Pressure on water resources has reached unprecedented levels during the last decades because of climate change, industrialization, and population growth. As a result, vulnerability to inappropriate water availability and/or quality is increasing worldwide. In this paper, a Soil & Water Assessment Tool (SWAT) model of the Carp River watershed located in the city of Ottawa, Ontario was calibrated and validated. The model was then used to evaluate the individual and coupled impacts of urbanization and climate change on water quantity (discharge) and quality (nitrogen and phosphorus loads). While most of the watershed is currently rural, the headwaters will undergo rapid urbanization in the future, and there are concerns about possible negative impacts on water quantity and quality. Seven scenarios were developed to represent various watershed configurations in terms of land use and climate regime. Future climate time series were obtained by statistically downscaling the outputs of nine regional climate models, run under representative concentration pathways (RCP)4.5 and RCP8.5. The impacts were evaluated at the main outlet and at the outlet of an upstream sub-watershed that would be most affected by urbanization. Results show that climate change and urbanization's impacts vary greatly depending on the spatial scale and geographic location. Globally, the annual average discharge will increase between 6.75 and 9.34% by 2050, while changes in annual average nitrogen and phosphorus loads will vary between −1.20 and 24.84%, and 19.15 and 23.81%, respectively. Local impacts in sub-watersheds undergoing rapid urbanization would be often much larger than watershed-scale impacts.

  • A global-local approach was used to assess the impacts.

  • Local impacts can be an order of magnitude higher than watershed-scale impacts.

  • Impacts at the local and watershed scale might have opposite directions of change.

  • Climate change will likely drive the flow and nitrogen load in the Carp River watershed.

  • Urbanization will control the phosphorus load.

During the last decades, pressure on natural resources has been steadily increasing, mainly driven by industrialization and population growth. The United Nations estimated that the world population would increase by 2 billion in the next 30 years, from 7.7 billion in 2020 to 9.7 billion in 2050 (United Nations 2019). This demographic change will be associated with higher standards of living, increased demand for land and water, and increased pollution. This will inevitably affect water supply, possibly disrupting domestic and industrial water uses, as well as environmental sustainability. The consequence will be lower access to essential water-related services, decreased economic prosperity, and increased conflicts around water bodies. For instance, Munia et al. (2020) reported that the number of people exposed to water stress would increase by 50–100%, depending on the emission scenario and population growth rate.

The Mississippi Valley Conservation Authority (MVCA) is the responsible authority for the sustainable management of the Carp River watershed located in Ontario, Canada (Figure 1). The watershed is currently mainly in a rural state; however, the headwaters are expected to undergo rapid urbanization in the future. The MVCA anticipates significant urban developments in the upcoming decades. It is expected that both urbanization and climate change will induce substantial changes in the watershed's hydrology, which in turn will affect its ecosystem health through changes in water quantity and quality. The results of this study could support authorities in planning and managing water resources and providing information for sound decision-making.

Figure 1

Carp River watershed: (a) location and topography, (b) land-use, and (c) soil classes.

Figure 1

Carp River watershed: (a) location and topography, (b) land-use, and (c) soil classes.

Close modal

The combined effects of climate change and land-use change on water quantity and/or quality have been examined by many authors (Tu 2009; Dunn et al. 2012; El-Khoury et al. 2015; Karlsson et al. 2016; Kundu et al. 2017; Čerkasova et al. 2018; Choukri et al. 2020; Guo et al. 2020; Wang et al. 2020). These studies differ in the location of the study area, the hydrological model, the type of assessed water quantity and quality variables, and the way climate change and land-use scenarios were generated. These studies involved numerical experiments where calibrated hydrological models incorporated climatic inputs representing one or several global warming scenarios and using current and/or projected land-use maps. Although these studies confirmed the impact of urban development and climate change on streamflow, the evaluation was only limited to the effects at the main outlet (global level). In this study, the impacts at the local scale were also considered by estimating the change at the outlet of the sub-watershed that was most affected by urbanization.

Since 2005, several studies related to the preparation of the Carp River model were conducted: City of Ottawa (2016), Greenland International Consulting Ltd (2009, 2010, 2011, 2014), Stantec Consulting Ltd (2006), and CH2M Hill (2005, 2006). All these works focused on flood level analysis and restoration plans, but not climate change or urbanization. A limited number of studies addressing the combined effects of climate change and urbanization in Canada can be found in the literature. Of particular interest is El-Khoury et al. (2015), who assessed the impact of climate change and deforestation on the South Nation watershed in opposition to urbanization.

Hydrological impacts are generally assessed by simulating hydrological processes and water exchange within an area. The impact of natural variability and anthropogenic factors on a hydrologic system can be assessed by changing the model's climatic inputs or watershed configuration (Yu 2015). The application of rainfall–runoff models varies from straightforward water balance models to more complex watershed models, such as the Annualized Agricultural Non-Point Source model (Young et al. 1989) and the Soil Water & Assessment Tool (SWAT) (Arnold et al. 1998), which can simulate loads and concentrations of various water quality variables. The selection of the appropriate model depends on several factors such as scale of the problem, computational cost, and robustness of the model. There is no single best model, as there are many plausible solutions (Ogden 2021). Therefore, a typical model selection is more guided by familiarity with the model than appropriateness (Addor & Melsen 2019). The SWAT model was selected in this research, as it has been widely used to assess the hydrology in small- and large-scale catchments: Huo & Li (2013), Khoiab & Thomb (2015), and Jajarmizadeh et al. (2017) in Asia; Arnold et al. (2012), El-Khoury et al. (2015), and Havrylenko et al. (2016) in America; Abbaspour et al. (2015) in Europe; and Mengistu et al. (2019), Choukri et al. (2020), and Muluneh (2020) in Africa. The SWAT is also used to investigate the relative impact of climate, land-use & land-cover (LULC) changes, and management practices on the available water resources (Arnold et al. 1998; Neitsch et al. 2009; Tirupathi & Shashidhar 2020). In addition, its structure and applicability to address different problems make it more flexible. The SWAT also allows for the simulation of water quantity and quality processes within the same model (e.g., flow, N, and P) without the contribution of other programs. For instance, the precedent model of the municipality developed by the municipality combines several hydrologic platforms and a separate hydraulic model.

Several approaches have been reported in the literature for generating climate change projections: Wang et al. (2020) utilized the Delta-change method, which consisted of adding an arbitrarily chosen (but realistic) variation to historical observations to represent global warming. Others used the outputs of global and regional climate models (RCMs) without any processing (Tu 2009; Čerkasova et al. 2018), or in combination with a statistical downscaling approach such as the Delta-change method (e.g., Dunn et al. 2012) and bias correction (El-Khoury et al. 2015; Karlsson et al. 2016; Kundu et al. 2017; Choukri et al. 2020; Guo et al. 2020). The Delta-change approach and the bias correction method have their advantages and weaknesses. They are straightforward in the generation of the climate output. However, data generated from raw global data outputs with the Delta method cannot capture changes in climate variability, and bias correction does not remove distortion because the approaches may be too simplistic to characterize climate trends (El-Khoury et al. 2015). In addition, the choice of the downscaling method is very determinant, as it significantly affects the outputs. For instance, Miralha et al. (2020) have demonstrated that the choice of bias correction method affects the direction of change and the magnitude of nutrient loads (N and P) and hydrological processes. Chen et al. (2012) have found that for the same global climate model (GCM), the simulated runoffs vary significantly when using rainfall provided by different statistical downscaling techniques as the input to the hydrological models. Therefore, reasonable care must be taken in the selection of downscaling method to represent future hydrological scenarios that are more realistic.

This paper aims to evaluate the individual and coupled impacts of urbanization and climate change on water quantity (discharge) and quality (nitrogen and phosphorus loads) in the carp watershed. The outputs of nine RCMs were downscaled using quantile mapping. The results are examined at two scales: the watershed scale (i.e., at the outlet of the watershed) and the local scale (i.e., at the outlet of the sub-watershed which undergoes the most drastic urbanization).

Study area

The Carp River is an important natural ecosystem located west of the City of Ottawa, ON, Canada. It is 42 km long and drains an area of approximately 265 km². It has its headwaters in the Glen Cairn area of Kanata and flows north into the Ottawa River at Fitzroy Harbour. The Carp River is one of the main sources of the City of Ottawa's drinking water. The annual average precipitation of the historical period of simulation (1990–2018) is 933 mm. The daily minimum, mean, and maximum of the observed streamflow at outlet 8 (Figure 2) during the same period are 0.032, 2.95, and 23.7 m3/s, respectively. The highest elevation has an altitude of 203 m above sea level, while the outlet lies at 59 m. The average elevation measures 117 m above sea level. Based on the latest provincial land-use update in 2017 (Ministry of Natural Resources and Forestry 2019), the watershed is classified into 10 different land-use types with a dominance of agricultural land, covering more than half of the total area (52%). Urbanized area represents 10% (Figure 1). Mixed forests consisted of two types (FRST and FOMI) and only differ on biomass die-fraction. Tables 1 and 2 show the percentage of coverage of each land-use/-cover and soil type within the watershed.

Table 1

Land-use characteristics of the Carp River watershed

Land use/cover typeSWAT codeArea (%)
1. Agricultural land AGRC 52.34 
2. Wooded wetland WEWO 18.02 
3. Evergreen needle leaf forest FOEN 9.93 
4. Urban URBN 9.90 
5. Forest – deciduous FRSD 4.16 
6. Forest-mixeda FRST 3.25 
7. Barren or sparsely vegetated BSVG 0.82 
8. Water WATR 0.69 
9. Mixed foresta FOMI 0.55 
10. Cropland/woodland mosaic CRWO 0.34 
Land use/cover typeSWAT codeArea (%)
1. Agricultural land AGRC 52.34 
2. Wooded wetland WEWO 18.02 
3. Evergreen needle leaf forest FOEN 9.93 
4. Urban URBN 9.90 
5. Forest – deciduous FRSD 4.16 
6. Forest-mixeda FRST 3.25 
7. Barren or sparsely vegetated BSVG 0.82 
8. Water WATR 0.69 
9. Mixed foresta FOMI 0.55 
10. Cropland/woodland mosaic CRWO 0.34 

aTwo types of mixed forests defined in the SWAT with two different codes (FRST and FOMI).

Table 2

Soil characteristics

Soil nameSWAT codeArea (%)
Loam*1 Be1-2a-4649 51.29 
Loam*2 Be1-2a-4648 40.30 
Sandy Loam Po2-1-2b-4971 6.33 
Clay Gm3-3a-3070 2.08 
Soil nameSWAT codeArea (%)
Loam*1 Be1-2a-4649 51.29 
Loam*2 Be1-2a-4648 40.30 
Sandy Loam Po2-1-2b-4971 6.33 
Clay Gm3-3a-3070 2.08 

Note: The two types of loam differ in their available water capacity: 0.08 mm/mm (*1) and 0.175 mm/mm (*2).

Figure 2

Location of meteorological, hydrometric, and water quality stations.

Figure 2

Location of meteorological, hydrometric, and water quality stations.

Close modal

Model setup

The SWAT (Arnold et al. 1998) is a physically-based, semi-distributed, and continuous model used to evaluate and predict the impacts of climate change and land-use management on water quantity and quality. The SWAT operates on a daily time step and can simulate continuously over a long period. The procedure used by the SWAT consists of dividing the watershed into several sub-watersheds, which are further subdivided into hydrologic response units (HRUs). An HRU is a unique combination of topographical, land-use management, and soil characteristics (Arnold et al. 2012).

Hydrological simulations in the SWAT are based on the water balance equation, which is applied to the HRU scale at each time step:
(1)
where is soil water content at the final time, t, and time 0, is the amount of precipitation on day i, is the amount of surface runoff on day i, is the amount of evapotranspiration on day i, is the amount of water entering the vadose zone from the soil profile on day i, and is the amount of return flow on day i, in mm. The calculated water, sediment, and nutrient fluxes are then routed through the stream network, where the SWAT tracks mass flow and the transformation of chemicals in the stream and streambed (Neitsch et al. 2009). Rainfall is partitioned into infiltration and surface runoff using the curve number (CN) method (Neitsch et al. 2009):
(2)
where P is the rainfall (mm), is the accumulated runoff (mm), and S is the retention parameter (mm) – representing the maximum amount of water that can be abstracted by watershed.

S is expressed in terms of a CN, a dimensionless watershed parameter ranging from 0 to 100. Groundwater is represented by two reservoirs (a shallow and a deep aquifer), which interact with surface water through infiltration, percolation, evaporation, lateral flow, and baseflow. The SWAT also simulates the complete nutrient cycle (transformation and movement) for nitrogen and phosphorus as well as the degradation of any pesticides applied in an HRU. Nutrient levels are computed on a mass basis, while it allows the input data as concentrations.

The SWAT model was set up with ArcSWAT, a GIS preprocessor of the SWAT model that combines the spatial and climatic inputs and converts them into a SWAT model. ArcSWAT requires a few subjective user inputs, including the location of sub-watershed outlets and the HRUs definition method (i.e., how soil, land use, and slope are classified and used to define HRUs). Options for HRU definition are:

  • (1)

    multiple HRUs per sub-watershed representing all combinations of the slope, land-use, and soil classes;

  • (2)

    a single HRU per sub-watershed corresponding to the dominant slope class, dominant land-use, and dominant soil classes; and

  • (3)

    a single HRU corresponding to the most extensive combinations of the slope, land-use, and soil classes.

In this paper, the first option was selected. Given the relatively flat topography of the area, a single slope class was considered.

Different land-use changes and climate change scenarios were developed by downscaling the outputs of nine RCMs; the projected climate time series were used to force the SWAT model, and changes in water quantity and water quality parameters were calculated.

Input data

The model setup requires three types of input data (described in Table 3). Observed water quantity and quality time series of the parameters of interest are required for calibration and validation. In this paper, time series of streamflow, total nitrogen, and total phosphorus were used. The initial land-use classes were reviewed and reclassified based on land-use types defined in the SWAT database. Observed daily precipitations and temperature were obtained from the Environment and Climate Change Canada (Environment and Natural Resources Canada 2020) at two locations in the area of Ottawa Airport. Other meteorological parameters (humidity, wind speed, and solar radiations) were retrieved from the Watch-Forcing Data ERA-Interim (Weedom et al. 2014). Streamflow, Total N, and Total P load data were available for different periods: measured streamflow at outlet 8 covers the 1990–2015 period, while records of total N and total P for outlets 8 and 30 were available for 2000–2018 and 2000–2007 (Figure 4). These observed water quantity and quality data were used for model calibration and validation. Table 4 presents the characteristics of the record stations used.

Table 3

Data characteristics

DataDescriptionTemporal scaleSpatial scalePeriodSource
Topography Elevation – LiDAR N/A 1 m 2015 City of Ottawa 
Land use and cover Categories of land occupation (wood, urban, and agricultural) N/A 30 m 2017 Ministry of Natural Resources and Forestry of Canada (2019)  
Soil Soil types and physical properties N/A 10 km 2007 Food and Agricultural Organization (2019)  
Weather Precipitation and temperature Daily Point 1990–2018 Environment and Climate Change Canada (ECCC) 
Solar radiation, relative humidity, and wind speed Daily 0.5° 1990–2018 WFDEI 
Water quantity Streamflow Daily (one measurement per month) Point 1990–2015 HYDAT – Environment Canada 
Water quality N, P Monthly  2000–2018
2000–2007 
Ministry of Environment (Ontario) 
DataDescriptionTemporal scaleSpatial scalePeriodSource
Topography Elevation – LiDAR N/A 1 m 2015 City of Ottawa 
Land use and cover Categories of land occupation (wood, urban, and agricultural) N/A 30 m 2017 Ministry of Natural Resources and Forestry of Canada (2019)  
Soil Soil types and physical properties N/A 10 km 2007 Food and Agricultural Organization (2019)  
Weather Precipitation and temperature Daily Point 1990–2018 Environment and Climate Change Canada (ECCC) 
Solar radiation, relative humidity, and wind speed Daily 0.5° 1990–2018 WFDEI 
Water quantity Streamflow Daily (one measurement per month) Point 1990–2015 HYDAT – Environment Canada 
Water quality N, P Monthly  2000–2018
2000–2007 
Ministry of Environment (Ontario) 

N/A, not applicable.

Figure 4

Carp River watershed delineation. The numbers represent the assigned index to the sub-watersheds.

Figure 4

Carp River watershed delineation. The numbers represent the assigned index to the sub-watersheds.

Close modal

Model performance measures

The performance of the calibration/validation is generally evaluated using statistical and graphical methods. Recent works such as Jajarmizadeh et al. (2017) and Čerkasova et al. (2018) selected NS/R2 and PBIAS, combined with time-series plots to evaluate the model performance. This study used Nash–Sutcliffe efficiency (NSE or NS, Nash & Sutcliffe 1970) and percentage bias (PBIAS, Gupta et al. 1999) as statistical measures. They are given by the following equations:
(3)
(4)
where is the variable (e.g., discharge, N, and P quantity), the bar stands for average, is the measured data, is the simulated data, i is the time, and is the total number of periods.

NS ranges from –∞ to 1. The optimal value is 1, corresponding to the situation where the plot of observed data fits the simulation (Khoiab & Thomb 2015) perfectly, while values less than 0 indicate that the observed data mean is a more accurate predictor than the simulated output (Jajarmizadeh et al. 2017). The target interval for PBIAS is zero to ±25%, with zero corresponding to the optimum. Positive values indicate the model is underestimating the observations, while negative values indicate an overestimation.

The statistical measures are compared to the criteria illustrated in Table 5 (Moriasi et al. 2007) to classify the model's performance.

Table 4

Weather stations characteristics

Station name (Climate ID)LongitudeLatitudeDistance to the watershed boundary (km)
Ottawa CDA (6105976) −75.72 45.38 16.5 
Ottawa Macdonald-Cartier Int'l (6106000) −75.67 45.32 22 
– −75.25 45.75 30 
– −75.75 45.75 42 
– −75.25 45.25 11 
– −75.75 45.25 11 
Station name (Climate ID)LongitudeLatitudeDistance to the watershed boundary (km)
Ottawa CDA (6105976) −75.72 45.38 16.5 
Ottawa Macdonald-Cartier Int'l (6106000) −75.67 45.32 22 
– −75.25 45.75 30 
– −75.75 45.75 42 
– −75.25 45.25 11 
– −75.75 45.25 11 
Table 5

Model performance criteria from Moriasi et al. (2007) 

SatisfactoryGoodVery good
NS 0.5–0.7 0.7–0.8 0.8–1.0 
PBIAS (%) 15–25 10–15 Less than 10% 
SatisfactoryGoodVery good
NS 0.5–0.7 0.7–0.8 0.8–1.0 
PBIAS (%) 15–25 10–15 Less than 10% 

Sensitivity analysis, calibration, and validation

Calibration consists of modifying model parameters to achieve a good fit between the simulated output and the observed data. Validation checks the model's performance using the calibrated parameters and an independent data set (not used in the calibration process). The simulation period is split into calibration and validation periods. Table 6 presents how the period of data was used for the calibration and the validation.

Table 6

Data repartition for calibration and validation

VariablePeriod
CalibrationValidation
Discharge Outlet 8 1990–2005 2007–2015 
N Outlet 8 2000–2012 2013–2018 
Outlet 30 2000–2003 2004–2006 
P Outlet 8 2000–2012 2013–2018 
Outlet 30 2000–2003 2004–2006 
VariablePeriod
CalibrationValidation
Discharge Outlet 8 1990–2005 2007–2015 
N Outlet 8 2000–2012 2013–2018 
Outlet 30 2000–2003 2004–2006 
P Outlet 8 2000–2012 2013–2018 
Outlet 30 2000–2003 2004–2006 
Given that complex models such as SWAT have hundreds of parameters, sensitivity analysis is used to select a small set of parameters to use for the calibration process. A parameter sensitivity analysis provides insights on which parameters contribute most to the output variance due to input variability (Holvoet et al. 2005). It is computed by altering each parameter, one by one, while all other parameters remain fixed. It is then evaluated using the values of t-stat and p-value determined with a multiple regression analysis using the following equation (Abbaspour 2007):
(5)
where g is the objective function (e.g., correlation coefficient, absolute or relative bias, and Nash–Sutcliffe model efficiency) value used to evaluate the model calibration effectiveness; b is the parameter; is the regression constant; β corresponds to the technical coefficient attached to the variable b, and m is equal to the number of parameters.

