Variations of streamflow and sediment yield in the Mosul-Makhool Basin, North Iraq under climate change: a pre-dam construction study Open Access

To adapt to and mitigate the impacts of climate change, communities require reliable climate data. General Circulation Models (GCMs) serve as a valuable spatial and temporal climate data resource. Essential information about the climate system can be obtained from the GCMs. This information would be difficult to derive from observational data alone (Hamed et al. 2022a; Reddy et al. 2023). Earlier studies utilized GCM datasets to construct the representative concentration pathways (RCPs) scenarios of the CMIP5 project. However, more recent data from Phase Six of the Coupled Model Intercomparison Project (CMIP6) have become available to the global scientific community, significantly advancing the analysis of global climate change. Within CMIP6, the Scenario Model Intercomparison Project (ScenarioMIP) has developed five narratives representing the predicted worldwide social development by the end of the 21st century. These narratives, along with the new RCPs, have produced eight comprehensive scenarios addressing both climate change and society. Collectively, these scenarios are referred to as Shared Socioeconomic Pathways (SSPs) (O'Neill et al. 2017).

The description of SSP1 and SSP5 portrays a future that is characterized by a positive outlook. These scenarios envision the presence of effective institutions, notable acceleration in economic growth, and significant investments in health and education. However, SSP1 presumes a transition toward sustainable energy consumption, whereas SSP5 anticipates a continued reliance on fossil-based energy sources. In contrast, SSP3 and SSP4 depict a less optimistic trajectory, characterized by the limited allocation of resources toward education and health, resulting in heightened increased disparities and population growth. Consequently, these communities are particularly susceptible to the effects of climate change. SSP4 recognizes the presence of severe inter- and intranational inequalities, whereas SSP3 emphasizes regional security as the highest priority. The SSP2 scenario represents the primary pathway, assuming continuing historical trends without significant deviations (O'Neill et al. 2017). The superior performance of CMIP6 compared to CMIP5 has been documented in numerous worldwide studies. Among these, studies are Zamani et al. (2020) in Iran, Kamruzzaman et al. (2021) in Bangladesh, Pour et al. (2022) in Peninsular Malaysia, Hamed et al. (2022a) in Southeast Asia, and Hamed et al. (2022b) in the Middle East and North Africa (MENA). Hamed et al. (2022b) studied the accuracy of the CMIP5 and CMIP6 models in predicting precipitation and temperature patterns in the MENA region. The study results revealed that CMIP6 models demonstrated enhanced simulation capabilities compared to CMIP5 models. Based on the results of Rivera & Arnould (2020), it was observed that most GCMs in the CMIP6 precipitation simulations could effectively replicate climate attributes and long-term precipitation fluctuations. Nevertheless, the utilization of CMIP6 for evaluating streamflow and sediment yield is infrequent, necessitating further investigation to improve the accuracy of simulations in this context.

