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Traditionally, flood models are based on physical and hydrodynamic processes, which require massive data. However, this information is not always easily accessible. As an alternative, the development and application of data-driven models, such as Artificial Intelligence (AI) techniques, have grown. These have the advantage of requiring less parameterization than traditional models. However, some of those models demand long time series data for model training. We present comparison of traditional and AI approaches based on a bibliometric review. We used the Scopus database, selecting papers from 1990 to 2020, divided into two groups: physical models, with thekeywords: GIS-based model, Hydrodynamic model, HEC-RAS and PCSWMM, and AI models, with the keywords: Artificial Intelligence, Machine learning, and Neural. To restrict the search, the following keywords were also inserted: Urban flood, Urban rainstorm flood, Flood mapping, Inundation mapping, Flood hazard, Flood, Pluvial, and Fluvial. Table 1 shows the results for each search string. The main metrics evaluated were the number of papers per year (Figure 1) and co-citation networks (Figure 2). For physical models, the main keyword found was “HEC-RAS”, with twice as many occurrences compared to the second most found “Hydrodynamic model”, demonstrating its wide application. The United States has the highest production of papers, followed by China and the United Kingdom. However, the United Kingdom has a more central role when evaluating the co-citation network. P. D. Bates (Bristol University, UK) stands out regarding the authors with the highest citation. For the AI models, the results for the keyword "Machine Learning" and the keywords "Artificial Neural Network" and "Neural Network" together have an equal count. China and the United States also presented the highest production. However, in the co-citation network, Iran has greater centrality and citations. The main authors include B. Pradhan (University of Sydney, Australia) and K. Khosravi (Sari Agricultural Science and Natural Resources University, Iran). In Figure 3, we present word clouds for each category. This review identifies the most relevant methods in flood modeling, which will be used to select solutions for comparative case studies in megacities.

In Latin American and Caribbean (LAC) countries, Disaster Risk Reduction (DRR) options have accelerated “education for preparedness” initiatives for 100 million people living at >90,000 water-risk prone areas. Before and during COVID pandemic, innovative T-shaped (“breadth-and-depth”) labs of open science have promoted education towards the Agenda 2030. Here we depict actions delivered by the Water-Adaptive Design & Innovation lab (@TheWADILab) to provide citizen science with inclusiveness (CC+), as a learning accelerator of human-water coevolution under change (i.e. ‘Panta Rhei Open Science for Future Earth’, PROSFE). We summarize CC+PROSFE examples of practical learning with a blend of options at the DRR Center for Education and Research on Disasters (CEPED, www.ceped.eesc.usp.br) through: open access to data and tools, new mechanisms, hybrid curation platforms, competency-skill curricula, and blended education. First, examples have been applied during >1,200 hours of lectures of hydrology to >600 students, ranging from “puzzling 3D transboundary aquifer” for undergraduate students (Fig 1) to competency learning (skills, fundamentals, attitudes) of blended education for professionals (Fig 2). Second, CC+PROSFE aims to: (1) mobilize international cooperation to improve knowledge and innovation to address water security challenges at “Panta Rhei Schools", i.e. welcoming visiting scientists to the School of Advanced Studies in Water & Society Under Change (SASW≻ http://www1.eesc.usp.br/ppgshs/internacionalizacao), (2) strengthen science-policy interface to reach water security at local-to-global levels through the new UNESCO Chair in Urban Waters and the new WMO-Americas Regional Climate Centre hosted by Prohimet (https://www.prohimet.org/), (3) pave new pathways for water security knowledge to empower and activate citizenry at the local level using the Socio-Hydrological Observatory for Water Security (SHOWS), cycles of #OneDropOfScience #OneDoseOfResilience and Academia-Community’s Waters for Our World! Project (https://prg.usp.br/academia-comunidade-wow-waters-for-our-world/), and (4) develop institutional and human capacities for water security, sustainability and inspiration through water literacy and communication in the joint initiative of UNESCO-IHP-LAC & Pechakucha (https://www.pechakucha.com/events/agua-todavia-unesco-x-pechakucha).

Cities must develop actions that reduce flood risk in the face of extreme rainfall events. In this study, the dynamic resilience of the Gregorio catchment (São Carlos, Brazil) was assessed. The catchment lacks environmental monitoring and suffers from recurrent floods. The resilience curves were made considering the water depth in the drainage system as the performance index, obtained by simulations with SWMM and HEC-RAS. The calibration of the flood extension was performed using citizen science data. The contribution to increasing the dynamic resilience by implementing decentralized low impact development (LID) practices was also evaluated. For this purpose, bioretention cells were added to the SWMM simulations. The resilience curves were then calculated for the current and future climate scenario, with and without LID, for return periods of 5, 10, 50, and 100 years and duration of 30, 60, and 120 min. Intensity–duration–frequency curves (IDFs) updated by the regional climate model MIROC5 for 2050 and 2100 were used. The results showed a significant improvement in the system’s resilience for light storms and the current period due to LID practice interventions. Efficiencies were reduced for moderate and heavy storms with no significant drops in floodwater depth and resilience regardless of the scenario.

