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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-418
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 29 Aug 2018

Technical note | 29 Aug 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Technical note: A novel technique to improve the hydrological estimates at ungauged basins by swapping workspaces

Muhammad Uzair Qamar1, Muhammad Azmat2, Muhammad Usman1,3, Daniele Ganora4, Muhammad Adnan Shahid5, Faisal Baig6, and Sumra Mushtaq7 Muhammad Uzair Qamar et al.
  • 1Department of Irrigation and Drainage, Faculty of Agricultural Engineering and Technology
  • 2Institute of Geographical Information Systems (IGIS), School of Civil & Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), IGIS Building (2nd Floor), 44000 Islamabad, Pakistan
  • 3Department of Remote Sensing, Institute for Geography and Geology, Julius Maximillian's University Wuerzburg, Oswald Külpe Weg 86, 97074 Wuerzburg, Germany
  • 4Dipartimento di Idraulica, Trasporti ed Infrastrutture Civili, Politecnico di TorinoTurin, Italy
  • 5Water Management Research Centre, University of Agriculture, 38040 Faisalabad, Pakistan
  • 6Department of Agricultural Engineering, Bahauddin Zakariya University, 60800 Multan, Pakistan
  • 7Department of Catchment Hydrology, Helmholtz Centre for Environmental Research UFZ

Abstract. The dissimilarity-based methods to perform prediction of flow regimes in ungauged basins have become quite popular in the recent times. Generally, these methods use geomorphological and climatic characteristics of the basins to translate their hydrological properties. However, the methods have been criticized for using selective basin characteristics for the prediction of hydrological data of the basins in the entire study area. Incase these selected descriptors are not strongly related to the hydrological properties of the considered basin; as opposed to the general perception, a considerable magnitude of localized error may be introduced in the final results. To address these drawbacks, we propose a novel technique which assists in identifying a better individual regional model for the prediction of hydrological data at each ungauged basin. The new procedure treats each flow regime as a complete hydrological object. Whereas, the variability in regime shape is determined by using dissimilarity values arranged in a distance matrix executed by considering normalized values of three types of dissimilarities viz; point-to-point dissimilarity, vertical dissimilarity and lateral dissimilarity. On the basis of defined statistical routines, the flow distance matrix is linked with the distance matrices of basin characteristics, acquired by simple comparison of descriptors values, to select most suitable descriptors from the pool of 74 descriptors to form regionalized models. The dissimilarity-based regionalization model thus obtained is primarily coupled with nearest neighbor algorithm to constitute a model space for the initial predictions of the monthly flow regimes. Afterwards, based on the orientation of nearest neighbors of ungauged basin in descriptor space __ the prediction is improved by swapping the model space with the other available models provided the set criteria are fulfilled. The proposed study is conducted in northwestern Italy and the proposed method is tested on the dataset of 124 basins. The basins where the set criteria of model swapping are complied with; the results obtained are statistically better than the initial estimates.

Muhammad Uzair Qamar et al.
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Muhammad Uzair Qamar et al.
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Short summary
The dissimilarity methods use basins' descriptors to translate their hydrologic data. The methods use selective basin parameters for the prediction. We coupled dissimilarity model with nearest neighbor algorithm to constitute a model space for the predictions of the flow data. Afterwards, based on the orientation of nearest neighbors of ungauged basin in descriptor space __ the prediction is improved by swapping the model space with the other available models provided the set criteria are met.
The dissimilarity methods use basins' descriptors to translate their hydrologic data. The...
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