Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

Mercado‐Bettín, Daniel; Clayer, François; Shikhani, Muhammed; Moore, Tadhg; Frías, M. D. Rodríguez; Jackson-Blake, Leah; Sample, James Edward; Iturbide, Maialen; Herrera, Sixto; French, Andrew S.; Norling, Magnus Dahler; Rinke, Karsten; Marcé, Rafael

doi:10.1016/j.watres.2021.117286

Cited by 18 publications

(33 citation statements)

References 37 publications

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“…However, as we did not find a strong sensitivity to seasonal climate, this aim became redundant. In systems which are more sensitive to seasonal climate, a next step would be to assess GBN predictive performance using seasonal climate model hindcasts when making predictions (as in Mercado-Bettín et al, 2021). A comparison of model forecasting skill using seasonal climate model hindcasts versus observed weather would then allow for an assessment of the value of seasonal climate model hindcasts.…”

Section: Operational Forecasting Using Seasonal Climate Forecastingmentioning

confidence: 99%

“…Tools were co-developed with water managers at five catchmentlake case study sites, with four in Europe and one in South Australia (Jackson-Blake et al, 2022). Tools linked seasonal climate forecasts with models for predicting river discharge, lake water level and water temperature (Mercado-Bettín et al, 2021), water quality, algal bloom risk, and fish migration. Here, we describe the model developed to forecast lake water quality at one of the case study sites, Vansjø in Norway.…”

Section: Introductionmentioning

confidence: 99%

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

Jackson-Blake

Clayer

Haande

et al. 2022

Hydrol. Earth Syst. Sci.

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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.

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Section: Operational Forecasting Using Seasonal Climate Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jackson-Blake

Clayer

Haande

et al. 2022

Hydrol. Earth Syst. Sci.

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“…Water quality forecasting tools were developed for four regulated water lakes/reservoirs in Europe and Australia which have been described earlier (Mercado-Bettin et al, 2021;Table 1). Briefly, Sau (Spain) and Mount Bold (Australia) reservoirs large water supplies for the cities of Barcelona and Adelaide, respectively.…”

Section: Case Study Sitesmentioning

confidence: 99%

“…quality in lakes and reservoirs has been limited so far, where the focus has been on water quantity (Arnal et al, 2018;Giuliani et al, 2020;Greuell et al, 2019;Pechlivanidis et al, 2020). Studies forecasting water temperature, a fundamental water quality variable, are rare in the literature (Mercado-Bettin et al, 2021;Zhu et al, 2020), despite the diverse influence of this variable on lake ecosystem structure and functioning (Dokulil et al, 2021). Nevertheless, a simple lumped model (air2water; Piccolroaz et al, 2013), previously developed to estimate surface lake water temperature as a function of air temperature, has been applied to predict water temperature in thousands of lakes (Zhu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Inertia and seasonal climate prediction as sources of skill in lake temperature, discharge and ice-off forecasting tools

Clayer

Jackson-Blake²,

Mercado‐Bettín³

et al. 2022

Preprint

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Abstract. Despite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lake and reservoir set up at four sites in Australia and Europe. These tools, as previously presented, consist of coupled hydrological catchment and lake models forced with seasonal climate forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools’ predictive skill and an apportionment of the predictability between legacy effects and input data. To this end, models were forced with two meteorological datasets from the European Centre for Medium Range Weather Forecasts (ECMWF), the seasonal forecasts SEAS5 and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e., seasonal lake hindcasts (forced with SEAS5) and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low, but higher than SEAS5 climate hindcasts. Nevertheless, lake and SEAS5 windows of opportunity were identified, although they were not always synchronous, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the target season. When SEAS5 hindcasts were skillful, additional predictability originates from the interaction between legacy and SEAS5 skill. We conclude that a climatology driven forecast is currently likely to yield higher quality forecasts.

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Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

Wander,

Thomas,

Moore

et al. 2024

Ecosphere

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Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data.

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Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

Cited by 18 publications

References 37 publications

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

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

Inertia and seasonal climate prediction as sources of skill in lake temperature, discharge and ice-off forecasting tools

Data assimilation experiments inform monitoring needs for near‐term ecological forecasts in a eutrophic reservoir

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