The effect of sampling frequency and strategy on water quality modelling

driven by continuous monitoring data in a boreal catchment

Paweł Marcinkowski1, Mikołaj Piniewski1, Jari Koskiaho2, Sirkka Tattari2

1. Warsaw University of Life Sciences (email: p.marcinkowski@levis.sggw.pl)

2. Finnish Environment Institute

Water quality monitoring

• Two broad categories of water quality sampling:• Base-flow sampling used for routine monitoring

programmes

• Event-based sampling, used for estimating soil and nutrient erosion rates and constituent load exports

• HELCOM (2015) recommends that the riverine water sampling strategy used by monitoring agencies should be designed to cover the entire flow cycle, i.e. low, mean and high flow.

Figure 1: Riverine monitoring during 2010-2016 in Baltic Sea Region (BSR) countries. Source:HELCOM Pollution Load Compilation

Low-frequency sampling is likely to miss many storm events, and thus fail to disclose the close coupling between the hydrological and chemical dynamics…

Examples of sampling strategies

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Tota

l P lo

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Regular sampling, 5th day of every month (12 samples per year)

PtotOBSkg

Water sample taken

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Flow-proprtional sampling (12 samples per year)

PtotOBSkg

Water sample taken

High-frequency monitoring

• These issues can be addressed by field-deployable water quality analysers.

• Automatic monitoring devices produce reliable, high-frequency water quality data, provided that quality assurance is taken care of.

Objective

Evaluating the continuous, six-year-long, high-frequency water quality monitoring data set as a source for the calibration and testing of the SWATmodel in a case study carried out in a medium-sized, boreal catchment in southern Finland.

Tasks

• calibration and validation of the model against thehigh-frequency data set of TSS, NO3-N, TN and TP loads.

• Developing a set of 'sub-sampling' scenariosfocused on quantifying the effect of the sampling frequency (12 vs. 26 samples per year) and strategy (regular vs. flow-proportional sampling) on the model performance and output uncertainty

Study area

• Vantaanjoki catchment

• One of the case study catchments of the Bonus RETURN project (www.bonusreturn.com)

• The river flows through Helsinki metropolitan area of ½ million people huge recreational value Figure 2: Case study location

Major point sources, tributaries & lakes

• Catchment area: 1,680 km2

• 75% of the catchmentenclosed by automatic monitoring station

Land use and soil map

Land use (as in 2012)AGRR 17%FRST 56%HAY 7%URBN 9%UIDU 8%WETL 1%WATR 2%

Soil Clay 45%Moraines 12%Rocky 21%Peat 7%Coarse 12%Water 3%

Pitkäkoski automatic monitoring station

• S::can nitro-lyser sensor(www.s-can.at/products/spectrometer-probes#)

• Hourly data from years 2011–2016, virtually without breaks

• Turbidity (FNU)• NO3-N (mg/l)• TOC & DOC (mg/l)• High correlation between

turbidity and water-sampled total P (and sediment) conversions with linear relationships

Assembling the sensor

in October 2010

y = 1,35x + 1,69R² = 0,82

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Turbidity (FNU), raw sensor data

TSS

y = 2,03x + 45,28R² = 0,73

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Turbidity (FNU), raw sensor data

Ptot

y = 0,61xR² = 0,86

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NO3-N (mg/l), raw sensor data

NO3-N

y = 0,83xR² = 0,87

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NO3-N (mg/l), raw sensor data

Ntot

Raw sensor data vs. water samples

conversions to TSS, Ptot and Ntot

concentrations by linear regression

equations

conversion into average daily loads

Sub-sampling scenarios

• 1. Flow-proportional sampling with 26 samples per year (FP-26)

• 2. Flow-proportional sampling with 12 samples per year (FP-12)

• 3. Regular sampling with 12 samples per year (R-12)

• All tested through6 realizations

Work flow

Modelcalibration

against high-frequency data considered as ‘benchmark’

Sub-sampled observational

data set model calibration using

SUFI-2.

54 scenarios

Comparison of calibration

results based on sub-sampleddata with the benchmark

Model performance

KGE = 0.88 (cal) 0.86 (val) KGE = 0.79 (cal) 0.73 (val)

KGE = 0.76 (cal) 0.72 (val) KGE = 0.83 (cal) 0.69 (val)

Model performance in sub-samplingscenarios

PBIAS vs KGE

Model output uncertainty under sub-sampling scenarios

Conclusions

• The model calibrated against low-frequency data performed worse when evaluated against high-frequency data and compared with the benchmark model for each of the studied water qualityparameters.

• Increasing the sampling frequency from 12 to 26 samples per year led to an improvement in the model performance (as quantified by KGE),particularly for TSS and TP loads.

Conclusions

• Several of the lowest KGE values were usually associated with high over- or under-estimation bias

• In contrast to the sampling frequency, no strong evidence for the effect of sampling strategy on the model performance was found.

• More attention should be paid to calibration data as a source of uncertainty in model predictions

• Evidence for a wider adoption of modern sensors providing high-frequency data

• Evidence for increasing the grab sampling frequency to reduce the uncertainty of loading estimates (particularly in rivers with flashy flow regime)

Funding

• BONUS RETURN has received funding from BONUS (Art 185), funded jointly by the EU and Swedish Foundation for Strategic Environmental ResearchFORMAS, Innovation Fund Denmark, Academy of Finland and National Centre for Research and Development in Poland.