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: 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 This model won first prize for data models at the 2006 ESRI User Conference

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

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

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

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 http://www.cuahsi.org/HO/prospectus_list.htm

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. www.esri.com/datamodels

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

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.

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. http://www.edwardsaquifer.net/geology.html

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

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...

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)

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

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.

Framework data model Core classes for representing spatial groundwater data

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

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

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

Measurements along boreholes Core classes for representing spatial groundwater data

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

3D geospatial context Core classes for representing spatial groundwater data

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.

HydroGeologicUnit table Core classes for representing spatial groundwater data

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

Time Series Core classes for representing spatial groundwater data

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

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

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

Example 1 – Representing hydrostratigraphy in the North Carolina coastal plain aquifer system Ten aquifers and nine confining units Giese et al., 1997. Simulation of ground-water flow in the coastal plain aquifer system of North Carolina. USGS.

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

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

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 http://www.npwd.org/new_page_2.htm

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: www.twdb.state.tx.us/GwRD/waterwell/well_info.asp

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

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

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 Wells in 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.

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

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)

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 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)

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

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

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

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

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.

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

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