The primary goal of this research is to evaluate and identify proper calibration approaches, skillful hydrological models, and suitable weather forecast inputs to improve the accuracy and reliability of hydrological forecasting in different types of watersheds. The research started by formulating an approach that examined single- and multi-site, and single- and multi-objective optimization methods for calibrating an event-based hydrological model to improve flood prediction in a semi-urban catchment. Then it assessed whether reservoir inflow in a large complex watershed could be accurately and reliably forecasted by simple lumped, medium-level distributed, or advanced land-surface based hydrological models. Then it is followed by a comparison of multiple combinations of hydrological models and weather forecast inputs to identify the best possible model-input integration for an enhanced short-range flood forecasting in a semi-urban catchment. In the end, Numerical Weather Predictions (NWPs) with different spatial and temporal resolutions were evaluated across Canada’s varied geographical environments to find candidate precipitation input products for improved flood forecasting. Results indicated that aggregating the objective functions across multiple sites into a single objective function provided better representative parameter sets of a semi-distributed hydrological model for an enhanced peak flow simulation. Proficient lumped hydrological models with proper forecast inputs appeared to show better hydrological forecast performance than distributed and land-surface models in two distinct watersheds. For example, forcing the simple lumped model (SACSMA) with bias-corrected ensemble inputs offered a reliable reservoir inflow forecast in a sizeable complex Prairie watershed; and a combination of the lumped model (MACHBV) with the high-resolution weather forecast input (HRDPS) provided skillful and economically viable short-term flood forecasts in a small semi-urban catchment. The comprehensive verification has identified low-resolution NWPs (GEFSv2 and GFS) over Western and Central parts of Canada and high-resolution NWPs (HRRR and HRDPS) in Southern Ontario regions that have a promising potential for forecasting the timing, intensity, and volume of floods. / Thesis / Doctor of Philosophy (PhD) / Accurate hydrological models and inputs play essential roles in creating a successful flood forecasting and early warning system. The main objective of this research is to identify adequately calibrated hydrological models and skillful weather forecast inputs to improve the accuracy of hydrological forecasting in various watershed landscapes. The key contributions include: (1) A finding that a combination of efficient optimization tools with a series of calibration steps is essential in obtaining representative parameters sets of hydrological models; (2) Simple lumped hydrological models, if used appropriately, can provide accurate and reliable hydrological forecasts in different watershed types, besides being computationally efficient; and (3) Candidate weather forecast products identified in Canada’s diverse geographical regions can be used as inputs to hydrological models for improved flood forecasting. The findings from this thesis are expected to benefit hydrological forecasting centers and researchers working on model and input improvements.

The research project described in this dissertation studies the modelling techniques employed for the Mlazi River in the context of flood analysis and flood forecasting in order to model flood inundation. These techniques are applicable to an environment where there is uncertainty due to a lack of historical input data for calibration and validation purposes. This uncertainty is best explained by understanding the process and data required to model flood inundation. In order to model flood inundation in real time, forecasted flood flows would be required as input to a hydraulic river model used for simulating flood inundation levels. During this process, forecasted flood flows would be obtained from a flood-forecasting model that would need to be calibrated and validated. The calibration process would require historical rainfall data correlating with streamflow data and subsequently, the validation process would require real time streamflow data. In the context of the Mlazi Catchment, there are only two stream gauges located in the upper subcatchments. Although these stream gauges have recorded data for 20 years, the streamflow data does not correlate with disaggregated daily rainfall data, of which there are records for at least 40 years. Therefore it would be difficult to develop the forecasting model based on the rainfall and streamflow data available. In this instance, a more realistic approach to modelling flood inundation involved the integration of GIS technology, a physically based hydrological model for flood analysis, a conceptual forecasting model for real time forecasting and a hydraulic model for computation of inundation levels. The integration of modelling techniques are better explained by categorising the process into three phases: Phase 1 Desktop catchment modelling: A continuous, physically based simulation model (HEC-HMS Model) was set up using GIS technology. The model applied the SCS-UH method for the estimation of peak discharges. Synthetic hyetographs for various recurrence intervals were used as input to the model. A sensitivity analysis was implemented and subsequently the HEC-HMS model was calibrated against output SCS-UH method and peak discharges simulated. The synthetic hyetographs together with results from the HEC-HMS model were used for validation of the Mlazi Meta Model (MMM) used for real time flood forecasting. Phase 2 Implementation of the Inundation Model: The hydraulic model (HEC-RAS) was created using a Digital Elevation Model (DEM). A field survey was conducted for the purpose of capturing the roughness coefficients and hydraulic structures, which were incorporated into the model and also for the confirmation of the terrain cross sections from the DEM. Flow data for the computation of levels of inundation were obtained from the HEC-HMS model. The levels of inundation for the natural channel of Mlazi River were simulated using the one dimensional steady state analysis, whereas for the canal overbank areas, simulation was conducted for unsteady state conditions. Phase 3 Creation of the Mlazi Meta Model (MMM): The MMM used for real time flood forecasting is a linear catchment model which consists of a semi-distributed three reservoir cell model (Pegram and Sinclair, 2002). The MMM parameters were initially adjusted using the HEC-HMS model so that it became representative of the Mlazi catchment. This approach sounds unreasonable because a model is being validated by another model but it gave the best initial estimate of the parameters rather than using trial and error. The MMM will be further updated using record radar data and streamflow data once all structures have been put in place. The confidence in the applicability of the HEC-HMS model is based on the intensive efforts applied in setting it up. Furthermore, the output results from the calibrated HEC-HMS model were compared with other reliable methods of computing design peak discharges and also validated with frequency analysis conducted on one of the subcatchments. / Thesis (M.Sc.)-University of Natal, Durban,2003.

