Simple statistical models for relating river discharge with precipitation and air temperature—Case study of River Vouga (Portugal)
- 99 Downloads
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.
Keywordsmultiple regression atmospheric precipitation river discharge runoff Aveiro Lagoon
Unable to display preview. Download preview PDF.
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.
- 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
- 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
- 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
- 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
- ESBN (2005). Soil Atlas of Europe. Luxembourg: European CommissionGoogle Scholar
- 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
- IPMA (2015). Normais climatológicas 1971–2000 [Weather and Climate 1971–2000]. Retrieved from: http://www.ipma.pt/en/oclima/normais.clima/Google Scholar
- 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
- 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
- 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
- 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
- Sen Z (2010). Fuzzy Logic and Hydrological Modelling. Boca Raton: Taylor and FrancisGoogle Scholar
- 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