Hydrological modelling research at NCAR · Hydrological modelling research at NCAR CCRN Modelling...
Transcript of Hydrological modelling research at NCAR · Hydrological modelling research at NCAR CCRN Modelling...
Hydrological modelling research at NCAR
CCRN Modelling Workshop, Saskatoon Canada15 September 2014
Martyn Clark (NCAR/RAL)
• 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
Outline
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-continuoushydrometeorological 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
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….
▫ Need extensible, multi-physics framework…foster experimentation and expose process uncertainty…
1-10’s km 100’s m - 1’s km 1-10’s m
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…)
WRF-Hydro Architecture Description:
Basic Concepts• Modes of operation..1-way
vs. 2-way
• Model forcing and feedback components:
Forcings: T, Press, Precip., wind, radiation, humidity, BGC-scalars
Feedbacks: Sensible, latent, momentum, radiation, BGC-scalars
One-way (‘uncoupled’) →
Two-way (‘coupled’) ↔
Output products: Forecasts of water
cycle components
Clouds & WeatherPrecipitationSnowpack : SWESoil MoistureEvapotranspiration
Channel Flows at spatial resolutions of
10s to 100s of meters
Output products: Forecasts of water
cycle components
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
• 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
Outline
The method of multiple working hypotheses
• Scientists often develop “parental affection” for their theories
T.C. Chamberlain
• 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)
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
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 sub-domains
▫ The governing equations are scale-invariant
2. Differences among models relate toa) 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.
General schematic of the terrestrial water cycle,
showing dominant fluxes of water and energy
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
The unified approach to hydrologic modeling
Governing equations
Hydrology
Thermodynamics
Physical processes
XXX Model options
Evapo-transpiration
Infiltration
Surface runoff
SolverCanopy storage
Aquifer storage
Snow temperature
Snow Unloading
Canopy interception
Canopy evaporation
Water table (TOPMODEL)
Xinanjiang (VIC)
Rooting profile
Green-AmptDarcy
Frozen ground
Richards’Gravity drainage
Multi-domain
Boussinesq
Conceptual aquifer
Instant outflow
Gravity drainage
Capacity limited
Wetted area
Soil water characteristics
Explicit overland flow
Atmospheric stability
Canopy radiation
Net energy fluxes
Beer’s Law
2-stream vis+nir
2-stream broadband
Kinematic
Liquid drainage
Linear above threshold
Soil Stress function
Ball-Berry
Snow drifting
Louis
Obukhov
Melt drip Linear reservoir
Topographic drift factors
Blowing snowmodel
Snowstorage
Soil water content
Canopy temperature
Soil temperature
Phase change
Horizontal redistribution
Water flow through snow
Canopy turbulence
Supercooledliquid water
K-theory
L-theory
Vertical redistribution
16
(1) Model architecture
soil soil
aquifer
(e.g., Noah) (e.g., VIC)
aquifer
soil
soil
(e.g., PRMS) (e.g., DHSVM)
aquifer
soil
- spatial variability and hydrologic connectivity
a) GRU configuration b) HRU configuration
(2) Process parameterizations (and parameters)
Data from research basins: Interception of snow on
the vegetation canopy
• Weighing tree experiments at Umpqua
Different interception formulations
Simulations of canopy interception (Umpqua)
Data from research basins: Interplay
between model representations of
biogeophysics and hydrology
Data from Reynolds Creek, Idaho
Interplay between model parameters
and model parameterizations
Rooting
depth
Hydrologic
connectivity
Soil stress
function
• 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
Outline
Data to enable community modelingMeteorological 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
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
CONUS-wide watershed Dataset• Basin Selection
▫ Used GAGES-II, Hydro-climatic data network (HCDN)-2009
671 primarily headwater basins (median size ~330 km2)
▫ Forcing data derived from Daymet(http://daymet.ornl.gov/)
▫ 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
Benchmark Simulations
• Calibrated Sacramento/SNOW-17 model
Data to enable community modeling #1Spatial 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
CLM simulations coupled with network-based routing
model configured for the USGS geospatial fabric
• 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
Outline
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.
Skill of Mean 6mo Runoff Forecast
SC
F U
nce
rta
inty
(F
raction
of
Clim
o V
ari
an
ce
)
IHC Uncertainty (Fraction of Climo Variance)
Crystal River Ab Avalance Crk Nr Redstone CO
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
R2
0.0
0.2
0.4
0.6
0.8
1.0
Oct 1
P
CE
rE
Nov 1 Dec 1
0.0
0.2
0.4
0.6
0.8
1.0
Jan 1 Feb 1 Mar 1
0.0
0.2
0.4
0.6
0.8
1.0
Apr 1 May 1 Jun 1
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Jul 1
0.0 0.2 0.4 0.6 0.8 1.0
Aug 1
0.0 0.2 0.4 0.6 0.8 1.0
Sep 1
Wood et al, VESPA, 2014…
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…
Diurnal temp. range comparison (WY1980-08)DJF JJAMAM SON
Wet-day fraction comparison (WY1980-08)DJF JJAMAM SON
Biases in model forcing variables
°C
32CLM VIC
Analysis: Water balance
• 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
Summary: opportunities for collaboration?