Hydrologic Measurement Techniques

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Hydrologic Measurement Techniques

• Introduction to Hydrologic Data Collection
• Fritz R. Fiedler

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Hydrologic Data Collection

• Why do we collect hydrologic data?
• What forms do the data take?
• What do we do with the data?
• How are the data handled?

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Why? Many, many reasons!

• Hydrology linked to ecology, e.g., fish habitat
  (Congress has defined essential fish habitat for
  federally managed fish species as "those waters
  and substrate necessary to fish for spawning,
  breeding, feeding, or growth to maturity.)

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Why continued

• Watershed management

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Why continued...

• Hydrology linked to water quality

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Why continued...

• Hydrology and natural disasters, e.g., flooding

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Why continued...

• Hydrology and water resources, e.g., Moscow water
  supply

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Why continued...

• Hydrology plays an essential role in regional and
  global climate systems

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Why continued...

• Life on other planets??

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Why - summary

• Type of data collected is in part dictated by
  some purpose
• Purpose in this class?
• To learn about hydrologic processes in natural
  systems which, in turn, assist us in development
  of conceptual and mathematical descriptions of
  hydrology
• To use in developed models

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What forms do data take?

• Hydrologic processes vary in time and space at
  different scales, e.g., precipitation

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Forms of data continued

• Time series data any variable vs. time

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Forms...

• Spatial data variables distributed in space

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Exercise

• With a partner (or two), identify time and space
  scales important to each of the following with
  respect to precipitation
• Fish habitat in the Columbia River System
• Hydrologic changes in response to logging
• Flooding in the Mississippi River
• Moscow water supply
• Climate change

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Exercise hints

• Time Scales
• minute
• hour
• day
• month
• seasonal
• year
• decade
• century

• Space Scales
• foot
• 100 feet
• mile
• 10 miles
• 100 miles
• 1000 miles
• 10000 miles

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Time and Space Scales
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What do we do with the data?

• Statistical Analysis (learn about natural system
  behavior)
• temporal
• spatial
• Modeling
• steady-state vs. transient
• lumped vs. distributed

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Statistical Analysis

• Temporal Mean (a.k.a. average)
• average annual rainfall
• mean daily streamflow
• Spatial Mean
• mean areal precipitation

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Mean Areal Precipitation
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Time Series Sampling

• If observations are taken at a frequency
  (number/time period) of f, then any information
  with a frequency greater than 1/2f can not be
  recovered.
• If you take temperature measurements every 24
  hours, can you learn anything about diurnal
  variations?
• Does this concept apply to spatial sampling?

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Precision and Accuracy

• Precision how close the measurements are to each
  other
• Accuracy how close the measurements are to the
  true value

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Hydrologic Modeling

• Steady-State models
• may require temporal mean data
• may or may not be spatially averaged
• Transient models
• requires data that vary in time
• may or may not be spatially averaged

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Hydrologic Modeling

• Lumped spatially averaged
• Distributed variables vary in space

input
output
Model
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How are data handled?

• Hydrologic Measurement Sequence
• sensing translates intensity of phenomenon into
  signal
• recording preserves signal
• transmission move recorded signal to central
  location
• translation convert recorded signal into usable
  (electronic) form

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Data handling continued...

• editing quality control procedures
• storage archive data in some form of database
• retrieval obtain data from storage
• More on this in a later class...

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Some Practical Aspects

• What to bring to field/lab exercises?
• proper clothes (is it going to rain?)
• boots or other suitable shoes
• sun protection
• bug protection
• notepaper (waterproof?), pen/pencil(s)
• watch
• calculator

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More Practical Aspects

• What to record in addition to the actual data?
• date, time, general location of measurements
• project (lab) name and number
• personnel on site (partner, contractor,
  instructor)
• weather conditions
• type of equipment used
• type and purpose of measurements
• measurement number, precise measurement locations

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Still More Practical Aspects

• Data Organization and Formatting
• a header that describes the data should precede
  the data themselves (metadata).
• Time series example

IDENTIFIERPTPX-31-1055 DESCRIPTIONBREVARD, NC
PERIOD OF RECORD10/1959 THRU 09/1962
SYMBOL FOR MISSING DATA-999.00 SYMBOL FOR
ACCUMULATED DATA-998.00 TYPEPTPX UNITSIN
DIMENSIONSL DATA TIME INTERVAL24 HOURS
OUTPUT FORMAT(3A4,2I2,I4,6F10.3)
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Even More...

• Spatial metadata (partial) example - top level

• Pajaro River Survey
• Metadata
• Identification_Information
• Data_Quality_Information
• Spatial_Data_Organization_Information
• Entity_and_Attribute_Information
• Distribution_Information
• Metadata_Reference_Information

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Spatial Metadata continued...
Identification_Information Citation
Citation_Information Originator Ken Thompson
Publication_Date Unpublished material
Publication_Time Unknown Title Pajaro River
Survey Edition 1st Geospatial_Data_Presentation
_Form map Series_Information Series_Name 1
Issue_Identification 1 Publication_Information
Publication_Place Not Published Publisher
None Larger_Work_Citation Citation_Information
Series_Information Publication_Information
Description Abstract Map of the lower Pajaro
River Basin near Watsonville, CA. Purpose Flood
control improvement studies. Time_Period_of_Conte
nt Time_Period_Information Single_Date/Time
Range_of_Dates/Times Multiple_Dates/Times
Calendar_Date 19950328 Calendar_Date 19950808
Currentness_Reference Dates of photography
Status Progress Complete Maintenance_and_Upda
te_Frequency Unknown Spatial_Domain
Bounding_Coordinates West_Bounding_Coordinate
-121.8303 East_Bounding_Coordinate -121.6687
North_Bounding_Coordinate 36.9517
South_Bounding_Coordinate 36.8488 Keywords
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Data Handling Summary

• Information about the data (metadata) as
  important as the actual data
• Data organization is critical
• Expected use of the data will in part dictate how
  data are collected (e.g., measurement frequency)
  and handled (e.g., organization)

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