Journal of Hydrology 486 (2013) 293–305

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Journal of Hydrology

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Modeling the hydrologic effects of roadside ditch networks on receiving waters

Brian Buchanan a, Zachary M. Easton b, Rebecca L. Schneider a, M. Todd Walter c,⇑a Dept. of Natural Resources, Cornell University, United Statesb Dept. of Biological Systems Engineering, Virginia Polytechnic Institute and State University, United Statesc Dept. of Biological and Environmental Engineering, Cornell University, United States

a r t i c l e i n f o s u m m a r y

Article history:Received 14 November 2011Received in revised form 9 January 2013Accepted 30 January 2013Available online 8 February 2013This manuscript was handled by Peter K.Kitanidis, Editor-in-Chief, with theassistance of Magdeline Laba, AssociateEditor

Keywords:HydrologyModelingArtificial drainageContaminant transportVariable source area hydrologyDitches

0022-1694/$ - see front matter � 2013 Elsevier B.V. Ahttp://dx.doi.org/10.1016/j.jhydrol.2013.01.040

⇑ Corresponding author. Tel.: +1 607 255 2488; faxE-mail address: mtw5@cornell.edu (M.T. Walter).

Roadside drainage networks can affect deleterious changes to watershed hydrology and water quality.However, very few studies have explored these effects in agricultural settings where the potential fornonpoint source pollution and the implications of tighter hydrologic linkages between landscape andstream are more critical. In this study, we apply a GIS-based, spatially distributed hydrologic model undera variety of drainage scenarios to quantify the hydrologic effects of roadside ditches at multiple spatialscales. We also qualitatively investigate the relative impacts of ditches on phosphorus transport. Ourprinciple findings indicate that roadside ditches: (i) substantially alter basin morphometry and naturalflow pathways, (ii) increase peak and total event discharge and (iii) expedite the transport of agriculturalpollutants, thereby short-circuiting natural degradation processes that would otherwise have mitigatedtheir effects. These findings underscore how relatively fine-scale alterations to natural flow pathways canresult in substantial impacts to watershed scale hydrology and water quality and may help to inform spa-tially-targeted water resource management decisions and future modeling efforts.

� 2013 Elsevier B.V. All rights reserved.

1. Introduction

Roadside ditch networks, ubiquitous across much of the US, aredesigned to minimize local flooding risk by collecting and effi-ciently conveying road runoff to downstream surface water bodies.Unfortunately, runoff associated with road systems may be ladenwith contaminants from vehicles, road maintenance activities,and atmospheric deposition that may adversely affect sensitivereceiving water bodies (Forman and Alexander, 1998; Formanet al., 2003).

In addition to transporting road runoff, road ditches alter hill-slope and watershed hydrology by re-routing and concentratinglandscape-derived runoff and by lowering water table depthsdownslope of roads (Forman and Alexander, 1998; Jones et al.,2000). The hydrologic effects of road ditches have been corrobo-rated through both modeling and empirical observation at multiplespatial scales. However, previous studies have primarily been lim-ited to unpaved logging road networks in the US Pacific Northwest.At the watershed scale, these field and modeling studies have col-lectively shown that logging road ditches substantially alter natu-ral drainage patterns, increase peak storm flows and result inecologically significant soil moisture changes due, in part, to

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: +1 607 255 4449.

ditch-interception of shallow stormflow (Harr et al., 1975, 1979;King and Tennyson, 1984; Jones and Grant, 1996; Storck et al.,1998; Bowling and Lettenmaier, 2001; La Marche and Lettenmaier,2001; Tague and Band, 2001; Wigmosta and Perkins, 2001). Ditch-scale studies have also shown that logging road ditches carry sub-stantial quantities of landscape- and road surface-derived runoff(Gilbert, 2002; Wemple and Jones, 2003; Toman, 2004; Royer,2006) and have generally found ditch effects are amplified at smal-ler scales. With over 6.3 million km of public roads in the US (For-man et al., 2003), it is probable that road ditch effects may be morepervasive than previously thought. Despite these serious implica-tions, there has been little scientific investigation regarding thehydrological effects of roadside ditches in the context of land usesand physiographic regions other than forested logging roads in theUS Pacific Northwest.

Moreover, by altering natural flow paths and routing efficien-cies, road ditches may play critical roles in the transport of non-point source (NPS) pollution, which is the leading threat tosurface water quality in the US (USEPA, 2009). Two facts are of par-ticular concern in this regard: (i) the majority of NPS pollutionstems from agricultural runoff (Stamm, 1998; USEPA, 1998) and(ii) roadside ditches are generally not separated from agriculturalfields by a buffer strip and are therefore in a unique position tofacilitate the rapid transport of agricultural runoff to sensitivereceiving water bodies. Despite the serious repercussions of

294 B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305

enhanced agricultural field-stream connectivity, only a handful ofstudies have examined the water quality effects of road ditchesin agricultural settings (e.g. Diaz-Robles, 2007; Falbo, 2010).Importantly, these studies found that ditches transport significantquantities of sediments, nutrients and pathogens from adjoiningagricultural fields. They also detected viable Escherichia coli in ditchsediment between storm events, indicating that ditches are poten-tial refuges for bacteria, which may be re-suspended and trans-ported in subsequent storm-runoff events (Falbo, 2010). So, inaddition to expediting transport of storm runoff and associatedcontaminants from the landscape to streams, ditches may alsofunction as pollutant source-sink reservoirs. Some additional in-sights into the potential effects of roadside ditches are offered byexisting studies of agricultural drainage structures. For instance,numerous researchers have found that agricultural drainageimprovements can lead to increased peak flows as well as in-creased sediment, pesticide, pathogen and nutrient delivery to nat-ural waterways (Gilliam et al., 1999; Skaggs et al., 1994). It followsthen that roadside ditches, which can be similar in form and func-tion, may be somewhat analogous.

The primary objective of this study is to quantify the hydrologiceffects of roadside ditch networks in an agricultural landscape inthe Northeastern US. We address this objective through a geospa-tial analysis and the application of a distributed hydrologic model.A secondary goal is to explore the implications of road ditches forNPS pollution transport via storm runoff.

