Data for Hydrologic Synthesis Project

Data for Hydrologic Synthesis Project

Expanded Datasets for 431 MOPEX Basins (Working Document)

2009 Summer Institute at the University of British Columbia, Vancouver, Canada

Prepared by

Matej Durcik

Peter Troch Paul Brooks

Hydrology and Water Resources, University of Arizona

Murugesu Sivapalan

Department of Civil and Environmental Engineering, University of Illinois

http://www.hwr.arizona.edu/

http://www.arizona.edu/

http://cee.illinois.edu/

http://illinois.edu/index.html

Data Access

Data access is public, but it requires login:

Username: public

Password: public

Acknowledgment

When using these data in any publication, please acknowledge the website by including

text such as: “Data for Hydrologic Synthesis Project were prepared by Matej Durcik and

supported by SAHRA and Water Cycle Dynamics in a Changing Environment:

Advancing Hydrologic Science through Synthesis project and are available at

http://nero.hwr.arizona.edu/mopex/.”

and cite:

Durcik M., Troch P.A., Brooks P.D., and Sivapalan M. (2009) Data for Hydrologic

Synthesis Project. Retrieved dd Mon YYYY, from http://nero.hwr.arizona.edu/mopex/.

or

Brooks P.D., Troch P.A., Durcik M., Gallo E., and Schlegel M. (2011) Quantifying

regional-scale ecosystem response to changes in precipitation: Not all rain is created

equal. Water Resources Research, 47, W00J08 doi: 10.1029/2010WR009762.

or

Voepel H., Ruddell B., Schumer R., Troch P.A., Brooks P.D., Neal A., Durcik M., and

Sivapalan M. (2011) Quantifying the role of climate and landscape characteristics on

hydrologic partitioning and vegetation response. Water Resources Research doi:

10.1029/2010WR009944.

http://nero.hwr.arizona.edu/mopex/


This data collection consists of historical hydro-meteorological and remotely sensed data

for 431 MOPEX catchments (Schaake et al., 2006; Duan et al., 2006).

Time Period:

10/01/1999 – 12/31/2008

Datasets:

Data type Dataset Source

Hydrological

Daily streamflow USGS

Daily baseflow

Daily storm runoff Computed

Meteorological

Daily site observations:

precipitation, minimal and maximal temperatures

NCDC, NRCS,

USGS

Monthly PRISM:

precipitation, minimal and maximal temperatures Oregon State

Monthly potential evapotranspiration (PRISM) Computed

MODIS

normalized difference vegetation index (NDVI)

enhanced vegetation index (EVI)

leaf area index (LAI)

fraction of photosynthetically active radiation (Fpar)

gross primary production (GPP)

net photosynthesis (PsnNet)

surface reflectance (band1 – band7)

NASA,

USGS

Additional Daily snow water equivalent (SWQ) NRCS

NARR short- and long-wave radiations NCEP

Needed

Daily gridded high resolution (~ 4 km) data:

precipitation, minimal and maximal temperatures

(to expand MOPEX database for the period from

1/1/2004 to 12/31/2008)

Data Location:


and

ftp://nero.hwr.arizona.edu/mopex/

Note: Authentication for http and ftp is required.



USGS Streamflows

Streamflov data are obtained from the USGS National Water Information System

(NWIS) (available at http://water.usgs.gov/nwis).

Data are located in sub-directories USGSStreamFlows431 and

USGSStreamFlowsSelected92.

Data are organized by USGS gage numbers with extension dsf. Each text file contains 5

columns:

1: date (yyyymmdd)

2: streamflow discharge in m3/s (10.3f)

3: daily streamflow discharge in mm (10.4f)

4: daily baseflow in mm (10.4f)

5: daily storm runoff in mm (10.4f)

The one parameter filter developed by Lyne and Hollick (1979) was used to partition the

streamflow into two components, baseflow and storm runoff. Mathematically written:

kkk fby

where y is a streamflow, b is a baseflow, f is a storm runoff and k is a time step number.

