Predicting Gran alkalinity and calcium concentrations in river waters over a national scale using a novel modification to the G-BASH model

Cresser, Malcolm S.; Ahmed, Nayan; Smart, Richard P.; Arowolo, T. A.; Calver, Louise J.; Chapman, P. J.

doi:10.1016/j.envpol.2005.11.020

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…A comparison of our results with the earlier G‐BASH models based on continuous characterizations of geology demonstrates both the advantages of the G‐BASH approach, and its limitations. The G‐BASH model performed well when applied to subcatchments within the River Dee basin [ Cresser et al , 2000; Smart et al , 2001], but application to another basin by Cresser et al [2006] produced systematic overpredictions. Once differences in dilution due to runoff were accounted for and the model reparameterized with data from both locations, the model predicted Ca and Gran alkalinity with slightly more precision than our models.…”

Section: Discussionmentioning

confidence: 99%

“…G‐BASH models were based on the measured CaO or MgO content of each formation mapped at 1:50,000, whereas our models used average lithology values for map units often consisting of multiple formations mapped at 1:250,000 or greater. This difference in approach occurred partly because Cresser et al [2006] had access to high‐resolution geologic data and partly because of the practical limitations of applying that resolution to an area 20 times larger than the one used by Cresser et al The other key difference in approaches is our explicit inclusion of other geologic and environmental factors in our models as opposed to the post hoc correction for differences in precipitation applied by Cresser et al [2006]. The limited amount of climatic variation within the study area of Cresser et al also reduced the need to account for variations in temperature or vegetation.…”

Section: Discussionmentioning

confidence: 99%

“…We also characterized possible spatial interactions between geology and climate by dividing the derived grids of rock chemical properties (section 2.1) by the amount of precipitation within each grid cell. Atmospheric deposition can also be an important driver of stream chemistry, especially near coasts [ Cresser et al , 2006] and urban areas [ Chae et al , 2004]. We therefore calculated long‐term average atmospheric wet deposition from data obtained from the National Atmospheric Deposition Program National Trends Network.…”

Section: Methodsmentioning

confidence: 99%

“…To overcome the inherent limitations of process‐based approaches in predicting spatial variation in water chemistry, Cresser et al [2000] and Smart et al [2001] developed the empirical G‐BASH model to predict water chemistry attributes for the River Dee in Scotland from rock geochemistry. They subsequently underscored the need to also account for variation in climate and atmospheric deposition when applying their model to other catchments [ Cresser et al , 2006]. Other empirical models have been developed to predict spatial variation in water chemistry across regions from land use data, but these models primarily predict water chemistry variation associated with differences in land use, not variation in natural background conditions.…”

Section: Introductionmentioning

confidence: 99%

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Predicting natural base‐flow stream water chemistry in the western United States

Olson

Hawkins

2012

Water Resources Research

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[1] Robust predictions of stream solute concentrations expected under natural (reference) conditions would help establish more realistic water quality standards and improve stream ecological assessments. Models predicting solute concentrations from environmental factors would also help identify the relative importance of different factors that influence water chemistry. Although data are available describing the major factors controlling water chemistry (i.e., geology, climate, atmospheric deposition, soils, vegetation, topography), geologic maps do not adequately convey how rocks vary in their chemical and physical properties. We addressed this issue by associating rock chemical and physical properties with geological map units to produce continuous maps of percentages of CaO, MgO, S, uniaxial compressive strength, and hydraulic conductivity for western United States lithologies. We used catchment summaries of these geologic properties and other environmental factors to develop multiple linear regression (LR) and random forest (RF) models to predict base flow electrical conductivity (EC), acid neutralization capacity (ANC), Ca, Mg, and SO 4 . Models were derived from observations at 1414 reference-quality streams. RF models were superior to LR models, explaining 71% of the variance in EC, 61% in ANC, 92% in Ca, 58% in Mg, and 74% in SO 4 when assessed with independent observations. The root-mean-square error for predictions on validation sites were all <11% of the range of observed values. The relative importance of different environmental factors in predicting stream chemistry varied among models, but on average rock chemistry > temperature > precipitation > soil ¼ atmospheric deposition > vegetation > amount of rock/water contact > topography.Citation: Olson, J. R., and C. P. Hawkins (2012), Predicting natural base-flow stream water chemistry in the western United States,

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting natural base‐flow stream water chemistry in the western United States

Olson

Hawkins

2012

Water Resources Research

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“…), and the River Dee‐based G‐BASH model (Cresser et al. ), as well as for poor transfers of model parameters (e.g., Duan et al. , Gan and Burges ).…”

Section: Discussionmentioning

confidence: 99%

Model application niche analysis: assessing the transferability and generalizability of ecological models

et al. 2017

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. 2017. Model application niche analysis: assessing the transferability and generalizability of ecological models. Ecosphere 8(10):e01974. 10. 1002/ecs2.1974 Abstract. The use of models by ecologists and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 yr. Due to logistical, economical, and theoretical benefits, model users frequently transfer preexisting models to new sites where data are scarce. Modelers have made significant progress in understanding how to improve model generalizability during model development. However, models are always imperfect representations of systems and are constrained by the contextual frameworks used during their development. Thus, model users need better ways to evaluate the possibility of unintentional misapplication when transferring models to new sites. We propose a method of describing a model's application niche for use during the model selection process. Using this method, model users synthesize information from databases, past studies, and/or past model transfers to create model performance curves and heat maps. We demonstrated this method using an empirical model developed to predict the ecological condition of plant communities in riverine wetlands of the Appalachian Highland physiographic region, USA. We assessed this model's transferability and generalizability across (1) riverine wetlands in the contiguous United States, (2) wetland types in the Appalachian Highland physiographic region, and (3) wetland types in the contiguous United States. With this methodology and a discussion of its critical steps, we set the stage for further inquiries into the development of consistent and transparent practices for model selection when transferring a model.

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A spatial and seasonal assessment of river water chemistry across North West England

Rothwell

Dise

Taylor

et al. 2010

Science of The Total Environment

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Predicting Gran alkalinity and calcium concentrations in river waters over a national scale using a novel modification to the G-BASH model

Cited by 5 publications

References 20 publications

Predicting natural base‐flow stream water chemistry in the western United States

Predicting natural base‐flow stream water chemistry in the western United States

Model application niche analysis: assessing the transferability and generalizability of ecological models

A spatial and seasonal assessment of river water chemistry across North West England

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