2. Methods

We mapped the artificial drainage network, including all agri-cultural and roadside ditches, in a small agricultural catchmentin central NY, allowing us to quantify how man-made drainages al-tered the physical hydrography of the study catchment. We thenincorporated the mapped ditch data into a time–area-based dis-tributed hydrologic model (Buchanan et al., 2012a). To assesshow ditches modified the hydrologic response of the basin weran the model under different drainage scenarios, which either in-cluded or omitted the ditches.

2.1. Site description

Located in central New York State, Paines Creek, drains a 38 km2

watershed that discharges into Cayuga Lake (Fig. 1). Agriculture isthe dominant land use (68.6%), with the remaining land a combina-tion of mixed and evergreen forest (17.2% and 14%, respectively)and residential (0.2%). The average slope is 5% with elevationsranging from 116 to 359 m. The region receives, on average,900 mm yr�1 of precipitation, with mid-latitude frontal systemsand cyclonic events common. Anthropogenic modification to thenatural drainage network in Paines Creek, like many rural water-sheds in the Northeast, consists of a complex network of agricul-tural and roadside ditches that alter and/or extend the naturaldrainage system.

2.2. Field measurements

2.2.1. Ditch network mappingWe characterized the artificial drainage network in Paines Creek

by recording the location, connectivity, flow direction, vegetatedcondition, and channel geometries (i.e. depth and width) of allroadside ditches and many agricultural ditches using Trimble™GeoExplorer XT GPS units (sub-meter accuracy). Roadside ditchesand relevant agricultural ditches (i.e. those connected directly toroadside ditches or natural streams) were then digitized from acombination of field GPS data and aerial photos (1:2000 scale). In

cases where property access issues prohibited direct field mappingof agricultural ditches, we used a combination of high-resolutioncolor orthophotos and Light Detection and Ranging (LiDAR) data(±0.15 m vertical accuracy). The orthophoto/LiDAR-derived agri-cultural drainage network was validated using available field sur-vey data. It should be noted that, unlike the road ditches, theagricultural ditch network was generally comprised of naturaldrainages that had been straightened and re-routed to facilitateagricultural development. In many cases, the agricultural ditcheswere developed in response to the concentrated discharges fromthe road ditches.

2.2.2. Rainfall and discharge monitoringTru-Trak™ capacitance probes, housed in PVC stilling wells,

were used to monitor stage height at the watershed outlet and atseven subbasins (Fig. 1). The mid-section method was used toestablish stage–discharge relationships for the instrumentedstreams over a range of flows. A 3-pass recursive filter was usedto separate the discharge data into its base- and quick-flow compo-nents (Lyne and Hollick, 1979). Hourly and total cumulative rain-fall data were obtained from tipping-bucket and Tru-Chek™ raingauges located within and proximal to the watershed boundary(Fig. 1).

2.3. Geospatial data

2.3.1. Soils, land cover and surface roughnessLand cover maps were extracted from a high resolution geospa-

tial data layer developed for a recent modeling project of the Cayu-ga Lake Basin (Haith et al., 2009). The Soil Data Viewer application(USDA-NRCS, 2009) was used to create raster maps of saturatedhydraulic conductivity (Ksat), effective porosity and soil hydrologicgroup from the USDA-NRCS Soil Survey Geographic (SSURGO)database. The Ksat values from the SSURGO database were in-creased by a factor of 10 to reflect rapid conductivities associatedwith preferential flow through macropores (Brutsaert and Nieber,1977; Brutsaert and Lopez, 1998; Frankenberger et al., 1999;Mehta et al., 2004).

2.3.2. Digital terrain analysis and scenario generation2.3.2.1. Road Ditch Scenario. This scenario represents the currentdrainage conditions in the watershed. To derive this scenario, wegenerated a 3 � 3 m DEM from a high-resolution LiDAR dataset(15 cm vertical resolution). The DEM was then filtered to minimizeanomalous elevation averages and all pits (i.e. cells with an unde-fined drainage direction) were filled. Although the topographic ef-fects of the roads (i.e. road berms) were generally well-representedby the 3 m DEM, the fine-scale topographic effects of the roadditches themselves were often not captured correctly. Thus, itwas necessary to ‘‘burn’’ the road and agricultural ditch networksinto the DEM, i.e., we manually lowered the elevation of DEM gridcells associated with agricultural and road ditches. The channelnetwork, watershed and sub-watersheds were then delineatedusing ArcHydro, a suite of terrain preprocessing tools that aid inthe creation and manipulation of digital terrain models (Maidment,2002). Channel delineation for the Road Ditch Scenario was carriedout by iteratively varying the channel threshold until the totalchannel length equaled that of the Natural Scenario (see below),plus the road ditch network (Table 1).

2.3.2.2. No-Road Ditch Scenario. For this scenario, we retained allthe current conditions, including the impervious road system,but removed the road ditches. By comparing this scenario to theRoad Ditch Scenario, we can ascertain the specific influence of theroad ditches. To do so, we started with the Natural Scenario DEM(described in the next section) and ‘‘burned’’ the agricultural

Fig. 1. Location map depicting the stream (blue solid lines) and road (black double lines) networks, elevation, flow monitoring sites (stars) and precipitation gauges (circles)of Paines Creek watershed. One stream and one roadside ditch were monitored at each of the seven flow monitoring sites. NHD stream overlay derived from the NationalHydrography Dataset (USGS, 2009). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 1Attributes of the three drainage scenarios.

Scenario Channellength

Roadland use

Ag. ditchesenforced

Road ditchesenforced

Natural 103No-Road Ditch 103 X XRoad Ditch 173 X X X

B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305 295

ditches into the DEM. Channel delineation for the No-Road DitchScenario was carried out by iteratively varying the channel thresh-old until the total channel length equaled that of the natural drain-age network (Table 1). Because we were interested in isolating therouting effects of the roadside ditches in particular, we retained theroad land use – meaning that roads could generate storm runoffbut it could not be routed via the road ditches (Table 1).

