Combined Remote Sensing and GIS for Assessment of ... · Combined Remote Sensing and GIS for Assessment of Groundwater Volumes Pumped for Irrigation in the Plain of Sidi Bouzid, Tunisia

European Journal of Scientific Research ISSN 1450-216X / 1450-202X Vol. 149 No 3 June, 2018, pp. 289-301 http://www. europeanjournalofscientificresearch.com

Combined Remote Sensing and GIS for Assessment of

Groundwater Volumes Pumped for Irrigation in the

Plain of Sidi Bouzid, Tunisia

Manel Boughanmi

Université de Sousse, Institut Supérieur Agronomique de Chott Mariem BP 47

4042 Chott Mariem, Tunisie

Laboratoire d’Hydrologie et de Géochimie de Strasbourg (LHyGeS)

UMR 7517 Centre National de la Recherche Scientifique

Université de Strasbourg/EOST, Strasbourg, France-

E-mail: [email protected] Tel: +216 21275361

Lotfi Dridi



E-mail: [email protected]

Nouha Jridi

Université de Carthage, Institut National Agronomique de Tunisie

43 Avenue Charles Nicolle, Tunis 1082, Tunisie


Gerhard Schäfer

Laboratoire d’Hydrologie et de Géochimie de Strasbourg (LHyGeS)

UMR 7517 Centre National de la Recherche Scientifique

Université de Strasbourg/EOST, Strasbourg, France


Rajouene Majdoub




Abstract

The use of groundwater for irrigation is considered as the key asset of agricultural

development in the plain of Sidi Bouzid. Consequently, the sustainability of groundwater resources requires a good estimation of groundwater extraction. This work aimed to assess the groundwater volume pumped for irrigation during the hydrological year 2013-2014, based on combined remote sensing and GIS; and to compare it with annually pumped volumes estimated by the Tunisian water management authority. High-resolution spatial Landsat-8 images have been used. The supervised classification was carried out to obtain the land use map. This map allowed us to determine the different irrigated crops and to calculate the Irrigation Water Requirements (IWR) of the crops. The estimation of the

Combined Remote Sensing and GIS for Assessment of Groundwater Volumes Pumped for Irrigation in the Plain of Sidi Bouzid, Tunisia 290

pumped volume was determined after subtracting the Crop Water Requirements (CWR) from efficient rainfall (ER) and floodwater (FW) used for irrigation in the plain of Sidi Bouzid. The results showed that the groundwater pumping volume is approximately 63 Mm3.This volume is 1.4 times higher than that stated by the Tunisian water management authority which is equal to 44 Mm3. This significant difference may be due to the increasing number of illegal wells, the absence of water metering at the wells and the uncertainties of the classification method.

Keywords: Irrigation, pumping, remote sensing, land use, FAO-method, Sidi Bouzid

1. Introduction Agriculture is confronting many challenges, amongst which are the maintenance of a highly-productive system and the protection of water resources (Duchemin et al., 2015). In fact, agriculture may lead to realize livelihood opportunities, generate income and contribute to economic productivity (WWAP, 2015). One of the main factors limiting agricultural development is water scarcity. Indeed, countries suffering from water scarcity are often not able to meet their food requirements using the water resource available within their boundaries (Qadir et al., 2007), and the number of countries and regions without enough water to produce their food is continually rising as the population increases and as climatic variability is greater. The use of groundwater for land irrigation can be considered as the key asset of agricultural development (Chandra, 2012).

In semi-arid areas like Tunisia, managing and controlling water resources is the main challenge for the governments. In fact, due to irregular spatial and temporal distribution of rainfall, Tunisia is faced with great water problems in many regions. The potential resources per capita were estimated to less than 400 m3 per person per year (World Bank, 2013). In addition, the demand of water is high and agriculture is by far the largest consumer of water with about 80% of the renewable resource used for irrigation. Irrigated areas present less than one-tenth (1/10) of cultivated area, while it produces around one-third (1/3) of the total Tunisian food production (Chahed et al., 2008; Guermazi et al., 2016).

