SCADA & Hydro Data Management Department ......PROCEDURE: Q205 DATE: 10/30/06 REVISION: Original Page 5 of 70 QA/QC OF GROUNDWATER LEVEL DATA PROCEDURES 1.0 PURPOSE This procedure

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DATE: 10/30/06 REVISION: Original

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SCADA & Hydro Data Management Department OPERATIONS & HYDRO DATA MANAGEMENT DIVISION

Quality Assurance/Quality Control Section

Procedure: Q205 Title: QA/QC of Groundwater Level Data Procedures

Revision Chart

REV DESCRIPTION DATE PREPARED BY REVIEWED BY APPROVED BY

- -

Original Issue 10/30/06 T. Sangoyomi/ D. Lambright

C. Bevier/ E. Damisse/

M. Ansar

M. Ansar

Only current revisions of this document are authorized for use. Users are responsible for checking the master list for the current revision of this document.

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TABLE OF CONTENTS

1.0 PURPOSE...................................................................................................................... 5

2.0 SCOPE........................................................................................................................... 5

3.0 REFERENCES ............................................................................................................... 5

4.0 RESPONSIBILITIES ...................................................................................................... 6

4.1 Primary Responsibility for the Process and this Procedure...............................................6 4.2 Secondary Responsibility.......................................................................................................6

5.0 DEFINITIONS................................................................................................................. 8

5.1 Acronyms .................................................................................................................................8 5.2 Terms........................................................................................................................................9

6.0 INTRODUCTION .......................................................................................................... 12

6.1 Groundwater Monitoring Well Networks .............................................................................12 6.2 South Florida Water Management District – Aquifer Systems..........................................13

6.2.1 Groundwater Level Data ............................................................................................................... 15 6.2.2 Datum.............................................................................................................................................. 20

6.3 Data Acquisition Methods ....................................................................................................20 6.4 DBHYDRO ..............................................................................................................................21 6.5 DBKeys...................................................................................................................................22 6.6 Data Standardization – Metadata .........................................................................................23 6.7 Data Categories .....................................................................................................................23 6.8 Data Quality............................................................................................................................23 6.9 Significance of Site/Station Registry...................................................................................23 6.10 Data Collection/Validation Pre-Processing System (DCVP).............................................24 6.11 Data Processing Activity .....................................................................................................24 6.12 QA/QC Post-Processing Analyses......................................................................................25

7.0 PROCEDURES FOR QA/QC OF GROUNDWATER LEVEL DATA............................ 27

7.1 General ...................................................................................................................................27 7.1.1 QA/QC Schedules.......................................................................................................................... 27 7.1.2 Internal/External Communications .............................................................................................. 27

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7.1.3 Site/Station Folders....................................................................................................................... 28 7.2 Methodology ..........................................................................................................................28

7.2.1 Site Description and Site Selection ............................................................................................. 28 7.2.2 Data Extraction and External Agency Data Acquisition & Validation ...................................... 32 7.2.3 QA/QC Data Investigation and Analysis ..................................................................................... 33 7.2.4 Missing Data Values...................................................................................................................... 43 7.2.5 Preferred DBKey and Regional Modeling (MOD1) DBKey Development................................. 44 7.2.6 Load Modeling (MOD) Data Sets into DBHYDRO....................................................................... 47 7.2.7 Extract/Verify MOD DBKey Data Sets.......................................................................................... 50

7.3 Accessing QA/QC Post-Processing Applications..............................................................51 7.3.1 DBHYDRO Browser Application .................................................................................................. 51 7.3.2 Data Collection/Validation Pre-processing (DCVP) System Application................................. 61

8.0 APPENDICES .............................................................................................................. 64

Appendix A: Possible Reasons for Groundwater Level Data Problems and Data Changes___ 65 Appendix B: Description of Common DBHYDRO Codes/Tags (Data Qualifiers) ____________ 66 Appendix C: District Database Applications (DBHYDRO & DCVP) Naming Conventions ____ 67 Appendix D: Listing of QA/QC Post-Processing Applications___________________________ 68 Appendix E: Data Analysis Methods: Mass Balance & Statistical Analysis ________________ 69 Appendix F: Methodology for the Evaluation of Outliers (Box Plot Method) _______________ 70

List of Figures Figure 1: QA/QC Groundwater Level Data Process Flow Chart. _________________________________7 Figure 2: Three-dimensional View of Florida’s Aquifer Systems (Source: Berndt, 1988) ______________ 14 Figure 3: Potable and Nonpotable Water in the Floridan Aquifer System. (Source: FDEP). ___________ 15 Figure 4: Groundwater Level or Head in a Well in an Unconfined Aquifer (Well 1) and in a Confined

Aquifer (Well 2) (Source: modified from Taylor and Alley, 2001). ________________________ 16 Figure 5: An In-Situ® Level Troll Pressure Transducer. _______________________________________ 17 Figure 6: Rittmeyer ® Pressure Transducers Connected to a Floridan Aquifer System Dual Zone Well. __ 17 Figure 7: A Campbell Scientific® CR10 Data Logger. _________________________________________ 19 Figure 8: Groundwater Well with Ruler attached to clear PVC pipe. _____________________________ 19 Figure 9: Valid Benchmark (USACE survey mark). __________________________________________ 20 Figure 10: SCADA Installations and the Operations Control Center (OCC). ________________________ 21 Figure 11: SIM Maintenance Database Screens available at http://e41165/Esdastart.aspx?index=0._____ 30 Figure 12: USGS Florida Groundwater Data available at http://waterdata.usgs.gov/fl/nwis/gw.__________ 31 Figure 13: Example Hydrogeologic Data Screens from the DBHYDRO Database. ___________________ 32 Figure 14: Example of Groundwater Level and Rainfall Graphical Plot Comparison.__________________ 35 Figure 15: Effect of Periodic Valve Opening on a Flowing Floridan Well.___________________________ 37

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Figure 16: Effects of Pumping from Nearby Municipal Wells on a Floridan Monitoring Well. ____________ 37 Figure 17: Effects of Errors in Sensor Performance Following Water Quality Sampling Episodes on

6/22/04, 10/6/04 and 2/3/05. ____________________________________________________ 38 Figure 18: Groundwater Levels at CHAPMAN_G Compared with Levels from Adjacent Station C36 and

Rainfall Data (CHAPMAN_R). The Change in Reference Elevation for CHAPMAN_G Occurred in October 1980. _____________________________________________________ 39

Figure 19: Groundwater Levels at Station TOHO13_GW Compared with Levels from Adjacent Groundwater Stations and with Precipitation Data from an Adjacent Rainfall Station. ________ 41

Figure 20: Chisholm Estates Construction Site Adjacent to TOHO13_GW._________________________ 41 Figure 21: REG_TO_PREF Program.______________________________________________________ 45 Figure 22: Reflections TextEditor used to edit UNIX text file. ____________________________________ 47 Figure 23: Steps for Loading Modeling (MOD) Data Sets into DBHYDRO Database__________________ 48 Figure 24: Example of a .csv (comma delimited) File. _________________________________________ 49 Figure 25: Example of a Control File to Load Single Series Data to Temporary Table in wrep.__________ 49 Figure 26: DBHYDRO Browser Menu______________________________________________________ 51 Figure 27: Select Search Parameters______________________________________________________ 52 Figure 28: DBHYDRO Query Criteria ______________________________________________________ 53 Figure 29: DBHYDRO Query Criteria Results _______________________________________________ 54 Figure 30: Station Information Field Link ___________________________________________________ 55 Figure 31: DBHYDRO Query Data Selection ________________________________________________ 56 Figure 32: Single Time Series Format _____________________________________________________ 57 Figure 33: DBHYDRO Query Data Selection ________________________________________________ 58 Figure 34: Time Series Graph for Groundwater Well MHGW_GW1 Comparing Daily Average Data and

Instantaneous Data (Breakpoint Data). ____________________________________________ 59 Figure 35: Time Series Graph for Four Groundwater Wells._____________________________________ 60 Figure 36: Instructions for Accessing DCVP Database. ________________________________________ 61 Figure 37: DCVP System Main Menu. _____________________________________________________ 61 Figure 38: Reference Information Menu. ___________________________________________________ 62 Figure 39: DCVP Annotations Screen _____________________________________________________ 63

List of Tables Table 1. Aquifer System Thicknesses (in feet) Throughout the District. __________________________ 13 Table 2. DCVP Applications (Abbreviated).________________________________________________ 24 Table 3. Links for Groundwater Well Information. ___________________________________________ 29

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QA/QC OF GROUNDWATER LEVEL DATA PROCEDURES

1.0 PURPOSE

This procedure supports the SCADA & Hydro Data Management Department (SHDM), Operations & Hydro Data Management Division (OHDM), Quality Assurance and Quality Control (QA/QC) Section Policy Q200. It specifies the QA/QC of Groundwater Data Procedures that will be used by the QA/QC Engineer during post-processing data analysis and reporting.

The primary purpose of this procedure is to provide reliable groundwater level data to meet the needs of the District and its customers (USACE, USGS, etc.) These data are the most appropriate combination of data available from any known source and are produced through a series of QA/QC post-processing statistical analyses.

2.0 SCOPE

The goal of this procedure is to provide the QA/QC Engineer with a baseline to follow during post-processing performance of quality assurance and quality control (QA/QC) of groundwater level data series. QA/QC post-processing includes analysis of the hydro-geologic system through application of modeling tools, performance of hydrologic mass balance analyses, spatial and temporal statistical analyses of the data, evaluation of groundwater level measurements and filling missing data.

Refer to Figure 1, for an overview of the QA/QC Groundwater Level Data process. The scope of the process includes: (1) External agency data acquisition and validation, (2) Data extraction, (3) Data analysis, examination, and validation, (4) Erroneous data replaced with higher quality data, or deleted, or qualified and tagged, (5) Single time series generation, (6) Data upload to DBHYDRO database, and (7) Data verification after upload.

3.0 REFERENCES Procedure Title Q200 OHDM Quality Assurance/Quality Control Section Policy Q201 QA/QC of Stage Data Procedures Q202 QA/QC of Rainfall Data Procedures – Rainfall Gauges and NEXRAD Q203 QA/QC of Flow Data Procedures – Flow Ratings & Associated Input

Parameters Q204 QA/QC of Meteorological and Evapotranspiration Data Procedures Q205 QA/QC of Groundwater Level Data Procedures (this document)

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Additional Reference Material

Article I. Floridan Aquifer System (F.A.S.) Artesian Wells Calibration SOP, dated 2006 Hydrologic Monitoring Network of SFWMD, Technical Publication # OHDM – xxx,

dated September 2006 Groundwater and Surface Water Interaction, Section 7.0, H&H Division, dated 3/6/2003 Ground Water Monitor Well Network Assessment, SFWMD Technical Memorandum

WS-12. Groundwater Level Monitoring and the Importance of Long-Term Water Level Data.

USGS Circular 1217. Q105 Site Registration Procedure, Operations and Hydrologic Data Processing Distribution and Origin of Salinity in the Surficial and Intermediate Aquifer Systems,

Southwestern Florida, USGS Water-Resources Investigation Report 01-4159.

4.0 RESPONSIBILITIES

4.1 Primary Responsibility for the Process and this Procedure

The Operations & Hydro Data Management Quality Assurance/Quality Control (QA/QC) Technical Lead Engineer (or designee) is responsible for the QA/QC of Groundwater Level Data process and maintains this document.

Awareness and compliance to this standard operating procedure (SOP) is required by all applicable SFWMD organizations. This issue and all subsequent revisions of this procedure must have evidence of approval.

4.2 Secondary Responsibility

The QA/QC Senior Engineers and QA/QC Engineering Associates are responsible for understanding this procedure and its contents. They are also responsible for ensuring that personnel in their sections are trained in, and comply with, this procedure as written.

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QA/QC Procedures for Groundwater Level Data

Figure 1: QA/QC Groundwater Level Data Process Flow Chart.

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5.0 DEFINITIONS

5.1 Acronyms

ADAPS Automated Data Processing System of the U.S. Geological Survey ARDAMS Automatic Remote Data Acquisition and Monitoring System CERP Comprehensive Everglades Restoration Plan CR10 Campbell Scientific Data Recorders/Loggers DBHYDRO District Corporate Environmental Database DCVP Data Collection/Validation Preprocessing System DTW Distance-to-Water DWR Daily Water Readings EAA Everglades Agricultural Area EST Eastern Standard Time FDEP Florida Department of Environmental Protection FTP File Transfer Protocol GIS Geographic Information System GPS Global Positioning System GVA Graphical Verification Analysis Program IWEB District’s Internal Internet Website (Intranet) MIRMaid Maintenance/Inventory/Recorder Malfunction Aid Reports MOSCAD Motorola SCADA (Supervisory Control and Data Acquisition) MSL Mean Sea Level NAVD 88 North American Vertical Datum 1988 NGVD 29 National Geodetic Vertical Datum 1929 NEXRAD Next Generation Radar O&M Operations & Maintenance Department OHDM Operations & Hydro Data Management Division OHDP Operations & Hydrologic Data Processing p.o.r. Period-of-record psi Pounds per square inch QA/QC Quality Assurance/Quality Control RACU Remote Acquisition Control Unit RECOVER Restoration, Coordination and Verification RTU Remote Terminal Unit SCADA Supervisory Control and Data Acquisition SFWMD South Florida Water Management District SEHS Streamgauging, Engineering & Hydraulic Support SHDM SCADA & Hydro Data Management Department

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SIM SCADA & Instrumentation Management Division SOP Standard Operating Procedure TCP/IP Transmission Control Protocol/Internet Protocol USACE U.S. Army Corps of Engineers USDA U.S. Department of Agriculture USGS U.S. Geological Survey WCA Water Conservation Areas WMSS District Water Management SCADA System WSR-88D Weather Surveillance Radar 1988 Doppler (a.k.a. NEXRAD) XWEB District’s External Internet Website

5.2 Terms

Aquifer An aquifer can be defined as a natural geologic formation of permeable layers of underground rock or sand that hold or transmit groundwater below the water table, and that will yield water to a well in sufficient quantities to produce water for beneficial use. Aquifers are classified as either confined or unconfined.

Breakpoint Breakpoint data is the name given to fine time resolution data collected by SFWMD. The term is used to describe data collected at random intervals throughout a given observed duration of hydrologic conditions. The breakpoint data format for groundwater levels simply records the times when the groundwater level changes from one steady value to another; the rates are also recorded.

Confined Aquifer An aquifer that is between two impermeable layers. The water is confined (under pressure) and when a well is drilled, the water will rise above the top of the aquifer. Water may also flow out of wells freely at the surface; these wells are called flowing artesian wells.

Data Processing A set of activities that are performed on raw time series data collected through the District’s monitoring network to correct anomalies.

DBKey A DBKey is a database key containing 5-alphanumeric characters that identify a unique combination of station, data type, recorder type, frequency, statistic, agency and other database information.

DCVP The Data Collection/Validation Preprocessing System is used to process all of the raw data received through the various data collection methods used by the District. DCVP was developed to handle manually and electronically collected data.

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Groundwater Water found beneath the Earth’s surface, usually in aquifers, which supplies wells and springs; water in the zone of saturation where all openings in rocks and soil are filled, the upper surface of which forms the water table.

Head Groundwater level reported in feet or psi. Hydrometeorology Hydrometeorology is the study of atmospheric and land

phases of the hydrological cycle, with emphasis on the interrelationships involved.

Metadata Includes descriptive information about the context, quality and conditions, or characteristics of the data. In the case of DBHYDRO, each of the attributes of a time series is metadata.

Monitoring Well Any man-made excavation by any method for the purpose of monitoring fluctuations in groundwater levels, quality of underground waters, or the concentration of contaminants in underground water.

Preferred Data Synthesized from SFWMD and other sources. Represents best source of continuous period of record. Reviewed quarterly by QA/QC Engineers, and designated by recorder type ‘PREF’ in DBHYDRO (only used for mandated sites).

Quality Assurance The utilization and evaluation of quality control results to verify that a system is operating within acceptable limits.

Quality Control Represents the procedures and practices developed and implemented to produce data of a required level of reliability.

Reference Elevation Represents the surveyed elevation to which the water depth measurement is tied. It is usually represented as the elevation of the top of the casing or of the transducer in the groundwater well.

Site A representative area used to designate one or more Stations that are associated by proximity or project. Site level representation is to provide clarity for small scale mapping in lieu of displaying a high density of associated stations. The Site location is often based on the position of a recording device, such as a remote terminal unit (RTU), or can be derived from a “common sense” location between the associated stations. A Site should not be viewed as an area feature with specific boundaries but simply as a representative location of activities. A site is named.

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Station A specific coordinate that indicates where data (observations, sampling or monitoring) are collected. Data are usually assigned to stations by various classifications such as Stage, Flow, Weather, Well, and Water Quality. A coordinate may have more than one Station associated to it. The name given to a Station has traditionally been similar to, or an exact duplicate of, the corresponding Site name.

Strata A positive number that indicates the depth (in feet) below land surface to the bottom of the monitored interval.

Sublocation A monitoring zone or screened interval in a groundwater well station which has multiple screened or open intervals. These multi-zoned wells have substation names attached to the different open zones so that groundwater level and water quality data collected may be assigned to the proper depth in the well.

Surface Water Water that is visible above the Earth’s surface. Telemetry Transmission and collection of data obtained by sensing

conditions in a real-time environment. Telvent OASyS A monitoring software platform, customized for the

District’s Supervisory Control and Data Acquisition (SCADA) system, which facilitates real-time, liberal two-way information exchange between the field and the command center.

Unconfined Aquifer An aquifer in which water is free to percolate through an unsaturated zone of soil or rock to the water table. Wells drilled in unconfined aquifers require pumps to extract the water.

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6.0 INTRODUCTION

The South Florida Water Management District (SFWMD or the District) is a regional governmental agency that oversees the water resources in the southern half of the state – 16 counties from Orlando to the Florida Keys, covering nearly 18,000 square miles. The District maintains an extensive hydrometeorology monitoring program that includes instrumentation to measure the quantity and quality of the District’s water resources. Hydrology measurements include rainfall, evapotranspiration and other meteorological records, water quality, hydrogeologic information, groundwater levels in the aquifer systems, and flow (discharge) from lakes, rivers, streams, canals and approximately 500 water control structures. Water level data are also monitored at various points on rivers, lakes, streams, in coastal areas, and throughout the roughly 1,800 miles of primary canal systems. These data, accumulated during many years, constitute a valuable database for developing an improved understanding of the complex hydrology of Central and South Florida.

6.1 Groundwater Monitoring Well Networks

The SFWMD and the USGS jointly manage and fund an extensive groundwater monitoring well network in South Florida to assess regional groundwater conditions. The current network consists of approximately 975 wells, more than 600 of these are District wells. The wells penetrate the principal aquifer systems in Florida. Groundwater monitoring wells are principally used for observing groundwater levels and flow conditions, obtaining samples for determining groundwater quality, and for evaluating hydraulic properties of water-bearing strata (rocks). There are three general classes of data collected at a District groundwater monitoring well site: hydrogeologic, hydrologic, and water quality.

Data from other groundwater wells are also available through the District’s DBHYDRO database. These wells include those monitored by the USACE, specific District projects, public water utilities, waste water utilities, and privately owned agricultural wells.

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6.2 South Florida Water Management District – Aquifer Systems

The principal aquifer systems monitored by the District and the USGS are the Surficial Aquifer System (SAS), the Floridan Aquifer System (FAS), and the Intermediate Aquifer System (IAS). The relative depths of each aquifer system are summarized in Table 1. The spatial relationship of these aquifer systems in Florida is depicted in Figure 2.

Table 1. Aquifer System Thicknesses (in feet) Throughout the District.

Aquifer System Aquifer Unit Thickness (in feet)

Surficial Water Table

Biscayne Lower Tamiami

40 to 400(a)

Intermediate Sandstone

Mid-Hawthorn 0 to 260(b)

Floridan

Lower Hawthorn Upper Floridan Middle Floridan Lower Floridan

1,800 to 3,600(c)

a) http://www.dep.state.fl.us/swapp/Aquifer.asp b) Tables 3 to 5, SFWMD, 2000 c) Figure on page 50, Fernald and Purdum, 1998

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Figure 2: Three-dimensional View of Florida’s Aquifer Systems (Source: Berndt, 1988)

The Surficial Aquifer System is an aquifer system that includes the Biscayne Aquifer, Lower Tamiami Aquifer, and all the otherwise undefined aquifers that are generally under water table conditions. With the exception of some parts of the Lower Tamiami Aquifer in the Lower West Coast area, the Surficial Aquifer System is unconfined, lies closest to the land surface, and is present throughout most of Florida. Most municipal and irrigation water is obtained from the SAS (the primary potable water source) in the Lower East Coast of Florida. The SAS is directly connected to (and influenced by) surface water variations such as canal stages, precipitation, and evapotranspiration. The Biscayne Aquifer is the primary source of water for all of Dade and Broward counties and for the southern portion of Palm Beach County. Water from the Biscayne Aquifer is also transported by pipeline to the Florida Keys.

The Floridan Aquifer System (FAS) is one of the highest producing aquifers in the world and underlies the entire District. The FAS includes the Lower Hawthorn Aquifer, Suwannee Aquifer, and the Upper, Middle, and Lower Floridan aquifers. It is the primary water supply source for the Upper East Coast, the Kissimmee Basin, and northern Palm Beach County. Although the FAS is a confined artesian aquifer, not all wells that penetrate the FAS are artesian wells (i.e., free flowing at land surface). The FAS has been divided into upper, middle, and lower aquifers separated by units of relatively lower permeability. The Upper Floridan Aquifer is the principal source of water supply in central Florida and wells in this aquifer in South Florida usually flow at land surface. In the southern part of the District, the FAS consists of non-potable,

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highly mineralized, saline water (Figure 3). Because salt water is denser than freshwater, the wells penetrating the lower FAS are referred to as static wells.

Figure 3: Potable and Nonpotable Water in the Floridan Aquifer System. (Source: FDEP).

The Intermediate Aquifer System (IAS) lies between the FAS and the SAS and is only present in southwestern Florida. This is a confined aquifer system (like the FAS) and consists of one or more water-bearing units separated by confining units. This system includes the Sandstone and Mid-Hawthorn aquifers. The IAS is the main source of water for Sarasota, Charlotte, and Lee counties. Much of the water pumped from the IAS is used for agricultural purposes. In most cases, recharge occurs through percolation from the SAS; however, upward recharge from the Upper Floridan Aquifer may also occur.

6.2.1 Groundwater Level Data

Groundwater level (head) is defined as the groundwater height (elevation) in feet relative to a surveyed reference elevation (Figure 4). The reference elevation is a measuring point that represents the elevation of the top of the well casing or transducer in the well relative to the location of a datum or benchmark. Groundwater levels in an unconfined aquifer, such as the SAS, reflect the elevation of the upper groundwater surface level, and are usually expressed in feet and recorded in DBHYDRO as Data Type WELL. For wells in a confined aquifer, such as the FAS, the groundwater is under pressure so the groundwater level (head) is measured in pounds per square inch (psi) and is then converted to feet. This elevation corresponds to pressure at the transducer in feet of fresh water (1 psi = 2.31 ft of fresh water). If the hydraulic head in the confined aquifer is above the land surface, this calculated fresh water elevation

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is added to the elevation of the transducer (usually placed on top of the well) and is recorded in DBHYDRO as Data Type UNHD.

Figure 4: Groundwater Level or Head in a Well in an Unconfined Aquifer (Well 1) and in a Confined Aquifer (Well 2) (Source: modified from Taylor and Alley, 2001).

Groundwater wells are constructed to accommodate various means of water-level measurement, including floats for mechanically operated, continuous water level recorders, slender sensors for submergence in static wells, or a screw-on sensor that can be attached to the outside of flowing artesian wells (Figures 5 and 6). Groundwater wells range in size from 2-inches to over 36-inches in diameter.

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Figure 5: An In-Situ® Level Troll Pressure Transducer.

Figure 6: Rittmeyer ® Pressure Transducers Connected to a Floridan Aquifer System Dual Zone Well.

The actual accuracy and precision of groundwater level measurements depends on the type of instrumentation used. There are several types of water level measuring devices available, but the two basic types are recording and non-recording. Recording type instruments keep track of groundwater levels at preset intervals and non-recording gauges require a field observer to read head elevation from a gauge.

