Advanced searchCitation search
Skip main navigation
Close Drawer MenuOpen Drawer Menu
Previous article
Next article
Open access
Technical Papers
Jul 1, 2022

Runoff Quantity and Quality from Amended Urban Residential Lawns

Authors: Jovana Radovanovic [email protected] and Eban Bean, Ph.D., M.ASCEAuthor Affiliations
Publication: Journal of Sustainable Water in the Built Environment
Volume 8, Issue 4
https://doi.org/10.1061/JSWBAY.0000995
PDF

Abstract

New residential landscapes in Florida are often installed on sandy fill soil that has been compacted (bulk densities >1.60  g/cm3) during the home construction process. The goal of this study was to determine whether incorporating compost into new residential lawns could reduce stormwater runoff volumes and nutrient loadings. Before landscape installation, one of three treatments (null, tilled, and tilled with compost) were applied to the lawn area (60% of landscape) and after landscape installation was completed, approximately half of the lots were topdressed. Overall, drainage areas with compost-amended soils produced 2.5% less runoff than tilled soils, but did not produce less runoff than null soils. However, considering variability across storm events, amending soils resulted in lower event runoff depths compared with tilled and null treatments, with effective curve numbers of 77, 88, and 82, respectively. Neither total nitrogen (TN) nor total phosphorus (TP) concentrations or loadings were significantly different based on treatment or topdressing. Annualized TN loading rates were highest for tilled soils (5.14  kg N/ha/year) and lowest for composted soils (4.21  kg N/ha/year). Tilled soils produced the greatest annualized phosphorus loading rates (1.18  kg P/ha/year), with null soils producing the lowest loading rates (0.73  kg P/ha/year). Results of this study indicate that amending compacted fill soils with compost prior to landscape installation decreased runoff volumes without increasing nutrient concentrations.

Introduction

Urbanization leads to increased runoff from watersheds due to the increase in total impervious area (TIA), particularly when TIA is directly connected impervious area (DCIA), where impervious surfaces are linked in series (e.g., roofs, driveways, gutters, stormwater collection systems) to prevent runoff from infiltrating into pervious areas, and efficiently convey stormwater from source areas to receiving waters. Runoff bypasses soil when conveyed more efficiently over impervious surfaces, eliminating opportunities for nutrient removal via filtering, uptake, and biological processes. Urban stormwater runoff is the second leading cause of impairment to streams, and as runoff increases due to lack of infiltration, it increases the potential of transporting nutrients into nearby surface waters (Paul and Meyer 2001; Nagy et al. 2012; Gregory et al. 2006). The increase in nitrogen loadings has the potential to accelerate eutrophication and algal blooms, leading to degraded surface waters (Beman et al. 2005; Lusk et al. 2020; Collins et al. 2010; Leopold 1986; Paul and Meyer 2001). The National Rivers and Streams Assessment found that 46% of the rivers and streams evaluated in the study were considered to be in “poor biological condition” (EPA 2016a) and that 21% of lakes are hypereutrophic (EPA 2016b), with nitrogen and phosphorus being the most prominent stressors for both.
Disconnecting impervious areas allows pervious areas to capture and infiltrate runoff, reducing runoff volumes and potential for nutrient exports. In medium- to high-density residential developments, on-lot pervious areas comprise lawns and landscape beds. After the residential development process, soils are often left more compacted than predevelopment and commonly function hydrologically less like pervious areas than credited (Gregory et al. 2006; Pitt et al. 1999; Bean and Dukes 2015; Chen et al. 2014). During the initial grading of a site before construction begins, or master grading of the site, the existing topsoil is either removed or buried under imported fill material that is generally low in organic matter (Chen et al. 2014; Hamilton and Waddington 1999; Shober and Toor 2008). During the construction process, soil compaction is necessary to provide a stable foundation by increasing load-bearing capacity, and reduces soil settling after construction (Multiquip 2011). However, vehicle traffic and vibrations also compact soils that will underlie landscape material (Kozlowski 1999). This, along with wetting and drying cycles, cause pores to consolidate and collapse, which increases bulk density and decreases porosity, infiltration rates, water-holding capacity, and plant available water (Kozlowski 1999; Cogger 2005; Gregory et al. 2006).
Mitigating compacted sandy soils by mechanically incorporating compost has been shown to increase water-holding capacity and infiltration rates, thus decreasing the runoff (Loper 2009; Landschoot and Mcnitt 1994; Pitt et al. 1999; Bean and Dukes 2015; Olson et al. 2013; Pandey 2005). However, the Bean and Dukes (2015) study only spanned 4 months and did not include vegetation on the mesocosm lysimeters, which may have affected infiltration and runoff rates. The study by Olson et al. (2013) assessed potential runoff only by simulation analysis, rather than direct runoff.
In Florida, where at least half of residential water use goes to landscape irrigation, improved soil quality could lead to significant water conservation if amended soils retained more water for landscape vegetation. However, studies evaluating the runoff reduction potential from compost amendments have been limited to the plot (Olson et al. 2013) or mesocosm scale (Pitt et al. 1999; Bean and Dukes 2015) rather than the catchment or subwatershed scale. These larger-scale implementations within real-world context can include factors omitted from smaller-scale controlled studies, such as effects from impervious areas or gutters, as well as homeowner behaviors and practices.
Amending soils with compost introduces a mostly stable source of nutrients. While the addition of compost to sandy soils increases organic matter and long-term water and nutrient retention (Cogger 2005), a labile fraction of nutrients is initially present within mature compost and may be flushed or leached from the material (Mullane et al. 2015). Nitrogen and phosphorus are common nutrients of concerns in receiving waters, and increasing nutrient exports could offset the benefits of runoff and irrigation reductions. Tilling without addition of any amendment is effective at mitigating soil compaction and may have similar benefits to incorporation of compost into soils without introducing a potential nutrient source. This would be a cheaper alternative as well because it avoids material and transport costs associated with compost. Topdressing, or spreading of finely screened material (3–6 mm) that can fall through the vegetative canopy (i.e., turfgrass blades), is another method for applying compost to turfgrass areas, typically at lower rates than when coarser amendments (6–12 mm) are incorporated into soil. Topdressing has received less attention from researchers. As opposed to destroying the surficial soil structure and existing vegetation with tillage when compost is incorporated into soil, topdressing of compost does not disturb the existing soil and vegetation structure, and may offer an alternative for established residential landscapes to reduce runoff by increasing soil organic matter, improving infiltration rates. Florida’s landscape industry has begun including compost topdressing in its landscape maintenance services as an alternative to conventional fertilizers to improve plant and soil health.
The goal of this study was to better understand the effects of compost introduction, via soil incorporation or topdressing, into residential landscapes in well-drained, sandy soils on runoff characteristics at the neighborhood scale or storm-drain inlet scale. The objectives of the research reported in this paper were to
•
evaluate effects of tilling alone, tilling with compost, and topdressing with compost on the hydrologic responses of residential soils; and
•
evaluate effects of tilling alone, tilling with compost, and topdressing with compost on runoff water quality.

