Balling Jr., R. C., Kiany, M. S. K., & Roy, S. S. (2016). Anthropogenic signals in Iranian extreme temperature indices. Atmospheric Research, 169, 96–101.
Abstract: We analyzed spatial and temporal patterns in temperature extremes from 31 stations located throughout Iran for the period 1961 to 2010. As with many other parts of the globe, we found that the number of days (a) with high maximum temperatures was rising, (b) with high minimum temperatures was rising, and (c) with low minimum temperatures was declining; all of these trends were statistically significant at the 0.05 level of confidence. Population records from 1956 to 2011 at the station locations allowed us to reveal that the rate of human population growth was positively related to the increase in the number of days with high maximum temperatures and negatively related to days with low maximum temperatures. Our research shows a number of identifiable anthropogenic signals in the temperature records from Iran, but unlike most other studies, the signals are stronger with indices related to maximum, not minimum, temperatures.
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Collins, J. M., Paxton, C. H., Wahl, T., & Emrich, C. T. (2017). Climate and weather extremes. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 579–615). Gainesville, FL: Florida Climate Institute.
Abstract: This chapter examines Florida’s extreme weather hazards: 1) why they happen, 2) their relation to interannual to multidecadal climate variability, and 3) the potential of each hazard and spatial variability across the state. The weather hazards indicated are under these broad categories: precipitation (rainfall, flooding, droughts), thunderstorms (lightning, hail, convective wind, tornadoes), tropical weather (tropical storms and hurricanes), and temperatures (extreme highs and lows). The conclusions section mainly addresses the challenge of attributing extreme events to human-induced climate change.
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Elliott, J., Glotter, M., Ruane, A. C., Boote, K. J., Hatfield, J. L., Jones, J. W., et al. (2018). Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. Agricultural Systems, 159, 275–281.
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Keellings, D. (2016). Evaluation of downscaled CMIP5 model skill in simulating daily maximum temperature over the southeastern United States. Int. J. Climatol., 36(12), 4172–4180.
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McOmber, C., Audia, C., & Crowley, F. (2019). Building resilience by challenging social norms: integrating a transformative approach within the BRACED consortia. Disasters, 43(53), S271–S294.
Abstract: Resilience is a complex phenomenon whereby a multitude of social and environmental factors, including gender, combine to shape the ways that shocks affect people. Looking at two BRACED (Building Resilience and Adaptation to Climate Extremes and Disasters) projects, in Burkina Faso and in Ethiopia, this article uses a desk review and primary data from partners and people at risk to explore how a gender-transformative approach can be an integral part of resilience-building projects, particularly those implemented by multi-stakeholder consortia. It also suggests ways to incorporate a stronger gender component in similar future projects. The article argues that donors and programme managers must provide clear principles and guidelines for achieving gender equity within resilience-building efforts. However, these must allow flexibility to adapt to norms, needs and resources as determined by implementing partners. The right balance can be achieved by facilitating spaces for individual and collective goal-setting; assessing current capacity and trajectories; and lesson-sharing as an iterative process for institutional learning.
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Salas, J. D., Obeysekera, J., & Vogel, R. M. (2018). Techniques for assessing water infrastructure for nonstationary extreme events: a review. Hydrological Sciences Journal, .
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Shangyong, S., & Misra, V. (2020). The role of extreme rain events in Peninsular Florida's seasonal hydroclimate variations. Journal of Hydrology, 589(125182).
Abstract: The role of extreme rains over Peninsular Florida (PF) in modulating the seasonal rainfall characteristics is
investigated in this study. The paper is motivated on its potential implication on the seasonal predictability of
the hydroclimate of PF by relatively coarse global seasonal climate models. A majority of these climate models
are unable to resolve the weather events like tropical cyclones that produce such extreme rain events. Therefore,
a legitimate question to ask is if this limits the model's seasonal predictability of the hydroclimate of PF.
