Data and Software for "Probabilistic Trade-offs Analysis for Sustainable and Equitable Management of Climate-Induced Water Risks"

Research data supporting the study "Probabilistic trade-offs analysis for sustainable and equitable management of climate-induced water risks"

This repository provides data of the Stochastic Dual Dynamic Programming (SDDP) model, and the output results of the simulations of the various policies and climate scenarios considered in this study, as well as the code used for postprocessing and visualizing the results.

Contents

1. Data: Model Inputs
   This folder contains the physical river network, reservoir and water demand, and economic data derived from the observed database.
   The key files are:
• Input_HydrologicalData
• Input_SystemData
2. Results: Model Output Analysis
   This folder includes outputs from the Stochastic Dual Dynamic Programming (SDDP) model under various policies and climate scenarios. The results showcase optimized sectoral water use, including irrigated areas, hydropower generation, and allocations for agriculture, energy, and urban demands across spatial locations (upstream and downstream).
   Key files include:
• SDDP Model Outputs (MATLAB format):
• EnergyPriority_Baseline.mat
• EnergyPriority_2070.mat
• EnergyPriority_2100.mat
• AgriculturePriority_Baseline.mat
• AgriculturePriority_2070.mat
• AgriculturePriority_2100.mat
• Extracted Model Results (Excel format):
• Organized for each policy and climate scenario to facilitate analysis.
3. Software: Data Analysis and Visualization
   Python scripts designed for outputs data analysis and visualization are included to reproduce the primary figures from the study.
   Scripts provided:
• CDF_outflow.py: Analyzes cumulative distribution functions for river discharge.
• CDF_sectors.py: Examines sectoral water use distributions.
• PCP_SI.py: Generates Parallel Coordinate Plots for trade-offs analysis.
4. Instructions: README File
   A comprehensive README file explains:
• Details of model input data.
• Instructions to interpret the SDDP model outputs.

Instructions:
The Python scripts process Excel files from the model output results folder to generate and visualize the figures for the paper. Each step is clearly documented within the scripts.