2.3.2.3. Natural Scenario. This scenario was intended to representnatural conditions, i.e., no impervious surfaces or modificationsto the natural drainage network. By comparing these results tothe other two, we can specifically evaluate the influence of roadsand ditches. To generate the hydrography for ‘‘natural’’ conditionswe first created a 15 m buffer around all road centerlines. Next, weused the buffer layer to remove all LiDAR data within 15 m ofeither side of the road. Deleting LiDAR data associated with roadseffectively removed the topographic and hydrological effects ofroads and roadside ditches. We then interpolated the remaining Li-DAR data to fill the missing areas, thereby creating an approxima-tion of the DEM for natural, un-road ditched conditions. The extentof the natural channel network was approximated by iterativelyvarying the contributing area threshold to define the uppermostpoint of each stream channel. The National Hydrography Dataset(NHD; USGS, 2009), served as a reasonable guide for establishingthe larger, more perennial streams, while our field survey dataand orthophoto analysis provided a reference for the smaller inter-mittent and ephemeral channels. Note: the DEM did not capture

the micro-topography associated with agricultural ditches – thusit was unnecessary to explicitly remove them in the NaturalScenario.

2.3.3. Channel attributesAccurate estimation of channel travel times (Eq. (9)) necessi-

tated the creation of raster coverages of channel bottom widthsand Manning’s roughness values for both the natural channelsand man-made drainages. Agricultural and road ditch bottomwidths were assigned from surveyed field data. Bottom widthsfor the natural stream network were determined via empiricalregression analysis of flow accumulation and channel width(r2 = 0.89; Buchanan et al., 2012a). Manning’s n roughness values(Eq. (9)) for agricultural and roadside ditches were based on tab-ulated values (Chow, 1959; Chin, 2006). Roughness values for thenatural stream network were linearly interpolated from tabulatedvalues based on stream order with 0.06 s m�1/3 for the lowest or-der and 0.03 s m�1/3 for the highest order (Bahremand et al.,2007).

2.4. Hydrographic analysis

The results of the ditch field mapping and digital terrain analy-sis were used to evaluate how the presence of artificial drainage al-tered the hydrographic characteristics of the watershed. Inaddition to the extent and hydrologic connectivity of the ditch net-work, we also quantified how ditches altered total channel length,drainage density, number of first order streams and flow distanceto open channels.

2.5. Model description

To evaluate the hydrological effects of roadside ditches in thehumid northeast, we required a model capable of simulating vari-able source area hydrology (VSA) (e.g., Dunne and Black, 1970), thepredominant storm runoff generating mechanism in the study re-gion (Walter et al., 2003; Easton et al., 2007), and also capable of

296 B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305

routing that storm runoff at spatial resolutions fine enough to cap-ture roadside ditches. We employed the Spatio-temporally Distrib-uted Direct Hydrograph (SDDH) VSA model (SDDH-VSA, Buchananet al., 2012a) because it satisfied these requirements.

The SDDH-VSA model builds upon the work of Maidment(1992a,b, 1993), Melesse and Graham (2004), and Du et al.(2009) to calculate hydrographs based on the time–area conceptoriginally proposed by Ross (1921). The approach consists of threemain stages: (i) watershed-scale storm runoff generation, (ii) spa-tial distribution of storm runoff and (iii) storm runoff-routing. Theapproach is briefly outlined below and discussed in detail in Bu-chanan et al. (2012a).

2.5.1. Storm runoff generationFirst, the model calculates the total watershed-wide runoff vol-

ume (Qv,t) at each time-step by summing all volumetric flows fromeach grid cell via a distributed application of the NRCS Curve Num-ber equation (Du et al., 2009; Buchanan et al., 2012a):

Qv;t ¼XN

i¼1

P2t

Pt þ Se;i

!� P2

t�1

Pt�1 þ Se;i

! !Ai for Pt > Ia ð1Þ

where Pt is the cumulative depth of effective rainfall (m) at time-step t (computed as rainfall – initial abstraction, Ia (m)), Se,i is thedepth of effective available storage in the ith cell (m), typicallydetermined from tabulated curve numbers (e.g., USDA, 1972), Ai isthe area of grid cell i contributing storm runoff at time t (m2) and,N is the total number of storm runoff generating cells. Ia is generallycomputed as 0.2S, however recent research suggests Ia = 0.05Syields more accurate estimations of storm runoff (Woodwardet al., 2003; Lim et al., 2006; Shaw and Walter, 2009; Shi et al.,2009). Hereafter, Ia = 0.05S is assumed.

2.5.2. Storm runoff distributionOnce the catchment-wide storm runoff is determined for each

time-step (Qv,t), it is then spatially distributed based on the frac-tional runoff-contributing area following Steenhuis et al. (1995):

Aft ¼ 1� S2e

ðPt þ SeÞ2ð2Þ

where Aft is the storm runoff generating fraction of the watershed ateach time-step. The watershed-wide effective storage term, Se, wasback-calculated from the total event storm runoff computed fromEq. (1) (e.g., McCool et al., 1995); although it not necessary for thisstudy, functions can be developed to relate these storage terms toantecedent conditions (e.g., Shaw and Walter, 2009). Fractionalstorm runoff contributing areas (Eq. (2)) are then spatially distrib-uted based on a soil topographic index (STI) (e.g. Lyon et al., 2004):

STI ¼ lna

D � K � tanðbÞ

� �ð3Þ

where a is the upslope contributing area (m2), b is the local topo-graphic slope (m m�1), and D is the soil depth (m) and K is the sat-urated hydraulic conductivity of the soil above the shallowestrestrictive layer (m d�1) (Walter et al., 2002). For ease of calcula-tions, the STI is binned into m equal-area wetness classes (wc) asper Schneiderman et al. (2007) and Easton et al. (2008). Next, thelocal effective storage in each wetness class (re,i) is calculated asa function of the maximum total watershed storage, Se (Schneider-man et al., 2007):

re;i ¼2ðSeÞ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1� Awc;i

p�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1� Awc;iþ1

p� �ðAwc;iþ1 � Awc;iÞ

� ðSeÞ ð4Þ

where each area is defined by a specific wc that is bounded on oneside by the fraction of the watershed that is wetter, Awc,i, i.e., has

less local effective moisture storage, and on the other side by thefraction of the watershed that is dryer, Awc,i+1, i.e., has greater localeffective moisture storage. Total storm runoff volumes at each time-step (Qv,t, Eq. (1)) are then partitioned into the appropriate wc as afraction of the total effective soil water storage:

