© 2023 The Authors
Water Practice & Technology Vol 18 No 11, 2818 doi: 10.2166/wpt.2023.175
Projections of drought characteristics based on combined drought index under
CMIP6 models
Mahrokh Shafieia, Mahnoosh Moghaddasib,c, * and Kimia Naderid
a
Hungarian University of Agriculture and Life Sciences, Godollo, Hungary
Department of Water Science and Engineering, Faculty of Agriculture and Environment, Arak University, Arak, Iran
c
Research Institute for Water Science and Engineering, Arak University, Arak, Iran
d
Arak University, Arak, Iran
*Corresponding author. E-mail: mah_moghaddasi@hotmail.com; m-moghaddasi@araku.ac.ir
b
ABSTRACT
This study assesses climate change’s impact on drought in Iran’s Dez Basin. It introduces the Hydro-Meteorological Drought
Index (HMDI), integrating the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI).
Using Climatic Research Unit Time Series (CRU TS) data (1980-2012) and downscaling forecasted data from three CMIP6
models (2020-2052) for SSP1-2.6 and SSP5-8.5 scenarios, we employ the rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning Hydrological Bureau’s Water Balance Model (HBV)-Light model to predict future streamflow. Drought characteristics
are analyzed. Under SSP5-8.5, CanEsm5 shows substantial temperature and runoff increases, notably in Bakhtiari and Borujerd
sub-basins (63% and 56%). Future droughts are expected to intensify, particularly under SSP5-8.5. The most severe HMDIderived drought (HMDI 12) in Borujerd station is projected to increase from -43.44 to -44.05. SSP5-8.5 is likelier to cause
severe and prolonged HMDI-derived droughts than SSP1-2.6 or the historical period. The analysis suggests that normal drought
levels will persist, while mild and severe drought levels will rise in the future.
Key words: climate change, drought, HBV, HMDI, SPEI, SRI
HIGHLIGHTS
• Using CMIP6 to assess the impact of climate change.
• Using compound drought index to monitor hydrological and meteorological drought.
1. INTRODUCTION
Drought disasters are frequent under the effects of both human activity and global climate change, and they significantly affect the nation’s economic, social production, and the natural environment over the long term
(Scanlon et al. 2017; Hameed et al. 2020). Therefore, drought monitoring and assessment globally or locally
are crucial to reduce its impact (Wang, et al. 2019). To achieve this purpose, several indices were developed
using a single variable (for example, precipitation; SPI1; McKee et al. 1993, runoff; SRI2; Shukla & Wood
2008), multiple variables (for example, precipitation and temperature; SPEI3; Vicente-Serrano et al. 2010), and
composite indices, such as the multivariate standardized drought index (MSDI)4; (Varol et al. 2023) for soil
moisture, temperature, and precipitation (Svoboda & Fuchs 2016). Composite indices have been created
(Yang et al. 2018) using linear and nonlinear methods (Beersma & Buishand 2004; Li et al. 2015), including
copula functions, scalogram models, principal component analysis (PCA) (Fahimirad & Shahkarami 2021),
entropy theory (Waseem et al. 2015; Naderi & Moghaddasi 2022), and Markov chain approaches.
Global climate models (GCMs) are essential tools for climate projection and attribution, predicting future climate change and reproducing historical climatic conditions. The IPCC assessment reports have been historically
based on the coupled model intercomparison projects (CMIPs). CMIP’s Phase 3 (CMIP3) and Phase 5 (CMIP5)
1
Standardized Precipitation Index
2
Standardized Runoff Index
3
Standardized Precipitation Evapotranspiration Index
4
Multivariate Standardized Drought Index
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying,
adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
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Water Practice & Technology Vol 18 No 11, 2819
provided the present and future climatic data analyzed in IPCC’s 4th and 5th assessment reports, respectively.
The sixth phase of the CMIP’s new multi-model datasets is now available, with CMIP6 models significantly
improved, resulting in higher spatial and vertical resolutions, updated microphysics parameterizations, more
sophisticated deep convective schemes, and altered ocean ice models (Eyring et al. 2016; Carvalho et al.
2022). Instead of using representative concentration pathway (RCP) scenarios, CMIP6 should use shared socioeconomic pathway (SSP) scenarios for future projections (O’Neill et al. 2016), offering a more detailed
description of future socioeconomic development and more precise climate projections. CMIP6 models achieve
better results when simulating historical climate (Lun et al. 2021).
Recent studies investigate how climate change affects rainfall and runoff in meteorological and hydrological
drought, which greatly decreases the amount of water supplied in all forms, including streamflow. In the Cheongmicheon watershed in South Korea, Abdulai & Chung (2019) investigated the meteorological and hydrological
droughts caused by climate change under the RCP 4.5 scenario. Their conclusions were based on the standardized precipitation evapotranspiration index (SPEI) and streamflow drought index (SDI), which measure the
frequency of short-term severe or extreme droughts. In another research, Atallah et al. 2023 investigated that
how the Wadi Louza in northwest Algeria responded to conditions of hydrological drought using an HBVlight hydrological model and using the SRI. According to the result, the two driest hydrological years were
1991–1993 and 2005–2006, and a 12-month period was ideal for developing efficient drought mitigation plans.
