HydroCNHS User Manual

The Hydrological model for Coupled Natural-Human Systems (HydroCNHS) is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS.

Manual contributors: Chung-Yi Lin & Ali Ghaffari

Contents:

• Installation
• 101 classroom
  • Watershed/Basin
  • Subbasin
  • Semi-distributed hydrological model
  • Rainfall-runoff process modeling
  • Routing & Routing outlets
  • A water system in node-link structure
  • Design a water system with the supporting APIs
• Input/Output
  • Inputs
  • Outputs
  • Daily climate data
  • Model configuration file
  • ABM modules
• Build a hydrological model
  • Step 1: Create a model configuration file
  • Step 2: Complete a model configuration file
  • Step 3: Run a calibration
  • Step 4: Run a simulation
• Integrate an ABM
  • Step 1: Create a model configuration file
  • Step 2: Complete a model configuration file
  • Step 3: Program ABM module (.py)
  • Step 4: Run a calibration
  • Step 5: Run a simulation
• Data collector
• Calibration
  • Calibration with the genetic algorithm (GA)
  • Converter
• Advanced ABM coding tips
  • Enhancing computational speed
  • Logging
• hydrocnhs package
  • Subpackages
  • Submodules
  • hydrocnhs.abm module
  • hydrocnhs.abm_script module
  • hydrocnhs.data_collector module
  • hydrocnhs.hydrocnhs module
  • hydrocnhs.indicators module
  • hydrocnhs.model_builder module
  • hydrocnhs.routing module
  • hydrocnhs.sediment module
  • hydrocnhs.util module
  • hydrocnhs.visual module
  • Module contents

How to cite?

Lin, C. Y., Yang, Y. C. E., & Wi S. (2022). HydroCNHS: A Python Package of Hydrological Model for Coupled Natural Human Systems, Journal of Water Resources Planning and Management, 148(12), 6022005.

Related studies

Lin, C. Y., & Yang, Y. E. (2022). The effects of model complexity on model output uncertainty in co-evolved coupled natural-human systems. Earth’s Future, e2021EF002403. Lin, C.-Y., Yang, Y. E., & Chaudhary, A. K. (2023). Pay-for-practice or Pay-for-performance? A coupled agent-based evaluation tool for assessing sediment management incentive policies. Journal of Hydrology, 624, 129959.

Indices and tables

• Index

• Module Index

• Search Page