Changes in the temporal variability of precipitation at all timescales are expected due to global warming. Such changes affect urban water infrastructure by potentially influencing their performance and risk of failure. Unfortunately, there is considerable uncertainty about how hydrological variables will change in the future. While uncertainty is present at all timescales, the climate signal in the daily time series simulated by climate models, for instance, can be estimated with much greater certainty than in the simulated hourly time series. That is problematic as sub-daily precipitation time series are essential to solving specific water resource engineering problems, especially in urban hydrology, where times of concentrations are typically less than a day. For instance, hourly or sub-hourly precipitation time series are routinely used to design stormwater and road drainage systems. Rainfall variability at sub-daily time steps is often represented as Intensity-Duration-Frequency (IDF) curves, relating precipitation duration (of basin time of concentration) to return period and average precipitation intensity. Naturally, several researchers investigated the integration of climate change in IDF curves, leading to methods of variable complexity and variable performance. This thesis aims to a) make a critical analysis of the most commonly used methods for IDF curves under climate change in Canada and b) identify the methods with optimal performance for a set of stations located in the South Nation watershed in Ottawa, Ontario, and c) perform a case study highlighting the effect of the choice of the temporal disaggregation method on the estimated risk of failure/performance of an urban water system. The first part of the thesis examines Equidistant Quantile Mapping (EQM) used in the IDF_CC tool developed for the Canadian Water Network project. Two conceptual flaws in the method that led to a systematic underestimation of extreme events were discovered. Two corrections are proposed to the EQM, leading to the development of two new methods for IDF generation. The output of EQM and its improved version is a time series of annual maximum precipitation intensity for different durations that can be used to derive IDF curves. These time series generated using the above approach are not appropriate for rainfall-runoff models for which continuous time series of precipitation (not only maximums) are required. The second part of the thesis tackles the issue, which examines a different approach to evaluating the risk of failure/performance of urban water systems under a changing climate. This second approach yields continuous time series of precipitation that can be fed in rainfall-runoff models used for IDF curve generation. The proposed method is applied in three steps: i) projections of future daily precipitation are generated by downscaling the output of climate models; ii) the downscaled daily precipitation time series are temporally disaggregated to an hourly time step using various techniques; iii) finally, the disaggregated future precipitation time series are used as inputs to rainfall-runoff models or used to generate IDF curves. This approach relaxes several strong assumptions made to develop the EQM approach, such as the implicit (and strong) assumption that the annual maximum precipitation at two different time steps occurs during the same event. That assumption is not necessarily valid and can affect the realism of the generated IDF curves. The method's performance is obviously dependent on the temporal disaggregation technique used in step 3. In this thesis, a simple steady-state stochastic disaggregation model that generates wet/dry day occurrence using a binomial distribution and precipitation intensity using an exponential distribution is proposed and compared to widely used temporal disaggregation methods: the multiplicative random cascade model (MRC), the Hurst-Kolmogorov process (HKP), and three versions of the K-nearest neighbor model (KNN) using the nonparametric Kolmogorov- Smirnov (KS) test. The six disaggregation techniques were assessed at four stations located in South Nation River Watershed located in Eastern Ontario, Canada. The third part of the thesis is a case study of the impact of climate change on stormwater management. First, a stormwater management model (SWMM) of St. Catharines, Ontario, developed in a previous study, was selected to simulate its stormwater and sanitary system. The model was forced with downscaled and temporally disaggregated precipitation outputs of the Canadian Regional Climate Model at the Port Dalhousie station, simulated under emission scenario RCP8.5. The temporal disaggregation was done using the Fahad-Ousmane and the KNN (30) methods developed in the previous chapter. The impact of climate change on the frequency, volume, and quality of combined sewer overflows and other hydraulic parameters is examined. Results suggest an increase in the total volume, flow frequency percentage, maximum flow, and average flow in the stormwater system due to climate change. Therefore, adaptation measures should be implemented for the distribution network and wastewater treatment plant to convey and treat the wastewater resulting from wet and dry events.

No description available.