Extreme droughts were predicted by the HBV-light model for the basin.
In order to reduce the impact of hydro-meteorological drought under climate change and enhance its impact
mechanisms, it is important to developing awareness of hydro-meteorological drought variations. Therefore, in
this study, the hydrological and meteorological drought indices (SRI and SPEI) for the Dez Dam Basin in Iran
are combined to form a new composite index for this study dubbed the hydro-meteorological drought index
(HMDI). The necessary data were retrieved from three CMIP6 models and the Climatic Research Unit Time
Series (CRU TS) models using the principal component analysis (PCA) method under the SSP1-2.6 and SSP58.5 scenarios, respectively. In addition to the HBV-light model, streamflow simulations under real and imagined
circumstances are run. In addition to researching the past and present effects of climate change, the run theory
was used to calculate drought characteristics.
2. MATERIAL AND METHODS
2.1. Study area and evaluation datasets
The Dez River Catchment is located in Khuzestan, southwest Iran. It is a sub-basin of the Karun Basin with semiarid climate(Adib et al. 2021) between 48° and 9 min and 15 s to 50° and 18 min and 37 s east and wide 31° and
35 min and 51 s to 34° and 7 min and 46 s located North. It is a significant break in the Persian Gulf Basin and the
central portion of the Zagros Mountains (Figure 1). The Karkheh basin (from the west), the Qurichay and Zayandehroud basins (from the north), and the Karun basin (from the east and south), are the basins that surround the
Dez Dam basin region which has four sub-basins of Tireh, Marbereh, Sezar, and Bakhtiari. This basin’s flow is
anticipated to provide water to a number of industries, including aquaculture, manufacturing, fishing, and hydropower generation. Snowmelt from the end of winter to the start of spring supplies the majority of the basin’s
yearly water production via precipitation (Gholami et al. 2022). The primary challenges of this research have
been the lack of long-term data with sufficient distribution since certain sites in the distribution region were
inadequate and some stations were closed. Regarding this limitation, CRU data sets were used. Sezar and Bakhtiari, the basin’s two principal rivers, join to form the Dez River, which drains the entire basin, close to Tange
Panj. The basin has an annual average temperature of 24.2 °C. Figure 1 also depicts the locations of meteorological and hydrometric stations for the four sub-basins. Note that a representative meteorological station from each
sub-basin was chosen to represent the results (Table 1). As seen, the Bakhtiari and Tireh sub-basins have the highest and lowest average annual rainfall and runoff, respectively.
2.2. Datasets
- Station-based observations
The Iranian Ministry of Energy provided the monthly temperature, precipitation, and runoff records for the
chosen stations in the Dez River basin. These documents served as proof of the expected data.
- Climate research unit (CRU)
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Water Practice & Technology Vol 18 No 11, 2820
Figure 1 | The case study.
Table 1 | The description sub-basin Dez basin and the selected meteorological stations
Sub-basin
Station
Area (km2)
Q (mm/month)
P (mm/month)
Tireh
Borujerd
3,477
13.56
39.61
Marbereh
Dorud
2,553
15.56
60.12
Sezar
Sepiddasht Sezar
3,281
24.67
58.12
Bakhtiari
Bakhtiari
5,973
46.98
94.80
- The CRU created a time series of monthly climate data with a spatial resolution of 0.5° from 1901 to 2016 (New
et al. 1999; Mitchell & Jones 2005). Utilizing monthly ground-based climatic variables over land, data were
griddled. These data were interpolated using inverse distance weighted interpolation (IDW). The observational
point-based data from the four sub-basin stations in the Dez Basin were compared to this dataset. A representative station from each sub-basin was validated using the normalized root mean square error (NRMSE), mean
bias error (MBE), and coefficient of determination (r) standards. As can be observed, statistical analysis demonstrated the dependability and correctness of CRU data (Table 2). For instance, the amounts of r, NRMSE, and
MBE in the Bakhtiari station are 0.96, 0.48, and 0.05, respectively. As a result, this dataset was utilized to
obtain monthly temperature and precipitation data (https://data.ceda.ac.uk).
- Future climate data
From https://esgf-node.llnl.gov/search/cmip6/, the outputs of the CMIP6 climate model were downloaded.
RCPs and Shared Socioeconomic Pathways (SSPs) scenarios are combined in the IPCC’s sixth assessment
report’s scenarios to study climate change (Eyring et al. 2016). The historical simulation period spans the time
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Water Practice & Technology Vol 18 No 11, 2821
Table 2 | Assessing the performance of the CRU dataset over the case study
Variable
Station
r
NRMSE
MBE
Precipitation
Borujerd
Dorud
Sepiddasht Sezar
Bakhtiari
0.66
0.81
0.68
0.85
0.92
0.71
0.93
0.67
0.07
0.09
0.11
1.68
Temperature
Borujerd
Dorud
Sepiddasht Sezar
Bakhtiari
0.8
0.93
0.89
0.96
0.14
0.12
0.52
0.48
0.04
1.23
2.35
0.05
of temperature observation, making it ideal for comparison with the records of actual temperature measurements.
