Representing the Integrated Water Cycle

in Community Earth System Model

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Hong-Yi Li, L. Ruby Leung, Maoyi Huang, Nathalie Voisin,

Teklu Tesfa, Mohamad Hejazi, and Lu Liu

Pacific Northwest National Laboratory

Water in the human-Earth system

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Water underlies and influences many important climate processes and

feedbacks – a leading cause of uncertainty in projecting future climate

Water vapor and

cloud feedback Snow-albedo feedback Aerosol-cloud

interactions Carbon-water

interactions

Water is essential for energy systems, ecosystem services, and a

wide range of life sustaining and other critical human activities

Global and regional water cycles are influenced by natural processes

as well as the human systems – how will they co-evolve in the future?

Processes in the integrated water cycle

Challenge: How to represent the multi-scale, dynamic interactions

among the atmosphere, terrestrial, and human systems

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Modeling the integrated water cycle

Objectives

Represent the dynamic interactions between the human

and earth systems and their influence on the water

cycle

Use the models to investigate the nexus of climate,

energy, water, and land under climatic and societal

changes for sustainable energy and water in the future

Approach

Improve model scalability to address the multi-scale

atmospheric and terrestrial water cycle processes

Add human components (water management, water

use, water demand) of water cycle processes in CESM 4

5 5

Model Structure

R-GCAM

River Routing/

Water Management

Land Use

Electric Infrastructure/

Electricity Operations

Weather Weather

Atmosphere and ocean

boundary conditions

ROMS CLM

Flux Coupler

Atmospheric forcing

Surface fluxes

RESM WRF

POP CLM

Flux Coupler

Atmospheric forcing

Surface fluxes

CESM CAM

GCAM

CLM coupled with river routing and water

management

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River Routing Model Coupled CLM-Routing Simulation

Evaporation

Melt

Sublimation

Throughfall

Infiltration Surface

runoff

Evaporation

Transpiration

Precipitation CLM Hydrology

Aquifer recharge

Water table

Soil

Saturated fraction

Water Management Model

Irrigation water demand Reservoir operations

Improve and add new capabilities in Community Land Model (CLM) to represent

hydrology and human – water cycle interactions at multiple time and space scales

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CLM4 •Based on TOPMODEL

•Assumptions:

High-resolution topographic data are available;

Subsurface flow is topographic driven.

A quasi-steady state to approximate saturated zone dynamics;

Recharge to ground water is spatially uniform;

These assumptions are invalid, e.g., over flat terrain or arid regions

VIC • Conceptual

• Limited assumptions: – land surface, and therefore

surface runoff generation, is heterogeneous;

– Subsurface flow is a nonlinear function of deep-layer water availability

• Calibration of parameters are

recommended

Runoff parameterizations in LSMs

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VIC as an hydrologic option in CLM To be released in Summer 2013

Li et al., 2011

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Comparison of observed and simulated annual LH over

MOPEX basins: CLM, CLMVIC, and other NLDAS models

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0.0

20.0

40.0

60.0

80.0

100.0

120.0

0.0 20.0 40.0 60.0 80.0 100.0 120.0

Late

nt

hea

t (W

/m2)

MODIS latent heat (W/m2)

Mean latent heat (2000-2007)

CLM (50.6)

CLMVIC (48.1)

NOAH (57.0)

VIC (62.1)

MOSAIC (80.1)

1:1 line MODIS (46.2)

Runoff schemes affect C cycle modeling through

interactions among water, energy, and C cycles

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Relative difference: -20.4% (CLM4VIC – CLM4)

MODIS: 112 Pg C/year

CLMVIC: 114 Pg C/year

CLM4.0: 143 Pg C/year

Lei, H., M. Huang, L.R. Leung, et al. under review in JGR-Biogeosciences

Both structural and parameter uncertainties in the runoff generation schemes can lead to large uncertainty in carbon modeling, highlighting the significant interactions among the water, energy, and carbon cycles and the need for improving hydrologic parameterizations in land surface models.

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Enhancing the CLM spatial structure

Grid cell/Subbasin

Landunits

Glacier Wetland Vegetated Lake Urban

Columns

PFTs

Permanent

channel

Floodplain Floodplain Upland Upland

Subbasins vs lat/lon grids as computational units

Subgrid structure: PFT/elevation, permanent channel/floodplain

Subgrid atmospheric forcing

3D radiative transfer in mountains

PFTs/Ele

vation

Subbasin

representation

Comparison of grid vs subbasin representations

Land surface heterogeneity such as topography has a dominant

influence on hydrological processes

Using subbasins as the computational units eliminates the needs to

represent the redistribution of soil moisture between units and

improve the accuracy for estimating the topographic index used in the

TOPMODEL parameterizations of surface and subsurface runoff

Grid-based representation (CLM) Subbasin-based representation (DCLM)

1o

0.5o

0.25o

0.125o

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The subbasin representation improves scalability

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Subbasin (DCLM) Grid (CLM)

Runoff

Latent

heat

flux

Model skills (MAE)

at different

resolutions are

more strongly

correlated and

model skill

increases

systematically with

resolution in the

subbasin

representation but

not the grid

representation

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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).

