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Article

Temperature and Precipitation Change Assessment in the North of Iraq Using LARS-WG and CMIP6 Models

by
Sura Mohammed Abdulsahib
1,
Salah L. Zubaidi
1,2,*,
Yousif Almamalachy
3 and
Anmar Dulaimi
4,5,*
1
Department of Civil Engineering, Wasit University, Wasit 52001, Iraq
2
College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
3
National Center for Water Resources Management, Ministry of Water Resources, Baghdad 10001, Iraq
4
College of Engineering, University of Kerbala, Karbala 56001, Iraq
5
School of Civil Engineering and Built Environment, Liverpool John Moores University, Liverpool L3 2ET, UK
*
Authors to whom correspondence should be addressed.
Water 2024, 16(19), 2869; https://doi.org/10.3390/w16192869
Submission received: 11 August 2024 / Revised: 3 October 2024 / Accepted: 8 October 2024 / Published: 9 October 2024
(This article belongs to the Section Water and Climate Change)
Browse Figures
Versions Notes

Abstract

:
Investigating the spatial-temporal evolutionary trends of future temperature and precipitation considering various emission scenarios is crucial for developing effective responses to climate change. However, researchers in Iraq have not treated this issue under CMIP6 in much detail. This research aims to examine the spatiotemporal characteristics of temperature and rainfall in northern Iraq by applying LARS-WG (8) under CMIP6 general circulation models (GCMs). Five GCMs (ACCESS-ESM1-5, CNRM-CM6-1, MPI-ESM1-2-LR, HadGEM3-GC31-LL, and MRI-ESM2-0) and two emissions scenarios (SSP245 and SSP585) were applied to project the upcoming climate variables for the period from 2021 to 2040. The research relied on satellite data from fifteen weather sites spread over northern Iraq from 1985 to 2015 to calibrate and validate the LARS-WG model. Analysis of spatial-temporal evolutionary trends of future temperature and precipitation compared with the baseline period revealed that seasonal mean temperatures will increase throughout the year for both scenarios. However, the SSP585 scenario reveals the highest increase during autumn when the spatial coverage of class (15–20) °C increased from 27.7 to 96.29%. At the same time, the average seasonal rainfall will rise in all seasons for both scenarios except autumn for the SSP585 scenario. The highest rainfall increment percentage is obtained using the SSP585 for class (120–140) mm during winter. The spatial extent of the class increased from 25.49 to 50.19%.

