Grid Scale Research Articles

Levator Advancement Surgery with or without Lateral Horn Incision: Effect of Lateral Horn Incision for Severe Involutional Blepharoptosis.

The lateral portion of the levator aponeurosis transforms into fibrous tissue called the lateral horn. Conventional levator advancement surgery for involutional blepharoptosis preserves the integrity of the lateral horn. The impact of incising the lateral horn has not been thoroughly studied. Sixty-nine patients who had undergone levator advancement surgery for involutional blepharoptosis between 2020 and 2023 were included in the study. In cases where the levator aponeurosis could not be well advanced during surgery, we incised the lateral horn to increase the advancement. We then retrospectively reviewed cases with or without the incision. We also measured the effect of the incision intraoperatively using a sheet with a scale grid. Lateral horn incisions were performed in 32 of the 69 cases. The preoperative degree of blepharoptosis was significantly more severe in cases that necessitated the incision (margin reflex distance 1; 0.5 vs 1.0 mm, P < 0.05). Patients receiving the incision were significantly older than those without (73.4 vs 61.7 years, P < 0.05). There were no significant differences between the two groups in postoperative margin reflex distance 1 (2.8 vs 3.0 mm, P = 0.20). Intraoperative measurements revealed that the levator aponeurosis was advanced 4.3 mm more caudally and 3.1 mm more medially as a result of the lateral horn incision. Lateral horn incision is effective in levator advancement surgery for severe involutional blepharoptosis. This technique aids in preventing undercorrection of palpebral fissures, especially in the medial region.

Coupling Coordination Measurement and Multi-dimensional Conflict Diagnosis among Territorial Space Functions

Exploring the relationship between urban, agricultural, and ecological functions is crucial for optimizing the territorial spatial patterns and achieving balanced territorial spatial development. Based on the classification system of "urban-agricultural-ecological" territorial spatial planning and with the help of multi-source data, including land use, population density, nighttime light, road network, precipitation, and county statistical data from 2010 to 2020, we constructed an evaluation index system of territorial space functions at the grid scale in the Poyang Lake Ecological Economic Zone. The coupling coordination levels among territorial space functions was measured using the coupling coordination degree model and spatial autocorrelation analysis. Moreover, the multi-dimensional function conflict identification model was used to diagnose the type and intensity of function conflicts. The results showed that： ① The urban function was mainly improved, accounting for 75.67%. The grids with high function evaluation index were mainly located in the south of the study area, whereas the intensity of urban function improvement in the north was higher than that in the south. Otherwise, the agricultural and ecological functions were mainly reduced, accounting for 77.44 % and 57.66%, respectively, and the majority of grids with a significant decrease were distributed in the south of Poyang Lake. ② From 2010 to 2020, the coupling coordination type was dominated by an imbalance recession, accounting for 53.87% and 49.89 %, respectively. However, the number of grids with crucial imbalance and primary coordination was reduced, and the coupling coordination types tended to change from both ends to the middle. The coupling coordination hot spots were mainly concentrated in the southern plain, whereas the cold spots were mainly distributed in the northern mountainous and hilly areas, both of which showed a reducing trend. However, in the north of Poyang Lake, a small number of hot spots, distributed along the lake area with relatively flat terrain, showed an expanding trend. ③ The intensity of four territorial space function conflicts was mainly moderate or severe. In addition to the slight positive trend of 'urban-agricultural' function conflict, the function conflicts of "urban-ecological", "agricultural-ecological", and "urban-agricultural-ecological" were all aggravated. The grids with aggravating conflicts were mainly distributed in the Ganjiang River and Fuhe River Basin. The major reason for the aggravation was that the urban function increased obviously, whereas the ecological and agricultural functions decreased significantly. In conclusion, in both the coupling coordination types and multi-dimensional conflicts among territorial space functions, significant spatial differences existed； thus, proposing optimization strategies according to local conditions is necessary. Additionally, the protection of ecological and agricultural spaces during each stage of urbanization development must be focused.

