Satellite-based monitoring of drought conditions#
Drought is a complex phenomenon that encompasses various elements including meteorological, hydrological, and agricultural components, each with its own characteristics and impacts. As such, no single index can perfectly capture and quantify all aspects of a drought. Many indices have been developed to measure different features of droughts, but each has its limitations due to the multifaceted nature of drought.
One widely used drought index is the Standardized Precipitation Index (SPI). The SPI is a simple, robust, and versatile index that allows for comparison across different regions and climates. The SPI can be useful for detecting meteorological droughts, as it primarily focuses on rainfall deficits.
However, the SPI does not account for other critical elements such as temperature, evaporation, soil moisture, and the impact on vegetation, which are important for understanding agricultural and hydrological droughts. Therefore, while the SPI is a valuable tool in drought monitoring, it should be used in conjunction with other indices and measurements to gain a comprehensive understanding of drought conditions. This illustrates the principle that while individual indices can provide valuable insights, no single index can perfectly capture the entirety of a drought.
The emergence of satellite-based climate data has added a new dimension to drought analysis. Satellite-derived precipitation estimates, for instance, can be utilized to enhance the SPI’s capabilities, offering more comprehensive insights.
Standardized Precipitation Index#
The Standardized Precipitation Index (SPI) is a normalized index first developed by McKee et al. (1993). The SPI is used for estimating wet or dry conditions based on the precipitation variable. This wet or dry condition can be monitored by the SPI on a variety of time scales from subseasonal to interannual scales.
The SPI calculation is based on the long-term precipitation record for a particular location and long-term period (longer than 30 years is desirable). The calculation method consists of a transformation of one frequency distribution (e.g., gamma) to another frequency distribution (normal, or Gaussian). The first step to calculate SPI is to adequately choose a particular probability distribution (e.g., gamma distribution, incomplete beta distribution (McKee et al. 1993, 1995), and Pearson III distribution (Guttman 1998, 1999) that reliably fits the long-term precipitation time series and conducts fitting to that distribution.
Gamma distribution has been widely used, as the gamma distribution has been understood as the reliable fit to the precipitation distribution. The fitting can be achieved through the maximum likelihood estimation of the gamma distribution parameters. The percentile value from this probability distribution is then transformed to the corresponding value in the new probability distribution. As a result, the probability that the rainfall is less than or equal to any rainfall amount will be the same as the probability that the new variate is less than or equal to the corresponding value of that rainfall amount. The normal distribution is usually used for this another transformation so that the mean and standard deviation of the SPI for a certain station and long-term period is zero and one, respectively (Edwards and McKee 1997).
Interpretation#
The SPI maps can be interpreted at various time scales. This in turn indicates that the SPI is useful in both short-term and long-term applications. These time scales reflect the impact of drought on the availability of the different water resources. For instance, soil moisture conditions respond to precipitation anomalies on a relatively short scale. Groundwater, streamflow, and reservoir storage reflect the longer-term precipitation anomalies.
The threshold and the symbology for the SPI can follow below color codes and image.
Class |
Threshold |
Hex |
RGB |
|---|---|---|---|
Exceptionally Dry |
-2.00 and below |
|
rgb(118, 0, 5) |
Extremely Dry |
-2.00 to -1.50 |
|
rgb(236, 0, 19) |
Severely Dry |
-1.50 to -1.20 |
|
rgb(255, 169, 56) |
Moderately Dry |
-1.20 to -0.70 |
|
rgb(253, 210, 138) |
Abnormally Dry |
-0.70 to -0.50 |
|
rgb(254, 254, 83) |
Near Normal |
-0.50 to +0.50 |
|
rgb(255, 255, 255) |
Abnormally Moist |
+0.50 to +0.70 |
|
rgb(162, 253, 110) |
Moderately Moist |
+0.70 to +1.20 |
|
rgb(0, 180, 74) |
Very Moist |
+1.20 to +1.50 |
|
rgb(0, 129, 128) |
Extremely Moist |
+1.50 to +2.00 |
|
rgb(42, 35, 235) |
Exceptionally Moist |
+2.00 and above |
|
rgb(162, 31, 236) |
Strengths and Limitations#
Used for estimating meteorological conditions based on precipitation alone.
Wet or dry conditions can be monitored on a variety of time scales from sub seasonal to interannual
Can be compared across regions with markedly difference climates
Does not consider the intensity of precipitation and its potential impacts on runoff, streamflow, and water availability
Expressed as the number of standard deviations from the long term mean, for a normally distributed random variable, and fitted probability distribution for the actual precipitation record
SPI values < -1 indicate a condition of drought, the more negative the value the more severe the drought condition. SPI values > +1 indicate wetter conditions compared to a climatology
Examples#
Below is the SPI example for different time scale and how to interpret the value, following the guideline from the US National Drought Mitigation Center
SPI 1-month
Reflects relatively short term conditions. Its application can be related closely with short term soil moisture and crop stress. Interpretation of the 1-month SPI may be misleading unless climatology is understood. In regions where rainfall is normally low during a month, large negative or positive SPIs may result even though the departure from the mean is relatively small.
