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Climate, land use and hydrologic sensitivities ofstormwater quantity and quality in a complex coastal-urban watershedOmar I. Abdul-Aziza & Shams Al-Amina

a Department of Civil & Environmental Engineering, Florida International University (FIU),Miami, FL, USAPublished online: 03 Jan 2015.

To cite this article: Omar I. Abdul-Aziz & Shams Al-Amin (2015): Climate, land use and hydrologic sensitivities of stormwaterquantity and quality in a complex coastal-urban watershed, Urban Water Journal, DOI: 10.1080/1573062X.2014.991328

To link to this article: http://dx.doi.org/10.1080/1573062X.2014.991328

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CASE REPORT

Climate, land use and hydrologic sensitivities of stormwater quantity and qualityin a complex coastal-urban watershed

Omar I. Abdul-Aziz* and Shams Al-Amin

Department of Civil & Environmental Engineering, Florida International University (FIU), Miami, FL, USA

(Received 20 November 2013; accepted 29 September 2014)

We determined reference hydro-climatic and land use/cover sensitivities of stormwater runoff and quality in the MiamiRiver Basin of Florida by developing a dynamic rainfall-runoff model with the EPA Storm Water Management Model.Potential storm runoff in the complex coastal-urban basin exhibited high and notably different seasonal sensitivities torainfall; with stronger responses in the drier early winter and wetter late summer months. Basin runoff and pollutant loadsshowed moderate sensitivities to the hydrologic and land cover parameters; imperviousness and roughness exhibited moredominant influence than slope. Sensitivity to potential changes in land use patterns was relatively low. The changes in runoffand pollutants under simultaneous hydro-climatic or climate-land use perturbations were notably different than thesummations of their individual contributions. The quantified sensitivities can be useful for appropriate management ofstormwater quantity and quality in complex urban basins under a changing climate, land use/cover, and hydrology aroundthe world.

Keywords: stormwater; sensitivity; climate; land use; hydrology; SWMM

1. Introduction

Stormwater is often held responsible for urban flooding

and poor water quality around the world. It has been

ranked as a leading cause of water quality impairments in

US lakes and streams (Novotny, 1991; Novotny & Olem,

2003; Tsihrintzis & Hamid, 1998; USEPA, 1990).

Although urban stream flows are dictated by climatic

variations, both climate and land use changes can notably

influence pollutant loads (Tu, 2009). Therefore, research

integrating land use/cover and climate variables in the

backdrop of highly altered and engineered catchment

hydrology is important for achieving insights into

stormwater generation and management strategies.

Many studies have been conducted for understanding

processes underlying urban storm runoff quantity and

quality. Example of relatively recent research includes

characterization of stormwater first flush (Bach et al.,

2010), assessment of hydrologic limitations of conven-

tional urban stormwater management (Burns et al., 2012),

and identification of dominant perspectives on urban

stormwater futures (Winz et al., 2011). Analyzing the US

National Stormwater Quality Database, Maestre and Pitt

(2006) investigated the effective role of different factors

such as land uses, imperviousness, watershed controls,

sampling methods, and seasons, to drive stormwater

quality. Other studies (e.g., Goonetilleke et al., 2005; Hatt

et al., 2004) demonstrated strong influence of land use

types, urban density, and drainage infrastructure on the

stormwater pollutant loads. Significantly higher storm

runoff and pollutant loads were reported from asphalt

driveways than their paved and crushed-stone counterparts

(Gilbert & Calusen, 2006). Overall, watershed land uses

can largely determine the primary stormwater pollutants

such as sediments (Nelson & Booth, 2002) and metals

(Helsel et al., 1979), significantly affecting stream

nutrients and secondary stream responses (aquatic biota)

(Lenat & Crawford, 1994).

The entire process of stormwater runoff and quality

can be impacted by the changing climatic conditions and

catchment hydrology (Berggren et al., 2011; Borris et al.

2013; Willems et al. 2012). Ferguson (1990) demonstrated

that the continuous, low-level background flows and long-

term water balance can significantly modulate the short-

term stormwater infiltration management. Conducting a

field-experimental study of urban roads in Melbourne

(Australia), Vaze and Chiew (2002) found build-up and

wash-off of surface pollutants critically depending on the

rainfall and runoff characteristics. Complex water quality

processes such as the metal element speciation in urban

rainfall-runoff are also effected by watershed hydrology

(Dean et al., 2005). Backstrom et al. (2003) reported a

winter driven climatic pattern for speciation and transport

of heavy metals in road runoff and roadside total

deposition at two Swedish sites. Field-experimental

q 2014 Taylor & Francis

*Corresponding author. Email: oabdulaz@fiu.edu

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simulations with anticipated rainfall regimes (associated

with projected climate change) in the Australian Gold

Coast region showed dominating controls of moderate to

high rainfalls, low to moderate rainfalls, and low to high

rainfalls on the wash-off of, respectively, heavy metals

(Mahbub et al., 2010), semi- and non-volatile organic

compounds (Mahbub et al., 2012), and volatile organic

compounds (Mahbub et al., 2011) from urban roads.

Mathematical modeling is a useful tool to integrate

different components of stormwater processes and

investigate the responses of runoff and quality to changes

in relevant sources and drivers. Both mechanistic (i.e.,

process-based) and empirical (i.e., data-driven) models

have been used to analyze stormwater runoff generation

and pollutant transport features. Mechanistic models

attempt to describe mainly the physico-chemical processes

associated with watershed hydro-climate, pollutant build-

up and wash-off, while empirical stormwater models

typically involve the Rational method, rating curves, unit

hydrograph method, and regression-based statistical

models (Mays, 2011). Exemplary stormwater modeling

research includes the determination of urban impervious-

ness substantially contributing to the total storm runoff and

pollutant loads (Bhaduri et al., 2000; Lee & Heanay,

2003), application of artificial neural network to

investigate stormwater quality and quantity response to

climate change (He et al., 2011), stormwater management

implementation using the Penn State Runoff Model

(Shamsi 1996), and numerous applications of the US

Environmental Protection Agency (US EPA)’s Storm

Water Management Model (SWMM) (Rossman, 2010).

The objective of this paper is to quantify the relative,

as well as the combined, influence of dominant stressors

on the potential stormwater runoff and major pollutant

loads in the Miami River Basin of Florida. The reference

hydro-climatic and land use/cover sensitivities of storm-

water runoff and quality are determined considering the

Basin as a case study of complex coastal-urban

watersheds. In contrast to a conventional short-term,

event-based application of the EPA SWMM, we have used

a year-round, continuous climate and hydrology data to

calibrate the model and elucidate seasonal, as well as

annual responses, of potential stormwater runoff and

associated pollutant loads to reference changes in climate,

land use and hydrologic parameters. Although the research

considered the Miami Basin as a pilot study area, it can

provide important insights for appropriate management of

stormwater runoff and quality in complex coastal-urban

environments in the US and around the world.

2. Materials and methods

2.1 Case study area

The case study area comprises of the complex urban

watershed of Miami River, located in Miami-Dade County

on the southeast coast of Florida, USA (Figure 1). The

juxtaposition of eastern coastal urban developments,

Figure 1. Land uses and major canals in the Miami River Basin of Florida, U.S.A.

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including the City of Miami, one of the largest US

metropolises and theUS citymost vulnerable to sea level rise,

with several surrounding National Parks and natural areas

makes the Miami Basin a unique location and living

laboratory for analysis of complex climate-environmental

interactions. The Miami River has a length of approximately

24.5 km from itsmouth at the BiscayneBay (Atlantic Ocean)

and a drainage area of around 175 km2. The Tamiami,

Comfort, and Wagner Creek Canals are examples of the

primary canals draining into the Miami River.

