Schematic Processor v2.0 documentation

Processing Ops

Processing ops are snippets of Python code that simulate RECEIVE or PASS behavior in a schematic network. Ops can be written in as little as one line of code or can be as complex as you like.

Several ops are included with the schematic processor. These ops were created and applied in example applications as described in Whiteaker et al., 2006 and Johnson, 2009. These ops are summarized below. For information about how to write your own ops, please see Writing Processing Ops.

Simple Accumulation

accumulation.py

This op sums all input values to produce a total output value and is the default op applied to each feature.

First-Order Decay

decay.py

This op applies first-order decay to input values. The feature associated with this op must include TravelTime and DecayConst attributes to represent travel time and the first-order decay coefficient, respectively. The values of these attributes must be in compatible units of measure.

This op is best applied as PASS behavior.

Tidal Prism

tidal_prism.py

The tidal prism approach is a one-dimensional method to model tidal waterbodies, simulating estuarine flushing as a function of freshwater inflows and tidal interactions over one or more tidal cycles. This op was programmed and applied as described in Johnson, 2009 and is provided to extend the application of the schematic processor in tidal systems. Features associated with this op must include the following fields in their attribute table:

  • FloodQ - the volume of “new” water entering the feature during flood tide
  • FloodC - the pollutant concentration in the “new” water entering the feature during flood tide
  • DecayConst - the first-order decay coefficient experienced at the feature
  • Volume - the average volume of the feature
  • EbbQ - the volume of water exiting the feature during ebb tide

The values of these attributes must be in compatible units of measure. This op is best applied as PASS behavior.

CSTR

cstr.py

The CSTR op simulates decay in a continuously stirred tank reactor. This approach simulates pollutant concentrations as a function of flushing from upstream features and first-order decay within the feature. The feature associated with this op must include these fields in their attribute table:

  • DecayConst - the first-order decay coefficient experienced at the feature
  • Volume - the average volume of the feature
  • Qf - the volume of water flowing through the feature during the modeled timestep

The values of these attributes must be in compatible units of measure. This op is best applied as PASS behavior.

References

Johnson, S. (2009). “A General Method for Modeling Coastal Water Pollutant Loadings.” Ph.D. Dissertation, The University of Texas at Austin, Austin, Texas.

Whiteaker, T., D. R. Maidment, J. L. Goodall, M. Takamatsu (2006). “Integrating Arc Hydro Features with a Schematic Network.” Transactions in GIS 10(2) pp. 219-237. (DOI: 10.1111/j.1467-9671.2006.00254.x)