Introduction
Catchment models are valuable tools for understanding natural processes
occurring at basin scales and for simulating the effects of different
management regimes on soil and water resources (e.g. Cao et al., 2006).
Model applications may have uncertainties as a result of errors associated
with the forcing variables, measurements used for calibration, and
conceptualisation of the model itself (Lindenschmidt et al., 2007). The
ability of catchment models to simulate hydrological processes and pollutant
loads can be assessed through analysis of uncertainty or errors during a
calibration process that is specific to the application domain (White and Chaubey, 2005).
The Soil and Water Assessment Tool (SWAT) model is increasingly used to
predict discharge, sediment and nutrient loads on a temporally resolved
basis and to quantify material fluxes from a catchment to the downstream
receiving environment such as a lake (e.g. Nielsen et al., 2013). The SWAT
model is physically based and provides distributed descriptions of
hydrologic processes at sub-basin scale (Arnold et al., 1998; Neitsch et
al., 2011). It has numerous parameters, some of which can be fixed on the
basis of pre-existing catchment data (e.g. soil maps) or knowledge gained
in other studies. However, values for other parameters need to be assigned
during a calibration process as a result of complex spatial and temporal
variations that are not readily captured either through measurements or
within the model algorithms themselves (Boyle et al., 2000). Such parameter
values assigned during calibration are therefore lumped, i.e. they
integrate variations in space and/or time and thus provide an approximation
for real values which often vary widely within a study catchment. Model
calibration is an iterative process whereby parameters are adjusted to the
system of interest by refining model predictions to fit closely with
observations under a given set of conditions (Moriasi et al., 2007). Manual
calibration depends on the system used for model application, the experience
of the modellers, and knowledge of the model algorithms. It tends to be
subjective and time-consuming. By contrast, auto-calibration provides a
less labour-intensive approach by using optimisation algorithms (Eckhardt
and Arnold, 2001). The Sequential Uncertainty Fitting (SUFI-2) procedure
has previously been applied to auto-calibrate discharge parameters in a
SWAT application for the Thur River, Switzerland (Abbaspour et al., 2007),
as well as for groundwater recharge, evapotranspiration and soil storage
water considerations in western Africa (Schuol et al., 2008). Model validation
is subsequently performed using measured data that are independent of those
used for calibration (Moriasi et al., 2007).
Values for hydrological parameter values in the SWAT model can vary
temporally. Cibin et al. (2010) found that the optimum calibrated values for
hydrological parameters varied with different flow regimes (low, medium and
high), thus suggesting that SWAT model performance can be optimised by
assigning parameter values based on hydrological characteristics. Other work
has similarly demonstrated benefits from assigning separate parameter values
to low, medium, and high discharge periods (Yilmaz et al., 2008), or based
on whether a catchment is in a dry, drying, wet or wetting state (Choi and
Beven, 2007). Such temporal dependence of model parameterisation on
hydrologic conditions has implications for model performance. Krause et al. (2005)
compared different statistical metrics of hydrological model
performance separately for base flow periods and storm events to evaluate
the performance. The authors found that the logarithmic form of the
Nash–Sutcliffe efficiency (NSE) value provided more information
on the sensitivity of model performance for discharge simulations during
storm events, while the relative form of NSE was better for base
flow periods. Similarly, Guse et al. (2014) investigated temporal dynamics
of sensitivity of hydrological parameters and SWAT model performance using
a Fourier amplitude sensitivity test (Reusser et al., 2011) and cluster
analysis (Reusser et al., 2009). The authors found that three groundwater
parameters were highly sensitive during quick flow, while one evaporation
parameter was most sensitive during base flow, and model performance was
also found to vary significantly for the two flow regimes. Zhang et al. (2011)
calibrated SWAT hydrological parameters for periods separated on the
basis of six climatic indexes. Model performance improved when different
values were assigned to parameters based on six hydroclimatic periods.
Similarly, Pfannerstill et al. (2014) found that assessment of model
performance was improved by considering an additional performance statistic
for very low flow simulations amongst five hydrologically separated regimes.
To date, analysis of temporal dynamics of SWAT parameters has predominantly
focussed on simulations of discharge rather than water quality constituents.
This partly reflects the paucity of comprehensive water quality data for
many catchments; near-continuous discharge data can readily be collected
but this is not the case for water quality parameters such as suspended
sediment or nutrient concentrations. Data collected in monitoring programmes
that involve sampling at regular time intervals (e.g. monthly) are often
used to calibrate water quality models, but these are unlikely to fully
represent the range of hydrologic conditions in a catchment (Bieroza et al.,
2014). In particular, water quality data collected during storm flow
periods are rarely available for SWAT calibration, thus prohibiting
opportunities to investigate how parameter sensitivity varies under
conditions which can contribute disproportionately to nutrient or sediment
transport, particularly in lower-order catchments (Chiwa et al., 2010;
Abell et al., 2013). Failure to fully consider storm flow processes could
therefore result in overestimation of model performance. Thus, further
research is required to examine how water quality parameters vary during
different flow regimes and to understand how model uncertainty may vary
under future climatic conditions that affect discharge regimes (Brigode et al., 2013).
(a) Location of Puarenga Stream surface catchment in New Zealand,
Kaituna rain gauge, climate station and managed land areas for which
management schedules were prescribed in SWAT. (b) Location of the
Puarenga Stream, major tributaries, monitoring stream gauges, two
cold-water springs and the Whakarewarewa geothermal contribution.
Measurement data (Table 3) used to calibrate the SWAT model were from the
Forest Research Institute (FRI) stream gauge and were considered
representative of the downstream/outlet conditions of the Puarenga Stream.
In this study, the SWAT model was configured to a relatively small, mixed
land use catchment in New Zealand that has been the subject of an intensive
water quality sampling programme designed to target a wide range of
hydrologic conditions. A catchment-wide set of parameters was calibrated
using the SUFI-2 procedure which is integrated into the SWAT Calibration
and Uncertainty Program (SWAT-CUP). The objectives of this study were to
(1) quantify the performance of the model in simulating discharge and fluxes
of suspended sediments and nutrients at the catchment outlet, (2) rigorously
evaluate model performance by comparing daily simulation output with
monitoring data collected under a range of hydrologic conditions, and
(3) quantify whether parameter sensitivity varies between base flow and quick
flow conditions.
Methods
Study area
The Puarenga Stream is the second-largest surface inflow to Lake Rotorua
(Bay of Plenty, New Zealand) and drains a catchment of 77 km2. The
catchment is situated in the central North Island of New Zealand, which has
a warm temperate climate. Annual mean temperature at Rotorua Airport
(Fig. 1a) is 15 ± 4 ∘C and annual mean evapotranspiration is
714 mm yr-1 (1993–2012; National Climatic Data Centre; available at
http://cliflo.niwa.co.nz/). Annual mean precipitation at the Kaituna rain gauge
(Fig. 1a) is 1500 mm yr-1 (1993–2012; Bay of Plenty Regional Council).
The catchment is relatively steep (mean slope = 9 %; Bay of Plenty
Regional Council) with predominantly pumice soils that have high
macroporosity, resulting in high infiltration rates and substantial
subsurface lateral flow contributions to stream channels. Two cold-water
springs (Waipa Spring and Hemo Spring) and one geothermal spring (Fig. 1b)
are located in the catchment area. Two cold-water springs have annual mean
discharge of ∼ 0.19 m3 s-1 (Rotorua District
Council) and one geothermal spring has annual mean discharge of
∼ 0.12 m3 s-1 (White et al., 2004).
