Search for: All records

The dynamics of water and solutes were investigated in two northern bog ponds using sensor networks and discrete water samples. Embedded sensors monitored water level (S), precipitation (P), evaporation (E), water temperature (T) and specific conductivity (SC) in the peatlands and encircled ponds at 30 min time intervals from 2009 to 2015. Pond water chemistry was monitored seasonally from 2000 to 2020. Daily hydrographs and water budgets indicated that both bogs are ombrotrophic systems, perched above the local water table. Although the predominant flowpath for liquid water was precipitation → pond → peatland → underlying glacial deposits, evaporation accounted for 70% to 90% of water losses. High dissolved organic matter (DOM) in the ponds resulted from transient reversals of flowpath and from molecular diffusion across the peatland/pond interface (a tea bag effect). DOM of peatland origin dominated pond water chemistry, regulating the concentration of important metals, major nutrients and the acid–base status of both bog ponds. Elevated concentrations of Fe, Hg and MeHg in the ponds reflected ligand binding by DOM. The formation of DOM‐Fe‐PO₄complexes likely accounted for >3‐fold higher P concentration relative to nearby clearwater lakes. Linear regression of dissolved organic carbon (DOC) against the anion deficit indicated that DOM contributed up to 6.6 mEq of strong acid per gramme carbon in pond waters. Winter maxima in the seasonal cycles of DOC, Ca, Mg, N, P, Hg and MeHg in both bog ponds were attributable, in large part, to salting out during ice formation. We conclude that multiple methods are needed to understand the dynamics of water and solutes in bog ecosystems.

 

The data are associated with the following manuscript: Hanson, P. C., Ladwig, R., Buelo, C., Albright, E. A., Delany, A. D., & Carey, C. (2023). Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake. Lake water and ice observational data and lake bathymetry are from the North Temperate Lakes Long Term Ecological Research program. Brief abstract of the work: To investigate how water quality in Lake Mendota might respond to nutrient pollution reduction, we used computer models to simulate the elimination of phosphorus inputs from the catchment and track water quality change. The data herein are used to drive and calibrate the model. In addition, model code and simulation output are included as "other entities." 

Abstract. Hypolimnetic oxygen depletion during summer stratification in lakes can lead to hypoxic and anoxic conditions. Hypolimnetic anoxia is a water quality issue with many consequences, including reduced habitat for cold-water fish species, reduced quality of drinking water, and increased nutrient and organic carbon (OC) release from sediments. Both allochthonous and autochthonous OC loads contribute to oxygen depletion by providing substrate for microbial respiration; however, their relative contributions to oxygen depletion across diverse lake systems remain uncertain. Lake characteristics, such as trophic state, hydrology, and morphometry, are also influential in carbon-cycling processes and may impact oxygen depletion dynamics. To investigate the effects of carbon cycling on hypolimnetic oxygen depletion, we used a two-layer process-based lake model to simulate daily metabolism dynamics for six Wisconsin lakes over 20 years (1995–2014). Physical processes and internal metabolic processes were included in the model and were used to predict dissolved oxygen (DO), particulate OC (POC), and dissolved OC (DOC). In our study of oligotrophic, mesotrophic, and eutrophic lakes, we found autochthony to be far more important than allochthony to hypolimnetic oxygen depletion. Autochthonous POC respiration in the water column contributed the most towards hypolimnetic oxygen depletion in the eutrophic study lakes. POC water column respiration and sediment respiration had similar contributions in the mesotrophic and oligotrophic study lakes. Differences in terms of source of respiration are discussed with consideration of lake productivity and the processing and fates of organic carbon loads.

 

This repository includes the setup and output from the analysis ran on Lake Mendota to explore the trophic cascade caused by invasion of spiny water flea in 2010. Scripts to run the model are located under /src, and the processed results for the discussion of the paper are located under /data_processed.

 

Species invasions can disrupt aquatic ecosystems by re‐wiring food webs. A trophic cascade triggered by the invasion of the predatory zooplankter spiny water flea (Bythotrephes cederströmii) resulted in increased phytoplankton due to decreased zooplankton grazing. Here, we show that increased phytoplankton biomass led to an increase in lake anoxia. The temporal and spatial extent of anoxia experienced a step change increase coincident with the invasion, and anoxic factor increased by 11 d. Post‐invasion, anoxia established more quickly following spring stratification, driven by an increase in phytoplankton biomass. A shift in spring phytoplankton phenology encompassed both abundance and community composition. Diatoms (Bacillaryophyta) drove the increase in spring phytoplankton biomass, but not all phytoplankton community members increased, shifting the community composition. We infer that increased phytoplankton biomass increased labile organic matter and drove hypolimnetic oxygen consumption. These results demonstrate how a species invasion can shift lake phenology and biogeochemistry.

 

Abstract Lake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream–lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake’s epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2–2.5 mg O 2 L −1  day −1 (9–670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream–lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulated littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors. 

Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.