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River flow prediction through rainfall-runoff modelling with a probability-distributed model (PDM) in Flanders, Belgium

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  • Cabus, Pieter

Abstract

The hydrological probability-distributed model (PDM) is widely used all over the world and its applicability has also been investigated in Flanders, Belgium. Rainfall-discharge relations for all gauging stations installed on non-navigable watercourses were modelled over a long succession of monitoring years. In all, 1456 years were modelled. Typical characteristics (peak flow, volumes) of modelled series are compared with observations. Based on the relatively long time series, reliable discharge values can be generated with the PDM. Water volumes and peak characteristics are very close to the observed values. The set of 98 PDMs was analysed and clustered. Three cluster approaches were considered: a single-parameter approach, a parameter set approach and an approach with known cluster zones, delineated on hydrological flow characteristics. The single-parameter approach, the parameter set approach and the combination of both gave less detailed regional information than the clustering on hydrological characteristics.

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  • Cabus, Pieter, 2008. "River flow prediction through rainfall-runoff modelling with a probability-distributed model (PDM) in Flanders, Belgium," Agricultural Water Management, Elsevier, vol. 95(7), pages 859-868, July.
    • Handle: RePEc:eee:agiwat:v:95:y:2008:i:7:p:859-868
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    1. Biswas, Partha P. & Suganthan, P.N. & Qu, B.Y. & Amaratunga, Gehan A.J., 2018. "Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power," Energy, Elsevier, vol. 150(C), pages 1039-1057.
    2. Prashant Srivastava & Dawei Han & Miguel Rico-Ramirez & Deleen Al-Shrafany & Tanvir Islam, 2013. "Data Fusion Techniques for Improving Soil Moisture Deficit Using SMOS Satellite and WRF-NOAH Land Surface Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5069-5087, December.
    3. Getnet, Mezegebu & Hengsdijk, Huib & van Ittersum, Martin, 2014. "Disentangling the impacts of climate change, land use change and irrigation on the Central Rift Valley water system of Ethiopia," Agricultural Water Management, Elsevier, vol. 137(C), pages 104-115.
    4. Jordan Labbe & Hélène Celle & Jean-Luc Devidal & Julie Albaric & Gilles Mailhot, 2023. "Combined Impacts of Climate Change and Water Withdrawals on the Water Balance at the Watershed Scale—The Case of the Allier Alluvial Hydrosystem (France)," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    5. Elkin D. Reyes & Arturo S. Bretas & Sergio Rivera, 2020. "Marginal Uncertainty Cost Functions for Solar Photovoltaic, Wind Energy, Hydro Generators, and Plug-In Electric Vehicles," Energies, MDPI, vol. 13(23), pages 1-20, December.
    6. Mohammad Lotfi Akbarabadi & Reza Sirjani, 2023. "Achieving Sustainability and Cost-Effectiveness in Power Generation: Multi-Objective Dispatch of Solar, Wind, and Hydro Units," Sustainability, MDPI, vol. 15(3), pages 1-33, January.
    7. Daniel Losada & Ameena Al-Sumaiti & Sergio Rivera, 2021. "Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments," Energies, MDPI, vol. 14(10), pages 1-22, May.

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