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Operational hydrological forecasting during the IPHEx-IOP campaign – Meet the challenge
- Tao, Jing, Wu, Di, Gourley, Jonathan, Zhang, Sara Q., Crow, Wade, Peters-Lidard, Christa, Barros, Ana P.
- Journal of hydrology 2016 v.541 pp. 434-456
- basins, hydrologic models, prediction, radar, rain, rain gauges, rivers, satellites, soil water, stream flow, uncertainty, watersheds, Appalachian region
- An operational streamflow forecasting testbed was implemented during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP) in May–June 2014 to characterize flood predictability in complex terrain. Specifically, hydrological forecasts were issued daily for 12 headwater catchments in the Southern Appalachians using the Duke Coupled surface–groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs (Quantitative Precipitation Forecasts) produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs (Quantitative Precipitation Estimates) were used to provide initial conditions for present day forecasts. This manuscript first describes the operational testbed framework and workflow during the IPHEx-IOP including a synthesis of results. Second, various data assimilation approaches are explored a posteriori (post-IOP) to improve operational (flash) flood forecasting. Although all flood events during the IOP were predicted by the IPHEx operational testbed with lead times of up to 6h, significant errors of over- and, or under-prediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. To improve operational flood prediction, three data-merging strategies were pursued post-IOP: (1) the spatial patterns of QPFs were improved through assimilation of satellite-based microwave radiances into NU-WRF; (2) QPEs were improved by merging raingauge observations with ground-based radar observations using bias-correction methods to produce streamflow hindcasts and associated uncertainty envelope capturing the streamflow observations, and (3) river discharge observations were assimilated into the DCHM to improve streamflow forecasts using the Ensemble Kalman Filter (EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) methods. Both flood hindcasts and forecasts were significantly improved by assimilating discharge observations into the DCHM. Specifically, Nash–Sutcliff Efficiency (NSE) values as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region demonstrating that the assimilation of discharge observations at the basin’s outlet can reduce the errors and uncertainties in soil moisture at very small scales. Success in operational flood forecasting at lead times of 6, 9, 12 and 15h was also achieved through discharge assimilation with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. Analysis of experiments using various data assimilation system configurations indicates that the optimal assimilation time window depends both on basin properties and storm-specific space–time–structure of rainfall, and therefore adaptive, context-aware configurations of the data assimilation system are recommended to address the challenges of flood prediction in headwater basins.