Scaling and Hydrologic Modeling

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CUAHSI

Fall 2004 Vision Paper Cyberseminar Series www.cuahsi.org

CUAHSI

Fall 2004 Vision Paper Cyberseminar Series www.cuahsi.org

Geoff

Thyne

Coming to you from

Golden, CO Golden, CO October 14 October 14thth_{, 2004}_{, 2004} To begin at 3:05 ET To begin at 3:05 ET

Scaling and Hydrologic Modeling

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Welcome to the 3

rdrd

Semester of

CUAHSI

Education and Outreach

Distinguished Lectures

Host: Jon Duncan

CUAHSI Communications

Director

Problems?

Send a chat to Host

Feedback?

Please send an email to

[email protected]

Director

The Presentation can be downloaded From www.cuahsi.org

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Fall Schedule

•

Intensively Managed LandscapesIntensively Managed Landscapes Bill Simpkins, ISU. October 19

Bill Simpkins, ISU. October 19thth

•

EcohydrologyEcohydrology of Semiof Semi--Arid EnvironmentsArid Environments Brent Newman, LANL. October 21

Brent Newman, LANL. October 21stst

•

Remote SensingRemote Sensing Witold

Witold KrajewskiKrajewski, U Iowa October 26, U Iowa October 26thth

Go to CUAHSI website for complete calendar, links to

papers, presentations, and discussion forums

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A framework for interdisciplinary

watershed research

Geoffrey

Geoffrey ThyneThyne, , AdriaAdria BodourBodour, Wendy Gordon, , Wendy Gordon, John

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Authors

Wendy Gordon

Wendy Gordon -- Water Planning Coordinator Water Planning Coordinator -- Texas Parks & WildlifeTexas Parks & Wildlife Kyle Murray

Kyle Murray -- Department of Earth and Environmental Science Department of Earth and Environmental Science -- UT at San AntonioUT at San Antonio Adria

Adria BodourBodour -- Department of Earth and Environmental Science Department of Earth and Environmental Science -- UT at San AntonioUT at San Antonio Geoffrey

Geoffrey ThyneThyne –– Geology and Geological Engineering Geology and Geological Engineering -- CSM CSM John McCray

John McCray -- Hydrology Program Hydrology Program -- Environmental Science and Engineering Division Environmental Science and Engineering Division –– CSMCSM

Acknowledgements

Kathleen Nicoll - School of Geography & The Environment - University of Oxford CUASHI – Richard Hooper and Jon Duncan

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Authors Multidisciplinary Education and Training

Biology Biology Botany Botany Chemistry Chemistry Electrical Engineering, Electrical Engineering, Environmental Science Environmental Science

Environmental Systems Engineering Environmental Systems Engineering Geography and Environmental Studies Geography and Environmental Studies

Geological Engineering Geological Engineering

Geological Sciences

Hydrology and Water Resources. Hydrology and Water Resources.

Natural Resource Policy Natural Resource Policy

Oceanography Oceanography

Soil, Water and Environmental Science Soil, Water and Environmental Science

Zoology Zoology

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Objectives

•

• Propose an approach to scaling problems associated with quantitaPropose an approach to scaling problems associated with quantitative tive watershed modeling in the context of the CUASHI Hydrologic

watershed modeling in the context of the CUASHI Hydrologic

Observatories

Observatories •

• Approach should be applicable over next tenApproach should be applicable over next ten--fifteen yearsfifteen years

•

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Future Watershed Model Goals

•

The successful watershed model of the future will have The successful watershed model of the future will have to address relevant societal issues such as surface

to address relevant societal issues such as surface

discharge rates, vulnerability and contaminant transport,

and sustainability of water resources both within the

watershed and for exports to downstream watersheds

over a broad of time scales.

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The Problem

•

• Our observations are generally made at spatial and temporal scalOur observations are generally made at spatial and temporal scales es much smaller than the watershed and long

much smaller than the watershed and long--term climatic term climatic

predictions. Thus, the effect of scale when trying to determine

predictions. Thus, the effect of scale when trying to determine the the mathematical formulations for watershed models remains an issue.

mathematical formulations for watershed models remains an issue. •

• If we cannot predict watershed response to changes in inputs If we cannot predict watershed response to changes in inputs because of data from different scales, then data at the same sca

because of data from different scales, then data at the same scale le or at least at scales as similar as possible is required.