The mean of the variations in the objective function estimates the sensitivity. The t-stat is the regression coefficient of a parameter divided by its standard error. The p-value for each parameter tests the null hypothesis that the regression coefficient is equal to zero. The higher the absolute value of t-stat and the smaller the value of p-value, the more sensitive the parameter is (Abbaspour 2007). A small p-value, typically lower than 0.05, means the sensitivity of the parameter is statistically significant (Verhagen et al. 2004), while a value of 0.05 indicates that there is a 95% probability that a parameter change will affect the dependent variable (Abbaspour et al. 2009).

Both the sensitivity analysis and the calibration were conducted using the SUFI2 (Sequential Uncertainty Fitting) algorithm implemented in the SWAT-CUP program (Abbaspour 2007). The parameters included in the sensitivity analysis are shown in Table 7.

Table 7

Parameters used for calibration and validation

ParametersDescriptionVariation range
r__CN2.mgt Initial SCS CNII value −0.2 to 0.5 
v__ESCO.hru Soil evaporation compensation factor 0.01–1 
v__GWQMN.gw Threshold water depth in the shallow aquifer for flow 0–2,500 
v__ALPHA_BF.gw Baseflow alpha factor 0–1 
a__REVAPMN.gw Threshold depth of water in the shallow aquifer for ‘revap’ to occur (mm) 0–500 
v__SOL_AWC.sol Available water capacity 0–0.8 
a__OV_N.hru Manning's n value for overland flow 0.01–20 
v__GW_REVAP.gw Groundwater ‘revap’ coefficient 0.02–0.15 
a__SLSUBBSN.hru Average slope length 10–130 
v__SOL_K.sol Saturated hydraulic conductivity 0–1 
a__RCN.bsn Concentration of N in rainfall 0–15 
a__ERORGN.hru Organic N enrichment ratio 0–5 
a__SHALLST_N.gw Concentration of nitrate in groundwater contribution to streamflow from sub-basin (mg N/l) 0–1,000 
a__N_UPDIS.bsn N uptake distribution parameter 0–100 
a__NPERCO.bsn N percolation coefficient 0–1 
a__ANION_EXCL.sol Fraction of porosity (void space) from which anions are excluded 0.01–1 
a__P_UPDIS.bsn P uptake distribution parameter 0–100 
a__PPERCO.bsn P percolation coefficient 10–17.5 
a__ERORGP.hru Organic P enrichment ratio 0–5 
a__GWSOLP.gw Concentration of soluble P in groundwater contribution to streamflow from sub-basin (mg P/l) 0–1,000 
a__PSP.bsn P sorption coefficient 0.01–0.7 
a__PHOSKD.bsn P soil partitioning coefficient 100–200 
ParametersDescriptionVariation range
r__CN2.mgt Initial SCS CNII value −0.2 to 0.5 
v__ESCO.hru Soil evaporation compensation factor 0.01–1 
v__GWQMN.gw Threshold water depth in the shallow aquifer for flow 0–2,500 
v__ALPHA_BF.gw Baseflow alpha factor 0–1 
a__REVAPMN.gw Threshold depth of water in the shallow aquifer for ‘revap’ to occur (mm) 0–500 
v__SOL_AWC.sol Available water capacity 0–0.8 
a__OV_N.hru Manning's n value for overland flow 0.01–20 
v__GW_REVAP.gw Groundwater ‘revap’ coefficient 0.02–0.15 
a__SLSUBBSN.hru Average slope length 10–130 
v__SOL_K.sol Saturated hydraulic conductivity 0–1 
a__RCN.bsn Concentration of N in rainfall 0–15 
a__ERORGN.hru Organic N enrichment ratio 0–5 
a__SHALLST_N.gw Concentration of nitrate in groundwater contribution to streamflow from sub-basin (mg N/l) 0–1,000 
a__N_UPDIS.bsn N uptake distribution parameter 0–100 
a__NPERCO.bsn N percolation coefficient 0–1 
a__ANION_EXCL.sol Fraction of porosity (void space) from which anions are excluded 0.01–1 
a__P_UPDIS.bsn P uptake distribution parameter 0–100 
a__PPERCO.bsn P percolation coefficient 10–17.5 
a__ERORGP.hru Organic P enrichment ratio 0–5 
a__GWSOLP.gw Concentration of soluble P in groundwater contribution to streamflow from sub-basin (mg P/l) 0–1,000 
a__PSP.bsn P sorption coefficient 0.01–0.7 
a__PHOSKD.bsn P soil partitioning coefficient 100–200 

Replace or v_: the existing parameter value is to be replaced by the given value.

Absolute or a_: the existing parameter value is added to a given value.

Relative or r_: (1+a given value) multiply the existing parameter value.

Land-use projections

The projected land-use map was elaborated by the City of Ottawa in collaboration with the MVCA based on projections of the next 30 years (by 2050). The land-use scenario consisted of a change in the land-use area ratios and the type of urban area: from residential-medium density to commercial in the future. This conversion allows predicting impacts in severe conditions. The two land-use types differ in several characteristics, including the fraction of total impervious area, fraction directly connected to impervious, mass fraction of total N and total P in suspended solid load from the impervious area. No change in management operations in agricultural lands was completed between current and future projected conditions.

Table 8 presents the percentages of change between the current land uses and projected land uses within the watershed (map shown in Figure 3).

Table 8

Projected land-use change in the Carp River watershed by 2050

Land-use typeChange (%)
1. Agricultural land (2.63
2. Wooded wetland (0.54
3. Evergreen needle leaf forest (0.13
4. Urban (+3.64
5. Forest – deciduous (0.17
6. Forest-mixed (FRST) (0.17
7. Barren or sparsely vegetated (+0
8. Water (+0
9. Mixed forest (FOMI) (−0.02
10. Cropland/woodland mosaic (+0
Land-use typeChange (%)
1. Agricultural land (2.63
2. Wooded wetland (0.54
3. Evergreen needle leaf forest (0.13
4. Urban (+3.64
5. Forest – deciduous (0.17
6. Forest-mixed (FRST) (0.17
7. Barren or sparsely vegetated (+0
8. Water (+0
9. Mixed forest (FOMI) (−0.02
10. Cropland/woodland mosaic (+0

bold: increase; italic: decrease

Figure 3

Current and projected land uses within the Carp River watershed. The current land-use map corresponds to the land-use repartition in 2017; the projected map includes the anticipated extension of urban development in the watershed.

Figure 3

Current and projected land uses within the Carp River watershed. The current land-use map corresponds to the land-use repartition in 2017; the projected map includes the anticipated extension of urban development in the watershed.

Close modal

Climate change scenarios

Developing a climate change scenario involves considerable inherent uncertainties (Shrestha et al. 2020). As these scenarios will be used in planning and decision-making processes, it is crucial to estimate a range of plausible future climate conditions in studies of climate change impact. Therefore, using a single RCM or carbon emission scenario is not sufficient for assessment purposes, since it cannot provide an uncertainty range. Consequently, a combination of three RCMs with two emission scenarios were used for this study. Scenarios are defined based on IPCC AR5 representative concentration pathway (RCP)4.5 and RCP8.5 (Collins et al. 2013). The RCP4.5 represents medium emissions, while RCP8.5 provides future climate data with high emissions assuming high population and relatively slow income growth with modest rates of technological change and energy intensity improvements resulting in the long term to high-energy demand and greenhouse gas (GHG) emissions without any climate change policies (Wayne 2013). Various RCMs were used to generate the projected future climate data at smaller scale driven by GCMs from several institutes as listed in Table 9.

Table 9

Characteristics of the GCM/RCM model combinations

No.InstituteInstitute acronymDriving GCMsRCMs
1 Canadian Centre for Climate Modeling and Analysis CCCma CanESM2 CANRCM4 
2 Institut Catholique des Hautes Études Commerciales ICHEC EC-EARTH RCA4, HIRHAM5 
3 Met Office Hadley Centre MOHC HadGEM2-ES WFR, RegCM4 
4 Max Planck Institute for Meteorology MPI-M MPI-ESM-LR WFR, RegCM4 
5 National Oceanic and Atmospheric Administration – Geophysical Fluid Dynamics Laboratory NOAA-GFDL GFDL-ESM2M WFR, RegCM4 
No.InstituteInstitute acronymDriving GCMsRCMs
1 Canadian Centre for Climate Modeling and Analysis CCCma CanESM2 CANRCM4 
2 Institut Catholique des Hautes Études Commerciales ICHEC EC-EARTH RCA4, HIRHAM5 
3 Met Office Hadley Centre MOHC HadGEM2-ES WFR, RegCM4 
4 Max Planck Institute for Meteorology MPI-M MPI-ESM-LR WFR, RegCM4 
5 National Oceanic and Atmospheric Administration – Geophysical Fluid Dynamics Laboratory NOAA-GFDL GFDL-ESM2M WFR, RegCM4 
Table 10

Description and characteristics of scenarios

No.ScenariosClimate data frameLand-use mapDescription
1. S0o Historical observations (1990–2018) 2017 Current model (Baseline) 
2. S0m Historical model (1990–2018) 2017 Baseline scenario for climate change and combined effects scenarios 
3. S1 Historical observations (1990–2018) 2050 Land-use change 
4. S0M45 RCP4.5 (2021–2050) 2017 RCP4.5 climate change 
5. S0M85 RCP8.5 (2021–2050) 2017 RCP8.5 climate change 
6. S1M45 RCP4.5 (2021–2050) 2050 Combined land-use change and climate change under RCP4.5 
7. S1M85 RCP8.5 (2021–2050) 2050 Combined land-use change and climate change under RCP8.5 
No.ScenariosClimate data frameLand-use mapDescription
1. S0o Historical observations (1990–2018) 2017 Current model (Baseline) 
2. S0m Historical model (1990–2018) 2017 Baseline scenario for climate change and combined effects scenarios 
3. S1 Historical observations (1990–2018) 2050 Land-use change 
4. S0M45 RCP4.5 (2021–2050) 2017 RCP4.5 climate change 
5. S0M85 RCP8.5 (2021–2050) 2017 RCP8.5 climate change 
6. S1M45 RCP4.5 (2021–2050) 2050 Combined land-use change and climate change under RCP4.5 
7. S1M85 RCP8.5 (2021–2050) 2050 Combined land-use change and climate change under RCP8.5 

The 1985–2100 precipitation, temperature (maximum and minimum), solar radiation, relative humidity, and wind speed data sets were simulated using the RCMs under RCP4.5 and RCP8.5 scenarios. Data were downloaded from North America – CORDEX (Coordinated Downscaling Experiment: Mearns et al. 2017). The time series for the five variables were extracted for the period of 2021–2050 and downscaled using the quantile mapping, also known as quantile–quantile or quantile matching.

