2.1. Study area

1. Introduction

136 Water Quality

In arid and semiarid regions, freshwater resources are under the ever increasing pressure of many current issues such as population increase, economic development, climate change, and pollution [1]. Water quality is a major concern and expressed by its biological, chemical, physical, and aesthetic properties [2]. The water quality is determined by a number of factors such as electrical conductivity, pH, amount of salts, dissolved oxygen, levels of microorganisms, nutrients, heavy metals, quantities of pesticides, and herbicides [3]. These factors can lead to the problems (salinity, infiltration, toxicity, and nutrients), which are extensively pre-

Nitrogen leaching from agricultural land is a main pollutant in many countries in the world [7, 8]. In agricultural areas of the European Union (EU), fertilizer contribution as nonpoint source pollution to the surface water is estimated to be 55% [9]. The European Union Water Framework Directive (WFD) has issued important regulations in order to reduce the environmental impact of nitrogen due to agriculture and to keep water bodies in good quality state; based on the EU Drinking Water Directive (80/778/EEC), the accepted maximum admissible concentra-

On the other hand, nitrogen is an essential nutrient for adequate plant growth, and mostly used as type of fertilizer [11]. During the N cycle, it undergoes many processes in soil, water, and atmosphere level [12–14]. Nitrogen cannot be used directly by the plants and animals until it is converted into its available compounds and forms. Nitrate ions in soil are usually in dissolved form in the soil solution, and it can easily be lost to leaching as water moves through

Understanding of nitrogen dynamics in the nature, nitrogen balance or nitrogen budget becomes more of an issue about prevention of environmental pollution and economic losses on a country basis. Nitrogen balance studies have been continued for over 170 years [17]. There are different ways of defining nitrogen budgets in empirical statistical methods, depending on the measurements and modeling. Calculation of N budget in agricultural systems by this way is a common practice in OECD and EU countries. This method does not include explaining the processes of nutrient cycle in the soil-plant-atmosphere system but follows statistical method-

Measured nitrogen budgets in soil-plant-atmosphere level are based on the conservation of mass of nitrogen in the system. A previous study carried out [21, 22] aimed at evaluating nitrogen fluxes by measuring agronomic system in Akarsu Study Area in southern Turkey. As part of the findings, it was found that considerable amounts of nitrate are lost to drainage and shallow groundwater. During the study years, nitrogen budget calculations resulted in unac-

As known, Mediterranean climate is characterized by mild rainy winters and hot dry summers [24]. Annual and interannual changes in dry and wet periods result in change of water balance and water level fluctuations especially in the areas where Mediterranean climate is dominating [25]. Based on the recent years' ongoing drought events and therefore water scarcity, irrigation scheduling and types need to be reevaluated. Recently, best management techniques such as

ology at national and regional levels to determine nitrogen budget [18–20].

sent in many watersheds with irrigated agriculture [4–7].

tion for the nitrate was set as 50 mg l−<sup>1</sup> [10].

the soil profile due to the rapid dynamism [15, 16].

counted values ranging from 40 to 60 kg N ha−<sup>1</sup> [23].

The Akarsu Irrigation District (AID) study area is located in the Mediterranean coastal region, between 36°51′45″ and 36°57′35″ N latitudes, and 35°24′10″ and 35°36′20″ E longitudes in Turkey. The district covers an area of 9495 ha (irrigation area), and hydrological area is 11,308 ha in the Lower Seyhan Plain (LSP) and has been irrigated for over 60 years under conventional irrigation and drainage infrastructures. Until 1994, the national irrigation agency, i.e., State Hydraulic Works (DSI), was responsible for the management, operation, and maintenance of the district. Management of the irrigation and drainage system in the district was taken over by the water users in 1994. Akarsu Water User Association has been responsible for the irrigation management, operation, and repairing issues in the district since 1994. Irrigation water has been provided from Seyhan Dam (L6, L3, and L7 in Figure 1), in case of water shortage in the system during the peak irrigation season or if irrigation water is not diverted to the main irrigation canal through L6, then pumping station is activated and some water is diverted from Ceyhan River (Abdioglu Pumping Station, L9 in Figure 1). The irrigation water in Seyhan Dam has excellent water quality (0.33 ≤ EC ≤ 0.50 dS m−<sup>1</sup> , EC = 0.43 dS m−<sup>1</sup> ). However, electrical conductivity (EC) of Ceyhan River is slightly higher than Seyhan (0.41 ≤ EC ≤ 0.80 dS m−<sup>1</sup> , EC = 0.58 dS m−<sup>1</sup> ). The drainage water flows through open ditches along the downstream areas and finally discharges into the Mediterranean Sea.