Investigating the impacts of multi-dimensional urbanization processes on ecosystem services in mega-urban agglomerations at the grid scale

Investigating the impacts of multi-dimensional urbanization processes on ecosystem services in mega-urban agglomerations at the grid scale

Application of a finite element method variant in nonconvex domains to parabolic problems

In this paper we address one of the major difficulties which is the nonconvex behavior of the domains while finding the solution of the problems. The part of the domain where the nonsmoothness appears is where the challenge arises and the way that area is handled using different numerical methods reveals the effectiveness of these techniques. Here in this article, we study the semilinear parabolic problem in nonconvex polygonal domain. For the approximation of the solution we use the Composite Finite Element (CFE) method, which is a classification of the Finite Element Method. CFE discusses the two-scale discretization — the larger mesh also known as the coarse mesh with the size H and the smaller mesh, also known as the fine mesh with the size h. It helps in reducing the dimension of the domain space of consideration. The fine scale grid is used to resolve the nonconvexity of the boundary whereas the coarse scale grid is comprised of larger grids at an appropriate distance from the boundary. The degrees of freedom depends on the coarse grid. This is the precedence of CFE over other methods, i.e., it eases the task of reducing the domain complexity. In this article, we consider two approaches — the semi discrete analysis where only space discretization is carried out, and the fully discrete analysis where both the time and space discretization is done using both backward Euler and Crank–Nicolson method. We study the error analysis in the L∞(L2)-norm and in the L∞(H1)-norm for the semidiscrete case whereas for the fully discrete case, we study the error analysis in the L∞(L2)-norm. Also, we check for the optimal results. For the CFE technique in the L∞(L2)-norm, we derive the convergence having optimal order in time and almost optimal order in space even if the domain is nonconvex. We consider a T-shaped domain and another star shaped domain to carry out the theoretical findings. Thereafter, numerical computations are implemented to validate the theoretical results.

A Stable Implementation of a Data‐Driven Scale‐Aware Mesoscale Parameterization

AbstractOcean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020), https://doi.org/10.1029/2020gl088376 (ZB20) applied an equation‐discovery algorithm to reveal an interpretable expression parameterizing the subgrid momentum fluxes by mesoscale eddies through the components of the velocity‐gradient tensor. In this work, we implement the ZB20 parameterization into the primitive‐equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different dynamical regimes and topography. The original parameterization was found to generate excessive numerical noise near the grid scale. We propose two filtering approaches to avoid the numerical issues and additionally enhance the strength of large‐scale energy backscatter. The filtered ZB20 parameterizations led to improved climatological mean state and energy distributions, compared to the current state‐of‐the‐art energy backscatter parameterizations. The filtered ZB20 parameterizations are scale‐aware and, consequently, can be used with a single value of the non‐dimensional scaling coefficient for a range of resolutions. The successful application of the filtered ZB20 parameterizations to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long‐standing biases in global ocean simulations in future studies.

Tower and power line segmentation method based on RandLA-Net

Abstract The scale of the power grid of China has gradually expanded, and the number of long-distance and high-level transmission lines has also increased. However, it is very hard to detect the power infrastructure transmission lines because of the complex laying environment and various ground features. Lines and towers in the power system are important components of power transmission, and their safety directly affects the operation of the entire power system. Reasonable management of lines and towers can reduce the occurrence of faults and system safety risks. At present, both manual inspection efficiency and visual inspection processing accuracy are low. An airborne LiDAR point cloud has become a key means for monitoring towers and power lines. In response to the above problems, we propose a tower and power line segmentation algorithm based on RandLA-Net. Experimental results demonstrate that the proposed method gets precise detection of power poles and lines, and enhances the efficiency in identifying hazardous areas along power lines. The highest Mean Intersection over Union (mloU) recorded is 95.34%, establishing a clear correlation between power towers and lines. Power line data holds significant importance for the power sector, facilitating the monitoring and management of transmission lines.

Posterior comparison of model dynamics in several hybrid turbulence model forms

Hybrid turbulence models that can accurately reproduce unsteady three-dimensional flow physics across the entire range of grid scales and turbulence dynamics from Reynolds-averaged Navier–Stokes (RANS), through large-eddy simulation (LES), down to direct numerical simulations (DNS) are of increasing interest to the turbulence modeling community. However, despite decades of research and development, the basic tasks of eliminating poor-performing hybrid RANS-LES models and accelerating adoption of superior models through well-designed validation and verification have yet to occur. As a step in this direction, in this work we evaluate thirteen different hybrid RANS-LES models via systematic grid refinement of decaying homogeneous isotropic turbulence. We further derive a novel mathematical framework for assessing the energy partitioning dynamics of each Hybrid RANS-LES model, wherein model-to-model variations in energy partitioning can be interpreted as different feedback mechanisms operating on a low-dimensional nonlinear dynamical system. We found that model forms similar to the flow simulation methodology—also often termed very-large eddy simulation—are dynamically inconsistent with DNS at all resolutions. Additionally, we found a strong dynamical similarity in the feedback mechanisms of all models related to detached eddy simulation and partially averaged Navier–Stokes that is inherent to their general model forms.

