Uncertainty of nitrate-N load computations for agricultural watersheds

Yiping Guo, Momcilo Markus, Misganaw Demissie

Research output: Contribution to journalArticlepeer-review

Abstract

Water quality samples for most streams are collected at variable frequencies within monitoring periods of different lengths. On the basis of discrete concentration data obtained from these monitoring studies, loads of various pollutants passing through a gaging station for selected periods may be calculated using various load estimation methods. In this paper, nitrate-N load estimates were compared with their "true" values, calculated using 6 years of daily nitrate-N concentration and average discharge data at an agricultural watershed in central Illinois. A Monte Carlo subsampling study was conducted to simulate different sampling scenarios for variable sampling frequencies and different monitoring durations. Load calculations were compared for each sampling scenario based on rating curve, ratio estimator, and flow-weighted average estimator. In addition, two bias correction techniques (minimum variance unbiased estimator (MVUE) and smearing estimator) were applied to the rating curve method. The monitoring durations were 1, 2, 3, and 6 years, and the sampling frequencies ranged from weekly to bimonthly. The results demonstrated that a desired accuracy of the estimates could be achieved either by sampling more frequently or by monitoring the site longer. Although the ratio and the flow-weighted average estimators had a small negative bias, in most cases rating curve estimators were positively biased when applied to the study site. Also, neither of the two bias correction techniques, MVUE and smearing estimator, decreased this positive bias. On the contrary, those techniques produced a higher bias, which resulted in increased root-mean-square error (RMSE). The rating curve uncorrected for bias, the simple ratio, and the flow-weighted estimator had a significantly smaller RMSE for all sampling frequencies and all periods of record than the bias-corrected rating curve methods.

Original languageEnglish (US)
Pages (from-to)3-1-3-12
JournalWater Resources Research
Volume38
Issue number10
DOIs
StatePublished - Oct 1 2002

Keywords

  • Load estimation
  • Monte Carlo
  • Nitrate-N
  • Sampling frequency
  • Uncertainty

ASJC Scopus subject areas

  • Water Science and Technology

Fingerprint Dive into the research topics of 'Uncertainty of nitrate-N load computations for agricultural watersheds'. Together they form a unique fingerprint.

Cite this