Global hydrological models can fill crucial gaps for providing essential information on water resources management, flood and drought forecasting, and assessing the impacts of climate change. However, these models face several challenges that must be addressed to ensure their applicability at local scales. These challenges include effectively managing Big Data, proper communication, adoption, and achieving accuracy in their results. Achieving accuracy in global hydrological models is critical for acceptance in decision-making, but poses the most significant challenge due to the extensive amount of observed data required and the complexity of obtaining and preparing such data for model evaluation. In this study, I conducted an evaluation of the GEOGloWS ECMWF Streamflow Services (GESS) historical simulation and forecast. The evaluation revealed the presence of systematic biases inherent in global models, which restrict their accuracy and reliability for local applications. To address this limitation, I propose a bias correction methodology that uses local data and employs a quantile-mapping approach to correct the systematic biases in the GESS model. I applied this methodology to the +40 years historical simulation dataset and forecast files released between January 1, 2014, and December 31, 2019, demonstrating its effectiveness in correcting the magnitude and seasonality of simulated streamflow values. Additionally, to enhance communication and adoption of the GESS model, I developed a web application called Historical Validation Tool (HVT) that processes and visualizes observed and simulated historical stream discharge data from the GESS model, performs bias correction on the historical simulation, computes goodness-of-fit metrics, and applies forward bias correction to subsequent forecasts. This web application was customized specifically for Brazil, Colombia, Ecuador, and Peru within the framework of the NASA SERVIR Amazonia Project. HVT enables users from these countries to get adjusted GESS historical simulations and forecasts, enhancing the reliability of GESS modeling results at the local scale. The results demonstrate that the bias correction method significantly improves the accuracy of the GESS historical simulation and forecast, as evidenced by the Kling Gupta Efficiency, making it a valuable tool for hydrological studies and water resources management. Furthermore, HVT with its user-friendly graphical interface, rapid performance, and flood alert capabilities, effectively communicates the improvements in GESS historical and forecasted data.

Arctic sea ice has undergone a dramatic transformation in recent decades, including a substantial reduction in sea ice extent in summer months. Such changes, combined with relatively recent advancements in seasonal (1-12 months) to decadal forecasting, have prompted a rapidly-growing body of research on forecasting Arctic sea ice on seasonal timescales. These forecasts are anticipated to benefit a vast array of end-users whose activities are dependent on Arctic sea ice conditions. The research goal of this thesis is to address fundamental challenges pertaining to seasonal forecasts of Arcitc sea ice, with a particular focus placed on improving operational sea ice forecasts in the Canadian Seasonal to Interannual Prediction System (CanSIPS). Seasonal forecasts are strongly dependent on the accuracy of observations used as initial condition inputs. A key challenge initializing Arctic sea ice is the sparse availability of Arctic sea ice thickness (SIT) observations. I present on the development of three statistical models that can be used for estimating Arctic SIT in real time for sea ice forecast initialization. The three statistical models are shown to vary in their ability to capture the recent thinning of sea ice, as well as their ability to capture interannual variations in SIT anomalies; however, each of the models is shown to dramatically improve the representation of SIT compared to the climatological SIT estimates used to initialize CanSIPS. I conduct a thorough assessment of sea ice hindcast skill using the Canadian Climate Model, version 3 (one of two models used in CanSIPS), in which the dependence of hindcast skill on SIT initialization is investigated. From this assessment, it can be concluded that all three statistical models are able to estimate SIT sufficiently to improve hindcast skill relative to the climatological initialization. However, the accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC), with the most robust improvements obtained with two statistical models that adequately represent both processes. The final goal of this thesis is to improve the quantification of uncertainty in seasonal forecasts of regional Arctic sea ice coverage. Information regarding forecast uncertainty is crucial for end-users who want to quantify the risk associated with trusting a particular forecast. I develop statistical post-processing methodology for improving probabilistic forecasts of Arctic SIC. The first of these improvements is intended to reduce sampling uncertainty by fitting ensemble SIC forecasts to a parametric probability distribution, namely the zero- and one- inflated beta (BEINF) distribution. It is shown that overall, probabilistic forecast skill is improved using the parametric distribution relative to a simpler count-based approach; however, model biases can degrade this skill improvement. The second of these improvements is the introduction of a novel calibration method, called trend-adjusted quantile mapping (TAQM), that explicitly accounts for SIC trends and is specifically designed for the BEINF distribution. It is shown that applying TAQM greatly reduces model errors, and results in probabilistic forecast skill that generally surpasses that of a climatological reference forecast, and to some degree that of a trend-adjusted climatological reference forecast, particularly at shorter lead times. / Graduate

No description available.

