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.

Estimating the landscape and soil freeze-thaw (FT) dynamics in the Northern Hemisphere (NH) is crucial for understanding permafrost response to global warming and changes in regional and global carbon budgets. A new framework for surface FT-cycle retrievals using L-band microwave radiometry based on a deep convolutional autoencoder neural network is presented. This framework defines the landscape FT-cycle retrieval as a time-series anomaly detection problem, considering the frozen states as normal and the thawed states as anomalies. The autoencoder retrieves the FT-cycle probabilistically through supervised reconstruction of the brightness temperature (TB) time series using a contrastive loss function that minimizes (maximizes) the reconstruction error for the peak winter (summer). Using the data provided by the Soil Moisture Active Passive (SMAP) satellite, it is demonstrated that the framework learns to isolate the landscape FT states over different land surface types with varying complexities related to the radiometric characteristics of snow cover, lake-ice phenology, and vegetation canopy. The consistency of the retrievals is assessed over Alaska using in situ observations, demonstrating an 11% improvement in accuracy and reduced uncertainties compared to traditional methods that rely on thresholding the normalized polarization ratio (NPR).

Glacial responses to climate change exhibit considerable heterogeneity. Although global glaciers are generally thinning and retreat, glaciers in the Karakoram region are distinct in their surging or advancing, exhibiting nearly zero or positive mass balance-a phenomenon known as the Karakoram Anomaly. This anomaly has sparked significant scientific interest, prompting extensive research into glacier anomalies. However, the dynamics of the Karakoram anomaly, particularly its evolution and persistence, remain insufficiently explored. In this study, we employed Landsat reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 albedo products to developed high-resolution albedo retrieval models using two machine learning (ML) regressions--random forest regression (RFR) and back-propagation neural network regression (BPNNR). The optimal BPNNR model (Pearson correlation coefficient [r] = 0.77-0.97, unbiased root mean squared error [ubRMSE] = 0.056-0.077, RMSE = 0.055-0.168, Bias = -0.149 similar to -0.001) was implemented on the Google Earth Engine cloud-based platform to estimate summer albedo at a 30-m resolution for the Karakoram region from 1990 to 2021. Validation against in-situ albedo measurements on three glaciers (Batura, Mulungutti and Yala Glacier) demonstrated that the model achieved an average ubRMSE of 0.069 (p < 0.001), with RMSE and ubRMSE improvements of 0.027 compared to MODIS albedo products. The high-resolution data was then used to identify firn/snow extents using a 0.37 threshold, facilitating the extraction of long-term firn-line altitudes (FLA) to indicate the glacier dynamics. Our findings revealed that a consistent decline in summer albedo across the Karakoram over the past three decades, signifying a darkening of glacier surfaces that increased solar radiation absorption and intensified melting. The reduction in albedo showed spatial heterogeneity, with slower reductions in the western and central Karakoram (-0.0005-0.0005 yr(-1)) compared to the eastern Karakoram (-0.006 similar to -0.01 yr(-1)). Notably, surge- or advance-type glaciers, avalanche-fed glaciers and debris-covered glaciers exhibited slower albedo reduction rates, which decreased further with increasing glacier size. Additionally, albedo reduction accelerated with altitude, peaking near the equilibrium-line altitude. Fluctuations in the albedo-derived FLAs suggest a transition in the dynamics of Karakoram glaciers from anomalous behavior to retreat. Most glaciers exhibited anomalous behavior from 1995 to 2010, peaking in 2003, but they have shown signs of retreat since the 2010s, marking the end of the Karakoram anomaly. These insights deepen our understanding of the Karakoram anomaly and provide a theoretical basis for assessing the effect of glacier anomaly to retreat dynamics on the water resources and adaptation strategies for the Indus and Tarim Rivers.

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.

The tau -omega model is expanded to properly simulate L -band microwave emission of the soil-snow-vegetation continuum through a closed -form solution of Maxwell's equations, considering the intervening dry snow layer as a loss -less medium. The error standard deviations of a least -squared inversion are 0.1 and 3.5 for VOD and ground permittivity, over moderately dense vegetation and a snow density ranging from 100 to 400 kg m -3 , considering noisy brightness temperatures with a standard deviation of 1 kelvin. Using the Soil Moisture Active Passive (SMAP) satellite observations, new global estimates of VOD and ground permittivity are presented over the Arctic boreal forests and permafrost areas. In the absence of dense in situ observations of ground permittivity and VOD, the retrievals are causally validated using ancillary variables including ground temperature, above -ground biomass, tree height, and net ecosystem exchange of carbon dioxide. Time -series analyses promise that the new data set can expand our understanding of the land-atmosphere interactions and exchange of carbon fluxes over Arctic landscapes.