The two SSP scenarios for the future – SSP1-2.6 as a low forcing scenario (sustainable development) and SSP58.5 as a large forcing scenario – were examined using the three CMIP6 models that were chosen for analysis due
to their accurate simulating regional climate patterns, capturing complex interactions, and reproducing past
trends, particularly within this study region. Table 3 provides thorough details on these models. It should be high-
Table 3. | List of the CMIP6 models that were used in this study
CMIP6
Source
Resolution
CANESM5
Canadian Center for Climate Modeling and Analysis, Canada
2.81*2.81°
BCC-CSM2-MR
Beijing Climate Center, China
1.125°*1.125°
IPSL-CM6A-LR
Institute Pierre-Simon Laplace, France
1.26°*2.5°
lighted that the historical simulation is valid for comparison with the existing data because it spans the
temperature observation period from 1980 to 2012.
2.3. Methodology
2.3.1. Methodology framework
An overview of the methodology of the current research is as follows (Figure 2).
This flowchart shows how the necessary information was taken from the CRU dataset, weather station, and
AR6 models. Climate projection creates climate scenarios for both the historical and the future. Furthermore,
the HBV-light model is calibrated and validated as part of the hydrological model, and monthly runoff simulation
calculations are made under historical and projected climate scenarios. After the calculation of SPEI and SRI,
combined drought index HMDI were estimated. The severity and duration of the drought were determined
using various indices and run theory.
2.3.2. HBV-light model
The rainfall–runoff model called the hydrologiska byråns vattenbalansavdelning (HBV) is a semi-distributed one.
It mimics streamflow by accounting for variables such as temperature, precipitation, and potential evapotranspiration (PET) (Bergström 1976, 1995; Seibert 1997). The model is composed of runoff generation, soil
moisture, and snow and glacier routines (Figure 3). Five elevation zones, spaced roughly 500 m apart, had
their snowfall and melted calculated using the degree-day approach. This temperature-based strategy is helpful
since the HBV-light model does not require significant datasets for the high upstream elevations, in contrast to
physically based energy balance models (containing climate, snow depth, or snow water equivalent data to distribute the climate variables spatially). Real evapotranspiration and groundwater recharge serve as the
mechanisms for storing water in the soil moisture cycle. As the last step, the lumped response function converts
recharge to discharge. An in-depth examination of the model is given in Seibert & Vis (2012). Several software
programs have included the HBV concept. HBV-light was used in this instance (Seibert & Vis 2012). With parameter ranges similar to those previously proposed, the model was calibrated using a genetic algorithm (Seibert
2000). The 14 free parameter values, for instance, were chosen by Seibert & Vis (2012) after 3,500 model
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Water Practice & Technology Vol 18 No 11, 2822
Figure 2 | The research flowchart.
Figure 3 | The structure of the HBV-light model (Seibert 2000).
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Water Practice & Technology Vol 18 No 11, 2823
iterations. Based on the non-parametric variation of Kling–Gupta efficiency, Non-Precipitation Events (NPE),
100 different calibration attempts were made for each catchment, resulting in ensembles with 100 parameter
sets (Pool et al. 2018).
2.3.3. Downscaling
The change factor (CF) technique, which also produced precipitation and temperature records for subsequent
intervals, was used to downscale the results of the general circulation model (GCM). The CF method is typically
used to transform historical temperature and precipitation series, as well as climate change scenarios, into
monthly ratios. Equations (1) and (2) calculate the ‘difference’ and ‘ratio’ for temperature and precipitation
based on the long-term monthly average of the future period and historical period that the same GCM has simulated in each cell of the computational grid, respectively (IPCC-TGCIA 2007).
P ¼ Pobs PGCMs,fut,i
!
PGCMs,base,i
T ¼ Tobs þ (TGCMs,fut,i TGCMs,base,i )
(1)
(2)
where Pobs and Tobs indicate the observed precipitation and temperature time series in the historical (baseline)
period, PGCMs,fut,i and TGCMs,fut,i are the average time series of future precipitation and temperature, and
PGCMs,base,i and TGCMs,base,i are the average time series of historical (baseline) precipitation and temperature.
2.3.4. Standardized precipitation evapotranspiration index
Based on a monthly climate water balance, which is the total of PET and precipitation (P), this index is computed
using the standard values of the probability distribution function of the deficit or surplus accumulation of a climate water balance at various time scales (McKee et al. 1993; McKee 1995). The monthly mean temperature is a
function of the Thorn–Thwaite equation, which is used to calculate PET (Thornthwaite 1948). For the month I,
the difference between P and PET is determined as follows:
Di ¼ Pi PETi
(3)
where the following relationship, applied over various time periods, is used to calculate D values:
Dkn ¼
k1
X
Pn1 PETni
(4)
n¼0
where k (months) is the time frame being considered, and n is the month that the calculation considers. SPEI is
calculated using the three-parameter log-logistic distribution. Estimating the parameters of the log-logistic distribution can be done using the L-Moment method (Ahmad et al. 1988). The following equation is the probability
distribution function for the D series according to the log-logistic distribution
1
a
(x) ¼ 1 þ
xg
(5)
where α, β, and γ are scale, shape, and location parameters, respectively, for D values in the range (γ , x , ∞).
