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Volume 35, Nº 1 - maio 2014

 

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  • Abstract / Resumo
  • References / Bibliografia
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Revista Recursos Hídricos

DOI:10.5894/rh35n1-3
O texto deste artigo foi submetido para revisão e possível publicação em abril de 2014, tendo sido aceite pela Comissão de Editores Científicos Associados em maio de 2014. Este artigo é parte integrante da Revista Recursos Hídricos, Vol. 35, Nº 1, 37-52, maio de 2014.

Uma forma alternativa de enfrentar a escassez de dados na bacia do rio Zambeze com vista à calibração de modelos hidrológicos

An alternative approach to face the scarcity of data in the Zambezi River basin aiming at calibrating hydrological models

J. P. Matos1, M. M. Portela2, D. Juízo3


1 - Doutor em Engenharia Civil, Instituto Superior Técnico /// Doutor em Ciências, École Polytechnique Fédérale de Lausanne /// [email protected]
2 - Doutora em Engenheira Civil, Professora Auxiliar, Instituto Superior Técnico /// [email protected]
3 - Doutor em Engenharia Civil, Universidade Eduardo Mondlane /// [email protected]


RESUMO
No âmbito da estimação de escoamentos à escala diária numa extensa bacia hidrográfica escassamente monitorizada – na bacia hidrográfica do rio Zambeze, em Moçambique, com cerda de 1 400 000 km2 –, sistematizam-se as etapas fundamentais da modelação, compreendendo, para além do modelo hidrológico propriamente dito, a aquisição dos dados de base, a caracterização espacial da precipitação, a calibração de parâmetros e a validação de resultados. São especificados e brevemente discutidos alguns dos modelos aplicáveis às diferentes etapas. Tendo em conta a relevância da fase de calibração, é explorada a possibilidade de recorrer a superfícies de precipitação interpoladas a partir de dados históricos de acordo com a técnica POM (interpolação por memória orientada por padrões, ou, do Inglês, Pattern Oriented Memory) de modo a aumentar o período de calibração sustentando-o em informação compatível, quer decorrente daquela interpolação, quer, após 1998, derivada de dados de satélite. Não obstante o reconhecimento da necessidade de investigação adicional, o artigo evidencia as potencialidades da adopção de períodos de calibração alargados possibilitada pela nova técnica de interpolação espacial POM.

Palavras-chave: Rio Zambeze, escoamento, interpolação da precipitação, POM, modelo hidrológico, calibração, validação.

ABSTRACT
The main steps of an approach towards the evaluation of flows at a daily time scale in a large ungauged watershed – the Zambezi River basin, in Mozambique, with approx. 1 400 000 km2 – is presented, comprehending, besides the hydrological model, the data acquisition, the spatial characterization of the rainfall, the parameters’ calibration and the results’ validation. Due to the relevance of the calibration step, the possibility of using rainfall surfaces interpolated from satellite data, according to the POM technique (Pattern Oriented Interpolation), is explored. In this way, the rainfall data obtained either by the POM technique, prior to 1998, or from satellite, after 1998, is made compatible, thus allowing to lengthen the calibration period. Despite the need for additional research, the paper stresses the advantage of larger calibration periods as a result of the POM spatial interpolation technique.

Keywords: Zambezi River, surface runoff, rainfall interpolation, POM, hydrological model, calibration, validation.

 

Aonashi, K., J. Awaka, M. Hirose, T. Kozu, T. Kubota, G. LIU, S. Shige, S. Kida, S. Seto e N. Takahashi (2009). GSMaP passive microwave precipitation retrieval algorithm: algorithm description and validation. 87:119-136.

Arnold, J. G., R. Srinivasan, R. S. Muttiah e J. Williams (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association 34:73-89.

Beven, K. (1993). Prophecy, reality and uncertainty in distributed hydrological modelling. Advances in Water Resources 16:41-51.