Recording devices measure groundwater levels (heads) continuously (as time series data) with automatic sensors (float/counterweight, pressure transducers, etc.) interfaced with remote-terminal-units (RTUs). Recording devices can be grouped into one of the following categories: (1) mechanical, (2) electronic, or (3) combination. Mechanical types include digital punched paper tape and analog graphical strip chart recorders. Analog strip chart recorders convert rotational shaft positions into the position of an ink pen on a graphic chart. As the chart moves by the pen, an analog graph representing the action of the groundwater level over time is generated. Punched paper tape recorders convert rotational shaft position into coded digital information and periodically record this information as punched holes in paper tape. Combination recording devices use both mechanical and electronic technology. The shaft is positioned mechanically, but the position of the shaft is sensed and recorded electronically.

The majority of the recording devices used are totally electronic devices (i.e., pressure transducers) which use the liquid/air interface as the measuring point and therefore require no mechanics. Both combination and electronic recording devices record water-level measurements digitally and store the values in the remote-terminal-unit (e.g. solid-state data logger) memory (Figure 7).

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Figure 7: A Campbell Scientific® CR10 Data Logger.

A non-recording device requires a field observer to recorder the measurements. For example, rulers can be installed and are usually observed and recorded at a predetermined frequency (Figure 8). Another example is the use of an electronic well sounder to measure groundwater levels.

Figure 8: Groundwater Well with Ruler attached to clear PVC pipe.

The District and other governmental agencies acquire groundwater level data at various time intervals depending on the “type” of instrumentation actually installed at a specific location within the District’s regional monitoring network. The majority of groundwater wells monitored by the District (67%) are fitted with continuous data recorders. The remaining wells are monitored monthly (26%) or more frequently than monthly (7%).

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The reasons for groundwater level data problems and data changes are varied. The most common are datum adjustments (reference elevation changes) and instrumentation problems (missing data, equipment malfunctions, etc). (Refer to Appendix A.)

6.2.2 Datum

A datum can be considered as a “coordinate system” of geo-spatial data. Vertical datums have traditionally been based on a form of mean sea level (MSL), but can also be based on a form of tidal datums, and three-dimensional datums such as global positioning systems (GPS). District topographic maps generally have elevations referenced to mean sea level, using either the North American Vertical Datum 1988 (NAVD88) or the older National Geodetic Vertical Datum 1929 (NGVD29).

Figure 9 illustrates a valid benchmark which is located within 100 feet of a District groundwater well.

Figure 9: Valid Benchmark (USACE survey mark).

6.3 Data Acquisition Methods

The District’s SCADA (supervisory control and data acquisition) system transmits and receives information on water levels, rainfall, wind velocities, water temperature, salinity levels and other hydrometeorological data. The system operates on a 24-hour basis (via microwave and radio frequency telemetry, and other electronic/telephonic/radio data acquisition systems) and provides the wireless communications necessary to monitor and control water levels, water-control gate positions, and pumping activities (Figure 10). Telemetry sites continuously provide updated data every 50-minutes to 1-hour depending on the site.

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Figure 10: SCADA Installations and the Operations Control Center (OCC).

The District currently utilizes three types of remote-terminal-units (RTUs) within the SCADA system:

• Campbell Scientific, Inc. CR10 data loggers. Data from the traditional CR10X array-based configuration can be collected either manually once a month or daily by telemetry through the Automatic Remote Data Acquisition and Monitoring System (ARDAMS). Data from the CR10X-TD table-based configuration are collected in real-time through the LoggerNet System.

• Motorola SCADA (MOSCAD). Data are collected from these units in real-time. • Legacy MCU/RACU (Master Concentrator Unit/ Remote Access and Control Unit).

Data are collected from these units in real-time.

The District is currently modernizing its Water Management SCADA System (WMSS) to support SCADA communications nodes primarily using transmission control protocol/internet protocol (TCP/IP) and other modern networking protocols. The implementation of the new WMSS is based on Telvent’s OASyS DNA monitoring software platform, which has been customized for the WMSS project. The new system facilitates real-time, two-way information exchange between the field and the command center. As a result of this modernization program, the MCU/RACU and ARDAMS systems are being phased out and only the methods used by LoggerNet and MOSCAD system will be used for new installations.

6.4 DBHYDRO

DBHYDRO is the District’s corporate environmental database which stores hydrologic, meteorologic, surface water, groundwater, hydrogeologic, water quality, and permit-required data. In addition, biological data are being collected to support ecosystem restoration. This database is the source of historical and up-to-date scientific information for the 16-county region (approximately 18,000 square miles) covered by the District. The DBHYDRO database stores extensive information on monitoring sites,

RTU: CR10X Datalogger

RTU: Motorola SCADA (MOSCAD) Telemetry

(Microwave Tower)

Operations Control Center

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stations and water control structures including basin, latitude, longitude, state plane coordinates, quad sheet location, county, section, township, range, and other useful data. It is also populated with data from external agencies such as the U.S. Geological Survey (USGS), U.S. Army Corps of Engineers (USACE), Everglades National Park (ENP), U.S. Department of Agriculture (USDA), and others. These data are utilized to support daily District activities, the Comprehensive Everglades Restoration Plan (CERP) and Restoration Coordination and Verification (RECOVER) Projects, as well the needs of external users.

There are many end-users of the DBHYDRO database, including researchers, modelers, and external customers. Various groups within the agency utilize the database to publish reports summarizing data, findings and information on a wide variety of programs (e.g. Kissimmee River Restoration) and for mandated permits in support of the District’s mission. Some examples of published reports include: Water Shortage Conditions Report, South Florida Environmental Report, and Everglades Drought Report.

6.5 DBKeys

With the exception of hydrogeologic data, which do not vary with time, all hydrometeorologic data in the DBHYDRO database are linked to specific source database keys (DBKeys) that serve to identify the time series. A DBKey contains 5-alphanumeric characters that identify a unique combination of site, data type, sensor type, frequency (e.g. breakpoint, daily, etc.), statistic (e.g. maximum, minimum, mean, etc.), agency and other database information.

NOTE: DBKeys are programmatically assigned during the site/station registration process in a sequential manner.

Preferred DBKeys are selected based on legally mandated sites and are comprised of stations located in the Water Conservation Areas (WCAs), Storm Treatment Areas (STAs), Everglades Agriculture Area (EAA), and around Lake Okeechobee. These data sets are identified with “PREF” under the ‘recorder’ column in the DBHYDRO database. Although PREF data are not currently used for groundwater data, some groundwater stations may become part of the legally mandated sites. Another type of data set is required for regional scale modeling and has been subjected to rigorous post-processing quality assurance/quality control. These data sets are identified with “MOD” (e.g. MOD1) under the “recorder” column in the DBHYDRO database.

The Operations & Hydrologic Data Processing (OHDP) Section maintains data saved in “source” DBKeys, whereas the QA/QC Section of Operations & Hydro Data Management Division (OHDM) maintains the “PREF” and “MOD” data. The QA/QC Section notifies the user community of any changes made to preferred data, including the corresponding preferred DBKey, by posting the relevant information on the District’s Internal Internet Website (IWEB) and External Internet Website (XWEB).

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6.6 Data Standardization – Metadata

Data standardization is required such that data from different agencies, projects, or investigations can be interpreted and correlated with each other whenever necessary. Standardization ensures that a common set of attributes, or metadata, are captured for each measurement, sample, or observation. In the case of DBHYDRO, each of the attributes of a time series is metadata. Metadata facilitates indexing and retrieval by engineers, scientists, planners, consultants and other data users. Metadata includes descriptive information about the context, quality and condition, or characteristics of the data. Metadata (e.g. site name, station name, start date, end date, period-of-record, etc.) are required so that data may be indexed and retrieved regardless of the project.

6.7 Data Categories

All groundwater level data are categorized as time series data. This includes supplementary data received from partnering agencies, such as the USACE, which are used for comparison purposes. Time series data is defined as a single data variable that changes with time.

6.8 Data Quality

It is often difficult to quantitatively assess the quality of groundwater level data. In DBHYDRO, daily data values may be tagged with “null” or a single character. For example, the “M” tag indicates that data are missing, and the “E” tag indicates that data values have been estimated. Refer to Appendix B for a listing of DBHYDRO tags/codes (or data qualifiers) used and their respective meaning.

6.9 Significance of Site/Station Registry

New site installations are planned for and budgeted by the District. New site installations can be installed in-house by the SCADA & Hydro Data Management Department, or contracted out to an approved vendor. To minimize the impact of new installations a Site Registration Procedure has been implemented. The Site Registration process involves the administration of all aspects of environmental-monitoring and quality control of data.

Primarily this administration covers field remote terminal unit (RTU) registration for hydrologic and meteorological data and equipment, SCADA (supervisory control and data acquisition) system registrations, and the registration of new data acquisition sites, modifications to existing installations, and equipment deactivations. For additional information on the Site/Station Registry process refer to Q105 Site Registration procedure.

NOTE: It is important to understand the site/station registry and naming conventions that are currently used for data collection, loading, processing, extractions, plotting, verification, validation, and reporting purposes. (Refer to Appendix C.)

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6.10 Data Collection/Validation Pre-Processing System (DCVP)

The Data Collection/Validation Preprocessing System (DCVP) is used to process all of the raw data received through various collection methods by the District. DCVP was developed to handle manually and electronically collected data. At this writing, the DCVP system is composed of 6 applications: SG1, SG2, SG3, SG4, RF2 and RF3. (Refer to Table 2.)

Table 2. DCVP Applications (Abbreviated).

DCVP Application Name Type of Recorded Data

SG1 (Stage/Gate 1) Manual recorder that uses punched paper tape to record surface water or groundwater levels. Type of equipment includes Stevens Digital Punch-Tape recorders.

SG2 (Stage/Gate 2) Manual recorder that continuously records water level (e.g. surface, groundwater, and rainfall) data on a graphical strip chart. Type of equipment includes Stevens A35 and 2A35 Analog Graphic Recorders.

SG3 (Stage/Gate 3)

Data are collected through the Automatic Remote Data Acquisition and Monitoring System (ARDAMS), LoggerNet CR10X and traditional CR10X configuration RTUs. Data includes a variety of hydrologic and meteorologic information such as groundwater level, gate activity, pump operation, etc. The SG3 application is also used for manually observed data (e.g. stage, gate, flashboard, pump, etc.) recorded onto a log sheet.

SG4 (Stage/Gate 4)

District’s microwave telemetry network provides real-time data for the purpose of groundwater level management. The network is used to record a variety of hydrologic and hydraulic data (e.g. groundwater level, gate position, pump operation, rainfall, conductivity, etc.). Monitoring network includes the Remote Acquisition and Control Units (RACU), Motorola SCADA (Supervisory Control and Data Acquisition RTUs), traditional CR10X configuration RTUs, and Tipping-bucket type rainfall gauges.

RF2 (Rainfall 2) Manual recorder that continuously records rainfall data on a graphical strip chart. Type of equipment includes: Belfort Universal Recording Rain Gages and Stevens 2A35 Analog Graphic Recorders.

RF3 (Rainfall 3) Manually observed rainfall/evaporation data recorded onto a log sheet.

Each DCVP application is structured to process a particular type of data. Each application is also designed with a system of validation and checks that are performed on all data sets that are manually or automatically loaded. These validation and check processes are used for data verifications. Each program provides interactive editing and verification of each data set. The verification process is invoked by the Operations & Hydrologic Data Processing (OHDP) staff while working in the DCVP processing system.

6.11 Data Processing Activity

The data processing activity is an important component of groundwater level data management. Data processing is a “set of activities” that are performed on raw time series data collected within the District’s monitoring networks. These data are reviewed by data processing associates who apply various validation procedures and processes to assure the quality of the data values. Many of these procedures and processes are automatic and all are done with various software applications, such as the Data Collection/Validation Preprocessing (DCVP) system and the Graphical Verification Analysis (GVA) program. Standard Operating Procedures (SOPs) have been

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developed and implemented for the Operations & Hydrologic Data Processing (OHDP) Section of the Operations & Hydro Data Management Division, to ensure the integrity of the data during collection, data entry, processing analysis, validation and update to the database. At this writing, the OHDP Standard Operating Procedures are accessible through the District IWEB Learning Center located at:

http://iweb/iwebB501/scada_hdm/hh/learning_center/index.html

6.12 QA/QC Post-Processing Analyses

Hydrometeorologic data collected at selected sites undergo rigorous post-processing analyses by the Quality Assurance/Quality Control (QA/QC) Section to ascertain and/or improve its quality. The QA/QC post-processing examination is performed after the Operations & Hydrologic Data Processing (OHDP) function and initial QA/QC. The post-processing examination process is considered a global function that checks the entire District data acquisition process from data measurement, collection, transmission, processing, and flow computation and streamgauging.

