Spatio-temporal variability of air temperature lapse rate in the glacierised catchment of the Chandra basin, western Himalaya using in-situ measurements

The air temperature lapse rate (TLR) plays an important role in estimating ice and snow melt in high mountain regions. The TLR can vary depending on several factors, including the topography of the catchments and the microclimate. TLR calculations are typically not precise in the Himalayan glacierised regions due to a lack of in-situ observation of meteorological parameters. Therefore, a dense in-situ monitoring network over a high altitudinal gradient is needed to estimate the TLR accurately. We have obtained in-situ measurements of air temperature data from five automatic weather stations (AWS) installed at the best possible locations in the Chandra basin catchment of the semi-arid zone of the western Himalaya from October 2019 to September 2022. The altitudinal range for air temperature measurement varied between ~4000 and 5000 m a.s.l. We utilise the air temperature data to estimate the TLR by regressing the temperature with the corresponding elevations.
Comparing all the estimated TLR, the mean annual value (4.9°C/km) was significantly lower than the standard environmental lapse rate (6.5 °C/km) with substantial seasonality. The maximum TLR (~6.8 °C/km) during the summer is likely due to the high-altitude range and thin air and the presence of cold air pools at higher altitudes. However, the significantly lower TLR (~1.9 °C/km) during winters is likely due to the low air temperature and high moisture content in the region due to western disturbance. Further, we observed strong diurnal variations of TLR, which was highest during the daytime and lowest at night. This study highlighted that the TLR was potentially influenced by the local topography, particularly with higher lapse rates at higher elevations. TLR vary topographically and temporally significantly in the Chandra basin, indicating that the air temperature in this region is more sensitive to climatic variations. The findings of this study will play an important role in glacio-hydrological models, where TLR is one of the essential inputs.