Beven, K. (2006). A manifesto for the equifinality thesis. Journal of Hydrology 320:18-36.

Beven, K. e A. Binley (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6:279-298.

Broomhead, D. e D. Lowe (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal signals and radar establishment Malvern, England.

Cohen Liechti, T. (2013). Influence of dam operation on water resources management under different scenarios in the Zambezi River Basin considering environmental objectives and hydropower. Ph.D. thesis. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

Cohen Liechti, T., J. P. Matos, J.-L. Boillat e A. J. Schleiss (2011). Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin. Hydrology and Earth System Sciences 8:8173-8201.

CRC Catchment Hydrology (2005). Series on Model Choice: 1. General approaches to modelling and practical issues of model choice. Cooperative Research Centre for Catchment Hydrology.

Cressie, N. (1988). Spatial prediction and ordinary kriging. Mathematical Geology 20:405-421.

Cressie, N. (1990). The origins of kriging. Mathematical Geology 22:239-252.

Deb, K., A. Pratap, S. Agarwal e T. Meyarivan (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on 6:182-197.

Denconsult (1998). Sector studies under ZACPLAN.

Diskin, M. H. e E. Simon (1977). A procedure for the selection of objective functions for hydrologic simulation models. Journal of Hydrology 34:129-149.

Duan, Q., S. Sorooshian e V. Gupta (1992). Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28:1015-1031.

Efstratiadis, A. e D. Koutsoyiannis. 2010. One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrological Sciences Journal–Journal des Sciences Hydrologiques 55:58-78.

Food and Agriculture Organization of the United Nations, F. (1995). Digital soil map of the World and derived soil properties (version 3.5) [CD-ROM]. Rome, Italy.

Gerrits, A. M. J. (2005). Hydrological modelling of the Zambezi catchment for gravity measurements. Master. Techinical University Delft, Delft, The Netherlands.

Global Runoff Data Centre. River discharge time series D-56002 Koblenz, Germany.

Goldberg, D. e J. Holland (1988). Genetic Algorithms and Machine Learning. Machine learning 3:95-99.

Hansen, N. (2006). The CMA evolution strategy: A comparing review. Pages 75-102 in J. Lozano, P. Larrañaga, I. Inza e E. Bengoetxea, editors. Towards a new evolutionary computation. Springer Berlin / Heidelberg.

Hansen, N. (2010). The CMA evolution strategy: A tutorial.

Hansen, N. e A. Ostermeier (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary computation 9:159-195.

Herman, A., V. B. Kumar, P. A. Arkin e J. V. Kousky (1997). Objectively determined 10-day African rainfall estimates created for famine early warning systems. International Journal of Remote Sensing 18:2147-2159.

Huffman, G. J., R. F. Adler, D. T. Bolvin, E. J. Nelkin, F. Hossain e M. Gebremichael (2010). The TRMM multi-satellite precipitation analysis (TMPA). Pages 3-22 in M. Gebremichael e F. Hossain, editors. Satellite rainfall applications for surface hydrology. Springer.

Huffman, G. J., R. F. Adler, B. Rudolf, U. Schneider e P. R. Keehn (1995). Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information. Journal of Climate 8:1284-1295.

Huffman, G. J., D. T. Bolvin, E. J. Nelkin, D. B. Wolff, R. F. Adler, G. Gu, Y. Hong, K. P. Bowman e E. F. Stocker (2007). The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. Journal of Hydrometeorology 8:38-55.

Joyce, R. J., J. E. Janowiak, P. A. Arkin e P. Xie (2004). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology 5:487-503.

Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino e G. L. Potter (2002). NCEP-DOE AMIP-II reanalysis (R-2). Bulletin of the American Meteorological Society 83:1631-1644.

Kennedy, J. e R. Eberhart (1995). Particle swarm optimization. Pages 1942-1948 in Neural Networks, 1995. Proceedings., IEEE International Conference on. IEEE.

