97
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Combining a data-driven approach with seasonal forecast data to predict reservoir water volume in the Mediterranean area

ORCID Icon, ORCID Icon & ORCID Icon
Pages 764-781 | Received 02 Jul 2022, Accepted 07 Feb 2023, Published online: 12 Apr 2023

References

  • Ahmad, A., et al., 2014. Reservoir optimization in water resources: a review. Water Resources Management, 28 (11), 3391–3405. doi:10.1007/s11269-014-0700-5.
  • Arnal, L., et al., 2018. Skilful seasonal forecasts of streamflow over Europe? Hydrology and Earth System Sciences, 22 (4), 2057–2072. doi:10.5194/hess-22-2057-2018.
  • Arnone, E., et al., 2014. Strategies investigation in using artificial neural network for landslide susceptibility mapping: application to a sicilian catchment. Journal of Hydroinformatics, 16 (2), 502–515. doi:10.2166/hydro.2013.191.
  • Arnone, E., et al., 2020a. Droughts prediction: a methodology based on climate seasonal forecasts. Water Resources Management, 34 (14), 4313–4328. doi:10.1007/s11269-020-02623-3.
  • Arnone, E., et al., 2020b. The drought-alert decision support system for water resources management. Desalination and Water Treatment, 194, 304–314. doi:10.5004/dwt.2020.26033.
  • Awchi, T.A., 2014. River discharges forecasting In Northern Iraq using different ANN techniques. Water Resources Management, 28 (3), 801–814. doi:10.1007/s11269-014-0516-3.
  • Bonaccorso, B., Cancelliere, A., and Rossi, G., 2015. Probabilistic forecasting of drought class transitions in Sicily (Italy) using standardized precipitation index and North Atlantic oscillation index. Journal of Hydrology, 526, 136–150. doi:10.1016/j.jhydrol.2015.01.070.
  • Buontempo, C., et al., 2018. What have we learnt from EUPORIAS climate service prototypes? Climate Services, 9, 21–32. doi:10.1016/j.cliser.2017.06.003.
  • Caloiero, T., et al., 2018. Drought analysis in Europe and in the mediterranean basin using the standardized precipitation index. Water, 10 (8), 1043. doi:10.3390/w10081043.
  • Chaves, P. and Chang, F.-J., 2008. Intelligent reservoir operation system based on evolving artificial neural networks. Advances in Water Resources, 31 (6), 926–936. doi:10.1016/j.advwatres.2008.03.002.
  • Clark, R.T., et al., 2017. Skilful seasonal predictions for the European energy industry. Environmental Research Letters, 12 (2), 024002. doi:10.1088/1748-9326/aa57ab.
  • Crochemore, L., et al., 2017. Seasonal streamflow forecasting by conditioning climatology with precipitation indices. Hydrology and Earth System Sciences, 21 (3), 1573–1591. doi:10.5194/hess-21-1573-2017.
  • De Felice, M., Alessandri, A., and Catalano, F., 2015. Seasonal climate forecasts for medium-term electricity demand forecasting. Applied Energy, 137 (c), 435–444. doi:10.1016/j.apenergy.2014.10.030.
  • Doblas-Reyes, F.J., et al., 2013. Seasonal climate predictability and forecasting: status and prospects. WIREs Climate Change, 4 (4), 245–268. doi:10.1002/wcc.217.
  • Ehret, U., et al., 2012. HESS Opinions “Should we apply bias correction to global and regional climate model data?” Hydrology and Earth System Sciences, 16 (9), 3391–3404. doi:10.5194/hess-16-3391-2012.
  • EIA, 2010. International energy outlook 2010. Paris, France: Energy Information Administration.
  • El-Shafie, A., et al., 2012. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia. Hydrology and Earth System Sciences, 16 (4), 1151–1169. doi:10.5194/hess-16-1151-2012.
  • El-Shafie, A., Taha, M.R., and Noureldin, A., 2007. A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resources Management, 21 (3), 533–556. doi:10.1007/s11269-006-9027-1.
  • Essenfelder, A.H., et al., 2020. Smart climate hydropower tool: a machine-learning seasonal forecasting climate service to support cost-benefit analysis of reservoir management. Atmosphere, 11 (12), 12. doi:10.3390/atmos11121305.
  • Fei, T. and Shuang-Qing, X., 2012. Definition of business as usual and its impacts on assessment of mitigation efforts. Advances in Climate Change Research, 3 (4), 212–219. doi:10.3724/SP.J.1248.2012.00212.
  • Forestieri, A., et al., 2018. The impact of climate change on extreme precipitation in Sicily, Italy. Hydrological Processes, 32 (3), 332–348. doi:10.1002/hyp.11421.
  • Fowler, H.J., et al., 2021. Anthropogenic intensification of short-duration rainfall extremes. Nature Reviews Earth & Environment, 2 (2), 107–122. doi:10.1038/s43017-020-00128-6.
  • Gharib, A. and Davies, E.G.R., 2021. A workflow to address pitfalls and challenges in applying machine learning models to hydrology. Advances in Water Resources, 152.
  • Glahn, H.R. and Lowry, D.A., 1972. The use of model output statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology and Climatology, 11 (8), 1203–1211. doi:10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2.