The Miami Basin is highly urbanized and characterized

by a variety of land use types, including agricultural lands

(in the northwest) and a mixture of park, single/multi-family

residential, commercial and industrial areas (Figure 1). The

complex Basin land uses, as well as the inflow from the

Miami Canal, cause a variety of pollutant loadings into

Miami River (FDEP, 2001). The Basin is characterized by a

tropical monsoon climate with hot, humid summers and

short, dry winters (Kottek et al., 2006). On average, the

Basin receives approximately 52 inches of rainfall annually;

elevation varies between 0 to 12 feet (relative to the vertical

datum, NAVD, 1988) for most of the Basin.

2.2 Datasets

The Miami Basin was extracted from the most recent,

smallest available (12-digit) hydrologic unit codes (HUC)

(ID: 030902061405), as delineated nationally by the US

Geological Survey (USGS) (USGS 2013). The study area

was further divided into 33 subbasins following the zoning

map of the Miami-Dade Department of Regulatory and

Economic Resources (Miami-Dade County, 2013). The

land use features (i.e., slope, area) were extracted by

analyzing the 10 ft Florida Division of Emergency

Management (FDEM) LiDAR Data, as obtained from

the South Florida Water Management District (SFWMD),

on an ESRI ArcGIS 9.0 platform. The various land uses of

each subbasin were aggregated into the five broad land use

types of open lands (park þ agricultural) area, single

family residential area, multifamily residential area,

commercial area, and industrial area. Industrial-commer-

cial areas, as well as residential-commercial areas, were

equally divided between the two respective categories. All

office and institutional areas were considered commercial

areas. The “general” areas in a subbasin were distributed

among the five land use categories based on their percent

distributions in that subbasin. The imperviousness of each

subbasin was calculated using an area-weighted average of

runoff coefficients for different land uses, as obtained from

UDFCD (2001). According to this approach (Gironas

et al., 2009), major land use types for an individual

subbasin were categorized; assigning each land use area

(Aj is area of the land use type j) an appropriate runoff

coefficient (Cj). The overall imperviousness (I) for that

subbasin was then estimated as the area weighted average

of the runoff coefficients for all existing land uses (i.e.,

I ¼ fPCjAj}=A, where A is the total area of the subbasin).

Canal flow and stage (water depth relative to the river

bed) data for the calendar year of 2010 were obtained from

DBHYDRO, the environmental database of SFWMD.Daily

average flows (m3/sec) at C6.L30 (upstream station of

Miami River), C4.CORAL (Tamiami Canal) and C2.74

(Snapper Creek Canal), as well as average stage (m) time-

series at G.93 (Coral Gables Canal), were treated as the

inflow boundary conditions in model development (see

Figure 2). Stage data at the downstream station of MRMS4

(near the outlet of Miami River) were used as the outflow

boundary condition. Flow data at the Miami River station,

S26 (Figure 2) were used for model calibration and

validation. The stage datawere converted to flow time-series

(m3/sec) usingManning’s equation based on the correspond-

ing channel cross section, bed roughness and slope data. The

canal geometry (cross section, slope) and bathymetry (bed

elevation) data were extracted from the most recent survey

data, as collected by the SFWMD for the C-4 (Tamiami

Canal) Flood Control Operations Modeling Project; the data

were shared by R. Arteaga (personal communication, 30

July, 2012). Themodel domain includes two gated spillways

and one gated culvert. The operational data for the control

structures were obtained from DBHYDRO and the C-4

Flood Control Operations Modeling Project.

In order to reflect the spatio-temporal rainfall

variability, daily data from three rainfall stations

(DBHYDRO station IDs of MIA, K8673 and 16632)

within and around the Miami River Basin were used for

the year of 2010 (Figure 2). Hourly data were available for

only the MIA station from the NCDC database of the

National Oceanic and Atmospheric Administration. The

daily data at other stations were approximately distributed

among different hours based on the observed, hourly

distribution at the MIA station. The approximation was

necessary in order to generate hourly precipitation to run

the SWMM at an hourly time-step. Although there was a

noticeable difference in total monthly precipitation among

the three stations (Figure 2), the approximate method of

hourly disaggregation of daily rainfalls is acceptable since

the observed total daily precipitation at the individual

stations were conserved. Observed daily evapotranspira-

tion (ET) data for a USGS station near Hialeah were used

to incorporate ET from the basin (Figure 2). SWMM

allows average monthly ET input (mm/day), which was

computed by averaging the daily observations.

The observed groundwater levels (i.e., water table) of

the Miami Basin were considered to reflect the role of

groundwater in stormwater generation. The 2010 data

from eight USGS observation wells (G-3466, G-3566,

G-3567, G-968 G, G-973G, G-1368A, F-239 G, S-68) and

two SFWMD observation wells (G-3264A, G-1166) were

used as the initial water tables (Figure 3); the model

dynamically updated the subsequent groundwater levels.

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2.3 Development of a StormWater Management Model(EPA SWMM 5.0)

The US EPA Storm Water Management Model (SWMM

5.0) is a largely mechanistic, one-dimensional (longitudi-

nal) dynamic rainfall-runoff model that links climate, land

use, and surface and subsurface hydrologic processes

(Rossman, 2010). Since a comprehensive documentation

of SWMM 5.0 development and application can be found

in Rossman (2010) and Gironas et al. (2009), a brief

overview is presented (Figure 4).

We considered 33 subbasins, 81 nodes, and 105 links to

reasonably represent the land uses, topography, and drainage

network of the Miami Basin (Figure 4). Since our objective

was to determine the basin-scale sensitivity of the total

potential stormwater runoff and quality (rather than the

actual flooding or pollutant loads on the ground), we did not

explicitly incorporate management infrastructures such as

catch-basins or underground sewers in model development.

Instead, we assumed stormwater from the individual

subbasins and their temporary retentions (e.g., catch-basin)

will eventually flow into the drainage canals, which were

represented in the model by a network of nodes and links.

Each subbasin was associated with the closest of the three

rainfall stations and 10 groundwater wells. Water table

observations were input into SWMM as initial conditions

and the model computes the subsequent groundwater levels;

flow between the groundwater and transport compartments

(Figure 4) occurs according to a hydraulic head based

transfer function (Rossman, 2010). This approximation of

groundwater flow is reasonable since storm runoff in a

highly urbanized area is mainly a surface process, which is

rigorously parameterized by SWMM 5.0.

The developed SWMM model was constrained by

observed time-series of canal discharges at four gauging

stations as the boundary conditions, which can be

significantly impacted by external factors such as inflows

from the upstream (or neighboring) basins and tidal flows at

the Basin outlet (Figure 2); the boundary constraints helped

to avoid external biases in computation of Miami Basin

runoff. The model was run in an hourly time-step,

simulating the surface runoff in each subbasin and

discharges at all nodes on the drainage canals. Hourly

simulated flow rates (volume per time) were averaged to

compute themean daily discharges at the calibration station

(Figure 2). The total stormwater generated for the Miami

Basin was computed by summing the runoffs of all

subbasins at each time-step. The hourly, basin-scale

stormwater flow rates were aggregated to computemonthly

and annual stormwater (in volume units), which were later

used for determining stormwater sensitivity in the Basin.

SWMM 5.0 has a substantial capability to model build-

up, wash-off and transport of stormwater pollutants in a

basin. Subject to the lack of adequate data for building a

comprehensive process-based water quality model for the

Miami Basin, we estimated the potential pollutant load

rates (in units of mass per time) from each subbasin by

multiplying the simulated hourly runoff with a pollutant-

specific event mean concentration (EMC) (Table 1); EMC

Figure 2. Climate (rainfall and ET) and canal discharge (boundary conditions and calibration point) gauging stations used for the MiamiBasin SWMM model.