Methods

Study Site

This research study was conducted in North Central Florida at On Top of the World (OTOW) Communities in southwest Ocala, Florida (29°05′41″ N, 82°16′13″ W), within the Sanctuary neighborhood. The dominant soil in this area is a Candler sand (hyperthermic, uncoated lamellic quartzipsamments; 95% sand, 4% silt, 1% clay) characterized as very deep, excessively drained, and very permeable (NRCS 2013). Although bulk density and infiltration rates were not measured in the exact study area, the bulk density was measured in the neighborhood’s model homes just north of the study area (Fig. 1). The average bulk densities for control, tilled, and composted soils were 1.76, 1.78, and 1.51  g/cm3, respectively. The soil’s light color indicates low organic content. The climate is characterized as subtropical, receiving on average 132 cm of rainfall per year.
Fig. 1. Aerial view of research area including three treatments across 24 homes at On Top of the World Communities in Ocala, Florida. (Base map from Map data ©2022 Google Imagery ©2022, Maxar Technologies, Sanborn, US Geological Survey.)
Eight subwatersheds were in this study, which included 24 residential lots where homes were constructed between 2018 and 2019. Each home’s landscape complied with Florida Water Star requirements and was approximately 60% lawn (Empire zoysiagrass) (Florida Water Star 2021). The 24 lots were divided into three soil treatments: tilled, tilled with compost (from now on referred to as “compost”), and null (Fig. 1). Additionally, lawns for 11 of the 24 lots were topdressed with compost on four occasions during the study. The compost product was COMAND Natural Soil Builder provided by LifeSoils (Sumterville, Florida), made from feedstocks of used horse stall bedding, yard waste, and landscape debris, and certified under the US Composting Council’s Seal of Testing Assurance. For incorporation, 1.3 cm screened compost was spread at 3.3  m3/100  m2 and then tilled to a 15-cm depth prior to installation of the irrigation system and sod laying (Empire zoysiagrass). Topdressing material was screened to 0.64 cm and applied to the lawn areas at a rate of 0.4  m3/100  m2 in May and September 2019 and June and September 2020.
A limitation of this study was the lack of treatment replicates. Due to budget and feasibility constraints, we were unable to capture runoff at the lot level and left to measure and collect runoff within storm drains. Thus, treatments were applied to uniformly to all lawns within a drainage area, rather than randomized across the study areas. This was done to ensure that there was only one treatment per storm drain.

Rainfall Data

Rainfall data were collected from an Onset RG3 tipping bucket rain gauge (±0.3  mm) located 380 m to the northeast at the OTOW Landscape Operations facility from July 2018 until May 2019, when a separate Onset RG3 tipping bucket rain gauge was installed on the northeast end of the neighborhood (Fig. 1), and provided data through October 2020. Incremental rainfall records were summed to determine storm event depths. A 6-h interevent dry period was used to distinguish rainfall records into separate, independent events. Over the course of 24 months, samples were taken from 24 storm events. The time between each storm event that was sampled from varied from 24 h to 2 months, ensuring that the storm events were independent of each other.

