Water Resource Applications

BACKGROUND

Scientists and engineers in RAL’s Hydrometeorological Applications Program at the National Center for Atmospheric Research are collaborating with the U.S. Army Corps of Engineers, the Bureau of Reclamation, the National Atmospheric and Oceanic Administration, the National Aeronautics and Space Administration, the Department of Energy, the U.S. Geological Survey, the U.S. Forest Service and multiple universities to build new community hydrologic datasets, models and methods for water resources research and applications that will advance our nation’s capability to monitor, predict and project hydrology and to inform water management and planning.  The work strives to address scientific gaps and serve practical needs across time and space scales – from quantifying long-term trends and variability, to predicting real-time flood and drought risk and characterizing uncertainties arising from a multitude of sources. Through developing improved methods, models, and datasets, this research improves the fundamental building blocks on which hydrometeorological analyses and applications depend. It provides useful tools and data resources for both researchers and practitioners to better manage current climate and flood risk, reveal future climate change risks, and to more effectively evaluate future change and adaptation options.

ACCOMPLISHMENTS

Models, methods, and datasets

In the last 5 years, RAL/HAP scientists have made widely recognized advances in developing models, methods, and datasets. These science advances collectively provide a strong foundation for understanding and adapting to future environmental change, servicing multiple needs for multiple users. The key advances are as follows:

Meteorological forcing data

Moving from deterministic to probabilistic national-domain meteorological datasets. NCAR has further developed the Gridded Meteorological Ensemble Tool (GMET), which generates high-quality, probabilistic gridded meteorological fields that can be used to quantify uncertainty of meteorological forcings useful for climate model evaluation, hydrologic model parameter estimation, and hydrologic data assimilation. The initial application of GMET is a first-of-its-kind ensemble gridded dataset of precipitation and temperature for the period 1980-2012, was described by Newman et al. (2015) and is available at http://dx.doi.org/10.5065/D6TH8JR2. Subsequent applications of GMET include the probabilistic evaluation of WRF model simulations (Prein et al., 2016; Liu et al., 2016) and hydrologic data assimilation for initializing short-range streamflow forecasts (Clark E. et al., 2017).  In the past year, GMET has been extended to make use of climatologically aided interpolation methods in regions with sparse observational networks (e.g. Alaska) and extreme meteorological gradients (e.g. Hawaii) (Newman et al. 2018).

More details on GMET are available at

https://ncar.github.io/hydrology/projects/meteorological_datasets

Local Scale Weather and Climate Prediction

Advancing a new, powerful statistical weather and climate downscaling tool for high-resolution weather prediction and localizing climate projections.  NCAR’s Ensemble Generalized Analog Regression Downscaling (En-GARD) is a generalized ensemble downscaling utility that can apply most common downscaling methods, e.g. regression, analogs, and hybrid analog-regression method on any number of variables and spatial configuration. It is being used both for forecasting and climate downscaling applications. The En-GARD approach derives from previous papers on climate downscaling and probabilistic quantitative precipitation estimation (Clark and Hay, 2004; Clark and Slater, 2006; Gangopadhyay et al., 2005). The development of En-GARD and the assessment of forecasting and climate downscaling performance will be documented in a series of papers in the next year.  In the past year, En-GARD has been used to produce a large ensemble of downscaled climate projections, and this work is currently being documented in two papers (Gutmann et al in prep.; Hamman et al. in prep).  The En-GARD source code is available at https://github.com/NCAR/GARD, and is already being used by university and agency researchers around the world with support from NCAR.

Creating the first community quasi-dynamical weather and climate downscaling model.  NCAR has developed the Intermediate Complexity Atmospheric Research (ICAR) model, a quasi-dynamical downscaling approach that uses simplified wind dynamics to perform high-resolution meteorological simulations 100 to 1000 times faster than a traditional atmospheric model and can therefore be used to better characterize uncertainty across numerical weather prediction models and climate models, and in dynamical downscaling. Gutmann et al. (2016) describes the development of ICAR.  In the last year, this code has been significantly updated through the use of object-oriented programming and modern parallelization techniques.  The newer parallel features of ICAR overlap communication between parallel processes with computation within a process to permit the simulation to scale to 100,000 cores (Rouson et al. 2017).  As a result, a simulation that used to take 30 minutes can be performed in half a second (figure 1). In addition, new supporting infrastructure and documentation has been developed to make the setup and configuration of ICAR simulations easier for new users. The ICAR source code is available at https://github.com/NCAR/icar.  ICAR is being used by university and agency researchers around the world with support from NCAR, see for example, Horak et al (2018) and Bernhardt et al (2018). The effort is supported by USACE and Reclamation to improve their understanding of future climate at scales relevant to water resource managers.