The filter equation is

112

1

kkkk yy

aabb

subject to kk yb , where a = 0.925 is the filter parameter (Lyne and Hollick, 1979;

Arnold and Allen, 1999; and Eckhardt, 2005).

Database Format

Data are also available in the ms access database which uses in the Observations Data

Model (ODM) developed by CUAHSI to store values and necessary information about

data. Database documentation and schema is at http://his.cuahsi.org/odmdatabases.html.

Database file StreamFlows4Mopex431.zip is in the sub-directory MSAccessDB.

Variables

VariableID VariableCode VariableName

100003 Q Discharge, daily

100013 Qmm Discharge, daily

100014 BF Base Flow, daily

100015 SR Storm Runoff, daily

http://water.usgs.gov/nwis

http://his.cuahsi.org/odmdatabases.html

MODIS Data

MODIS data are obtained from ftp://e4ftl01u.ecs.nasa.gov/MOLT/:

Product Dataset MOD Spatial

resolution

Temporal

resolution

Vegetation Indices NDVI, EVI MOD13A2 1 km 16 days

LAI/Fpar LAI, Fpar MOD15A2 1 km 8 days

Gross Primary Productivity GPP, PsnNet MOD17A2 1 km 8 days

More detail information about the data and other product can be found at http://modis-

land.gsfc.nasa.gov/index.htm.

The MODIS data were spatially averaged for each catchment and derived data are located

in directories MODIS431 and MODISSelected92. Each directory contains three sub-

directories qHigh, qMedium and qLow where data are computed using different data

quality filtering.

Derived data located in qLow are computed from all valid values.

For so called medium and high quality, the following filtering schema is used:

Product Medium quality High quality

Vegetation Indices Usefulness <= 8 (1000) Usefulness <= 4 (0100)

LAI/Fpar QC <= 81 (01010001) QC <= 48 (00110000)

Gross Primary Productivity QC <= 81 (01010001) QC <= 48 (00110000)

Data are organized by USGS gage numbers with extension mod. Each text file contains

19 columns:

1: date (yyyymmdd)

2: NDVI (9.4f)

3: EVI (9.4f)

4: Fpar in% (9.4f)

5: LAI in m2/m2 (9.4f)

6: GPP in kg/m2 (9.4f)

7: PsnNet in kg/m2 (9.4f)

8: Std of NDVI (9.4f)

9: Std of EVI (9.4f)

10: Std of Fpar in% (9.4f)

11: Std of LAI in m2/m2 (9.4f)

12: Std of GPP in kg/m2 (9.4f)

13: Std of PsnNet in kg/m2 (9.4f)

14: Percentage of grid points used to derive NDVI (4d)

15: Percentage of grid points used to derive EVI (4d)

ftp://e4ftl01u.ecs.nasa.gov/MOLT/

http://modis-land.gsfc.nasa.gov/index.htm


16: Percentage of grid points used to derive Fpar (4d)

17: Percentage of grid points used to derive LAI (4d)

18: Percentage of grid points used to derive GPP (4d)

19: Percentage of grid points used to derive PsnNet (4d)

Database Format

Data are also available in the ms access database which uses in the Observations Data

Model (ODM) developed by CUAHSI to store values and necessary information about

data. Database documentation and schema is at http://his.cuahsi.org/odmdatabases.html.

File Modis4Mopex431.zip contains database files for each quality level and is located in

the sub-directory MSAccessDB.

Variables


1 NDVI Normalized Difference Vegetation Index, 16 days

2 EVI Enhanced Vegetation Index, 16 days

3 FPAR Fraction of Photosynthetically Active Radiation, 8 days

4 LAI Leaf Area Index, 8 days

5 GPP Gross Primary Production, as carbon, 8 days

6 PSN Net Photosynthesis, as carbon, 8 days

DataValues table is a central table which stores data (see ODM description). The column

ValueAccuracy stores standard deviation of the spatially averaged value (column

DataValue) and the column CensorCode represents percentage of all grid points located

in a selected site (catchment) which value was included in the average.

