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Examples and opportunities for syntheses of long-term cross site data Experimental Forest Network LTER Examples and opportunities for syntheses of long-term cross site data Experimental Forest Network LTER Network Lotic Intersite Nitrogen Experiment

Early Efforts in LTER Climate Committee 1986 Objectives • Establish baseline meteorological measurements – Early Efforts in LTER Climate Committee 1986 Objectives • Establish baseline meteorological measurements – Characterize each LTER site – Enable intersite comparisons • Document both cyclic and long-term changes • Provide a detailed climatic history – Correlate with bio-ecological phenomena – Provide data for modeling

Climate and Hydrology Database harvester Objectives – Promote use of data for science, management Climate and Hydrology Database harvester Objectives – Promote use of data for science, management and education – Maintain a current data warehouse of multi-site, multinetwork, long-term climate and streamflow data – Provide critical background data in learning about and mitigating environmental change on the continental scale – Provide accessibility to data through a single portal – Provide a web interface to download, graphically display, and view data – Facilitate intersite science and foster development of multi-network datasets

Clim. DB/Hydro. DB Web Pages Implemented in 2002 Funded provided by LTER and USFS Clim. DB/Hydro. DB Web Pages Implemented in 2002 Funded provided by LTER and USFS Small amts of funding to sites to organize data into format for harvester Funding for programming of harvester http: //www. fsl. orst. edu/hydrodb/

Clim. DB and Hydro. DB 41 Sites – 24 LTER sites + 2 International Clim. DB and Hydro. DB 41 Sites – 24 LTER sites + 2 International LTER sites – 22 USFS sites – 12 sites include USGS gauging stations 327 Measurement Stations – 154 meteorological – 173 stream gauging (includes 65 USGS) 21 daily measurement parameters – Primarily streamflow, air temperature, precipitation Over 10 million daily values

Clim. DB/Hydro. DB User downloads by year: Year 2003 2004 2005 2006 2007 2008 Clim. DB/Hydro. DB User downloads by year: Year 2003 2004 2005 2006 2007 2008 2009* Total Files Plots Views 1840 309 1240 291 931 267 566 98 1737 717 829 191 3199 1886 978 335 1972 946 816 210 2494 1259 888 347 1597 856 537 204 Total 13, 770 6240 5854 1676 * Through 8 -19 -2009

Parameter Code airtemp atmpressure dewpoint globalrad Units Precipitation Relative Humidity Resultant Wind Direction Resultant Parameter Code airtemp atmpressure dewpoint globalrad Units Precipitation Relative Humidity Resultant Wind Direction Resultant Wind Speed Snow Depth (water equivalence) precip rh reswinddir Millimeters (mm) Percent (%) Degrees Azimuth reswindsp snowh 2 o Meters per second (m/sec) Millimeters (mm) Soil Moisture Soil Temperature Stream Discharge Vapor Pressure Water Temperature Wind Direction Wind Speed sm soiltemp discharge vappressure watertemp winddir windsp Megapascal (MPa) Degrees Celsius ( C) Liters per second (l/sec) Hectopascals (h. Pa) Degrees Celsius ( C) Degrees Azimuth Meters per second (m/sec) Air Temperature Atmospheric Pressure Dew point Temperature Global Radiation Degrees Celsius ( C) Hectopascals (h. Pa) Degrees Celsius ( C) Megajoules per square meter (MJm-2)

Metadata categories about sites • • • Research Area Information Watershed Spatial Characteristics Watershed Metadata categories about sites • • • Research Area Information Watershed Spatial Characteristics Watershed Ecological Characteristics Watershed Descriptions Hydrologic Gauging Station Meteorological Station

Data Aggregation Rules • • • Values flagged with “Q” or “M” will not Data Aggregation Rules • • • Values flagged with “Q” or “M” will not be included in monthly or yearly aggregation. Values tagged with “E” will be included. The number of valid values used in the aggregation will be displayed. Sites are encouraged to estimate data values rather than reporting questionable or missing data. If all data values (e. g. , data values listed in the header line) are all missing for a period of days, it is not necessary to “fill in” these periods with null data and “missing” flags. Each field in the data is parsed and has its leading and trailing spaces removed before inspection. Then in this order these operations occur: If a data value of 9999 is encountered, its flag will be forced to M. If an invalid flag code is encountered, an error message will be logged and the record ignored. If a data value of NULL (nothing) is encountered, the flag will be forced to M If a flag value is G, the flag will be forced to NULL. If a flag value is M, the data value will be forced to NULL, In the case of precipitation, if the flag is T but the data value is NULL (e. g. , blank), the flag will be forced to M and a warning message will be logged.

Data Acquisition Download or Graphical Display Data Acquisition Download or Graphical Display

More cross-site comparisons LTER Network More cross-site comparisons LTER Network

LTER Eco. Trends Data summary and comparison across sites LTER Eco. Trends Data summary and comparison across sites

LTER Eco. Trends LTER Eco. Trends

LTER ECOTRENDS EXAMPLE Lots more issues than expected Problem! LTER ECOTRENDS EXAMPLE Lots more issues than expected Problem!