The aim of this research project was to estimate parameters for the distribution of annual maximum flood levels for the Zambezi River at Katima Mulilo. The estimation of parameters was done by using the maximum likelihood method. The study aimed to explore data of the Zambezi's annual maximum flood heights at Katima Mulilo by means of fitting the Gumbel, Weibull and the generalized extreme value distributions and evaluated their goodness of fit.

This thesis provides a basic framework for probabilistic real-time urban flood forecasting based on data of varying degree of quality and quantity. The framework was developed based on precipitation data from two case study areas:Aarhus Denmark and Castries St. Lucia. Many practitioners have acknowledged that a combination of structural and non-structural measures are required to reduce the effects of flooding on urban environments, but the general dearth of the desired data and models makes the development of a flood forecasting system seem unattainable. Needless to say, high resolution data and models are not always achievable and it may be necessary to override accuracy in order to reduce flood risk in urban areas and focus on estimating and communicating the uncertainty in the available resource. Thus, in order to develop a pertinent framework, both primary and secondary data sources were used to discover the current practices and to identify relevant data sources. Results from an online survey revealed that we currently have the resources to make a flood forecast and also pointed to potential open source quantitative precipitation forecast (QPF) which is the single most important component in order to make a flood forecast. The design of a flood forecasting system entails the consideration of several factors, thus the framework provides an overview of the considerations and provides a description of the proposed methods that apply specifically to each component. In particular, this thesis focuses extensively on the verification of QPF and QPE from NWP weather radar and highlights a method for estimating the uncertainty in the QPF from NWP models based on a retrospective comparison of observed and forecasted rainfall in the form of probability distributions. The results from the application of the uncertainty model suggest that the rainfall forecasts has a large contribution to the uncertainty in the flood forecast and applying a method which bias corrects and estimates confidence levels in the forecast looks promising for real-time flood forecasting. This work also describes a method used to generate rainfall ensembles based on a catalogue of observed rain events at suitable temporal scales. Results from model calibration and validation highlights the invaluable potential in using images extracted from social network sites for model calibration and validation. This framework provides innovative possibilities for real-time urban flood forecasting.