The plain of Sidi Bouzid in Central Tunisia has been selected as the investigation area for this study. This region is subjected to the combined effect of intensive water withdrawal and scarcity of water supply. Therefore, estimation of groundwater volumes pumped as a fundamental requirement in the local and global assessment and management irrigation is of both interest and concern (Abourida et

al., 2009). Rainfall occurs on an irregular basis in brief, high-intensity events causing strong floods mainly in Wadi El Fekka. Thus, the spreading perimeters, located on both banks of Wadi El Fekka are irrigated by the resulting surface runoff.

One of the main important strategies of irrigation management to deal with the shortage of water is the use of water efficiently and effectively (Sari et al., 2013; Briggs and Shantz, 1913). The design of tools that provide regional estimates of the groundwater volumes pumped may help the sustainable management of water for this region. The water volume pumped for irrigation is being approximated by crop water needs. It is of a great matter for water managers to assess the volumes of irrigation water and particularly the volume of pumping (Simonneaux et al., 2009). In addition, an improved quantification of groundwater withdrawal enables a better understanding of the global water balance. The impact of water withdrawals on water storage variations is significant in semi-arid and arid regions especially with intensive irrigation. The estimation of groundwater volumes pumped is an important parameter in groundwater flow models and vulnerability studies in many regions in the world.

Numerous innovative techniques and tools are applied and promoted to increase the efficiency of irrigation and to improve the irrigation performance. In fact, in this context, some authors proposed the assessment of crop water needs based on the combination of reference evapotranspiration (ET0) and

291 Manel Boughanmi, Lotfi Dridi, Nouha Jridi, Gerhard Schäfer and Rajouene Majdoub crop coefficients (Kc) to improve the irrigation management (Allen et al., 2006). Besides, various scientists have applied computer technology and models to predict water crop requirements such as CRIWAR (Mostajeran, 1994; Bos et al., 2002) and CROPWAT (FAO, 2009; Stancalie, 2010; Surendran et al., 2015). In some specific studies, they have applied remote sensing techniques and Geographic Information Systems (GIS) for monitoring of water resources (Ma et al., 2001; Todorovic and Steduto, 2003; Johnson and Belitz, 2012; Sari et al., 2013; Saadi et al., 2015). Remote sensing may be the only feasible means of providing such information on a consistent space and time basis (Courault et al., 2005). Its potential for monitoring of water resources is well known and there is a huge number of successful applications in different contexts in the last decades (Belmonte et al., 1999; Stehman and Milliken, 2007).

Nowadays, the recommended FAO-56 method is used in numerous countries (Courault et al., 2005). Coupling this method with remotely sensed crop coefficients is very promoting since the determination of crop coefficients is also debatable because many factors occur such as crop type, crop variety, crop development stage, ground cover and root system development (Neale et al., 1989). The FAO-56 may use two coefficients to separate the respective contribution of plant transpiration (Kcb) and soil evaporation (Ke). However, standard basal crop coefficients (Kcb) profiles provided by FAO tables are average values not suited for specific growth conditions that can largely differ between plots. It has been shown that the crop coefficients were linked to the spectral response of the cover, especially vegetation indices (Bausch and Neale, 1987). The crop coefficient could also be estimated by relating Normalized Difference Vegetation index (NDVI) and Kcb (Er-raki et al., 2007), or by combining Soil Adjusted Vegetation index (SAVI) and Kcb (Chattaraj et al., 2013).

In this study, water volume pumped for irrigation was estimated over an irrigated large area located in the Sidi Bouzid by combining remote sensing, GIS and ground truth observation. It aimed to analyse the potential of the integration and application of remote sensing for water irrigation management in the plain of Sidi Bouzid. The objective of the work was to assess the groundwater volume pumped for irrigation during the hydrological year 2013-2014 and to compare it with the pumping volume estimated by the Tunisian water management authority. The land use map was obtained by satellite images and the crop water need was determined based on the FAO-56 method.