2.3.5. Standardized runoff index
Shukla & Wood (2008) created the standardized runoff index (SRI) to recognize hydrological droughts. Similar
to the standardized precipitation index (SPI), SRI is calculated (Gao & Zhang 2016). The normalizing function
was determined to be the two-parameter logarithmic function (Bą k & Kubiak-Wójcicka 2016). Cumulative runoff
values for each month and n months must be gathered in order to calculate SRI values on various time scales. A
total of five different time series – 1, 3, 6, 9, and 12 months – were examined. SRI values are used in the
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Water Practice & Technology Vol 18 No 11, 2824
calculation (6) to express the departure from the median in standard deviation units:
SRI ¼
f(x) m
d
(6)
In this case, f (X ) is the transformed runoff total, μ is the mean value of the normalized X, and δ is the X’s standard deviation.
2.3.6. Principal component analysis
Principal component analysis (PCA), a linear statistical method, uses an orthogonal linear transformation to
transform observation data into a new dataset with filtered elements while maintaining the maximum variability
of the original dataset. Principle component analysis (PCA) was used to extract the pertinent drought data from
prospective hydro-agro-meteorological variables X1, X2, … , Xn, and principal components (PC) were constructed
for index computation (Keyantash & Dracup 2004). Months were examined separately to account for how seasonality may impact drought. The following major components are produced when the original data are merged
with the eigenvectors E for month j ( j ¼ 1, … , 12):
Z ¼ XE
(7)
where X is a m n matrix of normalized observational data and Z is a m n matrix of PCs (each element is processed by subtracting the mean value and then dividing by the column standard deviation). E, sometimes referred
to as loading, is a n n unit eigenvector matrix produced by PCA that can illustrate the connections between PCs
and the original dataset. It is clear that PCs are essentially uncorrelated vectors with orthogonal treatments;
hence it is inappropriate to represent them all numerically as a single entity.
2.3.7. Hydro-meteorological drought index
This index is proposed as an integrated index for evaluating and monitoring droughts since it can
accurately characterize the performance of hydro-meteorological drought. Equations (5) and (6) are used
to determine the monthly values of SPEI and SRI for each time series in order to achieve this objective.
Then, in the form of a matrix with predetermined dimensions, the value of the combined drought index
is calculated for each month. It should be noted that the classification of HDMI is similar to the selected
individual indices.
2.3.8. Evaluation criteria
In this research, evaluation criteria including r, NRMSE, and mean MBE were used for data analysis and model
evaluation. Their relationships are presented in the following equations:
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u2
32
n
u 1X
u
(Xp m0 )(Xp m0 )7
u6
u6n m¼1
7
r¼u
7
u6
5
sXp sX0
t4
(8)
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
uP
u N
u
(X X0 )2
tm¼1 P
NRMSE ¼ 100 n
P
MBE ¼ m¼1
n
X0 max X0 min
(9)
(XP X0 )
n
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(10)
Water Practice & Technology Vol 18 No 11, 2825
where n is the calculated average, standard deviation, and different numbering of the data, and X stands for simulated data. The data that are simulated and observed are indicated by the subscripts P and o, respectively. The
linear relationship between the simulated and observed data is represented by the number r, which ranges
from 0 to 1. The linear relationship between the simulated and observed data is stronger when r is close to
1. The linear relationship between the simulated and observed data is stronger when r is close to 1. Metrics
like NRMSE and MBE are used by researchers to measure model biases and accuracy, enhancing the validity
of climate projections and intercomparison studies.
3. RESULTS AND DISCUSSION
3.1. Future climate projections
The precipitation and temperature data of three CMIP6 models were downscaled using the CF approach
under two scenarios in order to generate future climatic data. Then, the evaluation criteria were calculated
for them (Equations (9)–(11)). For instance, in Bakhtiari station, the amount of r was 0.98 and 0.32 for temperature and precipitation, respectively. Additionally, the least MBE of precipitation and temperature were
seen at Borujerd station, equal to 1.41 and 0.66. The lowest NRMSE amounts were observed in Borujerd
and Sezar stations with less precipitation compared to two other stations (Figure 4), because the model simulations were more accurate with less bias in regions with less amount of precipitation. In order to provide
future climate data, the CANESM5 model was selected since it had the lowest NRMSE and MBE and the
Figure 4 | Result of evaluation criteria for the three CMIP6 models at the selected stations.
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Water Practice & Technology Vol 18 No 11, 2826
highest r. As a result, climatic information was predicted for the chosen stations for the years 2020–2052
using the SSP1-2.6 and SSP5-8.5 scenarios. As seen in Figure 5, there are no discernible patterns in the
monthly precipitation at the chosen stations when compared to the historical period. November (3.1) and
August (0.1) saw the most historical increases and decreases in average monthly precipitation, respectively.
It should be observed that the majority of the months show a growing tendency for SSP5-8.5 scenarios as
Figure 5 | Changes in monthly mean temperature and precipitation compared to the historical period for SSP1-2.6 and SSP58.5 scenarios: (a) Bakhtiari, (b) Borujerd, (c) Dorud, and (d) Sepiddasht Sezar station.
