Hydrological modelling of the Zambezi catchment for ... - TU Delft

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HYDROLOGICAL MODEL INPUT DATA Monthly precipitation data (average of 10 stations) 450.00 400.00 350.00 300.00 [mm/month] 250.00 200.00 150.00 100.00 50.00 0.00 jan-93 mei-94 sep-95 feb-97 jun-98 nov-99 mrt-01 aug-02 dec-03 [month] RAIN GAUGES MIRA Figure 3.4: Output of MIRA compared with rain gauges in the Zambezi catchment. cum. Observed 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 MIRA [mm/month] y = 0.8693x R 2 = 0.9991 0 2000 4000 6000 8000 10000 cum MIRA Figure 3.5: Double Mass Curve of MIRA. 26
HYDROLOGICAL MODEL INPUT DATA MIRA [mm/month] Observed 500 450 400 350 300 250 200 150 100 50 0 0 100 200 300 400 500 MIRA Figure 3.6: Scatter plot of MIRA with 45° line As can be seen from Figure 3.5 MIRA over-estimates the rainfall with circa 15%. The correlation coefficient between the satellite estimates and the rain gauges is 0.93, which is very high. 3.1.2 Famine Early Warning Systems (FEWS) In framework to identify problems in the food supply system a network named ‘U.S. Agency for International Development, Famine Early Warning System Network’ (USAID/FEWSNET) has been developed. This network has the objective to provide timely and accurate information regarding potential food-insecure conditions. Together with the ‘National Aeronautic and Space Administration’ (NASA) and the ‘National Oceanic and Atmospheric Administration’ (NOAA) they collect and process satellite data to provide rainfall estimations. The FEWS rainfall estimations [11] are operational from July 1995. Since then there have been two algorithm versions. The first version, RFE 1.0 (Herman et al., 1997), was operational since July 1995 till December 2000 and the second, RFE 2.0 (Xie and Arkin, 1997), started at January 2001 and is still operational. RFE 1.0 The RFE 1.0 algorithm uses for its rainfall estimates: • METEOSAT 5 thermal Infrared (IR) satellite data, 27
Page 1: Master's Thesis Hydrological modell
Page 5 and 6: PREFACE Preface This Master’s The
Page 7 and 8: TABLE OF CONTENTS Table of contents
Page 9 and 10: SUMMARY Summary Hydrologists are in
Page 11 and 12: SUMMARY The results of the GLUE for
Page 13 and 14: ACRONYMS & SYMBOLS Acronyms & symbo
Page 15 and 16: ACRONYMS & SYMBOLS qflo Quick flow
Page 17 and 18: INTRODUCTION 1 Introduction High pr
Page 19 and 20: PROBLEM ANALYSIS 2 Problem analysis
Page 21 and 22: PROBLEM ANALYSIS GRACE As explained
Page 23 and 24: PROBLEM ANALYSIS Hence it is not po
Page 25 and 26: PROBLEM ANALYSIS As study case the
Page 27 and 28: PROBLEM ANALYSIS Hydrological error
Page 29 and 30: PROBLEM ANALYSIS UPPER ZAMBEZI KAFU
Page 31 and 32: PROBLEM ANALYSIS Just before the in
Page 33 and 34: PROBLEM ANALYSIS In the center of t
Page 35 and 36: PROBLEM ANALYSIS Table 2.2: Land co
Page 37 and 38: HYDROLOGICAL MODEL INPUT DATA 3 Hyd
Page 39 and 40: HYDROLOGICAL MODEL INPUT DATA R c A
Page 41: HYDROLOGICAL MODEL INPUT DATA 1. If
Page 45 and 46: HYDROLOGICAL MODEL INPUT DATA The l
Page 47 and 48: HYDROLOGICAL MODEL INPUT DATA near
Page 49 and 50: HYDROLOGICAL MODEL INPUT DATA FEWS
Page 51 and 52: HYDROLOGICAL MODEL INPUT DATA Looki
Page 53 and 54: HYDROLOGICAL MODEL INPUT DATA As ca
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Page 57 and 58: HYDROLOGICAL MODEL DESCRIPTION 4 Hy
Page 59 and 60: HYDROLOGICAL MODEL DESCRIPTION The
Page 61 and 62: HYDROLOGICAL MODEL DESCRIPTION 4.2.
