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Editorial on the Research Topic Past, Present, and Future Impacts of Climate on Infrastructure Climate change is one of the biggest challenges that the global community faces. The changing climate may lead to the infrastructure bring exposed to unprecedented climate with an increase in the frequency and intensity of extreme weather events, such as more intense rain events and flooding, extreme winds, landslides, and other hazards, that could result in infrastructure damage and failure (Stocker et al., 2013). The consequences of failure can be quite significant and cause fatalities, injuries, and illnesses, disruption or loss of service, increased costs to infrastructure owners, and unforeseen costs to infrastructure users, and considerable negative socioeconomic impacts to the governments. Infrastructure systems are primarily located in urban areas. The urban climate is often different from the surrounding rural climate. It is generally warmer, rainier, less windy, and more polluted. This means that more drastic effects of changing climate will be experienced by the urban infrastructure systems than the surrounding areas (Krayenhoff et al., 2018). The cities and infrastructure systems will also be overburdened in the future due to ongoing rapid urbanization. It is predicted that by the 2050s, 66% of the world's population will live in urban areas, up from about 50% living in the urban areas in the year 2007, making the infrastructure systems increasingly strained in the future also due to increases in urban population (UN, 2014). To design urban infrastructure systems considering the non-stationarity in climate, it is essential to assess the impacts of past, current, and future climate on the infrastructure systems. This will entail developing approaches to reliably model the extreme climate hazards and their interactions with the complex urban systems. The papers part of this Research Topic aims to provide new knowledge in these areas. Saha and Ghosh study the relative impacts of future projected climate and land-use change on the hydrological response of the Ganga river basin in India. The complex chain of analysis performed included: generation of future climate projections following different global warming scenarios and socioeconomic pathways, preparation of future land-use scenarios using a land allocation model and performing hydrologic simulations using a semi-distributed hydrologic model, followed by application of Bodyko framework to understand the relative impacts of climate and land-use changes on the basin characteristics. The study found that as a consequence of global warming, the Ganga river basin will become more arid in the future. However, the basin's future hydrologic response will mostly be governed by projected changes in climate. Land-use changes will have minimal effect on its hydrologic response. Yan et al. provided a review of a recently developed science-driven engineering product: next-generation Intensity-Duration-Frequency (NG-IDF) curve to establish a consistent IDF design methodology for both rain-dominated and snow-dominated regions. The NG-IDF captures multiple flood-generating mechanisms, including rainfall, snowmelt, and rain-on-snow as opposed to the typical precipitation-based IDF curves (PREC-IDF), which only captures flood occurrences due to extreme rainfall. NG-IDF is the outcome of a coordinated effort from climate scientists developing necessary climate information with global and regional scale climate models, hydrologists simulating snow processes and estimating water available for runoff using hydrologic models, the civil engineering community on integrating the snow processes into the IDF design process. Recent developments toward validating the NG-IDF curves on a larger spatiotemporal domain and incorporating future projected effects of climate change more accurately in them are discussed. Bondank and Chester advocate that infrastructure systems and not merely complicated systems that contain many parts and there is uncertainty included in the system, they are complex systems characterized by “unpredictability and the presence of unknown unknowns,” and so the common cause-and-effect approach of managing the uncertainty of the failure of infrastructure systems in the face of climate change hazards may not be best suited to model them. They recommend that best practices from complex system sciences such as Decision Making Under Deep Uncertainty and Safe-to-Fail frameworks should be used to improve the decision-making when managing the complex infrastructure systems. Besides, it is highlighted that the communication and coordination between managers of different infrastructure systems need to be enhanced to better implement strategies. Data is central in the climate change debate. Especially data that is multidimensional and explores the societal impacts are crucial for informed decision making. Using information as evidence to derive social vulnerability is much needed. Barankin et al. describe this in their work on an evidence-driven approach for assessing social vulnerability during extreme events. A novel data-driven predictive approach is forwarded that overcomes over-generalization or aggregation in the indicator-based method. Using the case of Hurricane Sandy in the State of New Jersey, the authors demonstrate variability in the vulnerability among the Minorities” is substantial, with a low approval rate in the insurance claims. The study successfully showed that using the need-based, evidence-driven method provides a validation route for vulnerability assessments and is scalable across geographies. The universality of the process is worth reproducing. It can be considered the new direction of research on climate-related vulnerability measurements unbiased from the statistical inflation of indicators. Markolf et al., while exploring the opportunities and challenges for artificial intelligence applications in...
Abhishek Gaur; Ronita Bardhan. Editorial: Past, Present, and Future Impacts of Climate on Infrastructure. Frontiers in Water 2021, 3, 1 .
