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Amoussou e 20150708_1730_upmc_jussieu_-_room_207

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Amoussou e 20150708_1730_upmc_jussieu_-_room_207

  1. 1. Our Common Future Under Climate Change International Scientific Conference 7-10 July 2015 Paris, France. Analysis hydrometeorological events of floods in the watershed Mono shared Benin-Togo (West Africa) / Analyse hydrométéorologique des crues dans le bassin-versant du Mono en Afrique de l’Ouest avec un modèle conceptuel pluie-débit Amoussou Ernest, Tramblay Yves, Totin V. S. Henri, Mahé Gil, Paturel Jean-Emmanuel, Ribstein Pierre, Descroix Luc, Houndénou Constant & Boko Michel Paris, 7-10 july 2015
  2. 2. Context  The change / climate variability resulting in a plan amendment and rate of precipitation (frequency and intensity). Rainfall instability observed in the hydrological basin  variations with a recurrence of floods in recent years. 1 & 2: Climate bimodal; 3 & 4: unimodal climate Source: Amoussou & al., 2012
  3. 3.  The recurrence of extreme precipitation anomalies resulting in flooding or drought is a normal component of natural climate variability. The adverse effects of floods in recent years have strong socio-economic and ecological impacts  loss of life and property damage.
  4. 4.  Vulnerability to natural hazards is high in West Africa and the Mono Basin in particular, where populations tend to occupy more and more the most exposed areas. The goal is to analyze the dynamics of the basin Hydrometeorological and distribution of incoming flood dam Nangbéto through the hydrological model GR4J in daily time to assess the risk of flooding in the lower valley of the river.
  5. 5. Between 06 ° 16 'and 09 ° 20'N and between 0 ° 42' and 2 ° 25'E Area: 27,870 km²  Two rivers in basin: the Mono and Couffo Where is the study area?
  6. 6. The sub-basin of Nangbéto: 9952 km² The launch in September 1987 of dam
  7. 7. TOOLS GR4J Hydrological modeling of floods Calculation of ETP (Oudin et al., 2005)  Daily climate data (rain, ETP); 14 rainfall stations were selected  Daily flow data (flow) at the inlet and outlet of the dam Correlation between annual floods and hydro-climatic indicators Period:1988- 2010 Data and methods Ordinary kriging block by precipitation (climatological variogram) Detecting trends Mann-Kendall and non- stationary GEV
  8. 8. Fonctionnement GR4J Source : Perrin & al., 2003  Two production parameters (X1: Production capacity of the tank and F (X2): parameter exchange (gain or loss); Two transfer parameters (X3: the routing capacity tank and X4: time base (maximum) of the hydrograph. Model parameters are calibrated automatically with the Simplex algorithm with multi-objective function :    BiasRRMSENashf peak  11
  9. 9. RESULTS
  10. 10. 1. Dynamic flood and Trends
  11. 11. Climate trends in the basin  A slight non-significant decrease in rainfall during the late 2010s and a slight increase in water evaporated blades  decline in flood flows?
  12. 12. Interannual variability of the maximum 24-hour rainfall from 1988 to 2010 in the Mono Basin Nangbéto A net increase in the annual maximum daily water slides precipitated significant at the 95% level.
  13. 13. A marked seasonal rhythm with almost all flow rates recorded during the summer between June and October  small role but significant dam (Payan, 2007 and Amoussou, 2010) Seasonal dynamics of flow (m3 / s) flows at the dam Nangbéto
  14. 14. Dam Influence Nangbéto on floods Empirical distributions of annual maximum flows measured upstream and downstream of the dam Nangbéto Annual maximum flows out of the dam and Nangbéto rate corresponding incoming Good correlation between incoming and outgoing flows of the same day R² = 0.77  the dam does not protect against floods and waits almost all daily maximum flood, especially for high flow rates
