Software:RS MINERVE

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RS MINERVE
RS MINERVE Logo.svg
MINERVE system.jpg
Interface of RS MINERVE
Developer(s)CREALP, HydroCosmos
Written inC#
Operating systemWindows
Available inEnglish
TypeHydrological modeling
WebsiteDownload RS MINERVE

RS MINERVE is a software for the simulation of free surface run-off flow generation and propagation.[1][2] It allows to build complex hydrological and hydraulic models following a semi-distributed approach. The software is capable of representing not only the main hydrological processes such as snow and glacier melt, surface and sub-surface runoff, but also hydraulic elements such as reservoirs, spillways, water intakes, turbines and pumps, among others. This modeling platform can be used as a standalone tool, or as part of complex hydrological forecast systems.

RS MINERVE is based on the same concept as Routing System II,[3][4] a software developed at the Laboratory of Hydraulic Constructions (LCH) at the Swiss Federal Institute of Technology in Lausanne (EPFL). The RS MINERVE software has been jointly developed by CREALP and HydroCosmos, with the collaboration of the Swiss Federal Institute of Technology in Lausanne (EPFL), the Polytechnic University of Valencia (UPV) and the Hydro10 Association.

RS MINERVE program is freely distributed and can be downloaded from the CREALP's website.

Functionality

The global analysis of a hydrologic-hydraulic network is essential in numerous decision-making situations such as the management of water resources, the optimization of hydropower plant operations, the design and regulation of spillways or the development of appropriate flood protection concepts. RS MINERVE makes such analyses accessible to a broad public through its user-friendly interface and its valuable possibilities. In addition, thanks to its modular framework, the software can be developed and adapted to specific needs.

Main features

Hydrological models

The tool contains different hydrological models for rainfall-runoff that can be used simultaneously, such as GSM (Glacial Snow Melt), SWMM,[5] SOCONT,[6] SAC-SMA,[7][8] GR4J[9] and HBV.[10][11][12]

Hydraulic structures

The combination of hydraulic structure models (reservoirs, turbines, spillways,…) can also reproduce complex hydropower schemes. In addition, a hydropower model computes the net height and the linear pressure losses, providing energy production values and total income based on the turbine performance and on the sale price of energy. A consumption model calculates water deficits for consumptive uses of cities, industries and/or agriculture. A structure efficiency model computes discharge losses in a structure such a canal or a pipe by considering a simple efficiency coefficient.

Expert modules

RS MINERVE - GIS user interface

The Expert modules, specifically created for research or complex studies, enable in-depth evaluation of hydrologic and hydraulic results.

  • The Calibrator module allows to automatically calibrate the model parameters using different optimization algorithms: Shuffled Complex Evolution (SCE-UA),[13][14] Coupled Latin Hipercube[15] with Rosenbrock,[16] and Uniform Adaptive Monte Carlo.[17][18] The best set of hydrological parameters can then be defined based on a user-defined objective function.
  • The Scenario simulation tool, used to assess the response of the model results to the initial values of the state variables, to the model parameters or to the hydrometeorological inputs.
  • In order to analyse the sensitivity of the model when parameters and/or initial conditions change, the Stochastic simulation module launches multiple simulations based on the statistical behaviour of the analysed variables. Different predefined probability distributions can be used to statistically characterise the parameters and/or the initial conditions: uniform, normal, log-normal and exponential.
  • The Time-slice simulation module helps partitioning the simulation length in shorter periods (or time-slices), thus reducing the computation duration and making it possible to run complex models even with limited computation capacities.
  • The GIS module allows to easily create complete hydrological models from structured vector layers.

Others

  • Ability to work from the command line in R or VBS languages for the automatization of simulations.
  • Optimization of hydraulic management scenarios.
  • Implementation of external plugins to extend the functionality of the software for tailored needs.
  • Possibility of inserting images into the models' graphical interface.

Applications

Hydrological modelling

RS MINERVE software has been used for a multitude of hydrological models in numerous case studies in Switzerland,[19][20][21] Peru,[22][23][24] Spain,[25][26] France,[27] Mexico[28] or Buthan,[29] among others.

Since its modeling scheme can be adapted to any context, its field of application spans different domains such as the evaluation of water resources, the definition of maximum floods, or the assessment of climate change impacts on hydrographic basins.

