Klodian ZAİMİ, Sergio FATTORELLİ, Francesca RAMAZZİNA

Use of EURO-CORDEX models for analyses of the future water resources in Bovilla catchment

Bovilla Lake catchment is part of the Ishëm River. Bovilla catchment is part of the Ishëm River. The artificial lake created in 1998 from the Bovilla dam is one of the most important water resources in Albania because is used as the main source from the Tirana Municipality water supply system. About 60% of Bovilla catchment is covered by forest (both broad-leaved and coniferous) and 5% of the area is bare or only barely wooded. Grasslands and pastures cover about 8-9% of the area, while 18% are dedicated to agriculture. Climate change, growing population, unsustainable development, and inappropriate land use threaten to induce or intensify natural hazards’ exposure and vulnerability with disastrous consequences for the environment and societies. The performance and the spatial resolution of General Circulation Models (GCMs) have continuously improved in the recent years, but the typical state of the art spatial scale is still too coarse to realistically reproduce present climate and project climate change signals on local scales, especially in the presence of complex orography. In order to provide seasonal and annual water balance with different climate change scenarios for Bovilla catchment, the hydrological rainfall-runoff model HEC-HMS was implemented. In the absence of measured data, the parameters’ values of the hydrological model were assigned on the basis of acceptable data ranges from the manual, from literature, and on the expert experience. The parameters have been finalized in order to well match the simulated and observed data.

Keywords:

climate change general circulation models, hydrological model, rainfall-runoff,

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Cardona, O.D., M.K. van Aalst, J. Birkmann, M. Fordham, G. McGregor, R. Perez, R.S. Pulwarty, E.L.F. Schipper, and B.T. Sinh,2012: Determinants of risk: exposure and vulnerability. In: Managing the Risks of Extreme Events and Disasters to AdvanceClimate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach,G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA,pp. 65-108.2Determinants of Risk:Exposure and Vulnerability Maraun, D., & Widmann, M. (2018). Statistical Downscaling and Bias Correction for Climate Research. Cambridge: Cambridge University Press. Doi: 10.1017/9781107588783.
Cyril Ray, Clément Iphar, Aldo Napoli. Methodology for Real-Time Detection of AIS Falsification.Maritime Knowledge Discovery and Anomaly Detection Workshop, Jul 2016, Ispra, Italy. pp.74-77 -ISBN 978-92-79-61301-2. hal-01421910.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E. & Engen Skaugen, T. (2012) Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences 9, 6185-6201.
Jacob, D. et al. (2013), EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg Environ Change, 14(2), 563–578, doi: 10.1007/s10113-013-0499-2.
Scoccimarro, Enrico & Gualdi, Silvio & Bellucci, Alessio & Zampieri, Matteo & Navarra, Antonio. (2013). Intense precipitation extremes in a warmer climate: results from CMIP5 models.
Teutschbein, C. and Seibert, J.: Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? DOI:10.5194/hess-17-5061-2013. Hydrol. Earth Syst. Sci., 17, 5061–5077, 2013 (www.hydrol-earth-syst-sci.net/17/5061/2013/).
USACE (2000). HEC-HMS hydrologic modeling system user’s manual. Hydrologic Engineering Center, Davis, CA.