Skip to main content

Tools to perform flow naturalisation in Caterbury

Project description

This git repository contains project code for the flow naturalisation procedure. The procedure has several modules for performing different tasks that ultimately combine for the naturalisation.

The base class (FlowNat) initialises the tool with a from_date, to_date, min_gaugings, input_sites, and output_path. This sets up and prepares a lot of datasets for the successive modules.

Modules:
  • Querying and/or estimating flow at the input_sites

  • Catchment delineation above the input_sites

  • Selecting the upstream water abstraction sites from the catchment delineation

  • Querying and Estimating water usage when the usage doesn’t exist

  • Flow naturalisation

Input Parameters

The base class (FlowNat) initialises the tool with a from_date, to_date, min_gaugings, input_sites, rec_data_code, and output_path. This sets up and prepares a lot of datasets for the successive modules. If all of those input parameters are defined at initialisation, then all of the successive modules/methods will not require any other input.

Methods

The modules use several python packages for their procedures.

The catchment delineation module uses the python package gistools which has a catchment delineation function. This functions uses the REC stream network version 2 and the associated catchments for determining the catchments above specific points. The flow locations are used to delineate the upstream catchments. The upstream catchments are then used to select the WAPs that are within each catchment. The WAPs were taken from a summary of Accela.

Not all flow locations have a continuous record from a recorder. Consequently, the flow sites with only gaugings need to be correlated to flow sites with (nearly) continuous recorders. This is done via the hydrolm package that uses ordinary least squares regressions of one or two recorders. The F statistic is used to determine the best regression.

Water usage data also needs to be estimated when it doesn’t already exist. This was done by grouping the consents by SWAZ and use type and estimating the ratio of usage to allocation. These ratios were then applied at all consents without existing water usage data. This analysis was performed on a monthly scale.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

FlowNaturalisation-0.1.1.tar.gz (1.3 MB view details)

Uploaded Source

File details

Details for the file FlowNaturalisation-0.1.1.tar.gz.

File metadata

  • Download URL: FlowNaturalisation-0.1.1.tar.gz
  • Upload date:
  • Size: 1.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Python-urllib/3.6

File hashes

Hashes for FlowNaturalisation-0.1.1.tar.gz
Algorithm Hash digest
SHA256 8774f86936c02a9ac644af46be17c1b9967f031f8c9845aff814ef3ae73b41e6
MD5 75514206fdf4eca24936d6a01ed23f5a
BLAKE2b-256 6e6ab1261f0d115fcef36bdbed5113db689dcd8506036cb41ce6779da6da949c

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page