Remote Sensing of Hydro-Meteorology

Flood/drought, risk management, and policy: decision-making under uncertainty. Hydrometeorological extremes and their impact on human–environment systems. Regional and nonstationary frequency analysis of extreme events. Detection and prediction of hydrometeorological extremes with observational and model-based approaches. Vulnerability and impact assessment for adaptation to climate change

A Distributed Hydrological Modelling System to Support Hydrological Production in Northern Environments under Current and Changing Climate Conditions

The overarching goal of this project was to implement a distributed hydrological modelling system to support hydroelectric production in Yukon under current and changing climate conditions. Building from previous collaboration between YU and YEC, the project has increased the capacity for short and mid-term inflow forecasts for the Whitehorse (including Marsh Lake), Aishihik and Mayo Facilities and assess potential change in flow volume and extreme events due to climate change in terms of severity, timing and frequency.ReportThis report, including any associated maps, tables and figures (the “Information”) conveys general comments and observation only. The Information is provided by the Institut national de la recherche scientifique Eau Terre Environnement (INRS-ETE) on an “AS IS” basis without any warranty or representation, express or implied, as to its accuracy or completeness. Any reliance you place upon the information contained here is your sole responsibility and strictly at your own risk. In no event will the INRS-ETE be liable for any loss or damage whatsoever, including without limitation, indirect or consequential loss or damage, arising from reliance upon the Information.Final Report presented to: Yukon Energ

A hydroclimatic assessment of the U.S. corn belt across spatial and temporal scales

The term hydroclimate is used to describe the climate of a given location as determined by the incident radiant energy (temperature) and the existence of water in its various forms on Earth. Two types of climate comprise the science of hydroclimatology: the climate as established by general global circulation patterns at specific locations on Earth (large-scale climate) and the climate established at Earth\u27s surface resulting from the daily fluxes of radiant energy and water in its various forms between the atmosphere, Earth\u27s surface, and the subsurface (local-scale climate) (Shelton 2009). This dissertation investigates different spatial and temporal scales of the U.S. Corn Belt hydroclimate and includes analysis of large- and local-scale hydroclimatic feedbacks. Large-scale hydroclimate research in this assessment investigates how general circulation patterns and teleconnections, specifically the El Ni?o-Southern Oscillation and the Arctic Oscillation, influence climate variability in the form of temperature and precipitation patterns across the U.S. Corn Belt with findings applicable to agricultural decision making. A large- and local-scale hydroclimatic assessment examines the rainfall contribution of land-falling tropical cyclones to the Eastern U.S. Corn Belt. Locale-scale hydroclimate research considers the role of land-surface feedbacks in the life cycle of land-falling tropical cyclones. Results from the assessments that comprise this dissertation show that the spatial and temporal scales at which hydroclimatic feedbacks are examined are important to the understanding of hydroclimate system interactions. It is suggested from the results of this comprehensive assessment that the newly identified, large- and local-scale hydroclimatic feedbacks be given stronger consideration in forecasts and climate projection models. Additionally, it is suggested that more hydroclimate assessments across spatial and temporal scales be completed to better prepare for and mitigate the effects of projected climate variability and climate change. A framework for climatological applications to agronomy is discussed in the first chapter, with the findings of the hydroclimatological assessments in subsequent chapters primarily applied to agronomic decision making

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

Statistical physics approaches to the complex Earth system

Global climate change, extreme climate events, earthquakes and their accompanying natural disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, strategic interactions and complex structure of the Earth system, the understanding and in particular the predicting of such disruptive events represent formidable challenges for both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge for a better understanding of the Earth system, including climate extreme events, earthquakes and Earth geometric relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as, critical phenomena, network theory, percolation, tipping points analysis, as well as entropy can be applied to complex Earth systems (climate, earthquakes, etc.). Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics approaches can be useful in the field of Earth system science

Forecasting of rainfall using different input selection methods on climate signals for neural network inputs

Long-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this purpose, climatic data (large-scale signals) and meteorological data (local precipitation and temperature) with 3 to 12 months lead-times are used as inputs to predict precipitation for 3, 6, 9 and 12 months periods in 6 selected stations across Iran. A genetic algorithm (GA) and self-organized neural network (SOM) along with the application of winGamma software were comparatively used as input selection methods to choose the appropriate input variables. Examining the results, out of 96 predictions performed at all stations, in 43 cases, GA, in 28 cases, winGamma, and in 25 cases SOM have the best results compared to the other two methods. According to this, as a generalized assumption, it can be said that at least for the selected stations in this paper, the GA method is more reliable than the other two methods, and can be used to make predictions for future applications as a reliable input selection method. Moreover, among different climatic signals, Pacific Decadal Oscillation (PDO), Trans-Niño Index (TNI) and Eastern Tropical Pacific SST (NINO3) are the most repetitive indices for the most accurate forecast of each station

Statistical physics approaches to the complex Earth system

Global warming, extreme climate events, earthquakes and their accompanying socioeconomic disasters pose significant risks to humanity. Yet due to the nonlinear feedbacks, multiple interactions and complex structures of the Earth system, the understanding and, in particular, the prediction of such disruptive events represent formidable challenges to both scientific and policy communities. During the past years, the emergence and evolution of Earth system science has attracted much attention and produced new concepts and frameworks. Especially, novel statistical physics and complex networks-based techniques have been developed and implemented to substantially advance our knowledge of the Earth system, including climate extreme events, earthquakes and geological relief features, leading to substantially improved predictive performances. We present here a comprehensive review on the recent scientific progress in the development and application of how combined statistical physics and complex systems science approaches such as critical phenomena, network theory, percolation, tipping points analysis, and entropy can be applied to complex Earth systems. Notably, these integrating tools and approaches provide new insights and perspectives for understanding the dynamics of the Earth systems. The overall aim of this review is to offer readers the knowledge on how statistical physics concepts and theories can be useful in the field of Earth system science