One-Arc-Second DEMs and Field Survey Data for Small Dams Using Terrain Metrics: A Comparative Study

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This study evaluates the effectiveness of one-arc-second digital elevation models (DEMs) in determining reservoir volume-elevation data, comparing these satellite-derived data with field survey data, which are known to be costly and time-consuming. Focused on small dams, the research compares field survey data with data derived from the one-arc-second DEM to address the lack of validation for these studies in small reservoirs. The methodology involves analyzing ten reservoirs in two different locations, namely Erbil and Sulaymaniyah governorates in Northern Iraq, using ArcGIS and Remote Sensing for digital elevation model processing. The volume-elevation data for the reservoirs are determined using ArcGIS. Consequently, evaluations of terrain metrics and sensitivity analyses support the adoption of one-arc-second DEMs for determining volume-elevation data in early-stage dam planning and reservoir assessments. Key innovations include addressing the limitations of low-resolution DEMs by using a wider 5 km radius for terrain analysis, which enables a thorough evaluation of landform features. The Terrain Ruggedness Index (TRI) is introduced as a key metric for evaluating terrain complexity, regional variations, and sensitivity to absolute error percentages. Morris’s sensitivity analysis highlights TRI's significance as the decisive parameter by examining how different terrain parameters affect error rates. These advancements enhance the accuracy of reservoir and terrain evaluations, offering valuable insights for improved dam design and water resource management. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards One-Arc-Second DEM Small Dam Terrain Ruggedness Index (TRI) Sensitivity Analysis Volume-Elevation Data Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Topography is a key land-surface feature that significantly impacts the water balance within a watershed and the capacity of reservoirs. [ 1 ]. Surveying the topography of reservoir and dam locations is essential for determining site suitability for dam construction; however, it is a time-consuming and expensive process.[ 2 ]. Highly accurate Digital Elevation Models DEMs are not commonly available, particularly in developing countries.[ 3 ] Meanwhile, one-arc-second resolution DEMs are freely available and easily downloadable, which offers more efficient and cost-effective alternatives. [ 3 ]. Numerous studies have been published using DEMs and Remote Sensing technologies to estimate water volumes in large reservoirs across various global locations. [ 4 – 13 ]. Moreover, extensive research has been conducted to ascertain the volume-elevation data for the reservoirs. For instance, Ahmet Irvem generated a DEM from a topographic map of the Buyuk Karacay basin using the Ripple method. [ 14 ]. In a subsequent study conducted in 2020, Irvem investigated the effects of DEM resolution on dam storage capacity determination using Geographic Information Systems (GIS). He concluded that a 10-meter resolution provides the most accurate estimates of reservoir volume[ 15 ]. Yao Li et al. presented a framework to derive reservoir volume-area-elevation data from TanDEM-X data and integrated the volume values from the top to bottom layers to generate the reservoir volume-area-elevation data. [ 16 ]. Mengfei Mu et al. combined the Global Reservoir and Dam (GRanD) database with Landsat-based global surface water extent (GSW) data to derive area-volume-elevation data of reservoirs, and the resulting data were validated with in situ data from the US and China, showing accurate results for reservoirs larger than one km² and with various shapes[ 17 ]. Despite numerous studies employing DEMs for estimating reservoir volume, a need remain to validate the benefits of using one-arc-second DEMs in reservoir volume-elevation data determination as a replacement for the traditional surveying data, especially when reservoir volume is small [ 18 ]. While there is a lack of research validating the use of one-arc-second Digital Elevation Models (DEMs) for calculating volume-elevation data in small reservoirs, this study seeks to address this gap by evaluating and validating the effectiveness of one-arc-second DEMs in determining reservoir volume-elevation data. The primary objective is to establish one-arc-second DEMs as a feasible and efficient alternative to traditional on-ground surveying techniques during the feasibility phase of dam locations. To achieve this, the study will employ a set of parameters not used in previous research, thereby enhancing the novelty and depth of the analysis. The approach aims to provide a comprehensive assessment of the accuracy and reliability of one-arc-second DEMs in capturing the intricate details of reservoir volumes and elevations, potentially offering significant advantages in terms of cost, time efficiency, and practicality over conventional surveying methods. The findings of this study will contribute to the body of knowledge in the field and support the adoption of one-arc-second DEMs as a viable tool in the early stages of small dam planning. 1.1 Study Area Data from on-ground surveying with GPS were gathered from the regional water resources authorities for ten locations in Erbil and Sulaymaniyah governorates, the Kurdistan Region of Iraq, as shown in Fig. 1 . The reservoir volume-elevation data of the reservoirs were compiled using the prismatic method.[ 19 ]. The coordinates of the small dams are provided in Table 1 . Table 1 Small dams UTM coordinates, heights, and reservoir volumes. No. Location Dam Name Easting Northing Dam Height m Reservoir Volume m 3 1 Erbil Kollak 424293.3 4012323 11 160,000 2 Bneslawa 430726 4002579 20 2,200,000 3 Mzoryan 426795 3991235 14 365,000 4 Klkan 417989.7 4018297 10 250,000 5 Tarin 438509 4014585 15 131,546 6 Sulaymaniyah Hamza Romi 518813 3906954 14 190,000 7 Serchnar 474758 3963599 18 160,000 8 Nwrey Serw 490349 3922875 15 365,000 9 Taqtaq 479864 3947315 15 106,700 10 Zarda 479040 3897258 14 525,000 The reservoirs vary in volume, surface area, height, length, shape, and topography (Fig. 2 ). Furthermore, the heights of the small dams vary based on considerations such as designer preferences, site suitability, and the demand for water in the respective areas. The maximum capacity of the small dams' reservoirs exhibits notable disparities across different geographic locations. Taqtaq Dam in Sulaymaniyah registers a reservoir volume of 106,700 m 3 , whereas the Bneslawa Dam in Erbil boasts a maximum volume of 2,200,000 m 3 . Concurrently, the height of the small dams ranges from 10 meters to 20 meters. Notably, the lower-lying small dams are situated in Erbil, whereas those in Sulaymaniyah tend to possess slightly higher heights. This discrepancy in height is attributed to the general topographical distinctions between the two regions, with Erbil featuring flatter landforms compared to the more undulating terrain of Sulaymaniyah.[ 20 ]. 2. Materials and Methodology This methodology outlines the comparison between data derived from DEMs and actual topographic data. The study employs a multi-step approach involving the acquisition of one-arc-second resolution DEMs and field-collected topographic measurements. On the other hand, this study involves a systematic approach to analyze the accuracy and reliability of remote sensing and GIS tools in estimating the volume-elevation data of reservoirs. The following steps outline the methodology: 2.1 Data collection The data used in this study consists of topographic information, specifically volume-elevation data, for ten distinct reservoirs selected from Erbil and Sulaymaniyah governorates. Data was obtained from two different sources: 1. Actual Data (Field Data): The General Irrigation Directorate gathered comprehensive on-site survey data for the 10 reservoirs (small dams). This dataset is used to determine the volume-elevation data for each site. These field measurements ensure accurate and precise elevation data directly obtained from the terrain. 