This thesis explores key aspects for synthesizing data across spatiotemporal scales relevant for water resources management in an Eastern Africa context. Specifically, the potential of large scale global precipitation datasets (GPDs) in data limited regions to overcome spatial and temporal data gaps is considered. The thesis also explores the potential to utilize limited and non-continuous streamflow and stream water chemistry observations to increase hydrological process understanding. The information gained is then used to build a coherent hydro-climatic framework for streamflow modelling. In this thesis, Kilombero Valley Drainage Basin (KVDB) in Tanzania is used as an example of a data limited region targeted for rapid development, intensification and expansion of agriculture. As such, it is representative for many regions across the Eastern Africa. With regards to the data synthesis, two satellite products, three reanalysis products and three interpolated products were evaluated based on their spatial and temporal precipitation patterns. Streamflow data from KVDB and eight subcatchments were then assessed for quality with regards to missing data. Furthermore, recession analysis was used to estimate catchment-scale characteristic drainage timescale. Results from these streamflow analyses, in conjunction with a hydrological tracer-based analysis, were then used for improved understanding of streamflow generation in the region. Finally, a coherent modelling framework using the HBV rainfall-runoff model was implemented and evaluated based on daily streamflow simulation. Despite the challenges of data limited regions and the often large uncertainty in results, this thesis demonstrates that improved process understanding could be obtained from limited streamflow records and a focused hydrochemical sampling when experimental design natural variability were leveraged to gain a large  signal to noise ratio. Combining results across all investigations rendered information useful for the conceptualization and implementation of the hydro-climatic modelling framework relevant in Kilombero Valley. For example, when synthesized into a coherent framework the GPDs could be downscaled and used for daily streamflow simulations at the catchment scale with moderate success. This is promising when considering the need for estimating impacts of potential future land use and climate change as well as agricultural intensification. / Denna avhandling utforskar aspekter på att syntetisera data med olika rumslig och temporal upplösning, vilket är centralt för vattenförvaltning i östra Afrika. Särskilt fokus ligger på att undersöka möjligheten till att använda globala nederbördsdataset för att fylla rumsliga och temporala luckor där data saknas. Avhandlingen undersökeräven möjligheten till att använda flödesdata med icke-kompletta tidsserier samt kemidata från vattendrag för att utöka kunskap-en om hydrologiska processer. Informationen används för att bygga upp ett integrerande ram-verk för hydro-klimatologisk modellering som exempelvis kan användas för att utforska ef-fekten av ett utökat och intensifierat jordburk på vattenresurser. I denna avhandling användes Kilomberodalens avrinningsområde (Tanzania) som exempel på ett databegränsat område där det pågår en intensiv utökning av jordbruksverksamhet. Detta område kan ses som representa-tivt för ett stort antal områden inom östra Afrika.Datasyntesen innefattade två nederbördsprodukter baserade på satellitdata, tre baserade på återanalysprodukter samt två baserade på interpolering av observervationsdata från regnmä-tare. Dessa åtta produkter utvärderades baserat på deras nederbördsmönster i rum och tid. Ut-över detta utvärderades vattenföringsdata från Kilomberodalens avrinningsområde samt åtta delavrinningsområden utifrån mängden saknad data i respektive tidsserie. Vidare användes resultaten från hydrologisk recessionsanalysför att uppskatta den karaktäristiska avrinningsti-den för avrinningsområden. Resultaten från recessionsanalysensamthydrologiskt spårämnes-försök användessedan för att utöka kunskapen om avrinningsbildning och vattenföring i om-rådet samt som stöd i valet av hydrologiskt modelleringsverktyg. Avslutningsvis användes HBV-avrinningsmodellen för att simulera daglig vattenföring. Trots utmaningen i att arbeta iett databegränsat område och de osäkerheter i resultat som detta tenderar att leda till visar resultaten att det var möjligt att använda begränsad vattenfö-ringsdata och vattenkemidata för att utöka den hydrologiska processförståelsen av området. Detta möjliggjordes genom ett experimentellt upplägg som utnyttjade till ett stort signal-till-brusförhållande under rådande förhållanden av naturlig variabilitet. Kombinerade resultat från alla genomförda studier kunde utnyttjas vid konceptualiseringen och implementeringen av ramverket för hydroklimatologisk modellering av Kilomberodalens avrinningsområde. Till exempel kunde de globala nederbördsdataseten användas för lokal modellering av flödesdata med viss framgång efter syntes och implementering i det integrerande ramverket för hydro-klimatologisk modellering. Detta är lovande med tanke på behovet av att undersöka vilken påverkan möjliga framtida förändringar i markanvändning, klimat samt jordbruk har på den lokala och regionala miljön. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.</p>