Large Biases in Regression‐Based Constituent Flux Estimates: Causes and Diagnostic Tools1. (21st May 2014)

• Record Type: Journal Article
• Authors: Hirsch, Robert M.
• Is Part Of: Journal of the American Water Resources Association. Volume 50:Number 6(2014)
• Page Start: 1401
• Page End: 1424
• Publication Date: 2014-05-21
• Subjects: Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/jawr.12195
• Languages: English
• ISSNs: 1093-474X

Abstract

<abstract abstract-type="main" id="jawr12195-abs-0001"> <title>Abstract</title> <p>It has been documented in the literature that, in some cases, widely used regression‐based models can produce severely biased estimates of long‐term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven‐parameter model, LOADEST five‐parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST‐7 and LOADEST‐5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration <italic>vs</italic>. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.</p> </abstract>