eric f. wood - lancaster university · 2012-07-05 · princeton university hyper‐resolution,...

Princeton University

Hyper‐Resolution, Global Land Surface Modeling:  Are there pathways for addressing this need and will such models improve predictive capabilities?

Eric F. WoodDepartment of Civil and Environmental Engineering

Princeton University, NJ, USA

Catchment Change Network Conference(Hyper‐resolution Modelling of the Impacts of Change)

The Lancaster Environmental Center

June 25‐27, 2012

Princeton University

Motivation for hyper-resolution (<1km) LSM

Look at the climate modeling community – who wants 10-km climate projections

Princeton University

Motivation for Hyper-Resolution (<1km) (HypR LSM)

Look at the climate modeling community – who wants 10-km climate projections

Princeton University

•How do land areas respond to the large scale climate system?•How do atmosphere-land surface interactions operate and feed back onto the regional and larger scale climate system?•How do these interactions vary with spatial scale? •How do these interactions operate over the annual cycle and what are their most critical periods in terms of feedbacks?

Understanding the role of the terrestrial hydrosphere-biosphere in Earth’s climate system

Hydrologic Science Issues that require HypR-LSM

Princeton University

•How do land areas respond to the large scale climate system?•How do atmosphere-land surface interactions operate and feed back onto the regional and larger scale climate system?•How do these interactions vary with spatial scale? •How do these interactions operate over the annual cycle and what are their most critical periods in terms of feedbacks?

Understanding the role of the terrestrial hydrosphere-biosphere in Earth’s climate system

Hydrologic Science Issues that require HypR-LSM

Understanding the role of the land surface in climate variability and climate extremes.

•What is the nature and extent of changes to the terrestrial water and energy balance due to changing climate and land surface characteristics?

Princeton University

•How do land areas respond to the large scale climate system?•How do atmosphere-land surface interactions operate and feed back onto the regional and larger scale climate system?•How do these interactions vary with spatial scale? •How do these interactions operate over the annual cycle and what are their most critical periods in terms of feedbacks?

Understanding the role of the terrestrial hydrosphere-biosphere in Earth’s climate system

Hydrologic Science Issues that require HypR-LSM

Understanding the role of the land surface in climate variability and climate extremes.

•What is the nature and extent of changes to the terrestrial water and energy balance due to changing climate and land surface characteristics?

Understanding the linkages amongst the terrestrial water-energy and biogeochemical cycles within the physical climate system.

•How are the terrestrial and oceanic systems linked through water and biogeochemical fluxes?

Princeton University

1. “Develop an observational field programme in highly instrumented basins”

2. “Increase awareness of the value of data”

3. “Advance the capability to make predictions and their uncertainty”

4. “Advance understanding of hydrologic variability”

PUB Science Drivers

PUB science plan has as objectives:

What should be measured, for how long and what will be learned?

Are some data more ‘valuable’ than others, if so which?

How can we estimate predictive skill for ungauged basins?

How can we estimate the sources of hydrologic variability, especially the role of climatic forcings versus landscape controls?

Princeton University

1. “Develop an observational field programme in highly instrumented basins”

2. “Increase awareness of the value of data”

3. “Advance the capability to make predictions and their uncertainty”

4. “Advance understanding of hydrologic variability”

PUB Science Drivers

PUB science plan has as objectives:

What should be measured, for how long and what will be learned?

Are some data more ‘valuable’ than others, if so which?

How can we estimate predictive skill for ungauged basins?

How can we estimate the sources of hydrologic variability, especially the role of climatic forcings versus landscape controls?

I claim little progress will b

e made through

observational studies alone.

Princeton University

An integrated remote sensing W/E/B cycles testbed

1. What are the potential benefits of an integrated water‐energy‐biogeochemical remote sensing testbed?

2. Can a testbed help us pose science questions related to these cycles at regional/continental/global scales?

3. Can a testbed help the development of applications based on integrated remote sensing observations?

4. How can we create a terrestrial water cycle testbed?

RunoffPrecip. Evap.

GW FlowRecharge

Princeton University

Integrated Water Cycle Modeling•Modeling all the major stocks and fluxes of the terrestrial water 

cycle in a comprehensive and interactive manner.-Snow, surface water, soil moisture, groundwater-Evapotranspiration, runoff, streamflow, floodplain hydrodynamics, energy fluxes, interfacial water fluxes

•Links to in situ and remotely‐sensed data, hydrologic information systems, etc.-Streamflow, soil moisture, well levels-SWOT, SMAP, SCLP/CoReH2O, GRACE, AMSR‐E, MODIS, etc.

