Post on 15-Jan-2016
Regional water cycle studies:Current activities and future plans
Water System Retreat, NCAR14 January 2015
Martyn Clark, Naoki Mizukami, Andy Newman, Pablo Mendoza, Andy Wood (NCAR)
Luis Samaniego (UFZ)
Bart Nijssen (UW)
Outline• Motivation
▫ Large inter-model differences in representation of the land component of the water cycle
▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty
• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing
• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex
process-based models make adequate use of the data on meteorology, vegetation, soils and topography?
▫ Use of simple models (statistical, bucket) as benchmarks
• Summary
Basins of interest for this study
The Colorado Headwaters Region offers a major renewable water supply in the southwestern United States, with approximately 85 % of the streamflow coming from snowmelt. Hence, we conduct this research over three basins located in this area:
- Yampa at Steamboat Springs
- East at Almont
- Animas at Durango
Simulations in the Colorado Headwaters
How does hydrologic model choice affect the magnitude and direction of climate change signal?
4
Uncalibrated model simulations Calibrated model simulations
Uncalibrated models: Climate change signal in Noah (↑ET and ↑Runoff) differs from the rest of models (↑ET and ↓Runoff).
After calibration, signal direction from Noah-LSM switches to ↑ET and ↓Runoff.
Inter-model agreement does not necessarily improve in terms of magnitude and direction.
Results: hydrology
CONUS-scale simulationsinterplay between downscaling methodology and hydrology simulations
UCO
MR
AR
GBCA
LCO RIO
NLDAS 12km domain- 205 x 462 grid boxes
Understanding different sources of uncertainty
GCM initial conditions
EmissionsScenario(s)
Global ClimateModel(s)
Downscalingmethod (s)
HydrologicModel
Structure(s)
HydrologicModel
Parameter(s)projection
PD
F
projection
PD
F
projection
PD
F
projection
PD
F
projection
PD
F
projection
PD
F
Combined uncertainty
projection
PD
F
66
Inter-model difference in canopy evaporation Submitted to JHM
Impact on Annual water balance – statistical downscaling methods and models
8Extreme runoff – inter-forcing differenceHigh flow
20yr Daily Max. flow [mm/day]
Low flow 7Q10 [mm/day]
9Extreme runoff – Inter-model difference
Low flow estimate is more dependent on models
High flow 20yr Daily Max. flow [mm/day]
Low flow 7Q10 [mm/day]
10SWE – Inter-model difference vs. inter-forcing difference
Inter-model differences are larger than inter-forcing
Inter-model comparison in peak SWE [mm]
Inter-forcing comparison in peak SWE [mm]
Outline• Motivation
▫ Large inter-model differences in representation of the land component of the water cycle
▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty
• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing
• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex
process-based models make adequate use of the data on meteorology, vegetation, soils and topography?
▫ Use of simple models (statistical, bucket) as benchmarks
• Summary
What are the key issues that constrain progress in model development?• Unsatisfactory process representation
▫ Missing processes (e.g., spatial heterogeneity, groundwater)▫ Dated/simplistic representation of some processes
• Limited capabilities to isolate and evaluate competing model hypotheses▫ The failure of MIPs and the need for a controlled approach to model
evaluation
• Insufficient recognition of the interplay between different modeling decisions▫ The interplay between model parameters and process
parameterizations▫ Interactions among different model components
• Inadequate attention to model implementation▫ Impact of operator-splitting approximations in complex models▫ Bad behavior of conceptual hydrology models
• Ignorance of uncertainty in models and data▫ To what extent does data uncertainty constrain our capabilities to
effectively discriminate among competing modeling approaches?▫ Are we so “over-confident” in some parts of our model that we may
reject modeling advances in another part of the model?
Modeling approachPropositions: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
Clark et al. (WRR 2011); Clark et al. (under review)
soil soil
aquiferaquifer
soilsoil
aquifer
soil
c) Column organization
a) GRUs b) HRUs
i) lump ii) grid
iii) polygon
The Structure for Unifying Multiple Modeling Alternatives (SUMMA)
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
LouisObukhov
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
Supercooled liquid water
K-theory
L-theory
Vertical redistribution
Example simulationsImpact of model parameters, process parameterizations and model architecture on simulations of transpiration
Stomatal resistance parameterizations
Rooting profilesSubsurface flow
among soil columns
Outline• Motivation
▫ Large inter-model differences in representation of the land component of the water cycle
▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty
• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing
• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex
process-based models make adequate use of the data on meteorology, vegetation, soils and topography?