Figure 1: Scaling characteristics of core ICAR algorithm as run on a supercomputer at DOE’s National Energy Research Scientific Computing Center (NERSC)
Figure 1: Scaling characteristics of core ICAR algorithm as run on a supercomputer at DOE’s National Energy Research Scientific Computing Center (NERSC)

More details on ICAR are available at

https://ncar.github.io/hydrology/projects/intermediate-complexity_downscaling

Hydrologic Modeling

Advanced a comprehensive new community hydrologic modeling framework that for the first time provides the hydrology community with a structured approach for investigating and developing theories about hydrologic processes.  NCAR’s Structure for Unifying Multiple Modeling Alternatives (SUMMA)is a framework that provides multiple options to generate models that simulate a wide range of biophysical and hydrologic processes from the treetops to the stream. It will be particularly useful to characterize model and parameter uncertainty in hydrologic model simulations, and to identify strengths as weaknesses in our existing hydrologic understanding. Clark et al. (2015a; b; c) describes the development of SUMMA; the SUMMA source code is available at https://github.com/NCAR/summa. Many of the concepts developed with SUMMA are now being used to unify land modeling activities across NCAR as part of the developing Community Terrestrial Systems Model (CTSM).  In addition, RAL scientists are collaborating with NASA to implement SUMMA within the NASA Land Information System (LIS).

More details on SUMMA are available at

https://www.ral.ucar.edu/projects/summa.

Developed a multi-scale and multi-physics land surface modeling framework.  To permit multi-scale and multi-physics representation of terrestrial hydrologic processes and provide a coupling interface to link hydrologic process models to weather and climate models, NCAR has developed the Community WRF-Hydro Modeling System (Gochis et al., 2015; Yucel et al., 2015; Senatore et al., 2015). WRF-Hydro is an open-source terrestrial hydrologic modeling system that provides the capability to perform coupled (to the atmosphere) and uncoupled simulations of water cycle processes and their impacts on a wide range of spatial and temporal scales.  The code structure has been designed to scale well on high performance computing platforms.  WRF-Hydro currently serves as the underlying modeling architecture for the NOAA National Water Model. (see the WRF-Hydro and the National Water Model section of this report for more information).

More details on WRF-Hydro are available at:

https://www.ral.ucar.edu/projects/wrf_hydro

Developed an advanced hydrologic model parameter estimation tool to address the long-standing challenge of model implementation over regional domains. NCAR’s Multi-scale Parameter Regionalization Flex (MPR-flex) is a model-independent, flexible parameter estimation application that enables continental-domain application of multiple hydrologic models in a spatially consistent way (Mizukami et al., 2017). In the past year, MPR-flex has been applied to multiple hydrological models, a step which significantly expands the potential use of MPR-flex by the broader community, and permits a consistent evaluation of calibration issues across different hydrologic modeling approaches. NCAR has also investigated the properties of MPR calibrated models, particularly for flood frequency analysis (e.g. Wobus et al 2017) and shown how model calibration can be modified to improve robustness of the parameter set across a broader range of metrics through the use of a weighted Kling-Gupta Efficiency objective function.

More details on MPR-Flex are available at

https://ncar.github.io/hydrology/projects/parameter_estimation.

Developed a flexible multi-method, continental-domain routing model,  mizuRoute, which efficiently routes streamflow from any distributed hydrologic model through river networks. It has been used to provide streamflow values at 54,000 river segments across the contiguous United States. In the past year, mizuroute has been extended to operate on the more detailed National Hydrography Dataset (NHDplus), which contains millions of river segments across the United States. To enable this effort, mizuroute has been parallelized to make better use of available High Performance Computing resources. Mizukami et al. (2016a) describes the development of mizuRoute; the mizuRoute source code is available at https://github.com/NCAR/mizuRoute.

