Home Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
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Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model

  • Nasser Kh. Muhaisen EMAIL logo , Thair Sh. Khayyun , Mustafa Al Mukhtar and Waqed H. Hassan
Published/Copyright: March 15, 2024
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Open Engineering
From the journal Open Engineering Volume 14 Issue 1

Abstract

Regions characterized by an arid or semi-arid climate are highly susceptible to prospective climate change impacts worldwide. Therefore, evaluating the effects of global warming on water availability in such regions must be accurately addressed to identify the optimal operation policy of water management facilities. This study used the weather generator model LARS-WG6.0 to forecast possible variations in precipitation and temperature of the Mosul Dam Reservoir in northern Iraq. Future climate change was predicted using three greenhouse gas emission scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) for four time intervals (2021–2040, 2041–2060, 2061–2080, and 2081–2100) using five Global climate models (GCMs): CSIRO-Mk3.6.0, HadGEM2-ES, CanESM2, BCC-CSM1-1, and NorESM1-M. The model’s calibration and validation were conducted using data from 2001 to 2020 from eight meteorological stations in the study area. The results showed that the weather generator model’s performance was outstanding in predicting daily climate variables. The results also showed that the highest increase in maximum and minimum temperatures was 5.70°C in July and 5.30°C in September, respectively, for the future period 2081–2100 under RCP8.5. The highly forecasted minimum and maximum temperatures were extracted from the CanESM2 and HadGEM2-ES GCM models. It was demonstrated that the study region would experience different patterns of precipitation change during the wet seasons in the evaluated periods. Finally, the variations in precipitation and temperatures in the Mosul dam region would significantly impact the amount of freshwater obtained in these areas due to rising loss rates of evaporation. This could lead to a water shortage and mismanagement of the sustainable operations of the dam.

Keywords: Mosul dam; arid and semi-arid climate; GCM models; RCP; weather generator model

1 Introduction

Climate change is a paramount environmental concern currently confronting the world [1]. Furthermore, climate change is defined as a spatial and temporal alteration in the distribution of rainfall and snowfall patterns within a specific geographical area [2]. Climate change can seriously affect many essential aspects of the global economy, including water resources, agricultural sector, energy production, and tourism [3,4,5]. Due to the high concentration of greenhouse gases resulting from fossil fuels, human activities, and land use, global warming is responsible for changing global climate [6].

The Intergovernmental Panel on Climate Change (IPCC) 2014 approved the RCP 2.6, 4.5, 6, and 8.5 as inferential greenhouse emissions in the present century based on demographics, financial habits, landscape circumstances, energy utilization, and climate policy. The Coupled Model Intercomparison Project, also known as CMIP, is a structure for a collaborative effort established to enhance the development of Global Climate Models (GCMs) [7]. The five-phase Coupled Model Intercomparison Project, also known as CMIP5, has more characteristics than the older iteration in the carbon cycle simulation, anticipating the system in one-decade steps and identifying the reaction of climate models [8,9]. The GCMs provided predictions of the climate worldwide, allowing policymakers to adjust better strategies to address the consequences of global warming based on the information supplied by the GCMs. GCMs use the complexity of the global system to produce valuable results at the international, hemispheric, and continental levels. However, these global processes cannot be shown at the local sub-grid level [10]. GCMs are extensively used tools to determine the impact of global warming; however, because of their loose geographic resolution, their direct application to the sustainable management of resources at the regional or local scale is limited [11]. The downscaling approaches are necessary for increasing the geographic resolution of GCMs from a coarse level to a satisfactory level, which is required for their direct application on a local or regional scale [12]. The two downscaling techniques that are most commonly used are statistical downscaling and dynamic downscaling. Both of these methods connect the coarse resolution of GCMs to local environmental variables [13].

In previous research works, statistical downscaling methods were chosen because they are more convenient to use, have more computing power than dynamic downscaling techniques, and are cheaper. These are the three main reasons why statistical downscaling methods were chosen. Two frequently employed software tools for climate predictions and the downscaled analysis of past and future climate data are the Stochastic Weather Generator (Long Ashton Research Station Weather Generator; LARS-WG), Climate Generator (CLIGEN), and Statistical Downscaling Models (SDSM) [14,15]. SDSM can also analyze past climate data [16]. The LARS-WG model is a type of random weather generator. It is one of the most widespread models created to assess climate change’s effects. It has been committed to investigating various environmental circumstances, during which it has performed admirably compared to other generators [1].

There are several studies on the world used the results from various GCMs to investigate the effect of global warming on the water resources [17,18,19]. For example, Osman et al. [3] studied the effect of climate changes on Baghdad City in Iraq based on two scenarios of the seven general circulation model and concluded that the trend of precipitation increased in the autumn and winter seasons while the trend decreased in the spring season for all periods considered in the research. Mohammed and Scholz [20] utilized LARS-WG to study the potential impacts of global warming on northern Iraq’s Lower Zab River watershed. Compared to the reference period, the findings showed that the maximum temperatures are expected to rise from 3.17 to 3.70°C between 2046 and 2065, while the minimum temperatures are expected to increase from 3.02 to 3.33°C during that same period.

