Predicting Outflow Hydrographs of Potential Dike Breaches in a Bifurcating River System Using NARX Neural Networks

SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided.

Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas.

Making Room for Our Forthcoming Rivers

This paper provides a schematic, conceptual trip across a set of paradigms that can be adopted to design flood control actions and the associated river setting, including the space allocated to the river. By building on such paradigms, it eventually delineates an integrated approach to identify a socially desirable river setting, under a climate changing reality. The key point addressed is that when residual Risk and Operation, Management and Replacement costs are considered to their full extent, even a basic economic analysis may suggest alternative river settings that can be more attractive, particularly if accompanied by suitable economic-administrative management measures. Emphasis is put on the deep uncertainty characterizing the whole decision problem and on the need for a drastic change of paradigm. The approach proposed can greatly improve current Flood Risk Management Plans responding to the European Flood Directive (Directive 2007/60/EC). It can also help to develop constructive dialogues with stakeholders, while enhancing the understanding of the problem. Although mainly intended to address a conceptual level, it also aims at providing an applicable method.

Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change: A Review

The modelling and management of flood risk in urban areas are increasingly recognized as global challenges. The complexity of these issues is a consequence of the existence of several distinct sources of risk, including not only fluvial, tidal and coastal flooding, but also exposure to urban runoff and local drainage failure, and the various management strategies that can be proposed. The high degree of vulnerability that characterizes such areas is expected to increase in the future due to the effects of climate change, the growth of the population living in cities, and urban densification. An increasing awareness of the socio-economic losses and environmental impact of urban flooding is clearly reflected in the recent expansion of the number of studies related to the modelling and management of urban flooding, sometimes within the framework of adaptation to climate change. The goal of the current paper is to provide a general review of the recent advances in flood-risk modelling and management, while also exploring future perspectives in these fields of research.