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A reliable climate dataset is a backbone for modeling the essential processes of the water cycle and predicting future conditions. Although a number of gridded climate datasets are available for the North American content, which provide reasonable estimates of climatic conditions in the region, there are inherent inconsistencies in these available climate datasets (e.g., spatial and temporal varying data accuracies, meteorological parameters, length of records, spatial coverage, temporal resolution, etc). These inconsistencies raise a valid question as to which datasets are the most suitable for the study area and how to systematically combine these datasets to produce a reliable climate dataset for climate studies and hydrological modeling. This study suggested a framework, called reference reliability evaluation system (REFRES), that systematically determines a ranking of multiple climate datasets to generate a hybrid climate dataset for a region. To demonstrate the usefulness of the proposed framework, REFRES was applied to produce a historical hybrid climate dataset for the Athabasca River basin in Alberta, Canada. A proxy validation was also conducted to prove the applicability of the generated hybrid climate datasets to hydrologic simulations. This study evaluated five climate datasets, including station-based gridded climate datasets (ANUSPLIN, Alberta Township, and PNWNAmet), a multi-source gridded dataset (Canadian Precipiation Analysis – CaPA), and a reanalysis-based dataset (NARR). The results showed that the gridded climate interpolated from station data performed better than multi-source and reanalysis based climate datasets. For the Athabasca River basin, Township and ANUSPLIN were mostly ranked first for precipitation and temperature, respectively. The proxy validation also confirmed the superior performance of hybrid climate datasets compared with the other five individual climate datasets investigated in this study. These results indicate that the hybrid climate dataset provides a better representation of historical climatic conditions and thus, enhancing the reliability of hydrologic simulations.

The climate is changing and these changes may induce severe impacts on both, global and local scales. The Public Infrastructure Engineering Vulnerability Committee (PIEVC) established by Engineers Canada conducted an assessment of the vulnerability of Canadian Public Infrastructure to changing climatic conditions. The major conclusion of the assessment is that water resources infrastructure failures due to the climate change will be common across Canada. As a follow up, the City of London took an initiative to evaluate the impacts of climate change on its municipal infrastructure. An original systematic procedure is used to gather and examine available data in order to develop an understanding of the relevant climate effects and their interactions with infrastructure. The key steps of the procedure include: (i) Inventory of infrastructure components; (ii) Data gathering and sufficiency; (iii) Qualitative vulnerability assessment; (iv) Quantitative vulnerability assessment; and (v) Prioritization of the infrastructure components based on the level of risk. The assessment work is based on the results of the previously completed climate change impact study and focuses on infrastructure vulnerability to flooding. Assessment methodology requires identification of climate loading on the municipal infrastructure. Climate and hydrologic modeling methodology and results are presented in this report as the basis for the impact assessment work. A weather generator model combined with principle component analysis (WGPCA) and HEC-HMS hydrologic model are used in this study. The WG-PCA model is used to generate two different climate scenarios named: (a) historic scenario and (b) wet scenario, representing the lower and the upper bound of potential climate change, respectively. Generated meteorological data (precipitation and temperature) is used with the hydrologic model (HEC-HMS) and transformed into flow at multiple locations within the study region. Lastly, the flow frequency analysis is conducted to provide input into a hydraulic model that is used in mapping the floodplains for two climate scenarios considered (Sredojevic and Simonovic, 2009). Using 43-years of historical data from 1964 to 2006 at 15 stations in the Upper Thames River basin, the WG-PCA generates a feasible future scenario of precipitation and temperature for 200 years – the historic climate scenario. The historic data is used to represent the business-as-usual condition that assumes there is no change in the social-economic-climatic system in the future. This scenario simulates climate change that may occur as a consequence of the already existing conditions and is considered in this study as the lower bound of climate change impact on the region under consideration. The second climate scenario employs CCSRNIES global climate model (GCM) with B21 emission scenario for time slice of 2040-2069 together with the historical data to generate a feasible future scenario we named in this work as a wet climate scenario – upper bound of climate change impact on the region under consideration. The results demonstrate the WG-PCA regenerates well the 25th, 50th, 75th percentile statistical values of precipitation and temperature for the historic scenario. Use of data perturbation process within the weather generator model generates data out of the range of values within the observed data. For the wet scenario, the WG-PCA generates the future that reflects the monthly climate shift of GCM model used (CCSRNIES B21) in the study. The generated annual precipitation extreme values for 200 years are processed to extract the largest annual flood event for the entire basin and corresponding annual peak flow is used in flood frequency analysis. An assumption introduced in this work is that the largest floods are generated from extreme precipitation events. Several probability distributions including Gumbel, LP3, and GEV are utilized in this study. The flood frequency analysis results obtained using Gumbel distribution for the historic and the wet climate scenarios are compared with the current data used by the Upper Thames River Conservation Authority (UTRCA) for flood plain management. The difference exists between the current data and the data generated for two scenarios, as expected, and the direction of change varies with the location in the basin. During the work on this study, the major deficiency in observed flow data is noticed across the basin – specially for the locations within the City of London. Therefore, continuous monitoring system at the various sites in the basin is needed to provide the accurate hydrologic information that should enhance the results of modeling work. If and when the new observed data is collected, the hydrologic modeling analysis can enhanced and consequently flood flow frequency analysis can be verified. https://ir.lib.uwo.ca/wrrr/1030/thumbnail.jpg

Climate change is one of the more pressing issues that attract the attention of scientists and policy makers. Many scientists are developing necessary methodologies to better understand the impacts of climate change, and support the development of appropriate adaptation measures. Literature on the application of adaptation measures to changing climatic conditions is very limited and the need for more work is evident on the development of adaptation strategies for mitigating negative impacts of climate change in water resources management practice. This study presents an integrated reservoir management system for the Upper Thames River basin that includes: (1) a Weather Generator (WG) model; (2) a hydrologic model; and (3) a differential evolutionary optimization model. It is used to develop the alternative optimal operating rule curves for three reservoirs in the basin that will take into consideration the impact of climate change. Alternative curves developed using the proposed methodology represent one of the possible climate change adaptation strategies for the use of existing storage in the basin. Three different weather scenarios are employed to verify the integrated reservoir management system; (1) Case 1: scenarios set | generated with the original WG model of Sharif and Burn (2006) with one variable (precipitation); (2) Case 2: scenario set ||: generated with original WG model with three variables named WG3; (3) scenario set |||: generated with the modified WG that is combined with Principal Component Analysis using three variables WG-PCA3. The results of this study indicate that the rule curves developed using B11(dry) climate scenario show the best result for the scenarios set | because there is no significant flood events in the case 1 and for the scenario set || and the scenario set ||| generated by WG3 and WG-PCA3, the B11 (PCA) rule curves provide the best result for B11, B11(PCA), and historic(PCA) scenarios and the B21 rule curves represent the best results for B21 and B21(PCA) scenarios. Another notable result is that the flood operations would be required until April if the B21(wet) scenario occurs in the future. In addition, the WG-PCA3 provides more wet weather conditions than the original WG model. https://ir.lib.uwo.ca/wrrr/1024/thumbnail.jpg