Source code for risk analysis of farmland irrigation decision system based on the AquaCrop model

Published: 2 July 2025| Version 1 | DOI: 10.17632/rhj89pgst6.1
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Description

To quantitatively assess the impact of uncertainties in weather forecasting, crop characteristics, management practices, and soil properties on irrigation decision results, we first developed a real-time irrigation decision-making system (RDIT) based on AquaCrop model. The system incorporates 7-day-ahead weather forecast data to provide irrigation decisions several days in advance, thereby creating sufficient lead time for optimal water resource allocation and scheduling within irrigation districts. When the predicted available soil water falls below a predefined threshold, the system activates its irrigation decision-making mechanism. The irrigation amount is calculated to replenish the total available water (TAW) in the root zone to field capacity. During the risk analysis, a virtual real-case scenario is first established based on the fundamental characteristics of a specific irrigation area. Perturbations are then introduced to management, crop, and soil parameters relative to this baseline. Irrigation decisions are made using the perturbed system, and the resulting decisions are applied to the virtual real case to evaluate the impacts of uncertainties in management, crops, and soil on irrigation performance. The farmland irrigation decision risk analysis system was developed through secondary development based on the AquaCrop-OS Matlab version. Key configuration instructions: 1. Parameter uncertainty ranges can be set in the ​Main.m​ file. 2. Other crop model parameters (crop, meteorological, irrigation, etc.) can be modified in the corresponding files within the ​Maize_sample​ folder. 3. The system requires long-term daily average weather data and weather forecast data in the ​Weather​ folder. 4. Additional system settings are documented in the code comments.

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Institutions

  • Ningxia University

Categories

Risk Analysis, Crop Simulation Model, Agricultural Irrigation

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