AMA StyleAbhishek Gaur, Ronita Bardhan. Editorial: Past, Present, and Future Impacts of Climate on Infrastructure. Frontiers in Water. 2021; 3 ():1.
Chicago/Turabian StyleAbhishek Gaur; Ronita Bardhan. 2021. "Editorial: Past, Present, and Future Impacts of Climate on Infrastructure." Frontiers in Water 3, no. : 1.
There is a significant share of greenhouse gas (GHG) emissions attributed to the built environment, either for production or the operation of buildings. Various initiatives are being implemented to reduce the release of GHGs into the atmosphere relying on the evaluation, tracking, recording and verification of GHG emissions and removals. The annual accounting of GHG flows associated with buildings should be conducted in a lifecycle context to ensure that policies are effective at mitigating climate change. Buildings operate for decades and both the climate and electricity grid mix are expected to change significantly during such a time frame. This study aims to support the design of resource- as well as energy-efficient buildings using a sound life cycle assessment (LCA) methodology in the preliminary design stage. A straightforward method that can be applied for a detailed understanding of the effects of climate change and prospective electricity grid mix on building energy use is presented. The novelty of this study was to integrate long-term energy projections in a high temporal resolution LCA for buildings and taking into account different future climates and prospective electricity mixes across Canada. The research integrated the dynamic LCA capabilities directly into a Building Information Model (BIM). Such dynamic considerations as climate and energy mix improves the environmental importance and scientific robustness of LCA metrics. The proposed methodology will assist users to apply a clear framework that helps to define an optimized design alternative through a dynamic energy analysis and future weather forecasting simulation. An example of an actual office building is provided to demonstrate the capabilities and usefulness of the developed integrated framework.
Farzad Jalaei; Geoffrey Guest; Abhishek Gaur; Jieying Zhang. Exploring the effects that a non-stationary climate and dynamic electricity grid mix has on whole building life cycle assessment: A multi-city comparison. Sustainable Cities and Society 2020, 61, 102294 .
AMA StyleFarzad Jalaei, Geoffrey Guest, Abhishek Gaur, Jieying Zhang. Exploring the effects that a non-stationary climate and dynamic electricity grid mix has on whole building life cycle assessment: A multi-city comparison. Sustainable Cities and Society. 2020; 61 ():102294.
Chicago/Turabian StyleFarzad Jalaei; Geoffrey Guest; Abhishek Gaur; Jieying Zhang. 2020. "Exploring the effects that a non-stationary climate and dynamic electricity grid mix has on whole building life cycle assessment: A multi-city comparison." Sustainable Cities and Society 61, no. : 102294.
Subdaily precipitation gauging stations are limited and unevenly distributed across Canada. To support the design of sustainable stormwater infrastructure, especially in the data-sparse regions of Canada, this study presents a novel, gridded intensity–duration–frequency (IDF) dataset of precipitation storms of 5, 10, 15, 30, and 60 min and 1, 2, 6, 12, and 24 h durations and 2, 5, 10, 25, 50, and 100 year return periods. The dataset has been prepared using atmospheric variable (AVs) estimates from two reanalysis products: the North American Regional Reanalysis (NARR) and ERA-Interim. A state-of-the-art machine-learning algorithm, named a support vector machine (SVM), is used to establish the link between AVs and extreme precipitation magnitudes. First, the most relevant AVs shaping precipitation extremes in different parts of Canada are identified, and preliminary estimates of gridded IDFs are produced. The preliminary IDF estimates are corrected for systematic distribution of spatial errors to obtain corrected gridded IDF estimates. Modeled gridded IDF estimates are compared with observations and are found to exhibit a root mean squared error varying between 5% and 25% across different regions of Canada. The gridded IDFs are also found to capture the observed spatial pattern of extreme precipitation reasonably well.
Abhishek Gaur; Andre Schardong; Slobodan P. Simonovic. Gridded Extreme Precipitation Intensity–Duration–Frequency Estimates for the Canadian Landmass. Journal of Hydrologic Engineering 2020, 25, 05020006 .
AMA StyleAbhishek Gaur, Andre Schardong, Slobodan P. Simonovic. Gridded Extreme Precipitation Intensity–Duration–Frequency Estimates for the Canadian Landmass. Journal of Hydrologic Engineering. 2020; 25 (6):05020006.
Chicago/Turabian StyleAbhishek Gaur; Andre Schardong; Slobodan P. Simonovic. 2020. "Gridded Extreme Precipitation Intensity–Duration–Frequency Estimates for the Canadian Landmass." Journal of Hydrologic Engineering 25, no. 6: 05020006.
