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Glacial meltwater directly influences glacier dynamics. However, in the case of debris-covered glaciers, the drivers of glacier velocity and the influence of supraglacial lakes have not yet been sufficiently analysed and understood. We present a spatio-temporal analysis of key glacier characteristics for Baltoro Glacier in the Karakoram from October 2016 to September 2022 based on Earth observation data and climate parameters extracted from the High Asia Refined analysis (HAR) data set. For the glacier variables, we used surface velocity, supraglacial lake extent, melt of snow and ice, and proglacial run-off index. For climate variables, we focused on air temperature and precipitation. The surface velocity of Baltoro Glacier was characterized by a spring speed-up, summer peak, and fall speed-up with a relative increase in summer of 0.2–0.3 m d -1 (75 %–100 %) in relation to winter velocities, triggered by the onset of or an increase in basal sliding. Snow and ice melt have the largest impact on the spring speed-up, summer velocity peak, and the transition from inefficient to efficient subglacial drainage. The melt covered up to 64 % (353 km 2) of the entirety (debris-covered and debris-free) of Baltoro Glacier and reached up to 4700 m a.s.l. during the first melt peak and up to 5600 m a.s.l. during summer. The temporal delay between the initial peak of seasonal melt and the first relative velocity maximum decreases downglacier. Drainage from supraglacial lakes (3.6–5.9 km 2) contributed to the fall speed-up, which showed a 0.1–0.2 m d -1 (20 %–30 %) lower magnitude compared to the summer velocity peak. Most of the run-off can be attributed to the melt of snow and ice. However, from mid-June onward, the lakes play an increasing role, even though their contribution is estimated to be only about half of that of the melt. The observed increase in summer air temperatures leads to a greater extent of melt, as well as to a rise in the number and total area of supraglacial lakes. This tendency is expected to intensify in a future warming climate. [ABSTRACT FROM AUTHOR]

Surface meltwater directly influences glacier velocity, as liquid water at the bed allows the glacier to slide. However, prolonged discharge of water at the bed increases the efficiency of the drainage system and decreases the amount of sliding. Due to the presence of an insulating debris mantle, debris-covered glaciers respond in a more complex way to changes in climate than those that are debris-free. The influence of long-lasting high temperatures on melt processes and, subsequently, supraglacial lake formation, and the triggers of basal sliding have not yet been sufficiently analyzed and understood. We present a spatio-temporal analysis of key glacier characteristics for the Baltoro Glacier in the Karakoram, from October 2016 to September 2022 based on Earth Observation data and climate parameters extracted from the High Asia Refined analysis (HAR) data set. For the glacier variables, we used surface velocity, supraglacial lake extent, snowmelt, and runoff derived from Earth Observation data. For climate variables, we focused on air temperature and precipitation. The relationship and dependency between the variables were examined with Pearson correlation and linear regression, respectively. Additionally, the temporal delay between snowmelt peak and glacier flow acceleration was determined. The surface velocity of the Baltoro Glacier was characterized by a spring speed-up, summer peak, and fall speed-up with the largest increase in spring of 1.0-1.7 m d-1 triggered by the onset or an increase of basal sliding. Snowmelt has the largest impact to the spring speed-up, summer velocity peak, and to the transition from inefficient to efficient drainage. It covered up to 64 % (353 km²) of the complete (debris-covered and debris-free) Baltoro Glacier and reached up to 4700 m a.s.l. during the first melt peak and up to 5600 m a.s.l. during summer. The temporal delay between the initial peak of seasonal snowmelt and the first relative velocity maximum decreases downglacier. Drainage from supraglacial lakes (3.6-5.9 km²) contributed to the fall speed-up, which showed a lower magnitude by 0.1-0.2 m d-1 than the summer velocity peak. Most of the runoff can be attributed to snowmelt. However from mid-June onwards, the lakes play an increasing role, even though their contribution is estimated to be only about half of that of the snowmelt. The observed increase in summer temperatures influences the magnitude of snowmelt, as well as the formation of supraglacial lakes. This tendency is expected to intensify in the future. [ABSTRACT FROM AUTHOR]

The latest epidemiological studies have revealed that the adverse health effects of PM2.5 have impacts beyond respiratory and cardio-vascular diseases and also affect the development of the brain and metabolic diseases. The need for accurate and spatio-temporally resolved PM2.5 data has thus been substantiated. While the selective information provided by station measurements is mostly insufficient for area-wide monitoring, satellite data have been increasingly applied to comprehensively monitor PM2.5 distributions. Although the accuracy and reliability of satellite-based PM2.5 estimations have increased, most studies still rely on a single sensor. However, several datasets have become available in the meantime, which raises the need for a systematic analysis. This study presents the first systematic evaluation of four satellite-based AOD datasets obtained from different sensors and retrieval methodologies to derive ground-level PM2.5 concentrations. We apply a random forest approach and analyze the effect of the resolution and coverage of the satellite data and the impact of proxy data on the performance. We examine AOD data from the Moderate resolution Imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites, including Dark Target (DT) algorithm products and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. Additionally, we explore more recent datasets from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3a and from the Tropospheric Monitoring Instrument (TROPOMI) operating on the Sentinel-5 precursor (S5p). The method is demonstrated for Germany and the year 2018, where a dense in situ measurement network and relevant proxy data are available. Overall, the model performance is satisfactory for all four datasets with cross-validated R2 values ranging from 0.68 to 0.77 and excellent for MODIS AOD reaching correlations of almost 0.9. We find a strong dependency of the model performance on the coverage and resolution of the AOD training data. Feature importance rankings show that AOD has less weight compared to proxy data for SLSTR and TROPOMI. [ABSTRACT FROM AUTHOR]