More details on mizuRoute are available at

https://ncar.github.io/hydrology/projects/streamflow_routing.

 

CLIMATE SCENARIO APPLICATIONS

 

Analyzed existing climate scenarios and developed a broader suite of projections. To aid water resource managers in assessing projections of future hydrologic scenarios, NCAR has performed detailed assessments of existing climate scenarios and developed a large-ensemble of downscaled hydrologic predictions. In performing these assessments, the first step has been to review hydrologic metrics of interest, in collaboration with researchers in the university community and MMM (Ekström et al 2017).  This work has been extended with broad discussion of the properties of ensembles of climate projections, with particular attention to the independence and representativeness of different climate models (Abramowitz et al 2018).  Next, NCAR has worked with the university community in the evaluation of hydrologic projections to understand the role of hydrologic models in controlling climate change signals (Melsen et al 2018). Most recently, a very large ensemble of climate projections have been developed, making use of ICAR and En-GARD as well as MPR and Mizuroute to improve the physical representation of both local climate and hydrology.  This work is being disseminated at the American Geophysical Union and will be published in the coming year.  Likewise, advanced projections are being developed over Alaska and Hawaii to provide these communities with a larger ensemble of likely hydro-climate projections than they have had available in the past.

 

Outreach to water resource managers. NCAR has performed extensive outreach to water resource managers, working closely at every stage in the process with collaborators at the US Bureau of Reclamation and the Army Corps of Engineers.  This has included developing guidance for water resource manager (Vano et al. 2018), as well as presenting webinars on the analysis of current datasets (Hamman et al 2018).

 

HYDROLOGIC FORECASTING APPLICATIONS

 

Developed an integrative system for real-time assessment and demonstration of advanced streamflow forecasting approaches.  The System for Hydromet Analysis Research and Prediction (SHARP) provides an integrative platform for assessment and demonstration of many of the modeling and methodological advances outlined above to evaluate new opportunities for streamflow and water prediction applications, including operational forecasting for water systems support for development of climate adaptation though better anticipation of climate and water extremes. The effort is supported by USACE and Reclamation to provide science-based solutions to long-standing challenges in streamflow forecasting to support water management.

 

More details on SHARP are available at 

https://ncar.github.io/hydrology/projects/streamflow_forecasting.

Assessed CONUS-wide seasonal streamflow predictability.  To develop and benchmark new methods of climate and seasonal streamflow prediction, NCAR has conducted a comprehensive assessment and intercomparison of new and existing strategies for leveraging climate and hydrologic predictability to advance operational seasonal flow prediction, in collaboration with water management offices of the two largest US federal water agencies, USACE and Reclamation (Mendoza et al, 2017).  This research effort also included a comprehensive nationwide seasonal hydrologic predictability assessments, as described by Wood et al. (2016), and assessment of hybrid methods in climate prediction (Madadgar, et al, 2016). 

Advanced new methods in hydrologic data assimilation, which is a critical strategy for improving short to seasonal range streamflow predictions. With support from USACE and Reclamation, NCAR has comprehensively assessed capabilities for assimilating snow observations (Huang et al., 2016; Clark et al., 2006; Clark and Slater, 2006; Wood and Lettenmaier, 2006) to improve seasonal hydrologic prediction; and the particle filter for streamflow assimilation to enhance shorter range operational flow forecasting (E. Clark et al, 2017). In support of community modeling, NCAR has developed Hydro-DART, an open-source, ensemble based data assimilation architecture (DART) that has been configured to support hydrologic data assimilation in the community WRF-hydro modeling system.  The diversity of different data assimilation methodologies and filtering algorithms in DART provides users with significant flexibility in applying data assimilation to a host of environmental data assimilation problems.  Using this new HYDRO-DART system work is now proceeding on assimilation of remotely sensed snowpack estimates using NOAA JPSS satellite data.  HYDRO-DART is also being employed in WRF-Hydro parameter estimation activities.  (The DART system is developed and supported by the NCAR Computational and Information Systems Laboratory). 

