Abstract. 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, mean total phosphorus (TP) and chlorophyll a (chl a) concentration, mean water colour, and maximum cyanobacteria biovolume for the upcoming growing season (May–October) in Vansjø, a shallow nutrient-rich lake in southeastern Norway. To develop the model, we first identified controls on interannual variability in seasonally aggregated water quality. These variables were then included in a GBN, and conditional probability densities were fit using observations (≤39 years). GBN predictions had R2 values of 0.37 (chl a) to 0.75 (colour) and classification errors of 32 % (TP) to 17 % (cyanobacteria). For all but lake colour, including weather variables did not improve the 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 sensibly parameterised using only the observed data, despite the small dataset. Developing a comparable discrete BN was much more subjective and time-consuming. Although low interannual variability and high temporal autocorrelation in the study lake meant the GBN performed only slightly better than a seasonal naïve forecast (where the forecasted value is simply the value observed the previous growing season), we believe that the forecasting approach presented here could be particularly useful in areas with higher sensitivity to catchment nutrient delivery and seasonal climate and for forecasting at shorter (daily or monthly) timescales. 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 with continuous variables. 

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

Abstract Water temperature, ice cover, and lake stratification are important physical properties of lakes and reservoirs that control mixing as well as bio-geo-chemical processes and thus influence the water quality. We used an ensemble of vertical one-dimensional hydrodynamic lake models driven with regional climate projections to calculate water temperature, stratification, and ice cover under the A1B emission scenario for the German drinking water reservoir Lichtenberg. We used an analysis of variance method to estimate the contributions of the considered sources of uncertainty on the ensemble output. For all simulated variables, epistemic uncertainty, which is related to the model structure, is the dominant source throughout the simulation period. Nonetheless, the calculated trends are coherent among the five models and in line with historical observations. The ensemble predicts an increase in surface water temperature of 0.34 K per decade, a lengthening of the summer stratification of 3.2 days per decade, as well as decreased probabilities of the occurrence of ice cover and winter inverse stratification by 2100. These expected changes are likely to influence the water quality of the reservoir. Similar trends are to be expected in other reservoirs and lakes in comparable regions. 

/pdf/ensemble-of-models-shows-coherent-response-of-a-reservoirs-u64tl5jh.pdf

Ensemble of models shows coherent response of a reservoir’s stratification and ice cover to climate warming

Abstract. Heat storage within the Earth system is a fundamental metric for understanding climate change. The current energy imbalance at the top of the atmosphere causes changes in energy storage within the ocean, the atmosphere, the cryosphere, and the continental landmasses. After the ocean, heat storage in land is the second largest term of the Earth heat inventory, affecting physical processes relevant to society and ecosystems, such as the stability of the soil carbon pool. Here, we present an update of the continental heat storage, combining for the first time the heat in the land subsurface, inland water bodies, and permafrost thawing. The continental landmasses stored 23.8 ± 2.0 × 1021 J during the period 1960–2020, but the distribution of heat among the three components is not homogeneous. The sensible diffusion of heat through the ground accounts for ∼90 % of the continental heat storage, with inland water bodies and permafrost degradation (i.e. latent heat) accounting for ∼0.7 % and ∼9 % of the continental heat, respectively. Although the inland water bodies and permafrost soils store less heat than the solid ground, we argue that their associated climate phenomena justify their monitoring and inclusion in the Earth heat inventory.


Continental heat storage: contributions from the ground, inland waters, and permafrost thawing

Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring data set covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68% of the cyanobacterial biomass proliferation, while lake warming contributed to 32%, including direct effects via promoting growth (18%) and synergistic effects via intensifying internal P loading (14%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes.

Synergistic Effects of Warming and Internal Nutrient Loading Interfere with the Long-Term Stability of Lake Restoration and Induce Sudden Re-eutrophication.

Lake Ice Will Be Less Safe for Recreation and Transportation Under Future Warming

Abstract. 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 for ISIMIP phases 2 and 3. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various representative greenhouse gas concentration pathways (RCPs), 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 using uncalibrated models for 17 500 lakes defined for all global grid cells containing lakes. 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. 

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

Abstract. Advance warning of seasonal conditions has the potential to assist water management in planning and risk mitigation, with large potential social, economic, and ecological benefits. In this study, we explore the value of seasonal forecasting for decision-making at five case study sites located in extratropical regions. The forecasting tools used integrate seasonal climate model forecasts with freshwater impact models of catchment hydrology, lake conditions (temperature, water level, chemistry, and ecology), and fish migration timing and were co-developed together with water managers. To explore the decision-making value of forecasts, we carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management. Reductions in forecast uncertainty and a need to develop practical, hands-on experience were identified as key requirements before forecasts would be used in operational decision-making. Seasonal climate forecasts provided little added value to freshwater forecasts in these extratropical study sites, and we discuss the conditions under which seasonal climate forecasts with only limited skill are most likely to be worth incorporating into freshwater forecasting workflows. 

/pdf/opportunities-for-seasonal-forecasting-to-support-water-er51mbax.pdf

Opportunities for seasonal forecasting to support water management outside the tropics

&amp;lt;p&amp;gt;Global surface water bodies suffer multitude of human induced pressures that lead to the deterioration of water status through conditions of anoxia, eutrophication, pollution, depletion, among others, which compound water scarcity. Climatic and socio-economic changes will most likely exacerbate the water crises. However, effective water governance strategies can be deployed to mitigate water crisis and guarantee sustainable water use and ecosystem health.&amp;amp;#160; Such strategies can be developed and implemented using a holistic modelling approach.&amp;amp;#160; In this study, we aim at developing &amp;amp;#8211; through re-analysis and modelling &amp;amp;#8211; an adaptive water governance framework that can be utilized to ensure sustainable water-use and ecosystem health. The study will be piloted at the M&amp;amp;#246;hne reservoir in the North Rhine-Westphalia state, Germany &amp;amp;#8211; representing a multi-decadal time machine for hydroclimatic changes, socioeconomic dynamics and water governance by the Ruhrverband. The study will address five key questions; i) How did the drivers and pressures of the reservoir water status change over time? ii) How did the water governance structures change/respond over time? iii) How did the water quality and quantity change over time? iv) What are the likely future drivers, pressures and governance structures/responses? v) Are there strategies to compare with or transfer to/from other systems?&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In order to reconstruct the drivers, pressures, status and impacts of the M&amp;amp;#246;hne reservoir waters, with the respective management and policy responses over time, we start by re-analyzing the multi-decadal trajectory of a few representative variables. In this poster we highlight this using air temperature and precipitation, reservoir water level and population changes in the catchment.&amp;lt;/p&amp;gt; 

Fatima Kakkar

Papers

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

Opportunities for seasonal forecasting to support water management outside the tropics

Solutions for existing and future challenges in water governance