Additionally, Mohammed et al. [1] investigated the climate change impact on southern Iraq by utilizing the LARS-WG model based on two emission scenarios of the five general circulation model, and annual minimum and maximum temperatures are projected to rise approximately 6°C under RCP8.5 and 1.5°C under RCP4.5 by the end of this century, across all survey sites. The outcomes also reveal that the five GCMs have conflicting projections for precipitation decline patterns.

The novelty of this study is that it provides a conceivable exploration of climate change’s impact on northern Iraq’s water resources, thereby aiding in decision-making processes related to water resource management, land-use planning, and infrastructure development. In this study, LARS-WG 6.0 was utilized to predict precipitation, minimum, and maximum temperatures for Northern Iraq (Mosul Dam Watershed) over four periods: 2021–2040, 2041–2060, 2061–2080, and 2081–2100. Five CMIP5 models, i.e., CanESM2, CSIRO_Mk3.6, HadGEM2-ES, NorESM1-M, and BCC-CSM1-1 were applied considering three emission scenarios of RCP2.6, RCP4.5, and RCP8.5.

2 Study area and data

The Tigris River, originating in the Taurus Mountains of eastern Turkey approximately 25 km southeast of Elazig and about 30 km away from the source of the Euphrates, spans a length of 1,800 km. This study was focused on the Tigris River basin, which is located between latitude 36°35′20″ to 37°48′00 N and longitude 41°46′33″ to 43°29′17″ as illustrated in Figure 1. The area of the Mosul Dam watershed is 11,108 km2, about 48% of which is located within the Iraqi borders, 44% in Turkey, and 8% in Syria. Climate stations in this region recorded the highest temperature in August, 46°C, while the lowest was in January. The rainy season starts in October and continues until May. The annual rainfall in this region is more than 1,000 mm [21]. This study selected eight meteorological stations to investigate the forthcoming precipitation and temperature patterns, as presented in Table 1. The Climate Hazards Group Infrared Precipitation with Station data and National Aeronautics and Space Administration were consulted to obtain historical data on daily precipitation and maximum and minimum temperatures. These two sources of historical data were collected from two different websites. The weather generator model was calibrated and validated using daily climate data covering two decades, extended from 2001 to 2020.

Figure 1 Location of the study area and meteorological stations within it.
Figure 1

Location of the study area and meteorological stations within it.

Table 1

Geographical coordinates of the selected meteorological stations

Station name Lat. Long. Length of record Location
Station 1 37°08′00 42°43′12″ 2001–2020 Iraq
Station 2 36°52′12″ 43°00′00″ 2001–2020 Iraq
Station 3 37°15′36″ 43°10′00″ 2001–2020 Iraq
Station 4 37°30′00″ 42°54′00″ 2001–2020 Turkey
Station 5 37°24′00″ 42°30′00″ 2001–2020 Turkey
Station 6 37°12′00″ 42°24′00″ 2001–2020 Turkey
Station 7 36°57′00″ 42°30′00″ 2001–2020 Iraq
Station 8 36°42′00″ 42°45′00″ 2001–2020 Iraq

3 Methodology

3.1 Overview of the LARS-WG model

The LARS-WG is a randomized weather generator that can reproduce weather data at a particular location [22,23,24] under present and future climate conditions. These data are presented in the format of daily time series for several different climate variables, including precipitation measured in millimeters, maximum and minimum temperatures measured in °C, and solar radiation. The initial development of stochastic weather generators was motivated primarily by two objectives:

  1. Develop a methodology that enables the generation of synthetic weather time series, possessing statistical characteristics that closely match the historical statistics at a specific location. The generated time series should also have sufficient length to assess risks in environmental or agricultural contexts.

  2. For offering a technique for expanding the generation of weather time series to locations that have not been observed by using extrapolation of the weather generator parameters received. Executing the models in nearby locations will extend the simulation to areas that have not been followed.

LARS-WG applies a semi-empirical distribution, the cumulative probability distribution function, to estimate the distributions of dry and moist sequences of daily precipitation and maximum and minimum temperatures. This allows for a more accurate representation of the cumulative probability distribution function. So, to determine the value of the climatic variable v i , which corresponds to the probability p i for each climatic variable v, the following formula is used:

(1) v i = min [ v : P ( v obs ≤ v ) ≥ p i ] i = 0 , … , n ,

(2) v 0 = min { v obs } ,

(3) v n = max { v obs } .