Advanced new methods in streamflow forecast post-processing, another critical strategy for improving short to medium range streamflow forecasts, NCAR has leveraged support from USACE and Reclamation to develop a first-of-its-kind multi-method streamflow forecast post-processing application.  Working within SHARP, the application provides a retrospective and real-time assessment of a broad range of forecast post-processing approaches being explored by the streamflow forecasting community.  NCAR has also developed and implemented a major, CONUS-wide ‘nudging’ technique for operational deployment of the National Water Model, in which observed streamflows are used to adjust the simulation and forecast outputs of the NWM to improve NWM forecast skill. 

Leadership of HEPEX, an international initiative in ensemble hydrologic prediction.  NCAR’s Andy Wood is current chair of the Hydrologic Ensemble Prediction Experiment (HEPEX; http://www.hepex.org/), together with other leads from the European Center for Medium Range Forecasting, Irstea (France), and the Commonwealth Science, Industry and Research Organization (CSIRO, Australia).  With over 400 members, HEPEX promotes the development and operational application of ensemble hydrologic forecasting to support water, hazard and energy management.  In 2018, HEPEX organized international workshops on ensemble prediction (Melbourne, Australia).

Water Resource APPLICATIONS OF COMPUTATIONAL HYDROLOGY RESEARCH AND TOOLS

Many of the models, methods, datasets and tools described above are motivated by the need for new applications to serve important societal needs.  Two key needs and application areas are Operational Streamflow Prediction and Assessing Climate Change Impacts on Hydrology and Water Resources. 

1.  In the Streamflow Prediction area, a major effort has been the development of hyper-resolution modeling and prediction capabilities for the National Weather Service, and in particular the National Water Model that was launched operationally in summer 2016 at the National Water Center.  This effort leveraged elements described above, including the WRF-Hydro model, the visualization tool RWRFhydro, and the Meteorological Forcing Engine.  A second major effort has been the deployment of real-time short-range and seasonal ensemble streamflow forecasts to support collaborations with the two major US water agencies (USACE and Reclamation).  The central objective is to demonstrate and understand the viability of new ‘Over-the-Loop’ forecasting methods for water management.  This effort employed the SHARP system, running operationally at NCAR and integrating tools such as GMET, SUMMA, mizuRoute, En-GARD, and several data assimilation and post-processing capabilities.  Streamflow prediction applications are described in more detail in the Streamflow Prediction LAR.

2.  In the Climate Change Impacts area, NCAR has used many of the tools described above to undertake a major CONUS-wide effort (extending to Alaska and Hawaii) to characterize and communicate uncertainties in the projection of future hydrology, given climate change and variability.  Recognizing that key scientific challenges persist in estimating future climate at the large scale, downscaling climate to the local scale, and representing hydrologic sensitivities to climate, NCAR has developed an assessment strategy that reveals uncertainties in each of these areas that have been previously under-estimated, and then reduces these uncertainties through application of tools and models described above, including ICAR, GMET, SUMMA, MizuRoute, MPR-flex, among others.  Outcomes from this effort are informing federal water agency guidance to support water management decisionmaking and risk assessment.   

Each of these opportunities is expected to be pursued in future work (next section).

FUTURE PLANS

Looking ahead there is a vast array of possibilities in improving the fidelity and skill of our hydrologic modeling and prediction tools. RAL scientists will continue to work with their academic and government partners to advance this research and also to bring new water prediction technologies and capabilities into societal applications.  There are number of emerging capabilities that will provide significant advances in accuracy and usability of hydrologic modeling and prediction products. These include:

  • Improve probabilistic spatial meteorological fields, to both improve the quality and probabilistic information content of hydrologic model inputs;
  • Advance model-agnostic methods to generate spatial fields of model parameters, to improve the fidelity of hydrologic model simulations;
  • Increase the computational agility of process-based hydrologic models, to support computationally intensive tasks such as hydrologic data assimilation and parameter estimation;
  • Build more robust hydrologic data assimilation capabilities to reduce errors in model initialization states;
  • Developing a broader understanding of tradeoffs in hydrologic prediction approaches at scales from flash flooding to seasonal forecasting
  • Utilize a new generation of meter scale terrain data from airborne lidar and incorporating that information into flow routing and inundation algorithms;
  • Improve the physics of snowpack accumulation and ablation;
  • Explore the use of remotely sensed meter scale snowpack products for model parameter estimation and data-assimilation.
  • Advance the representation of water management and infrastructure influences on runoff generation and streamflow;
  • Integrate more real-time information on land cover and land cover disturbance characteristics into real-time prediction systems.