1. Introduction

The sixth assessment report (AR6) declared that the worldwide surface temperature increased by 1.1 °C by 2020 due to human activity and greenhouse gas emissions, compared to 1850–1900 levels. This warming trend is expected to continue until the end of the century, when it will reach 1.5 °C [1]. Many regions of the world have observed different consequences of climate change, including ice melting, rising sea levels, raised temperatures, degraded air quality, and fluctuation in atmospheric and oceanic dynamics. These impacts are considered the greatest risk to human life [2].
The Earth’s climate has been changed for over a century because of high amounts of greenhouse gases (GHG) produced by various anthropogenic activities, such as burning fossil fuels and land use change [3]. The problem of climate change will be more prominent during the 21st century, according to all the Intergovernmental Panel on Climate Change (IPCC) emission scenarios. Thus, the Earth’s temperature will rise, and the hydrological systems will be affected significantly [4,5].
Because of its semi-arid and arid climate and the probability of water shortages brought on by sharply reducing rainfall and increasing temperatures, the Middle East could be the most influenced region by the consequences of climate change in recent years [6,7]. Iraq is one of the Middle Eastern countries most affected by the consequences of global warming. According to Nile et al. [8], Iraq suffers from environmental difficulties such as freshwater scarcity, frequent sandstorms, intense heat waves, and unanticipated floods. These problems will be worsened by increased extreme weather events that cause environmental degradation in a large portion of the country in general. Thus, reviewing research conducted in the Middle East, assessing climate change’s effect on water resource availability, and evaluating hydrological parameters is crucial. Furthermore, recent studies by several academics have confirmed that observed extreme occurrences are rising in Iran, Turkey, Syria, Iraq, and Saudi Arabia due to the impacts of global warming [9,10,11,12,13].
Researchers widely depend on general climate models (GCMs) to project the climate state both at the current time and in the future using different scenarios of emission [14,15,16]. Because GCM grids are so coarse, these grids should be downscaled to a resolution proper for local and regional scales [17,18,19]. Downscaling techniques fall into two classes: statistical and dynamic downscaling [20,21]. The GCM outputs obtained from the Coupled Model Intercomparison Project (CMIP) are the most extensively utilized [22,23,24]. The Working Group on Coupled Modelling (WGCM) has presented the newest CMIP6 scenarios, which is the latest group of emission scenarios known as shared socio-economic pathways (SSPs), depending on several socio-economic hypotheses [25].
One of the Statistical Downscaling Models (SD) that has been proven to be reliable in identifying intricate patterns for the prediction of spatial-temporal data is the Long Ashton Research Station Weather Generator (LARS-WG) model since it is less computationally expensive, easier to use, and demands fewer computer resources than dynamical downscaling strategies. The model of LARS-WG was used successfully in different regions worldwide; for example, in Asia [26,27,28,29,30], Africa [31,32,33,34], Europe [35,36,37,38], Australia [39], and North America [40,41,42].
Besides, Iraq falls in a semi-arid to arid climate marked by limited water resources and dispersed low annual rainfall [43,44]. Many regions in Iraq, especially in the north, depend on permaculture and produce strategic crops; therefore, the climate change that the country witnesses directly impacts the region’s productivity [45]. Mohammed and Scholz [46] employed the LARS-WG 5.5 model to study potential changes caused by climate change in the Lower Zab River watershed in Northern Iraq for three periods until 2100, depending on the reference period (from 1980 to 2010). The investigation used seven GCMs under two scenarios (SRA2 and SRA1B). The two climate scenarios showed almost the same results for the future years from 2046 to 2065, indicating that there may be a growth in the minimum temperature between 3.0 and 3.3 °C and an increase in the maximum temperature between 3.2 and 3.7 °C as vs. a reference period. Hassan and Hashim [12] applied the LARS-WG technique to determine the consequences of climate change in the southwest part of Iraq. The study is based on two GCMs (HadCM3 and CanESM2) under scenarios B2, A2, and RCP 8.5, 2.6, and 4.5. The model simulated future climate scenarios and projected that temperatures will climb steadily till the end of the 21st century. According to the prediction using the RCP 4.5 scenario, the yearly temperature will rise by 1.78 to 2.07 °C for the period from 2040 to 2069 and 2.2 to 2.63 °C for the years between 2070 and 2099. Meanwhile, the rise in annual temperature by RCP 8.5 for the first period will be 2.18 to 2.85 °C and 2.93 to 3.7 °C for the second period. Saeed et al. [29] applied the LARS-WG 6 model in Iraq to downscale five GCM outputs using three scenarios (RCPs) 2.6, 4.5, and 8.5 for three future periods (2021–2040), (2041–2060), and (2061–2080) to forecast the rainfall and temperature (minimum and maximum) measured in four metrological stations. The outcomes revealed that the studied areas tend to become drier and hotter because of reduced rainfall and the increased temperature. Moreover, under the RCP 8.5 and for the period from 2061 to 2080, the study pointed out an accelerated rise in the maximum and minimum temperature in northern regions (Kirkuk and Mosul) by approximately 80 and 35%, respectively. A lower increase can be observed in southern zones (Kut and Khalis) for the minimum and maximum temperatures of around 40 and 20%, respectively. The greatest rates of rise in maximum and minimum temperatures were observed in the winter months for all the weather stations. Mohammed and Hassan [47] employed the LARS-WG 6 model for forecasting temperature and precipitation. Five GCMs were used to predict climate change depending on the RCP8.5 and RCP4.5 emission scenarios over three time periods (2021–2040), (2051–2070), and (2081–2100). The technique was calibrated and validated employing the 1980–2010 data from three weather stations in southern Iraq. Results indicated that 5.67 and 5.91 °C will increase in annual minimum and maximum temperatures, respectively, by the end of the current century under the RCP8.5, while the increase will be 1.41 and 1.50 °C under the RCP4.5 at all research stations. The results also demonstrated that all five GCM models forecast different precipitation drop trends.
Also, there are some studies related to climate change impacts on temperatures and precipitation in nearby areas, such as Kuglitsch et al. [48], who used data from 246 stations in the eastern Mediterranean area (Turkey, Romania, Albania, Bosnia-Herzegovina, Slovenia, Greece, Bulgaria, Cyprus, Croatia, and Serbia) to identify variations in heat wave number, intensity, and length from 1960 to 2006. After accounting for warm-biased readings in the 1960s, regional heat wave patterns were expected to be up to 8% higher. Substantial variations occur in the western Balkans, southwestern and western Turkey, and the southern Black Sea coastline. Since the 1960s, heat wave intensity, length, and number in the eastern Mediterranean area have grown by 7.6 ± 1.3, 7.5 ± 1.3, and 6.2 ± 1.1, respectively. These results indicate a greater heat wave increase in this region than previously reported. Selek et al. [49] mentioned that global warming expressively affects water resources. Different studies predict diminishing rainfall and rising temperatures in Turkey’s Mediterranean region. This research examined climate change adaptation options using Seyhan Dam in this location. The study briefly discusses the criteria for building the Seyhan Dam, the basin and dam, climate change assessments for the basin, and a larger assessment of adaptation techniques and policies. First, the paper discusses how the Seyhan Dam increases climate change resilience. Second, the paper examines recent legal and institutional changes in the water industry and their effects on global warming.
From this perspective, the aim of this research is to project how northern Iraq’s rainfall and mean temperature will vary due to climate change by applying state-of-the-art climate modeling tools. Particularly, the study postulates that various climate scenarios will bring about substantial shifts in climatic patterns, mainly in terms of rainfall and mean temperature. These future predictions are vital to comprehend the probable effects on water supplies and the regularity of severe hydrological occurrences in the area. In pursuit of this aim, the following objectives will be taken:
  • To apply the LARS-WG (8) technique to project climatic factors (i.e., rainfall and temperature) over the period (2021–2040) covering fifteen meteorological locations in the north of Iraq.
  • To assess the impact of two climate scenarios (i.e., SSP245 and SSP585) by integrating CMIP6 predictions from five GCMs to capture a wide variety of possible results and to lessen the uncertainty surrounding future predictions.
  • To examine the projected and historical spatial-temporal seasonal variability of rainfall and mean temperature to discover the potential effects of future SSP245 and SSP585 scenarios.
  • To contribute essential information for environmental planning at the local level, focusing on the management of water resources and the reduction of hazards associated with extreme hydrological occurrences in the near to medium term (2021–2040).

