Study region: The study focuses on the Indus River Basin and southern Pakistan, severely affected by flooding in 2022. Study focus: This study assessed how land surface temperature, snow cover, soil moisture, and precipitation contributed to the deluge of 2022. This study mainly investigated MODIS-AIRS land surface temperature, MODIS snow cover (NDSI), SMAP soil moisture, and GPM IMERG precipitation accumulation. Furthermore, different flood visualization and mapping techniques were applied to delineate the flood extent map using Landsat 8-9, Sentinel-2 MSI, and Sentinel-1 SAR data. New hydrological insights for the region: The region experienced some of the most anomalous climatic events in 2022, such as prolonged heatwaves as observed with higher-than-average land surface temperatures and subsequent rapid decline in snow cover extent during the spring, increased soil moisture followed by an abnormal amount of extreme monsoon precipitation in the summer. The upper subbasins experienced more than 8 degrees C in positive temperature anomaly, indicating a warmer climate in spring. Subsequently, the snow cover declined by more than 25 % in the upper subbasins. Further, higher surface soil moisture values (> 0.3 m3/m3) were observed in the basin during the spring due to the rapid snow and ice melt. Furthermore, the basin received more than 200 mm of rainfall compared to the long-term average rainfall of about 98 mm, translating to about 300 % more rainfall than usual in July and August. The analysis helps understand the spatial and temporal variability within the basin and facilitates the understanding of factors and their intricate connections contributing to flooding.

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).

Background . The paper is devoted to the analysis of vertical displacements based on remote sensing data as an identifier of hazardous engineering-geological processes in areas with underground infrastructure. The study was carried out on the example of the of the tunnel between Demiivska and Lybidska stations of the Kyiv subway. In December 2023, processes of uneven compaction and vibration creep of the soil massif around the tunnel lining were detected, and there was a risk of loss of stability of the tunnel structures and an emergency. Methods . This study employs the Differential Interferometric Synthetic Aperture Radar (D-InSAR) method which allows monitoring of soil surface deformations through phase change analysis among radar images. The correction procedures were applied to mitigate noise in processed images caused by temporal and geometric decorrelation, atmospheric disturbances, and other noise interferences. Correction and filtering method, specifically Canny and Sobel linear filters, were used to improve accuracy. Their application to processed satellite images enhances the contours of recorded vertical displacements and reduces geometric distortion noise, preserving the structural integrity of the images. According to our calculations, effective anomaly detection in images of urbanized areas requires a minimum threshold of 25 % in image contrast and clarity. The filters' application for highlighting significant intensity changes achieved a 28 % increase in clarity, indicating high processing effectiveness for further analysis of displacement maps and other parameters related to vertical shifts. Results . Abnormal zones of vertical displacements were identified within the study area based on vertical displacement data. During the 2022-2023 observation period, these zones shifted toward the metro tunnel axis. Vertical displacements directly above the area of subsidence near the 'Rozetka' store were detected during the fifth observation period, October-December 2023, coinciding with the tunnel closure for repairs. Overall, displacement values shifted from negative in 2022 to positive in 2023, suggesting that displacements may have served as an early indicator of underground structure deformation activation. The use of filters allowed for more precise identification of displacement dynamics and localization of deformation zones throughout the observation periods. In the final period, the anomalous zone aligned with the location of tunnel deformations and recorded surface subsidence. Conclusions . Using the example of the distillation tunnel section, we demonstrate the possibility of using the analysis of vertical surface displacements performed by D-InSAR together with a combination of Kenny and Sobel filters to track vertical surface displacements, which is important for monitoring the condition of underground facilities and preventing possible accidents. This study lays the foundation for further development of methodological approaches to the analysis of potential deformations of underground structures based on surface dynamics (vertical displacements). Further improvement of the methodology will help to ensure the accuracy and reliability of data in the context of monitoring underground structures.

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.

Monitoring surface albedo at a fine spatial resolution in forests can enrich process understanding and benefit ecosystem modeling and climate-oriented forest management. Direct estimation of surface albedo using 10 m reflectance imagery from Sentinel-2 is a promising research avenue to this extent, although questions remain regarding the representativeness of the underlying model of surface reflectance anisotropy originating from coarser-resolution imagery (e.g., MODIS). Here, using Fennoscandia (Norway, Sweden, Finland) as a case region, we test the hypothesis that systematic stratification of the forested landscape into similar species compositions and physical structures prior to the step of carrying out angular bin regressions can lead to improved albedo estimation accuracy of direct estimation algorithms. We find that such stratification does not lead to statistically meaningful improvement over stratification based on conventional land cover classification, suggesting that factors other than forest structure (e.g., soils, understory vegetation) may be equally important in explaining within-forest variations in surface reflectance anisotropy. Nevertheless, for Sentinel-2-based direct estimation based on conventional forest classification, we document total-sky surface albedo errors (RMSE) during snowfree and snow-covered conditions of 0.015 (15 %) and 0.037 (21 %), respectively, which align with those of the coarser spatial resolution products in current operation.