Seven scenarios

Based on the climate change and land-use change projections, the seven following experiments, presented in Table 10, were developed: the first scenario (S0o) is the reference (baseline scenario) with observed climate, while S0 m is generated with simulated climate data of RCMs. S1 represents the impact of urbanization on the current model, while S0M45 and S0M85 represent climate change impacts under RCP4.5 and RCP8.5, respectively. The effects of combined urbanization and climate change are assessed with S1M45 and S1M85 under RCP4.5 and RCP8.5, respectively. S1 outputs are compared to S0o, while the scenario involving climate change will be compared to S0 m. This is to ensure the comparison between the two scenarios is consistent with the climate data frame (observations or simulations).

The simulated outputs were compared to the baseline model (current conditions) outputs at global and local levels using the percentage of change, estimated according to the following equation:
(6)
A negative percent change corresponds to a reduction of the quantity from the current condition scenario to the corresponding future scenario, while a positive value is an indication that the quantity increases.

The local/watershed-scale approach consisted of evaluating the impact of each scenario at the main outlet (watershed scale) and the outlet of sub-watershed 46 (local scale). This upstream sub-watershed was selected because it is the most affected by urbanization.

Monthly averages are calculated by averaging the values for a specific month during the simulation period excluding the warmup period, as the model does not include it in the output time series. Annual maximum boxplots are created based on the maximum discharge observed at each year of simulation.

Parameter sensitivity

A total of 55 sub-watersheds were delineated (Figure 4).

Parameter selection and sensitivity analysis

Recommendations of similar studies in the literature (e.g., Abbaspour 2007; Arnold et al. 2012; Abbaspour et al. 2015; El-Khoury et al. 2015; Khoiab & Thomb 2015) led to the selection of 22 parameters to use in the calibration. First, a sensitivity analysis was performed to assess the influence of these parameters on the objective function, which in this case is the sum of the NS coefficient for streamflow, total nitrogen, and total phosphorus in their respective calibration periods. The sensitivity analysis provides information about the most important process drivers in the study region, depending on local characteristics (Abbaspour et al. 2018). The results are shown in Table 11, where it can be observed that the most sensitive parameter is N_UPDIS with a p-value and t-stat of 0.00 and 35.17, respectively. On the other hand, GWQMN.gw is the parameter with the least influence on the performance of the model. In this case, the uptake distribution of N seemed to affect the model performance the most.

Table 11

Sensitivity analysis results

Parameter namet-statp-valueRank
A__N_UPDIS.bsn 35.17 0.00 1 (most sensitive
A__ERORGP.hru −23.95 0.00 
A__ERORGN.hru 19.98 0.00 
R__CN2.mgt −9.30 0.00 
A__OV_N.hru 6.37 0.00 
V__ESCO.hru −2.04 0.04 
A__RCN.bsn −1.37 0.17 
V__GW_REVAP.gw −1.29 0.20 
A__REVAPMN.gw 0.93 0.35 
A__P_UPDIS.bsn 0.85 0.40 10 
V__SOL_K(..).sol −0.78 0.43 11 
A__PPERCO.bsn −0.77 0.44 12 
A__SLSUBBSN.hru 0.65 0.52 13 
R__GWSOLP.gw 0.64 0.52 14 
V__SOL_AWC.sol 0.48 0.63 15 
A__PHOSKD.bsn 0.47 0.64 16 
A__PSP.bsn 0.47 0.64 17 
A__ANION_EXCL.sol −0.43 0.67 18 
R__SHALLST_N.gw −0.30 0.76 19 
V__ALPHA_BF.gw −0.26 0.79 20 
A__NPERCO.bsn 0.15 0.88 21 
A__GWQMN.gw −0.03 0.98 22 (least sensitive
Parameter namet-statp-valueRank
A__N_UPDIS.bsn 35.17 0.00 1 (most sensitive
A__ERORGP.hru −23.95 0.00 
A__ERORGN.hru 19.98 0.00 
R__CN2.mgt −9.30 0.00 
A__OV_N.hru 6.37 0.00 
V__ESCO.hru −2.04 0.04 
A__RCN.bsn −1.37 0.17 
V__GW_REVAP.gw −1.29 0.20 
A__REVAPMN.gw 0.93 0.35 
A__P_UPDIS.bsn 0.85 0.40 10 
V__SOL_K(..).sol −0.78 0.43 11 
A__PPERCO.bsn −0.77 0.44 12 
A__SLSUBBSN.hru 0.65 0.52 13 
R__GWSOLP.gw 0.64 0.52 14 
V__SOL_AWC.sol 0.48 0.63 15 
A__PHOSKD.bsn 0.47 0.64 16 
A__PSP.bsn 0.47 0.64 17 
A__ANION_EXCL.sol −0.43 0.67 18 
R__SHALLST_N.gw −0.30 0.76 19 
V__ALPHA_BF.gw −0.26 0.79 20 
A__NPERCO.bsn 0.15 0.88 21 
A__GWQMN.gw −0.03 0.98 22 (least sensitive

Model performance

The calibration was performed using the 22 parameters identified in the previous section. The optimal range for each parameter as well as the fitted values are shown in Table 12. The outputs of the calibrated model are shown in Figure 5.

Table 12

Calibration of parameters

ParametersOptimal rangeFitted values
r__CN2.mgt [0.11713, 0.12033] 0.11873 
a__GWQMN.gw [−1347.63, −1136.19] −1241.9 
v__GW_REVAP.gw [0.097516, 0.100094] 0.098805 
v__ESCO.hru [0.997944, 1.010913] 1.0044 
v__ALPHA_BF.gw [0.482128, 0.54378] 0.51295 
v__SOL_AWC().sol [0.974373, 1.001545] 0.98796 
a__REVAPMN.gw [187.1984, 212.1717] 199.69 
v__SOL_K().sol [0.577949, 0.600627] 0.58929 
a__OV_N.hru [7.371342, 8.932384] 8.1519 
a__SLSUBBSN.hru [135.1207, 137.2007] 136.16 
a__RCN.bsn [14.5449, 16.35953] 15.452 
a__N_UPDIS.bsn [−20.5479, −16.0446] −18.296 
r__SHALLST_N.gw [−135.764, −86.191] −110.98 
a__ERORGN.hru [−0.13804, 0.148926] 0.005445 
a__NPERCO.bsn 1.144399, 1.161047] 1.1527 
a__ANION_EXCL.sol [0.763784, 0.786918] 0.77535 
a__PSP.bsn [0.570577, 0.594221] 0.5824 
a__PHOSKD.bsn [116.2567, 120.4304] 118.34 
a__P_UPDIS.bsn [89.41322, 99.38607] 94.400 
a__PPERCO.bsn [12.34229, 12.44181] 12.392 
a__ERORGP.hru [0.011739, 0.019001] 0.01537 
r__GWSOLP.gw [297.5318, 319.4124 308.47 
ParametersOptimal rangeFitted values
r__CN2.mgt [0.11713, 0.12033] 0.11873 
a__GWQMN.gw [−1347.63, −1136.19] −1241.9 
v__GW_REVAP.gw [0.097516, 0.100094] 0.098805 
v__ESCO.hru [0.997944, 1.010913] 1.0044 
v__ALPHA_BF.gw [0.482128, 0.54378] 0.51295 
v__SOL_AWC().sol [0.974373, 1.001545] 0.98796 
a__REVAPMN.gw [187.1984, 212.1717] 199.69 
v__SOL_K().sol [0.577949, 0.600627] 0.58929 
a__OV_N.hru [7.371342, 8.932384] 8.1519 
a__SLSUBBSN.hru [135.1207, 137.2007] 136.16 
a__RCN.bsn [14.5449, 16.35953] 15.452 
a__N_UPDIS.bsn [−20.5479, −16.0446] −18.296 
r__SHALLST_N.gw [−135.764, −86.191] −110.98 
a__ERORGN.hru [−0.13804, 0.148926] 0.005445 
a__NPERCO.bsn 1.144399, 1.161047] 1.1527 
a__ANION_EXCL.sol [0.763784, 0.786918] 0.77535 
a__PSP.bsn [0.570577, 0.594221] 0.5824 
a__PHOSKD.bsn [116.2567, 120.4304] 118.34 
a__P_UPDIS.bsn [89.41322, 99.38607] 94.400 
a__PPERCO.bsn [12.34229, 12.44181] 12.392 
a__ERORGP.hru [0.011739, 0.019001] 0.01537 
r__GWSOLP.gw [297.5318, 319.4124 308.47 
Table 13

Summary of the results describing the impacts of land-use change, climate change, and combined effects at the watershed scale and local levels

