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Land Surface Hydrology and the Earth System Christopher Taylor Eleanor Blyth Doug Clark Richard Harding Centre for Ecology and Hydrology, Wallingford Nic Gedney Hadley Centre Dave Lawrence CGAM, University of Reading. Land Surface Hydrology and the Earth System.

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Land Surface Hydrology and the Earth System Christopher Taylor Eleanor Blyth Doug Clark Richard HardingCentre for Ecology and Hydrology, WallingfordNic GedneyHadley CentreDave LawrenceCGAM, University of Reading

land surface hydrology and the earth system
Land Surface Hydrology and the Earth System
  • Role of land surface in hydrological cycle
  • Impacts of variability (spatial and temporal) on atmosphere
  • Modelling
  • requirements
  • point processes
  • sensitivities of soil hydrology in GCM
  • horizontal complexity
  • Conclusions
role of land surface in hydrological cycle




Soil moisture store



Role of land surface in hydrological cycle
  • After rain/snow, surface hydrology determines when water available for evaporation back into atmosphere, or runoff into ocean
  • Continental regions, surface evaporation is important part of atmospheric moisture budget, + controls diurnal heating
  • Potential for feedbacks between soil wetness and atmosphere
temporal variability
Temporal Variability

Impact of Interactive Soil Moisture on Climate Variability in GCM - North America

RH spectrum: interactive soil moisture experiment

RH spectrum: prescribed soil moisture experiment

Delworth and Manabe (1993)

Soil moisture-atmosphere interactions increase variability of atmosphere and modify time scales of variability

temporal variability5
Temporal Variability

Impact of Improved Surface Description on Sahelian Weather Systems in GCM

  • Introduced more realistic controls on evaporation:
  • diurnal timescale - stomatal conductance
  • daily timescale – soil drainage and bare soil evaporation
  • Affected diurnal and synoptic weather systems

Power spectra of rainfall - Sahel

Taylor and Clark, QJRMS, 2001

spatial variability

~2000 km

Spatial Variability

Patterns of soil moisture in Sahel inferred from Meteosat

Red (warm): soil surface dried

Blue (cool): recent rain

Taylor et al, QJRMS, 2003

Rainfall variability at range of scales produces surface heterogeneity

impacts of spatial variability on atmosphere convective scale
Impacts of Spatial Variability on Atmosphere: Convective Scale

Annual Rainfall 1992 (HAPEX-Sahel)

Extreme gradients rain can develop over series of storms

Not random, nor forced by topography

Soil moisture feedback:

memory of past storms retained in soil moisture + atmospheric humidity gradients

Feedbacks appear even at 10km!

Taylor and Lebel, MWR, 1998

impacts of spatial variability on atmosphere convective scale8
Impacts of Spatial Variability on Atmosphere: Convective Scale

A Sahelian squall line approaching

  • Use cloud-resolving model to show that rainfall from organised convective systems very sensitive to surface variability
  • Dynamical feedbacks at scale of individual convective cells reinforce soil moisture variability, particularly when surface length scales matches convective cell length scale

Rainfall increase

Wet patch

Rainfall decrease

Clark et al, QJRMS, 2003

impacts of spatial variability on atmosphere synoptic scale
Impacts of Spatial Variability on Atmosphere: Synoptic Scale

1000 km

Satellite analysis shows that wet and dry patches occur at large scale.

Composite “hotspot”


Atmospheric analyses suggest:

higher temperatures

lower surface pressure



vortex develops

subsequent rainfall



Degrees longitude

Taylor et al, submitted QJRMS

modelling of surface hydrology
Modelling of Surface Hydrology
  • Land surface hydrology schemes need to partition:
  • net radiation into sensible and latent heat
  • rainfall into evaporation, runoff, storage
  • Requires modelling of:
  • controls on evaporation - stomatal opening
  • pathways of water (+ snow) through vegetation canopy, soil and landscape
  • These processes complex + not always well understood at large scale
  • Parameters not universal but depend on:
  • soil properties
  • vegetation properties
  • topography
  • These properties vary widely over globe.
modelling point processes need for complexity


MOSES + snow/canopy processes

Standard MOSES

Essery and Clark, GPC 2003


Modelling point processes: need for complexity

Example of interception of snow by forest canopy in Swedish catchment.

Simple model, moisture rapidly recycled to atmosphere

Adding complexity, model runoff is enhanced and delayed

Month of year

comparison of surface schemes within gcms

Evaporative fraction

Runoff (mm/day)

Gedney et al JClim 2000

Critical soil moisture

Scaled soil moisture

Scaled soil moisture

Comparison of Surface Schemes Within GCMs

4 GCMs run, each with 2 versions of own surface scheme

Diagnosis over Amazonia (similar results elsewhere):

(i) Evaporative fraction increases to “critical point”, tends to flatten off for wetter soils

(ii) Runoff response quite different in different schemes

(iii) Models occupy characteristic soil moisture regimes, some unrealistic

– partly due to forcing (precip)

Soil moisture state is closely linked to runoff at critical point in scheme

soil moisture stress in unified model
Soil moisture stress in Unified Model

Large parts of world are either very dry (purple) or very wet (red)

Not v. realistic

Extreme soil moisture states can produce positive feedbacks on precip. to exacerbate problem

Using alternative soil hydrology parameterisation (incl. greater runoff at critical point), reduce number of extreme points

Still left with unrealistic soil moisture where precip. poor

horizontal versus vertical complexity
Horizontal versus vertical complexity

Surface schemes tended to focus on vertical processes (e.g. evaporation)

Can be compared with field observations

But landscape has obvious horizontal complexity

Comparison of impact of complexity in vertical (soil type) with horizontal (topography)

Suggests that horizontal complexity at least as important

Blyth, IAHS 2001

sub grid variability
Sub-grid variability

GCM grid cell

Sub-grid variations in near-surface moisture due to recent rain

- large variability in direct evaporation from soil

- large variability in drainage rates

Soil moisture using mean rainfall

Sub-grid rainfall distribution assumed

“True” surface soil moisture

Day of year

Compare length-scale of forcing on surface simulations

Taylor and Blyth JGR 2000

concluding remarks
Concluding Remarks
  • Land surface hydrology plays important role in earth system
  • Modelling requires:
  • appropriate complexity and scale
  • realistic forcing
  • Use of new global datasets (for parameters and validation) will produce better models