The of the Yellow River between its source and Hekou Town in Inner Mongolia is known as the Upper Yellow River Basin. It is the main source area of water resources in the Yellow River Basin, providing reliable water resources for 120 million people. Studying the hydrometeorological changes in the Upper Yellow River Basin is crucial for the development of human society. However, in the past, there has been limited research on hydrometeorological changes in the Upper Yellow River Basin. In order to clarify the four-dimensional spatiotemporal variation characteristics of hydrometeorological elements in the Upper Yellow River Basin, satellite and reanalysis hydrometeorological elements products need to be used. Unfortunately, there is currently a lack of precise evaluation studies on satellite and reanalysis hydrometeorological elements products in the Upper Yellow River Basin, and the geomorphic characteristics of this area have raised doubts about the accuracy of satellite and reanalysis hydrometeorological elements products. Thus, the evaluation study in the Upper Yellow River Basin is an important prerequisite for studying the four-dimensional spatiotemporal changes of hydrometeorological elements. When conducting evaluation study, we found that previous evaluation studies had a very confusing understanding of the spatiotemporal characteristics of datasets. Some papers even treated the spatiotemporal characteristics of evaluation metrics as the spatiotemporal characteristics of datasets. Therefore, we introduced a four-dimensional spacetime of both datasets and evaluation metrics to rectify the chaotic spatiotemporal view in the past. Our research results show that satellite and reanalysis hydrometeorological elements products have different abilities in describing the temporal and spatial distribution and change characteristics of hydrometeorological elements. The difference in the ability of satellite and reanalysis hydrometeorological elements products to describe temporal and spatial distribution and change characteristics requires us to select data at different temporal and spatial scales according to research needs when conducting hydrometeorological research, in order to ensure the credibility of the research results.

The southeastern Tibetan Plateau (SETP), which hosts the most extensive marine glaciers on the Tibetan Plateau (TP), exhibits enhanced sensitivity to climatic fluctuations. Under global warming, persistent glacier mass depletion within the SETP poses a risk to water resource security and sustainability in adjacent nations and regions. This study deployed a high-precision ICESat-2 satellite altimetry technique to evaluate SETP glacier thickness changes from 2018 to 2022. Our results show that the average change rate in glacier thickness in the SETP is -0.91 +/- 0.18 m/yr, and the corresponding glacier mass change is -7.61 +/- 1.52 Gt/yr. In the SETP, the glacier mass loss obtained via ICESat-2 data is larger than the mass change in total land water storage observed by the Gravity Recovery and Climate Experiment follow-on satellite (GRACE-FO), -5.13 +/- 2.55 Gt/yr, which underscores the changes occurring in other land water components, including snow (-0.44 +/- 0.09 Gt/yr), lakes (-0.06 +/- 0.02 Gt/yr), soil moisture (1.88 +/- 1.83 Gt/yr), and groundwater (1.45 +/- 0.70 Gt/yr), with a closure error of -0.35 Gt/yr. This demonstrates that this dramatic glacier mass loss is the main reason for the decrease in total land water storage in the SETP. Generally, there are decreasing trends in solid water storage (glacier and snow) against stable or increasing trends in liquid water storage (lakes, soil moisture, and groundwater) in the SETP. This persistent decrease in solid water is linked to the enhanced melting induced by rising temperatures. Given the decreasing trend in summer precipitation, the surge in liquid water in the SETP should be principally ascribed to the increased melting of solid water.

Objective Light-absorbing aerosols have a huge impact on visibility. The atmospheric pollution they cause can pose serious risks to human health. Quantitatively assessing the optical properties and spatiotemporal distribution of light-absorbing aerosols is of vital importance for decision-making in the management and control of complex air pollution. The dynamic changes in the physicochemical properties of light-absorbing aerosols, along with their temporal and spatial heterogeneity, introduce significant uncertainties in simulating their radiative forcing. The challenges arise from difficulties in accurately estimating particle size distribution, chemical composition, and mixed state, impeding precise retrievals through satellite remote sensing, with common model simulations and radiative transfer equations assuming the presence of external mixing for light-absorbing aerosols. However, research indicates that, especially in regions prone to pollution events like East Asia, South Asia, and Southeast Asia, a core-shell mixed state, with black carbon as the core and scattering aerosols like sulfates and nitrates as the shell, best represents the prevailing state of light-absorbing aerosols. Rough assumptions about aerosol states not only introduce significant errors in simulating aerosol number and mass concentrations in the atmosphere but also lead to substantial uncertainties in estimating overall radiative forcing. Methods Data from both satellite and in situ measurements are employed in the present study. First, we employ the AERONET aerosol optical depth (AOD) dataset to identify polluted days at three selected sites, and we match it in space and time with the single scattering albedo (SSA) dataset combined with the TROPOMI ultraviolet (UV) SSA dataset. Second, we utilize the Mie optical model across various combinations of core and shell sizes to establish a preliminary SSA map. Subsequently, we use SSA data from six different wavebands to constrain the SSA output from the Mie model. All calculations are conducted at a daily and grid-level resolution. Upon obtaining probability distributions for core size, shell size, and their corresponding SSA and absorption coefficient (ABS) values, we then apply spatial relationships between the column total absorbing aerosol optical depth (AAOD) from TROPOMI, single-particle absorption, and size distribution. This allows us to assess the column value of black carbon mass concentration and particle number concentration. Results and Discussions Spatial distribution of the mean absorption coefficient obtained from the Mie model simulations during periods of severe pollution shows that the absorption coefficient of the Beijing station is generally higher, with values mainly concentrated between 0. 05 and 0. 07. This indicates a higher presence of light-absorbing aerosols during this period. For the Hong Kong station, most of the absorption coefficients are below 0. 1, with the majority falling below 0. 2 and a low standard deviation of less than 0. 02. Factors related to topography and wind patterns are the primary reasons for the lower values observed in the Hong Kong station (Fig. 3). After applying spatial relationships between the column total AAOD from TROPOMI, the results show that the particle concentrations in the column at the Beijing station generally fall within the range of 3 x 10(19)-5 x 10(19) grid(-1). The number concentrations in Hong Kong are relatively lower than those in Beijing. Except for a few grid points where concentrations reach 2. 5 x 10(19) grid(-1), the overall value range in Hong Kong between 1 x 10(19) and 2 x 10(19) grid(-1). For the Seoul station, particle concentration range is from 1. 5 x 10(19) to 3. 0x 10(19) grid(-1) (Fig. 4). By considering the particle size distribution of black carbon aerosols under the core-shell mixed state simulated by the Mie model, the results of the spatial distribution of black carbon aerosol column mass concentration at each grid point (Fig. 6) shows that over 60% of the area of Beijing have concentrations exceeding 500 kg/grid. In the Hong Kong area, apart from certain regions within the Pearl River Delta urban cluster where black carbon column mass exceeds 500 kg/grid, the values in other areas are below 300 kg/grid. In addition, Seoul has an overall column mass concentration of less than 300 kg/grid.

