Monthly lake oxygen profiles capture seasonal patterns but make depletion-rate estimates timing-sensitive

Approximately monthly oxygen profiles captured broad seasonal changes and the vertical pattern of low-oxygen exposure in a small stratified Alpine lake, but estimates of how quickly deep-water oxygen was disappearing became increasingly sensitive to the timing of observations. Researchers compared three years of hourly sensor measurements with 36 discrete depth profiles collected from Lake Piburg, a dimictic subalpine lake in Austria’s Ötztal Alps, between January 2022 and December 2024. Their results indicate that the appropriate monitoring frequency depends on the oxygen metric: monthly measurements can support seasonal assessments and long-term comparisons, whereas depletion-rate estimates are less robust when observations are widely spaced.

Lake Piburg covers about 0.13 square kilometres, reaches a maximum depth of roughly 24.6 metres and sits at 913 metres above sea level. The lake undergoes pronounced seasonal thermal stratification, with a relatively stable hypolimnion during the stratified period and complete or near-complete overturn during spring and autumn mixing. Its high-frequency dataset came from in situ dissolved-oxygen and temperature loggers deployed at depths from 1 to 24 metres. The lower-frequency dataset consisted of samples collected at 3-metre intervals and measured by Winkler titration. For comparison, the researchers interpolated both datasets onto a common 1-metre depth grid and calculated daily layer-level values.

When the approximately monthly profiles were interpolated through time, their volume-weighted oxygen concentrations followed the major seasonal trajectory in all three operational depth zones. Pearson correlations between the reconstructed monthly series and the hourly-sensor series were 0.928 in the epilimnion, 0.945 in the metalimnion and 0.916 in the hypolimnion. Those strong correlations did not mean the measurements were identical. Root mean square errors were 0.67, 0.84 and 1.09 milligrams per litre, respectively, while mean differences between sensor and reconstructed values were +0.25 milligrams per litre in the upper layer, −0.57 in the middle layer and −0.97 in the deep layer. The hypolimnion therefore showed both the lowest correlation and the largest negative bias among the three layers.

Differences were particularly pronounced at individual depths close to the boundary between the metalimnion and hypolimnion. In an analysis of 229 paired date-depth observations from 29 original sampling dates, the largest root mean square error occurred at 9 metres. The mean sensor-minus-profile difference there was −2.49 milligrams per litre, and the maximum absolute difference reached 6.61 milligrams per litre on 5 November 2024. At 12 metres, the mean difference was −1.42 milligrams per litre. Errors were much smaller at most other depths, including 0.23 milligrams per litre at 24 metres. The study’s authors said that differences in sampling depth, sensor and sampler positions, vertical integration, moving gradients and analytical uncertainty could all contribute, but the available data could not separate those effects quantitatively.

The clearest divergence appeared in estimates of hypolimnetic oxygen depletion rate. The rate was calculated by regressing daily, volume-weighted deep-water oxygen concentration against elapsed time during sustained depletion periods. The full hourly-sensor reference rates were −0.0170, −0.0187 and −0.0319 milligrams per litre per day in 2022, 2023 and 2024, respectively. To isolate the effect of temporal sampling, the researchers repeatedly thinned the hourly record to 7-, 14-, 30- and 60-day intervals, shifting the first sampling date through every possible starting-day offset within fixed annual fitting periods. These schedules produced both faster and slower depletion estimates than the full hourly record, demonstrating that the inferred slope depended on where the sparse observations happened to fall within the depletion period.

The average within-year range of signed deviations from the full-sensor reference expanded sharply as sampling intervals lengthened. It was 8.0 percentage points for 7-day sampling, 20.5 percentage points for 14-day sampling, 47.5 percentage points for 30-day sampling and 48.6 percentage points for 60-day sampling. The increase was not strictly monotonic in every year, and the 60-day result was based on fewer estimable schedules because several long-interval fits relied on only two sampling dates. Traditional regression goodness-of-fit measures could not be calculated for those schedules, so the researchers used deviation from the full hourly-sensor depletion rate as the primary indicator of sensitivity. A separate comparison using the actual monthly sampling dates introduced both frequency and method differences, because the monthly reconstruction relied on discrete Winkler measurements rather than in situ sensors.

Depth-specific counts of days with oxygen below 2 milligrams per litre showed a more robust vertical pattern than the absolute duration of low-oxygen conditions. Counts generally increased with depth, and no daily mean below the threshold appeared at depths shallower than 10 metres in either dataset. The shallowest affected depth in the sensor record was 10 metres in 2022, 11 metres in 2023 and 10 metres in 2024, compared with 12 metres in every year for the reconstructed monthly profiles. Correlations between paired depth-wise counts were 0.947, 0.933 and 0.977 for 2022, 2023 and 2024, respectively, using 24 paired depths per year. Both approaches therefore identified the depths where prolonged oxygen deficits occurred, even when the estimated number of affected days differed.

Those threshold counts cannot be treated as equivalent full-year exposure estimates. The sensor dataset contained 212, 317 and 324 available days in 2022, 2023 and 2024, respectively, while the interpolated monthly record covered 263, 365 and 340 days. Root mean square differences between depth-wise counts were 40.1, 55.9 and 29.7 days, and mean reconstructed-minus-sensor differences were +5.6, −15.0 and −12.3 days. Because the observation windows and missing-data patterns differed, the analysis compared the shape of oxygen exposure through the water column rather than providing an unbiased estimate of annual low-oxygen duration. The authors used the counts descriptively because adjacent depths in a reconstructed profile are not independent replicates.

The findings support metric-specific monitoring rather than a universal optimum sampling interval. Monthly profiles remained informative for seasonal context, historical comparison and locating persistent low-oxygen layers, and they retained much of the broad lake-wide oxygen trajectory. Higher-frequency observations became more valuable when investigators wanted to estimate depletion rates, identify the beginning or end of depletion, detect reoxygenation or characterize short-lived changes between visits. The study did not establish whether monthly observations alone would identify the same depletion windows as the hourly record. Instead, the researchers selected fitting periods from the high-frequency data and held them fixed during subsampling, so the analysis isolated sensitivity within a known depletion interval rather than testing the detection of that interval.

Generalization beyond Lake Piburg remains uncertain. The study examined one small lake over three years, whereas oxygen dynamics can differ with depth, trophic state, mixing regime, climate and the timing of stratification. The authors therefore described their results as evidence that sampling frequency should be matched to the metric being estimated, not as a prescription for all lakes. For Lake Piburg specifically, the study indicates that monthly profiling can remain valuable for broad seasonal and long-term assessment, while denser observations provide more stable estimates of deep-water oxygen loss and better resolve rapid depletion and replenishment events. The findings also preserve the value of historical monthly records, which were collected before continuous sensors became practical for routine lake monitoring.

Subject of Research: Effects of monitoring frequency on dissolved-oxygen metrics in a seasonally stratified lake

Article Title: Robustness of oxygen metrics to sampling frequency in a stratified lake

Article References: Weniger, E., & Sommaruga, R. (2026). Robustness of oxygen metrics to sampling frequency in a stratified lake. Environmental Monitoring and Assessment, 198(11), Article 1239. https://doi.org/10.1007/s10661-026-16081-8

Image Credits: AI Generated

DOI: 10.1007/s10661-026-16081-8

Keywords: lake monitoring, dissolved oxygen, sampling frequency, oxygen depletion, high-frequency sensors, depth profiles, Lake Piburg, environmental sensors, water quality, seasonal stratification, Robustness, oxygen