A comparative study of flood inundation mapping using hydrologic-hydrodynamic modelling and remote sensing-based datasets in the Kabul River Basin, Pakistan | |
| Author | Adnan, Muhammad |
| Call Number | AIT Thesis no.WM-24-20 |
| Subject(s) | Hydrogeological modeling--Pakistan--Kabul River Basin Floods--Pakistan--Kabul River Basin--Remote sensing |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management |
| Publisher | Asian Institute of Technology |
| Abstract | The transboundary Kabul River Basin (KRB) straddled the borders with Afghanistan and Pakistan is profoundly vulnerable to pluvial and fluvial floods due to the changing climate in the region. The 2010 and 2022 floods are major examples that caused huge devastation downstream cities such as Charsadda, Peshawar and Nowshera. However, flood inundation maps are not readily available for developing emergency action plans, evacuation plans, response and flood damage assessment. Therefore, this study for the first time applied a combination of conventional modelling and remote sensing to develop flood inundation maps. The PCSWMM hydrological-hydrodynamic model was calibrated for the 2010 flood and validated for the 2022 flood considering both observed as well as MERRA-II re-analysis precipitation separately. Model performance was evaluated using different indicators such as Integral Square Error (ISE), Nach Sutcliffe Efficiency (NSE), and Percentage Bias (PBIAS). The model performed well during calibration (validation), for example, ISE, NSE, and PBIAS were 0.42 (0.54), 0.77 (0.84), and -0.56% (-2.607%) for observed precipitation while 0.32 (0.51), 0.86 (0.86), and -12.12 (-8.26) for re-analysis precipitation respectively. The model captured the peak flows of the 2010 and 2022 floods. Flood analysis was run to get flood inundation maps from observed and re-analysis precipitation for the 2022 extreme rainfall event. Alternatively, this study also explores processing Synthetic Aperture Radar (SAR C-band) remote sensing data using the Sentinel Application Platform (SNAP) to map flood inundation of the same rainfall event. The flood inundation maps from the model and SAR were compared with the referenced map by creating a Confusion Matrix. Ten different indices like Mathews Correlation Coefficient, error rate, specificity, false positive rate etc. were calculated where the model gives improved performance for simulating flood inundation. SAR imagery was not available during the flooding days and hence missed the peak flood resulting in reduced inundation. Despite being underestimated in high-altitude areas, MERRA-2 precipitation data overall improved model performance by incorporating more observation points where no observations were available and revealed a correlation with observed precipitation thereby making it suitable for data-scarce flat regions. The results from this study can be used for informed decisions. |
| Year | 2024 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Water Engineering and Management (WM) |
| Chairperson(s) | Shanmugam, Mohana Sundaram, |
| Examination Committee(s) | Shrestha, Sangam;Natthachat Tangdamrongsub;Virdis, Salvatore G.P |
| Scholarship Donor(s) | Asian Development Bank-Japan Scholarship Program (ADB-JSP) |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2024 |