Qj;t ¼ Qv ;tPt � rjPmj¼1Pt � rj

!ð5Þ

where Qj,t is the volumetric storm runoff from the jth wc at timestep t (m3) and rj is the local effective storage in the jth wc andm is the total number of wcs. The storm flow per unit horizontalarea in each wc and time-step (qj,t, m s�1) was then calculated as:

qj;t ¼Q j;t

AwcDtð6Þ

where Awc is the area of the wc (m2) and Dt is the time-step length.To ensure that impervious surfaces such as roads and water

bodies are correctly represented in the storm runoff generationprocess, the standard, infiltration-excess-based CN technique(e.g., Walter and Shaw, 2005) is applied to the impervious surfacesin the watershed and the distributed road runoff raster at eachtime-step is overlaid with saturation-excess raster created fromEq. (6).

2.5.3. Flow routingTo route the spatio-temporally distributed storm runoff, calcu-

lated above, to the watershed outlet, SDDH-VSA first computesstorm runoff travel-times for each grid cell at each time step viaa three-phase algorithm which accounts for the travel times asso-ciated with: (i) storm runoff (‘‘overland’’ flow), (ii) interflow trig-gered by infiltrating overland flow, and (iii) channel flow.

Overland travel times are estimated by combining the steadystate kinematic wave approximation with Manning’s equation:

Tj;t ¼ q�0:4j;t

L0:6n0:6

b0:3

!j

ð7Þ

where Tj,t is the overland travel time across cell j at time-step t (s), Lis the flow path distance in the grid cell (m), n is the Manning’sroughness value (s m�1/3), qj,t is the storm flow per unit area overthe grid cell at time-step t (m s�1) (Eq. (6)), and b is the topographicslope (m m�1). Note: although we are conceptualizing storm runoffas overland flow, it represents both overland flow and shallow, sub-surface storm flow (e.g., Lyon et al., 2006); this was also noted in theoriginal formulation of the SCS-CN method (USDA-SCS, 1972, chap-ter 10). Thus, we are assuming the ‘‘overland flow’’ equations arereasonable proxies for estimating travel times for these quickflow(overland and subsurface) storm runoff fluxes (henceforth referredto as storm runoff).

In some cases, grid cells will not generate storm runoff, yet willreceive it from neighboring upslope cells. In these instances, we as-sume storm runoff would infiltrate and proceed down-slope asshallow interflow. Ignoring tortuosity, shallow interflow traveltimes across the jth cell, Ti,j, (s) are estimated assuming steadystate flow through soil (e.g. Brosig et al., 2008):

Ti:j ¼L

K dhnedL

� �0@

1A

j

ð8Þ

where L is the flow path distance (m), K is the lateral saturatedhydraulic conductivity (m s�1), ne is the effective porosity, and dh/dL is the hydraulic gradient, approximated by the topographic slope(m m�1), i.e. b. For simplicity, we are implicitly assuming that stormrunoff to these cells is much greater than direct rainfall and, thus,direct rainfall is assumed negligible.

Table 2(A) Date, storm depth (mm) and duration (h) of observed storm events. (B) Returnperiods and percent smaller than statistics for selected design storms.

A BObserved storms 12-h design storms

Date Depth(mm)

Duration(h)

Depth(mm)

Returnperiod(yr)

%Smallerthan

5/16/2009 16 11 6a <1 235/26/2009 42 66 13 <1 506/30/2009 19 50 25 <1 738/2/2009 15 8 33 <1 8010/28/2009 33 12 46 1 884/25/2010 32 55 56 2 925/6/2010 22 9 97 25 985/12/2010 25 54 133 100 996/6/2010 27 17 – – –

a The smallest storm depth for which we observed ditch flow in the field.

Table 3Morphometric characteristics under different drainage conditions.

Hydrographic parameter Drainage scenario

Natural No-RoadDitch

RoadDitch

Drainage density (km/km2) 2.7 2.7 4.5# of Headwater streams 55 57 234Average flow length to a channel (m) 491 468 317

B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305 297

Channel flow velocities were calculated by combining Manning’sequation and the continuity equation for a wide channel (Muzik,1996; Melesse and Graham, 2004). The travel time across the jthchannel cell at each respective time interval (Tcj,t) was determinedas the flow length, L, divided by the channel velocity:

Tcj;t ¼ Lb0:5

nQt

B

� �0:67", #0:6

0@

1A

j

ð9Þ

where Qt is the cumulative discharge (m3 s�1) obtained from aweighted flow accumulation to the cell at time-step t and B is thechannel width (m) of the jth grid cell.

The cumulative travel times to the outlet at each time-step arecalculated as the sum of all grid cell travel times along each respec-tive flow path. Finally, the direct storm runoff flow at the outlet isdetermined as the sum of the volumetric flow from all cells arriv-ing at the outlet at each respective travel time.

2.6. Model application

2.6.1. Model parameterization, calibration, and performanceevaluation

The model was parameterized with the aforementioned rastercoverages of soils, land cover, export coefficient, surface roughnessand channel attributes. Nine storm events (Table 2A) were used toevaluate model performance and ensure it correctly captured thehydrological response at both the watershed- and subbasin-scale.Simulations of the observed storms (see Section 2.6.2) and designstorms (see Section 2.6.3) used back-calculated values for Se (Eq.(1)) based on the total event storm runoff computed from Eq. (1)(e.g., McCool et al., 1995). All other parameters were either directlymeasured or taken from publications, as discussed in the modeldescription. To asses model performance we employ three metrics:percent error in peak flow (PEP), Nash–Sutcliff Efficiency (NSE),and the percent increase in peak flow.