2. Materials and Methods

2.1. Area of the Study and the Data Set

Iraq is among the Middle Eastern nations that experience arid and semi-arid climates. In general, Iraq can be considered one of the hottest zones on the globe. However, the regions of northern Iraq are relatively cooler than those in southern Iraq [50].
The region under consideration in this study is the northern portion of Iraq (from 34°10′ to 37°20′ N, 41°16′ to 46°20′ E), which covers an area of 65,000 km2. The study area includes five governorates: Dohuk, Erbil, Sulaymaniyah, Mosul, and Kirkuk. The northern and northeastern parts of Iraq are mountainous, including Dohuk, Erbil, and Sulaymaniyah. At the same time, Kirkuk and Mosul are considered undulating areas. This area of northern Iraq is home to most of the Tigris River tributaries, including the Diyala, Adhaim, Lower Zab, Upper Zab, and Khabur. Thus, it is significant for the entire Iraqi land’s water resource management because it has several dams and reservoirs [51].
The climate of northern Iraq is often classified as semi-arid, with cold and humid winters and hot and dry summers. The warmest summer months, July, and August, have a mean maximum temperature of 39–43 °C and frequently approaches 50 °C, except for the high mountains, with moderate summers. Wintertime averages range from 7 to 13 °C for maximum temperatures and 2 to 7 °C for minimum temperatures, with the potential of frost. Seasonal rainfall takes place during the winter, from November to April. The study area’s average annual precipitation varies from more than 1000 mm in the east to less than 300 mm in the western locations [52]. The average yearly temperature observed in Iraq from 1971 to 2020 was less than the global average and showed fluctuations in the second half of the twentieth century. As a result of the effects of climate change, Iraq’s average temperature has been steadily climbing since the mid-1990s, and this trend continues to this day. However, beginning in 1998, it was clear that the amount of precipitation in Iraq was decreasing [53]. This situation matches temperature growth in 11 eastern Mediterranean countries between 1960 and 2006 [48] and reduced precipitation in Turkey [49].
The scarcity of meteorological data is a fundamental problem in developing countries. The majority of recorded meteorological data between 1990 and 2020 is discontinuous due to the exceptional circumstances of wars and terrorism in Iraq. According to Kadhim Tayyeh and Mohammed [54], satellite data have been considered an effective and promising alternative for climatic and hydrological studies; therefore, National Aeronautics and Space Administration (NASA) data is used in this study. Also, the satellite data were used successfully to project future climate data in different studies, such as [55,56].
Fifteen meteorological stations distributed over the five governorates were chosen to investigate climate change; their locations are displayed in Figure 1 and Table 1. In the current study, the satellite was used to provide historical information on the daily precipitation and the minimum (Tmin) and maximum (Tmax) temperatures from 1985 to 2015, which were taken as the baseline period to validate and calibrate the LARS-WG program (version 8.0). Table 2 presents statistical descriptions of important parameters, including maximum (max.), minimum (min.), and mean, over the baseline period for all stations.

2.2. Downscaling Using LARS-WG

The LARS-WG, which is one of the most common stochastic weather generator models, was used to downscale the GCMs outputs. The LARS-WG generates daily solar radiation, temperatures (minimum and maximum), and precipitation at a particular site for future and current climate periods [57]. In 1990, the Assessment of Agricultural Risk Project in Hungary included the initial release of LARS-WG. Semenov et al. [57] assessed and approved the LARS-WG model’s efficiency at eighteen meteorological stations across Europe, Asia, and the United States [58]. There are three processes to create synthetic weather data using the LARS-WG model: validation of the model, calibration, and creation of the synthetic data. These steps will be briefly explained below. Additional information regarding the modeling process is available in [59,60].
Firstly, the model must be calibrated. The LARS-WG function “SITE ANALYSIS” analyzes the measured weather data, such as rainfall and minimum and maximum temperatures, to ascertain their statistical properties. The LARS-WG saves this data in two parameter files. Secondly, in the validation process, the parameter files obtained by the measured data throughout the calibration process are utilized to produce synthetic weather data with the same statistical characteristics. To verify whether the LARS-WG model is appropriate or not for use in the study, the model validation includes analyzing and matching the statistical characteristics of the synthetic and observed data to estimate the model’s capability in simulating the weather factors within the selected sites. The Kolmogorov-Smirnov (K-S) goodness-of-fit test was applied to measure the probability distribution of the simulated and observed weather data to examine the similarity of the probability distribution between the measured data and the simulated data in the stations under study. Finally, the obtained parameter files by the calibration process are utilized to produce synthetic data analogous to a certain scenario simulated by the GCMs [61]. The methodology followed in this research is summarized in Figure 2. The latest LARS-WG software (i.e., version 8.0) was used with two SSPs in order to downscale the large-scale rainfall and temperature data from the five GCMs. Calibration, validation, and obtaining future climate data were the steps in producing synthetic precipitation and temperature data for both the current and future climates. To create the model parameters and produce the synthetic daily weather data of 20 years for the model validation, the LARS-WG was calibrated depending on the baseline data of 31 years (from 1985 to 2015) at each of the 15 stations within the nominated study area. The observed and synthetic daily weather data of each station were compared using the statistical test to determine the model’s validity. The statistical test applied in this study is the K-S test, which was utilized to decide whether the seasonal distribution of the wet and dry series (WD Series), the distribution of daily precipitation (PD), and the daily minimum and maximum temperature distributions (TminD and TmaxD) obtained from the observed and downscaled data are identical. After the calibration and validation of the model, the last stage is to generate future weather data series.
In the present research, the five models of GCMs available in the LARS-WG (version 8.0) model, as shown in Table 3, are used under two SSPs, as presented in Table 4, to forecast variations in rainfall and min-max temperatures. Single GCM climate simulations cannot accurately predict future hydrologic implications or depict their unknown ties [62]. The mean ensemble of GCMs output may yield accurate estimates. GCM biases will be eliminated, reducing uncertainty [63]. The mean ensemble of GCMs offers more accurate future estimates and is the most conservative risk-management technique [64,65].
Zhai et al. [66] described rising mean temperature as a warming indicator. The climate difference in temperature depicts the variations that happen up or under the average temperature along time [67]. Also, the research examines different techniques to calculate mean temperature, including arithmetic, geometric, and harmonic. Six statistical criteria were applied to assess techniques. For the mean of the two observations, the results reveal that geometric techniques (Equation (1)) slightly outperformed the other techniques.
G e o m e t r i c − 2 = T m a x T m i n

