Browsing by Author "Gupta, Anil"
Now showing 1 - 6 of 6
Results Per Page
Sort Options
- ItemOpen AccessEvaluation and Optimization of Weather Networks in Athabasca Oil Sands Region(2023-01-26) Deshmukh, Dhananjay; Hassan, Quazi; Gupta, Anil; Achari, GopalThe monitoring of weather is required for climate studies, research, and forecasting. For the monitoring purpose, three networks of 19 stations i.e., Water Quantity Program (WQP), Meteorological Towers (MT), and Edge Sites (ES) were operational in Athabasca oil sands region. The overall objective of the study was to identify similarities/redundancies in meteorological observations for the optimization of weather networks. For this, firstly similarity among meteorological parameters have been quantified for air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD) among station-pairs of each network. In this process, Pearson’s correlation coefficient (r) and average absolute error (AAE) were the best representative measures from the methods of association and coincidence while proposed percentage of similarity (PS%) was the best in comparison to r and AAE to quantify the similarity. Further, RH found to be the least variable with strong and acceptable similarity in each network while similarity was decreased in order of SD, BP, AT, SR, and PR respectively. Secondly, Wind data has been analyzed for these three networks to find the optimal network. Here, it has been revealed that wind rose diagram only appropriate for visual comparison of wind characteristics while r, AAE and PS measures were suitable for similarity analysis of wind. Later, it has been found that all station from these three networks were required to represent wind variability in the region due to very low and unacceptable PS values. Thirdly, influence of land cover and topography have been evaluated on meteorological parameters of these 19 stations where they categorised under seven groups based on similar kind of land cover and topography. In this evaluation, parameters AT and SR were shown strong correlation but limited similarity while RH exhibit the least variability in each group. Moreover, BP and SD have some similarities while PR and WSD were highly variable due to various locational factors other than similar land cover and topography.
- ItemOpen AccessForest Fire Danger/Risk Forecasting: A Remote Sensing Approach(2020-03) Ahmed, M. Razu; Hassan, Quazi K.; Gupta, Anil; Kibria, Md GolamForest/wildland fires are natural disasters that create a significant threat to the communities living in the vicinity of the forested landscape. To minimize the risk concerning resiliency of those urban communities to forest fires, my overall objective was to develop primarily remote sensing (RS)-based models assessing potential risks at the wildland-urban interface (WUI) and making predictions of danger conditions in the environs forest/vegetation. I investigated the risks associated with WUI for the Fort McMurray community and danger conditions in the northern part of Alberta, Canada. For developing the risk modelling framework at WUI, I employed primarily a WorldView-2 satellite image acquired on June 06, 2016. I estimated structural damages due to the devastating 2016 Horse River wildland fire (HRF) that entered the community on May 03, 2016. Besides, I analyzed the presence of vegetation at the WUI to identify the associated risks according to the FireSmart Canada guidelines. My remote sensing-based estimates of the number of structural damages identified a strong linear relationship (i.e., r2 value of 0.97) with the ground-based estimates. Besides, all damaged structures were found associated with the existence of vegetation within the 30m buffer/priority zone of the WUI. It was revealed that approximately 30% of the areas of the WUI were vulnerable due to the presence of vegetation, in which approximately 7% were burned during the 2016 HRF event that led the structural damages. In addition, I developed a new medium-term (i.e., four days) model to forecast forest fire danger conditions using RS-derived biophysical variables of vegetation. I primarily employed Terra MODIS (moderate resolution imaging spectroradiometer)-derived four-day composites of daily surface temperature, normalized difference vegetation index and normalized difference water index. The model was able to detect about 75% of the fire events in the top two danger classes (i.e., very high and high) when evaluated with the historical ground-based forest fire occurrences during the fire seasons of 2015–2017. Besides, the model was able to predict the 2016 HRF event with about 67% agreement. Finally, I developed an operational near real-time (NRT) model to forecast forest fire danger conditions for a day to the next 8 days. Here, I employed Terra MODIS-acquired NRT data from NASA's LANCE (land, atmosphere near real-time capability for earth observing system), where data are made available to the public domain within 2.5 hours of satellite observation. The NRT model was successful in producing forecasted forest fire danger maps at any given time. These developed risk/forecast models would be very useful for the stakeholders in the forest fires management strategies of saving life, property, and community.