DataValues

ValueID

DataValue

ValueAccuracy

LocalDateTime

UTCOffse

t

DateTimeUTC

SiteID

VariableID

OffsetValue

OffsetTypeID

CensorCode

QualifierID

MethodID

SourceID

SampleID

DerivedFromID

QualityControlLevelID

1 0.5048 0.0479 2/18/2000 0 2/18/2000 1 1 100 1 1 1 2

2 0.434 0.0522 2/18/2000 0 2/18/2000 2 1 88 1 1 1 2

Additional Data

MODIS data were recomputed using the highest possible filtering schema:

Vegetation Indices - Usefulness <= 1 (0001) AND ModLand_QA = 0 (00)

LAI/Fpar - QC = 0 (00000000)

Gross Primary Productivity - QC = 0 (00000000)

These data are stored in Modis4Mopex431_QH.zip in the sub-directory MSAccessDB.


MODIS Surface Reflectance

MODIS data are obtained from ftp://e4ftl01u.ecs.nasa.gov/MOLT/:

Product Dataset MOD Spatial

resolution

Temporal

resolution

Surface Reflectance

Band1 (620–670 nm)

Band2 (841–876 nm)

Band3 (459–479 nm)

Band4 (545–565 nm)

Band5 (1230–1250 nm)

Band6 (1628–1652 nm)

Band7 (2105–2155 nm)

MOD09A1 500 m 8 days

More detail information about the data and other product can be found at http://modis-

land.gsfc.nasa.gov/index.htm.

The MODIS data were spatially averaged for each catchment. They are located in the

directory MODIS431/mod09a1/SurfaceReflectance. Data were computed in two different

levels of quality (HighQuality and HighQualityClear) with the following filtering

schema:

HighQuality – band data quality <= 2 (0010) and QA bits = 0 (00);

HighQualityClear – band data quality <= 2 (0010) and QA bits <= 1 (01) and State QA

cloud = 0 (00);


22 columns:

1: date (yyyymmdd)

2: Band1 (9.4f)

3: Band2 (9.4f)

4: Band3 (9.4f)

5: Band4 (9.4f)

6: Band5 (9.4f)

7: Band6 (9.4f)

8: Band7 (9.4f)

9: Std of Band1 (9.4f)







16: Percentage of grid points used to derive Band1 (4d)


ftp://e4ftl01u.ecs.nasa.gov/MOLT/








Indexes derived from Surface Reflectance

Indexes were computed for each grid point from filtered surface reflectance data and then

spatially averaged for each catchment. They are located in the directory

MODIS431/mod09a1/DerivedProducts.


16 columns:

1: date (yyyymmdd)

2: NDVI (9.4f)

3: EVI (9.4f)

4: NDWI (9.4f)

5: SIWSI (9.4f)

6: MSAVI (9.4f)

7: Std of NDVI (9.4f)

8: Std of EVI (9.4f)

9: Std of NDWI (9.4f)

10: Std of SIWSI (9.4f)

11: Std of MSAVI (9.4f)

12: Percentage of grid points used to derive NDVI (4d)

13: Percentage of grid points used to derive EVI (4d)

14: Percentage of grid points used to derive NDWI (4d)

15: Percentage of grid points used to derive SIWSI (4d)

16: Percentage of grid points used to derive MSAVI (4d)

NDVI - Normalized Difference Vegetation Index

12

12

BandBand

BandBandNDVI

(Huete et al., 1999, 2002)

EVI - Enhanced Vegetation Index

0.135.710.62

120.2

BandBandBand

BandBandEVI (Huete et al., 1999, 2002)

NDWI - Normalized Difference Water Index

52

52

BandBand

BandBandNDWI

(Gao, 1996; Stimson et al., 2005; Cheng et al., 2006)

SIWSI - Shortwave Infrared Water Stress Index

26

26

BandBand

BandBandSIWSI

(Cheng et al., 2006; Fensholt and Sandholt, 2003)

MSAVI - Modified Soil Adjusted Vegetation Index

0.2

)12(0.8)0.120.2(0.120.2 2 BandBandBandBandMSAVI

(Qi et al.,

1994; Song X et al., 2007)

NARR Radiation Data

North American Regional Reanalysis (NARR) data were obtained from the

http://nomads.ncdc.noaa.gov/data/narr/ and some description can be found at

http://www.emc.ncep.noaa.gov/mmb/rreanl/.