In the study area, the Mediterranean climate is dominant, summers are hot and dry winters are mild and rainy. Precipitation is mostly in the form of rain (average of 659 mm) that usually falls during winter and spring [35]. Temperature in June, July, and August is very high (average 33.3°C); winter months are cool with reasonable temperatures (average 10.5°C) [36]. While the long-term (1929–2014) mean temperature is 27.4°C, the long-term mean total evaporation is about 1559 mm annually (coefficient of variation <27%). According to the long-term data, soil moisture and soil temperature regimes are defined as xeric and thermic by Ref. [37].

In the area, 1st April–30th September is defined as irrigation season (IS), while 1st October–1st April is defined as nonirrigation season (NIS). However, these dates may change a little by precipitation and climatic conditions.

The soils of Akarsu consist of 11 different soil series (Incirlik, Arikli, Yenice, Innapli, Arpaci, Canakci, Mursel, Ismailiye, Golyaka, Gemisure, and Misis). The model-related physical and chemical characteristics of these soil series are recorded from Ref. [37] and verified to be used in the SWAT model. As an example, only the data of six common soil series are given in Table 1. Arikli (29.5%), Incirlik (25.3%), and Yenice (12.2%) series cover 67% of the entire study area. Innapli (1.03%) and Mursel (0.7%) have got the minimum distributions.

Figure 1. The Akarsu study area.

Modeling Agricultural Land Management to Improve Understanding of Nitrogen Leaching in an Irrigated... http://dx.doi.org/10.5772/65809 139


<sup>1</sup> L, loam; C, clay; S, sand; Si, silt.

<sup>2</sup> Bulk density (g cm−<sup>3</sup> ).

<sup>3</sup> Organic matter (%).

long-term (1929–2014) mean temperature is 27.4°C, the long-term mean total evaporation is about 1559 mm annually (coefficient of variation <27%). According to the long-term data, soil

In the area, 1st April–30th September is defined as irrigation season (IS), while 1st October–1st April is defined as nonirrigation season (NIS). However, these dates may change a little by

The soils of Akarsu consist of 11 different soil series (Incirlik, Arikli, Yenice, Innapli, Arpaci, Canakci, Mursel, Ismailiye, Golyaka, Gemisure, and Misis). The model-related physical and chemical characteristics of these soil series are recorded from Ref. [37] and verified to be used in the SWAT model. As an example, only the data of six common soil series are given in Table 1. Arikli (29.5%), Incirlik (25.3%), and Yenice (12.2%) series cover 67% of the entire study

moisture and soil temperature regimes are defined as xeric and thermic by Ref. [37].

area. Innapli (1.03%) and Mursel (0.7%) have got the minimum distributions.

precipitation and climatic conditions.

138 Water Quality

Figure 1. The Akarsu study area.

<sup>4</sup> Plant available water capacity (mm H2O mm soil depth<sup>−</sup><sup>1</sup> ).

<sup>5</sup> Saturated hydraulic conductivity (mm h−<sup>1</sup> ).

Table 1. Soil properties for the Akarsu study area.

### 2.2. Database

The SWAT model input data, which is used in the project, is listed in Table 2. The 25 m resolution digital elevation model was derived by Akgul [38]. The chemical and physical properties of soils were gathered from Ref. [37], and these data were checked and verified with various measurements and laboratory analysis. Soil albedos and values of USLE were calculated by using the equations given in Ref. [39]. Soil series characteristics were interpreted and soil hydrologic group codes were assigned to each soil series based on the run-off generating characteristics. Daily irrigation return flow rates were determined by the data observed at the Inlet (L2, L11) and Outlet (L4) drainage monitoring stations. Nitrate concentrations were determined in water samples collected via automatic sampler located in L4 gauging site.


Table 2. Model input data and the sources.

### 2.3. Agricultural land management

The SWAT model has eight main components: hydrology, weather, sedimentation, soil temperature, crop growth, nutrients, pesticides, and agricultural management [30]. Watershed hydrology is affected by vegetation types, soil properties, geology, terrain, climate, land use practices, and spatial patterns of interactions among these factors [40].

2.2. Database

140 Water Quality

Land cover/land

Agricultural management practices

Daily irrigation return flow rate (outlet)

Daily irrigation return flow rate (inlet)

Daily irrigation return flow nitrate load (outlet)

Daily irrigation return flow nitrate load (inlet)

use

2.3. Agricultural land management

Table 2. Model input data and the sources.

The SWAT model input data, which is used in the project, is listed in Table 2. The 25 m resolution digital elevation model was derived by Akgul [38]. The chemical and physical properties of soils were gathered from Ref. [37], and these data were checked and verified with various measurements and laboratory analysis. Soil albedos and values of USLE were calculated by using the equations given in Ref. [39]. Soil series characteristics were interpreted and soil hydrologic group codes were assigned to each soil series based on the run-off generating characteristics. Daily irrigation return flow rates were determined by the data observed at the Inlet (L2, L11) and Outlet (L4) drainage monitoring stations. Nitrate concentrations were determined in water samples collected via automatic sampler located in L4 gauging site.