Interest in exploring stream chemistry responses to environmental gradients and land use change across Interest in exploring stream chemistry responses to environmental gradients and land use change across continent

Beginning cross site synthesis of long-term stream biogeochemistry across 10 Experimental Forests Beginning cross site synthesis of long-term stream biogeochemistry across 10 Experimental Forests

Drowning in Data Drowning in Data

Current Experimental Forest Synthesis Database Design Data tables Site web sites Contacts Site disclaimers Current Experimental Forest Synthesis Database Design Data tables Site web sites Contacts Site disclaimers & agreements Status of data Basins Disturbance details Chemistry parameters tables Discharge Names Samples - instantaneous Standardized methods Chem data – instantaneous concentrations Samples – integrated & aggregated Chem data – integrated & aggregated concentrations Chem data – fluxes Labs Equipment Methods

Challenges Standardizing data & issues of comparability Preliminary List Of Analytes Nitrate Calcium Ammonium Challenges Standardizing data & issues of comparability Preliminary List Of Analytes Nitrate Calcium Ammonium Standard units and nomenclature Potassium Magnesium Dissolved Kjeldahl Nitrogen Methods Sodium Total Kjeldahl Nitrogen (unfiltered) Sulfate Total Dissolved Nitrogen Total Nitrogen (unfiltered) Detection limits Chloride Soluble Reactive Phosphorus Orthophosphate measured by IC Silica Bicarbonate Carbonate ANC/alkalinity Time steps / aggregation methods Total Dissolved Phosphorus Total Phosphorus (unfiltered) p. H Hardness Conductivity Dissolved Organic Carbon Total Organic Carbon Dissolved Inorganic Carbon Total Aluminum

Chemistry data standardization Examples of difficult issues Hierarchical parameter categories Importance of standard terminology Chemistry data standardization Examples of difficult issues Hierarchical parameter categories Importance of standard terminology Example: Total Nitrogen is ambiguous – Total Nitrogen (unfiltered) and – Total Dissolved Nitrogen chemistry databases can become unwieldy without organized parameters

Chemistry data standardization Examples of difficult issues Converting to standard units Clear labels of Chemistry data standardization Examples of difficult issues Converting to standard units Clear labels of original & standard units are important Example: NO 3 (mg/L) is ambiguous – nitrate as nitrogen, NO 3 -N (mg N/L) – nitrate as nitrate, NO 3 (mg NO 3/L) Pre-population conversion vs. stored procedures to convert on the fly

Chemistry data standardization Examples of difficult issues Detection Limits Documenting the detection limit of Chemistry data standardization Examples of difficult issues Detection Limits Documenting the detection limit of below detection values is important – different researchers /labs’ policies on reporting machine-read values below detection and detection limits vs. only reporting detection limits needs to be reconciled – a code indicating “below detection” is insufficient – treatment of historic data for which detection limits are unknown needs to be determined

Question driven, collaborative approach and harvester as a product Synthesis Papers and Products Topic Question driven, collaborative approach and harvester as a product Synthesis Papers and Products Topic 1: Informing national nutrient criteria using long term reference basin data Topic 2: Examining effects of forest disturbance on stream chemistry dynamics, concentrations and fluxes Topic 3: Cross site comparison of effects of different calculations of fluxes Products: peer reviewed papers, metadata on sites and methods, databases structured to allow future cross site harvesting

Poster presented at NAFEW, Logan UT, June 2009 Poster presented at NAFEW, Logan UT, June 2009

Chem DB Developed from desire for cross site stream chemistry synthesis Idea to build Chem DB Developed from desire for cross site stream chemistry synthesis Idea to build on Clim/Hydro. DB harvester but with increasing complexity

Observations GIS Climate Models Remote Sensing Hydro. Desktop Hydrologic Information System (HIS) Observations GIS Climate Models Remote Sensing Hydro. Desktop Hydrologic Information System (HIS)

Synthesis and Networks • Challenges involved in integrating legacy data • Planning for synthesis Synthesis and Networks • Challenges involved in integrating legacy data • Planning for synthesis at the beginning easier • Importance and value of metadata • Automated data scripts to keep data current • Huge benefits and learning from comparing dynamics cross site

Organizing from the start: Critical Zone Observatories Organizing from the start: Critical Zone Observatories

Opportunities for and syntheses of long-term cross site data Opportunities for and syntheses of long-term cross site data

Clim. DB Parameters • • • • Air temperature; daily minimum, maximum, and mean Clim. DB Parameters • • • • Air temperature; daily minimum, maximum, and mean in degrees Celsius ( C) Atmospheric pressure; daily mean in hectopascals (h. Pa) Dewpoint temperature; daily mean in degrees Celsius ( C) Global solar radiation; daily total in Mega. Joules per square meter (MJm-2) Precipitation; daily total in millimeters (mm) Relative humidity; daily mean in percent (%) Snow depth (water equivalence); daily instantaneous observation in millimeters (mm of water). Soil Moisture; daily mean in megapascals (MPa) Soil temperature; daily mean in degrees Celsius ( C) Stream Discharge; daily mean in liters per second (l/sec) Vapor pressure; daily mean in hectopascals (h. Pa) Water Temperature; daily minimum, maximum, and mean in degrees Celsius ( C) Wind direction and resultant wind direction; daily mean in degrees azimuths (deg) Wind speed and resultant wind speed; daily mean in meters per second (m/sec)