2.6.2. Scenarios using measured dataThe hydrologic effect of the ditch network was assessed by forc-

ing the three drainage scenarios with four of the nine observedstorms, which represented the range of storm sizes and antecedentconditions: two spring events representing wetter antecedent con-ditions were 5/16/2009 (relatively small storm) and 5/12/2010(relatively large storm) and two summer-fall events representingdrier antecedent conditions were 6/30/2009 (relatively smallstorm) and 10/28/2009 (relatively large storm) (Table 2A). Outputfrom the scenario runs was compared at both the watershed outletand at the outlet of the seven monitored subbasins.

2.6.3. Scenarios using design stormsWe also forced the model with a series of 12-h design storms

(Table 2B) to better examine how the watershed-scale effect ofthe ditch network varied with storm size. In addition, a 25 mm12-h design storm was used to illustrate the effect of ditch inter-ception on distributed flow timing at the subbasin scale. Designstorms greater than a 1-yr return interval were derived from alog-normal maximum exceedence frequency analysis of the pre-cipitation data (period of record: 1975–1997). Note, for the twolargest storms (25 yr and 100 yr) it is likely that some Hortonianflow would occur (e.g. Walter et al., 2003), which our model doesnot simulate. However, these storms are so large that most models(including ours) predict that virtually the whole landscape is gen-erating storm runoff, so the ultimate runoff response is likely thesame for both mechanisms. For the purposes of this analysis we as-sumed average antecedent moisture conditions and that all stormevents possessed a Type II distribution (viz. SCS, 1984).

3. Results and discussion

3.1. Hydrographic analysis

We documented a total of 179 roadside ditches during our fieldsurvey, which translates to almost 70 km of road ditch in the wa-tershed. Over 94% were hydrologically connected to the naturalstream network – either directly or via agricultural ditches. Theinclusion of road ditches substantially increases the drainage den-sity and number of headwater stream segments while substan-tially decreasing the average flow distance to open channels(Table 3, Fig. 2). Note, while some of these analyses are similar toBuchanan et al. (2012b), the underlying assumptions and thus,the results are different.

The reduced average flow distance to open channels, coupledwith the fact that over 75% of the total road ditch channel lengthwas directly adjacent to unbuffered agricultural fields, implies botha more efficient hydrological connection between field and streamand a greater risk of agricultural NPS pollution.

Approximately 27% of the total watershed area is drained by theroadside ditch network under the Road Ditch Scenario. Includingthe drainage area associated with agricultural ditches, the drainagearea of the entire man-made ditch network is almost 50% of the to-tal channel length (Fig. 3).

3.2. Model performance

The SDDH-VSA model accurately captured the storm magni-tudes and, for the most part, the timing of the stream hydrographsunder existing conditions (Fig. 4). For the nine monitored storms in2009 and 2010, the model reproduced the hourly stream dischargewith an r2 = 0.65 and an average NSE of 0.69, with most of the erroroccurring in the first few hours of the event when the modeltended to over-predict discharge (Buchanan et al., 2012a). Full

Fig. 2. Flow distance to open channels under the three different drainage scenarios. Note the inverse relationship between degree of anthropogenic alteration and flowdistance (i.e. increasing blue from right to left). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. Subbasins draining to natural streams (white subbasins, solid blue lines),agricultural ditches (light grey, green dashed lines), and road ditches (dark grey,black dotted lines). (For interpretation of the references to color in this figurelegend, the reader is referred to the web version of this article.)

Fig. 4. Predicted vs. observed streamflow for all nine storms (r2 = 0.65).

298 B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305

model diagnostics and limitations are presented in Buchanan et al.(2012a).

The SDDH-VSA model also provided a reasonable approxima-tion of the observed ditch runoff response at the scale of individualditches (Fig. 5), which lends credence to the model’s representationof storm runoff generation and routing processes.

3.3. Watershed-scale effects: measured data

As anticipated, including the ditch network is critical to simu-lating the observed hydrograph, i.e. Natural and No-Road Ditch Sce-narios hydrographs did not match observations (Fig. 6) and thestatistical agreement with the observations was poor (Table 4).

The hydrographs for the Natural and No-Road Ditch Scenarios arecharacterized by lower peak flows and long, flat recession limbs,reflecting the slower storm runoff travel times in the absence ofroad ditches. The No-Road Ditch Scenario resulted in minor in-creases in peak flow and slightly steeper recession limbs relativeto the Natural Scenario. The hydrologic influence of road ditcheswas larger than agricultural ditches due to their higher densityand because they follow unnatural flow paths parallel to roadsand, quite often, perpendicular to the slope, increasing the likeli-hood of storm runoff and interflow interception. By contrast, themajority of agricultural ditches in Paines Creek tended to parallelthe slope, often as a straightened form of the ‘‘natural’’ channel.

On average, the presence of roads and road ditches increasedpeak flow at the watershed outlet by over 100% whereas the agri-cultural ditches alone resulted in a 16% peak flow increase over thefour modeled events (Table 4). The substantial hydrological effectsof the road ditches are, perhaps, unsurprising given the profoundchange in drainage network form and extent (e.g. road ditches al-most doubled the drainage density). Indeed, numerous studieshave demonstrated that increases in drainage density and hydro-logic connectivity result in enhanced peak flow rates and totalevent flows with concomitant declines in baseflow volumes (Carl-ston, 1963; Trainer, 1969; Jones and Grant, 1996).

Road surfaces contributed approximately 10% of the storm run-off over the four storms, suggesting the road effect operated pri-marily through increased routing efficiencies via the road ditchesas opposed to large storm runoff contributions from the road sur-faces themselves. Because the inclusion of agricultural ditchesalone did not result in substantial changes in storm runoff relativeto the Natural Scenario, henceforth we will often omit the No-RoadDitch Scenario for simplicity.

One effect of the road/road ditch system, which is captured bythe SDDH-VSA model, is to deplete water tables downslope ofroads that run perpendicular to the topographic slope (Fig. 7). Thishas been previously observed by other researchers (Bowling and

Fig. 5. Predicted vs. observed Qt (A) and Qp (B) for seven gauged subbasins.