Runoff Flow Monitoring

The 24 lots included in the study were split up into separate drainage areas so that each drain received runoff only from one treatment area (tilled, tilled with topdressing, compost, compost with topdressing, null, or null with topdressing) and the correct runoff volumes per treatment could be calculated. Four of the six treatment areas drained into individual catch basin inlets. The remaining two treatment areas (null with topdressing and non-topdressed composted) each drained to two catch basin inlets, resulting in eight total runoff collection points. The catch basin inlets were each equipped with a 60° weir box containing an Onset HOBO water level logger (U20-001-04) (Onset, Bourne, Massachusetts). A Visqueen plastic sheet (Uline, Prairie, Wisconsin) apron and a rigid plastic barrier were installed to direct all runoff into the weir box (Fig. 2). The apron was inspected and replaced as needed during the study. A separate HOBO logger was located on the side of a private residence for continuously recording atmospheric pressure for calculating water depths in each weir box.
Fig. 2. Weir box with plastic sheeting being installed into storm drain to capture stormwater runoff on the left, and fully installed weir box seen through the grate with barrier along lower edge of grate on the right.

Flow Analysis

Data from each water level logger were downloaded monthly, corrected for atmospheric fluctuations. Water-level time-series for each watershed were exported from HOBOware to Microsoft Excel. Water-level measurements were converted to flow rates using the Kindsvater-Shen equation (USBR 1997), which is written as
Q=4.28Cetanθ2h1e52
(1)
where Q = discharge over weir in f t3/s; Ce = effective discharge coefficient; h1e = effective head on the weir in ft; and θ = angle of V-notch. The flow rate was then converted from ft3/s to L/s.
Water-level and flow data were then reviewed for quality assurance and quality control. Most commonly, minor shifts in the depth to the weir invert were adjusted due to changes in the HOBO logger position between downloads. On a few occasions, sedimentation buried a logger and data were removed. Other validity checks included comparing flow patterns across watersheds and with rainfall patterns to reduce noise from water level fluctuations from sources other than precipitation (e.g., irrigation events). For precipitation events, water-level responses began and concluded at similar times for all storm drains. Flow records were compared across storm drains to identify flows that occurred independently of other storm drains; this step specifically screened nonprecipitation events where there were errors with the water-level loggers or during periods of ongoing construction within the drainage area. Runoff flow rates were used to calculate runoff volumes for individual storm events, which were then used to determine runoff depths based on drainage areas.
Drainage areas for each inlet were initially delineated using a GIS tool from the Marion County Property Appraiser website (MCPA 2022) and topographic surveys provided by OTOW, and subsequently verified in the field. Lot size information was gathered from the Marion County Property Appraiser site. The impervious area for each lot was also delineated from aerial imagery using the GIS tool from the Marion County site and aggregated for each drainage area. TIA ranged from 46% to 57% for all drainage areas.
Rainfall event totals were summed using a 6-h interevent dry period to separate storms. Event runoff volumes for each watershed were summed for each event and converted to depths. Storm events smaller than 1.27 cm were omitted from this analysis to screen out very small runoff depths and events that did not produce runoff. The SCS Curve Number Method is widely used to estimate runoff from rainfall depths, which was initially develop for agricultural applications but was later adapted and broadly applied to urban hydrologic modeling and designs (NRCS 1986). Curve number values range from 0 to 100, with greater values producing greater runoff depths, and depend on an area’s soil characteristics and land use, which includes amount and connectivity of impervious cover. Measured rainfall depths and runoff depths were used to estimate NRCS (1986) curve numbers for each storm event using Eqs. (2) and (3) (Hawkins 1993)
S=5[P+2Q−(4Q2+5PQ)]
(2)
CN=25,400254+S
(3)
where S = watershed storage depth; P = event rainfall depth; Q = runoff depth; and CN = curve number. An effective CN was determined for each treatment by running a regression against the inverse of rainfall depth based on Eqs. (2) and (3) (Hawkins 1993). For multiple drainage areas of the same soil and topdressing treatment, the CN for the two drainage areas was averaged to yield one treatment CN. For soil treatments, the two topdressed and non-topdressed CNs were averaged into one CN and, for topdressed or non-topdressed, the three respective CNs were averaged across soil treatments.