Successful water resource management, including drought and flood prediction and sustainable agriculture promotion, requires an accurate evaluation of climate change's effects on streamflow (Li & Fang 2021). Numerous studies have focused on investigating the impact of climate change on water resources (Mohseni et al. 2023), revealing that a significant proportion of world's 200 major rivers have experienced notable alterations in streamflow patterns since the 1950s. Rising greenhouse gas concentrations significantly increase the potential risks associated with climate change's consequences on freshwater availability. Many semi-arid and arid regions expect their sustainable surface water and groundwater resources to decrease considerably over the 21st century (Li & Fang 2021). Semi-arid regions exhibit heightened susceptibility to anthropogenic alterations in terrestrial ecosystems and environmental conditions (Stocker et al. 2014). Roughly 15% of the global population resides in these regions, making precipitation an essential resource for satisfying domestic, industrial, and agricultural demands (Schwinning et al. 2004). The influence may be strongly felt in arid and semi-arid regions, especially in western Asia (Salman et al. 2018). The dry climatic system is particularly vulnerable to even minor fluctuations in the current climate due to the fragility of its ecosystems (Salman et al. 2017; Ahmed et al. 2019). The intricate relationship between precipitation and sea surface temperatures in the region gives rise to irregular precipitation patterns and recurring drought conditions (Barlow et al. 2021). Previous research has demonstrated that even minor fluctuations in precipitation levels can lead to a substantial increase in extreme weather events associated with precipitation (Nashwan & Shahid 2020; Salman et al. 2022). Therefore, significant alterations in climatic factors, particularly rainfall, can result in the emergence of climate change-related challenges, such as hydro-meteorological hazards, within the western region of Asia (Salman et al. 2017, 2019; Salehie et al. 2022). Iraq, located in the western region of Asia, is globally recognized as one of the countries with the lowest level of climate resilience. The country has observed a substantial temperature increase, surpassing the global average temperature rise rate over the past 40 years (Salman et al. 2017). Furthermore, it has experienced a significant decrease in rainfall over time by 7.8% (Salman et al. 2022). These changes have substantially impacted various economic sectors and individuals' means of survival.

Hydrological conditions and water availability will change on regional and global scales due to climate change. These changes will influence rainfall, evaporation, water yield, and land surface runoff (Abbas et al. 2022). Droughts and floods threaten various sectors, particularly in developing countries where rain-fed agriculture substantially contributes to the economy (Oguntunde & Abiodun 2013). Multiple studies have provided evidence suggesting that climate change will have profound and significant effects on water resources worldwide, necessitating adaptation strategies (Lehner et al. 2019; Schilling et al. 2020; Li & Fang 2021). Effective adaptation plans require a thorough understanding of global climate change projections and their effects on water resources. Catchment area consequences from climate change are commonly evaluated using rainfall-runoff models and global climate change scenarios. The results highlight the importance of precise climate projections and robust hydrological models capable of withstanding changing climatic conditions (Ruelland et al. 2015). The Soil and Water Assessment Tool (SWAT) is a hydrological, basin-scale tool based on physics. Due to its effectiveness in assessing the impacts of global warming on runoff and sediment yield, it has gained broad acceptance (Zhang et al. 2016).

The future effects of global warming on streamflow and sediment yield have been the focus of much recent research using the SWAT for simulation. The investigation by Azari et al. (2016) focused on the Gorganroud River watershed in northern Iran. The study aimed to determine the impact of global warming on sediment yield and streamflow in this area; the climate change data were derived mostly from the CMIP5 model. De Oliveira et al. (2019) in the Upper Paranabá River Basin, Southeastern Brazil, Tian et al. (2020) in the Tibet Lhasa River Basin, and Sharafati et al. (2020) in the Iran Dehbar River Basin used the CMIP5 model for future climate data under two emission scenarios (RCP4.5 and RCP8.5) to evaluate the effects of global warming on sediment yield and streamflow. Climate data from CMIP5 were also used by Shrestha et al. (2022) in the Songkhram River Basin, Thailand, and dos Santos et al. (2021) in the Caatinga/Atlantic forest ecotone, Brazil to study climate change effects on streamflow and sediment yield.

Numerous studies indicate that variations in precipitation and temperature may have a considerable influence on watershed processes, while the magnitude and nature of this impact can vary widely from region to region. Given the anticipated global negative effects of climate change across all regions, it is crucial to quantify the hydrological impacts to enhance our understanding and predictive capabilities concerning sediment yield and streamflow processes. This is particularly important as part of a pre-dam construction study. Therefore, the objective of this study is to assess the potential consequences of climate change on streamflow and sediment yield in the Tigris River Basin, with specific emphasis on the Mosul-Makhool Basin (MMB). To achieve this, we will utilize the SWAT model and CMIP6 data from three GCMs, analyzing the time period from 2021 to 2100. The analysis will be conducted under two emissions scenarios, namely SSP2–4.5 and SSP5–8.5, and is divided into four time intervals: 2021–2040, 2041–2060, 2061–2080, and 2081–2100. The use of 20-year intervals serves as a practical and scientifically justifiable approach, aligning with the requirements of dam construction planning, risk management, and adaptation strategies. This approach allows us to capture the evolving nature of climate change and its impacts on streamflow and sediment yield, which are critical considerations for the proposed dam project. The findings from this study will significantly contribute to decision-making processes, particularly in relation to the proposed Makhool Dam. Moreover, the results will promote sustainable practices in water resource management in arid and semi-arid regions facing the challenges of climate change.