3 month
Provides a comparison of the precipitation over a specific 3 month period with the precipitation totals from the same 3 month period for all the years included in the historical record. Reflects short and medium term moisture conditions and provides a seasonal estimation of precipitation.
6 month
Compares the precipitation for that period with the same 6 month period over the historical record. A 6 month SPI can be very effective in showing the precipitation over distinct seasons and may be associated with anomalous streamflow and reservoir levels.
9 month
Provides an indication of precipitation patterns over a medium time scale. SPI values below 1.5 for these time scales are usually a good indication that significant impacts are occurring in agriculture and may be showing up in other sectors as well.
12 month
Reflects long term precipitation patterns. Longer SPIs tend toward zero unless a specific trend is taking place. SPIs of these time scales are probably tied to streamflow, reservoir levels, and even groundwater levels at the longer time scales. In some locations of the country, the 12 month SPI is most closely related with the Palmer Index, and the two indices should reflect similar conditions.
Below is the latest SPI-12 condition based on CHIRPS as of May 2023.
In addition to geographical maps, time series charts offer a compelling visualization tool that vividly captures the evolution of drought conditions. These charts, with their month-by-month and year-by-year granularity, effectively illustrate the changing dynamics of drought. Furthermore, they provide insightful details on the percentage of regions affected over time, painting a comprehensive picture of how droughts impact different areas periodically.
National
Admin1
Admin2
Other chart for different time scale from National, Admin1 and Admin2 level are available in folder
SPI-based drought characteristics#
Droughts usually take a season or more to develop. Longer time scales (>12 months) are better to measure the effects of a precipitation deficit on different water resource components (stream flow, soil moisture, groundwater and reservoir storage) and the impact to agricultural practices in longer term.
Numerous indices for measuring drought, including SPI, SPEI, PDSI, among others, have been suggested in various research studies. The choice of a particular index often depends on the data that is available. Even though the interpretation and definition varies for each index, most of them can be described using the run theory methodology.
The run theory was initially introduced by Yevjevich in 1967 and has since been used to identify drought characteristics. The paper from Le, et al in 2019 provide better explanation about it: duration, severity, intensity, and interarrival.
Illustration of drought events and characteristics based on run theory (adapted from Yevjevich, 1967).
Event: Number of months in which the SPEI is less than a threshold.
Duration D: the number of consecutive months in which the SPEI is below the threshold.
Inter-arrival T: the duration (month) between the initiation time of two successive drought events (regardless of the length) in the same drought category. It includes the drought and subsequent non-drought periods. Therefore, T characterizes the timing variability of drought events.
Magnitude M: the absolute cumulative SPEI value during drought events. Unitless
Severity S: the number came from magnitude divided by duration to get level of severity. Unitless
Note
Currently we are still not sure on Inter-arrival time usefulness, even though we are confident with the algorithm and calculation for this following above paper.
Let say within 5 year in different periods (1991-1995 and 2015-2019), there are drought event A which start from Jan 1991 and end in May 1991, then start again in Dec 1995. Then drought event B start from Jan 2015 and end in May 2019, then start again in Dec 2019. Both event will have Inter-arrivale time value 59, although the drought duration are very different. Further exploration are needed before we can use this variable.
We have no problem with the Magnitude (the run sum of negatives deviations) variable, everything is correct and feasible to use to see the magnitude of drought events.
On the Severity, as the equation is simple: magnitude divide by duration, but we feel this is not good enough or good way to measure the severity. It’s because the duration came from cumulative sum of the event, it means the duration value always higher at the end of drought event. While we are expecting the Severity pattern is following the Magnitude that varies month to month, to give a sense of how severity changes as the drought develops and then subsides.
Actually, what we doubted above only happens if we use monthly time series data for monitoring. But if we focus on a single drought event and look for its characteristics, we will get accurate results.
References#
McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration of time scales. Eighth Conference on Applied Climatology, American Meteorological Society, Jan17-23, 1993, Anaheim CA, pp.179-186.
McKee, T. B., N. J. Doesken, and J. Kleist, 1995: Drought monitoring with multiple time scales. Ninth Conference on Applied Climatology, American Meteorological Society, Jan15-20, 1995, Dallas TX, pp.233-236.
Guttman, N. B., 1998: Comparing the Palmer Drought Index and the Standardized Precipitation Index. J. Amer. Water Resources Assoc., 34(1), 113-121.
Guttman, N. B., 1999: Accepting the Standardized Precipitation Index: A calculation algorithm. J. Amer. Water Resources Assoc., 35(2), 311-322.
Yevjevich, V. M. (1967). Objective approach to definitions and investigations of continental hydrologic droughts, An. Hydrology papers (Colorado State University); no. 23. https://www.engr.colostate.edu/ce/facultystaff/yevjevich/papers/HydrologyPapers_n23_1967.pdf
Le, P. V. V., Phan‐Van, T., Mai, K. V., & Tran, D. Q. (2019). Space‐time variability of drought over Vietnam. International Journal of Climatology. https://doi.org/10.1002/joc.6164