the indices
The monitoring system is developed by integrating state-of-the-art science and advances in technologies, and selecting a set of coupled meteo-climatic and vegetation satellite-derived indices, together with other indices related to soil and socio-economic factors, following the “convergence of evidence” concept.
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 1, 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.
From April 2022 the CHIRPS quasi-global dataset is replaced by the MSWEP (Multi-Source Weighted-Ensemble Precipitation) global dataset, due to its better accuracy compared to other datasets.
MSWEP merges gauge, satellite, and reanalysis data.
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.
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Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. M., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F. MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bulletin of the American Meteorological Society 100(3), 473–500, 2019.
____Funk C.C., Peterson P.J., Landsfeld M.F., Pedreros D.H., Verdin J.P., Rowland J.D., Romero B.E., Husak G.J., Michaelsen J.C., and Verdin A.P. (2014). A quasi-global precipitation time series for drought monitoring. U.S. Geological Survey Data Series 832, 4 p.
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 Land Surface Temperature – LST is the temperature of the top of any surface, from roofs to the top of the trees canopy, to the streets or pastures.
It differs from the “classical” temperature that generally measures air temperature at 2m height.
The LST anomalies for a specific period (week, month, year, etc.) are calculated compared to the 30 years climatic average (actually 1991-2020) of the same period.
For the LST anomalies computation, we use the ERA5 Land Copernicus dataset which delivers LST data in quasi real time, with a time lag of about 5-6 days and a spatial resolution of 0.09° (about 9 km).
“The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast-response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. The ESI also demonstrates the capability for capturing early signals of flash drought, brought on by extended periods of hot, dry, and windy conditions leading to rapid soil moisture depletion“ (National Integrated Drought Information System, n.d.).
ESI composite over short time scales (e.g. 4 weeks) can be representative of fast-changing conditions, while ESI composite over longer time scales (e.g. 12 weeks) can be more representative of slower changes.
Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007a: A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: I. Model formulation. J. Geophys. Res., 112, D10117, doi:10110.11029/12006JD007506.
Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. P. Otkin, and W. P. Kustas, 2007b: A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: II. Surface moisture climatology. J. Geophys. Res., 112, D11112, doi:11110.11029/12006JD007507.
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.
The vegetation indices dataset (DOI: 10.5067/MODIS/MOD13Q1.006) used to calculate VCI comes from the elaboration of MODIS instrument images (Moderate Resolution Imaging Spectroradiometer) of Terra satellite (EOS AM-1).
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.
The LST dataset (DOI: 10.5067/MODIS/MOD11A2.006) used to calculate TCI comes from the elaboration of MODIS instrument images (Moderate Resolution Imaging Spectroradiometer) of Terra satellite (EOS AM-1).
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.
The EDI-Effective Drought 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.