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Assumptions

•

• The future trend will be an earth system approach that integrateThe future trend will be an earth system approach that integrates s multidisciplinary data in a single model (e.g. hydrologic, biolo

multidisciplinary data in a single model (e.g. hydrologic, biological, gical, geochemical, ecological and geomorphologic).

geochemical, ecological and geomorphologic). •

• Both empirical and fundamental approaches will be used to Both empirical and fundamental approaches will be used to

formulate models, but models will use the distributed approach.

formulate models, but models will use the distributed approach. •

• The scale of measurements in watersheds will be limited to existThe scale of measurements in watersheds will be limited to existing ing or emerging technologies.

or emerging technologies. •

• Hydrologists will typically continue to make point to small catcHydrologists will typically continue to make point to small catchment hment scale measurements that must be up

scale measurements that must be up--scaled to model grids (i.e. scaled to model grids (i.e. model grids will remain coarser than measurement scales).

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Assumptions, cont.

•

• The framework should try and limit the problems with scaling by The framework should try and limit the problems with scaling by limiting the degree of scaling (up or down).

limiting the degree of scaling (up or down). •

• The framework should facilitate merging data from the smaller The framework should facilitate merging data from the smaller research sites to the much larger

research sites to the much larger HOHO’’ss..

•

• Investigators will be provided core data from Investigators will be provided core data from HOHO’’ss, but the core , but the core data must be supplemented with any public

data must be supplemented with any public--domain information and domain information and future investigations.

future investigations. •

• GIS will be a fundamental tool for constructing future watershedGIS will be a fundamental tool for constructing future watershed models.

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Types of models

•

physicallyphysically--based models with rigorous mass and energy based models with rigorous mass and energy constraints

constraints

•

conceptual models with empiricallyconceptual models with empirically--calibrated lumped calibrated lumped parameters that enable prediction, but often lack the

parameters that enable prediction, but often lack the

ability to evaluate fundamental relationships and extend

our understanding

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Scale Dependence

•

Types of scale

dependence

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Dispersion

Fundamental property of aquifer material

that influences transport by groundwater

Shown to be linear function of Shown to be linear function of distance

distance--fromfrom--source at scales source at scales

between about 1 m and 100 m for a between about 1 m and 100 m for a variety of different subsurface

variety of different subsurface

materials of varying heterogeneity. materials of varying heterogeneity. Highly non

Highly non--linear, and difficult to linear, and difficult to predict at scales less than 1 m or predict at scales less than 1 m or greater than 100m.

greater than 100m.

Can be predicted reasonably well at Can be predicted reasonably well at the 30

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Scaling Approaches

•

Approaches to deal with the scale problem have

focused on

upscaling

and downscaling, a.k.a.

aggregation and

disaggregation

,

disaggregation

-

-aggregation, fractal or using characteristic

aggregation, fractal or using characteristic

scales.

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Upscaling

(aggregation)

•

Used when trying to apply smallUsed when trying to apply small--scale data from scale data from experimental or site

experimental or site--scale relationships of fundamental scale relationships of fundamental processes such as dispersion or adsorption to the larger

processes such as dispersion or adsorption to the larger

scale of a watershed model.

Downscaling (

disaggreagation

)

•

Developed to deal with linking largeDeveloped to deal with linking large--scale datasets such scale datasets such as those from remote sensing and atmospheric

as those from remote sensing and atmospheric

circulation models (300

circulation models (300--500 km scale) to point500 km scale) to point--scale scale rainfall events, stream flow and soil properties.

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Disaggregation

-

aggregation approach

•

Tries to downscale catchmentTries to downscale catchment--scale variables to pointscale variables to point- -scale, applies physical models to the downscaled data

scale, applies physical models to the downscaled data

and

and upscalesupscales the pointthe point--scale responses back to scale responses back to catchment scale.

catchment scale.

Fractal approach

•

Fractal approach has tried to find fundamental, scaleFractal approach has tried to find fundamental, scale- -invariant relationships in hydrology.

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Framework

•

Choose standard grid size for the models that minimizes Choose standard grid size for the models that minimizes upscaling

upscaling or downscaling requirements.or downscaling requirements.

•

Consider what data common to any watershed problem Consider what data common to any watershed problem is already available and at what scale.

is already available and at what scale.

•

Example is for spatial scale.Example is for spatial scale.

•

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Fundamental Data

Data Source Resolution Precipitation Nexrad ?

Elevation DEM 10-30 m

Soil STATSGO/SSURGO digitized to 30 m Vegetation Landsat 10-30 m

Land use NLCD ?