- ItemOpen AccessHydrological Modelling of River Flow Forecasting in Cold Regions and Its Application over the Athabasca River(2023-03-23) Belvederesi, Chiara; Hassan, Quazi; Achari, Gopal; Rangelova, Elena; Gupta, AnilFloods are disasters that represent a growing threat to the communities living close to rivers. To maximize community resilience, the main objective was to formulate a transferable framework for river flow forecasting in cold and poorly gauged/ungauged regions. First, the literature was reviewed, summarizing the recent findings in river flow forecasting in these regions. Here, hydrological processes greatly vary seasonally and annually, translating into increased model uncertainty. Regionalization, spatial calibration, and other methods were implemented into process-based and empirical models. Although process-based models provided a wide understanding of a watershed’s hydrology, they were often complex and computationally demanding. Empirical models produced fewer calibration parameters although generated biased results when insufficient descriptors were available. The results from this review highlighted some efforts necessary to improve river flow forecasting, including: coping with limited data; providing user-friendly interfaces; advancing model structure; developing a universal method for transferring parameters; standardizing calibration and validation; integrating process-based and empirical models. In addition, a machine learning-based model was developed using a single-input sequential adaptive neuro-fuzzy inference system (ANFIS) in the Athabasca River Basin (ARB) in Alberta, Canada. After estimating the optimal lead time between four hydrometric stations, data measured near the source were used to compute flows near the mouth, over approximately 1,000 km. This technique was compared to nonsequential and multi-input ANFIS, which used data from all the four hydrometric stations. The results showed that sequential ANFIS could accurately predict flows (r2 = 0.99, Nash–Sutcliffe = 0.98) with a longer lead time (6 days) using a single input. Finally, a novel simplistic method for short-term (6 days) forecasting called Flow Difference Model (FDM) was developed and compared against existing hydrological models (i.e., Regression Models (RM) and Base Difference Model (BDM)), to demonstrate that simplistic modelling can achieve acceptable accuracy. The results showed that the FDM outperformed the other models (Nash–Sutcliffe = 0.95) using limited inputs and calibration parameters. Moreover, the FDM had similar performance to machine learning techniques, demonstrating the forecasting capability of simplistic methods. These findings could be utilized towards flood prevention and planning, operations, maintenance, and administration of water resource systems.
- ItemOpen AccessIntegrated Environmental Modelling Framework for Cumulative Effects Assessment(University of Calgary Press, 2021-01) Gupta, Anil; Farjad, Babak; Wang, George; Eum, Hyung; Dubé, MoniqueA thorough and detailed examination of integrated environmental modelling and integrated environmental modelling frameworks for cumulative effects assessment of complex environmental problems. Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment.
- ItemOpen AccessRemote Sensing of Forest Fire Danger Forecasting(2019-04-26) Abdollahi, Masoud; Hassan, Quazi K; Hass; Gupta, Anil; Islam, Tanvir; Nowicki, Edwin Peter; Govind, AjitForest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, my aim was to employ primarily remote sensing data in forecasting the forest fire danger conditions in the Canadian province of Alberta. Thus, I followed three specific objectives. Firstly, I generated topography-based static fire danger (SFD) map upon exploring the relationship between topographical elements (i.e., elevation, slope, and aspect) and fire occurrences. Since, the slope was found to be the best predictor for fire occurrences; I generated a slope-derived probability of forest fire occurrences. However, I did not incorporate the obtained map in the final specific objective as it had very small low fire danger areas. Secondly, I examined the possibility of lightning-caused fires modelling using remote sensing-derived vegetation moisture content in natural subregion level. I employed 8-day composite of normalized differences water index (NDWI) at 500 m spatial resolution along with historical lightning-caused fire occurrences during the 2005-2016 period. Employing the cumulative frequency cumulative-values of natural subregion-specific median NDWI and lightning-caused fire frequencies from snow disappearance date to the peak of the growing season, I found strong agreements (i.e., R2 ≥ 0.96) between these two frequencies for each of the subregions. Finally, I developed an advanced forest fire danger forecasting system upon applying three modifications on the exiting FFDFS, and incorporating the outcomes in the scope of the previous specific objectives. Then I examined the outcomes of the different combinations against the actual fire spots during the fire seasons of 2009–2011. Among all of the combinations, I found that the integration of modified FFDFS and human-caused SFD map demonstrated the most effective results in fire detection, i.e., about 82% on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. I strongly believe that my developments would be useful in the forest fire management.
- ItemOpen AccessSequencing Abandoned Wellsite Reclamations(2018-07-27) Thiessen, Ronald James; Achari, Gopal; Jergeas, George Farage; Gupta, Anil; Chen, Bing; Dann, Markus R.Alberta has 69,100 abandoned oil and gas wells. Once a well is abandoned according to provincial regulations, the wellsite must be reclaimed. This means soil and groundwater contamination caused by well drilling and operations must be removed, and the wellsite returned to a condition equivalent to the vegetation, soil quality, and topography of the surrounding land. Provincial regulations currently do not specify a maximum time to reclaim a wellsite after abandonment. Other jurisdictions have similar issues as well. An appeal heard by the Supreme Court of Canada involving federal bankruptcy and insolvency legislation and provincial energy legislation will likely motivate changes in how and when wellsites are moved from abandoned to reclaimed status. Assuming these changes will occur and since concurrently reclaiming all abandoned wellsites is not practical, this thesis presents a method of prioritising abandoned wellsite reclamations based on indicators of negative economic, environmental, and social impacts. This practical method can be adapted to similar problems elsewhere around the globe. Using publicly available data on wellsite reclamation costs, environmental liability as defined in Canadian accounting guidance is considered an indicator of negative economic impact. Property value reduction is also treated as a negative economic impact indicator and land transfer data from the provincial land titles office is referenced. The adverse environmental effect of an abandoned wellsite is a negative environmental impact indicator and is estimated using preliminary quantitative risk assessment methods. Data supporting this assessment is from publicly available environmental reports on completed wellsite reclamations. Adverse public responses to abandoned wellsites are treated as indicators of negative social impacts and land damage and compensation claims by landowners, public complaints to the provincial energy regulator, and regulatory orders are referenced. These indicators are analysed by censored data and ordinal logistic regression statistical methods as well as a priori classification techniques. They are combined using partial order methods to yield a wellsite reclamation sequencing model that requires three pieces of readily-available wellsite information. The model is illustrated with information on 100 abandoned wellsites in Alberta. Model validation and regulatory policy recommendations are provided.