Operational hydrological forecasting during the 2 IPHEx-IOP campaign – meet the challenge

Authors: TAO, JING - Duke University; WU, DI - National Aeronautics And Space Administration (NASA); GOURLEY, J. - National Oceanic & Atmospheric Administration (NOAA); ZHANG, S. - National Aeronautics And Space Administration (NASA); Crow, Wade; PETERS-LIDARD, C. - National Aeronautics And Space Administration (NASA); BARROS, A. - Duke University

Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2016
Publication Date: 10/1/2016
Citation: Tao, J., Wu, D., Gourley, J., Zhang, S., Crow, W.T., Peters-Lidard, C., Barros, A. 2016. Operational hydrological forecasting during the 2 IPHEx-IOP campaign – meet the challenge. Journal of Hydrology. doi:10.1016/j.jhydrol.2016.02.019.

Interpretive Summary: Hydrologic forecasting of streamflow is important for water resources monitoring and flood prediction within agricultural watersheds. Such forecasts can potentially be enhanced through the more efficient integration of observational data (e.g. remotely-sensed soil moisture estimates, ground-based rain gauge observations and ground-based streamflow observations). This paper demonstrates that such data integration has the potential for significantly improving the quality of streamflow forecasts issued by operational hydrologic forecasters and makes specific recommendations as to how this potential can be reached. This information will eventually be used by hydrologists at the National Weather Service (and elsewhere) to improve the quality of streamflow forecasts available for agricultural regions. Such improved forecasting will, in turn, lead to more efficient water resource management in such areas.

Technical Abstract: 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 skill in complex terrain and to investigate the propagation of uncertainty in quantitative precipitation forecasts (QPFs) and estimates (QPEs) to streamflow forecast uncertainty using a distributed hydrologic model. Specifically, hydrological forecasts for the 24 hour period beginning at 12:00 UTC were issued daily for 12 headwater catchments in the Southern Appalachians with drainage areas ranging from 71 km2 to 520 km2 using the Duke Coupled surface-groundwater Hydrology Model (DCHM) forced by hourly atmospheric fields and QPFs produced by the NASA-Unified Weather Research and Forecasting (NU-WRF) model. Previous day hindcasts forced by radar-based QPEs were used to provide initial conditions (e.g. soil moisture) for the 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 flash flood events during the IOP were predicted by the IPHEx operational testbed results with lead times of up to 6 hours, 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) to improve QPFs through assimilation of satellite-based microwave radiances into NU-WRF; 2) to improve QPEs by merging raingauge observations using simple but effective bias-correction methods, and 3) to improve streamflow forecasts by assimilating river discharge observations into the DCHM using the Ensemble Kalman Filter(EnKF), the fixed-lag Ensemble Kalman Smoother (EnKS), and the Asynchronous EnKF (i.e. AEnKF) data assimilation techniques. The NU-WRF simulations assimilating satellite data resulted in improved QPF spatial patterns; hydrologic simulations forced by ensembles of merged QPEs from satellite and ground-based radar observations produced streamflow hindcasts and associated uncertainty envelope capturing the observations; and, finally, 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 15hrs 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 assimilating 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.