A Nowcasting System for Hydrometeorological Hazard Assessment of Landslides and Flooding – Part 2: On Verification and Validation Process
DOI:
https://doi.org/10.11137/1982-3908_2024_47_62979Keywords:
Data integration, Dynamical risk mapping, Validation methodsAbstract
This work presents a comprehensive analysis of the verification and evaluation of a system designed for forecasting hydrometeorological risks, with a specific focus on landslides and floods in a defined region. The proposed system continuously integrates real-time meteorological and hydrological data to provide precise and timely information on potential risk events. Through a meticulously conducted case study, the practical application of the system is highlighted, demonstrating its effectiveness in monitoring and forecasting risk events in real-world scenarios. The work addresses fundamental challenges associated with the validation of complex systems, emphasizing the imperative need for robust verification and validation methods. Furthermore, the unique characteristics of complex systems are discussed, and their implications for effective modeling and validation processes are elucidated. A detailed presentation of the benchmark case study results includes analyses of rainfall intensity, dynamic mapping of landslide susceptibility, river height monitoring, and forecast comparisons. These findings are complemented by visual aids in a web interface that facilitate a comprehensive understanding of the system’s performance under real conditions. Key insights are emphasized, highlighting the crucial role of the proposed system in advancing knowledge in the field of hydrometeorological risk assessment and forecasting. The conclusions succinctly summarize the main results and underscore the critical importance of systems like the proposed one in mitigating these risks.
References
Beven, K.J. 2012, Rainfall-Runoff Modelling: the Primer, 2nd edn, John Wiley & Sons, Hoboken.
Britto, A.L., Quintslr, S. & Pereira, M.S. 2019, 'Dossier: Rivers and Societies', Revista Brasileira de História, vol. 39, no. 81.
Bockstaller, C. & Girardin, P. 2003, 'How to validate environmental indicators', Agricultural Systems, vol. 76, no. 2, pp. 639-53.
Bourguignon, D.A.S., Fraga, M.S., Lyra, G.B., Cecílio, R.A., Pereira, C.R. & Abreu, M.C. 2023, 'Assessment of Water Quality and Identification of Priority Areas for Intervention in Guanabara Basin, Rio De Janeiro, Brazil, Using Nonparametric and Multivariate Statistical Methods', Earth Science Research Network EarthSciRN, vol. 196, no. 10, e-920.
Brazil 247 News 2024, Ministry of Cities to include drainage project in PAC for region affected by rains in Rio de Janeiro, viewed 16 February 2024, <https://www.brasil247.com/regionais/sudeste/ministerio-das-cidades-deve-incluir-no-pac-projeto-de-drenagem-em-regiao-afetada-pelas-chuvas-no-rio-de-janeiro>.
Brazil 247 News 2024a, Lula guarantees emergency support and resources to cities affected by heavy rains in RJ, viewed 16 February 2024, <https://www.brasil247.com/regionais/sudeste/lula-garante-apoio-emergencial-e-recursos-as-cidades-atingidas-por-fortes-chuvas-no-rj>.
Brazil 247 News 2024b, Number of deaths from Rio rains reaches 12, viewed 16 February 2024, <https://www.brasil247.com/regionais/sudeste/chega-a-12-o-numero-de-mortos-pelas-chuvas-no-rio>.
Brazil 2024, 63rd CEMADEN/MCIT Impact Meeting, viewed 16 February 2024, <https://www.youtube.com/watch?v=8xm1LTvEUYE>.
Brown, B., Jensen, T., Gotway, J.H., Bullock, R., Gilleland, E., Fowler, T., Newman, K., Adriaansen, D., Blank, L., Burek, T. & Harrold, M., 2021, ‘The Model Evaluation Tools (MET): More than a decade of community-supported forecast verification’, Bulletin of the American Meteorological Society, vol. 102, no. 4, pp. E782-E807.
CEMADEN 2024, Estação Pluviométrica: Jardim Metrópole - Cidade: São João de Meriti/RJ, Brasil, <https://resources.cemaden.gov.br/graficos/interativo/>.
