In Pursuit of Improving Peak Flow Prediction in the Canadian Prairies

The prairies were subjected to multiple unprecedented floods over the past decade that caused major damages to agricultural and residential areas. Accurate prediction of the magnitude and timing of floods is important as it is an essential component of flood risk management programs. However, the accuracy of predicting floods and the associated flooding extents have not drawn much attention in the prairies due to difficulties in predicting prairie streamflow in general. Such difficulties are caused, mainly, by the limitations of the currently available modeling approaches in handling the pothole complexities – a dominant feature in prairie watersheds. This thesis focuses on improving the prediction of floods (peak flows), in particular, and the streamflow in general, along with the associated landscape pluvial and nival flooding extents that frequently occur in the complex pothole-dominated environment of the Canadian prairies. This aim is achieved through adapting/developing a set of models that are built and tested for the prairies to contribute to solving the flood prediction problem in the prairies. The first model is a new Hydrological model for the Prairie Region (HYPR), which is proposed as an engineering solution for the prediction of the flood peak in the prairies. HYPR is a modified version of the HBV model, developed by coupling the conceptual HBV model, for hydrological processes representation, and the Probability Distribution Model based RunOFf generation algorithm (PDMROF) for pothole representation. The second model is a novel Prairie Region Inundation MApping model (PRIMA), which is developed as a distributed hydrologic routing model for more accurate and comprehensive storage dynamics simulation and inundation mapping in the prairies. PRIMA uses a set of rules along with Manning’s equation (iteratively) to route the water over the landscape. The third model is the Modelisation Environmentale Communautaire (MEC)—Surface and Hydrology (MESH), which is modified by coupling it with PRIMA to improve the non-contributing area and potholes dynamic representation in complex land surface models for better prediction of peak flows and the associated flooding extents. In this model, called MESH-PRIMA, MESH handles the vertical fluxes calculations based on physically based equations and PRIMA routes the water over the landscape and accounts for the effect of potholes on changing the net runoff reaching the stream network. HYPR shows good simulation of the overall hydrograph and peak flows, on a daily resolution, as indicated by the Nash-Sutcliffe Efficiency (NSE) of 0.72 and NSE for flows over threshold (NSEOT) of 0.78, respectively, averaged over multiple prairie watersheds for the entire simulation period. Although HYPR’s process representation is simple, it shows acceptable simulation of internal hydrologic variables (e.g., accumulated snow on ground) when compared against field measurements. HYPR can be useful when data or computational resources are limited. As for PRIMA, it shows potential for simulating the inundation extents when compared against remote sensing observations of water extents with an accuracy of 85 % averaged over two prairie basins in Saskatchewan, Canada. PRIMA is three to eight times as computationally efficient as the recently developed Wetland DEM Ponding Model (WDPM). The MESH-PRIMA model shows an improved hydrograph and flood simulation on a daily resolution (NSE = 0.55 and NSEOT = 0.60, respectively) compared to the MESH model with its current prairie algorithm (NSE = 0.49 and NSEOT = 0.55, respectively) for the entire simulation period. More importantly, MESH-PRIMA can identify the spatial distribution of water over the landscape and quantify the spatial non-contributing area for different flood events. The proposed models in this thesis can be used for efficient pothole storage dynamics simulation, inundation mapping, streamflow, and peak flow prediction in the prairies. The models can be used for a wide spectrum of hydrologic or hydraulic purposes ranging from limited data, conceptual-lumped-operational mode (e.g., HYPR) to detailed data, physically based research mode (e.g., MESH-PRIMA). These models, especially MESH-PRIMA, improve our understanding of the complexities of the prairie hydrology and the impacts of land depressions on changing the watershed response. More importantly, the methods proposed in MESH-PIMA can be explicitly used in most land-surface schemes within earth system models, allowing for important application in climate change and numerical prediction systems that typically ignore this important prairie phenomenon

Modelling the high-resolution dynamic exposure to flood in city-region

Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering residents' travel behavior. First, a flood simulation was conducted using the LISFLOOD-FP model to predict the spatiotemporal distribution of flooding. Second, an agent-based model was used to simulate residents' movements during the urban flooding period. Finally, to study the evolution and patterns of urban flooding exposure, the exposure of population, roads, and buildings to urban flooding was simulated using Lishui, China, as a case study. The results showed that water depth was the major factor affecting total urban exposure in Lishui. Urban exposure to fluvial flooding was concentrated along the river, while exposure to pluvial flooding was dispersed throughout the area (independent from the river). Additionally, the population distribution on weekends was more variable than on weekdays and was more sensitive to floods. In addition, residents' response behavior (based on their subjective consciousness) may result in increased overall exposure. This study presents the first fully formulated method for dynamic urban flood exposure simulation at a high spatiotemporal resolution. The quantitative results of this study can provide fundamental information for urban flood disaster vulnerability assessment, socioeconomic loss assessment, urban disaster risk management, and emergency response plan establishment

