Multi-Scale Modelling of Cold Regions Hydrology

Numerical computer simulations are increasingly important tools required to address both research and operational water resource issues related to the hydrological cycle. Cold region hydrological models have requirements to calculate phase change in water via consideration of the energy balance which has high spatial variability. This motivates the inclusion of explicit spatial heterogeneity and field-testable process representations in such models. However, standard techniques for spatial representation such as raster discretization can lead to prohibitively large computational costs and increased uncertainty due to increased degrees of freedom. As well, semi-distributed approaches may not sufficiently represent all the spatial variability. Further, there is uncertainty regarding which process conceptualizations are used and the degree of required complexity, motivating modelling approaches that allow testing multiple working hypotheses. This thesis considers two themes. In the first, the development of improved modelling techniques to efficiently include spatial heterogeneity, investigate warranted model complexity, and appropriate process representation in cold region models is addressed. In the second, the issues of non-linear process cascades, emergence, and compensatory behaviours in cold regions hydrological process representations is addressed. To address these themes, a new modelling framework, the Canadian Hydrological Model (CHM), is presented. Key design goals for CHM include the ability to: capture spatial heterogeneity in an efficient manner, include multiple process representations, be able to change, remove, and decouple hydrological process algorithms, work both at point and spatially distributed scales, reduce computational overhead to facilitate uncertainty analysis, scale over multiple spatial extents, and utilize a variety of boundary and initial conditions. To enable multi-scale modelling in CHM, a novel multi-objective unstructured mesh generation software *mesher* is presented. Mesher represents the landscape using a multi-scale, variable resolution surface mesh. It was found that this explicitly captured the spatial heterogeneity important for emergent behaviours and cold regions processes, and reduced the total number of computational elements by 50\% to 90\% from that of a uniform mesh. Four energy balance snowpack models of varying complexity and degree of coupling of the energy and mass budget were used to simulate SWE in a forest clearing in the Canadian Rocky Mountains. It was found that 1) a compensatory response was present in the fully coupled models’ energy and mass balance that reduced their sensitivity to errors in meteorology and albedo and 2) the weakly coupled models produced less accurate simulations and were more sensitive to errors in forcing meteorology and albedo. The results suggest that the inclusion of a fully coupled mass and energy budget improves prediction of snow accumulation and ablation, but there was little advantage by introducing a multi-layered snowpack scheme. This helps define warranted complexity model decisions for this region. Lastly, a 3-D advection-diffusion blowing snow transport and sublimation model using a finite volume method discretization via a variable resolution unstructured mesh was developed. This found that the blowing snow calculation was able to represent the spatial redistribution of SWE over a sub-arctic mountain basin when compared to detailed snow surveys and the use of the unstructured mesh provided a 62\% reduction in computational elements. Without the inclusion of blowing snow, unrealistic homogeneous snow covers were simulated which would lead to incorrect melt rates and runoff contributions. This thesis shows that there is a need to: use fully coupled energy and mass balance models in mountains terrain, capture snow-drift resolving scales in next-generation hydrological models, employ variable resolution unstructured meshes as a way to reduce computational time, and consider cascading process interactions

Unstructured grid generation using LiDAR data for urban flood inundation modelling

Inundation disasters, caused by sudden water level rises or rapid flow, occur frequently in various parts of the world. Such catastrophes strike not only in thinly populated flood plains or farmland but also in populated villages or urban areas. Inundation of the populated areas causes severe damage to the economy, injury, and loss of life; therefore, a proper management scheme for the disaster has to be developed. To predict and manage such adversity, an understanding of the dynamic processes of inundation flow is necessary because risk estimation is performed based on inundation flow information. In this study, we developed a comprehensive method to conduct detailed inundation flow simulations for a populated area with quite complex topographical features using LiDAR data. Detailed geospatial information including the location and shape of each building was extracted from the LiDAR data and used for the grid generation. The developed approach can distinguish buildings from vegetation and treat them differently in the flow model. With this method, a fine unstructured grid can be generated representing the complicated urban land features precisely without exhausting labor for data preparation. The accuracy of the generated grid with different grid spacing and grid type is discussed and the optimal range of grid spacing for direct representation of urban topography is investigated. The developed method is applied to the estimation of inundation flows, which occurred in the river basin of the Shin-minato River. A detailed inundation flow structure is represented by the flow model and the flow characteristics with respect to topographic features are discussed

Modeling the impacts of climate extremes and multiple water uses to support water management in the Icó-Mandantes Bay, Northeast Brazil

The hydropower production, water supply and aquaculture services of the Itaparica Reservoir are of immense importance for the Brazilian Northeast. Uncontrolled water resources consumption (e.g. irrigation, water supply), climate and land use change effects deteriorated the water quantity and quality in the reservoir, leading to socio-economic and environmental problems. In this work, a depth-averaged shallow water model was set up for the Icó-Mandantes Bay, one major branch of the reservoir, using the open TELEMAC-MASCARET system. The aim was to assess the impacts of the newly built water diversion channel, as well as the effects of a flood and tracer transport from an intermittent tributary, both located in the bay. An alternative approach to estimate the water retention times was additionally implemented. The simulations showed that though the diversion channel did not significantly influence the hydrodynamics of the bay, it is necessary to continuously monitor water quality parameters in the withdrawal, especially during rainy periods after droughts, because of the nutrient inputs from the tributary and the overflows of the nearby drainage systems. Management measures adapting to the continuously changing natural conditions and anthropogenic impacts are thus indispensable and the model presented can be a valuable supporting tool for this purpose.BMBF, 01LL0904A, Verbundvorhaben INNOVATE: Nachhaltige Nutzung von Stauseen durch innovative Kopplung von aquatischen und terrestrischen Ökosystemfunktionen - Teilvorhaben 1: Verbundkoordination, Grüne Leber und Ökonomi

Estimation of flood-exposed population in data-scarce regions combining satellite imagery and high resolution hydrological-hydraulic modelling: A case study in the Licungo basin (Mozambique)

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract:] Study Region Licungo basin (Mozambique) Study Focus The Licungo basin (23,263 km2) suffers frequent severe flooding due to tropical storms, in a country that is among the world’s most vulnerable in terms of exposure to weather-related hazards and climate change. We propose a methodology for the estimation of the population exposed to flooding at the catchment scale in data-scarce regions, combining satellite imagery with integrated high-resolution hydrological-hydraulic modelling. All the input data needed are retrieved from freely-available global satellite products. The numerical model is also freeware. The methodology is therefore replicable worldwide. An estimate of the flood extent and exposed population during Tropical Storm Ana (January 2022) is presented as a case study. New Hydrological Insights for the Region Current freely-available satellite products in combination with high-resolution hydrological-hydraulic models can be used to estimate the population exposed to flooding in the whole catchment. This estimate is more realistic than the one obtained using satellite imagery alone, since satellite images are very rarely taken at the time of maximum flooding. Using the proposed methodology, we estimate that over 273,000 people (out of 1.5 million) were exposed to flooding in the Licungo basin during Tropical Storm Ana. This represents 18% of the basin population and is 8 times larger than the estimate obtained using only the available satellite images.European Civil Protection and Humanitarian Operations (ECHO); ECHO/-SF/BUD/2018/9100