Assessing ways to combat eutrophication in a Chinese drinking water reservoir using SWAT

Nielsen, Anders; Trolle, Dennis; Me, Wang; Ling, Luo; Han, Bo; Liu, Zhengwen; Olesen, Jørgen E.

doi:10.1071/mf12106

Cited by 37 publications

(17 citation statements)

References 55 publications

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“…OP loading was a significant (p < 0.05) predictor of OP concentrations in all four reservoirs. Nielsen et al (2013) observed a similar linear relationship between SWAT-generated phosphate loading and total phosphate concentrations in a reservoir in China. The regression and 95 % prediction limits allow for the estimation of annual loading limits that ensure mean annual OP concentrations fall below certain trophic status indicators.…”

Section: Impact Of Op Effluent Standard Scenarios On Trophic Statussupporting

confidence: 57%

Applying SWAT to predict ortho-phosphate loads and trophic status in four reservoirs in the upper Olifants catchment, South Africa

Dabrowski

2014

Hydrol. Earth Syst. Sci.

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Abstract. Excessive phosphate loading in the upper Olifants River, South Africa has resulted in widespread eutrophication and associated ecosystem health impacts. The majority of sewage treatment works (STWs) are operating poorly and are likely an important source of bioavailable orthophosphate (OP) in the catchment. The Soil Water Assessment Tool (SWAT) was used to identify important sources of OP loading in the catchment and to predict changes in the trophic status of four reservoirs associated with three STW effluent OP standards (the current average of 4 mg L −1 and standards of 1 and 0.1 mg L −1 ). Nash-Sutcliffe and PBIAS statistics showed good model performance for simulations of flow and OP loading using a multi-site calibration and validation approach. Key sub-catchments responsible for high OP loading were identified. Three of the four reservoirs are heavily impacted by OP loading originating from STWs. Two reservoirs show mean annual OP concentrations indicative of eutrophic conditions and a 1 mg L −1 effluent standard would reduce concentrations to well within the mesotrophic range. There was little difference in OP loads and concentrations associated with the 1 and 0.1 mg L −1 effluent standards, indicating that attention to nonpoint sources would be required to realise any benefit associated with the strictest effluent standard. Regression analyses and associated 95 % prediction limits between simulated OP loads and concentrations for all effluent treatment scenarios from 2002 to 2010 allowed for the quantification of OP loading that would ensure a specific trophic status in each reservoir. This study is one of the first to apply SWAT in simulating OP loading and concentrations in large reservoirs, and its application in South Africa provides further support for its utility throughout a wide geographical area.

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Section: Impact Of Op Effluent Standard Scenarios On Trophic Statussupporting

confidence: 57%

Applying SWAT to predict ortho-phosphate loads and trophic status in four reservoirs in the upper Olifants catchment, South Africa

Dabrowski

2014

Hydrol. Earth Syst. Sci.

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“…Nielsen et al, 2013). The SWAT model is physically based and provides distributed descriptions of hydrologic processes at sub-basin scale (Arnold et al, 1998;Neitsch et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Abell

Hamilton

2015

Hydrol. Earth Syst. Sci.

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Abstract. The Soil Water Assessment Tool (SWAT) was configured for the Puarenga Stream catchment (77 km 2 ), Rotorua, New Zealand. The catchment land use is mostly plantation forest, some of which is spray-irrigated with treated wastewater. A Sequential Uncertainty Fitting (SUFI-2) procedure was used to auto-calibrate unknown parameter values in the SWAT model. Model validation was performed using two data sets: (1) monthly instantaneous measurements of suspended sediment (SS), total phosphorus (TP) and total nitrogen (TN) concentrations; and (2) high-frequency (1-2 h) data measured during rainfall events. Monthly instantaneous TP and TN concentrations were generally not reproduced well (24 % bias for TP, 27 % bias for TN, and R 2 < 0.1, NSE < 0 for both TP and TN), in contrast to SS concentrations (< 1 % bias; R 2 and NSE both > 0.75) during model validation. Comparison of simulated daily mean SS, TP and TN concentrations with daily mean dischargeweighted high-frequency measurements during storm events indicated that model predictions during the high rainfall period considerably underestimated concentrations of SS (44 % bias) and TP (70 % bias), while TN concentrations were comparable (< 1 % bias; R 2 and NSE both ∼ 0.5). This comparison highlighted the potential for model error associated with quick flow fluxes in flashy lower-order streams to be underestimated compared with low-frequency (e.g. monthly) measurements derived predominantly from base flow measurements. To address this, we recommend that high-frequency, event-based monitoring data are used to support calibration and validation. Simulated discharge, SS, TP and TN loads were partitioned into two components (base flow and quick flow) based on hydrograph separation. A manual procedure (one-at-a-time sensitivity analysis) was used to quantify parameter sensitivity for the two hydrologically separated regimes. Several SWAT parameters were found to have different sensitivities between base flow and quick flow. Parameters relating to main channel processes were more sensitive for the base flow estimates, while those relating to overland processes were more sensitive for the quick flow estimates. This study has important implications for identifying uncertainties in parameter sensitivity and performance of hydrological models applied to catchments with large fluctuations in stream flow and in cases where models are used to examine scenarios that involve substantial changes to the existing flow regime.

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“…Lake management was also the subject of modelling work done to mitigate P loads and maintain drinking water quality in the subtropical Kaiping (Dashahe) reservoir in China (Nielsen et al 2013). By adapting a surface water catchment model, a powerful scenario analysis was conducted of different mitigation technologies and practices.…”

Section: Linking Nutrient Losses To Effects and Management Across Scalesmentioning

confidence: 99%

Nutrients and eutrophication: introduction

McDowell

Hamilton

2013

Mar. Freshwater Res.

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Assessing ways to combat eutrophication in a Chinese drinking water reservoir using SWAT

Cited by 37 publications

References 55 publications

Applying SWAT to predict ortho-phosphate loads and trophic status in four reservoirs in the upper Olifants catchment, South Africa

Applying SWAT to predict ortho-phosphate loads and trophic status in four reservoirs in the upper Olifants catchment, South Africa

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Nutrients and eutrophication: introduction

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