Data type Resolution Source Description/properties

station and meteorological monitoring gage (L8)

Farmer questionnaires in Akarsu and field surveys (face

1 monitoring and sampling station (L4 in Figure 1)

2 monitoring and sampling stations (L2, L11)

1 monitoring and sampling

Two monitoring and sampling

stations (L2, L11)

to face)

station

Climate data Adana State meteorological

Topography (DEM) 25 m × 25 m [38] Elevation, slope, channel slopes, overland

Soils 10 m × 10 m [37] Spatial soil variability, soil types, soil physical

Drainage network [35] Drain spacing, length of cannels, drainage divides,

etc.

Daily flow (m3 day−<sup>1</sup>

Daily flow (m3 day−<sup>1</sup>

Daily NO3-N load (kg day<sup>−</sup><sup>1</sup>

Daily NO3-N load (kg day<sup>−</sup><sup>1</sup>

10 m × 10 m [35] Land cover, land use classification

properties; bulk density, texture, saturated hydraulic conductivity classes, etc.

Daily precipitation, temperature (max., min.), solar radiation, wind speed, relative humidity

Planting, fertilizer application rates and timing, tillage, harvesting dates, irrigation water management and amount, etc.

)

)

)

)

The SWAT model has eight main components: hydrology, weather, sedimentation, soil temperature, crop growth, nutrients, pesticides, and agricultural management [30]. Watershed The area is suitable for various agricultural productions with its favorable climatic and productive land conditions. Cropping pattern data have been assessed since 2006, and the likely crop rotation has been decided for the modeling practices. According to the data, land use and cropping pattern varied from year to year depending on the market and cultivation conditions. Based on the assessments, we have set five different crop rotations plus fruit orchards and citrus plantations (Table 3), which have been well adopted by the farmers in the region. Based on the recent years' evaluation, the main crops in the area were wheat, corn, citrus, cotton, and vegetables (Table 3). Agricultural management practices were determined based on the current surveys carried out at the local field and farmers' level.



<sup>1</sup> C1, first crop corn.

<sup>2</sup> WW, winter wheat.

<sup>3</sup> S2, second crop soybean.

<sup>4</sup> Co, cotton.

<sup>5</sup> P1, first crop peanut.

<sup>6</sup> P2, second crop peanut.

<sup>7</sup> C2, second crop corn.

<sup>+</sup> All kinds of operations done to orchards and citrus between these dates.

Table 3. Agricultural land management crop rotations used in the model.

The proportion of this land use type in the hydrological model area (11,308 ha) is: AGRL (Agricultural Area) (64.56%), ORAN (Citrus) (21.49%), ORCD (Orchards) (1.74%), WPAS (Winter Pastures) (9.20%), URMD (Settlement area (Medium Density)) (1.64%), and URLD (Settlement area (Low Density) (1.36%)). The agricultural areas in the study area contain various annual crops such as first crop corn, second crop corn, winter wheat, first crop soybean, second crop soybean, peanuts, and cotton.

### 2.4. SWAT model description

The soil and water assessment tool is one of the recent models, known as a catchment area or watershed scale model, developed by Arnold et al. [31] and improved in the last 30 years [41]. It is a semidistributed hydrological model, which is a physically based, long period of simulation, lumped parameter, and derived from agriculture management systems models such as CREAMS, EPIC, and GLEAMS [41, 42]. The model separates selected basin to subbasins and hydrologic response units (HRU) comprised of identical hydrological properties such as land use, soil, and slope [43]. SWAT is an efficient tool to predict the impact of nitrogen cycle and land management practices on water, sediment, nutrient, and pesticide with the ArcSWAT module [44]. The nitrogen cycle can be represented by the SWAT model in the soil profile and shallow aquifer. SWAT comprises two pools that are inorganic forms of nitrogen (NH4 <sup>+</sup> and NO3 − ) and three pools that are organic forms of nitrogen in the soil [45–47]. Nitrate and organic N into the nitrogen cycle, N removal from soil to water sources, and amounts of NO3- N included in lateral flow, runoff, and percolation can also be represented by the SWAT model [45]. The SWAT model could sufficiently predict sediment and nutrient statuses as well as tile drainage NO3-N losses [48, 49].

The prediction of land management practices is important as well as nitrogen cycle to provide the progress of future socioeconomic stability and sustainable use of natural resources and to search the impact of human activities on a given basin [50, 39]. SWAT has a capability to estimate the effects of land management practices on sediment, water, and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions over a long-term time [43, 51–54].