ScenariosImpactAnnual maximum flow
Annual mean flow
Annual nitrogen load
Annual phosphorus load
Value (m3/s)Change (%)Value (m3/s)Change (%)Value (×105 Kg)Change (%)Value (×103 Kg)Change (%)
S0o Watershed scale 10.75 – 35.40 – 8.71 – 6.62 – 
Local 0.57 – 1.97 – 0.19 – 0.62 – 
S0 m Watershed scale 12.63 – 3.15 – 8.96 – 6.65 – 
Local 0.68 – 0.18 – 0.19 – 0.62 – 
S1 Watershed scale 10.83 0.73 3.09 1.57 8.55 − 1.88 8.38 26.49 
Local 0.60 4.78 0.18 9.45 0.14 − 29.00 1.08 73.56 
S0M45 Watershed scale 13.11 3.76 3.32 5.49 11.61 29.62 6.72 1.07 
Local 0.67 − 1.05 0.18 2.61 0.24 28.24 0.62 − 0.55 
S0M85 Watershed scale 12.75 0.90 3.38 7.52 9.14 2.03 6.35 − 4.49 
Local 0.66 − 3.54 0.18 1.13 0.19 − 0.96 0.55 − 11.19 
S1M45 Watershed scale 13.13 3.97 3.36 6.75 11.19 24.84 8.24 23.81 
Local 0.69 1.61 0.19 11.10 0.16 − 12.92 1.03 66.15 
S1M85 Watershed scale 12.79 1.29 3.44 9.34 8.85 − 1.20 7.93 19.15 
Local 0.68 − 0.36 0.20 13.07 0.14 − 24.80 0.98 57.97 
ScenariosImpactAnnual maximum flow
Annual mean flow
Annual nitrogen load
Annual phosphorus load
Value (m3/s)Change (%)Value (m3/s)Change (%)Value (×105 Kg)Change (%)Value (×103 Kg)Change (%)
S0o Watershed scale 10.75 – 35.40 – 8.71 – 6.62 – 
Local 0.57 – 1.97 – 0.19 – 0.62 – 
S0 m Watershed scale 12.63 – 3.15 – 8.96 – 6.65 – 
Local 0.68 – 0.18 – 0.19 – 0.62 – 
S1 Watershed scale 10.83 0.73 3.09 1.57 8.55 − 1.88 8.38 26.49 
Local 0.60 4.78 0.18 9.45 0.14 − 29.00 1.08 73.56 
S0M45 Watershed scale 13.11 3.76 3.32 5.49 11.61 29.62 6.72 1.07 
Local 0.67 − 1.05 0.18 2.61 0.24 28.24 0.62 − 0.55 
S0M85 Watershed scale 12.75 0.90 3.38 7.52 9.14 2.03 6.35 − 4.49 
Local 0.66 − 3.54 0.18 1.13 0.19 − 0.96 0.55 − 11.19 
S1M45 Watershed scale 13.13 3.97 3.36 6.75 11.19 24.84 8.24 23.81 
Local 0.69 1.61 0.19 11.10 0.16 − 12.92 1.03 66.15 
S1M85 Watershed scale 12.79 1.29 3.44 9.34 8.85 − 1.20 7.93 19.15 
Local 0.68 − 0.36 0.20 13.07 0.14 − 24.80 0.98 57.97 
Figure 5

Calibration and validation performance at the two outlets: outlet 8 – (a) discharge, (b) nitrogen, and (c) phosphorus; Outlet 30 – (d) nitrogen and (e) phosphorus.

Figure 5

Calibration and validation performance at the two outlets: outlet 8 – (a) discharge, (b) nitrogen, and (c) phosphorus; Outlet 30 – (d) nitrogen and (e) phosphorus.

Close modal

Overall, the model shows a good performance for the discharge at outlet 8 in calibration: NS is in the satisfactory range (0.5<NS=0.67<0.7) according to Moriasi et al. (2007), while the PBIAS is excellent (close to 0). In validation, a relatively satisfactory performance was reached with an NS of 0.48 and a PBIAS of +16.9% (Figure 5(a)). However, the graphical comparison shows the model tends to underestimate the peaks and overestimate low flows. This is a common problem in rainfall–runoff modeling, which is linked to the quality of climatic inputs and the spatial discretization of the study area (Ghosh & Hellweger 2012; Sharma & Regonda 2021). In addition, the spatial repartition of rain gauges is a factor (Lobligeois et al. 2014) as a sparse number of rain gauges (as in this paper) or very large sub-watershed may lead to some extreme precipitation events being overlooked or extreme occurring in one small area that happens to have a rain gauge assumed to occur over a larger area.

As expected, a lower performance was obtained for water quality parameters, especially for P. According to the NS (0.54 for outlet 8 and 0.55 for outlet 30), the simulation of the N at both outlets showed acceptable results in calibration. Meanwhile, both PBIAS were excellent: 5.2 and −9.9%. Unsurprisingly, discharge is better simulated than nutrients by the model. A few studies (El-Khoury et al. 2015; Zhou et al. 2015; Čerkasova et al. 2018) have also obtained better performance for streamflow calibration. The reason for the low performance in predicting water quality parameters is the limited available data for a large number of parameters, highlighted in Hoang et al. (2019) and Noori et al. (2020). There are also greater uncertainties in nutrient data associated with errors in streamflow measurements, sample collection, storage, and analysis (Harmel & Smith 2007). For instance, in this study, the length of validation period for discharge was 11 years (2007–2015), while only 6 and 3 years of data were used for N and P loads at outlets 8 and 30, respectively.

All the simulations for water quality parameters showed unsatisfactory performance in the validation phase except for P at outlet 30, where PBIAS was 16.5%. The graphical comparison shows the model better simulates the average value of N at outlet 8 (Figure 5(b)), but not the peaks. It is important to note that these results are in the same range (or even better) as some published studies such as El-Khoury et al. (2015) with NS=0.24 and 0.28 in the calibration of nitrite-nitrogen and organic P, respectively; Shrestha et al. (2021) with NS=−0.71 and PBIAS=60.3% for P calibration.

Impacts of the scenarios on discharge, N load, and P load

This section presents the impacts of each scenario in terms of annual maximum discharge and monthly average discharge, N load, and P load at the main outlet (global) and at the outlet of sub-watershed 46 (local). This sub-watershed, located upstream (Figure 4), was selected because it was one of the most affected by the developments, with an important difference in the urbanization ratio compared to the global watershed. This allows an observable difference in the impacts at the two scales when comparing them. It also helps to investigate how different the effects between the global and the local scales would be. Figure 6 illustrates the repartition of the land uses before and after the developments in terms of watershed and local scales.

Figure 6

Repartition of mainland uses/covers in the entire watershed (watershed scale) and the selected sub-watershed (local, sub46) under urbanization.

Figure 6

Repartition of mainland uses/covers in the entire watershed (watershed scale) and the selected sub-watershed (local, sub46) under urbanization.

Close modal
Impact of urbanization (S0o vs. S1) on discharge, N load, and P load

Figure 7 shows the annual maximum discharge, while Figures 8 and 9 present the average monthly outputs related to discharge, N, and P for the experiments S0o and S1. Globally, urbanization slightly increased the averages of discharge and P load by 1.9 and 23%, respectively, while the average N load decreased by 4.4%. The N load decreases particularly in July–August, where higher precipitation amounts are observed. In terms of local impacts, the variation of the monthly averages has the same direction of change for all the three variables except that local impact is more significant. Annual averages in local increased by 9.6 and 69.5% for discharge and P load, respectively, and decreased by 30.2 for N load (higher than the previous percent changes). The high contrast change in P load compared to the N load is not surprising, since the model performance in simulating P was low (Figure 5(c) and 5(e)).

Figure 7

(a) Global and (b) local (sub46) impacts of land-use change on annual maximum discharge. Column height corresponds to the median, top and bottom whiskers are the errors with 95% confidence, and red markers represent the mean discharge. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wcc.2021.158.

Figure 7

(a) Global and (b) local (sub46) impacts of land-use change on annual maximum discharge. Column height corresponds to the median, top and bottom whiskers are the errors with 95% confidence, and red markers represent the mean discharge. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wcc.2021.158.

Close modal
Figure 8

Watershed-scale impact of land-use change on monthly average discharge (a), nitrogen load (b), and phosphorus load (c).

Figure 8

Watershed-scale impact of land-use change on monthly average discharge (a), nitrogen load (b), and phosphorus load (c).

Close modal
Figure 9

Local impact (sub46) of land-use change on monthly average discharge (a), nitrogen load (b), and phosphorus load (c).

Figure 9

Local impact (sub46) of land-use change on monthly average discharge (a), nitrogen load (b), and phosphorus load (c).

Close modal

Peak values for discharge and P are observed during the summer–spring period (April–August), while the same is observed in July–August for N. This is due to the increase of total precipitations (combination of snow melting and rainfall). In terms of water quantity, extreme events are more likely to occur around April. They are expected to vary by +4.78% at the local scale, while a relatively small difference of +0.73% is estimated at the watershed scale (Figure 7).

Impacts of climate change (S0 m vs. S0M45/85) on discharge, N load, and P load

Unlike the land-use change scenario, the change in climate change scenario is calculated and compared to S0 m output instead of S0o, although the S0o results are also presented on graphs only as a reference.

In terms of annual maximum, the discharge has experienced a relatively low change under the two RCPs, as presented in Figure 10. On average, at the watershed scale, the maximum discharge reached 12.63 m3/s in S0 m, and the percentages of change under RCP4.5 and RCP8.5 scenarios are +3.76 and +0.90%, respectively. However, an opposite change is observed at the local scale with −1.06 and −3.54% for S0M45 and S0M85, respectively. Annual average discharge presented a similar trend in S0M45 at the watershed scale and the local scale with an increase of 5.49 and 2.61%, respectively, while in S0M85, the change was 7.52% (watershed scale) and 1.13% (local) increase. In terms of water quality, overall the climate change seems to have a significant impact on N in S0M45 with an increase of 29.62% (+265 tons) globally and 28.24% (+5 tons) locally, while the change is less important in S0M85 with +2.03% (+18 tons) and −0.96% (−0.18 tons) globally and locally, respectively. Monthly results are illustrated in Figure 11 and Figure 12.

Figure 10

(a) Global and (b) local (sub46) impacts of climate change on annual maximum discharge under RCP4.5 and RCP8.5. Column height corresponds to the median, top and bottom whiskers are the errors with 95% confidence, and red markers represent the mean discharge. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wcc.2021.158.