The predominant land use (47 %) is exotic forest (Pinus radiata). Approximately 26 %
is managed pastoral farmland, 11 % mixed scrub and 9 % indigenous
forest. Since 1991, treated wastewater has been pumped from the Rotorua
Wastewater Treatment Plant and spray-irrigated over 16 blocks of total area
of 1.93 km2 in the Whakarewarewa Forest (Fig. 1a). Following this, it
took approximately 4 years before elevated nitrate concentrations were
measured in the receiving waters of the Puarenga Stream (Lowe et al., 2007).
Prior to 2002, the irrigation schedule entailed applying wastewater to two
blocks per day so that each block was irrigated approximately weekly. Since
2002, 10–14 blocks have been irrigated simultaneously at daily frequency.
Over the entire period of irrigation, nutrient concentrations in the
irrigated water have gradually decreased as improvements in treatment of the
wastewater have been made (Lowe et al., 2007).
Measurements from the Forest Research Institute (FRI) stream gauge (1.7 km
upstream of Lake Rotorua; Fig. 1b) were considered representative of the
downstream/outlet conditions of the Puarenga Stream. The FRI stream gauge
was closed in mid 1997, then reopened late in 2004 (Environment Bay of
Plenty, 2007). Annual mean discharge at this site is 2.0 m3 s-1
(1994–1997 and 2004–2008; Bay of Plenty Regional Council). The Puarenga
Stream receives a high proportion of flow from groundwater stores and has
only moderate seasonality in discharge. On average, the lowest mean daily
discharge is during summer (December–February; 1.7 m3 s-1) and
the highest mean daily discharge is during winter (June–August;
2.4 m3 s-1). Discharge records during 1998–2004 were intermittent and
this precluded a detailed comparison of measured and simulated discharge
during that period. In July 2010, the gauge was repositioned 720 m
downstream to the State Highway 30 (SH 30) bridge (Fig. 1b).
Model configuration
SWAT input data requirements included a digital elevation model (DEM),
meteorological records, records of springs and water abstraction, soil
characteristics, land use classification, and management schedules for key
land uses (pastoral farming, wastewater irrigation, and timber harvesting).
The SWAT model (version SWAT2009_rev488) was run on an hourly
time step, but daily mean simulation outputs were used for this study.
The DEM was used to delineate boundaries of the whole catchment and
individual sub-catchments, with a stream map used to “burn-in” channel
locations to create accurate flow routings. Hourly rainfall estimates were
used as hydrologic forcing data. The Penman–Monteith method (Monteith,
1965) was used to calculate evapotranspiration (ET) and potential ET. The
Green and Ampt (1911) method was used to calculate infiltration, rather than
the SCS (Soil Conservation Service) curve number method. Therefore, the hourly rainfall/Green and Ampt
infiltration/hourly routing method (Neitsch et al., 2011) was chosen to
simulate upland and in-stream processes. Ten sub-catchments were
represented in the Puarenga Stream catchment, each comprising numerous
hydrologic response units (HRUs). Each HRU aggregates cells with the same
combination of land cover, soil, and slope. A total of 404 HRUs was defined
in the model. Runoff and nutrient transport were predicted separately within
SWAT for each HRU, with predictions summed to obtain the total for each sub-catchment.
Descriptions and sources of the data used to configure the SWAT model are
given in Table 1. There were a total of 197 model parameters. Values of SWAT
parameters were assigned based on (i) measured data (e.g. some of the soil
parameters; Table 1), (ii) literature values from published studies of
similar catchments (e.g. parameters for dominant land uses; Table 2), or
(iii) by calibration where parameters were not otherwise prescribed.
SWAT simulates loads of “mineral phosphorus” (MINP) and “organic
phosphorus” (ORGP) of which the sum is total phosphorus (TP). The MINP fraction
represents soluble P either in mineral or in organic form, while ORGP refers
to particulate P bound either by algae or by sediment (White et al., 2014).
Soluble P may be taken up during algae growth, or released from benthic
sediment. This fraction can be transformed to particulate P contained in
algae or sediment.
Description of data used to configure the SWAT model.
Data
Application
Data description and configuration details
Source
Digital elevation
Sub-basin
25 m resolution. Used to define five slope classes:
Bay of Plenty Regional Council
model (DEM) and
delineation
0–4, 4–10, 10–17, 17–26 and > 26 %.
(BoPRC)
digitised stream
(Fig. 1b)
network
Spring discharge
Point source
Constant daily discharge and nutrient concentrations
White et al. (2004), Proffit (2009)
and nutrient loads
(Fig. 1b)
assigned to two cold-water springs (Waipa Spring and
Paku (2001), Mahon (1985), Glover
Hemo Spring) and one geothermal spring.
(1993), Rotorua District Council
(personal communication)
Water abstraction
Water use
Monthly water abstraction assigned to two cold-water
Kusabs and Shaw (2008),
volumes
springs.
Jowett (2008)
Land use
HRU definition
25 m resolution, 10 basic land-cover categories. Some
New Zealand Land Cover Database
particular land-cover parameters were previously estimated
Version 2; BoPRC
(Table 2).
Soil characteristics
HRU definition
22 soil types. Properties were quantified based on
New Zealand Land Resource
measurements (if available) or estimated using
Inventory and digital soil map
regression analysis to estimate properties for
(available at:
unmeasured functional horizons.
http://smap.landcareresearch.co.nz)
Meteorological
Meteorological
Daily maximum and minimum temperature, daily
Rotorua Airport Automatic
data
forcing
mean relative humidity, daily global solar radiation,
Weather Station, National Climate
daily (09:00 LT) surface wind speed and hourly
Database (available at:
precipitation.
http://cliflo.niwa.co.nz/); Kaituna
rain gauge (Fig. 1a)
Agricultural
Agricultural
Stock density
Statistics New Zealand (2006),
management
management
Ledgard and Thorrold (1998)
practices
schedules
Applications of urea and diammonium phosphate
Statistics New Zealand (2006),
Fert Research (2009)
Applications of manure-associated nutrients
Dairying Research Corporation
(1999)
Nutrient loading
Nonpoint-
Wastewater application rates and effluent composition
Rotorua District Council (2006)
by wastewater
source from land
(i.e. concentrations of total nitrogen and total phosphorus)
application
treatment
for 16 spray blocks from 1996 to 2012. Each spray block
irrigation
was assigned an individual management schedule specifying
daily application rates.
Forest stand map
Forestry
Planting and harvesting data for 472 ha forestry stands.
Timberlands Limited, Rotorua,
and harvest dates
planting and
Prior to 2007 we assumed stands were cleared
New Zealand (personal
harvesting
1 year prior to the establishment year. Post-2007,
communication)
operations
harvesting date was assigned to the first day of
harvesting month.
Previously estimated parameter values for three dominant types of
land cover in the Puarenga Stream catchment. Values of other land use
parameters were based on the default values in the SWAT database.
