Effects of subsurface water infiltration systems on land movement dynamics in Dutch peat meadows

Large-scale drainage and cultivation of peat soils over the last centuries, occurring worldwide, have resulted in substantial CO₂ emission and land subsidence caused by peat decomposition by microbial activity, shrinkage, and soil compaction. In addition, seasonal reversible vertical soil movement is caused by shrink and swell in the unsaturated zone and by poroelastic deformation in the saturated zone. To reduce CO₂ emissions and land subsidence in drained peat soils, subsurface water infiltration systems (WISs) are expected to be a suitable measure. In this study, effects of WIS on seasonal vertical soil movements are evaluated, based on field measurements from five locations in Dutch peat meadows, for the years 2021 to 2023. First estimates of long-term land subsidence have also been made. At each study location, vertical soil movement has been measured using spirit leveling and extensometers in both a parcel with a WIS and a nearby reference parcel without any measure. Phreatic groundwater level fluctuations are found to induce soil volume decreases and increases in both the saturated and the unsaturated zone, which cause vertical land movement dynamics of up to 10 cm in the dry summer of 2022 at a location with a relatively thick (6 m) peat layer. Poroelastic deformation processes in the deeper saturated soil contribute substantially to surface-level movement and are largely reversible for the relatively short time period considered in this study. In peat meadows, subsurface water infiltration systems, if correctly applied, reduce seasonal vertical soil movements while (potentially) reducing soils’ resilience to drought-induced volume losses. Seasonal vertical soil surface dynamics are about an order of magnitude higher than longer-term (years to decades) land subsidence rates, which are commonly of the order of millimeters per year in the Dutch drained peat areas as is supported by estimates of land subsidence from this study. Therefore, multiyear data series are needed to filter out variations in seasonal dynamics, which are mainly introduced by annual variations in weather conditions, and more accurately estimate long-term land subsidence.