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Water Practice & Technology Vol 18 No 11, 2827
opposed to SSP1-2.6 situations. At the chosen stations, however, there is a trend toward higher monthly
temperatures when compared to the historical period. Under all scenarios, the selected stations recorded
an increase in August of the greatest magnitude (1.9 and 2.8 °C, respectively). Additionally, under SSP1-2.6
and SSP5-8.5, respectively, the amount of average monthly temperature above historical levels is rising by
around 1.2 and 1.9 °C.
3.2. Runoff model performance
Using monthly data on precipitation, temperature, and evapotranspiration, the HBV model simulates streamflow
at four sub-basins from 1987 to 2010. Note that the model calibration and validation periods at these basins were
different. Table 4 shows the model performance for each sub-basin as well as calibration and validation period
details. Streamflow data gathered over a minimum of 13 years was used to calibrate the model. The model’s performance was evaluated after calibration using at least 6 years of observed flow data. In each station, the
correlation coefficient of the models was greater than 0.8, and the NRMSE was less than 1. At the Bakhtiari
station, for example, the correlation coefficients for the calibration and validation periods were 0.88 and 0.85,
respectively. According to the statistical criteria, the calibrated model looks to perform adequately. Figure 6
depicts the streamflow in each of the four sub-basins throughout the calibration and validation periods. As can
Table 4 | Evaluation criteria for the observed and simulated streamflow by HBV-Light model over the case study
r
NRMSE
MBE
1987–2000
2001–2007
0.87
0.88
0.094
0.093
1.96
1.70
Calibration
Validation
1989–2001
2002–2008
0.85
0.82
0.09
0.10
2.6
0.63
Sepiddasht Sezar
Calibration
Validation
1987–2000
2001–2010
0.93
0.87
0.08
0.09
6.7
5.09
Bakhtiari
Calibration
Validation
1989–2001
2002–2008
0.88
0.85
0.09
0.11
2.42
1.9
Stations
Period
Borujerd
Calibration
Validation
Dorud
Figure 6 | The observed and simulated runoff during calibration and validation periods.
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Water Practice & Technology Vol 18 No 11, 2828
be seen, the model has occasionally forecasted less runoff than was actually witnessed. This is due to the model’s
conceptually basic and comparably uncomplicated structure, which results in the runoff with a groundwater layer
(Abebe et al. 2010; Al-Safi & Sarukkalige 2018; Moghaddasi et al. 2022). Then, the future runoff was simulated
based on it under two scenarios. Figure 7 displays that the average monthly runoff will increase than historical
runoff over the case study except for Sepiddasht Sezar station (Sezar basin) under two scenarios. It should be
noted that the highest increase occurred in Dorud (about 2.7) and Borujerd station (about 2). In addition to,
the largest and lowest variability of runoff was observed at Bakhtiari and Borujerd stations. The trend of
runoff values was bigger than the median, as can be shown (positive skewness).
Figure 7 | Changes of monthly mean runoff compared to the historical period for SSP1-2.6 and SSP5-8.5 scenarios: (a) (left)
Borujerd, (right) Dorud, (b) (left) Sepiddasht Sezar, (right) Bakhtiari stations.
3.3. HMDI calculation
Firstly, the existing drought indices including SPEI and SRI were computed at the 3- and 12-month time scale for
historical and future periods. In addition, the HMDI was developed based on the mentioned individual indices.
As an example of the future, 12-month time scale is mentioned in Figure 8. According to this figure, the HMDI
indicated similar variation patterns with existing indices during the historical period for four sub-basins, such as
the 1990–1994 and 2008–2012 dry periods. Then, the most important characteristics of drought including severity
and duration were extracted based on Run theory (Yevjevich 1967) using HMDI. Note that a threshold of 0.5
was chosen, with smaller values indicating a drought event. As it can be seen, Table 5 shows the number, severity,
and duration (month) of drought events for the historical (1980–2012) and future periods during the period)
2020–2052) under SSP1-2.6 and SSP5-8.5 scenarios for four sub-basins. In general, the frequency of dry periods
decreases as the time scale lengthens, which causes the severity and duration of drought to increase. The most
severe drought in recorded history for all sub-basins has severity, duration, and number ranging from 15.56
to 83.42, 15 to 61 months, and 6 to 24. For example, in Bakhtiari station, the number of droughts decreased
from HMDI 3 to HMDI 12 from 18 to 8 in the historical period and from 20 to 9 in SSP5-8.5. Meanwhile,
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Water Practice & Technology Vol 18 No 11, 2829
Figure 8 | The monthly SPEI-12, SRI-12, HMDI-12 in historical and future periods: (a) Borujerd, (b) Dorud, (c) Sepiddasht Sezar,
and (d) Bakhtiari stations.
Table 5 | The number, highest severity, and duration (month) of drought events under the historical and future periods
Historical
SSP1-2.6
SSP5-8.5
Station
Index
Number
Duration
Max
Severity
Max
Number
Duration
Max
Severity
Max
Number
Duration
Max
Severity
Max
Bakhtiari
HMDI 3
HMDI 12
18
8
32
57
1.44
1.56
24
13
7
16
1.43
1.46
23
12
8
17
1.45
1.42
Borujerd
HMDI 3
HMDI 12
24
10
15
60
1.47
1.33
36
9
6
21
1.37
1.31
30
10
6
18
1.35
1.40
Dorud
HMDI 3
HMDI 12
17
8
29
47
1.35
1.43
23
11
8
19
1.41
1.36
24
11
8
17
1.42
1.39
Sepiddasht Sezar
HMDI 3
HMDI 12
15
6
20
61
1.43
1.37
22
11
8
17
1.43
1.44
21
11
8
19
1.42
1.44
duration and severity increase from 32 to 57 and 44.76 to 83.42, respectively. The results revealed that the
probability of severity and duration of the HMDI-derived drought generated by the SSP5-8.5 scenario is increased
in comparison with SSP1-2.6 and during the historical period in Dez basin, so the watershed would face a modest
increase in runoff in the future. According to the study’s findings, the effects of climate change can differ depending on where it happens.
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Water Practice & Technology Vol 18 No 11, 2830
As illustrated in Figure 9, the most and least incidence percentage of severe drought class (extreme) occurred at
the Dorud (6.75) and Borujerd (3.09) stations during the historical period. The percentage total of mild and severe
drought classes was increased under two scenarios than the historical period at the selected stations except for
Sepidasht Sezar station. For instance, the incidence percentage of the mild and severe drought classes ranged
Figure 9 | The percentage change of drought classes under the historical and future periods at (a) Borujerd, (b) Bakhtiari,
(c) Sepiddasht Sezar, and (d) Dorud and 12-month time scale.
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Water Practice & Technology Vol 18 No 11, 2831
from 11.95 to 16.62 and 3.09 to 5.19, respectively, at the Borujerd station. The extreme drought class rarely
occurred in the case study, especially at Borujerd station. It can also be said that the region will probably not
experience the extreme drought class in the future period.
4. CONCLUSION
This study investigated the impact of climate change on drought conditions in Iran’s Dez Basin, which is significantly important for hydropower production and water supply for drinking, industry, and agriculture. The
positive aspect of the current research is the division of the basin area into four sections, which enables a separate
examination of each region’s climatic change. In this study, monthly data sets including precipitation and temperature for a period of 22 years (1980–2012) were considered. Moreover, three GCM models, CANESM5, BCCCSM2-MR, and IPSL-CM6A-LR models were chosen for analysis due to their accurate simulating regional climate patterns. The hydrological response of the Dez basin to climate change was simulated using the
conceptual HBV rainfall–runoff model. Then two meteorological (SPEI) and hydrological (SRI) indices were
used to calculate streamflow historical and future drought. Finally, the newly combined drought index HMDI
was built using the prior individual indexes on 3- and 12-month time scales for characterizing past and future
drought periods.
The result showed that CANESM5 was chosen as the best model because it had the highest r and the lowest
NRMSE and MBE. In order to extract data for future climate under the SSP1-2.6 and SSP5-8.5 scenarios, revealing a significant increasing trend in temperature and precipitation. The HBV-light model performed well during
the calibration phase, demonstrating its suitability for predicting the hydrological state of the watershed in the
future. The average yearly streamflow increase was measured under two scenarios, and the SPEI and SRI drought
indices were derived. The HMDI index on 3- and 12-month time scales for past and future periods showed the
most severe drought produced by the SSP5-8.5 scenario in Borujerd, 12-month scale, and the least one with
SSP5-8.5 scenario in Borujerd station from 11.71 to 12.75. Therefore, the drought severity in SSP5-8.5 was predicted more than in SSP1-2.6. The findings suggest the need to examine how climate change affects local scales
and predict a significant increase in runoff in the future. Additionally, the responses of the four sub-basins were
found to vary in many situations. Finally, the drought classes were extracted, and under the historical period, the
Dorud (6.75) and Borujerd (3.09) stations had the highest and lowest incidence percentages of the severe drought
classes in the future. Under the two scenarios, the incidence percentage total of the mild and severe drought
classes increased in the selected stations, with the exception of the Sepidasht Sezar station. These findings
point to an impending drought in the Dez basin. The detrimental effects of climate change, according to this
study, will cause substantial droughts to occur in the investigated region in the future. Consequently, it is essential
to develop a management strategy to eradicate its inescapable adverse impacts. It is recommended that future
research will integrate nonlinear approaches and utilize more variables, such as evapotranspiration and runoff
in order to create new combination indices and compare their results.
DATA AVAILABILITY STATEMENT
All relevant data are available from an online repository or repositories. CRU Data: https://data.ceda.ac.uk/;
CMIP 6 data: https://esgf-node.llnl.gov/search/cmip6/.
CONFLICT OF INTEREST
The authors declare there is no conflict.
REFERENCES
Abdulai, P. J. & Chung, E. S. 2019 Uncertainty assessment in drought severities for the Cheongmicheon watershed using
multiple GCMs and the reliability ensemble averaging method. Sustainability 11(16), 4283. https://doi.org/10.3390/
su11164283.
Abebe, N. A., Ogden, F. L. & Pradhan, N. R. 2010 Sensitivity and uncertainty analysis of the conceptual HBV rainfall–runoff
model: Implications for parameter estimation. Journal of Hydrology 389(3–4), 301–310. https://doi.org/10.1016/j.jhydrol.
