Hidrología e hidrogeología

In this study, the evaluation of wind climate has been based on two different lines of research. Both started from National Network data, but while the first approach considered a direct evaluation of those data, the second one implicated the Fryberger’s methods elaboration.

For what concerns the first, more general, evaluation, both years analyzed were split in two seasons, “summer” and “winter”, to highlight wind climate characteristics and to synchronize this evaluation to the volumetric seasonal monitoring applied to dunes.

Yearly data, already shown in previous chapter, indicated in general which winds are to be considered “regnant”, more frequent, and which are “dominant”, stronger. In this case offshore winds, resulted to be the yearly regnant winds: both in 2012 and 2013 these winds came from W and WNW quadrants, with a respective frequency of 14,9 % and 11.0 %, in 2012, 14.4 % and 13.1 % in 2013. The less represented winds were those blowing from N and NNW (both 2.1 % in 2012, 2.6 % in 2013).

Examining dominant winds, the study revealed that the highest velocities were recorded in correspondence of those winds which blew from NE/NNE and SE, but with much more less frequency. In 2012 the maximum was 21.7 m/s (from NE), while in 2013 it was 22.1 m/s (NE). To examine wind climate with more accuracy, the two summer periods, as well as the two winter periods have been isolated (figures 5.2 and 5.3).

During the summer, even if it is the period when dominant onshore winds are more frequent, the maximum wind velocity recorded was lower than winter: 20.2 m/s for 2012, 20.1 for 2013. Most frequent winds blew from West (12.1 % in 2012; 12.8 % in 2013), but it was also recorded a considerable percentage of onshore wind, coming from E (7.1% in 2012; 7.0 % in 2013), ESE (9.4 % in 2012; 10.0 % in 2013), SE (8.2 % in 2012; 9.1 % in 2013) and SSE (7.1 % in 2012; 7.7 % in 2013).

Figure 5.2: wind rose summer diagram for 2012 and 2013.

During winters (figure 5.3) most frequent winds were, as before, coming from West, but in this case the slice of provenience (and the frequencies) was wider. In both 2012 and 2013 regnant winds blew from W (respectively 18.7 % and 14.1 %), WNW (17.5 % and 19.9 %) and NW (7.16 % and 8.3 %). All other directions recorded low frequency, from a minimum of 1.9 to a maximum of 6.45. In spite of this, highest wind velocities were recorded in these seasons and blew from North-East.

The Fryberger’s model application to these wind data was very useful to understand some other characteristics about aeolian local climate. In this meaning Fryberger’s method helps in two ways: giving a reference to have an idea of the real quantity of sand transported, on a yearly time scale; on the other hand, the interpolation of numeric indexes and relative sand roses gives several information about the real influence of winds in modifying sand distribution (drift potential transport local climate).

Remembering what wrote in the paragraph 4.3 about the Lettau and Lettau (1978) equation, it is important to specify something about annual rate of potential sediment drift: in effect Q is not technically a true measure of sediment flux, because it is measured in terms of Volume/Area/Time, while it should be used a mass dimension (i.e. Kg). In this form it is a relative measure of sediment transport potential (in VU) defined solely by available wind energy. This can be converted readily to a flux value if wind speed units are converted to m/s and an appropriate value for bulk density of the sand in transport is applied (Bullard, 1997).

To convert the volume (m3) to kg is sufficient to use the bulk density of the sand in question. Arens (2004) studied this complication and found a value of 1600 kg/m3. It is assumed that the period taken in consideration is one year long (to be as much as possible representative) and that sediment surfaces consist of dry, loose quartz sand (0.25–0.30 mm diameter) with sparse vegetation cover and no bed form roughness greater than ripples.

In figure 5.3 yearly Sand Roses and relative values are reported. Following what just written, the annual rate of potential sand drift is 32.25 m3/m2/year for 2012, and 35.22 m3/m2/year, for 2013. To have these results in term of mass sediment flux, the author converted them multiplying the values for the quartz sand density:

 2012 32.25 * 1.6 = 51.6 Kg/m2/yr  2013 35.22 * 1.6 = 56.3 Kg/m2/yr

The annual Sand Rose graphs can be consulted in figure 5.4. As reported in “Materials and Methods” chapter, it is assumed that, in a yearly time scale, they describe the wind environment (Fryberger, 1979) and basing on relative results, the author had the opportunity to classify the local transport climate.

Looking at the diagrams two preferential directions were individuated, from which the most effective winds blow. These, which are called “peak directions”, correspond to NE/NNE and SE. Thus the wind environment is “Bimodal”, because of these two peak directions. The angle measure between these two directions is about 106°, so wider than a right angle. That defines the second feature of the local climate: Wide Bimodal.

Figure 5.4: 2012 and 2013 sand roses. Segments represents the principal wind (blowing from) components of the total DP; values lower than 0.5 were not drawn. The arrow represent the Resultant Drift Potential and its direction is determined by the Resultant Drift Direction.

133 Dott. Stefano Fabbri

For a better comprehension of the environment, seasonal sand roses were plotted too (figure 5.5). These data are much more variables and don’t indicate any trend. It is clear that winter diagrams are more similar to the yearly ones, and very similar to each other. The summer, instead, seems to be much more variable and the roses are very different one from each other and both from the yearly ones.

During summer 2013 the climate seemed to be particularly mild, but data presented at the beginning of this paragraph deny that happening. The reason is probably to be researched in wind frequency distribution subdivided by quadrant: the frequency distribution was so that the different components compensate, at least in part, each other. This is decisively confirmed by the low Directional Variability Index value (0.52).