Flood forecasting

Switzerland

Platform to access hydrological forecast for the Upper Rhone River based on RS MINERVE simulations

A hydrologic-hydraulic model has been developed for the Upper Rhone River basin in Switzerland. The so-called MINERVE system[30][31] is currently operational in the Canton of Valais for real-time flood forecasting and management, providing automatic warnings to the crisis cell of the Canton as well as proposing preventive emptying operations of dam reservoirs to reduce potential flood damage. The system is connected with a database for real-time data transfer and a website has been created to provide information for flood management, such as warning levels, hydrological forecasts at the main control points of the Rhone River and its tributaries, precipitation forecasts over the whole basin, snow cover state and reservoirs water levels. Besides, a hydrological call center has been established for supporting the crisis cell during risked event situations.

Peru

RS MINERVE is used in real-time for the hydrological forecasting of several basins monitored by the Peruvian National Meteorological and Hydrological Service (SENAMHI). The information is made available to the public[32] and represents the flow forecasts in basins where hydrological data exist and hydrological models are calibrated through the RS MINERVE platform, which is used to forecast short-term flows considering the ETA-SENAMHI and GFS rainfall forecasts.

Extreme floods analysis

A hydrological modelling tool such as RS MINERVE can be used for the analysis of extreme floods as well. For instance, the CRUEX++ methodology,[33] completed in 2017, allows for estimation of the hydrograph of extreme floods in alpine catchments and for verification of dam safety regarding floods. A CRUEX++ Plugin was then implemented in RS MINERVE to allow combining different user defined rainfall events with initial terrain conditions for the hydrological simulation.

The CRUEX++ Plugin provides an overview of the hydrographs and the related water levels to facilitate the determination of design and safety floods for dams. Furthermore, the plugin allows fitting statistical distributions to systematic flow data. Both conventional statistical distributions such as Generalized Extreme Value, Generalized Pareto and Pearson III and the particular type of upper-bounded statistical distributions have been implemented in the plugin. Upper-bounded statistical distributions allow for taking into account the probable maximum flood (PMF) in statistical analyses.

Advantages

  • User-friendly style and design of the interface.
  • The program has a simple import of data in the database module.
  • Parameters and initial conditions of individual elements can also be quickly imported and exported.
  • It allows building semi-distributed hydrological models through its GIS tool using vector layers of sub-basins and rivers.
  • It allows long-range analyses thanks to its command line management by defining scripts to launch the calculation module without having to open the program.
  • You can define a wide variety of water resource management options, including temporary limitations or assignments based on the result of any other element.
  • It is constantly developed based on the feedback of users.

Disadvantages

  • There is no water management module based on consumption priorities.
  • It uses a specific module for the management of databases and it is not possible to edit them without it.
  • The program interface is only available in English.