Land-cover type
Parameter
Definition
Value
Source
PINE
HVSTI
Percentage of biomass harvested
0.65
Ximenes et al. (2008)
(Pinus radiata)
T_OPT (∘C)
Optimal temperature for plant growth
15
Kirschbaum and Watt (2011)
T_BASE (∘C)
Minimum temperature for plant growth
4
Kirschbaum and Watt (2011)
MAT_YRS
Number of years to reach full development
30
Kirschbaum and Watt (2011)
BMX_TREES (t ha-1)
Maximum biomass for a forest
400
Bi et al. (2010)
GSI (m s-1)
Maximum stomatal conductance
0.00198
Whitehead et al. (1994)
BLAI (m2 m-2)
Maximum leaf area index
5.2
Watt et al. (2008)
BP3
Proportion of phosphorus in biomass at maturity
0.000163
Hopmans and Elms (2009)
BN3
Proportion of nitrogen in biomass at maturity
0.00139
Hopmans and Elms (2009)
FRSE
HVSTI
Percentage of biomass harvested
0
–
(evergreen forest)
BMX_TREES (t ha-1)
Maximum biomass for a forest
372
Hall et al. (2001)
MAT_YRS (years)
Number of years for tree to reach full development
100
–
PAST
T_OPT (∘C)
Optimal temperature for plant growth
25
McKenzie et al. (1999)
(pastoral farm)
T_BASE (∘C)
Minimum temperature for plant growth
5
McKenzie et al. (1999)
Description of data used to calibrate the SWAT model. Data were
measured at the Forest Research Institute (FRI) stream gauge and were
considered representative of the downstream/outlet conditions of the
Puarenga Stream.
Data
Application
Measurement data details
Source
Stream discharge
Calibration
15 min stream discharge data were measured at FRI stream
Bay of Plenty Regional Council
measurements
(2004–2008)
gauge (Fig. 1b) within the catchment and aggregated as daily
(BoPRC); Abell et al. (2013)
Validation
mean values (1994–1997; 2004–2008).
(1994–1997)
Stream water quality
Calibration
Monthly grab samples for determination of suspended
BoPRC; Abell et al. (2013)
measurements
(2004–2008)
sediment (SS), total phosphorus (TP) and total nitrogen (TN)
Validation*
concentrations (1994–1997; 2004–2008), and high-
(1994–1997;
frequency event-based samples for concentrations of SS
2010–2012)
(9 events), TP and TN (both 14 events) at 1–2 h frequency
(2010–2012), were also measured at FRI stream gauge
(Fig. 1b) within the catchment.
* Model validation was undertaken using two different data sets.
The monthly measurements (1994–1997) were predominantly collected when base
flow was the dominant contributor to stream discharge. Data from high-frequency
sampling during rain events (2010–2012) were also used to validate model
performance during periods when quick flow was high.
Summary of calibrated SWAT parameters. Discharge (Q), suspended
sediment (SS) and total nitrogen (TN) parameter values were assigned using
auto-calibration, while total phosphorus (TP) parameters were manually
calibrated. SWAT default ranges and input file extensions are shown for each parameter.
Parameter
Definition
Unit
Default
Calibrated
range
value
Q
EVRCH.bsn
Reach evaporation adjustment factor
0.5–1
0.9
SURLAG.bsn
Surface runoff lag coefficient
0.05–24
15
ALPHA_BF.gw
Base flow alpha factor (0–1)
0.0071–0.0161
0.01
GW_DELAY.gw
Groundwater delay
day
0–500
500
GW_REVAP.gw
Groundwater “revap” coefficient
0.02–0.2
0.08
GW_SPYLD.gw
Special yield of the shallow aquifer
m3 m-3
0–0.4
0.13
GWHT.gw
Initial groundwater height
m
0–25
14
GWQMN.gw
Threshold depth of water in the shallow aquifer required for return
mm
0–5000
372
flow to occur
RCHRG_DP.gw
Deep aquifer percolation fraction
0–1
0.87
REVAPMN.gw
Threshold depth of water in the shallow aquifer required for “revap”
mm
0–500
260
to occur
CANMX.hru
Maximum canopy storage
mm
0–100
0.6
EPCO.hru
Plant uptake compensation factor
0–1
0.34
ESCO.hru
Soil evaporation compensation factor
0–1
0.9
HRU_SLP.hru
Average slope steepness
m m-1
0–0.6
0.5
LAT_TTIME.hru
Lateral flow travel time
day
0–180
3
RSDIN.hru
Initial residue cover
kg ha-1
0–10 000
1
SLSOIL.hru
Slope length for lateral subsurface flow
m
0–150
40
CH_K2.rte
Effective hydraulic conductivity in the main channel alluvium
mm h-1
0–500
20
CH_N2.rte
Manning's N value for the main channel
0–0.3
0.16
CH_K1.sub
Effective hydraulic conductivity in the tributary channel alluvium
mm h-1
0–300
100
CH_N1.sub
Manning's N value for the tributary channel
0.01–30
20
SS
USLE_P.mgt
USLE equation support practice factor
0–1
0.5
PRF.bsn
Peak rate adjustment factor for sediment routing in the main channel
0–2
1.9
SPCON.bsn
Linear parameter for calculating the maximum amount of sediment
0.0001–0.01
0.001
that can be re-entrained during channel sediment routing
SPEXP.bsn
Exponent parameter for calculating sediment re-entrained in channel
1–1.5
1.26
sediment routing
LAT_SED.hru
Sediment concentration in lateral flow and groundwater flow
mg L-1
0–5000
5.7
OV_N.hru
Manning's N value for overland flow
0.01–30
28
SLSUBBSN.hru
Average slope length
m
10–150
92
CH_COV1.rte
Channel erodibility factor
0–0.6
0.17
CH_COV2.rte
Channel cover factor
0–1
0.6
TP
P_UPDIS.bsn
Phosphorus uptake distribution parameter
0–100
0.5
PHOSKD.bsn
Phosphorus soil partitioning coefficient
100–200
174
PPERCO.bsn
Phosphorus percolation coefficient
10–17.5
14
PSP.bsn
Phosphorus sorption coefficient
0.01–0.7
0.5
GWSOLP.gw
Soluble phosphorus concentration in groundwater loading
mg P L-1
0–1000
0.063
LAT_ORGP.gw
Organic phosphorus in the base flow
mg P L-1
0–200
0.01
ERORGP.hru
Organic phosphorus enrichment ratio
0–5
2.5
CH_OPCO.rte
Organic phosphorus concentration in the channel
mg P L-1
0–100
0.02
BC4.swq
Rate constant for mineralisation of organic phosphorus to dissolved
day-1
0.01–0.7
0.3
phosphorus in the reach at 20 ∘C
RS2.swq
Benthic (sediment) source rate for dissolved phosphorus in the reach at 20 ∘C
mg m-2 day-1
0.001–0.1
0.02
RS5.swq
Organic phosphorus settling rate in the reach at 20 ∘C
day-1
0.001–0.1
0.05
Continued.
Parameter
Definition
Unit
Default
Calibrated
range
value
TN
RSDCO.bsn
Residue decomposition coefficient
0.02–0.1
0.09
CDN.bsn
Denitrification exponential rate coefficient
0–3
0.3
CMN.bsn
Rate factor for humus mineralisation of active organic nitrogen
0.001–0.003
0.002
N_UPDIS.bsn
Nitrogen uptake distribution parameter
0–100
0.5
NPERCO.bsn
Nitrogen percolation coefficient
0–1
0.0003
RCN.bsn
Concentration of nitrogen in rainfall
mg N L-1
0–15
0.34
SDNCO.bsn
Denitrification threshold water content
0–1
0.02
HLIFE_NGW.gw
Half-life of nitrate–nitrogen in the shallow aquifer
day
0–200
195
LAT_ORGN.gw
Organic nitrogen in the base flow
mg N L-1
0–200
0.055
SHALLST_N.gw
Nitrate–nitrogen concentration in the shallow aquifer
mg N L-1
0–1000
1
ERORGN.hru
Organic nitrogen enrichment ratio
0–5
3
CH_ONCO.rte
Organic nitrogen concentration in the channel
mg N L-1
0–100
0.01
BC1.swq
Rate constant for biological oxidation of ammonium–nitrogen to
day-1
0.1–1
1
nitrite–nitrogen in the reach at 20 ∘C
BC2.swq
Rate constant for biological oxidation of nitrite–nitrogen to
day-1
0.2–2
0.7
nitrate–nitrogen in the reach at 20 ∘C
BC3.swq
Rate constant for hydrolysis of organic nitrogen to
day-1
0.2–0.4
0.4
ammonium–nitrogen in the reach at 20 ∘C
RS3.swq
Benthic (sediment) source rate for ammonium–nitrogen in the reach
mg m-2 day-1
0–1
0.2
at 20∘C
RS4.swq
Rate coefficient for organic nitrogen settling in the reach at 20∘C
day-1
0.001–0.1
0.05
SWAT simulates loads of nitrate–nitrogen (NO3–N), ammonium–nitrogen
(NH4–N) and organic nitrogen (ORGN), the sum of which is total
nitrogen (TN). Nitrogen parameters were auto-calibrated for each N
fraction. The SWAT model does not account for the initial nitrate
concentration in shallow aquifers, as also noted by Conan et al. (2003).
