Research Article

Modelling Hydrodynamic Changes in the Salinity of the Hammar Marsh and Shatt al-Arab Waters Using Dimensional Analysis and Remote Sensing

Forqan Khalid Al-Daraji1, Husam Hasan Abdulaali2*, Yousif Sh. J. Al-Jorani and Muayad H. M. Albehadili1

1Department of Applied Marine Sciences, College of Marine Sciences, University of Basrah, Basrah Governorate, Iraq; 2Soil Science and Water Resources Department, Faculty of Agriculture, University of Basrah.

Received | January 07, 2026; Accepted | January 26, 2026; Published | March 26, 2026

*Correspondence | Husam Hasan Abdulaali, Soil Science and Water Resources Department, Faculty of Agriculture, University of Basrah; Email: [email protected]

Citation | Al-Daraji, F.K., H.H. Abdulaali, Y.S.J. Al-Jorani and M.H.M. Albehadili. 2026. Modelling hydrodynamic changes in the salinity of the Hammar Marsh and Shatt Al-Arab waters using dimensional analysis and remote sensing. Pakistan Journal of Agricultural Research, 39(1): 203-214.

DOI | https://dx.doi.org/10.17582/journal.pjar/2026/39.1.203.214

Keywords | Water salinity, Remote sensing, Dimensional analysis, World heritage sites, Climate change

Copyright: 2026 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

The Shatt al-Arab River and the marshlands of southern Iraq are important natural features that have heavily influenced the region’s historical environmental and social identity, owing to their economic and heritage significance (Qadra, 2020). The Shatt al-Arab River, created by the confluence of the Tigris and Euphrates rivers north of Basrah at Qurna. It runs for about 200 km before it empties into the Arabian Gulf (Danboos et al., 2023). It functions as an important channel for trade and irrigation, thereby increasing farming output (Hamdan et al., 2018). The Iraqi Marshes are among the largest wetlands in the Middle East. In addition, they harbour high biodiversity, serve as breeding grounds for migratory birds and fish, and provide sustenance to local communities relying on fishing and farming. These marshes were added to UNESCO’s World Heritage List for their habitat-related values and cultural heritage, as well as for their association with the civilisation of Mesopotamia (Smaysim and Slewa, 2014). Yet, the marsh reeds are under increasing environmental threats, associated with both drought and climate change, which negatively affect the hydrological regime of the Shatt al-Arab River and its discharge (Al-Jiburi and Al-Basrawi, 2009). The intake of the Shatt al-Arab into the Persian Gulf, additionally intensified by climate change (Al-Mahmood and Al-Mahmood, 2021), has significantly reduced the flow of the Tigris and Euphrates since dam construction in Turkey and Syria (Haleem and Al-Muhyi, 2018). Human interventions such as the marsh drainage campaigns of the 1990s, excessive water use, and industrial pollution have further intensified salinity challenges and complicated traditional monitoring efforts (Hamdan et al., 2020). Remote sensors have been a useful means of collecting real-time, accurate information from remote locations (Vijayan et al., 2010). Projection models help estimate future salinisation indicators and support effective monitoring of environmental degradation and its effects on agriculture and biodiversity (Saleh, 2017; Alqasemi et al., 2021). Salinity in the Shatt al-Arab River has increased to 10–12 ppm, clearly demonstrating the need for desalination projects and enhanced local cooperation (Hamdan et al., 2020). Over the last 20 years, temperatures in southern Iraq have increased by 1.5°C, while rainfall has decreased by 20% (Al-Daraji et al., 2025), highlighting the imperative nature of formulating successful water management strategies to address climate change (Al-Khalidi et al., 2018). Salinity in the marshes reaches 12 ppm, implying a growth in freshwater inflow and sound water management (Mohamed et al., 2016). Other research also reports a 40% decrease in streamflow (Tigris-Euphrates) resulting from the construction of dams upstream (Adamo et al., 2018). On the other hand, conventional water quality monitoring approaches in southern Iraq incur annual expenses exceeding $3 million, thereby calling for the application of cost-effective geomatics tools to improve data correctness (Harmel et al., 2023). Reported that high temperatures and scant rainfall led to a 20% increase in river salinity in southern Iraq, prompting researchers to propose improved water management to reduce the impact of climate change (Al-Salihi et al., 2024). Reported that water salinity resulted in a 50% drop in crop yield in some locations, and recommended advanced technologies for water management and salinity reduction (Van Zandwijk et al., 2021). The Shatt al-Arab, water salinity was 18.5 ppm in 2009 and was increasing further, they recommended better water management in the area. Noted that salinity in the Karma Ali River increased by 15% due to industrial and sewage pollution, and recommended the use of modern technology for controlling pollution impacts on the environment (Al-Jawad et al., 2018). Several investigators have monitored river water salinity using satellite data (Harmel et al., 2023).