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represents the average pollutant concentration (i.e., total

pollutant mass divided by the total runoff volume) during a

storm event (Huber et al., 1988). Migliaccio and Castro

(2009) reported the monitored EMC data for the Biscayne

Watershed and overall Florida using different land use

categories such as the agricultural area (i.e., open lands),

single family residential area, multifamily residential area,

commercial area, and industrial area. Based on the existing

land uses, the EMCs of the Miami Basin (which is a

subbasin of the Biscayne Watershed) for four major

pollutants (total suspended solids, total nitrogen, bio-

chemical oxygen demand, and copper) were obtained from

Migliaccio and Castro (2009). It should be noted that the

collected values of EMC did not represent the overall

concentrations of pollutants at the basin (or any subbasin)

outlet and were not necessarily tied to any particular

composition of basin land uses. Rather, the EMC values

were specific to different land use types (see Table 1); and

appropriate EMCs were reassigned in SWMM when the

relative fractions of different land use types in each

subbasin had been changed under various land use change

scenarios (e.g., a reference 20% conversion of open lands

to residential areas). Thus, the current EMCs were utilized

to estimate the changes in pollutant loads in response to

different conversion scenarios of basin land uses. The

hourly load rates of all subbasins were aggregated to

compute potential monthly and annual pollutant loads

(in mass units) for determining pollutant-load sensitivities

to hydro-climatic and land use/cover variations.

2.4 Model calibration and validation

Considering the main focus of the study on the monthly

variation and sensitivity of stormwater throughout a year

Figure 4. Summary of dataset preparation and SWMM model development for the Miami River Basin.

Figure 3. Groundwater observation wells used for the Miami Basin SWMM model.

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(and the overall annual response), the model was

calibrated for January 1 to June 30 and validated for July

1 to December 31 of 2010 with daily stream flow data

measured at a lower mainstem location (S26; see Figure 2)

of the Miami River. The parameters of watershed and

channel roughness (Manning’s n), depression storage,

characteristics width, percent runoff routed from imper-

vious to pervious areas, groundwater transfer function, and

Green-Ampt infiltration model were adjusted to obtain the

calibration (see Table 2 for adjusted parameter values).

The characteristics width was calculated by multiplying

the square root of subbasin area by a coefficient of 0.5 (i.e.,

0:5*ffiffiffiffiffiffiffiffiffiarea

p), as obtained through calibration. The cali-

bration performance was evaluated by the Nash-Sutcliffe

Efficiency (NSE) (Nash & Sutcliffe, 1970), root-mean-

square error (RMSE) based coefficient of variation (CV

(RMSE)), and Pearson correlation coefficient (r). The

NSE, which measures the goodness of model fit, is unity

(1.0) if simulations are perfectly representing the

observations. A negative NSE indicates that model

predictions are worse than a prediction performed using

the average of all observations as an alternative model.

The CV(RMSE), which measures model accuracy (i.e.,

deviations between predictions and observations), was

computed by dividing the RMSE by the mean of

observations; a low CV(RMSE) indicates high prediction

accuracy. The r indicates extent of linear correspondence

between observed and predicted flow rates; a value of ^1

indicates a perfect correspondence.

2.5 Definition of sensitivity coefficients

The changes in computed, basin-scale potential storm-

water runoff and pollutant loads for any changes in

model parameters and variables can be determined by

defining dimensionless sensitivity coefficients. Following

Abdul-Aziz et al. (2010), the relative sensitivity

coefficients (S *), which shows both the magnitude and

direction (i.e., increase or decrease) of model responses,

were defined as follows:

S* ¼ DM=M

DP=Pð1Þ

Table 2. Ranges of major input parameters and variables for the Miami Basin SWMM.

Stressor Parameter Subbasins Overall basin

Climate Rainfall (mm)* 0.25–59a, 0.00–85b, 1.5–40c

Evapotranspiration (mm/day)* 3.9–10.4d

Land use Open lands* 0–100% 20%Single family residential areas* 0–96% 29%Multi-family residential areas* 0–100% 14%Commercial areas* 0–100% 9%Industrial areas* 0–34% 28%

Hydrology and land cover Characteristics width (m) 442–2409Depression storage (mm) 0.508e, 5.08f

Slope* 0.0002–0.0054Imperviousness* 5–85.65%Roughness coefficient* (Manning’s n) 0.04e, 0.30f

Percent runoff routed from impervious to pervious areas* 25%Suction head (mm) 101.6Conductivity (mm/hr) 124.5Initial deficit 0.3Groundwater flow coefficient 0.01Surface water flow coefficient 0.01Groundwater flow exponent 1Surface water flow exponent 1Surface-GW interaction coefficient 0

Notes: aHourly precipitation at Station 16632; bhourly precipitation at Station K8673; chourly precipitation at Station MIA; dmonthly averageevapotranspiration; eimpervious areas; fpervious areas; *parameters used for sensitivity analysis.

Table 1. Event mean concentrations (EMCs) for differentpollutants of the Miami River Basin (Source: Migliaccio &Castro, 2009).

Runoff concentrations (mg/L)

Land use category TSS TN BOD Cu

Single family residential areas 37.5 2.07 7.9 0.016Multi-family residential areas 77.8 2.32 11.3 0.009Commercial areas 69.7 2.40 11.3 0.015Industrial areas 60.0 1.20 7.6 0.003Open lands 94.3 3.47 5.1 0.013

Note: TSS ¼ total suspended solids; TN ¼ total nitrogen; BOD ¼biochemical oxygen demand; Cu ¼ copper.

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where,P ¼ a base value of the model parameter or forcing

variable (e.g., rainfall, ET, imperviousness, roughness,

land use fractions);

DP ¼ change in the base value of a model parameter or

forcing variable;

M ¼ base-line model response (i.e., in stormwater

runoff or pollutant loads); and

DM ¼ change in the model response.

Relative sensitivity coefficients of potential storm-

water runoff and pollutant loads were computed by

changing one parameter at a time, while combined

sensitivity coefficients were computed by changing

multiple parameters simultaneously. Percent changes in

runoff and pollutant loads in each month were calculated

for up to 20% change in each parameter. The 20% change

is considered as an arbitrary reference so that model

responses to different parameters can be compared on a

common perturbation basis. The variation of model

sensitivity over a year was reflected by the range

between maximum and minimum value of sensitivity

coefficients. Mean annual sensitivities were computed by

dividing the percent changes of annual runoff (or pollutant

loads) by the specified changes in model parameters/

variables.

2.6 Selected parameters for sensitivity analysis

We computed relative sensitivity coefficients (see Table A

in the Appendix) of potential stormwater runoff and

pollutant loads for two climate and four hydrologic

parameters/variables and nine land use conversions (see

Table 2). The parameters were selected based on relevance

to storm water generation and transport processes in the

Miami Basin (Al-Amin & Abdul-Aziz, 2013) after

carefully reviewing the parameterization and methodo-

logical details of EPA SWMM 5.0. The percent

imperviousness, which indicates the subbasins’ land

cover types, has higher values for pavements and lower

values for vegetated areas. The roughness coefficient

reflects land cover roughness, with higher values for

vegetated areas and lower values for pavements. The

parameter of percent runoff routed from impervious areas

to pervious areas allows the runoff to infiltrate and be

stored in depressions in the pervious subareas before

reaching the basin outlet. Routing from impervious to

pervious areas, as well as basin roughness, can be used to

implicitly represent effect of management practices such

as the low impact development (LID) controls. Nine land

use conversions among the five land use types were

considered: open lands to (i) single family residential, (ii)

multi-family residential, (iii) industrial, and (iv) commer-

cial areas; single family residential to (v) multi-family

residential, (vi) industrial, and (vii) commercial areas; and

multi-family residential to (viii) industrial, and (ix)

commercial areas.

