Coupled water-carbon modelling in data-limited sites: a new approach to explore future agroforestry scenarios

Abstract. Agroforestry is considered an important strategy for mitigating against, and adapting to, climate change. Questions yet remain regarding the potential impacts of different tree species on water/carbon cycling at different locations, scales and under different climatic conditions. There is an urgent need for numerical models capable of quantifying agroforestry impacts on a host ecosystem services including carbon sequestration and soil water/river flow regulation. A key challenge in modelling agroforestry systems is that they depend heavily on soil moisture as the main driver of many biogeochemical processes. Soil moisture itself is highly variable with soil properties (and therefore with location) but also with depth. Given that target sites for agroforestry are often ungauged, location-specific agroforestry modelling must inevitably rely only on data available from satellites and/or nearby weather stations which do not typically cover the subsurface, i.e., there is an incommensurability between data-availability and system complexity. To overcome this, we propose RSEEP, a new ecohydrological model that only requires rainfall, potential evapotranspiration, and surface soil moisture for its calibration. We demonstrate RSEEP’s capability in water cycling for a site in Scotland where soil moisture observations are available for different depths and vegetation types. We then couple RSEEP to the well-known RothC soil carbon model to (i) test RothC’s sensitivity to water cycling method, and to (ii) simulate water-carbon dynamics of three different silvo-pastoral agroforestry systems (all at 400 stems/ha density) in Scotland; these systems are: with evergreen conifer (Scots Pine), deciduous conifer (Hybrid Larch), and deciduous broadleaf (Sycamore) trees. We find that not including more accurate soil moisture accounting methods in RothC can significantly overestimate soil carbon stocks. Under the current future climate pathway (RCP6.0), 40 years after planting trees, above+below ground carbon storage can be 2–5 times (100–250 t/ha) higher under silvo-pasture than under pasture depending on species, with Larch having the highest potential and Sycamore the lowest. Larch also exhibits the highest potential for preserving soil moisture under drier conditions, but Pine shows the highest potential for river flow regulation under both wet and dry conditions at our site. The choice of species is therefore important and should be made site-specifically and based on the ecosystem service and management priorities/objectives. Examining our scenarios under drought- and flood-relevant conditions and scales is a logical next step.