Figure 10

(a) Global and (b) local (sub46) impacts of climate change on annual maximum discharge under RCP4.5 and RCP8.5. Column height corresponds to the median, top and bottom whiskers are the errors with 95% confidence, and red markers represent the mean discharge. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wcc.2021.158.

Close modal
Figure 11

Watershed-scale climate change impact on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Figure 11

Watershed-scale climate change impact on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Close modal
Figure 12

Local (sub46) climate change impact on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Figure 12

Local (sub46) climate change impact on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Close modal
Combined impacts of land-use and climate changes (S0 m vs. S1M45/85) on discharge, N load, and P load

Separately assessing the impacts of land-use change and climate change provides a better understanding of the level of impact caused by each of the two scenarios on the water quantity and quality. However, it is also important to determine and quantify the impact when the two effects are coupled. Figure 13 shows that the maximum discharge is not significantly affected by the combined effects of climate and land-use changes. The difference between the means (cross mark inside the boxes) of the scenarios S0 m, S1M45, and S1M85 is relatively small, both in watershed and local scales, although the RCP4.5 has a higher impact compared to the RCP8.5.

Figure 13

(a) Global and (b) local (sub46) impacts of combined climate and land-use changes on annual maximum discharge under RCP4.5 and RCP8.5. Column height corresponds to the median, top and bottom whiskers are the errors with 95% confidence, and red markers represent the mean discharge. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wcc.2021.158.

Figure 13

(a) Global and (b) local (sub46) impacts of combined climate and land-use changes on annual maximum discharge under RCP4.5 and RCP8.5. Column height corresponds to the median, top and bottom whiskers are the errors with 95% confidence, and red markers represent the mean discharge. Please refer to the online version of this paper to see this figure in color: http://dx.doi.org/10.2166/wcc.2021.158.

Close modal

In contrast, the annual discharge is more sensitive to the RCP8.5 than RCP4.5. The annual average changed from 3.15 m3/s in the baseline conditions to 3.44 m3/s (+9.34%) under RCP8.5, while RCP4.5 caused an increase of 0.21 m3/s (+6.75%). This is also the same at the local scale, where an increase of 13.07 and 11.10% is observed under RCP8.5 and RCP4.5, respectively. The coupled effect caused a reduction of the annual N load except in S1M45, where a significant increase of 24.84% was observed at the watershed scale. The local impact under RCP8.5 is also likely to be important, as the load decreased by 24.80% compared to scenario S0 m. However, the results indicated the annual load at the main outlet under RCP8.5 was not significantly impacted, and only a 1.20% decrease (−10.8 tons) of the baseline condition load is expected. The quantity of P faced a watershed-scale significant increase and was even higher at the local outlet (in terms of percentage of change). The load increase at the main outlet was estimated at 1.6 tons (23.81%) in S1M45 and 1.3 tons in S1M85 (19.15%), while in the upstream sub-watershed, it was 0.411 tons (66.15%) and 0.360 tons (57.97%). Monthly results are illustrated in Figure 14 and Figure 15.

Summary of global and local impacts

The summary of the results is presented in Figure 16 and Table 13.

Discussion

Results suggest that water quantity was more sensitive to climate change than urbanization in the Carp River watershed when the factors are taken individually (S1 and S0M45/85). When climate change alone is considered in the watershed, the increase in maximum discharge is expected to be within 0.90–3.76% at the main outlet. Urbanization will only lead to a 0.76% increase in the maximum discharge. Similarly, urbanization is expected to increase the average discharge by 1.57%, while this change is expected to be between 5.49 and 7.42% under climate change. This is not surprising as similar studies on areas located in Canada have come up with the same conclusion: Kaykhosravi et al. (2020) in Montreal and Toronto; El-Khoury et al. (2015) for the South Nation watershed in Ontario. For instance, the impact caused by the land-use and climate changes in the South Nation watershed was, respectively, +1.2 and +11.2%. Although the percentage of the urban area increased in the urbanization scenario, the agricultural lands are expected to remain dominant with 49.7% of the total area against only 13.5% for urban zones in 2050. This land-use repartition minimized the upstream developments’ effect and could explain the small impact observed in the Carp River watershed. A different urbanization rate or a different location of the developments within the same watershed may lead to very different results.

Monthly output analysis indicated extreme events were likely to occur at the end of the winter (around April) and during the summer. This is very common in cold areas. This study has shown that snow contribution to the total precipitations is more than rainfall, as the peaks of April are higher than during the summer, meaning that floods were expected to be more severe in April. According to the model, this characteristic of the watershed is not expected to change by 2050 because the predicted monthly hydrograph in the future conditions (S1M45/85) follows the same variation as the historical period (1990–2018). The variation of the peak flow during April is expected to be within −27.1 to 2.38%. Therefore, no significant increase is expected, but on the contrary, peaks may decrease up to 27.1% on average over the next 30 years. Although the peaks of July (due to rainfall events) are less severe, unlike April peaks, they were predicted to increase on average by 8.60%.

In terms of water quality, the sources of pollution of N and P are generally related to land uses such as urban and agricultural. Based on the impacts observed in the urbanization scenario, N seemed more sensitive to agricultural lands during July–August when agricultural activities occur. As an important amount of fertilizers is used for these activities, it is unsurprising to observe a drastic increase in the monthly graphs for all the scenarios. The sensitivity is also demonstrated by a 1.88% decrease in the annual total load at the main outlet when the ratio of the agricultural land is reduced by 2.63% (see Table 8). Similarly, a more significant decrease was observed at the local scale when the agricultural land ratio dropped from 49.7 to 10.6%, resulting in a reduction of the impact from 1.88 to 29.00%.

In the same perspective, as the increase of urban areas led to an increase of the annual P load (Figure 16(a)), the pollution of P seems to be dominated by the urban areas. This is consistent with what is observed at the local scale where a higher urban area ratio caused a larger increase (from 26.49% globally to 73.56% locally when urban ratio passed from 13.5 to 47.9%, Figure 6).

Figure 14

Watershed-scale impacts of combined climate and land-use changes on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Figure 14

Watershed-scale impacts of combined climate and land-use changes on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Close modal
Figure 15

Local impacts (sub46) of combined climate and land-use changes on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Figure 15

Local impacts (sub46) of combined climate and land-use changes on annual average discharge (a), nitrogen (b), and phosphorus (c) loads under RCP4.5 and RCP8.5.

Close modal
Figure 16

Annual average variation of discharge, N and P. Variation due to land-use change (a), climate change (b,c), and combined effects (d,e) compared to the reference conditions. LUC, land-use change, CC, climate change.

Figure 16

Annual average variation of discharge, N and P. Variation due to land-use change (a), climate change (b,c), and combined effects (d,e) compared to the reference conditions. LUC, land-use change, CC, climate change.

Close modal

Considering the most severe conditions in the future (S1M85), a decrease in the annual average of N load (−1.20%) is expected in opposition to an increase of 24.84% in the scenario S1M45. Therefore, a contrasting impact on N is likely to occur under the two RCPs. As a justification, the increase of the temperatures in the future conditions, especially in RCP8.5, influences the evaporation and transpiration (indispensable for the growth of plants) from natural (forests and water bodies) and agricultural lands. Given that plant cells control the openings where water is released to the atmosphere, higher temperatures stimulate them to open, enhancing water release. Consequently, the rate of transpiration, defined as the water loss from living plant surfaces (Manashi 2016), goes up with the temperature (USGS 2020). Plant growth is influenced by the availability of N, a vital element in the photosynthesis process. The higher the plant growth rate, the higher the demand in N. Considering the expected increase in transpiration and N availability, the plant's demand for N is likely to increase in the future. Consequently, this will decrease the annual N load, as observed in the S1M85 scenario with a −1.20% reduction.

The above justification is also valid for P, as the increase of the load at the main outlet is smaller in the hottest scenario (S1M85) than that of the coolest (S1M45) with 19.15 and 23.81%, respectively. The difference is that P is less soluble than N, and consequently, it is less sensitive to temperature. N and P are both partially lost in plant uptake, but in N's case, the losses also include denitrification and volatilization. Given that higher temperatures enhance their decomposition and accumulation (Hong & Tang 2014; Geng et al. 2017), denitrification and volatilization are promoted, and consequently, N loss may be more significant, which is the case in the Carp River watershed. In addition, even though having similar sources of pollution, N and P seemed to have different transport pathways. Due to its solubility, N is more likely to be retained within the watershed by infiltration into the groundwater and/or in the atmosphere by volatilization, while the P will mostly go into the surface water, especially for urbanized areas. This was demonstrated for the Mississippi River watershed in the USA (Hobbie et al. 2017) where only 22% of P inputs was retained vs. 80% of the N inputs. Nieder et al. (2018) also stated that nitrate is the most common chemical contaminant in the world's groundwater aquifers, and surface water is particularly affected by P, confirming the findings in the Carp River watershed.

The contrast in N and P variation (Figure 16) observed in our study area differs from some published studies such as Mehdi & Lehner (2014), El-Khoury et al. (2015), Eum et al. (2016), and Luo et al. (2020) and with the different direction of change. For instance, El-Khoury et al. (2015) found out that P will increase regardless of the type of conditions (LUC, CC, or the two combined), which contradicts what is observed in the Carp River watershed. This divergence indicates the impact of climate change, land-use change, or both must be evaluated case by case. It cannot be generalized even for watersheds located in the same region because every study area has different characteristics in terms of topography, land-use occupation, and climate conditions. Therefore, water quality is considerably affected by the entire watershed characteristics (Liu et al. 2018).

