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Boreal lakes are the most abundant lakes on Earth. Changes in acid rain deposition, climate, and catchment land use have increased lateral fluxes of terrestrial dissolved organic matter (DOM), resulting in a widespread browning of boreal freshwaters. This browning affects the aqueous communities and ecosystem processes, and boost emissions of the greenhouse gases (GHG) CH 4 , CO 2 , and N 2 O. In this study, we predicted biotic saturation of GHGs in boreal lakes by using a set of chemical, hydrological, climate, and land use parameters. For this purpose, concentrations of GHGs and nutrients (organic C, -P, and -N) were determined in surface water samples from 73 lakes in south-eastern Norway covering wide ranges in DOM and nutrient concentrations, as well as catchment properties and land use. The spatial variation in saturation of each GHG is related to explanatory variables. Catchment characteristics (hydrological and climate parameters) such as lake size and summer precipitation, as well as NDVI, were key determinants when fitting GAM models for CH 4 and CO 2 saturation (explaining 71 and 54%, respectively), while summer precipitation and land use data were the best predictors for the N 2 O saturation, explaining almost 50% of deviance. Our results suggest that lake size, precipitation, and terrestrial primary production in the watershed control the saturation of GHG in boreal lakes. These predictions based on the 73-lake dataset was validated against an independent dataset from 46 lakes in the same region. Together, this provides an improved understanding of drivers and spatial variation in GHG saturation in boreal lakes across wide gradients of lake and catchment properties. The assessment highlights the need to incorporate multiple explanatory parameters in prediction models of GHGs for extrapolation across the boreal biome.
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This content will become publicly available on October 1, 2025
The Critical Role of Hydrological Distance in Shaping Nutrient Dynamics Along the Watershed‐Lake Continuum
Abstract Terrestrial hydrological and nutrient cycles are subjected to major disturbances by agricultural operations and urbanization that profoundly influence freshwater resources. Non‐point source pollution is one of the primary causes for water quality deterioration, and thus an emerging imperative in limnology is establishing empirical models that connect watershed attributes and hydrological drivers with lake nutrient dynamics. Here, we compiled three nation‐wide nutrient, meteorological, and watershed‐landscape data sets, to develop Generalized Linear Models that predict lake phosphorus and nitrogen concentrations as a function of the surrounding watershed characteristics within various hydrological distances across 104 Chinese lakes and reservoirs. Our national‐scale investigation revealed that lake nutrient concentrations can be satisfactorily predicted by proxies of natural drivers and anthropogenic activities, reflecting the properties of the surrounding watershed. Counter to previous studies, we found that China's lake nutrient concentrations strongly depend on watershed characteristics within a hydrological distance of less than 45 km rather than the entire watershed. Furthermore, extensive human activities in watersheds not only compromise our predictive capacity, but also increase the hydrological distance that is relevant to predict lake nutrients. This national‐scale characterization can inform one of the most contentious issues in the context of China's lake management, that is, the determination of the extent of the nearshore area, where nutrient control should be prioritized. As far as we know, our study represents the first attempt to apply the concept of hydrological distance and establish statistical models that can delineate the critical spatial domain primarily responsible for the nutrient conditions along the watershed‐lake continuum.
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- PAR ID:
- 10562285
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Earth's Future
- Volume:
- 12
- Issue:
- 10
- ISSN:
- 2328-4277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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