Understanding of future changes in drought characteristics is crucial for climate change adaptation and drought impact mitigation. We analyzed the projected changes in drought characteristics relative to historical drought conditions in Central Asia using the Standardized Precipitation Evapotranspiration Index (SPEI) based on both gridded observations and an ensemble of bias-corrected and spatially-disaggregated Global Circulation Models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). The results suggest that precipitation and potential evapotranspiration are projected to increase across Central Asia. Even though the change in wetness may not be significant and robust, a ‘dry gets drier and wet gets wetter’ pattern may emerge in future under different scenarios. Drought events in Central Asia's semi-arid and arid regions (aridity index < 0.5) are projected to become more frequent (>125%), with longer duration (>55%), higher severity (>74%) and intensity (>8%) by the end of the 21^st century under four Shared Socioeconomic Pathway (SSP) scenarios. In the coming period of 2021-2050, Central Asia is expected to have more drought events with a longer duration but lower intensity. Comparisons between different SSP scenarios stress the importance of climate change mitigation strategies to avoid more drought occurrence, longer drought duration and higher drought severity. The long-term mitigation and adaptation studies for increasing drought impacts are also imperative in Central Asia.

Detritus geochemical information has been proven through research to be an effective prospecting method in mineral exploration. However, the traditional detritus metal content monitoring methods based on field sampling and laboratory chemical analysis are time-consuming and may not meet the requirements of large-scale metal content monitoring. In this study, we obtained 95 detritus samples and seven HySpex hyperspectral imagery scenes with a spatial resolution of 1 m from Karatag Gobi area, Xinjiang, China, and used partial least squares and wavebands selection methods to explore the usefulness of super-low-altitude HySpex hyperspectral images in estimating detritus feasibility and effectiveness of Cu element content. The results show that: (1) among all the inversion models of transformed spectra, power-logarithm transformation spectrum was the optimal prediction model (coefficient of determination(R²) = 0.586, mean absolute error(MAE) = 21.405); (2) compared to the genetic algorithm (GA) and continuous projection algorithm (SPA), the competitive weighted resampling algorithm (CARS) was the optimal feature band-screening method. The R² of the inversion model was constructed based on the characteristic bands selected by CARS reaching 0.734, which was higher than that of GA (0.519) and SPA (0.691), and the MAE (19.926) was the lowest. Only 20 bands were used in the model construction, which is lower than that of GA (105) and SPA (42); (3) The power-logarithm transforms, and CARS combined with the model of HySpex hyperspectral images and the Cu content distribution in the study area were obtained, consistent with the actual survey results on the ground. Our results prove that the method incorporating the HySpex hyperspectral data to invert copper content in detritus is feasible and effective, and provides data and a reference method for obtaining geochemical element distribution in a large area and for reducing key areas of geological exploration in the future.