▫ Use of simple models (statistical, bucket) as benchmarks
• Summary
The parameter estimation problem…• Many CONUS-scale applications based on
very uncertain a-priori model parameters
• Basin-by-basin calibration efforts provide patchwork-quilt of model parameters (no physical realism)
• Traditional model calibration leads to the “right answers for the wrong reasons” (compensatory effects)
Solutions?• One of the “unsolved” problems of large-
scale hydrology
• Need systematic evaluation of existing methods (currently limited understanding of what works)
• Need to reduce dimensionality of the parameter estimation problem (multiple signatures)
• Need to represent multi-scale behavior
Initial computational infrastructure
Continental-scale parameter estimation
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Soil Data (e.g., STATSGO space)
βi
Adjust TF coefficients
Model Layers (e.g., 3 Layers)
𝑃 𝑖
Horisontal upscaling
Model Params (e.g., 3 Layers)
𝑃 𝑖
Vertical upscaling
Model Params (e.g., STATSGO space)
Pi
(Pedo-) transfer function
simulations
Individual basins; donor catchments
21
A priori parameter NLDAS Calibrated parameters single basin
Max Soil Moisture Storage in bottom layer
Calibrated parameters – region
Nearest Neighbor
Calibrated multiplier• Cρbulk(basini)• Cd1(basini)• Cd2(basini)• Cztot(basini)i = 1,…15
Estimation with default TF coefficients Estimation with calibrated TF coefficients
Max Soil Moisture Storage in bottom layer
Calibrated TF coef. • aρbulk
• ad1
• ad2
• aztot
Transfer function calibration
Outline• Motivation
▫ Large inter-model differences in representation of the land component of the water cycle
▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty
• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing
• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex
process-based models make adequate use of the data on meteorology, vegetation, soils and topography?
▫ Use of simple models (statistical, bucket) as benchmarks
• Summary
Uncertainties in model forcing data
• N-LDAS vs. Maurer▫ Gridded meteorological forcing
fields (12-km grid) across the CONUS, 1979-present
• Opportunities to improve these products▫ 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)
CLM simulations over the Upper Colorado River basin for three elevation bands, using two different meteorological forcing datasetsMizukami et al. (JHM, 2013)
See Andy Newman’s presentationon ensemble forcing (next)
RoutingCLM simulations coupled with network-based routing model configured for the USGS geospatial fabric
Outline• Motivation
▫ Large inter-model differences in representation of the land component of the water cycle
▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty
• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing
• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex
process-based models make adequate use of the data on meteorology, vegetation, soils and topography?
▫ Use of simple models (statistical, bucket) as benchmarks
• Summary
Simple models as benchmarks•The NERD approach (statistical models as
benchmarks)•Bucket-style models as a statistical model
Can more complex models extract the same information content from the available data on meteorology, vegetation, soils and topography?
If not, why not?
What work do we need to do in order to ensure that physically realistic models perform better than models with inadequate process representations?
Newman et al., HESS (in press)
Model constraints?
Hard coded parameters are the most sensitive ones
Outline• Motivation
▫ Large inter-model differences in representation of the land component of the water cycle
▫ Opportunities to improve both fidelity of process representations and characterization of model uncertainty
• Model development activities▫ Model architecture and process parameterizations▫ Continental-scale parameter estimation▫ Ensemble forcing▫ Routing
• Continental-scale model benchmarks▫ Data, information, knowledge and wisdom: Can complex
process-based models make adequate use of the data on meteorology, vegetation, soils and topography?
▫ Use of simple models (statistical, bucket) as benchmarks
• Summary
Summary and future plans • Work underway for a continental-scale implementation of the
flexible hydrologic modeling approach, improving continental-scale parameter estimates and improving characterization of forcing uncertainty▫ Applications: Improved representation of hydrologic processes in
climate risk assessments and in streamflow prediction systems
• Work started on improving representation of hydrologic processes in CLM▫ Collaboration with CUAHSI
• Collaboration with the Canadians (University of Saskaskewan)▫ Cold season hydrologic processes; interest in WRF-Hydro
• New focal areas: Alaska and Hawaii▫ Extend methods developed in the CONUS to “more challenging”
modeling environments
• Funding from Bureau of Reclamation, US Army Corps of Engineers, NASA, NOAA, NSF, and (hopefully) DOE