(Li et al., JHM, 2013, in press)

Conceptualized network

Model for Scale Adaptive River Transport

(MOSART)

Case Study: Columbia River Basin

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Daily runoff generation from Variable Infiltration Capacity model (VIC) at 1/16 degree resolution

Off-line evaluation against monthly naturalized streamflow data at selected major stations

Model for Scale Adaptive River Transport

(MOSART)

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MOSART is more skillful in simulating streamflow compared to RTM

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0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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DALLE ICEHA PRIRA CHIEF BROWN WANET CORRA ARROW

NS coeff. for monthly mean streamflow -- subbasin based representation

RTM(1/2)

MOSART_grid(1/8)

MOSART_subbasin

VIC(1/16)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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DALLE ICEHA PRIRA CHIEF BROWN WANET CORRA ARROW

NS coeff. for monthly mean streamflow – grid based representation Q_RTM(1/2)

Q_RTM(1/4)

Q_RTM(1/8)

Q_RTM(1/16)

Q_MOSART(1/2)

Q_MOSART(1/4)

Q_MOSART(1/8)

Q_MOSART(1/16)

Q_VIC(1/16)

Large drainage area Small drainage area

Model for Scale Adaptive River Transport

(MOSART)

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MOSART reproduces monthly variation of channel velocity with minimum calibration

Model for Scale Adaptive River Transport

(MOSART)

Coupling MOSART to CLM4

Grid-based representation: replacing RTM with MOSART, keeping

remapping and parallel algorithms

Subbasin-based representation: one-one mapping between CLM and

MOSART grids

Supporting global multi-resolution database

Hydrography parameters (flow direction, channel length and slope etc.) directly derived from high resolution DEM (1km) at 1/16, 1/8, ¼, ½, 1 and 2 degree resolutions

Other parameters (channel geometry and Manning’s coefficients) derived based on landcover and empirical Hydraulic Geometry relationships

Global testing using NCAR benchmarking case (I-2000 )

Global run done for 1948-2004 period (spatial resolution of CLM4 0.9*1.25 degree, MOSART 0.5 degree)

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1129 GRDC stations were geo-referenced to the new river network (drainage area error no more than 10%)

NSC > 0 for 470 out of 1129 GRDC stations for monthly streamflow

Global testing/evaluation of CLM4-MOSART

-- Evaluation with GRDC observed streamflow

1.0E+04

1.0E+05

1.0E+06

1.0E+07

1.0E+04 1.0E+05 1.0E+06 1.0E+07

DR

T e

sti

mate

d u

pstr

eam

are

a

(km

2)

GRDC provided upstream area (km2)

Preservation of upstream area

1.0E+00

1.0E+01

1.0E+02

1.0E+03

1.0E+04

1.0E+05

1.0E+06

1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 1.0E+06

Mean

of

sim

ula

ted

flo

w (

m3/s

)

Mean of observed flow (m3/s)

Preservation of water balance

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CLM Water

column

Active layer

Water column

River bank

Active layer

River bank

Inundated area

Water

Sediment

Nutrient

Main channel routing Sub-network

routing Hillslope routing

Couple riverine biogeochemistry into MOSART

Water management model (Voisin et al.

2013, HESS, submitted)

Designed for full coupling in an earth system models

Assume no knowledge of future inflow

Use generic operating rules

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Water management model

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Total water demand Reservoir dependency

CLM

Routing model Natural flow in each units

WRM

Regulated flow

Natural and regulated flow

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Combining flood control and irrigation objectives in operating rules

best capture the observed regulated flow in the Columbia river basin

Reservoir storage and supply deficit

Reservoir storage is only reproduced using operating rules that

combine flood control and irrigation priorities

At the American Falls, supply deficit is related to groundwater use 24

Integrating with IAM: Water market and

water demand

Water demand for various

sectors driven by energy

demand, GDP, and

agricultural land demand is

simulated by IAM (GCAM)

GCAM also solves the

energy market, land market,

and water market

simultaneously to establish

prices and shares by source

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CLM

Routing model Natural flow in each units

WRM

Regulated flow

Water demand from

sectors other than

agriculture

Land use

Summary

On hydrologic modeling and model scalability:

Subbasin representation offers some advantage in scalability

The new river routing model (MOSART) represents hillslope,

tributary, and channel routing and works well across scales

A new CLM subgrid structure is being developed to account

for subgrid PFT, elevation, and inundation

On representing the dynamic interactions between

human and earth systems:

Developed a coupled system including CLM, river routing,

and water management to represent irrigation water use and

water management

Ongoing research to represent water demand, water use, and

water market using the coupled CESM - GCAM

Global implementation and evaluation underway 26