Vegetation Change and Its Response to Climate Change between 2000 and 2016 in Marshes of the Songnen Plain, Northeast China

Nitrogen and Phosphorus Stoichiometry of Bolboschoenus planiculmis Plants in Soda-Alkali Wetlands Undergoing Agricultural Drainage Water Input in a Semi-Arid Region

In semi-arid regions, wetlands often face water scarcity, salinity, and alkalinity stresses. Agricultural drainage water has been used to restore degraded wetlands, but it alters water quality and plant growth and resource distribution. Nitrogen (N) and phosphorus (P) stoichiometry reflect plant resource strategies. In China's Songnen Plain, Bolboschoenus planiculmis, a key plant in soda-alkali wetlands and food for the rare white crane (Grus leucogeranus), is impacted by agricultural water input. However, the N and P stoichiometry in B. planiculmis and the influencing water variables remain unclear. This study analyzed N and P contents in B. planiculmis leaves, stems, tubers, and roots, and water variables. Results showed that leaf N content was highest, while tuber P content exceeded that of other organs. Leaf nitrogen to phosphorus (N:P) ratio was highest, and tuber's was the lowest. N and P contents in plants were positively correlated, except between roots and stems. Redundancy analysis (RDA) revealed water temperature (T), oxidation-reduction potential (ORP), N contents, and water depth (WD) as key factors influencing N and P stoichiometry. Structural equation models (SEMs) indicated water T negatively affected plant N, while water nitrate nitrogen positively affected it. Water P content directly influenced leaf and stem P, and ammonium nitrogen affected aboveground P accumulation. Water T and WD directly impacted N:P ratios. These findings show that while agricultural drainage water alleviated aridification and salinization in degraded soda-alkali wetlands, exogenous N and P inputs significantly affected vegetation's N and P utilization strategies.

Spatial and temporal variation of net primary productivity of herbaceous marshes and its climatic drivers in China

Herbaceous marshes are widely distributed in China and are vital to regional ecological security and sustainable development. Vegetation net primary productivity (NPP) is a vital indicator of vegetation growth. Climatic change can significantly affect NPP, but variations in NPP of herbaceous marsh and their responses to climate change in China remain unclear. Using meteorological data and MODIS NPP data during 2000-2020, this study analyzed the spatial and temporal variations of NPP and their responses to climate change in Chinese herbaceous marshes. We found that the annual NPP of herbaceous marshes in China increased significantly at a rate of 3.34 g C/m2/a from 2000 to 2020, with an average value of 336.60 g C/m2. The increased annual total precipitation enhanced the national average NPP, whereas annual mean temperature had no significant effect on the national average NPP. Regionally, precipitation had a significant positive effect on the NPP in temperate semi-arid and arid and temperate semi-humid and humid marsh regions. For the first time, we discovered asymmetry effects of daytime and nighttime temperatures on NPP in herbaceous marshes of China. In temperate humid and semi-humid marsh regions, increased summer daytime temperature decreased the NPP while increased summer nighttime temperature increased the NPP. In the Tibetan Plateau, increased autumn daytime temperature, as well as summer daytime and nighttime temperatures could increase the NPP of herbaceous marshes. This study highlights the different influences of seasonal climate change on the NPP of herbaceous marshes in China and indicates that the differential effects of daytime and nighttime temperatures should be considering in simulating the NPP of herbaceous marshes in terrestrial ecosystem models, especially under the background of global asymmetric diurnal warming.

Heterogeneous responses of wetland vegetation to climate change in the Amur River basin characterized by normalized difference vegetation index from 1982 to 2020