2. Materials and Methods 2.1 Study Area

The Sidi Bouzid plain is located in the central-western part of Tunisia. The study area is presented like an almost closed basin, surrounded by El Kabar Mountain at its South, El Hfey Mountain to its South-West, Rakhmat Mountain in the West, Hamra Mountain at its North-West and Liswda Mountain at the North-Est (Figure 1). The area is characterized by a semi-arid climate. Mean annual evaporation of about 1600 mm exceeds considerably annual precipitation estimated to approximately 276 mm. Surface flow is done through Wadi El Fekka, which is the main river in the region with Southwest Northeast direction.

The study area is underlain by recent alluvial deposits of Mio-Plio-Quaternay age. It is characterized by Pleistocene sediments which are mainly a mixing of sand, clay, and gravel. It is mainly occupied by agricultural lands with vegetable crops and olive whose production is one of the largest in the country both for local consumption and for export.


Figure 1: Location of the study area

2.2 Methodology

The overall procedure adopted to quantify the water volume required for irrigation during the hydrological year 2013-2014 is presented in Figure 2. To evaluate the Crop Water Requirement (CWR), the method suggested by FAO was adopted. Based on remote sensing and GIS, the methodology employs LANDSAT images, meteorological data and geographical vectorial layers determined by a field survey.

Figure 2: Flow chart of the methodology adopted for the present study

293 Manel Boughanmi, Lotfi Dridi, Nouha Jridi, Gerhard Schäfer and Rajouene Majdoub 2.3 Image Classification

2.3.1 Remote Sensing Data

Remote sensing provides spatial coverage by measurement of reflected and emitted electromagnetic radiations, through a wide range of wavebands, from the earth’s surface and the surrounding atmosphere. The used images were acquired by two different satellites sensors of Landsat 8 including Landsat OLI and Landsat 8 TIRS. These data are freely provided by the NASA and the United States Geological Survey (USGS). Landsat 8 images include nine spectral bands with a spatial resolution of 30 m, a panchromatic band with a resolution of 15 m. Several available scenes selected from September, October 2013 and from June, April, July, August 2014 were included in the present study. Spectral characteristics of Landsat 8 satellite images are shown in Table 1. Table 1: Characteristics of Landsat 8 satellite sensors

Sensor Date of acquisition Bands Spatial resolution (m) Wavelength (μm)

Operational Land imager (OLI)

29/09/2013

B1 (Coastal aerosol) 30 0.433-0.453

B2 (Blue) 30 0.450-0.515

B3 (green) 30 0.525-0.600

31/10/2013 B4 (red) 30 0.630-0.680

B5 (NIR) 30 0.845-0.885

19/01/2014

B6 (SWIR 1) 30 1.560-1.660

B7 (SWIR 2) 30 2.100-2.300

B8 (panchromatic) 15 0.500-0.680

27/04/2014 B9 (cirrus) 30 1.360-1.390

Thermal Infrared Sensor (TIRS)

30/07/2014 B10 (TIRS 1) 100 10.30-11.30

16/04/2014 B11 (TIRS 2) 100 11.50-12.50

2.3.2 Pre-processing

The radiometric calibration of Landsat 8 images was achieved in two steps: the conversion of the digital number (DN) values into radiance and the atmospheric correction. The processing included conversion of DN values into radiance as follows (Markham and Barker, 1987):

�� = � ��

� × �� − �� + �� (1)

where, �� is the top of atmosphere (TOA) band spectral radiance (W/(m2 sr µm)), QCAL is the quantized calibrated pixel value in DN, LMIN is the spectral radiance scaled to QCALMIN at QCAL = 0, LMIN is the spectral radiance scaled to QCALMAX at QCAL = QCALMAX, QCALMIN is the minimum quantized calibrated pixel value, and QCALMAX is the maximum quantized calibrated pixel value.

The atmospheric correction was applied on Landsat 8 images using the Dark-Object Subtracting (DOS) method. The Dark-Object Subtraction (DOS) method is an image-based technique to cancel out the haze component caused by additive scattering from remote sensing data (Chavez, 1988; Gilmore et al., 2012). This method is found to be data dependent and well accepted by the geospatial community to correct light scattering in remote sensing data (Song et al., 2001). This correction method was carried out with the software ENVI4. After the processing procedure, the plain of Sidi Bouzid boundary map was overlaid on the radiance image to extract the study area. 2.3.3 Supervised Classification

Babamaaji and Lee (2013) affirmed that the choice of supervised classifiers depends on the nature of the input data, the environment and the desired output. Several studies confirm the performance of the probabilistic maximum likelihood classifier (MLC) (Oommen et al., 2008). The radiance image was subjected to supervised classification.