Rainfall Intensity–Duration–Frequency (IDF) curves are among the most essential datasets used in water resources management across the globe. Traditionally, they are derived from observations of historical rainfall, under the assumption of stationarity. Change of climatic conditions makes use of historical data for development of IDFs for the future unreliable, and in some cases, may lead to underestimated infrastructure designs. The IDF_CC tool is designed to assist water professionals and engineers in producing IDF estimates under changing climatic conditions. The latest version of the tool (Version 4) provides updated IDF curve estimates for gauged locations (rainfall monitoring stations) and ungauged sites using a new gridded dataset of IDF curves for the land mass of Canada. The tool has been developed using web-based technologies and takes the form of a decision support system (DSS). The main modifications and improvements between version 1 and the latest version of the IDF_CC tool include: (i) introduction of the Generalized Extreme value (GEV) distribution; (ii) updated equidistant matching algorithm (QM); (iii) gridded IDF curves dataset for ungauged location and (iv) updated Climate Models.
Andre Schardong; Slobodan P. Simonovic; Abhishek Gaur; Dan Sandink. Web-Based Tool for the Development of Intensity Duration Frequency Curves under Changing Climate at Gauged and Ungauged Locations. Water 2020, 12, 1243 .
AMA StyleAndre Schardong, Slobodan P. Simonovic, Abhishek Gaur, Dan Sandink. Web-Based Tool for the Development of Intensity Duration Frequency Curves under Changing Climate at Gauged and Ungauged Locations. Water. 2020; 12 (5):1243.
Chicago/Turabian StyleAndre Schardong; Slobodan P. Simonovic; Abhishek Gaur; Dan Sandink. 2020. "Web-Based Tool for the Development of Intensity Duration Frequency Curves under Changing Climate at Gauged and Ungauged Locations." Water 12, no. 5: 1243.
Sustainable building practices are rooted in the need for reliable information on the long-term performance of building materials; specifically, the expected service-life of building materials, components, and assemblies. This need is ever more evident given the anticipated effects of climate change on the built environment and the many governmental initiatives world-wide focused on ensuring that structures are not only resilient at their inception but also, can maintain their resilience over the long-term. The Government of Canada has funded an initiative now being completed at the National Research Council of Canada’s (NRC) Construction Research Centre on “Climate Resilience of Buildings and Core Public infrastructure”. The outcomes from this work will help permit integrating climate resilience of buildings into guides and codes for practitioners of building and infrastructure design. In this paper, the impacts of climate change on buildings are discussed and a review of studies on the durability of building envelope materials and elements is provided in consideration of the expected effects of climate change on the longevity and resilience of such products over time. Projected changes in key climate variables affecting the durability of building materials is presented such that specifications for the selection of products given climate change effects can be offered. Implications in regard to the maintainability of buildings when considering the potential effects of climate change on the durability of buildings and its components is also discussed.
Michael A. Lacasse; Abhishek Gaur; Travis V. Moore. Durability and Climate Change—Implications for Service Life Prediction and the Maintainability of Buildings. Buildings 2020, 10, 53 .
AMA StyleMichael A. Lacasse, Abhishek Gaur, Travis V. Moore. Durability and Climate Change—Implications for Service Life Prediction and the Maintainability of Buildings. Buildings. 2020; 10 (3):53.
Chicago/Turabian StyleMichael A. Lacasse; Abhishek Gaur; Travis V. Moore. 2020. "Durability and Climate Change—Implications for Service Life Prediction and the Maintainability of Buildings." Buildings 10, no. 3: 53.
Overheating in buildings arising from climatic extreme heat events has been identified as a health concern to vulnerable occupants. However, there have been very limited studies to generate suitable weather data to evaluate by simulation the overheating risk and its effect on the comfort and health of occupants. This paper develops a methodology to identify reference summer weather years (RSWY) for overheating risk analysis. The methodology includes generation of historical climate data, and development of a heat stress metric for the definition and characterization of heat events. The Standard Effective Temperature was selected among a short list of popular metrics, modified and named t-SET to account for transient heat events, activity levels of occupants, and thermoregulatory controls of sleeping subjects. The t-SET model predictions compared well with measured body temperatures of subjects undergoing multi-stage activities under hot conditions. The t-SET index was used to generate RSWY for selected Canadian cities.
A. Laouadi; Abhishek Gaur; M. A. Lacasse; M. Bartko; M. Armstrong. Development of reference summer weather years for analysis of overheating risk in buildings. Journal of Building Performance Simulation 2020, 13, 301 -319.
AMA StyleA. Laouadi, Abhishek Gaur, M. A. Lacasse, M. Bartko, M. Armstrong. Development of reference summer weather years for analysis of overheating risk in buildings. Journal of Building Performance Simulation. 2020; 13 (3):301-319.