Climate change affects wetland vegetation dramatically in mid- and high- latitudes, especially in the Amur River basin (ARB), straddling three countries and distributing abundance wetlands. In this study, spatiotemporal changes in average normalized difference vegetation index (NDVI) of wetland during the annual growing season were examined in the ARB from 1982 to 2020, and the responses of wetland vegetation to climatic change (temperature and precipitation) in different countries, geographic gradients, and time periods were analyzed by correlation analysis. The NDVI of wetland in the ARB increased significantly (p < 0.01) at the rate of 0.023 per decade from 1982 to 2020, and the NDVI on the Russian side (0.03 per decade) increased faster than that on the Chinese side (0.02 per decade). The NDVI of wetland was significantly positively correlated with daily mean temperature (p < 0.05, r = 0.701) and negatively correlated with precipitation, although the correlation was not significant (p > 0.05, r = -0.12). However, the asymmetric effects of diurnal warming on wetland vegetation were weak in the ARB. Correlations between the NDVI of wetland and climatic factors were zonal in latitudinal and longitudinal directions, and 49°N and 130°E were the points for a shift between increasing and decreasing correlation coefficients, closely related to the climatic zone. Under climate warming scenarios, the NDVI of wetland is predicted to continue to increase until 2080. The findings of this study are expected to deepen the understanding on response of wetland ecosystem to global change and promote regional wetland ecological protection.

Wetland vegetation cover changes and its response to climate changes across Heilongjiang-Amur River Basin

The Heilongjiang-Amur River Basin is one of the largest and most complex aquatic systems in Asia, comprising diverse wetland resources. The wetland vegetation in mid-high latitude areas has high natural value and is sensitive to climate changes. In this study, we investigated the wetland vegetation cover changes and associated responses to climate change in the Heilongjiang-Amur River Basin from 2000 to 2018 based on the growing season (May to September) climate and LAI data. Our results indicated that the wetland LAI increased at 0.014 m2·m-2/yr across Heilongjiang-Amur River Basin with the regional climate showed wetting and warming trends. On a regional scale, wetland vegetation in China and Russia had positive partial correlation with solar radiation and minimum air temperature, with precipitation showing a slight lag effect. In contrast, wetland vegetation in Mongolia had positive partial correlation with precipitation. These correlations were further investigated at different climate intervals. We found the precipitation is positively correlated with LAI in the warm regions while is negatively correlated with LAI in the wet regions, indicating an increase in precipitation is beneficial for the growth of wetland vegetation in heat sufficient areas, and when precipitation exceeds a certain threshold, it will hinder the growth of wetland vegetation. In the cold regions, we found solar radiation and minimum air temperature are positively correlated with LAI, suggesting SR and minimum air temperature instead of mean air temperature and maximum air temperature play more important roles in affecting the wetland vegetation growth in the heat limited areas. The LAI was found to be negatively correlated with maximum air temperature in the arid areas, indicating excessive temperature would inhibit the wetland vegetation growth when the water is limited. Our investigation can provide a scientific foundation for the trilateral region in wetland ecosystem protection and is beneficial for a more comprehensive understanding of the responses of wetlands in the middle and high latitudes to climate change.

Spatial-Temporal Characteristics of Spring Maize Drought in Songnen Plain, Northeast China

With the intensification of global warming, food production will face serious drought risk. In view of the insufficient applicability of the existing crop drought index, a standardized crop water deficit index (SCWDI) was constructed based on the construction idea of the standardized precipitation evapotranspiration index (SPEI) and the crop water deficit index (CWDI) in this study. On this basis, the spatial and temporal characteristics of spring maize drought in Songnen Plain were explored by the slope trend analysis and Morlet wavelet analysis methods. The results show the following: (1) Compared with the existing drought index, the SCWDI shows obvious advantages in drought monitoring of spring maize. (2) In the whole growth stage of spring maize, the change trend of SCWDI was small in the temporal series (−0.012/10a). Spatially, the drought trend of spring maize was mainly decreasing (−0.14~0/10a). The drought frequency of spring maize in each growth stage was mainly light drought in most regions. (3) The three main drought cycles of spring maize in Songnen Plain were 29 years, 10 years, and 4 years. In the next few years, the drought of spring maize in Songnen Plain was controlled by the first main cycle, and the drought years may increase, which should be prevented. The research was expected to provide technical support for crop drought monitoring and agricultural disaster prevention.

