In the Afghanistan Drought Early Warning (AF-DEWS) online portal , the user can select range of drought monitoring and early warning product derived from the Google Earth Engine (hereafter refer as GEE) data catalogue and Google Cloud Storage (for third party datasets).
At first, user can select different drought indices and indicators on three major drought types as well as weather forecasts and drought impacts type as -
Further above mentioned categories divided into sub-categories and the data can be populated as per following sub categories
Within each sub-categories, user can select different indices or indicators to assess the current or historical drought situation at different spatial and temporal scale. The platform has the provision to display time-series data and plot the raster values both in space and time for easy interpretation.
User can select, click on the button "SHOW LAYER" under the Map Option to display the customized maps of different drought types and its sub-categories. In Graphical Option, user can plot time series data with multiple geometry (point, polygon or district) and download those time series data as CSV, JPG and many other formats.
Climate, vegetation, and drought can be monitored many different ways through ground observations, gridded weather and climate data, and airborne and satellite remote sensing. Access to GEE’s satellite image and meteorological collections allow for efficient near real-time drought monitoring and early warning using indicators such as surface temperature, vegetation, precipitation, snow cover, surface water, soil moisture, evapotranspiration and many more.
The product calculates accumulated rainfall using satellite-based rainfall i.e CHIRPS for a given period example weekly, monthly and seasonal.
Reference: 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.Rainfall anomaly index (RAI)
Rainfall anomaly index (RAI) is a normalized precipitation values based upon the historical rainfall of a particular location. It is a comparison of current rainfall variation from the historical period.
RAI = (RFCurrent - RFMean)/(RFStdDev)
Where, RFCurrent is Current rainfall, RFMean and RFStdDev are the mean and standard deviation of long-term rainfall.
Reference: Kraus, E.B., 1977. Subtropical droughts and cross-equatorial energy transports. Monthly weather review, 105(8), pp.1009-1018.The Soil Water Anomaly Drought Index (SWDI)
The Soil Water Anomaly Drought Index (SWADI) monitors the changes of soil water anomaly with reference to long-term extremities. It is pixel based calculation that efficiently separates the short term changes in SWI from the long-term ecological changes. SWADI has proved to more efficient in identifying drought condition and following is the equation for calculating SWADI
SWADIijk = (SWIijk - SWI(mean)ijn)/SWI(std)ijn
Where, SWADIijk is SWADI for pixel i in composite j of year k; SWIijk is SWI for pixel i in composite j of year k; SWI(mean)ijn is the long-term mean of SWI for pixel i in composite j and SWI(std)ijx is the long-term standard deviation of SWI for pixel i in composite j. The value range varies between “-3” to “+3”. The value nearby “-2” reveals extreme drought situation and nearby +1 expresses healthy situation.Normalize Difference Vegetation Index (NDVI )
Although there are, several vegetation indices, one of the most widely used is the Normalized Difference Vegetation Index (NDVI). NDVI values range from +1.0 to -1.0. Areas of barren rock, sand, or snow usually show very low NDVI values (for example, 0.1 or less). Sparse vegetation such as shrubs and grasslands or senescing crops may result in moderate NDVI values (approximately 0.2 to 0.5).
NDVI = (NIR-RED)/(NIR+RED)
Where NIR = near infrared band, RED = red band
Reference: Rouse Jr, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1973. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation.NDVI Anomaly
NDVI anomaly is normalized vegetation values based upon the historical vegetation of a particular location. It is a comparison of current vegetation variation from the historical period.
NDVI Anomaly = (NDVICurrent - NDVIMean)/(NDVIStdDev)
Where, NDVICurrent is current vegetation, NDVIMean and NDVIStdDev are mean and slandered devotion of long-term vegetation conditionEnhanced Vegetation Index (EVI)
EVI can also be used to quantify vegetation greenness. However, EVI corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation or high biomass. EVI can be calculated as below
EVI = G * ((NIR - R) / (NIR + C1 * R – C2 * B + L))
The formula incorporates an “L” value to adjust for canopy background, “C” values as coefficients for atmospheric resistance, and values from the blue band (B). These enhancements allow for index calculation as a ratio between the R and NIR values, while reducing the background noise, atmospheric noise, and saturation in most cases.
