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The value of streamflow data in improving TSS predictions – Bayesian multi-objective calibration


Sikorska, Anna E; Del Giudice, Dario; Banasik, Kazimierz; Rieckermann, Jörg (2015). The value of streamflow data in improving TSS predictions – Bayesian multi-objective calibration. Journal of Hydrology, 530:241-254.

Abstract

The concentration of total suspended solids (TSS) in surface waters is a commonly used indicator of water quality impairments. Its accurate prediction remains, however, problematic because: (i) TSS build-up, erosion, and wash-off are not easily identifiable; (ii) calibrating a TSS model requires observations of sed- iment loads, which are rare, and streamflow observations to calculate concentrations; and (iii) predicted TSS usually deviate systematically from observations, an effect which is commonly neglected. Ignoring systematic errors during calibration can lead to overconfident (i.e. unreliable) uncertainty estimates during predictions. In this paper, we therefore investigate whether a statistical description of systematic model errors makes it possible to generate reliable predictions for TSS. In addition, we explore how the reliability of TSS predictions increases when streamflow data are additionally used in model calibration. A key aspect of our study is that we use a Bayesian multi-output calibration and a novel autoregressive error model, which describes the model predictive error as a sum of independent random noise and autocorrelated bias. Our results show that using a statistical description of model bias provides more reliable uncertainty estimates of TSS than before and including streamflow data into calibration makes TSS predictions more precise. For a case study of a small ungauged catchment, this improvement was as much as 15%. Our approach can be easily implemented for other water quality variables which are dependent on streamflow.

Abstract

The concentration of total suspended solids (TSS) in surface waters is a commonly used indicator of water quality impairments. Its accurate prediction remains, however, problematic because: (i) TSS build-up, erosion, and wash-off are not easily identifiable; (ii) calibrating a TSS model requires observations of sed- iment loads, which are rare, and streamflow observations to calculate concentrations; and (iii) predicted TSS usually deviate systematically from observations, an effect which is commonly neglected. Ignoring systematic errors during calibration can lead to overconfident (i.e. unreliable) uncertainty estimates during predictions. In this paper, we therefore investigate whether a statistical description of systematic model errors makes it possible to generate reliable predictions for TSS. In addition, we explore how the reliability of TSS predictions increases when streamflow data are additionally used in model calibration. A key aspect of our study is that we use a Bayesian multi-output calibration and a novel autoregressive error model, which describes the model predictive error as a sum of independent random noise and autocorrelated bias. Our results show that using a statistical description of model bias provides more reliable uncertainty estimates of TSS than before and including streamflow data into calibration makes TSS predictions more precise. For a case study of a small ungauged catchment, this improvement was as much as 15%. Our approach can be easily implemented for other water quality variables which are dependent on streamflow.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2015
Deposited On:08 Dec 2015 14:55
Last Modified:05 Apr 2016 19:35
Publisher:Elsevier
ISSN:0022-1694
Publisher DOI:https://doi.org/10.1016/j.jhydrol.2015.09.051

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