A “one stop shop” runoff simulation model blending hydrological data boundaries

K. Risva, D. Nikolopoulos, E. Michailidi, and A. Efstratiadis, A “one stop shop” runoff simulation model blending hydrological data boundaries, Water, 18 (20), 2583, doi:10.3390/w18202583, 2026.

[doc_id=2660]

[English]

While literature offers a plethora of hydrological models, their practical implementation is still hampered by the amount of information needed, either to support calibration or to provide the essential inputs for a physically-based description of underlying pro-cesses. To leverage the diverse data sources that may be available across a river basin at multiple levels of detail, a hybrid simulation scheme is introduced. Its architecture synthesizes the three characteristic schematization levels, i.e., distributed, semi-distributed and lumped. The first involves rainfall-runoff transformations and soil moisture balance processes, which are represented through a modified NRCS-CN scheme on a cell basis. The semi-distributed level involves surface runoff routing over the soil cells and along the river network, via a novel velocity method that accounts for the concept of event-varying time of concentration. Finally, a lumped approach is ap-plied for soil and groundwater processes, by means of conceptual layers receiving the distributed vertical fluxes from the overlying cells to produce interflow and baseflow. The model runs in event-based mode and can easily be extended for continuous simu-lations, to support associated engineering studies. Its overall advantage is the flexibility of acquiring its inputs from easily retrievable spatial information, thus ensuring balance between data requirements, parsimony with respect to parameter setting, and accuracy in the description of process heterogeneity.

PDF Full text (9164 KB)

See also: https://www.mdpi.com/2073-4441/18/20/2583

Our works referenced by this work:

1. A. Efstratiadis, and D. Koutsoyiannis, An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, UK, 1423–1428, doi:10.13140/RG.2.1.1038.6162, International Water Association, 2002.
2. A. Efstratiadis, I. Nalbantis, A. Koukouvinos, E. Rozos, and D. Koutsoyiannis, HYDROGEIOS: A semi-distributed GIS-based hydrological model for modified river basins, Hydrology and Earth System Sciences, 12, 989–1006, doi:10.5194/hess-12-989-2008, 2008.
3. A. Efstratiadis, A. D. Koussis, D. Koutsoyiannis, and N. Mamassis, Flood design recipes vs. reality: can predictions for ungauged basins be trusted?, Natural Hazards and Earth System Sciences, 14, 1417–1428, doi:10.5194/nhess-14-1417-2014, 2014.
4. A. Efstratiadis, A. Koukouvinos, E. Michailidi, E. Galiouna, K. Tzouka, A. D. Koussis, N. Mamassis, and D. Koutsoyiannis, Description of regional approaches for the estimation of characteristic hydrological quantities, DEUCALION – Assessment of flood flows in Greece under conditions of hydroclimatic variability: Development of physically-established conceptual-probabilistic framework and computational tools, Contractors: ETME: Peppas & Collaborators, Grafeio Mahera, Department of Water Resources and Environmental Engineering – National Technical University of Athens, National Observatory of Athens, 146 pages, September 2014.
5. E. Savvidou, A. Efstratiadis, A. D. Koussis, A. Koukouvinos, and D. Skarlatos, The curve number concept as a driver for delineating hydrological response units, Water, 10 (2), 194, doi:10.3390/w10020194, 2018.
6. E. Michailidi, S. Antoniadi, A. Koukouvinos, B. Bacchi, and A. Efstratiadis, Timing the time of concentration: shedding light on a paradox, Hydrological Sciences Journal, 63 (5), 721–740, doi:10.1080/02626667.2018.1450985, 2018.
7. K. Risva, D. Nikolopoulos, A. Efstratiadis, and I. Nalbantis, A framework for dry period low flow forecasting in Mediterranean streams, Water Resources Management, 32 (15), 4911–1432, doi:10.1007/s11269-018-2060-z, 2018.
8. K. Risva, D. Nikolopoulos, and A. Efstratiadis, Distributed hydrological modelling using spatiotemporally varying velocities, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-13402, doi:10.5194/egusphere-egu2020-13402, 2020.
9. A. Efstratiadis, P. Dimas, G. Pouliasis, I. Tsoukalas, P. Kossieris, V. Bellos, G.-K. Sakki, C. Makropoulos, and S. Michas, Revisiting flood hazard assessment practices under a hybrid stochastic simulation framework, Water, 14 (3), 457, doi:10.3390/w14030457, 2022.

Tagged under: Hydrological models