Research on Integrated Earth System
Modeling at Global and Regional Scales
L. Ruby Leung
Pacific Northwest National Laboratory, Richland, WA
2nd RASM Workshop
Monterey, CA, May 15 – 17, 2012
1
The needs for model coupling and new
development
Understand the role of biosphere-atmosphere
feedbacks on droughts in the southwestern U.S.
Asses the impacts of climate change in the
southeastern U.S. (e.g., hurricanes)
Impacts of land-atmosphere (Amazon) and
atmosphere-ocean (Atlantic) interactions on the
tropical Atlantic biases
Develop an integrated model to represent
human-earth system interactions for modeling
and analysis of climate change mitigation and
adaptation, with a focus on the nexus of energy,
water, and land use
2
Regional Earth System Model (RESM)
A regional earth system model is being developed using
WRF, CLM, and ROMS, following the flux coupling
approach used in CESM
Global
CESM
Consistent
representations of
land processes at
global and regional
scales
CAM
Atmospheric conditions
Flux Coupler
Surface fluxes
CLM
POP
Regional
Atmosphere and ocean
boundary conditions
RESM
Integrate human
systems in CLM
Atmospheric conditions
Flux Coupler
Surface fluxes
CLM
3
WRF
Flexibility to model
land processes using
resolution or grid
different from the
atmospheric model
ROMS
Facilitate air-sea
coupling at regional
scale
Model development
Developed global high resolution (0.05o) input data for
CLM based on MODIS
Implemented the VIC surface/subsurface runoff and
groundwater parameterizations to CLM
Tested grid based vs subbasin based approaches
Developed a new river routing model for CLM for both
grid based and subbasin based approaches (including
global input data at 6 different resolutions)
Developing a water management model for CLM
Adding subgrid elevation classification in CLM
Applied UQ to understand model sensitivity to hydrologic
parameters
Developing WRF-ROMS coupling through CPL7
4
A 0.05-degree input dataset for CLM
5
Comparison of new and old CLM input data
Bare soil
Trees
Shrubs
Grass
Crop
6
Introducing VIC soil hydrology to CLM
Infiltration excess runoff
Surface- and groundwater
interactions
Saturation excess runoff
ARNO baseflow curve
Hydraulic redistribution
Interactions of water
movement between the root
system and soil porous media
7
Dynamic representation of surface and
groundwater interactions
Change of
soil moisture
Diffusion
term
Drainage
term
  
  K ( )
  D( )

t z 
z 
z
Change of water
table depth
s porosity
ne(t) effective porosity
1
 (t  t )   (t ) 