Fig. 6. Predicted vs. observed (thin solid line) hydrographs under the Natural (dashed line), No-Road Ditch (solid grey line) and Road Ditch (black solid line) scenarios for fourselected storm events. Hourly rainfall is depicted as bars, using the right-hand y-axis.

B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305 299

Lettenmaier, 1997; Tague and Band, 2001; Wigmosta and Perkins,2001) and is the result of road ditch interception of upslope stormrunoff and shallow interflow.

Assuming the volume of water associated with increased peak-flow minus road runoff represents a re-allocation of water thatwould otherwise have contributed to soil or groundwater recharge,the mean groundwater depletion due to artificial drainage is esti-mated at 15% of the total storm runoff. This estimation is likely

conservative (low) because we did not model the interception ofdeeper groundwater flow by the ditches.

The relative contributions from roads are a function of anteced-ent moisture conditions and the particular point in the storm(Fig. 8). This is most notable during the early periods of the stormdue to the time lag associated with wetting up of the landscapeprior to initiating storm runoff (Fig. 8); note, however, that this isalso the period when the model systematically over-predicts so

Table 4Percent error in peak flow (PEP) and Nash–Sutcliff Efficiencies (NSE) under the three drainage scenarios, as well as the percent increase in peak flow due to artificial drainage.

Storm Natural No-Road Ditcha Road Ditchb % Increase due to ag. ditches % Increase due to road and ag. ditches

PEP NSE PEP NSE PEP NSE

5/12/2010 �44.6 0.63 �28.3 0.70 20.9 0.67 29.4 117.66/30/2009 �54.4 0.59 �41.8 0.68 8.6 0.91 26.9 138.55/16/2009 �41.8 0.61 �38.3 0.6 16.7 0.65 5.4 10010/28/2009 �51.9 0.68 �51.2 0.68 �19.9 0.73 1.3 66.4

Average �49.4 0.63 �39.9 0.65 6.6 0.74 15.8 105.6

a Ag. ditches only.b Includes ag. ditches.

300 B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305

the actual percentages of early-event road contributions are likelysomewhat lower than shown in the figure. The percent road contri-bution drops precipitously as cumulative discharge increases –emphasizing that the landscape is the primary driver of storm run-off in the watershed and that road effects are primarily associatedwith ditch routing and not road surface runoff. Note also that thedark, dashed lines represent the May 16th storm, which occurredduring higher antecedent moisture conditions than the June 30thstorm (solid, dark lines; Fig. 8). Under wetter conditions, storageand initial abstraction is greatly reduced and, thus, the percentcontribution from the roads is far less because the landscape rap-idly begins contributing storm runoff. This implies a transitionfrom predominantly road-derived to landscape-derived contami-nants as storms progress or as storm magnitude and/or antecedentmoisture increase.

Our finding that roadside ditches resulted in a significant in-crease in peak flow rates and steeper recession limbs, yet littlechange in time-to-peak is consistent with numerous other fieldand modeling studies (e.g. Bowling and Lettenmaier, 1997; Storcket al., 1998; Thomas and Megahan, 1998; Carluer and De Marsily,2004; La Marche and Lettenmaier, 2001; Buchanan et al., 2012b).In most cases, the simulated increase in peak discharge at our wa-

Fig. 7. Close-up view of the result of subtracting the Natural STI from the Road Ditch STgreater soil moisture deficits and thus reduced probability of saturation-excess runoff geto color in this figure legend, the reader is referred to the web version of this article.)

tershed outlet was greater than previous research. Disparities be-tween our results and those of previous work are likelyattributable to the fact that the other studies were conducted onunpaved logging roads in heavily forested catchments with <50%road drainage-stream connectivity (e.g. Bilby et al., 1989; Bowlingand Lettenmaier, 1997; Skaugset and Allen, 1998) and a 21–63% in-crease in channel density as a result of road drainage (e.g. Wempleet al., 1996; Storck et al., 1998) – whereas the current study consid-ered impervious road surfaces in a predominantly agricultural set-ting with >67% road ditch-stream connectivity and a 68% increasein channel density resulting from road drainage.

3.4. Watershed-scale effects: design storms

To gain a better understanding of how the influence of roaddrainage changed with storm size we plotted a range of 12-h de-sign storm depths against the percent difference in peak flow be-tween the two most extreme drainage scenarios (Natural vs. RoadDitch) and the rainfall frequency (Fig. 9A). The relative influenceof road drainage on peak discharge is inversely related to stormsize and the degree of influence is greatest during the small, mostfrequent storms (e.g. generally <13 mm). The inverse relationship

I. Black linear features represent roads and road ditches. Darker red areas indicateneration resulting from road-ditch interception. (For interpretation of the references

Fig. 8. Percent of the total runoff contributed by the road surfaces (primary y-axis) and cumulative discharge at the watershed outlet (secondary y-axis) at each time step.Dashed and solid lines represent wet (May 16th storm) and dry (June 30th storm) antecedent moisture conditions, respectively.

B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305 301

between ditch influence and storm size (Fig. 9), was corroboratedby the empirical observations of Wright et al. (1990), Jones andGrant (1996), Thomas and Megahan (1998), Beschta et al. (2000)and the modeling results of Alila and Schnorbus (2005). In contrast,La Marche and Lettenmaier (2001) and Cuo et al. (2006) foundincreasing ditch effects with increasing return period. The lack ofconsensus is difficult to account for, especially considering thatmany of the conflicting studies employed the same distributedhydrologic model (i.e. DHSVM, Wigmosta et al., 1994). Alila andSchnorbus (2005) explain that the short-circuiting effect of theroad network will have the greatest impact on peak discharge dur-ing small storms where matrix flow dominates (i.e. return periods<0.17 yrs). As event size increases, however, the hydrologic effectsof road drainage will diminish because a larger portion of hillsloperunoff is routed via fast-moving preferential flow paths (Alila andSchnorbus, 2005), which are prevalent in the study region (Harpoldet al., 2010; Dahlke et al., 2012). The transition from matrix to pref-erential flow paths in Paines Creek is likely compounded by thefact that the relative contribution of road surfaces will diminishas more of the pervious (e.g., non-road surface) landscape saturatesand contributes storm runoff under larger and larger storms (Zie-gler and Giambelluca, 1997).