Chicago/Turabian StyleA. Laouadi; Abhishek Gaur; M. A. Lacasse; M. Bartko; M. Armstrong. 2020. "Development of reference summer weather years for analysis of overheating risk in buildings." Journal of Building Performance Simulation 13, no. 3: 301-319.
This chapter identifies key research priorities for fulfilling a subset of the work under the Climate-Resilient Buildings and Core Public Infrastructure project that relate to the health and safety of building occupants under projected changes in climate. In particular, (dis)comfort and mortality of occupants because of future projected overheating of building enclosures and the performance of common wall assemblies used in Canada under extreme wind-driven rainfall events are of interest. These climate hazards were selected because Canada is projected to undergo large increases in temperature in the future. Premier health agencies have identified future increases in temperature as a potential health risk for Canadians. Similarly, wind-driven rain has been identified as a critical climate load for the assessment of hygrothermal performance of building envelopes.
Abhishek Gaur; Michael Lacasse. Towards Formulating a National Guideline on the Design of Building Enclosures Subjected to Climate Change in Canada. Engineering Methods for Precipitation under a Changing Climate 2020, 97 -113.
AMA StyleAbhishek Gaur, Michael Lacasse. Towards Formulating a National Guideline on the Design of Building Enclosures Subjected to Climate Change in Canada. Engineering Methods for Precipitation under a Changing Climate. 2020; ():97-113.
Chicago/Turabian StyleAbhishek Gaur; Michael Lacasse. 2020. "Towards Formulating a National Guideline on the Design of Building Enclosures Subjected to Climate Change in Canada." Engineering Methods for Precipitation under a Changing Climate , no. : 97-113.
Buildings and homes in Canada will be exposed to unprecedented climatic conditions in the future as a consequence of global climate change. To improve the climate resiliency of existing and new buildings, it is important to evaluate their performance over current and projected future climates. Hygrothermal and whole building simulation models, which are important tools for assessing performance, require continuous climate records at high temporal frequencies of a wide range of climate variables for input into the kinds of models that relate to solar radiation, cloud-cover, wind, humidity, rainfall, temperature, and snow-cover. In this study, climate data that can be used to assess the performance of building envelopes under current and projected future climates, concurrent with 2 °C and 3.5 °C increases in global temperatures, are generated for 11 major Canadian cities. The datasets capture the internal variability of the climate as they are comprised of 15 realizations of the future climate generated by dynamically downscaling future projections from the CanESM2 global climate model and thereafter bias-corrected with reference to observations. An assessment of the bias-corrected projections suggests, as a consequence of global warming, future increases in the temperatures and precipitation, and decreases in the snow-cover and wind-speed for all cities.
Abhishek Gaur; Michael Lacasse; Marianne Armstrong. Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities. Data 2019, 4, 72 .
AMA StyleAbhishek Gaur, Michael Lacasse, Marianne Armstrong. Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities. Data. 2019; 4 (2):72.
Chicago/Turabian StyleAbhishek Gaur; Michael Lacasse; Marianne Armstrong. 2019. "Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities." Data 4, no. 2: 72.
This study discusses the flooding related consequences of climate change on most populous Canadian cities and flow regulation infrastructure (FRI). The discussion is based on the aggregated results of historical and projected future flooding frequencies and flood timing as generated by Canada-wide hydrodynamic modelling in a previous study. Impact assessment on 100 most populous Canadian cities indicate that future flooding frequencies in some of the most populous cities such as Toronto and Montreal can be expected to increase from 100 (250) years to 15 (22) years by the end of the 21st century making these cities highest at risk to projected changes in flooding frequencies as a consequence of climate change. Overall 40–60% of the analyzed cities are found to be associated with future increases in flooding frequencies and associated increases in flood hazard and flood risk. The flooding related impacts of climate change on 1072 FRIs located across Canada are assessed both in terms of projected changes in future flooding frequencies and changes in flood timings. Results suggest that 40–50% of the FRIs especially those located in southern Ontario, western coastal regions, and northern regions of Canada can be expected to experience future increases in flooding frequencies. FRIs located in many of these regions are also projected to experience future changes in flood timing underlining that operating rules for those FRIs may need to be reassessed to make them resilient to changing climate.
Ayushi Gaur; Abhishek Gaur; Dai Yamazaki; Slobodan P. Simonovic. Flooding Related Consequences of Climate Change on Canadian Cities and Flow Regulation Infrastructure. Water 2019, 11, 63 .
AMA StyleAyushi Gaur, Abhishek Gaur, Dai Yamazaki, Slobodan P. Simonovic. Flooding Related Consequences of Climate Change on Canadian Cities and Flow Regulation Infrastructure. Water. 2019; 11 (1):63.
