The Department of Arequipa, in Peru, is a region with limited water resources making freshwater management critical and requiring the development of water-demand models, which can be valuable tools for policymakers. This study developed a monthly agricultural water-demand mapping algorithm for the agricultural districts surrounding the city of Arequipa. It was accomplished by:(1) developing a ground-reference data collection method;(2) creating a crop mapping algorithm, which incorporates supervised classification methods, as well as spatial-and temporal-consistency correction methods to create crop maps out of high resolution (~3 m) PlanetScope satellite images; (3) integrating a crop growth-stage prediction algorithm for the crop maps and; (4) applying an algorithm for the estimation of the agricultural-water-demand maps using the results of steps 2 and 3, local climate data, and an irrigation demand estimation tool. The crop mapping algorithm was shown to create maps with acceptable accuracy, with 5 out of 6 months with available data having mean monthly classification accuracies of 69% to 77%for those classes which had available data. Growth stage predictions had mean absolute prediction errors of 0.55 to 0.69 months in 5 out of 6 months.The6th month (the first with ground reference data collection) had a mean absolute prediction error of 0.90 months because it lacked prior month information to correctly identify planting month. Water demand maps were produced with high spatial (3.0m) and temporal (monthly) resolution, allowing for a detailed look at local agricultural water demands. This study provides a framework for future large-scale agricultural-water demand mapping for the Department of Arequipa and similar regions around the world.

A crucial decision in the real-time management of today’s irrigation systems involves the coordination of diversions and delivery of water to croplands. Since most irrigation systems experience significant lags between when water is diverted and when it should be delivered, an important technical innovation in the next few years will involve improvements in short-term irrigation demand forecasting. The main objective of the researches presented was the development of these critically important models: (1) potential evapotranspiration forecasting; (2) hydraulic model error correction; and (3) estimation of aggregate water demands. These tools are based on statistical machine learning or data-driven modeling. These, of wide application in several areas of engineering analysis, can be used in irrigation and system management to provide improved and timely information to water managers. The development of such models is based on a Bayesian data-driven algorithm called the Relevance Vector Machine (RVM), and an extension of it, the Multivariate Relevance Vector Machine (MVRVM). The use of these types of learning machines has the advantage of avoidance of model overfitting, high robustness in the presence of unseen data, and uncertainty estimation for the results (error bars). The models were applied in an irrigation system located in the Lower Sevier River Basin near Delta, Utah. For the first model, the proposed method allows for estimation of future crop water demand values up to four days in advance. The model uses only daily air temperatures and the MVRVM as mapping algorithm. The second model minimizes the lumped error occurring in hydraulic simulation models. The RVM is applied as an error modeler, providing estimations of the occurring errors during the simulation runs. The third model provides estimation of future water releases for an entire agricultural area based on local data and satellite imagery up to two days in advance. The results obtained indicate the excellent adequacy in terms of accuracy, robustness, and stability, especially in the presence of unseen data. The comparison provided against another data-driven algorithm, of wide use in engineering, the Multilayer Perceptron, further validates the adequacy of use of the RVM and MVRVM for these types of processes.