Using the mathematical methodology (SCS-CN) and the linear regression algorithm to estimate the amount of surface water runoff and its harvesting potential for human investments in the Wadi Takiyat Basin, an applied study
Using the mathematical methodology (SCS-CN) and the linear regression algorithm to estimate the amount of surface water runoff and its harvesting potential for human investments in the Wadi Takiyat Basin, an applied study
Pages
828-803Keywords:
Abstract
The current research aims to estimate the volume of runoff in the Takiyat basin by using digital elevation models inspired by satellite imagery and analysing them using geographic information systems (GIS) to reach the morphometric and hydrological features of the basin and then simulating these results with artificial intelligence software using a linear regression algorithm to reach the most accurate results. Using a linear regression algorithm that simulates water harvesting to reach the most accurate results, the research results indicated the possibility of applying a water harvesting system in the region, by selecting the proposed catchment sites to store water and benefit from it for various human purposes, as the water runoff was calculated according to the American model (SCS- CN). The research concluded that the basin enjoys the presence of three optimal sites for water storage for the purpose of using it in periods of drought, whose storage capacity ranged between (6377400-16074900 m3) with a surface runoff volume of (6407-2480168) mm, which is sufficient quantitative data, which can be utilised in developing agricultural lands, expanding pastures, establishing human settlements and raising groundwater levels, while the linear regression model shows R² value of 0. 99 that the model accounts for 99% of the variance in the data, which means that the model is very accurate in predicting runoff according to the independent variables CN_value and area. From the coefficients, when CN_value increases by one unit, Q increases by 2.24 units, while an increase in area by one unit (km²) increases Q by 0.039 units.
References
- Al-Eisawi, Abdulhamid Wali Abdul Bati, & Al-Ani, Salah Othman. (2018). Selecting water catchment sites for the Wadi Tabl Basin between Iraq and Saudi Arabia based on hydrogeomorphological indicators and their impact on desert development using geomatics techniques. Journal of Educational and Scientific Studies, College of Education, Iraqi University, Issue (11), Volume (2), p. 242.
- Al-Kharabsheh, Atef Ali Hamed, & Ghoneim, Othman Mohammed. (2009). Water harvesting in arid and semi-arid regions in the Arab world. Amman: Dar Safa for Publishing and Distribution, p. 61.
- Khudair, Zainab Wanas. (2007). Composition, lithology, and sedimentology of the upper Euphrates River valley and their impact on changes in salt concentrations in river water between the regions of Husaybah and Ramadi. Al-Ustadh Journal, Issue (65), p. 110.
- Siskian, Varoujan Khajik, & Hafez, Shaker Qanbar. (1994). Report on the Boukamal and Wadi Al-Miyah maps (Report No. 2323). Baghdad: General Company for Geological Survey and Mining, p. 3.
- Al-Nafie, Haifa Mohammed. (2010). Estimation of surface runoff and its flood hazards in the upper basin of Wadi Arafah, east of Makkah, using remote sensing and GIS (Unpublished master's thesis). Umm Al-Qura University, College of Social Sciences, p. 91.
- Al-Karbouli, I. D. A. S. (2022). The Analysis of the hydro morphometric properties of the valley Halewat Basin in Anbar Governorate using remote sensing and geographic information systems. Midad Al-Adab Refereed Journal, 1(25).
- Karbouli, A. S. E. A. (2022). Hydromorphometric Characteristics of the Milan Valley Basin in the Western Plateau and the Possibility of Investing Them for the Purposes of Water Harvesting. Journal of Tikrit University for Humanities, 29.
- Arzek, A. S. (2018). Evaluating the qualitative characteristics of groundwater and its suitability for human use in the Kirkuk district. Journal of Education and Scientific Studies, 2(11).
- (*) To calculate surface runoff depth, the following equation is used:
- Q = (P - Ia)² / ((P - Ia) + S)
- Where:
- • Q = Surface runoff depth (in inches)
- • P = Precipitation (in inches)
- • Ia = Initial abstractions before runoff begins (such as infiltration, surface depressions, evaporation, and transpiration) (in inches)
- • S = Maximum potential retention after runoff begins (in inches)
- Since Ia is typically considered to be 20% of S, it is calculated as:
- Ia = 0.2 × S
- To calculate the value of S, the following equation is used:
- S = (1000 / CN) - 10
- Where:
- • CN = Curve Number
- Because all inputs are in inches and the metric system is used for consistency, the values are converted to millimeters by multiplying the equation by 25.4, resulting in the modified equation:
- S = (25400 / CN) - 25.4
- After generating the spatial layers for S, Ia, and Q, the results of the equations were entered into ArcGIS 10.4, using the Raster Calculator tool to compute the volume of surface runoff using the following formula:
- Qv = (Q × A) / 1000
- Where:
- • Qv = Surface runoff volume (in cubic meters)
- • Q = Surface runoff depth (in millimeters)
- • A = Basin area (in square kilometers)
- • 1000 = Conversion factor to ensure the final unit is in cubic meters
Identifiers
Statistics
How to Cite
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
