Streamflow Prediction

BACKGROUND

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.

Table 1 – RAL projects linked to water management and streamflow forecasting.
Table 1 – RAL projects linked to water management and streamflow forecasting. 

Selected projects are described in more detail below.

Development and evaluation of extended range ensemble streamflow and water resources forecast products for the National Water Model

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)

Figure 2 – Depiction of snow water equivalent simulated on a HUC12 scale to by a coarser configuration of the National Water Model that can support ensemble streamflow predictions (inset).
Figure 1 – Depiction of snow water equivalent simulated on a HUC12 scale to by a coarser configuration of the National Water Model that can support ensemble streamflow predictions (inset).

ASSESSING THE VIABILITY OF OVER-THE-LOOP STREAMFLOW FORECASTING TO SUPPORT RISK-BASED WATER, ENERGY, AND HAZARD MANAGEMENT

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.

FY2018 Accomplishments

  • Running the SHARP system to explore new techniques for hydrologic data assimilation (DA), and in particular, the use of a particle filter to assimilate streamflow for improved hydrologic state estimation, and a novel analog-particle-filter approach to avoid DA challenges such as filter collapse.
  • Generating reservoir inflow hindcasts for the Klamath River basin and the Crooked River watersheds of Oregon to support a Bureau of Reclamation effort to develop forecast-informed reservoir operating rules.
  • Developing distributed regional watershed forecasting models using an implementation of the SUMMA model on the USGS HUC12 in the Western US.
  • Initial explorations of the ability of the pseudo-dynamical Intermediate Complexity Atmospheric Research (ICAR) model for downscaling regional weather predictions.
  • Implementation of SUMMA-based SHARP outputs in an online display via the NCAR HydroInspector tool.

Development of new parameter optimization techniques using Ostrich

Publications

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).

Figure 3 – Examples of SHARP forecast system elements and outputs.  Top left: An ensemble member of daily precipitation at the USGS Geospatial Fabric resolution using the SUMMA modeling implementation.  Top right: An over-the-loop ensemble forecast from the SHARP system for the Hungry Horse Reservoir, compared with a forecast from the River Forecast Center.  Bottom: A forecast display using the HydroInspector to show routed streamflow along the Geospatial Fabric.
Figure 2 – Examples of SHARP forecast system elements and outputs.  Top left: An ensemble member of daily precipitation at the USGS Geospatial Fabric resolution using the SUMMA modeling implementation.  Top right: An over-the-loop ensemble forecast from the SHARP system for the Hungry Horse Reservoir, compared with a forecast from the River Forecast Center.  Bottom: A forecast display using the HydroInspector to show routed streamflow along the Geospatial Fabric.

FY2018 Plans

  • Build out the SHARP real-time medium range forecast evaluation domain to the western US (Reclamation service area), including real-time 1/16th degree ensemble forcings.
  • Apply forecasting techniques developed in pilot watersheds to regional modeling efforts using SUMMA.
  • Demonstrate the value of NCAR modeling and prediction tools via SHARP, including medium-range forecasts based on ICAR downscaling of GEFS, SUMMA and Mizuroute modeling and routing, and model calibration through the Multiscale Parameter Regionalization.
  • Continue to raise awareness and promote discussion about tradeoffs in modeling and streamflow forecasting approaches within the research, operational and management communities.
Figure 4: Schematic of the components of SHARP, a research system now running in real-time at NCAR to produce daily-updating ensemble flood forecasts (7-day lead times) and bi-weekly seasonal streamflow predictions for a small number of pilot basins in the Western US.
Figure 3: Schematic of the components of SHARP, a research system now running in real-time at NCAR to produce daily-updating ensemble flood forecasts (7-day lead times) and bi-weekly seasonal streamflow predictions for a small number of pilot basins in the Western US.

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.  

IMPROVING SUB-SEASONAL TO SEASONAL STREAMFLOW PREDICTIONS IN THE LOWER COLORADO AND RIO GRANDE RIVER BASINS

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).

Figure 5: (a) March-July mean temperature anomalies relative to 1982-2016 from observations, 40 CMIP5 models, and seasonal prediction models (NMME+ECMWF), averaged over the box indicated in Fig. 1a. The red line is the mean across NMME-ECMWF models, the gray line is the mean across CMIP5 models, and the black line is observations. Shading indicates the 5-95% range. (b) Correlation between observed and forecasted temperature for different temperature targets and seasonal prediction models for 1982-2016. Forecasts are initialized at the start of each predicted period. All correlations are significant at 95% confidence.
Figure 4: (a) March-July mean temperature anomalies relative to 1982-2016 from observations, 40 CMIP5 models, and seasonal prediction models (NMME+ECMWF), averaged over the box indicated in Fig. 1a. The red line is the mean across NMME-ECMWF models, the gray line is the mean across CMIP5 models, and the black line is observations. Shading indicates the 5-95% range. (b) Correlation between observed and forecasted temperature for different temperature targets and seasonal prediction models for 1982-2016. Forecasts are initialized at the start of each predicted period. All correlations are significant at 95% confidence.

