Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in Sweden
Accurate monitoring data of phosphorus concentrations in the water will benefit the evaluation of measures taken to reduce the problems of eutrophication. Today, the quantification of phosphorus loads from diffuse sources involves large uncertainties. Traditional grab sampling could underestimate th...
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| Formato: | H2 |
| Lenguaje: | Inglés sueco |
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SLU/Dept. of Aquatic Sciences and Assessment
2017
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| Materias: |
| _version_ | 1855571717883166720 |
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| author | Hoang, Cham |
| author_browse | Hoang, Cham |
| author_facet | Hoang, Cham |
| author_sort | Hoang, Cham |
| collection | Epsilon Archive for Student Projects |
| description | Accurate monitoring data of phosphorus concentrations in the water will benefit the evaluation of measures taken to reduce the problems of eutrophication. Today, the quantification of phosphorus loads from diffuse sources involves large uncertainties. Traditional grab sampling could underestimate the loads exported from diffuse sources due to the flow dependence of particulate phosphorus (PP) concentrations. Technical innovations allow continuous monitoring of certain parameters in the water and by that generate data of high temporal resolution. These parameters have the potential to act as surrogate measurements for total phosphorus (TP). One parameter that is often argued to have strong correlations with total phosphorus is turbidity, which might reflect the PP fraction in the water. However, poorer correlations have also been reported. Possibly due to land-use or soil type within the catchments. By finding landscape factors related to good/poor performance of in-situ sensors, in the prediction of TP, will benefit the strategical planning of monitoring programs. In this study, data from available sensor parameters were used to create regression models for TP at 194 monitoring stations in Sweden. Turbidity, water temperature and total organic carbon were most frequently included in the significant regression models. The correlation coefficient from the most significant TP regression model at each station was compiled into a response dataset for multivariate analysis. The correlation coefficients were then predicted with landscape data from the catchments of the monitoring stations with multivariate analysis. A separate dataset of landscape data within 100 m buffer zone was also used for comparison. The results indicated weak associations between the landscape factors and the performance of the TP prediction models. The landscape factor of most significance relation to high correlation coefficients was forest on mire, whereas water and open wetlands along the buffer zone were related to lower correlation coefficient values. No preferences were found between significant catchment factors and sensor parameters included in the regression model at the stations. Data of the whole catchment explained larger variations in the TP regression model’s strength than the buffer zone data, possibly due to low data resolution in the buffer zone. |
| format | H2 |
| id | RepoSLU10597 |
| institution | Swedish University of Agricultural Sciences |
| language | Inglés swe |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher | SLU/Dept. of Aquatic Sciences and Assessment |
| publisherStr | SLU/Dept. of Aquatic Sciences and Assessment |
| record_format | eprints |
| spelling | RepoSLU105972017-08-17T14:03:33Z Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in Sweden Hoang, Cham surface water monitoring surrogate parameters total Phosphorous turbidity catchment land-use soil type Accurate monitoring data of phosphorus concentrations in the water will benefit the evaluation of measures taken to reduce the problems of eutrophication. Today, the quantification of phosphorus loads from diffuse sources involves large uncertainties. Traditional grab sampling could underestimate the loads exported from diffuse sources due to the flow dependence of particulate phosphorus (PP) concentrations. Technical innovations allow continuous monitoring of certain parameters in the water and by that generate data of high temporal resolution. These parameters have the potential to act as surrogate measurements for total phosphorus (TP). One parameter that is often argued to have strong correlations with total phosphorus is turbidity, which might reflect the PP fraction in the water. However, poorer correlations have also been reported. Possibly due to land-use or soil type within the catchments. By finding landscape factors