Grid Scale Research Articles

Statistical Analysis of Levelized Round Trip Cost of Grid Scale Electrical Energy Storage in Batteries with Different Chemistries

Electrical energy storage is a crucial component of the clean energy transition for integrating high share of renewable electricity generators into the supply mix. In this study, the round-trip costs of grid scale electrochemical energy storage from 2 up to 24 hours for peak power ratings of 1 MW and 10 MW in lithium-ion LFP, lithium-ion NMC, Pb-acid and vanadium redox flow batteries are compared using their currently projected techno-economic characteristics for year 2030. A statistical approach is used for estimating mean costs and quantifying the uncertainties in these estimates, since many of the techno-commercial features are still evolving and are uncertain. Using Monte Carlo simulations to derive the input parameters, the levelized round trip cost of energy storage is found to vary from Rs 10.73±0.77/kWh(e) to Rs 15.96±1.12/kWh(e) for Li-LFP batteries, Rs 14±1/kWh(e) to Rs 20.3±1.46/kWh(e) for Li-NMC batteries, Rs 19.1±1.39/kWh(e) to Rs 58.3±4.28/kWh(e) for Pb-acid batteries and Rs 18.7±1.37/kWh(e) to Rs 44.2±3.19/kWh(e) for Vanadium redox flow batteries, for the range of input values considered in this study. Through sensitivity analysis and calculation of correlation coefficients, the battery installed capital cost and useful lifetime are found to be the two most influential parameters affecting life cycle electrical energy storage cost.

Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R2 of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2

Abstract. Recent studies have suggested that injection of solid particles such as alumina and calcite particles for stratospheric aerosol injection (SAI) instead of sulfur-based injections could reduce some of the adverse side effects of SAI such as ozone depletion and stratospheric heating. Here, we present a version of the global aerosol–chemistry–climate model SOCOL-AERv2 and the Earth system model (ESM) SOCOLv4 which incorporate a solid-particle microphysics scheme for assessment of SAI of solid particles. Microphysical interactions of the solid particle with the stratospheric sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code (RTC) for the first time within an ESM. Therefore, the model allows simulation of heterogeneous chemistry at the particle surface as well as feedbacks between microphysics, chemistry, radiation and climate. We show that sulfur-based SAI results in a doubling of the stratospheric aerosol burden compared to the same mass injection rate of calcite and alumina particles with a radius of 240 nm. Most of the sulfuric acid aerosol mass resulting from SO2 injection does not need to be lifted to the stratosphere but is formed after in situ oxidation and subsequent water uptake in the stratosphere. Therefore, to achieve the same radiative forcing, larger injection rates are needed for calcite and alumina particle injection than for sulfur-based SAI. The stratospheric sulfur cycle would be significantly perturbed, with a reduction in stratospheric sulfuric acid burden by 53 %, when injecting 5 Mt yr−1 (megatons per year) of alumina or calcite particles of 240 nm radius. We show that alumina particles will acquire a sulfuric acid coating equivalent to about 10 nm thickness if the sulfuric acid is equally distributed over the whole available particle surface area in the lower stratosphere. However, due to the steep contact angle of sulfuric acid on alumina particles, the sulfuric acid coating would likely not cover the entire alumina surface, which would result in available surface for heterogeneous reactions other than the ones on sulfuric acid. When applying realistic uptake coefficients of 1.0, 10−5 and 10−4 for H2SO4, HCl and HNO3, respectively, the same scenario with injections of calcite particles results in 94 % of the particle mass remaining in the form of CaCO3. This likely keeps the optical properties of the calcite particles intact but could significantly alter the heterogeneous reactions occurring on the particle surfaces. The major process uncertainties of solid-particle SAI are (1) the solid-particle microphysics in the injection plume and degree of agglomeration of solid particles on the sub-ESM grid scale, (2) the scattering properties of the resulting agglomerates, (3) heterogeneous chemistry on the particle surface, and (4) aerosol–cloud interactions. These uncertainties can only be addressed with extensive, coordinated experimental and modelling research efforts. The model presented in this work offers a useful tool for sensitivity studies and incorporating new experimental results on SAI of solid particles.