A study by Ahmed et al. (2023) utilised Landsat and Sentinel-2 data to assess water salinity in the Tigris River at 15 locations, disclosing a strong correlation (r = 0.85) between salinity and medium-wave infrared (MWIR) data with 90% accuracy. In a study by Mukhamediev et al. (2023), Landsat 8 data were used to measure water salinity in the Kazakhstan River at 10 locations, achieving 85% accuracy with a moderate correlation (r= 0.72) between salinity and near-infrared (NIR) data (Li et al., 2021). A quantitative model for salinity was developed using visible–near-infrared spectral data processed with the Savitzky–Golay filter, MSC, and combined transformations. Spectral metrics (DI, RI, NDI) were calculated, and the most informative (Spearman’s r > 0.8) were selected. Results from the RBF neural network model showed that its accuracy was relatively good (R²= 0.950, RMSE= 1.014, RPD= 4.479) and that it could effectively predict salinity values. The combination of remote sensing (Landsat 9 and Sentinel-2 bands/salinity indices) with environmental predictors (ENVI) largely improved soil salinity estimates, whereas the RS + ENVI approach using Sentinel-2 showed the best model effectiveness (R² = 0.86), compared to single RS or ENVI models (Jia et al., 2024). A study created a multivariable linear algorithm to estimate sea surface salinity (SSS) using Landsat 8 OLI bands 1 to 4. The method achieved high accuracy (R²= 0.74 and RMSE < 2%) even though a certain algorithm parameters and data were limited. It identified SSS changes during extreme events at higher spatial and temporal scales, whereas numerical models overestimated salinity by 3.4%, showing how high-resolution satellites can monitor small-scale marine features (Zhao et al., 2017). The (MPNN SSS model), constructed from both MODIS and SeaWiFS data (Rrs at 412-667 nm, SST), performed well with RMSE= 1.2, R²= 0.86, and the bias close to zero to resolve gradients at a spatial resolution of 1 km from coastal to offshore regions in the northern Gulf of Mexico. Its reliability was assessed through validation and sensitivity tests (RMSE= 1.1 on independent data) that also showed that the obtained map, compared to Aquarius maps, had an improved ability to resolve fine-scale features and salinity gradients. However, the application in algal bloom and upwelling areas was restricted due to interference from blue-band Rrs (Chen and Hu, 2017), reported that high levels of soil and water salinity were increasingly threatening crop productivity and quality, particularly in arid or semi-arid climates with high evaporation and intensive irrigation. They used Landsat-5 TM satellite images in their study conducted on the Neretva River Delta, Croatia, to investigate low to moderately salt-affected agricultural land. The results indicated that remotely sensed data could explain 43% and 62% of the variation in water salinity using the SLR and MLR models, respectively, suggesting the applicability of this technique for assessing environmental quality in salt-affected agricultural lands (Racetin et al., 2020). Reported that the saline balance conditions of the estuarine ecosystems, which are altered suddenly by storms and hurricanes, lead to disturbances in aquatic life (Wang and Xu, 2011). Although measuring salinity directly using remote sensing is challenging, studies have found a correlation between salinity, colored dissolved organic matter (CDOM) and suspended solids, allowing the development of indirect estimative models for monitoring salinity changes. Due regarding challenges in direct data collection, machine learning techniques were applied to assess salinity in East Sivash Bay using 93 in situ samples and 6 Sentinel-2 datasets. The best simple linear regression model attained an accuracy of 0.8797, while the random forest, along with AdaBoost models, showed lower accuracies. The study also found that as salinity increases, light absorption shifts to the infrared spectrum, permitting regular surveillance of hypersaline water bodies using remote sensing (Borovskaya et al., 2022). Established strategies commonly fail to meet the effectiveness along with exactness needs in complex basins. The USalt system is proposed to utilise UAV mobility and IR-UWB radar for fast and accurate salinity sensing. The system eliminates signal contamination and extracts salinity-related features with a neural network model (ssNet) for exact estimation. An advanced learning framework (mssNet) was developed for ecological flexibility. Field experiments have shown that USalt achieves an MAE of 0.39 g/100 mL in water salinity sensing (Wang et al., 2024b).A study uses Landsat 8 OLI imagery and 102 in situ salinity data points to map salinity in the Karun River, Iran. including spectral bands and salinity indices were assessed using Random Forest Variable Significance Score (RFFIS), Sobol’s sensitivity analysis, and correlation with salinity. Key features for salinity estimation included the Red and Green bands, salinity indices 2-6 NSMI and EGRI. Spatial autocorrelation analysis suggested that models including spatial terms produced better results. Landsat 8 OLI effectively mapped salinity changes and increased the salinity due to agriculture (Ansari et al., 2025). However, the use of remote sensing-based studies with mathematical models to evaluate river salinity and improve water management in climate change is one commonality in all the articles, providing several suggestions for developing a model that includes more climatic and seasonal agents to increase forecast precision and guarantee planning for renewable water resources. The objectives are to derive a work method for integrating ground measurements and remote sensing products into a hydrodynamic mathematical model to monitor/simulate the changes in water salinity in these Al-Hamaar Marshes / Shatt al-Arab River UNESCO World Heritage site areas.