Millions of data records are collected and posted to the DBHYDRO database after data processing, initial QA/QC, and groundwater level computation. Post-processing QA/QC is performed on a subset of those data to meet various legally mandated data requirements, such as: (a) Everglades Agricultural Area (EAA) Rulemaking, Chapter 40E-63, (b) Stormwater Treatment Areas (STA), and (c) Everglades Construction Project (ECP). Post-processing of legally mandated selected sites is conducted monthly or quarterly according to pre-established and published schedules. At this time, the groundwater level monitoring stations undergo post-processing QA/QC for modeling purposes and not to satisfy legally mandated requirements.

The QA/QC post-processing function is used to support many District activities, including (a) water supply, water budget, and water quality analyses, (b) flood plain studies, flood control planning, and flood frequency analyses, (c) hydrologic modeling, (d) assessment of ecological restoration efforts, and (e) design of new water control (hydraulic) structures.

These needs are met by providing what is known as “preferred or MOD1 data.” Preferred or MOD1 data are the “best available data” which are composed of the most appropriate combinations of data available from any known source. Production of preferred data is accomplished through a series of QA/QC post-processing statistical analyses. It involves data investigation to detect anomalies, correct the anomalies, and initiate steps to prevent them from occurring again. Groundwater level data do not currently receive monthly or quarterly post-processing QA/QC. Post-processing QA/QC for groundwater level is performed annually, and the results are stored in MOD1 DBKeys.

QA/QC post-processing tools used include: (1) comparison of historical patterns; (b) site knowledge; (c) graphical inspections; (d) communication with data processing, field technicians, hydrogeologists, and hydraulic engineering staff; (e) alternate data sets; (f) mass balance analysis; and (g) engineering know-how.

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In post-processing, missing groundwater level data may be estimated with data estimation techniques and processes such as spatial and temporal interpolation, and statistical or simulation model applications. Erroneous data may be replaced with higher quality data, be deleted, or qualified and tagged.

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7.0 PROCEDURES FOR QA/QC OF GROUNDWATER LEVEL DATA

7.1 General

The Operations & Hydro Data Management Division (OHDM) has required time series data on groundwater levels at selected sites within the District. The data series is used in the development of baseline data for the Comprehensive Everglades Restoration Plan (CERP) and Restoration Coordination and Verification (RECOVER) process, and for regional modeling purposes. The data are available from the District or other external agencies. The time series data are needed at each of the selected sites. In order to develop these time series, the source DBKeys need to undergo a QA/QC process.

Upon completing the development of SINGLE data series for a SINGLE station, they may be archived in a separate database “table” in DBHYDRO, and identified as modeling (MOD1) or preferred (PREF) DBKey data. At this time, none of the groundwater level data are identified as PREF data.

7.1.1 QA/QC Schedules

Post-processing QA/QC of groundwater level data is conducted yearly by contractors as funding is available. It is the responsibility of the QA/QC Engineer to ensure that the contractor completes all deliverables in the time frames required.

7.1.2 Internal/External Communications

Operations & Hydrologic Data Processing – When the QA/QC Engineer discovers questionable or erroneous data, interaction with the Operations & Hydrologic Data Processing (OHDP) staff is essential. The engineering associate processing the breakpoint time series data is familiar with the history of the station and can provide invaluable information to support the QA/QC process. After conferring with the associate and/or supervisor, the data in question shall be reviewed and corrected to the QA/QC Engineer’s recommendations to insure that the integrity of the District databases is maintained.

7.1.2.1 SCADA and Instrumentation Management – When the QA/QC Engineer identifies instrumentation problems with District data collection platforms or reference elevation issues, interaction with the SCADA and Instrumentation Management (SIM) Division field technician responsible for the equipment and calibrations at the specific site is essential.

7.1.2.2 Water Supply Department - When the QA/QC Engineer detects data inconsistencies, interaction with the Water Supply Department Hydrogeologists, the project manager for the well, and the Groundwater Data Steward is essential.

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7.1.3 Site/Station Folders

Documentation is an extremely important factor in communicating the condition of any District site and/or data collection station. Site/Station folders are generated and maintained by the applicable QA/QC Engineer, and may contain copies of the following information: groundwater well information, maintenance records, site map, photos, sensor schematic, DCVP annotation tables, DBHYDRO data extracts, real-time resolution data, correspondence, phone numbers, etc.

7.2 Methodology

The QA/QC post-processing procedure includes the following steps: (1) site description and selection, (2) data extraction, (3) extraction and validation of data from external agencies, (4) data analysis, investigation, estimating missing data, and data qualifying, (5) single time series generation, (6) data upload to DBHYDRO, and (7) extraction and re-verification of data sets.

NOTE: Refer to Figure 1 for an “overview” of the QA/QC Groundwater Level Data process.

7.2.1 Site Description and Site Selection

The QA/QC Engineer should develop a complete understanding of the selected groundwater monitoring station and its relevant site information such as: site name, site description, site location, aquifer type(s), reference elevation, station description, station identification (station name, latitude and longitude, and state plane x and y coordinates), alternate identification, well depth, depth of open or screened interval(s), agency, county, sensor type, recorder type, DBKey, history, and frequent problems. This information is extremely helpful in understanding the overall data collection, storage and QA/QC processes.

7.2.1.1 Consult with District experts for information on the site, instrumentation, and operations. Query the DCVP database, and talk to the data processing associate processing the data for more insights on the trends of groundwater level data.

7.2.1.2 Whenever possible, going on field trips to visit the site and station is highly recommended.

7.2.1.3 For historical information on groundwater data review the USGS Water Resources Data Volumes published annually, if USGS data are available for the site. The groundwater data are usually available in the “B” series of data volumes that pertain to Ground-Water Wells. Determine the maximum and minimum water levels that can occur at the well for that year.

7.2.1.4 For current information on groundwater data visit the USGS website http://www.USGS.gov/ (Figure 12) and/or download current data from the

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U.S. Geological Services Automated Data Processing System (ADAPS).

7.2.1.5 Review the District SCADA and Instrumentation Management (SIM) Division’s Maintenance Database available on the District IWEB. Review the Site Schematic and locate the sensors installed within the same aquifer and watershed. (Refer to Table 3 and Figure 11.)

7.2.1.6 Review the Registration Worksheets for information about the well.

7.2.1.7 Review the Hydrogeologic Data section of the DBHYDRO Database which contains well construction data, aquifer identification and testing information, photographs, field notes, reports, and other useful information (see Figure 13).

Table 3. Links for Groundwater Well Information.

Source Type Location/Link Access SFWMD SCADA and Instrumentation Management Division

For current information about groundwater well maintenance records, photos, site notes, station worksheets, and monthly data files visit the SFWMD SIM Maintenance Database: http://e41165/Esdastart.aspx?index=0. See example screens in Figure 11.

SFWMD Map of Groundwater Monitoring Sites

The map depicting SFWMD groundwater well monitoring sites can be accessed at http://www.sfwmd.gov.curre/sitemaps/wellmonitoring.pdf

SFWMD Registration Worksheets \\dataserv\570\5730\5732\registrations\

USGS Website For current information on groundwater level data visit the USGS website: http://www.USGS.gov/. See example screen in Figure 12.

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Figure 11: SIM Maintenance Database Screens available at http://e41165/Esdastart.aspx?index=0.

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Figure 12: USGS Florida Groundwater Data available at http://waterdata.usgs.gov/fl/nwis/gw.

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Figure 13: Example Hydrogeologic Data Screens from the DBHYDRO Database.

7.2.2 Data Extraction and External Agency Data Acquisition & Validation

The hydrologic data to be reviewed during QA/QC post-processing are primarily downloaded from the DBHYDRO database. Each data set is stored in DBHYDRO under a unique DBKey (database identification) that serves to identify the time series.

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Source data obtained from external agencies, such as the U.S. Geological Survey (USGS) and others, are loaded and stored in the DBHYDRO database. These source data may have been downloaded erroneously and will require validation.

7.2.2.1 DBHYDRO Data Extraction

Extract (download) the appropriate groundwater level data from DBHYDRO for all the available DBKeys. In many instances, a site or station could have varying period-of-record (p.o.r.) data sets; and multiple source DBKeys resulting from multiple instruments installed at that location (e.g. telemetry, solid-state CR10 datalogger, graphic chart recorder, manually observed data, etc.), or if similar data are being collected at the same site by an external agency.

Use the search option in DBHYDRO that queries by “station” and “data type.” If using a single source DBKey, retrieve the data from DBHYDRO using Format 6. If using multiple source DBKeys, retrieve the data from DBHYDRO using Format 7.

7.2.2.2 External Agency Data Acquisition and Validation

Source data from external agencies (e.g. USGS) that were previously loaded into DBHYDRO will need to be verified to ensure that the data coming (downloaded) from the external agency are the current data. Compare the source data existing in the DBHYDRO DBKey with the external agency source data; and if the data do not match, then upload the current external agency data into DBHYDRO. External agency data may be obtained from their respective databases via file transfer protocol (FTP). For example, the USGS external agency source data may be retrieved using the Automated Data Processing System (ADAPS) database.

NOTE: Accessibility to these external agencies source databases are restricted to specific groups only, and require supervisory assistance and permission from the cooperator.

7.2.3 QA/QC Data Investigation and Analysis

Refer to Appendix D for a listing of applications currently available to the QA/QC Engineer for rigorous post-processing analysis of hydrometeorological data.

7.2.3.1 Graphical Plotting

(A) After the site/station data are downloaded from DBHYDRO into EXCEL using the DBHYDRO browser or EXCEL ODBC, the remainder of the QA/QC analysis may be performed in an EXCEL spreadsheet environment. The EXCEL spreadsheet will contain

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the station information where all the computations are carried out. The single time series is built in the work area. EXCEL scripts will need to be created to evaluate the various “tags” existing in the source data set. Ancillary information may be added to the spreadsheets as necessary for clarification.

NOTE: Open DataBase Connectivity (ODBC) builds a query with EXCEL and brings the data from DBHYDRO directly into either EXCEL or Access. This eliminates creating a text file and importing it into EXCEL.

(B) The following “file naming convention” may be adopted for each EXCEL Workbook. This allows easy data access and post-processing tasks such as uploading the “preferred” data set into DBHYDRO.

Example: TOHO13_GW_Well_XXXXX_Name_Series_por.xls,

TOHO13_GW is the station identification (ID)

Well signifies that Well data is being processed

XXXXX is the “target” station DBKey

Name: QA/QC Engineer performing the QA/QC process

Series: indicates time series data

por period-of-record (optional)

(C) The examination of the time series groundwater level data is performed through graphical plotting (daily, historical, and monthly). Gaps, overlaps and relationships are depicted. Plot and examine the source groundwater level time series data set with at least three (3) adjacent vicinity groundwater well stations with at least one of the three stations monitoring the same strata (aquifer), whenever possible, for the period-of-interest of the data being analyzed.

NOTE: As a preview for adjacent site/station selection, the DBHYDRO environment may be accessed to graph, view, and compare the source groundwater level data and the three (3) vicinity groundwater level well stations.

Figure 14 illustrates an abbreviated graphical Groundwater Level and Rain plot comparing two FAS groundwater well stations (GLF-6 and MHGW_GW1), two SAS groundwater well stations (CRSO3FM and CRSO3FS), and a rainfall station

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(S478_R) in the vicinity for the period-of-interest (06/15/2006 through 07/15/2006). The SAS wells track each other and reflect the changes in precipitation. The FAS wells also track each other, however, the changes in precipitation and increased water levels in the SAS wells are not reflected in these wells because of the impervious confining layer that separates the SAS from the FAS groundwater wells.

Figure 14: Example of Groundwater Level and Rainfall Graphical Plot Comparison.

NOTE: Hydraulic connection between groundwater wells (i.e., located in the same basin or the same aquifer) in addition to station proximity is very important for adjacent site/station selection.

(D) Plot at least one (1) and up to three (3) adjacent (within the same basin) rainfall gauging stations, whenever possible, to obtain matching data for the period-of-record (p.o.r.) of the groundwater level data being analyzed. The rainfall stations are all plotted on the same graph in an EXCEL spreadsheet. For additional information on rainfall post-processing, refer to standard operating procedure, Q202 QA/QC of Rainfall Data Procedures – Rainfall Gauges and NEXRAD.

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NOTE: NEXRAD stands for National Weather Service deployed Next Generation Radar (a.k.a. WSR-88D Weather Radar). This technology improves the spatial estimation of rainfall depth. NEXRAD data can be found through DBHYDRO Browser Menu under “Radar-Based Rainfall Data”.

