Impact of Engineered Filter Bed Substrate Composition and Plants on Stormwater Remediation within a Rain Garden System1

Abstract Thirty-two rain-garden-engineered filter-bed substrates (EFBS) resulting from combinations of two substrate bases (sand and slate), two organic matter amendments [composted yard waste (CYW) and pine bark (PB)], two combination methods (banding and incorporation), and four combination amounts [2.5 cm/5%, 5.1 cm/10%, 7.6 cm/15%, and 10.2 cm/20% (by vol.)] were evaluated using three plant species (Betula nigra L. ‘Duraheat', Monarda fistulosa L. and Panicum virgatum L. ‘Shenandoah'). The impact of particle size distribution, saturated hydraulic conductivity (Ksat), volume of effluent, evapotranspiration, EFBS composition, and plant growth on water movement within a rain garden was determined. Sand EFBS maintained a numerically lower Ksat compared to slate EFBS regardless of composition. Using CYW and banding reduced effluent volume and increased evapotranspiration. Each EFBS was also evaluated for its ability to support plant growth and nutrient uptake. Shoot dry weight and shoot nutrient content (nitrogen and phosphorus) trends were similar and were highest for all species when grown in sand amended with banded CYW. Higher levels of total soluble nitrogen (TSN) were in the effluent from CYW compared to PB, regardless of substrate base. Sand generally had lower concentrations of TSN and PO4−3-P present in the effluent than slate. Index words: bioretention cell; saturated hydraulic conductivity (Ksat); effluent volume; effluent nutrient concentration; evapotranspiration; particle size distribution; total soluble nitrogen; ortho-phosphate; nitrate; ammonium. Species used in this study: ‘Duraheat' river birch (Betula nigra L.); wild bee balm (Monarda fistulosa L.); and ‘Shenandoah' switch grass (Panicum virgatum L.).

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Relationship between the Hydraulic Conductivity Function and the Particle-Size Distribution

Soil Science Society of America Journal ◽

10.2136/sssaj2003.0373 ◽

2003 ◽

Vol 67 (1) ◽

pp. 373

Author(s):

Lalit M. Arya ◽

Feike J. Leij ◽

Peter J. Shouse ◽

Martinus Th. van Genuchten

Keyword(s):

Particle Size ◽

Hydraulic Conductivity ◽

Particle Size Distribution ◽

Size Distribution ◽

Conductivity Function

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Simplified estimation of unsaturated soil hydraulic conductivity using bulk electrical conductivity and particle size distribution

Soil Research ◽

10.1071/sr12158 ◽

2013 ◽

Vol 51 (1) ◽

pp. 23 ◽

Cited By ~ 8

Author(s):

Mohammad Reza Neyshabouri ◽

Mehdi Rahmati ◽

Claude Doussan ◽

Boshra Behroozinezhad

Keyword(s):

Electrical Conductivity ◽

Particle Size ◽

Hydraulic Conductivity ◽

Particle Size Distribution ◽

Size Distribution ◽

Unsaturated Soil ◽

Soil Core ◽

Soil Hydraulic Conductivity ◽

Core Samples ◽

Soil Hydraulic

Unsaturated soil hydraulic conductivity K is a fundamental transfer property of soil but its measurement is costly, difficult, and time-consuming due to its large variations with water content (θ) or matric potential (h). Recently, C. Doussan and S. Ruy proposed a method/model using measurements of the electrical conductivity of soil core samples to predict K(h). This method requires the measurement or the setting of a range of matric potentials h in the core samples—a possible lengthy process requiring specialised devices. To avoid h estimation, we propose to simplify that method by introducing the particle-size distribution (PSD) of the soil as a proxy for soil pore diameters and matric potentials, with the Arya and Paris (AP) model. Tests of this simplified model (SM) with laboratory data on a broad range of soils and using the AP model with available, previously defined parameters showed that the accuracy was lower for the SM than for the original model (DR) in predicting K (RMSE of logK = 1.10 for SM v. 0.30 for DR; K in m s–1). However, accuracy was increased for SM when considering coarse- and medium-textured soils only (RMSE of logK = 0.61 for SM v. 0.26 for DR). Further tests with 51 soils from the UNSODA database and our own measurements, with estimated electrical properties, confirmed good agreement of the SM for coarse–medium-textured soils (<35–40% clay). For these textures, the SM also performed well compared with the van Genuchten–Mualem model. Error analysis of SM results and fitting of the AP parameter showed that most of the error for fine-textured soils came from poorer adequacy of the AP model’s previously defined parameters for defining the water retention curve, whereas this was much less so for coarse-textured soils. The SM, using readily accessible soil data, could be a relatively straightforward way to estimate, in situ or in the laboratory, K(h) for coarse–medium-textured soils. This requires, however, a prior check of the predictive efficacy of the AP model for the specific soil investigated, in particular for fine-textured/structured soils and when using previously defined AP parameters.

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