Runoff Quality

Approximately monthly stormwater runoff samples were collected via seven ISCO 6,712 and one ISCO 3,700 autosamplers (Teledyne ISCO, Lincoln, Nebraska) during rainfall events for each of the eight storm drains. The sample tube that collected the runoff was secured to the inner side of the weir box at the same elevation as the weir invert (notch). Autosamplers were programmed to collect 200 mL of runoff (if available) every 5 min, which were composited into a 10-L carboy. Composite samples were subsampled into two 20-mL vials within 3 h of a storm ending or the following morning for overnight storms. One duplicate sample was collected randomly from one of the eight ISCO autosamplers that collected runoff from storm drains for each storm event to ensure sample-to-sample consistency in analytical results. Subsamples were put on ice while transported to the UF/IFAS Environmental Water Quality Lab (EWQL) where they were stored a refrigerator at 4°C until they were analyzed.
Samples were analyzed by EWQL for nitrate (NO3−N) (EPA method 353.2), ammonium (NH4+-N) (EPA method 350.1), total Kjeldahl nitrogen (TKN) (EPA method 351.2), and total phosphorus (TP) (EPA method 365.1). Organic nitrogen (Org N) was calculated as the difference between TKN and NH4+-N concentrations, and total nitrogen (TN) was calculated as the sum of TKN and NO3−-N concentrations.
While samples were collected on a time-weighted basis, due to the relatively small watershed size and short time of concentration, the resulting event concentrations were assumed to be reasonable estimates for flow-weighted samples and event mean concentrations. Event mean concentrations (EMC) were calculated using the following equation:
EMC=∑(Ci×Ri)∑Ri
(4)
where EMC = long-term event mean concentration in mg/L; Ci = event concentration in mg/L; and Ri = cumulative event runoff volume (L).
Gaps in runoff data for part of the study period limited the direct calculation of loadings and loading rates. To fill the gaps, loading rates were estimated by multiplying the cumulative loadings from sampled events by the ratio of cumulative rainfall over the study period (PT) to cumulative rainfall for events with runoff data (PS). Loadings were then divided by the study period in years (TSP) and the drainage area (Da) to determine the annualized loading rates per area, as in
Mt=(Mc×PTPS)×ATSP
(5)
where Mt = loading rate in kg/ha/year; Mc = cumulative loading from sampled events in kg; PT = cumulative rainfall over the study period in cm; PS = cumulative rainfall for sampled events in cm; A = drainage area in ha; and TSP = study period duration in years. This scaled up the sum of directly calculated loadings by the ratio of total rainfall to rainfall for sampled events.
Of the 24 storms sampled, 23 had TN concentrations under 5.0  mg/L. One runoff sample in the tilled, non-topdressed treatment was identified as an outlier (19  mg N/L, 10,389  μg P/L, October 20, 2019 event), as the concentration was three times greater than the next greatest concentration from that drainage are. This storm event, for all treatments, has been omitted from the data analyses of concentrations and loadings.

Statistical Analysis

In the field study, the ranges and means of CN, total runoff depths, and total runoff volumes were examined without looking at statistical differences, because they did not have enough replicates to be analyzed statistically. The following parameters were analyzed by running a mixed-effects model in RStudio version 1.3.959 (RStudio Team 2020) using lmer (Kuznetsova et al. 2017) and Tukey’s post hoc analysis to determine if they were significantly different (p=0.05) based on treatment type (compost, tilled, null), if topdressing were applied (yes, no), and if date was significant: NH4+-N, NO3−-N, TKN, Org. N, TN, and TP event concentrations and loadings, and annualized TN and TP loading rates. In this study, separate storm events were replicates and identified by their date, which was treated as a categorical variable. Treatment and topdressing were fixed effects, and drain numbers were random effects.
Nutrient concentrations and loadings were normalized by either square root (SR) transformation or log-transformation, and the lmer test and Tukey’s post hoc analyses were run. With the ANOVA model for concentrations and nutrient loadings, nonsignificant interaction terms were dropped, and the model was rerun to determine if any of the parameters (treatment, topdressing, date) were significant.
Differences in treatment area runoff concentrations were also compared by developing probability exceedance plots of TN and TP to assess whether the distributions of TN and TP differed.

Results and Discussion

Runoff Quantity

In our study, we found that lots that were amended with compost had a lower percent runoff produced and CN compared with tilled and null lots (Tables 1 and 2; Fig. 3). Similar patterns of compost reducing CN was found in other studies, e.g., Bean and Dukes (2015) and Olson et al. (2013), where CN and percent runoff produced were lowest in the compost-amended treatments compared with the other treatments, which were fly ash amendment and null, and tilled and null, respectively.
Table 1. Treatment and topdressing present drainage area, rainfall depth, runoff depth, and runoff percentage for each drainage area included in this study
TreatmentDrainage area (hectares)Rainfall depth (cm)Runoff depth (cm)Runoff (%)
C-Y0.2753385917
C-N10.2273388926
C-N20.2103388325
T-Y0.3282176932
T-N0.3933388926
N-Y10.2553389929
N-Y20.3972356929
N-N0.4572916221

Note: C = compost; T = tilled; N = null; Y = topdressed; and N = not topdressed.

