3. Results and discussion

### 3.1. Calibration of drainage flows

Calibration process of the model used in this specific research was first completed with hydrologic calibration and followed by the drainage nitrogen. In general, calibration and validation of water quality models are typically performed with data collected at the outlet of a watershed to be able to assess possible pollution risks. In Akarsu, daily measured data were used during the model processes. The most sensitive parameters for hydrologic calibration process were SURLAG, GW\_Delay, Revapmn, GW\_Revap, and Esco (Table 4), while Nperco, Cmn, Hlife, and Ngw are the sensitive ones for nitrogen calibration.


Table 4. SWAT input parameters for river flow and nitrogen calibrations.

Based on the model outputs, the SWAT model is reliable enough to be used in nonnatural catchments such as Akarsu Irrigation District where drainage network is not topographydriven but man-made. Additionally, hydrologic water dynamics such as inflows, outflows, and the whole water balance are well defined since 2006. The area is affected by routine agricultural management activities, i.e., irrigation and fertilization in specific.

Three recommended quantitative statistics, determination (R<sup>2</sup> ), Nash-Sutcliffe efficiency (NSE), and PBIAS, in addition to the graphical techniques for visual examination have been used to assess the hydrologic model performance [59], i.e., model calibration and validation. These performance indicators of the model (R<sup>2</sup> , NSE, and PBIAS) during calibration period of 2009–2012 have been found as 0.62, 0.57, and 6.3, respectively (Table 5). Typically, values of R<sup>2</sup> greater than 0.50, while values of NSE between 0.0 and 1.0, and values of PBIAS ±25% for streamflow calibration are generally considered as acceptable levels [59]. In addition, model validation was made by utilizing the daily data for 2013 and 2014 period. The performance statistics for the validation period were 0.67, 0.59, and −10.04 for R<sup>2</sup> , NSE, and PBIAS, respectively (Table 5).


Table 5. Objective function statistics for drainage flow and nitrogen in drainage.

3. Results and discussion

144 Water Quality

3.1. Calibration of drainage flows

Parameter File extensions Explanation

Ch\_N2 .rte Manning's n

Alpha\_BF .gw Base flow recession factor, days GW\_DELAY .gw Groundwater delay, days

SURLAG .bsn Surface runoff lag coefficient, days ESCO .bsn Soil evaporation compensation factor

GW\_REVAP .gw Groundwater "revap" coefficient

SOL\_AWC .sol Available water capacity

NPERCO .bsn Nitrate percolation coefficient

Table 4. SWAT input parameters for river flow and nitrogen calibrations.

HLIFE\_NGW .gw Half-life of nitrate in shallow aquifer (days)

Calibration process of the model used in this specific research was first completed with hydrologic calibration and followed by the drainage nitrogen. In general, calibration and validation of water quality models are typically performed with data collected at the outlet of a watershed to be able to assess possible pollution risks. In Akarsu, daily measured data were used during the model processes. The most sensitive parameters for hydrologic calibration process were SURLAG, GW\_Delay, Revapmn, GW\_Revap, and Esco (Table 4), while Nperco,

Based on the model outputs, the SWAT model is reliable enough to be used in nonnatural catchments such as Akarsu Irrigation District where drainage network is not topographydriven but man-made. Additionally, hydrologic water dynamics such as inflows, outflows, and the whole water balance are well defined since 2006. The area is affected by routine

(NSE), and PBIAS, in addition to the graphical techniques for visual examination have been used to assess the hydrologic model performance [59], i.e., model calibration and validation.

2009–2012 have been found as 0.62, 0.57, and 6.3, respectively (Table 5). Typically, values of R<sup>2</sup>

), Nash-Sutcliffe efficiency

).

, NSE, and PBIAS) during calibration period of

agricultural management activities, i.e., irrigation and fertilization in specific.

Three recommended quantitative statistics, determination (R<sup>2</sup>

These performance indicators of the model (R<sup>2</sup>

Cmn, Hlife, and Ngw are the sensitive ones for nitrogen calibration.

GWQMN .gw Threshold depth for ground water flow to occur, mm

CN2 .mgt SCS curve number, antecedent moisture condition II, for crop land use

CMN .bsn Rate factor for humus mineralization of active organic nutrients (N).

CH\_K2 .rte Effective hydraulic conductivity in main channel alluvium

AI1 .wwq Fraction of algal biomass that is nitrogen (mg N mg alg−<sup>1</sup>

REVAPMN .gw Threshold water depth in shallow aquifer for percolation to deep aquifer to occur

Descriptive statistics for observed and simulated (calibration and validation) were resented in Table 6, indicating that model performance was satisfactory with the mean values of 3.51, 2.98 m3 s <sup>−</sup><sup>1</sup> for calibration period and 2.71 and 2.98 for validation period. Similarly, other descriptive statistics for observed and simulated flow values were in good agreement.

The visual examination of observed versus predicted drainage flows for the calibration (Figure 2) and validation periods (Figure 3) indicated adequate calibration and validation. Therefore, SWAT simulations and observed data were in good agreement visually and statistically. SWAT-CUP automatic calibration results for the sensitive parameters were presented in Table 7. These parameters are reasonable enough to accept performance of the model [56] in a well-defined agricultural catchment of Akarsu where anthropogenic factors affecting hydrological processes are very preponderant.

Because the study area is under irrigation in dry periods of the year, it was necessary to consider irrigation amounts of field and horticultural crops grown in the region. Therefore, during the calibration period, irrigation requirements of the crops were estimated by using universal reference evapotranspiration method of Penman-Monteith. Then, using the crop coefficients of FAO [60], net irrigation requirements of irrigated crops were obtained and used in management files as a model input. For the calibration, the created base model with net irrigation amounts and routine fertilizer rates were saved in crop rotations. The actual irrigation bypass flows were determined through running different simulations by adapting calibrated SWAT parameters given in Table 7. Finally, it was determined that 40% of the total diverted irrigation water to the district at any time was directly draining into the drainage system as bypass flow.