Reference: Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), pp.195-213.EVI anomaly
EVI anomaly is normalized vegetation values based upon the historical vegetation of a particular location. It is a comparison of current vegetation variation from the historical period.
EVI Anomaly = (EVICurrent - EVIMean)/(EVIStdDev)
Where, EVICurrent is current vegetation, EVIMean and EVIStdDev are mean and slandered devotion of long-term vegetation conditionNormalize Difference Water Index (NDWI)
The normalized difference water index can be utilized for evaluating vegetation liquid water contents or water inundated areas (Gao, 1996). NDWI is useful for evaluating reflectance from vegetation canopies that have similar scattering properties, but slightly different liquid water absorption due to canopy water content. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies and open water areas. The common range of NDWI for green vegetation is -0.1 to 0.4
There are different way of NDWI calculation as follows
NDWI = (GREEN-NIR)/(GREEN+NIR)
Where NIR = near infrared band, GREEN = green band
Reference: Gao, B.C., 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.NDWI anomaly
NDWI anomaly is normalized NDWI values based upon the historical NDWI of a particular location. It is a comparison of current NDWI variation from the historical period.
NDWI Anomaly = (NDWICurrent - NDWIMean)/(NDWIStdDev)
Where, NDWICurrent is current vegetation, NDWIMean and NDWIStdDev are mean and slandered devotion of long-term vegetation conditionLand Surface Water Index (LSWI)
The near-infrared (NIR: 841–876 nm) and the shortwave infrared (SWIR1: 1628–1652 nm) bands are used to calculate LSWI. The NIR spectral region serves as a moisture reference band and the SWIR spectral domain is used as the moisture measuring band. SWI was found to correspond well with the drought severities in many studies. LSWI can be calculated as below
LSWI = (NIR-SWIR1)/(NIR+SWIR1)
Higher LSWI values represent wet condition and lower values indicated dry condition.
Reference: Xiao, X.; Hollinger, D.; Aber, J.; Goltz, M.; Davidson, E.A.; Zhang, Q.; Moore, B., III. Satellite-based modeling of gross primary production in an evergreen needle leaf forest. Remote Sens. Environ. 2004, 89, 519–534.Normalize Difference Snow Index (NDSI)
Calculation of the Normalize Difference Snow Index (NDSI) is mainly to understand presence of snow pixels in satellite images and more accurately, it is known as detection of Fractional Snow Cover (FSC).
NDSI = (Green-SWIR) / (Green+SWIR)
The pixels which are having NDSI value >0 is considered as to have some snow.
Reference: Hall D.K., Riggs, G.A., Salomonson V.V., DiGirolamo N.E., and Bayr K.J., 2002. MODIS snow-cover products. Remote Sensing of Environment 83: 181–194.
A dry spell is defined as the number of consecutive days with a daily precipitation amount below a certain threshold, such as 0.1, 1, 5, 10 mm, preceded and followed by at least one day with rainfall exceeding the threshold. The app uses rainfall product from CHIRPS to calculate the dry spell for specific time period ranging from few days to months.
Reference: Suppiah, R. and Hennessy, K.J., 1998. Trends in total rainfall, heavy rain events and number of dry days in Australia, 1910–1990. International Journal of Climatology: A Journal of the Royal Meteorological Society, 18(10), pp.1141-1164.Standardized Precipitation Index (SPI)
The Standardized Precipitation Index (SPI) the measure of the number of standard deviations of observed cumulative precipitation deviates from the climatological average. SPI calculation involves the estimation of probability distribution function over desired rainfall intervals. SPI can be calculated for any time scale vary from monthly to multi-months.
Reference: McKee T.B., Doesken N.J., and 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.Precipitation Condition Index (PCI)
The PCI compares present rainfall reference with long-term extreme rainfall values. The PCI value range varies between 0-100, where the value nearby 0 represents extreme stress, while values close to 100 expresses a healthy situation. RFmin and RFmax are the long-term minimum and maximum RF for a given pixel and the RFi is the current RF for the same pixel.
