E3S Web Conf.
Volume 264, 2021International Scientific Conference “Construction Mechanics, Hydraulics and Water Resources Engineering” (CONMECHYDRO - 2021)
|Number of page(s)||14|
|Section||Ecology, Hydropower Engineering and Modeling of Physical Processes|
|Published online||02 June 2021|
Reanalysis dataset-based hydrologic predictions for ungauged basins
National Research Moscow State University of Civil Engineering, Moscow, Russia
* Corresponding author: firstname.lastname@example.org
In the present study, Soil and Water Assessment Tool (SWAT) is employed to simulate streamflows from watershed with a semi-arid climate, using Climate Forecast System Reanalysis (CFSR) as forcing data input. To this end, two streamflow simulation scenarios, with and without readjustment of the reanalysis datasets, were investigated depending on available ground information. The findings indicate that the performance of the model is slightly improved when the former scenario, with readjustment of precipitation, is considered. Despite improvement in the overall model prediction, uncertainties during calibration and validation partially remained far less than the permissible limits. The reason seems to be associated with the mismatch between in-situ data and reanalysis datasets with respect to time and space. Irrespective of the sources of prediction uncertainties, the use of readjusted reanalysis datasets are deemed to be the best option for streamflow simulations in poorly gauged or ungauged watersheds. However, to underpin the findings with supportive and sound evidence, further investigation on the reanalysis datasets for hydrological predictions from similar regions with sufficient and reliable ground information becomes imminent. The study also underscores the need for undertaking pre-emptive measures to reverse the quantitative decline of hydrometric networks and existing management practices in the region.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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