HydroProg: a system for hydraulic forecasting in real time, based on the multimodelling approach

• Autorzy: Niedzielski T. , Miziński B. , Kryza M. , Netzel P. , Wieczorek M. , Kasprzak M. , Migoń P. , Szymanowski M. , Jeziorska J. , Witek M. , Kosek W
• Języki publikacji: EN

Abstrakty

EN

Aleja Mickiewicza 24/28, 30-059 Kraków, Poland Abstract: The objective of this paper is to present the concept of a novel system, known as HydroProg, that aims to issue flood warnings in real time on the basis of numerous hydrological predictions computed using various models. The core infrastructure of the system is hosted by the University of Wrocław, Poland. A newly-established computational centre provides in real time, courtesy of the project Partners, various modelling groups, referred to as “project Participants”, with hydrometeorological data. The project Participants, having downloaded the most recent observations, are requested to run their hydrologic models on their machines and to provide the HydroProg system with the most up-to-date prediction of riverflow. The system gathers individual forecasts derived by the Participants and processes them in order to compute the ensemble prediction based on multiple models, following the approach known as multimodelling. The system is implemented in R and, in order to attain the above-mentioned functionality, is equipped with numerous scripts that manipulate PostgreSQL- and MySQL-managed databases and control the data quality as well as the data processing flow. As a result, the Participants are provided with multivariate hydrometeorological time series with sparse outliers and without missing values, and they may use these data to run their models. The first strategic project Partner is the County Office in Kłodzko, Poland, owner of the Local System for Flood Monitoring in Kłodzko County. The experimental implementation of the HydroProg system in the Nysa Kłodzka river basin has been completed, and six hydrologic models are run by scientists or research groups from the University of Wrocław, Poland, who act as Participants. Herein, we shows a single prediction exercise which serves as an example of the HydroProg performance.

Słowa kluczowe

EN

hydrology; ensemble prediction; multimodelling; real time prognosis; Kłodzko District

• Wydawca: Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
• Czasopismo: Meteorology Hydrology and Water Management. Research and Operational Applications
• Rocznik: 2014
• Tom: Vol. 2, Iss. 2
• Strony: 65--72
• Opis fizyczny: Bibliogr. 38 poz., rys., wykr.

Twórcy

autor

Niedzielski T.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Miziński B.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Kryza M.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Netzel P.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Wieczorek M.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Kasprzak M.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Migoń P.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Szymanowski M.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Jeziorska J.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Witek M.

• University of Wrocław, Institute of Geography and Regional Development, Plac Uniwersytecki 1, 50-137 Wrocław, Poland

autor

Kosek W

• University of Agriculture in Kraków, Faculty of Environmental Engineering and Land Surveying, Aleja Mickiewicza 24/28, 30-059 Kraków, Poland

