1. Arc Hydro Groundwater: a geographic data model for groundwater systems
By Gil Strassberg, David Maidment and Norman Jones
These slides are taken from the PhD Dissertation
defense of Gil Strassberg in Nov 2005
Reference:
We are discussing with ESRI the transformation of this
work into an ESRI Press Book in 2007
This model won first prize for data models at the 2006 ESRI User Conference

2. Research questions
What are the primary hydrogeologic features common to groundwater studies in regional and site scales, and what is the best conceptual approach for describing them?
What are the basic features required for representing structures of groundwater simulation models, their inputs and outputs, and how can these structures be integrated within GIS?
What is the most efficient way to store, view, access, and analyze these features using current GIS technology?
The data model design and implementation is the process through which these questions are answered

3. Outline Introduction and data model goals
Arc Hydro groundwater data model design
Case studies (4 examples)
Conclusions

4. What is a data model?
Booch et al. defined a model: “a simplification of reality created to better understand the system being created”
Objects
Aquifer
stream
Well
Volume
R.M. Hirsch, USGS

5. Why do we need data models?
Proposed hydrologic observatories (CUAHSI):
26 proposed hydrologic observatories
Data needs to be integrated across observatories and from state and national data sources
Standardize:
Concepts
Data structures
Terminology
Basis for development of applications

6. ArcGIS Geographic data models
About 30 ArcGIS data models for a variety of disciplines
Geosciences Network
Here is the core Arc Hydro data model for surface water.
The hydrological system is described through a set of components that characterize the system.
These include:
Drainage systems – define the drainage areas – catchments, watersheds and basins
Hydrography – describe features such as streams, lakes and monitoring points
Hydro network – a geometric network that creates connectivity between features.
Channels – a detailed description of channels, cross sections and profile lines.
On top of the geographical representation arc hydro also includes temporal data in the format of time series. There is a structure to define a variety of time series and the relationships between the temporal data and the geographical features.
The idea here is that this format is useful for a variety of studies and models, and it is easy to access this information and apply tools because it is in a standard format.
As you can see the existing framework only deals with surface water, and it has become apparent that groundwater features should be added to better represent reality.

7. Arc Hydro surface water
A data model for representing surface water systems
Here is the core Arc Hydro data model for surface water.
The hydrological system is described through a set of components that characterize the system.
These include:
Drainage systems – define the drainage areas – catchments, watersheds and basins
Hydrography – describe features such as streams, lakes and monitoring points
Hydro network – a geometric network that creates connectivity between features.
Channels – a detailed description of channels, cross sections and profile lines.
On top of the geographical representation arc hydro also includes temporal data in the format of time series. There is a structure to define a variety of time series and the relationships between the temporal data and the geographical features.
The idea here is that this format is useful for a variety of studies and models, and it is easy to access this information and apply tools because it is in a standard format.
As you can see the existing framework only deals with surface water, and it has become apparent that groundwater features should be added to better represent reality.
Published by ESRI press, 2002
Experience from the surface water data model design provides basic design concepts for the groundwater component

8. Goals of the Arc Hydro groundwater data model
Objective
Develop a geographic data model for representing groundwater systems.
Data model goals
Support representation of regional groundwater systems.
Support the representation of site scale groundwater data.
Enable the integration of surface water and groundwater data.
Facilitate the Integration of groundwater simulation models with GIS.

9. Regional groundwater systems
Describe groundwater systems from recharge to discharge
In many cases assumed as 2D systems, vertical scale >> horizontal scale
Eckhardt, G. Hydrogeology of the Edwards Aquifer.

10. Site scale data Describe groundwater data in a small area of interest.
Usually includes 3D data (e.g. multilevel samplers, cores).
Multilevel samplers in the MADE site in Mississippi
Photographs provided by Chunmiao Zheng

11. Integration of surface water and groundwater data
Describe the relationship between surface water features ( e.g. streams and waterbodies) with groundwater features (aquifers, wells).
Enable the connection with the surface water data model
Hydro network
Aquifers
In the future go to 3D...

12. Integration of groundwater simulation models with GIS
Define data structures for representing groundwater simulation models within GIS.
Support spatial and temporal referencing of model data – allows the display and analysis of model data within a “real” geospatial and temporal context.
Focus on modflow as the standard model used in the groundwater community
Non spatial representation (layer, row, column)
Geospatial representation (x, y, and z coordinates)

13. Outline Introduction and data model goals
Arc Hydro groundwater data model design
Case studies (4 examples)
Conclusions

14. Full data model
Hydrogeology – 2D and 3D features, tables, and rasters to describe hydrogeologic features such as wells, aquifers, cross sections, volumes, streams, land surface etc.
Simulation – Objects for georeferencing grids/meshes of simulation models.
Time Series – Temporal information stored in tables and as cataloged rasters.

