Global Optimization-Based Calibration Algorithm for a 2D Distributed Hydrologic-Hydrodynamic and Water Quality Model

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive. This makes the use of automatic calibration algorithms hard to apply due to the large number of model runs. However, with the recent advances in parallel processing, computational resources, and increasing high-resolution climatologic and GIS data, high-resolution hydrodynamic models can be used for optimization-based calibration. This paper presents a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm can find a near-optimal set of parameters to explain observed gauged data. The modeling framework presented here, although applied in a poorly-gauged catchment, can be adapted for catchments with more detailed observations. We applied the algorithm in different cases of the V-Tilted Catchment, the Wooden-Board catchment, and in an existing urban catchment with heterogeneous data. The results of automatic calibration indicate $\mathrm{NSE} = 0.99$ for the V-Tilted catchment, $\mathrm{RMSE} = 830~\mathrm{mgL^{-1}}$ for salt concentration pollutographs (i.e., 8.3% of the event mean concentration), and $\mathrm{NSE} = 0.89$ for the urban catchment case study. This paper also explores the issue of equifinality in modeling calibration (EqMC). Equifinality is defined as the set of different parameter combinations that can provide equally good or accepted results, within the physical parameter ranges. EqMC decreases with the number of events and increases with the choice of partially or nonproducing runoff ones. Furthermore, results indicate that providing more accurate parameter ranges based on a priori knowledge of the catchment is fundamental to reduce the chances of finding a set of parameters with equifinality.Comment: Preprint submitted to Journal of Hydrolog

Low Impact Development practices in the context of United Nations Sustainable Development Goals: A new concept, lessons learned and challenges

The increase in urbanization and climate change brings new challenges to the cities’ sustainability and resilience, mainly related to flood and drought events. Among these challenges, it can be highlighted the physical and health damage to the population, interruption of water, energy and food supply services, damage to basic infrastructure, economic losses and contamination of urban rivers. To contribute to the increase of resilience in urban centers, LID practices have been used as a new approach of mitigation and adaptation within urban drainage systems, aiming at runoff retention, peak flow attenuation, pollutant removal and ecosystem services restoration (e.g., resources recycling, carbon sequestration, thermal comfort and landscape integration). These different mitigation purposes and complementary benefits provided by LID practices can be related to the different Sustainable Development Goals (SDG) presented by the United Nations (UN), to achieve countries’ systemic sustainability. The identification of local techniques that contribute to the different SDG helps to achieve their territorialization and application as public policy. Therefore, this paper presents a literature review, categorizing the studies into different generations based on their main application purpose and presents a linkage of the LID benefits to different SDG. Some challenges were identified requiring further investigation, such as the need to identify and quantify the energy demands for LID practices maintenance and their incorporation in the system final energy balance, identification of processes that contribute to carbon sequestration and emission, and risks of emerging pollutants for human health from water reuse and nutrient cycling for sustainable agriculture

Comparação entre equações empíricas para estimativa da evapotranspiração de referência na Bacia do Rio Jacupiranga

A evapotranspiração de referência (ETo) representa a perda de água do solo vegetado para a atmosfera devido à evaporação e à transpiração. O modelo de Penman-Monteith demanda variados elementos meteorológicos em sua solução, o que dificulta sua aplicação em estudos agrometeorológicos e hidrológicos em regiões com poucas estações meteorológicas, como a bacia do rio Jacupiranga, SP, Brasil. O estudo foi realizado com o objetivo de se verificar a precisão dos métodos de estimativa de ETo propostos por Camargo, Blaney-Criddle, Hamon, Hargreaves, Thornthwaite e Kharrufa, definindo-se coeficientes de ajuste regional. Dados meteorológicos de duas estações climatológicas locais foram usados nas estimativas. Na comparação das equações com o método FAO Penman-Monteith, analisaram-se coeficientes de determinação, correlação concordância, confiança e erro padrão experimental. Os resultados obtidos indicam que, na região, os métodos de Hargreaves e Camargo podem ser aplicados tanto na forma original como na formulação modificada. A equação de Hargreaves com coeficientes regionais apresentou índices de confiança superiores a 0,995 para a bacia do rio Jacupiranga e é recomendada devido às suas exeqüibilidade e simplicidade

A Qualitative Analysis of the Early Warning Process in Disaster Management

Early warning systems are an important means of improving the efficiency of disaster response and preparedness. However, in its analysis of the technological aspects of the infrastructure, the literature has failed to carry out an investigation of early warning process. This paper has sought to take a step toward understanding this issue by carrying out a qualitative analysis of the early warning process in disaster management. This has involved participatory observations and conducting interviews with practitioners from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN). The results have shown that this research area is a promising way of increasing efficiency and reducing the response time to warnings. This might be achieved by conducting a process analysis, which could provide evidence and information about bottlenecks or investigate the misuse of information systems or tasks by the players involved

Bridging the gap between decision-making and emerging big data sources: an application of a model-based framework to disaster management in Brazil

With the emergence of big data and new data sources, a challenge posed to today's organizations consists of identifying how to align their decision-making and organizational processes to data that could help them make better-informed decisions. This paper presents a study in the context of disaster management in Brazil that applies oDMN +, a framework that connects decision-making with data sources through an extended modeling notation and a modeling process. The study results revealed that the framework is an effective approach for improving the understanding of how to leverage big data in the organization's decision-making

