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In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters. Plain Language Summary Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future. Key Points High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity losses Estimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacity Potential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters

Alpine lakes in the interior of Tibet, the endorheic Changtang Plateau (CP), serve as 'sentinels' of regional climate change. Recent studies indicated that accelerated climate change has driven a widespread area expansion in lakes across the CP, but comprehensive and accurate quantifications of their storage changes are hitherto rare. This study integrated optical imagery and digital elevation models to uncover the fine spatial details of lake water storage (LWS) changes across the CP at an annual timescale after the new millennium (from 2002-2015). Validated by hypsometric information based on long-term altimetry measurements, our estimated LWS variations outperform some existing studies with reduced estimation biases and improved spatiotemporal coverages. The net LWS increased at an average rate of 7.34 ± 0.62 Gt yr−1 (cumulatively 95.42 ± 8.06 Gt), manifested as a dramatic monotonic increase of 9.05 ± 0.65 Gt yr−1 before 2012, a deceleration and pause in 2013-2014, and then an intriguing decline after 2014. Observations from the Gravity Recovery and Climate Experiment satellites reveal that the LWS pattern is in remarkable agreement with that of regional mass changes: a net effect of precipitation minus evapotranspiration (P-ET) in endorheic basins. Despite some regional variations, P-ET explains ~70% of the net LWS gain from 2002-2012 and the entire LWS loss after 2013. These findings clearly suggest that the water budget from net precipitation (i.e. P-ET) dominates those of glacier melt and permafrost degradation, and thus acts as the primary contributor to recent lake area/volume variations in endorheic Tibet. The produced lake areas and volume change dataset is freely available through PANAGEA (https://doi.pangaea.de/10.1594/PANGAEA.888706).

High-quality graphene-based van der Waals superlattices are crucial for investigating physical properties and developing functional devices. However, achieving homogeneous wafer-scale graphene-based superlattices with controlled twist angles is challenging. Here, we present a flat-to-flat transfer method for fabricating wafer-scale graphene and graphene-based superlattices. The aqueous solution between graphene and substrate is removed by a two-step spinning-assisted dehydration procedure with the optimal wetting angle. Proton-assisted treatment is further used to clean graphene surfaces and interfaces, which also decouples graphene and neutralizes the doping levels. Twist angles between different layers are accurately controlled by adjusting the macroscopic stacking angle through their wafer flats. Transferred films exhibit minimal defects, homogeneous morphology, and uniform electrical properties over wafer scale. Even at room temperature, robust quantum Hall effects are observed in graphene films with centimetre-scale linewidth. Our stacking transfer method can facilitate the fabrication of graphene-based van der Waals superlattices and accelerate functional device applications. The large-scale fabrication of twisted van der Waals heterostructures remains challenging due to the formation of defects and contaminations during the transfer process. Here, the authors report a transfer method to fabricate graphene-based van der Waals superlattices at the wafer scale, showing controllable twist angles and robust quantum Hall effect.

Accurate information of urban surface water is important for assessing the role it plays in urban ecosystem services under the content of urbanization and climate change. However, high-resolution monitoring of urban water bodies using remote sensing remains a challenge because of the limitation of previous water indices and the dark building shadow effect. To address this problem, we proposed an automated urban water extraction method (UWEM) which combines a new water index, together with a building shadow detection method. Firstly, we trained the parameters of UWEM using ZY-3 imagery of Qingdao, China. Then we verified the algorithm using five other sub-scenes (Aksu, Fuzhou, Hanyang, Huangpo and Huainan) ZY-3 imagery. The performance was compared with that of the Normalized Difference Water Index (NDWI). Results indicated that UWEM performed significantly better at the sub-scenes with kappa coefficients improved by 7.87%, 32.35%, 12.64%, 29.72%, 14.29%, respectively, and total omission and commission error reduced by 61.53%, 65.74%, 83.51%, 82.44%, and 74.40%, respectively. Furthermore, UWEM has more stable performances than NDWI’s in a range of thresholds near zero. It reduces the over- and under-estimation issues which often accompany previous water indices when mapping urban surface water under complex environmental conditions.

Lakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods. Key Points Landsat images and laser altimeters were leveraged to reconstruct multi‐decadal lake levels of both large and small lakes Reconstructed water levels were validated against observed levels on 342 global lakes with a median error of 0.66 m Most of the reconstructed lake level time series have a bi‐monthly or higher frequency

The tetraploid Avena species in the section Pachycarpa Baum, including A . insularis , A . maroccana , and A . murphyi , are thought to be involved in the evolution of hexaploid oats; however, their genome designations are still being debated. Repetitive DNA sequences play an important role in genome structuring and evolution, so understanding the chromosomal organization and distribution of these sequences in Avena species could provide valuable information concerning genome evolution in this genus. In this study, the chromosomal organizations and distributions of six repetitive DNA sequences (including three SSR motifs (TTC, AAC, CAG), one 5S rRNA gene fragment, and two oat A and C genome specific repeats) were investigated using non-denaturing fluorescence in situ hybridization (ND-FISH) in the three tetraploid species mentioned above and in two hexaploid oat species. Preferential distribution of the SSRs in centromeric regions was seen in the A and D genomes, whereas few signals were detected in the C genomes. Some intergenomic translocations were observed in the tetraploids; such translocations were also detected between the C and D genomes in the hexaploids. These results provide robust evidence for the presence of the D genome in all three tetraploids, strongly suggesting that the genomic constitution of these species is DC and not AC, as had been thought previously.

Snow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5-year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave-based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine-scale and high-frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Improving high-resolution (meter-scale) mapping of snow-covered areas in complex and forested terrains is critical to understanding the responses of species and water systems to climate change. Commercial high-resolution imagery from Planet Labs, Inc. (Planet, San Francisco, CA, USA) can be used in environmental science, as it has both high spatial (0.7–3.0 m) and temporal (1–2 day) resolution. Deriving snow-covered areas from Planet imagery using traditional radiometric techniques have limitations due to the lack of a shortwave infrared band that is needed to fully exploit the difference in reflectance to discriminate between snow and clouds. However, recent work demonstrated that snow cover area (SCA) can be successfully mapped using only the PlanetScope 4-band (Red, Green, Blue and NIR) reflectance products and a machine learning (ML) approach based on convolutional neural networks (CNN). To evaluate how additional features improve the existing model performance, we: (1) build on previous work to augment a CNN model with additional input data including vegetation metrics (Normalized Difference Vegetation Index) and DEM-derived metrics (elevation, slope and aspect) to improve SCA mapping in forested and open terrain, (2) evaluate the model performance at two geographically diverse sites (Gunnison, Colorado, USA and Engadin, Switzerland), and (3) evaluate the model performance over different land-cover types. The best augmented model used the Normalized Difference Vegetation Index (NDVI) along with visible (red, green, and blue) and NIR bands, with an F-score of 0.89 (Gunnison) and 0.93 (Engadin) and was found to be 4% and 2% better than when using canopy height- and terrain-derived measures at Gunnison, respectively. The NDVI-based model improves not only upon the original band-only model’s ability to detect snow in forests, but also across other various land-cover types (gaps and canopy edges). We examined the model’s performance in forested areas using three forest canopy quantification metrics and found that augmented models can better identify snow in canopy edges and open areas but still underpredict snow cover under forest canopies. While the new features improve model performance over band-only options, the models still have challenges identifying the snow under trees in dense forests, with performance varying as a function of the geographic area. The improved high-resolution snow maps in forested environments can support studies involving climate change effects on mountain ecosystems and evaluations of hydrological impacts in snow-dominated river basins.

The Integrated Valuation of Ecosystem Services and Trade-Offs (InVEST) model with the distributed hydrological model Soil and Water Assessment Tool (SWAT) were implemented. The SWAT model quantifies and visualizes water production and groundwater reserves in the Mudanjiang River Basin, employing the groundwater runoff modulus method to calculate groundwater recharge in the basin. This study aims to assess the model’s applicability in cold basins and subsequently analyze groundwater distribution characteristics, water reserves, and the exploitable volume. It serves as a reference for the judicious allocation of groundwater resources and the preservation of the local aquatic ecosystem. The study indicates the following: (1) Utilizing the monthly runoff data from the Mudan River hydrologic station, SWAT simulation and calibration were conducted, yielding a determination coefficient (R2) of 0.75 and a Nash–Sutcliffe efficiency coefficient (NS) of 0.77, thereby satisfying fundamental scientific research criteria. The water yield predicted by the InVEST model aligns closely with the water resources bulletin of the research region. (2) The data from the water production module of the InVEST model indicate that the average annual water production during the research period was 6.725 billion m3, with an average annual water production depth of 148 mm. In 2018, characterized by ample water supply, the water output was at its peak, with a depth of 242 mm. In 2014, the water depth recorded was merely 16 mm. (3) Throughout the study period, the average annual flow of the Mudan River was 4.2 billion m3, whereas the groundwater reserve was 24.13 (108 m3·a−1). In 2013, the maximum groundwater reserve was 38.42 (108 m3·a−1), while the minimum reserve in 2014 was 2.36 (108 m3·a−1), suggesting that the region was predominantly experiencing sustainable exploitation. (4) The mean groundwater runoff modulus is 0.28 L/(s·km2), with a peak annual recharge of 15.4 (108 m3·a−1) in 2013 and a lowest recharge of just 3.2 (108 m3·a−1) in 2011.