A Convolutional Neural Network-Based Defect Recognition Method for Power Insulator

As the scale of the power grid rapidly expands, its operation becomes increasingly complex, with higher demands on personnel proficiency, grid stability, equipment safety, and operational efficiency. In this study, a novel power insulator defect detection method based on convolutional neural networks (CNNs) is proposed. This method innovatively combines the feature extraction advantages of deep learning to build an efficient binary classification model capable of accurately detecting defects in power insulators in complex backgrounds. To avoid the impact of a small dataset on model performance, transfer learning was employed during model training to enhance the model’s generalization ability. A combination of Grid Search and Random Search was used for hyperparameter tuning, and the Early Stopping strategy was introduced to effectively prevent the model from overfitting to the training set, ensuring generalization performance on the validation set. Experimental results show that the proposed method achieves an average accuracy of 98.6%, a recall of 96.8%, and an F1 score of 97.7% on the test set. Compared to traditional Faster RCNN and PCA-SVM methods, the proposed CNN model significantly improves detection accuracy and computational efficiency in complex backgrounds, exhibiting superior recognition precision and model generalization ability for efficiently and accurately identifying defective insulators.

Dynamic vulnerability assessment framework for urban flood disasters considering real-time population changes

ABSTRACT Flood vulnerability assessment is an important means to cope with urban flood disasters. However, urban population mobility and land-use changes bring significant uncertainty to vulnerability assessments. In order to respond to the refined and dynamic needs of urban flood disaster prevention and control, a multi-scale and multi-categorized urban flood dynamic vulnerability assessment framework that considers real-time population changes was proposed in this study. The framework used the comprehensive function of exposure, sensitivity, and coping ability under population dynamic changes to characterize urban flood dynamic vulnerability, and constructed urban flood disaster vulnerability assessment models at both grid and regional scales. The results show that population mobility has a significant impact on the vulnerability of urban flood disasters. The vulnerability of flood disasters in Zhengzhou City is highest at 8:00 and 18:00, and high-vulnerability areas are mainly concentrated in residential areas in the central part of the city and main transportation channels. From 10:00 to 15:00, the high-vulnerability areas migrate to public service areas in the eastern part of Zhengzhou City. In the evening, the vulnerability degree of flood disasters is relatively low. The research results can provide a theoretical basis for urban flood managers to prevent and control flood disasters.

Optimization of an Urban Microgreen Space Distribution Based on the PS-ACO Algorithm: A Case Study of Shenyang, China

In this study, optimization of the microgreen space distribution through multistage regulation is investigated, with the goal of alleviating the imbalance between the supply and demand of green resources in the central urban area of Shenyang. An optimized evaluation model of green space supply and demand is employed to calculate the green space accessibility index at a 100-m grid scale and identify different levels of green space resource supply and demand. Priority is given to supplementing resources for the elderly population by balancing the green space supply in vulnerable areas. Particle swarm—ant colony optimization (PS-ACO) is used to select microgreen space sites within each priority level. On the basis of the “important-urgent” quadrant analysis, S1-priority residential areas account for 8.12% of the grid, S2-priority areas account for 67.01%, and S3-priority areas account for 24.87%. The PS-ACO algorithm outputs potential microgreen space sites within each priority level to accurately regulate the green space distribution in residential areas with different supply pressures and limited land availability. A spatial correlation analysis of the new sites reveals good spatial dispersion within service units, effectively alleviating demand pressures, and good aggregation at a regional scale to address imbalances in the supply of green space in a targeted manner. Thus, the optimized results of the PS-ACO algorithm are effective, providing reliable site-selection references for subsequent urban microgreen space distributions.