Using the input and output data for 2010 and

calibrated SWMM, we computed the dimensionless,

monthly and annual mean sensitivity coefficients of

potential storm runoff and loads of four major pollutants

(total suspended solids, total nitrogen, biochemical

oxygen demand, and copper) for the Miami Basin; in

response to a range of reference climate, land uses/cover,

and hydrologic variations. Although the computed

relative sensitivity coefficients (Equation (1)) can be

used to estimate model responses for any changes

(increases or decreases) in parameters or variables, we

reported (see Section 3) the percent changes in runoff and

pollutant loads caused by an increase in model

parameters/variables as examples. This choice was also

inspired by anticipated (or projected) future changes in

the Miami Basin. For example, both rainfall and ET

can increase from the current levels due to global

warming (Obeysekera et al., 2011). Subject to an

increasing population, urbanization is likely to continue,

converting the open lands to residential and commercial

areas that will increase basin imperviousness. In contrast,

increasing basin roughness and percent runoff routed

from impervious to pervious areas will reflect the effect

of relatively green developments and management

practices.

Preliminary analyses of the relative sensitivity identified

imperviousness and roughness as the most important

hydrologic parameters for storm runoff generation in the

Miami Basin. Increased imperviousness is a consequence of

urbanization and causes shorter lag-times and higher runoff

(Shuster et al., 2005). Increased roughness refers to creation

of more pervious areas (e.g., lawns, rain gardens), increasing

infiltration and decreasing runoff (Darboux & Huang, 2005).

To examine basin runoff responses under combined hydro-

climatic changes, we analyzed the effects of increasing

imperviousness and roughness, in concert with increasing

rainfall scenarios. Unlike the relative sensitivity analysis, the

combined sensitivity was determined by changing both the

climate (rainfall) and a hydrologic parameter (impervious-

ness or roughness) simultaneously. Changes in total annual

basin-runoff were analyzed for a 5% increment in both

climate and hydrologic parameters.

Based on the preliminary analyses of relative

sensitivity, storm runoff and potential pollutant loads

were most responsive to conversion of open lands to

residential, commercial and industrial areas; conversion of

open lands to industrial and commercial areas showed

nearly equivalent sensitivities. We, therefore, analyzed the

combined climatic and land use sensitivities for conver-

sion of open lands to residential (equally distributed

between single and multi-family areas) and commercial

areas as examples. Analyses of changes in pollutants were

done for four pollutants (TSS, TN, BOD, and CU) to

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represent sensitivities of the four major categories of

pollutants (i.e., sediment, nutrient, organics, and metal).

3. Results and discussion

3.1 Model calibration and validation

The NSE, CV(RMSE), and r for the Miami Basin SWMM

model calibration were, respectively, 0.89, 0.20, and 0.95;

the same for the validation were 0.90, 0.32, and 0.96 (NSE,

CV(RMSE), and r, respectively). The model performance

indicates high quality simulations of the flow rates

(Figure 5) and consistent model predictions throughout the

year. Overall, the application of inflow and outflow

boundary conditions minimized external biases in

computation of basin-runoff, while the calibration and

validation with measured data ensured a realistic model

response to the hydro-climate, watershed, and land use/

cover forcing variables.

3.2 Climate sensitivities

For the model calibration year of 2010, the Miami Basin

received lower rainfalls from October to January and

higher rainfalls during February to September (Figure 6a).

The monthly variation of the number of precipitation

events, depths and durations, in concert with resulting soil

saturation, appeared to have caused differential sensi-

tivities of potential stormwater runoff in the Basin

(Figure 6b). For each month, runoff increased almost

linearly for increasing the corresponding hourly rainfall

depths by 5% to 20% while keeping the respective number

of rainfall events and durations unchanged. However,

slope of the sensitivity curves (i.e., sensitivity coefficient,

which is defined as the percent change in runoff for a

percent change in rainfall depth) was the steepest for

December and the flattest for October. Defining the

number of rainfall events by the number of clock-hours

experiencing rainfall in (or adjacent to) the Miami Basin

(Ignaccolo & De Michele, 2010), December experienced

34 aggregated hourly rainfall events of lowest depths and

short durations, leading to the smallest base-line monthly

runoff compared to that of other months. Increasing the

hourly rainfall depths by 5–20%, therefore, resulted in

very high relative increases (14–62%, respectively) and

sensitivity for December runoff. Although January

experienced nearly the same number of aggregated hourly

rainfall events and total amounts (i.e., depth) as that of

December, the higher intensities (mm/hour) (i.e., shorter

durations) of January rainfalls led to much higher base-line

monthly runoff. Increasing the small rainfall depths caused

Flo

w r

ate

(m3 /s

) 0

5

10

15

20

25

0 30 60 90 120 150 180

Observation

Simulation

NSE = 0.89; CV(RMSE) = 0.20; r = 0.95

0

5

10

15

20

25

30

181 211 241 271 301 331 361

NSE = 0.90; CV(RMSE) = 0.32; r = 0.96

(a)

(b)

Julian day

Observation

Simulation

Figure 5. Model calibration with daily discharges from January 1 to June 30, 2010 (a) and validation with daily discharges from July 1 toDecember 31, 2010 (b) at the Miami River station, S-26.

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smaller relative increases (10% to 42%) in total monthly

runoff, leading tomuch lower runoff sensitivity for January.

Compared to December, November experienced slightly

less number of rainfall events (total 27) of much higher

intensities, which resulted in an order of magnitude (i.e., 10

times) higher base-line monthly runoff. However, increas-

ing the high rainfall intensities by 5 to 20% led to high

relative increases (12% to 51%, respectively) and

sensitivity for November runoff. October experienced

only 14 rainfall events (nearly half of that of November)

with much higher depths and durations, generating nearly

three times higher monthly base-line runoff than that of

November. Increasing the rainfall depths by 5% to 20%,

therefore, resulted in the lowest relative increases (8% to

33%, respectively) and sensitivity of October runoff.

Rainfall in the wetter months (except for February)

occurred with very high intensities for longer durations,

leading to early soil saturations and high base-line runoffs.

In response to the 5% to 20% increases in the hourly

rainfall depths and intensities (while keeping the number

of events and durations unchanged), the relative runoff

increased by 10% to 58% (respectively), resulting in

moderate to very high runoff sensitivities for March to

September. In contrast, February experienced 77 aggre-

gated hourly rainfall events of relatively low intensities

distributed nearly over the entire month, leading to high

2010 base-line runoff for the month. The 5% to 20%

increase in the hourly rainfall depths, therefore, resulted in

much smaller relative increase (9% to 37%, respectively)

and the second lowest sensitivity (next to October) for

February runoff.

Relative runoff changes due to standalone increases in

the two major climatic drives of rainfall and evapotran-

spiration (ET) showed opposite effects on runoff

sensitivities, as expected (not shown). Annual basin-runoff

increased by around 47% and decreased by around 14% for

a reference 20% increase in, respectively, rainfall

intensities and ET. The monthly variation of relative

changes in runoff for changes in rainfall was much higher

compared to that for ET, indicating that the temporal

variation of runoff is strongly dictated by rainfall variability

rather than by that in the meteorological variables (e.g.,

solar radiation, temperature, wind speed, humidity) driving

ET. On average, rainfall had around 3.4 times stronger

influence on the annual stormwater runoff than that of ET in

the basin.

Changes in climatic variables can cause variations in

pollutant build-up and wash-off processes. Pollutant

transport can also change due to the varied amount of

generated runoff. Since we used an EMC based modelling

0

50

100

150

200

250

300

350

400

450

Jan Feb Mar April May June July Aug Sep Oct Nov Dec

Tota

l rai

nfal

l (m

m)

Station MIAStation 16632Station K8673

0

10

20

30

40

50

60

70

5 10 15 20

Incr

ease

in r

unof

f (%

)

Increase in rainfall (%)

JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberAnnual

(a)

(b)

Figure 6. (a) Monthly rainfall variation in the Miami River Basin; and (b) predicted relative changes in basin-runoffs for differentchanges in rainfall.

