Ecohydrological Service Characteristics of Qilian Mountain Ecosystem in the Next 30 Years Based on Scenario Simulation

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

Spatio-temporal changes in vegetation net primary productivity and its responses to climatic factors in Jiangsu Province, Eastern China

Vegetation net primary productivity (NPP) is an important indicator in determining the ecological functions and carbon cycle of terrestrial ecosystems. As an important part of Chinese Yangtze River Delta region, Jiangsu is one of main grain producing areas in China. Therefore, understanding of spatio-temporal changes in NPP has a practical significance to ensure ecological sustainability in this region. In this study, we used satellite-based vegetation productivity model, the Carnegie-Ames-Stanford Approach (CASA) to assess spatio-temporal variations in NPP and analyzed the relationships between NPP and climatic factors over Jiangsu. The results showed that annual mean NPP reached up to 745.26 ± 69.21 g C m-2 year-1, with high NPP values mainly in the central and southwestern regions. Besides that, annual mean NPP increased from 2000 to 2015, with a rate of 6.54 g C m-2 year-1. The increasing trends with higher changing rate were mainly found in the northern regions of Jiangsu, whereas the decreasing trends were mainly found in central and southern Jiangsu. Moreover, the correlation analysis indicated that the mean temperature and total precipitation in spring had a significant relationship with the corresponding NPP in most part of Jiangsu. The findings will have an important significance for improving the ecosystem management in Jiangsu

Relationships between Ecosystem Services and Urbanization in Jiangsu Province, Eastern China

Ecosystem services are comprehensive and quantitative indicators for describing ecosystem–human interactions. China has experienced rapid urbanization in the past 30 years, which has created a significant impact on regional ecosystem services. However, whether the impact is linear is not clear as yet. In this study, the Jiangsu province, a main body of the Chinese Yangtze River Delta city cluster, was chosen as a case study. Multi-source remotely-sensed geospatial data, including meteorological, land use, vegetation, and socio-economic data, were collected to estimate the total amount of ecosystem services (TESV) and urbanization levels. Subsequently, the relationships between TESV and urbanization indices (i.e., gross domestic product (GDP) per unit area, GPUA; population per unit area, PPUA; and built-up land proportion, BULP) were determined using the Pearson correlation analysis and piecewise linear regression. The primary findings of this study were as follows: (1) There was a distinct spatial pattern in TESV, which gradually increased from west to east with high-value areas located in eastern coastal areas of Jiangsu. Among different land use types, cropland and woodland contributed the most to TESV; (2) The three urbanization indices had spatial patterns, indicating higher urbanization in southern Jiangsu than in central or northern Jiangsu; and (3) Once GPUA and PPUA exceeded threshold values of 3719.55 × 104 yuan/km2 and 744.37 person/km2, respectively, TESV sharply decreased with an increase in these indices. However, the BULP showed a linear and significantly negative relationship with TESV at all values, which indicated that the impacts of economic and population growth on TESV lagged behind that of built-up land expansion. These findings provide a potentially significant reference for decision-makers to rationally enhance regional ecosystem services during rapid urbanization processes

Assessment of Climatic Impact on Vegetation Spring Phenology in Northern China

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

Responses of Vegetation Autumn Phenology to Climatic Factors 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

Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems

Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R-2 0.84), than in the temperate steppe (R-2 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts