3.2. Calibration of nitrogen in drainage water

After calibrating the hydrologic part of the model with a successful performance, nitrate simulation was confidently applied with the appropriate water parameters. All the nitrogen inputs were incorporated in the management files as fertilizer, water, and soil point sources.


Average daily NO3-N loads (kg day<sup>−</sup><sup>1</sup> ) were selected as water quality parameter and calculated based on daily discharge data (m3 day−<sup>1</sup> ) at L4 gauging station (outlet).

Table 6. Descriptive statistics of drainage flows and nitrogen loads in drainage for observed and simulated during calibration and validation periods.

Objective function statistics, R<sup>2</sup> , NSE, and PBIAS in specific, for nitrogen in drainage were defined as 0.47, −0.63, and 88.1% for the calibration and 0.50, −0.20, and 72.9% for validation, respectively (Table 5). As indicated by Moriasi et al. [59], the PBIAS ±70% for N is accepted as a performance criteria.

Average daily NO3-N loads (kg day<sup>−</sup><sup>1</sup> ) of the selected water quality parameter was also calculated based on daily discharge data (m3 day−<sup>1</sup> ) at L4 gauging station at the outlet of the district. Based on the graphical presentation in Figures 4 and 5, overlapping of the both measured and calibrated lines for N cannot be considered as perfect because nature and dynamics of N in the whole system, even though the statistics are reasonably acceptable. Similar underestimation with the data of only 2009 and 2010 was also recorded in the same location [38]. It is important to point out that calibration and validation of the model are sensitive to time periods, instead of using daily data, monthly data were more suitable to modeling purpose of N [61].

Average daily NO3-N loads (kg day<sup>−</sup><sup>1</sup>

146 Water Quality

Objective function statistics, R<sup>2</sup>

calibration and validation periods.

Average daily NO3-N loads (kg day<sup>−</sup><sup>1</sup>

calculated based on daily discharge data (m3 day−<sup>1</sup>

performance criteria.

based on daily discharge data (m3 day−<sup>1</sup>

) were selected as water quality parameter and calculated

−1 )

)

, NSE, and PBIAS in specific, for nitrogen in drainage were

) of the selected water quality parameter was also

) at L4 gauging station at the outlet of the

defined as 0.47, −0.63, and 88.1% for the calibration and 0.50, −0.20, and 72.9% for validation, respectively (Table 5). As indicated by Moriasi et al. [59], the PBIAS ±70% for N is accepted as a

Table 6. Descriptive statistics of drainage flows and nitrogen loads in drainage for observed and simulated during

district. Based on the graphical presentation in Figures 4 and 5, overlapping of the both measured and calibrated lines for N cannot be considered as perfect because nature and

) at L4 gauging station (outlet).

Observed Simulated Observed Simulated Drainage flows (m<sup>3</sup> s

Nitrogen in drainage (kg d−<sup>1</sup>

Calibration period (2009–2012) Validation period (2013–2014)

Mean 3.51 2.98 2.71 2.98 Median 3.48 2.69 2.74 2.62 Mode 1.04 1.86 1.23 2.35 Standard dev. 2.02 1.93 1.45 1.46 Kurtosis 5.54 10.43 −1.01 −0.62 Skewness 1.70 1.96 0.33 0.38 Minimum 0.73 0.09 0.58 0.31 Maximum 14.06 18.65 6.05 7.27 CV% 58 65 54 49

Mean 1810.2 443.1 938.7 552.6 Median 1376.4 243.9 774.0 457.6 Mode 1775.5 229.3 − 1135.0 Standard dev. 1659.0 639.9 608.9 533.2 Kurtosis 15.8 21.9 5.8 57.8 Skewness 3.4 4.0 2.0 5.8 Minimum 143.4 10.4 64.9 39.7 Maximum 14024.7 6403.0 4826.0 7599.0 CV% 92 144 65 96 Sample size (n) 1461 730

Figure 2. Daily drainage discharge (m<sup>3</sup> s −1 ) calibration for the Akarsu catchment outlet L4.

This basin is not natural instead it is a man-made hydrologically well-defined area in a semiarid Mediterranean region where it is subjected to intensive irrigation and fertilizer applications by anthropogenic activities. Imported N loads by irrigation water, rainfall, and inorganic fertilizer inputs make the calibration and validation difficult and relatively weak. There are three district-specific conditions in the area to be pointed out for nitrogen and nitrogen balance: the canals being open despite high rates of ET, irrigation also taking place outside of irrigation season, and the possible loses of irrigation water to drainage.

In terms of management practices, there are two planting seasons in a year; the crop rotations used in the model include all the planting and harvesting dates. Except for perennial crops, the crop pattern (land use) varies from year to year. The model permits use of only one land use map in HRU delineation; for this reason, rotation calendars were made to be utilized within the model. Farmer behavior and knowledge are diverse, and the use of nitrogen fertilizers and irrigation is intense.

Figure 3. Daily drainage discharge (m<sup>3</sup> s −1 ) validation for the Akarsu catchment outlet at L4.


Table 7. Sensitive hydrologic model parameters for SWAT.

Figure 4. Nitrogen load (kg day−<sup>1</sup> ) at L4 (outlet) calibration and validation period on monthly level.

### 3.3. Nitrogen balance

the model. Farmer behavior and knowledge are diverse, and the use of nitrogen fertilizers and

irrigation is intense.

148 Water Quality

Figure 3. Daily drainage discharge (m<sup>3</sup> s

Table 7. Sensitive hydrologic model parameters for SWAT.