Surface water NWIS 10-30 m Surface temperature RS radiometer 30 m

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Additional Data

point point

Data Source Resolution Streamflow USGS, State, County, EPA, etc. point

Groundwater level USGS, State, County, EPA, etc. point Water quality USGS, State, County, EPA, etc. point ET USGS, State, County, EPA, etc. point Imports and diversions USGS, State, County, EPA, etc. point Point source discharge EPA database point Soil moisture USDA, State, County point

Wind NWS point

Solar radiation NOAA 3-30 m

Humidity NWS point

Hydraulic conductivity State, County, EPA, private Point (<100m) Porosity EPA Point (<1m3₎

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Sources of more watershed

information

•

Non

-

academic sources such as contaminant

studies (subsurface geology, hydraulic

conductivity, tracers, soil properties, etc.).

•

Remote sensing.

•

Scale of experiments/sensors can be adapted to

30m.

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Contaminant Sites

We understand and can measure important contaminant

transport processes at scales up to about 50 m

Data and/or wells exist (without drilling additional costly

wells) in most watersheds to investigate fate and

transport at scales up to about 100 m.

Understanding transport processes at scales larger than

100 m is a real challenge because of cost, not

technology, limitations.

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Contaminant Transport

Important contaminant sources in watersheds are often

“point” sources with respect to the watershed.

The data from many of these sites are public

The data from many of these sites are public--domain.domain. Examples:

Examples: •

•Housing Developments: Housing Developments: –

–Wastewater pollutants from septic tanks Wastewater pollutants from septic tanks

•

•Pharmaceuticals, phosphorus, nitrates Pharmaceuticals, phosphorus, nitrates –

–Runoff from constructionRunoff from construction –

–Wood and coal smokeWood and coal smoke

•

•Industrial SourcesIndustrial Sources –

–Chlorinated solvent spills (cleaners, garages)Chlorinated solvent spills (cleaners, garages) –

–Gasoline spillsGasoline spills –

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Pollutant Transport Dynamics

•

Pollutant transport mechanisms & reactions Pollutant transport mechanisms & reactions – –BiodegradationBiodegradation – –SorptionSorption – –MineralizationMineralization – –DispersionDispersion

•

These processes are well studied only at the “beaker” These processes are well studied only at the “beaker” scale (except dispersion, which has been studied up to

scale (except dispersion, which has been studied up to

a scale of about 2000 m).

•

Ground water heterogeneities control contaminant Ground water heterogeneities control contaminant transport at scales from 5 m to 1000 m.

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•

Typical areas of most sources are 100 m

2 2

to 10

66

m

10 m x 10 m to 1000m x 1000 m

Source Area: Drums with solvent and metal waste in Idaho.

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Little Creek Amphibious Base

Virginia Beach, Virginia

0.001 0.010 0.100 1.000 0 1 2 3 4 5 6 7 8 Time (days) C/Co Field Data Model

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Perhaps the largest well

Perhaps the largest well- -studied contaminant plume

studied contaminant plume

(Washington state).

Scale ~ 2000 x 2000 m

More than 20 years of

study

Plume behavior still

confounds researchers.

Represents upper end of

possible scale for

contaminant transport

processes.

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Hydraulic Conductivity:

Another example

•

Critical to understand ground water Critical to understand ground water flow.

flow.

•

Pump tests work well at scales less than Pump tests work well at scales less than 100 m, generally.

100 m, generally.

•

Well logs provide means to estimate K Well logs provide means to estimate K (driller’s pump tests) for entire

(driller’s pump tests) for entire

watershed.

•

Scale of measurement is Scale of measurement is between homes.

between homes.

•

Many sites exist with groundwater wells Many sites exist with groundwater wells already installed at

already installed at spacingsspacings of 10m to of 10m to 100m. These can be used to obtain

100m. These can be used to obtain

aquifer information at ~ 30 m scale.

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Remote Sensing

–

linking topography

to other variables of interest

Topographic Variable Derived Variable

Elevation Precipitation (e.g., PRISM (Daly et al. 1994)); snow

characteristics (e.g., Liston and Sturm 1998, Prasad et al. 2001); wind speeds; air temperature; soil depth.

Slope Incident solar radiation (e.g,. Dozier and Frew 1990); soil properties and preferential flow pathways (e.g., Beven and Kirby 1979); soil moisture; transpiration rates (e.g., Beven 1995).

Aspect Incident solar radiation; precipitation; snow accumulation and melting; wind speeds; air temperature; soil moisture;

transpiration rates.

Upslope catchment area Precipitation and runoff volume; soil moisture; soil surface properties; stream network order and main channel length (e.g., Tarboton et al. 1988, 1989, 1991, 1992, Peckham 1995). Slope curvature Snow accumulation; subsurface water flow; soil moisture; soil

litter accumulation; soil erosion/deposition rates; soil depth and texture; water-holding capacity; nutrient availability.