Historical reconstruction of aquatic vegetation of typical lakes in Northeast China based on an improved CA-Markov model

Aquatic vegetation is an important marker of the change in the lake ecosystem. It plays an important supporting role in the lake ecosystem, and its abundance and cover changes affect the ecosystem balance. Collecting accurate long-term distribution data on aquatic vegetation can help monitor the change in the lake ecosystem, thereby providing scientific support for efforts to maintain the balance of the ecosystem. This work aimed to establish an improved CA-Markov model to reconstruct historical potential distribution of aquatic vegetation in the two typical lakes (Xingkai Lake and Hulun Lake) in Northeast China during 1950s to 1960s. We firstly analyzed remote sensing data on the spatial distribution of aquatic vegetation data in two lakes in six periods from the 1970 to 2015. Then, we built a transfer probability matrix for changes in hydrothermal conditions (temperature and precipitation) based on similar periods, and we designed suitability images using the spatial frequency and temporal continuity of the constraints. Finally, we established an improved CA-Markov model based on the transfer probability matrix and suitability images to reconstruct the potential distributions of aquatic vegetation in the two northeastern lakes during the 1950s and 1960s. The results showed the areas of aquatic vegetation in the 1950s and 1960s were 102.37 km2 and 100.7 km2 for Xingkai Lake and 90.81 km2 and 88.15 km2 for Hulun Lake, respectively. Compared with the traditional CA-Markov model, the overall accuracy of the improved model increased by more than 50%, which proved the improved CA-Markov model can be used to effectively reconstruct the historical potential distribution of aquatic vegetation. This study provides an accurate methodology for simulating the potential historical distributions of aquatic vegetation to enrich the study of the historical evolution of lake ecosystem.

Response of vegetation variation to climate change and human activities in semi-arid swamps

Vegetation is a sensitive factor in marsh ecosystems, which can provide nesting sites, foraging areas, and hiding places for waterfowl and can affect their survival environment. The Jilin Momoge National Nature Reserve, which consists of large areas of marshes, is located in the semi-arid region of northeast China and is an important stopover site for the critically endangered species of the Siberian Crane (Grus leucogeranus). Global climate change, extreme droughts and floods, and large differences in evaporation and precipitation in this region can cause rapid vegetation succession. In recent years, increased grain production and river-lake connectivity projects carried out in this area to increase grain outputs and restore wetlands have caused significant changes in the hydrological and landscape patterns. Therefore, research on the response of variation trends in vegetation patterns to the main driving factors (climate change and human activities) is critical for the conservation of the Siberian Crane. Based on the Google Earth Engine (GEE) platform, we obtained and processed the Normalized difference vegetation index (NDVI) data of the study area during the peak summer vegetation period for each year from 1984 to 2020, estimated the annual vegetation cover using Maximum value composites (MVC) method and the image dichotomy method, calculated and analyzed the spatial and temporal trends of vegetation cover, explored the response of vegetation cover change in terms of climate change and human activities, and quantified the relative contribution of both. The results revealed that first, from the spatial and temporal changes, the average annual growth rate of regional vegetation was 0.002/a, and 71.14% of the study area was improved. The vegetation cover showed a trend of degradation and then recovery, in which the percentage of high vegetation cover area decreased from 51.22% (1984-2000) to 28.33% (2001-2005), and then recovered to 55.69% (2006-2020). Second, among climate change factors, precipitation was more correlated with the growth of vegetation in the study area than temperature, and the increase in precipitation during the growing season could promote the growth of marsh vegetation in the Momoge Reserve. Third, overall, human activities have contributed to the improvement of vegetation cover in the study area with the implementation of important ecological projects, such as the return of farmland to wetlands, the return of grazing to grass, and the connection of rivers and lakes. Fourth, climate change and human activities jointly drive vegetation change, but the contribution of human activities in both vegetation improvement and degradation areas (85.68% and 78.29%, respectively) is higher than that of climate change (14.32% and 21.71%, respectively), which is the main reason for vegetation improvement or degradation in the study area. The analysis of vegetation pattern change within an intensive time series in semi-arid regions can provide a reference and basis for studying the driving factors in regions with rapid changes in vegetation and hydrological conditions.