This paper presents a convolutional autoencoder deep learning framework for probabilistic characterization of the ground freeze-thaw (FT) dynamics in the Northern Hemisphere to enhance our understanding of permafrost response to global warming and shifts in the high-latitude carbon cycle, using Soil Moisture Active Passive (SMAP) satellite brightness temperatures (TB) observations. The autoencoder recasts the FT-cycle retrieval as an anomaly detection problem in which the peak winter (summer) represents the normal (anomaly) segments of the TB time series. The results demonstrate that the new framework outperforms the widely used fixed-thresholding of the Normalized Polarization Ratio (NPR) by learning the land surface structural and radiometric complexities that might arise in TB times series due to snow cover and vegetation. Validation against ground-based measurements over Alaska shows that the accuracy of the FT-cycle retrievals can be improved by 12%, primarily due to a marked reduction in false detection of short snowmelt episodes as ground thawing by the NPR thresholding approach.

Study region: The Northwest inland basins of China (NWC).Study focus: Terrestrial water resources, especially groundwater resources, are the main source of water for human activities and for maintaining the stability of the ecological environment in NWC. Excessive consumption of water resources will seriously affect the sustainable utilization of water resources and ecological security in this region. Therefore, it is urgent to clarify the long-term changes in water storage in this area in order to handle the pressure of future water re-sources and the natural environment. Using GRACE satellite datasets and global hydrological models (GHMs) products, this study analyzed spatiotemporal variations in terrestrial water storage anomalies (TWSA), groundwater storage anomalies (GWSA), soil moisture, snow water equivalent, and canopy interception combined anomalies (SSCA) in NWC through the application of the water balance, trend decomposition, and empirical orthogonal decomposition methods. Furthermore, the driving factors of water storage change and feasible water resource manage-ment strategies were discussed. New hydrological insights for the region: TWSA in the NWC has experienced a continuous decline over the past nearly 40 years, while SSCA has shown a weak increasing trend (0.03 cm yr-1). Since the availability of glacial retreat data (2003-2016), glacial water storage in the NWC has decreased by 0.09 cm per year, while TWSA, SSCA, and GWSA have changed at rates of -0.25, 0.02, and -0.18 cm yr-1, respectively. The North Tianshan Rivers Basin has become one of the areas with the most severe groundwater depletion in China. 2005-2010 was a turning period in the changes of TWSA, followed by widespread water loss across the NWC. Glacier and snow melt are the most important factors for the decline of TWSA in the Tianshan mountains area, and over -exploitation of groundwater by human activities is a secondary factor. For other regions, Groundwater losses remain the most significant contributor to TWSA losses. The massive loss of water storage in the Tianshan Mountains area, especially the accelerated retreat of glaciers, will affect the stable water supply to the middle and lower reaches of the oasis region, perhaps leading to increased groundwater extraction, which will threaten regional water security and sustainable development. Developing a water-saving society and implementing inter-basin water transfer arefeasible ways to alleviate the water resource crisis. Conducting a comprehensive analysis of all inland rivers in China helps to facilitate horizontal comparisons between various basins, thereby providing more comprehensive insights of water storage fluctuations. The data on water storage changes, extending back to 1980, provide a longer-term perspective on water resource changes in the region, which can contribute to enhancing water resource security and ecological environ-mental protection.