  • Gudmundsson, L., et al., 2012. Technical note: downscaling RCM precipitation to the station scale using statistical transformatio–s - a comparison of methods. Hydrology and Earth System Sciences, 16 (9), 3383–3390. doi:10.5194/hess-16-3383-2012.
  • Hadiyan, P.P., Moeini, R., and Ehsanzadeh, E., 2020. Application of static and dynamic artificial neural networks for forecasting inflow discharges, case study: Sefidroud Dam reservoir. Sustainable Computing-Informatics & Systems, 8, 337–352.
  • Hassan, M., et al., 2015. Predicting streamflows to a multipurpose reservoir using artificial neural networks and regression techniques. Earth Science Informatics, 8 (2), 337–352. doi:10.1007/s12145-014-0161-7.
  • Hoskins, B., 2013. The potential for skill across the range of the seamless weather-climate prediction problem: a stimulus for our science. Quarterly Journal of the Royal Meteorological Society, 139 (672), 573–584. doi:10.1002/qj.1991.
  • ISPRA, 2016. Bollettino Siccità, Italia. Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA). Available from: https://www.isprambiente.gov.it/pre_meteo/siccitas/html/2016/index_2016.html.
  • Johnson, S.J., et al., 2019. SEAS5: the new ECMWF seasonal forecast system. Geoscientific Model Development, 12 (3), 1087–1117. doi:10.5194/gmd-12-1087-2019.
  • Klein, W.H. and Glahn, H.R., 1974. Forecasting local weather by means of model output statistics. Bulletin of the American Meteorological Society, 55 (10), 1217–1227. doi:10.1175/1520-0477(1974)055<1217:FLWBMO>2.0.CO;2.
  • Kouhestani, S., et al., 2016. Projection of climate change impacts on precipitation using soft-computing techniques: a case study in Zayandeh-rud Basin, Iran. Global and Planetary Change, 144, 158–170. doi:10.1016/j.gloplacha.2016.07.013.
  • Maraun, D., et al., 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48, RG3003. doi:10.1029/2009RG000314
  • Maraun, D., 2012. Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophysical Research Letters, 39, L06706. doi:10.1029/2012GL051210.
  • Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the Asabe, 50 (3), 885–900. doi:10.13031/2013.23153.
  • Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6.
  • Niu, W.-J. and Feng, Z.-K., 2021. Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management. Sustainable Cities and Society, 64, 102562. doi:10.1016/j.scs.2020.102562.
  • Pearson, K., 1895. Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240–242. doi:10.1098/rspl.1895.0041.
  • Peñuela, A., Hutton, C., and Pianosi, F., 2020. Assessing the value of seasonal hydrological forecasts for improving water resource management: insights from a pilot application in the UK. Hydrology and Earth System Sciences, 24 (12), 6059–6073. doi:10.5194/hess-24-6059-2020.
  • Piani, C., et al., 2010. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395 (3–4), 199–215. doi:10.1016/j.jhydrol.2010.10.024.
  • Pyrina, M., et al., 2021. Statistical seasonal prediction of European summer mean temperature using observational, reanalysis, and satellite data. Weather and Forecasting, 36 (4), 1537–1560.
  • Raoult, B., et al., 2017. Climate service develops user-friendly data store [online]. ECMWF. Available from: https://climate.copernicus.eu/sites/default/files/2018-04/CDS.pdf.
  • Rozos, E., 2019. Machine learning, urban water resources management and operating policy. Resources, 8 (4), 173. doi:10.3390/resources8040173.
  • Sanchez, G.M., et al., 2020. Forecasting water demand across a rapidly urbanizing region. Science of the Total Environment, 730.
  • SIAS, 2016. Siccità invernale 2015-2016: siccità periodo Dicembre 20–5 - Febbraio 2016 Temperature anomale Febbraio 2016. Servizio Informativo Agrometereologico Siciliano (SIAS) - Regione Siciliana. Available from: http://www.sias.regione.sicilia.it/NHEOWL007_78.htm.
  • Treppiedi, D., et al., 2021. Detecting precipitation trend using a multiscale approach based on quantile regression over a Mediterranean area. International Journal of Climatology, 41 (13), 5938–5955. doi:10.1002/joc.7161.
  • Vannitsem, S. and Nicolis, C., 2008. Dynamical properties of model output statistics forecasts. Monthly Weather Review, 136 (2), 405–419. doi:10.1175/2007MWR2104.1.
  • Viel, C., et al., 2016. How seasonal forecast could help a decision maker:an example of climateservice for water resource management. Advances in Science and Research, 13, 51–55. doi:10.5194/asr-13-51-2016.
  • WWA, 2017. Euro-Mediterranean he–t - summer 2017. World Weather Attribution (WWA). Available from: https://www.worldweatherattribution.org/euro-mediterranean-heat-summer-2017/.
  • Yang, S., et al., 2019. Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. Journal of Hydrology, 579, 124229. doi:10.1016/j.jhydrol.2019.124229.
  • Zribi, M., et al., 2020. Introduction, and M. Zribi, eds. Water resources in the Mediterranean region. Amsterdam, Netherlands: Elsevier, xv–xix.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.