Aboveground Biomass of Wetland Vegetation Under Climate Change in the Western Songnen Plain

Understanding the spatiotemporal dynamics of aboveground biomass (AGB) is crucial for investigating the wetland ecosystem carbon cycle. In this paper, we explored the spatiotemporal change of aboveground biomass and its response to climate change in a marsh wetland of western Songen Plain by using field measured AGB data and vegetation index derived from MODIS datasets. The results showed that the AGB could be established by the power function between measured AGB density and the annual maximum NDVI (NDVImax) of marsh: Y = 302.06 × NDVImax 1.9817. The averaged AGB of marshes showed a significant increase of 2.04 g⋅C/m2/a, with an average AGB value of about 111.01 g⋅C/m2 over the entire western Songnen Plain. For the influence of precipitation and temperature, we found that the annual mean temperature had a smaller effect on the distribution of marsh AGB than that of the total precipitation in the western Songnen Plain. Increased precipitation in summer and autumn would increase AGB by promoting marshes' vegetation growth. In addition, we found that the minimum temperature (Tmin) and maximum temperatures (Tmax) have an asymmetric effect on marsh AGB on the western Songnen Plain: warming Tmax has a significant impact on AGB of marsh vegetation, while warming at night can non-significantly increase the AGB of marsh wetland. This research is expected to provide theoretical guidance for the restoration, protection, and adaptive management of wetland vegetation in the western Songnen Plain.

Spatiotemporal differences in climate change impacts on vegetation cover in China from 1982 to 2015

The impacts of climate change on vegetation cover in different regions in China are not entirely clear because of the interference of non-climatic factors, such as human activity. This study aims to analyze the spatiotemporal differences in climate impacts qualitatively and quantitatively by applying trend, correlation, and multiple linear regression (MLR) analyses to the data of Normalized Difference Vegetation Index (NDVI) and two climatic factors (air temperature and precipitation) during 1982-2015 in China. The MLR equation linking two climatic variables with NDVI was used to identify the NDVI trend caused by climate change. We demonstrated that the central and eastern regions of China, dominated by deciduous and evergreen broadleaf forests, experienced a rapid increase in NDVI from 1982 to 2015. The response of NDVI to variations in temperature and precipitation exhibited large spatiotemporal differences across China, which was closely related to climatic conditions and vegetation types. Overall, warming, particularly the sharp rise in spring, was the main climatic driving force behind China's NDVI increase, and precipitation also influenced the NDVI increase in temperate grassland and desert regions due to the relatively arid climate, particularly in summer. The contributions of climate change to the total NDVI trend (CC) showed a large spatiotemporal heterogeneity across China. Overall, only 45% of the pixels (with a resolution of 8 km) in the study area showed that the MLR equations between NDVI and two climatic factors were significant at the 0.05 significance level during the growing season (April-October), and the average CC of these pixels was 38%. Among the eight vegetation sub-regions of China, the temperate desert and Qinghai-Tibet Plateau alpine meadow regions generally exhibited relatively larger CCs than other vegetation sub-regions in different seasons. At a national scale, the regional average CC reached 64% during the growing season. These results at multiple scales can help to deeply understand the mechanisms of regional environmental variation and sustainability.

Exploring the long-term vegetation dynamics of different ecological zones in the farming-pastoral ecotone in northern China

The vegetation in the farming-pastoral ecotone in northern China is influenced by both natural and anthropogenic factors and has undergone drastic changes in the past decades. The farming-pastoral ecotone is the transition zone from agriculture to animal husbandry. The ecological environment of this ecotone is complex and fragile. Most researches have primarily focused on the entire farming-pastoral ecotone, seldomly considering the differences between different ecological zones characterized by soil, climate, and biome conditions. Based on the long time series of leaf area index (LAI) data, meteorological data, and land-use dataset, this study analyzed LAI variation trends, the correlations between LAI and climate factors, and the impact of land-use type change on vegetation in the farming-pastoral ecotone in northern China. Moreover, this paper makes a full study of the changes of the whole study area from the perspective of the differences between different ecological zones. The results showed that over 36 years, areas with vegetation improvements were considerably larger than those with degradations. However, there were still 49.56% of the total area showing no significant vegetation change. There are differences in vegetation change and response to climate between the forest ecological zones and the grassland ecological zones. The vegetation improvement trends of the forest ecological zones were larger and more sensitive to temperature, while the vegetation improvements of the grassland ecological zones were relatively small, and were more sensitive to precipitation. Human activities promote LAI changes in areas close to the forest ecological zones. The change of land use indicates that the decrease of the overall natural vegetation area has not resulted in decreasing LAI. And there is a growing trend of woodland area in the grassland ecological zones. The study provides a theoretical basis for the management of the environment and vegetation in the farming-pastoral ecotone in northern China.