Materials and Methods

Study location

The study took place in southern Iraq, in the alluvial plain of the Mesopotamian (Wadi al-Rafidain) region, located in the northern part of Basrah Province (Sissakian et al., 2014). The study concentrated on the branch of the Shatt al-Arab River and the southern section of Hawr al-Himar (Al-Daraji et al., 2024). Samples were collected in two phases, with 40 water samples taken per phase. In each phase, 20 water samples were randomly distributed at sites whose coordinates were recorded using GPS, covering an area with a total land surface of approximately 153 km² and an aquatic surface (including water bodies and the river network) of about 20.51 km², as shown in Figure 1.

Satellite imagery and information processing

Landsat 8 satellite imagery was downloaded from the United States Geological Survey (USGS) website on April 5, 2024, and April 18, 2024. These dates correspond to the data acquisition times for each row and satellite orbit that provided a scene covering the study area. ArcGIS 10.8 software was used to perform spectral correction on the satellite imagery and to extract the targeted study area. Subsequently, digital data processing was conducted, and the spectral reflectance for bands 2 through 10 was computed (Tajudin et al., 2021). From these data, the water salinity index (Al-Khakani et al., 2018) and the normalised difference chlorophyll index (NDCI) were derived, both of which are considered among the most important variables in model construction (Pałas and Zawadzki, 2020).

 

Variables and mathematical model construction

The variables were identified, and Buckingham’s π-theorem was applied for the purpose of deriving the variables and obtaining the governing Equation 1 as shown in Table 1, as follows (Phull and Babar, 2012):

Sm = f (S_index, NDCI, L, D, g, T, E, ρ) …(1)

Several remote sensing variables were computed as follows:

Salinity Index (Equation 2):

Band 3: Reflectance in the green wavelength range (0.53–0.59 µm), Band 4: Reflectance in the red wavelength range (0.64–0.67 µm).

Normalised Difference Chlorophyll Index (NDCI, Equation 3):

Band 5: Reflectance in the Near-Infrared (NIR) range, corresponding to Band 5 in Landsat 8. Band 2: Reflectance in the Red band, corresponding to Band 2 in Landsat 8.

Evapotranspiration in the study area was calculated using remote sensing data and the Eq. developed by Al-Daraji et al. (2025), which is based on the modified Thornthwaite method that utilises thermal bands and satellite imagery.

 

Table 1: Shows the variables of the proposed model.