3. Results

3.1. Calibration and Validation of the Model

The daily temperature and precipitation data in the fifteen meteorological sites in northern Iraq from 1985 to 2015 were utilized to validate and calibrate the model of LARS-WG. The model’s ability to downscale GCMs in the research area was examined using a statistical test. The Kolmogorov-Smirnov (K-S) test was conducted to ensure that the distributions of daily climatic variables computed from observed and simulated data were similar. Additionally, this study used a p-value to accept or reject the hypothesis that both data sets (measured and projected) may have originated from the same distribution. It can accept the simulated climate as being identical to the measured environment if the low K-S value and highly high p-value [60]. Table 5 illustrates the statistical tests for the Zakho station as an example.
According to the assessment results shown in the table, one can conclude that the technique’s performance in projecting the distribution of the daily Tmin, Tmax, and rainfall is generally perfect (P) except for some very good (V G) performance. Consequently, the downscaling model was more confidently used in this investigation.
The confidence in the technique’s capability to anticipate future rainfall and (minimum and maximum) temperature at Zakho station is demonstrated by examining the monthly standard deviations (Std) and averages for the measured and projected series (Figure 3). Generally, the outcomes are reasonable, primarily because of the difficulty of modeling Std at an acceptable level in previous research [68]. Figure 3 reveals that the LARS-WG technique performs well for values of monthly Std and mean values for the minimum and maximum temperature and rainfall, suggesting that it possesses a notable capacity for predicting climatic factors. Consequently, the conclusion is that the LARS-WG technique performs well when simulating daily rainfall and (minimum and maximum) temperature data for the future.
Four statistical criteria (Table 6), including mean absolute error (MAE), root means square error (RMSE), mean bias error (MBE), and correlation coefficient (R), were applied to assess the model calibration for monthly precipitation and temperature (minimum and maximum) for Zakho station over the baseline period 1985–2015. The outcomes show that the LARS-WG technique can project all climate factors [69,70], especially for Tmax and Tmin, and these outcomes are identical to the results presented in Figure 3.
To further evaluate the LARS-WG model, the Box-Whisker plot was employed. Box plots of monthly measured and predicted data for Tmax, Tmin, and rainfall time series over the baseline period are shown in Figure 4. The LARS-WG technique can be deemed suitable if it reproduces the measured box reliably. The results exposed the capability of the LARS-WG technique to reproduce the measured box for all climate factors well based on multiple parameters, including median, upper, and lower box borders, and upper and lower limits of whisker, especially for Tmax and Tmin.

3.2. Projection Results

According to the verification and calibration findings acquired from the LARS-WG model (Section 3.1), it was concluded that the efficiency of the LARS-WG was adequate for downscaling and forecasting the daily rainfall and the max-min temperatures at the fifteen weather stations. Thus, the model was applied to project the daily climatic factors for each station between 2021 and 2040 based on five GCMs and two scenarios (i.e., SSP245 and SSP585).
A well-known approach in climate change impact studies is to assess this phenomenon seasonally; hence, the climatic data temporal granularity for each year was converted from monthly to seasonal by averaging the monthly values of each season’s corresponding months for temperature and accumulating values for rainfall. Accordingly, the observed and projected (i.e., SSP245, and SSP585) data are separated into four seasons: autumn (September–November), winter (December–February), spring (March–May), and summer (June–August). At this phase, seasonal data was generated for each year across the whole period. By computing the mean of climatic factors (i.e., precipitation and mean temperature) for each season (autumn, winter, spring, and summer) throughout all years to provide each nominated location with four values for mean temperature and four for precipitation. These values characterize the seasonal difference in precipitation and mean temperature for each location.
Up to this point, all the previous analyses were conducted on each selected site separately. ArcGIS 10.8.2 software with Python coding was used to represent the data spatially. All observed and projected variables are spatially interpolated across the study area using the Inverse Distance Weighting (IDW) method and mapped in different classes [51,71]. The authors’ cartographic expertise and previous investigations [72,73,74,75] determined the division of classes. For example, when choosing classes for rainfall maps, some major points were considered: 1—The classes should range from the highest to the lowest possible rainfall value so that the classes (color themes) can be fixed across all maps, and the reader can easily compare the maps only by looking at them. 2—In order to prevent class colors from being confused, the study limited to seven distinct classes that can more easily distinguish ones. 3—No less than seven classes were chosen to make the map as informative as it can be efficiently. Since reducing classes narrows the amount of information that the map can show. 4—To make all the values fit in the seven-class criteria, one of the classes was slightly extended (by 10 mm). The middle class, 90–120, was chosen to be extended to keep the extreme classes (low or high ones) more distinguishable on the map with a 20 mm period.
As per the previous research studies [72,76], after determining the classes for mean temperature or rainfall, the nominated area (km2) for each class was calculated, allowing us to know each class’s seasonal area. Lastly, the percentage of areas (%) for each class relative to the overall area was determined as the area altered over time.
The letter R denotes the seasonal precipitation, while the mean temperature is represented by the letter T. In conclusion, the seasons represented by the numbers 1, 2, 3, and 4 are autumn, winter, spring, and summer, in that order. Eight seasonal spatiotemporal distribution figures of observed and simulated precipitation and mean temperature forecasts for the selected period are revealed in the following sections.