Asymmetry of Daytime and Nighttime Warming in Typical Climatic Zones along the Eastern Coast of China and Its Influence on Vegetation Activities

In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons on vegetation activities during the growing season period according to the copula function theory optimized based on Markov chain Monte Carlo (MCMC). The main conclusions are as follows: (1) The seasonal daytime and nighttime warming trends of Guangdong, Jiangsu and Liaoning over the past 35 years were significant, and the daytime and nighttime warming rates were asymmetric. In spring and summer of Guangdong province, the warming rate in the daytime was higher than that at night, while, in autumn, the opposite law was observed. However, the warming rate in the daytime was lower than that at night in Jiangsu and Liaoning provinces. There were latitude differences in diurnal and nocturnal warming rate. (2) The daytime and nighttime warming influences on vegetation showed significant seasonal differences in these three regions. In Guangdong, the influence of nighttime warming on vegetation growth in spring is greater than that in summer, and the influences of daytime warming on vegetation growth from strong to weak were spring, summer and autumn. In Jiangsu, both the influences of daytime and nighttime warming on vegetation growth in summer were less than that in autumn. In Liaoning, both the influences of daytime and nighttime warming on vegetation growth from strong to weak were autumn, spring and summer. (3) In Guangdong, Jiangsu and Liaoning provinces, their maximum temperature (Tmax) and minimum temperature (Tmin) and the joint probability distribution functions of NDVI, all had little effect on NDVI when Tmax and Tmin respectively reached their minimum values, but their influences on NDVI were obvious when Tmax and Tmin respectively reached their maximum values. (4) The smaller the return period, the larger the range of climate factor and NDVI, which has indicated that when the climate factor is certain, the NDVI is more likely to have a smaller return period, and the frequency of NDVI over a certain period is higher. In addition, the larger the climate factor, the greater the return period is and NDVI is less frequent over a certain period of time. This research can help with deep understanding of the dynamic influence of seasonal daytime and nighttime asymmetric warming on the vegetation in typical coastal temperature zones of China under the background of global climate change.

Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors

Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.

Land–Sea Interaction: Integrating Climate Adaptation Planning and Maritime Spatial Planning in the North Adriatic Basin

Land–sea interaction dynamics are physiologically regulated by an exchange of matter (and energy) between the anthropic system and the natural environment. Therefore, the appropriate management of land–sea interaction (LSI)contexts should base on those planning approaches which can holistically support coastal development, such as Maritime Spatial Planning (MSP) and Climate Adaptation Planning (CAP). One of the main limiting factors for this integration is the fragmentation of existing databases and information sources, which compose the territorial knowledge framework. Investigations have sought to address the representation and assessment of “wicked” and interconnected coastal problems. The present research focuses on the production of the necessary information to fill sectorial knowledge gaps and to merge the available data into a single framework. The research methodology is based on remote sensing assessment techniques and is designed to be replicated in other coastal areas to integrate CAP and MSP. The output maps are a result of the empirical application of the integration of the assessment techniques and are meant to support local decision-making processes. The result aims at illustrating and highlighting the relationships between climate change impact vulnerabilities their spatial relation to marine resources and maritime activities. This can support effective actions aimed at environmental and urban protection, the organization of the uses of the sea and adaptation to climate impacts. The application of the assessment techniques is developed on a case study in the north Adriatic Basin. The Gulf of Trieste constitutes a representative case study for the Mediterranean Basin due to its transboundary nature. The relationship and the ongoing projects between Slovenia and Italy make the case study an interesting context in which to test and train the proposed integrated planning approach. Therefore, the study investigates local vulnerability to climate impacts, i.e., Urban Heat Island (UHI) and urban runoff, and the existing relationship between the urban fabrics and the marine environment.