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Catchment classification and streamflow predictions in ungauged basins

Keywords: design-flood, low-flow indices, flow-duration curves, statistical regionalization, rainfall-runoff modelling
Fig. 1. Unsupervised classification of ~300 Italian gauged catchments into nine hydrological classes (Authors: Di Prinzio, A. Castellarin).
Fig. 2. Metauro river: prediction of low-flows along the stream network through geostatistical interpolation (Authors: S. Castiglioni, A. Castellarin)

The research activities aim at improving our current level of knowledge in the field of streamflow prediction (e.g. the flood occurs on average once every T years, low-flow indices, flow-duration curves, etc.) for catchment lacking streamflow observations (i.e. ungauged basins). This is a fundamental practical issue that needs to be addressed when dealing with several water related problems (e.g. flood-risk management; water-quality and water-availability assessments; feasibility of hydropower plants; design of drinking-water supply, irrigation and reclamation systems, etc.).

The main research activities are:

(A) Classification of river basins to improve the representation of spatio-temporal variability of streamflows. We are focusing on objective classification techniques that combine multivariate analysis techniques (e.g. Principal Component Analysis, Canonical Correlation Analysis) with unsupervised artificial neural networks, such as the Self Organising Maps (see e.g. Fig. 1).

(B) Statistical regionalization. We develop and test regionalization procedures to transfer hydrological information from donor gauged basins to ungauged basins. In particular, we are currently testing the potential of innovative geostatistical procedures (e.g. Fig. 2).

(C) Simulation of streamflows in ungauged catchments through mathematical rainfall-runoff models, whose parameters are identified through innovative techniques that do not require concurrent rainfall and streamflow observations for the site of interest, and are therefore suitable for ungauged basins.

Research activities are carried out in close collaboration with other national and international universities and research institutes (e.g. Polytechnic of Turin, Research Institute for Geo- Hydrological Protection, Italian NRC; GeoForschungsZentrum, GFZ, Potsdam, Germany; Technische Universität Wien, Vienna, Austria; Centre for Ecology & Hydrology, Wallingford, UK; U.S. Geological Survey, Northborough, MA, U.S.; Institute for Environment and Sustainability, JRC, European Commission).

Main publications

Archfield, S.A., A. Pugliese, A. Castellarin, J. O. Skøien, and J. E. Kiang (2013): Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach? Hydrol. Earth Syst. Sci., 17, 1575-1588

Castellarin, A., 2014. Regional prediction of flow-duration curves using a three-dimensional kriging. J. Hydrol. 513, 179–191.

Castellarin, A., G. Botter, D.A. Hughes, S. Liu, T.B.M.J. Ouarda, J. Parajka, D. Post, M. Sivapalan, C. Spence, A. Viglione and R. Vogel (2013): Prediction of flow duration curves in ungauged basins, Chp. 7 in Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales (Eds. G. Blöschl, M. Sivapalan, T. Wagener, A. Viglione, H. Savenije, ISBN-13: 9781107028180, 135-162.

Castiglioni, S., A. Castellarin, A. Montanari, J.O. Skøien, G. Laaha, G. Blöschl (2011): Smooth regional estimation of low-flow indices: physiographical space based interpolation and topkriging, Hydrol. Earth Syst. Sci., 15, 715-727.

Ceola, S., Arheimer, B., Baratti, E., Blöschl, G., Capell, R., Castellarin, A., Freer, J., Han, D., Hrachowitz, M., Hundecha, Y., Hutton, C., Lindström, G., Montanari, A., Nijzink, R., Parajka, J., Toth, E., Viglione, A., Wagener, T., 2015. Virtual laboratories: new opportunities for collaborative water science. Hydrol Earth Syst Sci 19, 2101–2117.

Ceola, S., Bertuzzo, E., Singer, G., Battin, T.J., Montanari, A., Rinaldo, A. (2014). Hydrologic controls on basin-scale distribution of benthic invertebrates. Water Resour. Res. 50, 2903–2920.

Di Prinzio, M., A. Castellarin, E. Toth (2011): Data-driven catchment classification: application to the PUB problem, Hydrol. Earth Syst. Sci., 15, 1921-1935.

Lombardi, L., E .Toth, A. Castellarin, A.Montanari, A. Brath (2012): Calibration of a rainfall–runoff model at regional scale by optimising river discharge statistics: Performance analysis for the average/low flow regime, Physics and Chemistry of the Earth, 42-44, 77-84.

Pugliese, A., Farmer, W.H., Castellarin, A., Archfield, S.A., Vogel, R.M. (2016): Regional flow duration curves: Geostatistical techniques versus multivariate regression. Adv. Water Resour. 96, 11–22.

Pugliese, A., Castellarin, A., Brath, A., 2014. Geostatistical prediction of flow–duration curves in an index-flow framework. Hydrol Earth Syst Sci 18, 3801–3816.

Toth, E. (2009): Classification of hydrometeorological conditions and multiple artificial neural networks for streamflow forecasting, Hydrol. Earth Syst. Sci., 13, 1555-1566.

Toth, E.(2013) Catchment classification based on characterisation of streamflow and precipitation time series, Hydrol. Earth Syst. Sci., 17, 1149- 1159.

Viglione, A., A. Castellarin, M. Rogger, R. Merz, and G. Blöschl (2012): Extreme rainstorms: Comparing regional envelope curves to stochastically generated events, Water Resources Research, 48, W01509.

Wagener, T., and A. Montanari (2011), Convergence of approaches toward reducing uncertainty in predictions in ungauged basins, Water Resour. Res., 47, W06301.

Research projects

FP7-ENV-2013 ID. 603587-2 Project: SWITCHON Sharing Water-related Information to Tackle Changes in the Hydrosphere - for Operational Needs.

POR-FESR 2014-2020 Project: INFRASAFE – Monitoraggio intelligente per infrastrutture sicure.