Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Abstract

Abstract. Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind/storms have devastating effects each year. One of the key challenges for society is understanding how these extremes are evolving and likely to unfold beyond their historical distributions under the influence of multiple drivers such as changes in climate, land cover, and other human factors. Methods for analysing hydroclimatic extremes have advanced considerably in recent decades. Here we provide a review of the drivers, metrics and methods for the detection, attribution, prediction and projection of nonstationary hydroclimatic extremes. We discuss issues and uncertainty associated with these approaches (e.g arising from insufficient record length, spurious nonstationarities, or incomplete representation of nonstationary sources in modelling frameworks), examine empirical and simulation-based frameworks for analysis of nonstationary extremes, and identify gaps for future research.

Do small and large floods have the same drivers of change? A regional attribution analysis in Europe

“…In this latter case, the sample uncertainty resulting from the adjusted likelihood will be larger, compared to the model in which spatial cross-correlation is not accounted for. For further details on the adjustment to the likelihood and its application to hydrological data, see Smith (1990), Ribatet et al (2012) and Sharkey and Winter (2019).…”

“…the 2-year and the 100-year flood) and potential drivers of flood change and to separate the effects of drivers on selected flood quantiles. For ease of interpretation, the quantiles are expressed here in terms of return periods, although alternative metrics are available under non-stationarity conditions (see, for example, Read and Vogel, 2015;Slater et al, 2020). We adopt a non-stationary flood frequency approach to attribute observed flood changes to potential drivers, used as covariates of the parameters of the regional probability distribution of floods.…”

Global Changes in 20‐Year, 50‐Year, and 100‐Year River Floods

“…There is growing acceptance that stationary concepts like a fixed 1‐in‐100‐year flood are too easily misinterpreted (HM Government, 2016) and new methods are required to better represent time‐varying probability distributions reflecting the ubiquity of global changes in climate and land cover (see e.g., Slater et al. [2020] for a review).…”

Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios

“…However, these studies are often smaller than 1000 km 2 in size, and it is unknown whether the impacts of afforestation may scale up over larger catchments, given its complex influence on streamflow [16][17][18] . Furthermore, few systematic evaluations exist of how afforestation location and extent may influence catchment hydrology across a wide range of climatic and physiographic conditions 19,20 .…”