2010.06.007.
Adib, A., Kisi, O., Khoramgah, S., Gafouri, H. R., Liaghat, A., Lotfirad, M. & Moayyeri, N. 2021 A new approach for suspended
sediment load calculation based on generated flow discharge considering climate change. Water Supply 21(5), 2400–2413.
https://doi.org/10.2166/ws.2021.069.
Downloaded from http://iwaponline.com/wpt/article-pdf/18/11/2818/1331263/wpt0182818.pdf
by guest
Water Practice & Technology Vol 18 No 11, 2832
Al-Safi, H. I. J. & Sarukkalige, P. R. 2018 Evaluation of the impacts of future hydrological changes on the sustainable
water resources management of the Richmond River catchment. Journal of Water and Climate Change 9(1), 137–155.
https://doi.org/10.2166/wcc.2017.144.
Atallah, M. H., Djellouli, F., Bouanani, A., Baba-Hamed, K., Faisal, A. A. & Hasan, K. 2023 Assessment of catchment behavior
of the Wadi Louza in NW-Algeria under hydrological drought conditions. Earth Systems and Environment 7(1), 297–306.
Ahmad, M. I., Sinclair, C. D. & Werritty, A. 1988 Log-logistic flood frequency analysis. Journal of Hydrology 98(3-4),
205–224.
Bąk, B. & Kubiak-Wójcicka, K. 2016 Assessment of meteorological and hydrological drought in Torun (central Poland town) in
1971–2010 based on standardized indicators. In: 3rd International Conference Water Resources and Wetlands Conference
Proceedings, pp. 164–170.
Bergström, S. 1976 Development and application of a conceptual runoff model for Scandinavian catchments, Bulletin Series A,
No. 52 – SMHI report RH07, Norrköping: Swedish Meteorological and Hydrological Institute, 134.
Bergström, S., 1995 The HBV model. In: Computer Models of Watershed Hydrology (Singh, V. P., ed.). Water Resources
Publication, Highlands Ranch, pp 443–476.
Beersma, J. J. & Buishand, T. A. 2004 Joint probability of precipitation and discharge deficits in the Netherlands. Water
resources research 40(12).
Carvalho, D., Rafael, S., Monteiro, A., Rodrigues, V., Lopes, M. & Rocha, A. 2022 How well have CMIP3, CMIP5 and CMIP6
future climate projections portrayed the recently observed warming. Scientific Reports 12(1), 11983.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J. & Taylor, K. E. 2016 Overview of the Coupled Model
Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9(5),
1937–1958. https://doi.org/10.5194/gmd-9-1937-2016.
Fahimirad, Z. & Shahkarami, N. 2021 The impact of climate change on hydro-meteorological droughts using copula functions.
Water Resources Management 35(12), 3969–3993. https://doi.org/10.1007/s11269-021-02918-z.
Gao, L. & Zhang, Y. 2016 Spatio-temporal variation of hydrological drought under climate change during the period 1960–2013
in the Hexi Corridor, China. Journal of Arid Land 8(2), 157–171. https://doi.org/10.1007/s40333-015-0022-3.
Gholami, H., Moradi, Y., Lotfirad, M., Gandomi, M. A., Bazgir, N. & Shokrian Hajibehzad, M. 2022 Detection of abrupt shift
and non-parametric analyses of trends in runoff time series in the Dez river basin. Water Supply 22(2), 1216–1230. https://
doi.org/10.2166/ws.2021.357.
Hameed, M., Ahmadalipour, A. & Moradkhani, H. 2020 Drought and food security in the Middle East: An analytical
framework. Agricultural and Forest Meteorology 281, 107816. https://doi.org/10.1016/j.agrformet.2019.107816.
Intergovernmental Panel on Climate Change, (IPCC): Climate Change 2007 2007 Synthesis Report of the Fourth Assessment
Report, IPCC.
Keyantash, J. A. & Dracup, J. A. 2004 An aggregate drought index: Assessing drought severity based on fluctuations in the
hydrologic cycle and surface water storage. Water Resources Research 40(9). https://doi. org/10.1029/2003WR002610.
Li, Q., Li, P., Li, H. & Yu, M. 2015 Drought assessment using a multivariate drought index in the Luanhe River basin of
Northern China. Stochastic Environmental Research and Risk Assessment 29(6), 1509–1520. https://doi.org/10.1007/
s00477-014-0982-4.
Lun, Y., Liu, L., Cheng, L., Li, X., Li, H. & Xu, Z. 2021 Assessment of GCMs simulation performance for precipitation and
temperature from CMIP5 to CMIP6 over the Tibetan Plateau. International Journal of Climatology 41(7), 3994–4018.
https://doi.org/10.1002/joc.7055.
McKee, T. B. 1995 Drought monitoring with multiple time scales. In: Proceedings of 9th Conference on Applied Climatology,
Boston, 1995.McKee TB, Doesken NJ, Kleist J. 1995 Drought monitoring with multiple time scales. In: 482 9ths Conference
on Applied Climatology, Am Meteor Soc 233–236.
McKee, T. B., Doesken, N. J. & Kleist, J. 1993 The relationship of drought frequency and duration to time scales. In: Proceedings
of the 8th Conference on Applied Climatology, Vol. 17, No. 22, pp. 179–183.