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.

Figure 6: (a) Absolute skill improvement of the temperature-aided forecast relative to the baseline forecast at individual gages for issue date 1 March as an illustrative example. (b) Absolute skill improvement for all gages as a function of issue date. (c) Relative skill improvement for all gages as a function of issue date. Solid lines are the median across (black) all gages and (colors) the three basins. Dashed line is the median across all gages when using observed temperature, mimicking the hypothetical case where the future temperature is known at the time of forecast issue, and dotted line is the median when using only the linear trend of observed temperature.
Figure 5: (a) Absolute skill improvement of the temperature-aided forecast relative to the baseline forecast at individual gages for issue date 1 March as an illustrative example. (b) Absolute skill improvement for all gages as a function of issue date. (c) Relative skill improvement for all gages as a function of issue date. Solid lines are the median across (black) all gages and (colors) the three basins. Dashed line is the median across all gages when using observed temperature, mimicking the hypothetical case where the future temperature is known at the time of forecast issue, and dotted line is the median when using only the linear trend of observed temperature.

Key Findings:

  • Seasonal temperature forecasts from climate prediction models are skillful over the headwaters of the Colorado and Rio Grande River basins
  • Adding temperature information to current operational seasonal streamflow forecasts in snowmelt-driven basins improves forecast skill
  • Temperature forecasts help mitigate impacts of non-stationarity on US Southwest streamflow predictability under increasing temperatures
Figure 7: Runoff ratio at the Rio Grande River at Otowi Bridge (colors) as a function of water year precipitation and annual mean temperature from (a) reconstructions, (b) observations, and (c) CESM control simulation (1,800 years total). All time series are relative to their median; in the case of observations, relative to the median of the reconstructions. The colored numbers give the percentage of very low (< 10th percentile), low (< 30th), high (> 70th), and very high (> 90th) runoff ratio years that fall within a given quadrant of precipitation and temperature anomalies.
Figure 6: Runoff ratio at the Rio Grande River at Otowi Bridge (colors) as a function of water year precipitation and annual mean temperature from (a) reconstructions, (b) observations, and (c) CESM control simulation (1,800 years total). All time series are relative to their median; in the case of observations, relative to the median of the reconstructions. The colored numbers give the percentage of very low (< 10th percentile), low (< 30th), high (> 70th), and very high (> 90th) runoff ratio years that fall within a given quadrant of precipitation and temperature anomalies.

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:

  • The decreasing runoff efficiency trend from 1986-2015 in the Upper Rio Grande River basin is unprecedented in the last 440 years
  • Very low runoff ratios are 2.5 to 3 times more likely when temperatures are above- normal than when they are below-normal
  • The trend arises primarily from natural variability but runoff sensitivity to temperature implies further declines should warming continue

Publications

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

SUB-SEASONAL TO SEASONAL CLIMATE PRODUCTS FOR HYDROLOGY AND WATER MANAGEMENT

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:

  • misalignment of forecast products with users' space-time analysis needs, which often follow specific watershed boundaries;
  • use of data formats that users cannot easily input to their analysis tools;
  • systematic biases in products relative to user climatologies; and
  • verification information that is tailored toward forecast producer diagnostic needs but is unusable or not relevant to water sector users.

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.

Figure 8:  S2S Climate Outlooks for Watersheds website (http://hydro.rap.ucar.edu/s2s/), showing real-time S2S forecasts based on the CFSv2 and NMME operational products.
Figure 7:  S2S Climate Outlooks for Watersheds website (http://hydro.rap.ucar.edu/s2s/), showing real-time S2S forecasts based on the CFSv2 and NMME operational products.

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...).

Publications

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).

SUPPORT FOR INTERNATIONAL STREAMFLOW FORECAST INITIATIVES AND PROGRAMS

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:

  • Hydrologic Ensemble Prediction Experiment (HEPEX:  http://www.hepex.org/) – A. Wood, Co-Chair
  • US CLIVAR Predictability, Predictions, Applications Interface (PPAI) Panel – A. Wood, Co-Chair
  • European Union (EU) Improving Predictions and Management of Hydrological Extremes (IMPREX) – A. Wood, Advisory Board Member
  • ECMWF European Flood Awareness System (EFAS) and Global Flood Awareness System (GLOFAS) Project – A. Wood, Advisory Board Member
  • WMO Hydrological Status and Outlook System (HydroSOS) Initiative – A. Wood, Task Lead
  • AMS 100th Anniversary Monograph
  • CUAHSI Workshop on the Science and Practice of Ensemble Streamflow Prediction

Publications

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