Water Distribution and Climate change issues

Transcription

1 Water Distribution and Climate change issues Alejandro Chamorro, Msc. University of Stuttgart, Germany IWS Institut für Wasserbau Lehrstuhl für Hydrologie und Geohydrologie Prof. Dr. rer. nat. Dr.-Ing. András Bárdossy Pfaffenwaldring 61, Stuttgart, Deutschland

2 2 Liwa Project: Sustainable Water and Wastewater Management in Urban Growth Centres Coping with CLimate Change - Conceps for Lima Metropolitana (Perú)- Liwa Main task (IWS): - Simulation of the water and energy resources under changing climate General tasks: - Interpolation methods - Downscaling methods 2

3 Fig 1: Comparacion de los patrones de Pp 3

4 Cuencas Rimac Lima 4

5 Estaciones Z x 1 Z x 2 X 3 Z x k X k X Z x i, j i 1, m; j 1, n n m 5

6 Interpolation Kriging-Method i. Estimation error has zero mean ii. The variance of the estimation error is minimum i. Z e Z x Z 0 x 0 n x Z 0 i x i i1 Z x 1 X 1 Z x 2 X 3 X n Z x n EZ n 0 0 i i1 x Zx 0 1 Abbildung 2: 2-d Schema 6

7 7 ii.- Mit Var Z x Zx min Var Zx0 Zx0 i jcij 2iCi0 ij e C : Cov Z, i j i x Zx i j min Var Kriging System K-S n j1 n i1 C 1 i j ij C i0 i 1,...,n Aditional knowledge: 7

8 Ng (mm) - Ordinary kriging - Drift kriging Time period: 40 years Elevation as a drift Monthly rainfall as a drift Smoothed data Elevation-Precipitation Elevation (m) 8 Fig 2: monthly average rainfall

9 Precipitacion anual regionalizada. Anos Fig 3: Cartas de precipitacion 9

10 4.1 EDK with original data 4.2 EDK with smoothed data 4.3 EDK with OK Fig 4: comparacion de la interpolacion entre diferentes metodos 10

11 Distribucion de la precipitacion en las 3 cuencas principales (Chillón, Rimac, Lurín) y subcuencas en Mantaro Figura 5: Distribucion de la precipitacion Figura 4: Cuencas y subcuencas 11

12 12 Definicion de las cuencas Station Höhe Sheque 3150 Tamboraque 3000 Surco 1812 Chosica 850 Fig 4: Puntos de balance 12

13 13 Subcuencas Fig. 5.1: Sheque 13

14 14 Fig. 5.2: Tamboraque 14

15 15 Fig. 5.3: Surco 15

16 16 Fig. 5.4: Chosica 16

17 17 Fig. 5.5: Subcuencas 17

18 Pronostico futuro: Downscaling Monthly GCM precipitation distributions Control time period Scenario runs Interpolated sub catchment precipitation and temperature Diferent scenarios A2 Echam B2 A1B A2 Had B2 A1B Fig5: models and scenarios 18

19 Downscaling Resolucion GCM q=q(v) Resolucion cuencas 19

20 20 Downscaling GCM resolution q=q(v) Catchment resolution Diferentes metodos Transformacion Quantile-Quantile - Ajuste de distribution para precipitacion y temperatura: Parametrico y no parametrico 20

21 21 Downscaling GCM resolution q=q(v) Catchment resolution Diferentes metodos Transformacion Quantile-Quantile - Ajuste de distribution para precipitacion y temperatura: Parametrico y no parametrico Ajuste (October)

22 Used distributions Precipitation: Weibull fit for each month - GCM control run - Interpolated areal precipitation 12 weibull fit using maximum Likelihood Temperature: Parametric and non parametric aproach - Normal distribution - Kernel function f h ( x) 1 n n i1 K h ( x x i ) K x 1 2h h( x) e 2 h

23 Chillon HAD A2 Had A1B Echam A2 Echam A1B Echam B1 Observed Rimac Had A2 Had A1B Echam A2 Echam A1B Echam B1 Observados 23 Fig 8: Resultados Downscaling. Promedio mensual de precipitacion

24 24 Lurin Had A2 Had A1B Echam A2 Echam A1B Echam B1 Observed Cuencas Had A2 Had A1B Echam A2 Echam A1B Echam B1 Observados 24 Fig 8: Resultados Downscaling. Promedio mensual de precipitacion

25 25 Pp(mm) Had_A2 Had_A1B Echam_A2 Echam_A1B Echam_B1 Observed Model Cuencas principales. Promedio anual de precipitacion 25

26 26 Fig 7: Resultado Downscaling para Echam A2, periodo

27 27 Trabajo actual - Analisis de diferentes metodos de interpolacion - downscaling basedo en patrones de circulacion y fuzzy rule systems. - Modelamiento Hidrologico. 27

28 28 Gracias 28