 s  ne (t )
t  t


 t  t    (t )   ( p  R  Qb  E t )  dt 
t


Liang et al., JGR, 2003
8
Change of total soil moisture
in the unsaturated zone
Net water recharge to
the groundwater body
Implementation of VICGROUND to CLM
A runtime
option
activated
through the
namelist
9
Simulated water budget at Tonzi Ranch
10
Global testing of CLMVIC
CLM4-SP
Forcing: Qian et al. 2004
Land cover: current (i.e., 2000)
Simulation period: 1995-2004
Resolution: standard one-degree (i.e., 0.9 x 1.25)
CLM-CN
Forcing: CRU-NCEP
Land cover: potential vegetation (pre-industrial)
Simulation period: 1800-1900 (by randomizing 19011930)
Resolution: 0.5-degree grid
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CLM4-SP: Summer LH, 1995-2004
CLM4
CLM4VIC – CLM4
12
CLM4VIC
CLM4VIC – CLM4, global mean
CLM4-CN: Summer LH
CLM4CN
CLM4VICCN – CLM4CN
13
CLM4VICCN
CLM4VICCN – CLM4CN
global mean, stabilized
Motivation for a new runoff routing model
To provide more accurate freshwater flux to the ocean from
subdaily to daily time scales
To provide a linkage between the human (e.g., surface
water withdrawal, reservoir operation) and natural systems
For transport of nutrients and sediments
Features
Consistent process representation across various scales
(global, regional, local)
Easy to be coupled with water management model
Easy to be coupled with other fluxes
14
River Transport Model (RTM) in CLM 4.0
Study area divided into cells
Flow direction is determined
by D8 algorithm
Cell-to-cell routing with a
linear advection model
Limitations
Over-simplification of river network
Over-simplification of physical processes
Global constant channel velocity (0.35m/s)
No account for sub-grid heterogeneity
15
Model for Scale-Adaptive River Transport (MOSART)
Grid-based approach
16
Subbasin-based approach
This hierarchical dominant river tracing
method preserves the baseline high resolution
hydrography (flow direction, flow length,
upstream drainage area) at any coarse
resolution (Wu et al. 2011)
Subbasin representation
preserves the natural
boundaries of runoff
accumulation and river system
organization
Model for Scale-Adaptive River Transport (MOSART)
Grid-based approach
Subbasin-based approach
Conceptualized network
Hillslope routing
Sub-network routing
Main channel routing
17
Hillslope routing to account for event dynamics and impacts of
overland flow on soil erosion, nutrient loading, etc.
Sub-network routing: scale adaptive across different resolutions
to reduce scale dependence
Main channel routing: explicit estimation of in-stream status
(velocity, water depth, etc.)
Inputs and Parameters
Daily runoff generation from UW VIC at 1/16o
resolution for the Columbia River Basin
Spatial delineation and network based on
HydroSHEDS
DRT algorithm for grid-based representation 1/16, 1/8,
¼ and ½ degree resolutions (available globally)
ArcSWAT package for subbasin-based representation
(average size ~109km2)
Manning’s roughness for hillslope and channel
routing set to 0.4 and 0.05, respectively
Evaluate against monthly naturalized streamflow
data at selected major stations
18
Improved streamflow simulations
NS coefficient for monthly mean streamflow – grid based representation
1
Q_RTM(1/2)
0.9
0.8
Q_RTM(1/4)
0.7
Q_RTM(1/8)
0.6
Q_RTM(1/16)
0.5
Q_MOSART(1/2)
0.4
Q_MOSART(1/4)
0.3
0.2
Q_MOSART(1/8)
0.1
Q_MOSART(1/16)
0
DALLE
ICEHA
PRIRA
CHIEF
BROWN
WANET
CORRA
Q_VIC(1/16)
ARROW
NS coefficient for monthly mean streamflow – subbasin based representation
1
0.9
0.8
RTM(1/2)
0.7
0.6
MOSART_grid(1/8)
0.5
0.4
MOSART_subbasin
0.3
0.2
VIC(1/16)
0.1
0
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DALLE
ICEHA
PRIRA
Large drainage area
CHIEF
BROWN
WANET
CORRA
ARROW
Small drainage area
Water Resource Management Model: Conceptual Design
For full coupling in an earth system model:
Assume no knowledge of future inflow
Use generic operating rules
Two components:
Regulation module: extraction of water at the reservoir
Storage: stores water over extended period of time
Regulation: Follows monthly operating rules for flood
control, environmental flow, irrigation and hydropower
Constrained extraction: Daily partitioning of reservoir
releases for irrigation water supply, other consumptive uses
and environmental constraints. It includes the distribution
across demanding units.
Local surface water extraction module: extracts water at the
unit
Hillslope surface runoff: represent irrigation retention ponds
Unit main stem if unpounded by an upstream reservoir
20
CLM-MOSART-WRM coupling
Routing + reservoir model (T- Δt )
CLM (t)
Loop over PFTs
Aggregated
demand
(t- Δt)
Local surface water (t- Δt) contribution to
irrigation demand (t). Remaining
demand?
NO
Irrigated fraction found
NO
YES
Need
irrigation
YES
YES
Aggregated
supply (t)
NO
Extraction from main stem if not
impounded. Remaining demand?
YES
Updated ET, runoff,
baseflow, irrigation demand
Extraction from reservoir release to
complement the local supply
End of loop
Generated runoff,
Agg. irrigation demand
CLM
Routing model
Agg. irrigation supply
21
Natural flow in each units;
irrigation demand
PFTs: vegetation types
WRM
Regulated flow;
Irrigation supply at each unit
Data Preprocessing
Create a “unit-reservoir dependency database”:
- Local approach: independent tributaries, elevation
constraint,
constrained distance-based buffer
- Global approach: elevation constraint and distance-based
buffer
Distribute the demand across the reservoir based on the
dependency database and maximum storage capacity of each
dependent dam
22
Downscaling CCSM Simulations
WRF-CLM is being used to downscale CMIP5 CCSM
historical, RCP4.5, and RCP8.5 simulations from 1975 - 2100
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Uncertainty quantification framework
24
Ranks of significance of input parameters
over 10 Flux Tower Sites
Larger sensitivity to
parameters of
subsurface processes
25
Effects of Barrier Layers on TC Intensification
Balaguru et al. 2012 PNAS (in revision)
26
TC intensification rate is higher by 20% for TC
that passes over BL than over non-BL
27