In this paper, extreme rain events over PF within a season are defined as days with daily rain amount at or
above the 95th percentile over 39 years from 1979 to 2017 at the grid resolution of the observed rainfall dataset
(0.5° × 0.5°). The thresholds for extreme rain days range from 16 mmday-1 to 36 mmday-1 depending on the
season and the location over PF, while the heaviest rainfall range from 58 mmday-1 to 278 mmday-1. These
extreme rain events occur most often across PF in the boreal summer season followed by the fall season with the
least in the boreal winter season. Our study reveals that removing the days of extreme rain events has the largest
impact on the corresponding seasonal anomalies and daily rainfall distribution in the dry winter season and least
in the wet summer season.
The impact of El Niño and the Southern Oscillation (ENSO) on the extreme rain events was evaluated by
contrasting the differences in the shape and the scale parameters of the fitted Gamma distribution on daily
rainfall in winter/spring seasons during warm and cold phases. Results revealed that the warm ENSO phases
make the tails of the daily rainfall distribution over PF heavier and longer relative to the cold ENSO phases in the
winter and spring seasons. In essence, our study reveals that the extreme rain events that are critical for the
overall seasonal distribution of rainfall over PF in the first half of the year is modulated by large-scale phenomenon
(e.g., ENSO). In the latter half of the year (summer and fall), the extreme rain events are not as critical
to the seasonal rainfall anomaly or the overall seasonal distribution of rainfall over PF. Therefore, resolving the
extreme rain events need not be as critical for the seasonal predictability of the hydroclimate of PF.
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Shi, S., & Misra, V. (2020). The role of extreme rain events in Peninsular Floridas seasonal hydroclimate variations. Journal of Hydrology, 589.
Abstract: The role of extreme rains over Peninsular Florida (PF) in modulating the seasonal rainfall characteristics is investigated in this study. The paper is motivated on its potential implication on the seasonal predictability of the hydroclimate of PF by relatively coarse global seasonal climate models. A majority of these climate models are unable to resolve the weather events like tropical cyclones that produce such extreme rain events. Therefore, a legitimate question to ask is if this limits the model’s seasonal predictability of the hydroclimate of PF.
In this paper, extreme rain events over PF within a season are defined as days with daily rain amount at or above the 95th percentile over 39 years from 1979 to 2017 at the grid resolution of the observed rainfall dataset (0.5° × 0.5°). The thresholds for extreme rain days range from 16 mmday−1 to 36 mmday−1 depending on the season and the location over PF, while the heaviest rainfall range from 58 mmday−1 to 278 mmday−1. These extreme rain events occur most often across PF in the boreal summer season followed by the fall season with the least in the boreal winter season. Our study reveals that removing the days of extreme rain events has the largest impact on the corresponding seasonal anomalies and daily rainfall distribution in the dry winter season and least in the wet summer season.
The impact of El Niño and the Southern Oscillation (ENSO) on the extreme rain events was evaluated by contrasting the differences in the shape and the scale parameters of the fitted Gamma distribution on daily rainfall in winter/spring seasons during warm and cold phases. Results revealed that the warm ENSO phases make the tails of the daily rainfall distribution over PF heavier and longer relative to the cold ENSO phases in the winter and spring seasons. In essence, our study reveals that the extreme rain events that are critical for the overall seasonal distribution of rainfall over PF in the first half of the year is modulated by large-scale phenomenon (e.g., ENSO). In the latter half of the year (summer and fall), the extreme rain events are not as critical to the seasonal rainfall anomaly or the overall seasonal distribution of rainfall over PF. Therefore, resolving the extreme rain events need not be as critical for the seasonal predictability of the hydroclimate of PF.
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Teegavarapu, R. S. V. (2013). Climate change-sensitive hydrologic design under uncertain future precipitation extremes. Water Resour. Res., 49(11), 7804–7814.
Abstract: Precipitation as an important component of hydrologic cycle bears a significant influence on hydrologic design and water resources management. The uncertainties associated with future climate change coupled with limitations of climate change models and uncertainties in projections, our inability to quantify these introduce additional complexities in hydrologic design using future precipitation extremes. A new optimal compromise hydrologic design of a stormsewer system using a fuzzy mixed integer nonlinear mathematical programming (MINLP) model with discrete, binary variables and logical constraints is developed and evaluated in this study. Preferences of hydrologists toward expected uncertain future changes in precipitation extremes are modeled using linear, nonlinear, triangular and Gaussian fuzzy membership functions. Methods for deriving membership functions based on multimodel multiple scenario GCM-based simulations are also suggested. Incorporation of triangular functions in optimization formulation required the use of binary variables. A hypothetical hydrologic design example with realistic parameter values is used to obtain compromise elements of stormsewer system. Results from the study suggest a compromise design is possible based on the preferences and a balance between over and under design of stormsewer infrastructure is achievable. The nature of membership functions reflecting the preferences attached to future extremes influence the optimal solutions obtained resulting in changes in cost-based performance measures.
