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the indices

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

types of drought

availability of data

consistency of data

geographical characteristics

time and spatial variability

final users

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.

McKee T.B., Doesken N. J., Kliest J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference of Applied Climatology, 17-22 January, Anaheim, CA. American Meteorological Society, Boston, MA. 179-184.
Guttman, N. B. (1999). Accepting the Standardized Precipitation Index: a calculation algorithm. J. Amer. Water Resour. Assoc., 35 (2), 311-322.
Svoboda M., Hayes M., & Wood D. (2012). Standardized precipitation index user guide. World Meteorological Organization Geneva, Switzerland.
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.

The EDI index, calculated with a daily time step, is more sensitive to each single rainfall event and shows a more detailed influence of precipitation on the recovery from an accumulated deficit.

  • EP is the Effective precipitation;
  • Pm is rainfall of m days before;
  • i is the number of days (usually equal to 365 days) along which rainfall is summed in order to calculate the drought intensity.
  • MEP is the mean climatological effective precipitation (calculated over a 30-years period);
  • DEP is the deviation of the effective precipitation from the MEP indicating a water deficit/surplus for a specific day.

EDI is the standardized value of DEP, where ST(DEP) is the standard deviation of each daily DEP. Additionally, EDI is effective to spatially recognize the onset of a drought episode consequently can be used at the punctual level for further specific information.

 Morid, S.; Smakhtin, V.; Moghaddasi, M. Comparison of seven meteorological indices for drought monitoring in Iran. International journal of climatology 2006, 26, 971–985.
Byun, H.R.; Wilhite, D.A. (1999). Objective quantification of drought severity and duration. Journal of Climate. 12, 2747–2756.

The Vegetation Condition Index

where NDVIi, NDVImin, and NDVImax are respectively the last NDVI image available and the absolute minimum and maximum values along the time series, related to the same period.

Although the NDVI is calculated over the whole year, during the autumn-winter period satellite images are more influenced by the higher cloud cover characterizing these colder months.

 Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research. 15, 91-100.

Temperature Condition Index

where LSTi, LSTmin, and LSTmax are respectively the last LST image available and the absolute minimum and maximum values along the time series, related to the same period. In accord with the study of Sun and Kafatos, we use daytime LST instead of brightness temperature for calculating TCI.

Although the LST is calculated over the whole year, during the autumn-winter period satellite images are more influenced by the higher cloud cover characterizing these colder months.

Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research. 15, 91-100.
Sun D., Kafatos M. (2007). Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America. Geophysical Research Letters, 34.

Vegetation Health Index

where a, and b are coefficients that quantify the VCI and TCI contributions to the vegetation response, respectively. Since our environment is complex and characterized by different vegetation types (from Mediterranean evergreen coniferous and broad-leaf forests to temperate coniferous and deciduous broad-leaf ones) responding differently to temperature and water availability, we assigned the same weight (0.5) to the coefficients to simplify the computation of the index.

Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research. 15, 91-100.
Kogan F.N. (2001). Operational space technology for global vegetation assessment. Bulletin of the American Meteorological Society. 82 (9), 1949-1964.

Effective-Vegetation Condition Index

The E-VCI is calculated as the VCI, but the EVI (Enhanced Vegetation Index) is used instead of the NDVI.
Compared to NDVI, EVI is less influenced by scattering related to aerosols [Huete et al., 2002] and less susceptible to saturation [Xiao et al., 2003] in forests with high vegetation cover.

Although the EVI is calculated over the whole year, during the autumn-winter period satellite images are more influenced by the higher cloud cover characterizing these colder months.

Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213.
Xiao, X.; Braswell, B.; Zhang, Q.; Boles, S.; Frolking, S.; Moore, B. (2003). Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia. Remote Sens. Environ. 84, 385–392.

Enhanced-Vegetation Health Index

The E-VHI is calculated as the VHI, but E-VCI (Enhanced-Vegetation Condition Index) is used instead of the VCI.