Computational Hydrology

Introduction

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 U.S. Geological Survey, the U.S. Forest Service and multiple universities to build new community hydrologic research and applications datasets, models and methods 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, to characterizing uncertainties in climate impacts assessments arising from a myriad 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

  1. Moving from deterministic to probabilistic national-domain meteorological datasets. The CHG has 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., in prep).  More details on GMET are available at

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

  1. Statistical post-processing of meteorological analyses and forecasts prior to use in hydrologic models. The CHG has developed the Meteorological Forcing Engine (MFE), a generic open-source software package that ingests gridded meteorological data from radars, satellites, re-analyses and numerical weather and climate prediction models, performs a wide range of regridding, downscaling and bias correction methods, and outputs the data in formats ready for ingest into the community WRF-Hydro system.  The MFE is utilizes generalizable Earth System Modeling Framework (ESMF) regridding libraries and efficient I/O capabilities within the ncl programming language to provide a robust set of data processing tools. The MFE is the underlying meteorological data pre-processor for the current NOAA National Water Model.  Documentation and a publicly accessible github repository for the MFE code is currently under development.
  2. Creating a new, powerful statistical weather and climate downscaling tool for high-resolution weather prediction and localizing climate projections.  The CHG’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. 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 GARD and the assessment of forecasting and climate downscaling performance will be documented in a series of papers in the next year.  Although it is still at an early stage, the GARD source code is available at https://github.com/NCAR/GARD.
  3. Creating the first community quasi-dynamical weather and climate downscaling model.  The CHG 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; the ICAR source code is available at https://github.com/NCAR/icar.

Weather and Climate Downscaling

More details on ICAR are available at

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

Hydrologic Modeling

  1. Developed a suite of pre-processing tools to derive physiographic and hydrographic data fields for use within hydrologic models, including WRF-Hydro.  These tools use a combination of ArcGIS and open-source python geospatial processing tools to manage and manipulate a number of geospatial input datasets including terrain, hydrography, vegetation, soils and water management features, mapping utilities for conservative regridding of model quantities across irregular polygons, along with information on digital river networks to support distributed streamflow routing.  Work is ongoing to provide an entirely open-source pre-processing toolkit to support process-based hydrologic modeling. A popular pre-processing tool is the Geographic Information System Pre-processor (GISPp) which be found on the ’Downloads’ page of the WRF-Hydro website:

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

  1. 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.  The CHG’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. Some of the SUMMA concepts are used to unify land modeling activities across NCAR, using the ideas in Clark et al. (2015d) and Clark et al. (2016a).

More details on SUMMA are available at

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

  1. 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, the CHG has developed the Community WRF-Hydro Modeling System(Gochis et al., 2015; Yucel et al., 2015; Senatore et al., 2015; Gochis et al., 2016-in preparation). 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 Streamflow Prediction section of this report for more details on the National Water Model application of the WRF-Hydro system).

More details on WRF-Hydro are available at:

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

  1. Developed an advanced hydrologic model parameter estimation tool to address the long-standing challenge of model implementation over regional domains. . The CHG’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).

More details on MPR-Flex are available at

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

  1. 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. 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.

New research and systems supporting hydrologic applications

  1. Developed an integrative system for real-time assessment and demonstration of advanced streamflow forecasting approaches.  The new 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.

  1. Assessed CONUS-wide seasonal streamflow predictability.  To develop and benchmark new methods of climate and seasonal streamflow prediction, the CHG 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). 
  2. 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, the CHG 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, in prep). In support of community modeling, the CHG 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). 
  3. Advanced new methods in streamflow forecast post-processing, another critical strategy for improving short to medium range streamflow forecasts, the CHG 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.  The CHG 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. 
  4. Leadership of HEPEX, an international initiative in ensemble hydrologic prediction.  CHG’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 2016, HEPEX organized international workshops on data assimilation (Bonn, Germany) and ensemble prediction (Quebec City, Canada), and NCAR collaborated in a number of publications on various aspects of forecasting (Arnal et al, 2016; Zhao et al, 2016; Pagano et al, 2016; Emerton et al, 2016)
  5. Visualizing and evaluating hydrologic model output.  To support hydrologic model evaluation and verification, the CHG has developed Rwrfhydro, a generalizable, open-source, model-agnostic hydrologic model evaluation and verification tool set written in the R programming environment. Rwrfhydro supports the ingest of multiple streams of observational data and provides quantification and visualization of hydrologic model evaluation metrics for a wide variety of hydrologic variables such as streamflow, precipitation, evapotranspiration, snowpack and soil moisture.  Rwrfhydro currently serves as the underlying model evaluation system for the NOAA National Water Model development effort at NCAR. 

More details on Rwrfhydro can be found at:

 https://github.com/mccreigh/rwrfhydro/

  1. Web-based dissemination of hydrologic modeling and forecasting results.  To enable model visualization and increase the usability of model output, the CHG has developed HydroInspector, a modern, web mapping display system which provides online, dynamic and customizable web-mapping capabilities for the visualization of observational data, geospatial feature data and weather and hydrologic model forecast information. HydroInspector utilizes a modern geodatabase and THREDDS data servers to efficiently access and render images of large model datasets.  Originally developed for the display of WRF-Hydro model output, the HydroInspector has recently been enhanced to ingest and display a wider, more general variety of model analysis and forecast data. It is also being expanded to allow for a wide range of user configuration, geospatial querying, statistical processing and data exporting capabilities.  It is expected that the HydroInspector will be a valuable resource for projects across NCAR. 

More details on HydroInspector can be found on the HydroInspector page of the WRF-Hydro web page at: https://www.ral.ucar.edu/projects/wrf_hydro..

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 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.   Climate-change related applications are described in more detail in the Climate Change and Water LAR.   

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 cumbersome 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
  • Develop better process representations in hydrologic models to characterize runoff generation and flow routing mechanisms across the landscape and in stream and river channels;
  • 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;
  • 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;
  • Provide resources for the entire Nation: these tools provide consistent, continental-domain datasets and are currently being implemented in Alaska and Hawaii. Example WRF simulations over Alaska are provided in the figure below.
Figure 1.  Average precipitation from September 2002 to August 2003 from the WRF model (WRF simulations) and gridded observations from the Scenarios Network for Alaska+Arctic Planning (SNAP observations).
Figure 1.  Average precipitation from September 2002 to August 2003 from the WRF model (WRF simulations) and gridded observations from the Scenarios Network for Alaska+Arctic Planning (SNAP observations).

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 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 publically available and will be accompanied with documentation for how to use them to support adaptation planning and decision-making.

References

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

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.

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/.

Gochis, D.J., D.N. Yates, A. Dugger, J. McCreight, W. Yu, K. Sampson, B. Cosgrove, L. Karsten, L. Pan, A. Rafieei-Nasab, F. Salas, A. Newman, A. Wood, Z. Cui, L. Read, C. Phan, D. Kitzmiller, E. Clark, D. Maidment, T. Graziano, M. Clark, R. Rasmussen, 2016: The NOAA National Water Model: A continental domain, high resolution hydrologic prediction system for the U.S.  In preparation.

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.

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. (accepted)

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 

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.

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.

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 (accepted).