Flood Risk Mitigation and Valve Control in Stormwater Systems: State-Space Modeling, Control Algorithms, and Case Studies

The increasing access to non-expensive sensors, computing power, and more accurate forecasting of storm events provides unique opportunities to shift flood management practices from static approaches to an optimization-based real-time control (RTC) of urban drainage systems. Recent studies have addressed a plethora of strategies for flood control in stormwater reservoirs; however, advanced control theoretic techniques are not yet fully investigated and applied to these systems. In addition, there is an absence of a coupled integrated control model for systems composed of watersheds, reservoirs, and channels for flood mitigation. To this end, we develop a novel state-space model of hydrologic and hydrodynamic processes in reservoirs and one-dimensional channels. The model is tested under different types of reservoir control strategies based on real-time measurements (reactive control), and based on predictions of the future behavior of the system (predictive control) using rainfall forecastings. We apply the modeling approach in a system composed by a single watershed, reservoir, and a channel connected in series, respectively, for the San Antonio observed rainfall data. Results indicate that for flood mitigation, the predictive control strategy outperforms the reactive controls not only when applied for synthetic design storm events, but also for a continuous simulation. Moreover, the predictive control strategy requires smaller valve operations, while still guaranteeing efficient hydrological performance. From the results, we recommend the use of the model predictive control strategy to control stormwater systems due to the ability to handle different objective functions, which can be altered according to rainfall forecasting and shift the reservoir operation from flood-based control to strategies focused on increasing detention times, depending on the forecasting

Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks

Effective flood risk management requires updated information to ensure that the correct decisions can be made. This can be provided by Wireless Sensor Networks (WSN) which are a low-cost means of collecting updated information about rivers. Another valuable resource is Volunteered Geographic Information (VGI) which is a comparatively new means of improving the coverage of monitored areas because it is able to supply supplementary information to the WSN and thus support decision-making in flood risk management. However, there still remains the problem of how to combine WSN data with VGI. In this paper, an attempt is made to investigate AGORA-DS, which is a Spatial Decision Support System (SDSS) that is able to make flood risk management more effective by combining these data sources, i.e. WSN with VGI. This approach is built over a conceptual model that complies with the interoperable standards laid down by the Open Geospatial Consortium (OGC) – e.g. Sensor Observation Service (SOS) and Web Feature Service (WFS) – and seeks to combine and present unified information in a web-based decision support tool. This work was deployed in a real scenario of flood risk management in the town of São Carlos in Brazil. The evidence obtained from this deployment confirmed that interoperable standards can support the integration of data from distinct data sources. In addition, they also show that VGI is able to provide information about areas of the river basin which lack data since there is no appropriate station in the area. Hence it provides a valuable support for the WSN data. It can thus be concluded that AGORA-DS is able to combine information provided by WSN and VGI, and provide useful information for supporting flood risk management

A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios

This work proposes a data-driven theoretical framework for addressing: (i) extreme climate events prediction through multi-hazard risk mapping using remote sensing, artificial intelligence, and hydrological models, considering multiple hazards; and (ii) environmental monitoring using on-site data collection and IoT technologies. The framework considers the possibility of evaluating multiple climate change scenarios for improving decision-making in terms of Government policies and farm planning. Its main requirements are gathered based on a literature review. Several essential metrics that can be evaluated, considering both supervised and unsupervised metrics and key performance indicators considering the triple bottom line aspects, are also proposed. The framework also adopts multi-hazard (considering several hazards) and multi-risk (considering several relevant stakeholders) aspects and can be used to simulate different scenarios, an essential task for improving decision-making

Generalizing rapid flood predictions to unseen urban catchments with conditional generative adversarial networks

Two-dimensional hydrodynamic models are computationally expensive. This drawback can limit their application to solving problems requiring real-time predictions or several simulation runs. Although the literature presented improvements in using Deep Learning as an alternative to hydrodynamic models, Artificial Neural Networks applications for flood prediction cannot satisfactorily predict floods for areas outside the training datasets with different boundary conditions. In this paper, we used a conditional generative adversarial network (cGAN) aiming to generalize flood predictions in catchments not included in the training process. The proposed method, called cGAN-Flood, uses two cGAN models to solve a rain-on-grid problem by first identifying wet cells and then estimating the water depths. The cGANs were trained using HEC-RAS outputs as ground truth. cGAN-Flood distributes a target flood volume (vt) in a given catchment, which can be calculated via water balance from hydrological simulations. Our approach was trained on ten and tested on five urban catchments with distinct characteristics. The cGAN-Flood was compared to HEC-RAS for different rainfall magnitudes and surface roughness. We also compared our approach to the Weighted Cellular Automata 2D (WCA2D), a rapid flood model (RFM) used for rain-on-grid simulations. Our method successfully predicted water depths in the testing areas, showing that cGAN-Flood could generalize to different locations. However, cGAN-Flood tended to underestimate depths in channels in some areas for events with a small peak of precipitation intensity. cGAN-Flood was 50 and 250 times faster than WCA2D and HEC-RAS, respectively. Due to its computational efficiency and accuracy, we suggest that cGAN-Flood can be applied when fast simulations are necessary, and it can be a viable modeling solution for flood forecasts in large-scale watersheds.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Sanitary Engineerin