Even in the remote Med Sea, all background TM inputs presented an anthropogenic signature, except for Fe, Mn and Ti. The first rain sample collected in the Ionian Sea (Rain ION) was a typical regional background wet deposition event, whereas the second rain sample collected in the Algerian Basin (Rain FAST) was a Saharan dust wet deposition event. Rain samples were analysed for Al, 12 TMs (Co, Cd, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Ti, V and Zn) and nutrient (N, P, dissolved organic carbon) concentrations. This study reports the only recent characterization of two contrasted wet deposition events collected during the PEACETIME (ProcEss studies at the Air–sEa Interface after dust deposition in the MEditerranean Sea) cruise in the open Mediterranean Sea (Med Sea) and their impact on trace metal (TM) marine stocks. To map the spatial variation in the catchmentĬharacteristics that are needed for the waterĮrosion data analyses, with relatively high Soil sampling, autocorrelation analysis, and We present the procedures forĪpplying spatial data models in a discreteįashion, as well as the equations necessary for Geoinformatics data models needed for waterĮrosion studies. Of noise, and not all variables are spatiallyĬontinuous. The roles of various climatic and environmentalįactors in water erosion processes. Variation in space, and offer an account of Strategy, introduce and quantify the Tobler Several reasons: they can improve sampling Geostatistical models are advantageous for Techniques (with or without ancillary data). Moran’s I analyses for testing spatial autocorrelation, Properties from point measurements, using To these four discrete units, a set of continuousĭata analysis tools can be applied to map soil Pedogeomorphological unit of the hillslopeĬatena, the subcatchment unit, the parcel (anĪgricultural field) representing data aggregation, Fourĭiscrete units are discussed in this chapter: the Known points, using geostatistical tools. Quantifying autocorrelation in space from It is concluded that identi-fication of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure.ĭividing the space into discrete units, or by Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors and 2) model uncertainties. Increased time-and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. The multiplicative (a) and exponent (b) parameters are said to be ''scale dependent'' if applying the observed and calculated rainfall intensities to objective function at different scale results in different ''optimal'' parameters. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions.
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