15. Framework data model
Core classes for representing spatial groundwater data

16. Common data structures highlighted by the literature review
Data type
Public Petroleum Data Model (PPDM)
ArcGIS Marine data model
EarthFX data model
Water Resources Information Project (WRIP) data model
Wells /Observation points
3D Line features with measures
2D point features (marine points)
Borehole table
2D point features (3D lines are optional for display and are created from the attributes of the borehole)
3D interval data along a well
Line events along the well
Not included
Tabular information related to the borehole
Borehole interval sample table
3D point data along a well
Point events along the well
Measurement table with Z coordinates related to the marine point
Borehole point sample table
Temporal information
Not available
Time series related to measurements
Time series related to intervals
Time series related to borehole points or intervals
Well
3D point data
Time series
3D interval data

17. Representing well and aquifer features
Core classes for representing spatial groundwater data

18. Representation of wells and aquifers
Wells are represented as 2D points with attributes describing the 3D geometry of the well (elevation, depth) and the related aquifer.
Aquifers are represented as 2D polygons with subtypes for confined, unconfined, and aquifer and aquitard boundaries
The AquiferID of well features is the HydroID of an aquifer (one to many relationship)
Aquifer
Well
HydroID
AquiferID

19. Measurements along boreholes
Core classes for representing spatial groundwater data

20. Representing measurements along boreholes
Vertical data is stored in the VerticalMeasurements table and tools are applied to create the spatial features.
BorePoint is a 3D point representing point data along a borehole.
BoreLine is a 3D line representing interval data along a borehole.
BorePoints and BoreLines are related to well features
Well
BorePoint
HydroID
WellID
BoreLine
Well
VerticalMeasurements table

21. 3D geospatial context
Core classes for representing spatial groundwater data

22. 3D geospatial context
GeoVolumes created by defining a Boundary on the land surface (GeoRaster) and extruding the boundary area into the subsurface.
Boundary
Land surface (GeoRasters)
GeoVolume
The GeoVolume, boundary, and the land surface provide the geospatial context to groundwater data.

23. HydroGeologicUnit table
Core classes for representing spatial groundwater data

24. HydroGeologicUnit table
Table for storing attributes of hydrogeologic units.
Hydrogeologic units represented in the table are linked to spatial features.
The HGUID field is the key attribute for linking spatial features with hydrogeologic units

25. Time Series
Core classes for representing spatial groundwater data

26. Time Series TSType - describes the type of time series
TimeSeries - stores time series related to features
Bromide (mg/l)
Arsenic (mg/l)
Spatial-temporal views are created by linking time series with spatial features

27. Tools for implementing the data model
Arc Hydro groundwater tools
ArcScene toolbar for creating three-dimensional features such as BoreLines, GeoSections, and GeoVolumes
MODFLOW geoprocessing tools
Geoprocessing tools to create Cell2D, Cell3D, and Node features and integrate modflow inputs and outputs into GIS
SQL based tools for creating spatial-temporal views of time series data
Link spatial features such as wells and BorePoints with time series data to create 2D and 3D geospatial views of time series

28. Outline Introduction and data model goals
Arc Hydro groundwater data model design
Case studies (4 examples)
Conclusions

29. Example 1 – Representing hydrostratigraphy in the North Carolina coastal plain aquifer system
Ten aquifers and nine confining units
Giese et al., Simulation of ground-water flow in the coastal plain aquifer system of North Carolina. USGS.

30. Creating wells and BoreLines
Tabular data: 496 wells with hydrostratigraphy
HydroID = 1137, Deppe station
BoreLines representing hydrostratigraphy

31. Interpolated data BoreLines Wells Vertical measurements GeoSection
BorePoints created from wells and vertical measurements
GeoVolume
GeoRasters representing top and bottom of a formation
GeoSection from GeoVolumes

32. Example 2 – Regional scale 2D mapping of time series in the Ogallala aquifer, Texas
Boundary of the Ogallala aquifer
Boundary of the aquifer within Texas

33. Wells in the Ogallala aquifer
Data is from the TWDB groundwater database. The database contains tables describing well locations and attributes, and water level and water quality time series. There are about 21,000 wells designated in the Ogallala aquifer.
Wells in the Ogallala aquifer
Wells categorized by water use
Number of wells in each water use category
FType
Description
Count 10
MINING 1 6
FIRE 14
AQUACULTURE 5
POWER 2 11
MEDICINAL 3
COMMERCIAL 17
INSTITUTION 19 9
INDUSTRIAL (COOLING) 4
DEWATER 25 15
RECREATION 32 20
OTHER (see remarks)
Blank
324 12
INDUSTRIAL
385
AIR CONDITIONING
463 13
PUBLIC SUPPLY
1106 7
DOMESTIC
1817 16
STOCK
1928 18
UNUSED
2971 8
IRRIGATION
11824
Data is from the TWDB groundwater database:

