Estimates of reservoir methane emissions based on a spatially balanced probabilistic‐survey

Abstract

Global estimates of methane (CH 4 ) emissions from reservoirs are poorly constrained, partly due to the challenges of accounting for intra-reservoir spatial variability. Reservoir-scale emission rates are often estimated by extrapolating from measurement made at a few locations; however, error and bias associated with this approach can be large and difficult to quantify. Here, we use a generalized random tessellation survey (GRTS) design to generate unbiased estimates of reservoir-CH 4 emissions rates (695% CI) for areas below tributary inflows, open-waters, and at the whole-reservoir scale. Total CH 4 emission rates (i.e., sum of ebullition and diffusive emissions) were 4. 2011), partly due to the large range of CH 4 emission rates reported for reservoirs. Some of the variability in reported emission rates among systems can be attributed to differences in water temperature, reservoir age, and productivity (Barros et al. 2011;West et al. 2012), but another potentially large source of variability among studies is differences in how intra-reservoir spatial patterns in emission rates are assessed and incorporated into system-scale estimates. This source of variability is attributable to methodology, rather than true differences in emission rates, and therefore obscures spatial and temporal patterns in emission rates and adds uncertainty to emission rate estimates. The factors that govern CH 4 production and emission from aquatic systems can lead to pronounced spatial patterns in CH 4 emission rates. Methane is produced in reservoirs from the degradation of organic matter under anoxic conditions, therefore one factor that governs CH 4 production rates is the quantity and quality of carbon delivered to the sediments (Sobek et al. 2012). Sediment deposition rates in reservoirs are often greatest in the transition zone between tributaries and open waters where water velocities rapidly decrease and suspended solids consequently settle (Thornton 1990). Also,

The Magnitude and Drivers of Methane Ebullition and Diffusion Vary on a Longitudinal Gradient in a Small Freshwater Reservoir

“…By comparison, ebullition at transects T2 and T3, which are just downstream of FCR's primary inflow, was driven by variables characteristic of riverine and transitional sites in larger reservoirs (Beaulieu et al, ). Ebullition rates at both T2 and T3 sites were driven by a negative association with inflow discharge, and ebullition at T2 also had a positive association with SWI temperature.…”

“…Within reservoirs, CH 4 ebullition and diffusion rates can vary substantially along a longitudinal gradient from the upstream areas near the major inflows to the downstream areas near the dam (Beaulieu et al, , ; Huang et al, ; Sobek et al, ; Tušer et al, ). Ebullition is generally highest in shallow areas upstream, with much lower (but still detectable) rates in downstream areas (Beaulieu et al, , ). Similarly, studies have also demonstrated that the highest diffusion rates occur in shallow upstream sites and decrease toward the deeper sites in larger reservoirs (Beaulieu et al, ; Yang et al, ).…”

“…Ebullition is generally highest in shallow areas upstream, with much lower (but still detectable) rates in downstream areas (Beaulieu et al, , ). Similarly, studies have also demonstrated that the highest diffusion rates occur in shallow upstream sites and decrease toward the deeper sites in larger reservoirs (Beaulieu et al, ; Yang et al, ). This longitudinal heterogeneity has been well documented in large reservoirs (surface area > 0.5 km 2 ; DelSontro et al, ; Demarty et al, ; Marcelino et al, 2015; Soumis et al, ; Tušer et al, ), but it remains unknown how variable CH 4 emissions are on a longitudinal gradient within small reservoirs (surface area < 0.5 km 2 ), which may exhibit less predictable reservoir zonation because of their size.…”

“…Similar to ebullition, diffusion rates are also influenced by multiple factors that can vary spatially (Beaulieu et al, ). For example, increasing near‐surface turbulence and increasing winds can increase the magnitude of CH 4 diffusing across the air‐water interface (Bastviken et al, ; Poindexter et al, ; Poindexter & Variano, ; Yang et al, ).…”

“…Quantifying ecosystem‐scale CH 4 emission rates in combination with their drivers is difficult because of spatial heterogeneity in environmental variables, which necessitates extensive spatial sampling to avoid biased estimates (Beaulieu et al, ; Wik et al, ). Previous studies that have estimated spatial variation in the drivers of CH 4 emissions in reservoirs have primarily been conducted in larger reservoirs (Deemer et al, ).…”