No	Variable	Variable symbol	Type of variable	Unit of measurement	Dimensions	Notes
1	Water salinity	Sm	Dependent	g L-3	ML−3	Measurements were conducted in the field and subsequently converted to EC ds m-1
2	Water salinity index	S index	Independent	Non Unit	1	Measured by using remote sensing, Equations 2, 3
3	Normalized difference chlorophyll index in water	NDCI
4	Distance from the main river	L		m	L	Measured from satellite images using GIS
5	Water depth	D			L	Measured in the field using a measuring wire
6	Land surface temperature	T		Celsius	θ	Measured using the remote sensing thermal equilibrium method
7	Daily evapotranspiration	Et		mm day-1	LT−1	Measured using remote sensing, applying the Thornthwaite method as modified by Al-Daraji et al. (2024)
8	Water density	ρ		kg m-1	ML−3	Measured in the field
9	Gravitational acceleration	g		m s-2	LT−2	Constant value

 

The number of dimensionless groups (π) is determined as follows:

The total number of variables (n) is nine, and the number of fundamental dimensions (k) is four, namely mass (M), length (L), time (T), and temperature (θ). Therefore, the number of dimensionless groups (π) is 5, according to Equation 4:

π-groups = n – k => 9- 4 = 5 …(4)

The fundamental variables that cover all the basic dimensions are selected as follows: for mass (M), water density (ρ) is chosen; for length (L), the distance from the main river (L) is chosen; for time (T), gravitational acceleration (g) is selected and for temperature (θ), air temperature (T) is selected. In order to compute the exponents in each dimensionless group (π-group) using the Buckingham π-theorem, the following steps are followed:

Sm = ρa.Lb.gc.Td ….(6)

ML−3 = (ML−3)a. (L)b. (LT−2)c. (θ)d ….(7)

Dimensional equilibrium:

Thus, the steps described above can be repeated to compute π2, π₃, π₄, and π₅, as illustrated in Table 2:

 

Table 2: The π-groups and their exponents.

Exponents Variables (a, b, c, d)	π-group	Variable
a= 1, b=0, c=0, d=0	π1= Sm/ρ	Sm
a= 0, b=0, c=0, d=0	π2= Sindex	S index
a= 0, b=0, c=0, d=0	π3= NDCI	NDCI
a= 0, b=1, c=0, d=0	π4= D/L	D, L
a= 0, b= 0.5, c= 0.5, d=0	π5= Et/√gL	Et, g

 

About the temperature T, it was chosen as the fundamental variable to represent the dimension (θ). However, it does not appear in the other dimensionless groups because the non-fundamental variables do not depend on temperature, which results in the temperature exponent, d, being zero in all groups for any spatial dimensions. After establishing the dimensionless groups, the final governing equations are obtained. Can be formulated as follows:

The regression analysis between the computed (Sm) values and the field values (Sf) is repeated using curve fitting with the SPSS statistical analysis program (Table 3).

 

Table 3: The type of regression relationship between π-groups.

Variable	Symbol	Regression relationship	Constants
Sm/p	π1	-	a0= -30.410
S index	π2	Inverse	a1= 0.255
NDCI	π3	Cubic	a2=-2.475
D/L	π4	Exponential (ln)	a3= -0.270
Et/√(gL)	π5	Inverse	a4= 141.159

 

The governing relationship was exponential, yielding constants a= 3.698 and b= –15488.251, with a correlation coefficient of R= 0.714 (Sahbeni, 2021). Upon substituting these values into Equation 15, the final Eq. becomes as follows:

It can be applied by employing remote sensing and establishing water salinity monitoring (in units of EC ds·m-¹) in the Shatt al-Arab River branch and the al-Hammar marsh.

Results and Discussion

Integration of field and satellite data in the evaluation of the aquatic system

Table 4 presents a comprehensive study that integrates field data with satellite imagery-derived information to assess the condition of the aquatic system. The research considered two main variables: water depth (D), Normalised Difference Chlorophyll Index (NDCI), and Salinity Index (SI), along with some climate indicators: Land Surface Temperature (LST) and Evapotranspiration (ET). Coordinates and distances from the mainstream were collected for spatial analysis of the sites. Depth ranged from 0.75 to 9.00 m, and NDCI ranged between –0.22 and 0.20.

 

The field-measured salinity (Sf) ranged from 3.04 dS/m, and the modelled values (Sm) varied between 2.51 dS/m and 0.98 dS/m. The land top-layer temperatures fluctuated between 26.18 °C and 22.42 °C, and evapotranspiration rates changed between time intervals. The estimated water density ranged from 1017.66 kg/m³ to 1000.05 kg/m³. These figures vary based on numerous factors such as the hydrology, climate, and geology (Soler et al., 2021). Changes in water depth are linked to variations in flow circulation and sedimentation (Serra et al., 2002). This study shows that differences in how flow and sediment are distributed cause ongoing changes in the bottom surface, leading causing alterations in water depth and salinity. Changes in NDCI are linked to factors such as light intensity and organic matter concentration, as noted in (Shourabi et al., 2023). On the other hand, salinity differences across water bodies are related to evaporation and recharge rates under local climate conditions (Suchan and Azam, 2021).

