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 erence: http://www.ce.utexas.edu/prof/maidment/giswr2006/docs/strassberg.pdf We are discussing with ESRI the transformation of this work into an ESRI Press Book in 2007 s model won first prize for data models at the 2006 ESRI User Confe
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Arc Hydro Groundwater: a geographic data model for groundwater systems
By Gil Strassberg, David Maidment and Norman Jones
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
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.
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)
MS Access SQL query relating wells with time series
The query is embedded within ArcObjects to create geospatial-temporal views
of time series data
Average water level in 2000
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
Example 3 – 3D time series in the MADE site, Mississippi
Location of the MADE site Wells within the MADE site
Harvey, C., and S. M. Gorelick. 2000. Rate-limited mass transfer or macrodispersion: Which dominates plume evolution at the Macrodispersion Experiment (MADE) site? Water Resources Research 36:637-650.
Wells in the MADE site
Wells and BorePoints
Within the site there are two types of wells: multilevel samplers for
monitoring tracer concentrations and water level wells.
Wells with tracer data
BorePoints
Well features
BorePoints represent the multilevel sampling ports
148 water level monitoring wells and 245 multilevel sampling wells for monitoring tracer concentrations
Spatial-temporal views of 3D time series3D 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
Bromide (mg/L)
Isosurfaces created using ArcGIS 3D interpolation tools
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
Geospatially referencing the modelIntegrating the model within GIS requires creating a 3D geospatial reference system in which
the model grid is represented
1. Define the model boundary
2. Create 2D cells and read attributes from model files (active
cells, elevations)
3. Create 3D cells by extruding 2D cells
4. Create Nodes at the centroid of the 3D cells
(1) (2)
(3)
(4)
Temporally referencing the modelIn 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
MODFLOW stress periods Date time
Recharge Well discharge1. Temporally reference model stress periods
2. Read stress data into Arc Hydro Time Series tables
3. Create geospatial views of stress data
Representing model resultsSimulated heads are read into the Arc Hydro time series tables and can
be analyzed using GIS tools
Simulated head values are associated with model nodes
Raster of interpolated heads
Head contours
Outflow terms
Inflow terms
(a)
(b)
Creating water budgets
ZONEBUDGET is used to create water budgets for zones defined within GIS
Cells selected for defining a budget zone
Cells within the Barton Creek lower watershed
Water budget terms for the defined zone
Outline
1. Introduction and data model goals
2. Arc Hydro groundwater data model design
(focus on the framework)
3. Case studies (4 examples)
4. Conclusions
Conclusions1. 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?
TableVerticalMeasurements
Polygon feature classAquifer
Subtypes are Aquifer boundary, Outcroparea, Confined area
Line feature classBoreLine
Point feature classBorePoint
Point feature classWell
Relationship class
One to many
WellHasBoreLines
Relationship class
One to many
WellHasBorePointsRelationship class
One to many
AquiferHasWells
Geospatial context
Aquifers and wells Measurements in Boreholes
TableTimeSeries
TableTSType
Relationship class
One to many
TSTypeHasTimeSeries
Time Series
Polygon feature classBoundary
Raster catalogGeoRasters
TableHydroGeologicUnit
Hydrogeologic units
Multipatch feature classGeoVolume
• 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.
Conclusions2. 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
Conclusions
3. What is the most efficient way to store, view, access, and analyze these features
using current GIS technology?
• 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