Affiliation:
1. Free University of Bozen-Bolzano
2. AiAqua S.r.l.
Abstract
Abstract
Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 hours of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies.
Publisher
Research Square Platform LLC
Reference70 articles.
1. Abera, Wuletawu and Antonello, Andrea and Franceschi, Silvia and Formetta, Giuseppe and Rigon, Riccardo (2014) The uDig Spatial Toolbox for Hydro-Geomorphic Analysis. Geomorphological Techniques 2(4): 19 British Society for Geomorphology, eng, Hydrology;geomorphology;GIS;Open source;catchment analysis;network extraction, 2047-0371
2. Avesani, Diego and Zanfei, Ariele and Di Marco, Nicola and Galletti, Andrea and Ravazzolo, Francesco and Righetti, Maurizio and Majone, Bruno (2022) Short-term hydropower optimization driven by innovative time-adapting econometric model. Applied Energy 310: 118510 https://doi.org/10.1016/j.apenergy.2021.118510, eco, Electricity prices forecast, hydro, Hydropower generation, Short-term hydropower optimization, Storage reservoir management, Time-adapting econometric models, March, 2023-04-17, en, 0306-2619
3. Beven, K. (2012) Rainfall-{Runoff} {Modelling}: {The} {Primer}: {Second} {Edition}. John Wiley and Sons, Pages: 457, 10.1002/9781119951001, English, Rainfall-{Runoff} {Modelling}, 978-0-470-71459-1, Rainfall-{Runoff} {Modelling}: {The} {Primer}: {Second} {Edition}
4. Beven, Keith and Feyen, Jan (2002) The {Future} of {Distributed} {Modelling}. Hydrological Processes 16(2): 169--172 https://doi.org/10.1002/hyp.325, 2023-12-04, en, 1099-1085, Copyright © 2002 John Wiley & Sons, Ltd.
5. Bl öschl, G ünter and Reszler, Christian and Komma, J ürgen (2008) A spatially distributed flash flood forecasting model. Environmental Modelling & Software 23(4): 464--478 https://doi.org/10.1016/j.envsoft.2007.06.010, Distributed modelling, Dominant processes concept, Floods, Forecasting, Kalman Filter, Model accuracy, Parameter identification, Stream routing, April, 2023-12-04, 1364-8152