These efforts are actively being worked to support a number of practical initiatives, including the NOAA National Water Model development effort, the Reclamation Reservoir Pilot Operations Study and West Wide Risk Assessment, and Over-the-Loop ensemble streamflow forecasting demonstration project, sub-seasonal to seasonal hydrologic and water supply forecasting, and longer-term 50-state water security assessment projects by federal agencies under the federal Secure Water Act.  As these tools and datasets mature they become publicly available and will be accompanied with documentation for how to use them to support adaptation planning and decision-making.

REFERENCES

Abramowitz, G.; Nadja Herger, Ethan Gutmann, Dorit Hammerling, Reto Knutti, Martin Leduc, Ruth Lorenz, Gavin A. Schmidt. 2018: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing. Earth System Dynamics Discussion paper in review doi:10.5194/esd-2018-51

Arnal, L, AW Wood, E Stephens, H Cloke, F Pappenberger, 2016, Decomposing the sources of seasonal streamflow predictability, Hydrol. Earth Syst. Sci.

Bernhardt, M., Härer, S., Feigl, M., and Schulz, K. (2018). Der Wert Alpiner Forschungseinzugsgebiete im Bereich der Fernerkundung, der Schneedeckenmodellierung und der lokalen Klimamodellierung. Österreichische Wasserund Abfallwirtschaft.

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, L. D. Brekke, J. R. Arnold, D. J. Gochis, and R. M. Rasmussen, 2015a: A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51, 4, 2498-2514, doi: 10.1002/2015wr017198.

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, D. J. Gochis, R. M. Rasmussen, D. G. Tarboton, V. Mahat, G. N. Flerchinger, and D. G. Marks, 2015b: A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies. Water Resources Research, 51, 4, 2515-2542, doi: 10.1002/2015wr017200.

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, and L. D. Brekke, 2015c: The Structure for Unifying Multiple Modeling Alternatives (SUMMA), version 1: Technical description. NCAR Technical Note NCAR/TN-514+STR, 54 pp., National Center for Atmospheric Research, Boulder, Colo., doi:10.5065/D6WQ01TD.

Clark, M. P., Y. Fan, D. M. Lawrence, J. C. Adam, D. Bolster, D. J. Gochis, R. P. Hooper, M. Kumar, L. R. Leung, and D. S. Mackay, 2015d: Improving the representation of hydrologic processes in Earth System Models. Water Resources Research, 51, doi: 10.1002/2015WR017096.

Clark, M. P., B. Schaefli, S. Schymanski, L. Samaniego, C. Luce, B. Jackson, J. Freer, J. R. Arnold, D. Moore, E. Istanbulluoglu, and S. Ceola, 2016a: Improving the theoretical underpinnings of process-based hydrologic models. Water Resources Research, 52, doi: 10.1002/2015WR017910.

Clark, M. P., R. L. Wilby, E. D. Gutmann, J. A. Vano, S. Gangopadhyay, A. W. Wood, H. J. Fowler, C. Prudhomme, J. R. Arnold, and L. D. Brekke, 2016b: Characterizing uncertainty of the hydrologic impacts of climate change. Current Climate Change Reports, 2, 2, 55-64, doi: 10.1007/s40641-016-0034-x.

Ekström M, Gutmann ED, Wilby RL, Tye MR, Kirono DGC. 2017: Robustness of hydroclimate metrics for climate change impact research. WIREs Water. e1288. doi:10.1002/wat2.1288

Emerton, R, EM Stephens, F Pappenberger, TC Pagano, AH Weerts, AW Wood, P Salamon, JD Brown, N Hjerdt, C Donnelly and HL Cloke, 2016.  Continental and Global Scale Flood Forecasting Systems, WIREs Water 3:391–418. doi: 10.1002/wat2.1137.

