Multiscale Variability and the Comparison of Ground and Satellite Radar Based Measures of Peatland Surface Motion for Peatland Monitoring

Peatland surface motion is highly diagnostic of peatland condition. Interferometric Synthetic Aperture Radar (InSAR) can measure this at the landscape scale but requires ground validation. This necessitates upscaling from point to areal measures (80 × 90 m) but is hampered by a lack of data regarding the spatial variability of peat surface motion characteristics. Using a nested precise leveling approach within two areas of upland and low-lying blanket peatland within the Flow Country, Scotland, we examine the multiscale variability of peat surface motion. We then compare this with InSAR timeseries data. We find that peat surface motion varies at multiple scales within blanket peatland with decreasing dynamism with height above the water table e.g., hummocks < lawn < hollows. This trend is dependent upon a number of factors including ecohydrology, pool size/density, peat density, and slope. At the site scale motion can be grouped into central, marginal, and upland peatlands with each showing characteristic amplitude, peak timing, and response to climate events. Ground measurements which incorporate local variability show good comparability with satellite radar derived timeseries. However, current limitations of phase unwrapping in interferometry means that during an extreme drought/event InSAR readings can only qualitatively replicate peat movement in the most dynamic parts of the peatland e.g., pool systems, quaking bog.

GEODYNAMICS

The most dangerous exogenous geological processes (EGP) in terms of the amount of damage caused to economic objects include: landslides, karst, flooding, abrasion, mudslides, etc. The distribution and intensity of EGP are determined by the peculiarities of geological and geomorphological structure of the territory, its tectonic, neotectonic and seismic regime, as well as hydrological, climatic, hydrogeological paleo- and modern conditions. Solotvynsky salt mine is one of the oldest enterprises in Transcarpathia. The field has been exploited since the Roman Empire. In 1360, a settlement of salt miners, Solotvyno, was founded on the site of the mine, which later became a center of salt production and a royal monopoly. There are a total of nine mines in the field. In 1995-1996 and 2001, floods began flooding mines. In 2005, landslides and karst abysses intensified in Solotvyno, leading to damage to residential buildings, roads and infrastructure. There was a complete flooding of the mines of two mines. Currently, dangerous natural and man-made processes are observed on the territory of the salt mine and adjacent territories. This is mainly salt karst, both underground and surface, the collapse of areas in the location of mines, as well as landslides. Therefore, the purpose of the research is to conduct a geodynamic audit of SOLOTVYNSKY SALT MINE SE and the surrounding area with the possibility of identifying areas with subsidence or rise of the earth's surface, which are gradually slowing down, accelerating or developing at a constant rate. Output data. Radar interferometry data in the period from April 30, 2016 to June 25, 2018 were used for research and performance of geodynamic audit of SOLOTVYNSKY SALT MINE SE and the adjacent territory. Modern methods of interferometric processing of satellite radar data are used in the work: the method of "PS" – the method of constant scatterers, and the method SBAS – the method of small baselines. The method of geometric leveling was used to measure vertical displacements in some places on the earth's surface in order to verify interferometric data. Monitoring of the area of interest was carried out using modern technologies of satellite radar interferometry. According to the results of observations of landslides and individual objects by space (radar interferometry) and ground (geometric leveling) methods, a high correlation of data was recorded and the presence of zones of active subsidence in the mining area was confirmed.

Permafrost Dynamics and Degradation in Polar Arctic From Satellite Radar Observations, Yamal Peninsula

We investigate permafrost surface features revealed from satellite radar data in the Siberian arctic at the Yamal peninsula. Surface dynamics analysis based on SRTM and TanDEM-X DEMs shows up to 2 m net loss of surface relief between 2000 and 2014 indicating a highly dynamic landscape. Surface features for the past 14 years reflect an increase in small stream channels and a number of new lakes that developed, likely caused by permafrost thaw. We used Sentinel-1 SAR imagery to measure permafrost surface changes. Owing to limited observation data we analyzed only 2 years. The InSAR time-series has detected surface displacements in three distinct spatial locations during 2017 and 2018. At these three locations, 60–120 mm/yr rates of seasonal surface permafrost changes are observed. Spatial location of seasonal ground displacements aligns well with lithology. One of them is located on marine sediments and is linked to anthropogenic impact on permafrost stability. Two other areas are located within alluvial sediments and are at the top of topographic elevated zones. We discuss the influence of the geologic environment and the potential effect of local upwelling of gas. These combined analyses of InSAR time-series with analysis of geomorphic features from DEMs present an important tool for continuous process monitoring of surface dynamics as part of a global warming risk assessment.

Calculation of vertical displacement of the earth surface in the “Vostochny” open pit using radar data

The paper deals with the issues of assessment of the condition and changes in the land surface on the territory of the Vostochny open pit (Kemerovo region). The application of the multi-pass series of Sentinel-1 satellite radar data using the Small Baseline Subset (SBaS) method to determine the Earth surface displacement dynamics using constructed vertical displacement maps is demonstrated.

Statistical and machine learning methods applied to the prediction of different tropical rainfall types

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.