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 HMDI-derived 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.
HIGHLIGHTS
Using CMIP6 to assess the impact of climate change.
Using compound drought index to monitor hydrological and meteorological drought.
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) 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 HBV-light 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 SSP5-8.5 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.
MATERIAL AND METHODS
Study area and evaluation datasets
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 |
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 |
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)
- 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
Variable . | Station . | r . | NRMSE . | MBE . |
---|---|---|---|---|
Precipitation | Borujerd | 0.66 | 0.92 | 0.07 |
Dorud | 0.81 | 0.71 | 0.09 | |
Sepiddasht Sezar | 0.68 | 0.93 | 0.11 | |
Bakhtiari | 0.85 | 0.67 | 1.68 | |
Temperature | Borujerd | 0.8 | 0.14 | −0.04 |
Dorud | 0.93 | 0.12 | 1.23 | |
Sepiddasht Sezar | 0.89 | 0.52 | −2.35 | |
Bakhtiari | 0.96 | 0.48 | 0.05 |
Variable . | Station . | r . | NRMSE . | MBE . |
---|---|---|---|---|
Precipitation | Borujerd | 0.66 | 0.92 | 0.07 |
Dorud | 0.81 | 0.71 | 0.09 | |
Sepiddasht Sezar | 0.68 | 0.93 | 0.11 | |
Bakhtiari | 0.85 | 0.67 | 1.68 | |
Temperature | Borujerd | 0.8 | 0.14 | −0.04 |
Dorud | 0.93 | 0.12 | 1.23 | |
Sepiddasht Sezar | 0.89 | 0.52 | −2.35 | |
Bakhtiari | 0.96 | 0.48 | 0.05 |
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 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 SSP5-8.5 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 highlighted that the historical simulation is valid for comparison with the existing data because it spans the temperature observation period from 1980 to 2012.
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° |
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° |
Methodology
Methodology framework
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.
HBV-light model
Downscaling
Standardized precipitation evapotranspiration index
where α, β, and γ are scale, shape, and location parameters, respectively, for D values in the range (γ < x < ∞).
Standardized runoff index
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.
Principal component analysis
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.
Evaluation criteria
RESULTS AND DISCUSSION
Future climate projections
Runoff model performance
Stations . | Period . | r . | NRMSE . | MBE . | |
---|---|---|---|---|---|
Borujerd | Calibration | 1987–2000 | 0.87 | 0.094 | −1.96 |
Validation | 2001–2007 | 0.88 | 0.093 | −1.70 | |
Dorud | Calibration | 1989–2001 | 0.85 | 0.09 | −2.6 |
Validation | 2002–2008 | 0.82 | 0.10 | −0.63 | |
Sepiddasht Sezar | Calibration | 1987–2000 | 0.93 | 0.08 | −6.7 |
Validation | 2001–2010 | 0.87 | 0.09 | 5.09 | |
Bakhtiari | Calibration | 1989–2001 | 0.88 | 0.09 | −2.42 |
Validation | 2002–2008 | 0.85 | 0.11 | −1.9 |
Stations . | Period . | r . | NRMSE . | MBE . | |
---|---|---|---|---|---|
Borujerd | Calibration | 1987–2000 | 0.87 | 0.094 | −1.96 |
Validation | 2001–2007 | 0.88 | 0.093 | −1.70 | |
Dorud | Calibration | 1989–2001 | 0.85 | 0.09 | −2.6 |
Validation | 2002–2008 | 0.82 | 0.10 | −0.63 | |
Sepiddasht Sezar | Calibration | 1987–2000 | 0.93 | 0.08 | −6.7 |
Validation | 2001–2010 | 0.87 | 0.09 | 5.09 | |
Bakhtiari | Calibration | 1989–2001 | 0.88 | 0.09 | −2.42 |
Validation | 2002–2008 | 0.85 | 0.11 | −1.9 |
HMDI calculation
. | Historical . | SSP1-2.6 . | SSP5-8.5 . | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Station . | Index . | Number . | Duration . | Severity . | Number . | Duration . | Severity . | Number . | Duration . | Severity . |
Max . | Max . | Max . | Max . | Max . | Max . | |||||
Bakhtiari | HMDI 3 | 18 | 32 | −1.44 | 24 | 7 | −1.43 | 23 | 8 | −1.45 |
HMDI 12 | 8 | 57 | −1.56 | 13 | 16 | −1.46 | 12 | 17 | −1.42 | |
Borujerd | HMDI 3 | 24 | 15 | −1.47 | 36 | 6 | −1.37 | 30 | 6 | −1.35 |
HMDI 12 | 10 | 60 | −1.33 | 9 | 21 | −1.31 | 10 | 18 | −1.40 | |
Dorud | HMDI 3 | 17 | 29 | −1.35 | 23 | 8 | −1.41 | 24 | 8 | −1.42 |
HMDI 12 | 8 | 47 | −1.43 | 11 | 19 | −1.36 | 11 | 17 | −1.39 | |
Sepiddasht Sezar | HMDI 3 | 15 | 20 | −1.43 | 22 | 8 | −1.43 | 21 | 8 | −1.42 |
HMDI 12 | 6 | 61 | −1.37 | 11 | 17 | −1.44 | 11 | 19 | −1.44 |
. | Historical . | SSP1-2.6 . | SSP5-8.5 . | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Station . | Index . | Number . | Duration . | Severity . | Number . | Duration . | Severity . | Number . | Duration . | Severity . |
Max . | Max . | Max . | Max . | Max . | Max . | |||||
Bakhtiari | HMDI 3 | 18 | 32 | −1.44 | 24 | 7 | −1.43 | 23 | 8 | −1.45 |
HMDI 12 | 8 | 57 | −1.56 | 13 | 16 | −1.46 | 12 | 17 | −1.42 | |
Borujerd | HMDI 3 | 24 | 15 | −1.47 | 36 | 6 | −1.37 | 30 | 6 | −1.35 |
HMDI 12 | 10 | 60 | −1.33 | 9 | 21 | −1.31 | 10 | 18 | −1.40 | |
Dorud | HMDI 3 | 17 | 29 | −1.35 | 23 | 8 | −1.41 | 24 | 8 | −1.42 |
HMDI 12 | 8 | 47 | −1.43 | 11 | 19 | −1.36 | 11 | 17 | −1.39 | |
Sepiddasht Sezar | HMDI 3 | 15 | 20 | −1.43 | 22 | 8 | −1.43 | 21 | 8 | −1.42 |
HMDI 12 | 6 | 61 | −1.37 | 11 | 17 | −1.44 | 11 | 19 | −1.44 |
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, BCC-CSM2-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.
Standardized Precipitation Index
Standardized Runoff Index
Standardized Precipitation Evapotranspiration Index
Multivariate Standardized Drought Index