•Water management, consumptive use and urban areas must be included

-Irrigation, reservoir storage, withdrawal rates•Links to water quality, biogeochemical, ecological and climate 

models •Models should be available to the community

-e.g. Community Hydrologic Modeling Platform (CHyMP), PUB

The Need for Integrated W/E/B Cycle Modeling

From Famiglietti (SWOT workshop)

Princeton University

Science Drivers for Integrated W/E/B Cycle Modeling

•How is fresh water distributed over and through the land surface, and how will this change over the next century?

•How can water management best adapt to changes in global hydrology, and what are the local‐ to global‐scale feedbacks?

•What are the effects of a changing landscape and changes in water management on hydrologic processes?

•Can remote sensing be used to test hydrologic theory on spatial hydrologic processes, and can it provide us with mult‐iscalemeasurements, so hydrologic processes are transferred correctly across scales?

Grand challenge: modeling the storage, movement and quality of water at every point on the landscape.

There is simply no way to accomplish this without global remote sensing water cycle assimilation of in situ and remotely sensed data into a HypR‐LSM.

Princeton University

Remotely sensed photosynthetic activity as a measure for ET

Rascher, Crewell

et al., (2009)

Agricultural test site of the Research Center TR32 close to Jülich, Germany

We encounter variance at all scales

ANDhave to deal with coupled 

nonlinear physics.

Provided by S Kollet

Princeton University

Increasing resolution more realistic soil moisture?

1km

350m

~1500km2

Can we go to higher resolution?

Kollet & Maxwell, WRR (2008)

Little Washita

Princeton University

Analysis of Pathways and Residence Time Distribution from Hyper-resolution Simulations shows Fractal Scaling

Residence Times Pathways

Land surface

20 400

Travel time (y)

Z (m)

Kollet & Maxwell, GRL (2008)

Princeton University

Simulating the Carbon Cycle:Carbon Out-gassing from the Amazon

(8S,72W)

(0,72W) (0,54W)

(8S,54W)(8S,72W)

(0,72W) (0,54W)

(8S,54W)

(0,54W)

(8S,72W)

(0,72W)

(8S,54W)

(Amazonia inundated area from JERS imagery)Richey et al. (Nature,,2002) 

Richey et al (2002) estimated that 0.5 Gt of carbon per year is out‐gassed from  stream  surfaces  in  the  Amazon  basin  ‐‐ an  order  of magnitude larger  than  the  export  to  the  ocean  via  the  channel  system.    Because much of  the  total  surface  area  is made up  of  relatively  small  streams, accounting for this term requires a Hyper‐resolution LSM that represents the stream channel systems, and their dynamics.

Princeton University

Ground Surface

Water Table

Infiltration Front

Saturated Zone

Vadose Zone

Vegetation

Atmosphere

Examples of massively parallel computer simulations

Princeton University

Land Surface Model requirements

• Distributed (represent hydrologic processes at 100 – 1000m scale)

• Water/energy/biogeochemistry/water quality• Cold season (snow/frozen soil/permafrost)• Lakes/reservoirs/wetlands• Water management, irrigation, urban areas, and??

Potential model frameworks?? (VIC, TOPLATS, G2G, tRIBS and/or??)

Princeton University

Land Surface Model requirements

• Distributed (represent hydrologic processes at 100 – 1000m scale)

• Water/energy/biogeochemistry/water quality• Cold season (snow/frozen soil/permafrost)• Lakes/reservoirs/wetlands• Water management, irrigation, urban areas, and??

Potential model frameworks?? (VIC, TOPLATS, G2G, tRIBS and/or??)

Princeton University

HypR-LSM Model ParametersSp

atia

l Sca

le (k

m)

20001000500250100

502510

521

Vegetation Soil Topography Lakes/wetlandsUrban areas

AVHRRMODISLandSat

ISLSCP (100km)(global)

(1km)NRCS (U.S.)FAO (global)

Hydro1K (1km)HydroSHEDS (90m)

Princeton University

Global Reservoir and Dam (GRanD) Database

coordinated by Global Water System Project (GWSP)

~ 7000 largest reservoirs – Total storage capacity ~ 5000 km3

referenced to SWBD polygons and HydroSHEDS river networkBernhard Lehner,McGill

Princeton University

Global Reservoir and Dam (GRanD) Database

coordinated by Global Water System Project (GWSP)

~ 7000 largest reservoirs – Total storage capacity ~ 5000 km3

referenced to SWBD polygons and HydroSHEDS river networkBernhard Lehner,McGill

~ 4.2 mill. km2

~ 2.8% of land area~ 304 million> 0.1 ha

~ 3.5 mill. km215-26 million> 1 ha

~ 2.9 mill. km21-2 million> 0.1 km2

~ 2.3 mill. km2155,000> 1 km2

Area of lakesNr. of lakesSize class

Princeton UniversityBernhard Lehner,McGill

Topography, and extracting river networks

Princeton University

TM classification of land cover at ~30 m resolution in the vicinity of Leavenworth, WA.  Right panel:  mosaic equivalent.

Land cover and its current representation in LSM

Princeton University

Global Meteorological Forcing Dataset

ReanalysisHigh temporal/low spatial resolution

ObservationsGenerally low temporal/high spatial resolution

Global Forcing DatasetHigh temporal/high

spatial resolution, bias corrected, trend corrected, etc…

CRU1901-2000, Monthly, 0.5degP, T, Tmin, Tmax, CldGPCP

1997-, Daily, 1.0degP UW

1979-2000, Daily, 2.0degP TRMM

2002-, 3hr, 0.25degP SRB

1985-2000, 3hr, 1.0degLw, Sw

Bias Correct and Downscale• corrected rainday statistics, gauge undercatch• removal of biases in monthly P, T, DTR, SW, LW• removal of spurious trends in SW • adjustment for elevation effects• downscale in time and space• consistency check between P and radiations

Precipitation

Temperature

SW Radiation

Precipitation

Temperature

SW Radiation

Diurnal Cycle

Princeton University

Global Meteorological Forcing Dataset

ReanalysisHigh temporal/low spatial resolution

ObservationsGenerally low temporal/high spatial resolution

Global Forcing DatasetHigh temporal/high

spatial resolution, bias corrected, trend corrected, etc…

CRU1901-2000, Monthly, 0.5degP, T, Tmin, Tmax, CldGPCP

1997-, Daily, 1.0degP UW

1979-2000, Daily, 2.0degP TRMM

2002-, 3hr, 0.25degP SRB

1985-2000, 3hr, 1.0degLw, Sw

Bias Correct and Downscale• corrected rainday statistics, gauge undercatch• removal of biases in monthly P, T, DTR, SW, LW• removal of spurious trends in SW • adjustment for elevation effects• downscale in time and space• consistency check between P and radiations

Precipitation

Temperature

SW Radiation

Precipitation

Temperature

SW Radiation

Diurnal Cycle

How to go from ~25 km resolution to the 1 km scale?  Satellite precipitation is available at ~2km (CMORPH, JAXA), solar radiation ~2 km from geostationary satellites, regional model output at 1km, BUT WE MUST DEMAND MORE AND BETTER OBSERVATIONS.

Princeton University

Some examples: TOPLATS over the US

catchments

river network

simulated water table depth

simulated inundation extent

Goteti et al., 2008

Princeton University(Cole et al, EGU 2009)

Some examples: G2G at 1km over the UK

Princeton University

5‐minute Soil Type (FAO)

Some examples: 5-km over Africa

Princeton University

Some examples: VIC at 5-km over Africa

Princeton University

Some examples: VIC at 5-km over Africa

Princeton University

Observation System Simulation Experiment (OSSE)Similar to the call for virtual hydrologic laboratories of Weiler and McDonnell 

(“Virtual experiments: a new approach for improving process  conceptualization in hillslope hydrology” J. Hydrology 285(2004): 3‐18.)

Goals – to test out the value of new integrated W/E/B cycle observations for addressing science and applications:

• Sampling (orbit/antenna) patterns;• Sensor/retrieval errors and

correlation structure;• Data assimilation techniques.• Retrievals and their usefulness in

applications

Contribution to Water Cycle OSSE Studies

Princeton University

Observation System Simulation Experiment (OSSE)

Experiment Configurations:

• Continental to global water cycle simulations;

• 1-3 km resolution;• Hourly for at least 1 year;• FOR ALL THE WATER CYCLE

VARIABLES

1-minute Topographic Index

LIS 1km vs0.25o LSM simulation

Princeton University

Need for Data Assimilation

Data assimilation techniques for large-scale problems:

103~106 pixels on computing grid

For example, multi-scale estimation

sγ

s1 s2 sq

… … …

sαqsα2sα1

.............

Each fine scale tree node represents a group of pixels

Princeton University

Moore’s law and its limits

New Systems must integrate a large number of processors and various kinds of memory types (hierarchic).

Princeton University

The vision (and need) for hyper‐resolution, global land surface modeling: A Grand 

Challenge for the Community

Vision(\vi‐zhən\ (noun)“something seen in a dream, trance…object of imagination”

Princeton University

Illusion\i‐lü‐zhən\ (noun)   “perception that deceives or misleads intellectually”

The vision (and need) for hyper‐resolution, global land surface modeling: A Grand 

Challenge for the Community

Princeton University

1. We already are, albeit at small scales.  How to scale up to regional and continental areas?

2. How do we acquire the information needed for such models (process representation; parameters; forcing data)?  We must be more demanding regarding needs.

3. How do we assess the uncertainty in predictions as we change scales?  When and where would such models provide useful for improved scientific understanding and/or management decisions?

4. How can the international community come together for such an initiative? (A good problem for our young colleagues)

Can we do hyper-resolution W/E/B land surface modeling?

Potential Pathways Forward and the Challenges