Scientists and engineers in RAL’s Hydrometeorological Applications Program at the National Center for Atmospheric Research are undertaking research to facilitate the transition of advances in land surface modeling, weather and climate prediction and downscaling, data assimilation, post-processing and other areas into operational streamflow forecasting practice in the US. With strong external funding from Reclamation, US Army Corps of Engineers, NASA, and NOAA, a number of projects are underway that support our nation’s water prediction capabilities and water management. Much of this work centers on the development and application of ensemble forecasting techniques (meteorological and hydrological). Table 1 lists examples of current projects.
Selected projects are described in more detail below.
To improve the NWM’s utility for providing S2S predictions to support extended range water management decisions, we propose developing, transitioning, and testing a low-risk, alternative NWM configuration for long range ensemble (LRE) forecasting that is designed to meet the requirements of the extended range forecast use case. Notably, this configuration would offer the computational agility to apply a full range of community methods (such as additional model calibration methods, large ensembles, hydrologic model ensemble data assimilation, multi-year hindcasting and verification) that have long been relied upon by existing operational extended range forecasting systems in the US and internationally. The LRE configuration will be based on the HUC12 spatial unit (approximately 200,000 small watersheds) with extensions for transboundary rivers in Canada and Mexico. The work will adapt the existing the latest long-range configuration and associated data streams and workflows, as well as existing NWM-supporting methods and infrastructure at NCAR, as a starting point, and target integration (in coordination with other NWM development activities) to enable testing and review at NWC. (Co-Leads / Team – A Wood, B Nijssen, M Clark, D. Gochis, M. Barlage, K. Sampson, A. Dugger, T. Flowers)
The U.S. Army Corps of Engineers and Bureau of Reclamation have jointly sponsored a multi-year project to evaluate and provide a real-time demonstration of the viability of new science-based techniques and strategies for real-time hydrologic flood and drought forecasting in support of real-time water decisions. NCAR has collaborated with both agencies and the University of Washington to develop and run a fully automated streamflow forecast system called SHARP (System for Hydromet Analysis, Research, and Prediction). The system produces real-time ensemble streamflow predictions for lead times of days to seasons, using a variety of weather and climate forecast datasets, hydrologic models, statistical methods and other tools. The project is focused on exploring ways to enhance the physical realism of real-time watershed monitoring and prediction, while still maintaining the computational agility needed to depict uncertainties (e.g., using ensemble techniques). The research will help us demonstrate the tradeoffs of making components of our nation’s hydrologic monitoring and prediction workflows more automated and objective, opening the door to advances such as more complex watershed models and uncertainty-aware products. The overarching goal is to strengthen our nation’s scientific foundation for operational hydrologic prediction to better manage resources and risks in the face of changing weather and climate extremes.
Clark, EA, AW Wood, and B Nijssen, 2017, Assessing ensemble particle filters for the estimation of model states for streamflow forecasting, Wat. Res. Rsrch. (in review).
Mendoza, PA, AW Wood, EA Clark, N Voisin, B Nijssen, MH Ramos, 2017, An assessment of streamflow post-processing techniques for short-range ensemble streamflow forecasts, in prep.
Clark, E. A., A. W. Wood, B. Nijssen, and M. P. Clark, Analog Resampling For Particle Filter Data Assimilation In Hydrologic State Estimation, Hydrol. Earth Syst. Sci (in prep)
Zhu E, X Yuan and AW Wood, 2018, Benchmark Decadal Forecast Skill for Terrestrial Water Storage Estimated by an Elasticity Framework, Nature Geosciences (in review).
Mizukami, N, O Rakovec, A Newman, M Clark, AW Wood, H Gupta, and R Kumar, 2018, On the choice of calibration metrics for “high flow” estimation using hydrologic models, HESS (in review)
Mazrooei, Amirhossein, S Arumugam and AW Wood, 2018, Variational Assimilation of Gauge-Measured Streamflow Records in Monthly Streamflow Simulation and Forecasting, Hydrology and Earth System Sciences (submitted).
Clark, EA, AW Wood, and B Nijssen, and M. P. Clark. Implications of streamflow data assimilation via particle filter on streamflow forecasts in basins with seasonal snow, 2018. Hydrology and Earth System Sciences (in review).
NCAR has teamed with scientists from Purdue and NASA, as well as forecasters from the NWRFC, to use satellite data primarily from the MODIS sensors (see below right) and LandSat to derive estimates of surface ponded water volume and extent, and use them to update real-time streamflow forecasting models. It has upgraded the NASA Land Information System (LIS) -based VIC hydrology model to include surface ponding schemes, and is beginning to evaluate strategies for improving hydrologic simulation and prediction through data assimilation of the ponded water datasets.
Operational streamflow forecasts at daily-to-seasonal lead times are critical to Reclamation’s management of reservoirs in the Colorado River basin that store and allocate water to serve water needs worth billions of dollars annually in seven southwestern states. These forecasts sometime fail to predict the real-time conditions that are later observed by Reclamation operators, which can lead to sub-optimal outcomes for decisions in water operations, especially during extreme events such as droughts or floods. Recent studies show that changes in climatic conditions have resulted in changes to temperature and precipitation patterns throughout the West. Anecdotal evidence suggests that differences between streamflow forecasts versus observation is increasing, perhaps due to the fact that existing forecast methodologies must incorporate increasing variability and uncertainty, and extreme weather events. In addition, some current forecast methods (such as regression-based forecasting) are to some extent dependent on the assumption that climate and weather patterns are stationary over multi-decadal periods, but this is traditional concept has been all but abandoned in the hydrometeorological sciences in recent years.