(E) In cases, where multiple data sources are used, the data review will include all available sources. Plot the data sources side-by-side using an EXCEL spreadsheet. Compare the plot of the primary source to the other sources to see if there is general agreement in the plots. The primary source is usually pre-selected. In the case where there are multiple District sources, the order of preference in selecting the primary source is (1) telemetry (MOSCAD or RACU), (2) solid state recorder (e.g. CR10 datalogger), (3) graphics, and finally (4) manual (observer data).

(F) The QA/QC Engineer will conduct a review of source data sets provided by other (external) agencies, such as the USGS. Plot the source data in DBHYDRO and the latest data downloaded from other agencies’ respective databases (e.g. ADAPS) side-by-side using an EXCEL spreadsheet. Compare the plots to see if there is general agreement. Differences occur when the external agency changes data after reporting it to the District. Therefore, if the data do not match, the latest (most current) external agency data will need to be reloaded into DBHYDRO.

7.2.3.2 Detailed QA/QC Analysis

Quality assurance and quality control procedures emphasize the use of statistical tools such as correlation and regression analyses to ensure that the groundwater level data are as reliable as is technically possible; and to ascertain and/or improve data quality. (Refer to Appendix E, Data Analysis Methods: Mass Balance & Statistical Analysis.)

(A) Identify which strata/aquifer the groundwater well sensor is monitoring. Use data from groundwater wells located in the same basin that monitor the same strata/aquifer. Check data for gaps and overlaps in information, outliers, and groundwater level relationships with respect to rainfall events (see Figure 14) and groundwater pumping/wellfield activities (see Figures 15, 16, and 17).

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Figure 15: Effect of Periodic Valve Opening on a Flowing Floridan Well.

Figure 16: Effects of Pumping from Nearby Municipal Wells on a Floridan Monitoring Well.

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Figure 17: Effects of Errors in Sensor Performance Following Water Quality Sampling Episodes on 6/22/04, 10/6/04 and 2/3/05.

(B) Determine the groundwater surface elevation from DBHYDRO, or from topographic maps, or digital elevation models (e.g., http://nmviewogc.cr.usgs.gov/viewer.htm). Verify the groundwater surface elevation in NGVD29 or NAVD88.

(C) Determine the reference elevation for the groundwater well from the DCVP Station Reference Table, DCVP Reference Elevation Form, and the USGS Water Resources Data Volumes. Verify the vertical datum to be NGVD29 or NAVD88 reference datum, and the groundwater elevation in units of feet. Changes in reference elevations at a station can cause breaks in the groundwater level data (see Figure 18).

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Figure 18: Groundwater Levels at CHAPMAN_G Compared with Levels from Adjacent Station C36 and Rainfall Data (CHAPMAN_R). The Change in Reference Elevation for CHAPMAN_G Occurred in October 1980.

NOTE: The marked increase (almost 10 feet) in groundwater level for Chapman_G is not reflected in the groundwater levels for the adjacent station, C36. Similarly, the precipitation values for the area do not support the increased water level.

(D) The groundwater levels for wells monitoring unconfined aquifers (SAS) should normally be lower than the groundwater surface elevation. The groundwater level, however, can be higher than the groundwater surface elevation if the site is flooded. If the groundwater level is higher than the ground surface elevation, confirm that flooding occurred at the site.

(E) The groundwater levels for wells monitoring confined aquifers (IAS and FAS) may be higher than the ground surface elevation due to the artesian nature of the wells.

(F) If during the period-of-record the groundwater level values are flat for long periods of time, these values should be labeled as missing, since it is very likely that either the groundwater level has dropped below the level of the sensor or the sensor has malfunctioned.

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(G) Examine the groundwater level data for outliers. Hydrologic data (e.g. groundwater level, stage, flow, and rainfall) that can be measured are all subject to outliers.

NOTE: An outlier is a single observation far away from the rest of the data. The outlier may be a discordant observation. Outliers can be found on the upper end or lower end of the data spectrum. The existence of outliers in the data will bias statistical results. Possible source of outliers are: recording and measurement errors, unknown data structure, a new phenomenon occurring in the data, or response of the groundwater level monitoring system to seasonal fluctuations. Outliers may represent peaks in the groundwater level as the result of extraordinary rain events, such as tropical storms and hurricanes. Other outliers may represent dry seasonal periods from stations that are capable of recording the groundwater level data. Suspected mild or extreme outliers need to be examined thoroughly before a final decision is made to remove them from the data set.

Figure 19 illustrates an inconsistent groundwater level for Station TOHO13_GW. For most of the period of record, groundwater levels at TOHO13_GW are consistent with the groundwater levels at adjacent groundwater stations. However, between June 2004 and February 2005, the levels at TOHO13_GW dropped while the levels at adjacent stations rose. The drop in groundwater levels is also inconsistent with the precipitation data during this period. A high amount of precipitation was recorded during this period when the groundwater levels at Station TOHO13_GW were dropping. In this example, a review of District permitting records revealed that a large construction project at an adjacent property was conducting dewatering operations (Figure 20) during the same period groundwater levels in TOHO13_GW appear anomalously low. This is a good example of how new phenomena occurring in the physical system can create unexpected changes in groundwater level data.

During the summer and fall East Lake Toho was very high due to hurricane-related storms. Discharge was routed off site during construction because infiltration in the onsite retention area was inadequate and the site was flooding. The water table was lowered substantial during this dewatering (27 feet below land surface). Construction site dewatering could be responsible for the decline in groundwater levels at this time and for the slow recovery. The groundwater levels still reacted to rainfall events that occurred during this dewatering period so there were no sensor problems.

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Figure 19: Groundwater Levels at Station TOHO13_GW Compared with Levels from Adjacent Groundwater Stations and with Precipitation Data from an Adjacent Rainfall Station.

Figure 20: Chisholm Estates Construction Site Adjacent to TOHO13_GW.

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The QA/QC Engineer needs to perform statistical analysis of each time series to identify outliers and investigate their validity before making a final decision to remove them from the data set. The outlier evaluation may be performed by examining the groundwater level and rainfall data at the station of interest and by comparing the behavior or patterns at nearby stations from the same aquifer and at groundwater levels from other aquifers in the same well. Available information from source agencies such as the USGS is also reviewed. The Box Plot method (Appendix F) may be used for identifying outliers.

(H) Examine the groundwater data for sudden sharp peaks or troughs. For groundwater wells monitoring the SAS or Biscayne Aquifer (or even the IAS or FAS), if there is a sharp peak, examine the rainfall data at nearby rain gauges and ascertain whether the peak is justifiable. Also look for nearby canal discharges caused by sudden operations of gates or other structures. This may involve looking at the operations data for that nearby structure.

If there is a sharp trough, look for groundwater or public water supply wells nearby that may cause a drawdown in the aquifer. Public water supply wells can be located from the District geographic information system (GIS) maps and coverages.

(I) If the peaks and troughs cannot be explained by looking at nearby rain gauges or water supply wells; look for staff gauges nearby and check whether these gauges also exhibit the same behavior for the groundwater level data. If the behavior is seen in other stations, then accept the data.

(J) Examine the groundwater level data for extreme sharp peaks of small duration (lasting a few days), and if these cannot be corroborated from other stations nearby, treat that part of the record as missing data.

(K) If there is a higher elevation or lower elevation groundwater monitoring station in the same well or in nearby groundwater wells monitoring the same strata, a relationship between groundwater levels at those two locations may be developed using regression techniques. The relationship may then be used to confirm groundwater level values for the station being examined.

(L) Determine reasons for outliers, missing data, and other anomalies. Examine the MIRMaid (Maintenance/ Inventory/ Recorder/ Malfunction/Aid) Reports and the Data Annotations in the DCVP system, and/or consult with the SCADA & Instrumentation Management field personnel, Operations & Hydrologic Data Processing staff, and Water Supply Department staff.

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NOTE: The MIRMaid application is used to report data recorder malfunctions and data collection errors. This tool was created to expedite and systemize the problem reporting process. The interface used to enter problems to the MIRMaid system is called REQUEST (Recorder Equipment problem Entry system).

(M) Work to resolve the problem at the source, this will help to prevent the data problem from occurring again. Inaccuracies in groundwater level data may be caused by: datum adjustments (reference elevation changes), structure or sensor maintenance problems, instrumentation calibration problems, data acquisition and loading problems, and data processing problems.

NOTE: Any unresolved problems may lead to future data changes in the DBHYDRO database. However doing so is not a trivial issue, as the historical data are likely to have already been used in critical projects of national importance.

7.2.4 Missing Data Values

After completion of the time series examination through graphical plotting, most of the gaps, overlaps and relationships problems should be depicted. The missing daily data may be estimated using one of the following methodologies. However, the main purpose of QA/QC data analysis should be to determine whether the source data is acceptable or not, prior to replacing the erroneous data with higher quality data. Remember, good judgment should always prevail.

NOTE: Missing groundwater level information includes: N (existing data not processed/not yet available), and M (missing) tags in the DBHYDRO database.

CAUTION: If data are tagged “N” existing data may not yet be available, consult with the data processing staff to determine if the data can be processed and/or re-loaded into DBHYDRO for QA/QC re-evaluation.

7.2.4.1 If groundwater level data are available for the same station from another DBKey, then the missing data may be estimated from the alternate DBKey, provided a relationship can be established between the water levels at the two stations.

7.2.4.2 If the period-of-record (p.o.r.) with missing data is short (seven days or less) and the data have a well defined trend over that period, and there is no rainfall for that period, consider estimating the missing values by linear interpolation between the end points.

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7.2.4.3 If the period-of-record (p.o.r.) with missing data is more than seven days that part of the record should be treated as missing data. Missing data may be estimated based on the process described in step 7.2.4.4.

7.2.4.4 If nearby stations have groundwater level data, and a relationship may be established by linear regression, then the data from these stations may be used to estimate the missing data.

7.2.4.5 For all missing daily data not within these three cases, an “M” (missing) tag may be re-assigned to the data.

7.2.4.6 The QA/QC Engineer will assign an “E” (estimated) tag for data that has been “filled in”.

7.2.5 Preferred DBKey and Regional Modeling (MOD1) DBKey Development

After performing an EXCEL spreadsheet data set analysis, the QA/QC Engineer should create the “preferred or MOD1” data set from the “evaluated” source data set. All data changes should represent significant improvements, and resolve inconsistencies so that the accuracy, precision and reliability of data are increased. Any data changes (edits) shall be identified with qualifiers (tags/codes).

NOTE: If the QA/QC Engineer requires assignment of a new preferred DBKey, the QA/QC Section supervisor will need to be contacted. Preferred DBKeys are created only upon the QA/QC Section supervisor’s request to the Site Registration Administrator.

NOTE: All Regional Modeling requests for post-processing of preferred data, designated as MOD1 DBKeys, shall be approved by the Operations & Hydro Data Management Division (OHDM) Director. MOD1 data sets are processed in the same manner as preferred data sets, with the exception of having the opportunity of being assigned two additional modeling tags/codes, and require a different DBHYDRO loading process.

7.2.5.1 Preparation for Loading PREF Data Sets into DBHYDRO

Preferred data sets will need to be prepared for loading into the DBHYDRO database. The following steps have been provided to aid the QA/QC Engineer during this process.

Perform the following steps:

(A) Go into DBHYDRO and extract (download) the source DBKey data file using <Single Time Series Format 6>.

(B) Save the data file. Example: g204_h_1004.f6

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(C) Convert to load format into DBHYDRO using the reg_to_pref program. (Refer to Figure 21.)

The reg_to_pref program converts the data format from the source DBKey into the preferred DBKey format. This application can be accessed from a UNIX workstation or PC using either E-Term 32 Terminal Emulator window or Reflections software.

The following prompts will be displayed:

Enter <Input File Name> (e.g. g204_h_1004.f6) Enter <Output File Name> (e.g. g204_h_1004.pref) Enter <Target DBKey> (e.g. JW230) Enter <Data Type> S=Stage Enter <Selected Format> W=DBHYDRO Web Browser Enter <Yes or No> if user wants to skip more than 7 lines at the beginning of the file. (The normal default is 7 lines.)