The probability, denoted as P(), relies on the historical observation {v obs}. For each climatic variable, fixed values, p 0 and p n, are established as p 0 = 0 and p n = 1, respectively, with corresponding values of v 0 and v n representing the variable’s values at the start and end of the intervals. The climatic variable can accurately approximate extreme values by assigning some p i near zero for significantly low values and near one for extremely high values. The remaining p i values are evenly distributed across the probability scale.

Due to the relatively high probability of experiencing very low daily precipitation (less than 1 mm) and the minimal impact of such low precipitation on the output of a process-based effect model, only two values were utilized: v 1 = 0.5 mm and v 2 = 1 mm. The associated probabilities were computed as p i = P(v obs v i ) for i = 1 and 2. This approach is based on the fact that the daily precipitation probabilities being insufficient, i.e., less than 1 mm, is comparatively high in most cases. Additionally, such little precipitation has minimal impact on the result of a process-based effect model. For constructing Synthetic Exceedance Distributions representing wet and dry series, two values close to 1 were employed: p n−1 = 0.99 and p n−2 = 0.98. These values were chosen to account for extremely long dry and wet periods. Regarding minimum and maximum temperatures, two values close to 0 and two values close to 1 were utilized, respectively. This choice allows the model to encompass extremely low to extremely high temperatures.

The sixth version of LARS-WG6, developed and published in 2018, incorporates climatic forecasts from 19 GCMs utilized in the IPCC’s (AR5) report, known as CMIP5 models. For this particular study on climate change, five models described in Table 2 were selected to forecast changes in precipitation and maximum and minimum temperatures. This reduced the overall uncertainty associated with GCMs.

Table 2

Five GCM models used in this study

No. GCMs’ name Resolution
1 Canadian Center for Climate Modeling and Analysis, Canada (CanESM2) 2.79° × 2.81°
2 Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO-Mk3.6) 1.8° × 1.8°
3 Met Office Hadley Center, United Kingdom (HadGEM2-ES) 1.25° × 1.87°
4 Norwegian Climate Center, Norway (NorESM1-M) 1.89° × 2.5°
5 Beijing Climate Center, China (BCC-CSM1-1) 2.8° × 2.8°

Future precipitation, minimum, and maximum temperatures were determined by LARS-WG6.0 based on daily historical data of these climate parameters from a particular location for both the prospect distribution of climate parameters and their relations.

3.2 Performance criteria of the model

This study applied a non-parametrized Kolmogorov–Smirnov (K–S) statistical test to evaluate the model’s performance. It measures whether the seasonal distributions of the series of wet and dry days and the daily distributions of precipitation obtained from observations and simulations are equal. This test provides a p-value, which is utilized to decide whether to agree or disagree with the assumption that the two sets of data were derived from an identical distribution. High and low values of (K–S) and (p-value) statistics indicate that the model is unlikely because the acquired data are inaccurate and cannot be used to evaluate the model. Katz et al. [25] considered a value of 0.01 for the parameter p to be a reasonable range for the model outputs. According to Mohammed and Hassan [1], the model was considered to have a perfect fit when the p-value was equal to 1, and it was supposed to have a very good fit when the p-value was more significant than 0.7 but smaller than 1.0. When the p-value was less than 0.7 but greater than 0.4, it was determined that the model had a good fit; on the other hand, it was determined that the model had a poor fit when the p-value fell within the range of 0–0.4. This statistical index is insufficient to assess the model calibration and validation, so, in this study, three other statistical parameters were used to make the calibration and validation processes of the LARS-WG model, i.e., the coefficient of determination (R 2), the Nash–Sutcliffe (NS), and the root mean square error (RMSE) to the standard deviation of observed data (STDobs) ratio (RSR). These statistical parameters are used to compare the simulated results with the actual results of the observed data. The coefficient of determination (R 2) measures how efficiently the model duplicated observed outcomes. The values of R 2 range from zero to one, and better model performance is when the value of R 2 is closer to one, as in previous literature [26,27]

A Nash–Sutcliffe efficiency (N SE) value equal to or greater than zero signifies that the simulated value provides more accurate predictions of the concerned component than the mean observed value. On the other hand, an N SE value of one signifies the attainment of ideal modeling. The NSE serves as a measure to compare the variation between the measured and modeled data against a best-fit line with a 1:1 ratio.

The ratio between the RMSE and the standard deviation of the observed data serves as a statistical criterion for error evaluation. Hence, a simulation is acceptable if the RSR value is below 0.5, as in the study by Moriasi et al. [28]. These statistical parameters are determined from the following equations:

(4) R 2 = ∑ i = 1 N ( Q i − Q a ) ( M i − M a ) 2 ∑ i = 1 N ( Q i − Q a ) 2 ∑ i = 1 N ( M i − M a ) 2 ,

(5) N SE = 1 − ∑ i = 1 N ( Q i − M i ) 2 ∑ i = 1 N ( Q i − Q a ) 2 ,

(6) RSR = RMSE STD ob = ∑ i = 1 N ( Q i − M i ) 2 ∑ i = 1 N ( Q i − Q a ) 2 .

where Q i and M i are the observed and simulated stream flow values for the ith pair of stream flow values, respectively. Q a is the mean value of the observed stream flow values, N is the total number of paired stream flow values, and M a is the mean value of the modeled stream flow.

