1 / 23

Sedimentation Data Analysis

WASER/IAHR/IRTCES Three Gorges Project Study Tour Yichang, 8-15 August 2005. Sedimentation Data Analysis. Paolo Ronco Phd Student, University of Padua - Italy. The Context. Global Evaluation of Sediment Transport (GEST) DATA BASE UNESCO International Hydrological Programme – IHP

tyler
Download Presentation

Sedimentation Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. WASER/IAHR/IRTCES Three Gorges Project Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco Phd Student, University of Padua - Italy

  2. The Context Global Evaluation of Sediment Transport (GEST) DATA BASE UNESCO International Hydrological Programme – IHP International Sediment Initiative – ISI Introduction Data Quality Data Analysis A Case Study: Zambesi River SMIO - Sediment Management International Observatory (Italy) • Main scope: sediment data analysis and processing • Development of filing- and retrieval procedures (Information and library techniques) • Review of simulation models (Various degrees of space- and time resolution) • Integration of surface-erosion and sediment-transport data (Watershed scale) • Evaluation and comparison of different data sources (Suspended transport records, occasional field measurements, reservoir sedimentation…) • Treating uncertain information (Theory of identification, Fuzzy logic, Signal analysis…) • Processing scarce, sparse, incomplete, inaccurate data, • Recognizing missing information in sediment transport data (e.g. Sediment grain size or geotechnical data) WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

  3. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River spatial (no data from less developed countries) and temporal (few long term records) scarcity make difficult to analyse the trends • Scarce WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

  4. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River • Scarce • Non - Homogeneous different data typologies (bibliography, iconography , photography, cartography, remote sensing, analogical and numerical data, etc..) WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

  5. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River • Scarce • Non - Homogeneous • Scattered many different institutions with no “archive” or useful database WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

  6. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River • Scarce • Non - Homogeneous • Scattered • Non - Correlated different disciplines (hydraulic, hydrology, meteorology, geology, ecology, lithology, geology, economy, etc…) with no linking tentative WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

  7. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River • Scarce • Non - Homogeneous • Scattered • Non - Correlated • Discontinuous no spatial continuity (reservoirs gaps..). no temporal continuity: no long, continuous historical series, make difficult the statistic analysis WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

  8. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River • Scarce • Non - Homogeneous • Scattered • Non - Correlated • Discontinuous • Non - Accurate WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) no standard collect procedures and equipments (the use of sediment rating curve from infrequent sampling introduce errors..)

  9. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River • Scarce • Non - Homogeneous • Scattered • Non - Correlated • Discontinuous • Non - Accurate • Variably Reliable WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) different approaches on calculations and measurement procedures

  10. Problems Of Data Reliability And Accuracy Characteristics of Data Introduction Data Quality Data Analysis A Case Study: Zambesi River • Scarce • Non - Homogeneous • Scattered • Non - Correlated • Discontinuous • Non - Accurate • Variably Reliable • Relevant To Different Scales WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) high natural variability of the erosion/sedimentation/solid transport phenomena; temporal and spatial downscaling/upscaling concept Uncertain and Inadequate Sedimentation Data!!!

  11. Uncertain and Inadequate Data Introduction Data Quality Data Analysis A Case Study: Zambesi River Costs of Data Uncertain Models WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) Alternative Approaches with Statistical Analysis Limits and Reliability of Measures and Data: Maximize Informations from Available Data

  12. Statistical Instruments Analysis Hypothesis: 1D Gaussian Distribution Introduction Data Quality Data Analysis A Case Study: Zambesi River the variable x could be: • bed load sediment concentration • suspended sediment concentration, • sediment yield, etc… WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) • Expectation • Variance

  13. Statistical Instruments Analysis Hypothesis: 2D Gaussian Distribution Introduction Data Quality Data Analysis A Case Study: Zambesi River • the variable x could be: • bed load sediment conc. • suspended sediment conc. • sediment yield, etc… the variable y could be: • hydrology (lots of data available) • soil use • sediment delivery ratio, etc… WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) • Expectation • Variance • Covariance • Covariance Matrix

  14. 0 1 perfect correlation Cxx=0 non correlation reducing uncertainty Statistical Instruments Analysis Hypothesis: 2D Gaussian Distribution Introduction Data Quality Data Analysis A Case Study: Zambesi River • the variable x could be: • bed load sediment conc. • suspended sediment conc. • sediment yield, etc… the variable y could be: • hydrology (lots of data available) • soil use • sediment delivery ratio, etc… WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) • Correlation

  15. phenomena noise measures errors Statistical Instruments Analysis Theory Of Identification Introduction Data Quality Data Analysis A Case Study: Zambesi River modelling the evolution of a system under solicitations measures WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) making an estimation signify

