Extreme value analysis dilemma for climate change impact assessment on global flood and extreme precipitation

Abstract

A reliable estimation of hydrological extremes with potentially severe socio-economic impacts is of crucial importance for efficient planning and design of hydraulic structures. Extreme value theory provides a firm theoretical foundation for the statistical modelling of extreme hydrological events. The dilemma in the modelling is on whether to use block maxima (BM) or peak-over-threshold (POT) method, each with its own cons and pros. It remains unexplored to what extent future projected changes in extreme hydrological events are influenced by the method choice, especially when some simplifications are made to lessen the computational burden for large-scale studies. This study addresses this research question by a comparative analysis between the BM and POT methods on future climate change impact on global flood and extreme precipitation. The extreme precipitation analysis is performed using 24 CMIP5 general circulation models (GCMs), while the flood analysis is based on a multi-model ensemble of 20 members including five global impact models forced by four CMIP5 GCMs. The results reveal that the BM and POT methods agree on the sign of changes in flood and extreme precipitation intensities, but disagree on the magnitude. The discrepancy between the BM and POT results increases with event extremity. The difference also varies with season, where the difference in the global land area with increasing signals peaks in winter at the rates of 11% for extreme precipitation and in summer at the rates of 3.5% for flood.

Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought

“…However, in recent climate change projection studies this threshold is often chosen to be higher, at 1 mm (Raymond et al, 2018;Tabari and Willems, 2018a;Kendon et al, 2019;Han et al, 2019). This is done to counter the tendency of coarse climate models (GCMs) to overestimate the number of days with low precipitation (Tabari and Willems, 2018a), also known as the so-called drizzle problem (Moon et al, 2018). Following the definition used in the climate change study by Raymond et al (2018), a dry spell is defined as consecutive dry days with less than 1 mm of precipitation.…”

“…The number of dry days and total precipitation are both straightforward indicators that are widely used in the literature for drought assessment (e.g. Tabari and Willems, 2018a;Hänsel et al, 2019). Both have proven to be useful for comparing statistical downscaling methods (e.g.…”

“…One of the aspects of drought is a lack of precipitation. As the projected decrease in total precipitation does not systematically correspond to an increase in dry days and longest dry spell length (Tabari and Willems, 2018a), apart from total precipitation, dry spells and its building blocks, dry days, should be studied to evaluate the impact of climate change on drought. It is clear that prolonged periods of consecutive dry days can play an important role, for example, in replenishing groundwater levels in time for the dry summer season (Raymond et al, 2019).…”

The Detection of Flood Characteristics Alteration Induced by the Danjiangkou Reservoir at Han River, China

“…In terms of model simulations, the models have made obvious progress in regional-scale compatibility [12], stability [13], and uncertainty reductions [14], which has led to a significant improvement in the accuracy and applicability of flood simulations. In terms of hydrological indicator statistics, extreme value theory provides a firm theoretical foundation for the statistical modeling of extreme hydrological events; currently, the most commonly used indicators are block maxima (BM) and peak-over-threshold (POF) [15]. Moreover, some studies have combined meteorological and hydrological elements to establish some simplified indicators for characterizing flood processes, such as monthly rainfall [16], rainy season rainfall [17], maximum daily annual floods, and precipitation [18,19].…”

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

“…The existence of such rare events can affect data distribution characteristics, such as in the form of high skewness and kurtosis, which correspond to long-and heavy-tail behaviors. In environmental phenomena, the occurrence of extreme events can be observed in various fields, such as extreme precipitation and flooding [1], extreme wind speeds [2], droughts [3], extreme temperatures [4], natural hazards [5], and air pollution events [6,7].…”