Uncertainty quantification of multi-source hydrological data products for the improvement of water budget estimations in small-scale Sakarya basin, Turkey

ABSTRACT

The present study aims to improve the efficacy of water budget (WB) estimations from various hydrological data products, by (1) evaluating the uncertainties of hydrological data products, (2) merging four precipitation and six evapotranspiration products using their error variances, and (3) employing the constrained Kalman filter (CKF) method to distribute residual errors among water budget components based on their relative uncertainties. The results show that applying bias correction before the merging process improved estimations of precipitation products with decreasing root mean square error (RMSE), except Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Variable Infiltration Capacity (VIC) and bias-corrected Climate Prediction Center Morphing Technique (CMORPH) products outperformed other evapotranspiration and bias-corrected precipitation products, respectively, in terms of mean merging weights. The terrestrial water storage change is the primary reason for non-closure errors, mainly caused by the coarse resolution of Gravity Recovery and Climate Experiment (GRACE). The CKF results were insensitive to variations in uncertainties of runoff. Precipitation derived from the CKF was the best precipitation output, with the highest correlation coefficient (CC) and smallest root mean square deviation (RMSD).

KEYWORDS:

• water budget
• hydrological data products
• uncertainty quantification
• dynamic modelling

Editor S. Archfield Associate Editor A. Domeneghetti

Editor S. Archfield Associate Editor A. Domeneghetti

Acknowledgements

The authors are thankful to the State Hydraulic Works of Turkey and the Turkish State Meteorological Service for providing runoff and precipitation data for this work.

Disclosure statement

No potential conflict of interest was reported by the authors.