Fig. 9. (A) Percent difference in peakflow between the Natural and Road Ditch drainage sca function of storm depth. Predicted hydrographs with and without the roadside ditch netC have different y-axis scales.

Fig. 9B and C provide a more detailed comparison of the Naturaland Road Ditch hydrographs for a small, frequent, storm (6.4 mm,return period = <1 yr; Fig. 9B) verses a large, infrequent storm(152 mm, return period = 100 yr; Fig. 9C). This further highlightsthe amplified relative effect of ditches during smaller storms. Thesubstantial difference between the Natural and Road Ditch scenar-ios in Fig. 9B is due to the large storm runoff contribution of theroad surface relative to the landscape during smaller storms. Note,in the larger storm, the presence of road ditches in the Road DitchScenario serves to re-allocate storm runoff from the recession limbin the Natural Scenario to the peak flow portions of the Road DitchScenario hydrograph. Thus, as storm magnitude increases, the prin-ciple effect of roads and their associated drainage networks transi-tions from increased storm runoff volumes to shortened stormrunoff travel-times due to flow interception and rapid open chan-nel transport. In addition, the recession curve is steeper for theroad-ditched scenario, reflecting the faster drainage of storm run-off by the road ditches.

The shortening of distributed storm runoff travel times by theditch network can be seen in the dark blue linear features in theRoadDitch Scenario in Fig. 10, as well as by the overall reductionin slower moving storm runoff (red pixels). During larger storms,

enarios (Qp; primary axis) and percentage of storms smaller than (secondary axis) aswork for a small 6.4 mm storm (B) and a large 152 mm storm (C). Note figures B and

302 B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305

road ditches have a more profound impact on reducing travel timesfor areas high in the watershed, which generate storm runoffmainly during large events.

To some extent, the conclusions drawn from this analysis mustbe tempered by the fact that several of the design storms were out-side the range of tested storm depths.

3.5. Subbasin-scale

Road ditches significantly altered the effective drainage areaupslope of stream crossings by re-routing surface and subsurfaceflow between subbasins. In some cases this resulted in a significantincrease in drainage area, while in others drainage area was re-duced. In addition to changing contributing areas, ditches also al-tered routing efficiencies at the subbasin-scale. To illustrate theeffect of ditch interception on distributed flow timing at the subba-sin scale, Natural storm runoff travel times were subtracted fromthe Road Ditch travel time raster for a representative ditch subbasinduring a 25 mm, 12-h design storm (Fig. 11). Had the road ditchbeen absent (i.e. Natural Scenario), storm runoff from the agricul-tural field would have proceeded downslope as slower movingoverland and shallow interflow. With the roadside ditch in place,however, storm runoff is shunted directly to the neighboringstream, resulting in an average decrease in travel time to the wa-tershed outlet of 21 h.

Collectively, the changes in subbasin contributing areas and in-tra-subbasin routing efficiencies translated to substantial modifi-cation of peak and total event flows in the receiving streams. Toexamine these effects more closely, we modeled seven differentstream subbasins under the Natural, No-Road Ditch, and Road Ditchscenarios during the October 28th, 2009 storm (see Fig. 1 for loca-tions). In general, agricultural ditches resulted in only minorchanges to the seven streams (i.e. average percent change in Qt,Qp and drainage area of �2%, 8%, and 4%, respectively; Table 5).Road ditches, on the other hand, caused considerable increases inQt, Qp and drainage area (i.e. average percent change in Qt, Qp anddrainage area of 40%, 123%, and 24%, respectively; Table 5). Therewas, however, considerable variability in the results among themodeled subbasins (Table 5). In addition, subbasins 3 and 4 expe-rienced a decrease in drainage area and Qt due to a reduction in thecontributing area of the watershed when ditches were introduced.Interestingly, although the drainage areas were reduced by asmuch as 14%, the peak flow rates still increased by 42% and 27%,respectively, indicating that increased drainage efficiency morethan compensated for the reduction in contributing area.

It is interesting and somewhat counterintuitive that despiteclear changes to distributed storm runoff timing resulting fromthe road ditches (Figs. 10 and 11), SDDH-VSA, and indeed, most

Fig. 10. Runoff travel time distributions under the three drainage scenarios for the 100-ydark blue linear features in the Road Ditch Scenario map. (For interpretation of the referearticle.)

modeling efforts, have been unable to detect significant changesin peak flow timing in the natural stream network. This discrep-ancy may be explained by the complex and likely compensatoryhydrologic effect of ditch networks. For example, increased drain-age efficiency associated with the conversion of overland and shal-low subsurface flow to quick-moving open channel flow may beoffset by: (i) the delaying effect of reduced soil moisture down-slope of roads, (ii) increased available water capacity upslope ofroads due to ditch interception and drainage of the water tableand (iii) increased drainage areas at the subbasin scale due to ditchrouting must come at the cost of a reduction in area from neighbor-ing sub-catchments (Tague and Band, 2001). Also, an analysis ofstorm runoff generation in relation to travel-time isochrones re-veals that the majority of peak flow is contributed by near-streamareas, which are often the first to saturate. The road ditches inPaines Creek do not significantly affect these areas. Instead, theyserve to intercept and reroute storm runoff from upslope areas.

Despite some variations in findings regarding the hydrologic ef-fects of roadside ditch networks (discussed earlier in this paper),one result has held true across the vast majority of relevant stud-ies: roadside ditch networks affect appreciable and potentially eco-logically significant changes to natural hydrologic regimes.Although it is difficult to demonstrate causal ecological effects,some researchers have found strong negative correlations betweenthe number of road crossings per km of stream and habitat integ-rity and macroinvertebrate health (e.g., Avolio, 2003; Alberti et al.,2007). We speculate that these ecohydrologic effects are likelyaccentuated in landscapes characterized by shallow restrictive lay-ers (promoting rapid sub-surface flow), humid climates, dense andwell-connected road drainage networks, and land uses with a highpropensity to generate storm runoff.

