Study area : the Kairouan plain (central Tunisia)

Conclusion

Comparison between measured groundwater withdrawals for irrigation and estimated crop

water consumption using remote sensing in the Kairouan plain

Fradi Fajr 1* , Massuel S.2 , Simonneaux V.3 , Calvez R.2 , Oueslati I.1

1 Université de Carthage/ INAT, Tunisie; 2 UMR G-EAU, France; 3 CESBIO, FranceE-mail: fradifajr@gmail.com

Introduction

Approaches

• Mean Rainfall rate:

300mm/year (irregular);

• Mean ET0 : 1600 mm/year;

• Abundance of irrigated agriculture;

• Common Farm type: mixed

(vegetables + field crops + trees);

• Main irrigation source : groundwater;

• Irrigated perimeters status: public and

private;

IB01Total area: 26 ha Crops: • 2013: 56% field crops + 41% chili• 2014: 56% field crops + 16% chili

IB02 Total area: 41 ha Crops: • 2013: 39% field crops+ 20% vegetables + 7% olives• 2014: 34% field crops+ 22% vegetables+ 7% olives

IB00 Total area: 38 haCrops: • 2013: 63% olives + 17% peach• 2014: 63% olives + 37% peach

March 2014March 2013

IB00 : the “tree” farm

What is compared?

Central Tunisia is predominantly semi-arid, characterized by a low rate of rainfall, intermittent surface water and high potential evapotranspiration rate. These factors encourage the use ofgroundwater exploitation for the development of irrigated agriculture. In the Kairouan plain, the strong irrigated agriculture development leads to intensive exploitation of the aquifers andcontribute to the water level drop. Up to know, the water management plans cannot rely on accurate estimations of the groundwater draft from the large number of individual farms.

The objective was to estimate the crop water consumption based on the FAO-56 method coupled with satellite estimates of crop coefficients and to compare it with measured pumpedvolumes at farm scale. This first step should provide insights into irrigation strategies of the farmers in the Kairouan plain.

General context

In this work, we deal with private irrigated perimeters.

VS

SAMIR tool( Satellite Monitoring of Irrigation)

Temperature monitoring + discharge measurement

Our approach consists of a comparison between an observed irrigation schedule

(groundwater withdrawals measured) and theoretical ones (SAMIR tool simulations).

Simulated Crop water requirements Groundwater draft measurements

A transducer records the temperature ofthe pipe every 15min. The pumping periodsare identified by a change in temperaturegetting close to GW

The discharge is measured periodically with anultrasonic flowmeter.

The SAMIR tool uses basal crop coefficients(Kcb) from satellite NDVI values. SAMIR computesat the daily step the crop evapotranspiration,upadates the soil water content and estimatesirrigation inputs from water budget.

SAMIR simulation

Crop proprieties

(root depth, sowing date…)

Spatial Data

(NDVI)Kcb = a* NDVI +b

Soil parameters

(Layers depths, diffusions between

layers…)

Climatic data

(Rainfall, ET0 )

Irrigation parameters

(Fw, water depth,%TAW, Kcb

start/ stop irrigation)

Soil water balance

Irrigation schedule

In this work, our objective was to see if models based on remote sensing image time series were able to estimate irrigationconsumption at farm scale. Due to uncertainties in the model parameters, we choose to test various scenarios from the minimum to themaximum possible water consumption. The results show strong differences in simulated irrigation inputs using these scenarios, but it isclear that compared to simulated crop requirements, strong periodical over-irrigation occurred for some farms. This suggests that thepumping rates are significantly driven by other features than the crop water requirement. Further research is needed to understand thereasons of what could be interpreted as a waste of water. In addition, the results highlight the strong variability in irrigation practices.

• Season 2013: overall coherence between observationand simulations. Over-irrigation observed in May can bedue to the budding period (critical period for olives);

• Season 2014: slightly lower inputs for both simulationsand observations is due to the higher rainfall. HoweverSAMIR decreases inputs after the strong rain in March,but the farmer doesn’t.

Acknowledgments: We are grateful to the CNES for supplying image timeseries in the frame of the SPOT4-Take5 experiment, and also for helping usacquiring SPOT5 images thanks to several ISIS actions. We also thank theTunisian Ministry of Research for granting students involved in this study.Financial support from the MISTRALS/SICMED program for the ReSAMEdproject, from the CNES/TOSCA program for the EVA2IRT project and fromthe ANR/TRANSMED program for the AMETHYST project (ANR-12-TMED-0006-01) are gratefully acknowledge.

• Season 2013 + 2014: the observed pumping rate is stableand decrease only during the wet winter while there arelarge variations of crop water demand in the period(SAMIR’s supply varies according to these variations). Themaximum capacity of the pumping gear prevent thefarmer from supplying water according to the picdemand.

• Season 2013: maximum irrigation is simulated forvegetables until July. Then, a large difference betweenobserved and simulated irrigations is observed at the endof the summer time;

• Season 2014: observed inputs are much higher thansimulated ones. Again the pumping capacity is used at itsmaximum, decreasing only during the wet winter.

IB01 : the “field crops” farm IB02 : the “mixed” farm

Results: monthly pumped volumes for irrigation

Observed irrigation schedule which are

the groundwater withdrawals since the

measured well irrigates only the studied

farm.

Observed pumping volumes

Irrigation schedule simulated according

to parameters calibrated on the Kairouan

plain in previous studies (Simonneaux et

al., 2009; Saadi et al., 2015);

SAMIR medium parameters

The models parameters (Kcb, irrigation

parameters, etc.) are set to produce

maximum irrigation amount

SAMIR maximum irrigation

The models parameters (Kcb, irrigation

parameters, etc.) are set to produce

minimum irrigation amount

SAMIR economic irrigation

Deg.C

pumping

pumpingpumping

Groundwater

temperature

Temperaturelogger

tQV nni

ipump

Pumping

Discharge Q1

Volume

Pumping Pumping Pumping

Discharge Q1

Pumping

Discharge Q1 Discharge Q2 Discharge Q2

Time

tQ

t1 t2 t3 t4 t5

Ultrasonicflowmeter

August 2013May 2013 April 2014 January 2014March 2013 April 2014