4 Results and discussion

4.1 LARS-WG model calibration and validation

In this study, daily climate variables (i.e., precipitation, maximum temperature, and minimum temperature) for eight stations in the study area were collected for 20 years as a reference period from 2001–2020 in calibrating and validating the LARS-WG model. The calibration and validation process can be summarized in two steps as follows: (1) using the ANALYSE SITE window, the daily observed meteorological data (precipitation, maximum and minimum temperatures, and solar radiation) of the Mosul Dam watershed are analyzed to compute their statistical parameters and save these data to two separate parameters files; (2) from two parameter files derived in step (1), the statistical parameters of eight weather stations were automatically calculated by LARS-WG version 6.0 as a TST file after using the generated data in the generation process. Table 3 outlines the statistical analysis of the measured seasonal climate variable data (precipitation, maximum, and minimum temperatures) within the validation process. Similarly, the statistical analysis for the observed monthly precipitation within the same process is shown in Table 4. The value of N in each table indicates the total number of tests performed.

Table 3

Assessment of the LARS-WG model according to the seasonal wet and dry distribution series

Season Wet/dry N K–S P-value Assessment Season Wet/dry N K–S P-value Assessment
Station 1 Station 2
DJF Wet 12 0.12 0.7384 Very good DJF Wet 12 0.05 1.00 Perfect
DJF Dry 12 0.05 1.00 Perfect DJF Dry 12 0.08 1.00 Perfect
MAM Wet 12 0.04 1.00 Perfect MAM Wet 12 0.04 1.00 Perfect
MAM Dry 12 0.10 0.99 Very good MAM Dry 12 0.08 1.00 Perfect
JJA Wet 12 0.17 0.71 Very good JJA Wet 12 0.08 0.97 Very good
JJA Dry 12 0.17 0.82 Very good JJA Dry 12 0.18 0.80 Very good
SON Wet 12 0.05 1.00 Perfect SON Wet 12 0.05 1.00 Perfect
SON Dry 12 0.05 1.00 Perfect SON Dry 12 0.06 1.00 Very good
Station 3 Station 4
DJF Wet 12 0.03 1.00 Perfect DJF Wet 12 0.05 1.00 Perfect
DJF Dry 12 0.07 1.00 Perfect DJF Dry 12 0.07 1.00 Perfect
MAM Wet 12 0.03 1.00 Perfect MAM Wet 12 0.05 1.00 Perfect
MAM Dry 12 0.07 1.00 Very good MAM Dry 12 0.05 1.00 Perfect
JJA Wet 12 0.79 0.00 Poor JJA Wet 12 0.68 0.00 Poor
JJA Dry 12 0.17 0.79 Very good JJA Dry 12 0.17 0.80 Very good
SON Wet 12 0.05 1.00 Perfect SON Wet 12 0.06 1.00 Perfect
SON Dry 12 0.07 1.00 Perfect SON Dry 12 0.08 1.00 Perfect
Station 5 Station 6
DJF Wet 12 0.08 1.00 Very good DJF Wet 12 0.05 1.00 Perfect
DJF Dry 12 0.06 1.00 Perfect DJF Dry 12 0.09 1.00 Very good
MAM Wet 12 0.03 1.00 Perfect MAM Wet 12 0.06 1.00 Perfect
MAM Dry 12 0.08 1.00 Very good MAM Dry 12 0.08 1.00 Perfect
JJA Wet 12 0.40 0.20 Poor JJA Wet 12 0.30 0.60 Good
JJA Dry 12 0.16 0.86 Very good JJA Dry 12 0.14 0.92 Very good
SON Wet 12 0.02 1.00 Perfect SON Wet 12 0.02 1.00 Perfect
SON Dry 12 0.07 1.00 Perfect SON Dry 12 0.07 1.00 Perfect
Station 7 Station 8
DJF Wet 12 0.03 1.00 Perfect DJF Wet 12 0.05 1.00 Perfect
DJF Dry 12 0.09 1.00 Perfect DJF Dry 12 0.06 1.00 Perfect
MAM Wet 12 0.03 1.00 Perfect MAM Wet 12 0.04 1.00 Perfect
MAM Dry 12 0.09 1.00 Perfect MAM Dry 12 0.08 1.00 Perfect
JJA Wet 12 0.06 1.00 Perfect JJA Wet 12 0.04 1.00 Perfect
JJA Dry 12 0.27 0.41 Good JJA Dry 12 0.17 0.75 Very good
SON Wet 12 0.07 0.96 Very good SON Wet 12 0.03 1.00 Perfect
SON Dry 12 0.06 1.00 Perfect SON Dry 12 0.08 1.00 Perfect
Table 4