Ground truth data have been collected during the period of 2013-2014 and were provided as reference sites for various land use types (Figure 3a). The field surveys were conducted at the same time as the satellite image capturing. Ground-truth points were first recorded using Global Positioning Systems (GPS) to identify locations of different crop types, then digitized and georeferenced. Polygon layers were used to obtain the regions of interest (ROI) that will be useful for the supervised classification (Figure 3b).

Cohen’s kappa coefficient (K) was used to assess the accuracy of the final result. Kappa coefficients are widely used as a measure of map accuracy of remote sensing data classification (Hudson and Ramm 1987; Alexandridis et al., 2008; Ndehedehe et al., 2013). Cohen (1960) proposed to interpret kappa results as follows: values < 0 as no agreement, 0.01–0.20 as none to slight agreement, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as substantial and 0.81–1.00 as almost perfect agreement. Overall accuracy and kappa coefficient are used to validate the performance of classified data.

Figure 3: (a) Ground truth samples

295 Manel Boughanmi, Lotfi Dridi, Nouha Jridi, Gerhard Schäfer and Rajouene Majdoub

Figure 4: (b): polygon layers located in the study area

3. Penman-Montieth Method 3.1 Estimation of Irrigation Water Requirement

The reference evapotranspiration Et0 was calculated from meteorological data using the Penman-Monteith formula. Numerous weather variables such as air temperature, relative humidity, wind speed and solar radiation are required to estimate ETo. Consequently, ETo is often estimated by means of empirical equations based on air temperature, relative humidity, extraterrestrial radiation and/or precipitation (Droogers et al., 2002; Popova et al., 2005).

�� = �.!�"∆�$%&�'( )**+,-./0-�1213�

∆'(�4'�.5!6-� (2)

where, ETo is the reference evapotranspiration [mm day−1], Rn is the net radiation at the crop surface [MJ·m−2·day−1], G is the soil heat flux density [MJ·m−2·day−1], T is the mean daily air temperature at 2 m height [˚C], u2 is the wind speed at 2 m height [m·s−1], es is the saturation vapour pressure [kPa], ea is the actual vapour pressure [kPa], es - ea is the saturation vapour pressure deficit [kPa], ∆ is the slope vapour pressure curve [kPa·˚C−1], γ is the psychrometric constant [kPa·˚C −1].

The meteorological parameters used in Equation 2 were taken from the meteorological station of Sidi Bouzid. Daily reference evapotranspiration (ET0) data were determined by the software ET0 calculator tool developed by the Land and Water Division of the FAO. The daily values were aggregated to monthly values for 12 months during the hydrological year 2013-2014 (Figure 4).

Combined Remote Sensing and GIS for Assessment of Groundwater Volumes Pumped for Irrigation in the

3.2

The crop coefficientKcunder typical irrigation management and soil wetting.crop coefficients are su Table

PistachioCornOatTomatoOliveBarley

3.3

The Net Water Requirement (Nrainfall (ER) that corresponds to the

estimation procedures (esti

Thus, to obtain the real to be subtracted from the ne


Figure

3.2 Crop Coefficient

The crop coefficientKcend) are given inunder typical irrigation management and soil wetting.crop coefficients are su

Table 2: Duration of phonological stages and values of crop coefficients for various crops present at the study area

Crops

Pistachio Corn Oat Tomato Olive Barley

3.3 Net Water Requirement


Many methods described in literature make use of the estimation procedures (estimated to 80% of total rainfall.