3.2.1. Projection of Precipitation

In this section, spatial-temporal precipitation maps were prepared in a seasonal time scale that was divided into seven classes, from 0–20 mm (the lowest) to 140–160 mm (the highest). Afterwards, the area percentages of each precipitation class were taken using spatial distribution maps. The prepared precipitation maps for observed and projected (i.e., SSP245 and SSPS285) data in autumn are represented in Figure 5. As seen in the figure, for observed data during 1985–2015, precipitation class (90–120) mm had the highest area percentage (54.54%), followed by class (70–90) mm with (45.01%) and class (50–70) mm with (0.45%). For the SSP245 scenario during 2021–2040, the mean of the ensemble of multi-GCM models reveals that class (90–120) mm has the highest proportion and increased slightly (1.54%) compared with observed data. Meanwhile, class (70–90) mm slightly dropped from (45.01%) in the observed to (43.16%). Small portions went to classes (50–70) mm and (120–140) mm with (0.56%) and (0.2%), respectively. For the SSP585 scenario during 2021–2040, the mean of the ensemble of multi-GCM models reveals that (57%) of the study area covered by class (70–90) mm, around (11.99%) higher than the same class in observed data. The incidence of class (90–120) mm has been estimated as (41.78%), which decreased (12.76%) in comparison with observed data—followed by class (50–70) mm with (1.22%).
Generally, the Northeastern part of the study area has the highest percentage of rainfall. Class (90–120) mm is dominant in the map of the observed and SSP245 scenario, while class (70–90) mm is dominant in the map of the SSP585 scenario. Also, there are some shifts, such as class (50–70) mm being shifted from Haweja in the observed period to Daquq in the SSP245 scenario.
Figure 6 presents the seasonal spatial-temporal precipitation maps for the observed and projected winter data. Generally, the winter maps have two main dominant classes: (90–120) and (120–140), and the Northwest had the highest percentage of rainfall for all winter maps, and the Southeast had the highest rainfall for both projected scenarios. In both projection scenarios, the rainfall in Darbandakhan and Sulaymaniyah stations has shown a class change from 90–120 to 120–140 mm. Similarly, the rainfall class in Mosul station has shifted from 120–140 to 140–160 mm. According to the SSP245 scenario, the precipitation in Rabia station has increased from class 120–140 to 140–160 mm. It is worth mentioning that the region around Haweja station shows the same rainfall category but with different coverage areas.
Generally, class (90–120) mm is dominant in the map of the observed and SSP245 scenario, while class (120–140) mm is dominant in the map of the SSP585 scenario. Although the observed and SSP245 scenario had the same dominant class, class (120–140) mm increased from (25.49%) in the observed to (42.33%) in the SSP245 scenario. Accordingly, both scenarios exhibit higher projected rainfall than the observed map. Also, precipitation in winter is higher than in autumn.
Figure 7 presents the seasonal spatial-temporal precipitation maps for the observed and projected Spring data. Generally, the results of this season show that more than 75% of the stations were in the rainfall class (50–70) mm. However, the spatial extent of the rainfall class (70–90) mm, which is the highest-class during spring, represents 20.05, 17.71, and 11.35% of the study area in the SSP245, SSP585, and observed map, respectively. So, both scenarios project higher precipitation than the observed map.
Generally, the Northern part of the study area has the highest percentage of rainfall. The rainfall in the study area during spring is significantly lower than that of winter and autumn. Also, there is one shift in class (20–50) mm that was shifted from Sinjar in the observed period to Rabia in the SSP585 scenario.
Finally, the seasonal spatial-temporal precipitation maps for the observed and projected summer data are shown in Figure 8. All the investigated stations recorded less than 20 mm of rainfall in the two projection scenarios and the observed rainfall. This result is attributed to the fact that summer is not a rainy season in the study area.
In general, precipitation is expected to rise in both scenarios, except in autumn under SSP585. The increase in precipitation is a variation based on the season and scenario; for example, the SSP245 will offer more rainfall than the SSP585 in spring and vice versa in winter.

3.2.2. Projection of Mean Temperature

In this section, spatial-temporal mean temperature maps were prepared in a seasonal time scale that was divided into seven classes, from 5–10 °C (the lowest) to 35–40 °C (the highest). Afterwards, the area percentages of each temperature class were taken using spatial distribution maps. The prepared temperature maps for observed and projected (i.e., SSP245 and SSPS285) data in autumn are represented in Figure 9. The spatial extent of the temperature class (15–20 °C), which is the warmest class during this season, increases from 27.70% in the observed map to 98.29% and 92.98% using the SSP585 and the SSP245, respectively. So, compared to the observed maps, both the projection scenarios show temperature increases during the studied period. The SSP585 exhibits a higher increase than the SSP245, resulting from unmitigated GHGs.
Regarding the winter season, Figure 10 illustrates the observed and projected seasonal mean temperatures. The dominant and coolest class in the observed map is a class (5–10) °C for the observed and projected data but with different percentages. It has the highest proportion for the observed period (1985–2015) with (92.75%) while it will be (79.78%) and (59.71%) of the mean of the ensemble of multi-GCM models for SSP245 and SSP585 scenarios during 2021–2040, respectively.
Generally, the Northern part of the study area has the coolest class of seasonal mean temperatures. Class (5–10) °C is dominant in the map of the observed, SSP245, and SSP585 scenarios. However, the projected data is hotter than the observed data, and the SSP585 scenario is the hottest one. Also, the SSP585 scenario shows a spatial shift in Rabiaa, Aqra, Mosul, Makhmour, Alton Kopry, and Darbandakhan stations, while the SSP245 exhibits a spatial shifting only in Makhmour, Alton Kopry, and Darbandakhan stations.
The prepared mean temperature maps for observed and projected (i.e., SSP245 and SSPS285) data in spring are represented in Figure 11. As seen in the figure, there are two classes, including (20–25) and (25–30) °C. Class (20–25) °C considered the dominant for observed data during 1985–2015 and the mean of the ensemble of multi-GCM models of the SSP245 scenario during 2021–2040 of (85.9%) and (51.17%), respectively. In comparison, class (25–30) °C is dominant for the SSP585 scenario during 2021–2040 at (74.44%).
Generally, the region to the north of the research site has the coldest mean temperature. Compared to the observed maps, both the projection scenarios show temperature increases during the studied period. The SSP585 exhibits a higher increase than the SSP245, resulting from unmitigated GHGs. Also, there are some spatial shifts between observed and projected periods.
Moving on to the summer, Figure 12 depicts the average mean temperatures with two new classes (30–35) and (35–40) °C. Class (30–35) °C has the highest percentage for observed SSP245, and SSP585 scenarios of (97.42%), (93.26%), and (85.97%), respectively. The hot class (35–40) °C of mean temperature has appeared by the SSP585, covering a total area of 14.03%, including Mosul, Makhmour, Hawija, Daquq, and Alton Koprey stations. On the other hand, according to the SSP245 scenario, the same class also appeared but with a smaller area of 6.74%, involving only Kirkuk Governorate (i.e., Alton Koprey, Hawija, and Daquq stations). In general, the mean temperature is expected to rise in both scenarios. The mean temperature will likely increase under SSP585 more than SSP245 for all seasons.

