Stochastic simulation of phytoplankton biomass using eighteen years of daily data - predictability of phytoplankton growth in a large, shallow lake.

During the past decades, on-line monitoring of freshwater lakes has developed rapidly. To use high frequency time-series in lake management, novel models are needed that are simple and provide insight into the complexity of phytoplankton dynamics. Chlorophyll a (Chl), a proxy for phytoplankton biomass and environmental drivers were monitored on-line in large, shallow Lake Balaton during the vegetation periods between 2001 and 2018. Growth and non-growth (G and non-G) states of algae were deduced from daily change in Chl. Random forests (RF) were used to find stochastic response rules of phytoplankton to growth-supporting environmental habitat templates. The stochastic G/non-G state was translated into long-term daily biomass dynamics by a deterministic biomass model to assess uncertainty and to distinguish between inevitable and unpredictable blooms. A biomass peak was qualified as inevitable or unpredictable if the lower 95% confidence limit of simulations exceeded or remained at the baseline Chl level, respectively. Compared to a stochastic null model based on monthly Markovian transition probabilities, RF-based models captured wax and wane of biomass realistically. Timing of peaks could be better simulated than their magnitude, likely because habitat templates were primarily determined by light whereas peak sizes might depend on unmeasured processes, such as phosphorus availability. In general, algal growth was favored by wind-induced sediment resuspension that decreased light availability but simultaneously enhanced the P supply. Seasonal temperature and an integral of departures from the "normal" seasonal temperature over 2 to 3 generations were important drivers of phytoplankton growth, whereas short-term (diel and day to day) changes in water temperature appeared to be irrelevant. Four types of years could be distinguished during the study period with respect to algal growth conditions. The present modeling approach can reasonably be used even in highly variable aquatic environments when 3 to 4 years of daily data are available.

Automatic High Frequency Monitoring for Improved Lake and Reservoir Management.

Recent technological developments have increased the number of variables being monitored in lakes and reservoirs using automatic high frequency monitoring (AHFM). However, design of AHFM systems and posterior data handling and interpretation are currently being developed on a site-by-site and issue-by-issue basis with minimal standardization of protocols or knowledge sharing. As a result, many deployments become short-lived or underutilized, and many new scientific developments that are potentially useful for water management and environmental legislation remain underexplored. This Critical Review bridges scientific uses of AHFM with their applications by providing an overview of the current AHFM capabilities, together with examples of successful applications. We review the use of AHFM for maximizing the provision of ecosystem services supplied by lakes and reservoirs (consumptive and non consumptive uses, food production, and recreation), and for reporting lake status in the EU Water Framework Directive. We also highlight critical issues to enhance the application of AHFM, and suggest the establishment of appropriate networks to facilitate knowledge sharing and technological transfer between potential users. Finally, we give advice on how modern sensor technology can successfully be applied on a larger scale to the management of lakes and reservoirs and maximize the ecosystem services they provide.

Balancing between retention and flushing in river networks--optimizing nutrient management to improve trophic state.

River basin management can frequently involve decisive situations, when conflicting interests must be resolved. In the Zala River catchment (Western Hungary) local efforts to improve water quality by reducing algal biomass are not always harmonized with the requirement of sustaining the same objective in its recipient, Lake Balaton. The PhosFate catchment model is a GIS tool designed to estimate the spatial variability and fate of diffuse phosphorus emission during transport. Besides diffuse pollution, a simplified annual hydrologic balance is also calculated. A new module was added to PhosFate that tracked the development of entrained algae during their travel downstream. The extended model was used to simulate the current average algal concentrations in the river network. The numerous small reservoirs and impoundments on the tributaries of the Zala River were identified as the key elements in determining algal biomass, since they fundamentally increase the water residence time (WRT) in the system. Without reservoirs, the short WRT in the drainage network would successfully prevent the development of suspended algal biomass despite the fairly high SRP concentrations. However, the removal of such standing waters is impossible for socio-economic reasons and reducing the overall P load to Lake Balaton would also require increasing WRT in the system. As a resolution to these conflicting interests, a hybrid management strategy was designed to simultaneously reach both goals: (i) switching from WRT to P limitation in reservoirs responsible for most of algal growth, and (ii) optimized deployment of buffer zones and the introduction of best agricultural practices on the remaining majority of the catchment to reduce the overall P load. The suggested management approach could be applied in other river catchments too, due to the extensive presence of reservoirs and impoundments in many stream networks.

Cyanobacteria-mediated internal eutrophication in shallow Lake Balaton after load reduction.

Nutrient loads to large, shallow Lake Balaton have been reduced by 45-50% since mid-1980s. While a delayed, but still surprisingly fast recovery was observed in the hypertrophic western areas of the lake, eutrophication followed sewage diversion from the mesotrophic northern basins. We assessed factors that could lead to this unusual response. The prime reason of the observed biomass increase might be a trend of increasing mean water temperature during late summers and the concurrent invasion of the subtropical cyanobacterium, Cylindrospermopsis raciborskii with superior light and nutrient utilisation capabilities. In the mesotrophic areas, the prerequisite of the unforeseen success of C. raciborskii was the exceptionally high potential of this species to generate internal P load. Specific morphometric features of the lake rather than nutrient loads might substantiate the increasing dominance of the cyanobacterium in these areas. Our results stress the need to consider individual characteristics of aquatic ecosystems during eutrophication management.