Bibliografia

• Abbott M.B., Bathurst J.C., Cunge J.A., O’Connell P.E., Rasmussen J., 1986, An introduction to the European Hydrological System – Système Hydrologique Européen, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system, Journal of Hydrology, 87 (1-2), 45-59, DOI: 10.1016/0022-1694(86)90114-9
• Babovic V., 2005, Data mining in hydrology, Hydrological Processes, 19 (7), 1511-1515, DOI: 10.1002/hyp.5862
• Beven K., 1997, TOPMODEL: A critique, Hydrological processes, 11 (9), 1069-1085, DOI: 10.1002/(SICI)1099-1085(199707)11:9<1069::AID-HYP545>3.0.CO;2-O
• Beven K.J., 2001, Rainfall-Runoff Modelling, The Primer, Wiley, Chichester
• Beven K.J., Kirkby M.J., 1979, A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du basin versant, Hydrological Sciences Bulletin, 24 (1), 43-69, DOI: 10.1080/02626667909491834
• Bougeault P., Toth Z., Bishop C., Brown B., Burridge D., Chen D.H., Ebert B., Fuentes M., Hamill T.M., Mylne K., Nicolau J., Paccagnella T., Park Y.-Y., Parsons D., Raoult B., Schuster D., Dias P.S., Swinbank R., Takeuchi Y., Tennant W., Wilson L., Worley S., 2010, The THORPEX interactive grand global ensemble, Bulletin of the American Meteorological Society, 91 (8), 1059-1072, DOI: 10.1175/2010BAMS2853.1
• Bugaets A.N., 2014, Using the OpenMI standard for developing integrated systems of hydrological modelling, Russian Meteorology and Hydrology, 39 (7), 498-506, DOI: 10.3103/S1068373914070097
• Butts M.B., Overgaard J., Dubicki A., Strońska K., Lewandowski A., Olszewski T., Kolerski T., 2006a, Intercomparison of distributed hydrological models for flood forecasting in the Odra River basin, Geophysical Research Abstracts, 8, 07250
• Butts M.B., Falk A.K., Madsen H., Hartnack J., van Kalken T., Mulholland M., 2006b, Ensemble-based flood forecasting with real-time observations for data assimilation, Geophysical Research Abstracts, 8, 07519
• Butts M., Dubicki A., Strońska K., Jørgensen G., Nalberczynski A., Lewandowski A., Van Kalken T., 2007, Flood forecasting for the upper and middle Odra River basin, [in:] Flood Risk Management in Europe, S. Begum et al. (eds.), Advances in Natural and Technological Hazards Research, 25, 353-384, DOI: 10.1007/978-1-4020-4200-3_19
• Castronova A.M., Goodall J.L., Ercan M.B., 2013, Integrated modelling within a Hydrologic Information System: An OpenMI based approach, Environmental Modelling & Software, 39, 263-273, DOI: 10.1016/j.envsoft.2012.02.011
• Castronova A.M., Goodall J.L., 2013, Simulating watersheds using loosely integrated model components: Evaluation of computational scaling using OpenMI, Environmental Modelling & Software, 39, 304-313, DOI: 10.1016/j.envsoft.2012.01.020
• Clarke R.T., 1973, A review of some mathematical models used in hydrology, with observations on their calibration and use, Journal of Hydrology, 19 (5), 1-20, 10.1016/0022-1694(73)90089-9
• Cloke H.L., Pappenberger F., 2009, Ensemble flood forecasting: A review, Journal of Hydrology, 375 (3-4), 613-626, DOI: 10.1016/j.jhydrol.2009.06.005
• Dawson C.W., Wilby R.L., 2001, Hydrological modelling using artificial neural networks, Progress in Physical Geography, 25 (1), 80-108, DOI: 10.1177/030913330102500104
• Estevez J., Gavilan P., Giraldez J.V., 2011, Guidelines on validation procedures for meteorological data from automatic weather stations, Journal of Hydrology, 402 (5), 144-154, DOI: 10.1016/j.jhydrol.2011.02.031
• Franz K., Ajami N., Schaake J., Buizza R., 2005, Hydrologic ensemble prediction experiment focuses on reliable forecasts, Eos Transactions American Geophysical Union, 86 (25), 239, DOI: 10.1029/2005EO250004
• Kosek W., 2002, Autocovariance prediction of complex-valued polar motion time series, Advances in Space Research, 30, 375-380
• Koutsoyiannis D., Yao H., Georgakakos A., 2008, Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods/Prévision du débit du Nil à moyen terme: une comparaison de méthodes stochastiques et determinists, Hydrological Sciences Journal, 53 (1), 142-164, DOI: 10.1623/hysj.53.1.142
• Kryza M., Werner M., Wałaszek K., Dore A.J., 2013, Application and evaluation of the WRF model for high-resolution forecasting of rainfall – a case study of SW Poland, Meteorologische Zeitschrift, 22 (5), 595-601, DOI: 10.1127/0941-2948/2013/0444
• Krzysztofowicz R., 2001, The case for probabilistic forecasting in hydrology, Journal of Hydrology, 249 (1-4), 2-9, DOI: 10.1016/S0022-1694(01)00420-6
• Laurenson E.M., 1976, Modeling of stochastic-deterministic hydrologic systems, Water Resources Research, 10 (5), 955-961, DOI: 10.1029/WR010i005p00955
• Lawrance A.J., Kottegoda N.T., 1977, Stochastic modelling of riverflow time series, Journal of the Royal Statistical Society. Series A (General), 140 (1), 1-47, DOI: 10.2307/2344516
• Maier H.R., Dandy G.C., 2000, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environmental Modelling & Software, 15 (1), 101-124, DOI: 10.1016/S1364-8152(99)00007-9
• McCuen R.H., 2003, Modeling hydrologic change: statistical methods, Lewis Publishers, London, NY, Washington, DC
• Niedzielski T., 2007, A data-based regional scale autoregressive rainfall-runoff model: a study from the Odra River, Stochastic Environmental Research and Risk Assessment, 21 (6), 649-664, DOI: 10.1007/s00477-006-0077-y
• Niedzielski T., 2010, Empirical hydrologic predictions for Southwestern Poland and their relation to ENSO teleconnections, Artificial Satellites, 45 (1), 11-26, DOI: 10.2478/v10018-010-0002-y
• Niedzielski T., 2011, Is there any teleconnection between surface hydrology in Poland and El Niño/Southern Oscillation?, Pure and Applied Geophysics, 168 (5), 871-886, DOI: 10.1007/ s00024-010-0171-4
• Özcelik C., Baykan O., 2009, An improved time series model for monthly stream flows, Stochastic Environmental Research and Risk Assessment, 23 (5), 587-601, DOI: 10.1007/ s00477-008-0244-4
• Piepiora Z., 2011, Lokalna polityka przeciwdziałania skutkom katastrof naturalnych w powiecie kłodzkim, Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, 152, 378-394
• Savic D.A., Walters G.A., Davidson J.W., 1999, A genetic programming approach to rainfall-runoff modelling, Water Resources Management, 13 (3), 219-231, DOI: 10.1023/A:1008132509589
• Schaake J., Franz K., Bradley A., Buizza R., 2006, The Hydrologic Ensemble Prediction EXperiment (HEPEX), Hydrology and Earth System Sciences Discussions, 3, 3321-3332
• Shafer M.A., Fiebrich C.A., Arndt D.S., Fredrickson S.E., Hughes T.W., 2000, Quality assurance procedures in the Oklahoma Mesonetwork, Journal of Atmospheric and Oceanic Technology, 17, 474-494, DOI: 10.1175/1520-0426(2000)017<0474:QA PITO>2.0.CO;2
• Whigham P.A., Crapper P.F., 2001, Modelling rainfall-runoff using genetic programming, Mathematical and Computer Modelling, 33 (6-7), 707-721, DOI: 10.1016/S08957177(00)00274-0
• Witek M., Jeziorska J., Niedzielski T., 2014, Experimental approach to verify prognoses of floods using the unmanned aerial vehicle, Meteorology Hydrology and Water Management – Research and Operational Applications, 2, 3-11
• Yevjevich V., 1987, Stochastic models in hydrology, Stochastic Hydrology and Hydraulics, 1 (1), 17-36, DOI: 10.1007/ BF01543907
• Zappa M., Rotach M.W., Arpagaus M., Dorninger M., Hegg C., Montani A., Ranzi R., Ament F., Germann U., Grossi G., Jaun S., Rossa A., Vogt S., Walser A., Wehrhan J., Wunram C., 2008, MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems, Atmospheric Science Letters, 9 (2), 80-87, DOI: 10.1002/asl.183
• Zhang Q., Wang C., Shibasaki R., 2005, Distributed modeling of hydrologic system based on digital river basin, Environmental Informatics Archives, 3, 92-97