4. Discussion

This research applied the LARS-WG (8) technique with five GCMs and two emission scenarios (i.e., SSP245 and SSP585) to project daily climatic factors between 2021 and 2040 for 15 metrological sites covering five governorates in northern Iraq. Based line period data from 1985 to 2015 were utilized to validate and calibrate the model of LARS-WG. Climate techniques such as LARS-WG and the CMIP6 model simulation framework both use the years 1985–2015 as their baseline. To properly portray long-term climatic trends and variation, this period—which encompasses a thorough 31-year span—is essential. Research shows that this time frame is crucial for making reliable climate evaluations and predictions.
After verification and calibration of the LARS-WG model, daily climate factors were simulated, and the mean of the ensemble of multi-GCM models was calculated. After that, the mean temperature data was calculated considering the maximum and minimum temperatures. Average seasonal mean temperature and average cumulative seasonal rainfall data were calculated for observed and projected time series. Then, the ArcGIS and IDW techniques were employed to understand better fluctuation among the stations for observed and simulated periods.
The rainfall results show that the SSP245 scenario has projected a clear increase in rainfall during autumn, winter and spring compared to the observed data. The highest rainfall increment percentage is obtained for class (120–140) mm during winter, with the spatial extent of the class increasing from 25.49 to 42.33%. On the other hand, the SSP585 scenario exhibits the same variation as the SSP245 scenario except during autumn, when precipitation is less. The highest rainfall increment percentage is obtained for class (120–140) mm during winter, with the spatial extent of the class increasing from 25.49 to 50.19%. In addition, there is a spatial shift for different classes in all seasons. These findings agree with research on climate change conducted in Iraq and other nearby countries [7,47,77]. For mean temperature, compared to the observed maps, both the projection scenarios show temperature increases during the studied period throughout the year. The SSP585 exhibits a higher increase than the SSP245, resulting from unmitigated GHGs. Also, there are some spatial shifts between observed and projected periods.
Increasing future precipitation could allow rainfed farmers to boost crop production and enhance their lives. However, it must be understood that the expected increment in rainfall could also bring a few difficulties, including a higher chance of floods and soil erosion [78]. Also, the probable rise in temperature in north Iraq needs extreme attention as it may lead to water stress, primarily if rainfall is reduced. Thus, it is imperative to thoroughly evaluate the possible consequences of rising temperatures in northern Iraq and create plans to lessen the negative impacts of such an increase. This increase can lead to implementing policies to save water resources, such as raising drought-resistant crops and introducing better water management techniques. Climate change slowly affects water supplies, agriculture, livestock, and regional climate factors. Global warming brought about by increasing greenhouse gas emissions will eventually result in higher ocean temperatures and water levels, boosting sea evaporation and raising global humidity and rainfall [79].
The current study’s findings indicate that the pattern of temperature alteration and future precipitation alteration are spatially compatible. Accordingly, the outcomes reveal that the future relationships between mean temperature and rainfall will be positive for both scenarios throughout the year except for autumn, which will show a negative relationship between mean temperature and rainfall under SSP585. Consequently, places receiving more rainfall will see warmer temperatures. This correlation exists because more precipitation is accompanied by greater cloud cover and humidity, which slows down the cooling process by trapping heat close to the surface. Nighttime temperatures can be higher in areas with a lot of rainfall because of the warming impact of the extra moisture in the air. Another factor that might amplify local warming is a rise in latent heat release during condensation processes in regions that experience heavy rainfall [80,81,82]. However, these two occurrences occurring at the same time will have an impact on several agroclimatic indices. The trends of each parameter in this paper’s investigated stations for 2021–2040 are consistent with the findings of the previous literature [27,78].
There is one potential limitation concerning the results of this study. This limitation concerns using satellite data instead of observed data, which is unavailable along the baseline period for 15 stations due to wars. Despite these limitations, the present study has enhanced our understanding of the impact of climate change using the last CMIP6 climate model. In terms of future research, it would be useful to extend the current findings by examining (1) applying another source of satellite data, such as CHRIPS, (2) using another statistical downscaling model. (3) Employing other drought indices, such as RDI, eRDI, SPI, and aSPI. We hope that the current research will stimulate further investigation of this crucial area. (4) Reviewing the scientific literature, we can infer that IDW is a well-known spatial interpolation method for climatic variables [83,84,85]. However, it suffers from the bull’s eye effect that may result from the limited number of metrological stations. This issue can be mitigated by applying another software technique, such as QGis (3.38.0). (5). Although the seasons were defined statically in this research, it is acknowledged that climate change is altering season timing and duration, which conventional static models may not completely reflect. In light of these continuous changes, it would be prudent for future research to think about how to incorporate dynamic, climate-driven definitions of seasons, enabling a more precise evaluation of the effects of climate change. Therefore, more accurate projections of the effects of changing seasons on ecosystems, farming methods, and human activities could be made in future studies, leading to better adaptation tactics. Accordingly, it is crucial to delve further into these changing patterns to improve our knowledge of and reaction to climate change.