Chicago/Turabian StyleAyushi Gaur; Abhishek Gaur; Dai Yamazaki; Slobodan P. Simonovic. 2019. "Flooding Related Consequences of Climate Change on Canadian Cities and Flow Regulation Infrastructure." Water 11, no. 1: 63.
Climate change has induced considerable changes in the dynamics of key hydro-climatic variables across Canada, including floods. In this study, runoff projections made by 21 General Climate Models (GCMs) under four Representative Concentration Pathways (RCPs) are used to generate 25 km resolution streamflow estimates across Canada for historical (1961–2005) and future (2061–2100) time-periods. These estimates are used to calculate future projected changes in flood magnitudes and timings across Canada. Results obtained indicate that flood frequencies in the northernmost regions of Canada, and south-western Ontario can be expected to increase in the future. As an example, the historical 100-year return period events in these regions are expected to become 10–60 year return period events. On the other hand, northern prairies and north-central Ontario can be expected to experience decreases in flooding frequencies in future. The historical 100-year return period flood events in these regions are expected to become 160–200 year return period events in future. Furthermore, prairies, parts of Quebec, Ontario, Nunavut, and Yukon territories can be expected to experience earlier snowmelt-driven floods in the future. The results from this study will help decision-makers to effectively manage and design municipal and civil infrastructure in Canada under a changing climate.
Ayushi Gaur; Abhishek Gaur; Slobodan P. Simonovic. Future Changes in Flood Hazards across Canada under a Changing Climate. Water 2018, 10, 1441 .
AMA StyleAyushi Gaur, Abhishek Gaur, Slobodan P. Simonovic. Future Changes in Flood Hazards across Canada under a Changing Climate. Water. 2018; 10 (10):1441.
Chicago/Turabian StyleAyushi Gaur; Abhishek Gaur; Slobodan P. Simonovic. 2018. "Future Changes in Flood Hazards across Canada under a Changing Climate." Water 10, no. 10: 1441.
Abhishek Gaur; Rao S. Govindaraju; Ganeshchandra Mallya; P.P. Mujumdar; Chandra R. Rupa; Alejandra R. Schmidt; Ashish Sharma; Slobodan P. Simonovic; Ramesh S.V. Teegavarapu; Shivam Tripathi; Conrad Wasko. List of contributors. Trends and Changes in Hydroclimatic Variables 2018, 1 .
AMA StyleAbhishek Gaur, Rao S. Govindaraju, Ganeshchandra Mallya, P.P. Mujumdar, Chandra R. Rupa, Alejandra R. Schmidt, Ashish Sharma, Slobodan P. Simonovic, Ramesh S.V. Teegavarapu, Shivam Tripathi, Conrad Wasko. List of contributors. Trends and Changes in Hydroclimatic Variables. 2018; ():1.
Chicago/Turabian StyleAbhishek Gaur; Rao S. Govindaraju; Ganeshchandra Mallya; P.P. Mujumdar; Chandra R. Rupa; Alejandra R. Schmidt; Ashish Sharma; Slobodan P. Simonovic; Ramesh S.V. Teegavarapu; Shivam Tripathi; Conrad Wasko. 2018. "List of contributors." Trends and Changes in Hydroclimatic Variables , no. : 1.
Downscaling is performed to estimate higher resolution climatic projections from global climate odel modeled climate output. Two different classes of models have been used in the past to perform downscaling: statistical and dynamic. Recently a novel statistical downscaling model, physical scaling, which attempts to build on the strengths of both statistical and dynamic downscaling, has been proposed. The model has been extensively validated and has been used to identify land-cover change–induced climatic changes in four catchments in Saskatchewan. This chapter provides a detailed description of the physical scaling model, presents results from model validation and application studies, as well as details a step-by-step procedure of building the model from scratch in the R programming language.
Abhishek Gaur; Slobodan P. Simonovic. Introduction to Physical Scaling. Trends and Changes in Hydroclimatic Variables 2018, 199 -273.
AMA StyleAbhishek Gaur, Slobodan P. Simonovic. Introduction to Physical Scaling. Trends and Changes in Hydroclimatic Variables. 2018; ():199-273.
Chicago/Turabian StyleAbhishek Gaur; Slobodan P. Simonovic. 2018. "Introduction to Physical Scaling." Trends and Changes in Hydroclimatic Variables , no. : 199-273.
