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Datasmith Research Paper

Abstract

We describe a method for using spatially referenced regressions of contaminant transport on watershed attributes (SPARROW) in regional water-quality assessment. The method is designed to reduce the problems of data interpretation caused by sparse sampling, network bias, and basin heterogeneity. The regression equation relates measured transport rates in streams to spatially referenced descriptors of pollution sources and land-surface and stream-channel characteristics. Regression models of total phosphorus (TP) and total nitrogen (TN) transport are constructed for a region defined as the nontidal conterminous United States. Observed TN and TP transport rates are derived from water-quality records for 414 stations in the National Stream Quality Accounting Network. Nutrient sources identified in the equations include point sources, applied fertilizer, livestock waste, nonagricultural land, and atmospheric deposition (TN only). Surface characteristics found to be significant predictors of land-water delivery include soil permeability, stream density, and temperature (TN only). Estimated instream decay coefficients for the two contaminants decrease monotonically with increasing stream size. TP transport is found to be significantly reduced by reservoir retention. Spatial referencing of basin attributes in relation to the stream channel network greatly increases their statistical significance and model accuracy. The method is used to estimate the proportion of watersheds in the conterminous United States (i.e., hydrologic cataloging units) with outflow TP concentrations less than the criterion of 0.1 mg/L, and to classify cataloging units according to local TN yield (kg/km2/yr).

Abstract

Peatlands globally are at risk of degradation through increased susceptibility to erosion as a result of climate change. Quantification of peat erosion and an understanding of the processes responsible for their degradation is required if eroded peatlands are to be protected and restored. Owing to the unique material properties of peat, fine-scale microtopographic expressions of surface processes are especially pronounced and present a potentially rich source of geomorphological information, providing valuable insights into the stability and dominant surface process regimes. We present a new process-form conceptual framework to rigorously describe bare peat microtopography and use Structure-from-Motion (SfM) surveys to quantify roughness for different peat surfaces. Through the first geomorphological application of a survey-grade structured-light hand-held 3D imager (HhI), which can represent sub-millimetre topographic variability in field conditions, we demonstrate that SfM identifies roughness signatures reliably over bare peat plots (<1 m2), although some smoothing is observed. Across 55 plots, the roughness of microtopographic types is quantified using a suite of roughness metrics and an objective classification system derived from decision tree analysis with 98% success. This objective classification requires just five roughness metrics, each of which quantifies a different aspect of the surface morphology. We show that through a combination of roughness metrics, microtopographic types can be identified objectively from high resolution survey data, providing a much-needed geomorphological process-perspective to observations of eroded peat volumes and earth surface change. Copyright © 2018 John Wiley & Sons, Ltd.