Geostatistical Discharge Estimation in Hydroelectric Canals: Integration Kriging, Conditional Simulation, and Sampling Design (software and derived data)

Read full text
Authors: Dominique Tapsoba, Xavier Emery
Cited by: 1

Abstract

Python software and derived data accompanying the article "Geostatistical Discharge Estimation in Hydroelectric Canals: Integration Kriging, Conditional Simulation, and Sampling Design" (Water Resources Research). Includes the integration-kriging and conditional-simulation pipeline, the validation code over 72 tests (36 Section A + 36 Section B), and the derived per-test results (discharge estimates, kriging and simulation coefficients of variation, and their ratio). Raw current-meter velocity data are governed by BC Hydro / CEATI agreements and are available on request; see the article's Data Availability Statement.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 July 2026