The MMB is a sub-basin of the Tigris River Basin, which is located in the northeastern region of Iraq. It borders Iran and Turkey to the east and the north, respectively (Figure 1). The basin spans 4.51 × 10⁴ km² at the location of the Makhool Dam at 140 meters above mean sea level (m.a.s.l.). The basin has various climatic conditions, from low-lying parts at 140 m.a.s.l. to the high-altitude Zagros Mountains at 4,168 m.a.s.l. (Osman et al. 2019). The climatic conditions in the northern and northeastern regions of Iraq are generally typical of a Mediterranean climate. The annual precipitation exhibits significant variability, with the mountainous areas in the northeast experiencing precipitation levels exceeding 1,500 mm, while the location of the Makhool Dam receives less than 350 mm. The predominant precipitation in the river basin is observed during the spring and winter seasons, mostly as rain and snowmelt in Turkey and Iran's mountainous headwater regions. Winter temperatures in the basin can drop below freezing (0 °C) in January, while temperatures in February rise above 18 °C. Conversely, the summer season consistently witnesses extremely high temperatures, frequently surpassing 45 °C (Al-Hasani 2019). The average annual streamflow in the Baji station is 779 m³/s, varying from 8,947 m³/s in spring to 409 m³/s in autumn. The geological composition of the basin's mountainous regions is primarily distinguished by the prevalence of carbonate rocks and clastic rocks. The western part of the watershed mainly consists of silt, clay, rare sand, and pebbles (Sissakian et al. 2018). The MMB is currently facing significant challenges related to soil erosion, sediment yields, floods, and debris flow. These problems stem from rainfall-induced erosion in sensitive soils, aggravated by extensive changes in land use, specifically the change of range lands and forests into arid areas. These factors have caused increased runoff, leading to significant soil erosion and sediment accumulation within the watershed.

The daily precipitation data used in the study were sourced from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset, which has a spatial resolution of 0.05° × 0.05°. CHIRPS is a comprehensive precipitation dataset that offers high-resolution information from 1981 to the present. This dataset represents a gridded time series of rainfall information derived from a combination of satellite data and data from in situ rain gauge stations. By incorporating both sources of information, this approach offers a comprehensive and precise depiction of precipitation patterns (Katsanos et al. 2016). The CHIRPS product and the related data are freely available on the official website: http://chg.geog.ucsb.edu/data/chirps/. Numerous studies have underscored the superior performance of CHIRPS compared to other datasets in simulating streamflow within the SWAT framework (Duan et al. 2019; Dhanesh et al. 2020; Mengistu et al. 2022).

The daily maximum and minimum temperature (Tmax and Tmin) data for this study were obtained from the reanalysis products provided by the National Aeronautics and Space Administration Prediction of Worldwide Energy Resource (NASA POWER). These datasets offer extensive information on daily, monthly, and annual Tmin and Tmax at a spatial resolution of 0.5° × 0.5° (Natumanya et al. 2022). The NASA POWER datasets can be accessed through the data access viewer at the following URL: https://power.larc.nasa.gov/data-access-viewer/. Given the limited number of ground observation stations and the associated data quality concerns, 45 meteorological stations were used in the MMB, which were provided by the CHIRPS and NASA POWER.

