Journal ArticleDOI
Cumulative uncertainty in measured streamflow and water quality data for small watersheds
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TLDR
In this article, a root mean square error propagation method was used to compare the uncertainty introduced by each procedural category, and then the error propagation was employed to determine the cumulative probable uncertainty in measured streamflow, sediment and nutrient data.Abstract:
The scientific community has not established an adequate understanding of the uncertainty inherent in measured
water quality data, which is introduced by four procedural categories: streamflow measurement, sample collection, sample
preservation/storage, and laboratory analysis. Although previous research has produced valuable information on relative
differences in procedures within these categories, little information is available that compares the procedural categories or
presents the cumulative uncertainty in resulting water quality data. As a result, quality control emphasis is often misdirected,
and data uncertainty is typically either ignored or accounted for with an arbitrary margin of safety. Faced with the need for
scientifically defensible estimates of data uncertainty to support water resource management, the objectives of this research
were to: (1) compile selected published information on uncertainty related to measured streamflow and water quality data
for small watersheds, (2) use a root mean square error propagation method to compare the uncertainty introduced by each
procedural category, and (3) use the error propagation method to determine the cumulative probable uncertainty in measured
streamflow, sediment, and nutrient data. Best case, typical, and worst case “data quality” scenarios were examined. Averaged
across all constituents, the calculated cumulative probable uncertainty (±%) contributed under typical scenarios ranged
from 6% to 19% for streamflow measurement, from 4% to 48% for sample collection, from 2% to 16% for sample
preservation/storage, and from 5% to 21% for laboratory analysis. Under typical conditions, errors in storm loads ranged
from 8% to 104% for dissolved nutrients, from 8% to 110% for total N and P, and from 7% to 53% for TSS. Results indicated
that uncertainty can increase substantially under poor measurement conditions and limited quality control effort. This
research provides introductory scientific estimates of uncertainty in measured water quality data. The results and procedures
presented should also assist modelers in quantifying the “quality” of calibration and evaluation data sets, determining model
accuracy goals, and evaluating model performance.read more
Citations
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Journal ArticleDOI
Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
Daniel N. Moriasi,Jeffrey G. Arnold,M. W. Van Liew,Ronald L. Bingner,R. D. Harmel,Tamie L. Veith +5 more
TL;DR: In this paper, the authors present guidelines for watershed model evaluation based on the review results and project-specific considerations, including single-event simulation, quality and quantity of measured data, model calibration procedure, evaluation time step, and project scope and magnitude.
Journal ArticleDOI
The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions
TL;DR: The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the USDA Agricultural Research Service (ARS) and has gained international acceptance as a robust interdisciplinary watershed modeling tool.
Posted Content
Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions, The
TL;DR: The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the U.S. Department of Agriculture (USDA), Agricultural Research Service.
Journal ArticleDOI
SWAT: Model Use, Calibration, and Validation
Jeffrey G. Arnold,Daniel N. Moriasi,Philip W. Gassman,Karim C. Abbaspour,Michael J. White,Raghavan Srinivasan,C. Santhi,R. D. Harmel,A. van Griensven,M. W. Van Liew,Narayanan Kannan,Manoj Jha +11 more
TL;DR: The SWAT-CUP tool as discussed by the authors is a semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration, and is used to provide statistics for goodness-of-fit.
Journal ArticleDOI
Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria
TL;DR: In this paper, a meta-analysis of performance data reported in recent peer-reviewed literature for three widely published watershed-scale models (SWAT, HSPF, WARMF), and one field-scale model (ADAPT) is performed.
References
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Journal ArticleDOI
The future of distributed models: model calibration and uncertainty prediction.
Keith Beven,Andrew Binley +1 more
TL;DR: The GLUE procedure works with multiple sets of parameter values and allows that, within the limitations of a given model structure and errors in boundary conditions and field observations, different sets of values may be equally likely as simulators of a catchment.
Journal ArticleDOI
Water quality modeling: A review of the analysis of uncertainty
TL;DR: A review of the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction can be found in this paper, where four problem areas are examined in detail: uncertainty about model structure, uncertainty in estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model.
Journal ArticleDOI
Quality assurance in the analysis of foods for trace constituents
TL;DR: An examination of the results of over 50 interlaboratory collaborative studies conducted by the AOAC on various commodities for numerous analytes shows a relationship between the mean coefficient of variation (CV) and the mean concentration measured, expressed as powers of 10, independent of the determinative method.
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