Modeling the high-resolution dynamic exposure to flooding in a city region

Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering residents' travel behavior. First, a flood simulation was conducted using the LISFLOOD-FP model to predict the spatiotemporal distribution of flooding. Second, an agent-based model was used to simulate residents' movements during the urban flooding period. Finally, to study the evolution and patterns of urban flooding exposure, the exposure of population, roads, and buildings to urban flooding was simulated using Lishui, China, as a case study. The results showed that water depth was the major factor affecting total urban exposure in Lishui. Urban exposure to fluvial flooding was concentrated along the river, while exposure to pluvial flooding was dispersed throughout the area (independent from the river). Additionally, the population distribution on weekends was more variable than on weekdays and was more sensitive to floods. In addition, residents' response behavior (based on their subjective consciousness) may result in increased overall exposure. This study presents the first fully formulated method for dynamic urban flood exposure simulation at a high spatiotemporal resolution. The quantitative results of this study can provide fundamental information for urban flood disaster vulnerability assessment, socioeconomic loss assessment, urban disaster risk management, and emergency response plan establishment

Spatiotemporal modeling of interactions between urbanization and flood risk: a multi-level approach

The main goal of this PhD research is to investigate the expected flood damage for future urban patterns at different scales. Four main steps are followed to accomplish this goal. In the first step, a retrospective analysis is performed for the evolution of the urban development in Wallonia (Belgium) as a case study. Afterward, two land use change models, cellular automata-based, and agent-based are proposed and compared. Based on this comparison, the agent-based model is employed to simulate future urbanization scenarios. An important feature of this research is evident in the consideration of the multiple densities of built-up areas, which enables to study both expansion and densification processes. As the model simulates urbanization up to 2100, forecasting land use change over such time frames entails very significant uncertainties. In this regard, uncertainty in land use change models has been considered. In the third step, 24 urbanization scenarios that differed in terms of spatial policies and urbanization rate are generated. The simulated scenarios have then been integrated with a hydrological model. The results suggest that urban development will continue within flood-prone zones in a number of scenarios. Therefore, in the fourth and last step, a procedural urban generation system is developed to analyze the respective influence of various urban layout characteristics on inundation flow, which assists in designing flood-resistant urban layouts within the flood-prone zones.This thesis was funded through the ARC grant for Concerted Research Actions for project number 13/17-01 entitled "Land-use change and future flood risk: influence of micro-scale spatial patterns (FloodLand)" financed by the French Community of Belgium (Wallonia-Brussels Federation)

Modeling riparian vegetation responses to flow alteration by dams and and climate change

2013 Fall.Includes bibliographical references.As the interface between freshwater and terrestrial ecosystems, riparian vegetation is a critical influence on biodiversity maintenance and ecosystem service production along river corridors. Understanding how altered environmental drivers will affect this vegetation is therefore central to sound watershed management. A river's flow regime exerts a primary control on the type and abundance of riparian vegetation, as differing adaptations to changing discharge levels mediate plant recruitment and persistence. Models of the relationships between flow and vegetation, generalized across species in terms of flow response traits such as flood tolerance, provide a means to explore the consequences of hydrologic alteration resulting from dams and climate change. I addressed these issues through development of a stage-structured model of woody riparian vegetation driven by variation in annual high flows. Simulation experiments offered insight into the potential trajectories of competing vegetation trait types relative to scenarios of dam construction, re-operation and removal. Modifying the size and frequency of the floods responsible for both disturbance mortality and establishment opportunities altered the relative abundance of pioneer and upland cover. Yet, qualitative differences in simulated outcomes resulted from alternative assumptions regarding seed limitation and floodplain stabilization, illustrating the need to carefully consider how these factors may shape estimated and actual vegetation responses to river regulation. In addition, I linked this simulation approach with an integrated watershed-modeling framework to assess the relative risk of invasion by the introduced plant Tamarix under multiple climate change scenarios. Though warming may increase the potential for Tamarix range expansion by weakening thermal constraints, the results of this work supported the expectation that hydrogeomorphic variation will control how this potential is realized. With simulated invasion risk strongly dependent on shifts in both the magnitude and timing of high flows, model outcomes underscored the importance of accounting for multiple, interacting flow regime attributes when evaluating the spread of introduced species in river networks. This research suggested the utility of simplified but process-based simulations of riparian flow-ecology relationships, demonstrating that such models can establish a first approximation of the potential consequences of management decisions and can highlight key questions for additional research, particularly where data are scarce and uncertainty is high

Flood-pedestrian simulator: an agent-based modelling framework for urban evacuation planning

