Unaccounted CO2 leaks downstream of a large tropical hydroelectric reservoir

Recent studies show that tropical hydroelectric reservoirs may be responsible for substantial greenhouse gas emissions to the atmosphere, yet emissions from the surface of released water downstream of the dam are poorly characterized if not neglected entirely from most assessments. We found that carbon dioxide (CO2) emission downstream of Kariba Dam (southern Africa) varied widely over different timescales and that accounting for downstream emissions and their fluctuations is critically important to the reservoir carbon budget. Seasonal variation was driven by reservoir stratification and the accumulation of CO2 in hypolimnetic waters, while subdaily variation was driven by hydropeaking events caused by dam operation in response to daily electricity demand. This “carbopeaking” resulted in hourly variations of CO2 emission up to 200% during stratification. Failing to account for seasonal or subdaily variations in downstream carbon emissions could lead to errors of up to 90% when estimating the reservoir’s annual emissions. These results demonstrate the critical need to include both limnological seasonality and dam operation at subdaily time steps in the assessment of carbon budgeting of reservoirs and carbon cycling along the aquatic continuum.

The species composition of rotifers in three hydroelectric reservoirs of western highlands, Central Vietnam

The rotifer fauna in three hydroelectric reservoirs of western highlands, central Vietnam were studied. Among a total of 63 identified species and subspecies, beloging to 23 genera in 15 families, Brachionidae was the most diverse family with 15 taxa recorded (23.81%) followed by Lecanidae (14 taxa, 22.22%), and Synchaetidae (8 taxa, 12.7%). Species accumulation curve and species richness estimators suggested a relatively high level of biodiversity of rotifera assemblages in the studied area. Besides, results on species composition of rotifera community in this study were compared to those of other freshwater bodies in Vietnam using Jaccard similarity index. In particular, the highest similarity was found between reservoirs in western highlands and Phu Ninh lake, Quang Nam Province while the largest difference in species composition was observed between western highlands and Bau Thiem lake, Thua Thien Hue Province.

Understanding how hydrological forecast quality impacts the management of hydroelectric reservoirs

<p>The evaluation of inflow forecast quality and value is essential in hydroelectric reservoir management. Forecast value can be quantified by the economic gains obtained when optimizing hydroelectric reservoir operations informed by weather and hydrological forecasts. This study [1] investigates the impact of 7-day streamflow forecasts on the optimal management of hydroelectric reservoirs and the associated economic gains. Flows from ten catchments in France are synthetically generated over a 4-year period to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts define the inflows to ten hydroelectric reservoirs, which are conceptually parametrized. Each reservoir is associated to a downstream power plant with yield 1 which produces electricity valued with a price signal. The system is modelled using linear programming. Relationships between forecast quality and economic value (hydropower revenue) show that forecasts with a recurrent positive bias (overestimation) and low accuracy generate the highest economic losses when compared to the reference management system where forecasts are equal to observed inflows. The smallest losses are observed for forecast systems with under-dispersion reliability bias, while forecast systems with negative bias (underestimation) show intermediate losses. Overall, the losses (which amount to millions of Euros) represent approximately 1% to 3% of the revenue over the study period. Besides revenue, the forecast quality also impacts spillage, stock evolution, production hours and production rates. For instance, forecasting systems that present a positive bias result in a tendency of operations to keep the storage at lower levels so that the reservoir can be able to handle the high volumes expected. This impacts the optimal placement of production at the best hours (i.e. when prices are higher) and the opportunity to produce electricity at higher production rates. Our study showed that when using biased forecasting systems, hydropower production is not only planned during more hours at lower rates but also at hours with lower median prices of electricity. The modelling approaches adopted in our study are certainly far from representing all the complexity of hydropower management under uncertainty. However, they proved to be adapted to obtaining the first orders of magnitude of the value of inflow forecasts in elementary situations.</p><p>[1] https://doi.org/10.5194/hess-2020-410</p>

Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach

The improvement of a forecasting system and the continuous evaluation of its quality are recurrent steps in operational practice. However, the systematic evaluation of forecast value or usefulness for better decision-making is less frequent, even if it is also essential to guide strategic planning and investments. In the hydropower sector, several operational systems use medium-range hydrometeorological forecasts (up to 7–10 d ahead) and energy price predictions as input to models that optimize hydropower production. The operation of hydropower systems, including the management of water stored in reservoirs, is thus partially impacted by weather and hydrological conditions. Forecast value can be quantified by the economic gains obtained with the optimization of operations informed by the forecasts. In order to assess how much improving the quality of hydrometeorological forecasts will improve their economic value, it is essential to understand how the system and its optimization model are sensitive to sequences of input forecasts of different quality. This paper investigates the impact of 7 d streamflow forecasts of different quality on the management of hydroelectric reservoirs and the economic gains generated from a linear programming optimization model. The study is based on a conceptual approach. Flows from 10 catchments in France are synthetically generated over a 4-year period to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts define the inflows to 10 hydroelectric reservoirs, which are conceptually parameterized. Relationships between forecast quality and economic value (hydropower revenue) show that forecasts with a recurrent positive bias (overestimation) and low accuracy generate the highest economic losses when compared to the reference management system where forecasts are equal to observed inflows. The smallest losses are observed for forecast systems with underdispersion reliability bias, while forecast systems with negative bias (underestimation) show intermediate losses. Overall, the losses (which amount to millions of Euros) represent approximately 1 % to 3 % of the revenue over the study period. Besides revenue, the quality of the forecasts also impacts spillage, stock evolution, production hours and production rates, with systematic over- and underestimations being able to generate some extreme reservoir management situations.

Generation of prognostic interval estimates of water inflows to hydroelectric reservoirs using multiparametric neural network

The article discusses the practical application of the neural network for hydropower and water management systems. Various models of neural networks are understood, their advantages and disadvantages for a particular subject area. Method and operation of multiparametric neural network are described using practical examples, in particular, formation of interval estimates in reservoir of hydroelectric power station.