Analysing the Crop Model Inversion Technique in the AquaCrop model under varying levels of Rainfall, Initial Soil Moisture, and Soil Texture

Reliable identification of agro-hydrometeorological change in developing countries is hindered by sparse and declining monitoring networks as well as limited data-management capacity. Increasing access to accurate, high-resolution agro-hydrometeorological data would improve hydrological model predictions and ultimately support better decision-making. One promising strategy to address data scarcity is model inversion of crop simulation models, where time-resolved crop growth information at the field scale can act as a proxy for soil moisture and, by extension, irrigation amounts.

In this study, we evaluate a yield-based inversion approach within AquaCrop, in which the observed final crop yield is used as the inversion target to retrospectively estimate seasonal irrigation. Under uniform, continuously applied irrigation, inferred irrigation amounts were generally accurate, with errors within ±10%. Model performance was strongly affected by the soil’s available water storage capacity, which is governed by texture. Incorporating information on soil texture, irrigation pattern (continuous vs. non-continuous), and rainfall substantially improved inversion accuracy. In contrast, under non-uniform or non-continuous irrigation regimes, the method tended to overestimate irrigation substantially. These findings suggest that yield-constrained inversion can reliably estimate irrigation in controlled settings but is less robust under intermittent or spatially heterogeneous irrigation. As a next step, we will invert AquaCrop using temporally resolved vegetation data rather than final yield to better constrain soil-moisture dynamics and reduce bias under complex irrigation patterns.