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approach using the measured EMC values for the Miami

Basin, the climate sensitivities of four major pollutant

groups (total suspended solids, total nitrogen, copper, and

biochemical oxygen demand) were nearly same as that of

storm runoff. These sensitivity patterns of quality

parameters are consistent with previous, field-scale

experimental studies (e.g., Gilbert & Calusen, 2006),

which reported variation of pollutant loads reflecting the

relative differences in storm runoff rather than that in

pollutant concentrations.

3.3 Hydrologic and land cover sensitivities

The temporal variations of rainfall and associated soil

saturation appeared to drive the differential runoff

sensitivities to the hydrologic and land cover parameters

(except for percent routed from impervious to pervious

areas) in different months. Among various parameters,

watershed imperviousness and slope had positive impacts

on runoff generation (Figure 7). On average, monthly

runoff increased by around 10% and 4% for a reference

20% increase in imperviousness and slope, respectively;

the relatively low runoff changes and sensitivities can be

attributed to the high urbanization (i.e., high impervious-

ness) and relatively flat topography of the Miami Basin.

Roughness had a strong negative impact on potential

runoff, leading to an average decrease of 8% in monthly

runoff for a 20% increase in roughness; the finding

underscores greening (through plantations and landscap-

ing, for example) as an effective way of urban runoff

management. Increasing the percent runoff routed from

impervious to pervious areas by 20% decreased any

monthly runoff by an equivalent amount (i.e., 20%),

indicating high effectiveness of rain garden (as a LID

remedy) in runoff control. Variations in hydrologic and

land cover components can also change the pollutant

generation, build-up and transport. Since we modeled the

potential pollutant loads using relevant EMCs, which

depend on land uses rather than land cover, hydrologic and

land cover sensitivities of pollutants were nearly identical

to that of runoff.

3.4 Land use sensitivities

3.4.1 Runoff

Land use conversions in the Miami Basin changed the

runoff generation and transport due to associated changes

in imperviousness and surface roughness for different land

use types. Percentage changes in runoff due to a reference

20% conversion of open lands and residential areas to the

more urban land uses were compared (Figure 8). The

–30

–20

–10

0

10

20

30

Imperviousness Slope Roughness %routed

Cha

nge

in r

unof

f (%

)

A reference 20% increase in parameters

MinimumAverageMaximum

Figure 7. Relative effects of different hydrologic and land cover parameters on monthly runoffs of the Miami Basin.

0

2

4

6

8

10

OL toSFR

OL toMFR

OL toIND

OL toCOM

SFR toMFR

SFR toIND

SFR toCOM

MFR toIND

MFR toCOM

Cha

nge

in r

unof

f (%

) MinimumAverageMaximum

A reference 20% conversion of land uses

Figure 8. Relative effects of different land use conversions on monthly runoffs of the Miami Basin (OL ¼ open lands; SFR ¼ singlefamily residential areas; MFR ¼ multi-family residential areas; IND ¼ industrial areas; and COM ¼ commercial areas).

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temporal variations of rainfall and associated soil

saturation appeared to have resulted in differential runoff

sensitivities in different months, particularly in response to

the conversions of open lands and single family residential

areas. Storm runoff was most sensitive to the conversion of

open lands to other land use types primarily due to a sharp

increase in surface imperviousness and decrease in

roughness. On average, a 20% conversion of open lands

to single family residential areas, multi-family residential

areas, industrial areas, and commercial areas increased the

potential basin-runoff, respectively, by around 4.7%,

5.6%, 6.2%, and 6.5%. Conversion of single family

residential areas to multi-family residential areas,

commercial areas, and industrial areas increased the

runoff by around 1.4% to 2.2%. Furthermore, the temporal

(monthly) variability with conversions of single family

residential areas was much smaller than that with

conversions of open lands. Conversion of multi-family

residential areas to industrial and commercial areas caused

a little change (1% or less) in total storm runoff. Compared

to the climatic and hydrologic parameters, runoff showed

much less sensitivities to land use parameters.

3.4.2 Pollutants

Although hydro-climatic sensitivities of potential storm

runoff and pollutant loadings were nearly identical, they

exhibited different sensitivities in response to land use

conversions (Table 3; Figure 8). Sensitivity of the

pollutant loadings varied based on the coupling of the

relative changes in total runoff and that in event mean

concentrations (EMCs). For example, the Miami Basin

EMC values for TSS (i.e., sediments) were highest for

open lands followed by multi-family residential, commer-

cial, industrial and single family residential areas

(Table 1). The imperviousness, however, was highest for

industrial areas followed by commercial areas, multi-

family residential areas, single family residential areas,

and open lands. Conversions of open lands to other land

uses, therefore, produced higher runoffs at a lower

concentration in the Miami Basin. Since increases in

runoff due to a 20% conversion of open lands far

outweighed the corresponding decreases in EMCs (as

represented by other land uses), the average monthly

loading of TSS increased by around 4.5% to 6.5%

(Table 3). Since both runoffs and EMCs increased by a

20% conversion of single family residential areas to multi-

family residential, commercial and industrial areas, the

average monthly loads increased by around 3% to 3.5%.

In contrast, dominance of EMC decreases over increasing

runoff increased the monthly TSS load by less than 1% for

a 20% conversion of multi-family residential areas to

commercial or industrial areas.

Subject to the outweighing effects of runoff increases

over EMC decreases, the average monthly nutrient (total

nitrogen) loads increased by around 6.5% to 9% for a 20%

conversion of open lands to other areas. Conversion of

single family and multi-family residential areas to the

more urban land uses increased the average monthly loads

by, respectively, 2% to 4% and 1% to 2%. A 20%

reference conversion of open lands and single family

residential areas to other urban land uses also increased the

average monthly BOD loads by around 4% to 6% and 2%

to 3%, respectively (Table 3).

In case of heavy metal (Cu, for example), the EMC

value was highest for single family residential areas

followed by commercial areas, open lands, multi-family

residential areas, and industrial areas (Table 1). A 20%

conversion of open lands to other urban land uses

increased the runoff that either outweighed the effect of

decreased EMCs (for conversion to multi-family residen-

tial and industrial areas) or amplified the effect of

increased EMCs (for conversion to single family

residential and commercial areas); increasing the average

Table 3. Percent (%) changes in monthly pollutant loads for a reference 20% conversion among the land use types of Miami Basin.

Parameter ChangestatisticsOL toSFR

OL toMFR

OL toIND

OL toCOM

SFR toMFR

SFR toIND

SFR toCOM

MFR toIND

MFR toCOM

TSS Average 4.5 5.8 6.1 6.5 3.1 2.9 3.6 0.5 0.9Maximum 5.7 7.3 7.9 8.4 4.4 4.8 5.8 1.3 1.7Minimum 2.8 3.7 3.7 3.9 1.6 1.2 1.7 20.4 0.1

TN Average 6.7 7.8 8.2 8.9 3.0 2.3 4.0 1.3 2.4Maximum 10.5 11.8 12.2 13.1 6.3 5.5 7.4 4.5 5.5Minimum 4.3 4.8 4.6 5.2 1.0 0.4 1.4 20.1 0.9

BOD Average 4.3 5.4 5.5 6.1 2.5 1.9 3.4 0.2 1.1Maximum 5.4 6.8 7.0 7.7 3.9 3.9 5.6 1.1 1.9Minimum 2.9 3.6 3.3 3.8 1.3 0.3 1.4 20.8 0.4

Cu Average 5.3 4.9 5.4 7.3 20.1 21.7 2.4 0.8 1.0Maximum 5.4 6.5 7.0 7.7 3.9 3.9 5.6 1.1 1.9Minimum 3.5 2.0 2.2 4.2 21.8 23.8 0.4 0.2 0.3

Notes: OL ¼ open lands; SFR ¼ single family residential areas; MFR ¼ multi-family residential areas; IND ¼ industrial areas; and COM ¼ commercialareas; e.g., “OL to SFR” means conversion of OL to SFR areas.