Changes in climatic conditions are expected to affect the hydrological cycle with intensification of extreme rainfall events caused by the disturbance in temperature and other atmospheric variables linked to precipitation. Extreme rainfall change will affect the intensity-duration-frequency (IDF) relationship, used in the design, maintenance, and operation of water infrastructure in Canada. This study presents a comparative analysis of the results from two IDF updating methods: (1) the IDF_CC tool, which applies an equidistance quantile-matching precipitation downscaling algorithm, and (2) the Clausius-Clapeyron (C-C) precipitation-temperature relationship, used with a proposed constant temperature scaling rate. The analyses were conducted using 358 selected Environment Canada hydro-meteorological stations from the IDF_CC tool database with record length longer than 20 years. Results for the future period (2061–2100), are based on the multimodel ensemble of 24 global climate models (GCMs). The difference in (1) projected precipitation and (2) uncertainty range for both IDF updating methods are presented and analyzed. The uncertainty range is defined in this work as the difference between IDF relationships obtained using various GCMs. The C-C temperature scaling method resulted, overall, in higher extreme precipitation projections than the IDF_CC tool for the stations located in the Canadian Prairies (i.e., the provinces of Alberta, Saskatchewan, and Manitoba). Stations located at the east and west coasts of Canada show smaller difference in the projected extremes. A similar pattern is observed for the multimodel ensemble median and the all individual GCMs. The difference in projected uncertainty range for both methods was analyzed for the multimodel ensemble and for representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5 emission scenarios. The C-C scaling shows a smaller uncertainty range for RCP 2.6 and RCP 4.5, and the IDF_CC tool shows a smaller uncertainty range for the RCP 8.5 scenario (especially for stations located in the Canadian Prairies). The difference in percent uncertainty ranges from −75% to about 100%, considering all stations across Canada. Both methods show comparable uncertainty range in the future. One significant conclusion is that the high level of uncertainty cannot be avoided, regardless of the method selected for updating IDF curves for future conditions. Use of the precipitation-based IDF_CC tool is recommended because of serious issues in using a constant scaling rate with C-C temperature scaling.
Andre Schardong; Abhishek Gaur; Slobodan P. Simonovic. Comparison of the Theoretical Clausius–Clapeyron Scaling and IDF_CC Tool for Updating Intensity-Duration-Frequency Curves under Changing Climatic Conditions in Canada. Journal of Hydrologic Engineering 2018, 23, 04018036 .
AMA StyleAndre Schardong, Abhishek Gaur, Slobodan P. Simonovic. Comparison of the Theoretical Clausius–Clapeyron Scaling and IDF_CC Tool for Updating Intensity-Duration-Frequency Curves under Changing Climatic Conditions in Canada. Journal of Hydrologic Engineering. 2018; 23 (9):04018036.
Chicago/Turabian StyleAndre Schardong; Abhishek Gaur; Slobodan P. Simonovic. 2018. "Comparison of the Theoretical Clausius–Clapeyron Scaling and IDF_CC Tool for Updating Intensity-Duration-Frequency Curves under Changing Climatic Conditions in Canada." Journal of Hydrologic Engineering 23, no. 9: 04018036.
The Multiplicative Random Cascade (MRC) disaggregation model has been extensively used to disaggregate precipitation in many regions across the globe. In this study, it is adapted to disaggregate a range of climate variables (CV) relevant for hygrothermal modelling of building envelopes. This generalized MRC-G model is further improved by explicitly modelling the cross-correlation structure between CVs in the MRC-G-CV model. A thorough evaluation of MRC, MRC-G, and MRC-G-CV models is performed for five Canadian cities: Ottawa, Vancouver, Calgary, St. Johns, and Winnipeg. Results indicate that the MRC model is able to simulate grid-level statistics with >90% accuracy. Grid-level extreme magnitudes and spatial cross-correlation structures are also well simulated. Error magnitudes associated with hourly predictions indicate superior performance of the models in respect to thermal variables, followed by wind variables, and then moisture related variables. Finally, the performance of MRC-G model towards modelling cross-correlation among CVs is found to improve by >50% in terms of energy distance by explicitly modelling these relationships in the MRC-G-CV model. Results indicate that the MRC model variants demonstrated in this study have the potential to facilitate effective hygrothermal performance evaluation of building envelopes at locations where observational sub-daily climate records are unavailable.
Abhishek Gaur; Michael Lacasse. Multisite multivariate disaggregation of climate parameters using multiplicative random cascades. Urban Climate 2018, 26, 121 -132.
AMA StyleAbhishek Gaur, Michael Lacasse. Multisite multivariate disaggregation of climate parameters using multiplicative random cascades. Urban Climate. 2018; 26 ():121-132.
Chicago/Turabian StyleAbhishek Gaur; Michael Lacasse. 2018. "Multisite multivariate disaggregation of climate parameters using multiplicative random cascades." Urban Climate 26, no. : 121-132.
Ayushi Gaur; Abhishek Gaur; Slobodan P. Simonovic. MODELLING OF FUTURE FLOOD RISK ACROSS CANADA DUE TO CLIMATE CHANGE. Risk Analysis XI 2018, 1 .