Ekanayake and Davie (2005) indicated that SWAT underestimated N loading from
groundwater and suggested a modification by adding a background
concentration of nitrate in streamflow to represent groundwater nitrate
contributions. Over the period of the first 5 years of wastewater
irrigation, nitrate concentrations in shallow groundwater draining the Waipa
Stream sub-catchment were estimated to have increased by ∼ 0.44 mg N L-1
(Paku, 2001). SWAT has no capability to dynamically adjust the
groundwater concentration during a simulation run. Therefore, we added
0.44 mg N L-1 to all model simulations of TN concentration assuming that
groundwater concentrations had equilibrated with the applied wastewater nitrogen.
Model calibration and validation
Daily mean discharge was firstly calibrated based on daily mean values of
15 min measurements (Table 3). Water quality variables were then
calibrated in the sequence: SS, TP and TN. Modelled mean daily
concentrations were compared with concentrations measured during monthly
grab sampling, with monthly measurements assumed equal to daily mean
concentrations (Table 3). One year (1993) was used for model warmup. The
calibration period was from 2004 to 2008 and the validation period was from
1994 to 1997. A validation period that pre-dated the calibration period was
chosen because discharge records were available for two separate periods
(1994–1997 and post 2004). In addition, the operational regime for the
wastewater irrigation has varied since operations began in 1991, with a
marked change occurring in 2002 when operations switched from applying the
wastewater load to 2 blocks (rotated daily for a total of 14 blocks in a
week; i.e. each block irrigated weekly), to 10–14 blocks each irrigated
daily. This operational regime continues today and we therefore decided to
assign the most recent (post-2002) period (2004–2008) to calibration to
ensure that the model was configured to reflect current operations.
Parameter values that were not derived from measurements or the literature
were assigned based on either automated or manual calibration (Table 4).
Manual calibration was undertaken for 11 parameters related to TP, while a
Sequential Uncertainty Fitting (SUFI-2) procedure was applied to
auto-calibrate 21 parameters for discharge simulations, 9 parameters for
SS simulations, and 17 parameters related to TN. The SUFI-2 procedure has
been integrated into the SWAT Calibration and Uncertainty Program (SWAT-CUP).
SUFI-2 is a procedure that efficiently quantifies and
constrains parameter uncertainties/ranges from default ranges with the
fewest number of iterations (Abbaspour et al., 2004), and has been shown to
provide optimal results relative to the use of alternative algorithms (Wu
and Chen, 2015). SUFI-2 involves Latin hypercube sampling (LHS), which is a
method that generates a sample of plausible parameter values from
a multidimensional distribution and ensures that samples cover the entire
parameter space, therefore ensuring that the optimum solution is not a local
minimum (Marino et al., 2008).
The SUFI-2 procedure analyses relative sensitivities of parameters by
randomly generating combinations of values for model parameters (Abbaspour,
2015). A sample size of 1000 was chosen for each iteration of LHS,
resulting in 1000 combinations of parameters and 1000 simulations. Model
performance was quantified for each simulation based on the Nash–Sutcliffe
efficiency (NSE). An objective function was defined as a linear regression
of a combination of parameter values generated by each LHS against the NSE
value calculated from each simulation. Each compartment was not given weight
to formulate the objective function because only one variable was
specifically focused on at each time. A parameter sensitivity matrix was
then computed based on the changes in the objective function after
1000 simulations. Parameter sensitivity was quantified based on the p value from a
Student t test, which was used to compare the mean of simulated values
with the mean value of measurements (Rice, 2006). A parameter was deemed
sensitive if p ≤ 0.05 after 1000 simulations (one iteration). Numerous
iterations of LHS were conducted. Values of p from numerous iterations were
averaged for each parameter, and the frequency of iterations where a
parameter was deemed sensitive was summed. Rankings of relative
sensitivities of parameters were developed based on how frequently the
sensitive parameter was identified and the averaged value of p calculated
from several iterations. The most sensitive parameter was determined based
on the frequency that the parameter was deemed sensitive and the smallest
average p value from all iterations.
SUFI-2 considers two criteria to constrain uncertainty in each iteration.
One is the P factor, the percentage of measured data bracketed by 95 %
prediction uncertainty (95PPU). Another is the R factor, the average
thickness of the 95PPU band divided by the standard deviation of measured
data. A range was first defined for each parameter based on a synthesis of
ranges from similar studies or from the SWAT default range. Parameter ranges
were updated after each iteration based on the computation of upper and
lower 95 % confidence limits. The 95 % confidence interval and the
standard deviation of a parameter value were derived from the diagonal
elements of the covariance matrix, which was calculated from the sensitivity
matrix and the variance of the objective function. Steps and equations used
in the SUFI-2 procedure to constrain parameter ranges are outlined by
Abbaspour et al. (2004).
The total number of iterations performed for each simulated variable (Q,
SS, MINP, ORGN, NH4–N and NO3–N) reflected the numbers required
to ensure that > 90 % of measured data were bracketed by
simulated output and the R factor was close to one. The “optimal” parameter
value was obtained when the NSE
criterion was satisfied (NSE > 0.5; Moriasi et al.,
2007). Auto-calibrated parameters for simulations of Q, SS, and TN were
changed by absolute values within the given ranges. Some of those given
ranges were restricted based on the optimum values calibrated in similar
studies. Parameter values for TP simulations were manually calibrated based
on the relative percent deviation from the predetermined values of those
auto-calibrated parameters for MINP simulations, given by the objective
functions (e.g. NSE). Parameters related to the physical
characteristics of the catchment were not changed because their values were
considered to be representative of the catchment characteristics. In
addition, high-frequency (1–2 h) water quality sampling was undertaken at
the FRI stream gauge during 2010–2012 (Table 3) to derive estimates of
daily mean contaminant loads during storm events. Samples were analysed for
SS (9 events), TP and TN (both 14 events) over sampling periods of 24–73 h.
The sampling programme was designed to encompass pre-event base flow,
storm-generated quick flow and post-event base flow (Abell et al., 2013).
These data permitted calculation of daily discharge-weighted (Q-weighted)
mean concentrations to compare with modelled daily mean estimates. We did
not use the high-frequency observations to calibrate the model, because of
the limited number of high-frequency (1–2 h) samples (9 events for SS
and 14 events for TP and TN in 2010–2012). The use of the high-frequency
observations for model validation allowed examining how the model performed
during short (1–3 day) high flow periods. The Q-weighted mean
concentrations CQWM were calculated as
CQWM=∑i=1nCiQi∑i=1nQi,
where n is number of samples, Ci is contaminant concentration measured
at time i, and Qi is discharge measured at time i.