  15. 15. Correlations between annual floods to incoming Nangbéto and hydro-climatic indices R2 = 0,7483 0 200 400 600 800 1000 1200 1400 1600 0 20 40 60 80 100 120 140 160 180 200 Q moyen annuel Qmaxannuel Correlation mean annual flow and annual maximum flow Correlation between maximum rates and annual rainfall over several days max Good correlation (r = 0.86) with the annual flood mean annual flow The best correlation coefficient (r = 0.67) is with the annual rainfall averaged over 19 days max Greater influence of saturated soils that heavy rainfall on floods
  16. 16. Frequency analysis of the return period flood Paramètres Valeur Bornes intervalle de confiance 95% Nangbéto amont k -0.10 -0.33 0.13 sigma 329.33 242.98 446.38 mu 861.62 716.57 1006.66 Nangbéto aval k 0.44 -0.16 1.04 sigma 258.21 159.46 418.11 mu 372.69 238.35 507.03 Adjustment to GEV inflows and outbound Nangbéto Settings GEV distributions Quantiles de débit Période de retour (années) Nangbéto amont Nangbéto aval 2 961.21 526.68 5 1330.89 914.53 10 1577.45 1210.55 20 1815.30 1527.97 50 2125.15 1993.82 100 2358.82 2388.69 Return periods of annual maximum flows Type Frechet Type Weibull
  17. 17. 2. Modeling with GR4J
  18. 18. Modeling of flood flows in Nangbéto: Calibration GR4J Daily discharges observed and simulated from 1988 to 2010 Nangbéto Average flows observed and simulated Nangbéto 1988-2010 Model the distribution of incoming flood dam Nangbéto through the hydrological model GR4J the daily time over the period 1988-2010. The model reproduces well the seasonal dynamics of flows and daily values
  19. 19. The flow rates extracts raw data observed and simulated data by the calibrated model  the two distributions are similar and not statistically different (Kolmogorov-smirnov)  Empirical distributions of incoming annual flood flows to Nangbéto, observed and simulated by GR4J
  20. 20. Validation Test 1: Split-sample "classic" into 2 periods Calibration (1988-1999) Validation (2000-2010) NASH 0,80 0,71 RRMSE 0,28 0,57 BIAS 0,002 0,16 Statistique KS 0,27 0,40 p-value 0,74 0,31 x1 x2 x3 x4 Calibration 226,49 0,63 77,34 1,60 Validation 262,51 0,25 118,24 1,97 Parameter values of the calibration model and validation samples statistical model of highly degraded between the period and the timing of validation A bias on  volumes of 2% to 16% and especially a RRMSE on the tips of  flood 28% to 57%. Quantile-quantile distribution of annual maximum flows to Nangbéto
  21. 21. Validation Test 2: Calibration of each for each year to analyze the variability of the model parameters x1 0 100 200 300 400 500 600 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 x2 0 0,2 0,4 0,6 0,8 1 1,2 1,4 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Parameter x1 (capacity of the production) Parameter x2 (coefficient of underground exchanges) The parameters x1 and x2 are not stationary and have an upward trend for x1 and x2 for downward over time.
  22. 22. Validation Test 3: Split-sample of 3 periods Calibration Validation1 Validation2 NASH 0,80 0,78 0,62 RRMSE 0,31 0,28 0,70 BIAS 0,00 0,04 0,28 Statistique KS 0,25 0,25 0,57 pvalue 0,93 0,93 0,13 The Pettitt test indicates a break in the ETP since 1995. Three periods of 8 years from 1988 to 1995 (calibration), 1996-2003 (validation1) and 2004-2010 (validation 2). The further away from the calibration period 1988-1995, plus we get bad results, including the 2004-2010 period. Calculating the ETP formula Oudin et al., 2005 Rupture
  23. 23. Conclusion 1.No trends in flows and rainfall from 1988 to 2010, but upward trend temperatures. 2.The difference between the input and output rates of borderline significance  dam showing the low retention capacity of the reservoir dam at high water. 3. Flooding induced  annual rates maxima and  well correlated with mean annual flows and soil saturation conditions. 4.Good performance in calibration GR4J but degraded performance validation and temporal instability of the parameters
  24. 24. Continuos reflection !!!! Thank you!

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