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

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HYDROLOGICAL MODEL INPUT DATA Table 3.1: Validation statistics of rainfall estimates from MIRA and GPI compared with other rainfall estimation methods (Todd et al., 2001) Independent validation data MIRA GPI CC Ratio RMSE CC Ratio RMSE SSM/I BUC [1] 0.47 1.1 2.2 0.04 1.54 2.84 SSM/I BUC [2] EPSAT gauge data [3] GPCP gauge data [4] WetNet PIP-3 GPCP land based gauges [5] WetNet PIP-3 Comprehensive Pacific Rainfall Data Base [6] 0.54 1.01 2.02 0.24 1.97 1.98 0.96 1.08 2.04 0.76 1.4 5.3 0.54 1.76 25.17 0.66 2.74 36.9 0.8 1.24 82.6 0.69 1.69 147.5 0.85 0.96 74.6 0.75 1.08 100.8 [1] 140°-180°E, 20°S-20°N, July 1991, 0.25°, conditional instantaneous, n= 515, mean=1.7mmhr -1 [2] 140°-180°E, 20°S-20°N, July 1991, 0.5°, conditional instantaneous, n= 228, mean=1.04 mmhr -1 [3] 2°-3°E, 13°-14°N, July 1992, 1°, daily, n=30, mean=3.5mm.day -1 [4] Selected locations (see Todd et al., 2001; Table 2), July 1991,2.5°, pentad, n= 46, mean=8.7mm.pentad -1 [5] Selected locations (see Morrissey et al., 1994), 2.5°, monthly, n=159, mean =83.7mm [6] Selected locations (see Morrissey et al., 1994), 2.5°/monthly, n=17, mean=217.6mm Rain rate [mm/h] 40 30 20 10 0 1 Validation Validatation of of MIRA and GPI 4 7 10 13 16 19 22 25 Time [days] Gauge MIRA GPI 28 Figure 3.3: MIRA and GPI compared to rain gauges (Todd et al., 2001). 3.1.1.1 Data The MIRA algorithm has calculated daily rainfall estimates in mean mm/hour for the period from 1993 till 2002. It covers only the African continent and has a spatial resolution of 0.1° x 0.1°. The rainfall estimates from MIRA can be downloaded from the Southern African Regional Science Infinitive (SAFARI 2000)-site: http://ltpwww.gsfc.nasa.gov/s2k/html_pages/groups/precip/daily_rainfall_mira. html Although the high temporal resolution of the rainfall estimates, there are unfortunately some gaps (Table 3.2). These gaps are for example due to the fact that some data files became corrupt by archiving on CD. To repair the data one of the following actions is executed: 24
HYDROLOGICAL MODEL INPUT DATA 1. If the missing day(s) are in the dry season (May-October) there is no precipitation added to the month value. It is assumed that the precipitation in those days is zero. The month sum is easily obtained by summing all the daily values. 2. If one or two days successive are lacking data, the value of the day before and/or the day after, is filled in. Assumed is a high correlation between two successive days. Again is the month sum easily obtained by simply summing up all the daily values inclusive the new values. 3. If more then two days are missing and at least one rain station is available with a complete daily range of rainfall data, the percentage (p) of the amount of rainfall of the missing days is calculated from the rain station. The month sum is then divided by (1-p) to obtain a repaired new month value. 4. If more than two days are missing data and there are no rain stations available, the average percentage (p) of the amount of rainfall of the missing days is computed from other years of that month. The month sum is then divided by (1-p) to obtain a repaired new month value. 5. If a complete month is missing, a highly correlated year is looked for. The missing month is filled in with the same month of the found correlated year. A complete overview of the data transformations and gap repair is given in Appendix 1.2.6. Table 3.2: Number of gaps in the MIRA data. rainy dry rainy 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 January 0 0 0 1 5 0 9 2 10 0 February 0 0 0 10 1 0 0 0 2 0 March 1 0 0 0 1 0 1 0 0 0 April 0 0 9 0 6 2 0 0 0 0 May 2 10 0 0 0 0 2 0 0 0 June 0 0 4 0 0 5 0 0 0 0 July 0 0 31 10 0 1 0 0 0 11 August 0 0 10 0 0 0 0 0 0 21 September 0 0 0 0 0 0 0 0 0 0 October 0 0 0 0 0 0 0 0 0 21 November 0 0 8 5 1 0 0 0 0 11 December 0 0 0 9 0 4 12 0 0 31 Tot perc. of gaps 0.82% 2.74% 16.99% 9.56% 3.84% 3.29% 6.58% 0.55% 3.29% 26.03% rainy season 0.27% 0.00% 4.66% 6.83% 3.84% 1.64% 6.03% 0.55% 3.29% 11.51% dry season 0.55% 2.74% 12.33% 2.73% 0.00% 1.64% 0.55% 0.00% 0.00% 14.52% 3.1.1.2 Performance To verify the rainfall estimates of the MIRA algorithm, the output is compared with ten rain stations in the catchment. From the selected stations the average is plotted in time. The same is done to the values of MIRA on the locations of the rain stations. See Figure 3.4. Also a double mass curve and a scatter plot are made (Figure 3.5. and Figure 3.6). 25
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: HYDROLOGICAL MODEL INPUT DATA R c A
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Page 61 and 62: HYDROLOGICAL MODEL DESCRIPTION 4.2.
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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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