Abstract
Abstract. State updating of distributed rainfall-runoff models via streamflow assimilation is subject to overfitting because large dimensionality of the state space of the model may render the assimilation problem seriously under-determined. To examine the issue in the context of operational hydrologic forecasting, we carried out a set of real-world experiments in which streamflow data is assimilated into the gridded Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic-wave routing models of the US National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) via variational data assimilation (DA). The nine study basins include four in Oklahoma and five in Texas. To assess the sensitivity of the performance of DA to the dimensionality of the control vector, we used nine different spatiotemporal adjustment scales, with which the state variables are adjusted in a lumped, semi-distributed, or distributed fashion and biases in precipitation and PE are adjusted at hourly or 6-hourly scale, or at the scale of the fast response of the basin. For each adjustment scale, three different assimilation scenarios were carried out in which streamflow observations are assumed to be available at basin interior points only, at the basin outlet only, or at all locations. The results for the nine basins show that the optimum spatiotemporal adjustment scale varies from basin to basin and between streamflow analysis and prediction for all three streamflow assimilation scenarios. The most preferred adjustment scale for seven out of the nine basins is found to be distributed and hourly. It was found that basins with highly correlated flows between interior and outlet locations tend to be less sensitive to the adjustment scale and could benefit more from streamflow assimilation. In comparison with outlet flow assimilation, interior flow assimilation produced streamflow predictions whose spatial correlation structure is more consistent with that of observed flow for all adjustment scales. We also describe diagnosing the complexity of the assimilation problem using spatial correlation of streamflow and discuss the effect of timing errors in hydrograph simulation on the performance of the DA procedure.
Highlights
Improving flood forecasting has long been an important research topic for natural hazard mitigation (Droegemeier et al, 2000; NHWC, 2002; NRC, 2010; USACE, 2000)
This indicates difficulty of solving the inverse problem in distributed modelling based on the limited information available in the outlet flow data alone, and points out the need for different assimilation strategies to reduce the degrees of freedom associated with the problem
To address the aforementioned issues with data assimilation (DA) into distributed models in an operational setting and to develop an effective assimilation strategy in order to limit the degrees of freedom in DA with distributed models, in this study we investigate the effect of the spatiotemporal scale of adjustment on analysis and prediction of streamflow
Summary
Improving flood forecasting has long been an important research topic for natural hazard mitigation (Droegemeier et al, 2000; NHWC, 2002; NRC, 2010; USACE, 2000). Clark et al (2008) tested the impact of assimilating streamflow at one location on streamflow prediction at other locations in the Wairau River basin in New Zealand by using the ensemble square root Kalman filter (EnSRF) and the distributed model TopNet. In the following, we summarize previous studies that address state and/or parameter identifiability when applying DA techniques to distributed hydrologic modelling. Lee et al (2011) found in a synthetic experiment using the Eldon basin (ELDO2) in Oklahoma that assimilating outlet flow into the gridded SAC via variational assimilation degraded streamflow prediction at interior locations This indicates difficulty of solving the inverse problem in distributed modelling based on the limited information available in the outlet flow data alone, and points out the need for different assimilation strategies to reduce the degrees of freedom associated with the problem. The paper is organised as follows: Sect. 2 describes the methodology including the hydrologic model, the assimilation technique, and the evaluation metrics; Sect. 3 describes the study basins; Sect. 4 describes the multi-basin experiment and presents the results and discussions; and Sect. 5 summarises conclusions and future research recommendations
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