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Remote Sensing

–

Estimation of ET

Fundamental physical process controlled by the

amount of solar radiation and

amount of solar radiation and interactions of interactions of soil, soil, atmospheric, and plant media.

atmospheric, and plant media.

Spectral reflectance and

Spectral reflectance and emittanceemittance data measured by data measured by satellite or airborne remote sensing equipment can be satellite or airborne remote sensing equipment can be used to obtain parameters successfully used by

used to obtain parameters successfully used by Anderson et al. (2003). Anderson et al. (2003). Instrument Resolution (m) Coverage repeat interval Number of bands LA NDSA T 30 16 days 7 A VIRIS 17 On demand 224 A STER 15 - 90 On demand 15 A DS40 1 On demand 4

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Proposed Framework

•

Most common scale for Most common scale for geomorphologicalgeomorphological data is 30 m,data is 30 m,

•

some related data can be obtained at that scale,some related data can be obtained at that scale,

•

30 m scale is intermediate between many other 30 m scale is intermediate between many other measurements minimizing up or downscaling,

measurements minimizing up or downscaling,

•

30 m grid would require about 11 30 m grid would require about 11 milliionmilliion cells per layer cells per layer in a model which is tractable given increasing computer

in a model which is tractable given increasing computer

speeds and memory,

•

other related parameters could be measured at the 30 m other related parameters could be measured at the 30 m scale.

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Microbial Processes at 30

-

m scale

•

No data at this scale to date.No data at this scale to date.

•

Microbial processes are studied using small sample Microbial processes are studied using small sample sizes (< 1

sizes (< 1 –– 150 g of soil). 150 g of soil).

•

Investigations for microbial processes at field scale Investigations for microbial processes at field scale have focused on soil respiration (i.e. CO2

have focused on soil respiration (i.e. CO2

concentration).

–

– Closed and open chamber systems using alkali or Closed and open chamber systems using alkali or

infrared gas analysis (IRGA).

•

• Problem is small chamber Problem is small chamber -- surface area 0.009 surface area 0.009 -- 0.2 m2.0.2 m2.

•

Advances in chamber may provide precise and Advances in chamber may provide precise and accurate reading of soil respiration.

accurate reading of soil respiration.

•

• Geodesic dome surface area of 12.25 m2 Geodesic dome surface area of 12.25 m2 –– Desert Desert Research Institute,

Research Institute, ArnoneArnone and and ObristObrist, 2003. , 2003.

•

Other disciplines advance this area of researchOther disciplines advance this area of research

–

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Example: LI-COR LI-8100 Automated

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Example: Geodesic Dome Ecosystem

Arnone and Obrist (2003)

Gas flux chamber being carried to an experimental plot. Arrows indicate the various features of the chamber.

Potential design for microbial

respiration CO2 fluxes at 30-m scale.

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Intergrating

Data

Example from small mountain

watershed

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Integrating Data (using GIS)

Turkey Creek Basin,

Jefferson County

(37)

Integrating Data

-

GIS

30 m DEM serves as base map

Turkey Creek Basin,

Jefferson County

(38)

Integrating Data

-

GIS

Digitized geological map (raster) allows geological parameter for each cell

Turkey Creek Basin,

Jefferson County

(39)

GIS can combine datasets by

producing parameter values

for each grid cell

(40)

Integrating Data

-

GIS

Other types of data (recharge) can be derived from raster maps at the 30 m scale

Turkey Creek Basin,

Jefferson County

(41)

Integrating Data

-

GIS

Synthetic parameters such as water quality groups from point measurements can be aggregated by statistical techniques and the results kringed to generate raster images that give values for every 30m cell

Turkey Creek Basin,

Jefferson County

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Conclusions

•

Integrating multidisciplinary data in computer models Integrating multidisciplinary data in computer models will be facilitated by a standard grid size (spatial scale).

will be facilitated by a standard grid size (spatial scale).

•

Many types of data are available at the 30 m scale.Many types of data are available at the 30 m scale.

•

Data from nonData from non--traditional sources such as contaminant traditional sources such as contaminant studies are applicable at this scale.

studies are applicable at this scale.

•

The scale is intermediate, minimizing scaling from the The scale is intermediate, minimizing scaling from the smaller and larger scales.

smaller and larger scales.

•

Measurements for many processes of interest could be Measurements for many processes of interest could be made at this scale by indirect methods (microbial

made at this scale by indirect methods (microbial

respiration, ET).