Floods are the most common natural disaster in the U.S. as reported by the Federal Emergency Management Administration (FEMA), and there is a need to provide advance warning to vulnerable communities on the potential risks of flooding after intense storms. The key drivers of urban hydrological research include climate change impacts and adaption, city resilience to hydrological extremes, and integration with emergency management and city planning disciplines. Significant advances in modeling techniques and computational resources have made real-time flood forecasting tools in urban and rural areas an achievable goal, but there is no universal method for flood modeling. Urban landscapes pose a challenge because of fine-scale features and heterogeneities in the landscape including streets, buildings, pipes, and impervious land cover. A nested regional-local modeling approach was used to evaluate its capabilities to provide useful and accurate flood related information to a small community in Iowa. The advantage of a nested approach is the ability to harness the computational efficiency of the regional model while providing reasonably accurate streamflow boundary conditions to the local model. The nested model incorporates the tools and products maintained at the Iowa Flood Center (IFC) including the streamflow bridge sensors, rain gauges, radar rainfall product, and statewide model. A one-way connection was made between the regional model of the upper Maquoketa Watershed (275 mi2) and the local model of the City of Manchester (5 mi2). The uncalibrated, nested model was validated using photos and streamflow records for flood events that occurred in July 2010 and September 2016. Multiple radar rainfall estimates were used as input to the model to better understand the impacts of the spatial and temporal resolution and variations of rainfall on streamflow predictions. A local storm event analysis was completed to determine the vulnerable areas of the stormwater network in eastern Manchester. The two main sources of flooding in Manchester are from the river and from local runoff. During extreme flood events caused by the river, the hydrologic impacts of the urban catchment are masked and the stormwater network system is overwhelmed. The coarse, regional model is limited in producing streamflow results for the small tributaries draining the eastern areas of Manchester. In the case of localized rainfall, a fine resolution model that takes into account the stormwater network and rainfall-runoff dynamics are crucial to capturing the hydrologic response of the urban area. Overall, the nested model showed skill in reproducing the hydrographs and the flood extents. Using an ensemble of rainfall input, the multiple model realizations envelope the observed streamflow indicating that the uncertainty of the rainfall is implicitly captured in the model results. The simulated streamflow at the outlet varies significantly depending on the spatial resolution of the rainfall but shows small sensitivity to the temporal resolution of the rainfall input. However, the local rainfall-runoff volumes vary significantly depending on the spatial and temporal resolution of the rainfall input. Recommendations are given to Manchester to highlight areas at risk to flooding. Recommendations are given to the IFC on the capabilities of the nested regional-local modeling approach along with suggestions for future work to incorporate urban areas into the statewide flood forecasting system.

Design flood estimation in small to medium sized ungauged catchments is frequently required in hydrologic analysis and design and is of notable economic significance. For this task Australian Rainfall and Runoff (ARR) 1987, the National Guideline for Design Flow Estimation, recommends the Probabilistic Rational Method (PRM) for general use in South- East Australia. However, there have been recent developments that indicated significant potential to provide more meaningful and accurate design flood estimation in small to medium sized ungauged catchments. These include the L moments based index flood method and a range of quantile regression techniques. This thesis focuses on the quantile regression techniques and compares two methods: ordinary least squares (OLS) and generalised least squares (GLS) based regression techniques. It also makes comparison with the currently recommended Probabilistic Rational Method. The OLS model is used by hydrologists to estimate the parameters of regional hydrological models. However, more recent studies have indicated that the parameter estimates are usually unstable and that the OLS procedure often violates the assumption of homoskedasticity. The GLS based regression procedure accounts for the varying sampling error, correlation between concurrent flows, correlations between the residuals and the fitted quantiles and model error in the regional model, thus one would expect more accurate flood quantile estimation by this method. This thesis uses data from 133 catchments in the state of Victoria to develop prediction equations involving readily obtainable catchment characteristics data. The GLS regression procedure is explored further by carrying out a 4-stage generalised least squares analysis where the development of the prediction equations is based on relating hydrological statistics such as mean flows, standard deviations, skewness and flow quantiles to catchment characteristics. This study also presents the validation of the two techniques by carrying out a split-sample validation on a set of independent test catchments. The PRM is also tested by deriving an updated PRM technique with the new data set and carrying out a split sample validation on the test catchments. The results show that GLS based regression provides more accurate design flood estimates than the OLS regression procedure and the PRM. Based on the average variance of prediction, standard error of estimate, traditional statistics and new statistics, rankings and the median relative error values, the GLS method provided more accurate flood frequency estimates especially for the smaller catchments in the range of 1-300 km2. The predictive ability of the GLS model is also evident in the regression coefficient values when comparing with the OLS method. However, the performance of the PRM method, particularly for the larger catchments appears to be satisfactory as well. / Master of Engineering (Honours)

<p>This diploma work has been done as a part of the EC funded projects, MUSIC VK1- CT-2000-00058 and SmartDoc IST-2000-28137. The objective was to create an intuitive and easy to use visualization of flood forecasting data provided in the MUSIC project. This visualization is focused on the Visual User Interface and is built on small, reusable components. The visualization, FloodViewer, is small enough to ensure the possibility of distribution via the Internet, yet capable of enabling collaboration possibilities and embedment in electronic documents of the entire visualization. Thus, FloodViewer has been developed in three versions for different purposes. </p><p>Analysis and report generation (FloodViewer ) Collaborative analysis (FloodViewerNet ) Presentation and documentation (FloodViewerX).</p>

Thesis (doctoral)--Vrije Universiteit, Amsterdam, 2003. / Vita. At head of title: Vrije Universiteit te Amsterdam. Includes bibliographical references (p. [89]-93).

