Climate change impacts water supply dynamics in the Upper Rio Grande (URG) watersheds of the US Southwest, where declining snowpack and altered snowmelt patterns have been observed. While temperature and precipitation effects on streamflow often receive the primary focus, other hydroclimate variables may provide more specific insight into runoff processes, especially at regional scales and in mountainous terrain where snowpack is a dominant water storage. The study addresses the gap by examining the mechanisms of generating streamflow through multi-modal inferences, coupling the Bayesian Information Criterion (BIC) and Bayesian Model Averaging (BMA) techniques. We identified significant streamflow predictors, exploring their relative influences over time and space across the URG watersheds. Additionally, the study compared the BIC-BMA-based regression model with Random Forest Regression (RFR), an ensemble Machine Learning (RFML) model, and validated them against unseen data. The study analyzed seasonal and long-term changes in streamflow generation mechanisms and identified emergent variables that influence streamflow. Moreover, monthly time series simulations assessed the overall prediction accuracy of the models. We evaluated the significance of the predictor variables in the proposed model and used the Gini feature importance within RFML to understand better the factors driving the influences. Results revealed that the hydroclimate drivers of streamflow exhibited temporal and spatial variability with significant lag effects. The findings also highlighted the diminishing influence of snow parameters (i. e., snow cover, snow depth, snow albedo) on streamflow while increasing soil moisture influence, particularly in downstream areas moving towards upstream or elevated watersheds. The evolving dynamics of snowmelt-runoff hydrology in this mountainous environment suggest a potential shift in streamflow generation pathways. The study contributes to the broader effort to elucidate the complex interplay between hydroclimate variables and streamflow dynamics, aiding in informed water resource management decisions.

Multi-source precipitation products (MSPs) are critical for hydrologic modeling, but their spatial and temporal heterogeneity and uncertainty present challenges to simulation accuracy that need to be addressed urgently. This study assessed the impact of different precipitation data sources on hydrologic modeling in an arid basin. There were seven precipitation products and meteorological station interpolated data that were used to drive the hydrological model, and we evaluated their performance by fusing the six precipitation products through the dynamic bayesian averaging algorithm. Ultimately, the runoff simulation uncertainty was quantified based on the DREAM algorithm, and the information transfer entropy was used to quantify the differences in hydrologic simulation processes driven by different precipitation data. The results showed that CMFD and ERA5 weights were higher, and the DBMA fused precipitation annual mean value was about 309.83 mm with good simulation accuracy (RMSE of 1.46 and R-2 of 0.75). The simulation was satisfactory (NSE >0.80) after parameter calibration and data assimilation for all driving data, with CHIRPS and TRMM performed better in the common mode, and HRLT and CMFD performed excellently in the glacier mode. The DREAM algorithm indicated less uncertainty for DBMA, CHIRPS and HRLT data. The entropy of information transfer revealed that precipitation occupied a significant position in information transfer, especially affecting evapotranspiration and surface soil moisture. CMFD and TPS CMADS were highest in snow water equivalent information entropy, and CHIRPS and TPS CMADS were highest in evapotranspiration information entropy. CDR, CHIRPS, ERA5-Land and IDW STATION had the highest snow water equivalent information entropy, DBMA and CMORPH had the highest runoff information entropy, CHIRPS and TRMM had the highest soil moisture information entropy, whereas ERA5, HRLT, and TPS CMADS had the highest evapotranspiration information entropy in glacial mode. This study reveals significant differences between different precipitation data sources in hydrological modeling of arid basin, which is an important reference for future water resources management and climate change adaptation strategies.