Assessment of the LARS-WG model according to the fitting of monthly precipitation

Month N K–S P-value Assess. Month N K–S P-value Assess.
Station 1 Station 2
J 11.5 0.104 0.9904 Very good J 11.5 0.065 1 Perfect
F 11.5 0.117 0.9872 Very good F 11.5 0.091 0.9936 Very good
M 11.5 0.0656 1 Perfect M 11.5 0.078 0.9968 Very good
A 11.5 0.1062 0.991 Very good A 11.5 0.0576 1 Perfect
M 11.5 0.1126 0.9792 Very good M 11.5 0.117 0.9384 Very good
J 11.5 0.3742 0.197 Poor J 11.5 0.3742 0.197 Poor
J 11.5 0.435 0.017 Poor J 11.5 1 0 Poor
A 11.5 0.6002 0.0012 Poor A 11.5 0.783 0 Poor
S No precipitation S 11.5 0.3476 0.16 Poor
O 11.5 0.091 0.9936 Very good O 11.5 0.1774 0.8176 Very good
N 11.5 0.117 0.9388 Very good N 11.5 0.1482 0.903 Very good
D 11.5 0.0974 0.9956 Very good D 11.5 0.0856 0.9964 Very good
Station 3 Station 4
J 11.5 0.065 1 Perfect J 11.5 0.091 0.9936 Very good
F 11.5 0.065 1 Perfect F 11.5 0.065 1 Perfect
M 11.5 0.065 1 Perfect M 11.5 0.065 1 Perfect
A 11.5 0.0858 0.9954 Very good A 11.5 0.0704 1 Perfect
M 11.5 0.1518 0.9152 Very good M 11.5 0.1446 0.9276 Very good
J 11.5 0.261 0.359 Poor J 11.5 0.1408 0.909 Very good
J 11.5 0.5044 0.0134 Poor J 11.5 0.8524 0.0004 Poor
A 11.5 0.3824 0.0536 Poor A 11.5 1 0 Poor
S 11.5 0.2526 0.4444 Good S 11.5 0.2128 0.61 Good
O 11.5 0.104 0.953 Very good O 11.5 0.0892 0.993 Very good
N 11.5 0.1022 0.992 Very good N 11.5 0.117 0.9388 Very good
D 11.5 0.0978 0.995 Very good D 11.5 0.0764 0.9962 Very good
Station 5 Station 6
J 11.5 0.143 0.9324 Very good J 11.5 0.078 0.9968 Very good
F 11.5 0.104 0.9904 Very good F 11.5 0.078 0.9968 Very good
M 11.5 0.065 1 Perfect M 11.5 0.0668 1 Perfect
A 11.5 0.0646 1 Perfect A 11.5 0.0942 0.9898 Very good
M 11.5 0.1692 0.8362 Very good M 11.5 0.1182 0.9852 Very good
J 11.5 0.3824 0.0536 Poor J 11.5 0.523 0.216 Poor
J 11.5 1 0 Poor J No precipitation
A 11.5 0.6696 0 Poor A No precipitation
S 11.5 0.3564 0.1096 Poor S 11.5 0.609 0 Poor
O 11.5 0.077 1 Perfect O 11.5 0.1306 0.9614 Very good
N 11.5 0.091 0.9936 Very good N 11.5 0.117 0.9872 Very good
D 11.5 0.0794 0.9952 Very good D 11.5 0.0924 0.9936 Very good
Station 7 Station 8
J 11.5 0.091 0.9936 Very good J 11.5 0.104 0.9904 Very good
F 11.5 0.091 0.9936 Very good F 11.5 0.065 1 Perfect
M 11.5 0.0654 1 Perfect M 11.5 0.065 1 Perfect
A 11.5 0.0628 1 Perfect A 11.5 0.0772 0.9968 Very good
M 11.5 0.0874 0.9964 Very good M 11.5 0.0714 1 Perfect
J 11.5 0.5188 0.0094 Poor J 11.5 0.534 0.203 Poor
J No precipitation J No precipitation
A No precipitation A No precipitation
S 11.5 0 1 Perfect S No precipitation
O 11.5 0.1224 0.988 Very good O 11.5 0.1096 0.9512 Very good
N 11.5 0.1592 0.828 Very good N 11.5 0.134 0.9516 Very good
D 11.5 0.062 1 Perfect D 11.5 0.0928 0.9964 Very good

To sum up, the model performs well in assessing the wet and dry series distributions. All stations had either a perfect fit or a very good fit throughout the winter: December, January, February (DJF); autumn: September, October, November (SON); spring: March, April, May (MAM); and a poor to very good fit was found during the summer evaluation: June, July, August (JJA). According to Table 4, the model’s performance ranges from very good to perfect when simulating daily rain distributions, except in the summer; the value of N in each table indicates the total number of tests performed. According to Table 4, the model’s performance ranges from good to perfect when simulating daily rain distributions, except in the summer; the model cannot adjust for dry periods and accurately assess the weather due to the lack of precipitation during the dry season.