Mitchell, T. D. & Jones, P. D. 2005 An improved method of constructing a database of monthly climate observations and
associated high-resolution grids. International Journal of Climatology: A Journal of the Royal Meteorological Society 25(6),
693–712. https://doi.org/10.1002/joc.1181.
Moghaddasi, M., Anvari, S. & Akhondi, N. 2022 A trade-off analysis of adaptive and non-adaptive future optimized rule curves
based on simulation algorithm and hedging rules. Theoretical and Applied Climatology 148(1), 65–78. https://doi.org/10.
1007/s00704-022-03930-y.
Naderi, K. & Moghaddasi, M. 2022 Drought occurrence probability analysis using multivariate standardized drought index and
copula function under climate change. Water Resources Management, 1–24. https://doi.org/10.1007/s11269-022-03186-1.
New, M., Hulme, M. & Jones, P. 1999 Representing twentieth-century space–time climate variability. Part I: Development of a
1961–90 mean monthly terrestrial climatology. Journal of Climate 12(3), 829–856. https://doi.org/10.1175/15200442(1999)012%3C0829:RTCSTC%3E2.0.CO;2.
O’Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F.,
Lowe, J., Meehl, G. A., Moss, R., Riahi, K. & Sanderson, B. M. 2016 The scenario model intercomparison project
(ScenarioMIP) for CMIP6. Geoscientific Model Development 9, 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016.
Pool, S., Vis, M. & Seibert, J. 2018 Evaluating model performance: Towards a non-parametric variant of the Kling-Gupta
efficiency. Hydrological Sciences Journal 63(13–14), 1941–1953. https://doi.org/10.1080/02626667.2018.1552002.
Downloaded from http://iwaponline.com/wpt/article-pdf/18/11/2818/1331263/wpt0182818.pdf
by guest
Water Practice & Technology Vol 18 No 11, 2833
Scanlon, B. R., Ruddell, B. L., Reed, P. M., Hook, R. I., Zheng, C., Tidwell, V. C. & Siebert, S. 2017 The food-energy-water nexus:
Transforming science for society. Water Resources Research 53(5), 3550–3556. https://doi.org/10.1002/2017WR020889.
Seibert, J. 1997 Estimation of parameter uncertainty in the HBV model: Paper presented at the Nordic Hydrological Conference
(Akureyri, Iceland-August 1996). Hydrology Research 28(4–5), 247–262. https://doi.org/10.2166/nh.1998.15.
Seibert, J. 2000 Multi-criteria calibration of a conceptual runoff model using a genetic algorithm. Hydrology and Earth System
Sciences 4(2), 215–224. https://doi.org/10.5194/hess-4-215-2000.
Seibert, J. & Vis, M. J. 2012 Teaching hydrological modeling with a user-friendly catchment-runoff-model software package.
Hydrology and Earth System Sciences 16(9), 3315–3325. https://doi.org/10.5194/hess-16-3315-2012.
Shukla, S. & Wood, A. W. 2008 Use of a standardized runoff index for characterizing hydrologic drought. Geophysical research
letters 35(2).
Svoboda, M. & Fuchs, B. 2016 Handbook of drought indicators and indices. Drought and water crises: Integrating science,
management, and policy, 155–208.
Thornthwaite, C. W. 1948 An approach toward a rational classification of climate. Geographical Review 38(1), 55–94. https://
doi.org/10.2307/210739.
Varol, T., Atesoglu, A., Ozel, H. B. & Cetin, M. 2023 Copula-based multivariate standardized drought index (MSDI) and length,
severity, and frequency of hydrological drought in the Upper Sakarya Basin, Turkey. Natural Hazards 116(3), 3669–3683.
https://doi.org/10.1007/s11069-023-05830-4.
Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. 2010 A multiscalar drought index sensitive to global warming: The
standardized precipitation evapotranspiration index. Journal of Climate 23(7), 1696–1718. https://doi.org/10.1175/
2009JCLI2909.1.
Waseem, M., Ajmal, M. & Kim, T. W. 2015 Development of a new composite drought index for multivariate drought
assessment. Journal of Hydrology 527, 30–37. https://doi.org/10.1016/j.jhydrol.2015.04.044.
Wang, Q., Liu, Y. Y., Zhang, Y. Z., Tong, L. J., Li, X., Li, J. L. & Sun, Z. 2019 Assessment of spatial agglomeration of agricultural
drought disaster in China from 1978 to 2016. Scientific reports 9(1), 14393.
Yang, J., Chang, J., Wang, Y., Li, Y., Hu, H., Chen, Y., Huang, Q. & Yao, J. 2018 Comprehensive drought characteristics analysis
based on a nonlinear multivariate drought index. Journal of Hydrology 557, 651–667.
Yevjevich, V. M. 1967 Objective Approach to Definitions and Investigations of Continental Hydrologic Droughts.
Doctoral Dissertation, Colorado State University. Libraries.
First received 3 August 2023; accepted in revised form 6 October 2023. Available online 26 October 2023
Downloaded from http://iwaponline.com/wpt/article-pdf/18/11/2818/1331263/wpt0182818.pdf
by guest