Flood forecasting techniques have been widely studied as a tool to mitigate damage from extreme events. However, their nature in urban areas developed without properly drainage planning, coupled with the scarcity of hydrological monitoring data, becomes a significant challenge for real-time flood forecasting. This doctoral thesis proposes deterministic methods of data assimilation for real-time hydrological forecasting. The methodology is developed using the semi-distributed hydrodynamic Storm Water Management Model (SWMM). It also aims to evaluate the impact of using traditional monitoring data together with citizen science data for model updating. The first and the second chapter present the general introduction and methodology of the thesis. The third chapter presents an automatic calibration tool - SWMM calibrator - developed to allow the adjustment of SWMM model parameters with data from multiple sources and to use observed level data as a priori knowledge. The fourth chapter deals with the use of citizen science data for urban model updating through a real-time estimator. The fifth chapter presents a data assimilation method by updating hydrological model inputs based on water level observations and evaluates the effectiveness of the technique in a distributed manner in the catchment. The proposed methodologies are validated in a case study at the Monjolinho urban catchment. The sixth chapter discusses general conclusions and recommendations. In conclusion, SWMM calibrator tool provides flexibility in calibration, allowing shaping the process according to the real-world limitations, and achieved satisfactory calibration results at Monjolinho catchment. The deterministic data assimilation methods proposed in the fourth and fifth chapters have shown effective results in a significant improvement in simulations accuracy. A previsão de enchentes para a mitigação dos danos causados por eventos extremos vem sendo amplamente estudada. No entanto, sua natureza em áreas urbanas desenvolvidas sem planejamento adequado de drenagem, associada a escassez de dados de monitoramento hidrológico apresentam um grande desafio para previsão de enchentes em tempo real. Esta tese de doutorado propõe novos métodos determinísticos de assimilação de dados em tempo real na previsão hidrológica, através do modelo hidrodinâmico semi-distribuído Storm Water Management Model (SWMM). Estes métodos visam contornar as limitações na previsão de enchentes em tempo real, em curto prazo e em bacias urbanas com dados escassos. Foi também avaliado o impacto do uso de dados de monitoramento tradicionais aliados a dados de ciência cidadã na atualização das simulações do modelo. O primeiro capítulo e o segundo capítulo trazem a introdução e metodologia gerais da tese. O terceiro capítulo apresenta uma ferramenta de calibração automática – SWMM calibrator – desenvolvida para permitir o ajuste de parâmetros do modelo SWMM com dados provenientes de múltiplos locais de monitoramento e utilizando dados observados de nível como conhecimento a priori. O quarto capítulo aborda a utilização de dados de ciência cidadã na atualização do modelo através de um estimador em tempo real. O quinto capítulo apresenta um método de assimilação de dados através da correção das entradas do modelo hidrológico baseado em observações de nível, e avalia a eficácia do método de forma distribuída na bacia. As metodologias propostas foram aplicadas para um estudo de caso na bacia urbana do Monjolinho. O sexto capítulo apresenta as conclusões e recomendações gerais. Em conclusão, a ferramenta SWMM calibrator disponibiliza flexibilidade na calibração, permitindo moldar o processo de acordo com as limitações de problemas reais, e alcançou resultados satisfatórios na calibração da bacia do Monjolinho. Os métodos determinísticos de assimilação de dados propostos no quarto e quinto capítulo mostraram resultados eficazes na redução do erro das simulações de nível do modelo, bem como mostraram resultados satisfatórios ao assimilar dados com uma distribuição temporal maior que o passo de tempo do modelo.

Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimi...

The city of Sao Carlos, state of Sao Paulo, Brazil, has a historical coexistence between society and floods. Unplanned urbanization in this area is a representative feature of how Brazilian cities ...

Places with limited coverage of rainfall gauges could present the challenge of providing accurate predictions, especially in cases of urbanised areas with rapid responses to heavy rainfall events. Physically-based models can represent the physics and spatial distribution of rainfall events in urban watersheds. Data assimilation techniques have been widely used in hydraulic and hydrological models to update model states and provide a more reliable prediction. However, model updating in case of non-linear systems is considerably complex. In this study, we present an approach to update an urban model assimilating water level values. The preliminary results of this study show a significant improvement in the results of simulations when assimilating water level observation. The methodology is applied in the city of São Carlos, in Brazil, where the urban system is modelled using SWMM.