Hydrograph and contaminant load separation
The Web-based Hydrograph Analysis Tool (Lim et al., 2005) was applied to
partition both measured and simulated discharges into base flow
(Qb) and quick flow (Qq). An Eckhardt filter
parameter of 0.98 and ratio of base flow to total discharge of 0.8 were
assumed (cf. Lim et al., 2005). There was a total of 60 days without quick
flow during the calibration period (2004–2008) and 1379 days for which
hydrograph separation defined both base flow and quick flow.
Flow chart of methods used to separate hydrograph and contaminant
loads and to quantify parameter sensitivities for Q (discharge),
SS (suspended sediment), MINP (mineral phosphorus), ORGN (organic nitrogen),
NH4–N (ammonium–nitrogen), and NO3–N (nitrate–nitrogen).
NSE: Nash–Sutcliffe efficiency.
Contaminant (SS, TP and TN) concentrations (Csep) were
partitioned into base flow (Cb′) and quick flow
components (Cq′; cf. Rimmer and Hartmann, 2014) to
separately examine the sensitivity of water quality parameters during base
flow and quick flow:
Csep=Qq×Cq′+Qb×Cb′Qq+Qb.
Cb′ for each contaminant was estimated as the
average concentration for the 60 days with no quick flow.
Cq′ for each contaminant was calculated by rearranging Eq. (2).
To ensure that Cq′ is positive, Cb′ is constrained to be the minimum of
Csep‾ and Csep. Measured and simulated base flow and
quick flow contaminant loads were then calculated.
A one-at-a-time (OAT) routine proposed by Morris (1991) was applied to
investigate how parameter sensitivity varied between the two flow regimes
(base flow and quick flow), based on the ranking of relative sensitivities
of parameters that were identified by randomly generating combinations of
values for model parameters for each individual variable using the SUFI-2
procedure. OAT sensitivity analysis was then employed by varying the
parameter of interest among 10 equidistant values within the default range.
The natural logarithm was used by Krause et al. (2005) and therefore the
standard deviation (SD) of the ln-transformed NSE was used to
indicate parameter sensitivity for the two flow regimes.
Parameters were ranked from most to least sensitive on the basis of the
sensitivity metric (SD of ln-transformed NSE), using a value
of 0.2 as a threshold above which parameters were deemed particularly
“sensitive”. The threshold value of 0.2 was chosen in this study, based on
the median value derived from the calculations of the SD of
ln-transformed NSE. Methods used to separate the two flow
constituents and to quantify parameter sensitivity are illustrated in Fig. 2.
Model evaluation
Model goodness of fit was assessed graphically and quantified using
coefficient of determination (R2), NSE and percent bias (PBIAS; Table 5).
R2 (range from 0 to 1) and NSE (range from -∞ to 1) values are commonly used to evaluate SWAT model performance (Gassman
et al., 2007). The PBIAS value indicates the average tendency of
simulated outputs to be larger or smaller than observations (Gupta et al., 1999).
Model uncertainty was evaluated by two criteria: R factor and P factor
(see Sect. 2.3). These were used to constrain parameter ranges during the
calibration using measured Q and loads of SS, MINP, ORGN, NH4–N and
NO3–N in the SUFI-2 procedure. The R software (R Development Core
Team) was used to graphically show the 95 % confidence and prediction
intervals for measurement data (Neyman, 1937) and model prediction intervals
(Geisser, 1993) for Q and concentrations of SS, TP and TN during the
calibration period (2004–2008).
Criteria for model performance. Note: on is the nth-observed datum,
sn is the nth-simulated datum, o‾ is the observed mean value, s‾ is the simulated daily mean
value, and N is the total number of observed data.
Performance rating criteria are based on Moriasi et al. (2007) for Q:
discharge, SS: suspended sediment, TP: total phosphorus and TN: total
nitrogen. Moriasi et al. (2007) derived these criteria based on extensive
literature review and analysing the reported performance ratings for
recommended model evaluation statistics.
Statistic equation
Constituent
Performance ratings
Unsatisfactory
Satisfactory
Good
Very good
R2 = ∑n=1Nsn-s‾on-o‾2∑n=1Non-o‾2×∑n=1Nsn-s‾2 (3)
All
< 0.5
0.5–0.6
0.6–0.7
0.7–1
NSE = 1 - ∑n=1Non-sni∑n=1Non-o‾i i = 2 (4)
All
< 0.5
0.5–0.65
0.65–0.75
0.75–1
±PBIAS% = ∑n=1Non-sn∑n=1Non × 100 (5)
Q
> 25
15–25
10–15
< 10
SS
> 55
30–55
15–30
< 15
TP, TN
> 70
40–70
25–40
< 25
R2: coefficient of determination;
NSE: Nash–Sutcliffe efficiency; PBIAS: percent bias.
Measurements and daily mean simulated values of discharge, suspended
sediment (SS), total phosphorus (TP) and total nitrogen (TN) during calibration (a–d)
and validation (e–h). Measured daily mean discharge was calculated
from 15 min observations and measured concentrations of SS, TP and TN correspond
to monthly grab samples.
Results
Model performance and uncertainty
Numerous rounds (each comprising 1000 iterations) of LHS were conducted for
each simulated variable until the performance criteria were satisfied. The
total number of rounds of LHS for each simulated variable was as follows
(number in parentheses): Q (7), SS (7), MINP (11), ORGN (10), NH4–N (4)
and NO3–N (4). The parameters that provided the best statistical
outcomes (i.e. best match to observed data) are given in Table 4. Two
criteria (R factor and P factor) were used to show model uncertainties for
simulations of discharge and contaminant loads, with values as follows: Q (0.97, 0.43),
SS (0.48, 0.19), MINP (2.64, 0.14), ORGN (0.47, 0.17),
NH4–N (1.16, 0.56) and NO3–N (1.2, 0.29). Model uncertainties
for simulations of Q and SS, TP and TN concentrations are shown in Fig. 6.
Modelled and measured base flow showed high correspondence, although
measured daily mean discharge during storm peaks was often underestimated
(Fig. 3a, e). Annual mean percentages of lateral flow recharge, shallow
aquifer recharge and deep aquifer recharge to total water yield were
predicted by SWAT as 30, 10 and 58 %, respectively. Modelled SS
concentrations overestimated measurements of monthly grab samples by an
average of 18.3 % during calibration and 0.32 % during validation
(Fig. 3b, f). Measured TP concentrations in monthly grab samples were
underestimated by 23.8 % during calibration (Fig. 3c) and 24.5 % during
validation (Fig. 3g). Similarly, measured TP loads were underestimated by
34.5 and 38.4 % during calibration and validation, respectively.
Modelled and measured TN concentrations were generally better aligned during
base flow (Fig. 3d), apart from a mismatch prior to 1996 when monthly
measured TN concentrations were substantially lower than model predictions,
although the concentrations gradually increased (Fig. 3h) during the
validation period (1994–1997). The average measured TN load increased from
134 kg N day-1 prior to 1996 to 190 kg N day-1 post-1996, and the
comparable increase in modelled TN load was from 167 to 205 kg N day-1, respectively.
Statistical evaluations of goodness of fit are shown in Table 6. The
R2 values for discharge were 0.77 for calibration and
0.68 for validation, corresponding to model performance ratings (cf. Moriasi
et al., 2007) of “very good” and “good” (Table 5). Similarly, the
NSE values for discharge were 0.73 (good) for calibration and 0.62
(satisfactory) for validation. Positive PBIAS (7.8 % for
calibration and 8.8 % for validation) indicated a tendency for
underestimation of daily mean discharge; however, the low magnitude of
PBIAS values corresponded to a performance rating of “very good”.