The Karnak Temples complex, a monumental site dating back to approximately 1970 BC, faces significant preservation challenges due to a confluence of mechanical, environmental, and anthropogenic factors impacting its stone blocks. This study provides a comprehensive evaluation of the deterioration affecting the northeast corner of the complex, revealing that the primary forms of damage include split cracking and fracturing. Seismic activities have induced out-of-plane displacements, fractures, and chipping, while flooding has worsened structural instability through uplift and prolonged water exposure. Soil liquefaction and fluctuating groundwater levels have exacerbated the misalignment and embedding of stone blocks. Thermal stress and wind erosion have caused microstructural decay and surface degradation and contaminated water sources have led to salt weathering and chemical alterations. Multi-temporal satellite imagery has revealed the influence of vegetation, particularly invasive plant species, on physical and biochemical damage to the stone. This study utilized in situ assessments to document damage patterns and employed satellite imagery to assess environmental impacts, providing a multi-proxy approach to understanding the current state of the stone blocks. This analysis highlights the urgent need for a multi-faceted conservation strategy. Recommendations include constructing elevated platforms from durable materials to reduce soil and water contact, implementing non-invasive cleaning and consolidation techniques, and developing effective water management and contamination prevention measures. Restoration should focus on repairing severely affected blocks with historically accurate materials and establishing an open museum setting will enhance public engagement. Long-term preservation will benefit from regular monitoring using 3D scanning and a preventive conservation schedule. Future research should explore non-destructive testing and interdisciplinary collaboration to refine conservation strategies and ensure the sustained protection of this invaluable historical heritage.

Economic damages of hurricanes and tropical cyclones are increasing faster than the populations and wealth of many coastal areas. There is urgency to update priorities of agencies engaged with risk assessment, risk mitigation, and risk communication across hundreds or thousands of water basins. This paper evaluates hydrology and social vulnerability factors to develop a risk register at a subbasin scale for which the priorities of agencies vary by storm scenario using publicly available satellite-based Earth observations. The novelty and innovation of this approach is the quantification and mapping of risk as a disruption of system order, while using social vulnerability indices and sensor data from disparate sources. The results assist with allocating resources across basins under several scenarios of hydrology and social vulnerability. The approach is in several parts as follows: first, a baseline order of basins is defined using the CDC/ATSDR social vulnerability index (SVI). Next, a set of storm scenarios is defined using Earth Observations and modeled data. Next, a swing-weight technique is used to update factor weights under each scenario. Lastly, the importance order of basins relative to the baseline order is used to compare the risk of scenarios across the study area. The risk is thus quantified (by least squares difference of order) as a disruption to the ordering of basins by social and hydrologic factors (i.e., SVI, precipitation, wind speed, and soil moisture), with attention to the most disruptive scenarios. An application is described with extensive mapping of hydrologic basins for Hurricane Ian to demonstrate a versatile method to address uncertainty of scenarios of storm nature and extent across coastal mega-regions.

Particle-particle and particle-gas processes significantly impact planetary precursors such as dust aggregates and planetesimals. We investigate gas permeability (kappa) in 12 granular samples, mimicking planetesimal dust regoliths. Using parabolic flights, this study assesses how gravitational compression - and lack thereof - influences gas permeation, impacting the equilibrium state of low-gravity objects. Transitioning between micro- and hyper-gravity induces granular sedimentation dynamics, revealing collective dust-grain aerodynamics. Our experiments measure kappa across Knudsen number (Kn) ranges, reflecting transitional flow. Using mass and momentum conservation, we derive kappa and calculate pressure gradients within the granular matrix. Key findings: (i) As confinement pressure increases with gravitational load and mass flow, kappa and average pore space decrease. This implies that a planetesimal's unique dust-compaction history limits subsurface volatile outflows. (ii) The derived pressure gradient enables tensile strength determination for asteroid regolith simulants with cohesion. This offers a unique approach to studying dust-layer properties when suspended in confinement pressures comparable to the equilibrium state on planetesimals surfaces, which will be valuable for modelling their collisional evolution. (iii) We observe a dynamical flow symmetry breaking when granular material moves against the pressure gradient. This occurs even at low Reynolds numbers, suggesting that Stokes numbers for drifting dust aggregates near the Stokes-Epstein transition require a drag force modification based on permeability.

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.