Cook, D.A. & Skinner, J.M. 2005, ‘How to Perform Credible Verification, Validation, and Accreditation for Modeling and Simulation’, CROSSTALK The Journal of Defense Software Engineering, pp. 20-4.
De Freitas, A.S., Oliveira Santos, A.D., Santos, R.F., Nascimento, M.T.L., Fonseca, E.M., Félix, L.C., Bila, D.M., Aguiar, V.M.C. & Baptista Neto, J.A. 2023, ‘Chemical and biological indicators of environmental pollution in the Canal do Cunha (Guanabara Bay, Rio de Janeiro, Brazil): Analysis and determination of toxins’, Journal of Coastal Research, vol. 39, no. 6, pp. 1146-57.
Dixon, M. & Wiener G. 1993, TITAN: Thunderstorm Identification, Tracking, Analysis and Nowcasting - A Radar-Based Methodology’, Journal of Atmospheric and Oceanic Technology, vol. 10, no. 6, 785-97.
Gentil, S. & Blake, G. 1981, ‘Validation of complex ecosystems models’, Ecological Modelling, vol. 14, no. 1-2, pp. 21-38.
Groesser, S.N. & Schwaninger, M. 2012, ‘Contributions to model validation: Hierarchy, process, and cessation’, System Dynamics Review, vol. 28, no. 2, pp. 157-81.
Held, G. Gomes, A.M., Naccarato, K.P., Pinto Jr., O. & Nascimento, E.L. 2006, ‘The Structure of Three Tornado-Generating Storms Based in Doppler Radar and Lightning Observations in the State of São Paulo, Brazil’, Proceedings of 8 ICSHMO, Foz do Iguaçu, Brazil, April 24-28, INPE, p. 1787-97.
Li, H., Kalnay, E., Miyoshi, T. & Danforth, C.M. 2009, ‘Accounting for model errors in ensemble data assimilation’, Monthly Weather Review, vol. 137, no. 10, pp. 3407-19.
Jolliffe, I.T. & Stephenson, D.B. 2012, Forecast Verification: A Practitioner's Guide in Atmospheric Science, John Wiley & Sons, New York.
Kalnay, E. 2003, Atmospheric modeling, data assimilation and predictability, Cambridge University Press, Cambridge.
Malta, F.S. & Costa, E.D. 2021, ‘Socio-environmental vulnerability index: An application to Rio de Janeiro-Brazil’, International journal of public health, vol. 66, 584308.
Ota, Y., Derber, J.C., Kalnay, E. & Miyoshi, T. 2013, ‘Ensemble-based observation impact estimates using the NCEP GFS’, Tellus A: Dynamic Meteorology and Oceanography, vol. 65, no. 1, 20038.
Petty, M.D. 2018, ‘Modeling and validation challenges for Complex Systems’, in Engineering Emergence, CRC Press, Florida, pp. 199-216.
Sandholz, S., Lange, W. & Nehren, U. 2018, ‘Governing green change: Ecosystem-based measures for reducing landslide risk in Rio de Janeiro’, International journal of disaster risk reduction, vol. 32, pp. 75-86.
Stager, P. 1993, ‘Validation in Complex Systems: Behavioral Issues’, Verification and Validation of Complex Systems: Human Factors Issues/NATO ASI Series, Berlin, <https://doi.org/10.1007/978-3-662-02933-6_5>.
Tatar, M. & Mauss, J. 2014, ‘Systematic Test and Validation of Complex Embedded Systems’, ERTS 2014 - Embedded Real Time Software and Systems, Toulouse, France.
Teperi, A.M., Paajanen, T., Asikainen, I. & Lantto, E. 2023, ‘From must to mindset: outcomes of human factor practices in aviation and railway companies’, Safety science, vol. 158, 105968.
WWRP/WGNE Joint Working Group on Forecast Verification Research New 2017, 7th International Verification Methods Workshop: Forecast Verification Methods Across Time and Space Scales, Berlin, Germany, pp. 1-89.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
This journal is licensed under a Creative Commons — Attribution 4.0 International — CC BY 4.0, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.