To address these issues, RAL is working through a project co-funded by Reclamation and NOAA, “Postdocs Applying Climate Expertise” (PACE), to understand the implications of potential climate change in the US Southwest for subseasonal-to-seasonal (S2S) lead forecasts, and to apply this understanding to improve operation long-lead water supply predictions. This effort features close collaboration with Reclamation water managers in the Lower Colorado and Rio Grande River basins, as well as operational forecasting staff at the National Water and Climate Center (NWCC).
This project has applied state-of-the-art climate forecasts from the NOAA NMME and the ECWMF System 4 to show that the effects of temperature non-stationarity on seasonal streamflow prediction may be offset by including forecasts that represent the warming as inputs to the streamflow forecast approach. Work is proceeding to produce improved seasonal streamflow predictions for the Rio Grande basin and to operationalize some of the new forecasting approaches at NWCC and Reclamation offices.
Earlier, the project found, using a paleo analysis, that recent spring runoff declines in the Rio Grande Basin are highly unusual, and that warming temperature trends are contributing to this decline in efficiencies. Additional findings included that:
Lehner, F, ER Wahl, AW Wood, DB Blatchford, and D Llewellyn, 2017, Assessing recent declines in Upper Rio Grande River runoff efficiency from a paleoclimate perspective, Geophys. Res. Lett., 44, doi:10.1002/2017GL073253.
Lehner, F., AW Wood, Llewellyn, D., Blatchford, D. B., Goodbody, A. G. & Pappenberger, F. (2017). Mitigating the impacts of climate non-stationarity on seasonal streamflow predictability in the US Southwest. Geophysical Research Letters, 44. https://doi.org/10.1002/2017GL076043
This project's overarching goal is to improve the understanding and application of S2S climate forecast products in the hydrology and water management sector, and to create an operational initial product generation capability in this area at the NCEP Climate Prediction Center (CPC).
The potential value of the sub-seasonal to seasonal (S2S) prediction has not yet been fully realized by stakeholders in the water management applications sector. Hurdles to adoption, in addition to low forecast skill, Aside from situations in which the S2S forecasts have low skill, include:
In each of these areas, more work can be done to bridge the gap to potential stakeholders and enhance quality, specificity, and accessibility, and thus usability of S2S predictions. This project is addressing the hurdles described above and also investigating opportunities for skill enhancement through forecast post-processing as well as for developing products describing extremes at S2S time scales.
The project will use S2S reforecasts (including CFSv2 and NMME, and other forecast sources) and real-time forecasts to apply and assess various post-processing approaches that may enhance the skill and reliability of the raw climate outputs. It will develop verification data products characterizing the predictability of surface precipitation and temperature predictions at bi-weekly, monthly, and seasonal time steps over the CONUS domain, at watershed-focused USGS Hydrology Unit Code (HUC)-4 and other HUC-spatial units. The benefits of statistical post-processing will be assessed against benchmarks (or baselines) from raw model outputs. For the prediction of extremes potential, the project will apply spatial extremes models using hierarchical Bayesian framework with climate system covariates. A transition plan leading toward experimental operation of the forecast approaches within NOAA in FY18 has also been developed.
More information about this project can be found in a recent UCAR AtmosNews article (https://www2.ucar.edu/atmosnews/in-brief/129967/new-climate-forecasts-fo...).
Baker, SA, AW Wood, and B Rajagopalan, 2018. Developing sub-seasonal to seasonal watershed-scale climate forecast products for hydrology and water management, 2018, J. Amer. Wat. Res. Assoc. (in review).
In 2017, NCAR personnel supported the following national and international streamflow and hydrologic forecasting efforts and conferences through organizational roles, participation, leadership, and/or advisory board membership. These include:
Peters-Lidard, CD, F Hossain, LR Leung, N McDowell, M Rodell, FJ Tapiador, FJ Turk and AW Wood, 2018, One hundred years of progress in hydrology, Chapter 14 in AMS 100th Anniversary Monograph (in review).
Handbook of Hydrometeorological Ensemble Forecasting”, ed. Q Duan, H Cloke, JC Schaake, J Thielen, AW Wood, F Pappenberger. Springer-Verlag GmbH, Berlin Heidelberg (Live Reference ISBN 978-3-642-40457-3), doi:10.1007/978-3-642-40457-3_36-1
Wood, AW, S Arumugam, and P Mendoza, 2018, The post-processing of seasonal streamflow forecasts, Chapter 7.3 in the Handbook of Hydrometeorological Ensemble Forecasting”, ed. Q Duan, H Cloke, JC Schaake, J Thielen, AW Wood, F Pappenberger. Springer-Verlag GmbH, Berlin Heidelberg (Live Reference ISBN 978-3-642-40457-3)
Hopson, TM, AW Wood, and A Weerts, 2018, Motivation and Overview of Hydrological Ensemble Post-processing, Chapter 7.1 in the Handbook of Hydrometeorological Ensemble Forecasting”, ed. Q Duan, H Cloke, JC Schaake, J Thielen, AW Wood, F Pappenberger. Springer-Verlag GmbH, Berlin Heidelberg (Live Reference ISBN 978-3-642-40457-3), doi:10.1007/978-3-642-40457-3_36-1