Evaluating a prototype ensemble water quantity and quality forecasting system for the Fitzroy River Basin

[摘要] The eReefs initiative is developing a series of marine hydrodynamic and biogeochemical 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 report describes and evaluates one possible approach to the generation of water quantity and quality forecasts.The approach generates ensemble streamflow forecasts by integrating semi-distributed hydrological modelling, recent rainfall and streamflow observations, numerical weather predictions and rainfall and streamflow post-processing methods.The methods are consistent with those being implemented operationally by the Bureau of Meteorology as a part of the pilot short-term forecasting services. Streamflow forecasts are produced at hourly time steps for the coming ten days.Generalised Additive Models (GAMs) are developed to relate concurrent and lagged daily streamflow observations to daily sediment and nutrient concentrations.GAMs are established for Total Suspended Solids, Dissolved Organic Nitrogen, Dissolved Organic Phosphorus, Filterable Reactive Phosphorus, Ammonium, Nitrate, Particular Nitrogen and Particulate Phosphorus concentrations.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 24-month period concluding in December 2013.The ensemble streamflow forecasts have considerably lower errors than simple persistence, currently used as input for the 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 display large errors, often significantly overestimating the observed values, which may limit their value for marine modelling.Forecasts of sediment concentrations tend to have smaller errors than for nutrient concentrations.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 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.

[发布日期] 2014-11-20 [发布机构] CSIRO

[学科分类] 地球科学(综合)