Geothermal steam used for power production contains significant quantities of volatile mercury. Much of this mercury escapes to the atmosphere as elemental mercury vapor in cooling tower exhausts. Mercury emissions from geothermal power plants, on a per megawatt (electric) basis, are comparable to releases from coal-fired power plants.
Hydrological post-processors refer here to statistical models that are applied to hydrological model predictions to further reduce prediction errors and to quantify remaining uncertainty. For streamflow predictions, post-processors are generally applied to daily or sub-daily time scales. For many applications such as seasonal streamflow forecasting and water resources assessment, monthly volumes of streamflows are of primary interest. While it is possible to aggregate post-processed daily or sub-daily predictions to monthly time scales, the monthly volumes so produced may not have the least errors achievable and may not be reliable in uncertainty distributions. Post-processing directly at the monthly time scale is likely to be more effective. In this study, we investigate the use of a Bayesian joint probability modelling approach to directly post-process model predictions of monthly streamflow volumes. We apply the BJP post-processor to 18 catchments located in eastern Australia and demonstrate its effectiveness in reducing prediction errors and quantifying prediction uncertainty
Statistical methods traditionally applied for seasonal streamflow forecasting use predictors that represent the initial catchment condition and future climate influences on future streamflows. Observations of antecedent streamflows or rainfall commonly used to represent the initial catchment conditions are surrogates for the true source of predictability and can potentially have limitations. This study investigates a hybrid seasonal forecasting system that uses the simulations from a dynamic hydrological model as a predictor to represent the initial catchment condition in a statistical seasonal forecasting method. We compare the skill and reliability of forecasts made using the hybrid forecasting approach to those made using the existing operational practice of the Australian Bureau of Meteorology for 21 catchments in eastern Australia. We investigate the reasons for differences. In general, the hybrid forecasting system produces forecasts that are more skilful than the existing operational practice and as reliable. The greatest increases in forecast skill tend to be (1) when the catchment is wetting up but antecedent streamflows have not responded to antecedent rainfall, (2) when the catchment is drying and the dominant source of antecedent streamflow is in transition between surface runoff and base flow, and (3) when the initial catchment condition is near saturation intermittently throughout the historical record
The eReefs initiative is developing a series of marine hydrodynamic and biogeochemical models that will model and provide forecasts of rainfall and flooding impacts on the Great Barrier Reef. These models require real-time predictions and forecasts of riverine inflows and associated concentrations of fine sediments, speciated nutrients and carbon at each time step. This paper describes and evaluates one possible approach to the generation of water quantity and quality predictions and forecasts by linking ensemble streamflow forecasts and empirical Generalised Additive Models (GAMs). Forecasts of daily sediment and nutrient concentrations are generated by forcing GAMs with hourly streamflow forecasts that have been aggregated to daily totals. The streamflow and water quality forecasts are evaluated for over a 24month period concluding in December 2013. The ensemble streamflow forecasts have considerably lower errors than simple persistence, which is used as input for the prototype marine models in forecasting mode. This suggests that marine modellers can potentially improve their simulations by using the streamflow forecasts in place of simple persistence. The ensemble forecasts of nutrient concentrations however display large errors, often significantly overestimating the observed values, which may limit their value for marine modelling. Errors in sediment and nutrient concentration forecasts, and the forecast uncertainties tend to be largest when the GAMS are extrapolating beyond the range of observations used to fit the GAMS model. Therefore improvements in the performance of sediment and nutrient concentration forecasts are most likely to be realised by fitting the GAMS to a larger set of either modelled or observed data.
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