Radar-rainfall uncertainty quantification has been recognized as an intricate problem due to the complexity of the multi-dimensional error structure, which is also associated with space and time scale. The error structure is usually characterized by two moments of the error distribution: bias and error variance. Despite numerous efforts to investigate radar-rainfall uncertainties, many questions remain unanswered. This dissertation uses two statistical descriptions (mean and variance) of the error distribution to highlight and describe some of the remaining gaps in representing radar-rainfall uncertainties. The four central issues addressed in this dissertation include: 1. Investigation of radar relative bias caused by radar calibration. 2. Statistical modeling of range-dependent error arising from the radar beam geometry structure. 3. Scale-dependent variability of radar-rainfall and rain gauge error covariance. 4. Scale-dependence of radar-rainfall error variance. The first two issues describe systematic features of main error sources of radar-rainfall. The other two are associated with quantifying radar error variance using the error variance separation (EVS) method, which considers the spatial sampling mismatch between radar and rain gauge data. This study captures the main systematic features (systematic bias arising from radar calibration and range-dependent errors) of radar measurements without using ground reference data and the error variance structure with respect to the spatio-temporal transformation of the measurements for further applications to hydrologic fields. Such consideration of radar-rainfall uncertainties represented by error mean and variance can enhance the characterization of the uncertainty structure and yield a better understanding of the physical process of precipitation.

Spatial variability of rainfall is a significant factor in hydrologic and water quality modeling. In recent years, characterizing and analyzing the effect of spatial variability of rainfall in hydrologic applications has become vital with the advent of remotely sensed precipitation estimates that have high spatial resolution. In this study, the effect of spatial variability of rainfall in hourly runoff generation was analyzed using the Soil and Water Assessment Tool (SWAT) for Big Sandy Creek and Walnut Creek Watersheds in North Central Texas. The area of the study catchments was 808 km2 and 196 km2 for Big Sandy Creek and Walnut Creek Watersheds respectively. Hourly rainfall measurements obtained from raingauges and weather radars were used to estimate runoff for the years 1999 to 2003. Results from the study indicated that generated runoff from SWAT showed enormous volume bias when compared against observed runoff. The magnitude of bias increased as the area of the watershed increased and the spatial variability of rainfall diminished. Regardless of high spatial variability, rainfall estimates from weather radars resulted in increased volume of simulated runoff. Therefore, weather radar estimates were corrected for various systematic, range-dependent biases using three different interpolation methods: Inverse Distance Weighting (IDW), Spline, and Thiessen polygon. Runoff simulated using these bias adjusted radar rainfall estimates showed less volume bias compared to simulations using uncorrected radar rainfall. In addition to spatial variability of rainfall, SWAT model structures, such as overland flow, groundwater flow routing, and hourly evapotranspiration distribution, played vital roles in the accuracy of simulated runoff.

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.

abstract: The Phoenix Metropolitan region is subject to intense summer monsoon thunderstorms that cause highly localized flooding. Due to the challenges in predicting these meteorological phenomena and modeling rainfall-runoff transformations in urban areas, the ability of the current operational forecasting system to predict the exact occurrence in space and time of floods in the urban region is still very limited. This thesis contributes to addressing this limitation in two ways. First, the existing 4-km, 1-h Stage IV and the new 1-km, 2-min Multi-Radar Multi-Sensor (MRMS) radar products are compared using a network of 365 gages as reference. It is found that MRMS products consistently overestimate rainfall during both monsoonal and tropical storms compared to Stage IV and local rain gauge measurements, although once bias-corrected offer a reasonable estimate for true rainfall at a higher spatial and temporal resolution than rain gauges can offer. Second, a model that quantifies the uncertainty of the radar products is applied and used to assess the propagation of rainfall errors through a hydrologic-hydraulic model of a small urban catchment in Downtown Phoenix using a Monte Carlo simulation. The results of these simulations suggest that for this catchment, the magnitude of variability in the distribution of runoff values is proportional to that of the input rainfall values. / Dissertation/Thesis / Masters Thesis Civil, Environmental and Sustainable Engineering 2020

