A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector

Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various Representative Greenhouse Gas Concentration Pathways, all consistently bias-corrected on a 0.5° × 0.5° global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and for nearly 17,500 lakes using uncalibrated models and forcing data from the global grid where lakes are present. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.

Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network

Freshwater management is challenging, and advance warning that poor water quality was likely, a season ahead, could allow for preventative measures to be put in place. To this end, we developed a Bayesian network (BN) for seasonal lake water quality prediction. BNs have become popular in recent years, but the vast majority are discrete. Here we developed a Gaussian Bayesian network (GBN), a simple class of continuous BN. The aim was to forecast, in spring, total phosphorus (TP), chlorophyll-a (chl-a), cyanobacteria biovolume and water colour for the coming growing season (May–October) in lake Vansjø in southeast Norway. To develop the model, we first identified controls on inter-annual variability in water quality using correlations, scatterplots, regression tree based feature importance analysis and process knowledge. Key predictors identified were lake conditions the previous summer, a TP control on algal variables, a colour-cyanobacteria relationship, and weaker relationships between precipitation and colour and between wind and chl-a. These variables were then included in the GBN and conditional probability densities were fitted using observations (≤ 39 years). GBN predictions had R2 values of 0.37 (cyanobacteria) to 0.75 (colour) and classification errors of 32 % (TP) to 13 % (cyanobacteria). For all but lake colour, including weather nodes did not improve predictive performance (assessed through cross validation). Overall, we found the GBN approach to be well-suited to seasonal water quality forecasting. It was straightforward to produce probabilistic predictions, including the probability of exceeding management-relevant thresholds. The GBN could be purely parameterised using observed data, despite the small dataset. This wasn’t possible using a discrete BN, highlighting a particular advantage of using GBNs when sample sizes are small. Although low interannual variability and high temporal autocorrelation in the study lake meant the GBN performed similarly to a seasonal naïve forecast, we believe the forecasting approach presented could be useful in areas with higher sensitivity to catchment nutrient delivery and seasonal climate, and for forecasting at shorter time scales (e.g. daily to monthly). Despite the parametric constraints of GBNs, their simplicity, together with the relative accessibility of BN software with GBN handling, means they are a good first choice for BN development, particularly when datasets for model training are small.

Utilizing neural networks for image downscaling and water quality monitoring

Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods.

Multiple drivers and extreme events collapse social-ecological systems sooner

The world’s ecosystems are undergoing unprecedented changes due to the impact of climate change and local human activities. A major concern is the possibility of tipping points where ecosystems and landscapes change abruptly to undesirable states. We consider what happens to the timing of tipping points when current stresses strengthen whilst systems experience additional stresses and/or extreme events. We run experiments on four mathematical models that simulate tipping points in lake water quality, the Easter Island community, the Chilika lagoon fishery, and forest dieback. We show that the strongest impacts occur under increasing levels of primary stress, but additional and more extreme stresses in all four models bring the tipping points significantly closer to today. Translating the results to the real world underlines the need for humanity to reduce damaging disturbances and global warming, and to be vigilant for signs that natural systems are degrading more rapidly than previously thought.

Transfer Functions in Volume Rendering of Lake Water Quality Considering Frequency Distribution

To explore the distribution and diffusion of pollutants in lakes, the volume rendering technique was used to express the lake water quality model in three dimensions. Due to the narrow distribution ranges and small spatial differences of the scalar field of the lake water quality mode, the perspective expression of subtle differences in the volume rendering process becomes important but difficult. In view of the foregoing case, this paper proposed transfer functions (TFs) in volume rendering of lake water quality considering the frequency distribution. The frequency distribution of the lake water quality scalar field was counted, the voxel ratios and the coloring probabilities of the frequency ranges were calculated, and then the voxel values were effectively mapped to colors and transparencies according to the coloring probabilities, to realize the refined expression of the differences in the spatial distribution of lake water quality. Experiments showed that the perspective expression of the subtle differences of lake (especially shallow lakes) water quality was improved using this method, which is conducive to analyzing the characteristics and changing laws of lake water quality model.

Reconstructing Ecological Change, Catchment Disturbance, and Anthropogenic Impact over the last 3000 years at Lake Pounui, Wairarapa, New Zealand

<p>Increased eutrophication of freshwater lakes has been attributed to an intensification in agriculture, with global warming projected to further compound the problem. Determining pre-human conditions of a lake has major conservation implications for water quality management, such as setting evidence-based restoration targets. New Zealand’s historical monitoring records of lake water quality are short and typically only begin after the onset of deterioration. Paleolimnology offers a complementary means of evaluating historical trends that predate human impact. This thesis investigates the declining water quality of Lake Pounui. Lake Pounui possesses high ecological integrity, though the lake is experiencing an increased frequency of severe algal blooms. The primary aim of this thesis is to reconstruct the past environment of Lake Pounui using paleolimnological methods to extend the historical monitoring data beyond human arrival. The reconstruction is used to address whether algal blooms are a natural feature of the lake, and examine anthropogenic impact. This study then attempts to identify reference conditions and critical transitions within the lake environment. Using this information possible targets for lake health restoration are discussed. Based on elevated charcoal influx, palynological evidence, and catchment disturbance indicators, such as organic content (Loss-on-Ignition (LOI)), grain size, and micro-X-ray fluorescence (µ-XRF), Māori land clearance was identified at ~450-350 calendar years before present (cal yr BP) (95% confidence interval (CI): 515.2-202.3 cal yr BP). The appearance of Pinus pollen and the diatom Asterionella formosa placed European arrival at ~150 cal yr BP (95% CI: 243-39 cal yr BP). Pre- and post-human environments of Lake Pounui were characterised using diatom analysis, bacterial DNA analysis, and supporting evidence from µ-XRF data. It appears that the lake existed in both a higher nutrient (3000-2100 cal yr BP, 95% CI: 3210-1977 cal yr BP) and lower nutrient (1600-450 cal yr BP, 95% CI: 1737- 389 cal yr BP) state, separated by a period of natural disturbance, which could relate to a combination of earthquakes and increased storminess. During Māori occupation (450-150 cal yr BP, 95% CI: 515-57 cal yr BP) the quality of the lake remained relatively high; however from 150 cal yr BP, the lake appears to become more nutrient enriched, and the cyanobacteria responsible for today’s blooms (Dolichospermum and Phormidium) become abundant. Paleolimnological analysis identified that the decline in water quality seen at Lake Pounui is a trend that has occurred over the last 200 years. Dissimilarity and critical transition analysis support this finding and suggest that rapid decline began just prior to 1950 AD. Based on dissimilarity analysis, the period of Māori occupation provides the most realistic restoration target. Planting native vegetation, ceasing stock grazing, and removing perch should be investigated to control the phytoplankton biomass, at the owner’s discretion.</p>