Ranghui Wang

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring.

It is significant for the carbon emission reduction of bogie frame (BF) to establish a standardized carbon footprint accounting method for BF manufacturing and to analyze the carbon emissions of different materials, different production stages, and different functional constituent parts. This study is based on the theory of life cycle assessment (LCA), combined with the carbon emission factor method, to account for the carbon footprint and formulate emission reduction measures for the entire manufacturing process of BF. The results show that the carbon footprint of each BF produced is 3115.02 kgCO₂eq, of which the raw material consumption stage has the highest carbon emission of 2882.01 kgCO₂eq, accounting for 92.52% of the carbon emission of the whole process of production and manufacturing. Of all the materials, P355NL consumption produced the largest carbon footprint, 2083.08 kgCO₂eq. Among the auxiliary materials, the consumption of welding wire produced the highest carbon emissions at 119.10 kgCO₂eq. Among all the functional constituent parts, the Side Beam Inner Web Plate in the Side Beam Assembly produced the highest carbon emissions at 241.96 kgCO₂eq. Finally, it is proposed that optimizing the material composition and using clean energy can reduce the carbon emissions in the production process of BF. Accounting for the carbon footprint of urban rail transit BF can provide theoretical support for the green manufacturing of urban rail transit equipment, and can also provide data support for the development of industry standards.

The inland rivers under the geographical pattern of the mountain-basin system in the arid areas of China have special ecosystem types and landscape appearances. The inland river basin, represented by the Tarim River, has given birth to different ecosystem types and landscape appearances of river corridors-vegetation or farmland patches-desert matrix patterns. Through multi-source remote sensing(RS) data, geographic element monitoring data, ecological environment statistical survey data, model simulation and other multi-source data, the data verification and normalization are carried out, and the ecological environment quality(EEQ) characteristics are obtained through model operation and analysis.The results show that due to the constraints and influences of the mountain-basin system, precipitation shortage, strong evaporation, and severe drought lead to the widespread salinization of the basin. In addition, the sparse vegetation and the frequent occurrence of sandstorm disasters have led to drastic changes in the spatiotemporal dynamics of desertification. Under the multiple stresses of climate change and the development of man-made water resources and land resources, a series of changes have taken place in the EEQ. The analysis of panchromatic aerial RS images (1959), color aerial RS images (1992), JERS-1 OPS RS images (1995) and MODIS RS images (2023) shows that desert riparian forests show a discontinuous distribution on the north and south sides of the main stream corridor, and the vegetation tends to be degraded from the source area to the upper, middle and lower reaches of the main stream. Since 2000, the water resources allocation project in the basin has alleviated the vulnerability of water resources to spatiotemporal changes, slowed down the degradation trend of EEQ in the basin, and significantly improved the EEQ in some areas.Based on the systematic analysis of multi-source data such as hydrology, soil, climate, vegetation and landscape pattern changes in the basin, combined with the SSP climate scenario model, it is found that the future temperature and precipitation will show an upward trend under the SSP2-4.5 and SSP5-8.5 scenarios. Through the establishment of the ecological risk index (ERI) model, the quantitative evaluation showed that the ERI values of the Aksu River Basin in the headwaters were 0.08 and 0.06 in 1998 and 2023, respectively, indicating that the EEQ was in a stable and improving state in the past 25 years, and the EEQ continued to improve. It is estimated that by 2040, drought and flood disasters in the basin will be further aggravated, and the evolution of EEQ will be complex and uncertain.

This study simulated the spatiotemporal changes in coastal ecosystem services (ESs) in the Jiaodong Peninsula from 2000 to 2050 and analyzed the driving mechanisms of climate change and human activities with respect to ESs, aiming to provide policy recommendations that promote regional sustainable development. Future climate change and land use were forecast based on scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to assess ESs such as water yield (WY), carbon storage (CS), soil retention (SR), and habitat quality (HQ). Key drivers of ESs were identified using Structural Equation Modeling (SEM). Results demonstrate the following: (1) High WY services are concentrated in coastal built-up areas, while high CS, HQ, and SR services are mainly found in the mountainous and hilly regions with extensive forests and grasslands. (2) By 2050, CS and HQ will show a gradual degradation trend, while the annual variations in WY and SR are closely related to precipitation. Among the different scenarios, the most severe ES degradation occurs under the SSP5-8.5 scenario, while the SSP1-2.6 scenario shows relatively less degradation. (3) SEM analysis indicates that urbanization leads to continuous declines in CS and HQ, with human activities and topographic factors controlling the spatial distribution of the four ESs. Climate factors can directly influence WY and SR, and their impact on ESs is stronger in scenarios with higher human activity intensity than in those with lower human activity intensity. (4) Considering the combined effects of human activities and climate change on ESs, we recommend that future development decisions be made to rationally control the intensity of human activities and give greater consideration to the impact of climate factors on ESs in the context of climate change.

