period of 69 years, from Germany. Second-moment as well as third-moment properties of the rainfall are considered. The results show that the proposed model is capable of reproducing rainfall properties well at various sub-hourly resolutions. Incorporation of third-order moment properties in estimation showed a clear improvement in fitting. A good fit to the extremes is found at larger resolutions, both at 12-h and 24-h levels, despite underestimation at 5-min aggregation. The proportion of dry intervals is studied by comparing the proportion of time intervals, from the observed and simulated data, with rainfall depth below small thresholds. A good agreement was found at 5-min aggregation and for larger aggregation levels a closer fit was obtained when the threshold was increased. A simulation study is presented to assess the performance of the estimation method.     

On the frequency distribution of concurrent rainfall in a large area

Summary The simultaneous occurrences of precipitation among 140 synoptic stations covering an area of more than 1000 kms radius around Frankfurt a.M., Germany, during 5 months have been counted. The result is being interpreted by a theoretically derived frequency distribution. The question how such frequency distributions of concurrent rainfall depend upon the size of the area or the number of observation stations will be discussed and related to the problem of verifying precipitation forecasts.

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Zusammenfassung Es wurden die Fälle gleichzeitigen Niederschlags unter 140 synoptischen Stationen im Umkreis von mehr als 1000 km um Frankfurt a.M., Deutschland, ausgezählt, die während 5 Monaten eintraten. Das Ergebnis wird mittels einer theoretisch abgeleiteten Häufigkeitsverteilung gedeutet. Ferner wird die Frage diskutiert, wie solche Häufigkeitsverteilungen gleichzeitigen Niederschlags von der Flächengrösse oder Stationenzahl abhängen, und auf ihren Zusammenhang mit dem Problem der Prüfung von Niederschlagsprognosen hingewiesen.

     

Relationship of pollution to rocky substratum polychaetes on the French Mediterranean coast

The distribution of Polychaetous Annelids (Serpulidae excluded) from infralittoral rocky shores of the French Mediterranean coast has been studied for 18 yr. Several hundred rock scrapings, each measuring 400 cm², have been made and the results analysed for species composition, Shannon-Weaver index of diversity and Sanders' similarity coefficient. These mathematical measures are well correlated with the degree of pollution. In the levels considered, the annelid settlements are structurally identical. No unique group of species was associated with the degree of pollution as is typical in the sediments. An index of biological detectors is proposed which is based on the premise of the sensitive modification of the polychaete population as a whole and is defined as the ratio of the amount of dominances of ‘polluted waters biological detectors’ (Platynereis dumerili, Theostoma oerstedi, Dorvillea rudolphi, …) to ‘pure water’ ones (Syllis spp., Amphiglena mediterranea). This ratio increases with the intensity of pollution and is always higher than 1 in a polluted environment.     

Seasonal patterns of runoff and erosion responses to simulated rainfall in a badland area in Mediterranean mountain conditions (Vallcebre,southeastern Pyrenees)

Regolith surface characteristics and response were examined over a three‐year period in a badland area in a Mediterranean middle‐mountain zone near Vallcebre (Eastern Pyrenees). Preliminary work carried out in this area indicated clear seasonal patterns of regolith properties driven by frost heaving in winter and crusting and erosion in the rest of the year. Rainfall simulations were performed with a small portable nozzle simulator in order to study seasonal changes in runoff generation, erosion rates and raindrop effect on bulk density changes. The results showed large seasonal variations in runoff and erosion responses. In?ltration rates after runoff start were correlated with precipitation depth before runoff start; runoff generation was therefore related to regolith saturation only to a very limited extent. Erosion rates were more controlled by runoff rates than by the weakness of regolith against raindrop splash, and sediment grain size increased with concentration. The combined role of antecedent regolith moisture and bulk density explained most of the seasonal variability in in?ltration, bulk density changes during rainfall and erosion rates, but some seasonal differences in sediment detachability were not explained by these variables and may be attributed to changes in roughness. Overall, runoff and erosion responses were relatively stable during spring and autumn, whereas wide variations in in?ltration rates and sediment detachment occurred in winter and summer respectively. Experiments conducted in a single season would have produced poorly representative, if not erroneous, results. Copyright © 2004 John Wiley & Sons, Ltd.     

