Geochemical Characteristics of Sediment in Tropical Lake Sentani, Indonesia, Are Influenced by Spatial Differences in Catchment Geology and Water Column Stratification

Physical and (bio)chemical processes in the catchment as well as internal lake processes influence the composition of lacustrine sediments. Lake internal processes are a consequence of reactions and fluxes between sediment, porewater and the water column. Due to its separation into four interconnected sub-basins, Lake Sentani, Papua Province, Indonesia, is a unique tropical lake that reveals a wide range of geochemical conditions. The highly diverse geological catchment causes mineralogical and chemical differentiation of the sediment input into each sub-basin. Also, strong morphological differences between the sub-basins result in a unique water column structure for each sub-basin, ranging from fully mixed to meromictic. Given the strong differences in sediment composition and bottom water chemistry among the four sub-basins, Lake Sentani offers a unique chance to study multiple lacustrine systems under identical climate conditions and with a common surface water chemistry. We used sediment cores and water samples and measured physicochemical water column profiles to reveal the geochemical characteristics of the water column, the sediment and pore water for all four sub-basins of Lake Sentani. The chemical composition of the sediment reveals differentiation among the sub-basins according to their sediment input and water column structure. Catchment lithology mainly affects overall sediment composition, whereas pore water chemistry is also affected by water column structure, which is related to basin morphology and water depth. In the meromictic sub-basins the bottom water and sediment pore water appear to form a single continuous system, whereas in those sub-basins with oxygenated bottom water the sediment-water interface forms a pronounced chemical barrier.

An application of Bayesian Belief Networks to assess management scenarios for aquaculture in a complex tropical lake system in Indonesia

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.