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Tegegne G, Melesse AM, & Worqlul, A. W. (2020). Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes. Sci Total Environ, 704.
Abstract: The severity and frequency of climate extremes will change in the future owing to global warming. This can severely impact the natural environment. Therefore, it is common practice to project climate extremes with a global climate model (GCM) in order to quantify and manage the associated risks. Several studies have demonstrated that a multi-model ensemble approach increases the reliability of predictions by exploiting the strengths and discounting the weaknesses of each climate simulator. However, the available multi-model averaging approaches exhibit significant drawbacks as they are not capable of extracting different climate extreme characteristics from the climate models. This study proposes a new approach that combines multiple models for projecting climate extremes by accounting for different extreme indices in the climate model performance weighting scheme. The capability of this method was evaluated with respect to reliability ensemble averaging (REA) and Taylor diagram-based GCM skill approaches for reproducing wet and dry precipitation events. The proposed multi-model averaging approach outperformed the available approaches in reducing the root mean square error (RMSE) by 5% and 54% in the wet and dry precipitation conditions, respectively. Therefore, it can be concluded that incorporating the different precipitation extremes in a multi-model combination approach could enhance the assessment of climate change impacts on the climate extremes. The climate change impacts on the extreme events, based on the proposed multi-model ensembles, is thus assessed using the standardized precipitation indexes of 3month, 6month, and 12month durations. In general, the results exhibited that the frequency of wet events increases, whereas that of drought events decreases.
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Tegegne, G., & Melesse, A. M. (2020). Multimodel Ensemble Projection of Hydro-climatic Extremes for Climate Change Impact Assessment on Water Resources. Water Resour Manage, 34, 3019–3035.
Abstract: Projected changes in climatic extremes, compared to the mean climate, exhibit a greater negative impact on the natural environment. Several studies reported that multi-model ensemble approach can improve the reliability of hydro-climatic extreme projection by extracting important information from a large number of general circulation models (GCMs). However, most of the available multi-model assembling methods do not consider both the spatial and temporal variabilities. Thus, this study reflects both the spatial and temporal climate characteristics during multi-model averaging through the Taylor diagram skill metrics. The capability of the proposed multi-model assembling approach was evaluated for reproducing the multitude of climate extreme indices. Moreover, the reliability of a multi-model assembling approach was assessed for preserving the maximum variability of the GCMs output. In general, the results showed that multi-model assembling approach outperformed the individual climate models for reproducing the hydro-climatic extremes; however, it artificially corrupted and narrowed the projected climate extremes variability of the GCMs output. Thus, it is worthwhile to consider both the individual climate models and multi-model ensemble projections toward an improved projection of hydro-climatic extremes. In general, the study proved that the impacts of climate change on the hydro-climatic extremes are more amplified compared to the changes in mean climate. Hence, this study suggests that meaningful efforts should be put in the future to proactively manage the risks of climate extremes.
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Wong, K. V. (2016). Planning and Engineering Strategies to Mitigate Effects of Climate Change. J. Energy Resour. Technol, 138(1), 014701.
Abstract: Extreme weather events seem to have become more frequent with climate change. These anomalies throughout the world may generally be categorized as drought, heavy rain storms, landslides, heavy snow storms, sea level rise, ice melts from the polar regions, tornadoes and hurricanes. The environmental and real property damage caused may be minimized if proper planning and best practices are engineered into place before the catastrophic events occur. The management of vulnerable areas should definitely include such plans and strategies. The purpose of the current work is to point to the best practices already being carried out in some areas, and to draw attention to some of the knowledge embodied in the indigenous populations in particular regions, which have come by this knowledge via generations of survival through adverse climate/ environmental changes. The integration of this indigenous knowledge where applicable, with modern engineering tools and techniques will help the world better to face the climatic challenges ahead.
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