Complexity of climatic signals in tree-ring width proxies from Yenisei River basin applicable to modeling Arctic river flow and water balance

Abstract:

We approach modeling of river flow from tree-ring widths to be integrated into large-scale modeling of flux variations in time and space from Northern Eurasia watersheds based on the proxy (tree-ring) data and hydrological data (discharge, water temperature, water balance). This specific modeling addresses the global issue of Arctic Amplification and the long-term variability of the flux of freshwater and heat to the Arctic Ocean from large Arctic watersheds.

The case study uses three subsets of ring-width chronologies from the Yenisei River Basin to explore the hydrological signals in the tree-ring proxies from legacy, updated, and simulated networks (Map1).

The assemblage has the common coverage back to 1750 and includes 1) 19 new site chronologies (2019 first year), 2) 30 site chronologies from the International Tree-Ring Data Bank (ITRDB), and 3) 30 pseudo chronologies simulated forward with the Vaganov-Shaskin process-based model (VS-Lite). The climatic signals of the series have been screened with the VS-Lite and Seascorr modeling (Tolwinski-Ward et al. 2011; Meko et al. 2011).

All subsets of ring width chronologies show tremendous variability in strength and structure of signal for precipitation and temperature across the YRB networks. Comparison of two versions of chronologies (standard and residual) does not find much consistency in the fluctuation of the signal strength.

The complexity of growth response to precipitation and temperature is the curse and cure for the modeling. The ranking of climatic signals provides a wealth of new information through which we can screen large diverse networks of tree-ring proxies for field discharge reconstruction via the K-nearest neighbor nonparametric approach (Gangopadhyay et al. 2009). Fields of Water Balance Model runs on the YRB resembles the spatial pattern of chronologies with the strong hydrological signals. The potential of these networks for hydrological modeling is robust.

This research was supported by U.S. National Science Foundation award# 1917503 and #1917515 from OPP-Arctic System Science - Program.

References: Meko et al. Computers & Geosciences 2011, 37: 1234–1241. Tolwinski-Ward et al. Climate Dynamics 2011, 36: 2419–2439. Gangopadhyay et al. Water Resources Research 2009, 45: W06417. Shiklomanov et al. 2020, 10.1007/978-3-030-50930-9.