The result will be a UNIX Text file.

startrek:/k_wmp/ka_hm/ka04/achong/PREF_LOAD/WORK>reg_to_pref PROGRAM TO CREATE INPUT FILE FOR DBHYDRO_LOADER ENTER INPUT FILE NAME: g204_h_1004.f6 ENTER OUTPUT FILE NAME: g204_h_1004.pref ENTER TARGET DBKEY: JW230 DATA TYPE: PRECIPITATION, STAGE OR FLOW (P/S/F) ?: S DEFINE INPUT DATA FORMAT: DBHYDRO (D) SQLPLUS OMD RETRIEVED DATA (SO) DBHYDRO WEB BROWSER (W) SQLPLUS DBHYDRO RETRIEVED DATA (SD) USER DEFINED (U) ENTER SELECTED FORMAT: W 7 LINES WILL BE SKIPPED AT THE BEGINNING OF THE FILE BY DEFAULT DO YOU WANT TO SKIP MORE THAN 7 LINES? ENTER Y/y FOR YES AND N/n FOR NO: 7 PROGRAM COMPLETED SUCCESSFULLY startrek:/k_wmp/ka_hm/ka04/achong/PREF_LOAD/WORK>

textedit g204_h_1004.pref

Figure 21: REG_TO_PREF Program.

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7.2.5.2 Edit UNIX Text File

(A) Access the “UNIX text file”. Various tools are available to the QA/QC Engineer for editing the UNIX text file such as: Reflections Text Editor, Notepad or WordPad.

(B) Using the text edit tool, manually edit the UNIX text file, using the data compiled in the EXCEL spreadsheet. Correct any period-of-record disparities identified during the QA/QC analysis and evaluation, replacing all erroneous data with best available and/or higher quality data. Any data changes in the text file shall be qualified through the addition of “E” (estimated) or “M” (missing) tags/codes. (Refer to Figure 22.)

NOTE: Regional Modeling or MOD1 data series may also be assigned “Y” tags. The “Y” tag represents suspicious data that are being left in the data series because they can still provide information that may be useful to the user. For example, if during a dry spell the groundwater level drops below the measuring sensor in the well, the level recorded will be the limit of the recorder and it would appear that the data “flat-lines” on the plot. If groundwater level cannot be estimated, the recorded data can be kept and tagged with “Y” to indicate that it is suspicious data rather than deleted and tagged as “M” or missing. In this case, the data provide useful information because the user can infer that the groundwater levels are below the recorded or flat-lined levels.

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Figure 22: Reflections TextEditor used to edit UNIX text file.

(C) Once the information has been edited, the next step will be to save the UNIX text file.

(D) Import a copy of the preferred data file into the EXCEL spreadsheet.

7.2.6 Load Modeling (MOD) Data Sets into DBHYDRO

A multiple-step process is needed to load the quality assured Modeling (MOD) data series into the DBHYDRO database. An example of the process steps are illustrated in Figure 23.

Assignment of Tags/Codes (Data Qualifiers): “E” Tag = estimated data value.

Value PREF DBKey

DATE: Required format (YYYYDDMM)

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Steps for Loading Modeling (MOD) Data into DBHYDRO Step # 1: The QA/QC Engineer saves a .csv (comma delimited) file from the EXCEL file containing the data set requiring load/reload into DBHYDRO. The location of .csv file should be well known for drive mapping purposes. (See Figure 24.) Step # 2: The QA/QC Engineer runs a standard SQL (Structured Query Language) script to transfer the data set into a staging area (dm_daily_data_transfer) in the water resources evaluation production (wrep) database. The following steps assumes a Windows PC environment: 1) Start up Oracle sqlplus as dmdbase in wrep database. 2) Delete the data for that DBKey from dm_daily_data_transfer. (Refer to Maintain_Transfer_Table.sql) 3) Edit the control file (load_dm_daily_data_transfer.ctl) and update the infile list. The infile list will use

the drive letter assigned to the server location (e.g. M drive) where the .csv file resides. (Refer to Figure 25.)

4) Open up a windows “command prompt” in the same directory as the control file. The user may wish to create a shortcut for this purpose.

5) Run sqlloader from the users working area defined by the shortcut: C:\Oraclt\bin\sqlldr userid=username/password@wrep control=load_dm_daily_data_transfer.ctl errors=150

6) Examine the .bad files (any record improperly formatted) and the .log files. 7) Rename the .log file to the next highest sequence number and move to the logfiles subdirectory.

Note: .log file will be overwritten if it has the same name in the logfiles subdirectory. 8) Move the .bad file to the badfiles subdirectory.

Note: .bad file will be overwritten if it has the same name in the badfiles subdirectory. 9) Check DBKeys and source DBKeys to make sure they are valid, otherwise the masterinsert will fail. Helpful hints:

use sql queries like this: To check data in dbkey 12345

select * from dm_daily_data_transfer where dbkey =’12345;

To delete dbkey 12345 and the data in it Delete from dm_daily_data_transfer where dbkey=’12345; Commit

To get list of dkbeys in the table and the number of records in each dbkey select dbkey, count(dbkey) from dm_daily_data_transfer group by dbkey

Step #3: The QA/QC Engineer will notify the designated person (with special rights and privileges) that the data set has been updated in the dm_daily_data_transfer table located in the wrep database, and is ready for loading into the DBHYDRO database.

Figure 23: Steps for Loading Modeling (MOD) Data Sets into DBHYDRO Database

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Figure 24: Example of a .csv (comma delimited) File.

Figure 25: Example of a Control File to Load Single Series Data to Temporary Table in wrep.

MOD1 DBKey

Date must be in this format: (YYYYMMDD)

Value

Tag/Code must be a valid uppercase code.

Source DBKey

Comments

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7.2.7 Extract/Verify MOD DBKey Data Sets

A data review is required to ensure the quality and integrity of the “MOD” data sets.

7.2.7.1 Extract the updated QA data series from DBHYDRO, and review the data to verify that all tags were converted to the desired tags. The goal of the QA/QC post-processing analysis is to obtain an updated time series comprised of only “M” missing tags and “E” estimated tags. (Refer to Appendix B.)

NOTE: The exception will be regional modeling tags: “Y” (provisional use for regional scale modeling), and “Z” (not appropriate for regional scale modeling), they are not to be converted.

7.2.7.2 To be consistent with QA/QC goals, a comparison plot between the source DBKey and MOD DBKey data series may be performed.

7.2.7.3 Another method would be to load the source DBKey and MOD DBKey data sets into an EXCEL spreadsheet, and to analyze the differences between the minimum and maximum water levels. The minimum and maximum water level differences should be close to zero.

7.2.7.4 Print a copy of the extract, and file in the site/station folder to ensure a documentation trail. Where possible, QA/QC analysis results, as well as site photographs should also be included in the file.

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7.3 Accessing QA/QC Post-Processing Applications

Refer to Appendix D for a listing of applications currently available to the QA/QC Engineer for rigorous post-processing analyses of hydrometeorologic data.

7.3.1 DBHYDRO Browser Application

The DBHYDRO Browser is a web-based application that allows users to search DBHYDRO, using one or more criteria, and to generate a summary of the data from the available period of record. The data is accessed through use of a station or site name, x-y coordinates, basin name, county, etc. The selected time series data is dynamically displayed on the user’s screen in tables or graphs. The DBHYDRO Browser is accessed by first selecting “Business Online” from the District’s Home Page on the District’s intranet (IWEB) and then selecting “DBHYDRO Browser”.

The DBHYDRO Browser Menu (Figure 26) will be displayed. The user must select the type of data to be queried (e.g. Surface Water and Meteorological data, Groundwater data, Water Quality data, etc.). An easy to use User’s Guide is available from the main menu which allows the user to understand all the key search functions available.

NOTE: The DBHYDRO Browser is essential when performing a QA/QC post-processing of groundwater level data operation.

Figure 26: DBHYDRO Browser Menu

This block allows the user to select Surface Water Data, Meteorological Data, Groundwater Data, or combinations therein.

Link to Data Validation and Processing Utilities

Link to Well Construction Information, Aquifer Data, and Multimedia Files (i.e. photos and reports).

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7.3.1.1 Select Search Parameters

Selecting “Ground Water” data from the DBHYDRO Browser brings up the “Select Search Parameters” screen. This screen offers multiple search parameter queries. In Figure 27 the areas of interest are the <Site Name> and <Data Type>. Once search parameters queries are selected, click the <Submit> button.

Figure 27: Select Search Parameters

7.3.1.2 DBHYDRO Query Criteria

The criteria fields are filled in by selecting from the field of values. Figure 28 illustrates that <Site Name> ALL, <Data Type> ALL, <Agency> SFWMD, and <County> GLADES are areas of interest. Selection is made to <Pick Time Series Individually> and <Order By> STATION. At this point, the user may save a parameter file. Once all criteria fields are filled, click the <Submit> button.

Search Parameter selected (e.g. Site Name)

Search Parameter selected (e.g. Data Type)

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Figure 28: DBHYDRO Query Criteria

Clicking on the “Submit” button will result in a “metadata” list. Figure 29 illustrates that the Glades County SFWMD site query has returned 52 records. The <Get Data> block may be used to select one or more data sets for display. After selecting the DBKey(s) requiring retrieval, click the <Get Data> button.

For example, DBKey “SD017”, Station “MHGW_GW1”, Data Type “Uncorrected Hydraulic Head (UNHD)”, Frequency “Daily”, Statistics “Mean”, Recorder type Campbell datalogger (CR10)”, and agency “District (WMD)” have been selected.

Select County (e.g. GLADES)

Select Agency (e.g. SFWMD)

Select Data Type (e.g. ALL)

Select Site Name (e.g. ALL)

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Figure 29: DBHYDRO Query Criteria Results

Each of the underlined fields in Figure 29 is hyperlinked to additional information that explains its meaning. For example, by clicking on the station field link for Station MHGW_GW1, the “Station Information” screen (Figure 30) will be displayed. From here the user can display a map, go get data, display structure information, or go to a list of nearby vicinity stations.

Data Type: WELL – groundwater level (ft NGVD29) UNHD – uncorrected hydraulic head (ft of water)

Frequency: DA – Daily BK – Breakpoint

Statistics: MEAN – Mean value for interval INST – Instantaneous

Recorder: CR10 – Campbell datalogger MOD1 – regional modeling data TELE – telemetry

Strata: Depth of well (ft)

Select one or more data sets for query

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Figure 30: Station Information Field Link

7.3.1.3 DBHYDRO Query Date Selection – Single Time Series Format 6

This screen allows the user to make selections for retrieving the period-of-record (p.o.r.) for the data set and to plot it. There are multiple output formats and four destinations.

Figure 31 illustrates the DBHYDRO query date selection for eight DBKeys: L7467, L7496, L7466, L7495, SC005, SC006, SD016, and SD018. These DBKeys represent four different strata: 12.78, 58.19, 707, and 1560.

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Figure 31: DBHYDRO Query Data Selection

• Using the drop-down menu select the <Date Range>. Enter the <Start Date> and <End Date> of the p.o.r. requiring a data search. (Dates entered must use the YYYYMMDD format.)

• Using the drop-down menu, select the Report Format & Graph <Single Time Series Format (Format 6)>.

• Select the <Destination>. • Select the <Run Mode>. • Click on <Submit> button.

Figure 32 illustrates the results of the Single Time Series format query for DBKey SC005, Station ID GLF-6, UNHD data for one month. The query returned 31 records.

Enter Start Date and End Date of the p.o.r. requiring a data search.

Select “Single Time Series” Format (Format 6).

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Figure 32: Single Time Series Format

7.3.1.4 DBHYDRO Query Date Selection – Graph (Plot)

Figure 33 illustrates the DBHYDRO query data selection for multiple DBKeys for Station MHGW_GW1.

NOTE: Only five (5) parameters can be selected at one time to plot or graph.

• Using the drop-down menu select the <Date Range>. Enter the <Start Date> and <End Date> of the p.o.r. requiring a data search. (Dates entered must use the YYYYMMDD format.)

• Using the drop-down menu, select the Report Format & Graph <Graph>.

• Select the <Destination>. • Select the <Run Mode>. • Click on <Submit> button.

Data Value: Daily water levels (UNHD) given in feet of water

Daily Date: Date period-of-record was collected from the field

Quality Code: E = estimated value

Revision Date: Date period-of-record was reprocessed and re-loaded into DBHYDRO.

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Figure 33: DBHYDRO Query Data Selection

By clicking on the “Submit” button a graph will be generated directly from the DBHYDRO database for the parameters selected. This graph may be printed to any available printer, using landscape mode.

Figure 34 illustrates a time series graph for one of the stations previously selected in Figure 14. The two different colors represent the two DBKeys selected for Station MHGW_GW1. The Uncorrected Hydraulic Head (UNHC) levels are given in feet of water. The period-of-record (p.o.r.) is from June 21, 2006 through June 24, 2006.

