Academic Journal
Assessment of climate change impact on inflows to Amandara headwork using HEC-HMS and ANNs
| Title: | Assessment of climate change impact on inflows to Amandara headwork using HEC-HMS and ANNs |
|---|---|
| Authors: | Haider Ali Hassaan, Ateeq ur Rauf, Abdul Razzaq Ghumman, Saba Khan, Erum Aamir |
| Source: | Journal of Umm Al-Qura University for Engineering and Architecture. 15:403-420 |
| Publisher Information: | Springer Science and Business Media LLC, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Environmental Engineering, Rainfall-Runoff Modeling, 0207 environmental engineering, 02 engineering and technology, Oceanography, 01 natural sciences, Environmental science, Global Flood Risk Assessment and Management, Hydrological Modeling using Machine Learning Methods, Climate change, 14. Life underwater, Water Science and Technology, 0105 earth and related environmental sciences, Global and Planetary Change, FOS: Environmental engineering, Groundwater Level Forecasting, Hydrology (agriculture), Geology, FOS: Earth and related environmental sciences, 15. Life on land, Water resource management, 6. Clean water, Geotechnical engineering, Hydrological Modeling and Water Resource Management, 13. Climate action, Environmental Science, Physical Sciences |
| Description: | This research has assessed the impact of climate change on temperature, precipitation, and inflows to the Amandara headwork in Pakistan. Trend Analysis using the Mann–Kendall test and Innovative Trend Analysis has been performed. Rainfall-runoff modeling is executed using the Hydrological Engineering Centre-Hydrological Modeling System (HEC-HMS) and Artificial Neural Networks including Feed Forward Neural Network, Conjugate Gradient, Two-layer Backpropagation Neural Network, and Broyden Fletcher-Goldfarb-Shanno. Mean daily hydro-meteorological data (1992 to 2023) was utilized for this study in which 70% was employed for calibration while the remaining 30% was used for validation of the model. Two GCMs namely CSIROMk3-6–0 and HadGEM2-ES with four Representative Concentration Pathways; RCP 2.6, 4.5, 6.0, and 8.5, were employed for future forecasting of temperature and precipitation. This future predicted data was then used to forecast flows up to 2050 by HEC-HMS. The performance of the models was assessed using correlation coefficient (R), Root Mean Square Error, Mean Bias Error, and Nash Sutcliffe Efficiency. Significant patterns in the runoff and temperature with no trend in precipitation were found. GCMs showed an increase in the range of 3–9 °C in temperature, 300 to 500 mm in precipitation, and 45 to 54% in peak flows. |
| Document Type: | Article Other literature type |
| Language: | English |
| ISSN: | 1658-8150 2731-6688 |
| DOI: | 10.1007/s43995-024-00064-2 |
| DOI: | 10.60692/wwcpn-hxt40 |
| DOI: | 10.60692/f48mx-3ws45 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....b5d0e6cd65ca74c21f19e74c57beaa26 |
| Database: | OpenAIRE |
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