Abstract
Abstract. The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations.
Highlights
The streamflow prediction plays a central role in hydrology because it is an important element for water resources management, the design of hydraulic infrastructures and flood risk mapping (Srinivasan et al, 2010)
Because it is an important component in the terrestrial water budget, streamflow is a direct diagnostic variable measuring the impact of climate changes and human activities that act on a given watershed
In implementation of Soil and Water Assessment Tool (SWAT), a basin is partitioned into multiple subbasins that are divided into hydrologic response units (HRUs), which consist of unique land cover, management, and soil characteristics (Neitsch et al, 2001; Gassman et al, 2007)
Summary
The streamflow prediction plays a central role in hydrology because it is an important element for water resources management, the design of hydraulic infrastructures and flood risk mapping (Srinivasan et al, 2010). In addition to those parameter regionalization approaches, newly developed data assimilation methods are encouraging and are capable to address some issues associated with PUB They are generally based on physical correlations between the neighbouring basins, and they can combine multisource observations to transfer information from gauged to ungauged basins (Sivapalan et al, 2003; Troch et al, 2003; Chen et al, 2011). We present the application of the partitioned update scheme to improve streamflow predictions in ungauged locations by assimilating gauged streamflow This data assimilation algorithm is fully coupled with the distributed hydrological model, i.e. SWAT.
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