Note that tThus, to obtain the real to be subtracted from the ne


Figure 5: Average daily precipitation and reference evapotranspiration (2013

Coefficients

The crop coefficients (Kc) of different given in the Bulletin 56 by the FAO (

under typical irrigation management and soil wetting.crop coefficients are summarized in Table

Duration of phonological stages and values of crop coefficients for various crops present at the study area

Initial

Kc Duration (d)

0.44 0.36 0.3 1.2

0.65 0.3

Water Requirement


�78 = 978Many methods described in literature make use of the

estimation procedures (Dastane, 1974mated to 80% of total rainfall.

Note that the farmers of the Sidi Bouzid Plain use also floodwater (FW) to irrigate the crops. Thus, to obtain the real groundwater volume to be subtracted from the ne

Combined Remote Sensing and GIS for Assessment of Groundwater Volumes Pumped for Irrigation in the Plain of Sidi Bouzid, Tunisia

Average daily precipitation and reference evapotranspiration (2013

(Kc) of different the Bulletin 56 by the FAO (

under typical irrigation management and soil wetting.mmarized in Table

Duration of phonological stages and values of crop coefficients for various crops present at the

Initial

Duration (d)

20 20 15 30 30 15

Water Requirement

The Net Water Requirement (NWRrainfall (ER) that corresponds to the rainfall transferred to soil moisture

978 − �8 Many methods described in literature make use of the

Dastane, 1974mated to 80% of total rainfall.

farmers of the Sidi Bouzid Plain use also floodwater (FW) to irrigate the crops. groundwater volume

to be subtracted from the net water requirement. During the hydrological year 2013

Combined Remote Sensing and GIS for Assessment of Groundwater Volumes Plain of Sidi Bouzid, Tunisia


(Kc) of different crops that the Bulletin 56 by the FAO (Allen

under typical irrigation management and soil wetting.mmarized in Table 2.


Development

Kc Duration (d)

1.10 0.45 0.25 1.15 0.7

1.15

WR) represents the difference rainfall transferred to soil moisture

Many methods described in literature make use of the

Dastane, 1974). A widely used one is to consider that the effective rainfall is

farmers of the Sidi Bouzid Plain use also floodwater (FW) to irrigate the crops. groundwater volume that is pumped

t water requirement. During the hydrological year 2013

Combined Remote Sensing and GIS for Assessment of Groundwater Volumes Plain of Sidi Bouzid, Tunisia


that depend on the phonological stagAllen et al., 1998

under typical irrigation management and soil wetting. The duration of phonological stage and values of


Development

Duration (d)

60 1.3230 1.2325 0.6540 90 25 0.65

represents the difference rainfall transferred to soil moisture

Many methods described in literature make use of the ). A widely used one is to consider that the effective rainfall is

farmers of the Sidi Bouzid Plain use also floodwater (FW) to irrigate the crops. that is pumped


Combined Remote Sensing and GIS for Assessment of Groundwater Volumes


depend on the phonological stag1998). These values have been established

The duration of phonological stage and values of


Mid

Kc Duration (d)

1.32 301.23 600.65 500.8 400.7 60

0.65 50

represents the difference betweenrainfall transferred to soil moisture:

Many methods described in literature make use of the effective rainfall). A widely used one is to consider that the effective rainfall is

farmers of the Sidi Bouzid Plain use also floodwater (FW) to irrigate the crops. that is pumped for irrigation, the floodwater volume has


Combined Remote Sensing and GIS for Assessment of Groundwater Volumes

Average daily precipitation and reference evapotranspiration (2013-

depend on the phonological stage (K). These values have been established



Duration (d) Kc

30 0.4560 50 40 0.760 0.750

between the CWR

effective rainfall concept, using different ). A widely used one is to consider that the effective rainfall is

farmers of the Sidi Bouzid Plain use also floodwater (FW) to irrigate the crops. for irrigation, the floodwater volume has


-2014)

e (Kcini, Kcmid, Kc). These values have been established



End

Kc Duration (d)

0.45 402 401 30

0.7 200.7 901 30

the CWR and the effective

concept, using different ). A widely used one is to consider that the effective rainfall is


t water requirement. During the hydrological year 2013-2014, the

296

, Kcdevl, ). These values have been established



ion (d)

40 40 30 20 90 30

and the effective

(3) concept, using different

). A widely used one is to consider that the effective rainfall is


2014, the

297 Manel Boughanmi, Lotfi Dridi, Nouha Jridi, Gerhard Schäfer and Rajouene Majdoub floodwater volume distributed on the eleven spreading perimeters is estimated to approximately 11.8 Mm3 (Boughanmi et al., 2017).