Time period: 1979 – 2008

Temporal resolution: 3 hours

Spatial resolution: 32 km

The reanalysis data were re-sampled to higher resolution (~6 km) and spatially averaged

for each catchment. Derived data are located in the directory NARR/Radiation at the ftp

server ftp://nero.hwr.arizona.edu/mopex/.

Data are organized by USGS gage numbers with extension rad. Each text file contains 3

columns:

1: date (yyyymmddhh) in UTC

2: DSWRF:sfc:kpds=204,1,0:0-3hr ave:winds are N/S: Downward shortwave radiation

flux [W/m2]

3: DLWRF:sfc:kpds=205,1,0:0-3hr ave:winds are N/S: Downward longwave radiation

flux [W/m2]

http://nomads.ncdc.noaa.gov/data/narr/

http://www.emc.ncep.noaa.gov/mmb/rreanl/


Monthly Data

PRISM monthly precipitation, minimal and maximal temperatures were obtained from

the Oregon State University at http://www.prism.oregonstate.edu/. Data were spatially

aggregated (average, sum) for each catchment.

USGS daily streamflows (available at http://water.usgs.gov/nwis) and derived daily base

flows and storm runoffs were aggregate to monthly values.

Monthly potential evapotranspiration was computed from the monthly average

temperature using Hamon equation (Hamon 1961)

100

95.4

97.13

062.0

2

T

t

t

eW

WdDPET

Where d is the number of days in a month, D is the mean monthly hours of daylight in

units of 12 h, Wt is a saturated water vapor density and T is the monthly mean

temperature in oC. PET is computed in mm per month.

Monthly mean temperature was computed as (Tmax+Tmin)/2.

The monthly hours of daylight was modeled using algorithm described by Forsythe et al.

(1995).

Data are located in the directory MonthlyData431 at the ftp server

ftp://nero.hwr.arizona.edu/mopex/. They are organized by USGS gage numbers with

extension mly. Each text file contains 8 columns:

1: date (yyyymmdd)

2: monthly precipitation in mm (8.2f)

3: monthly potential evapotranspiration in mm (8.2f)

4: monthly stream flow in mm (8.2f)

5: monthly average maximal temperature in oC (8.2f)

6: monthly average minimal temperature in mm oC (8.2f)

7: monthly base flow in mm (8.2f)

8: monthly storm runoff in mm (8.2f)

Database Format

Data are also available in the ms access database (see MonthlyData4Mopex431.zip)

which uses in the Observations Data Model (ODM) developed by CUAHSI to store

values and necessary information about data. Database documentation and schema is at

http://his.cuahsi.org/odmdatabases.html.

http://www.prism.oregonstate.edu/

http://water.usgs.gov/nwis



Variables


2001 Prcp Precipitation, monthly

2002 Tmax Temperature, monthly, maximum

2003 Tmin Temperature, monthly, minimum

2007 PE Potential Evaportanspiration, monthly

2013 Qmm Discharge, monthly, minimum

2014 BF Base Flow, monthly, minimum

2015 SR Storm Runoff, monthly, minimum

Seasonal Data

This dataset contains computed Horton and Humidity Indexes (Troch et al., 2009) for

different seasons:

yearly;

season 1: April – September;

season 2: May – October;

season 3: April – October.

Horton Index - SRP

QPHI

Humidity Index - PET

PHuI

Stream flow (discharge): Q = BF + SR

Seasonal data (see Variable table below) were computed using the monthly data (see

description above).