This study utilized electrical resistivity imaging (ERI) to investigate subsurface characteristics near Nicolaus Copernicus University Polar Station on the western Spitsbergen-Kaffi & oslash;yra Plain island in the Svalbard archipelago. Surveys along two lines, LN (148 m) collected in 2022 and 2023, and ST (40 m) collected in 2023, were conducted to assess resistivity and its correlation with ground temperatures. The LN line revealed a 1- to 2-m-thick resistive unsaturated outwash sediment layer, potentially indicative of permafrost. Comparing the LN resistivity result between 2022 and 2023, a 600 Ohm.m decrease in the unsaturated active layer in 2023 was observed, attributed to a 5.8 degrees C temperature increase, suggesting a link to global warming. ERI along the ST line depicted resistivity, reaching its minimum at approximately 1.6 m, rising to over 200 Ohm.m at 4 m, and slightly decreasing to around 150 Ohm.m at 7 m. Temperature measurements from the ST line's monitoring strongly confirmed that the active layer extends to around 1.6 m, with permafrost located at greater depths. Additionally, water content distribution in the ST line was estimated after temperature correction, revealing a groundwater depth of approximately 1.06 m, consistent with measurements from the S4 borehole on the ST line. This study provides valuable insights into Arctic subsurface dynamics, emphasizing the sensitivity of resistivity patterns to climate change and offering a comprehensive understanding of permafrost behavior in the region.

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.

Soot particles released in the atmosphere have long been investigated for their ability to affect the radiative forcing. Although freshly emitted soot particles are generally considered to yield only positive contributions to the radiative forcing, atmospheric aging can activate them into efficient cloud condensation or ice nuclei, which can trigger the formation of persistent clouds and ultimately provide a negative contribution to the radiative forcing. Depending on their residence time in the atmosphere, soot particles can undergo several physical and chemical aging processes that affect their chemical composition, particle size distribution and morphology, and ultimately their optical and hygroscopic properties. The impact of the physical-chemical aging on the properties of soot particles is still difficult to quantify, as well as their effect on the radiative forcing of the atmosphere.This work investigates the hygroscopic properties of chemically aged soot particles obtained from the combustion of aviation fuel, and in particular the interplay between aging mechanisms initiated by two widespread atmospheric oxidizers (O-3 and SO2). Activation is measured in water supersaturation conditions using a cloud condensation nuclei counter. Once particle morphology and size distribution are taken into account, the hygroscopicity parameter kappa is derived using kappa-K & ouml;hler theory and correlated to the change of the chemical composition of the particles aged in a simulation chamber. While fresh soot particles are poor cloud condensation nuclei (kappa < 10(-4)) and are not significantly affected by either O-3 or SO2 at the timescale of the experiments, rapid activation is observed when they are simultaneously exposed to both oxidizers. Activated particles become efficient cloud condensation nuclei, comparable to the highly hygroscopic particulate matter typically found in the atmosphere (kappa = 0.2-0.6 at RH = 20 %). Statistical analysis reveals a correlation between the activation and sulfur-containing ions detected on the chemically aged particles that are absent from the fresh particles.

Effective density (peff) is an important property describing particle transportation in the atmosphere and in the human respiratory tract. In this study, the particle size dependency of peff was determined for fresh and photochemically aged particles from residential combustion of wood logs and brown coal, as well as from an aerosol standard (CAST) burner. peff increased considerably due to photochemical aging, especially for soot agglomerates larger than 100 nm in mobility diameter. The increase depends on the presence of condensable vapors and agglomerate size and can be explained by collapsing of chain-like agglomerates and filling of their voids and formation of secondary coating. The measured and modeled particle optical properties suggest that while light absorption, scattering, and the single-scattering albedo of soot particle increase during photochemical processing, their radiative forcing remains positive until the amount of nonabsorbing coating exceeds approximately 90% of the particle mass.

For the period 2001-2020, the interannual variability of the normalized difference vegetation index (NDVI) is investigated in connection to Indian summer monsoon rainfall (ISMR). According to Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data, the ISMR and the vegetative activity of the Indo-Gangetic plain (IGP) in the month of January show a significant negative association. We hypothesized that the January vegetation state affects the ISMR via a delayed hydrological response, in which the wet soil moisture anomaly formed throughout the winter to accommodate the water needs of intensive farming influences the ISMR. The soil moisture anomalies developed in the winter, particularly in the root zone, persisted throughout the summer. Evaporative cooling triggered by increasing soil moisture lowers the summer surface temperature across the IGP. The weakening of monsoon circulation as a result of the reduced intensity of land-sea temperature contrast led in rainfall suppression. Further investigation shows that moisture transport has increased significantly over the past two decades as a result of increasing westerly over the Arabian Sea, promoting rainfall over India. Agriculture activities, on the other hand, have resulted in greater vegetation in India's northwest and IGP during the last two decades, which has a detrimental impact on rainfall processes. Rainfall appears to have been trendless during the last two decades as a result of these competing influences. With a lead time of 5 months, this association between January's vegetation and ISMR could be one of the potential predictors of seasonal rainfall variability.