<p>In this dissertation, a Hydrologic Data Assimilation System (HDAS) relying on the Duke Coupled surface-groundwater Hydrology Model (DCHM) and various data assimilation techniques including EnKF (Ensemble Kalman Filter), the fixed-lag EnKS (Ensemble Kalman Smoother) and the Asynchronous EnKF (AEnKF) was developed to 1) investigate the hydrological predictability of precipitation-induced natural hazards (i.e. floods and landslides) in the Southern Appalachians in North Carolina, USA, and 2) to characterize the seasonal (wet/dry) and inter-annual variability of surface-groundwater interactions with implications for water resource management in the Upper Zambezi River Basin (UZRB) in southern Africa. The overarching research objective is to improve hydrologic predictability of precipitation-induced natural hazards and water resources in regions of complex terrain. The underlying research hypothesis is that hydrologic response in mountainous regions is governed by surface-subsurface interaction mechanisms, specifically interflow in soil-mantled slopes, surface-groundwater interactions in recharge areas, and wetland dynamics in alluvial floodplains at low elevations. The research approach is to investigate the modes of uncertainty propagation from atmospheric forcing and hydrologic states on processes at multiple scales using a parsimonious uncalibrated hydrologic model (i.e. the DCHM), and Monte Carlo and Data-Assimilation methods. In order to investigate the coupled surface-groundwater system and assess the predictability of precipitation-induced natural hazards (i.e. floods and landslides) in headwater basins, including the propagation of uncertainty in QPE/QPF (Quantitative Precipitation Estimates/Forecasts) to QFE/QFF (Quantitative Flood Estimates/Forecasts), the DCHM model was implemented first at high spatial resolution (250m) in the Southern Appalachian Mountains (SAM) in North Carolina, USA. The DCHM modeling system was implemented subsequently at coarse resolution (5 km) in the Upper Zambezi River Basin (UZRB) in southern Africa for decadal-scale simulations (i.e. water years from 2002 to 2012). </p><p>The research in the SAM showed that joint QPE-QFF distributions for flood response at the headwater catchment scale are highly non-linear with respect to the space-time structure of rainfall, exhibiting strong dependence on basin physiography, initial soil moisture conditions (transient basin storage capacity), the space-time organization of runoff generation and conveyance mechanisms, and in particular interflow dynamics. The errors associated with QPEs and QPFs were characterized using rainfall observations from a dense raingauge network in the Pigeon River Basin, resulting in a simple linear regression model for adjusting/improving QPEs. Deterministic QFEs simulated by the DCHM agree well with observations, with Nash–Sutcliffe (NS) coefficients of 0.8~0.9. Limitations with state-of-the-science operational QPF and the impact of even limited improvements in rainfall forcing was demonstrated through an experiment consisting of nudging satellite-like observations (i.e. Adjusted QPEs) into operational QPE/QPF that showed significant improvement in QFF performance, especially when the timing of satellite overpass is such that it captures transient episodes of heavy rainfall during the event. The research further showed that the dynamics of subsurface hydrologic processes play an important role as a trigger mechanism of shallow landslides through soil moisture redistribution by interflow. Specifically, transient mass fluxes associated with the temporal-spatial dynamics of interflow govern the timing of shallow landslide initiation, and subsequent debris flow mobilization, independently of storm characteristics such as precipitation intensity and duration. Interflow response was shown to be dominant at high elevations in the presence of deep soils as well as in basins with large alluvial fans or unconsolidated debris flow deposits. In recharge areas and where subsurface flow is an important contribution to streamflow, subsurface-groundwater interactions determine initial hydrologic conditions (e.g. soil moisture states and water table position), which in turn govern the timing and magnitude of flood response at the event scale. More generally, surface-groundwater interactions are essential to capture low flows in the summer season, and generally during persistent dry weather and drought conditions. Future advances in QFF and landslide monitoring remain principally constrained by progress in QPE and QPF at the spatial resolution necessary to resolve rainfall-interflow dynamics in mountainous regions.</p><p>The predictability of QFE/QFF was further scrutinized in a complete operational environment during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP), in order to investigate the predictability of floods (and flashfloods) in headwater catchments in the Southern Appalachians with various drainage sizes. With the DCHM, a variety of operational QPEs were used to produce hydrological hindcasts for the previous day, from which the final states were used as initial conditions in the hydrological forecast for the current day. Although the IPHEx operational testbed results were promising in terms of not having missed any of the flash flood events during the IOP with large lead times of up to 6 hours, significant errors of overprediction or underprediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. Furthermore, the added value of improving QFE/QFF through assimilating discharge observations into the DCHM was investigated for advancing flood forecasting skills in the operational mode. Both the flood hindcast/forecast results were significantly improved by assimilating the discharge observations into the DCHM using the EnKF (Ensemble Kalman Filter), the fixed-lag EnKS (Ensemble Kalman Smoother) and Asynchronous EnKF (AEnKF). The results not only demonstrate the utility of discharge assimilation in operational forecasts, but also reveal the importance of initial water storage in the basin for issuing flood forecasts. Specifically, hindcast NSEs as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region, demonstrating that assimilation of discharge observations at the basin’s outlet can reduce the errors and uncertainties in soil moisture. Success in operational flood forecasting at lead times of 6, 9, 12 and 15hrs was also achieved through discharge assimilation, with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. The discharge assimilation experiments indicate that the optimal assimilating time window not only depends on basin properties but also on the storm-specific space-time-structure of rainfall within the basin, and therefore adaptive, context-aware configurations of the data assimilation system should prove useful to address the challenges of flood prediction in headwater basins.</p><p>A physical parameterization of wetland hydrology was incorporated in the DCHM for water resource assessment studies in the UZRB. The spatial distribution of wetlands was introduced in the model using probability occurrence maps generated by logistic regression models using MODIS reflectance-based indices as predictor variables. Continuous model simulations for the 2002-2012 period show that the DCHM with wetland parameterization was able to reproduce wetland hydrology processes adequately, including surface-groundwater interactions. The modelled regional terrestrial water storage anomaly (TWSA) captured very well the inter- and intra-annual variability of the system water storage changes in good agreement with the NASA’s GRACE (Gravity Recovery and Climate Experiment) TWSA observations. Specifically, the positive trend of TWSA documented by GRACE was simulated independently by the DCHM. Furthermore, it was determined that the TSWA positive trend results from cumulative water storage in the sandy soils of the Cuando-Luana sub-basin when shifts in storm tracks move rainfall to the western sector of the Angolan High Plateau. </p><p>Overall, the dissertation study demonstrates the capability of the DCHM in predicting specific characteristics of hydrological response to extreme events and also the inter- and intra-annual variability of surface-groundwater interactions at a decadal scale. The DCHM, coupled with slope stability module and wetland module featuring surface-groundwater interaction mechanism, not only is of great potential in the context of developing a regional warning system for natural hazards (i.e. flashfloods and landslides), but also is promising in investigating regional water budgets at decadal scale. In addition, the DCHM-HDAS demonstrated the ability to reduce forecasting uncertainty and errors associated with forcing data and the model proper, thus significantly improving the predictability of natural hazards. The HDAS could also be used to investigate the regional water resource assessment especially in poorly-gauged regions (e.g. southern Africa), taking advantage of satellite observations.</p> / Dissertation

