Hydroclimatic aggregate drought index (HADI): a new approach for
identification and categorization of drought in cold climate regions
Mohammad Hadi Bazrkar1 ˖ Jianglong Zhang2 ˖ Xuefeng Chu1
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2020) 34:1847-1870
STUDENT NAME
Presented by:
Saira Akram M27-F22
Presented to:
Prof. Dr. Sohail Chand
College of Statistical Sciences (CSS),
University of the Punjab
OBJECTIVES
Develop a new integrated drought index, HADI, for the identification of drought
in cold climate regions. Customize drought categorization for cold climate
regions by deriving variable threshold levels that account for both temporal and
spatial distributions of droughts.
The results showed
a remarkable mixture
distribution of two
normal distributions
with different means
and variances.
METHODS AND MATERIALS
The Q-Q plots the quantiles of the normal
distribution and the empirical
distribution, where the areal averages of
precipitation, rainfall, snowpack,
snowmelt, surface runoff, and the HADI
are positively skewed, and temperature,
and soil water storage are negatively
skewed.
The Flowchart for the HADI
calculation and drought categorization
in Red River of North Basin (RRB):
ABSTRACT
The paper introduces a new integrated drought
index called the Hydroclimatic Aggregate
Drought Index (HADI) for identifying and
categorizing drought in cold climate regions
which combines multiple hydroclimatic
variables to assess drought anomalies in the
root zone, and the performance of HADI is
evaluated by comparing with existing drought
indices like the Palmer Drought Severity Index
(PDSI) and the U.S. Drought Monitor (USDM)
products. The results show that HADI
outperforms PDSI in identifying droughts in the
Red River of the North Basin (RRB) based on
their impacts on agriculture. The customized
categorization of droughts using variable
threshold levels accounts for temporal and
geographical variations, making HADI more
accurate for cold climate regions.
INTRODUCTION
Drought is defined as an anomaly or deviation from a
normal condition, and the determination of this
normal condition varies in different regions and time
periods due to climate diversity. The main climatic
variables in the hydroclimatic system are
precipitation (rainfall and snowfall) and temperature,
which can cause meteorological drought through
abnormal rising or falling trends. The impacts of
climatic variables on surface runoff and soil moisture
lead to hydrologic and agricultural droughts, with a
lag in occurrence. Recognizing the dominant
hydroclimatic processes and drought mechanisms in
cold climate regions is essential for defining normal
conditions and identifying droughts.
The scatterplot matrix confirms the results of
correlations of different hydroclimatic variables
and the HADI.
Table 1: shows the derived Threshold
levels for the Categorization of
Drought And wet conditions.
A grid-based hydrologic model (GHM) simulates hydrologic processes in cold
climate regions. The HADI is developed by coupling GHM with a correlationbased Principal Component Analysis (PCA) technique. The HADI incorporates
multiple hydroclimatic variables to assess drought anomalies in the root zone.
The R-mode PCA is employed to identify unique properties of climate
variables and avoid redundant information in deriving the HADI. The
performance of the HADI is evaluated by comparing it with existing drought
indices like the Palmer Drought Severity Index (PDSI) and the U.S. Drought
Monitor (USDM) products. The customized drought categorization based on
variable threshold levels accounts for temporal and geographical variations,
enhancing the accuracy of drought identification and characterization.
According to the results, starting from April 2004, a long dry period initiated in
the RRB.
The uniqueness of the drought drivers
in cold climates entitles the types of
regions to have special drought
indices such as the HADI.
The comparison of monthly PDSI and HADI values and annual values of the PDSI,
HADI, and agriculture-based GDP showed the extreme dry and wet years.
CONCLUSIONS
RESULTS AND DISCUSSION
The first PC had maximum information and the last PC layer showed less
information.
Table 3:
The drought types can be defined by comparing
the loadings of the hydroclimatic variables.
It showed how the HADI and USDM differ in identifying and categorizing droughts
in climate division 2102, 3203 ad 3206.
The HADI outperformed the PDSI in identifying droughts in RRB based on
their impacts on agriculture. The customized categorization of droughts
using variable threshold levels accounts for temporal and geographical
variations, making HADI more accurate for cold climate regions.
FUTURE RESEARCH
Future research can consider the second principal component (PC) and
explore methods to mitigate the issue of the low ratio of explained variances
during summer due to collinearity and enhance the accuracy of the index.
REFERENCES
Table 2:
The Pearson correlation results indicated
that rainfall, surface runoff, and soil
mixture were not the main indicators
of drought conditions.
1. Akinremi OO, Mcginn SM, Barr AG (1996) Evaluation of the Palmer
Drought Index on the Canadian prairies. J Clim 9:897-905.
2. Svoboda M, LeComte D, Hayes M (2002) The drought monitor. Bull Am
Meteor Soc 83(8):1181-1190.
3. USDM (2019) United States drought monitor. 27 June, 2019.
https://droughtmonitor.unl.edu/Data/Timeseries.aspx.