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monthly Cu load by approximately 5% to 7% (Table 3).

A similar conversion of single family residential to

commercial areas increased the average Cu load by around

2%. Conversion of single family residential to industrial

areas, however, reduced the load by around 2% due to the

outweighing effects of substantially lower EMC value

over higher runoff of the industrial areas.

Overall, the reference 20% conversion of open lands

(to commercial areas) resulted in the highest sensitivity of

potential runoff (up to 8% increase; see Figure 8) and

pollutant loads (up to around 13% increase in TN; see

Table 3). Conversion of single family residential areas to

the other, more intense urban land uses (commercial areas,

in particular) increased the potential runoff and loads,

respectively, by up to 4.5% (Figure 8) and 7% (Table 3).

3.5 Combined hydro-climatic sensitivities

Combined hydro-climatic changes led to non-linear

responses in average monthly basin runoff (Table 4). For

example, a standalone 20% increase in rainfall increased

the runoff by 47%, while a similar increase in

imperviousness and roughness, respectively, increased

the runoff by 10% and decreased the runoff by 8%. But,

when both rainfall and imperviousness were simul-

taneously increased by 20%, runoff increased by 64%,

which is higher than the sum of their standalone,

individual contributions (57%). In contrast, a similar

simultaneous increase in rainfall and roughness increased

the runoff by 37%, which is smaller than their individual

linear contributions (39%). The synergic effects of

simultaneous increase in rainfall and imperviousness/

roughness can, therefore, be significantly different than the

projected standalone impacts of changing climate or

growing/greening urbanization. The management strat-

egies developed without due consideration of such

combined effects pose a risk of overlooking a considerable

runoff margin, which may lead to insufficient or

ineffective mitigations of stormwater runoff and associ-

ated pollutions.

3.6 Combined climatic and land use sensitivities

The combined sensitivity analysis indicated a strongly

dictating effect of climatic changes over land use

conversions for generating monthly average runoff (not

shown) and pollutant loads (Table 5) in the Miami Basin.

For example, a standalone 20% increase in rainfall

increased the loads of different pollutants by around 50%

(not shown in Table 5), which is nearly equivalent to the

relative increase in runoff (Table 4). In contrast, a

standalone 20% conversion of open lands to residential

areas increased the pollutants by around 4.5% to 7%.

However, when both rainfall was increased and open lands

were converted to residential areas by 20% simul-

taneously, increases in TSS (56%), TN (59%), Cu

(55%), and BOD (56%) were slightly higher than the

summation of their individual contributions. Similar

patterns were observed for conversion of open lands to

commercial areas. When rainfall was increased by 20%

and simultaneously 20% of open lands were converted to

commercial areas, increases in TSS (58%), TN (62%), Cu

(59%), and BOD (58%) were higher than the summation

of their individual contributions. Overall, conversion of

open lands to commercial areas produced more pollutants

than conversion to residential areas.

3.7 Comparison with previous studies

Subject to the limited availability of observational data,

stormwater models are often developed for small

catchments for single storm events. We have used a

year-round, continuous climate, hydrologic, and sea level

data to elucidate seasonal, as well as annual, responses of

total potential stormwater runoff and pollutant loads to

changes in climate, land use/cover and hydrologic drivers/

sources. The model explored the stormwater responses in a

USGS HUC 12 watershed-scale (Miami River of Florida),

which can facilitate leveraging research findings and

knowledge transfer for similar watersheds in the US and

around the world.

Our research findings from the Miami Basin

complement the literature. Previous studies on small

basins (e.g., Beling et al., 2011; Jewell et al., 1978)

reported runoff being most sensitive to watershed

imperviousness among the hydrologic parameters. Beling

et al.’s SWMM studies for small periurban basins of the

southern Brazil further reported high sensitivities of storm

runoff to the subbasin roughness, width and infiltration

parameters. Our study with a larger basin found runoff

demonstrating the highest sensitivities to imperviousness

and percentage routed from pervious to impervious areas,

Table 4. Combined hydro-climatic sensitivities of potentialmonthly average storm runoff in the Miami River Basin.

Increase in rainfall (%)

Parameter 0 5 10 15 20

Increase in imperviousness (%) 0 0 11 23 34 475 3 14 26 38 51

10 5 17 29 42 5615 8 20 33 44 6020 10 23 36 49 64

Increase in roughness (%) 0 0 11 23 34 475 22 9 20 32 44

10 24 6 18 29 4115 26 4 15 27 3920 28 2 13 24 37

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followed by roughness. Yu et al. (2014) also ranked

imperviousness as the most sensitive parameter for

stormwater runoff in a large-scale SWMM application

for the Jinan City, China. A novel contribution of our study

is that we have not only computed the conventional, single

(e.g., annual) sensitivity coefficients of individual runoff

and quality parameters in a watershed-scale, but also

identified the corresponding ranges over which the

sensitivity varied in different months. Seasonal sensi-

tivities are particularly important because effect of climate

change and land development is more pronounced on the

seasonal distributions of the stream flow and pollutant

loads than on their average annual counterparts (Tu, 2009).

The precipitation characteristics (rainfall depth,

duration, and frequency), along with catchment properties

(e.g., topology, land cover, soil conditions), control the

runoff characteristics in a watershed. Previous studies

(e.g., Chiew et al., 1995) found that changes in rainfall

were always amplified in runoff responses. Nearing et al.

(2005) reported that relative change (i.e., sensitivity) in

runoff was higher for small storms, while the absolute

change in total runoff volume was higher for larger storms.

With the Miami Basin study, we also found different

runoff sensitivities in different months, with notably

stronger responses in the dry, winter months of November

and December. This emphasizes the need for seasonal

strategies of stormwater management based on number of

rainfall events, depth and durations. The strongly dictating

effects of climate change on runoff generation and

pollutant loads responses over land use/cover changes for

the Miami Basin is consistent with literature. For example,

a 10% decrease in the daily rainfall year-round decreased

the average annual runoff by about 30% in a tropical

African basin (Legesse et al., 2003). Our study, analyzing

a different land use and hydrologic regime, found a 24%

increase in annual runoff corresponding to a 10% increase

in rainfall. Since the Miami Basin is already highly

urbanized and the base percentage of open lands is

relatively small, the substantially lower land use

sensitivities of storm runoff and pollutant loads are as

expected.

3.8 Limitations and caveats of the study

The developed SWMM model was calibrated for the

Miami Basin using daily stream flow time-series at one

mainstem location. Although model calibration at multiple

gauging stations would be ideal, measured stream flows

were available for only three locations (C6.L30, S26, and

MRMS4) on the mainstem of the Miami River (see

Figure 2). We had to use the data from the upstream end

(C6.L30) and downstream end (MRMS4) stations as

boundary conditions in order to account for external

influences (e.g., inter-basin and tidal flows) from the

basin’s stormwater runoff computation. Flow data for the

remaining mainstem station (S26) were used for model

calibration and validation. Thus, the model made a

reasonable use of available observational data given the

size of the study area (,175 km2).

