Integration of quantitative precipitation forecasts with real-time hydrology and hydraulics modeling towards probabilistic forecasting of urban flooding

Brendel, Conrad; Dymond, Randel L.; Aguilar, Marcus F.

doi:10.1016/j.envsoft.2020.104864

Cited by 25 publications

(9 citation statements)

References 25 publications

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“…To better account for these impacts, a more comprehensive framework for evaluating plastic material losses should be developed in future studies. This framework should combine real-time meteorological and hydrological monitoring with long-term extrapolated data and analyze these elements using dynamic environmental assessment methodologies …”

Section: Resultsmentioning

confidence: 99%

“…This framework should combine real-time meteorological and hydrological monitoring with long-term extrapolated data and analyze these elements using dynamic environmental assessment methodologies. 62 Current Plastic EoL Option's Environmental Cost. Currently, the U.S. is one of the world's top plastic polluters, and 78.31% of the domestic PP waste ends in landfills, and only 2.73% is effectively recycled.…”

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confidence: 99%

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Cascading Polymer Macro-Debris Upcycling and Microparticle Removal as an Effective Life Cycle Plastic Pollution Mitigation Strategy

You

2023

Environ. Sci. Technol.

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Plastic pollution caused by material losses and their subsequent chemical emissions is pervasive in the natural environment and varies with age. Cascading the life cycles of plastic losses with solid waste reclamation via re-manufacturing virgin polymers or producing fuels and energy may extend resource availability while minimizing waste generation and environmental exposure. Here, we systematically investigate this cascaded plastic waste processing over other waste end-of-life management pathways by analyzing the environmental consequences of plastic losses across the entire life cycle. Plastic losses can form volatile organic chemicals via photo-degradation and pose non-negligible global warming, ecotoxicity, and air pollution effects that worsen by at least 189% in the long run. These environmental burdens increase by above 9.96% under high ultraviolet radiation levels and participation rates, which facilitate plastic particulate compartment transport and degradation. Cascaded plastic waste processing aided by fast pyrolysis upcycling technologies can effectively cut environmental losses and outperform landfills and incineration in reducing 23.35% ozone formation and 19.91% air pollution by offsetting the external monomer manufacturing and fuels and energy production while saving at least 25.75% fossil fuels.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Cascading Polymer Macro-Debris Upcycling and Microparticle Removal as an Effective Life Cycle Plastic Pollution Mitigation Strategy

You

2023

Environ. Sci. Technol.

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“…Song et al (2019) showed that, with the use of future rainfall values, regression models successfully predicted flood events. Brendel et al (2020) proved the effectiveness of integrating quantitative precipitation forecasts in an urban flooding hydrology model. In a similar study, Ko et al (2020) worked on enhancing short-term intensive rainfall forecasts to improve a hydrologic model's predictive ability.…”

Section: Introductionmentioning

confidence: 94%

Data-driven approaches to rainfall nowcasting for application in hydrological modelling

2021

MODSIM2021, 24th International Congress on Modelling and Simulation.

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Flash floods are amongst the most complex and destructive phenomena. An abundance of process-based and data-driven models was proposed to serve as decision support tools for flood management authorities. While various observed hydrological and meteorological characteristics were usually used as an input for flash flood modelling, it was also found that integrating rainfall forecasts could considerably enhance the models' predictive ability. This study focuses on finding reliable and efficient data-driven rainfall nowcasting models (0-2h lead time). These models could then be integrated into a short-term flash flood prediction framework to investigate the framework performance including the effect of the precipitation nowcasts on the reliability of the modelling results. It is important to note that only data from rain gauges located on the same watershed are used to predict future precipitation. Rainfall data obtained from two rain gauges installed in the Spring Creek watershed, Ontario, Canada were used in this study. The investigated watershed is highly urbanized and prone to flash floods. Investigated data spanned four years from 2013 to 2016. We tackled this data-driven modelling problem from two perspectives: (1) an algorithmic and (2) a datacentric. From the algorithmic perspective, a comparative study of three data-driven models was performed. These models included the status quo persistence model, the statistical AutoRegressive Integrated Moving Average (ARIMA) model and the deep learning Long Short-Term Memory (LSTM) model. These models were applied to each time series to predict rainfall in the respective rain gauge location (univariate modelling). Following the data-centric approach, data from both sensors were combined into one dataset to predict rainfall in each sensor location (multivariate modelling). Lagged rainfall values from the sensor at the target location and the adjacent sensor were fed into an LSTM model to predict rainfall at the target location. Models were created for each investigated year for lead times ranging from 15 minutes to 60 minutes (corresponding to the time scale of the investigated rainfall events). Data for each year were chronologically split into training and testing with a 70%:30% split ratio. Root Mean Square Error (RMSE) and Maximum Residual Error (MRE) were used as evaluation metrics. Obtained results showed that overall, according to the estimated RMSE, LSTM demonstrated a better performance for all years except the year 2015. Figure 1 depicts models' performance for 2013 at the Hart Lake location using single sensor data. Further analysis revealed that the year 2015 had major hydrological pattern difference between the training and testing sets. MRE did not indicate major variations between the years; it was found that all the models performed approximately at the same level as the persistence model. The models failed to predict extreme values accurately. The data-centric approach, however, showed different results. According to the RMSE and MRE metrics, LSTM model...

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“…They use mathematical equations and expressions to characterize the physical processes and provide very accurate flood inundation predictions. Brendel et al (2020) developed the Probabilistic Urban Flash Flood Information Nexis (PUFFIN) app to predict the probability of urban flash flood occurrence. However, H&H models have relatively high computation costs and require extensive physical attributes of the study site such as geological, atmospheric, and land use data which might not be readily accessible in underdeveloped areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive multi-watershed flood monitoring using deep learning on integrated physical and social sensors data

Dong

Farahmand

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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This paper presents a deep learning model based on the integration of physical and social sensors data for predictive watershed flood monitoring. The data from flood sensors and 3-1-1 reports data are mapped and fused through a multi-variate time series approach. This data format is able to increase the data availability (partially due to sparsely installed physical sensors and fewer reported flood incidents in less urbanized areas) and capture both spatial and temporal interactions between different watersheds and historical events. We use Harris County, TX as the study site and obtained 7 historical flood events data for training, validating, and testing the flood prediction model. The model predicts the flood probability of each watershed in the next 24 hours. By comparing the flood prediction performance of three different datasets (i.e., flood sensor only, 3-1-1 reports only, and integrated dataset), we conclude that the integrated dataset achieves the best flood prediction performance with an accuracy of 0.825, Area Under the Receiver operating characteristics Curve (AURC) of 0.902, Area Under the Precision-Recall Curve (AUPRC) of 0.883, Area Under the F-measure Curve (AUFC) of 0.762, and Max. F-measure of 0.788.

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Integration of quantitative precipitation forecasts with real-time hydrology and hydraulics modeling towards probabilistic forecasting of urban flooding

Cited by 25 publications

References 25 publications

Cascading Polymer Macro-Debris Upcycling and Microparticle Removal as an Effective Life Cycle Plastic Pollution Mitigation Strategy

Cascading Polymer Macro-Debris Upcycling and Microparticle Removal as an Effective Life Cycle Plastic Pollution Mitigation Strategy

Data-driven approaches to rainfall nowcasting for application in hydrological modelling

Predictive multi-watershed flood monitoring using deep learning on integrated physical and social sensors data

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