Mountain snowpack is one of the primary surface water sources for about one-sixth of the global population. More than 75% of the total runoff originates from mountain snowpacks in the Western U.S. Snowmelt water recharges reservoirs and aquifers gradually in the melting season, providing vital water supplies for urban and agricultural areas. Therefore, accurately monitoring the spatial and temporal distribution of mountain snowpack – often measured as snow water equivalent (SWE) – is crucial for effective water management. While existing SWE estimation approaches remain highly uncertain, particularly when applied over large mountainous regions, the remotely-sensed snow data provide new opportunities to better characterize the spatial distributions of mountain snowpack. This dissertation investigates the approaches that optimally blend satellite, airborne, and ground snow observations to improve (near) real-time SWE estimation over mountainous terrain. The second chapter of this dissertation evaluates the accuracy of existing SWE estimation models in Sierra Nevada California. Five large-scale SWE datasets at fine spatial resolutions (<= 1000 m) are comprehensively validated and compared with the Airborne Snow Observatory (ASO) SWE data in the Tuolumne River Basin (2013-2017), and ground snow pillow and snow course SWE observations across the Sierra Nevada (2004-2014). These SWE datasets include REC-INT, REC-ParBal, a Sierra Nevada SWE reanalysis (REC-DA), and two operational SWE datasets from the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE), respectively. The results show that the REC-DA overall provides the most accurate SWE estimates across the Sierra Nevada (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%), followed by the REC-ParBal (R2 = 0.73, MAE = 83 mm, PBIAS = -6.4%), which is the least biased SWE estimates. Generally, SNODAS (R2 = 0.66, MAE = 106 mm, PBIAS = 9.3%) and REC-INT (R2 = 0.61, MAE = 131 mm, PBIAS = -28.3%) exhibit comparable but lower accuracy than the earlier mentioned two datasets, while NWM-SWE (R2 = 0.49, MAE = 142 mm, PBIAS = -25.2%) shows the least accuracy among the five SWE datasets.Given that REC-DA is not applicable in real-time, in the third chapter, a SWE data-fusion framework is developed, which integrates the historical SWE patterns derived from REC-DA into a statistically-based linear regression model (LRM) to estimate SWE in real-time. To investigate the influence of satellite-observed daily mean fractional snow-covered area (DMFSCA) on SWE estimation accuracy, two LRMs are compared: a baseline regression model (LRM-baseline) in which physiographic data and historical SWE patterns are used as independent variables, and an FSCA-informed regression model (LRM-FSCA) in which the DMFSCA from MODIS satellite imagery is included as an additional independent variable. By incorporating DMFSCA, LRM-FSCA outperforms LRM-baseline with improved R2 from 0.54 to 0.60, and reduced PBIAS from 2.6% to 2.2% in snow pillow cross-validation. The improvement in LRM-FSCA’s performance is more significant during snow accumulation periods than during the snowmelt seasons. Compared to the ASO SWE, the LRM-FSCA explains 85% of the variance on average, which is at least 21% higher than the operational SNODAS (R2 = 0.64) and NWM-SWE (R2 = 0.33) in comparison.In chapter 4, a SWE bias correction framework (SWE-BCF) is developed that incorporates the ASO SWE and machine learning (ML) algorithms to further improve LRM SWE estimates in real-time. The performance of a wide range of commonly used machine learning algorithms is examined in the SWE-BCF including Gaussian Process Regression (GPR), Support Vector Machine (SVM), Bayesian Regularized Neural Networks (BRNN), Random Forest (RF), and Gradient Boosting Machine (GBM). The results indicate that all ML algorithms are capable of improving LRM-SWE accuracy substantially. While no single model performs significantly better than others, GPR, overall, shows the best performance with a 20% (0.14) increase in mean R2 value, a 31% (51 mm) reduction in mean RMSE, and a 61% (18.0%) reduction in absolute PBIAS compared with the original LRM using ASO SWE data for model validation. RF shows the most robust and stable performance in SWE bias correction with a 10% (0.08) increase in median R2 and a 41% (50 mm) reduction in median RMSE compared with the original LRM.