To monitor the Ecological Environment Quality (EEQ) of the Jiaodong Peninsula and provide a scientific basis for ecological environment governance and sustainable development in the region, this study evaluates the EEQ of Jiaodong using the Remote Sensing-based Ecological Index (RSEI) model and analyzes its spatiotemporal evolution patterns, building upon single-factor correlation analysis using the Random Sample Consensus (RANSAC) algorithm, using GeoDetector to analyze the driving mechanisms of human activities and natural factors in EEQ. The results indicate the following: (1) The average RSEI values for 2000, 2010, and 2020 are 0.60, 0.57, and 0.66, with Good or Excellent areas accounting for 56.48%, 51.02%, and 67.17%. From 2000 to 2020, RSEI strong improvement areas were predominantly distributed in the eastern hilly areas of the Jiaodong Peninsula. The RSEI of the Jiaodong Peninsula showed significant spatial autocorrelation. (2) The RANSAC algorithm effectively reduces noise interference in remote sensing data, thereby improving the accuracy of single-factor correlation analysis. (3) In 2000, the importance of natural factors exceeds that of human activity factors. The Standardized Precipitation Evapotranspiration Index (SPEI) is the most important driving factor; while in 2010 and 2020, human activity factors surpass natural factors in importance, with the Land Use Composite Index (LUCI) being the most significant driving factor. The driving factors exhibited double-factor and nonlinear enhancement. The most significant interaction factors affecting the EEQ of the Jiaodong Peninsula in 2000, 2010, and 2020 are SPEI∩GDP, LUCI∩SPEI, and LUCI∩GDP.

Spring phenology is often considered the start of season (SOS) for vegetation, which can affect ecosystem photosynthesis, respiration, and evapotranspiration. However, the long-run variation of SOS remains unclear at the regional scale. In this research, the long-term variation of SOS in northern China was explored by using the updated normalized difference vegetation index and monthly climatic data during 1982–2014. Furthermore, the relative importance of climatic factors on SOS was analyzed through partial correlation and multivariate regression methods. The main results were as follows: (1) average SOS largely ranged between day 120 and 165 of the year and varied widely for different vegetation types; (2) SOS during 1982–2014 showed an advancing trend, but it appeared to be reversed after 1998; (3) preseason minimum temperature was a dominant factor controlling SOS in most pixels in northern China, followed by maximum temperature (Tmx). However, impacts of radiation and precipitation on the trend of SOS primarily depended on vegetation types; (4) impacts of climatic factors on SOS declined in the period after 1998, especially for Tmx. These findings provide important support for modeling vegetation phenology and growth in northern China.

Understanding the dynamics of vegetation autumn phenology (i.e., the end of growing season, EOS) is crucial for evaluating impacts of climate change on vegetation growth. Nevertheless, responses of the EOS to climatic factors were unclear at the regional scale. In this study, northern China was chosen for our analysis, which is a typical ecologically fragile area. Using the Enhanced Vegetation Index (EVI) and climatic data from 1982 to 2016, we extracted the EOS and analyzed its trends in northern China by using the linear least-squares regression and the Bayesian change-point detection method. Furthermore, the partial correlation analysis and multivariate regression analysis were used to determine which climatic factor was more influential on EOS. The main findings were as follows: (1) multi-year average of EOS mainly varied between 275 and 305 day of year (DOY) and had complicated spatial differences for different vegetation types; (2) the percentage of the pixel showing delaying EOS (65.50%) was larger than that showing advancing EOS (34.50%), with a significant delaying trend of 0.21 days/year at the regional scale during the study period. As for different vegetation types, their EOS trends were similar in sign but different in magnitude; (3) temperature showed a dominant role in governing EOS trends from 1982 to 2016. The increase in minimum temperature led to the delayed EOS, whereas the increase in maximum temperature reversed the EOS trends. In addition to temperature, the impacts of precipitation and radiation on EOS trends were more complex and largely depended on the vegetation types. These findings can provide a crucial support for developing vegetation dynamics models in northern China.