Identification of driving mechanisms of actual evapotranspiration in the Yiluo River Basin based on structural equation modeling

BackgroundActual evapotranspiration (ETa) is a crucial aspect of the hydrological cycle. It serves as a vital link between the soil–vegetation–atmosphere continuum. Quantifying the leading factors of regional ETa change and revealing the multi-factor compound driving mechanism of ETa evolution is necessary. Structural equation modeling (SEM) has been widely used to study the structural relationships between variables in large-scale areas. However, there is an urgent need for more in-depth exploration of these complex relationships at the grid scale. Therefore, the Yiluo River Basin, a representative area of soil and water conservation engineering demonstration in the Loess Plateau, was selected as the study area, and the SEM at the basin scale and grid-scale were constructed to carry out the research.ResultsThe data indicate that ETa decreased at 1.97 mm per year at the watershed scale from 1982 to 2020. Climate change had the greatest impact on the change of ETa in the watershed, with a total impact coefficient of over 0.9. The direct impact of climate change on ETa increased by 0.571 from 1982–1992 to 1993–2020. The direct impact coefficients of vegetation cover and soil moisture decreased by 0.402 and 0.102, respectively, while the impact coefficient of the water body factors increased by 0.096. At the scale of individual grid cells, the ETa in the watershed was affected by changes in watershed climate, vegetation, and soil moisture, with contributions ranging from − 0.31 to 0.22, − 1.09 to − 0.08, and 0.61 to 0.90, respectively. Spatially, vegetation and soil moisture had a stronger impact on ETa in the upstream area, while climate change had a negative effect, and the downstream region had the opposite effect. Furthermore, the regulatory impact of large reservoirs mitigated the response of water surface evaporation to climate change in the upstream region.ConclusionsThe application of SEM at different spatial and temporal scales has effectively quantified the driving mechanisms behind actual evapotranspiration in the Yiluo River Basin, while visually representing the spatial distribution characteristics of various influencing factors on ETa. This research provides a theoretical foundation for studying slope water consumption processes and circulation mechanisms.

Understanding scale effects and differentiation mechanisms of ecosystem services tradeoffs and synergies relationship: A case study of the Lishui River Basin, China

Understanding the scale effect of relationships among ecosystem services (ESs) and the factors influencing these relationships, is vital for sustainable management of ESs. Previous research has focused less on multiple scale units, and the driving mechanisms behind ESs interactions remain unclear. Therefore, in this study, we assessed the spatial distribution of water yield (WY), water purification (WP), and soil conservation (SC) across four spatial scales (3 km × 3 km grid, 5 km × 5 km grid, sub-watershed, and county scales) in the Lishui River Basin of China during 2020. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, geographical weighted regression, and Pearson correlation analysis, we examined tradeoffs and synergies among these ESs at various scales. Additionally, we employed a geographic detector to analyze the reactions and scale effects among these tradeoffs/synergies and social-ecological factors. Our findings revealed the following: (1) Significant spatial heterogeneity in ESs was observed in the basin. The WY decreased from west (2511.65 mm) to east (890.06 mm), with high WP values concentrated in the eastern part, and high SC values (8.30 × 105 t/hm2) found in densely forested and grassland areas surrounding water bodies. Forested and cultivated lands were key contributors to ESs in the basin. (2) Across the four investigated scales, tradeoffs or synergies between the same ESs becoming more pronounced as the scale increased. Notably, most ES relationships displayed their strongest synergies at the county scale, with certain spatial areas demonstrating change in interaction directions. (3) The explanatory power of single- or double-factor interactions increased with scale. ESs interactions were found to be closely linked to climatic factors, as well as topographic features (including slope gradient and elevation), with precipitation exhibiting relatively high explanatory power. The interaction of natural and social factors significantly outweighed the combined interaction of dual social factors. This indicated that socio-ecological factors, especially climate and topography, jointly influenced the intensity and direction of ES tradeoffs/synergies. Scale effects arose from the “peak cutting and valley filling” phenomenon during scale transitions. These insights hold significant value for guiding hierarchical management strategies for regional ESs and informing decisions regarding resource allocation.

How well can satellite altimetry and firn models resolve Antarctic firn thickness variations?

