Using fine-scale climate process, we investigated the relationship between extreme surface water runoff and property damages caused by flood in the U.S. Special attention was paid to disentangle effects of extreme weather and social wealth accumulation. We find it is still premature to claim that extreme weather events becomes more destructive due to anthropological climate change, if take into...

Extreme Value Theory is the branch of statistics that is used to model extreme events. The topic is of interest to meteorologists because much of the recent literature on climate change has focussed on the possibility that extreme events (very high or low temperatures, high precipitation events, droughts, hurricanes etc.) may be changing in parallel with global warming. As a specific example, t...

introduction long-term annual extreme climate events, especially temperature and precipitation, have generally been used as indicators for the assessment of climate change. one of the most changeable climatic variables in time and space is precipitation. there are great number of research papers focusing on extreme precipitation on global, regional and national scales have been written. the res...

Increases in extreme precipitation events of floods and droughts are expected to occur worldwide. The increase in extreme events will result in changes in streamflow that are expected to affect water availability for human consumption and aquatic ecosystem function. We present an analysis that may greatly improve current streamflow models by quantifying the impact of the interaction between for...

Cambodia is prone to extreme weather events, especially floods, droughts and typhoons. Climate change is predicted to increase the frequency and intensity of such events. The Cambodian population is highly vulnerable to the impacts of these events due to poverty; malnutrition; agricultural dependence; settlements in flood-prone areas, and public health, governance and technological limitations....

Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme w...

Statistical downscaling is commonly used in climate modeling to obtain high-resolution spatial projections of future climate scenarios from the coarse-resolution outputs projected by global climate models. Unfortunately, most of the statistical downscaling approaches using standard regression methods tend to emphasize projecting the conditional mean of the data while paying scant attention to t...