Mountain ecosystems have special ecohydrological services, and the study of water conservation and soil conservation services in the Qilian Mountain Ecosystem (QLME) in China has important theoretical value for scientific understanding of the ecological processes and mechanisms of mountain ecosystems. In this study, we quantitatively estimated the spatial-temporal changes of water conservation and soil conservation services in the QLME based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and estimated the future ecosystem services (ESS) of the QLME under RCP4.5 (Representative Concentration Pathways) and RCP8.5 scenarios using the coupled Geosos-FLUS model. Firstly, the QLME ecohydrological service increased from 1985 to 2018, and its spatial heterogeneity was high in the east and low in the west. Among them, water conservation first decreased and then showed a trend of fluctuating increase, and soil conservation services decreased sharply from 2010 to 2015. Secondly, there are differences in the ecohydrological services of the QLME under different land-use types. The water conservation capacity in descending order is glacier snow, grassland, forest land, wetland, and cultivated land. The soil conservation intensity from strong to weak is woodland, grassland, arable land, glacier snow, and bare land. Thirdly, under different scenarios, QLME water conservation and soil conservation functions will increase to varying degrees over the next 30 years. The water conservation in the RCP4.5 scenario is higher than that in the RCP8.5 scenario, and the higher discharge scenario will lead to the decline of the water conservation service function. The increased rate of soil conservation was greater under the RCP8.5 scenario. With the development of Nationally Determined Contributions (NDCs) and scenarios below 2 °C, the future of QLME ecohydrological services will be further understood.

Water is an important factor that affects local ecological environments, especially in drylands. The hydrological cycle and vegetation dynamics in Central Asia (CA) have been severely affected by climate change. In this study, we employed data from Gravity Recovery and Climate Experiment (GRACE), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model, and Climate Research Unit to analyze the spatiotemporal changes in hydrological factors (terrestrial water storage (TWS), evapotranspiration, precipitation, and groundwater) in CA from 2003 to 2015. Additionally, the spatiotemporal changes in vegetation dynamics and the influence of hydrological variables on vegetation were analyzed. The results showed that the declining rates of precipitation, evapotranspiration, GRACE-TWS change, GLDAS-TWS change and GW change were 0.40 mm/year, 0.11 mm/year, 50.46 mm/year (p < 0.05), 8.38 mm/year, and 41.18 mm/year (p < 0.05), respectively. Human activity (e.g., groundwater pumping) was the dominant in determining the GW decline in CA. Precipitation dominated the changes in evapotranspiration, GRACE-TWS and GLDAS-TWS (p < 0.05). The 2- to 3-month lagging signal has to do with the transportation from the ground surface to groundwater. The change in the normalized difference vegetation index (NDVI) from 2003 to 2015 indicated the slight vegetation degradation in CA. The results highlighted that precipitation, terrestrial water storage, and soil moisture make important contributions to the vegetation dynamics changes in CA. The effect of precipitation on vegetation growth in spring was significant (p < 0.05), while the soil moisture effect on vegetation in summer and autumn was higher than that of precipitation.

Soil evaporation (Es) and crop transpiration (Tc) are important components of water balance in cropping systems. Comparing the accurate calculation by crop models of Es and Tc to the measured evaporation and transpiration has significant advances to the optimal configuration of water resource and evaluation of the accuracy of crop models in estimating water consumption. To evaluate the adaptation of APSIM (Agricultural Production Systems simulator) in calculating the Es and Tc in Nanjing, APSIM model parameters, including the meteorological and soil parameters, were measured from a two-year field experiment. The results showed that: (1) The simulated evaporation was basically consistent with the measured Es, and the regulated model can effectively present the field evaporation in the whole maize growth period (R² = 0.85, D = 0.96, p < 0.001); and (2) The trend of the simulated Tc can present the actual Tc variation, but the accuracy was not as high as the evaporation (R² = 0.74, D = 0.87, p < 0.001), therefore, the simulation of water balance process by APSIM will be helpful in calculating Es and Tc in a humid area of Nanjing, and its application also could predict the production of maize fields in Nanjing.