El nacimiento de la historiografía musical.

The first purpose of this study was to complete the existing ITHACA drought EWS, based on the monitoring of vegetation conditions based on phenological parameters, introducing proper procedures able to to monitorrainfall conditions in near real-time. Consequently, considering the available precipitation datasets the Standardized Precipitation Index (SPI) was selected as the parameter to identify the possible meteorological drought events directly related with the rainfall. Proper procedures for the calculation of the SPI on a global scale for different cumulative rainfall intervals have been implemented.

Furthermore, a research has been conducted to identify the possible relationships between vegetation and rainfall in the whole Africa through a correlation analysis of several phenological vegetation parameters and the cumulated rainfall corresponding to the cumulative intervals of 1, 3 and 6 months. The main purpose of this step of the study was to identify the areas and the parameters that correctly support the planning and definition of effective procedures for the integration, where it is meaningful, of the vegetation monitoring procedures and the rainfall anomalies calculated through the SPI in the final ITHACA drought EWS. Obviously, due to the simplifications related to the resolution of spatial data used for the conducted analysis (0.05 deg), final recommendations and operative proposals may need further validation on a regional scale using also local datasets, where available.

In order to implement the research and considering the impacts that the drought events generate in the population, the whole African continent was selected as study area. First of all, a preliminary analysis aimed at identifying the areas where the results of the vegetation monitoring activities proposed in the ITHACA drought EWS are produced with insufficient reliability, was conducted. Then, an analysis of the land cover dataset was performed. In particular, considering the spatial distribution of the land cover types and the spatial resolution of the datasets used in this study (0.05 deg), the Croplands, Grasslands, Savannas,

Woody Savannas, Open Shrublands and Cropland/Natural Vegetation Mosaic classes were selected for the

subsequent correlation analyses proposed in this study.

A preliminary test of statistical significance of the correlation values obtained has been carried out. The main outcome of this test was the identification of the rainfall cumulating interval, specifically the 1 month interval, that produced larger areas with positive and significant correlation values for all the examined phenological parameters and land cover classes. Consequently, the monitoring of rainfall anomalies calculated using the 1 month SPI, which presents a higher likelihood to be the correct and effective information to be used in order to complete the vegetation conditions monitoring, has been initially proposed for its use in the ITHACA drought EWS. In addition, for the Woody Savannas and Savannas land cover types considerable areas with significant and positive correlation values could not be identified,

therefore it can be expected that, in these areas, the alerts generated through the vegetation monitoring would be independent to the rainfall anomalies.

Based on the outcomes of the test of statistical significance, a correlation analysis was conducted. The vegetation Seasonal Small Integral (SmI) and the Length of the growing Season (Len) phenological parameters were found to be the more correlated for all the examined vegetation land cover types. In addition, it was observed that the level of correlation of the Base, Amplitude (Amp) and Increase (Incr) phenological parameters depends of the considered vegetation land cover type. In particular, in the Increase parameter case, the correlation level decreases considerably considering a 6 months rainfall cumulating interval. On the other hand, the decrease (Decr) parameter presents the lower correlation values for all the vegetation land cover type. Therefore, through results obtained in this phase, it was possible to confirm the effectiveness of the use of the vegetation Seasonal Small Integral (SmI) parameter for drought early warning purposes. However, it was also proposed to investigate, in the future, the possible use of the Length of the growing Season (Len) as an alternative vegetation parameter or as a possible linked parameter between the rainfall and the vegetation.

Moreover, it was observed that the Grasslands and the Open Shrublands land cover classes presented, as a whole, the higher correlation values considering all the phenological parameters, except in the southern regions of the Africa, where large areas without significant correlations have been identified. However, the

Grasslands land cover class presented large areas with high correlation values in the Horn of Africa area. In

this area, this vegetation type shown to be directly correlated with the rainfall behavior in both the existing vegetation growing seasons.

Considering that, for some land cover classes, the obtained high correlation values may indicate, with prudence, a greater vulnerability to the vegetation to rainfall anomalies, a proper Vulnerability Index has been defined based on the different correlation levels found for each examined land cover class and investigated. The highest values of this index were found to be in correspondence to the 1month and 3 months rainfall cumulating intervals. Specifically, the areas that showed the highest values in correspondence to the 3 months rainfall cumulating interval are located in the south of the African continent (specifically, in the area that extends between the Botswana, Namibia and South Africa countries) and in the Horn of Africa, while the remaining areas presented highest values of vulnerability in correspondence to the 1 month rainfall cumulating interval.

Therefore, considering these results, it was proposed the possibility to use, in the ITHACA drought EWS, the developed Vulnerability Index in order to weight rainfall anomalies detected using the SPI, before the production of the final drought hazard dataset, which will be based on vegetation and precipitation anomalies detected in near real-time. In addition, based on the analysis of the Vulnerability Index, were also definitely identified and proposed, for each land cover type, the rainfall cumulating intervals (1, 3 or 6 months) to be correctly and effectively used for SPI calculation purposes, in order to complete the vegetation conditions monitoring.