The R2 values for SS were 0.42 (unsatisfactory) for
calibration and 0.80 for validation (very good). Similarly, the
NSE values for SS were -0.08 (unsatisfactory) for calibration and 0.76
(very good) for validation. The model did not simulate trends well for
monthly measured TP and TN concentrations. The R2
values for TP and TN were both < 0.1 (unsatisfactory) during
calibration and validation and NSE values were both < 0
(unsatisfactory). Values of PBIAS corresponded to “good” or “very
good” performance ratings for TP and TN.
Observed Q-weighted daily mean concentrations derived from hourly
measurements and simulated daily mean concentrations of SS, TP and TN during
an example 2-day storm event are shown in Fig. 4a–c. The simulations of
SS and TN concentrations were somewhat better than for TP. Comparisons of
Q-weighted daily mean concentrations (CQWM) during storm
events from 2010 to 2012 are shown in Fig. 4d–f for SS (9 events), TP
and TN (both 14 events). The CQWM of TP exceeded the simulated
daily mean by between 0.02 and 0.2 mg P L-1 and, on average, the
model underestimated measurements by 69.4 % (Fig. 4e). Although
R2 and NSE values for CQWM
of TN were unsatisfactory (Table 6), they were both close to the threshold
for satisfactory performance (0.5). For CQWM of SS and TP,
R2 and NSE values indicated that the model
performance was unsatisfactory. The PBIAS value of -0.87 for
CQWM of TN corresponded to model performance ratings of “very
good”, while the PBIAS values for CQWM of SS and TP
were 43.9 and 69.4, respectively, indicating satisfactory model performance.
Example of a storm event showing derivation of discharge (Q)-weighted
daily mean concentrations (dashed horizontal line) based on hourly measured
concentrations (black dots) of suspended sediment (SS), total phosphorus (TP)
and total nitrogen (TN) over 2 days (a–c). Comparisons of Q-weighted
daily mean concentrations with simulated daily mean estimates of SS, TP and TN
(scatter plot, d–f). The horizontal bars show the ranges in hourly
measurements during each storm event in 2010–2012.
Measurements and simulations derived using the calibrated set of
parameter values. Data are shown separately for base flow and quick flow.
(a) Daily mean base flow and quick flow; (b) suspended sediment (SS)
load; (c) total phosphorus (TP) load; (d) total nitrogen (TN) load.
Vertical lines in (b)–(d) show the contaminant load in quick flow.
Time series relate to calibration (2004–2008) and validation (1994–1997)
periods (note time discontinuity). Measured instantaneous loads of SS, TP, and
TN correspond to monthly grab samples.
Regression of measured and simulated (a) discharge (Q),
concentrations of (b) suspended sediment (SS), (c) total phosphorus (TP),
and (d) total nitrogen (TN) including lower and upper 95 % confidence
limits (LCL and UCL) and lower and upper 95 % prediction limits (LPL and UPL).
Note that the “indistict” shape of confidence limits shown in (b)–(d)
resulted from the few data points (< 50) in the regressions of measured and
simulated SS, TP and TN concentrations.
The standard deviation (SD) of the ln-transformed
Nash–Sutcliffe efficiency (NSE) used to indicate parameter
sensitivity based on one-at-a-time (OAT) sensitivity analysis for separate
base flow and quick flow components: (a) Q (discharge); (b) SS (suspended
sediment); (c) MINP (mineral phosphorus); (d) NO3–N
(nitrate–nitrogen); (e) ORGN (organic nitrogen); (f) NH4–N
(ammonium–nitrogen). A median value (0.2) derived from the SD
of ln-transformed NSE was chosen as a threshold above which
parameters were deemed to be “sensitive”. Definitions of each parameter are
shown in Table 4.
Model performance ratings for simulations of discharge (Q),
concentrations of suspended sediment (SS), total phosphorus (TP) and total
nitrogen (TN). n indicates the number of measurements. Q-weighted mean
concentrations were calculated using Eq. (1).
Model performance
Statistics
Q
SS
TP
TN
Calibration with
n = 1439
n = 43
n = 45
n = 39
instantaneous measurements
R2
0.77
0.42
0.02
0.08
(2004–2008)
(very good)
(unsatisfactory)
(unsatisfactory)
(unsatisfactory)
NSE
0.73
-0.08
-1.31
-0.30
(good)
(unsatisfactory)
(unsatisfactory)
(unsatisfactory)
±PBIAS%
7.8
-18.3
23.8
-0.05
(very good)
(very good)
(very good)
(very good)
Validation with
n = 1294
n = 37
n = 37
n = 36
instantaneous measurements
R2
0.68
0.80
0.01
0.01
(1994–1997)
(good)
(very good)
(unsatisfactory)
(unsatisfactory)
NSE
0.62
0.76
-0.97
-2.67
(satisfactory)
(very good)
(unsatisfactory)
(unsatisfactory)
±PBIAS%
8.8
-0.32
24.5
-26.7
(very good)
(very good)
(very good)
(good)
Validation with
–
n = 12
n = 18
n = 18
Q-weighted mean concentrations
R2
–
0.38
0.06
0.46
(unsatisfactory)
(unsatisfactory)
(unsatisfactory)
(2010–2012)
NSE
–
-0.03
-4.88
0.42
(unsatisfactory)
(unsatisfactory)
(unsatisfactory)
±PBIAS%
–
43.9
69.4
-0.87
(satisfactory)
(satisfactory)
(very good)
Measured and simulated discharge and contaminant loads separated for the two
flow regimes (base flow and quick flow) are shown in Fig. 5. Model
performance statistics differed between the two flow regimes (Table 7).
Simulations of discharge and constituent loads under quick flow were more
closely related to the measurements (i.e. higher values of
R2 and NSE) than simulations under base
flow. Base flow TN load simulations during the validation period showed
better model performance than simulations under quick flow. Additionally,
measurements under quick flow were better reproduced by the model than the
measurements for the whole simulation period. Simulations of contaminant
loads matched measurements much better than for contaminant concentrations,
as indicated by statistical values for model performance given in Tables 6 and 7.
Separated parameter sensitivity
Based on the ranking of relative sensitivities of hydrological and water
quality parameters derived from the SUFI-2 procedure (see Table 8), the OAT
sensitivity analysis undertaken separately for base flow and quick flow
identified three parameters that most influenced the quick flow estimates,
and five parameters that most influenced the base flow estimates (parameters
above the dashed line in Fig. 7a). Channel hydraulic conductivity
(CH_K2) is used to estimate the peak runoff rate (Lane,
1983). Lateral flow slope length (SLSOIL) and lateral flow travel time
(LAT_TIME) have an important controlling effect on the amount
of lateral flow entering the stream reach during quick flow. Both slope
(HRU_SLP) and soil available water content
(SOL_AWC) were particularly sensitive for the base flow
simulation because they affect lateral flow within the kinematic storage
model in SWAT (Sloan and Moore, 1984). The aquifer percolation coefficient
(RCHRG_DP) and the base flow alpha factor
(ALPHA_BF) strongly influenced base flow calculations
(Sangrey et al., 1984), as did the channel's Manning N value
(CH_N2), which is used to estimate channel flow (Chow, 2008).