Abstract. Elevation changes of the Antarctic Ice Sheet (AIS) related to surface mass balance and firn processes vary strongly in space and time. Their subdecadal natural variability is large and hampers the detection of long-term climate trends. Firn models or satellite altimetry observations are typically used to investigate such firn thickness changes. However, there is a large spread among firn models. Further, they do not fully explain observed firn thickness changes, especially on smaller spatial scales. Reconciled firn thickness variations will facilitate the detection of long-term trends from satellite altimetry; the resolution of the spatial patterns of such trends; and, hence, their attribution to the underlying mechanisms. This study has two objectives. First, we quantify interannual Antarctic firn thickness variations on a 10 km grid scale. Second, we characterise errors in both the altimetry products and firn models. To achieve this, we jointly analyse satellite altimetry and firn modelling results in time and space. We use the timing of firn thickness variations from firn models and the satellite-observed amplitude of these variations to generate a combined product (“adjusted firn thickness variations”) over the AIS for 1992–2017. The combined product characterises spatially resolved variations better than either firn models alone or altimetry alone. It provides a higher resolution and a more precise spatial distribution of the variations compared to model-only solutions and eliminates most of the altimetry errors compared to altimetry-only solutions. Relative uncertainties in basin-mean time series of the adjusted firn thickness variations range from 20 % to 108 %. At the grid cell level, relative uncertainties are higher, with median values per basin in the range of 54 % to 186 %. This is due to the uncertainties in the large and very dry areas of central East Antarctica, especially over large megadune fields, where the low signal-to-noise ratio poses a challenge for both models and altimetry to resolve firn thickness variations. A large part of the variance in the altimetric time series is not explained by the adjusted firn thickness variations. Analysis of the altimetric residuals indicate that they contain firn model errors, such as firn signals not captured by the models, and altimetry errors, such as time-variable radar penetration effects and errors in intermission calibration. This highlights the need for improvements in firn modelling and altimetry analysis.

A Sensitivity Test on the Modifiable Areal Unit Problem in the Spatial Aggregation of Fossil Data

In paleobiology and macroevolution research, the spatial aggregation of fossil data can be influenced by the modifiable areal unit problem (MAUP), wherein the selection of different grid-cell sizes for data aggregation can lead to variations in statistical results. This study presents a case analysis focused on the spatial extent of marine bivalves and brachiopods over time across three Areas of Interest (AOIs) to evaluate the potential impact of the MAUP in grid-based fossil data processing. By employing rectangular grid matrices with cell sizes of 50, 100, 200, and 400 km, this research assesses the MAUP-related sensitivity of two commonly used grid-based proxies for species’ spatial distribution. The results reveal that the proxy based on the number of occupied grid cells (OGCs) is particularly sensitive to changes in cell size, whereas the proxy based on minimum-spanning-tree distance (MST distance) demonstrates greater robustness across varying grid scales. This study underscores that when constructing proxies for species’ spatial distribution ranges using grid matrices, the OGC method is more susceptible to MAUP effects than the MST distance method, warranting increased caution in studies employing the OGC approach.

Spatially varying effect of soil moisture-atmosphere feedback on spring streamflow under future warming in China

Soil moisture–atmosphere feedback (SAF) is expected to accelerate warming in the future, but its influences on the terrestrial water cycle over non-traditional hotspots of land–atmosphere coupling (e.g., East Asia) remains elusive due to strong heterogeneity and uncertainties at model grid scales. Here we show robust evidence of the integral effects of SAF on streamflow under future warming across major river basins in China. Based on high-resolution hydrological modelings driven by CMIP6 simulations with/without land–atmosphere coupling, SAF is projected to significantly increase spring streamflow by 28–58% in mid-latitude China, but decrease it by 12–48% in other regions. Such contrary effects are attributed to the soil moisture-precipitation interaction, which causes spatially opposite changes in precipitation through thermodynamic (e.g., ascending/descending vertical motions) and thermal (e.g., weakened meridional water vapor transport) atmospheric adjustments. Our result highlights the importance of SAF in altering freshwater resources even in locations not typically considered to be SAF hotspots.

TC-SRNet: Reconstruction and prediction of typhoon high-resolution turbulent fields based on meteorological numerical forecast scale wind fields and deep learning method

Meteorological numerical forecast models can provide a more accurate typhoon wind fields compared to engineering typhoon models due to their incorporate atmospheric multiphysical processes. However, the latest advancements in supercomputing power indicate that the current finest level of real-time weather forecasting typically operates on grid scales ranging from 1 to 4 km, while the convergence of typhoon intensity and turbulent field characteristics occurs at scales as fine as 62–185 m. Therefore, the primary scientific inquiry lies in determining how to achieve high-precision turbulent wind fields while considering the realistic atmospheric multiphysical processes, that means establish a “bridge” of typhoon wind field between kilometer-level and hundred-meter scales. This study investigates the super-resolution reconstruction of wind fields across different horizontal grid scales, utilizing a benchmark wind field at a 62 m horizontal grid scale (ground truth), which is based on a hybrid down-sampling skip connection (DSC)/multi-scale (MS) model. The research findings demonstrate that compared to traditional interpolation methods, the DSC/MS method significantly improves reconstruction accuracy, albeit with some residual high-frequency energy dissipation issues. Additionally, the DSC/MS method currently exhibits better reconstruction performance for 62 m scale wind fields based on kilometer-scale and smaller horizontal grid scales (1 km, 555 m, 185 m), with improved reconstruction as grid scale decreases. However, significant errors are observed in reconstructing fine turbulent fields at 62 m scale based on wind fields at 1.67 km horizontal grid scale. The findings presented in the present study can provide real and high-precision turbulent wind fields for structural wind engineering and wind energy assessment studies, thereby holding significant scientific and engineering application value.