  16. ~ + - + = + ˆ x ( t 1 ) x ( t 1 t ) x ( t 1 t ) Phenomena Noise Measures Errors Statistical Instruments Analysis Kalman Filter Estimation Model Introduction Data Quality Data Analysis A Case Study: Zambesi River 2 steps update step prediction step WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) Estimation Error (ER) Real Value Kalman Estimation Aleatory Vector Gaussian Distribution

  17. Estimation Error (ER) • ER Statistical Distribution • 1D or 2D Gaussian Distribution? • Gaussian Distrib. Values ( , , , ) • Minimize The ER • Best Estimation: represents better the “Population” of Data with few Measures , how many? • Most Efficient Estimation: Minimize the Variance Statistical Instruments Analysis Introduction Data Quality Data Analysis A Case Study: Zambesi River WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) Hypothesis Test and Optimum Test Maximum Likehood Estimation Model Kramer-Rao and Neyman-Fischer Models

  18. Next Steps • Characterize Historical Series of Measures with Gaussian Statistical Distribution Values Introduction Data Quality Data Analysis A Case Study: Zambesi River • Apply Kalman Filter Estimation Model to Historical Series of Sedimentation Data and Study the ER Distribution • Utilize others Statistical Instruments Analysis and Methods, such as: Stochastic Models, Signal Processing Analysis, Fuzzy Logic, Etc.. WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) • Collecting and Processing Data from Specific Large River Basins particularly interesting, as Case Studies, for Large Amount of High Quality Data Available (Rhine, Danube, Nile, etc..) or Low Amount of Data (Uncertain Data...) Zambesi River Basin

  19. Zambesi River Basin • fourth largest floodplain river in Africa • River: 2.574 km • Catchment Area: 1.570.000 km2 (8 countries) • max. Flood: 22.000 m3/s (Lower Zambesi) Introduction Data Quality Data Analysis A Case Study: Zambesi River WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) • 3 segments: • upper (1.078 km) • middle (853 km) 30% 60% • lower (593 km) • 2 large dam impoundments: • Kariba Dam • Cahora Bassa Dam

  20. type: double curvature concrete arch dam • height: 128 m • max. discharge capacity: 9.500 m3/s • length of reservoir: 280 km • total storage: 180,6 km3 • “live” storage: 64,8 km3 (35%) • surface area: 5577 km2 • total generation capacity: 1320 MW • estimated annual input of sediment (Stocking & Elwell, 1973): 7 - 70 •106 t • estimated annual rate of loss of storage capacity: 7 – 70 •106 m3 • “economic life” (all sediments reach the “dead” storage) 1.600 – 16.000 years Kariba Dam and Reservoir Introduction Data Quality Data Analysis A Case Study: Zambesi River WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

  21. type: double curvature concrete arch dam • height: 171 m • max. discharge capacity: 1.650 m3/s • length of reservoir: 270 km • total storage: 72,5 km3 • “live” storage: 35 km3 • surface area: 2.739 km2 • total generation capacity: 2.040 MW • estimated annual input of sediment (most from Luangwa Basin; Bolton, 1983): 20 - 200 •106 t (very uncertain estimate) • estimated annual rate of loss of storage capacity: 20 - 200 •106 m3 • “economic life” 350 – 3.500 years Cahora Bassa Dam and Reservoir Introduction Data Quality Data Analysis A Case Study: Zambesi River ½ accumulate within the “live” storage WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) sediment deposition has an appreciable effect on the project’s operation within its “economic life”

  22. Cahora Bassa Dam and Reservoir Further Examinations concerning the Sediment Deposition in the Reservoir: Introduction Data Quality Data Analysis A Case Study: Zambesi River • accurate monitoring and estimation of the rate of sediment inflow • identify the principal region of deposition in the reservoir (“live” storage or “dead” storage) WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy) Principal Effects of the Reservoir on the Lower Zambesi Sediment Balance • up to 70% reduction in sediment transport during floods, with strong modifications on the river morphology (strong erosion of upper gorge zone and high deposition on the braided zone) • strong coastal erosion coupled with upstream penetration of the estuarine salt wedge • decline in coastal fisheries and shrimp industries due to loss of silt and associated nutrients

  23. Bibliography • G. Di Silvio, GEST Data Base, IHP-ISI Steering Committee Meeting, Wien, 2005. • R. Frezza, Theory of Identification and Data Analysis. University of Padua, 2000. • P. Bolton, Sediment deposition in major Reservoirs in the Zambesi Basin. Challenges in African Hydrology and Water Resources, Proceedings of the Harare Symposium, 1984) • B.R. Davies, R.D. Beilfuss, M.C. Thoms, Cahora Bassa Retrospective, 1974-1997: effects of flows regulation on the Lower Zambesi River. Limnology in the developing word, Stuttgart, 2000. Introduction Data Quality Data Analysis A Case Study: Zambesi River WASER/IAHR/IRTCES TGP Study Tour Yichang, 8-15 August 2005 Sedimentation Data Analysis Paolo Ronco - University of Padua (Italy)

More Related