This study presents the development of a cell-based routing model. The model developed is a two parameter hydrological routing model that uses a coarse resolution stream network to route runoff from each cell in the watershed to the outlet. The watershed is divided into a number of equal cells, which are approximated as cascade of linear reservoirs or tanks. Water is routed from a cell downstream, depending on the flow direction of the cell, using the cascade of tanks. The routing model consists of two phases, first is the overland flow routing, which is followed by the channel flow routing. In this study, the cell-to-cell stream flow routing model is applied to the Brazos River Basin to demonstrate the impact of the cascade of tanks on the flow over a simple linear reservoir method. This watershed was tested with a uniform runoff depth in absence of observed runoff data. A case study on Waller Creek in Austin, Texas with observed runoff depths and stream flow is used to demonstrate the calibration and validation of model parameters.

This study presents the development of a cell-based routing model. The model developed is a two parameter hydrological routing model that uses a coarse resolution stream network to route runoff from each cell in the watershed to the outlet. The watershed is divided into a number of equal cells, which are approximated as cascade of linear reservoirs or tanks. Water is routed from a cell downstream, depending on the flow direction of the cell, using the cascade of tanks. The routing model consists of two phases, first is the overland flow routing, which is followed by the channel flow routing. In this study, the cell-to-cell stream flow routing model is applied to the Brazos River Basin to demonstrate the impact of the cascade of tanks on the flow over a simple linear reservoir method. This watershed was tested with a uniform runoff depth in absence of observed runoff data. A case study on Waller Creek in Austin, Texas with observed runoff depths and stream flow is used to demonstrate the calibration and validation of model parameters.

Uncertainty surrounds the understanding of natural variability in hydrologic extremes such as droughts and floods and how these events are projected to change in the future. This thesis leverages Global Climate Model (GCM) data to analyse 738 year streamflow scenarios in the Nelson-Churchill River Basin. Streamflow scenarios include a 500 year stationary period and future projections forced by two forcing scenarios. Fifty three GCM simulations are evaluated for performance in reproducing observed runoff characteristics. Runoff from a subset of nine simulations is routed to generate naturalized streamflow scenarios. Quantile mapping is then applied to reduce volume bias while maintaining the GCM’s sequencing of events. Results show evidence of future increases in mean annual streamflow and evidence that mean monthly streamflow variability has decreased from stationary conditions and is projected to decrease further into the future. There is less evidence of systematic change in droughts and floods. / May 2016

The future holds challenges for urban areas when it comes to handling pluvial floodings, occurring when the rainfall intensity exceeds both the man-made and natural infiltration and drainage capacity. To gain understanding of the effects and needed measures, tools for modelling the urban response to events such as cloudbursts are needed. The aim of this project was to build a model using the Spatially Distributed Travel Time (SDTT) approach to model the rainfall-runoff response of an urban watershed. The model was developed in ArcGIS Pro using a built-in module ArcPy allowing for the use of a Python script to ensure fast calculations and simulations on grid cell basis. In total six smaller watersheds within the larger catchment were modelled with a variety in size and degree of urbanisation. Unlike fully distributed models solving for both the continuity equation and momentum equation, the models save time by applying kinematic wave approximation solving the steady state, uniform continuity equation and the Manning’s equation. The study uses only one calibration parameter representing the upstream area contributing to runoff, used for adjusting the travel times to ensure they are not too slow which could generate a delay and underestimation of the peak discharge. The model was parameterized for a cloudburst event that occurred in the city of Gävle, in the year of 2021, and was validated against a fully distributed model (MIKE 21) simulating the same event. The generated response from the SDTT model successfully returns similar hydrographs to that of a fully distributed model in most cases. It performed very well in high urbanised areas with an even spatial distribution of the two land cover classes used, impervious and pervious surfaces, and small volumes of depressions. In areas with lesser degree of urbanisation and larger depression volumes collecting runoff, the simplified model struggled to capture the draining dynamics of these. However, the model managed to match the time to peak reasonably well in the struggling areas as well. To increase the applicability of the model the upstream area contributing to runoff should be based on physical characteristics and not calibration. Further, the model should be applied to other areas preferably using other rainfall event data or design storms, as well as investigate the performance using more than two land cover classes. Finally, a sensitivity analysis could be performed for parameters that were now set to fixed values, done so to reduce the calibration. / Framtiden kommer bjuda på utmaningar för urbana områden när det kommer till hanteringen av pluviala översvämningar, vilka inträffar när nederbördsintensiteten överstiger både den konstgjorda och naturliga infiltrations- och dräneringskapaciteten. För att få ökad förståelse av effekterna samt besluta om nödvändiga åtgärder behövs nya verktyg för att modellera den urbana responsen till följd av extrem nederbörd så som skyfall. Syftet med detta projekt vara att med hjälp av spatialt fördelade rinntider och kinematiska vågmodellen modellera nederbörden och avrinningen i ett urbant avrinningsområde. Modellen utvecklades i ArcGIS Pro med hjälp av den inbyggda modulen ArcPy vilken tillåter användningen av ett Python-skript som ger snabba beräkningar och korta simuleringar applicerade på cellnivå. Totalt modellerades sex mindre avrinningsområden inom det större området, alla med olika storlek och urbaniseringsgrad. Till skillnad från fullt distribuerade modeller som löser både kontinuitetsekvationen och rörelsemängdsekvationen, sparar modellen tid genom att tillämpa kinematisk vågteori, stationära kontinuitetsekvationen samt Mannings ekvation. Studien använder endast en kalibreringsparameter vilken representerar området uppströms om varje cell som bidrar till avrinning nedströms. Denna används för att justera rinntiderna för att säkerställa att modellen inte returnerar för långsamma tider vilket kan generera en fördröjning av responsen och underskattning av maxflödet. Modellen parametriserades för ett skyfall som inträffade i Gävle år 2021, och validerades mot en fullt distribuerad modell (MIKE 21) som simulerade samma händelse. Den modellerade responsen från avrinningsmodellen byggd på spatialt fördelade rinntider kunde framgångsrikt leverera liknande hydrografer som den fullt distribuerade modellen. Modellen presterade framför allt bra i områden med hög urbaniseringsgrad innehavande en jämn spatial fördelning av de två marktäckningsklasserna som användes, nämligen hårdgjorda och icke hårdgjorda ytor, samt små volymer av sänkor i området. I områden med en lägre urbaniseringsgrad och större volymer sänkor vilka fördröjer avrinningen hade modellen svårare att producera liknande hydrografer troligen då den förenklade modellen ej kan fånga dynamiken av att fylla och tömma dessa sänkor. Däremot lyckades den fortfarande att matcha tiden för maxflödet även för dessa områden. För att öka modellens tillämpbarhet bör området uppströms som bidrar till avrinning nedströms baseras på fysikaliska egenskaper och inte kalibrering. Vidare bör modellen tillämpas på andra områden, helst med hjälp av andra nederbördsdata eller designregn, samt undersöka prestandan om mer än två marktäckningsklasser används. Slutligen kan en känslighetsanalys utföras för parametrar som nu satts till fasta värden, detta för att minska kalibreringen.