2. Digital Elevation Model (DEM): The DEM data were acquired from the Shuttle Radar Topography Mission (SRTM) through the USGS Earth Explorer platform. Its resolution is one-arc-second, which translates to approximately 30 meters by 30 meters for the proposed locations. 2.2 Utilization of ArcGIS Algorithms and Remote Sensing Data: ArcGIS algorithms and remote sensing data are pivotal in modern geospatial analysis, providing robust tools for mapping, analyzing, and interpreting spatial information. The following steps illustrate the processing procedure: A. Contour Mapping: Contour maps for each watershed are created using ArcGIS tools to visualize the terrain and elevation details. B. Volume-Elevation Data: The Spatial Analyst Supplemental Tool in ArcGIS is applied to determine volume-elevation tables for each watershed. These tables are essential for estimating the volume-elevation data based on the collected elevation data and landform characteristics. 2.3 Comparison between on-ground surveys and the estimated volume-elevation data generated from ArcGIS: A comparison was conducted between the ground-truth data obtained through on-ground surveys and the estimated volume-elevation data generated from ArcGIS and remote sensing applications. The absolute relative error percentage between the two datasets was calculated, serving as the principal parameter to assess the proximity of the GIS data to the actual survey data. This step is crucial for validating the accuracy of the remote sensing and GIS-based methods; all other parameter evaluations are conducted based on the absolute and relative error percentages. 2.3.1 Assessment parameters: Different parameters will be used to highlight the differences between reservoirs with minor errors and those with higher errors. The reservoirs are compared by evaluating their planar shape and the morphology (complexity) of the terrain. Evaluation of the planner shape includes the following parameters: A. Area-to-Volume Ratio (AVR): The AVR parameter represents the ratio of the reservoir's area to its volume at a specific elevation. The larger the AVR, the closer the valley shape gets to an inverted triangle (the triangle peak at the valley bottom). The larger the base of the triangle, the fewer side slopes will exist. This ideally concludes with a more accurate DEM representing the valley. B. 2D shape explanation (complexity) using Open JUMP GIS software [21a] and both imported tools, PolyMorph-2D for Morphometric Analysis, which is utilized to calculate both Shape Factor (SF) and Solidity [21], and Maximum Inscribed Circle (MICGIS) [21, 22]. Two parameters were extracted: C. Shape Factor (SF) : The shape factor is one of the compound and complete parameters that uses length, width, area, and perimeter. The value of the shape factor is 1.0 for a perfect circle, 0.785 for an ideal square, 0.698 for an equilateral triangle, and the value can be larger than 1.0 when the shape is narrow and long. D. Solidity : provides insight into the shape's roughness. This parameter is defined as the ratio of the convex hull perimeter to the perimeter of the actual shape. The closer the value is to 1.0, the closer the shape boundary is to being a perfect circle. Evaluation of morphology includes the following parameters: A. Slope : The slope refers to the measure of a surface's degree of inclination. It is calculated based on the change in elevation from one cell to its neighboring cells within a digital elevation model (DEM). B. Curvature : Curvature is a measure of the rate of change of slope across a surface, indicating the concavity or convexity of the terrain[23]. Curvature is derived from a digital elevation model (DEM) and helps to understand the shape and form of the landscape. C. Vector Ruggedness Measure (VRM) : is a vector-based measure that characterizes terrain ruggedness by analyzing the orientation or alignment of terrain vectors within a defined area [24]. VRM is calculated by decomposing the surface gradient into its horizontal and vertical components and then measuring the dispersion of the vectors' orientations. It is determined using Geomorphology VRM from the Benthic Terrain Modeler (BTM) Tools, which are imported into the ArcGIS toolbox. D. Terrain Ruggedness Index (TRI) : is a scalar measure that represents terrain ruggedness based on the changeability of elevation values within the study area defined by the masked DEM[25]. TRI is determined using Arc Hydro Tools Python [26]. It was imported to the ArcGIS toolbox provided by the Esri Water Resources Team. The Morphology comparison is made for each small dam using a 5 Km buffer zone as an effective area from the center of the reservoir, and the sensitivity analysis is calculated for the parameters mentioned above to determine the most effective parameters for the decision about the suitability of the DEM. 2.4 Utilizing Morris Method and Python SALib Library In this quantitative research, sensitivity studies are performed using the Morris Method [27] and the Python SALib library [28]. This method estimates parameter sensitivity by perturbing one parameter at a time while holding all others constant, providing a straightforward assessment of the individual effects of each parameter. The Morris Method approach is performed using local sensitivity analysis. It is also referred to as a “One-at-a-Time” analysis. To generate the sampling and design trajectories required for the Morris analysis, Latin Hypercube Sampling (LHS) is utilized, ensuring a thorough and representative exploration of the input space. This approach enhances the robustness and reliability of the sensitivity analysis results [28]. A Decision Tree Regression approach is implemented using Python for predictive modeling. To optimize the decision tree model's performance, both Grid Search and Cross-Validation were used from Scikit-learn. [29]Grid Search is conducted to tune hyperparameters, while Cross-Validation is employed to validate the model’s generalizability and prevent overfitting. The rigorous optimization process ensures that the final model is both accurate and reliable, providing a solid foundation for interpreting the results of the sensitivity analysis and drawing meaningful conclusions about the most influential factors. The methodological framework of the study is given in Figure 3. 3. Results and Discussions This study primarily focuses on comparing field data and satellite data. The outcome of the first step of processing resulted in line charts showing the curve of the reservoir volume for both field surveying and the DEM. Further processing of the DEMs was followed to provide explanations of the landform shape and complexity using various measures. Two main processes were carried out to compare the dam terrain morphology and the difference between the two territories in which the dams were located by taking a 5 km range from the approximate center of each dam. Further processing and analysis were performed for the dam’s 5km range territory. 