Page 63 and 64: HYDROLOGICAL MODEL DESCRIPTION Tabl
Page 65 and 66: HYDROLOGICAL MODEL DESCRIPTION His
Page 67 and 68: HYDROLOGICAL MODEL DESCRIPTION Su =
Page 69 and 70: HYDROLOGICAL MODEL DESCRIPTION Tabl
Page 71 and 72: HYDROLOGICAL MODEL DESCRIPTION The
Page 73 and 74: HYDROLOGICAL MODEL DESCRIPTION grou
Page 75 and 76: HYDROLOGICAL MODEL DESCRIPTION and
Page 77 and 78: HYDROLOGICAL MODEL DESCRIPTION 4.2.
Page 79 and 80: HYDROLOGICAL MODEL DESCRIPTION di P
Page 81 and 82: HYDROLOGICAL MODEL DESCRIPTION With
Page 83 and 84: ERROR- & SENSITIVITY ANALYSIS 5 Err
Page 85 and 86: ERROR- & SENSITIVITY ANALYSIS To qu
Page 87 and 88: ERROR- & SENSITIVITY ANALYSIS Uncer
Page 89 and 90: ERROR- & SENSITIVITY ANALYSIS Lukul
Page 91 and 92: ERROR- & SENSITIVITY ANALYSIS Victo
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ERROR- & SENSITIVITY ANALYSIS locat
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ERROR- & SENSITIVITY ANALYSIS Janua
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ERROR- & SENSITIVITY ANALYSIS Colou
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ERROR- & SENSITIVITY ANALYSIS N 2
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ERROR- & SENSITIVITY ANALYSIS Figur
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ERROR- & SENSITIVITY ANALYSIS sets
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ERROR- & SENSITIVITY ANALYSIS SV i
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ERROR- & SENSITIVITY ANALYSIS 5.4.2
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ERROR- & SENSITIVITY ANALYSIS A rem
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ERROR- & SENSITIVITY ANALYSIS Sensi
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GRAVITY MEASUREMENTS AND HYDROLOGY
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CONCLUSIONS & RECOMMENDATIONS 7 Con
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CONCLUSIONS & RECOMMENDATIONS the m
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CONCLUSIONS & RECOMMENDATIONS Gener
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REFERENCES References 2.1 Literatur
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REFERENCES Lamb, T., Beven, K. and
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REFERENCES Wahr, J., Molenaar, M. a
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REFERENCES http://ltpwww.gsfc.nasa.
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APPENDIX 1: DATA TRANSFORMATIONS IN
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APPENDIX 2: FAO SOIL UNITS DESCRIPT
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APPENDIX 2: FAO SOIL UNITS DESCRIPT
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APPENDIX 3: ZAMBEZI SCRIPT Appendix
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APPENDIX 3: ZAMBEZI SCRIPT # Calcul
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APPENDIX 4: MODEL RESULTS Appendix
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APPENDIX 4: MODEL RESULTS 1.2 Resul
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APPENDIX 4: MODEL RESULTS confl. Lu
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APPENDIX 5: GLUE SCRIPT Appendix 5
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APPENDIX 5: GLUE SCRIPT % FitnessFu
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APPENDIX 5: GLUE SCRIPT run_options
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APPENDIX 6: ROUTING MODEL Appendix
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APPENDIX 6: ROUTING MODEL Sub model
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APPENDIX 7: GRACE RESULTS Appendix
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APPENDIX 7: GRACE RESULTS Oct Nov D
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APPENDIX 7: GRACE RESULTS Oct Nov D
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APPENDIX 7: GRACE RESULTS Jul Aug S
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