5. Conclusions

This study set out to better understand seasonal spatial-temporal distribution characteristics of mean temperature and precipitation (past and future) for 15 stations in Northern Iraq. The study examines the LARS-WG (8) performance to project the daily climatic variables from 2021 to 2040, depending on the baseline period from 1985 to 2015. Five CMIP6 GCMs with two emission scenarios (medium stabilization SSP245 and a high emission SSP585) were used to simulate future climatic data to reduce the uncertainty and boost the projection range. Based on the investigation, the following conclusions were drawn:
  • The LARS-WG (8) model can adequately downscale daily climatic variables for 15 stations based on statistical tests.
  • The seasonal spatial-temporal ensemble of five GCMs predicts inconsistent trends in downscaled rainfall across two emission scenarios compared with the historical baseline period. However, it will rise in all seasons except autumn for the SSP585 scenario.
  • The highest rainfall increment percentage is obtained using the SSP585 for class (120–140) mm during winter. The spatial extent of the class increased from 25.49 to 50.19%.
  • The seasonal spatial-temporal ensemble of five GCMs for mean temperature projected an upward trend across two emission scenarios compared with the historical baseline period. However, the change was generally more noticeable for the SSP585 scenario than for SSP245.
  • The highest percentage of increase in mean temperature is achieved using the SSP585 scenario during the autumn season when the spatial coverage of class (15–20) °C increased from 27.7 to 96.29%.
  • The alteration pattern of future temperature and precipitation change are spatially compatible. It will be positive for both scenarios throughout the year except for autumn, showing a negative relationship between mean temperature and rainfall under SSP585. Consequently, places receiving more rainfall will witness warmer temperatures.
  • A key strength of the present study was that the findings emphasized the value of taking spatial and temporal factors into account since datasets could record drought occurrences at different periods and places, exposing modest differences in the effects of drought. This study has provided a deeper insight into policymakers and managers on managing and planning water resources under climate change’s variability. It impacts the rainfed production and livestock directly when increasing temperature and decreasing rainfall or more frequent flooding and soil erosion when increasing rainfall.

