The policy failure to tackle water theft has been attributed to the nonlinear adaptive responses by economic agents such as irrigators, which can affect and be affected by other socioeconomic and ecological processes via feedback loops with cascading impacts that are difficult to foresee. This has led to adaptation surprises with unexpected policy consequences, which have increased rather than reduced water theft, thus depleting water bodies and hampering sustainable development.
The testing of the methods in different living labs will ensure the robustness and the adaptability of the WaterTheft approach.
We will gather, process and harmonize data on land use and yields, market prices, family and hired labor, other production costs, subsidies and other revenues, and market prices for every single relevant crop and across the three labs, for the Arenales Aquifer, Spain, Northern California, USA, and Barwon-Darling River, Australia. This data will allow us to setup the mathematical programming models, and will be complemented with data from behavioral economics experiments gathered in WP1.
Where the transition from rainfed cereals and vines to water intensive maize and horticulture crops has been partly supplied with illegal water resources that are causing aquifer depletion and nitrate pollution.
Where legalized, highly valuable and water intensive marijuana production is driving higher theft and negatively impacting legitimate irrigation operations, drinking water sources, Native American tribes, and small communities, amidst the driest years on record
Where the water bought by public institutions to restore environmental flows is being pumped out for cotton growing.
