Input Data and Controlled-Scenario Outputs for the Gone Abat Jap Irrigation Allocation Model

Description

This dataset contains cleaned input data and machine-readable outputs used to evaluate a graph-based two-stage fair irrigation water allocation model for the Gone Abat Jap canal network in Nukus District, Republic of Karakalpakstan, Uzbekistan. The network comprises 36 nodes, 35 directed canal edges, 20 terminal water-delivery points, and 20 anonymized terminal service blocks, analyzed over 16 active ten-day periods (periods 11–26). Input data are provided in JSON and Excel formats. The scenario output tables report the Stage 1 max–min fairness guarantee, final Stage 2 allocations, source utilization, canal-edge loadings, active bottlenecks, and unmet demand under controlled local-edge, source-limited, and mixed scarcity conditions. The dataset also includes baseline-comparison results and a one-factor-at-a-time sensitivity analysis. The sensitivity analysis evaluates the baseline together with six perturbations: all canal-edge efficiencies at −5% and +5%, all positive net demands at −10% and +10%, and the capacities of edges K5, K6, K8, K11, and K12 at −10% and +10%. For each perturbation, all other inputs, source limits, and controlled-scenario settings are held constant. The README file describes the dataset structure, field definitions, calculation settings, and reproduction procedure. Service-block identifiers are anonymized as F1–F20, while personal identifiers and local filesystem paths have been removed. The scarcity limits represent controlled analytical scenario parameters rather than historically observed shortages or field-validated operational constraints.

Steps to reproduce

1. Download all twelve files and keep their filenames unchanged. 2. Use gone_abat_jap.json as the machine-readable model input. The accompanying gone_abat_jap.xlsx workbook contains the equivalent input tables for inspection in spreadsheet software. 3. From the companion project root, launch the graphical interface with “python run_gui.py” and select gone_abat_jap.json as the input file. 4. Set epsilon to 1e-6, delta to 0.01, enable edge-load balancing, and select the required results folder. 5. Click “Run Model” to regenerate scenario_summary.csv, farmer_allocations.csv, and edge_results.csv in the results folder’s tables subdirectory. 6. Click “Run Sensitivity” to regenerate sensitivity_summary.csv, sensitivity_by_period.csv, and sensitivity_allocations.csv. This action automatically uses bisection and edge-load balancing to match the reported configuration. 7. Regenerate the three baseline-comparison tables from the same project root with “python -m scripts.generate_baseline_comparison”. This creates baseline_summary.csv, baseline_by_period.csv, and baseline_allocations.csv. 8. Compare the regenerated CSV files with the corresponding files in this dataset. Differences should be limited to floating-point representation if the same software configuration and input files are used.

Institutions

• Karakalpak State University

  Karakalpakstan, Nukus
• National University of Uzbekistan

  Tashkent, Tashkent
• Tashkent Institute of Irrigation and Agricultural Mechanization Engineers

  Tashkent, Tashkent

Categories

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