This study examined the influence of the land-use change and climate change individually and their coupled impact on the watershed of the Carp River using a hydrological model SWAT. The model was applied using input data from several sources and different resolutions, and overall, the calibrated model showed a satisfactory performance. The impacts were evaluated by developing realistic scenarios based on the 2050 year land-use map projected by the local authority and future climate conditions under RCP4.5 and RCP8.5. Results showed that climate change and urbanization's impacts vary greatly depending on the spatial scale and geographic location. In addition to that, the following points list the main other findings that emerged from this study: (a) climate change is likely to be the most dominant factor affecting the flow (peaks and trend) and N, while urbanization will control the P quantity in the future; (b) global and local impacts may significantly differ and it is crucial to check the impacts at both levels; (c) the local impact can be more significant than the watershed-scale impact; (d) impacts are not additive; and (e) for impacts assessment study, evaluating one effect alone without considering the other is incorrect and leads to a wrong understanding of the variation. In terms of quantity and quality:

  1. Expected global increase of the annual maximum flow from 1.29 to 3.97%.

  2. Expected increase of the annual average flow (global) from 6.75 to 9.34%.

  3. Expected global variation of the annual average N load from −1.20 to 24.84%.

  4. Expected global variation of the annual average P load from 19.15 to 23.81%.

As a recommendation, P management should emphasize reducing watershed inputs and transport from urban zones. In contrast, N management should reduce watershed inputs and transport from cultivated zones when they are active.

The authors gratefully acknowledge the data and financial support of the MVCA and financial support from MITACs. The authors also expressed their gratitude to the City of Ottawa for providing us with high-resolution topographic data and for sharing with us the future urban development map of the municipality.

B.-S.Z. is involved in conceptualization, data curation, formal analysis, methodology, software, visualization, writing – original draft, and writing – review & editing. O.S. is involved in conceptualization, supervision, resources, visualization, and writing – review & editing. M.S. is involved in conceptualization, supervision, resources, visualization, writing – review & editing, and funding acquisition. N.N. and K.S. are involved in investigation, data curation, and project administration.

This work was supported by the MVCA through the Mitacs Accelerate (No. IT15546) program.

All relevant data are included in the paper or its Supplementary Information.

