Prediction of Runoff in Watersheds Located within Data-Scarce Regions
arid environment; catchment; design flood; flood routing; hydrograph; hydrological modeling; peak flow; prediction; rainfall-runoff modeling; runoff; streamflow; watershed; Jordan
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2023
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my.uniten.dspace-268342023-05-29T17:37:04Z Prediction of Runoff in Watersheds Located within Data-Scarce Regions Ghanim A.A.J. Beddu S. Abd Manan T.S.B. Al Yami S.H. Irfan M. Mursal S.N.F. Mohd Kamal N.L. Mohamad D. Machmudah A. Yavari S. Mohtar W.H.M.W. Ahmad A. Rasdi N.W. Khan T. 57210192561 55812080500 57219650719 57782985200 35069404400 57219650352 56239107300 57200335404 36442829100 57521992400 57215829072 6506760282 56446926400 54991181500 arid environment; catchment; design flood; flood routing; hydrograph; hydrological modeling; peak flow; prediction; rainfall-runoff modeling; runoff; streamflow; watershed; Jordan The interest in the use of mathematical models for the simulation of hydrological processes has largely increased especially in the prediction of runoff. It is the subject of extreme research among engineers and hydrologists. This study attempts to develop a simple conceptual model that reflects the features of the arid environment where the availability of hydrological data is scarce. The model simulates an hourly streamflow hydrograph and the peak flow rate for any given storm. Hourly rainfall, potential evapotranspiration, and streamflow record are the significant input prerequisites for this model. The proposed model applied two (2) different hydrologic routing techniques: the time area curve method (wetted area of the catchment) and the Muskingum method (catchment main channel). The model was calibrated and analyzed based on the data collected from arid catchment in the center of Jordan. The model performance was evaluated via goodness of fit. The simulation of the proposed model fits both (a) observed and simulated streamflow and (b) observed and simulated peak flow rate. The model has the potential to be used for peak discharges� prediction during a storm period. The modeling approach described in this study has to be tested in additional catchments with appropriate data length in order to attain reliable model parameters. � 2022 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T09:37:04Z 2023-05-29T09:37:04Z 2022 Article 10.3390/su14137986 2-s2.0-85133551325 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133551325&doi=10.3390%2fsu14137986&partnerID=40&md5=610f27998c860a98fda1ee8d2d41a422 https://irepository.uniten.edu.my/handle/123456789/26834 14 13 7986 All Open Access, Gold MDPI Scopus |
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| description |
arid environment; catchment; design flood; flood routing; hydrograph; hydrological modeling; peak flow; prediction; rainfall-runoff modeling; runoff; streamflow; watershed; Jordan |
| author2 |
57210192561 |
| author_facet |
57210192561 Ghanim A.A.J. Beddu S. Abd Manan T.S.B. Al Yami S.H. Irfan M. Mursal S.N.F. Mohd Kamal N.L. Mohamad D. Machmudah A. Yavari S. Mohtar W.H.M.W. Ahmad A. Rasdi N.W. Khan T. |
| format |
Article |
| author |
Ghanim A.A.J. Beddu S. Abd Manan T.S.B. Al Yami S.H. Irfan M. Mursal S.N.F. Mohd Kamal N.L. Mohamad D. Machmudah A. Yavari S. Mohtar W.H.M.W. Ahmad A. Rasdi N.W. Khan T. |
| spellingShingle |
Ghanim A.A.J. Beddu S. Abd Manan T.S.B. Al Yami S.H. Irfan M. Mursal S.N.F. Mohd Kamal N.L. Mohamad D. Machmudah A. Yavari S. Mohtar W.H.M.W. Ahmad A. Rasdi N.W. Khan T. Prediction of Runoff in Watersheds Located within Data-Scarce Regions |
| author_sort |
Ghanim A.A.J. |
| title |
Prediction of Runoff in Watersheds Located within Data-Scarce Regions |
| title_short |
Prediction of Runoff in Watersheds Located within Data-Scarce Regions |
| title_full |
Prediction of Runoff in Watersheds Located within Data-Scarce Regions |
| title_fullStr |
Prediction of Runoff in Watersheds Located within Data-Scarce Regions |
| title_full_unstemmed |
Prediction of Runoff in Watersheds Located within Data-Scarce Regions |
| title_sort |
prediction of runoff in watersheds located within data-scarce regions |
| publisher |
MDPI |
| publishDate |
2023 |
| _version_ |
1806428442922582016 |
| score |
13.18916 |