Table 2. Maximum, median, minimum, mean, and standard deviation for CNs for all storm events from 2018 to 2020
ExtremaCompostTillNullTopdressingNo topdressing
Maximum8591879184
Median7788828777
Minimum6884778568
Mean7788828876
Standard deviation126738
Fig. 3. Cumulative rainfall and runoff depths per treatment over the monitoring period.
Overall, lots that were topdressed produced greater runoff percentages (Table 1) and CNs compared with lots that were not topdressed. Looking at the effects of topdressing on the individual treatments showed varied results. Due to limited replicates, the effects could not be statistically analyzed. However, null and tilled lots that were topdressed had increased runoff percentages and CNs compared to non-topdressed lots, while composted lots had increased CNs but lower runoff percentages. The mechanisms that caused greater runoff and increased CNs from topdressed null and tilled lots are not clear, although topdressing may somehow reduce storage within the vegetated layer above the soil or reduce the rate that rainfall is absorbed into the soil. These processes deserve further investigation because they are counter to expected runoff reductions due to increased soil organic matter and overall soil quality. As for the compost lots, increased CNs for topdressed lots correspond to null and tilled lots; however, overall runoff percentage was lower. While runoff percentages and CNs should typically increase or decrease together, CNs reflect the regressed runoff response to individual rainfall events, accounting for differences in rainfall amounts, while the runoff percentage reflects the cumulative runoff response regardless of rainfall characteristics. Future studies should focus on identifying and quantifying how topdressing applications affect mechanistic runoff processes from turfgrass landscapes.
Our study differed from that of Bean and Dukes (2015) by having a higher CN for compost amended lots (74) than Bean and Dukes did (40, 44, and 62) for their two soil types in question, Arredondo with compost amendment and Orangeburg with compost amendment. We also saw a smaller increase in percent of runoff produced between null and composted soils, an increase of 4%, compared with 53% increase in the Olson et al. (2013) study. Additionally, the Bean and Dukes (2015) and Olson et al. (2013) studies were carried out on sites that had different soils, which may be the reason for slight variation.
The main difference between the current study and previous studies analyzing amended soil and storm water runoff is that this study was carried out in the field, that is, in a full-scale residential development. Our study validates the extension of previous studies done on smaller scales to the catchment scale. This includes both pervious and impervious areas, whereas previous studies have only evaluated pervious areas. It demonstrates runoff reduction potential from amending urban soils with compost at the storm drain catchment scale.

Runoff Concentrations and Loadings

Overall, neither treatment nor topdressing had a significant effect on TN or TP concentrations in runoff. TN concentrations were log-transformed for analysis, and TP concentrations were square-root transformed for analysis. All but one value fell below 10  mg N/L (Table 3; Fig. 4), and the majority of concentrations were below 600  ug P/L (Table 4; Fig. 5). In comparison, the Florida Department of Environmental Protection (FDEP) uses 2.07  mg N/L and 327  μg P/L for estimating runoff loadings from medium density residential watersheds (Harper and Baker 2007). Date was the only significant factor for both TN and TP concentrations (TN: df: 22, F: 7.87, p<0.01; TP: df: 22, F: 4.52, p<0.01), which reflects the event-to-event variability in runoff concentrations. Additionally, nitrogen species concentrations were not significantly different across treatment or topdressing (Table 3).
Table 3. Geometric means for nitrogen species in stormwater runoff concentrations; no significant differences were found
Nitrogen speciesCompostTillNullTopdressingNo topdressing
NO3−-N (mgN/L)0.250.190.200.210.22
NH4+-N (mgN/L)0.100.070.090.090.08
Org N (mg/L)0.720.680.610.700.64
TN (mg/L)1.090.920.910.960.97

Note: Significant differences were evaluated separately for soil treatment and topdressing, but no significant differences were found based on a Tukey’s post hoc analysis.

Fig. 4. Probability of exceedance for TN concentrations for composted, tilled, and null watersheds. The vertical dashed line represents a value (2.07  mg N/L) used by the FDEP for estimating runoff concentrations in medium-density residential watersheds (Harper and Baker 2007).
Table 4. Maximum, median, minimum, mean, and standard deviation ranges for TP concentrations (μg P/L) from stormwater runoff samples; no significant differences were found
ExtremaCompostTillNullTopdressingNo Topdressing
Maximum2,1888941,32613,262,188
Median326202164232219
Minimum228372
Mean311186161226204
Standard deviation range244,378166,206114,208177,275144,264
Fig. 5. Probability of exceedance for raw TP concentrations for composted, tilled, and null watersheds. The vertical dashed line represents a value (327  μg P/L) used by the FDEP for estimating runoff concentrations in medium-density residential watersheds (Harper and Baker 2007).
The TN EMC for null lots was 1.04  mg N/L, which was 14% lower than composted lots and 40% greater than tilled lots (Table 5). This was lower than that found in Lusk et al. (2020) for null lots (1.61  mg N/L). Yang and Toor (2016), however, had a monthly mean concentration of 1.04  mg N/L, which is equivalent to results from this study. Similar to our results, Lusk et al. (2020) also showed that Org-N was the dominant form of nitrogen in stormwater runoff, making up about half of TN concentrations.
Table 5. Total rainfall (cm), TN event mean concentrations (mg/L), and TP event mean concentrations (mg/L) for each drain by treatment (treatment-topdressing)
ParametersC-YC-N1C-N2T-YT-NN-Y1N-Y2N-N
Total rainfall338338338217338338235291
TN event mean concentrations0.841.331.470.790.701.530.820.77
TP event mean concentrations0.330.470.440.340.190.660.290.29
Neither treatment nor topdressing had a significant effect on event TN or TP loadings (kg/ha), which was expected given that concentrations were not significantly different (Tables 6 and 7). TN loadings were log-transformed and TP loadings were square-root transformed. Date was significant for both TN and TP event loading, which was likely because no two storm events or their antecedent conditions are the same. For TP loadings, the significant interaction of treatment and topdressing with date meant that date had an impact on how treatment and topdressing reacted in terms of TP loadings.
Table 6. ANOVA summary of event runoff TN loadings (kg/ha)
ParametersDegrees of freedomSum of squaresMean of squaresF valueP-value
Treatment20.0040.0020.4350.929
Topdressing10.0200.0204.0730.549
Date221.5460.07013.99<0.001a
Treatment*Topdressing20.0310.0153.0700.380
Treatment*Date440.4120.0091.8650.658
Topdressing*Date210.2620.1242.4860.446

aSignificant factors or interactions.

