Author(s): Francesco Sapino; Carlos Dionisio Perez-Blanco
Linked Author(s): Francesco Sapino
Keywords: Water Framework Directive; Resource cost; Multi-model ensemble; Mathematical programming; Water policy
Abstract: Water scarcity is a growing issue across the world and especially in southern Europe, where agriculture, the sector that consumes the most (up to 70% of total water withdrawals), is a key economic sector. Among the policies for water management, water pricing (charging a fee for its use) is one of the most applied and effective, even though it is not in agriculture. This happens because irrigators are often subsidized and/or the fees are too low to have an effect in reducing water consumption or inducing a more efficient use. EU Water Framework Directive (WFD) forces member States to reach the full cost recovery of water services, but for irrigation this goal is still far away. The methodology proposed by WFD suggests that price is obtained with three components: the financial, the environmental, and the resource cost. The financial cost is usually the one charged to recover the cost of water services today, while environmental and resource costs are much more difficult to calculate and therefore normally ignored. This paper presents a methodology to calculate resource cost for irrigation water and an application in a sub-catchment of Duero River Basin, the Órbigo catchment, in central Spain. Following the socio-hydrology theory, a hydrological Decision Support System (DSS) model – AQUATOOL – is coupled with a multi-model ensemble of 4 microeconomic models (1 Linear Programming, 2 Positive Mathematical Programming, and 1 Positive Multi-Attribute Utility Programming) to evaluate the coevolution of the human and the natural systems. The DSS model simulates the water quantity provisioned to irrigators in a 38 years time series, considering the effect of climate change and the achievement of some environmental goals. Robust decision making theory suggests the use of multi-model ensemble to sample uncertainty through model spread and obtain a range of possible outcomes to evaluate the resource cost. Results show that the resource cost varies between 0.01 to 0.029 EUR/m3, depending on the model.
DOI: https://doi.org/10.3850/IAHR-39WC2521711920221701
Year: 2022