Implementation of artificial, groundwater-dependent ponds in Mediterranean Agro-silvo-pastoral Ecosystems as a nature-based solution - DRYAD EU project

Mediterranean Agro-silvo-pastoral Ecosystems (MAEs) are increasingly threatened by climate-related hazards such as droughts, heatwaves, water scarcity, soil degradation and tree mortality. The DRYAD project of Mission Adaptation to Climate Change initiative, addresses these challenges by demonstrating, replicating and upscaling climate-resilient Nature-based Solutions (NbS). In DRYAD, various innovative tools are leveraged to support NbS-implementation; these include real-time monitoring with LoRaWAN sensors, development of web-based geospatial database management system (AgroAquae) handling real-time data (field and remote sensing), coupling of SCOPE-STEMMUS-MODFLOW6 models for analyzing plant-soil-groundwater dynamics and for assessment of tree mortality, machine-learning to scale NbS from local to regional scale, and finally development of user-friendly DSS implemented not only in AgroAquae, but also on cell-phone apps, facilitating the NbS use by stakeholders.

The NbS addressed in DRYAD fall in three categories, water-related, soil-related and biodiversity-related. One, water-related NbS, focusing on implementation of artificial ponds in Mediterranean oak woodland called Dehesa in Spain and Montado in Portugal, is presented hereafter. Dehesa-Montado is the most extensive MAE in Europe, which provides multiple socio-economic usages, with the most important livestock-farming for high quality meat production, which however requires large amount of continuously supplied water. To address that demand, farmers excavate ponds. Unfortunately, the majority of such artificial ponds dry up during droughts, while only those hydraulically linked to groundwater (further referred to as groundwater dependent ponds, GDPs) maintain water. Besides, majority of artificial ponds are not fenced, so eutrophication from livestock-manure, reduces water quality. As only GDPs can guarantee continuous fresh-water supply, the proposed methodology of artificial pond implementation, involves four objectives/steps:

1) Identification of optimal location of GDPs (two sub-steps): i) multi-year comparative analysis (dry versus wet seasons) of very high-resolution satellite images, to locate existing GDPs; ii) use of machine-learning to define new GDP locations at the regional scale using: the existing GDPs as primary training points, any in-situ information about water table depth and if needed, additional data from satellite image-processing, geo-radar survey and field-augering.

2) Assessment of optimal size, excavation depth and sustainability of GDPs; small scale MODFLOW6 models will be set up in selected, representative areas to define: i) size of GDPs, because larger ponds have larger evaporation loses; ii) excavation depth, because only depth larger than the lowest, multi-year water table position, guarantees continuous pond water presence; and iii) pond sustainability, to make sure that combined water use by livestock and environmental losses are balanced by yearly, surface and groundwater inflow.

3) Off-pond livestock watering system designed by fencing ponds to preserve good quality of water and by LoRaWAN-based automated control of water-divergence outside fencing to troughs.

4) Minimizing water evaporation by windbreaks, such as tree planting at least at the most frequent wind direction side and by solar shade structures, which can also provide power for water-divergence outside pond-fencing.

The proposed NbS is being implemented in the Alentejo (Portugal) and will be replicated in the Sardón area (Spain).

Acknowledgments: This research has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No:101156076