Flash floods caused by localized thunderstorms are a natural hazard of the semi-arid Southwest, and many communities have responded by installing ALERT flood forecasting systems. This study explored a rainfall-runoff modeling approach thought to be appropriate for forecasting in such watersheds. The kinematic model KINEROS was evaluated because it is a distributed model developed specifically for desert regions, and can be applied to basins without historic data. This study examined the accuracy of KINEROS under data constraints that are typical of semi-arid ALERT watersheds. The model was validated at the 150 km², semi-arid Walnut Gulch experimental watershed. Under the conditions examined, KINEROS provided poor simulations of runoff volume and peak flow, but good simulations of time to peak. For peak flows, the standard error of estimate was nearly 100% of the observed mean. Surprisingly, when model parameters were based only on measurable watershed properties, simulated peak flows were as accurate as when parameters were calibrated on some historic data. The accuracy of KINEROS was compared to that of the SCS model. When calibrated, a distributed SCS model with a simple channel loss component was as accurate as KINEROS. Reasons for poor simulations were investigated by examining a) rainfall sampling errors, b) model sensitivity and dynamics, and c) trends in simulation accuracy. The cause of poor simulations was divided between rainfall sampling errors and other problems. It was found that when raingage densities are on the order of 1/20 km², rainfall sampling errors preclude the consistent and reliable simulation of runoff from localized thunderstorms. Even when rainfall errors were minimized, accuracy of simulations were still poor. Good results, however, have been obtained with KINEROS on small watersheds; the problem is not KINEROS itself but its application at larger scales. The study also examined the hydrology of thunderstorm-generated floods at Walnut Gulch. The space-time dynamics of rainfall and runoff were characterized and found to be of fundamental importance. Hillslope infiltration was found to exert a dominant control on runoff, although flow hydraulics, channel losses, and initial soil moisture are also important. Watershed response was found to be nonlinear.