2025/12/01 by Ronny Seidel, Ullrich Dettmann, Bärbel Tiemeyer · 1 voice
Environmental Science · #Coastal wetland ecosystem dynamics #Fire effects on ecosystems #Peatlands and Wetlands Ecology
paper · pdf · doi:10.1002/eco.70140
openalex publication_date 2025/12/01 · openalex created_date 2025/12/16 · openalex updated_date 2026/05/21
ABSTRACT The surface of peatlands is constantly in motion. While pristine mires are characterized by peat growth and reversible surface fluctuation, induced by water level fluctuations, drained peatlands show subsidence due to peat mineralization and physical compaction. Still, drained peatlands show smaller but marked short‐term surface fluctuation as the water level continues fluctuating after drainage. This concurrence of physical and biochemical processes complicates the determination of greenhouse gas emissions from subsidence measurements. Restored peatlands may regain surface motion dynamics of pristine sites. Besides informing on carbon exchange, surface motion data might serve as an indicator for ecohydrological conditions. This review study compiles the key processes causing surface motion, methods to determine surface motion and subsidence models. A global meta‐analysis of 670 data points on subsidence (121 studies) and 70 data points on surface oscillation (29 studies) revealed large variability, with subsidence rates from −3.2 to 37.5 cm yr −1 and annual peak‐to‐peak amplitudes from 0.1 to 20 cm. Subsidence was influenced by time since drainage, climate, drainage depth, peat thickness and land use intensity. The subsidence rate decreased with time, while the share of physical compaction decreased, and the share of mineralization increased. With increasing temperature, mineralization and hence subsidence rate increased. Increasing drainage depth and thus land use intensity positively influence both mineralization and physical compaction. Subsidence rate and the share of physical compaction increased with increasing peat thickness. By combining existing model approaches, using the most available variables, we introduce four models to estimate subsidence rates on a global scale.