Rating curve based assessment of seasonal variability of sulfate in streamflow
Though constituent concentrations and loads in rivers exhibit apparent seasonal fluctuations, they are characterized by event-driven nature of the fluctuations in respond to natural processes and seasonal anthropogenic activities. This study aimed at establishing relationship between streamflow and...
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| Veröffentlicht in: | Environmental monitoring and assessment 2018-08, Vol.190 (8), p.493-9, Article 493 |
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| description | Though constituent concentrations and loads in rivers exhibit apparent seasonal fluctuations, they are characterized by event-driven nature of the fluctuations in respond to natural processes and seasonal anthropogenic activities. This study aimed at establishing relationship between streamflow and sulfate load in Gin River, the major water source in southern Sri Lanka and assessing seasonal sulfate levels in the streamflow following monsoon pattern and cultivation seasons. Rating curve, a load-streamflow regression model, was developed using adjusted maximum likelihood estimation. Following the assumptions of model fit, the regression model showed low correlation among explanatory variables and good empirical agreement with the measured data exhibiting its applicability to deduce sulfate loads from streamflow data, during non-sampling periods. Sulfate loads, highly dependent on streamflow, peaked annually in April–June (south-west monsoon contributing to
Yala
cultivation season) and October–December (north-east monsoon contributing to
Maha
cultivation season), following the bimodal monsoon pattern in the catchment. Median sulfate load exhibited fourfold increase from the lowest value 8,888 kg/day in August (non-cultivation season) to the highest value 38,185 kg/day in November (
Maha
cultivation season), despite the twofold increase of median streamflow between the two months. Flow-weighted sulfate concentrations showed varying flow dependence attributed to the seasonality. At low streamflows (above 70th percentile), sulfate concentration and streamflow were inversely related and at high streamflows (below 30th percentile), and sulfate concentration and streamflow were directly related. Elevated sulfate concentrations attributed to less soluble sulfate irons were clearly evident during the two cultivation seasons which coincided with the monsoon periods. |
| doi_str_mv | 10.1007/s10661-018-6863-4 |
| format | Article |
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Yala
cultivation season) and October–December (north-east monsoon contributing to
Maha
cultivation season), following the bimodal monsoon pattern in the catchment. Median sulfate load exhibited fourfold increase from the lowest value 8,888 kg/day in August (non-cultivation season) to the highest value 38,185 kg/day in November (
Maha
cultivation season), despite the twofold increase of median streamflow between the two months. Flow-weighted sulfate concentrations showed varying flow dependence attributed to the seasonality. At low streamflows (above 70th percentile), sulfate concentration and streamflow were inversely related and at high streamflows (below 30th percentile), and sulfate concentration and streamflow were directly related. Elevated sulfate concentrations attributed to less soluble sulfate irons were clearly evident during the two cultivation seasons which coincided with the monsoon periods.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-018-6863-4</identifier><identifier>PMID: 30066145</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Anthropogenic factors ; Atmospheric Protection/Air Quality Control/Air Pollution ; Catchment area ; Cultivation ; Dependence ; Earth and Environmental Science ; Ecology ; Ecotoxicology ; Environment ; Environmental Management ; Environmental monitoring ; Environmental science ; Fluctuations ; Hydrologic data ; Load ; Loads (forces) ; Maximum likelihood estimation ; Monitoring/Environmental Analysis ; Monsoons ; Regression models ; Rivers ; Seasonal variability ; Seasonal variation ; Seasonal variations ; Seasonality ; Seasons ; Stream discharge ; Stream flow ; Streamflow data ; Sulfates ; Wind</subject><ispartof>Environmental monitoring and assessment, 2018-08, Vol.190 (8), p.493-9, Article 493</ispartof><rights>Springer Nature Switzerland AG 2018</rights><rights>Environmental Monitoring and Assessment is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-83664ff641ed6956e51a60cb85d9b7cbb60952f68a50de2f864a20982021fe0a3</citedby><cites>FETCH-LOGICAL-c372t-83664ff641ed6956e51a60cb85d9b7cbb60952f68a50de2f864a20982021fe0a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-018-6863-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-018-6863-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30066145$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wickramaarachchi, Thushara