To validate the model’s prediction of future climate variables at all locations, the mean value and standard deviation of two climate variable series (observed and generated) for all stations in this study were compared for each month, as illustrated in Figure 2.

Figure 2 Comparison between the monthly and standard deviation of two series for maximum and minimum temperatures, and precipitation, respectively.
Figure 2 Comparison between the monthly and standard deviation of two series for maximum and minimum temperatures, and precipitation, respectively.
Figure 2 Comparison between the monthly and standard deviation of two series for maximum and minimum temperatures, and precipitation, respectively.
Figure 2

Comparison between the monthly and standard deviation of two series for maximum and minimum temperatures, and precipitation, respectively.

Considering how challenging it has been to model standard deviations satisfactorily in previous studies, the results are generally encouraging. Figure 2 shows the model’s adequate record for predicting monthly averages and standard deviations for minimum and maximum temperatures across all locations. In addition, the model’s ability to predict low and high temperatures and precipitation would confirm what was previously discussed. This verifies the tool’s applicability across all areas in the current study and suggests it could be used elsewhere to predict future daily weather values.

The results for all stations’ average monthly values of Tmax, Tmin, and precipitation are shown in Table 5, along with the coefficient of determination (R 2), N SE, and RSR. Across all study stations, statistical indicators showed a high concordance between observed and down-scaled data from the downscaling model. As a result, all eight climate variables had a high coefficient of determination (R 2) and Nash–Sutcliffe coefficient values, between 0.9871 and 0.9996, and between 0.9996 and 0.9864, respectively, and low RSR between 0.0203 and 0.1214.

Table 5

Statistical parameter results for calibration and validation of the LARS-WG model over the baseline period (2001–2020)

Station name Climate variable R 2 N SE RSR
Station 1 Max. temp. 0.9996 0.9996 0.0203
Min. temp 0.9994 0.9993 0.0256
Precipitation 0.9955 0.9952 0.0694
Station 2 Max. temp. 0.9994 0.9994 0.0246
Min. temp 0.9995 0.9995 0.0225
Precipitation. 0.9886 0.9864 0.1164
Station 3 Max. temp. 0.9993 0.9993 0.0266
Min. temp 0.9992 0.9991 0.0292
Precipitation. 0.9871 0.9864 0.1163
Station 4 Max. temp. 0.9993 0.9993 0.0261
Min. temp 0.9994 0.9992 0.0278
Precipitation. 0.9953 0.9876 0.111
Station 5 Max. temp. 0.9993 0.9993 0.0266
Min. temp 0.9995 0.9994 0.0244
Precipitation. 0.9881 0.9852 0.1214
Station 6 Max. temp. 0.9994 0.9993 0.0256
Min. temp 0.9995 0.9994 0.0236
Precipitation. 0.9931 0.9930 0.0839
Station 7 Max. temp. 0.9995 0.9994 0.0233
Min. temp 0.9995 0.9995 0.0218
Precipitation. 0.9947 0.9943 0.0753
Station 8 Max. temp. 0.9995 0.9995 0.0223
Min. temp 0.9995 0.9995 0.0228
Precipitation. 0.9983 0.9978 0.0470

4.2 Future trends in climate variables

The calibrated weather generator model for all stations was utilized to project the observed min. temp., max. temp., and precipitation in the baseline period (2001–2020) onto (2021–2040), (2041–2060), (2061–2080), and (2081–2100). This study employed various scenarios of greenhouse gas emissions, namely, RCP2.6, RCP4.5, and RCP8.5. In addition, it utilizes five GCMs, reviewed in Table 2, to downscale the climate variables for the watershed. Finally, the watershed’s projected minimum and maximum temperatures are plotted as shown in Figures 3 and 4.

Figure 3 Projected minimum and maximum temperatures for four periods for all scenarios for GCM models.
Figure 3

Projected minimum and maximum temperatures for four periods for all scenarios for GCM models.

Figure 4 Future maximum and minimum temperatures for all periods under three climate change scenarios.
Figure 4

Future maximum and minimum temperatures for all periods under three climate change scenarios.

Figure 3 shows the simulated maximum and minimum temperatures concerning the GCM models for four periods under three greenhouse gas emission scenarios. It is clear from Figure 3 that there is a discrepancy in predicting the future minimum and maximum temperatures for each of the climate change models. The highest future prediction of these climate variables was those extracted from the CanESM2 model, then the HadGEM2-ES model, while the lowest future forecast was from the CSIRO-Mk3.6 model for all scenarios of greenhouse gas emissions. For this reason, the future change in minimum, maximum, and precipitation for the study area was calculated according to the mean value of five GCM model results.