3.6. Implications for NPS pollution

The linkage between the delivery of NPS pollution and thestorm runoff dynamics of a watershed (e.g. runoff timing and mag-nitude and spatial distribution of storm runoff source areas) is wellestablished. It follows then that alterations in hydrologic responseof a watershed due to the presence of artificial drainage will resultin changes in the fate and transport of NPS pollutants.

To illustrate the potential impact of artificial drainage ditcheson agricultural NPS pollution transport we plotted the cumulativedrainage area associated with runoff travel times from agriculturallands during the October 28, 2009 storm for both the Natural andRoad Ditch Scenarios (Fig. 12). Not only are ditches potentiallyincreasing the total pollutant load reaching the outlet because theyare draining more agricultural area, but also, the contaminants arebeing delivered much more rapidly; suggesting that ditches may

r storm. Reduction in runoff travel times by road ditches is clearly manifested as thences to color in this figure legend, the reader is referred to the web version of this

Fig. 11. Subtraction raster of runoff travel times (i.e. Road Ditch – Natural; units = hours). The redder, more negative numbers represent runoff travel times more significantlyreduced by the presence of the roadside ditch.

Table 5Total event discharge (Qt), peak discharge (Qp) and drainage areas of seven stream subbasins under the Natural, No-Road Ditch and Road Ditch drainage scenarios. The averagepercent difference between the Natural and the No-Road and Road Ditch scenarios are also presented.

Sub-basin Qt (m3) Qp (m3/s) Drainage area (km2)

Nat No Road Road Nat No Road Road Nat No Road Road

1 1632 1725 2403 0.028 0.031 0.057 0.34 0.34 0.382 3318 3436 4487 0.062 0.069 0.114 0.91 0.91 1.013 4067 3417 3948 0.088 0.089 0.125 1.2 1.2 1.14 4055 3356 2800 0.088 0.089 0.112 1.2 1.2 1.035 1026 1109 2776 0.013 0.014 0.075 0.34 0.34 0.726 24,405 24,570 32,574 0.512 0.625 0.868 6.23 7.96 7.887 2774 2849 3518 0.041 0.043 0.064 0.59 0.59 0.77

Average 5897 5780 7501 0.119 0.137 0.202 1.54 1.79 1.84Mean % diff. from Nat – �2 40 – 8 123 – 4 24

B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305 303

greatly reduce natural pollutant degradation processes. For exam-ple, over 35% more agricultural land was drained in the first 24 h ofthe storm due to the ditches (dashed vertical line in Fig. 12); 5.2 vs.8.1 km2 under Natural and Road Ditch scenarios, respectively.Moreover, the mean runoff travel time for the Natural Scenariowas 13.3 h, vs. 21.5 h under the Road Ditch Scenario – indicatinggreatly enhanced drainage efficiency.

This is particularly important when you consider that mostagricultural contaminants such as nutrients and pesticides degradeover time. Simply put: the more time spent in the environment, themore pollutant is broken down or sequestered as a result of myriadbiogeochemical decay processes (e.g. adsorption, absorption,microbial and chemical breakdown, photodegradation, and volatil-ization). Consider phosphorus (P), which has an effective ‘‘half-life’’of �3.5 days (e.g., Smith et al., 1997; USEPA, 2004); i.e., an effectivedegradation of 20% in 24 h. So, under the Natural Scenario, not onlyis 35% less agricultural land contributing storm runoff at 24 h(Fig. 12), but this non-runoff generating land has 20% less P avail-able to contribute to NPS pollution.

This simple example demonstrates that by increasing drain-age efficiency, agricultural and roadside ditches may indeed

short-circuit natural retardation mechanisms that would other-wise have mitigated their effects. Given the serious threat NPSpollution poses to freshwater resources, it is imperative thatwe develop tools to aid in the identification of not only the crit-ical pollutant source areas, but also the critical transport path-ways connecting source areas to the drainage network.Evaluating pollutant loading in the context of storm runoff traveltimes, which account for fine-scale routing through the actualdrainage network, not only opens the door to remediation strat-egies targeted at these critical transport pathways (e.g. infiltra-tion basins), but also ensures the prudent allocation of limitedBMP funding to pollutant generating areas that are actually con-nected to the drainage network.

4. Conclusions

Applying a spatially distributed travel time model to a small rur-al catchment in central New York State, we demonstrated consider-able hydrographic, hydrologic, and potential water quality effectsresulting from road ditch networks. Principle findings include:

Fig. 12. Cumulative drainage area of agricultural land uses contributing runoffunder the Natural and Road Ditch scenarios during the October 28, 2009 storm as afunction of runoff travel time.

304 B. Buchanan et al. / Journal of Hydrology 486 (2013) 293–305

� Roadside and agricultural ditches drained almost half of thetotal watershed area, greatly increased drainage density, anddecreased flow distance to channels.� Roadside ditches had far greater morphometric and hydrologic

impacts than agricultural ditches, owing to their far greaterdensity and orientation to natural drainage patterns (e.g., inPaines Creek, road ditches often run across the slope and agri-cultural ditches typically follow natural, downslope pathways).� Accurate representation of basin hydrography, including fine-

scale artificial drainages, is critical to the accurate simulationof hydrologic response.� The road ditch effect was focused primarily on increased peak

flows and total storm runoff. Little effect on time-to-peak wasdetected either at the subbasin or watershed scales.� Roads altered basin hydrology primarily through routing effects

of the ditches as opposed to storm runoff contributions fromroad surfaces.� The presence of roads and their associated ditches has the

potential to significantly increase NPS pollution problems inagricultural watersheds.� Accordance of results regarding the hydrological effect of road

ditches among the aforementioned studies warrants furtherresearch into: the mechanisms driving ditch hydrology, includ-ing interception of groundwater and tile drainage; more thor-ough quantification and validation of the water qualityeffects; and exploration of potential remediation practices suchas nested infiltration basins, controlled tile drainage or in-ditchcheck dams.

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