Navodani</creatorcontrib><title>Rating curve based assessment of seasonal variability of sulfate in streamflow</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>Though constituent concentrations and loads in rivers exhibit apparent seasonal fluctuations, they are characterized by event-driven nature of the fluctuations in respond to natural processes and seasonal anthropogenic activities. This study aimed at establishing relationship between streamflow and sulfate load in Gin River, the major water source in southern Sri Lanka and assessing seasonal sulfate levels in the streamflow following monsoon pattern and cultivation seasons. Rating curve, a load-streamflow regression model, was developed using adjusted maximum likelihood estimation. Following the assumptions of model fit, the regression model showed low correlation among explanatory variables and good empirical agreement with the measured data exhibiting its applicability to deduce sulfate loads from streamflow data, during non-sampling periods. Sulfate loads, highly dependent on streamflow, peaked annually in April–June (south-west monsoon contributing to
Yala
cultivation season) and October–December (north-east monsoon contributing to
Maha
cultivation season), following the bimodal monsoon pattern in the catchment. Median sulfate load exhibited fourfold increase from the lowest value 8,888 kg/day in August (non-cultivation season) to the highest value 38,185 kg/day in November (
Maha
cultivation season), despite the twofold increase of median streamflow between the two months. Flow-weighted sulfate concentrations showed varying flow dependence attributed to the seasonality. At low streamflows (above 70th percentile), sulfate concentration and streamflow were inversely related and at high streamflows (below 30th percentile), and sulfate concentration and streamflow were directly related. Elevated sulfate concentrations attributed to less soluble sulfate irons were clearly evident during the two cultivation seasons which coincided with the monsoon periods.</description><subject>Anthropogenic factors</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Catchment area</subject><subject>Cultivation</subject><subject>Dependence</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental monitoring</subject><subject>Environmental science</subject><subject>Fluctuations</subject><subject>Hydrologic data</subject><subject>Load</subject><subject>Loads (forces)</subject><subject>Maximum likelihood estimation</subject><subject>Monitoring/Environmental Analysis</subject><subject>Monsoons</subject><subject>Regression models</subject><subject>Rivers</subject><subject>Seasonal variability</subject><subject>Seasonal variation</subject><subject>Seasonal variations</subject><subject>Seasonality</subject><subject>Seasons</subject><subject>Stream discharge</subject><subject>Stream flow</subject><subject>Streamflow 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Yala
cultivation season) and October–December (north-east monsoon contributing to
Maha
cultivation season), following the bimodal monsoon pattern in the catchment. Median sulfate load exhibited fourfold increase from the lowest value 8,888 kg/day in August (non-cultivation season) to the highest value 38,185 kg/day in November (
Maha
cultivation season), despite the twofold increase of median streamflow between the two months. Flow-weighted sulfate concentrations showed varying flow dependence attributed to the seasonality. At low streamflows (above 70th percentile), sulfate concentration and streamflow were inversely related and at high streamflows (below 30th percentile), and sulfate concentration and streamflow were directly related. Elevated sulfate concentrations attributed to less soluble sulfate irons were clearly evident during the two cultivation seasons which coincided with the monsoon periods.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>30066145</pmid><doi>10.1007/s10661-018-6863-4</doi><tpages>9</tpages></addata></record> |
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| ispartof | Environmental monitoring and assessment, 2018-08, Vol.190 (8), p.493-9, Article 493 |
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| subjects | Anthropogenic factors Atmospheric Protection/Air Quality Control/Air Pollution Catchment area Cultivation Dependence Earth and Environmental Science Ecology Ecotoxicology Environment Environmental Management Environmental monitoring Environmental science Fluctuations Hydrologic data Load Loads (forces) Maximum likelihood estimation Monitoring/Environmental Analysis Monsoons Regression models Rivers Seasonal variability Seasonal variation Seasonal variations Seasonality Seasons Stream discharge Stream flow Streamflow data Sulfates Wind |
| title | Rating curve based assessment of seasonal variability of sulfate in streamflow |
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