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Frontiers of Earth Science

, Volume 11, Issue 2, pp 203–213 | Cite as

Simple statistical models for relating river discharge with precipitation and air temperature—Case study of River Vouga (Portugal)

  • T. Stoichev
  • J. Espinha Marques
  • C. M. Almeida
  • A. De Diego
  • M. C. P. Basto
  • R. Moura
  • V. M. Vasconcelos
Research Article

Abstract

Simple statistical models were developed to relate available meteorological data with daily river discharge (RD) for rivers not influenced by melting of ice and snow. In a case study of the Vouga River (Portugal), the RD could be determined by a linear combination of the recent (PR) and non-recent (PNR) atmospheric precipitation history. It was found that a simple linear model including only PR and PNR cannot account for low RD. The model was improved by including non-linear terms of precipitation that accounted for the water loss. Additional improvement of the models was possible by including average monthly air temperature (T). The best model was robust when up to 60% of the original data were randomly removed. The advantage is the simplicity of the models, which take into account only PR, PNR and T. These models can provide a useful tool for RD estimation from current meteorological data.

Keywords

multiple regression atmospheric precipitation river discharge runoff Aveiro Lagoon 

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Notes

Acknowledgements

This research was partially supported by the Strategic Funding UID/Multi/04423/2013 through national funds provided by FCT–Foundation for Science and Technology and European Regional Development Fund (ERDF), in the framework of the programme PT2020. T. Stoichev is grateful to FCT for his fellowship (SFRH/BPD/88675/2012), co-financed by Programa Operacional Potencial Humano (POPH) / Fundo Social Europeu (FSE). J. Espinha Marques and R. Moura acknowledge the funding provided by the Institute of Earth Sciences (ICT), under contract with FCT.