The predicted average monthly minimum and maximum temperatures over the four time periods are displayed in Figure 4. Figure 4 shows that most of the average monthly maximum temperature has risen in the summer months of July, August, and September, while the least of it has risen in the winter months of December and January. Minimum and maximum temperatures gradually increase between the observed values for 2001–2020 and those predicted by this century’s end for all future scenarios. These results agreed with that reported by Mohammed and Hassan [1].

The average monthly differences in minimum and maximum temperatures for all future periods under three scenarios of climate change are illustrated in Figure 5. The highest difference (increase) in maximum and minimum temperatures was 5.70°C in July and 5.30°C in September, respectively for the future period (2081–2100) under RCP8.5. In contrast, the lowest difference (increase) in maximum and minimum temperatures were 0.16°C and 0.05°C in December for the future period (2021–2040) under the greenhouse gas emission scenarios RCP2.6 and RCP4.5.

Figure 5 Average monthly differences in maximum and minimum temperature for all future periods under three climate change scenarios.
Figure 5

Average monthly differences in maximum and minimum temperature for all future periods under three climate change scenarios.

The prediction of precipitation for each scenario is depicted in Figure 6. However, it can be observed that there are variations in the projected precipitation values between each scenario throughout all periods in the future. These variations are because rainfall does not occur continuously during the year. As a result, there are missing values for some months, which makes it challenging to forecast rainfall for the upcoming months.

Figure 6 Average monthly future precipitation for four periods under three climate change scenarios.
Figure 6

Average monthly future precipitation for four periods under three climate change scenarios.

Figure 7 shows the precipitation trend averaged from the five GCMs across the entire watershed. In general, it can be seen that the future change in the precipitation has fluctuated between increases and decreases, e.g., example, under the RCP2.6 scenario, the highest increase was found to be 16 mm in November for the period (2021–2040), while for the same scenario, the highest decrease was found to be 8.9 mm in May for the period (2041–2060). Therefore, it is reasonable to infer that the North of Iraq will experience different patterns of precipitation change during the wet seasons in the future periods. Furthermore, the presented results were consistent with those reported by Osman et al. [3].

Figure 7 Average monthly difference in precipitation for four periods under three scenarios of climate change.
Figure 7

Average monthly difference in precipitation for four periods under three scenarios of climate change.

The study demonstrated that the evaluated watershed would witness an increase in the trend of maximum and minimum temperatures. For example, the stations located within the Turkish borders will witness an increase in maximum and minimum temperatures more than those located within the Iraqi borders under all climate change scenarios and for all study periods. This is due to several factors, including the topography and geography of the area where the topography of an area can significantly influence its temperature. Turkish regions may have higher elevations than Iraqi regions, and temperature changes tend to be more pronounced at higher altitudes. As the climate warms, this elevation effect can lead to amplified temperature increases in Turkish areas. On the other hand, precipitation in the region will witness a slight increase across the entire watershed, regardless of the position of the stations inside or outside Iraq (Table 6).

Table 6

Change in climate variables under corresponding scenarios in the study according to the position of the station

Scenario Period Change in Turkey stations % change in Iraq stations
Max. temp. (°C) Min. temp. (°C) % prec. Max. temp. (°C) Min. temp. (°C) % prec.
RCP2.6 2021–2040 0.81 0.61 7.34 0.63 0.78 7.43
2041–2060 1.36 1.00 4.58 1.04 1.33 7.48
2061–2080 1.45 1.11 4.40 1.11 1.40 7.73
2081–2100 1.52 1.20 5.43 1.21 1.47 8.22
RCP4.5 2021–2040 0.54 0.62 3.61 0.63 0.82 3.90
2041–2060 1.72 1.30 0.12 1.32 1.66 2.50
2061–2080 2.39 1.84 0.49 1.87 2.32 2.78
2081–2100 2.69 2.07 3.34 2.10 2.66 3.66
RCP8.5 2021–2040 1.04 0.76 3.61 0.80 0.99 3.70
2041–2060 2.23 1.78 2.61 1.81 2.18 2.89
2061–2080 3.63 3.03 2.12 3.10 3.58 4.47
2081–2100 4.92 4.18 1.07 4.23 4.87 3.38

5 Conclusion

This study tested the weather generator model (LARS-WG) application to downscale the daily climate variables, including maximum and minimum temperatures and precipitation in the north of Iraq (Mosul Dam watershed, which includes many regional areas). Subsequently, the model was utilized in generating the future daily climate variables for four periods (2021–2040), (2041–2060), (2061–2080), and (2081–2100) under three scenarios of greenhouse gas emission RCP2.6, RCP4.5, and RCP8.5. In addition, the observed daily climate variable data for the period (2001–2020) were used to calibrate and validate the weather generator model and compared with the future period’s data. From the analysis of the model in this study, it can be concluded that:

  1. LARS-WG is a statistical downscaling method. It can relate large-scale predictors with the local climate variables like rainfall and temperatures.

