Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes

Abstract

Describing the space‐time variability of hydrologic extremes in relation to climate is important for scientific and operational purposes. Many studies demonstrated the role of large‐scale modes of climate variability such as the El Niño–Southern Oscillation (ENSO) or the North Atlantic Oscillation (NAO), among many others. Climate indices have hence frequently been used as predictors in probabilistic models describing hydrologic extremes. However, standard climate indices such as ENSO/NAO are poor predictors in some regions. Consequently, this paper describes an innovative method to avoid relying on standard climate indices, based on the following idea: the relevant climate indices are effectively unknown (they are hidden), and they should therefore be estimated directly from hydrologic data. In statistical terms, this corresponds to a Bayesian hierarchical model describing extreme occurrences, with hidden climate indices treated as latent variables. This approach is illustrated using three case studies. A synthetic case study first shows that identifying hidden climate indices from occurrence data alone is feasible. A second case study using flood occurrences at 42 east Australian sites confirms that the model correctly identifies their ENSO‐related climate driver. The third case study is based on 207 sites in France, where standard climate indices poorly predict flood occurrence. The hidden climate indices model yields a reliable description of flood occurrences, in particular their clustering in space and their large interannual variability. Moreover, some hidden climate indices are linked with specific patterns in atmospheric variables, making them interpretable in terms of climate variability and opening the way for predictive applications.

Moving beyond the catchment scale: Value and opportunities in large‐scale hydrology to understand our changing world

“…In many locations, commonly invoked climate indices (including the NAO or ENSO) may also be poor hydrological predictors (e.g., Giuntoli, Renard, Vidal, & Bard, ). For these reasons, it is often advisable to begin large‐scale hydrological studies with an “environment‐to‐climate” approach to investigate climate drivers—that is, the detection of unknown or hidden climate indices directly from hydrological data (Renard & Thyer, ).…”

Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins

“…Another extension would be to implement a more complex climate informed approach using larger‐scale climate covariates. Currently, identifying such covariates for many basins over disparate hydro‐climatic regions is cumbersome, but this goal may be more attainable with recent developments that use machine learning to detect atmospheric circulation patterns associated with extreme floods (Schlef et al, 2019), that link fronts to more than half of the extreme precipitation events occurring throughout the United States (Kunkel & Champion, 2019; Kunkel et al, 2012), and that use Bayesian techniques to identify so‐called hidden climate indices (Renard & Thyer, 2019).…”

Bayesian Hierarchical Models for the Frequency of Winter Heavy Precipitation Events Over the Central United States: The Role of Atmospheric Rivers

“…Both Y and μ are of S × T dimension. Conditional on μ s , t , Y s , t are assumed to be independent; the conditional independence assumption is succinctly explained in section 2.1.4 of Renard and Thyer (2019). To clearly illustrate the spatial dependence and conditional independence in data layer, we use the terms climate dependence and weather dependence given by Renard (2011).…”

“…They also noted that, at many stations, the model that included the entire climate field performed better than the model that included just the climate indices (e.g., the North Atlantic Oscillation [NAO]). Along these lines, Renard and Thyer (2019) argued that the most relevant climate indices can be extracted from the hydrologic observations, rather than relying on the standard climate indices, such as Southern Oscillation Index (SOI), NAO, etc. Many valuable references on climate‐informed frequency analysis of heavy precipitation events can be found in the recent literature (e.g., Aryal et al, 2009; Cooley et al, 2007; Lima et al, 2015; Lima & Lall, 2009; Sun et al, 2015).…”