Towards benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables in predictive modelling contexts
- 1Department of Engineering, Roma Tre University, Rome, Italy (papacharalampous.georgia@gmail.com)
- 2Department of Civil Engineering, School of Engineering, University of Patras, Patras, Greece (geopap@upatras.gr)
- 3Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Zographou, Greece (gpapacharalampous@hydro.ntua.gr)
- 4Hellenic Air Force General Staff, Hellenic Air Force, Cholargos, Greece (montchrister@gmail.com)
- 5Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Zographou, Greece (hristos@itia.ntua.gr)
We discuss possible pathways towards reducing uncertainty in predictive modelling contexts in hydrology. Such pathways may require big datasets and multiple models, and may include (but are not limited to) large-scale benchmark experiments, forecast combinations, and predictive modelling frameworks with hydroclimatic time series analysis and clustering inputs. Emphasis is placed on the newest concepts and the most recent methodological advancements for benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables, derived by collectively exploiting diverse essentials of studying and modelling hydroclimatic variability and change (from both the descriptive and predictive perspectives). Our discussions are supported by big data (including global-scale) investigations, which are conducted for several hydroclimatic variables at several temporal scales.
How to cite: Papacharalampous, G. and Tyralis, H.: Towards benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables in predictive modelling contexts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1840, https://doi.org/10.5194/egusphere-egu21-1840, 2021.