3.1 Volume Elevation Data The volume of each reservoir, along with its corresponding elevation, is calculated using survey data and a GIS application, and the results are plotted in Fig. 3 . The height range of the dam falls between 10 meters and 20 meters. Consequently, the analysis prioritizes the comparison within this specific height range. Figure 4 shows the absolute relative error percentages for the Erbil and Sulaymaniyah dams. 3.2 Dam Terrain Complexity To make a comparison between the reservoirs, several measures were calculated, considering both the planar shape and morphology of the reservoir, which are formed by the DEMs (Table). The DEMs were processed to generate slope, curvature, VRM, and TRI. Then, after zonal statistics were performed for the generated raster files, including the DEMs' zonal statistics and their standard deviations (SD), they were chosen for further analysis. The parameters in Table 2 explain solely the 2D shape and morphological traits of the reservoirs that contribute to complexity. Based on the absolute relative error percentage, the results are sorted from the best fit to the poorest fit. Table 2 The reservoirs’ complexity parameters. The Area, AVR, SF, and Solidity determine the planar shape of the reservoir’s area, whereas the other parameters determine the morphology. Location Reservoir Area AVR SF Solidity Elevation SD Slope SD Curvature SD VRM SD TRI SD Absolute relative error (%) Erbil Kollak 35318 0.14 0.778 0.744 3.492 3.88 0.27 0.0014 2.86 0 Bneslawa 477186 0.195 0.198 0.605 4.599 4.02 0.28 0.0006 2.78 12 Klkan 125575 0.172 0.925 0.832 2.758 3.25 0.25 0.0003 2.17 13 Mzoryan 109878 0.182 0.209 0.512 4.373 5.41 0.35 0.0011 3.54 16 Tarin 30609 0.161 0.745 0.769 3.53 4.93 0.42 0.002 2.78 77 Sulaymaniyah Hamza Romi 41372 0.136 0.535 0.667 5.47 6.33 0.5 0.0013 3.84 83 Serchnar 32785 0.137 0.906 0.779 5.379 7.91 0.43 0.0014 3.97 95 Nwrey Serw 73376 0.147 0.439 0.587 3.793 7.2 0.57 0.005 4.89 150 Taqtaq 45275 0.149 0.796 0.691 4.714 4.17 0.51 0.0009 2.57 160 Zarda 126456 0.145 0.225 0.56 3.648 3.35 0.33 0.0006 2.29 177 Based on the results, the parameters do not show a positive or a negative trend to the absolute relative error percentage. This is mainly due to the low resolution of the DEM and the small area of the reservoirs. The nearly 30m resolution of the DEM makes the terrain roughness smoother during processing and averaging elevation values. This resolution degradation has a more significant effect on steeper terrain and smaller areas. Since the dam valleys are small in area, this approach of reservoir area processing and analysis does not give insight; thus, the reservoir territory approach was adopted. 3.3 Dam’s Territory Terrain Complexity In this approach, it was assumed that the geomorphology of the surrounding terrain of the reservoir can, to some extent, generally represent the ruggedness of the reservoirs as well. From the approximate center of each reservoir, a 5 km range DEM was extracted. The range was selected to cover the largest reservoir boundary without the reservoir 2D polygon being circumscribed by a circle. The DEM was processed to generate slopes, plan curvature, profile curvature, VRM, and TRI. Then, after zonal statistics were performed for the generated raster files, including the DEM's zonal statistics and their standard deviations (SD), were chosen for further analysis. Like the dam terrain complexity results, most of the parameters do not show a trend, except for the TRI, which shows a significant positive correlation with the percentage of the absolute relative error (Table 3 ). Table 3 the reservoirs’ territory complexity parameters up to 5km range from the center of the reservoirs. Location Reservoir Elevation SD Slope SD Curvature SD Plan SD Profile SD VRM SD TRI SD Absolute relative error (%) Erbil Kollak 117.454 3.39 0.584 0.308 0.169 0.004 0.001 0 Bneslawa 52.793 7.578 0.588 0.314 0.369 0.003 0.004 12 Klkan 51.717 8.824 0.285 0.151 0.369 0.001 0.006 13 Mzoryan 74.379 6.268 0.487 0.257 0.304 0.002 0.003 16 Tarin 115.307 8.632 0.556 0.295 0.35 0.003 0.005 77 Sulaymaniyah Hamza Romi 149.123 13.436 0.572 0.295 0.371 0.003 13.436 83 Serchnar 146.257 12.031 0.755 0.388 0.491 0.005 12.031 95 Nwrey Serw 61.937 6.8 0.493 0.256 0.315 0.002 6.8 150 Zarda 37.822 5.298 0.48 0.256 0.292 0.002 5.298 160 Taqtaq 57.382 6.894 0.469 0.244 0.3 0.002 6.894 177 The result shows that the first five (5) reservoirs in Erbil have TRI measures below 0.1, and those located in Sulaymaniyah are above 1 on a large scale. The TRI indicates that the first 5 DEMs (located in Erbil) have lower roughness; therefore, the low-resolution DEM has a lesser impact on the accuracy of the results in these regions. In contrast, the other reservoirs’ 5km range DEMs located in Sulaymaniyah are highly impacted by the DEM resolution. 3.4 Sensitivity Analysis To further study the significant impact of territorial parameters on the relative absolute error percentage, sensitivity analyses [ 8 ] were performed using the Morris method. Sample functions of the Morris method were used with design trajectories and 20 grid levels to create the sample data. A suitable training model, Decision Tree Regression, was chosen and trained using Python. The model was optimized by using Grid Search and Cross-Validation methods. The whole process was repeated for ten random seeds, and the minimum sample split of the Decision Tree Regressor was found to be six. The model was also manually evaluated for a smaller value of minimum sample split (5 splits). The SA process was repeated for 10 random seeds for both minimum sample splits 5 and 6, and the average values are given in Table 4 . Table 4 The result of the Sensitivity Analysis for the reservoirs’ territory. The values presented are an average of 10 seeds for two different sample splits 5 and 6. Samples Split Elevation SD Slope SD Curvature SD Plan SD Profile SD VRM SD TRI SD 5 8.44 24.03 18.26 10.16 17.84 16.30 83.42 6 0 0 0 0 0 0 86.65 The sensitivity analysis results show that the absolute relative error is susceptible to the TRI, with a sensitivity value exceeding 80; meanwhile, the other parameters have lower average values, ranging approximately from 0 to 25. The significant influence of the TRI is that it can be relied on during the site location of dams, as shown in Figs. 5 . Figure 6and 7 illustrates the differences in the dams’ TRI between Erbil and Sulaymaniyah cities, ordered from best fit to the poorest fit. 4 Conclusions This study highlights the value of integrating field data with satellite data to analyze dam reservoirs. Comparing survey data and Digital Elevation Models (DEMs) provided accurate insights into reservoir volumes, showcasing the strengths and limitations of each data source. The analysis, which focused on dams with heights between 10 and 20 meters, was more reliable for Erbil but less so for Sulaymaniyah. Measures such as slope, curvature, Vector Ruggedness Measure (VRM), and Terrain Ruggedness Index (TRI) were used to compare reservoirs. Zonal statistics applied to these measures did not show clear trends, primarily due to the low resolution of the 30m DEM, which smoothed terrain roughness and affected accuracy in steeper, smaller areas. Consequently, a reservoir territory approach was adopted. This approach assumed that the geomorphology of the surrounding terrain could represent the reservoir's ruggedness. A 5 km range DEM was extracted and processed to generate slope, curvature, plan curvature, profile curvature, VRM, and TRI. While most parameters did not reveal a trend, TRI showed a significant positive correlation with the absolute relative error. Results indicated that reservoirs in Erbil had lower TRI values, reflecting less roughness and reduced impact from DEM resolution, while Sulaymaniyah reservoirs had higher TRI values and were more affected by DEM resolution. This underscores the need for higher-resolution DEMs and refined methods for complex terrains. The sensitivity analyses (SA) using the Morris method were conducted to investigate further the impact of territorial parameters on absolute relative error. Decision tree regression models were trained and optimized using grid search and cross-validation. Sensitivity analysis revealed that TRI was highly sensitive to the absolute relative error percentages, with a sensitivity value over 80, while other parameters had lower sensitivity values. This highlights the importance of TRI in site location decisions for dams, providing a robust framework for more accurate dam site evaluations and reservoir assessments. Declarations Funding Not applicable. Data Availability Statement The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. Conflicts of Interest All authors declare that there is no conflict of interest. Declaration of Generative AI In preparing this work, the author used ChatGPT to enhance language quality. Subsequently, a meticulous review ensured precision, and the author took full responsibility for the publication's linguistic integrity. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7035830","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489306835,"identity":"2d766a9e-d753-4f31-a7a9-5f6124dde9ee","order_by":0,"name":"Peshawa Bakhtyar","email":"","orcid":"","institution":"University of Sulaymaniyah-College of Engineering- Water Resources Department","correspondingAuthor":false,"prefix":"","firstName":"Peshawa","middleName":"","lastName":"Bakhtyar","suffix":""},{"id":489306836,"identity":"afc0250b-1544-453e-9894-b50ac1f74af0","order_by":1,"name":"Nawbahar Faraj Mustafa","email":"","orcid":"","institution":"University of Sulaymaniyah-College of Engineering- Water Resources Department","correspondingAuthor":false,"prefix":"","firstName":"Nawbahar","middleName":"Faraj","lastName":"Mustafa","suffix":""},{"id":489306837,"identity":"940bc255-3171-481b-b961-b62158db68be","order_by":2,"name":"Shvan Fars Aziz","email":"","orcid":"","institution":"University of Sulaymaniyah-College of Engineering- Water Resources Department","correspondingAuthor":false,"prefix":"","firstName":"Shvan","middleName":"Fars","lastName":"Aziz","suffix":""},{"id":489306838,"identity":"8f9c7549-4f9a-4cda-819a-e41d003d1efb","order_by":3,"name":"Nadhir Al-Ansari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACgwMMZgwJQAYfhC8hx0dIiwVMCxtUizEbGwEtNiAtDAgtDIltBLUcP7ztwcM9DPJs7GcPf/i5xyK9Tb7HgPFHBW4tZmfSyg0SnjEYtvHkpUn2PJPIbWPjMWDmOYNHy4EcM4mEAwyMbQw5Zgw8B6BagFycwPj8G7AW+zb+N8Yf/xyQSGcDamH8+Q+3FsMbEFsS2yRyDKSBtiSAtDDwNuDT8qwMqEUiuU3ijZm0zAEJwza2tILDPMdwazE4n7xN8scBG9t+/hzjj28O1MnzMx/e+PBHDW4tUCCByj1AUMMoGAWjYBSMArwAAONvSvezNFGxAAAAAElFTkSuQmCC","orcid":"","institution":"Lulea University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Nadhir","middleName":"","lastName":"Al-Ansari","suffix":""}],"badges":[],"createdAt":"2025-07-03 08:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7035830/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7035830/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30483-7","type":"published","date":"2025-12-27T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87426857,"identity":"e87deb2d-c7b9-465c-8f46-f84d177cfb6a","added_by":"auto","created_at":"2025-07-23 16:40:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":661862,"visible":true,"origin":"","legend":"\u003cp\u003eThe Study Area.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/23d2273541388ffa65bd1811.jpg"},{"id":87426856,"identity":"26614a59-5e17-414a-8f44-bdcbba85649d","added_by":"auto","created_at":"2025-07-23 16:40:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":289885,"visible":true,"origin":"","legend":"\u003cp\u003eErbil and Sulaymaniyah reservoirs, top view, with small dam and spillway structures (from survey data).\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/4f878e8211794d32fca00231.jpg"},{"id":87427523,"identity":"293fce0a-02fc-479b-a7d4-896d1a27155f","added_by":"auto","created_at":"2025-07-23 16:48:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":394734,"visible":true,"origin":"","legend":"\u003cp\u003eThe methodological framework of the study.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/efd8f88532befce40c1c92b1.jpg"},{"id":87427524,"identity":"6f4d6774-f067-4ec6-b02b-3bc6b80076c1","added_by":"auto","created_at":"2025-07-23 16:48:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1139021,"visible":true,"origin":"","legend":"\u003cp\u003eVolume-Elevation data from the Survey and GIS application.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/9f391d012605caf52d746854.jpg"},{"id":87426863,"identity":"1455d21d-52da-4f55-964a-d173756f30be","added_by":"auto","created_at":"2025-07-23 16:40:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":230478,"visible":true,"origin":"","legend":"\u003cp\u003eErbil and Sulaymaniyah dams' absolute and relative error percentages.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/cfecda0422298a7cb2825195.jpg"},{"id":87429170,"identity":"3de8b9a0-a30e-4903-9334-c19b85b58465","added_by":"auto","created_at":"2025-07-23 17:04:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":185391,"visible":true,"origin":"","legend":"\u003cp\u003eMorris Sensitivity Analysis for the dam’s territories complexity parameters using an average of 50 seeds for two different sample splits, 5 and 6.\u003c/p\u003e","description":"","filename":"17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/3da71163ea983cdbdd5d8bf7.jpg"},{"id":87428649,"identity":"0d5bca7e-ea2a-483e-a49b-5dc42db267c6","added_by":"auto","created_at":"2025-07-23 16:56:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":808995,"visible":true,"origin":"","legend":"\u003cp\u003eThe Mapped TRI of the dams with the same classification and color scheme.\u003c/p\u003e","description":"","filename":"18.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/9b986296734c0641f27ea175.jpg"},{"id":99172331,"identity":"cdeb9965-1b29-4328-88a0-50964e228113","added_by":"auto","created_at":"2025-12-29 16:07:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4698359,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7035830/v1/64499ae3-87ec-4193-9c8f-97fb06613001.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"One-Arc-Second DEMs and Field Survey Data for Small Dams Using Terrain Metrics: A Comparative Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTopography is a key land-surface feature that significantly impacts the water balance within a watershed and the capacity of reservoirs. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Surveying the topography of reservoir and dam locations is essential for determining site suitability for dam construction; however, it is a time-consuming and expensive process.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Highly accurate Digital Elevation Models DEMs are not commonly available, particularly in developing countries.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Meanwhile, one-arc-second resolution DEMs are freely available and easily downloadable, which offers more efficient and cost-effective alternatives. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Numerous studies have been published using DEMs and Remote Sensing technologies to estimate water volumes in large reservoirs across various global locations. [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, extensive research has been conducted to ascertain the volume-elevation data for the reservoirs. For instance, Ahmet Irvem generated a DEM from a topographic map of the Buyuk Karacay basin using the Ripple method. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In a subsequent study conducted in 2020, Irvem investigated the effects of DEM resolution on dam storage capacity determination using Geographic Information Systems (GIS). He concluded that a 10-meter resolution provides the most accurate estimates of reservoir volume[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Yao Li et al. presented a framework to derive reservoir volume-area-elevation data from TanDEM-X data and integrated the volume values from the top to bottom layers to generate the reservoir volume-area-elevation data. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Mengfei Mu et al. combined the Global Reservoir and Dam (GRanD) database with Landsat-based global surface water extent (GSW) data to derive area-volume-elevation data of reservoirs, and the resulting data were validated with in situ data from the US and China, showing accurate results for reservoirs larger than one km\u0026sup2; and with various shapes[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite numerous studies employing DEMs for estimating reservoir volume, a need remain to validate the benefits of using one-arc-second DEMs in reservoir volume-elevation data determination as a replacement for the traditional surveying data, especially when reservoir volume is small [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile there is a lack of research validating the use of one-arc-second Digital Elevation Models (DEMs) for calculating volume-elevation data in small reservoirs, this study seeks to address this gap by evaluating and validating the effectiveness of one-arc-second DEMs in determining reservoir volume-elevation data. The primary objective is to establish one-arc-second DEMs as a feasible and efficient alternative to traditional on-ground surveying techniques during the feasibility phase of dam locations.\u003c/p\u003e\u003cp\u003eTo achieve this, the study will employ a set of parameters not used in previous research, thereby enhancing the novelty and depth of the analysis. The approach aims to provide a comprehensive assessment of the accuracy and reliability of one-arc-second DEMs in capturing the intricate details of reservoir volumes and elevations, potentially offering significant advantages in terms of cost, time efficiency, and practicality over conventional surveying methods. The findings of this study will contribute to the body of knowledge in the field and support the adoption of one-arc-second DEMs as a viable tool in the early stages of small dam planning.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Study Area\u003c/h2\u003e\u003cp\u003eData from on-ground surveying with GPS were gathered from the regional water resources authorities for ten locations in Erbil and Sulaymaniyah governorates, the Kurdistan Region of Iraq, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The reservoir volume-elevation data of the reservoirs were compiled using the prismatic method.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The coordinates of the small dams are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSmall dams UTM coordinates, heights, and reservoir volumes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDam Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEasting\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNorthing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDam Height m\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReservoir Volume m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eErbil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKollak\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e424293.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4012323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e160,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4014585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e131,546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSulaymaniyah\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHamza Romi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e518813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3906954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e190,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSerchnar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e474758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3963599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e160,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNwrey Serw\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e490349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3922875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e365,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTaqtaq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e479864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3947315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e106,700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZarda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e479040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3897258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e525,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe reservoirs vary in volume, surface area, height, length, shape, and topography (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, the heights of the small dams vary based on considerations such as designer preferences, site suitability, and the demand for water in the respective areas. The maximum capacity of the small dams' reservoirs exhibits notable disparities across different geographic locations. Taqtaq Dam in Sulaymaniyah registers a reservoir volume of 106,700 m\u003csup\u003e3\u003c/sup\u003e, whereas the Bneslawa Dam in Erbil boasts a maximum volume of 2,200,000 m\u003csup\u003e3\u003c/sup\u003e. Concurrently, the height of the small dams ranges from 10 meters to 20 meters. Notably, the lower-lying small dams are situated in Erbil, whereas those in Sulaymaniyah tend to possess slightly higher heights. This discrepancy in height is attributed to the general topographical distinctions between the two regions, with Erbil featuring flatter landforms compared to the more undulating terrain of Sulaymaniyah.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Materials and Methodology","content":"\u003cp\u003eThis methodology outlines the comparison between data derived from DEMs and actual topographic data. The study employs a multi-step approach involving the acquisition of one-arc-second resolution DEMs and field-collected topographic measurements. On the other hand, this study involves a systematic approach to analyze the accuracy and reliability of remote sensing and GIS tools in estimating the volume-elevation data of reservoirs. The following steps outline the methodology:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Data collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study consists of topographic information, specifically volume-elevation data, for\u0026nbsp;ten distinct reservoirs selected from Erbil and Sulaymaniyah governorates. Data was obtained from two different sources:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Actual Data (Field Data): The General Irrigation Directorate gathered comprehensive on-site survey data for the 10 reservoirs (small dams). This dataset is used to determine the volume-elevation data for each site. These field measurements ensure accurate and precise elevation data directly obtained from the terrain.