Further developments of a transient Poisson-cluster model for rainfall

Stochastic point processes for rainfall are known to be able to preserve the temporal variability of rainfall on several levels of aggregation (e.g. hourly, daily), especially when the cluster approach is used. One major assumption in most of the applications todate is the stationarity of the rainfall properties in time, which must be reconsidered under a climate change hypothesis. Here, we propose new theoretical developments of a Poisson-based model with cluster, namely the Neyman–Scott Rectangular Pulses Model, which treats storm frequency as a nonstationary function. In this paper, storm frequency is modelled as a linear function of time in order to reproduce an increase (or decrease) of the mean annual precipitation. The basic theory is reconsidered and the moments are derived up to the third order. Then, a calibration method based on the generalized method of moments is proposed and discussed. An application to a rainfall time series from Uccle illustrates how this model can reproduce a trend for the average rainfall. This work opens new avenues for future developments on transient stochastic rainfall models and highlights the major challenges linked to this approach.     

Statistical downscaling model for late-winter rainfall over Southwest China

A statistical downscaling model is built for the late-winter rainfall over Southwest China(SWC).A partial-correlation method is used for selecting factors.The results show that the selected factors for late-winter rainfall in SWC are sea level pressure in Western Europe(SNAO)and sea surface temperature in Western Pacific(WPT).SNAO is related to the southern pole of North Atlantic Oscillation(NAO)and excites Southern Eurasian teleconnection,which influences the development of the southern branch trough and the water vapor transport to SWC.WPT indicates the variability of ENSO in the tropical Western Pacific.WPT excites Pacific-East Asia teleconnection and an anticyclone(cyclone)is formed in the southern part of China and suppresses(enhances)rainfall over SWC.A regression statistical downscaling model using SNAO and WPT shows good performance in fitting the variability of late-winter rainfall in the whole SWC region and every observation station,and the model also shows strong robustness in the independent validation.The statistical model can be used for downscaling output from seasonal forecast numerical models and improve the SWC late winter rainfall prediction in the future.     

A crust-mantle model for the NORSAR area

Summary Elastic waves from explosions were recorded at NORSAR and at a number of field stations, and the data were used for determining a crust-mantle model under the array. The number of explosions was eleven distributed on seven shot points. The total number of recording points was fifty-one, and the interpretation was based on 350 individual records.The velocities obtained for the crustal phases were 6.2, 6.6 and 8.2 km/sec for theP _g ,P _g andP _n waves respectively. A deep crustal phase with a velocity of about 7.4 km/sec was observed. The mean depths to the discontinuities within the crust were determined to be 17 and 26 km. The depth to Moho varied greatly across the array from 31.5 km in the central part to 38 km under the C-ring. The maximum dip observed for the Moho was 12^o.Contribution No. 57 to Norwegian Geotraverse Project.     

Sequential assimilation of a year-long time-series of SeaWiFS chlorophyll data into a 3D biogeochemical model on the French Mediterranean coast

The objective of this paper was to explore the potentialities of sequential statistical estimation methods to assimilate ocean color observations in a primary production model coupled to a 3D hydrodynamic model. The study site was the gulf of Fos—Rhone delta region on the French Mediterranean coast. The high rate of primary production generally observed in this area is mainly due to strong nutrient inputs of the Rhone River. The assimilation method is derived from the singular evolutive extended Kalman filter (SEEK), which uses an error subspace represented by multivariate empirical orthogonal functions (EOF). SeaWiFS chlorophyll data were assimilated by the ecosystem model during a simulation performed under realistic meteorological conditions for the year 2001. An ‘adaptive’ computing method of the EOF was applied in order to lower the instabilities of the filter. Data assimilation system permitted to reduce the mean absolute error between model and data from 1.51 to 0.77 mg m⁻³ thanks to the SEEK filter, showing a substantial 49% gain. Efficiency of the SEEK filter was then investigated considering several areas of interest inside the modelled domain. Finally, impact of the assimilation scheme on non-observed variables was illustrated and discussed. Throughout this experimentation the data assimilation system showed its potential regarding operational systems.