Kubota, T., S. Shige, H. Hashizume, K. Aonashi, N. Takahashi, S. Seto, Y. Takayabu, T. Ushio, K. Nakagawa e K. Iwanami (2007). Global precipitation map using satellite-borne microwave radiometers by the GSMaP Project: Production and validation. Geoscience and Remote Sensing, IEEE Transactions on 45:2259-2275.

Landert, J. (2008). Modeling Biogeochemistry using the Soil and Water Assessment Tool in the Zambezi River Basin. Swiss Federal Institute of Technology Zurich.

Lehner, B., K. Verdin e A. Jarvis (2008). New global hydrography derived from spaceborne elevation data. Eos Trans. AGU 89:93-94.

Lin, G.-F. e L.-H. Chen (2004). A spatial interpolation method based on radial basis function networks incorporating a semivariogram model. Journal of Hydrology 288:288-298.

Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohlen, Z. Zhu, L. W. M. J. Yang e J. W. Merchant (2000). Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing 21:1303-1330.

Mantovan, P. e E. Todini (2006). Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology. Journal of Hydrology 330:368-381.

Matheron, G. (1969). Le krigeage universel. École nationale supérieure des mines de Paris.

Matondo, J. e P. Mortensen (1998). Water resource assessment for the Zambezi river basin. Water International 23:256-262.

Matos, J. P., T. Cohen Liechti, D. Juízo, M. M. Portela e A. J. Schleiss (2013). Can satellite based pattern-oriented memory improve the interpolation of sparse historical rainfall records? Journal of Hydrology 492:102-116.

Matos, J. P., T. Cohen Liechti, M. M. Portela e A. J. Schleiss (2014). Pattern-oriented memory interpolation of sparse historical rainfall records, in press. Journal of Hydrology Volume 510: 493–503.

Meier, P. (2012). Real-time hydrologic modelling and floodplain modelling in the Kafue river basin, Zambia Ph.D. Swiss Federal Institute of Technology, Zurich, Zurich, Switzerland.

Meier, P., A. Frömelt e W. Kinzelbach (2011). Hydrological real-time modelling in the Zambezi river basin using satellite-based soil moisture and rainfall data. Hydrology and Earth System Sciences 15:999-1008.

Michailovsky, C. I. 2008. Comparing GRACE water storage observations and regional-scale hydrological models for Southern Africa. Technical University of Denmark.

Michailovsky, C. I. e P. Bauer-Gottwein (2013). Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study. Hydrol. Earth Syst. Sci. Discuss. 10:9615-9644.

Michailovsky, C. I., S. McEnnis, P. A. M. Berry, R. Smith e P. Bauer-Gottwein (2012). River monitoring from satellite radar altimetry in the Zambezi River Basin. Hydrol. Earth Syst. Sci. Discuss. 9:3203-3235.

Montanari, A. (2007). What do we mean by ‘uncertainty’? The need for a consistent wording about uncertainty assessment in hydrology. Hydrological Processes 21:841-845.

Moradkhani, H. e S. Sorooshian (2008). General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis. Pages 1-24 in S. Sorooshian, K.-L. Hsu, E. Coppola, B. Tomassetti, M. Verdecchia e G. Visconti, editors. Hydrological modelling and the water cycle. Springer Berlin Heidelberg.

Moriasi, D., B. Wilson, K. Douglas-Mankin, J. Arnold e P. Gowda (2012). Hydrologic and water quality models: use, calibration, and validation. Transactions of the ASABE 55:1241-1247.

Nash, J. e J. Sutcliffe (1970). River flow forecasting through conceptual models part I - A discussion of principles. Journal of Hydrology 10:282-290.

Neitsch, S. L., J. G. Arnold, J. R. Kiniry e J. R. Williams (2011). Soil and water assessment tool theoretical documentation, version 2009. Grassland, Soil and Water Research Laboratory, Agricultural Research Service; Brackland Research Center, Texas Agricultural Experiment Station, Temple, Texas, USA.

