Data from: County-Level Irrigation Water Demand Estimation Using Machine Learning: Case Study of California

This study presents a machine learning approach to estimate annual irrigation water demand at the county level in California, using Gaussian Process Regression (GPR) for improved predictive accuracy. Key input variables include meteorological parameters, geographical characteristics, and irrigated cropped area. The GPR model demonstrated high predictive accuracy (R² > 0.97, RMSE as low as 0.06 km³), identifying temperature, vapor pressure deficit, and irrigated area as the most influential factors. This research offers a robust tool for decision support in regional agricultural water management, enabling efficient evaluation of climatic and agricultural scenarios to optimize water resource allocation.