The outflow data from the Mosul Dam and the Dokan Dam, covering the period from 1994 to 2020, were sourced from the National Center for Water Resources, an entity operating under the Iraqi Ministry of Water Resources. The streamflow observations used in this study were of high quality, and there were no missing data points in the dataset. An interpolation method was employed if minor gaps occurred to ensure data continuity and reliability.

This research utilized 30 m spatial resolution data of the Digital Elevation Model (DEM). The DEM was created by using Shuttle Radar Topography Mission (SRTM) data and obtained from the official United States Geological Survey (USGS) website at https://earthexplorer.usgs.gov/.

The study utilized the 1995 global soil map from the Food and Agriculture Organization of the United Nations for soil data (http://www.fao.org). The land use/land cover (LULC) data were sourced from the Environmental Systems Research Institute (ESRI), with a spatial resolution of 10 m. The official data source provided by the ESRI can be accessed at https://livingatlas.arcgis.com/landcover/. Throughout the model simulation process, the LULC remained constant, as the primary objective was to evaluate climate change's effect on streamflow and sediment yield.

Sensitivity analysis, calibration, and validation of models are essential to accurately predicting the potential impact of climate change on sediment yield and runoff. By reviewing the model's performance statistics within acceptable ranges, these procedures establish its acceptability.

This study used the SWAT Calibration and Uncertainty Program (SWAT-CUP) to calibrate and validate the hydrological model. The effectiveness of the model is assessed using various performance metrics. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R²), and root mean squared error-observation deviation ratio (RSR) are all utilized in this investigation. Detailed information regarding these performance indicators can be found in Moriasi et al. (2007). These metrics comprehensively assess the model's performance and capacity to accurately simulate streamflow and sediment yield.

Among the options available in the SWAT-CUP, the Sequential Uncertainty Fitting (SUFI-2) algorithm was selected for this investigation. Previous studies have demonstrated that the SUFI-2 algorithm exhibits fewer iterations when optimizing parameters than alternative methods like particle swarm optimization and Generalized Likelihood Uncertainty Estimation. The SUFI-2 algorithm has gained recognition for its notable efficacy in optimizing parameters and analyzing uncertainty within the SWAT model (Abbaspour et al. 2017). These findings indicate that the SUFI-2 algorithm offers a more efficient approach for parameter optimization within the context of SWAT-CUP.

Twenty parameters were selected to calibrate the model of the MMB (Table 1). The sensitivity parameters were evaluated utilizing p-values and t-stats. Parameters with lower p-values and higher t-stats indicate a heightened sensitivity to the optimization function, suggesting their substantial influence on the model's performance. This sensitivity analysis helps identify the crucial parameters that play a key role in improving the accuracy and reliability of the model through the calibration process (Bhatta et al. 2019). After completing sensitivity analysis, calibration and validation were carried out. The calibration phase utilized data spanning from 1994 to 2011, while the subsequent validation period extended from 2012 to 2020.

General circulation models (GCMs) are widely accepted as effective and reliable for assessing global climate change. The data from the CMIP6 were utilized to evaluate the potential future changes in temperature and precipitation. The CMIP6 was preferred due to its improved precision and resolution compared to the previous iteration (CMIP5) (Nourani et al. 2022) and the incorporation of new scenarios that integrate socioeconomic aspects of climate change. This research employed ensemble data from three global climate models to predict the future climate and spatiotemporal variations of hydromorphological changes in the study region (Table 2). The decision of using three specific GCMs in our research was informed by recent studies in the field of climate modeling, specifically Salman et al. (2022) and Hamed et al. (2023). These studies have identified these three GCMs as the most suitable choices for simulating climate conditions in the Middle East and Iraq. Their findings align with our research objectives and the geographic focus of our study. The historical period (1994–2020) and the future (2021–2100) period were used for the analysis.