Gochis, D.J., W. Yu, D.N. Yates, 2015:  The WRF-Hydro model technical description and user’s guide, version 3.0.  NCAR Technical Document. 120 pages. Available online at: http://www.ral.ucar.edu/projects/wrf_hydro/.

Gutmann, E., I. Barstad, M. Clark, J. Arnold, and R. Rasmussen, 2016: The Intermediate Complexity Atmospheric Research Model (ICAR). Journal of Hydrometeorology, 17, 2016, 957-973, doi: 10.1175/JHM-D-15-0155.1.

Hamman, J; ED Gutmann, N Mizukami, M Clark, A Wood. 2018: LOCA Hydrology Analysis. US Bureau of Reclamation Science & Technology Water Operations and Planning Monthly Webinar Series.

Horak J; Hoffer, Maussian, Gutmann, Gohm, Rotach (2018) Assessing the Added Value of the Intermediate Complexity Atmospheric Research Model (ICAR). Hydr.Earth Sys.Sci. (submitted)

Huang, C, AJ Newman, MP Clark, AW Wood and X Zheng, 2016, Evaluation of snow data assimilation using the ensemble Kalman Filter for seasonal streamflow prediction in the Western United States, Hydrol. Earth Syst. Sci.

Liu, C., K. Ikeda, R. Rasmussen, M. Barlage, G. Thompson, A. J. Newman, A. F. Prein, F. Chen, L. Chen, M. Clark, A. Dai, J. Dudhia, T. Eidhammer, D. Gochis, E. Gutmann, S. Kurkute, Y. Li, and D. Yates, 2016: The Current and Future Water Cycle over the Contiguous United States from Decadal Convection Permitting Simulations. Climate Dynamics, doi: 10.1007/s00382-016-3327-9

Madadgar, S, A AghaKouchak, S Shukla, S Sorooshian, K-L Hsu, M Svoboda, and AW Wood, 2016, A Hybrid Statistical-Dynamical Drought Prediction Framework: Application to for the Southwestern United States, Wat. Res. Rsrch (online early view) DOI: 10.1002/2015WR018547 

Melsen, L., N. Addor, N. Mizukami, A. J. Newman, P. Torfs, M. Clark, R. Uijlenhoet, and A. J. Teuling, 2018: Mapping (dis)agreement in hydrologic projections. HESS, 22, 1775-1791, doi:10.5194/hess-22-1775-2018

Mendoza, PA, AW Wood, E Rothwell, EA Clark, MP Clark, B Nijssen, LD Brekke, and JR Arnold, 2016, An intercomparison of approaches for harnessing sources of predictability in operational seasonal streamflow forecasting, HESS Discussions (submitted)

Mizukami, N., M. Clark, K. Sampson, B. Nijssen, Y. Mao, H. McMillan, R. Viger, S. Markstrom, L. Hay, and R. Woods, 2016b: mizuRoute version 1: a river network routing tool for a continental domain water resources applications. Geoscientific Model Development, 9, 2223-2238, doi: doi:10.5194/gmd-9-2223-2016.

Mizukami, N., M. Clark, E. Gutmann, P. A. Mendoza, A. Newman, B. Nijssen, B. Livneh, J. R. Arnold, L. Brekke, and L. Hay, 2016b: Implications of the methodological choices for hydrologic portrayals over the Contiguous United States: statistically downscaled forcing data and hydrologic models. Journal of Hydrometeorology 17, 73-98, doi: 10.1175/JHM-D-14-0187.1

Mizukami, N., M. Clark, A. Newman, A. Wood, E. Gutmann, B. Nijssen, O. Rakovec and L. Samaniego, 2017: Towards seamless large-domain parameter estimation for hydrologic models. Water Resources Research, doi:10.1002/2017WR020401.