Abbaspour
K.
2007
User Manual for SWAT-CUP, SWAT Calibration and Uncertainty Analysis Programs
.
Swiss Federal Institute of Aquatic Science and Technology, Eawag
,
Dübendorf
,
Switzerland
.
Abbaspour
K.
,
Faramarzi
M.
,
Ghasemi
S. S.
&
Yang
H.
2009
Assessing the impact of climate change on water resources in Iran
.
Water Resources Research
45
,
1
-
16
.
doi.org/10.1029/2008WR007615.
Abbaspour
K.
,
Rouholahnejad
E.
,
Vaghefi
S.
,
Srinivasan
R.
,
Yang
H.
&
Kløve
B.
2015
A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model
.
Journal of Hydrology
524
,
733
752
.
https://doi.org/10.1016/j.jhydrol.2015.03.027
.
Addor
N.
&
Melsen
L.
2019
Legacy, rather than adequacy, drives the selection of hydrological models
.
Water Resources Research
55
(
1
),
378
390
.
doi:10.1029/2018WR022958
.
Arnold
J.
,
Srinivasan
P.
,
Muttiah
R.
&
Williams
J.
1998
Large area hydrologic modeling and assessment, part I: model development
.
Journal of the American Water Resources Association
34
(
1
),
73
89
.
doi:10.1111/j.1752-1688.1998.tb05961.x
.
Arnold
J. G.
,
Moriasi
D. N.
,
Gassman
P. W.
,
Abbaspour
K.
,
White
M. J.
,
Srinivasan
R.
,
Santhi
C.
,
Harmel
R. D.
,
Griensven
A.
,
van Liew
M. W.
,
Kannan
N. J.
&
Jha
M. K.
2012
SWAT: model use, calibration, and validation
.
American Society of Agricultural and Biological Engineers
55
(
4
),
1491
1508
.
doi: 10.13031/2013.42256
.
Chen
H.
,
Xu
C.-Y.
&
Guo
S.
2012
Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff
.
Journal of Hydrology
434–435
,
36
45
.
https://doi.org/10.1016/j.jhydrol.2012.02.040
.
CH2M Hill
2005
Existing Conditions, Flow Characterization and Flood Level Analysis – Carp River, Feedmill Creek, and Poole Creek: Final Report
.
CH2M Hill
,
Ottawa
.
CH2M Hill
2006
Post Development Flow Characterisation and Flood Level Analysis for Carp River
.
CH2M Hill
,
Ottawa
.
Choukri
F.
,
Raclot
D.
,
Naimi
M.
,
Chikhaoui
M.
,
Nunes
J. P.
,
Huard
F.
,
Hérivaux
C.
,
Sabir
M.
&
Pépin
Y.
2020
Distinct and combined impacts of climate and land use scenarios on water availability and sediment loads for a water supply reservoir in northern Morocco
.
International Soil and Water Conservation Research
8
(
2
),
141
153
.
City of Ottawa
2016
Carp River PCSWMM Model Documentation
.
City of Ottawa
,
Ottawa
.
Collins
M.
,
Knutti
R.
,
Arblaster
J.
,
Dufresne
J.-L.
,
Fichefet
T.
,
Friedlingstein
P.
,
Gao
X.
,
Gutowski
W. J.
,
Johns
T.
,
Krinner
G.
,
Shongwe
M.
,
Tebaldi
C.
,
Weaver
A. J.
&
Wehner
M.
2013
Long-term climate change: projections, commitments and irreversibility. In: Climate Change 2013: The Physical Science Basis. IPCC Working Group I Contribution to AR5. (IPCC eds.), Ch. 12. Cambridge University Press, Cambridge, UK, pp. 1029–1136
.
Dunn
S.
,
Brown
I.
,
Sample
J.
&
Post
H.
2012, April
Relationships between climate, water resources, land use and diffuse pollution and the significance of uncertainty in climate change
.
Journal of Hydrology
434–435
,
19
35
.
https://doi.org/10.1016/j.jhydrol.2012.02.039
.
El-Khoury
A.
,
Seidou
O.
,
Lapen
D. R.
,
Que
Z.
,
Mohammadian
M.
,
Sunohara
M.
&
Bahram
D.
2015
Combined impacts of future climate and land use changes on discharge, nitrogen and phosphorus loads for a Canadian river basin
.
Journal of Environmental Management
151
,
76
86
.
https://doi.org/10.1016/j.jenvman.2014.12.012
.
Environment and Natural Resources Canada
2020
Historical Climate Data
.
Environment and Natural Resources Canada
:
Eum
H.-I.
,
Dibike
Y.
&
Prowse
T.
2016
Comparative evaluation of the effects of climate and land-cover changes on hydrologic responses of the Muskeg River, Alberta, Canada
.
Journal of Hydrology: Regional Studies
8
,
198
221
.
Food and Agricultural Organization, UNESCO
2019
FAO/UNESCO Soil Map of the World, November. http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/
Geng
Y.
,
Baumann
F.
,
Song
C.
,
Zhang
M.
,
Shi
Y.
,
Kühn
P.
,
Scholten
T.
&
He
J.-S.
2017
Increasing temperature reduces the coupling between available nitrogen and phosphorus in soils of Chinese grasslands
.
Scientific Reports
7
,
43524
.
doi:10.1038/srep43524
.
Ghosh
I.
&
Hellweger
F. L.
2012
Effects of spatial resolution in urban hydrologic simulations
.
Journal of Hydrologic Engineering
17
(
1
).
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000405
.
Greenland International Consulting Ltd
2009
Third-Party Review – Carp River Restoration Plan
.
City of Ottawa
,
Ottawa
.
Greenland International Consulting Ltd
2010
Carp River Restoration Plan, Widening Alternatives
.
City of Ottawa
,
Ottawa
.
Greenland International Consulting Ltd
2011
Carp River Model Calibration Validation Exercise Final Report
.
Ottawa
.
Greenland International Consulting Ltd
2014
Model Development Program – Carp River Restoration Plan Draft Report
.
Ottawa
.
Guo
Y.
,
Fang
G.
,
Xu
Y.-P.
,
Tian
X.
&
Xie
J.
2020
Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China
.
Science of the Total Environment
710
.
https://doi.org/10.1016/j.scitotenv.2019.136275
.
Gupta
H.
,
Sorooshian
S.
&
Yapo
P.
1999
Status of automatic calibration for hydrologic models: comparison with multi-level expert calibration
.
Journal of Hydrology
4
(
2
),
135
143
.
Harmel
R. D.
&
Smith
P. K.
2007
Consideration of measurement uncertainty in the evaluation of goodness-of-fit in hydrologic and water quality modeling
.
Journal of Hydrology
337
(
3–4
),
326
336
.
https://doi.org/10.1016/j.jhydrol.2007.01.043
.
Havrylenko
S. B.
,
Bodoque
J. M.
,
Srinivasan
R.
,
Zucarelli
G. V.
&
Mercuri
P.
2016
Assessment of the soil water content in the Pampas region using SWAT
.
CATENA
137
,
298
309
.
doi.org/10.1016/j.catena.2015.10.001.
Hoang
B. H.
,
Hien
H. N.
,
Dinh
N. T.
,
Thao
N. A.
,
Ha
P. T.
,
Kandasamy
J.
&
Nguyen
T. V.
2019
Integration of SWAT and QUAL2 K for water quality modeling in a data scarce basin of Cau River basin in Vietnam
.
Ecohydrology & Hydrobiology
19
(
2
),
210
223
.
https://doi.org/10.1016/j.ecohyd.2019.03.005
.
Hobbie
S. E.
,
Finlay
J. C.
,
Janke
B. D.
,
Nidzgorski
D. A.
,
Millet
D. B.
&
Baker
L. A.
2017
Contrasting nitrogen and phosphorus budgets in urban watersheds and implications for managing urban water pollution
.
National Academy of Sciences
114
(
16
),
4177
4182
. doi.org/10.1073/pnas.1618536114.
Holvoet
K.
,
Van Griensven
A.
,
Seuntjens
P.
&
Vanrolleghem
P.
2005
Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT
.
Physics and Chemistry of the Earth, Parts A/B/C
30
(
8–10
),
518
526
.
doi.org/10.1016/j.pce.2005.07.006
Hong
F.
&
Tang
J.
2014
Influence of Temperature and Moisture on Nitrogen Cycling in Soils From Experimentally Heated and Control Plots at the Harvard Forest, MA
.
Mount Holyoke College
,
Chicago
,
Marine Biological Laboratory. Available from: https://www.mbl.edu/ses/files/2015/04/Fangyuan-Hong.pdf (accessed December 2020)
.
Jajarmizadeh
M.
,
Sidek
L. M.
,
Harun
S.
&
Salarpour
M.
2017
Optimal calibration and uncertainty analysis of SWAT
.
Air, Soil and Water Research
10
,
1
14
.
https://doi.org/10.1177%2F1178622117731792
.
Karlsson
I. B.
,
Sonnenborg
T. O.
,
Refsgaard
J. C.
,
Trolle
D.
,
Børgesen
C. D.
,
Olesen
J. E.
,
Jeppesen
E.
&
Jensen
K. H.
2016
Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change
.
Journal of Hydrology
535
,
301
317
.
https://doi.org/10.1016/j.jhydrol.2016.01.069
.
Kaykhosravi
S.
,
Khan
U. T.
&
Jadidi
M. A.
2020
The effect of climate change and urbanization on the demand for low impact development for three Canadian cities
.
Water
12
(
5
),
1280
.
https://doi.org/10.3390/w12051280
.
Khoiab
D. N.
&
Thomb
V. T.
2015
Global ecology and conservation
.
Parameter Uncertainty Analysis for Simulating Streamflow in a River Catchment of Vietnam
4
,
538
548
.
https://doi.org/10.1016/j.gecco.2015.10.007
.
Kundu
S.
,
Khare
D.
&
Mondal
A.
2017
Individual and combined impacts of future climate and land use changes on the water balance
.
Ecological Engineering
105
,
42
57
.
https://doi.org/10.1016/j.ecoleng.2017.04.061
.
Liu
L.
,
Ma
C.
,
Huo
S.
,
Xi
B.
,
He
Z.
,
Zhang
H.
,
Zhang
J.
&
Xia
X.
2018
Impacts of climate change and land use on the development of nutrient criteria
.
Journal of Hydrology
563
,
533
542
.
https://doi.org/10.1016/j.jhydrol.2018.06.039
.
Lobligeois
F.
,
Andréassian
V.
,
Perrin
C.
,
Tabary
P.
&
Loumagne
C.
2014
When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events
.
Hydrology and Earth System Sciences
18
(
2
),
575
594
.
https://doi.org/10.5194/hess-18-575-2014
.
Luo
C.
,
Li
Z.
,
Liu
H.
,
Li
H.
,
Wan
R.
,
Pan
J.
&
Chen
X.
2020
Differences in the responses of flow and nutrient load to isolated and coupled future climate and land use changes
.
Journal of Environmental Management
256
,
109918
.
Manashi
P.
2016
Impacts of Land Use and Climate Changes on Hydrological Processes in South Dakota Watersheds
.
South Dakota State University, Agricultural and Biosystems Engineering. ProQuest Dissertations Publishing
.
Mearns
L.
,
McGinnis
S.
,
Korytina
D.
,
Arritt
R.
,
Biner
S.
,
Bukovsky
M.
,
Chang
H.-I.
,
Christensen
O.
,
Herzmann
D.
,
Jiao, Yanjun, K., Slava Lazare
M.
,
Nikulin
G.
,
Qian, Minwei
Q.
,
Scinocca
J.
&
Winger
J.
2017
The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway, Boulder, CO. Retrieved November 20, 2020. https://doi.org/10.5065/D6SJ1JCH
.
Mehdi
B. B.
&
Lehner
B.
2014
Scenarios and Implications of Land Use and Climate Change on Water Quality in Mesoscale Agricultural Watersheds
.
McGill University, Department of Geography
,
Montreal
.
doi:10.3390/w6103152
.
Mengistu
A. G.
,
Van Rensburg
L. D.
&
Woyessa
Y. E.
2019
Techniques for calibration and validation of SWAT model in data scarce arid and semi-arid catchments in South Africa
.
Journal of Hydrology: Regional Studies
25
,
100621
.
Ministry of Natural Resources and Forestry
2019
Ontario Flow Assessment Tool
.
Retrieved from
Ontario Flow Assessment Tool
.
Miralha
L.
,
Muenich
R. L.
,
Scavia
D.
,
Wells
K.
,
Steiner
A. L.
,
Kalcic
M.
,
Apostel
A.
,
Basile
S.
&
Kirchhoff
C. J.
2020
Bias correction of climate model outputs influences watershed model nutrient load predictions
.
Science of the Total Environment
.
https://doi.org/10.1016/j.scitotenv.2020.143039
.
Moriasi
D.
,
Arnold
J.
,
Van Liew
M.
,
Bingner
R.
,
Harmel
R.
&
Veith
T.
2007
Model evaluation guidelines for systematic quantification of accuracy in watershed simulations
.
Transactions of the ASABE
50
(
3
),
885
900
.
doi:10.13031/2013.23153
.
Munia
H. A.
,
Guillaume
J. H.
,
Wada
Y.
,
Veldkamp
T.
,
Virkki
V.
&
Kummu
M.
, (
2020, May
).
Future Transboundary Water Stress and Its Drivers Under Climate Change: A Global Study
.
Earth's Future
8
(
7
),
e2019EF001321
.
doi:10.1029/2019EF001321.
Muluneh
A.
2020
Impact of climate change on soil water balance, maize production, and potential adaptation measures in the Rift Valley drylands of Ethiopia
.
Journal of Arid Environments
179
,
104195. https://doi.org/10.1016/j.jaridenv.2020.104195.
Nash
J.
&
Sutcliffe
J.
1970
River flow forecasting through conceptual models part I – a discussion of principles
.
Journal of Hydrology
10
(
3
),
282
290
.
https://doi.org/10.1016/0022-1694(70)90255-6
.
Neitsch
S. L.
,
Arnold
J. G.
,
Kiniry
J. R.
&
Williams
J. R.
2009
Soil and Water Assessment Tool Theoritical Documentation Version 2009. Grassland, Soil and Water Research Laboratory, Agricultural Research Service,Blackland Research Center, Texas AgriLife Research , US Department of Agriculture
.
Texas Water Research Institute
,
College Station, TX
.
Nieder
R.
,
Benbi
D. K.
&
Reichl
F. X.
2018
Reactive water-soluble forms of nitrogen and phosphorus and their impacts on environment and human health
.
Soil Components and Human Health
2018
,
223
255
.
Noori
N.
,
Kalin
L.
&
Isik
S.
2020
Water quality prediction using SWAT-ANN coupled approach
.
Journal of Hydrology
590
.
https://doi.org/10.1016/j.jhydrol.2020.125220
.
Ogden
F. L.
2021
Geohydrology: hydrological modeling
. In:
Encyclopedia of Geology
, 2nd edn. pp.
457
476
.
https://doi.org/10.1016/B978-0-08-102908-4.00115-6
.
Sharma
V. C.
&
Regonda
S. K.
2021
Multi-spatial resolution rainfall-Runoff modelling – a case study of Sabari River Basin, India
.
Water
13
(
9
),
1224
.
https://doi.org/10.3390/w13091224
.
Shrestha
S.
,
Sattar
H.
,
Khattak
M. S.
,
Wang
G.
&
Babur
M.
2020
Evaluation of adaptation options for reducing soil erosion due to climate change in the Swat River Basin of Pakistan
.
Ecological Engineering
158
,
106017
.
https://doi.org/10.1016/j.ecoleng.2020.106017
.
Shrestha
N. K.
,
Rudra
R. P.
,
Daggupati
P.
,
Goel
P. K.
&
Shukla
R.
2021
A comparative evaluation of the continuous and event-based modelling approaches for identifying critical source areas for sediment and phosphorus losses
.
Journal of Environmental Management
277
.
Article 111427. https://doi.org/10.1016/j.jenvman.2020.111427
.
Stantec Consulting Ltd
2006
Kanata West Master Servicing Study
.
Stantec Consulting Ltd
,
Ottawa
.
Tirupathi
C.
&
Shashidhar
T.
2020
Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios
.
Science of the Total Environment
721
.
https://doi.org/10.1016/j.scitotenv.2020.137736
.
Tu
J.
2009
Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts, USA
.
Journal of Hydrology
379
(
3–4
),
268
-
283
.
doi.org/10.1016/j.jhydrol.2009.10.009.
United Nations
2019
World Population Prospects 2019 – Volume II: DemographicProfiles
.
Economic and Social Affairs, PopulationDivision. United Nations
. .
USGS
2020
Water Science School – Evapotranspiration and the Water Cycle
.
USGS – Science for a Changing World
. .
Verhagen
A. P.
,
Ostelo
R. W.
&
Rademaker
A.
2004
Is the p value really so significant?*
.
Australian Journal of Physiotherapy
50
(
4
),
261
262
.
doi.org/10.1016/S0004-9514(14)60122-7.
Wang
Q.
,
Xu
Y.
,
Wang
Y.
,
Zhang
Y.
,
Xiang
J.
,
Xu
Y.
&
Wang
J.
2020
Individual and combined impacts of future land-use and climate conditions on extreme hydrological events in a representative basin of the Yangtze River Delta, China
.
Atmospheric Research
236
,
104805
.
https://doi.org/10.1016/j.atmosres.2019.104805
.
Wayne
G.
2013
The Beginner's Guide to Representative Concentration Pathways
.
Skeptical Science
.
Weedom
G.
,
Balsamo
G.
,
Bellouin
N.
,
Gomes
S.
,
Best
M.
&
Viterbo
P.
2014
The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data
.
Water Resources Research
50
.
doi:10.1002/2014WR015638
.
Young
R.
,
Onstad
C.
&
Bosch
D.
1989
AGNPS: a nonpoint source pollution model for evaluating agricultural watersheds
.
Journal of Water and Soil Conservation
44
(
2
),
168
173
.
Yu
Z.
2015
Hydrology, floods and droughts: modeling and prediction
. In:
Encyclopedia of Atmospheric Sciences
, 2nd edn. pp.
217
223
.
doi:10.1016/B978-0-12-382225-3.00172-9
.
Zhou
X. V.
,
Clark
C. D.
,
Nair
S. S.
,
Hawkins
S. A.
&
Lambert
D. M.
2015
Environmental and economic analysis of using SWAT to simulate the effects of switchgrass production on water quality in an impaired watershed
.
Agricultural Water Management
160
,
1
13
.
https://doi.org/10.1016/j.agwat.2015.06.018
.
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