Table 7. ANOVA summary of event runoff TP loadings (kg/ha)
ParametersDegrees of freedomSum of squaresMean of squaresF valueP-value
Treatment20.0040.0020.4350.670
Topdressing10.0200.0204.0730.078
Date221.5460.07013.99<0.001a
Treatment*Topdressing20.0310.0153.0700.118
Treatment*Date440.4120.0091.8650.014a
Topdressing*Date210.2620.1242.4860.004a

aSignificant factors or interactions.

The highest annual loading rate for TN was produced by tilled lots (5.14  kg/ha/year) and least by compost amended lots (4.21  kg/ha/year) (Table 8; Fig. 6). In comparison, Obreza (2004) states that for a single-family residential area, the typical loading in north Florida for annual TN loading is 11.7  kg/ha, which is greater than the amount of TN loading from any treatments in the current study. For TP annual loading rates, tilled lots produced the highest, while null lots produced the least (Table 8; Fig. 7).
Table 8. Annual estimates of runoff TN and TP loading rates (kg/ha/year) by treatment (soil treatment-topdressing)
NutrientsC-YC-NT-YT-NN-YN-N
TN2.955.466.323.965.433.68
TP0.801.471.640.720.960.49
Fig. 6. Stacked bar graph showing TN loading (kg/ha/year) between different treatments (C = compost, T = tilled, N = null) and topdressing application (Y = topdressing, N = no topdressing).
Fig. 7. Bar graph showing TP loading (kg/ha/year) between different treatments (C = compost, T = tilled, N = null) and topdressing application (Y = topdressing, N = no topdressing).
Overall, lots that were topdressed had higher TN and TP annual loading rates than lots that were not topdressed. However, topdressed tilled and null lots had lower annual loading rates than their non-topdressed counterparts. This was not the case for composted lots, which had higher rates from the topdressed lots compared with the non-topdressed lots. Thus, further research is needed to better understand the effects that topdressing has on TN and TP annual loading rates, and to potentially further examine the effect topdressing has on lots that were already composted.
A drawback of this study was the limited sample size (eight storm drains) and low replicates in each treatment area (one or two storm drains per treatment), which would increase confidence of significant differences. To improve the study, there could have been a greater replication of treatments to better understand the impacts of treatment type and topdressing. A longer time frame, in addition, may have yielded more consistent results, given that there was a sample collected for every storm drain for only four of the 16 sampled storm events. There were also challenges with coordinating during construction and with landscaping crews within the development process.

Conclusions

This study demonstrated that incorporating compost into compacted sandy soils in residential landscapes reduced runoff depths. Tillage alone increased runoff depths compared with compacted soils in this study, and thus, including an amendment with tillage was necessary to mitigate the hydrologic effects of soil compaction that occurs during residential construction. The amendment likely helps establish a resilient soil structure for noncohesive soil particles that inhibits settling and reconsolidation under irrigation and landscape traffic.
Although compost can be a source of N and P, the combined reduction in runoff volumes resulted in similar runoff loadings for N and P across treatments. Future research should investigate the relationship between amendment rates, other than 3.3  m3/100  m2, on runoff volumes and nutrient loadings to optimize applications for intended outcomes. As results for topdressing were generally inconclusive, future research should also investigate the effect of topdressing lawns with compost and the impact of runoff volumes and loadings. Additionally, future studies should examine the implications that compost incorporation and topdressing have on nonsandy soils, as the responses may differ from our study.
Results from this study support the following conclusions and recommendations:
1.
Incorporating compost into compacted sandy soils with compost decreases runoff production from residential lots, with CNs of 77 compared with 82 for compacted soils.
2.
Compost incorporation of soils should be credited for reducing stormwater runoff volumes from impervious areas draining to them, as long as the amended areas are maintained in good condition. If gutters are used to direct runoff to amended areas, caution should be exercised as to avoid erosion or soil washout.
3.
Overall, runoff nutrient concentrations were similar across treatments, indicating that the driving factor determining loadings was volume. Therefore, any loading reduction should be dependent primarily on factors affecting runoff volumes, such as pervious area, contributing impervious area, amended soil depth, and soil porosity.
4.
Compost materials are widely available and vary greatly in quality. Compost should meet certain minimum criteria, such as the US Composting Council’s Seal of Testing Assurance, to be credited. Future research should identify properties of compost products that yield runoff reductions when used in soil amendment applications.
5.
Nutrient runoff loadings were not significantly lower for soils amended with compost at a rate of 3.3  m3/100  m2. Decreased rates may yield reduced runoff loadings compared with conventional lots and should be investigated.
6.
While runoff nutrient loadings were similar to null lots, this study did not consider the potential for leaching of nutrients from the amended soils and the impact on groundwater quality. Both surficial and subsurface nutrient pathways should be assessed, particularly for areas where groundwater is vulnerable to nitrogen loadings from the land surface.