related to good/poor performance of in-situ sensors, in the prediction of TP, will benefit the strategical planning of monitoring programs. In this study, data from available sensor parameters were used to create regression models for TP at 194 monitoring stations in Sweden. Turbidity, water temperature and total organic carbon were most frequently included in the significant regression models. The correlation coefficient from the most significant TP regression model at each station was compiled into a response dataset for multivariate analysis. The correlation coefficients were then predicted with landscape data from the catchments of the monitoring stations with multivariate analysis. A separate dataset of landscape data within 100 m buffer zone was also used for comparison. The results indicated weak associations between the landscape factors and the performance of the TP prediction models. The landscape factor of most significance relation to high correlation coefficients was forest on mire, whereas water and open wetlands along the buffer zone were related to lower correlation coefficient values. No preferences were found between significant catchment factors and sensor parameters included in the regression model at the stations. Data of the whole catchment explained larger variations in the TP regression model’s strength than the buffer zone data, possibly due to low data resolution in the buffer zone. Miljöövervakningen av sjöar och vattendrag är ett sätt för oss att ta tempen på rådande trender, bra liksom dåliga. Ett rådande problem är övergödningen av sjöar och vattendrag som orsakas av utsläpp av näringsämnen från jordbruk, skogsbruk, reningsverk, avlopp och tätorter. Vattenmätningar som ofta tas vid utvalda tillfällen riskerar att missa ökade koncentrationer av partikulär fosfor vid plötsligt ökad avrinning. Idag finns det möjlighet att ta kontinuerliga prover med sensorer av vissa parametrar i vatten. Provresultaten från dessa parametrar kan användas för beräkning av missade höga halter av t.ex. fosfor på grund av ökad avrinning. Det har t.ex. påvisats starka samband mellan turbiditet (ett mått vattnets grumlighet) och partikulär fosfor i vattendrag. I den här studien testades olika kombinationer av tillgängliga sensorparameterar, turbiditet, elektrisk konduktivitet, organisk kol, vattentemperatur och pH, för att hitta den optimala beräkningsmodellen av total fosfor för 194 mätstationer i Sverige. De parametrar som oftast förekom bland de optimala beräkningsmodellerna var turbiditet, organisk kol och vattentemperatur. Starka samband mellan organisk kol och total fosfor kan innebära en stor mängd organisk fosfor i den totala halten. Vattentemperaturen kan reflektera en säsongsbaserad fosforkoncentration i vattnet. Lägre flöden i vattendrag under sommaren kan bidra till högre koncentrationer av löst fosfor från reningsverk eller enskilda avlopp. En mindre andel stationer fick beräkningsmodeller med låg förklaringsgrad, vilket kan bero på olika markanvändningstyper inom stationernas avrinningsområden. Markanvändning och jordtyper inom stationernas avrinningsområden beräknades tillsammans med fosformodellernas förklaringsgrad. Resultatet visade att skog på myr och kalhyggen ofta förekom tillsammans i avrinningsområdena och en stor andel av dessa ofta sammanföll med starka fosformodeller. Fosfor kan exporteras till vattendrag från blöta dikade kalhyggen, och då främst i organisk form, vilket kan förklara varför organisk kol ofta var inkluderad i de starka fosformodellerna. Ett negativt samband hittades också mellan starka fosformodeller och större andel vatten och öppen våtmark. En förklaring är att sjöar och öppna våtmarker har en förmåga att bromsa ner effekten av höga flöden som bär med sig partikulär fosfor. SLU/Dept. of Aquatic Sciences and Assessment 2017 H2 eng swe https://stud.epsilon.slu.se/10597/ |
| spellingShingle | surface water monitoring surrogate parameters total Phosphorous turbidity catchment land-use soil type Hoang, Cham Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in Sweden |
| title | Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in
Sweden |
| title_full | Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in
Sweden |
| title_fullStr | Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in
Sweden |
| title_full_unstemmed | Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in
Sweden |
| title_short | Landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in
Sweden |
| title_sort | landscape factors related to performance of in-situ sensors for prediction of total phosphorus : a statistical analysis of data from 194 streams in
sweden |
| topic | surface water monitoring surrogate parameters total Phosphorous turbidity catchment land-use soil type |