SPARROW MODELING - Enhancing Understanding of the Nation's Water Quality

Preston, Stephen D.; Alexander, Richard B.; Woodside, Michael D.; Hamilton, Pixie A.

doi:10.3133/fs20093019

Cited by 40 publications

(43 citation statements)

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“…While other techniques for riverine nutrient export estimation exist (e.g., Alexander et al ., ; Preston et al ., ) and other approaches to model water fluxes in the BSDB have been developed (e.g., Graham and Bergström, ; Reckermann et al ., ; Arheimer et al ., ; Meier et al ., ), the NANI methodology coupled with the CSIM framework (with some further consideration of the results presented here) could offer a straightforward coherent approach for application at the sub‐annual and subbasin scale. The consistent database embedded within the CSIM framework paired with a general, parsimonious modeling approach makes for a good testing ground to explore various management strategies and development scenarios, and to isolate clear cause‐effect relationships.…”

Section: Discussionmentioning

confidence: 99%

Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model

Lyon

Meidani

Velde

et al. 2014

J American Water Resour Assoc

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This study evaluates the ability of the Catchment SIMulation (CSIM) hydrologic model to describe seasonal and regional variations in river discharge over the entire Baltic Sea drainage basin (BSDB) based on 31 years of monthly simulation from 1970 through 2000. To date, the model has been successfully applied to simulate annual fluxes of water from the catchments draining into the Baltic Sea. Here, we consider spatiotemporal bias in the distribution of monthly modeling errors across the BSDB since it could potentially reduce the fidelity of predictions and negatively affect the design and implementation of land-management strategies. Within the period considered, the CSIM model accurately reproduced the annual flows across the BSDB; however, it tended to underpredict the proportion of discharge during high-flow periods (i.e., spring months) and overpredict during the summer low flow periods. While the general overpredictions during summer periods are spread across all the subbasins of the BSDB, the underprediction during spring periods is seen largely in the northern regions. By implementing a genetic algorithm calibration procedure and/or seasonal parameterization of subsurface water flows for a subset of the catchments modeled, we demonstrate that it is possible to improve the model performance albeit at the cost of increased parameterization and potential loss of parsimony.(KEY TERMS: Baltic Sea drainage basin; Baltic NEST; hydrological modeling; generalized watershed loading function (GWLF).)

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Section: Discussionmentioning

confidence: 99%

Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model

Lyon

Meidani

Velde

et al. 2014

J American Water Resour Assoc

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“…Watersheds contributing phosphorus to the Gulf of Mexico. (58) samples from the alluvial aquifer ( fig. 6-6) are higher than concentrations commonly found in groundwater and could be a source of phosphorus to streams in the lower Mississippi watershed.…”

Section: Elevated Concentrations Of Nutrients In Groundwater Can Impamentioning

confidence: 99%

The quality of our Nation's waters: water quality in the Mississippi embayment-Texas coastal uplands aquifer system and Mississippi River Valley alluvial aquifer, south-central United States, 1994-2008

Kingsbury¹,

Barlow²,

Katz³

et al. 2015

Circular

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Both samples were clear, colorless, transparent and without odor, having soft, pleasant taste, even at the temperature of 80 degrees Fahr., when the experiment was made. They contained a small quantity of pulverulent, rusty sediment, which under the microscope was found to consist of exceedingly minute particles of sand with darker particles of iron. No organic debris was discovered, and only a trace of infusorial life. This water is somewhat similar in its character to that of a rain water which has been shed from a clean roof and has undergone the natural purifying process which takes place during storage in a sound, clean, underground cistern, and as such is fit for all domestic and economic purposes. There are, however, few cistern waters, perhaps none, that are as pure as this water. Its freedom from organic matter is such that I have rarely met with natural water which gives less response to the tests for organic matter. I know of no city which has a water supply as pure as the samples now reported on.

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“…An alternative approach is to use models such as the USGS SPARROW (SPAtially Referenced Regression On Watershed attributes) models [ 29 - 31 ] to estimate nutrient loads to lakes. SPARROW models are watershed based models that estimate nutrient loads to streams based on landscape characteristics and known or estimated nutrient sources [ 30 ]. An example of this is the Northeast SPARROW model [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Lake Depth and Volume

et al. 2013

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Global nutrient cycles have been altered by the use of fossil fuels and fertilizers resulting in increases in nutrient loads to aquatic systems. In the United States, excess nutrients have been repeatedly reported as the primary cause of lake water quality impairments. Setting nutrient criteria that are protective of a lakes ecological condition is one common solution; however, the data required to do this are not always easily available. A useful solution for this is to combine available field data (i.e., The United States Environmental Protection Agency (USEPA) National Lake Assessment (NLA)) with average annual nutrient load models (i.e., USGS SPARROW model) to estimate summer concentrations across a large number of lakes. In this paper we use this combined approach and compare the observed total nitrogen (TN) and total phosphorus (TN) concentrations in Northeastern lakes from the 2007 National Lake Assessment to those predicted by the Northeast SPARROW model. We successfully adjusted the SPARROW predictions to the NLA observations with the use of Vollenweider equations, simple input-output models that predict nutrient concentrations in lakes based on nutrient loads and hydraulic residence time. This allows us to better predict summer concentrations of TN and TP in Northeastern lakes and ponds. On average we improved our predicted concentrations of TN and TP with Vollenweider models by 18.7% for nitrogen and 19.0% for phosphorus. These improved predictions are being used in other studies to model ecosystem services (e.g., aesthetics) and dis-services (e.g. cyanobacterial blooms) for ~18,000 lakes in the Northeastern United States.

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SPARROW MODELING - Enhancing Understanding of the Nation's Water Quality

Cited by 40 publications

References 0 publications

Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model

Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model

The quality of our Nation's waters: water quality in the Mississippi embayment-Texas coastal uplands aquifer system and Mississippi River Valley alluvial aquifer, south-central United States, 1994-2008

Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Lake Depth and Volume

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