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dry periods Analysis for the dam management in the North of Tunisia

dry periods Analysis for the dam management in the North of Tunisia Lebdi Fethi, Magid Mathlouthi and Lamddalena.N INAT Tunisie 14 - 17 Février 2007 CIHEAM, Bari, Italy. WASAMED. SUMMARY. Identification of Dry Events: Case survey of the Ghezala Dam in Tunisia.

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dry periods Analysis for the dam management in the North of Tunisia

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  1. dry periods Analysis for the dam management in the North of Tunisia Lebdi Fethi,Magid Mathlouthi and Lamddalena.N INAT Tunisie 14 - 17 Février 2007 CIHEAM, Bari, Italy WASAMED

  2. SUMMARY • Identification of Dry Events: • Case survey of the Ghezala Dam in Tunisia Use of chronological sets of dry events for Dams management

  3. Problematic: To optimize a dam reservoir management rules when occur dry extreme events

  4. Survey case: Ghezala Dam in Tunisia Station pluviométrique

  5. The mean yearly rainfall recorded in the Ghézala dam pluviometer (1968 - 2004) is 680 mm  • The mean monthly rainfall is 56,8 mm • Among the 444 months that constitute the sample, 53 months have a rainfall lower then 1 mm (roughly 13% of the sample). • On the average the most humid month is December with 105,5 mms; the driest month is July with 3 mms of rain • The humid season spreads from September to beginning May.

  6. Dry events identification • A rainy event is defined according to a certain daily rain doorstep value; • A limit of 4 mm/j has been chosen, water quantity roughly corresponding to the middle daily evapotranspiration and indicating the lower physical limit thus considering rain that can produce a usable water surface resource; • the time between the end of a rain event and the beginning of the rain event according to is the event dry representative the number of days without rain between two consecutive events.

  7. The dry event distribution is represented by the negative binomial law (Fig. 1): où n = 0, 1, ………….

  8. Table 1. Dry events statistic • 19% of the dry events lasted only one day. The average is of 7,3 days. • Dry periods until 30 days and even more can to be recorded.

  9. The length of rain events follows a geometric law (Fig. 2):  n = 1, 2, . . .

  10. CONCLUSIONS • The dry event analysis permits to plan resources hydric on a different basis of the one of observations made in regular time intervals. • The analysis by event permits to wedge functions of uncertain variable distribution. • The analysis by event permits the generation of synthetic event by simulation for dams management more realistic.

  11. Use of the chronological sets of dry events for dams management • A rainy event is a vector Ri,j : Où: Di,j: length of a rainy event j in a humid season i; Zi,j: length of a dry event j in a humid season i; Hi,j: total height of rain accumulated in Di,j rainy days. Where: hkrepresents the daily total rain in mm.

  12. The length of the rainy season Li is defined as the period between the beginning of the first and the end of the last rainy event of a given season: Where: Ni: rainy event number in a humid season i.

  13. Generation of rainy sets and dry events: Table 2. Maximal values of r2 determination coefficients

  14. A middle report has been found between the length of the event Di,j and the height of rain Hi,j by event; • A non meaning interrelationship between Zi,j and the Di,j length and the height of Hi,j rain of events can be detected; • The number of events per season is practically independent of the other variables, exception of the total height, of rain that characterizes the rainy events of the humid season.

  15. Functions of probability distributions (fdp) Number of events by humid season The function of fish density describes the number of events sufficiently per season. • Hauteur de pluies par évènement • It exists a relation between the height of rain/event and the length, therefore it is necessary to distinguish between fdps of rain heights for different lengths of the event: 1, 2, 3, 4+5 and >=6 days.

  16. For an event of length 1 day the negative binomial fdp provides a good adjustment (Fig.3).

  17. Length of the hydrologic year Table 3. Statistical features of the length of the hydrologic year • The mathematical esperence confirms the yearly characteristic for this phenomenon. • The weak variation coefficient gotten by the analysis indicates the stability of this value. 

  18. Some synthetic event sequences were generated by simulation of probability laws (Bogardi and al., 1988). Table 4. statistical Features of sets of rainy events observed and generated (on a period of 50 years) a valeur observée ; b valeur générée

  19. CONCLUSIONS • The case of survey, confirm the concept of the independence of the length of a rain event and the one of a dry event. • The phenomenon of drought in the region of the Dam Ghezala seems to be described particularly well while adjusting the negative binomial law to the length of the dry event. • The distribution of the rain height associated with different classes of length seems to adjust to the theoretical waitings. • The association of the chronological sets of rainy évènements to a rain - out-flow model permits to get sets of contributions that one uses to studies of optimization of rules of dam management by events.

  20. FIN Merci

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