Next Article in Journal
Contribution of Small Phytoplankton to Primary Production in the Northern Bering and Chukchi Seas
Previous Article in Journal
Assessing Groundwater Withdrawal Sustainability in the Mexican Portion of the Transboundary Santa Cruz River Aquifer
Article

Surface Water Flow Balance of a River Basin Using a Shallow Water Approach and GPU Parallel Computing—Pescara River (Italy) as Test Case

1
Department of Engineering and Geology (INGEO), University of “G. D’Annunzio”, Chieti-Pescara, 66013 Chieti, Italy
2
INDAM Research Group GNCS, National Institute of Advanced Mathematics, National Group of Scientific Computing, University of “G. D’Annunzio”, Chieti-Pescara, 66013 Chieti, Italy
3
Academy of Sciences of Abruzzo Region, Via Santa Giusta 23, 67100 L’Aquila, Italy
4
Environmental and Maritime Hydraulic Laboratory (LIam), Civil, Construction-Architectural and Environmental Engineering Department (DICEAA), University of L’Aquila, 67100 L’Aquila, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Pankaj Kumar
Water 2022, 14(2), 234; https://doi.org/10.3390/w14020234
Received: 9 December 2021 / Revised: 4 January 2022 / Accepted: 9 January 2022 / Published: 14 January 2022
(This article belongs to the Section Hydrology)
The analysis and prevention of hydrogeological risks plays a very important role and, currently, much attention is paid to advanced numerical models that correspond more to physical reality and whose aim is to reproduce complex environmental phenomena even for long times and on large spatial scales. Within this context, the feasibility of performing an effective balance of surface water flow relating to several months was explored, based on accurate hydraulic and mathematical-numerical models applied to a system at the scale of a hydrographic basin. To pursue this target, a 2D Riemann–Godunov shallow-water approach, solved in parallel on a graphical processing unit (GPU), able to drastically reduce calculation time, and implemented into the RiverFlow2D code (2017 version), was selected. Infiltration and evapotranspiration were included but in a simplified way, in order to face the calibration and validation simulations and because, despite the parallel approach, it is very demanding even for the computer time requirement. As a test case the Pescara river basin, located in Abruzzo, Central Italy, covering an area of 813 km2 and well representative of a typical medium-sized basin, was selected. The topography was described by a 10 × 10 m digital terrain model (DTM), covered by about 1,700,000 triangular elements, equipped with 11 rain gauges, distributed over the entire area, with some hydrometers and some fluviometric stations. Calibration, and validation were performed considering the flow data measured at a station located in close proximity to the mouth of the river. The comparison between the numerical and measured data, and also from a statistical point of view, was quite satisfactory. A further important outcome was the capability to highlight any differences between the numerical flow-rate balance carried out on the basis of the contributions of all known sources and the values actually measured. This characteristic of the applied modeling allows better calibration and verification not only of the effectiveness of much more simplified approaches, but also the entire network of measurement stations and could suggest the need for a more in-depth exploration of the territory in question. It would also enable the eventual identification of further hidden supplies of water inventory from underground sources and, accordingly, to enlarge the hydrographic and hydrogeological border of the basin under study. Moreover, the parallel computing platform would also allow the development of effective early warning systems, for example, of floods. View Full-Text
Keywords: shallow water modeling; basin hydrological balance; finite volume method; hydrological risk; parallel modeling GPU shallow water modeling; basin hydrological balance; finite volume method; hydrological risk; parallel modeling GPU
Show Figures

Figure 1

MDPI and ACS Style

Pasculli, A.; Longo, R.; Sciarra, N.; Di Nucci, C. Surface Water Flow Balance of a River Basin Using a Shallow Water Approach and GPU Parallel Computing—Pescara River (Italy) as Test Case. Water 2022, 14, 234. https://doi.org/10.3390/w14020234

AMA Style

Pasculli A, Longo R, Sciarra N, Di Nucci C. Surface Water Flow Balance of a River Basin Using a Shallow Water Approach and GPU Parallel Computing—Pescara River (Italy) as Test Case. Water. 2022; 14(2):234. https://doi.org/10.3390/w14020234

Chicago/Turabian Style

Pasculli, Antonio, Roberto Longo, Nicola Sciarra, and Carmine Di Nucci. 2022. "Surface Water Flow Balance of a River Basin Using a Shallow Water Approach and GPU Parallel Computing—Pescara River (Italy) as Test Case" Water 14, no. 2: 234. https://doi.org/10.3390/w14020234

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop