Methods

CH4 data from streams and rivers are widely scattered, as values are often included as end-member in studies focused on other processes or types of ecosystems. Thus, while we sought to be as complete as possible in compiling existing data, some sources have undoubtedly been overlooked. Sources included journal articles, book chapters, dissertations, USGS open file reports, meeting proceedings, and unpublished results provided by individual investigators. Data incorporated into MethDB were strictly limited to surface waters of rivers and streams; values for groundwater, porewater, saturated soils, lakes, reservoirs, wetlands, estuaries, and floodplains were not included. Some papers were excluded because essential supporting information was missing (e.g., units), or extracting data from complex graphs was considered to be unwise. Data sources are listed in the Notes and Comments section below.

Methods

The spatially explicit Lakes, Uplands, Wetlands Integrator (LUWI) model of the NHLD was used to explore the interactions among climate, watershed connections, hydrology and carbon cycling. See Cardille et al. 2007 and Cardille et al. 2009 for details on the LUWI model. See Van de Bogert (2011) for a discussion of how these model outputs are used in conjunction with LUWI to predict the effects on lake carbon cycling under the current and "no freeze" climate scenarios.The climate data used in this modeling effort, precipitation and temperature, were obtained from Minoqua, Wisconsin, USA from 1948-2000. In order to test the effect of a climate without freezing temperatures on lake water and carbon cycling the current climate was modeled in addition to a “no freeze” scenario where a minimum air temperature of 0 degrees Celsius was imposed on the model. Note that Van de Bogert (2011) only focuses on the current and “no freeze” climate scenarios, but these data are representative of four climate scenarios: the current climate (base_minoqua_precip), the scenario where the current climate is artificially floored to zero degrees Celsius (no_below_zero), and the scenarios where the current climate is increased and decreased by 10 degrees Celsius (minus_10_degrees and plus_10_degrees).Furthermore, the temperature and precipitation data that was used for the current climate model runs was broken up into aggregates.The aggregates are the length of the 1948-2000 Minoqua temperature and precipitation data that was used in model runs. A total of seven different aggregates were used for model runs under each of the four climate scenarios. The aggregates include temperature and precipitation data from Minoqua, WI, USA for 1. the complete record from 1948-2000 (1948_2000) 2. the driest year which was 1976 (1976_driest) 3. The wettest year which was 1953 (1953_wettest) 4. the five driest years on record from 1948-2000 (5_driest) 5. the five wettest years on record from 1948-2000 (5_wettest) 6. the five coldest years on record for December, January, and February from 1948-2000 (5_coldest_djf) 7. the five warmest years on record for December, January, and February from 1948-2000 (5_warmest_djf).The volume and timing of precipitation to the region were unchanged between scenarios.Evaporation rates were derived from values obtained from the NTL-LTER study site, Sparkling Lake (46.01, -89.70). Refer to Van de Bogert (2011) for a more complete discussion of model inputs and a discussion of the results of the model output. Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156.Cardille, J.A., Carpenter, S.R., Coe, M.T., Foley, J.A., Hanson, P.C., Turner, M.G., Vano, J.A., 2007. Carbon and water cycling in lake-rich landscapes: Landscape connections, lake hydrology, and biogeochemistry. Journal of Geophysical Research-Biogeosciences 112.Cardille, J.A., Carpenter, S.R., Foley, J.A., Hanson, P.C., Turner, M.G., Vano, J.A., 2009. Climate change and lakes: Estimating sensitivities of water and carbon budgets. Journal of Geophysical Research-Biogeosciences 114.

Methods

Data were collected from two lakes, Sparkling Lake (46.008, -89.701) and Peter Lake (46.253, -89.504), both located in the northern highlands Lake District of Wisconsin and the Upper Peninsula of Michigan over a 10 day period on each lake in July and August of 2007. Refer to Van de Bogert et al. 2011 for limnological characteristics of the study lakes.Measurements of dissolved oxygen and temperature were made every 10 minutes using multiple sondes dispersed horizontally throughout the mixed-layer in the two lakes (n=35 sondes for Sparkling Lake and n=27 sondes for Peter Lake). Dissolved oxygen measurements were corrected for drift.Conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were also measured by a subset of sensors in each lake. Of the 35 sondes in Sparkling Lake, 31 were from YSI Incorporated: 15 of model 600XLM, 14 of model 6920, and 2 of model 6600). The remaining sondes placed in Sparkling Lake were 4 D-Opto sensors, Zebra-Tech, LTD. In Peter Lake, 14 YSI model 6920 and 13 YSI model 600XLM sondes were used.Sampling locations were stratified randomly so that a variety of water depths were represented, however, a higher density of sensors were placed in the littoral rather than pelagic zone. See Van de Bogert et al. 2012 for the thermal (stratification) profile of Sparkling Lake and Peter Lake during the period of observation, and for details on how locations were classified as littoral or pelagic. In Sparkling Lake, 11 sensors were placed within the shallowest zone, 12 in the off-shore littoral, and 6 in each of the remaining two zones, for a total of 23 littoral and 12 pelagic sensors. Similarly, 15 sensors were placed in the two littoral zones, and 12 sensors in the pelagic zone.Sensors were randomly assigned locations within each of the zones using rasterized bathymetric maps of the lakes and a random number generator in Matlab. Within each lake, one pelagic sensor was placed at the deep hole which is used for routine-long term sampling.Note that in Sparkling Lake this corresponds to the location of the long-term monitoring buoy. After locations were determined, sensors were randomly assigned to each location with the exception of the four D-Opto sensor is Sparkling Lake, which are a part of larger monitoring buoys used in the NTL-LTER program. One of these was located near the deep hole of the lake while the other three were assigned to random locations along the north shore, south shore and pelagic regions of the lake. Documentation: Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.