Les modèles hydrologiques sont des outils indispensables pour calculer les débits a l’exutoire des bassins versants, la gestion des aménagements hydrauliques ou encore la prévision et la prévention des inondations. Les précipitations représentent la variable climatique principale à l’origine des débits des cours d’eau qui s’écoulent au sein d’un bassin versant. De ce fait, la réponse hydrologique du bassin est fortement dépendante de la représentativité des données d’entrée de précipitation.Les radars météorologiques, qui permettent aujourd’hui d’accéder a des mesures a haute résolution spatiale et temporelle des champs de précipitation, sont de plus en plus utilises dans le domaine de la prévision, pour le suivi des situations hydrométéorologiques. Cependant, la mesure des précipitations par radar est entachée d’erreurs qui peuvent affecter gravement la qualité des simulations de débit. De ce fait, l’utilisation des données de précipitations a haute résolution spatiale pour la modélisation hydrologique est souvent limitée par rapport a l’utilisation des données pluviométriques.Récemment, Météo-France a développe une réanalyse des lames d’eau au pas de temps horaire, sur une durée de 10 ans, en combinant l’ensemble des données de précipitation radar et pluviométriques : les mesures radars ont été corrigées et étalonnées avec le réseau de mesure au sol horaire et journalier. Dans cette thèse, nous proposons d’étudier l’intérêt de cette nouvelle base de données à haute résolution spatiale pour la modélisation pluie-débit.Dans un premier temps, nous avons développe et valide un modèle hydrologique semi-distribue qui a la capacité de fonctionner pour différentes résolutions spatiales, de la représentation globale jusqu’a une discrétisation spatiale très fine des bassins. Dans un deuxième temps, l’impact de la résolution spatiale des données d’entrée de précipitation sur la simulation des débits a été analysé. L’apport de l’information radar pour l’estimation des précipitations a été évalue par rapport a une utilisation exclusive des pluviomètres, par le biais de la modélisation pluie-débit en termes de précision des débits a l’exutoire des bassins. Enfin, le modèle semi-distribue TGR a été comparé avec le modèle global GRP actuellement opérationnel dans les Services de Prévision des Crues. L’originalité de notre travail réside sur l’utilisation de données d’observation sur un large échantillon de 181 bassins versants français représentant une grande diversité de tailles et conditions climatiques, ce qui nous permet d’apporter un diagnostic robuste et des éléments de réponse sur les problématiques scientifiques traitées. / Hydrologic models are essential tools to compute the catchment rainfall-runoff response required for river management and flood forecast purposes. Precipitation dominates the high frequency hydrological response, and its simulation is thus dependent on the way rainfall is represented. In this context, the sensitivity of runoff hydrographs to the spatial variability of forcing data is a major concern of researchers. However, results from the abundant literature are contrasted and it is still difficult to reach a clear consensus.Weather radar is considered to be helpful for hydrological forecasting since it provides rainfall estimates with high temporal and spatial resolution. However, it has long been shown that quantitative errors inherent to the radar rainfall estimates greatly affect rainfall-runoff simulations. As a result, the benefit from improved spatial resolution of rainfall estimates is often limited for hydrological applications compared to the use of traditional ground networks.Recently, Météo-France developed a rainfall reanalysis over France at the hourly time step over a 10-year period combining radar data and raingauge measurements: weather radar data were corrected and adjusted with both hourly and daily raingauge data. Here we propose a framework to evaluate the improvement in streamflow simulation gained by using this new high resolution product.First, a model able to cope with different spatial resolutions, from lumped to semi-distributed, was developed and validated. Second, the impact of spatial rainfall resolution input on streamflow simulation was investigated. Then, the usefulness of spatial radar data measurements for rainfall estimates was compared with an exclusive use of ground raingauge measurements and evaluated through hydrological modelling in terms of streamflow simulation improvements. Finally, semi-distributed modelling with the TGR model was performed for flood forecasting and compared with the lumped forecasting GRP model currently in use in the French flood forecast services. The originality of our work is that it is based on actual measurements from a large set of 181 French catchments representing a variety of size and climate conditions, which allows to draw reliable conclusions.