NOTE: Breakpoint (BK) data have more data points then Daily (DA) data averages which have one data point per day

Using the drop down menu, select “Graph” to plot the p.o.r. for the data sets selected.

Enter Start Date and End Date of the p.o.r. requiring a data search.

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.

Figure 34: Time Series Graph for Groundwater Well MHGW_GW1 Comparing Daily Average Data and Instantaneous Data (Breakpoint Data).

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Figure 35 illustrates a time series graph for the four groundwater wells listed in Figure 14. The four different colors represent four groundwater wells in Glade County. Values for two of the wells (MHGW_GW1 and GLF-6) are reported as Uncorrected Hydraulic Head (UNHC) levels and are given in feet of water. The other two wells (CRSO3FM and CRSO3FS) are shallower and the groundwater (WELL) levels are presented in feet NGVD29. The period-of-record (p.o.r.) is from June 15, 2006 through July 15, 2006.

Figure 35: Time Series Graph for Four Groundwater Wells.

The Y-axis information for the shallower (WELL) groundwater wells.

The depth of well GLF-6 is 1560 feet.

The depth of well CRSO3FM is 58.19 feet.

The depth of well CRS03FS is 12.78 feet.

The Y-axis information for the deeper (UNHD) groundwater wells.

The depth of Well MHGW_GW1 is 707 feet.

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Accessing DCVP from a Workstation: Go into the Workspace Menu, open a UNIX Command Tool window. A new screen will appear (cmdtool-/bin/csh). Enter: <dcvp_welcome>. Press<Return or Enter>. A new screen will appear. Enter your <DCVP username> and <password>. In the database field enter <dcvp>. Press <Return or Enter>. The user will now be accessing DCVP from the UNIX environment. Accessing DCVP from a PC (Using E-Term32 or Reflections Software): 1) If using E-Term32, open an E-Term32 Terminal Emulator Window. Click on the <Connect Menu> and select <Connect>. In the Connecting dialog box, in the “Node Name” tab select the name of a workstation in your area. If none is listed, type the name of the workstation in the box, this will be added to the drop down list for future use. Click on the <Connect> button. Log-in using your <UNIX username> and <password>. Enter <dcvp_welcome>. Press <Return or Enter>. A new screen will appear. Enter your <DCVP username> and <password>. In the database field enter <dcvp>. Press <Return or Enter>. The user will now be accessing DCVP from the PC environment via E-Term32. 2) If using Reflections, click on Reflections Icon. An “X Client Manager” window will open. Enter connection setting <method> and <host name>. Press <Connect> button. A “Sun Log-On” screen will appear. Enter your <UNIX username> and press <Return or Enter>. Enter user <password> and press <Enter or Return>. Open a cmdtool window. Enter <dcvp_welcome>. Press <Return or Enter>. A new screen will appear. Enter your <DCVP username> and <password>. In the database field enter <dcvp>. Press <Return or Enter>. The user will now be accessing DCVP from the PC environment via Reflections.

7.3.2 Data Collection/Validation Pre-processing (DCVP) System Application

7.3.2.1 Accessing DCVP Database Using Workstation or PC

The Data Collection/Validation Pre-processing (DCVP) System is a UNIX application running on an Oracle platform. DCVP is accessed from a “Workstation” through the main menu or from a “PC” through an E-Term32 Terminal Emulator window or Reflections Software. (Refer to Figure 36 for instructions.)

Figure 36: Instructions for Accessing DCVP Database.

Figure 37: DCVP System Main Menu.

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7.3.2.2 Reference Information Menu

a) From the DCVP Main Menu (Figure 37): Select <Option 3> Reference Information Depress <Return or Enter> key.

b) The Reference Information Menu (Figure 38) will be displayed.

The following Information regarding data collection sites is available from this menu:

• Identification numbers for database reference • Station names, recorder types, station description, data value

ranges, etc. • Stations elevation with respect to sea level • Period of record listings • Daylight Savings time table • DCVP Application descriptions • Site parameters descriptions and units of measure • Screen listing of output data values • Output data tags descriptions • Listing of stations currently in the technical data review process • Explanatory notes on estimated and missing data

Figure 38: Reference Information Menu.

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7.3.2.3 Annotations Table Information

The DCVP Annotations table was created as a tool to provide historical information regarding tagged data. It lists the station name, the period of record, the name of the employee responsible for making the annotation and comments explaining the reason the data was tagged.

a) From the Reference Information Menu (Figure 37): Select <Option 18> Annotations Depress <Return or Enter> key

b) The Annotations Table (Figure 33) will be displayed.

Depress <F2> to enter a Query Insert <Station ID> Execute Query <F3> Tab over to “Comments” column. Depress <CTRL E> keys The “Editor” window will be displayed.

NOTE: Any annotations (comments) identifying problems observed by the data processing associate for the specific period-of-record (p.o.r.) will be identified in this window.

Figure 39: DCVP Annotations Screen

Comment Field

Editor Window

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8.0 APPENDICES

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Appendix A: Possible Reasons for Groundwater Level Data Problems and Data Changes

The reasons for groundwater level data problems and data changes are varied. The approach used here consists of determining whether one or a combination of these reasons justified the data change in the source key for each groundwater station under consideration, which would result in changes in the preferred or MOD1 data set.

Factors Reasons for Groundwater Level Data Problems & Data Changes

Datum Adjustments

• Site installation can introduce error into the recorded measurement and reference point elevation surveys may be a source of error. The reference elevation may be an assumed value and would need to be surveyed to get the true value, which would necessitate a datum adjustment to the data.

Changing Weather Conditions

• Changing weather conditions such as wind speed, wind direction, and rainfall may result in water turbulence and will impact the groundwater level data. This may occur at sites that are flooded where the groundwater level is above the ground surface elevation.

Changing Site Conditions

• The introduction of human activities in the vicinity, such as irrigation pumping or construction dewatering, can affect the expected patterns seen in groundwater level data.

Instrumentation Problems

• The errors in data measurements could come from malfunction of instruments including power surge, power failure, mechanical mechanism and/or electronic system failure, and require re-calibration.

• Sources of error associated with manual tape downs to measure the groundwater level include human error in reading the tape and recording the data, dirty and unreadable tapes.

• Sources of error may be associated with the method by which the groundwater level is brought into communication with the groundwater level-sensing component.

• Conversion errors such as mechanical movement or electrical signal, or the means by which the data are recorded and uploaded such as paper chart, punched tape or datalogger may result in data measurement problems.

Data Processing Errors

• Some errors in groundwater level data are introduced in the data acquisition and data processing phase. These data errors are mostly human incorporated errors occurring during data capture, data transmission, data entry, data processing and data archiving.

• Mechanical graphic (analog) recorders and manual operation logs are major sources of data processing errors. The most prevalent of these are Date/Time errors and missing and questionable data (e.g. skews and slippages, missing pen traces, missing start/end time stamps, etc.).

• Solid-state electronic dataloggers (CR10s) and Telemetric recording devices produce data anomalies such as spikes.

• Additional data processing errors can be attributed to shaft encoder (sensor) not being calibrated properly.

• Correlation problems may occur when comparing electronically acquired data and direct field observations of groundwater levels from wells sounders.

• Any unresolved anomalies may lead to data change in DBHYDRO database.

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Appendix B: Description of Common DBHYDRO Codes/Tags (Data Qualifiers)

The data in DBHYDRO comprise several codes/tags (data qualifiers) that need to be properly understood before undertaking a QA/QC analysis.

Description of Common DBHYDRO Codes/Tags (Data Qualifiers) TAG DESCRIPTION ACTION

E ESTIMATED

“E” tags are generally kept, unless there is a reason not to do so. Check estimated value by comparison with preceding and following data values. Ensure estimated value is acceptable. If values are not acceptable, try other estimation methods to obtain best data. Only if the data cannot be reasonably estimated, change “E” tag to “M” tag.

L LINE-AVERAGED

“L” tags are generally converted to “E” tags. Carefully check data. Does the data make sense by comparison to adjacent station(s) data? If so, change “L” tag to “E” tag in updated series. If data is not reasonable, temporarily tag data “M” and apply missing data estimation rule to data. Talk to OHDM staff if necessary.

M MISSING

“M” tags are generally kept, unless there is a reason not to do so. If number of days is greater than 7, do not change. If number of days is less than 7, change through regression with adjacent station data, interpolation, etc. Whenever data can be reasonably estimated, replace “M” tag with “E” tag. If no estimation is possible, keep “M” tag.

A ACCUMULATED “A” tags are used for “manually observed” rainfall data accumulated over a period of more than 24-hours. Talk to data processing if necessary. Change “A” tags to “E” tags when QA/QC post-processing rainfall data. (Note: “A” tags are not used on flow data.)

? QUESTIONABLE (DO NOT USE)

Carefully check data. Temporarily tag data “M” and apply missing data rule. (Note: It is advised not to use this data except if there is a solid reason to do so.)

> GREATER THAN Check data for accuracy. Use HDPLOT or DCVP tools to double check data. Talk to data processing associate if necessary. If no mistake was made in assigning “”>” tag, temporarily tag data “M”, and apply rule on estimating missing values. Keep “M” if no estimation is possible.

< LESS THAN

Check data for accuracy. Use HDPLOT or DCVP tools to double check data. Talk to data processing Associate if necessary. If no mistake was made in assigning “<” tag, temporarily tag data “M”, and apply rule on estimating missing values. Keep “M” tag if no estimation is possible. (Note: this occurs mostly in dry conditions, such as dry water levels or wells.)

P SUMMARY COMPUTED FROM PARTIAL RECORD

“P” tags are generally converted to “E” tags. Carefully check data. Does the data make sense by comparison to adjacent station(s) data? If so, change “P” tag to “E” tag in updated series. If data is not reasonable, temporarily tag data “M” and apply missing data estimation rule to data. Talk to OHDM staff if necessary.

T TRACE OF PRECIPITATION

“T” tags are used for “manually observed” data when the amount of rainfall is to minimal to measure. Talk to data processing if necessary. Change “T” tags to “E” tags when QA/QC post-processing rainfall data. (Note: ‘T” tags are not used on flow data.)

N DATA NOT YET AVAILABLE, OR NOT YET PROCESSED.

Check with data processing associate assigned to site/station, whether data is available for further processing. (a) If data is available, ask Associate to process the available data and load to DBHYDRO. (b) If data is not available, temporarily replace “N” tag with “M” tag and apply the rule in handling data tagged “M”. (c) When data has been processed and loaded to DBHYDRO, use new data to build time series. Replace “N” with blank tag and write comment: “N” tag changed to blank. Data found, processed and reloaded.

! “NORMAL” LIMITS EXCEEDED

“!” tags always required careful attention. The data must be checked for accuracy. Check maximum value in DBHYDRO. If existing maximum value exceeds the data tagged “!”, it is likely that data was loaded to DBHYDRO before current maximum was established. A reload would be appropriate to remove the “!” tag. Investigate carefully by looking at DCVP maximum; sometimes the DCVP maximum (although instantaneous) could give a hint of what the true maximum is. In case there is a solid argument that the current maximum is not appropriate, work with OHDM staff to change the current maximum in DBHYDRO; then reload data to obtain data without the “!” tag. If after careful investigation the data exceeds the normal limits temporarily tag the data “M” and use rule to estimate missing data to current data. Keep “M” tag if no estimation is possible. When there is confusion about the “!” tag contact QA/QC Project Manager.

X INCLUDED IN NEXT AMOUNT MARKED ‘A’

“x” tags are used for manually observed rainfall data accumulated over a period of more than 24-hours. “X” tags are a fraction of an aggregated rainfall quantity tagged “A”. “X” tags precede “A” tags. (Note: “X” tags are not used on stage or flow data.)

Y PROVISIONAL USE FOR REGIONAL SCALE MODELING

“Y” tags are inserted after QA/QC of modeling data. “Y” tags cannot be converted. (Note: “Y” tags are not used on preferred data.)

Z NOT APPROPRIATE FOR REGIONAL SCALE MODELING

“Z” tags are inserted after QA/QC of modeling data. “Z” tags cannot be converted. (Note: “Z” tags are not used on preferred data.)