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. Thus, there is still a gap in research with regard to the use of social media as a proxy for rainfall-runoff estimations and flood forecasting. To address this, we propose using a transformation function that creates a proxy variable for rainfall by analysing geo-social media messages and rainfall measurements from authoritative sources, which are later incorporated within a hydrological model for streamflow estimation. We found that the combined use of official rainfall values with the social media proxy variable as input for the Probability Distributed Model (PDM), improved streamflow simulations for flood monitoring. The combination of authoritative sources and transformed geo-social media data during flood events achieved a 71% degree of accuracy and a 29% underestimation rate in a comparison made with real streamflow measurements. This is a significant improvement on the respective values of 39% and 58%, achieved when only authoritative data were used for the modelling. This result is clear evidence of the potential use of derived geo-social media data as a proxy for environmental variables for improving flood early-warning systems.

Este trabalho apresenta uma nova proposta metodológica de previsão de enchentes: o Modelo de Alerta Hidrológico com Base Participativa (MAHP). O MAHP consiste em um modelo de previsão de enchentes em bacias urbanas que integra Informações Geográficas Voluntárias (VGI) e redes de sensores sem fio (WSN). A principal contribuição deste modelo é o uso de dados de fontes heterogêneas (sensores aliados a dados fornecidos por voluntários) com o objetivo de reduzir a incerteza na previsão de enchentes. O modelo MAHP foi dividido em módulos, cada um deles é responsável por uma atividade no processo de previsão de enchentes. Embora o modelo possua diversos módulos auxiliares, pode-se resumir o modelo MAHP em três módulos principais: aquisição de dados; previsão de precipitações e, por fim, o módulo responsável pela previsão das enchentes. Para o módulo de aquisição de dados foram desenvolvidas metodologias para uso de dados voluntários de nível da água e sensores medidores de nível foram instalados para a composição da rede de sensores sem fio em pontos estratégicos nos canais fluviais da cidade. No módulo de previsão da precipitação do modelo MAHP foram desenvolvidos dois softwares de previsão, sendo um modelo de previsão da precipitação conceitual e um empírico. Para o funcionamento do módulo responsável pela previsão das enchentes foi feita a modelagem da bacia urbana de São Carlos no modelo SWMM (Storm Water Management Model). As simulações chuva-vazão realizadas com a bacia modelada apresentaram ajustes satisfatórios quando comparadas com eventos de enchente reais. Como o uso de informações voluntárias na previsão de enchentes é um conceito bastante novo, outra importante contribuição do trabalho foi propor parâmetros espaço-temporais que influenciam na qualidade da previsão ocasionada pelo uso de dados VGI. Existem vários cenários e combinações de uso de informações voluntárias que podem influenciar na previsão de enchentes. Neste trabalho foi considerada apenas uma destas combinações. Devido à ausência de dados VGI reais em eventos de enchente recentes foram utilizados dados de nível medidos por sensores para simular dados voluntários. Foram levantadas diversas hipóteses para que a inserção de dados voluntários no modelo MAHP tenham maior influência na redução da incerteza na previsão de enchentes. This work presents a new approach forflood forecasting: Hydrological Alert Model with Participatory Base (HAMPB). The HAMPB consists of a flood forecasting model applied to urban basins integrating Volunteered Geographic Information (VGI) and Wireless Sensor Networks (WSN). The main contribution of this model is the use of heterogeneous data sources (convencional sensors and volunteered data) aiming to reduce the uncertainty in the flood forecasting. The HAMPB model was divided in modules, which are responsible for the forecasting process activities. Although the model has multiple auxiliary modules, we can summarize the HAMPB model in three modules: data acquisition; rainfall forecasting and, finally, the module responsible for flood forecasting. Telemetric water level sensors were installed at strategic points in river channels of the city to create the WSN. In order to use the volunteered information, a methodology was proposed tdevelop the acquisition module. The rainfall forecasting module consists of two forecasting models: an empirical model and a conceptual model. The conceptual prediction model presented closest predictions of observed rainfall compared to the forecast of the empirical model. In order to apply the flood forecasting methodology, we modelled the urban basin of São Carlos using SWMM model. The rainfall-runoff simulations performed with the basin model showed satisfactory adjustments compared with actual flood events. Since the use of voluntary information on flood forecasting is a fairly new concept, another important contribution of this work was the proposition of spatiotemporal parameters that influence on the forecast caused by the use of VGI data. There are many scenarios and combinations which using volunteered information can be helpful in the flood forecasting. In this work we consider only one combination. Due to absence of real volunteered data, we use sensor data to simulate VGI data. Several hypotheses have been raised to the inclusion of volunteers in HAMPB data model to produce more relevant results than using traditional methods of forecasting.