Reanalysis dataset-based hydrologic predictions for ungauged basins

Anghesom Ghebrehiwot,Dmitry Kozlov,D Bazarov

doi:10.1051/e3sconf/202126401001

Anghesom Ghebrehiwot, Dmitry Kozlov + Show 1 more

Open Access

https://doi.org/10.1051/e3sconf/202126401001

Copy DOI

Abstract

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.

Highlights

Rainfall-runoff models are extensively applied as predictive tools for generating hydrological responses during water resources development and management studies
The regions that suffer the greatest human impacts are usually those where hydrometric networks are least established. This is true of regions in developing countries where lack of hydrometric data coupled with climate and land use changes has led to a depletion of water resources and environmental degradation
Model uncertainty could be minimized if and only if we clearly identify the sources of uncertainty

Summary

Introduction

Rainfall-runoff models are extensively applied as predictive tools for generating hydrological responses during water resources development and management studies. Studies carried out in the East African region [8] emphasized that the political and socio-economic history of the region for the last several decades have not been conducive to the management of hydrometeorological records. These problems are compounded by the impacts of human-made changes to the land surface and climate, occurring at the local, regional and global scales. The regions that suffer the greatest human impacts are usually those where hydrometric networks are least established This is true of regions in developing countries where lack of hydrometric data coupled with climate and land use changes has led to a depletion of water resources and environmental degradation. A bulk of the available evidence points to the fact that a wide range of remote sensing data-based models’ effectiveness and accuracy

Methods

Results

Conclusion