Abstract

Abstract. Understanding how water resources vary in response to climate at different temporal and spatial scales is crucial to inform long-term management. Climate change impacts and induced trends may indeed be substantially modulated by low-frequency (multi-year) variations, whose strength varies in time and space, with large consequences for risk forecasting systems. In this study, we present a spatial classification of precipitation, temperature, and discharge variability in France, based on a fuzzy clustering and wavelet spectra of 152 near-natural watersheds between 1958 and 2008. We also explore phase–phase and phase–amplitude causal interactions between timescales of each homogeneous region. A total of three significant timescales of variability are found in precipitation, temperature, and discharge, i.e., 1, 2–4, and 5–8 years. The magnitude of these timescales of variability is, however, not constant over the different regions. For instance, southern regions are markedly different from other regions, with much lower (5–8 years) variability and much larger (2–4 years) variability. Several temporal changes in precipitation, temperature, and discharge variability are identified during the 1980s and 1990s. Notably, in the southern regions of France, we note a decrease in annual temperature variability in the mid 1990s. Investigating cross-scale interactions, our study reveals causal and bi-directional relationships between higher- and lower-frequency variability, which may feature interactions within the coupled land–ocean–atmosphere systems. Interestingly, however, even though time frequency patterns (occurrence and timing of timescales of variability) were similar between regions, cross-scale interactions are far much complex, differ between regions, and are not systematically transferred from climate (precipitation and temperature) to hydrological variability (discharge). Phase–amplitude interactions are indeed absent in discharge variability, although significant phase–amplitude interactions are found in precipitation and temperature. This suggests that watershed characteristics cancel the negative feedback systems found in precipitation and temperature. This study allows for a multi-timescale representation of hydroclimate variability in France and provides unique insight into the complex nonlinear dynamics of this variability and its predictability.

Highlights

  • Hydroclimate variability represents the spatiotemporal evolution of hydrological and climate variables which are directly impacting hydrological variability

  • Phase–amplitude interactions are absent in discharge variability, significant phase– amplitude interactions are found in precipitation and temperature

  • Hydrological variability is, by definition, nonlinear (Labat, 2000; Lavers et al, 2010; McGregor, 2017) as it results from complex interactions between atmospheric dynamics and catchment properties that may vary at different timescales

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Summary

Introduction

Hydroclimate variability represents the spatiotemporal evolution of hydrological (e.g., discharge and groundwater level) and climate variables (e.g., precipitation and temperature) which are directly impacting hydrological variability. Hydrological variability is, by definition, nonlinear (Labat, 2000; Lavers et al, 2010; McGregor, 2017) as it results from complex interactions between atmospheric dynamics and catchment properties that may vary at different timescales (e.g., soil characteristics, water table, karstic systems, and vegetation covers; Gudmundsson et al, 2011; Sidibe et al, 2019) Such interactions between processes at different timescales, i.e., cross-scale interactions (Palus, 2014; Jajcay et al, 2018), have never been studied to further understand hydrological variability. It has been shown that hydroclimate variability is inherently nonstationary, with the time dependence of the mean and variance due to changes in the controlling factors (e.g., Coulibaly and Burn, 2004; Labat, 2006; Dieppois et al, 2013, 2016; Massei et al, 2017) This results in difficulties in characterizing and predicting the hydrological variability at different spatiotemporal scales (Gentine et al, 2012; Blöschl et al, 2019).

Hydrological and climate data
Continuous wavelet transforms
Image Euclidean distance clustering
Fuzzy clustering
Cross-scale interactions
Spatiotemporal clustering of hydrological variability
Time frequency patterns
Spatial variability of homogeneous hydroclimate variability in France
Conclusion
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