For SS loads, 12 and four parameters, respectively, were identified as
sensitive in relation to the simulations of base flow and quick flow
(parameters above the dashed line in Fig. 7b). Parameters that control main
channel processes (e.g. CH_K2 and CH_N2) and
subsurface water transport processes (e.g. LAT_TIME and
SLSOIL) were found to be much more sensitive for base flow SS load
estimations. Exclusive parameters for SS estimations, such as SPCON (linear
parameter), PRF (peak rate adjustment factor), SPEXP (exponent parameter),
CH_COV1 (channel erodibility factor), and CH_COV2 (channel cover factor) were found to be much more sensitive in base
flow SS load, while LAT_SED (SS concentration in lateral flow
and groundwater flow) was more sensitive in quick flow SS load. Parameters
that control overland processes, e.g. CN2 (the curve number),
OV_N (overland flow of Manning's N value) and SLSUBBSN
(sub-basin slope length), were found to be much more sensitive for quick
flow SS load estimations.
Of the sensitive parameters, BC4 (ORGP mineralisation rate) was particularly
sensitive for the simulation of base flow MINP load (Fig. 7c). RCN (nitrogen
concentration in rainfall) related specifically to the dynamics of the base
flow NO3–N load and NPERCO (nitrogen percolation coefficient)
significantly affected the quick flow NO3–N load (Fig. 7d). Parameter
CH_ONCO (channel ORGN concentration) similarly affected both
flow components of ORGN load (Fig. 7e) and SOL_CBN (organic
carbon content) was most sensitive for the simulations of quick flow ORGN
and NH4–N loads. Parameter BC1 (nitrification rate in reach) was
particularly sensitive for the simulation of the base flow NH4–N load (Fig. 7f).
Discussion
This study examined temporal dynamics of model performance and parameter
sensitivity in a SWAT model application that was configured for a small,
relatively steep and lower-order stream catchment in New Zealand. This
country faces increasing pressures on freshwater resources (Parliamentary
Commissioner for the Environment, 2013) and models such as SWAT potentially
offer valuable tools to inform management of water resources although, to
date, the SWAT model has received limited consideration in New Zealand (Cao
et al., 2006). Model evaluation on the basis of the data collected during an
extended monitoring programme enabled a detailed examination of how model
performance varied during different flow regimes. It also permitted the error in
daily mean estimates of contaminant loads to be quantified with relative
precision, which allows assessing the ability of the SWAT model to simulate
contaminant loads during storm events when lower-order streams typically
exhibit considerable sub-daily variability in both discharge and
contaminant concentrations (Zhang et al., 2010). Separating discharge and
loads of sediments and nutrients into those associated with base flow and
quick flow for separate OAT sensitivity analyses provided important insights
into the varying dependency of parameter sensitivity on hydrologic conditions.
Temporal dynamics of model performance
The modelled estimates of deep aquifer recharge (58 %) and combined
lateral flow and shallow aquifer recharge (40 %) were comparable with
estimates derived by Rutherford et al. (2011), who used an alternative
catchment model to derive respective estimates of 30 and 70 % for
these two fluxes. Our decision to deliberately select a validation period
(1994–1997) during which the boundary conditions of the system
(specifically anthropogenic nutrient loading) differed considerably from the
calibration period allowed us to rigorously assess the capability of SWAT to
accurately predict water quality under an altered management scenario
(i.e. the purpose of most SWAT applications).
Overestimation of TN concentrations prior to 1996 reflects higher
NO3–N concentrations in groundwater during the calibration period
(2004–2008) due to the wastewater irrigation operation. Nitrate
concentrations appeared to reach a new quasi-steady state as wastewater
loads and in-stream attenuation came into balance. SWAT may not adequately
represent the dynamics of groundwater nutrient concentrations (Bain et al.,
2012) particularly in the presence of changes in catchment inputs
(e.g. with start-up of wastewater irrigation). The groundwater delay parameter
was set to 5 years (cf. Rotorua District Council, 2006), but this did not
appear to capture adequately the lag in response to increases in stream
nitrate concentrations following wastewater irrigation from 1991.
Model performance statistics for simulations of discharge (Q), and
loads of suspended sediment (SS), total phosphorus (TP) and total nitrogen (TN).
Statistics were calculated for both overall and separated simulations.
Qall and Lall indicate the overall simulations; Qb and
Lb indicate the base flow simulations; Qq and Lq indicate the
quick flow simulations.
Model performance
Statistics
Q
SS
TP
TN
Qb
Qq
Qall
Lb
Lq
Lall
Lb
Lq
Lall
Lb
Lq
Lall
Calibration (2004–2008)
R2
0.84
0.84
0.77
0.66
0.68
0.61
0.24
0.65
0.39
0.72
0.97
0.95
NSE
0.6
0.71
0.73
0.33
0.33
0.27
-6.2
0.09
-0.17
0.5
0.89
0.85
±PBIAS%
7.5
8.7
7.8
7.57
-23.4
-3.6
45.4
40.1
43.6
0.8
6.6
2.7
Validation (1994–1997)
R2
0.87
0.81
0.68
0.36
0.98
0.95
0.27
0.27
0.06
0.79
0.33
0.58
NSE
0.56
0.62
0.62
-0.03
0.43
0.85
-1.9
0.04
-0.64
0.58
-0.07
0.33
±PBIAS%
11.3
-1.2
8.8
34.5
-79.7
11.1
45.8
-9.3
37
-7.6
14.3
-2.5
R2: coefficient of determination; NSE: Nash–Sutcliffe efficiency; PBIAS: percent bias.
Rankings of relative sensitivities of parameters (from most to
least) for variables (header row) of Q (discharge), SS (suspended sediment),
MINP (mineral phosphorus), ORGN (organic nitrogen), NH4–N
(ammonium–nitrogen), and NO3–N (nitrate–nitrogen). Relative
sensitivities were identified by randomly generating combinations of values
for model parameters and comparing modelled and measured data with a
Student t test (p ≤ 0.05). Bold text denotes that a parameter was
deemed sensitive relative to more than one simulated variable. Italic text
denotes that parameter was deemed insensitive to any of the two flow components
(base flow and quick flow; see Fig. 7) using one-at-a-time sensitivity
analysis. Definitions and units for each parameter are shown in Table 4.
Q
SS
MINP
ORGN
NH4–N
NO3–N
SLSOIL
LAT_SED
CH_OPCO
CH_ONCO
CH_ONCO
NPERCO
CH_K2
CH_N2
BC4
BC3
BC1
CDN
HRU_SLP
SLSUBBSN
RS5
SOL_CBN(1)
CDN
ERORGN
LAT_TTIME
SPCON
ERORGP
RS4
RS3
CMN
SOL_AWC(1)
ESCO
PPERCO
RCN
RCN
RCN
RCHRG_DP
OV_N
RS2
N_UPDIS
RSDCO
GWQMN
SLSOIL
PHOSKD
USLE_P
GW_REVAP
LAT_TTIME
GWSOLP
SDNCO
GW_DELAY
SOL_AWC(1)
LAT_ORGP
SOL_NO3(1)
CH_COV1
EPCO
CMN
CH_COV2
CANMX
HLIFE_NGW
EPCO
CH_K2
RSDCO
SPEXP
GW_DELAY
USLE_K(1)
CANMX
ALPHA_BF
CH_N1
GW_REVAP
PRF
CH_COV1
SURLAG
The poor fit between simulated daily mean TP concentrations and monthly
instantaneous measurements may partly reflect a mismatch between the
dominant processes affecting phosphorus cycling in the stream and those
represented in SWAT. The ORGP fraction that is simulated in SWAT includes
both organic and inorganic forms of particulate phosphorus; however, the
representation of particulate phosphorus cycling only focusses on organic
phosphorus cycling, with limited consideration of interactions between
inorganic streambed sediments and dissolved reactive phosphorus in the
overlying water (White et al., 2014). This contrasts with phosphorus cycling
in the study stream where it has been shown that dynamic sorption processes
between the dissolved and particulate inorganic phosphorus pools exert major
control on phosphorus cycling (Abell and Hamilton, 2013).