4. Results and Discussion 4.1 Land use Map

The obtained land use map is shown in Figure 5. Using ground truth samples, we built up the confusion matrix. The accuracy classification was found to be 98.35% and the Kappa value was 0.9455.

Figure 6: Land use map for the plain of Sidi Bouzid

4.2 Spatial Distribution of Net Water Requirement

By linking, in a GIS environment, land use classes distribution data with corresponding crop coefficient Kc values found in the Bulletin 56 of FAO and the ET0, a digital map for the NWR spatial distribution was obtained. Figure 6 shows the distribution of net water requirement for the entire area of the plain of Sidi Bouzid. Total NWR vary within an interval ranging from zero to 8559 m3/ha. The NWR map shows that higher values occurred in the center of the plain, while border areas tended to have lower values. For example, in the south of the study area, agricultural crown lands are characterized by Rain-Fed olive trees.


Figure 7: Spatial distribution of net water requirement in (m3/ha) of the Sidi Bouzid plain. Spreading perimeters are shown in form of white contour lines

Table 3 represents the calculated values of NWR from the land use map as function of different crops. The lowest crop water requirement is attributed to the corn and barely crops. The highest water demanding crops are found to be arboriculture combined with vegetables farming having a net water requirement value of approximately 7611 m3/ha/an. Table 3: Net water requirement for different crops of the Sidi Bouzid plain

Crops NWR (m3/ha/an)

Arboriculture 3166 Corn 803 Vegetable farming 4781 Arboriculture combined with vegetable farming 7611 Olive 3917 Barley 45 Oat 446

4.3 Estimation of Pumped Groundwater Volumes

The irrigation water requirement is the total water volume to be supplied to the crops. In general, this water volume is pumped from the shallow groundwater. The pumped water volumes vary strongly in space and are highly correlated with the quantified spatial distribution of Net Water Requirement (see Figure 6). Figure 6 shows that the highest value of net water requirement is concentrated on the Central-Western part of the study area, where the eleven spreading perimeters are located. Taking into account the estimated floodwater volume distributed on the spreading perimeters of 11.8 Mm3 (Boughanmi et al., 2017), it is found that the water volume pumped from the groundwater required on

299 Manel Boughanmi, Lotfi Dridi, Nouha Jridi, Gerhard Schäfer and Rajouene Majdoub the spreading perimeters is estimated to approximately 36% of the total volume pumped in the whole area.

The results showed that the total water volume pumped from the groundwater is approximately 63 Mm3.This volume is 1.4 times higher than that stated by the Tunisian water management authority which is equal to 44 Mm3.

This significant difference between pumped volumes for irrigation is may be due to the increasing number of illegal wells, the absence of water metering at the wells and the uncertainties of the classification method.

5. Conclusions The management of water resources in the region of Sidi Bouzid needs reliable information and knowledge on water resources and water requirements. A good estimation of water volumes pumped from the groundwater is thus a crucial issue for a sustainable and sound water resources planning.

The present study aimed at providing an estimate of groundwater withdrawals due to irrigation in the plain of Sidi Bouzid during the wet hydrological year 2013-2014. A simple and economic approach, in terms of data and resources, based on remote sensing and GIS, was used.

Combing temporal Landsat images, meteorological data and ground truth samples, the integrated use of GIS functionality allowed the elaboration of a map of annual crop water requirement for the study area.

The total groundwater volume pumped from the shallow aquifer was estimated to approximately 63 Mm3, which is 1.4 higher than that declared by the Tunisian authority.

The present study provides useful insights into the water balance of the Sidi Bouzid aquifer and delivers a useful tool for groundwater authorities in developing more efficient strategies to manage the groundwater withdrawal. In addition, these results are essential for further hydrological studies especially the numerical modelling of the groundwater flow at the regional scale as the groundwater discharge is an important component of the water balance.

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