References

  1. Achleitner S, Schöber J, Rinderer M, Leonhardt G, Schöberl F, Kirnbauer R, Schönlaub H (2012). Analyzing the operational performance of the hydrological models in an alpine flood forecasting system. J Hydrol (Amst), 412–413: 90–100CrossRefGoogle Scholar
  2. AEMET-IM (2011). Iberian Climate Atlas-Air temperature and precipitation (1971–2000). Ministerio de Medio Ambiente y Medio Rural y Marino (Spain), Instituto de Meteorologia (Portugal)Google Scholar
  3. Agroconsultores and Geometral (1995). Carta dos solos e da aptidão da terra do Entre-Douro e Minho [Map of Soils and Land Suitability of Entre-Douro and Minho]. Lisbon: DRAEDMGoogle Scholar
  4. Bellanger L, Tomassone R (2014). Exploration de données et méthodes statistiques: data analysis & data mining avec le logiciel R [Data Exploration and Statistical Methods: Data Analysis & Data Mining Using R]. Paris: EllipsesGoogle Scholar
  5. Beven K J (1989). Changing ideas in hydrology: the case of physicallybased models. J Hydrol (Amst), 105(1–2): 157–172CrossRefGoogle Scholar
  6. Beven K J (2012). Rainfall–Runoff Modeling: The Primer. Chichester: WileyCrossRefGoogle Scholar
  7. Brath A, Montanari A, Toth E (2004). Analysis of the effects of different scenarios of historical data availability on the calibration of a spatially-distributed hydrological model. J Hydrol (Amst), 291(3–4): 232–253CrossRefGoogle Scholar
  8. Cerejo M, Dias J M (2007). Tidal transport and dispersal of marine toxic microalgae in a shallow, temperate coastal lagoon. Mar Environ Res, 63(4): 313–340CrossRefGoogle Scholar
  9. Crawley M J (2007). The R book. Chichester: WileyCrossRefGoogle Scholar
  10. Dias J M, Abrantes I, Rocha F (2007). Suspended particulate matter sources and residence time in a mesotidal lagoon. J Coast Res, 50(Special issue): 1034–1039Google Scholar
  11. Dias J M, Lopes J F, Dekeyser I (1999). Hydrological characterisation of Ria de Aveiro, Portugal, in early summer. Oceanol Acta, 22(5): 473–485CrossRefGoogle Scholar
  12. Du J, Xie S, Xu Y, Xu C, Singh V P (2007). Development and testing of a simple physically-based distributed rainfall-runoff model for storm runoff simulation in humid forested basins. J Hydrol (Amst), 336(3–4): 334–346CrossRefGoogle Scholar
  13. ESBN (2005). Soil Atlas of Europe. Luxembourg: European CommissionGoogle Scholar
  14. Espinha Marques J, Samper J, Pisani B, Alvares D, Carvalho J M, Chaminé H I, Marques J M, Vieira G T, Mora C, Sodré Borges F (2011). Evaluation of water resources in a high-mountain basin in Serra da Estrela, Central Portugal, using a semi-distributed hydrological model. Environmental Earth Sciences, 62(6): 1219–1234CrossRefGoogle Scholar
  15. Gemmer M, Jiang T, Su B, Kundzewicz Z W (2008). Seasonal precipitation changes in the wet season and their influence on flood/drought hazards in the Yangtze River Basin, China. Quat Int, 186(1): 12–21CrossRefGoogle Scholar
  16. Gourley J J, Vieux B E (2006). A method for identifying sources of model uncertainty in rainfall-runoff simulations. J Hydrol (Amst), 327(1–2): 68–80CrossRefGoogle Scholar
  17. Hiscock K M, Lister D H, Boar R R, Green F M L (2001). An integrated assessment of long-term changes in the hydrology of three lowland rivers in eastern England. J Environ Manage, 61(3): 195–214CrossRefGoogle Scholar
  18. Hurkmans R T W L, de Moel H, Aerts J C J H, Troch P A (2008). Water balance versus land surface model in the simulation of Rhine river discharges. Water Resour Res, 44(1): W01418CrossRefGoogle Scholar
  19. IPMA (2015). Normais climatológicas 1971–2000 [Weather and Climate 1971–2000]. Retrieved from: http://www.ipma.pt/en/oclima/normais.clima/Google Scholar
  20. Muthukrishnan S, Harbor J, Lim K J, Engel B A (2006). Calibration of a simple rainfall-runoff model for long-term hydrological impact evaluation. URISA Journal, 18(2): 35–42Google Scholar
  21. Oliveira J T, Pereira E, Ramalho M, Antunes M T, Monteiro J H (1992). Carta Geológica de Portugal 1/500 000 [Geological Map of Portugal 1/500 000]. 5th ed. Lisbon: Serviços Geológicos de PortugalGoogle Scholar
  22. R Core Team (2014). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org/Google Scholar
  23. Rogado N J O, Batalha J F C S, Simões J J M F, Ribeiro L M (1992). Esboço duma carta de solos da Região de Aveiro na escala 1/100 000 [Project of a soil map of Aveiro region on the scale 1/100 000]. Coimbra (Portugal): DRABLGoogle Scholar
  24. Sen Z (2010). Fuzzy Logic and Hydrological Modelling. Boca Raton: Taylor and FrancisGoogle Scholar
  25. USGS (2009). Shuttle Radar Topography Mission 3-arc second data (version 2.1). Retrieved from: http://dds.cr.usgs.gov/srtm/version2_ 1/SRTM3/Google Scholar
  26. Van der Weijden C H, Pacheco F A L (2006). Hydrogeochemistry in the Vouga River basin (central Portugal): pollution and chemical weathering. Appl Geochem, 21(4): 580–613CrossRefGoogle Scholar
  27. Vilaysane B, Takara K, Luo P, Akkharath I, Duan W (2015). Hydrological stream flow modelling for calibration and uncertainty analysis using SWAT model in the Xedone river basin, Lao PDR. Procedia Environ Sci, 28: 380–390CrossRefGoogle Scholar
  28. Xu H, Taylor R G, Xu Y (2011). Quantifying uncertainty in the impacts of climate change on river discharge in sub-catchments of the Yangtze and Yellow River Basins, China. Hydrol Earth Syst Sci, 15(1): 333–344CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • T. Stoichev
    • 1
  • J. Espinha Marques
    • 2
  • C. M. Almeida
    • 1
  • A. De Diego
    • 3
  • M. C. P. Basto
    • 4
  • R. Moura
    • 2
  • V. M. Vasconcelos
    • 1
  1. 1.Interdisciplinary Center of Marine and Environmental Research (CIIMAR/CIMAR)University of PortoMatosinhosPortugal
  2. 2.Institute of Earth Sciences (ICT) and Department of Geosciences, Environment and Land Planning, Faculty of SciencesUniversity of PortoPortoPortugal
  3. 3.Department of Analytical Chemistry, Faculty of Science and TechnologyUniversity of the Basque Country UPV/EHUBilbao, Basque CountrySpain
  4. 4.CIIMAR/CIMAR and Faculty of SciencesUniversity of PortoPortoPortugal

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