Our finding that measured Q-weighted mean concentrations
(CQWM) of TP and SS during storm events (2010–2012) were
greatly underestimated relative to simulated daily mean TP and SS
concentrations has important implications for studies that examine effects
of altered flow regimes on contaminant transport. For example, studies which
simulate scenarios comprising more frequent large rainfall events
(associated with climate change predictions for many regions; IPCC, 2013)
may considerably underestimate projected future loads of SS and associated
particulate nutrients if only base flow water quality measurements
(i.e. those predominantly collected during “state of environment” monitoring) are
used for calibration/validation (see Radcliffe et al. (2009) for a discussion
of this issue in relation to phosphorus). This is also reflected by the two
model performance statistics relating to validation of modelled SS
concentrations using monthly grab samples (predominantly base flow; “very
good”) and CQWM estimated during storm sampling
(“unsatisfactory”) based on R2 and NSE values.
Key uncertainties
Model uncertainty in this study may arise from four main factors: (1) model
parameters, (2) forcing data, (3) in measurements used for evaluation of model
fit, and (4) model structure or algorithms (Lindenschmidt et al., 2007). The
values of most parameters assigned for model calibration, although specific
to different soil types (e.g. soil parameters), were lumped across land uses
and slopes in this study. They integrated spatial and temporal variations,
thus neglecting any variability throughout the study catchment. In terms of
forcing data, the assumption of constant values of spring discharge rate and
nutrient concentrations may inadequately reflect the temporal variability
and therefore increase model uncertainty, although this should contribute
little to the model error term. Most water quality data used for model
calibration comprised monthly instantaneous samples taken during base flow
conditions. The use of those measurements for model calibration would likely
lead to considerable underestimation of constituent concentrations (notably
SS and TP) due to failure to account for short-term high flow events.
Inadequate representation of groundwater processes in the model structure is
another key factor that is likely to affect model uncertainty, particularly
for nitrogen simulations. The analysis of model performance based on
data sets separated into base flow and quick flow constituents enabled
uncertainties in the structure of hydrological models to be identified,
denoted by different model performance between these two flow constituents.
Furthermore, the disparity in goodness-of-fit statistics between discharge
(typically “good” or “very good”) and nutrient variables (often
“unsatisfactory”) highlights the potential for catchment models which
inadequately represent contaminant cycling processes (manifest in
unsatisfactory concentration estimates) to nevertheless produce
satisfactorily load predictions (e.g. compare model performance statistics
for prediction of nutrient concentrations in Table 6 with statistics for
prediction of loads in Table 7). This highlights the potential for model
uncertainty to be underestimated in studies which aim to predict the effects
of scenarios associated with changes in contaminant cycling, such as
increases in fertiliser application rates.
Temporal dynamics of parameter sensitivity
To date, studies of temporal variability of parameters have focused on
hydrological parameters, rather than on water quality parameters. The
characteristics of concentration–discharge relationships for SS and TP are
different to that for TN (Abell et al., 2013). In quick flow, there is a
positive relationship between Q and concentrations of SS and TP, reflecting
mobilisation of sediments and associated particulate P. Total nitrogen
concentrations declined slightly in quick flow, reflecting the dilution of
nitrate from groundwater. Defining separate contaminant concentrations in
base flow and quick flow enabled us to examine how the sensitivity of water
quality parameters varied depending on hydrologic conditions.
In a study of a lowland catchment (481 km2), Guse et al. (2014) found
that three groundwater parameters, RCHRG_DP (aquifer
percolation coefficient), GW_DELAY (groundwater delay) and
ALPHA_BF (base flow alpha factor) were highly sensitive in
relation to simulating discharge during quick flow, while ESCO (soil
evaporation compensation factor) was most sensitive during base flow. This
is counter to the findings of this study for which the base flow discharge
simulation was sensitive to RCHRG_DP and ALPHA_BF.
This result may reflect that, relative to our study catchment, the
catchment studied by Guse et al. (2014) had moderate precipitation (884 mm yr-1)
with less forest cover and flatter topography. Although the
GW_DELAY parameter reflects the time lag that it takes water
in the soil water to enter the shallow aquifers, its lack of sensitivity
under both base flow and quick flow conditions in this study is a reflection
of higher water infiltration rates and steeper slopes. The ESCO parameter
controls the upwards movement of water from lower soil layers to meet
evaporative demand (Neitsch et al., 2011). Its lack of sensitivity in our
study may reflect relatively high and seasonally consistent rainfall
(1500 mm yr-1), in addition to extensive forest cover in the Puarenga Stream
catchment, which reduces soil evaporative demand by shading. Soil texture is
also likely a contributor to this result. The predominant soil horizon type
in the Puarenga Stream catchment was A, indicating high macroporosity which
promotes high water infiltration rate and inhibits upward transport of water
by capillary action (Neitsch et al., 2011). The variability in the
sensitivity of the parameter SURLAG (surface runoff lag coefficient) between
this study (relatively insensitive) and that of Cibin et al. (2010;
relatively sensitive) likely reflects differences in catchment size. The
Puarenga Stream catchment (77 km2) is much smaller than the study
catchment (St Joseph River; 2800 km2) of Cibin et al. (2010) and,
consequently, distances to the main channel are much shorter, with less
potential for attenuation of surface runoff in off-channel storage sites.
The curve number (CN2) parameter was found to be insensitive in both this
study and Shen et al. (2012), because surface runoff was simulated based on
the Green and Ampt (1911) method requiring the hourly rainfall inputs,
rather than the curve number equation which is an empirical model. By
contrast, the most sensitive parameters in our study are those that
determine the extent of lateral flow, an important contributor to streamflow
in the catchment, due to a general lack of ground cover under plantation
trees and formation of gully networks on steep terrain.
Parameters that control surface water transport processes
(e.g. LAT_TIME and SLSOIL) were found to be much more sensitive for
base flow SS load estimation than parameters that control groundwater
processes (e.g. ALPHA_BF and RCHRG_DP),
reflecting the importance of surface flow processes for sediment transport.
Sensitive parameters for quick flow SS load estimation related to overland
flow processes (e.g. OV_N and SLSUBBSN), thus reflecting the
fact that sediment transport is largely dependent on rainfall-driven
processes, as is typical of steep and lower-order catchments. Modelled base
flow NO3–N loads were most sensitive to the RCN because of rainfall as a predominant contributor to
recharging base flow. The NPERCO was more
influential for quick flow NO3–N load estimation, probably indicating
that the quick flow NO3–N load is more influenced by the mobilisation
of concentrated nitrogen sources associated with agriculture or treated
wastewater distribution. High sensitivity of the organic carbon content
(SOL_CBN) for quick flow ORGN load estimates likely reflects
mobilisation of N associated with organic material following rainfall. The
finding that base flow NH4–N load was more sensitive to nitrification
rate in reach (BC1) likely reflects that base flow provides more favourable
conditions to complete this oxidation reaction, as NH4–N is less
readily leached and transported. Similarly, the ORGP mineralisation rate (BC4)
strongly influenced base flow MINP load estimation, reflecting that
base flow phosphorus transport is relatively more influenced by cycling from
channel bed stores, whereas quick flow phosphorus transport predominantly
reflects the transport of phosphorus that originated from sources distant
from the channel.