34. Water level and water quality time series
Water levels and arsenic concentrations from the TWDB database are imported into the Time Series table of the data model. Two TSTypes are created: (1) for water levels, and (2) for dissolved arsenic.
HydroID = 1461

35. Geospatial views of time series using SQL queries
SQL (Structured Query Language) queries are used to join spatial features (e.g. wells) with time series and summarize data values.
Average water level in 2000
MS Access SQL query relating wells with time series
Relationships between the tables
Aggregation by the well’s HydroID
Calculates the average water level for each well (feet above mean sea level)
Defines the criteria for the query (TSType, Date, and Aquifer)
The query is embedded within ArcObjects to create geospatial-temporal views of time series data

36. Geospatial views of Time Series to RasterSeries
Spatial views of time series are interpolated into rasters and stored and attributed in the RasterSeries raster catalog

37. Example 3 – 3D time series in the MADE site, Mississippi
Location of the MADE site
Wells within the MADE site
Wells in the MADE site
Harvey, C., and S. M. Gorelick Rate-limited mass transfer or macrodispersion: Which dominates plume evolution at the Macrodispersion Experiment (MADE) site? Water Resources Research 36:

38. BorePoints represent the multilevel sampling ports
Wells and BorePoints
Within the site there are two types of wells: multilevel samplers for monitoring tracer concentrations and water level wells.
148 water level monitoring wells and 245 multilevel sampling wells for monitoring tracer concentrations
Well features
BorePoints represent the multilevel sampling ports
Wells with tracer data
BorePoints

39. Spatial-temporal views of 3D time series
3D views of temporal information are created by relating time series with BorePoint features with SQL queries. These can then be interpolated to create isosurfaces.
ArcScene application for creating views of 3D time series
3D view of bromide concentrations
Isosurfaces created using ArcGIS 3D interpolation tools
Bromide (mg/L)

40. Example 4 – Representing a GAM model of the Barton Springs segment of the Edwards aquifer, Texas
MODFLOW model developed for the TWDB as part of the GAM program
Confined zone of the Edwards aquifer
Unconfined zone of the Edwards aquifer
Model boundary
Model is 1 layer, 120 by 120 cells each cell is 1000 x 500 feet

41. Geospatially referencing the model
Integrating the model within GIS requires creating a 3D geospatial reference system in which the model grid is represented
Define the model boundary
Create 2D cells and read attributes from model files (active cells, elevations)
Create 3D cells by extruding 2D cells
Create Nodes at the centroid of the 3D cells
(1)
(2)
(3)
(4)

42. Temporally referencing the model
In order to read data from modflow stress packages into the Arc Hydro time series table, modflow stress periods need to be referenced as “real” dates
Temporally reference model stress periods
Read stress data into Arc Hydro Time Series tables
Create geospatial views of stress data
Well discharge
Recharge
MODFLOW stress periods
Date time

43. Representing model results
Simulated heads are read into the Arc Hydro time series tables and can be analyzed using GIS tools
Raster of interpolated heads
Simulated head values are associated with model nodes
Head contours

44. Creating water budgets
ZONEBUDGET is used to create water budgets for zones defined within GIS
Cells selected for defining a budget zone
Water budget terms for the defined zone
Cells within the Barton Creek lower watershed

45. Outline Introduction and data model goals
Arc Hydro groundwater data model design (focus on the framework)
Case studies (4 examples)
Conclusions

46. Conclusions
What are the primary hydrogeologic features common to groundwater studies in regional and site scales, and what is the best conceptual approach for describing them?
The data model framework defines the core classes for representing spatial groundwater datasets. These include classes for representing data recorded at wells, aquifers, time series, and the 3D geospatial context of the data.

47. Conclusions
What are the basic features required for representing structures of groundwater simulation models, their inputs and outputs, and how can these structures be integrated within GIS?
To integrate simulation models with GIS the model has to be geospatially and temporally referenced. The feature classes in the simulation component include the model boundary, 2D and 3D cells, and model nodes.
Model origin
Angle
Boundary
Cell2D
Cell3D
Node

48. Conclusions
What is the most efficient way to store, view, access, and analyze these features using current GIS technology?
3D GIS
Combination of 2D features and related tables, and 3D features is most appropriate for managing 3D information.
Time Series structures of Arc Hydro is appropriate for managing groundwater time series, and the combination with SQL queries is useful for creating spatial-temporal views of time series data.
Raster catalogs are useful to store, attribute, and index grids. GeoRasters are indexed by the HGUID to relate with a hydrogeologic unit, and RasterSeries are indexed by TSType and Date and Time.
XML is valuable for data exchange between applications