The monitoring system is developed integrating state-of-the-art science and advances in technologies, and selecting a set of coupled rainfall-based and satellite-derived indices.
The indices selected take into account the following issues
Climate Based Indices
Precipitation, as the first and main parameter pointing out drought occurrence, is used in many indices, among which the most widespread Standardized Precipitation Index (SPI) and the less known Effective Drought Index (EDI). These indices are considered better then others as providing different time scales of drought occurrence, and detecting its variation and duration.
Vegetation Based Indices
These indices focus vegetation health monitoring and are related to temperature and moisture stresses throughout a combination of NDVI or EVI, and LST parameters/indices. They represents an indirect drought responsive way to analyze the phenomenon, and satellite-derived indices are widely used for their spatiotemporal characteristics of full ground cover and quasi-continuous time observations.
the set of indices selected
About the Indices
The Standardized Precipitation Index (SPI), widely considered a robust and reliable index, allows a multiple time scales tracking (usually 3, 6, 12, 24 months) of dry/wet periods, detects drought variation and duration, and provides a comparison between geographically different locations thanks to its standardization.
For these reasons, and to have the possibility of expanding the geographical area of analysis as necessary, the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) dataset was considered suitable. Indeed, working over larger territory or different climatic zones, since the spatial distribution of weather stations is often inadequate, encourages the employment of a dataset that merges precipitation detected both by satellites and rain gauges.
In order to validate the CHIRPS dataset for the Tuscany region, a comparison of monthly precipitation time series from 10 rain gauges and the corresponding values of the CHIRPS grid cells for the period 1981-2010 has been done. An overall underestimation of CHIRPS monthly rainfall emerges from the analysis (R correlation = 0.69; Mean Bias = -8mm; RMSE = 10.4mm), which is acceptable for gridded datasets and our purposes.
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Funk C., Peterson P., Landsfeld M., Pedreros D., Verdin J., Shukla S., Husak G., Rowland J., Harrison L., Hoell A. & Michaelsen J. (2015). The climate hazards infrared precipitation with stations – A new environmental record for monitoring extremes. Scientific Data 2, 150066. doi:10.1038/sdata.2015.66.