The CMIP6 has replaced the RCPs with the SSPs for exploring potential interactions between socioeconomic development pathways and resulting greenhouse gas emissions and climate change. These pathways incorporate factors, including population growth, economic development, energy use, and land use changes, to generate integrated assessments of future climate change by linking with global climate models. Potential consequences of global warming on sediment yield and streamflow in the studied basin were assessed utilizing two emission scenarios: SSP2–4.5 (medium emission) and SSP5–8.5 (high emission). The scenario known as SSP2–4.5 indicates a moderate degree of social vulnerability and radiative forcing, whereas SSP5–8.5 is distinguished by the highest level of anthropogenic radiative forcing, reaching 8.5 W/m² by the year 2100 (Zhang et al. 2019).

This study employed the Climate Model data for hydrologic modeling (CMhyd) tool for statistical bias correction. CMhyd is a Python-based software tool designed to downscale and bias-correct climate model data obtained from general circulation models (GCMs) for hydrologic modeling purposes. GCMs typically have coarse spatial resolution and may produce biased climate projections unsuitable for SWAT use (Kumar et al. 2022). CMhyd utilizes various statistical downscaling techniques to generate high-resolution climate projections. This paper specifically employed the distribution mapping method for bias correction. This method was chosen based on studies highlighting its effectiveness in correcting biases in climate models (Smitha et al. 2018; Enayati et al. 2021). The adjustments to precipitation and temperature were carried out using a transfer function derived from the monthly mean values of the projected GCM data. This approach aimed to enhance the accuracy of simulations by fine-tuning the GCMs' outputs and reducing the discrepancies between observed and simulated climate variables (Siabi et al. 2023). By implementing these bias correction techniques via CMhyd, the researchers intended to improve the accuracy and reliability of the hydrological model's simulations, thereby ensuring more realistic and significant findings for their study (Vaittinada Ayar et al. 2021).

Twenty parameters were selected to calibrate the SWAT-based model of the MMB (SWAT-MMB). These selected parameters are illustrated in Table 1. In SWAT-CUP, the calibration process involves conducting 500 simulations. The effectiveness of optimization trials depends on the results of sensitivity analyses performed on various model parameters. The sensitivity analysis of these parameters involves assessing their significance by considering p-values and t-stats. A large absolute t-stats indicates that the parameter significantly affects the model output, while a small p-value (typically less than 0.05) indicates that the parameter has a significant effect on the model output. In general, parameters with small p-values and large t-stats are considered significant contributors to the model (Abbaspour et al. 2017). Among the parameters considered, it was observed that the Alpha factor of the base flow in bank storage (ALPHA_BNK) exhibited the greatest sensitivity in the MMB. Following ALPHA_BNK, the parameters that displayed significant sensitivity were the main channel's effective hydraulic conductivity (CH_K2), Manning's value (n) for the main channel (CH_N2), SCS runoff curve number (CN2), and the compensating factor for soil evaporation (ESCO). It is worth mentioning that during the model calibration process, the parameters related to routing water flow (ALPHA_BNK, CH_K2, and CH_N2) and parameters associated with surface runoff (CN2) were found to be the most sensitive. The results agree with the studies of Abbas et al. (2016) and Tian et al. (2020). The SWAT-MMB model was found to be reasonably influenced by snow-related factors like SFTMP and SMTMP. This influence was especially pronounced in high-altitude areas where winter temperatures consistently drop below freezing, leading to an accumulation of snow cover that melts during spring months.

In this study, the validity of precipitation and temperature data obtained from CHIRPS and NASA-POWER datasets was verified by comparing them with measurements from two weather stations in the study region, such as Tikrit Station and Mosul Station. This comparison involved calculating NSE, R², PBIAS, and RSR metrics (Table 4). The results indicated significant consistency between observed data from the weather stations and corresponding data from CHIRPS and NASA-POWER datasets, confirming the reliability and suitability of these datasets for precipitation and temperature analysis in the study area.

