Hydrological modelling research at NCAR. Martyn Clark (NCAR/RAL)

Transcription

1 Hydrological modelling research at NCAR Martyn Clark (NCAR/RAL) CCRN Modelling Workshop, Saskatoon Canada 15 September 2014

2 Outline Topics Hydrologic model development WRF-Hydro SUMMA Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

3 Example modeling framework: WRF-Hydro WRF-Hydro is a community-based, supported coupling architecture designed to couple multi-scale process models of the atmosphere and terrestrial hydrology Seek to provide: 1. An Earth Systems-oriented capability to perform coupled and uncoupled multi-physics, multi-scale, spatially-continuous hydrometeorological simulations and predictions 2. Fully utilize high-performance computing platforms 3. Leverage existing and emerging standards in data formats and pre- /post-processing workflows 4. An consistent extensible, portable and scalable environment for hydrometeorological prediction, hypothesis testing, sensitivity analysis, data assimilation and observation impact research

4 Motivation for WRF-Hydro: Scientific Needs: Based on community support requests it was evident that there was a need integrated modeling capabilities for conservative prediction for complete predictions of the water cycle climate impacts Need multi-scale framework bridge atmosphere-hydro application scales s km 100 s m - 1 s km 1-10 s m Need extensible, multi-physics framework foster experimentation and expose process uncertainty

5 Motivation for WRF-Hydro: Prediction System Needs: Need rapid pathway to operational deployment Seamless hydrometeorological modeling tools for continuum prediction: Linkage to ensemble forecasting methodologies Utilization of HPC (on both local and distributed/cloud architectures )

6 WRF-Hydro Architecture Description: Basic Concepts One-way ( uncoupled ) Modes of operation..1-way vs. 2-way Model forcing and feedback components: Two-way ( coupled ) Forcings: T, Press, Precip., wind, radiation, humidity, BGC-scalars Feedbacks: Sensible, latent, momentum, radiation, BGC-scalars

7 Output products: Forecasts of water cycle components Clouds & Weather PrecipitationSnowpack : SWE Soil Moisture Evapotranspiration

8 Output products: Forecasts of water cycle components Channel Flows at spatial resolutions of 10s to 100s of meters

9 WRF-Hydro System: Summary Open source, community-contributed code Readily extensible for multiple physics options Multi-scale/multi-resolution Supported, documented, multiple test-cases Portable/scalable across multiple computing platforms Standards based I/O Pre-/Post-processing Support

10 Outline Topics Hydrologic model development WRF-Hydro SUMMA Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

11 The method of multiple working hypotheses Scientists often develop parental affection for their theories Chamberlin s method of multiple working hypotheses the effort is to bring up into view every rational explanation of new phenomena the investigator then becomes parent of a family of hypotheses: and, by his parental relation to all, he is forbidden to fasten his affections unduly upon any one Chamberlin (1890) T.C. Chamberlain

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13 The need for a unified approach to hydrologic modeling Poor understanding of differences among models Model inter-comparison experiments flawed because too many differences among participating models to meaningfully attribute differences in model behavior to differences in model equations Poor understanding of model limitations Most models not constructed to enable a controlled and systematic approach to model development and improvement Disparate (disciplinary) modeling efforts Poor representation of biophysical processes in hydrologic models Community cannot effectively work together, learn from each other, and accelerate model development

14 Possibilities for a unified modeling framework Propositions: 1. Most hydrologic modelers share a common understanding of how the dominant fluxes of water and energy affect the time evolution of thermodynamic and hydrologic states The collective understanding of the connectivity of state variables and fluxes allows us to formulate general governing model equations in different subdomains The governing equations are scale-invariant General schematic of the terrestrial water cycle, showing dominant fluxes of water and energy 2. Differences among models relate to a) the spatial discretization of the model domain; b) the approaches used to parameterize individual fluxes (including model parameter values); and c) the methods used to solve the governing model equations. Given these propositions, it is possible to develop a unifying model framework For example, by defining a single set of governing equations, with the capability to use different spatial discretizations (e.g., multi-scale grids, HRUs; connected or disconnected), different flux parameterizations and model parameters, and different time stepping schemes

15 The unified approach to hydrologic modeling Louis Obukhov K-theory L-theory Wetted area Capacity limited Linear above threshold Atmospheric stability Canopy interception Melt drip Linear reservoir Canopy evaporation Liquid drainage Snow Unloading Topographic drift factors Canopy turbulence Blowing snow model Snow drifting Net energy fluxes Canopy temperature Canopy storage Water flow through snow Instant outflow Gravity drainage Canopy radiation Snow storage Snow temperature Solver Soil water characteristics Soil water content Phase change Aquifer storage Beer s Law 2-stream broadband 2-stream vis+nir Supercooled liquid water Soil temperature Evapotranspiration Surface runoff XXX Horizontal redistribution Rooting profile Infiltration Ball-Berry Soil Stress function Hydrology Thermodynamics Governing equations Physical processes Model options Vertical redistribution Boussinesq Kinematic Conceptual aquifer Gravity drainage Richards Multi-domain Darcy Green-Ampt Frozen ground Explicit overland flow Water table (TOPMODEL) Xinanjiang (VIC)