See also

References

  1. ↑ Foehn, Alain; García Hernández, Javier; Roquier, Bastien; Fluixá Sanmartín, Javier; Brauchli, Tristan; Paredes Arquiola, Javier; De Cesare, Giovanni (2020). RS MINERVE – User manual. Switzerland: CREALP. 
  2. ↑ García Hernández, Javier; Foehn, Alain; Fluixá Sanmartín, Javier; Roquier, Bastien; Brauchli, Tristan; Paredes Arquiola, Javier; De Cesare, Giovanni (2020). RS MINERVE – Technical manual. Switzerland: CREALP. 
  3. ↑ Dubois, J. and Boillat, J.-L. (2000). Routing System - Modélisation du routage des crues dans des systèmes hydrauliques à surface libre. Communication 9 du Laboratoire de Constructions Hydrauliques, Ed. A. Schleiss, Lausanne.
  4. ↑ García Hernández, J., Jordan, F., Dubois, J. and Boillat, J.-L. (2007). Routing System II - Modélisation d’écoulements dans des systèmes hydrauliques. Communication 32 du Laboratoire de Constructions Hydrauliques, Ed. A. Schleiss, Lausanne.
  5. ↑ Metcalf and Eddy, Water Resources Engineers, and University of Florida 1971. Storm Water Management Model, US EPA, Washington, D.C. Vol. I - Final Report, 11024DOC 7/71. Vol. II - Verification and Testing, 11024DOC 8/71. Vol. III - User's Manual, 11024DOC 9/71. Vol. IV - Program Listing, 11024DOC 10/71.
  6. ↑ Schaefli, B.; Hingray, B.; Niggli, M.; Musy, A. (2005-07-05). "A conceptual glacio-hydrological model for high mountainous catchments" (in en). Hydrology and Earth System Sciences 9 (1/2): 95–109. doi:10.5194/hess-9-95-2005. ISSN 1607-7938. Bibcode: 2005HESS....9...95S. https://hess.copernicus.org/articles/9/95/2005/. 
  7. ↑ Burnash, R. J. C., Ferral, R. L., and McGuire, R. A. (1973). A generalized streamflow simulation system – Conceptual modelling for digital computers. US Department of Commerce, National Weather Service and State of California, Department of Water Resources, 204 pp.
  8. ↑ Burnash, R. (1995) The NWS River Forecast System-Catchment Modeling. In: Singh, V., Ed., Computer Models of Watershed Hydrology, Water Resources Publication, Colorado, 311-366. ISBN: 1-887201-74-2.
  9. ↑ Perrin, Charles; Michel, Claude; Andréassian, Vazken (2003). "Improvement of a parsimonious model for streamflow simulation". Journal of Hydrology 279 (1–4): 275–289. doi:10.1016/S0022-1694(03)00225-7. Bibcode: 2003JHyd..279..275P. https://doi.org/10.1016/S0022-1694(03)00225-7. 
  10. ↑ Bergström, S. (1976). Development and application of a conceptual runoff model for Scandinavian catchments, SMHI Report RHO 7, Norrköping, 134 pp. [1]
  11. ↑ Bergström, S. (1995). The HBV model (Chapter 13). Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water Resources Publications, Highlands Ranch, Colorado, USA, 443–476. ISBN: 1887201742.
  12. ↑ Bergström, Sten; Lindström, Göran (2015-05-26). "Interpretation of runoff processes in hydrological modelling-experience from the HBV approach". Hydrological Processes 29 (16): 3535–3545. doi:10.1002/hyp.10510. ISSN 0885-6087. Bibcode: 2015HyPr...29.3535B. 
  13. ↑ Nelder, J. A.; Mead, R. (1965-01-01). "A Simplex Method for Function Minimization" (in en). The Computer Journal 7 (4): 308–313. doi:10.1093/comjnl/7.4.308. ISSN 0010-4620. https://academic.oup.com/comjnl/article-lookup/doi/10.1093/comjnl/7.4.308. 
  14. ↑ Mariani, Viviana Cocco; Justi Luvizotto, Luiz Guilherme; Guerra, Fábio Alessandro; dos Santos Coelho, Leandro (2011). "A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization" (in en). Applied Mathematics and Computation 217 (12): 5822–5829. doi:10.1016/j.amc.2010.12.064. https://linkinghub.elsevier.com/retrieve/pii/S009630031001266X. 
  15. ↑ McKay, M. D.; Beckman, R. J.; Conover, W. J. (1979). "A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code". Technometrics 21 (2): 239–245. doi:10.2307/1268522. ISSN 0040-1706. https://www.jstor.org/stable/1268522. 
  16. ↑ Rosenbrock, H. H. (1960-03-01). "An Automatic Method for Finding the Greatest or Least Value of a Function" (in en). The Computer Journal 3 (3): 175–184. doi:10.1093/comjnl/3.3.175. ISSN 0010-4620. https://academic.oup.com/comjnl/article-lookup/doi/10.1093/comjnl/3.3.175. 
  17. ↑ Markov chain Monte Carlo in practice. W. R. Gilks, S. Richardson, D. J. Spiegelhalter. London: Chapman & Hall. 1996. ISBN 0-412-05551-1. OCLC 34281847. https://www.worldcat.org/oclc/34281847. 
  18. ↑ Liu, Jun S. (2001). Monte Carlo strategies in scientific computing. New York: Springer. ISBN 0-387-95230-6. OCLC 45583527. https://www.worldcat.org/oclc/45583527. 
  19. ↑ Garcia Hernandez, J., Boillat, J.-L., Feller, I., and Schleiss, A. (2013). “Présent et futur des prévisions hydrologiques pour la gestion des crues. Le cas du Rhône alpin.” Mémoires de la Société Vaudoise des Sciences Naturelles, (25), 55–70. ISSN: 0037-9611.