Furthermore, geological settings and the topography of the marsh bottom result in variations in water density and temperatures at the surface (Guimond and Tamborski, 2021). This variety is a clear indication of an in-depth understanding of the environmental processes that influence water salinity. The observed occurrences may be related to various, intertwined peculiarities of the complex environmental patterns in the research area. Alterations in water depth and distance from the source directly influence water renewal rates and the physical and chemical processes that produce salt accretion. This approach supports (Sun et al., 2020) the claims that combining data enables us to obtain a richer view of environmental processes and phenomena (Moustafa et al., 2024).Receding water depths, when far from the water source, result in poor water exchange and salt accumulation. This is related to the natural behavior of renewal processes in the hydrological system, where water on the surface contributes more to evaporation and concentration (Da De Cunha et al., 2021). Increases in LST increase evapotranspiration rates and eventually lead to saltwater concentration. This relationship has been documented in various studies, and (Shokri-Kuehni et al., 2017) reported that changes in climatic temperatures directly affect aquatic layer properties by prompting evaporation on hot days. The NDCI is a measure of the state of biological activity in the water and, therefore, may be indirectly connected to chemical processes in the water system. Greater biological activity can alter water properties, therewith altering the salt regime (Wang et al., 2024a).

Evaluating model effectiveness and how it relates to field measurements

Figure 2 indicatesa significant linear correlation between field-measured salinity (Sf) and model-estimated salinity with remote sensing data (Sm). The r-value was 0.714, indicating that the model accounted for 71.4% of salinity variability in the water. This emphasises the model’s strong capability to simulate, in particular, the general trend in salinity distribution in the water body. At the same time, differences have been observed between n-values from samples; however, these differences may indicate that standards calibration lacks standardization, e.g., sample No. 39 shows up to 8 ds/m difference between external and internal solutions. Such divergences could necessitate further improved atmospheric adjustment algorithms and a higher density of field measurements. These results agree with those of (Luo et al., 2024), who also pointed out the significance of precise atmospheric adjustment to improve fineness in simulation using satellite data.

 

The highest field-measured salinity (Sf) was 3.04 ds/m, and the lowest was about 0.74 ds/m. Model-estimated salinity (Sm) ranged from 0.98 to 2.51 ds/m. The two curves black for field values and red for modelled values are mostly similar, but there are some small differences. These differences may be due to differences in remote sensing data correctness, timing of sample collection, or field conditions. The figure also shows how salinity changed between April 5 and April 18, 2024, likely because of changes in climate or water conditions (Table 3). Combining field measurements with satellite-based models helps expand monitoring and analysis without needing as much fieldwork. This approach saves time and effort, but it further stresses the requirement to optimize the model correctness through improving atmospheric correction and calibration.

 

Simulation of the spatial and temporal distribution of water salinity

Figure 3 shows the dynamic allocation of salinity in the region on 5 and 18 April 2024, indicating that salinity varied over time in Al-Himar marsh and the Karamah River along the Shatt al-Arab branch. On 5 April, water salinity ranged from 3.01 to 6.00 dS m⁻¹, with the highest concentrations in the central basin. On the other hand, the distribution of this parameter on April 18 indicates a water-mass advance and an accumulation of these values towards the end of the study area and in the basin. This variation may also occur due to changes associated with water renewal impulses, physical and chemical variations during tidal influences in the Shatt Al-Arab, the main supply, as well as environmental and transitory climatic conditions (Asadi and Alhello, 2019), (Table 3). Moreover, dispersion by a zone of low salinity (0.88–1.50 dS m⁻¹) is visible, showing that salinity decreases towards areasclose to the Shatt Al-Arab River. It is possible that, by being close to water and continually running into the ocean, salt isn’t able to build up enough. On the other hand, in areas farther from the coast, higher evapotranspiration rates make the water saltier (Xin et al., 2022). We have confirmed that such spatial and temporal variations result from the combination of complex physical and hydrological factors that affect the salinity regime, water flow, and solute transport in salt marshes, which are mainly driven by tidal movements, with a moderate influence from rainfall, evapotranspiration, and sea level rise. Tidal variations play a central role in plant zonation by modifying soil aeration and salt transport. They contribute to the export of large amounts of carbon and nutrients to coastal waters. Variations in surface water and groundwater temperatures affect water flow, soil conditions, and biogeochemical exchanges.