16 (1) Model architecture - spatial variability and hydrologic connectivity soil soil soil soil soil aquifer aquifer aquifer (e.g., Noah) (e.g., VIC) (e.g., PRMS) (e.g., DHSVM) 16

17 a) GRU configuration b) HRU configuration

18 (2) Process parameterizations (and parameters)

19 Different interception formulations Data from research basins: Interception of snow on the vegetation canopy Simulations of canopy interception (Umpqua) Weighing tree experiments at Umpqua

20 Data from research basins: Interplay between model representations of biogeophysics and hydrology Rooting depth Soil stress function Hydrologic connectivity Interplay between model parameters and model parameterizations Data from Reynolds Creek, Idaho

21 Outline Topics Hydrologic model development WRF-Hydro SUMMA Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

22 Data to enable community modeling Meteorological forcing data for hydrologic models Example community activity: CONUS-wide forcing data e.g., different gridded meteorological forcing fields (12-km grid) across the CONUS, 1979-present Advantages Integrates data from stations, radar, NWP models, and satellites Opportunities Make more extensive use of data from stations (additional networks) and NWP models (finer spatial resolution) in a formal data fusion framework Provide quantitative estimates of data uncertainty (ensemble forcing) Undertake detailed watershed-scale evaluation CLM simulations over the Upper Colorado River basin for three elevation bands, using two different meteorological forcing datasets

23 Ensemble forcing data for the CONUS Ensemble of gauge-based forcing fields Should consider data uncertainty when evaluating the suitability of different model structures and model parameter sets

24 CONUS-wide watershed Dataset Basin Selection Used GAGES-II, Hydro-climatic data network (HCDN) primarily headwater basins (median size ~330 km 2 ) Forcing data derived from Daymet ( Span a wide range of hydro-climatic conditions Dryness ratios (PET/P) from 0.2 to 4 Three spatial configurations of forcing data Basin mean Elevation bands HRUs

25 Benchmark Simulations Calibrated Sacramento/SNOW-17 model

26 Data to enable community modeling #1 Spatial data on network topology and geophysical attributes Example community activity: The USGS geospatial fabric Aggregation of NHD-Plus basins into Hydrologic Response Units (HRUs) and associated stream segments Parameter values for the USGS PRMS model on each HRU Advantages HRU-stream topology can support multiple hydrologic models Opportunities Currently a single set of HRUs: Desire alternative spatial configurations HRUs based hydrologic similarity Nested HRUs/grids that can explicitly represent lateral flow within a basin Currently PRMS-specific parameters: Desire general geophysical attributes Enables different modeling groups to test alternative approaches to relate attributes to model parameters Stream segments across the contiguous USA: NHD: 3,000,000 flow lines; 2,600,000 catchments Geospatial fabric: 56,000 stream segments, 110,000 HRUs

27 CLM simulations coupled with network-based routing model configured for the USGS geospatial fabric

28 Outline Topics Hydrologic model development WRF-Hydro SUMMA Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting

29 Motivation The land surface is a major source of hydrologic (hence drought) predictability. In some locations and seasons, land-surface skill almost entirely determines runoff prediction skill. SCF Uncertainty (Fraction of Climo Variance) E P Skill of Mean 6mo Runoff Forecast Crystal River Ab Avalance Crk Nr Redstone CO Oct 1 Nov 1 Dec 1 C re Jan 1 Feb 1 Mar 1 Apr 1 May 1 Jun 1 Jul 1 Aug 1 Sep R Wood et al, VESPA, IHC Uncertainty (Fraction of Climo Variance)

30 Motivation Watershed-scale assessment across CONUS shows importance of watershed conditions in forecasting skill elasticities > 1 in many locations elasticity = unit change flow forecast skill / unit change predictor skill Wood et al, VESPA, 2014

31 C Biases in model forcing variables Wet-day fraction comparison (WY ) DJF MAM JJA SON Diurnal temp. range comparison (WY ) DJF MAM JJA SON

32 Analysis: Water balance CLM VIC 32

33 Summary: opportunities for collaboration? Hydrologic model development WRF-Hydro SUMMA Supporting datasets/models and evaluation framework Probabilistic QPE CONUS-wide testbed Hydrologic model applications Impacts of climate change on water resources Streamflow forecasting