Flood estimation is often required in hydrologic design and has important economic significance. For example, in Australia, the annual spending on infrastructure requiring flood estimation is of the order of $650 million ARR (I.E. Aust., 1998). Rainfall-based flood estimation techniques are most commonly adopted in practice. These require several inputs to convert design rainfalls to design floods. Of all the inputs, loss is an important one and defined as the amount of precipitation that does not appear as direct runoff. The concept of loss includes moisture intercepted by vegetation, infiltration into the soil, retention on the surface, evaporation and loss through the streambed and banks. As these loss components are dependent on topography, soils, vegetation and climate, the loss exhibits a high degree of temporal and spatial variability during the rainfall event. In design flood estimation, the simplified lumped conceptual loss models were used because of their simplicity and ability to approximate catchment runoff behaviour. In Australia, the most commonly adopted conceptual loss model is the initial losscontinuing loss model. For a specific part of the catchment, the initial loss occurs prior to the commencement of surface runoff, and can be considered to be composed of the interception loss, depression storage and infiltration that occur before the soil surface saturates. ARR (I. E. Aust., 1998) mentioned that the continuing loss is the average rate of loss throughout the remainder of the storm. At present, there is inadequate information on design losses in most parts of Australia and this is one of the greatest weaknesses in Australian flood hydrology. Currently recommended design losses are not compatible with design rainfall information in Australian Rainfall and Runoff. Also design losses for observed storms show a wide variability and it is always difficult to select an appropriate value of loss from this wide range for a particular application. Despite the wide variability of loss values, in the widely used Design Event Approach, a single value of initial and continuing losses is adopted. Because of the non-linearity in the rainfall-runoff process, this is likely to introduce a high degree of uncertainty and possible bias in the resulting flood estimates. In contrast, the Joint Probability Approach can consider probability-distributed losses in flood estimation. In ARR (I. E. Aust., 1998) it is recommended to use a constant continuing loss value in rainfall events. In this research it was observed that the continuing loss values in the rainfall events were not constant, rather than it decays with the duration of the rainfall event. The derived loss values from the 969 rainfall and streamflow events of Queensland catchments would provide better flood estimation than the recommended design loss values in ARR (I. E. Aust., 1998). In this research, both the initial and continuing losses were computed using IL-CL loss model and a single median loss value was used to estimate flood using Design Event Approach. Again both the initial and continuing losses were considered to be random variables and their probability distribution functions were determined. Hence, the research showed that the probability distributed loss values can be used for Queensland catchments in near future for better flood estimate. The research hypothesis tested was whether the new loss value for Queensland catchments provides significant improvement in design flood estimation. A total of 48 catchments, 82 pluviograph stations and 24 daily rainfall stations were selected from all over Queensland to test the research hypothesis. The research improved the recommended design loss values that will result in more precise design flood estimates. This will ultimately save millions of dollars in the construction of hydraulic infrastructures.