Subject to the lack of adequate data for building a

comprehensive process-based water quality model for the

Miami Basin, observed EMCs from the Migliaccio and

Castro (2009) were used to estimate potential pollutant

loads. The approach is consistent with previous, field-scale

experimental studies (e.g., Gilbert & Calusen, 2006) that

reported export of different pollutants following the

Table 5. Effects of simultaneous changes in rainfall and land uses on the monthly average pollutant loads of Miami River Basin.

Increase inrainfall (%)

Conversion (%) of open lands to residential areas

5 10 15 20

TSS TN Cu BOD TSS TN Cu BOD TSS TN Cu BOD TSS TN Cu BOD0 1.9 3.5 1.4 1.8 3.1 4.9 2.5 2.9 4.1 6.2 3.5 3.9 5.1 7.3 4.5 4.85 13.4 15.1 12.7 13.3 14.7 16.7 14.0 14.5 15.9 18.1 15.2 15.7 17.0 19.4 16.2 16.710 25.5 27.3 24.7 25.4 26.9 29.1 26.1 26.7 28.2 30.6 27.4 28.0 29.4 32.0 28.6 29.115 38.1 40.0 37.3 38.0 39.7 42.0 38.8 39.5 41.1 43.6 40.2 40.8 42.4 45.1 41.5 42.120 51.3 53.4 50.4 51.3 53.1 55.5 52.0 52.9 54.6 57.4 53.6 54.4 56.1 59.0 55.0 55.8

Conversion (%) of open lands to commercial areas

5 10 15 20

TSS TN Cu BOD TSS TN Cu BOD TSS TN Cu BOD TSS TN Cu BOD0 2.4 4.0 2.6 2.2 3.9 5.9 4.4 3.6 5.2 7.5 5.9 4.9 6.5 8.9 7.3 6.15 13.9 15.8 14.1 13.8 15.6 17.8 16.1 15.3 17.2 19.6 17.8 16.8 18.6 21.2 19.4 18.110 26.0 28.0 26.3 25.9 27.9 30.3 28.4 27.6 29.6 32.3 30.3 29.2 31.1 34.0 32.1 30.715 38.7 40.8 39.0 38.6 40.7 43.3 41.3 40.5 42.6 45.5 43.4 42.2 44.3 47.5 45.4 43.920 52.0 54.2 52.2 51.9 54.3 57.0 54.8 54.0 56.3 59.4 57.1 55.9 58.2 61.5 59.2 57.8

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relative differences in storm runoff. However, standard

EMCs within each land use may change over time due to

the changing pollutant sources. Although the EMC

approach served our objective of sensitivity analysis,

lack of a detailed water quality model (including pollutant

build-up, wash-off, and transport) development for the

Basin can be seen as a limitation of this study.

Although we differentiated land use parameters from

hydrologic and land cover parameters, a change in one

parameter can simultaneously change the others by

variable extents. For example, conversion of open lands

to residential, industrial or commercial areas would

inherently change the landscapes, altering imperviousness,

roughness, and possibly slope of the pertinent watersheds.

We have examined runoff and quality responses to a range

(e.g., 0% to 20%) of simultaneous changes in hydro-

climatic and land uses/cover parameters through a

combined sensitivity analysis. The potential changes,

however, may not follow a simultaneous timeline in

multiple parameters; i.e., a 20% change of land use/cover

and hydrologic parameters may not overlap with a 20%

change in climate variables. Therefore, changing multiple

parameters by the same reference range (0% to 20%) in the

combined sensitivity analysis can be seen as a caveat of our

study, because the reference changes do not necessarily

reflect the actual corresponding changes (which are

unknown) in different parameters.

The Miami Basin study was also undertaken under the

assumption of constant drainage density, which may not be

the case due to possible infrastructure upgrades under a

changing climate and land use. However, this assumption

is reasonable here since we analyzed runoff and pollutant

generations from the source/driver perspective and the

objective was to quantify sensitivities of potential runoff

and pollutant loads to possible hydro-climatic and land use

changes; indicating the extent of necessary upgrade in

drainage infrastructures and density. Although the applied

model, SWMM is a largely mechanistic (i.e., process-

based) stormwater model utilizing existing knowledge of

runoff processes and pollutant generation, some of our

findings could be tied to the model structure and

parameterizations of SWMM 5.0. A comprehensive

uncertainty analysis can be helpful to address this issue

of robust modelling and predictions in future studies.

This study adopted a sensitivity scenario (i.e., static

scaling or change factors) approach, instead of using

downscaled projections from the General Circulation

Models (GCMs). Although a relatively large range of

anticipated changes (e.g., stormwater changes due to 0%

to 20% changes in parameters) were addressed, the inter-

annual variability of precipitation and ET were not

explicitly incorporated. However, previous studies (e.g.,

Obeysekera et al., 2011) reported enormous discrepancies

between historically observed rainfalls in South Florida

and the corresponding simulations by various GCMs and

regional climate models (RCMs) with and without

corrections for model biases using the well-known

statistical downscaling methods (e.g., Maurer et al.,

2008; Woods et al., 2004). Apart from the large spatial

resolutions of GCMs, the discrepancies were attributed to

the inability of the GCMs and downscaling techniques to

account for the highly variable South Florida hydro-

climate; as influenced by El Nino-Southern Oscillation

(ENSO), Pacific Decadal Oscillation (PDO), Atlantic

Multi-decadal Oscillation (AMO), sea breeze, and tropical

storms/hurricanes. The quasi-periodic (inter-annual to

multi-decadal) ocean-atmospheric oscillations induce a

tremendous uncertainty in future climate prediction for the

region. A proper downscaling of GCMs projected

precipitations is, therefore, yet to be achieved for South

Florida watersheds. Under the circumstances, the constant

scaling factors of our study provide a range of possible

changes in rainfall scenarios and their impacts on a

relatively small basin-scale stormwater runoff and quality.

By applying due professional judgments and caution, the

study findings can be useful for a design-scale (20–30

years) planning and operation of stormwater management

infrastructure.

3.9 Implications for stormwater management

The quantified sensitivity indicates the vulnerability of

stormwater management infrastructures in complex urban

watersheds under a changing climate, land use/cover, and

hydrology. The results have important management

implications for the case study region, particularly for

developing stormwater master plan. For example, the

coarse-scale GCMs-RCMs projections (although quite

crude) suggest that the regional annual rainfall can change

(increase or decrease) by the mid-century (2050) by

around 10%, while the ET can increase by up to 10%,

compared to the current levels (Obeysekera et al., 2011).

Based on our sensitivity coefficients (Appendix: Table A)

and percent changes (Table 4 and Figure 6b), the Miami

Basin runoff and associated pollutant loads can change by

around 47% (increase) and 14% (decrease) in response to a

reference 20% increase in, respectively, rainfall and ET.

Assuming no significant changes in land uses/cover, a

linear interpolation and summation of individual responses

suggest that a 10% increase in both rainfall and ET can

increase the total basin-runoff in 2050 by around 16.5%

from the current level. This means that even if the land use/

cover conditions remained at the current day conditions, the

stormwater drainage infrastructure in theMiami Basin may

need an upgrade to carry around 16.5% additional runoff in

order to avoid freshwater flooding and associated water

quality concerns in 2050.

However, with an ever increasing population (,5%

increase of Miami-Dade County population between 2010

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and 2013; see US Census 2013), the Miami Basin is

undergoing increasing urbanization, and surface imper-

vious is likely to increase in the future. Our analysis of the

projected 2015–2025 land use, which is adopted by the

Miami-Dade Department of Regulatory and Economic

Resources (see Miami-Dade County, 2014), estimated the

potential land use fractions of the Miami River Basin as

follows: open lands (8%), single family residential areas

(31%), multi-family residential areas (14.5%), commercial

areas (21%), and industrial areas (25.5%). By 2025,

compared to the current land use fractions (see the “overall

basin” column of Table 2), the open lands and industrial

areas are projected to decrease, respectively, by around

59% and 10%; while the commercial and residential

(single and multi-family combined) areas are projected to

increase by 131% and 6%, respectively. This means a

substantial portion of open lands would be converted to

commercial and residential areas by 2025.

