Global sensitivity analysis for large-scale socio-hydrological models using Hadoop

Yao Hu, Oscar Garcia-Cabrejo, Ximing Cai, Albert J. Valocchi, Benjamin DuPont

Research output: Contribution to journalArticlepeer-review


A multi-agent system (MAS) model is coupled with a physically-based groundwater model to understand the declining water table in the heavily irrigated Republican River basin. Each agent in the MAS model is associated with five behavioral parameters, and we estimate their influences on the coupled models using Global Sensitivity Analysis (GSA). This paper utilizes Hadoop-based Cloud Computing techniques and Polynomial Chaos Expansion (PCE) based variance decomposition approach for the improvement of GSA with large-scale socio-hydrological models. With the techniques, running 1000 scenarios of the coupled models can be completed within two hours with Hadoop clusters, a substantial improvement over the 42 days required to run these scenarios sequentially on a desktop machine. Based on the model results, GSA is conducted with the surrogate model derived from using PCE to measure the impacts of the spatio-temporal variations of the behavioral parameters on crop profits and the water table, identifying influential parameters.

Original languageEnglish (US)
Pages (from-to)231-243
Number of pages13
JournalEnvironmental Modelling and Software
StatePublished - Nov 1 2015


  • Global Sensitivity Analysis
  • Hadoop
  • Multi-agent system
  • Polynomial Chaos Expansion
  • Socio-hydrological model

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling


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