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Digital Elevation Model (DEM): The DEM data were acquired from the Shuttle Radar Topography Mission (SRTM) through the USGS Earth Explorer platform. Its resolution is one-arc-second, which translates to approximately 30 meters by 30 meters for the proposed locations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Utilization of ArcGIS Algorithms and Remote Sensing Data:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArcGIS algorithms and remote sensing data are pivotal in modern geospatial analysis, providing robust tools for mapping, analyzing, and interpreting spatial information. The following steps illustrate the processing procedure:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u0026nbsp;\u0026nbsp;\u003c/strong\u003eContour Mapping: Contour maps for each watershed are created using ArcGIS tools to visualize the terrain and elevation details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u0026nbsp;\u0026nbsp;\u003c/strong\u003eVolume-Elevation Data: The Spatial Analyst Supplemental Tool in ArcGIS is applied to determine volume-elevation tables for each watershed. These tables are essential for estimating the volume-elevation data based on the collected elevation data and landform characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Comparison between on-ground surveys and the estimated volume-elevation data generated from ArcGIS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparison was conducted between the ground-truth data obtained through on-ground surveys and the estimated volume-elevation data generated from ArcGIS and remote sensing applications. The absolute relative error percentage between the two datasets was calculated, serving as the principal parameter to assess the proximity of the GIS data to the actual survey data. This step is crucial for validating the accuracy of the remote sensing and GIS-based methods; all other parameter evaluations are conducted based on the absolute and relative error percentages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1\u0026nbsp; \u0026nbsp;\u0026nbsp;Assessment parameters:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferent parameters will be used to highlight the differences between reservoirs with minor errors and those with higher errors. The reservoirs are compared by evaluating their planar shape and the morphology (complexity) of the terrain. Evaluation of the planner shape includes the following parameters:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Area-to-Volume Ratio (AVR):\u0026nbsp;\u003c/strong\u003eThe AVR parameter represents the ratio of the reservoir\u0026apos;s area to its volume at a specific elevation. The larger the AVR, the closer the valley shape gets to an inverted triangle (the triangle peak at the valley bottom). The larger the base of the triangle, the fewer side slopes will exist. This ideally concludes with a more accurate DEM representing the valley.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;2D shape explanation (complexity)\u003c/strong\u003e using Open JUMP GIS software [21a] and both imported tools, PolyMorph-2D for Morphometric Analysis, which is utilized to calculate both Shape Factor (SF) and Solidity [21], and Maximum Inscribed Circle (MICGIS) [21, 22]. Two parameters were extracted:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Shape Factor (SF)\u003c/strong\u003e: The shape factor is one of the compound and complete parameters that uses length, width, area, and perimeter. The value of the shape factor is 1.0 for a perfect circle, 0.785 for an ideal square, 0.698 for an equilateral triangle, and the value can be larger than 1.0 when the shape is narrow and long.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Solidity\u003c/strong\u003e: provides insight into the shape\u0026apos;s roughness. This parameter is defined as the ratio of the convex hull perimeter to the perimeter of the actual shape. The closer the value is to 1.0, the closer the shape boundary is to being a perfect circle.\u003c/p\u003e\n\u003cp\u003eEvaluation of morphology includes the following parameters:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Slope\u003c/strong\u003e: The slope refers to the measure of a surface\u0026apos;s degree of inclination. It is calculated based on the change in elevation from one cell to its neighboring cells within a digital elevation model (DEM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Curvature\u003c/strong\u003e: Curvature is a measure of the rate of change of slope across a surface, indicating the concavity or convexity of the terrain[23]. Curvature is derived from a digital elevation model (DEM) and helps to understand the shape and form of the landscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Vector Ruggedness Measure (VRM)\u003c/strong\u003e: is a vector-based measure that characterizes terrain ruggedness by analyzing the orientation or alignment of terrain vectors within a defined area [24]. VRM is calculated by decomposing the surface gradient into its horizontal and vertical components and then measuring the dispersion of the vectors\u0026apos; orientations. It is determined using Geomorphology VRM from the Benthic Terrain Modeler (BTM) Tools, which are imported into the ArcGIS toolbox.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Terrain Ruggedness Index (TRI)\u003c/strong\u003e: is a scalar measure that represents terrain ruggedness based on the changeability of elevation values within the study area defined by the masked DEM[25]. TRI is determined using Arc Hydro Tools Python [26]. It was imported to the ArcGIS toolbox provided by the Esri Water Resources Team.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Morphology comparison is made for each small dam using a 5 Km buffer zone as an effective area from the center of the reservoir, and the sensitivity analysis is calculated for the parameters mentioned above to determine the most effective parameters for the decision about the suitability of the DEM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Utilizing Morris Method and Python SALib Library\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this quantitative research, sensitivity studies are performed using the Morris Method [27] and the Python SALib library [28]. This method estimates parameter sensitivity by perturbing one parameter at a time while holding all others constant, providing a straightforward assessment of the individual effects of each parameter. The Morris Method approach is performed using local sensitivity analysis. It is also referred to as a \u0026ldquo;One-at-a-Time\u0026rdquo; analysis. To generate the sampling and design trajectories required for the Morris analysis, Latin Hypercube Sampling (LHS) is utilized, ensuring a thorough and representative exploration of the input space. This approach enhances the robustness and reliability of the sensitivity analysis results [28].\u003c/p\u003e\n\u003cp\u003eA Decision Tree Regression approach is implemented using Python for predictive modeling. To optimize the decision tree model\u0026apos;s performance, both Grid Search and Cross-Validation were used from Scikit-learn. [29]Grid Search is conducted to tune hyperparameters, while Cross-Validation is employed to validate the model\u0026rsquo;s generalizability and prevent overfitting. The rigorous optimization process ensures that the final model is both accurate and reliable, providing a solid foundation for interpreting the results of the sensitivity analysis and drawing meaningful conclusions about the most influential factors. The methodological framework of the study is given in Figure 3.\u003c/p\u003e"},{"header":"3. Results and Discussions","content":"\u003cp\u003eThis study primarily focuses on comparing field data and satellite data. The outcome of the first step of processing resulted in line charts showing the curve of the reservoir volume for both field surveying and the DEM. Further processing of the DEMs was followed to provide explanations of the landform shape and complexity using various measures. Two main processes were carried out to compare the dam terrain morphology and the difference between the two territories in which the dams were located by taking a 5 km range from the approximate center of each dam. Further processing and analysis were performed for the dam\u0026rsquo;s 5km range territory.