AMA StyleAyushi Gaur, Abhishek Gaur, Slobodan P. Simonovic. MODELLING OF FUTURE FLOOD RISK ACROSS CANADA DUE TO CLIMATE CHANGE. Risk Analysis XI. 2018; ():1.
Chicago/Turabian StyleAyushi Gaur; Abhishek Gaur; Slobodan P. Simonovic. 2018. "MODELLING OF FUTURE FLOOD RISK ACROSS CANADA DUE TO CLIMATE CHANGE." Risk Analysis XI , no. : 1.
This study investigates the relationship between historically observed changes in extreme precipitation magnitudes and temperature (Pex-T relationship) at multiple locations in Canada. The focus is on understanding the behavior of these relationships with regards to key storm characteristics such as its duration, season of occurrence, and location. To do so, three locations are chosen such that they have large amounts of moisture available near them whereas four locations are chosen such that they are located in the land-locked regions of Canada and subsequently have no nearby moisture source available on them. To investigate the effect of different storm durations on Pex-T relationship, storms of durations: 5, 10, 15, 30 min, 1, 2, 6, 12, 24 h are considered. Finally, Pex-T relationship is analyzed separately for summer and winter seasons to quantify the influence of seasons. Results indicate strong influences of storm duration, season of occurrence, and location on observed precipitation scaling rates. Drastic intensification of precipitation extremes with temperature is obtained for shorter duration precipitation events than for longer duration precipitation events, in summers than in the winters. Furthermore, in summertime, increases in the intensity of convection driven precipitation extremes is found highest at locations away from large waterbodies. On the other hand, in wintertime most drastic increases in extreme precipitation are obtained at locations near large waterbodies. These findings contribute towards increasing the current understanding of precipitation extremes in the context of rapidly increasing global temperatures.
Abhishek Gaur; Andre Schardong; Slobodan Simonovic. Effects of Global Warming on Precipitation Extremes: Dependence on Storm Characteristics. Water Resources Management 2018, 32, 2639 -2648.
AMA StyleAbhishek Gaur, Andre Schardong, Slobodan Simonovic. Effects of Global Warming on Precipitation Extremes: Dependence on Storm Characteristics. Water Resources Management. 2018; 32 (8):2639-2648.
Chicago/Turabian StyleAbhishek Gaur; Andre Schardong; Slobodan Simonovic. 2018. "Effects of Global Warming on Precipitation Extremes: Dependence on Storm Characteristics." Water Resources Management 32, no. 8: 2639-2648.
Surface Urban Heat Island (SUHI) is an urban climate phenomenon that is expected to respond to future climate and land-use land-cover change. It is important to further our understanding of physical mechanisms that govern SUHI phenomenon to enhance our ability to model future SUHI characteristics under changing geophysical conditions. In this study, SUHI phenomenon is quantified and modelled at 20 cities distributed across Canada. By analyzing MODerate Resolution Imaging Spectroradiometer (MODIS) sensed surface temperature at the cities over 2002-2012, it is found that 16 out of 20 selected cities have experienced a positive SUHI phenomenon while 4 cities located in the prairies region and high elevation locations have experienced a negative SUHI phenomenon in the past. A statistically significant relationship between observed SUHI magnitude and city elevation is also recorded over the observational period. A Physical Scaling downscaling model is then validated and used to downscale future surface temperature projections from 3 GCMs and 2 extreme Representative Concentration Pathways in the urban and rural areas of the cities. Future changes in SUHI magnitudes between historical (2006-2015) and future timelines: 2030s (2026-2035), 2050s (2046-2055), and 2090s (2091-2100) are estimated. Analysis of future projected changes indicate that 15 (13) out of 20 cities can be expected to experience increases in SUHI magnitudes in future under RCP 2.6 (RCP 8.5). A statistically significant relationship between projected future SUHI change and current size of the cities is also obtained. The study highlights the role of city properties (i.e. its size, elevation, and surrounding land-cover) towards shaping their current and future SUHI characteristics. The results from this analysis will help decision-makers to manage Canadian cities more efficiently under rapidly changing geophysical and demographical conditions.
Abhishek Gaur; Markus Kalev Eichenbaum; Slobodan P. Simonovic. Analysis and modelling of surface Urban Heat Island in 20 Canadian cities under climate and land-cover change. Journal of Environmental Management 2018, 206, 145 -157.
AMA StyleAbhishek Gaur, Markus Kalev Eichenbaum, Slobodan P. Simonovic. Analysis and modelling of surface Urban Heat Island in 20 Canadian cities under climate and land-cover change. Journal of Environmental Management. 2018; 206 ():145-157.