Black carbon (BC) is one important component contributing to global warming and its climate-related impacts strongly depend on mixing state. Previous observations at ground level indicated BC aging was at a fast rate in daytime with efficient photochemical reactions, while BC aging significantly weakened at night. Here we present evidences that BC aging still occurs efficiently at night in the residual layer (RL). The ratio of thickly coated refractory BC (rBC) in total rBC (f(BC)) increased from 51.3% at 00:00 LST to 61.5% at 07:00 LST at the CITIC station, which located in the RL at night, with an increasing rate of 1.4% per hour. Such an increasing rate was even higher than that during noontime (11:00 to 15:00 LST, 0.7% per hour). Similar trend also reflected in the coating thickness (Dp/Dc) of rBC particles, which increased from 1.52 at 00:00 LST to 1.63 at 07:00 LST. The aging of rBC in the RL at night enhances light absorption of rBC particles correspondingly; calculated absorption enhancement (E-abs) increased from 1.64 at 00:00 LST to 1.79 in at 07:00 LST. Further analysis indicated that the Eabs depends not only on the D-p/D-c of rBC particles, but also on its size. An increase in the size of rBC particles in polluted episode can also enhance the Eabs. Combined observations of development of boundary layer and pollutants at the CITIC station suggested that rBC particles were upwards transported in daytime and trapped in the RL at night, where they were aged efficiently. These results will improve our understanding on rBC aging in the atmosphere, and hence help to evaluate its radiative forcing.

The Karakoram Anomaly has been intensively investigated, but the factors that control this anomaly, such as the glacier velocity, topography, and mass balance, remain poorly understood. To improve our understanding of the velocity, topography, and mass balance of the Karakoram Glacier, in this study, the spatiotemporal variability of four glacier velocities in the Hunza Basin of the Karakoram range were surveyed using co-registration of optically sensed images and correlation (COSI-Corr) on Landsat imagery from 1993-2019. The results show that the velocity of the Gulmit Glacier increases with a rising altitude from the glacier terminal. The three other glaciers initially display high velocity, followed by a decrease from the glacier terminal, with the maximum velocity attained in the middle of the glacier. In addition, the Karakoram glaciers produced a slight mass gain, with all mountain glaciers exhibiting clear regional acceleration from 1993-2019. The ice deformation velocity of the Batura Glacier diminished at an average rate of 8.49 %. However, the topography of the glacier base and physical factors require further analysis to determine their contribution to the observed changes in glacier velocity. In the present work, multi-temporal remote sensing image interpretations were carried out to determine glacier kinematics, which could enhance our understanding of glacier change mechanisms.

Quantifying the concentration of absorbing aerosol is essential for pollution tracking and calculation of atmospheric radiative forcing. To quickly obtain absorbing aerosol optical depth (AAOD) with high-resolution and high-accuracy, the gradient boosted regression trees (GBRT) method based on the joint data from Ozone Monitoring Instrument (OMI), Moderate Resolution Imaging Spectro-Radiometer (MODIS), and AErosol RObotic NETwork (AERONET) is used for TROPOspheric Monitoring Instrument (TROPOMI). Compared with the ground-based data, the correlation coefficient of the results is greater than 0.6 and the difference is generally within +/- 0.04. Compared with OMI data, the underestimation has been greatly improved. By further restricting the impact factors, three valid conclusions can be drawn: 1) the model with more spatial difference information achieves better results than the model with more temporal difference information; 2) the training dataset with a high cloud fraction (0.1-0.4) can partly improve the performance of GBRT results; and 3) when aerosol optical depth (AOD) is less than 0.3, the perform of retrieved AAODs is still good by comparing with ground-based measurements. The novel finding is expected to contribute to regional and even urban anthropogenic pollution research.