Preprints
https://doi.org/10.5194/hess-2019-288
https://doi.org/10.5194/hess-2019-288

  11 Jul 2019

11 Jul 2019

Review status: this preprint was under review for the journal HESS but the revision was not accepted.

Variational Assimilation of Streamflow Observations in Improving Monthly Streamflow Forecasting

Amirhossein Mazrooei1, A. Sankarasubramanian1, and Andrew W. Wood2 Amirhossein Mazrooei et al.
  • 1Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina
  • 2Research Applications Laboratory, National Center for Atmospheric Research (NCAR), Boulder, Colorado

Abstract. Uncertainties associated with the initial conditions (e.g. soil moisture content) of a hydrologic model have been recognized as one of the main sources of errors in hydrologic predictions, specifically over a rainfall-runoff regime. Apart from the recent advances in Data Assimilation (DA) for improving hydrologic predictions, this study explores variational assimilation (VAR) of gauge-measured daily streamflow data for updating initial state of soil moisture content of Variable Infiltration Capacity (VIC) Land Surface Model (LSM) in order to improve streamflow simulations as well as monthly streamflow forecasting. The study is conducted for the Tar River basin in North Carolina over 20-year period (1991–2010). The role of two critical parameters of VAR DA – the frequency of DA application and the length of assimilation window – in determining the skill of DA-improved streamflow predictions is also assessed. We found that correcting VIC model's initial conditions using a 7-day assimilation window results in the highest improvement in the skill of streamflow predictions quantified by Kling-Gupta Efficiency (KGE) and Nash-Sutcliffe Efficiency (NSE) metrics. In addition, the potential gain from VAR DA framework is quantified and compared under two 1-month ahead streamflow forecasting schemes: 1) deterministic forecasts developed by using ECHAM4.5 GCM 1-month ahead precipitation forecasts and 2) Probabilistic forecasts from Ensemble Streamflow Prediction (ESP) approach. This study also examines the persistence of the DA impact in the monthly predictions by quantifying the enhanced accuracy in daily flows over extending forecast lead time blocks. Analyses show that the the corrected initial state conditions continually enhance the 7–8 days ahead predictions, but after that the errors in forcings dominate the DA effects. Still, the overall impact of VAR DA in monthly streamflow forecasting is positive.

Amirhossein Mazrooei et al.

 
Status: closed
Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Amirhossein Mazrooei et al.

Amirhossein Mazrooei et al.

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Short summary
Reliable long-range hydrologic forecasts (e.g. month-ahead streamflow conditions) greatly help to facilitate water resources management. Toward this, climate forecasts are utilized to implement hydrologic models, with past hydrologic simulations used to initialize the land-surface conditions of models. Since there are uncertainties associated with the simulated conditions, Data Assimilation (DA) techniques can be employed to reduce such errors and consequently improve hydrologic forecasting.