Data Availability Statement

All data that support the findings of this study are available for download from https://bit.ly/3lcBknu.

Acknowledgments

Financial support was provided by Southwest Florida Water Management District (SWFWMD). The authors thank On Top of the World Communities in Ocala, Florida, for allowing the research to take place there. The homeowners who took part in this research were vital to this study, as were the staff from the Agricultural and Biological Engineering Department at the University of Florida. We also thank the two anonymous reviewers whose comments and suggestions greatly improved this work.

References

Bean, E. Z., and M. D. Dukes. 2015. “Effect of amendment type and incorporation depth on runoff from compacted sandy soils.” J. Irrig. Drain. Eng. 141 (6): 04014074. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000840.
Google Scholar
Beman, J. M., K. R. Arrigo, and P. A. Matson. 2005. “Agricultural runoff fuels large phytoplankton blooms in vulnerable areas of the ocean.” Nature 434 (7030): 211–214. https://doi.org/10.1038/nature03370.
Google Scholar
Chen, Y., S. D. Day, A. F. Wick, and K. J. McGuire. 2014. “Influence of urban land development and subsequent soil rehabilitation on soil aggregates, carbon, and hydraulic conductivity.” Sci. Total Environ. 494 (Oct): 329–336. https://doi.org/10.1016/j.scitotenv.2014.06.099.
Google Scholar
Cogger, C. G. 2005. “Potential compost benefits for restoration of soils disturbed by urban development.” Compost Sci. Util. 13 (4): 243–251. https://doi.org/10.1080/1065657X.2005.10702248.
Google Scholar
Collins, K. A., T. J. Lawrence, E. K. Stander, R. J. Jontos, S. S. Kaushal, T. A. Newcomer, N. B. Grimm, and M. L. Cole Ekberg. 2010. “Opportunities and challenges for managing nitrogen in urban stormwater: A review and synthesis.” Ecol. Eng. 36 (11): 1507–1519. https://doi.org/10.1016/j.ecoleng.2010.03.015.
Google Scholar
EPA. 2016a. National lakes assessment 2012: A collaborative survey of lakes in the United States. EPA 841-R-16-113. Washington, DC: Office of Water and Office of Research and Development.
Google Scholar
EPA. 2016b. National rivers and streams assessment 2008–2009: A collaborative survey. EPA 841-R-16/007. Washington, DC: Office of Water and Office of Research and Development.
Google Scholar
Florida Water Star. 2021. “Florida water star technical manual.” Accessed July 25, 2019. https://floridawaterstar.com/technicalmanual/.
Google Scholar
Gregory, J. H., M. D. Dukes, P. H. Jones, and G. L. Miller. 2006. “Effect of urban soil compaction on infiltration rate.” J. Soil Water Conserv. 61 (3): 117–124.
Google Scholar
Hamilton, G. W., and D. V. Waddington. 1999. “Infiltration rates on residential lawns in central Pennsylvania.” J. Soil Water Conserv. 54 (3): 564–568.
Google Scholar
Harper, H. H., and D. Baker. 2007. “Evaluation of current stormwater design criteria within the state of Florida.” Accessed December 5, 2019. http://www.erd.org/ERD%20Publications/EVAL%20OF%20CURRENT%20SW%20DESIGN%20CRITERIA%20WITHIN%20THE%20STATE%20OF%20FLA-2007.pdf.
Google Scholar
Hawkins, R. 1993. “Asymptotic determination of runoff curve number from data.” J. Irrig. Drain. Eng. 119 (2): 334–345. https://doi.org/10.1061/(ASCE)0733-9437(1993)119:2(334).
Google Scholar
Kozlowski, T. T. 1999. “Soil compaction and growth of woody plants.” Scand. J. For. Res. 14 (6): 596–619. https://doi.org/10.1080/02827589908540825.
Google Scholar
Kuznetsova, A., P. B. Brockhoff, and R. H. B. Christensen. 2017. “lmerTest package: Tests in linear mixed effects models.” J. Stat. Software 82 (13): 1–26. https://doi.org/10.18637/jss.v082.i13.
Google Scholar
Landschoot, P., and M. A. Mcnitt. 1994. “Selecting composts to improve your turf.” Michigan Turfgrass Conf. Proc. 3 (12): 1–5.
Google Scholar
Leopold, L. B. 1986. Hydrology for urban land planning: A guidebook on the hydrologic effects of urban land use. Washington, DC: USGS.
Google Scholar
Loper, S. 2009. “Effects of compost and tillage on soils and nutrient losses in a simulated residential landscape.” M.S. thesis, Dept. of Soil and Water Science, Univ. of Florida.
Google Scholar
Lusk, M. G., G. S. Toor, and P. W. Inglett. 2020. “Organic nitrogen in residential stormwater runoff: Implications for stormwater management in urban watersheds.” Sci. Total Environ. 707 (Mar): 135962. https://doi.org/10.1016/j.scitotenv.2019.135962.
Google Scholar
MCPA (Marion County Property Appraiser). 2022. “Beta Map It+.” Accessed October 5, 2018. https://www.pa.marion.fl.us/patviewer/.
Google Scholar
Mullane, J. M., M. Flury, H. Iqbal, P. M. Freeze, C. Hinman, C. G. Cogger, and Z. Shi. 2015. “Intermittent rainstorms cause pulses of nitrogen, phosphorus, and copper in leachate from compost in bioretention systems.” Sci. Total Environ. 537 (Dec): 294–303. https://doi.org/10.1016/j.scitotenv.2015.07.157.
Google Scholar
Multiquip. 2011. “Soil compaction handbook.” Accessed September 15, 2018. http://www.multiquip.com/multiquip/pdfs/Soil_Compaction_Handbook_low_res_0212_DataId_59525_Version_1.pdf.
Google Scholar
Nagy, C. R., G. B. Lockaby, L. Kalin, and C. Anderson. 2012. “Effects of urbanization on stream hydrology and water quality: The Florida Gulf Coast.” Hydrol. Processes 26 (13): 2019–2030. https://doi.org/10.1002/hyp.8336.
Google Scholar
NRCS (National Resource Conservation Service). 1986. “Technicalrelease-55: Urban hydrology for small watersheds.” Accessed September 1, 2020. https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1044171.pdf.
Google Scholar
NRCS (Natural Resources Conservation Service). 2013. “United States Department of Agriculture. Web soil survey.” Accessed September 1, 2020. http://websoilsurvey.sc.egov.usda.gov/.
Google Scholar
Obreza, T. 2004. “Florida springs land use information tool.” Accessed January 20, 2020. https://ufdc.ufl.edu/IR00004236/00001.
Google Scholar
Olson, N. C., J. S. Gulliver, J. L. Nieber, and M. Kayhanian. 2013. “Remediation to improve infiltration into compact soils.” J. Environ. Manage. 117 (Mar): 85–95. https://doi.org/10.1016/j.jenvman.2012.10.057.
Google Scholar
Pandey, C. 2005. “Effects of water table management and organic amendment on water quantity and quality in south Florida.” M.S. thesis, Dept. of Agricultural and Biological Engineering, Univ. of Florida.
Google Scholar
Paul, M. J., and J. L. Meyer. 2001. “Streams in the urban landscape.” Annu. Rev. Ecol. Evol. Syst. 32 (1): 333–365. https://doi.org/10.1146/annurev.ecolsys.32.081501.114040.
Google Scholar
Pitt, R., R. Harrison, C. Henry, D. Xue, and T. O’Conner. 1999. Infiltration through disturbed urban soils and compost-amended soil effects on runoff quality and quantity. Washington, DC: US EPA.
Google Scholar
RStudio Team. 2020. “RStudio: Integrated development for R.” Accessed May 1, 2019. http://www.rstudio.com/.
Google Scholar
Shober, A. L., and G. S. Toor. 2008. Soils and fertilizers for master gardeners: Urban soils and their management issues. Gainesville, FL: Univ. of Florida Institute of Food and Agricultural Sciences.
Google Scholar
USBR (United States Bureau of Reclamation). 1997. “Water measurement manual: A water resources technical publication.” Accessed May 1, 2019. https://www.usbr.gov/tsc/techreferences/mands/wmm/WMM_3rd_2001.pdf.
Google Scholar
Yang, Y.-Y., and G. S. Toor. 2016. “δ15N and δ18O reveal the sources of nitrate-nitrogen in urban residential stormwater runoff.” Environ. Sci. Technol. 50 (6): 2881–2889. https://doi.org/10.1021/acs.est.5b05353.
Google Scholar

