Numerical Monte Carlo analysis of the influence of pore-scale dispersion on macrodispersion in 2-D heterogeneous porous media

9 p.International audienceWe investigate the influences of pore‐scale dispersion and of larger‐scale permeabilityheterogeneities on the macrodispersion without the molecular diffusion. Permeability followsa lognormal exponentially correlated distribution characterized by its correlation length land its lognormal variance s2. Macrodispersion is evaluated numerically by usingparallel simulations on grids of characteristic size ranging from 200l to 1600l. We noteaLand aTthe pore‐scale longitudinal and transversal dispersivities. For aL/l <10−2andaT/l <10−3, the influence of pore‐scale dispersion on the macrodispersion is smaller than5% of the macrodispersion due only to permeability heterogeneitie

Seasonal precipitation interpolation at the Valencia region with multivariate methods using geographic and topographic information

The spatial pattern of precipitation is a complex variable that strongly depends on other geographic and topographic factors. As precipitation is usually known only at certain locations, interpolation procedures are needed in order to predict this variable in other regions. The use of multivariate interpolation methods is usually preferred, as secondary variables generally derived using GIS tools correlated with precipitation can be included. In this paper, a comparative study on different univariate and multivariate interpolation methodologies is presented. Our study area is centred in the region of Valencia, located to the eastern Spanish Mediterranean coast. The followed methodology can be divided in three steps. First, secondary variables having significant correlations with the precipitation were derived, where the hillsides were used as influence areas of certain variables. Secondly, precipitation was interpolated with different methodologies. Finally, the derived models were compared in terms of predicted errors. Models were achieved for seasonal scales, considering a total of 179 raingauges; data of another 45 raingauges were also used to predict errors. Results prove that there is no ideal method for all the cases but it will depend on one hand, on the number of geographical factors that influence the rainfall and, on the other hand, on the major or minor spatial correlation within the rainfall

Sampling procedures for throughfall monitoring: a simulation study

What is the most appropriate sampling scheme to estimate event-based average throughfall? A satisfactory answer to this seemingly simple question has yet to be found, a failure which we attribute to previous efforts' dependence on empirical studies. Here we try to answer this question by simulating stochastic throughfall fields based on parameters for statistical models of large monitoring data sets. We subsequently sampled these fields with different sampling designs and variable sample supports. We evaluated the performance of a particular sampling scheme with respect to the uncertainty of possible estimated means of throughfall volumes. Even for a relative error limit of 20%, an impractically large number of small, funnel-type collectors would be required to estimate mean throughfall, particularly for small events. While stratification of the target area is not superior to simple random sampling, cluster random sampling involves the risk of being less efficient. A larger sample support, e.g., the use of trough-type collectors, considerably reduces the necessary sample sizes and eliminates the sensitivity of the mean to outliers. Since the gain in time associated with the manual handling of troughs versus funnels depends on the local precipitation regime, the employment of automatically recording clusters of long troughs emerges as the most promising sampling scheme. Even so, a relative error of less than 5% appears out of reach for throughfall under heterogeneous canopies. We therefore suspect a considerable uncertainty of input parameters for interception models derived from measured throughfall, in particular, for those requiring data of small throughfall events

Geostatistical analysis of rainfall

Rainfall can be modeled as a spatially correlated random field superimposed on a background mean value; therefore, geostatistical methods are appropriate for the analysis of rain gauge data. Nevertheless, there are certain typical features of these data that must be taken into account to produce useful results, including the generally non-Gaussian mixed distribution, the inhomogeneity and low density of observations, and the temporal and spatial variability of spatial correlation patterns. Many studies show that rigorous geostatistical analysis performs better than other available interpolation techniques for rain gauge data. Important elements are the use of climatological variograms and the appropriate treatment of rainy and nonrainy areas. Benefits of geostatistical analysis for rainfall include ease of estimating areal averages, estimation of uncertainties, and the possibility of using secondary information (e.g., topography). Geostatistical analysis also facilitates the generation of ensembles of rainfall fields that are consistent with a given set of observations, allowing for a more realistic exploration of errors and their propagation in downstream models, such as those used for agricultural or hydrological forecasting. This article provides a review of geostatistical methods used for kriging, exemplified where appropriate by daily rain gauge data from Ethiopia

Remote Sensing and GIS Contribution to the Investigation of Karst Landscapes in NW-Morocco

Remote sensing and geographic information system (GIS) methods were used for karst research in the coastal area of Northwest Morocco near the city of Safi in order to identify karst landscapes, to describe karst features and to detect geological structures relevant to karst development. The aim of this study was to investigate the use of different satellite data, such as Landsat, RapidEye and IKONOS imagery, as well as ASTER- and SRTM-derived digital elevation models (DEMs) for the analysis of karst features. Dolines were identified by visual interpretations based on high resolution satellite imagery and aerial photographs. Digital image processing of the satellite data, such as deriving vegetation and water index images, helped to identify regions with relatively higher surface water input, where karstification processes might be more intense than in surrounding areas. ArcGIS-integrated weighted overlay tools were used for this purpose as well by aggregating of morphometric, causal factors (lowest and flattest areas) influencing the susceptibility to higher surface water input. Lineament analysis based on the different satellite data contributed to the detection of near-surface fault and fracture zones with potential influence on dissolution processes in sub-terrain waterways

La percepción de los humedales en la literatura española. Una aproximación al Siglo de Oro español

The Spanish territory is dotted with numerous wetlands, which have left their imprint on literary works of great writers. In this work, is made a study of the different perceptions that the Spanish Golden Century’s society had of the wetlands, through the Topographic relations of the towns of Spain, made of order of Felipe II, and of the works of the greatest Spanish writers of that period.El territorio español se encuentra salpicado de numerosos humedales, que han dejado su impronta en obras literarias de grandes escritores. En este trabajo se hace un estudio de las distintas percepciones que tenía la sociedad española de los humedales en el Siglo de Oro español, a través de las Relaciones topográficas de los pueblos de España, hechas de orden de Felipe II, y de las obras de los más grandes literatos españoles de ese período