Recent years have witnessed considerable developments in multiple fields with the potential to enhance our capability of forecasting pluvial flash floods, one of the most costly environmental hazards in terms of both property damage and loss of life. This work provides a summary and description of recent advances related to insights on atmospheric conditions that precede extreme rainfall events, to the development of monitoring systems of relevant hydrometeorological parameters, and to the operational adoption of weather and hydrological models towards the prediction of flash floods. With the exponential increase of available data and computational power, most of the efforts are being directed towards the improvement of multi-source data blending and assimilation techniques, as well as assembling approaches for uncertainty estimation. For urban environments, in which the need for high-resolution simulations demands computationally expensive systems, query-based approaches have been explored for the timely retrieval of pre-simulated flood inundation forecasts. Within the concept of the Internet of Things, the extensive deployment of low-cost sensors opens opportunities from the perspective of denser monitoring capabilities. However, different environmental conditions and uneven distribution of data and resources usually leads to the adoption of site-specific solutions for flash flood forecasting in the context of early warning systems.

Timely generation of accurate and reliable forecasts of flash flood events is of paramount importance for flood early warning systems in urban areas. Although physically based models are able to provide realistic reproductions of fast-developing inundation maps in high resolutions, the high computational demand of such hydraulic models makes them difficult to be implemented as part of real-time forecasting systems. This paper evaluates the use of a hybrid machine learning approach as a surrogate of a quasi-2D urban flood inundation model developed in PCSWMM for an urban catchment located in Toronto (Ontario, Canada). The capability to replicate the behavior of the hydraulic model was evaluated through multiple performance metrics considering error, bias, correlation, and contingency table analysis. Results indicate that the surrogate system can provide useful forecasts for decision makers by rapidly generating future flood inundation maps comparable to the simulations of physically based models. The experimental tool developed can issue reliable alerts of upcoming inundation depths on traffic locations within one to two hours of lead time, which is sufficient for the adoption of important preventive actions. These promising outcomes were achieved in a deterministic setup and use only past records of precipitation and discharge as input during runtime.

We present a novel approach to determine spatially distributed routing parameters for the distributed hydrological Hillslope Link Model (HLM), an ordinary differential equations‐based streamflow forecasting model implemented and tested in Iowa. We being by developing a technique to determine two model parameters that control the channel routing equation in gauged catchments draining less than 1,300 km2. Then, we implement a parameter regionalization methodology using machine learning classification techniques and a bootstrap procedure, in which we trained 400 Random Forests (RFs) using physical and geomorphological features for classification. We made a regional interpolation using an ensemble of selected RF realizations that exhibited the best performance. We used as benchmark of our results a more straightforward interpolation technique based on USGS Hydrological Units Codes. We performed simulations of the HLM over the entire state of Iowa between 2012 and 2018 using the two regionalization methods, comparing them to the operational model used by the Iowa Flood Center, which applies a single set of parameter values to the entire domain. After evaluating the results at 148 USGS stations, the Random‐Forest approach captures the value of observed peak flows more precisely without losing performance in terms of the Kling Gupta Efficiency index. The improvements obtained using our proposed strategy that uses data, hydrological modeling, and a machine learning technique to identify and regionalize routing parameters are modest, indicating that the parameters that control the rainfall‐runoff transformation dominate uncertainty in our flood forecast model.

IFIS model-plus: a web-based GUI for visualization, comparison and evaluation of distributed hydrologic model outputs." MS (Master of Science) thesis, University of Iowa, 2017.ii To my parents, my friends and Lizbeth, who were always there to support me.iii ACKNOWLEDGMENTS I would like, first of all, to recognize the value of my supervisor, Professor Ricardo Mantilla (PhD), for making my work a success. More than providing all guidance with excellency for my academic development as his graduate student, Ricardo also offered the intimacy and personal concern to make me consider him more than an advisor, but also a friend -a close one. Secondly, I would like to express my gratitude for Professor Ibrahim Demir (PhD) with all the support with the use of the tool developed by him -IFISwhich is a central element of this work, and all the constructive suggestions offered during all the process of development of this thesis. But this work have never been successful without the valuable help of many others members of IIHR -Hydroscience and Engineering, mainly: Felipe Quintero Duque, who taught me almost all the details of the cyber infrastructure with which I dealt during most of the activities I developed related to this workhe is someone to be called a Professor for sure; Samuel Debionne, who, while being responsible for maintaining ASYNCH and real-time instances of the hydrological modelfundamental elements of this workwas always open to possible improvements to the tool that would potentially benefit my activity and to discuss general topics related to technology; and Radek Goska, whose readiness to help and to suggest improvements with database-related issues was immeasurably helpful for the establishment of the tool developed in this theses. This work would not have gone so far if they were not who they are. Finally, I want to thank Professors Witold Krajewski and Larry Weber not only for accepting being part of my Thesis Committee, which was my great honor, but, as directors of the IFC -Iowa Flood Centerand of IIHR (respectively), for the opportunity to be part of the these groups. There are no words to describe the gratitude I feel for this experience.iv ABSTRACT This work explores the use of hydroinformatics tools to provide a user friendly and accessible interface for executing, and visualizing the output of, distributed hydrological models for Iowa. It uses an IFIS-based web environment for graphical displays and it communicates with the ASYNCH ODE solver to provide input parameters and to gather modeling outputs. The distributed hydrologic models used here are based on the segmentation of the terrain into hillslope-link hydrologic units, for which water flow processes are represented by sets of nonlinear ordinary differential equations. This modeling strategy has shown promising results in modeling extreme flood events in the state of Iowa -USA, but the usage and evaluation of outputs from hillslope-link models (HLM) has been limited to a restrict group of academics due to the demand of high processing capabil...

The use of data-driven surrogate models to produce deterministic flood inundation maps in a timely manner has been investigated and proposed as an additional component for flood early warning systems. This study explores the potential of such surrogate models to forecast multiple inundation maps in order to generate probabilistic outputs and assesses the impact of including quantitative precipitation forecasts (QPFs) in the set of predictors. The use of a k-fold approach for training an ensemble of flood inundation surrogate models that replicate the behavior of a physics-based hydraulic model is proposed. The models are used to forecast the inundation maps resulting from three out-of-the-dataset intense rainfall events both using and not using QPFs as a predictor, and the outputs are compared against the maps produced by a physics-based hydrodynamic model. The results show that the k-fold ensemble approach has the potential to capture the uncertainties related to the process of surrogating a hydrodynamic model. Results also indicate that the inclusion of the QPFs has the potential to increase the sharpness, with the tread-off also increasing the bias of the forecasts issued for lead times longer than 2 h.