Pagano, TC, F Pappenberger, AW Wood, MH Ramos, A. Persson and B Anderson, 2016, Automation and human expertise in operational river forecasting. WIREs Water, 3: 692–705. doi:10.1002/wat2.1163

Prein, A. F., G. J. Holland, R. M. Rasmussen, M. P. Clark, and M. R. Tye, 2016: Running dry: The US Southwest's drift into a drier climate state. Geophysical Research Letters, 43, 3, 1272-1279, doi: 10.1002/2015GL066727.

Rouson, D; ED Gutmann; A Fanfarillo; B Friesen 2017: Performance portability of an intermediate-complexity atmospheric research model in coarray Fortran. Proceedings of the Second Annual PGAS Applications Workshop, 4 SC17. doi:10.1145/3144779.3169104

Senatore, A., G. Mendicino, D. J. Gochis, W. Yu, D. N. Yates, and H. Kunstmann. (2015), Fully coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales, J. Adv. Model. Earth Syst., 07, doi:10.1002/2015MS000510.

Vano, J.A., Jeffrey R. Arnold, Bart Nijssen, Martyn P. Clark, Andrew W. Wood, Ethan D. Gutmann, Nans Addor, Joseph Hamman, Flavio Lehner. 2018: DOs and DON'Ts for using climate change information for water resource planning and management: guidelines for study design, Climate Services, doi:10.1016/j.cliser.2018.07.002.

Wobus, C., and Coauthors, 2017: Climate change impacts on flood risk and asset damages within mapped 100-year floodplains of the contiguous United States. Natural Hazards and Earth System Sciences, 17, 2199-2211, doi:10.5194/nhess-17-2199-2017.

Wood, A., T. Hopson, A. Newman, J. R. Arnold, L. Brekke, and M. Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology 17, 651-668, doi: 10.1175/JHM-D-14-0213.1.

Yucel, I., Onen, A., Yilmaz, K. and Gochis, D. 2015. Calibration and evaluation of a flood forecasting system: Utility of numerical weather prediction model, data assimilation and satellite-based rainfall. J. Hydrol. 523, 49 – 66.

Zhao, T, J Bennett, QJ Wang, A Schepen, AW Wood, D Robertson and MH Ramos, 2016, How suitable is quantile mapping for post-processing GCM precipitation forecasts?  J. Climate.

 

Climate Scenario Applications

Analyzed existing climate scenarios and developed a broader suite of projections. To aid water resource managers in assessing projections of future hydrologic scenarios, NCAR has performed detailed assessments of existing climate scenarios and developed a large-ensemble of downscaled hydrologic predictions. In performing these assessments, the first step has been to review hydrologic metrics of interest, in collaboration with researchers in the university community and MMM (Ekström et al 2017).  This work has been extended with broad discussion of the properties of ensembles of climate projections, with particular attention to the independence and representativeness of different climate models (Abramowitz et al 2018).  Next, NCAR has worked with the university community in the evaluation of hydrologic projections to understand the role of hydrologic models in controlling climate change signals (Melsen et al 2018). Most recently, a very large ensemble of climate projections have been developed, making use of ICAR and En-GARD as well as MPR and Mizuroute to improve the physical representation of both local climate and hydrology.  This work is being disseminated at the American Geophysical Union and will be published in the coming year.  Likewise, advanced projections are being developed over Alaska and Hawaii to provide these communities with a larger ensemble of likely hydro-climate projections than they have had available in the past.

Outreach to water resource managers. NCAR has performed extensive outreach to water resource managers, working closely at every stage in the process with collaborators at the US Bureau of Reclamation and the Army Corps of Engineers.  This has included developing guidance for water resource manager (Vano et al. 2018), as well as presenting webinars on the analysis of current datasets (Hamman et al 2018).

HYDROLOGIC Forecasting APPLICATIONS

Developed an integrative system for real-time assessment and demonstration of advanced streamflow forecasting approaches.  The System for Hydromet Analysis Research and Prediction (SHARP) provides an integrative platform for assessment and demonstration of many of the modeling and methodological advances outlined above to evaluate new opportunities for streamflow and water prediction applications, including operational forecasting for water systems support for development of climate adaptation though better anticipation of climate and water extremes. The effort is supported by USACE and Reclamation to provide science-based solutions to long-standing challenges in streamflow forecasting to support water management.