Author Contributions

Conceptualization, S.L.Z. and A.D.; Methodology, S.M.A., S.L.Z. and Y.A.; Software, S.M.A. and Y.A.; Validation, S.M.A., S.L.Z. and Y.A.; Formal analysis, S.M.A. and S.L.Z.; Investigation, Y.A.; Resources, S.L.Z. and A.D.; Data curation, S.M.A., S.L.Z. and Y.A.; Writing – original draft, S.M.A., S.L.Z., Y.A. and A.D.; Writing – review & editing, S.M.A., S.L.Z., Y.A. and A.D.; Visualization, S.M.A. and S.L.Z.; Supervision, S.L.Z. and A.D.; Project administration, S.L.Z. and A.D.; Funding acquisition, A.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data were obtained from the National Oceanic and Atmospheric Administration (NASA) https://www.ncdc.noaa.gov/cdo-web/datatools/findstation (accessed on 27 October 2022).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations of the chosen meteorological sites in the research region.
Figure 1. Locations of the chosen meteorological sites in the research region.
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Figure 2. A flowchart represents the methodology of this research.
Figure 2. A flowchart represents the methodology of this research.
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Figure 3. A comparison of the monthly average and standard deviation of minimum and maximum temperature and rainfall data from observations and simulations.
Figure 3. A comparison of the monthly average and standard deviation of minimum and maximum temperature and rainfall data from observations and simulations.
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Figure 4. Box plots of measured and predicted data for Tmax, Tmin, and rainfall time series over the baseline period.
Figure 4. Box plots of measured and predicted data for Tmax, Tmin, and rainfall time series over the baseline period.
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Figure 5. Precipitation results of autumn; (a) observed data, (b) using SSP245, and (c) using SSP585.
Figure 5. Precipitation results of autumn; (a) observed data, (b) using SSP245, and (c) using SSP585.
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Figure 6. Precipitation results of winter; (a) observed data, (b) using SSP245, and (c) using SSP585.
Figure 6. Precipitation results of winter; (a) observed data, (b) using SSP245, and (c) using SSP585.
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Figure 7. Precipitation results of spring; (a) observed data, (b) using SSP245, and (c) using SSP585.
Figure 7. Precipitation results of spring; (a) observed data, (b) using SSP245, and (c) using SSP585.
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Figure 8. Precipitation results of summer; (a) observed data, (b) using SSP245, (c) and using SSP585.
Figure 8. Precipitation results of summer; (a) observed data, (b) using SSP245, (c) and using SSP585.
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Figure 9. The mean temperature of autumn; (a) observed data, (b) using SSP245, and (c) using SSP585.
Figure 9. The mean temperature of autumn; (a) observed data, (b) using SSP245, and (c) using SSP585.
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Figure 10. Mean temperature of winter; (a) observed data, (b) using SSP245, and (c) using SSP585.
Figure 10. Mean temperature of winter; (a) observed data, (b) using SSP245, and (c) using SSP585.
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Figure 11. Mean temperature of spring; (a) observed data, (b) using SSP245, and (c) using SSP585.
Figure 11. Mean temperature of spring; (a) observed data, (b) using SSP245, and (c) using SSP585.
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Figure 12. Mean temperature of summer; (a) observed data, (b) using SSP245, and (c) using SSP585.
Figure 12. Mean temperature of summer; (a) observed data, (b) using SSP245, and (c) using SSP585.
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Table 1. Geographical information of the fifteen weather stations studied.
Table 1. Geographical information of the fifteen weather stations studied.
ProvinceStationLongitudeLatitudeElevation
DuhokDuhok43.0336.93569
Aqra43.9036.75636
Zakho42.6937.14444
ErbelMakhmour43.5835.77261
Soran44.5436.69728
Koya44.6336.07557
SulaymaniyahSulaymaniyah City45.2735.33885
Darbandikhan Dam45.7135.11610
Dokan Dam44.9535.95489
MosulMosul City43.1636.33238
Sinjar41.8536.30517
Rabia42.2336.74368
KirkukDaquq44.4335.17227
Alton Koprey44.1535.73261
Haweja43.7635.30184
Table 2. Statistical descriptions of important parameters over the baseline period for all stations.
Table 2. Statistical descriptions of important parameters over the baseline period for all stations.
StationsTminTmaxRainfall
Max.Min.MeanMax.Min.MeanMax.Min.Mean
Duhok25.55−4.27410.91442.1023.3722.914122.67028.987
Aqra28.382−1.87913.28745.6607.49727.024153.58026.373
Zakho28.624−1.40813.63445.2086.55526.165148.24028.879
Makhmour28.987−1.19514.17646.3508.92227.967129.46020.820
Soran24.518−4.7579.855641.9654.47623.518168.61024.278
Koya27.903−1.83313.10645.1217.01426.607139.98022.598
Sulaymaniyah26.493−2.51912.16543.8606.62125.873135.43023.109
Darbandikhan26.778−1.80712.86944.8438.44127.328121.7022.537
Dokan25.505−3.59711.28442.4654.68224.133142.77023.939
Mosul29.253−1.62013.54245.9347.72127.203124.34024.003
Sinjar27.006−2.36712.46244.0477.26825.952119.21020.512
Rabia28.776−1.74013.59845.5877.80927.097125.01022.332
Daquq30.160−0.12715.63947.0910.86229.558115.9018.070
Alton Koprey29.008−0.95414.39646.2289.14528.225124.44020.198
Haweja29.599−0.76514.77546.78710.10028.923119.31016.303
Table 3. The five GCM models were employed in this research.
Table 3. The five GCM models were employed in this research.
No.GCM ModelsInstitution/CountrySpatial Resolution
1ACCESS-ESM1-5Australian Community Climate and Earth System Simulator, Acton, Australia192 × 144
2CNRM-CM6-1Centre National de Recherches Météorologiques, Toulouse, France256 × 128
3HadGEM3-GC31-LLMet Office, United Kingdom192 × 144
4MPI-ESM1-2-LRMax Planck Institute, Hamburg, Germany192 × 96
5MRI-ESM2-0Meteorological Research Institute, Tsukuba, Japan320 × 160
Table 4. The selected two SSP models in this study.
Table 4. The selected two SSP models in this study.
No.SSPDescription
1SSP2-4.5Moderate GHG emissions: CO2 emissions will remain at current levels until 2050, after which they will decline but not completely disappear by 2100.
2SSP5-8.5Extremely high GHG emissions: by 2075, CO2 emissions will quadruple. 2.4 °C 4.4 °C 3.3–5.7 GHG: Greenhouse gas.
Table 5. KS test and P-value for Zakho station.
Table 5. KS test and P-value for Zakho station.
A. K-S Test for Distributions of the Seasonal Wet and Dry Series.
SeasonWet/DryNK-Sp-ValueAssessment
Dec., Jan., and Feb.Wet“11.5”0.0801.000P
Dry11.50.0841.000P
Mar., Apr., and MayWet11.50.0590.984V G
Dry11.50.1301.000P
Jun., Jul., and Aug.Wet11.50.0791.000P
Dry11.50.0701.000P
Sep., Oct., and Nov.Wet11.50.0591.000P
Dry11.50.0651.000P
B. K-S-Test for Distributions of Daily Rainfall.
MonthNK-Sp-ValueAssessment
Jan.11.50.0651.000P
Feb.11.50.0501.000P
Mar.11.50.0281.000P
Apr.11.50.0451.000P
May11.50.0301.000P
Jun.11.50.0301.000P
Jul.11.50.0251.000P
Aug.11.50.0311.000P
Sep.11.50.0481.000P
Oct.11.50.0601.000P
Nov.11.50.0371.000P
Dec.11.50.0821.000P
C. KS-Test for Daily MIN Distributions.
MonthNK-Sp-ValueAssessment
Jan.11.50.0531.000P
Feb.11.50.0531.000P
Mar.11.50.0531.000P
Apr.11.50.0531.000P
May11.50.0531.000P
Jun.11.50.0531.000P
Jul.11.50.0531.000P
Aug.11.50.0531.000P
Sep.11.50.0531.000P
Oct.11.50.0531.000P
Nov.11.50.0531.000P
Dec.11.50.0531.000P
D. KS-Test for Daily MAX Distributions.
MonthNK-Sp-ValueAssessment
Jan.11.50.0531.000P
Feb.11.50.0531.000P
Mar.11.50.0531.000P
Apr.11.50.0531.000P
May11.50.0531.000P
Jun.11.50.0531.000P
Jul.11.50.0531.000P
Aug.11.50.1050.999V G
Sep.11.50.0531.000P
Oct.11.50.0531.000P
Nov.11.50.0531.000P
Dec.11.50.1050.999V G
Table 6. Statistical criteria outcomes for model calibration over baseline period (1985–2015).
Table 6. Statistical criteria outcomes for model calibration over baseline period (1985–2015).
Climate FactorsRRMSE (°C)MAE (°C)MBE (°C)
Maximum temperature0.991.83711.40380.0145
Minimum temperature0.991.49261.1478−0.0062
Climate FactorsRRMSE (mm)MAE (mm)MBE (mm)
Rainfall0.7429.259119.32590.6725
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Abdulsahib, S.M.; Zubaidi, S.L.; Almamalachy, Y.; Dulaimi, A. Temperature and Precipitation Change Assessment in the North of Iraq Using LARS-WG and CMIP6 Models. Water 2024, 16, 2869. https://doi.org/10.3390/w16192869

AMA Style

Abdulsahib SM, Zubaidi SL, Almamalachy Y, Dulaimi A. Temperature and Precipitation Change Assessment in the North of Iraq Using LARS-WG and CMIP6 Models. Water. 2024; 16(19):2869. https://doi.org/10.3390/w16192869

Chicago/Turabian Style

Abdulsahib, Sura Mohammed, Salah L. Zubaidi, Yousif Almamalachy, and Anmar Dulaimi. 2024. "Temperature and Precipitation Change Assessment in the North of Iraq Using LARS-WG and CMIP6 Models" Water 16, no. 19: 2869. https://doi.org/10.3390/w16192869

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