Forecasting groundwater pumping cap in an overexploited Mediterranean aquifer using seasonal meteorological forecasts from Copernicus Climate Change Service

The Requena-Utiel aquifer in the Jucar River Basin (Mediterranean Spain) is mined mainly for the irrigation of vineyards (Denominación de Origen Utiel-Requena), and some olive and nut trees. It has been recently declared as in bad quantitative status by the Jucar River Basin Agency (Confederación Hidrográfica del Júcar, CHJ). Among the measures taken to control water abstraction, a pumping cap for the irrigation season (May-September) has been agreed between the CHJ and the groundwater user association. This limit depends on the cumulative precipitation from December to April (classifying the year in wet, normal or dry), although that irrigation amount is in any case below the crop requirements. Consequently, predicting the type of year beforehand is a piece of valuable information for the water users in order to optimally schedule groundwater pumping and foresee crop production.

This study analyses the ability of seasonal meteorological forecasts from the Copernicus Climate Change Service (C3S) to anticipate the type of year in the agricultural areas of the Requena Utiel aquifer considering different periods ahead. The following seasonal forecasting services were used: ECMWF SEAS5, UKMO GloSEA5, MétéoFrance System, DWD GCFS, and CMCC SPS. Seasonal forecasts issued between November 1^st and April 1^st were downloaded and post-processed using a month-dependent linear scaling against historical records. Once post-processed, the skill of seasonal forecasts to predict the type of year has been evaluated for the 1995-2015 period, depending on the anticipation time.

Results show that, on a broader view, the type of year cannot be safely anticipated before April 1^st. However, we have identified that, for particular types of year and forecasting services, the anticipation time can be enlarged (e.g predicting wet years in December). Furthermore, we have found a direct relationship between the strength of the signal (number of ensemble members that predict the same type of year) and the forecasting skill, meaning that seasonal forecasts showing a strong signal, if properly identified, could offer valuable information months in advance to the beginning of the irrigation season.

Acknowledgements:

This study has received funding from the eGROUNDWATER project (GA n. 1921), part of the PRIMA programme supported by the European Union’s Horizon 2020 research and innovation programme. It has been also supported by the ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Economia y Competitividad (MINECO) of Spain and with EU FEDER funds.