Abstract
An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models).
Highlights
In hydrology, the discrepancy between simulated and observed streamflow (Q) data can be used to update a model’s state variables, which has applications in basin-wide estimations and hydrological forecasting [1,2,3,4]
Model, (ii) removal of the bias module, bias correction (BC), (iii) simulation of the Hydrologiska Byråns Vattenbalansavdelning (HBV), with or without updating, where updating the HBV was carried out using bias-corrected satellite soil moisture which replaced the proper state variable of the HBV, or by using an assimilation procedure to create an ensemble of the model states with or without unbiased errors using an ensemble Kalman filter (EnKF) or an ensemble Kalman filter with bias correction (EnKF-BC) filter, and (iv) simulation of the HBV in forecast mode with an updating procedure using assimilation of the satellite observations
The framework proposed in the study addressed the two main stages of the HBV simulation procedure, the bias correction method and the assimilation procedure, with regards to the potential application of unbiased perturbation to soil moisture state variables
Summary
The discrepancy between simulated and observed streamflow (Q) data can be used to update a model’s state variables, which has applications in basin-wide estimations and hydrological forecasting [1,2,3,4]. Data assimilation (DA) procedures, which allow for accurate modeling of hydrological variables, can be used to provide the necessary ground conditions (e.g., soil moisture (θ), snowpack and snow water equivalent) for mathematical models. These state variables, along with other past and present model states (e.g., the contents of upper and lower boxes in a response routine in the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model), can be used in the model’s updating procedures. A properly configured hydrological simulation model should assimilate these observations, rather than use them directly as inputs for the deterministic model This can be achieved through the application of the DA procedure, coupled with the use of model state variables
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