  20. ↑ Foehn, Alain; García Hernández, Javier; De Cesare, Giovanni; Rinaldo, Andrea; Schaefli, Bettina; Schleiss, Anton (2020). "Estimation spatiale des précipitations et assimilation de données de débit pour la prévision hydrologique en milieu alpin". Wasser, Energie, Luft 112 (3): 175–182. https://issuu.com/swv_wel/docs/wel_3-sep_2020. 
  21. ↑ Foehn, A.; García Hernández, J.; Schaefli, Bettina; De Cesare, G.; Schleiss, A.J. (2021). "Spatialization of precipitation data for flood forecasting applied to the Upper Rhone River basin". Proceedings of the Conference Hydro 2016, Montreux, Switzerland. doi:10.48350/151292. https://boris.unibe.ch/151292/. 
  22. ↑ Muñoz, Randy; Huggel, Christian; Drenkhan, Fabian; Vis, Marc; Viviroli, Daniel (2021). "Comparing model complexity for glacio-hydrological simulation in the data-scarce Peruvian Andes" (in en). Journal of Hydrology: Regional Studies 37: 100932. doi:10.1016/j.ejrh.2021.100932. https://linkinghub.elsevier.com/retrieve/pii/S2214581821001610. 
  23. ↑ Astorayme, M., García, J., Suarez, W., Felipe, O., Huggel, C., Molina, W. (2015). Modelización hidrológica con un enfoque semidistribuido en la cuenca del río Chillón, Perú. Rev. Peru. Geo Atmosférica RGPA, 4, 109–124
  24. ↑ Pino-Vargas, Edwin; Chávarri-Velarde, Eduardo; Ingol-Blanco, Eusebio; Mejía, Fabricio; Cruz, Ana; Vera, Alissa (2022). "Impacts of Climate Change and Variability on Precipitation and Maximum Flows in Devil's Creek, Tacna, Peru" (in en). Hydrology 9 (1): 10. doi:10.3390/hydrology9010010. ISSN 2306-5338. 
  25. ↑ Fluixá-Sanmartín, Javier; Morales-Torres, Adrián; Escuder-Bueno, Ignacio; Paredes-Arquiola, Javier (2019). "Quantification of climate change impact on dam failure risk under hydrological scenarios: a case study from a Spanish dam". Natural Hazards and Earth System Sciences. doi:10.5194/nhess-2019-141. https://nhess.copernicus.org/preprints/nhess-2019-141/nhess-2019-141.pdf. 
  26. ↑ Chuquin, Daniel; Chuquin, Nelson; Chuquin, Juan; Castro, Lidia; Cepeda, Ramiro (2019). "Desarrollo de un modelo de precipitación escorrentía semi-distribuido para la evaluación de los recursos hídricos del río Jalón". Ciencia Digital 3 (1): 115–126. doi:10.33262/cienciadigital.v3i3.1.680. ISSN 2602-8085. http://cienciadigital.org/revistacienciadigital2/index.php/CienciaDigital/article/view/680. 
  27. ↑ Alperen, Cagri Inan; Artigue, Guillaume; Kurtulus, Bedri; Pistre, Séverin; Johannet, Anne (2021). "A Hydrological Digital Twin by Artificial Neural Networks for Flood Simulation in Gardon de Sainte-Croix Basin, France". IOP Conference Series: Earth and Environmental Science 906 (1): 012112. doi:10.1088/1755-1315/906/1/012112. ISSN 1755-1307. Bibcode: 2021E&ES..906a2112A. https://iopscience.iop.org/article/10.1088/1755-1315/906/1/012112. 
  28. ↑ Perez Luna, G., García Hernández, J., Rubio Gutiérrez, H. y Fluixá-Sanmartín, J. (2016). Gestión de recursos hídricos con un modelo semi-distribuido en la cuenca del río Fuerte, México. Proceedings XXVII Congreso Latinoamericano de Hidráulica-LADHI. International Association of Hydraulic Engineering and Research, IAHR, 28-30 September, Lima, Perú. ID259, pp. 2931-2940. ISBN: 978-612-47527-0-4.
  29. ↑ Sharma, Vasker; Jorden, Jessica; Manso, Pedro; Cesare, Giovanni De (2021). "Development of a Semi-Distributed Hydrological Model for Glaciated Punatshangchu Basin in Bhutan". Journal of Applied Engineering, Technology and Management 1 (1): 1–13. doi:10.54417/jaetm.v1i1.19. ISSN 2789-0848. https://journal.jnec.edu.bt/index.php/jaetm/article/view/19. 
  30. ↑ García Hernández, J. (2011). Flood management in a complex river basin with a real-time decision support system based on hydrological forecasts. Communication 48 du Laboratoire de Constructions Hydrauliques, Ed. A. Schleiss, EPFL, Lausanne.
  31. ↑ García Hernández, J., Claude, A., Paredes Arquiola, J., Roquier, B., and Boillat, J. L. (2014). Integrated flood forecasting and management system in a complex catchment area in the Alps—implementation of the MINERVE project in the Canton of Valais. Swiss Competences in River Engineerig and Restoration, Schleiss, Speerli & Pfammatter, London, 87–97.
  32. ↑ "SENAMHI - Flow forecast". https://www.senamhi.gob.pe/?p=pronostico-caudales. 
  33. ↑ Zeimetz, Fränz. "Abschätzung von Extremhochwassern bei Talsperren nach der Methode CRUEX++". Wasser, Energie, Luft 109 (4): 261–270. https://issuu.com/swv_wel/docs/wel_4_2017. 

Download

RS MINERVE is a free software that can be downloaded from:

www.crealp.ch/fr/accueil/outils-services/logiciels/rs-minerve/telechargement-rsm.html


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