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Volume Elevation Data\u003c/h2\u003e\n \u003cp\u003eThe volume of each reservoir, along with its corresponding elevation, is calculated using survey data and a GIS application, and the results are plotted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The height range of the dam falls between 10 meters and 20 meters. Consequently, the analysis prioritizes the comparison within this specific height range. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the absolute relative error percentages for the Erbil and Sulaymaniyah dams.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Dam Terrain Complexity\u003c/h2\u003e\n \u003cp\u003eTo make a comparison between the reservoirs, several measures were calculated, considering both the planar shape and morphology of the reservoir, which are formed by the DEMs (Table). The DEMs were processed to generate slope, curvature, VRM, and TRI. Then, after zonal statistics were performed for the generated raster files, including the DEMs\u0026apos; zonal statistics and their standard deviations (SD), they were chosen for further analysis. The parameters in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e explain solely the 2D shape and morphological traits of the reservoirs that contribute to complexity. Based on the absolute relative error percentage, the results are sorted from the best fit to the poorest fit.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe reservoirs\u0026rsquo; complexity parameters. The Area, AVR, SF, and Solidity determine the planar shape of the reservoir\u0026rsquo;s area, whereas the other parameters determine the morphology.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReservoir\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSolidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCurvature SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVRM SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTRI SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbsolute relative error (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eErbil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKollak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBneslawa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e477186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKlkan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMzoryan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSulaymaniyah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHamza Romi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerchnar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNwrey Serw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaqtaq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZarda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the results, the parameters do not show a positive or a negative trend to the absolute relative error percentage. This is mainly due to the low resolution of the DEM and the small area of the reservoirs. The nearly 30m resolution of the DEM makes the terrain roughness smoother during processing and averaging elevation values. This resolution degradation has a more significant effect on steeper terrain and smaller areas. Since the dam valleys are small in area, this approach of reservoir area processing and analysis does not give insight; thus, the reservoir territory approach was adopted.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Dam\u0026rsquo;s Territory Terrain Complexity\u003c/h2\u003e\n \u003cp\u003eIn this approach, it was assumed that the geomorphology of the surrounding terrain of the reservoir can, to some extent, generally represent the ruggedness of the reservoirs as well. From the approximate center of each reservoir, a 5 km range DEM was extracted. The range was selected to cover the largest reservoir boundary without the reservoir 2D polygon being circumscribed by a circle.\u003c/p\u003e\n \u003cp\u003eThe DEM was processed to generate slopes, plan curvature, profile curvature, VRM, and TRI. Then, after zonal statistics were performed for the generated raster files, including the DEM\u0026apos;s zonal statistics and their standard deviations (SD), were chosen for further analysis. Like the dam terrain complexity results, most of the parameters do not show a trend, except for the TRI, which shows a significant positive correlation with the percentage of the absolute relative error (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ethe reservoirs\u0026rsquo; territory complexity parameters up to 5km range from the center of the reservoirs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReservoir\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCurvature SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlan SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProfile SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVRM SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTRI SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbsolute relative error (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eErbil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKollak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBneslawa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKlkan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMzoryan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSulaymaniyah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHamza Romi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerchnar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNwrey Serw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZarda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaqtaq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe result shows that the first five (5) reservoirs in Erbil have TRI measures below 0.1, and those located in Sulaymaniyah are above 1 on a large scale. The TRI indicates that the first 5 DEMs (located in Erbil) have lower roughness; therefore, the low-resolution DEM has a lesser impact on the accuracy of the results in these regions. In contrast, the other reservoirs\u0026rsquo; 5km range DEMs located in Sulaymaniyah are highly impacted by the DEM resolution.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eTo further study the significant impact of territorial parameters on the relative absolute error percentage, sensitivity analyses [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] were performed using the Morris method. Sample functions of the Morris method were used with design trajectories and 20 grid levels to create the sample data. A suitable training model, Decision Tree Regression, was chosen and trained using Python. The model was optimized by using Grid Search and Cross-Validation methods.\u003c/p\u003e\n \u003cp\u003eThe whole process was repeated for ten random seeds, and the minimum sample split of the Decision Tree Regressor was found to be six. The model was also manually evaluated for a smaller value of minimum sample split (5 splits). The SA process was repeated for 10 random seeds for both minimum sample splits 5 and 6, and the average values are given in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe result of the Sensitivity Analysis for the reservoirs\u0026rsquo; territory. The values presented are an average of 10 seeds for two different sample splits 5 and 6.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSamples Split\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCurvature SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlan SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProfile SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVRM SD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTRI SD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe sensitivity analysis results show that the absolute relative error is susceptible to the TRI, with a sensitivity value exceeding 80; meanwhile, the other parameters have lower average values, ranging approximately from 0 to 25. The significant influence of the TRI is that it can be relied on during the site location of dams, as shown in Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Figure 6and 7 illustrates the differences in the dams\u0026rsquo; TRI between Erbil and Sulaymaniyah cities, ordered from best fit to the poorest fit.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study highlights the value of integrating field data with satellite data to analyze dam reservoirs. Comparing survey data and Digital Elevation Models (DEMs) provided accurate insights into reservoir volumes, showcasing the strengths and limitations of each data source. The analysis, which focused on dams with heights between 10 and 20 meters, was more reliable for Erbil but less so for Sulaymaniyah.\u003c/p\u003e\u003cp\u003eMeasures such as slope, curvature, Vector Ruggedness Measure (VRM), and Terrain Ruggedness Index (TRI) were used to compare reservoirs. Zonal statistics applied to these measures did not show clear trends, primarily due to the low resolution of the 30m DEM, which smoothed terrain roughness and affected accuracy in steeper, smaller areas. Consequently, a reservoir territory approach was adopted.\u003c/p\u003e\u003cp\u003eThis approach assumed that the geomorphology of the surrounding terrain could represent the reservoir's ruggedness. A 5 km range DEM was extracted and processed to generate slope, curvature, plan curvature, profile curvature, VRM, and TRI. While most parameters did not reveal a trend, TRI showed a significant positive correlation with the absolute relative error.\u003c/p\u003e\u003cp\u003eResults indicated that reservoirs in Erbil had lower TRI values, reflecting less roughness and reduced impact from DEM resolution, while Sulaymaniyah reservoirs had higher TRI values and were more affected by DEM resolution. This underscores the need for higher-resolution DEMs and refined methods for complex terrains.\u003c/p\u003e\u003cp\u003eThe sensitivity analyses (SA) using the Morris method were conducted to investigate further the impact of territorial parameters on absolute relative error. Decision tree regression models were trained and optimized using grid search and cross-validation. Sensitivity analysis revealed that TRI was highly sensitive to the absolute relative error percentages, with a sensitivity value over 80, while other parameters had lower sensitivity values. This highlights the importance of TRI in site location decisions for dams, providing a robust framework for more accurate dam site evaluations and reservoir assessments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there is no conflict of \u0026nbsp;interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn preparing this work, the author used ChatGPT to enhance language quality. Subsequently, a meticulous review ensured precision, and the author took full responsibility for the publication's linguistic integrity.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, methodology, software, formal analysis, investigation, data curation: PBSA; NFM and SFAvalidation, visualization: NA writing: All authorsAll authors have read and agreed to the published version of the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVaze, J., Teng, J. \u0026amp; Spencer, G. Impact of DEM accuracy and resolution on topographic indices. \u003cem\u003eEnviron. Model. Softw.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e (10), 1086\u0026ndash;1098 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalchabhadel, R., Nakagawa, H., Kawaike, K., Yamanoi, K. \u0026amp; Thapa, B. R. 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Res.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 2825\u0026ndash;2830 (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"One-Arc-Second DEM, Small Dam, Terrain Ruggedness Index (TRI), Sensitivity Analysis, Volume-Elevation Data","lastPublishedDoi":"10.21203/rs.3.rs-7035830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7035830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change has assured the critical role of small dams in water resource management by enhancing local water storage, flood control, and irrigation, thereby improving resilience to climate variability. This study evaluates the effectiveness of one-arc-second digital elevation models (DEMs) in determining reservoir volume-elevation data, comparing these satellite-derived data with field survey data, which are known to be costly and time-consuming. Focused on small dams, the research compares field survey data with data derived from the one-arc-second DEM to address the lack of validation for these studies in small reservoirs. The methodology involves analyzing ten reservoirs in two different locations, namely Erbil and Sulaymaniyah governorates in Northern Iraq, using ArcGIS and Remote Sensing for digital elevation model processing. The volume-elevation data for the reservoirs are determined using ArcGIS. Consequently, evaluations of terrain metrics and sensitivity analyses support the adoption of one-arc-second DEMs for determining volume-elevation data in early-stage dam planning and reservoir assessments. Key innovations include addressing the limitations of low-resolution DEMs by using a wider 5 km radius for terrain analysis, which enables a thorough evaluation of landform features. The Terrain Ruggedness Index (TRI) is introduced as a key metric for evaluating terrain complexity, regional variations, and sensitivity to absolute error percentages. Morris\u0026rsquo;s sensitivity analysis highlights TRI's significance as the decisive parameter by examining how different terrain parameters affect error rates. These advancements enhance the accuracy of reservoir and terrain evaluations, offering valuable insights for improved dam design and water resource management.\u003c/p\u003e","manuscriptTitle":"One-Arc-Second DEMs and Field Survey Data for Small Dams Using Terrain Metrics: A Comparative Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 16:40:36","doi":"10.21203/rs.3.rs-7035830/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-20T04:36:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T10:25:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T16:22:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77289926315301996293378912344834043699","date":"2025-07-20T19:07:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111639496705120592732277485697723701116","date":"2025-07-20T18:49:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-18T18:47:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T10:36:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-03T12:12:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T10:19:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-03T08:19:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"acabc1ff-90c5-4654-b16f-2f22b7afd133","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51947140,"name":"Earth and environmental sciences/Climate sciences"},{"id":51947141,"name":"Earth and environmental sciences/Environmental sciences"},{"id":51947142,"name":"Earth and environmental sciences/Hydrology"},{"id":51947143,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2025-12-29T16:02:25+00:00","versionOfRecord":{"articleIdentity":"rs-7035830","link":"https://doi.org/10.1038/s41598-025-30483-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-27 15:58:07","publishedOnDateReadable":"December 27th, 2025"},"versionCreatedAt":"2025-07-23 16:40:36","video":"","vorDoi":"10.1038/s41598-025-30483-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30483-7","workflowStages":[]},"version":"v1","identity":"rs-7035830","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7035830","identity":"rs-7035830","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}