Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

Abstract

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

An intercomparison of approaches for improving operational seasonal streamflow forecasts

“…The experiment summarized here did assess the skill of CFSv2 9-month climate forecasts at an earlier stage, but such evaluation has been excluded from this paper because the results did not show significantly higher skill from the CFSv2 forecasts than the CFSR-based empirical predictions, as is consistent with prior skill assessments (e.g., Yuan et al, 2011). Nonetheless, the topic of augmenting hydrologic predictability from dynamical climate forecasts remains an appealing area for future study and comparison, as does the potential for including IHC data assimilation to enhance watershed modelbased predictability (e.g., DeChant and Moradkhani, 2011;Huang et al, 2017). Future work can also explore alternative methodological choices such as multiple hydrological models, different climate datasets, or smaller details such as alternative variable transformations in statistical approaches (e.g., Wang et al, 2012).…”

Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts

“…They report that the initial state of the system plays important role on short-term streamflow forecasting even though the degree of influence varies across seasons, locations, and hydrometeorologic characteristics of the study areas. DeChant and Moradkhani (2011) demonstrate the role of DA in representing accurately the total seasonal flow uncertainty in snow dominated basins through initialization and characterization of the uncertainty in the initial states of the system. However, implementation of the above methods in operational forecasting system is very limited (DeChant and Moradkhani, 2011).…”

Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

“…Several authors have already shown the added value of DA in snow-dominated watersheds to improve the estimation of the state of the watershed (De Lannoy et al, 2012;Dechant and Moradkhani, 2011;Nagler et al, 2008;Slater and Clark, 2006;Andreadis and Lettenmaier, 2006). Some studies have also integrated DA in ensemble forecast systems for relatively short-term (up to 5-10 days) hydrologic forecasts (Abaza et al, 2014(Abaza et al, , 2015He et al, 2012), but studies focusing on longer forecast periods are scarce even though the need exists for water resource managers.…”