Regarding temperature changes, Tmax is anticipated to increase by 2.5 °C under the SSP2–4.5 scenario and 4.8 °C under the SSP5–8.5 scenario in the northern region, while, in the southern region, it is projected to increase by 2.6 °C under SSP2–4.5 and 4.8 °C under SSP5–8.5. Similarly, Tmin is expected to increase by 3.4 and 6.1 °C under both the SSP2–4.5 and SSP5–8.5 scenarios in the northern region, and in the southern region, Tmin is projected to increase by 3.4 °C under SSP2–4.5 and 5.8 °C under SSP5–8.5.

Figure 11(b) and 11(c) illustrates the changes in streamflow during the wet and dry seasons across the four future periods under the SSP2–4.5 and SSP5–8.5 scenarios. The observed effects of climate change on streamflow reveal a consistent pattern for both wet and dry seasons. Specifically, in the wet season, a decrease in streamflow is projected across all future periods. Under the SSP2–4.5 scenario, percentage changes in streamflow are 14.1, 20.2, 14.1, and 6.7% during the periods of 2021–2040s, 2041–2060s, 2061–2080s, and 2081–2100s, respectively. Similarly, under the SSP5–8.5 scenario, streamflow exhibits a reduction of 17.0, 30.0, 19.9, and 32.9% during the same time periods (Figure 11(b)). In the dry season, streamflow consistently decreases across all future decades under both SSP scenarios (Figure 11(c)). This reduction in streamflow during the dry season is particularly concerning as it exacerbates challenges related to water scarcity and availability during a period when demand is often at its peak. A stepwise multiple linear regression analysis was conducted to examine the effects of temperature and precipitation on streamflow. The results revealed that during the wet season, the influence of precipitation on streamflow had greater significance than that of temperature. This finding clarifies that the decline in streamflow during the wet season is anticipated to be less severe than the reductions observed during the dry season. Temperature is projected to have a more significant impact than precipitation during the dry season in future climate change scenarios (Table 6). The insight that precipitation remains a more influential factor than temperature in driving streamflow during the wet season offers a ray of hope for water resource management strategies. While climate change is projected to bring about reductions in streamflow during this critical period, there is an opportunity for mitigation through the enhancement of precipitation harvesting and storage techniques. During the dry season, both maximum and minimum temperatures are negatively correlated with streamflow, adversely impacting the water flow in the streams. This observation suggests that evaporation rates increase as temperatures rise, surpassing the effect of precipitation and reducing streamflow (Bhatta et al. 2019). Consistent with this, Azari et al. (2016) and Li & Fang (2021) reported a reduction in streamflow during the dry season and an increase during the wet season in various regions worldwide.

Figure 14(b) and 14(c) illustrates the variations in sediment yield during wet and dry seasons over specific periods. The future projections indicate a decline in sediment during the wet season for all four future periods under the SSP5–8.5 and SSP2–4.5 scenarios, as depicted in Figure 14(b). Specifically, the sediment yield is projected to decrease by 14.5, 21.7, 21.0, and 12.1% for the periods of 2021–2040s, 2041–2060s, 2061–2080s, and 2081–2100s, respectively, under the SSP2–4.5 scenario. Similarly, under the SSP5–8.5 scenario, the decrease in sediment yield exceeds 18.5, 35.6, 24.0, and 39.7% during the same periods. However, during the dry season, the sediment yield is projected to decline even further than during the wet season throughout the entire study period under the SSP2–4.5 and SSP5–8.5 scenarios, as shown in Figure 14(c).