Construction of Long-Term Grid-Scale Decoupling Model: A Case Study of Beijing-Tianjin-Hebei Region

Against the backdrop of rapid global economic development, the Beijing-Tianjin-Hebei (BTH) region, a pivotal economic hub and environmentally sensitive area in China, faces significant challenges in sustaining its landscape ecosystem. Given the region’s strategic importance and vulnerability to environmental pressures, this study investigated the intricate relationships between landscape ecological risk, urban expansion, and economic growth (EG) in the BTH region. Utilizing the landscape as the focal point, we constructed a decoupling model at the grid scale to explore the decoupling relationship between the landscape ecological risk index (ERI), construction area growth (CAG), and EG. The results showed that (1) distinct stages and regional disparities were observed in the trends of ERI, CAG, and EG within the BTH region. The hot and cold spot patterns for these factors did not align consistently. (2) From 1995 to 2019, the coupling relationship between ERI, CAG, and EG in the BTH region underwent a fluctuating transition, initially moving from an undesirable state to an ideal state, and subsequently reverting to an undesirable state. Although the overall trends in these relationships showed some convergence, there were notable spatial distribution differences. (3) The spatial heterogeneity of the two decoupling relationships in the BTH region was relatively poor. Further analysis revealed that the evolution of these decoupling relationships was closely intertwined with regional policy shifts and adjustments.

Spatial benefit difference of fertilizer reduction and substitution policy based on grid scale: A case study of the Heihe River Basin

Agriculture green development faces the challenge of balancing the application of both organic and chemical fertilizers. Formulating effective policies to harmonize these applications is essential for ensuring food security and environmental sustainability. However, there is a lack of studies investigating the economic and environmental benefits of policy implementation at the grid scale, and insufficient research has been conducted on the subsequent impacts these policies have on diverse crops. Therefore, this paper develops the SIMPLE-G-Heihe model to simulate the impacts of green agricultural policies on multiple crops planting in arid regions. This model addresses limitations found in the SIMPLE-G model, which supported a single crop and lacked organic fertilizer substitution simulation. The results show that: (1) Merely increasing subsidies for organic fertilizers is insufficient to significantly enhance yields, and improving the efficiency of fertilizer utilization should also be emphasized. (2) Combining improvements in fertilizer technology with subsidies for organic fertilizers can concurrently support economic advancement and environmental mitigation. Research indicates that integrating a 3% organic fertilizer subsidy with an 8% improvement in chemical fertilizer technology increases vegetable output value by 0.27% and reduces N2O emissions by 1.09%. (3) The upstream areas of the Heihe River Basin show a significant reduction in fertilizer use. Fertilizer input is mainly reduced for vegetables in the upstream areas, while for wheat and maize, the reduction is primarily in the midstream and upstream areas. The findings discuss the spatial heterogeneity and flexibility of policy impacts, providing valuable insights for the implementation of green agriculture strategies in China.

Modeling the Spatial Flows of Nitrogen: The Case of Xiamen

Rapid city expansion and intensive human activities have remarkably affected nitrogen flow, leading to increasingly intricate spatial heterogeneity of nitrogen flow. Focused on the temporal characteristics of nitrogen flow at certain city scales, the existing research has missed comprehensive grid-scale spatial models for nitrogen flow. To address this gap, this study develops a comprehensive spatial model for nitrogen flow by incorporating both natural and anthropic processes. Taking Xiamen as its research case, this study utilizes grid technology and spatial analysis to build a detailed spatial model for nitrogen flow at the grid scale. The results of spatial characteristics of Xiamen in 2015 revealed that hotspots of nitrogen input were primarily located in the surrounding areas outside and east of Xiamen, with the maximum nitrogen input reaching 20.07 × 104 kg/ha. However, the hotspots of nitrogen load in the atmosphere were concentrated in the urban center (i.e., Xiamen Island) and the nearby sea areas. The maximum nitrogen outputs can reach 18.32 × 104 kg/ha, which is 18 times the total nitrogen output to the water environment. Additionally, it was found that a significant gradient correlation exists between nitrogen flow and population density. These findings provide support for low-nitrogen spatial planning and emission reduction policymaking.

