Hyperpycnal flows control the persistence and flushing of hypoxic high conductivity bottom water in a High Arctic lake

Abstract

Abstract:In the deepest portions of many lakes, zones of high-conductivity bottom water (HCBW) depleted in dissolved oxygen (DO) are present. HCBW and DO are important for determining benthic diversity and abundance, nutrients, and contaminant cycling and understanding the long-term evolution of lakes. We investigate the persistence and removal of HCBW and DO replenishment in a High Arctic lake using physical properties and flow velocity data along with hydrometric and suspended sediment inflow data over a 4 year monitoring period (2007)(2008)(2009)(2010) , inflowing river water was periodically denser than lake water; however, HCBW was not removed in these years. Hyperpycnal flows were likely either of insufficient strength or duration, deposited on the delta front, or followed paths that led away from the deepest portion of the lake. Results suggest that hyperpycnal flow frequency will increase and HCBW persistence will decrease with projected climate change due to an increase in fluvial SSC inflow. Water density changes resulting from increased electrical conductivity and water temperature are not likely to have a similarly large effect on hyperpycnal flow frequency and HCBW.

The hyperpycnite problem

“…During the past four decades, there has been an accelerated effort to understand these density plumes through (1) observational and/or interpretational (Arnau et al 2004;Bhattacharya and MacEachern 2009;Collins et al 2017;Gihm and Hwang 2016;Johnson et al 2001;Lewis et al 2018;Luo et al 2017;Milliman et al 2007;Mulder et al 2003;Mutti et al 1996;Ogston et al 2000;Pan et al 2017;Petter and Steel 2006;Pierce 2012;Puig et al 2014;Schillereff et al 2014;Shanmugam 2016aShanmugam , 2018aShanmugam , 2018bShanmugam , 2018cSoyinka and Slatt 2008;Steel et al 2016Steel et al , 2018Sun et al 2016;Talling 2014;Warrick et al 2013;Schieber 2014, 2017;Wright et al 1986Wright et al , 1988Yang et al 2017a;Zavala and Arcuri 2016;Zavala and Pan 2018;Zavala et al 2006; among others), (2) experimental (Kostic and Parker 2003;Kostic et al 2002;Lamb and Mohrig 2009;Lamb et al 2010;Parsons et al 2001), and (3) numerical (Chen et al 2013;…”