The smaller decrease in sediment yield during the wet season, as compared to the dry season, can be attributed to the elevated streamflow resulting from increased precipitation levels during this specific period. In contrast, during the dry season, the reduced streamflow can be attributed to the combined effects of increased Tmax and Tmin and an accelerated evaporation rate. The rise in precipitation during the wet season enhances the overall volume of water flowing through the watersheds, leading to increased streamflow. This increased flow has a greater capacity to transport sediments, resulting in higher sediment yield during this season. Conversely, the dry season experiences higher temperatures in terms of Tmax and Tmin. These increased temperatures contribute to an elevated evaporation rate, causing the water levels in watersheds to decrease. The reduced streamflow during this period limits the transport capacity of sediments, resulting in lower sediment yield. Piao et al. (2004) stated that rising temperatures can stimulate vegetation growth. An increase in vegetation coverage can subsequently lead to a reduction in sediment yield. Therefore, the contrasting patterns of sediment yield between the wet and dry seasons can be attributed to the opposite effects of precipitation and temperature-related factors, such as streamflow and evaporation rates. These findings also indicate that climate changes significantly impact sediment yield more than streamflow. The substantial reduction in sediment yield has important implications for reservoir siltation. Reduced sediment yield can lead to slower rates of reservoir infilling, potentially impacting water storage capacity and the long-term sustainability of water resources. Additionally, the reduction in sediment supply may impact habitat quality for aquatic species and alter the geomorphology of riverbeds. This significant impact on sediment yield underscores the need for adaptive strategies in managing sediment transport and considering sediment-related aspects in climate resilience planning.

Future climate projections using the CMIP6 model are considered to be more reliable than those of CMIP5 (Nourani et al. 2022). However, further investigation utilizing alternative models is still necessary. Al-Hasani (2019) highlighted that streamflow is more responsive to precipitation changes than the temperature in the Mediterranean catchments of the Tigris River Basin. In contrast, our study demonstrates a clear impact of temperature on streamflow, particularly during dry seasons. Furthermore, it is worth noting that Osman et al. (2019) conducted a study specifically focused on the Greater Zab watershed, which is a part of the Tigris River Basin. In this study, the obtained changes in streamflow projections differ from the present research's findings. In Osman et al. (2019), both increases and decreases in streamflow magnitudes were projected. Particularly, the effect of temperature on streamflow was ignored in this investigation. Notably, it indicated a rise in streamflow from July to November, contrasting with the streamflow changes observed in the present research. The sources of uncertainty in the projected streamflow can be attributed to various factors, including the climate models utilized, the downscaling techniques employed, and the structure of the hydrological model.

Indeed, temperature and precipitation projections play a crucial role in assessing the uncertainties related to water resources and basin hydrology. Changes in precipitation patterns and temperature levels directly influence streamflow, water availability, sediment yield, and overall basin hydrology. Accurate predictions of these climatic variables are imperative for precisely evaluating water resources and hydrological processes. Any uncertainties in these projections can significantly impact the understanding and management of water systems (Ahmadalipour et al. 2018). This research utilized three global climate models to project future climate conditions. These models are well recognized for their accuracy and reliability in Iraq's climate research. The distribution mapping downscaling method enhanced the accuracy of climate model projections. This strategy is known for its effectiveness in addressing biases in climate models (Smitha et al. 2018; Enayati et al. 2021). By utilizing this technique, the present research intended to eliminate any potential discrepancies between the observed data and climate model results, ultimately improving projection accuracy. By considering these factors and employing ACCESS-CM2, BCC-CSM2-MR, and MRI-ESM2–0 models, along with the distribution mapping downscaling method, this study sought to reduce uncertainties and provide more accurate projections of future climate conditions related to water resources and basin hydrology. Developing suitable models and utilizing statistical downscaling methods are crucial steps in reducing the uncertainties associated with projected temperature and precipitation. These approaches will help quantify the effect of natural climate variability and anthropogenic activities on streamflow. Nevertheless, due to the limitations of this study, further research is necessary to explore these dimensions. Additional analyses can enhance our understanding of specific impacts and refine projections, ultimately reducing uncertainties in assessing water resources, streamflow, and sediment yield dynamics.

