This research targets disentangling shallow causes of anthropogenically-induced subsidence in a reclaimed and urbanized coastal plain. The study area is around the city of Almere, in the South Flevoland polder, the Netherlands, which is among the countries’ fastest subsiding areas. The procedure consists of integrating synthetic Interferometric Synthetic Aperture Radar (InSAR) data with high-resolution phreatic groundwater and lithoclass models, and a database containing construction details. The two main parts of the workflow are isolation of the InSAR points of structures without a pile foundation and a data assimilation procedure by Ensemble Smoothing with Multiple Data Assimilation. The shrinkage of surficial clay beds by phreatic groundwater level lowering is identified to be the main cause of shallow subsidence in the area, with an average contribution of 6 mm/year. The history-matched physics-based model predicts that one meter drop in phreatic groundwater level now translates into 10 millimeter of subsidence in the next five years. Also, this study showed that a groundwater deficiency due to severe dry periods should be considered as an accelerator of subsidence in both the short- and long-term planning. To ensure a robust network to estimate future subsidence, we advise on a consistent monitoring strategy of the phreatic groundwater level.
Light-absorbing impurities such as mineral dust can play a major role in reducing the albedo of snow surfaces. Particularly in spring, deposited dust particles lead to increased snow melt and trigger further feedbacks at the land surface and in the atmosphere. Quantifying the extent of dust-induced variations is difficult due to the high variability in the spatial distribution of mineral dust and snow. We present an extension of a fully coupled atmospheric and land surface model system to address the impact of mineral dust on the snow albedo across Eurasia. We evaluated the short-term effects of Saharan dust in a case study. To obtain robust results, we performed an ensemble simulation followed by statistical analysis. Mountainous regions showed a strong impact of dust deposition on snow depth. We found a mean significant reduction of -1.4 cm in the Caucasus Mountains after one week. However, areas with flat terrain near the snow line also showed strong effects despite lower dust concentrations. Here, the feedback to dust deposition was more pronounced as increase in surface temperature and air temperature. In the region surrounding the snow line, we found an average significant surface warming of 0.9 K after one week. This study shows that the impact of mineral dust deposition depends on several factors. Primarily, these are altitude, slope, snow depth, and snow cover fraction. Especially in complex terrain, it is therefore necessary to use fully coupled models to investigate the effects of mineral dust on snow pack and the atmosphere.