Variables VariableID VariableCode VariableName

3001 Prcp_y Precipitation, water year

3002 Q_y Discharge, water year

3003 SR_y Storm Runoff, water year

3011 Prcp_s1 Precipitation, season Apr-Sep

3012 Q_s1 Discharge, season Apr-Sep

3013 SR_s1 Storm Runoff, season Apr-Sep

3021 Prcp_s2 Precipitation, season May-Oct

3022 Q_s2 Discharge, season May-Oct

3023 SR_s2 Storm Runoff, season May-Oct

3031 Prcp_s3 Precipitation, season Apr-Oct

3032 Q_s3 Discharge, season Apr-Oct

3033 SR_s3 Storm Runoff, season Apr-Oct

3501 HI_y Horton Index, water year

3502 HuI_y Humidity Index, water year

3511 HI_s1 Horton Index, season Apr-Sep

3512 HuI_s1 Humidity Index, season Apr-Sep

3521 HI_s2 Horton Index, season May-Oct

3522 HuI_s2 Humidity Index, season May-Oct

3531 HI_s3 Horton Index, season Apr-Oct

3532 HuI_s3 Humidity Index, season Apr-Oct

Data are available only in the ms access database (see Horton_PRISM.zip) which uses in

the Observations Data Model (ODM) developed by CUAHSI to store values and

necessary information about data. Database documentation and schema is at

http://his.cuahsi.org/odmdatabases.html.


References

Forsythe W.C., E.J. Rykiel Jr., R.S. Stahl, H. Wu, R.M. Schoolfield (1995) A model

comparison for daylength as a function of latitude and day of year. Ecological Modelling,

Volume 80, Issue 1, Pages 87-95.

Huete, A., Didan, K., Miura, T., Rodríguez, E.P., Gao, X., Ferreira, L.G. (2002)

Overview of the radiometric and biophysical performance of the MODIS vegetation

indices. Remote Sensing of Environment 83,195-213.

Huete A., Justice C., and van Leeuwen W (1999) Modis Vegetation Index (MOD 13)

Algorithm Theoretical Basis Document, Version 3.

http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf

Schaake J., S. Cong, and Q Duan (2006) U.S.MOPEX Data Set.

ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Documentation/.

Duan Q, Schaake J, Andreassian V, Franks S, Goteti G, Gupta HV, Gusev YM, Habets F,

Hall A, Hay L, Hogue T, Huang M, Leavesley G, Liang X, Nasonova ON, Noilhan J,

Oudin L, Sorooshian S, Wagener T, Wood EF (2006) Model Parameter Estimation

Experiment (MOPEX): An overview of science strategy and major results from the

second and third workshops. Journal of Hydrology, 320(1-2), 3-17.

Stimson H.C., D.D. Breshears, S.L. Ustin, S.C. Kefauver (2005) Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sensing of Environment, 96, 108– 118.

Cheng Y.B., P.J. Zarco-Tejada, D. Riano, C.A. Rueda, and S.L. Ustin (2006) Estimating

vegetation water content with hyperspectral data for different canopy scenarios:

Relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment,

104(3), 346-359.

Gao B.C. (1996) NDWI - A normalized difference water index for remote sensing of

vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257-266.

Fensholt R. and I. Sandholt (2003) Derivation of a shortwave infrared water stress index

from MODIS near- and shortwave infrared data in a semiarid environment. Remote

Sensing of Environment, 87(1), 111-121.

Qi J., A. Chehbouni, A. Huete, Y. Kerr, and S. Sorooshian (1994) A modified soil

adjusted vegetation index. Remote Sensing of the Environment, 48, 119-126.

Song X., S. Huang; L., Qinhuo, and L. Xiaotao (2007) Vegetation water inversion using

MODIS satellite data. In: Geoscience and Remote Sensing Symposium, 2007. IGARSS

2007. IEEE International.

http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf

ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Documentation/

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