The development of a novel approach for assessment of the first flush in urban stormwater discharges

2010 ◽  
Vol 61 (10) ◽  
pp. 2681-2688 ◽  
Author(s):  
P. M. Bach ◽  
D. T. McCarthy ◽  
A. Deletic
Keyword(s):  
Weighted Average ◽  
Quality Data ◽  
Background Concentration ◽  
First Flush ◽  
Storm Events ◽  
Sensitivity Testing ◽  
Water Quality Data ◽  
Runoff Volume ◽  
Novel Approach ◽  
The Absolute

The management of stormwater pollution has placed particular emphasis on the first flush phenomenon. However, definition and current methods of analyses of the phenomena contain serious limitations, the most important being their inability to capture a possible impact of the event size (total event volume) on the first flush. This paper presents the development of a novel approach in defining and assessing the first flush that should overcome these problems. The phenomenon is present in a catchment if the decrease in pollution concentration with the absolute cumulative volume of runoff from the catchment is statistically significant. Using data from seven diverse catchments around Melbourne, Australia, changes in pollutant concentrations for Total Suspended Solids (TSS) and Total Nitrogen (TN) were calculated over the absolute cumulative runoff and aggregated from a collection of different storm events. Due to the discrete nature of the water quality data, each concentration was calculated as a flow-weighted average at 2 mm runoff volume increments. The aggregated concentrations recorded in each increment (termed as a ‘slice’ of runoff) were statistically compared to each other across the absolute cumulative runoff volume. A first flush is then defined as the volume at which concentrations reach the ‘background concentration’ (i.e. the statistically significant minimum). Initial results clearly highlight first flush and background concentrations in all but one catchment supporting the validity of this new approach. Future work will need to address factors, which will help assess the first flush's magnitude and volume. Sensitivity testing and correlation with catchment characteristics should also be undertaken.

Effects of sampling frequency on estimation accuracies of annual loadings for water quality parameters in different sized watersheds

2020 ◽  
Vol 55 (3) ◽  
pp. 261-277
Author(s):  
Lin Gao ◽  
Junyu Qi ◽  
Sheng Li ◽  
Glenn Benoy ◽  
Zisheng Xing ◽  
...  
Keyword(s):  
Water Quality ◽  
Suspended Solids ◽  
Nitrate Nitrogen ◽  
Sampling Frequency ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Estimation Errors ◽  
Water Quality Data ◽  
Novel Approach

Abstract Potential errors or uncertainties of annual loading estimations for water quality parameters such as suspended solids (SS), nitrate-nitrogen (NO3-N), ortho-phosphorus (Ortho-P), potassium (K), calcium (Ca), and magnesium (Mg) can be greatly affected by sampling frequencies. In this study, annual loading estimation errors were assessed in terms of the coefficient of variation, relative bias, and probability of potential errors that were estimated with statistical samples taken at a series of sampling frequencies for a watershed in northwestern New Brunswick, Canada, and one of its sub-watersheds. Results indicate that annual loading estimation errors increased with decreasing sampling frequency for all water quality parameters. At the same sampling frequencies, the estimation errors were several times greater for the smaller watershed than those for the larger watershed, possibly due to the flushing nature of streamflows in the smaller watershed. We also found that low sampling frequency tended to underestimate the annual loadings of water quality parameters dominated by stormflow events (SS and K) and overestimate water quality parameters dominated by baseflow (Mg and Ca). These results can be used by hydrologists and water quality managers to determine sampling frequencies that minimize costs while providing acceptable estimation errors. This study also demonstrates a novel approach to assess potential errors when analyzing existing water quality data.

Effect of Hydrologic Export on Soil Carbon Turnover Rates

2020 ◽  
Author(s):  
Oleksandra Hararuk ◽  
Stuart Jones ◽  
Christopher Solomon
Keyword(s):  
Weighted Average ◽  
The United States ◽  
Substantial Effect ◽  
Quality Data ◽  
United States Geological Survey ◽  
Turnover Rates ◽  
Water Quality Data ◽  
Soil C ◽  
C Cycle ◽  
Soc Stocks

<p>Soil is the largest terrestrial carbon (C) reservoir and is an important component of climate-carbon feedbacks, potentially sequestering or releasing large amounts CO<sub>2</sub> from or to the atmosphere. In global land models soil C dynamics is determined by the long-term balance between C inputs and turnover rates, and the latter are usually a function of soil texture, temperature, and soil moisture, which represents environmental limitation of microbial soil organic carbon (SOC) mineralization. Hydrologic C export is often overlooked in the terrestrial C cycle models, likely because proportionally soils contain a very small amount of C that can be exported with runoff, contributing around 2.9 Pg C yr<sup>-1</sup> to aquatic systems globally. However, ignoring hydrologic C export in areas, where it has substantial effect on SOC turnover rate, could result in systematic overestimation of SOC stocks and inaccurate simulation of SOC responses to changing environmental conditions. We combined water quality data from the United States Geological Survey with hydrologic and soil chemistry data products to estimate the relative contribution of hydrologic export to bulk soil turnover rates across the continental USA. The catchment area weighted average of hydrologic export effect on SOC turnover was 5.2%. Hydrologic export accounted for 0-2% of the bulk SOC turnover in arid regions, 2-15% - in forests, and 20-40% - in wetland-rich areas. The SOC stocks generated for the continental U.S. using microbe-mediated turnover alone amounted to 88.3 Pg C and were 15.4% higher than the amount reported in the Harmonized World Soil Database (76.5 Pg C), thus illustrating the importance of accounting for hydrologic C export when simulating SOC dynamics.</p>

Water-quality data collected at Lake Anne, Reston, Virginia, 1997-1999

2000 ◽  
Author(s):  
Kathryn M. Conko ◽  
Margaret M. Kennedy ◽  
Karen C. Rice
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Water Quality Data
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