Multi-Scale Effects of LCZ and Urban Green Infrastructure on Diurnal Land Surface Temperature Dynamics

The impact of urban green infrastructure (UGI) on land surface temperature (LST) has been widely discussed as a strategy to improve urban thermal environments. However, most studies have focused primarily on daytime conditions. Due to the limitations of polar-orbiting satellites like the Landsat series, the influence of UGI on LST over a 24-hour cycle remains unclear. To assess the relative influence and interdependence of UGI characteristics on LST across four different grid scales over the diurnal cycle, this study, conducted within Beijing's Fifth Ring Road during the summer, utilized a random forest (RF) regression model. The results indicate that: (1) UGI's impact on LST follows two distinct patterns: daytime (morning and afternoon) and non-daytime (night and dawn), which indicates the intensity of sunlight as a key driving factor; (2) tree landscape pattern indices are the most significant factors affecting LST; (3) during non-daytime periods (night and dawn), cropland's cooling effect is equally important as that of trees. These findings are valuable for prioritizing and strategically placing different types of UGI in urban planning. However, as this study focuses on a specific area, future research should include comparative studies in cities with different climatic conditions.

Realization of Integrated Regional Ecological Management Based on Ecosystem Service Supply and Demand Flow Networks: An Example from a Dominant Mineral Resources Development Area

Understanding the flow processes and pattern optimization of ecosystem services (ESs) supply and demand is crucial for integrated regional ecological management. However, the understanding of the flow process of ESs at the 1 km grid scale is still limited, especially in areas dominated by mineral resource development. The landscape in these areas has undergone significant changes due to mining activities. It is urgent to construct a regional management model that integrates the flow of ecosystem services and mine restoration. This study developed a framework that links ecosystem service flows (ESFs) and ecological security patterns (ESP) based on multi-source ecological monitoring data, constructed an ES supply-demand flow network through the flow properties, and determined the sequence and optimization strategies for mine rehabilitation to achieve integrated regional management. The results show that, except for food production (FP), other services were in surplus overall, mostly in synergistic relationships, but the spatial distribution of their supply and demand was not coordinated. Surplus areas were located mainly in the eastern woodlands, and deficit areas were located in the northwestern production agglomeration centers, suggesting that areas of supply-demand imbalance can be mitigated through ecological integration. Among these, water yield (WY) had a small number of sources and sinks and is limited in area range. Habitat quality (HQ) sources and sinks had the largest area coverage and the highest number. The distribution of ESF corridors, influenced by factors such as the number of sources and sinks, flow characteristics, and spatial resistance, varied significantly. HQ exhibited a more uniform distribution range, while WY had a longer average length of flow path. Overlaying ecological and mining factors, we identified ecological strategic spots, important supply areas, beneficiary areas, and mine priority restoration areas to further optimize the overall layout and rationally allocate the intrinsic structure of the patches based on ES supply and demand.

Investigating the Spatial Distribution and Influencing Factors of Non-Grain Production of Farmland in South China Based on MaxEnt Modeling and Multisource Earth Observation Data.

Excessive non-grain production of farmland (NGPF) seriously affects food security and hinders progress toward Sustainable Development Goal 2 (Zero Hunger). Understanding the spatial distribution and influencing factors of NGPF is essential for food and agricultural management. However, previous studies on NGPF identification have mainly relied on high-cost methods (e.g., visual interpretation). Furthermore, common machine learning techniques have difficulty in accurately identifying NGPF based solely on spectral information, as NGPF is not merely a natural phenomenon. Accurately identifying the distribution of NGPF at a grid scale and elucidating its influencing factors have emerged as critical scientific challenges in current literature. Therefore, the aims of this study are to develop a grid-scale method that integrates multisource remote sensing data and spatial factors to enhance the precision of NGPF identification and provide a more comprehensive understanding of its influencing factors. To overcome these challenges, we combined multisource remote sensing images, natural/anthropogenic spatial factors, and the maximum entropy model to reveal the spatial distribution of NGPF and its influencing factors at the grid scale. This combination can reveal more detailed spatial information on NGPF and quantify the integrated influences of multiple spatial factors from a microscale perspective. In this case study of Foshan, China, the area under the receiver operating characteristic curve is 0.786, with results differing by only 1.74% from the statistical yearbook results, demonstrating the reliability of the method. Additionally, the total error of our NGPF identification result is lower than that of using only natural/anthropogenic information. Our method enhances the spatial resolution of NGPF identification and effectively detects small and fragmented farmlands. We identified elevation, farming radius, and population density as dominant factors affecting the spatial distribution of NGPF. These results offer targeted strategies to mitigate excessive NGPF. The advantage of our method lies in its independence from negative samples. This feature enhances its applicability to other cases, particularly in regions lacking high-resolution grain crop-related data.

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.