AbhigyanPandey08/Climate-Change-Greenhouse-Gas-Hypoxia-2026-Nature: Python Code for Riverine Climate Feedback Domain Greenhouse Gas Emission Research

This repository contains the full Python code, including the Bayesian Monte Carlo modeling framework, used to identify and quantify the Riverine Climate Feedback Domain (RCFD) estimates. The study integrates multi-source riverine dissolved oxygen (DO) data to analyze the synergistic effects of warming, hypoxia and eutrophication on greenhouse gas emissions from global river networks. The Python-based Bayesian Monte Carlo simulation is used to determine the critical thermal thresholds and posterior distributions for the feedback loops. The associated code allows for the replication of the mechanisms underlying RCFD, which identify the transition from carbon-sink to carbon-source behavior in riverine systems. It specifically quantifies how warming-induced deoxygenation triggers a climate feedback loop in riverine climates. Citation: If using this code, proper citation of the code and the associated manuscript of Pandey et al. (2026) must be cited duly.