Information & Authors

Information

Published In

Go to Journal of Sustainable Water in the Built Environment
Journal of Sustainable Water in the Built Environment
Volume 8 • Issue 4 • November 2022

Copyright

This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.

History

Received: Sep 10, 2021
Accepted: Apr 14, 2022
Published online: Jul 1, 2022
Published in print: Nov 1, 2022
Discussion open until: Dec 1, 2022

ASCE Technical Topics:

  1. Biological processes
  2. Compacted soils
  3. Composting
  4. Design (by type)
  5. Earth materials
  6. Engineering fundamentals
  7. Environmental engineering
  8. Fills
  9. Geomaterials
  10. Geomechanics
  11. Geotechnical engineering
  12. Hydrologic engineering
  13. Hydrology
  14. Infrastructure
  15. Landscaping
  16. Load factors
  17. Loading rates
  18. Nutrient pollution
  19. Pollution
  20. Runoff
  21. Soil mechanics
  22. Soils (by type)
  23. Structural design
  24. Urban and regional development
  25. Urban areas
  26. Waste management
  27. Water and water resources
  28. Water pollution

Authors

Affiliations

Jovana Radovanovic [email protected]
Research Associate, Dept. of Agricultural and Biological Engineering, Univ. of Florida, 1741 Museum Rd., Gainesville, FL 32611 (corresponding author). Email: [email protected]
View all articles by this author
Eban Bean, Ph.D., M.ASCE
P.E.
Assistant Professor and Extension Specialist, Dept. of Agricultural and Biological Engineering, Univ. of Florida, 1741 Museum Rd., Gainesville, FL 32611.
View all articles by this author

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share