Understanding the Impact of Observation Data Uncertainty on Probabilistic Streamflow Forecasts Using a Dynamic Hierarchical Model

Das Bhowmik, R and Ng, TL and Wang, JP (2020) Understanding the Impact of Observation Data Uncertainty on Probabilistic Streamflow Forecasts Using a Dynamic Hierarchical Model. In: Water Resources Research, 56 (4).

	PDF WAT_RES_RES_4.pdf - Published Version Restricted to Registered users only Download (7MB) | Request a copy
	PDF SUPPLIMENTARY.pdf - Published Version Restricted to Registered users only Download (544kB) | Request a copy
	PDF suppli..pdf - Published Version Restricted to Registered users only Download (177kB) | Request a copy

Abstract

Earlier researches have proposed algorithms to quantify the measurement uncertainty in rating curves and found that the magnitude of the uncertainty can be significant enough to impact hydrologic modeling. Therefore, they suggested frameworks to include measurement uncertainty in the rating curve to make it robust. Despite their efforts, a robust rating curve is often ignored in traditional practices, considering the investment of time and money as well as the resulting benefit from it. In the current research, we are interested in understanding the role of the measurement error variance in real-time streamflow forecasting. Our objectives are (i) to employ a state-of-the-art statistical forecasting model that can handle measurement uncertainty in daily streamflow and (ii) to understand the trade-off in forecasting performance when substantial knowledge regarding the measurement uncertainty is provided to the modeler. We apply the Bayesian dynamic hierarchical model (BDHM) on four gauging sites in the United States. Results show that the BDHM performs better than the daily climatology and local linear regression model. Also, the forecast variance changes proportionally with the change in the error variance as an input in the observation equation. Following this, we design a simulation-based study, which assigns the measurement error in the reported streamflow to obtain multiple realizations of the true streamflow. The inclusion of substantial knowledge about the true error improves the BDHM's performance by lowering the CRPS (continuous rank probability score) values. However, the inclusion increases the forecast variance to bring the true streamflow within the sampling variability of the forecasted streamflow. Overall, an improved trade-off between the success rate of forecasts and the forecast variance can be achieved by including the measurement error in the BDHM for rivers that witness less dispersed streamflow data. ©2020. American Geophysical Union. All Rights Reserved.

Item Type:	Journal Article
Publication:	Water Resources Research
Publisher:	Blackwell Publishing Ltd
Additional Information:	(Parikshita)Copyright of this article belongs to Blackwell Publishing Ltd
Keywords:	Economic and social effects; Forecasting; Hierarchical systems; Measurement errors; Regression analysis; Stream flow, Forecasting performance; Hydrologic modeling; Local linear regression; Measurement uncertainty; Observation equation; Statistical forecasting; Streamflow forecast; Streamflow forecasting, Uncertainty analysis, algorithm; error analysis; forecasting method; hierarchical system; hydrological modeling; investment; observational method; probability; research work; streamflow; uncertainty analysis, United States
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	01 Oct 2020 09:30
Last Modified:	01 Oct 2020 09:30
URI:	http://eprints.iisc.ac.in/id/eprint/65361

Actions (login required)

View Item