Abstract. Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency's 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-tovolume ratio) were important for explaining water quality (54-60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28-39% variance explained). Basin-scale land use and land cover explained between 45-62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.

Ecosystem metabolism and the contribution of carbon dioxide from lakes to the atmosphere can be estimated from free-water gas measurements through the use of mass balance models, which rely on a gas transfer coefficient (k) to model gas exchange with the atmosphere. Theoretical and empirically based models of k range in complexity from wind-driven power functions to complex surface renewal models; however, model choice is rarely considered in most studies of lake metabolism. This study used high-frequency data from 15 lakes provided by the Global Lake Ecological Observatory Network (GLEON) to study how model choice of k influenced estimates of lake metabolism and gas exchange with the atmosphere. We tested 6 models of k on lakes chosen to span broad gradients in surface area and trophic states; a metabolism model was then fit to all 6 outputs of k data. We found that hourly values for k were substantially different between models and, at an annual scale, resulted in significantly different estimates of lake metabolism and gas exchange with the atmosphere.

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Algae blooms have been recorded in Lake Maninjau in November 2000, October 2011 and recently on April 2018. These blooms were indicated by green scum formation on the lake surface with a very high chlorophyll-a concentration, as high as > 100 µg-L. We determined the characteristics of phytoplankton composition and abundance including environmental conditions during cyanobacterial blooms and non-cyanobacterial blooms in Lake Maninjau. During cyanobacterial blooms, phytoplankton were dominated by Microcystis aeruginosa with a maximum abundance of 24,320 x 103 individual L-1 (94.4 % of the total assemblage). While during the non-blooming period, cyanobacteria species were more diverse, represented by Cylindrospermopsis raciborskii, Anabaea affinis, Aphanizomeon sp, Planktolyngbya sp. and Chroococcus sp. Diatom (Synedra ulna) generally occurred in all conditions, however, desmids (green algae) disappeared during cyanobacteria blooms. It is highlighted that the occurrence of Microcystis blooms can be related to total phosphorous dynamics in the lake.

Lakes are known to be important to the global carbon balance as they are both CO2 sources to the atmosphere and also accumulate large amounts of carbon in their sediment. CO2 flux dynamics across air-water interface in 11 lakes of varying trophic state in the Rotorua region, New Zealand, derived from measured alkalinity, pH and wind speed at given temperature showed that lakes may shift from being atmospheric CO2 sources to sinks due to seasonal changes in phytoplankton productivity and lake mixing dynamics. Decreases in trophic state (i.e., improved water quality) in some of the lakes over the eight-year monitoring period were associated with increased surface water CO2 concentrations and as a consequence, increasing CO2 flux to the atmosphere. Organic carbon content analysis collected from bottom sediments revealed that lakes with high phytoplankton productivity, indicated by high chlorophyll a biomass, generally had high rates of carbon deposition to the sediments, but not all deposited carbon was permanently buried. Remineralization of the organic carbon accrual in productive lakes may potentially generate CO2, as well as CH4, in which this promotes lakes to act as greenhouse gas emitters.

A Bayesian Belief Network, validated using past observational data, is applied to conceptualize the ecological response of Lake Maninjau, a tropical lake ecosystem in Indonesia, to tilapia cage farms operating on the lake and to quantify its impacts to assist decision making. The model captures ecosystem services trade-offs between cage farming and native fish loss. It is used to appraise options for lake management related to the minimization of the impacts of the cage farms. The constructed model overcomes difficulties with limited data availability to illustrate the complex physical and biogeochemical interactions contributing to triggering mass fish kills due to upwelling and the loss in the production of native fish related to the operation of cage farming. The model highlights existing information gaps in the research related to the management of the farms in the study area, which is applicable to other tropical lakes in general. Model results suggest that internal phosphorous loading (IPL) should be recognized as one of the primary targets of the deep eutrophic tropical lake restoration efforts. Theoretical and practical contributions of the model and model expansions are discussed. Short- and longer-term actions to contribute to a more sustainable management are recommended and include epilimnion aeration and sediment capping.

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One of the key requirements of successful water quality management in lakes and reservoirs is a good understanding of the underlying processes within the system. Lake managers, however, need a very simple practical tool to support quality regulation and policy implementation in terms of protecting and restoring these ecosystems. Here, we communicate a starting point from which lake managers, particularly in Indonesia, can gain a better understanding of aquatic ecosystem processes through the integrated application of different models. Until now, numerical aquatic ecosystem models have been used rarely in designing lake and reservoir restoration programs in Indonesia. We highlight the importance of model applications, while noting the difficulties of advancing management plans for Indonesian lakes and reservoirs.