  2. The study has a limited number of weather stations which affect the accuracy of the prediction of climate data.

  3. The results obtained by increasing the temperature will negatively influence water and food security in Iraq.

  4. This study’s main finding can help assess the potential risks and challenges associated with changes in water availability, agricultural practices, and ecological balance. Moreover, this research provides valuable information for policymakers, water resource managers, and local communities who can benefit from the findings to develop strategies to adapt to and mitigate future climate change impacts in northern Iraq.

  1. Funding information: Authors declare that the manuscript was done depending on the personal effort of the authors, and there is no funding effort from any side or organization.

  2. Conflict of interest: The authors state no conflict of interest.

  3. Data availability statement: Most datasets generated and analyzed in this study are comprised in this submitted manuscript. The other datasets are available on reasonable request from the corresponding author with the attached information.

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Received: 2023-10-15
Revised: 2023-11-10
Accepted: 2023-11-15
Published Online: 2024-03-15

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/eng-2022-0567
Keywords for this article
Mosul dam; arid and semi-arid climate; GCM models; RCP; weather generator model
Creative Commons
BY 4.0

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  9. Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy
  10. Exploring the potential of ammonia and hydrogen as alternative fuels for transportation
  11. Impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings at various climate zones
  12. Advanced autopilot design with extremum-seeking control for aircraft control
  13. Adaptive multidimensional trust-based recommendation model for peer to peer applications
  14. Effects of CFRP sheets on the flexural behavior of high-strength concrete beam
  15. Enhancing urban sustainability through industrial synergy: A multidisciplinary framework for integrating sustainable industrial practices within urban settings – The case of Hamadan industrial city
  16. Advanced vibrant controller results of an energetic framework structure
  17. Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
  18. Improved correlation of soil modulus with SPT N values
  19. Technologies for high-temperature batch annealing of grain-oriented electrical steel: An overview
  20. Assessing the need for the adoption of digitalization in Indian small and medium enterprises
  21. A non-ideal hybridization issue for vertical TFET-based dielectric-modulated biosensor
  22. Optimizing data retrieval for enhanced data integrity verification in cloud environments
  23. Performance analysis of nonlinear crosstalk of WDM systems using modulation schemes criteria
  24. Nonlinear finite-element analysis of RC beams with various opening near supports
  25. Thermal analysis of Fe3O4–Cu/water over a cone: a fractional Maxwell model
  26. Radial–axial runner blade design using the coordinate slice technique
  27. Theoretical and experimental comparison between straight and curved continuous box girders
  28. Effect of the reinforcement ratio on the mechanical behaviour of textile-reinforced concrete composite: Experiment and numerical modeling
  29. Experimental and numerical investigation on composite beam–column joint connection behavior using different types of connection schemes
  30. Enhanced performance and robustness in anti-lock brake systems using barrier function-based integral sliding mode control
  31. Evaluation of the creep strength of samples produced by fused deposition modeling
  32. A combined feedforward-feedback controller design for nonlinear systems
  33. Effect of adjacent structures on footing settlement for different multi-building arrangements
  34. Analyzing the impact of curved tracks on wheel flange thickness reduction in railway systems
  35. Review Articles
  36. Mechanical and smart properties of cement nanocomposites containing nanomaterials: A brief review
  37. Applications of nanotechnology and nanoproduction techniques
  38. Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review
  39. Communication
  40. Techniques to mitigate the admission of radon inside buildings
  41. Erratum
  42. Erratum to “Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy”
  43. Special Issue: AESMT-3 - Part II
  44. Integrated fuzzy logic and multicriteria decision model methods for selecting suitable sites for wastewater treatment plant: A case study in the center of Basrah, Iraq
  45. Physical and mechanical response of porous metals composites with nano-natural additives
  46. Special Issue: AESMT-4 - Part II
  47. New recycling method of lubricant oil and the effect on the viscosity and viscous shear as an environmentally friendly
  48. Identify the effect of Fe2O3 nanoparticles on mechanical and microstructural characteristics of aluminum matrix composite produced by powder metallurgy technique
  49. Static behavior of piled raft foundation in clay
  50. Ultra-low-power CMOS ring oscillator with minimum power consumption of 2.9 pW using low-voltage biasing technique
  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
  74. Numerical and experimental study of the impact on aerodynamic characteristics of the NACA0012 airfoil
  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
  77. Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study
  78. Flexural behavior of RC beams externally reinforced with CFRP composites using various strategies
  79. Influence of fiber types on the properties of the artificial cold-bonded lightweight aggregates
  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
  82. An experimental and numerical study investigating sediment transport position in the bed of sewer pipes in Karbala
  83. Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach
  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
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