Chicago/Turabian StyleAbhishek Gaur; Markus Kalev Eichenbaum; Slobodan P. Simonovic. 2018. "Analysis and modelling of surface Urban Heat Island in 20 Canadian cities under climate and land-cover change." Journal of Environmental Management 206, no. : 145-157.
Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.
Abhishek Gaur; Slobodan P. Simonovic. Application of physical scaling towards downscaling climate model precipitation data. Theoretical and Applied Climatology 2017, 132, 287 -300.
AMA StyleAbhishek Gaur, Slobodan P. Simonovic. Application of physical scaling towards downscaling climate model precipitation data. Theoretical and Applied Climatology. 2017; 132 (1-2):287-300.
Chicago/Turabian StyleAbhishek Gaur; Slobodan P. Simonovic. 2017. "Application of physical scaling towards downscaling climate model precipitation data." Theoretical and Applied Climatology 132, no. 1-2: 287-300.
Physical scaling (SP) method is a statistical downscaling approach where model-based climate data are downscaled taking into consideration large-scale climate, elevation and land-cover at the location of interest. In this study, the downscaling skills of an ensemble of SP method and its variants and Statistical DownScaling Model (SDSM) towards downscaling North American Regional Reanalysis (NARR) temperature data are compared. Two downscaling approaches: direct and indirect, two versions: SP and surrounding pixel information and three functional forms: linear regression, quantile regression and generalized additive models are considered to prepare the method ensemble. To evaluate method performance, a leave-one-out cross-validation approach is adopted. Results indicate that SP method and its variants have comparable skill to SDSM. Further method skill is found to be only marginally influenced by the choice of method version and functional form, and considerably influenced by the choice of approach. The ensemble of models is thereafter used to downscale future air temperature projections made by a global climate model: FGOALS-s2. It is found that downscaled future projections are most significantly influenced by the choice of version, followed by the choice of approach and the choice of functional form in the decreasing order of importance.
Abhishek Gaur; Slobodan P. Simonovic. Extension of physical scaling method and its application towards downscaling climate model based near surface air temperature. International Journal of Climatology 2016, 37, 3353 -3366.
AMA StyleAbhishek Gaur, Slobodan P. Simonovic. Extension of physical scaling method and its application towards downscaling climate model based near surface air temperature. International Journal of Climatology. 2016; 37 (8):3353-3366.
Chicago/Turabian StyleAbhishek Gaur; Slobodan P. Simonovic. 2016. "Extension of physical scaling method and its application towards downscaling climate model based near surface air temperature." International Journal of Climatology 37, no. 8: 3353-3366.
The objective of this study is to investigate the vulnerability of different land-cover types to climate change. To this end, land-cover specific temperature change factors are quantified for the southern Saskatchewan region using a novel statistical downscaling model: physical scaling (SP). SP model considers large-scale climate and regional physical characteristics like land-cover, elevation in its formulation and hence can be used to predict future temperature for different land-cover types under changing large-scale climatic and land-cover conditions. The model is validated by assessing its ability to downscale North American Regional Reanalysis (NARR) derived surface (skin) temperature from an initial resolution of 32 km to 500 m. The downscaled NARR data are evaluated using a cross-validation approach over the period 2006–2013 with reference to MODerate-resolution Imaging Spectroradiometer (MODIS) derived surface temperature estimates and satisfactory model performance is obtained (average RMSE = 0.03 K). The validated model is used to predict future surface temperature across the study region. Future land-cover projections are derived by downscaling land-use projections for Representative Concentration Pathways (RCPs) 2.6 and 8.5 made by integrated assessment models: IMAGE and MESSAGE, respectively. An analysis of land-cover specific temperature changes between historical (2006–2013) and future (2081–2100) timelines indicate variations of up to 2 K between different land-cover classes. Vulnerability pattern of different land-cover classes differ significantly between day- and night-time. Further, variations of upto 1 K in projected changes are observed among different forest cover types. Closed shrubland is obtained as the most vulnerable forest-cover class whereas evergreen broadleaf forest is found to be the least vulnerable.
Abhishek Gaur; Slobodan P. Simonovic. Accessing vulnerability of land-cover types to climate change using physical scaling downscaling model. International Journal of Climatology 2016, 37, 2901 -2912.
AMA StyleAbhishek Gaur, Slobodan P. Simonovic. Accessing vulnerability of land-cover types to climate change using physical scaling downscaling model. International Journal of Climatology. 2016; 37 (6):2901-2912.
Chicago/Turabian StyleAbhishek Gaur; Slobodan P. Simonovic. 2016. "Accessing vulnerability of land-cover types to climate change using physical scaling downscaling model." International Journal of Climatology 37, no. 6: 2901-2912.