Impacts of rainfall on the water quality of the Newport River Estuary (Eastern North Carolina, USA)

Coulliette, Angela D.; Noble, Rachel T.

doi:10.2166/wh.2008.136

Cited by 64 publications

(50 citation statements)

References 23 publications

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“…Precipitation had stronger links to water quality compared to temperature. Precipitation rapidly drained all kinds of particulate and dissolved materials from the whole catchment into the nearby river [87]. Moreover, anthropogenic biomes were the strong predictors of CEWQ compared to surficial geology because anthropogenic biomes were directly associated with intense human activities.…”

Section: Linear Models Of Terrestrial Determinants and Cewqmentioning

confidence: 99%

How Do Terrestrial Determinants Impact the Response of Water Quality to Climate Drivers?—An Elasticity Perspective on the Water–Land–Climate Nexus

et al. 2017

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Investigating water-land-climate interactions is critical for urban development and watershed management. This study examined this nexus by elasticity and statistical approaches through the lens of three watersheds: The Yukon, Mekong and Murray. Here, this study reports the fundamental characteristics, explanations and ecological and management implications of terrestrial determinant influence on the response of water quality to climate drivers. The stability of the response, measured by climate elasticity of water quality (CEWQ), is highly dependent on terrestrial determinants, with strong impacts from anthropogenic biomes and low impacts from surficial geology. Compared to temperature elasticity, precipitation elasticity of water quality is more unstable due to its possible linkages with many terrestrial determinants. Correlation and linear models were developed for the interaction system, which uncovered many interesting scenarios. The results implied that watersheds with a higher ratio of rangeland biomes have a lower risk of instability as compared to watersheds with a higher proportion of dense settlement, cropland and forested biomes. This study discusses some of the most essential pathways where instability might adversely affect CEWQ parameters and recommends suggestions for policy makers to alleviate the instability impacts to bring sustainability to the water environment.

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Section: Linear Models Of Terrestrial Determinants and Cewqmentioning

confidence: 99%

How Do Terrestrial Determinants Impact the Response of Water Quality to Climate Drivers?—An Elasticity Perspective on the Water–Land–Climate Nexus

et al. 2017

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“…Samples were homogenized and kept under magnetic stirring. Following the procedures described by Feng et al (2002), aliquots were drawn at 10, 20, 30 and 60 min and the most probable number (MPN) of TTC was determined in order to evaluate the efficiency of the treatments. Saline solution at 0.85% was used to make serial decimal dilutions (10-1 to 10-5) of all aliquots.…”

Section: Water Treatmentmentioning

confidence: 99%

“…The occurrence of thermotolerant coliforms (TTC) in natural water bodies has been studied extensively around the world (PITKÄNEN et al, 2008;COULLIETTE and NOBLE, 2008;JAIANI et al, 2013). TTCs colonize the gastrointestinal tract of warm -blooded animals and include species of the genera Escherichia, Klebsiella and Enterobacter (TÔRRES, 2004).…”

Section: Introductionmentioning

confidence: 99%

Thermotolerant Coliform Die-Off in Water Treated With Moringa Oleifera Seeds

Costa

Silva²,

Peixoto³

et al. 2014

C & N

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“…The quantification of the rainfall-runoff process and the resulting NPS pollutants is essential for developing mitigation strategies, which are the basis for watershed management [3]. The rainfall process is the major driving force for NPS, thus rainfall-runoff-pollutant (R-R-P) relationships have become the focus of watershed research [4,5]. Many studies have been conducted in the fields of rainfall-runoff relationships but have rarely involved the runoff-pollutant relationship, especially for the event-based estimation of NPS loads [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

Chen

Cheng

Wang

et al. 2017

Entropy

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Event-based runoff-pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS) prediction and evaluation. The artificial neural network (ANN) was used to extend the runoff-pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.

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Impacts of rainfall on the water quality of the Newport River Estuary (Eastern North Carolina, USA)

Cited by 64 publications

References 23 publications

How Do Terrestrial Determinants Impact the Response of Water Quality to Climate Drivers?—An Elasticity Perspective on the Water–Land–Climate Nexus

How Do Terrestrial Determinants Impact the Response of Water Quality to Climate Drivers?—An Elasticity Perspective on the Water–Land–Climate Nexus

Thermotolerant Coliform Die-Off in Water Treated With Moringa Oleifera Seeds

Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

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