Synchronization and Delay Between Circulation Patterns and High Streamflow Events in Germany

Abstract

River floods cause extensive losses to economy, ecology, and society throughout the world.They are driven by the space-time structure of catchment rainfall, which is determined by large-scale, or even global-scale, atmospheric processes. The identification of coherent, large-scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall-induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns.

Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE)

“…Our overarching hypothesis is that intelligent blends of AI and physics can improve predictive understanding of crucialyet long-standingchallenges. Our team members are among the pioneers [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80] in developing integrated science-AI models for earth systems: we will build upon our foundational work to inform workshops and plans for DOE that will inspire the development of a suite of solutions in physics-guided AI (PGAI), AIenhanced physics (AIEP), including generative models, causal and interpretable AI, as well as uncertainty or risk assessments. Here we define 'physics' broadly to include biogeochemistry and process knowledge.…”

Development of a Stepwise‐Clustered Multi‐Catchment Hydrological Model for Quantifying Interactions in Regional Climate‐Runoff Relationships

“…Previously, there were many studies of data‐driven hydrological models for exploring the contributions of climate variables on concurrent variations of runoffs (Abatzoglou & Ficklin, 2017; Yang et al., 2014; Conticello et al., 2020). For instance, a neural network model was employed to simulate the hydrologic response of climate sequences (Khan & Coulibaly, 2006; Nayak et al., 2013).…”

“…Conticello et al. (2020) explored the synchronization and delay between circulation patterns and high streamflow events using multiple machine learning methods. Oppel and Fischer (2020) assessed the clusters of temporal distributions of rainfalls and their coherence with flood types through one clustering approach based on unsupervised learning.…”

“…relationships, differentiating the effects of spatial variabilities in climate variables, and enhancing the reliability of hydrological simulation (Fan et al, 2015;Okkan & Serbes, 2012). Previously, there were many studies of data-driven hydrological models for exploring the contributions of climate variables on concurrent variations of runoffs (Abatzoglou & Ficklin, 2017;Yang et al, 2014;Conticello et al, 2020). For instance, a neural network model was employed to simulate the hydrologic response of climate sequences (Khan & Coulibaly, 2006;Nayak et al, 2013).…”

“…More recently, Mathai and Mujumdar (2019) presented a time irreversibility dynamics-based multisite streamflow generating framework to simulate concurrent streamflow sequences at multiple sites in a basin. Conticello et al (2020) explored the synchronization and delay between circulation patterns and high streamflow events using multiple machine learning methods. Oppel and Fischer (2020) assessed the clusters of temporal distributions of rainfalls and their coherence with flood types through one clustering approach based on unsupervised learning.…”