 

Figure 4 shows a classification of the salinity area based on ranges of electrical conductivity dividing the region into three main categories 0.88–1.50 dS m⁻¹, 1.51–3.00 dS m⁻¹ and 3.01–6.00 dS m⁻¹ It is observed that on April 5 there is an increased spatial coverage of the 3.01–6.00 dS m⁻¹ category compared to the same category on April 18 which instead recorded a clear increase in the extent of the 1.51–3.00 dS m⁻¹ category. This variation shows that the study area is very dynamic, with water salinity in Al-Hammar marsh changing rapidly and frequently. These changes are mainly due to hydrological and hydraulic factors, as well as the direct effect of tides on water renewal in the Shatt al-Arab River, consistent with (Al-Mulla, and Al-Ali, 2015).

Difficulties and constraints of the modelling

Despite the model’s success in portraying the overall trend in water salinity distribution, several shortcomings emerge from differences between field and modelled measurements. These differences may result because of elements like calibration accuracy, limited atmospheric correction data, and the low spatial and temporal resolution of the satellite imagery used (Roohi, 2024). Also, using only a few variables might miss other important influences, such as wind and sudden changes in water flow (Liu et al., 2020). Consequently, there is a need to adopt more improved atmospheric correction models and incorporate additional variables to enhance modelling accuracy, as noted by Sola et al. (2018).

Conclusions

Combining field survey measurements with satellite data has enabled a holistic approach to the aquatic system, revealing spatial and temporal differences in its properties. The correlation coefficient between field observations and values predicted by the mathematical model is 0.714, indicating that the model may explain up to 71.4% of the variation in water salinity. The atmosphere-correction procedures should be further developed, and the density of measurement points should be increased. Salinity is influenced by physical and climatic conditions. The further from the source, the shallower and slower to renew water becomes, and so salt builds up.

In the meantime, warmer surface heat levels and higher precipitation rates result in greater salt content. Also, biological activity (as denoted by the NDCI index) is involved in chemical interactions as well as salt distribution. For the 5–18 April 2024 simulations, the time-space variability of the salinity distribution between source-proximal and distal areas posed a dynamic challenge for field versus modelled estimates due due to constraints in atmospheric correction and satellite constraints in space and time. This demonstrates the need to combine field data with RS information to make better decisions in water resources.

Future recommendations and practical approaches

The results imply that the field database should be extended both in area and in data detail, enabling us to recalibrate the model and improve its accuracy. It is also recommended to use multispectral sensing technology, including radar and infrared, to identify slight variations in water state. In addition, dynamic models including minute changes in natural factors are needed to provide more accurate salinity forecasts under the continuing effect of climate change. This evidence are important both from the point of view of scientific model development, but also for policy-relevant applications in water resources management and environmental planning, especially in vulnerable areas with strong reactions to climatic variability.

Acknowledgement

The authors would like to thank the College of Marine Sciences and the College of Agriculture / University of Basra for their support and for providing access to their laboratories and facilities, which helped complete the research.

Novelty Statement

This study combines dimensional analysis and remote sensing data to create a predictive model for tracking salinity changes in the Hammar Marsh–Shatt Al-Arab system. This method offers a new way to model water movement and monitor water quality in changing river and estuary environments.

Author’s Contribution

Forqan Khalid Al-Daraji, Husam Hasan Abdulaali, Yousif sh.J. Al-Jorani, and Mu’ayyad H. M. Al-Bahadli: handled the materials, collected the data, and carried out the analysis. The first draft of the manuscript focused on simulating and predicting hydrodynamic changes in water salinity in the Hammar Marsh and Shatt Al-Arab River using dimensional analysis and remote sensing. Husam Hasan Abdulaali revised the scientific content, improved the language and presentation, and gave advice to make the research suitable for peer-reviewed journals. All authors reviewed earlier versions of the chapter and approved the final version. All authors participated in the design and preparation of the study.

Funding declaration

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Generative AI and AI assisted technology statement

The authors declare that no generative AI and AI assisted technology was used in the creation of this manuscript.

Statement of conflict of interest

The authors have declared no conflict of interest.

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