Multi-Scale Analysis of Ecosystem Service Trade-Offs/Synergies in the Yangtze River Delta

The transformation of ecosystem structure leads to changes in ecosystem services (ESs) and their relationship. However, most research in this area has focused on particular scales and timeframes, often overlooking the significance of spatial and temporal variations. Therefore, we used the equivalent value method to evaluate seven typical ESs in the Yangtze River Delta (YRD) between 2000 and 2020: food production (FP), water supply (WS), climate regulation (CR), environmental purification (EP), soil conservation (SC), biodiversity maintenance (BM), and aesthetic landscape (AL). We further employed the Spearman correlation coefficient and bivariate Moran’s I to evaluate the relationship of ESs and their spatial heterogeneity at grid, township, county and city scales. Our results show that (1) All ESs except AL exhibited a fluctuating upward trend from 2000 to 2020, resulting in a total increase in ecosystem service (ES) value of RMB 650.63 billion. (2) Approximately 70% of the ES pairs demonstrated a synergistic relationship, with the exception of FP and other ESs, which mainly showed a trade-off. (3) With the scale increased from grid to city level, the degree of trade-off between FP and other ESs strengthened at different levels, while the synergy degree of among other ESs gradually decreased. (4) The relationship between ESs demonstrated strong spatial heterogeneity, with FP and other ESs exhibiting trade-offs primarily in the northern and southern YRD, while other ES pairs exhibited mostly synergy in these regions. This study provides scientific information for governments to optimize land use distribution and improve ESs.

Multicomponent hierarchical Cu-based advanced cathode for ultrahigh capacity rechargeable Cu-Zn battery

Rechargeable aqueous Zn-based batteries (RAZBs) have emerged as a practically attractive option for large-scale energy storage applications due to their cost-effectiveness, safety, eco-friendliness, and high theoretical capacity. Nonetheless, the application of RAZBs at the grid scale is restricted by the drawbacks of cathode that exhibit low capacities and poor durability. In this work, a multicomponent hierarchical Cu@CuO@Cu2O cathode with superior electrochemical energy storage performance has been successfully synthesized by a fairly simple calcination method. Benefiting from its large active area, fast ion diffusion channel, high conductivity, and synergetic effect between different components, the resulting Cu@CuO@Cu2O electrode exhibits ultrahigh specific capacity (40.2 mAh cm−2, 402 mAh cm−3 at 10 mA cm−2), excellent rate capability (24.8 mAh cm−2, 248 mAh cm−3 at 100 mA cm−2) and satisfactory cycling durability (71.5 % capacity retention after 3800 cycles). To our knowledge, such high areal/volumetric specific capacity ranks top among previously reported Cu-based electrode and other aqueous electrodes. Furthermore, the fabricated Cu@CuO@Cu2O//Zn battery achieves an extraordinary energy density of 24.5 mWh cm−2 and a remarkable cyclic capability of 91 % capacity retention after 3000 cycles, highlighting its potential for large-scale energy storage in RAZBs.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Subtle Land Subsidence Elevates Future Storm Surge Risks Along the Gulf Coast of the United States

AbstractWe developed a robust InSAR processing strategy that can effectively mitigate severe decorrelation noise in a large volume of InSAR data. We mapped the average land subsidence rate (2017–2020) over the 131,572 km2 Upper Texas and Louisiana coasts from Sentinel‐1 data, with ∼2 mm/yr accuracy based on independent GPS and tide gauge validation at 189 locations. The improved InSAR observations reveal widespread subsidence that was previously undetected in coastal wetlands and rural areas with small communities. Our InSAR surface deformation map is at the spatial scale that overlaps with the scale of hydrodynamic model grids. This allows us to integrate InSAR observations into operational storm surge models to analyze future flooding risks due to relative sea level change. We found that these subtle millimeter‐to‐centimeter subsidence features can substantially increase hurricane‐induced inundation, and passive flood mapping (known as the “bathtub” approach) can lead to inaccurate flood risk predictions.