Perrin, M. (2013). Gestion intégrée des ressources en eau du bassin du Zambèze. Master’s thesis. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

Roads, J., M. Kanamitsu e R. Stewart (2002). CSE water and energy budgets in the NCEP-DOE Reanalysis II. Journal of Hydrometeorology 3:227-248.

Schuol, J., K. C. Abbaspour, H. Yang, R. Srinivasan e A. J. B. Zehnder (2008). Modeling blue and green water availability in Africa. Water Resour. Res 44:W07406.

Shamseldin, A. Y. e K. M. O’Connor (2001). A non-linear neural network technique for updating of river flow forecasts. Hydrology and Earth System Sciences 5:577-597.

Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Pages 517-524 in Proceedings of the 1968 23rd ACM national conference

Sivapalan, M., K. Takeuchi, S. W. Franks, V. K. Gupta, H. Karambiri, V. Lakshmi, X. Liang, J. J. McDonnell, E. M. Mendiondo e P. E. O’connell (2003). IAHS decade on Predictions in Ungauged Basins (PUB), 2003–2012: shaping an exciting future for the hydrological sciences. Hydrological Sciences Journal 48:857-880.

Sorooshian, S. e V. K. Gupta (1983). Automatic calibration of conceptual rainfall-runoff models: The question of parameter observability and uniqueness. Water Resources Research 19:260-268.

Sorooshian, S., K. Hsu, X. Gao, H. Gupta, B. Imam e D. Braithwaite (2000). Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bulletin of the American Meteorological Society 81:2035-2046.

Srinivas, N. e K. Deb (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation 2:221-248.

Srinivasan, R., X. Zhang e J. Arnold (2010). SWAT ungauged: hydrological budget and crop yield predictions in the Upper Mississippi River Basin. Transactions of the ASABE 53:1533-1546.

Suykens, J. A. K., T. V. Gestel, J. D. Brabanter, B. D. Moor e J. Vandewalle (2002). Least squares support vector machines. World Scientific Pub. Co. Inc., Singapore.

SWRSD Zambezi Basin Joint Venture (2011). Dam synchronization and flood releases in the Zambezi River Basin project., Consultancy report for the German Federal Ministry for Economic Cooperation and Development (GTZ) and the UK Department for International Development (DIFD).

Thiessen, A. H. (1911). Precipitation averages for large areas. Monthly weather review 39:1082-1089.
U. S. Geologycal Survey (2010). HydroSHEDS.

Vörösmarty, C. J. e B. Moore (1991). Modelling basin-scale hydrology in support of physical climate and global biogeochemical studies: An example using the Zambezi River. Surveys in Geophysics 12:271-311.

Vrugt, J. A., H. V. Gupta, L. A. Bastidas, W. Bouten e S. Sorooshian (2003). Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resources Research 39:1214.

Vrugt, J. A. e B. A. Robinson (2007). Improved evolutionary optimization from genetically adaptive multimethod search. Proceedings of the National Academy of Sciences 104:708.

Vrugt, J. A., B. A. Robinson e J. M. Hyman (2009a). Self-adaptive multimethod search for global optimization in real-parameter spaces. Evolutionary Computation, IEEE Transactions on 13:243-259.

Vrugt, J. A., C. J. Ter Braak, M. P. Clark, J. M. Hyman e B. A. Robinson (2008). Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research 44.

Vrugt, J. A., C. J. Ter Braak, H. V. Gupta e B. A. Robinson (2009b). Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic environmental research and risk assessment 23:1011-1026.

Winkler, R. L. (2010). An introduction to Bayesian inference and decision, 2nd edition. Probabilistic Publishing, Florida.

Winsemius, H., B. Schaefli, A. Montanari e H. Savenije (2009). On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information. Water Resour. Res 45:W12422.

Xie, P. e P. Arkin (1996). Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. Journal of Climate 9:840-858.

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