Agent-Based Modelling (ABM) is an increasingly used approach for characterisation of human behaviour in evacuation simulation modelling. ABM-based evacuation models used in flood emergency are developed mostly for vehicular scenarios at regional scale. Only a few models exist for simulating evacuations of on-foot pedestrians responding to floods in small and congested urban areas. These models do not include the heterogeneity and variability of individuals’ behaviour influenced by their dynamic interactions with the floodwater properties. This limitation is due to the modelling restrictions pertaining to the computational complexity and the modelling flexibility for agent characterisation. This PhD research has aimed to develop a new ABM-based pedestrian evacuation model that overcomes these challenges through an ABM platform called Flexible Large-scale Agent Modelling Environment for the Graphics Processing Units (FLAME GPU). To achieve this aim, a hydrodynamic model has been integrated into a pedestrian model within the FLAME GPU framework. The dynamic interactions between the flood and pedestrians have been formulated based on a number of behavioural rules driving the mobility states and way-finding decisions of individuals in and around the floodwaters as well as the local changes in the floodwater properties as a result of pedestrians’ crowding. These rules have been progressively improved and their added value has been explored systematically by diagnostically comparing the simulation results obtained from the base setup and the augmented version of the model applied to a synthetic test case. A real-world case study has been further used to specifically evaluate the added value of rules relating the individuals’ way-finding mechanism to various levels of flood-risk perception. The findings from this research have shown that increasing the level of pedestrians’ heterogeneity and the effect of pedestrians’ crowding on the floodwater hydrodynamics yield to a considerably different prediction of flood risk and evacuation time. Besides, accounting for pedestrians’ various levels of flood-risk perception has been found to be one determinant factor in the analysis of flood risk and evacuation time when there are multiple destinations. Finally, the sensitivity analysis on the simulation results have shown that the deviations in the simulation outcomes increases in line with the increase in the sophistication of human behavioural rules

Enable High-resolution, Real-time Ensemble Simulation and Data Assimilation of Flood Inundation using Distributed GPU Parallelization

Numerical modeling of the intensity and evolution of flood events are affected by multiple sources of uncertainty such as precipitation and land surface conditions. To quantify and curb these uncertainties, an ensemble-based simulation and data assimilation model for pluvial flood inundation is constructed. The shallow water equation is decoupled in the x and y directions, and the inertial form of the Saint-Venant equation is chosen to realize fast computation. The probability distribution of the input and output factors is described using Monte Carlo samples. Subsequently, a particle filter is incorporated to enable the assimilation of hydrological observations and improve prediction accuracy. To achieve high-resolution, real-time ensemble simulation, heterogeneous computing technologies based on CUDA (compute unified device architecture) and a distributed storage multi-GPU (graphics processing unit) system are used. Multiple optimization skills are employed to ensure the parallel efficiency and scalability of the simulation program. Taking an urban area of Fuzhou, China as an example, a model with a 3-m spatial resolution and 4.0 million units is constructed, and 8 Tesla P100 GPUs are used for the parallel calculation of 96 model instances. Under these settings, the ensemble simulation of a 1-hour hydraulic process takes 2.0 minutes, which achieves a 2680 estimated speedup compared with a single-thread run on CPU. The calculation results indicate that the particle filter method effectively constrains simulation uncertainty while providing the confidence intervals of key hydrological elements such as streamflow, submerged area, and submerged water depth. The presented approaches show promising capabilities in handling the uncertainties in flood modeling as well as enhancing prediction efficiency

Multi-method Modeling Framework for Support of Integrated Water Resources Management

The existing definition of integrated water resources management (IWRM) promotes a holistic approach to water resources management practice. The IWRM deals with planning, design and operation of complex systems in order to control the quantity, quality, temporal and spatial distribution of water with the main objective of meeting human and ecological needs and providing protection from water disasters. One of the main challenges of IWRM is development of tools for operational implementation of the concept and dynamic coupling of physical and socio-economic components of water resources systems. This research examines the role of simulation in IWRM practices, analyses the advantages and limitations of existing modeling methods, and, as a result, suggests a new generic multi-method modeling framework that has the main goal to capture all structural complexities and interactions within water resources systems. Since traditional modeling methods solely do not provide sufficient support, this framework uses multi-method simulation approach to examine the co-dependence between natural resources and socio-economic environment. Designed framework consists of (i) a spatial database, (ii) a process-based model for representing the physical environment and changing conditions, and (iii) an agent-based model for representing spatially explicit socio-economic environment. The main idea behind multi-agent models is to build virtual complex systems composed of autonomous entities, which operate on local knowledge, possess limited abilities, affect and are affected by local environment, and thus enact the desired global system behavior. Based on the architecture of the generic multi-method modeling framework, an operational model is developed for the Upper Thames River basin, Southwestern Ontario, Canada. Six different experiments combine three climate and two socio-economic scenarios to analyze spatial dynamics of a complex physical-social-economic system. Obtained results present strong dependence between changes in hydrologic regime, in this case surface runoff and groundwater recharge rates, and regional socio-economic activities