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Appendix C: District Database Applications (DBHYDRO & DCVP) Naming Conventions

District Database Applications (DBHYDRO & DCVP) Naming Conventions Terms Description of Terms

SITE (Site Name = DCVP database) (Site = DBHYDRO database)

A SITE is a representative point (location) used to designate one or more stations that are associated by proximity or project. Site level representation is to provide clarity for small scale mapping in lieu of displaying a high density of associated stations. The Site location is often based on the position of a recording device, such as a remote terminal unit (RTU), or can be derived from a “common sense” location between the associated stations. A Site should not be viewed as an area feature with specific boundaries but simply as a representation location of activity. Each Site is assigned a specific name. Example: S99

STATION (DBHYDRO database)

A STATION refers to a specific coordinate that indicates where data (observations, sampling or monitoring) is collected. Data are usually assigned to Stations by various classifications such as Stage, Flow, Weather, and Water Quality. A coordinate may have more than one Station associated to it. The name given to a station has traditionally been similar to, or an exact duplicate of, the corresponding site name. • The majority of Station names have a “suffix” indicating the parameter that is being

monitored (e.g. Headwater _H, Rainfall _R, Tailwater _T, Pump _P, Evaporation _E, Groundwater _G). Example: S99_H, S99_R and S99_T

• The Station name is also the “primary identifier” for structure information. The FLOW program converts a Site Name or Site ID to the corresponding Station Name to enable access to structure data required for computation of flow estimates. Many Station Names often attach a “suffix” indicating structure type (e.g. Spillway _S, Flume _F, Open Channel _O, Culvert _C, Weir _W, Lock _L). Example: S99_S

SITE ID (DCVP database for Flow Combinations)

The Site Identifier (ID) is used for FLOW data time series which points to the individual DCVP Station ID used in the computation of discharge. A Site ID is an eight digit ID number associated with a group of Station IDs. Each Site ID refers to a specific set of instruments which gather all water level and operating unit measurements required by the FLOW program for estimating flow. Example: Site Name Station Name Site IDs S99 S99_S 50635391, 60635391, 20635391

STATION ID (DCVP database)

The Station Identifier (ID) is a unique “time series identifier”. In DCVP Station ID naming conventions often use symbols to separate the station name from the parameter or sensor identifier. These symbols are based on the type of recording methodology in use: Symbol Recording Methodology - Telemetry (RACU/MOSCAD) * Graphic or Digital Chart + LoggerNet/ARDAMS/CR10s @ Manual Measurement # Measurement from other source (Sutron) ^ Measurement from other source (Domsat) Example: Site ID Station IDs Site IDs Stations IDs Site ID Station IDs 20635391 S99-H 50635391 S99*H 60635391 S99+H S99-T S99*T S99+T S99-1 S99*1 S99+1 S99-2 S99@2 S99+2

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Appendix D: Listing of QA/QC Post-Processing Applications The following table lists several applications currently available to the QA/QC Engineer to ensure that “preferred or MOD1 DBKey” hydrometeorologic data are properly processed and validated.

Listing of QA/QC Post-Processing Applications Application Name Description

BKPTFLOW BKPTFLOW (Breakpoint Flow),

DCVP DCVP (Data Collection/Validation Preprocessing System) is used to process all of the raw data received through the various data collection methods used by the District. It handles manually and electronically collected data.

DMBACKUP DMBACKUP (wcle) directory is the “original” ARDAMS (daily) raw data file.

DBHYDRO BROWSER Web-based application that allows the user to browse DBHYDRO, using one or more search criteria to retrieve a summary listing of time series data. The user can then select a time series of interest and have the data dynamically displayed on-screen in tables or graphs.

DBHYDRO_LOADER UNIX script created to automate the process of loading hydrologic data from external agencies (USGS, EAA, USACE, etc.) into the SFWMD DBHYDRO database. Pre-requisites: the input file format from the external agency is specified. Input files should follow format specifications for proper loading execution.

FLOWCALC

Bourne Shell script based application which acts as a wrapper script to the HYDROGRAPH program, which in turn, retrieves current DCVP PROVISIONAL, DCVP ARCHIVE or OMD IMS data, runs the flow computation algorithms, and computes the most up-to-date instantaneous and mean daily discharges for water control and pump structures. This program can be run in either automatic or interactive mode. An option in the main menu of FLOWCALC will run the HYDROGRAPH program. The FLOWCALC script has been constructed in a way to easily allow additional sites to be added to the predefined group for the automatic flow computation mode.

GVA GVA (Graphical Verification Analysis), a time series verification program that works by extracting data from the Oracle output tables and creating a graphical on-screen reproduction of the processed data, enabling the data processing Engineering Associate to view and edit the data.

HYDROEXTRACT

Builds a formatted FLOW program input file from a set of hydroExtract output files (or similarly structured data files). Depending on the command line entered to invoke the program, it will produce a file meeting one of the two primary FLOW program input formats. The program reads the date and time of entries in each input file and creates a record for each unique date/time combination. Included date/time entries missing in one or more files are interpolated from data which immediately proceeds and follows the given date/time instance.

HYDROGRAPH

Script-based application that coordinates a group of programs to retrieve current DCVP Provisional, DCVP Archive or OMD IMS water level and operating unit data for user specified station Ids and time period, creates a FLOW program input file with the selected data, invokes the FLOW program to compute flow estimates, creates an IVG (Interval Value Generator) input file, and uses the FLOW program estimates to calculate mean daily flow. In addition, it prepares several plots which can be viewed with the TSPLOT application.

HDPLOT

HDPLOT (Hydrologic Data Plot) is an X-Windows based computer program that provides convenient and timely selection and graphic display of District hydrologic data. Data sources include DBHYDRO, which is the central hydrologic data base for summary data, Data Collection/Validation Preprocessing (DCVP) system data, and Operations data for breakpoint data. Up to seven time series may be plotted at once.

INTERP-BKPT

INTERP-BKPT (Interpolated Breakpoint), builds a formatted FLOW program input file from a set of HydroExtract output files (or similarly structured data files). Depending on the command line entered to invoke the program, it will produce a file meeting one of the two primary FLOW program input formats. The program reads the date and time of entries in each input file and creates a record for each unique date/time combination. Included date/time entries missing in one or more files are interpolated from data which immediately precedes and follows the given date/time instance.

IVG IVG (Interval Value Generator) computes summary statistics for measurement data retrieved from a FLOW program output file or the Oracle database.

Microsoft Access Microsoft Access is a powerful program to create and manage databases. It has many built in features to assist the user in constructing and viewing information.

Microsoft Excel Microsoft Excel is a spreadsheet style application for retrieving, analyzing, and reporting on data.

MIRMaid MIRMaid (Maintenance/Inventory/Recorder/Malfunction/Aid Reports) is used as the repository for reporting data recording malfunctions and data collection problems.

ODBC ODBC (Open DataBase Connectivity) is a database protocol for connecting applications to Structured Query Language (SQL) compliant databases (i.e. Oracle, Microsoft Access, Microsoft Excel).

REG_TO_PREF This application converts the source (regular) non-preferred data DBKey to a “Preferred” data DBKey; and creates an INPUT file for loading the preferred data into DBHYDRO using the DBHYDRO_LOADER.

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Appendix E: Data Analysis Methods: Mass Balance & Statistical Analysis

DATA ANALYSIS INCLUDES TWO PARTS: MASS BALANCE & STATISTICAL ANALYSIS DEFINITION WHERE USED?

MASS BALANCE

Mass balance is used to investigate the variability between flow values at various structures within the same basin (i.e. water level upstream is less than water levels downstream for a specific period. Could be caused by local pumping and local water withdrawals in a direction opposing normal flow direction).

Flow

STATISTICAL ANALYSIS DEFINITION WHERE USED?

Regression Analysis:

Linear Regression

Non-Linear

Regression

Regression analysis is an analytical procedure for deriving prediction equations for a variable (dependent) based on given values of one or more other variables (independent). It is also used to develop mathematical relations between SFWMD, USGS data and other agencies. Ultimately, the mathematical relations between data set will be used to estimate missing values. 1) Linear regression can be developed to relate groundwater levels at station A

to the groundwater levels at station B, i.e., StationA = StationB x Int + Cons, where Int and Cons are the intercept and constant values of the regression.

2) Non-linear regression can also be developed to relate groundwater levels between two or more stations. This relationship can be developed to include monthly and seasonal parameters.

Flow, Stage, Rainfall, ET, Groundwater Level

Correlation Correlation is a measure of the association between two or more variables. The correlation coefficient will help define the power of regression. (If R>0 positive correlation. If R=0 no correlation, If R<0 negative correlation) R=correlation coefficient.) Sometimes the coefficient of determination R2 is used instead of correlation coefficient. When R2 is close to 1 the correlation between variables is very good.

Rainfall, Stage, Flow, ET, Groundwater Level

Data Estimation Techniques:

Regression Interpolation Direct

Substitution Inverse

Distance Relationships

Data estimation is a technique that approximates a population variable (in a statistical sense) with a sample variable/parameter. (Population= total data set of interest, and Sample=portion of population data.) The “goal” is to minimize the error of estimation. Concept of estimation is also broadly used. Adjacent station data could be used to estimate the missing information at the station of interest.

Rainfall, Stage, Flow, ET, Soil and Moisture conditions, Groundwater Level

Frequency Analysis

Frequency analysis is used to determine the distribution of data spatially or temporally.


Mass Curve Analysis

Mass curve analysis is used to understand cumulative variability of the parameter. Rainfall, ET, Flow

Spatial and Temporal Trend Analysis:

Variance Regression Covariance Time Series

and Auto-Correlation Analysis

1. Analysis of variance techniques could be used to detect regional or year-to-year differences in water quality and rainfall.

2. Regression techniques could be used to test for a particular trend, such as linear trend overtime in water quality and/or nutrient loading.

3. Analysis of covariance could be use in detecting trends, while removing the effect of secondary source of variability in the response. (For example, analysis of covariance could be used to detect annual changes in total nitrogen concentration that would have existed if freshwater flow in all years was constant. Differences in total nitrogen could then be attributed to sources other than flow.)

4. Time series analysis could be applied to assess short term or long term temporal trends at a particular site. For example a series of observations that are equally spaced in time (monthly averages), and tests of significant seasonal or long term trends.


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Appendix F: Methodology for the Evaluation of Outliers (Box Plot Method) Box Plot Method

A Box Plot provides a graphical summary of a set of data. It shows the following statistical measures: • Median: Mid-point of the data set, or the 50th percentile of the data. • Lower Quartile (Q1): Is the median of the first 50% of the data, or the 25th percentile of the data. • Upper Quartile (Q3): Is the median of the last 50% of the data, or the 75th percentile of the data. • Minimum data value • Maximum data value

The following diagram shows the typical components of a Box Plot.

For each group of observations, the box limits represent the lower quartile Q1 (25th percentile) and the upper quartile Q3 (75th percentile). The median is typically displayed as a line across the box. Therefore ¼ of the distribution is between this line and the top of the box and ¼ of the distribution is between this line and the bottom of box.

The Hinge Spread or the Interquartile range (QR) is the value of the 75th percentile (Q3) minus the value of the 25th percentile (Q1). This measure of scale attempts to measure the variability of points near the center. If the median line within the box is not equidistant from the hinges, then the data is skewed.

Whiskers are drawn from the upper quartile (Q3) to the upper adjacent value and from the lower quartile (Q1) to the lower adjacent value. The whiskers must end at an observed value, therefore connecting all the values outside the box that are not more than 1.5 times the box width (or 1.5 QR) away from the box. The lower whisker runs from the lower hinge to the extreme low observation only if this value is less than 1.5 QR beyond the bottom quartile. Otherwise, the left whisker ends at the lower hinge minus 1.5 QR. Similarly, the upper whisker runs from the upper hinge to the extreme high observation only if this value is less than 1.5 QR beyond the top quartile. Otherwise, the right whisker ends at the upper hinge plus 1.5 QR. The points outside the ends of whiskers are outliers or suspected outliers.

“Mild” outliers are values that fall within the outer fences, and are typically represented by a “*” sign. The lower (left) outer fence is the interval between the lower (left) hinge minus 1.5 QR and the lower (left) hinge minus 3 QR. Similarly, the upper (right) outer fence is the interval between the upper (right) hinge plus 1.5 QR and the upper (right) hinge plus 3 QR. “Extreme” outliers are data values that fall below the left outer fence or above the right outer fence, and are typically represented by an “0” sign.

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SCADA & Hydro Data Management Department ......PROCEDURE: Q205 DATE: 10/30/06 REVISION: Original Page 5 of 70 QA/QC OF GROUNDWATER LEVEL DATA PROCEDURES 1.0 PURPOSE This procedure