Changes in land use and human activities, such as urban expansion and reservoir operations, significantly impact runoff generation, sediment yield, and water resource availability (Zhou et al. 2015). This investigation examined the potential consequences of climate change under the assumption of no changes in land use. Additionally, potential variations in dam releases resulting from modifications in the reservoir operation, which can consequently affect streamflow, were considered. The use of a calibrated streamflow model to estimate sediment yield introduces a level of uncertainty. However, employing such a model is a reasonable approach in regions where sediment yield measurements are unavailable (ungauged regions). If sediment data becomes accessible in subsequent periods, it is advisable to re-evaluate and enhance the model by incorporating it for calibration and validation. Despite these assumptions, these findings still serve as a valuable reference for understanding the potential effect of future climate change on streamflow and sediment yield.

The primary objective of this research was to investigate and evaluate how potential climate change would affect streamflow and sediment yield in the MMB that was located in northern Iraq during the 21^st century. The study also aimed to evaluate the possible changes in streamflow and sediment yield under two distinct climate scenarios. A SWAT-based model was employed to simulate and assess streamflow and sediment yield in the basin. The sensitivity analysis and model performance were evaluated using SWAT-CUP software. The CMIP6 was queried for precipitation and temperature projections in the future under two scenarios (SSP2–4.5 and SSP5–8.5). A distribution mapping bias correction method was utilized to enhance the precision of climate data.

In the late 21st century, the MMB is projected to experience a decline in average annual precipitation. Specifically, the projected decrease is approximately 9.5% in the SSP2–4.5 scenario, while under the SSP5–8.5 scenario, it is anticipated to decrease by around 18.7%. The results of this study demonstrate that the rate of increase in minimum temperature exceeds that of maximum temperature. Furthermore, the changes in both minimum and maximum temperatures during the summer, particularly in July and August, were more substantial compared to the rainy and winter seasons. The combined effects of reduced precipitation and increased temperatures synergistically influence the streamflow. According to the SSP2–4.5 scenario, the study projects a significant reduction in streamflow of more than 36.6% between 2021 and 2100. However, under the more severe SSP5–8.5 scenario, the streamflow exhibited an even higher rate of decline, with a reduction of 45.9% over the same period. It is worth noting that regardless of the emission scenario, there was a notable change in the pattern of peak flow. The peak flow will shift from May to April under both emission scenarios. The sediment yield decreased by 46.0% from 2021 to 2100 during the SSP2–4.5 scenario and decreased by 55.0% during the SSP5–8.5 scenario over the same period. The monthly analysis revealed that the runoff and sediment yield increases were more noticeable during the wet seasons but decreased during the dry season. The findings also indicate that climate changes significantly impact sediment yield more than streamflow.

The projected decrease in streamflow and sediment yield due to climate change could lead to challenges in maintaining sufficient reservoir levels for consistent water supply, especially in regions relying on dams for their water needs. Reduced sediment inflow into the reservoir can influence water quality by trapping and removing nutrients, pollutants, and other substances from the water. A decrease in sediment supply may lead to changes in water quality, which can affect the ecological health of the reservoir and the suitability of the water for various uses.

The findings of this paper provide valuable insights into how future climate scenarios may impact streamflow and sediment yield in the MMB, serving as a pre-dam construction study. Decision-makers could use this information to develop and implement proactive water resource management strategies in the MMB and the Makhool Dam. This will help in mitigating potential future challenges related to water resources. Decision-makers could develop adaptive strategies to address the potential impacts of climate change on water resources of more frequent droughts and changing precipitation patterns. This paper can serve as a useful reference for future studies focusing on the design and operation of the dams. Indeed, in addition to considering climate change impacts, it is essential to account for other factors, such as LULC changes. Incorporating these factors into future research efforts will provide a more comprehensive understanding of the dynamics affecting water resources in the region.

For their helpful and encouragement, the authors would like to extend their gratitude to the Civil Engineering Department professors and employees at the University of Technology in Iraq.

This research received no external funding.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.