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

Abstract

[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,

Developing site-specific nutrient criteria from empirical models

“…The data from many grab samples allowed us to develop models whose scope included a broad range of environments. Sites were originally identified as being in reference condition by the sampling agency, but to ensure consistency, we screened sites to verify that their catchments had little to no human disturbance except for atmospheric deposition (i.e., all sites had ,10% agriculture or urban land use, and 95% of sites had ,2% of either land use; see Olson and Hawkins 2012).…”

“…Dodds and Oakes (2004) called for consideration of spatially variable characteristics, such as geology, slope, and drainage area, to better account for natural variation in water chemistry within ecoregions. New spatial data describing environmental factors that can influence water chemistry have been produced (Olson and Hawkins 2012), and new modeling techniques that account for nonlinear and interacting predictors have been developed (e.g., Random Forests and Artificial Neural 720 J. R. OLSON AND C. P. HAWKINS [Volume 32…”

Captured streams and springs in combined sewers: A review of the evidence, consequences and opportunities

“…In other studies, topographic stream flowpaths have been used to quantify watercourse fragmentation caused by culverts and urban development, differentiating between lost streams with 9 perennial (year-round spring fed baseflow), intermittent (seasonal spring fed baseflow) and ephemeral (stormwater runoff only) regimes (Brooks andColburn 2011, Roy et al 2009), and predicting their likely water chemistry (Olson and Hawkins 2012). Elmore and Kaushal (2008) used aerial photography to verify modelled topographic flowpaths in the Baltimore area and develop a predictive model of buried headwater streams based on land use classifications.…”

Managing Salinity in Upper Colorado River Basin Streams: Selecting Catchments for Sediment Control Efforts Using Watershed Characteristics and Random Forests Models

“…Water 2018, 10, x FOR PEER REVIEW 3 of 17 used more frequently in hydrologic investigations (for example [28][29][30][31][32][33]). Random forests methods have advantages over other modeling methods, such as regression, in that they do not require data to be transformed, can use categorical data, can autonomously fit non-linear relations, and can automatically incorporate interactions between explanatory variables [24,34].…”

“…Random forests is a non-linear, multi-variate classification and regression process that uses a collection of independent decision trees to produce robust (low variance) and low bias predictions [24]. Random forests methods are popular classification tools in ecological studies (for example [25][26][27]), and are being used more frequently in hydrologic investigations (for example [28][29][30][31][32][33]). Random forests methods have advantages over other modeling methods, such as regression, in that they do not require data to be transformed, can use categorical data, can autonomously fit non-linear relations, and can automatically incorporate interactions between explanatory variables [24,34].…”