PCI = ((RFi - RFmin)/(RFmax - RFmin))X 100
Reference: Kogan F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11): 91–100. DOI: 10.1016/0273-1177(95)00079-T.Temperature Condition Index (TCI)
TCI derived from long-term historical extremes of LST and it assigns the values as normalized ranging from 0-100. Where values close to 0 depicts stress condition for vegetation growth and values closer to 100 reflects favorable conditions for vegetation growth. LSTmin and LSTmax are the long-term minimum and maximum LST for a given pixel and the LSTi is the current LST for the same pixel.
LST = ((LSTi - LSTmin)/(LSTmax - LSTmin))X 100
Reference: Kogan F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11): 91–100. DOI: 10.1016/0273-1177(95)00079-T.Vegetation Condition Index (VCI)
Pixel-based VCI calculation is more effective to identify the drought condition irrespective of the ecological region. The range of VCI values varies between 0-100 and the value 0 reveals extreme stress while 100 expresses healthy vegetation. NDVImin and NDVImax are the long-term minimum and maximum NDVI for given pixel and the NDVIi is the current NDVI for the same pixel.
VCI = ((NDVIi - NDVImin)/(NDVImax - NDVImin))X 100
Reference: Kogan F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11): 91–100. DOI: 10.1016/0273-1177(95)00079-T.Vegetation Health Index (VHI)
Vegetation Health Index (VHI) is an index characterizes the health of the vegetation by integrating NDVI and Temperature so that stressed conditions are linked to lower than normal NDVI and higher than normal temperature. The VHI and the sub-indices are used for various purposes, of which its applicability in detecting and monitoring the phenomenon of drought.
VHI = αVCI + (1-α)TCI
where VCI is the Vegetation Condition Index, TCI is the Thermal Condition Index and α is 0.5
Reference: Kogan F.N. 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11: 1405–1419. DOI: 10.1080/01431169008955102.Snow Condition Index (SCI)
NDSI anomaly is normalized snow cover values based upon the historical representation of snow of a particular location. It is a comparison of current snow variation from the historical period. Snow Condition Index (SCI) represents snow condition similar like VCI and can be calculated as below
SCI = ((NDSIi - NDSImin)/(NDSImax - NDSImin))X 100
The SCI value range varies between 0-100, where the value nearby 0 represents extreme lower snow with respect to longterm, while values close to 100 expresses a greater snow condition. NDSImin and NDSImax are the long-term minimum and maximum NDSI for a given pixel and the NDSIi is the current NDSI for the same pixel.Soil Moisture Condition Index (SMCI)
Soil Moisture Condition Index (SMCI) represents soil moisture condition similar like VCI and can be calculated as below
SMCI = ((SMi - SMmin)/(SMmax - SMmin))
The SMCI value range varies between 0-100, where the value nearby 0 represents extreme soil moisture stress, while values close to 100 expresses a healthy situation. SMmin and SMmax are the long-term minimum and maximum SM for a given pixel and the SMi is the current soil moisture for the same pixel.
Reference: Liu, Q., Zhang, S., Zhang, H., Bai, Y. and Zhang, J., 2020. Monitoring drought using composite drought indices based on remote sensing. Science of The Total Environment, 711, p.134585.Moisture Adequacy Index (MAI)
Moisture Adequacy Index (MAI) is the ratio of actual evapotranspiration (AET) to the potential evapotranspiration (PET). Thus, MAI represents moisture effectivity which has impact on vegetation in relation to the climate. The range of MAI values varies between 0-1; value near to 0 reveals extreme stress while 1 expresses ample amount of soil moisture availability.
MAI = AET/PET
Reference: Thornthwaite, C.W. and Mather, J.R., 1955. The water balance publications in Climatology, 8 (1). DIT, Laboratory of climatology, Centerton, NJ, USA.
IDSI is based on the integration of multi-scale VCI, PCI and TCI using data fusion techniques. The IDSI values range from 0 to 100, which is similar to all the prior indices produced. Values close to 0 represent extreme drought, whereas values near to 100 reveal healthy vegetation conditions (no drought). Integrated Drought Severity Index (IDSI) calculated using the following equation.
IDSIijk, VCIijk, TCIijk and PCIijk are IDSI, VCI, TCI and PCI values for a pixel (i) in a composite (j) of a year (k). L is the normalization factor to keep the output value in expected range and c is a constant to avoid a null in the denominator.