The drastically reduced fraction of projected open

lands and the strikingly increased commercial land uses by

2025 have important implications for stormwater manage-

ment in the Basin. For example, out of the 59% projected

losses in open lands, if 13% is converted to residential

areas (resulting in a 6% increase), around 46% of the

current open lands would be converted to commercial

areas by 2025. Based on the sensitivity coefficients

(Appendix: Table A) and percent changes (Figure 8), the

storm runoff would increase by around 5% (average for

single and multi-family residential) and 6.5% for a 20%

conversion of open lands to, respectively, residential and

commercial land uses. Under a scenario of rainfall and ET

staying at the current level, the results suggest that the

stormwater drainage infrastructure in the Miami Basin

should be upgraded to carry around 18% additional runoff

in order to avoid freshwater flooding and associated water

quality concerns in 2025. However, a linear coupling of

the projected land use changes with a scenario of 5%

increase in both rainfall and ET by 2025 (assuming a

monotonic linear trend in rainfall and ET based on the

GCMs projections of 10% increases by 2050) indicates

that the Basin may rather generate 25% additional runoff,

compared to the current level. Furthermore, the non-linear

responses of storm runoff and associated pollutant loads to

the simultaneous climate and land use changes (see

Tables 4 and 5) suggest that the Miami Basin stormwater

drainage network may have to carry more than 25%

additional runoff in order to avoid freshwater flooding and

associated water quality concerns in 2025.

Although the research considered the Miami River

Basin as a pilot study area, it can provide important

insights into appropriate management of stormwater

runoff and quality in complex coastal urban environments

around the world. For example, the results reemphasize

that decreasing imperviousness, as well as increasing

roughness and percentage routed from impervious to

pervious areas, can be effective ways of stormwater

runoff management, echoing the call for a greener urban

planning. In particular, inclusion of grass lawns in

residential, commercial and industrial areas, as well as

adding grass strips alongside the pavements can be

relatively low impact means for stormwater runoff and

pollution control in Miami or similar basins. Watershed

slope can be adjusted at the local scale (e.g., subbasin or

block level) through landscaping, leading stormwater to

flow and infiltrate in lawns and grass covers. The

temporal variation in sensitivities can provide important

guidelines for developing seasonal management strat-

egies, leading to an efficient utilization of time, money,

and manpower.

4. Conclusions

Considering the Miami River Basin of Florida as a case

study of complex coastal urban watersheds, we developed

a dynamic rainfall-runoff model with the EPA SWMM

5.0, and computed reference sensitivities of potential

stormwater runoff and quality to the hydro-climatic and

land use/cover changes. Potential storm runoff in the

complex urban basin exhibited high seasonal sensitivities

to rainfall; with stronger responses in the drier early

winter months (e.g., November, December) and wetter

late summer months (e.g., July, August). The temporal

variation of the number of precipitation events, depths

and durations, as well as consequent soil saturation,

appeared to have caused notably different sensitivities of

runoff (and associated pollutant loads) in different

months in the watershed. Potential basin runoff and

pollutant loads showed moderate sensitivity to the

hydrologic and land cover parameters; imperviousness

and roughness exhibited more dominant influence than

slope. Sensitivity to potential changes in land use was

relatively low, compared to that for climate and

hydrologic changes. Higher increases in runoff and

pollutants resulted from conversion of open lands to

residential, commercial or industrial areas; conversion

among residential, commercial, and industrial land uses

led to much less changes. The changes in runoff and

pollutants under simultaneous hydro-climatic or climate-

land use perturbations were notably different than the

summations of their individual contributions. The

quantified sensitivities can be useful for management of

stormwater quantity and quality in complex urban basins

under a changing climate, land use/cover, and hydrology

around the world. Subject to the availability of properly

downscaled climate projections (from GCMs or RCMs),

future studies should look into the potential changes in

the Basin runoff and pollutant loads under different IPCC

climate projections (e.g., Nakicenovic et al., 2000) and

anticipated land use/cover scenarios.

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Acknowledgements

The research was funded by a faculty start-up grant from theCollege of Engineering and Computing of the FloridaInternational University, Miami, U.S.A. Contributions of Dr.Abdul-Aziz were also funded by the State of Florida Office ofInsurance Regulations through the “Florida Public HurricaneLoss Model Enhancements” project. The statements, findings,conclusions, and recommendations are those of the authors anddo not necessarily reflect the views of the State of Florida or anyof its subagencies. We thank the reviewers and the Editor forproviding insightful comments.

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Appendix

Figure A. Relative effects of the reference 20% land use conversions on the monthly pollutant loads of the Miami Basin(OL ¼ open lands; SFR ¼ single family residential areas; MFR ¼ multi-family residential areas; IND ¼ industrial areas; andCOM ¼ commercial areas).

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Table

A.

Relativesensitivitycoefficients(dim

ensionless)ofmonthly

runoffsforareference

20%

increase

intheclim

ate,

hydrologic,andland/cover

param

eters.

Param

eters

January

February

March

April

May

June

July

August

September

October

Novem

ber

Decem

ber

Average

Rainfall

2.11

1.86

2.47

2.24

2.38

1.97

2.88

2.70

2.28

1.66

2.57

3.11

2.35

Evapotranspiration

20.29

20.50

20.65

20.61

20.62

20.59

20.86

21.02

20.71

20.69

21.02

20.98

20.71

Imperviousness

0.12

0.67

0.37

0.44

0.38

0.61

0.50

0.54

0.56

1.15

0.55

0.20

0.51

Roughnes

s20.73

20.27

20.53

20.47

20.51

20.33

20.41

20.38

20.37

0.12

20.37

20.67

20.41

Slope

0.40

0.13

0.28

0.24

0.27

0.16

0.22

0.19

0.19

20.08

0.19

0.37

0.21

%routed

21.00

21.00

21.00

21.00

21.00

21.00

21.00

21.00

21.00

21.00

21.00

21.00

21.00

OLto

SFR

0.15

0.24

0.19

0.26

0.27

0.28

0.21

0.26

0.27

0.17

0.30

0.23

0.24

OLto

MFR

0.17

0.30

0.23

0.31

0.32

0.34

0.25

0.31

0.33

0.22

0.36

0.26

0.28

OLto

IND

0.18

0.33

0.24

0.35

0.35

0.38

0.28

0.34

0.36

0.25

0.39

0.28

0.31

OLto

COM

0.18

0.34

0.25

0.36

0.37

0.39

0.29

0.35

0.38

0.26

0.41

0.29

0.32

SFRto

MFR

0.01

0.09

0.04

0.07

0.06

0.09

0.07

0.08

0.08

0.14

0.08

0.02

0.07

SFRto

IND

0.02

0.12

0.06

0.09

0.08

0.12

0.10

0.11

0.11

0.19

0.12

0.04

0.10

SFRto

COM

0.02

0.14

0.07

0.10

0.09

0.14

0.11

0.12

0.13

0.22

0.13

0.04

0.11

MFRto

IND

0.01

0.06

0.03

0.04

0.04

0.05

0.04

0.05

0.05

0.08

0.05

0.02

0.04

MFRto

COM

0.01

0.07

0.05

0.05

0.04

0.06

0.05

0.06

0.06

0.09

0.06

0.02

0.05

Notes:OL¼

open

lands;SFR¼

singlefamilyresidentialareas;MFR¼

multi-familyresidentialareas;IN

D¼

industrialareas;andCOM

¼commercialareas;e.g.,“O

LtoSFR”meansconversionofOLto

SFRareas.

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