−1

) validation for the Akarsu catchment outlet at L4.

Parameter Default Range Calibrated values

CN2 83 35–98 73.9 Alpha\_BF 0.048 0–1 0.55 GW\_Delay 31 0–500 36.08 Gwqmn 1000 0–5000 4187.5 Surlag 4 1–24 0.42 Esco 0.95 0–1 0.837 Revapmn 750 0–1000 488.75 Ch\_K2 0 −0.01 to 500 378.75 Gw\_Revap 0.02 0.02–0.2 0.089 Ch\_n2 0.014 −0.01 to 0.3 0.266

Nitrogen calibration was carried out on daily basis. Average daily NO3-N loads (kg day<sup>−</sup><sup>1</sup> ) of selected water quality parameter were calculated based on daily discharge data (m3 day−<sup>1</sup> ) at L4 gauging station. Table 5 and Figures 4 and 5 created for nitrogen did not show a strong relationship between measured and simulated values. One of the main reasons is that for hydrologic reasons inclusion of the two hilly pasture areas (Figure 1) into the 9495 ha hydrologically well-defined Akarsu irrigation district by extending the area to 11,308 ha. Therefore, when the actual N inputs were distributed in a larger area the prediction became lower. Also, since the soils are climatically suitable to nitrification, greater amount of nitrogen especially from the inorganic fertilizers may be quickly transformed to nitrate in a very short time period and leached to the drainage [62]. As also discussed by Abbaspour et al. [56], amount of nitrogen fertilizer leached below the root zone, which is 0–90 cm in the study, is under-estimated. In addition, fertilizer application level may be higher than that of the recorded from our three consecutive survey data. Therefore, it may cause higher measured NO3 concentrations in drainage. Overall, since the irrigated area is under very intensive agricultural management practices including irrigation and very dynamic fertilization, it is quite possible to underestimate the N leaching to the drainage. For example, SWAT model prediction was very successful for calibration (and validation) of rivers accounting the dynamics of nitrate transport [56].

Nitrogen balance variables are given in Table 8. The sums of nitrate nitrogen leached from the soil profile in kg NO3–N(NO3L) and N uptake by plants (NUP) from 2009 to 2014 are reasonably in agreement with the amount of applied nitrogen (N APP). The remaining inputs in the so-called man-made research area are coming from the N content of irrigation water, rainfall, mineralization of soil organic matter, and transforms of N forms into readily available NH4 − and NO3. Based on the climatic conditions, amount of rainfall, thus leaching to drainage, and groundwater, varies year to year. For example, in 2013, total rainfall was 349 mm, which was the lowest figure among the other years of the study (ranged 349–951 mm). The reflection of this unusual rainfall was clearly performed in Figure 5, which is for the simulation period. Figure 4 clearly indicates that impacts of rainfall in winter and irrigation applications in


\* N\_APP, NO3L, and NUP stand for applied, leached, and taken-up nitrogen at the catchment level.

Table 8. Temporal variability of nitrogen balance by SWAT modeling for the Akarsu region (2009–2014).

Figure 5. Nitrogen load (kg day−<sup>1</sup> ) at L4 (outlet) calibration and validation period on monthly level.

Modeling Agricultural Land Management to Improve Understanding of Nitrogen Leaching in an Irrigated... http://dx.doi.org/10.5772/65809 151

Nitrogen balance variables are given in Table 8. The sums of nitrate nitrogen leached from the soil profile in kg NO3–N(NO3L) and N uptake by plants (NUP) from 2009 to 2014 are reasonably in agreement with the amount of applied nitrogen (N APP). The remaining inputs in the so-called man-made research area are coming from the N content of irrigation water, rainfall, mineralization of soil organic matter, and transforms of N forms into readily available NH4

and NO3. Based on the climatic conditions, amount of rainfall, thus leaching to drainage, and groundwater, varies year to year. For example, in 2013, total rainfall was 349 mm, which was the lowest figure among the other years of the study (ranged 349–951 mm). The reflection of this unusual rainfall was clearly performed in Figure 5, which is for the simulation period. Figure 4 clearly indicates that impacts of rainfall in winter and irrigation applications in

)

Nitrogen balance variables (kg N ha−<sup>1</sup> year−<sup>1</sup>

Year N\_APP1\* NO3L NUP 329.2 196.8 270.0 368.1 212.8 228.3 310.9 234.7 181.3 368.1 256.3 175.1 329.2 159.2 277.7 368.1 249.3 254.6

\* N\_APP, NO3L, and NUP stand for applied, leached, and taken-up nitrogen at the catchment level.

Figure 5. Nitrogen load (kg day−<sup>1</sup>

150 Water Quality

Table 8. Temporal variability of nitrogen balance by SWAT modeling for the Akarsu region (2009–2014).

) at L4 (outlet) calibration and validation period on monthly level.

−

Figure 6. Comparison between average nitrogen fertilizers applied (kg ha−<sup>1</sup> ) and potential for nitrogen leaching (kg ha−<sup>1</sup> ) below the bottom of the soil profile in Akarsu study area in the period between 2009 and 2014.

summer are the most important drivers of the N leaching. Conflicting performance ratings of N calibration seen in Figures 4 and 5 might be attributed to above mentioned two drivers. In addition, routine fertilizer applications are exceedingly high than the recommended levels, i.e., 380 kg N ha−<sup>1</sup> is applied to corn while only 240 kg N ha−<sup>1</sup> is the expert recommendation for corn in the region [63]. This results in high potential for nitrogen leaching (Figure 6).
