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1.
Ying Yong Sheng Tai Xue Bao ; 34(11): 2929-2937, 2023 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-37997403

RESUMO

Ecological drought monitoring is important for regional status assessment and protection of water resources. In this study, we constructed a new ecological drought index, the kernel temperature vegetation drought index (kTVDI), by using the kernel normalized vegetation index (kNDVI) to improve the temperature vegetation drought index (TVDI) in Inner Mongolia. We further analyzed the spatial and temporal distribution of ecological drought in Inner Mongolia during 2000-2022 and the future trend of ecological drought by using segmented linear regression model, Theil-Sen median, Mann-Kendall test, and Hurst index. The results showed that kTVDI performed better in monitoring ecological drought than TVDI. From 2000 to 2022, kTVDI showed a decreasing trend in the growing season in Inner Mongolia, but the change was not significant, and a sudden change occurred in 2016, and the wetting trend after the sudden change was more obvious. During the study period, ecological drought in 23.6% of the areas of Inner Mongolia showed an aggravating trend, and ecological drought was alleviated in 46.5% of the area. In the future, ecological drought would be exacerbated in the eastern part but alleviated in the central and western parts of Inner Mongolia.


Assuntos
Mudança Climática , Secas , Temperatura , Estações do Ano , China , Previsões , Ecossistema
2.
Environ Res ; 236(Pt 1): 116643, 2023 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-37442253

RESUMO

Increased vegetation peak growth and phenological shifts toward spring have been observed in response to climate warming in the temperate regions. Such changes have the potential to modify warming by perturbing land‒atmosphere energy exchanges; however, the signs and magnitudes of biophysical feedback on surface temperature in different biomes are largely unknown. Here, we synthesized information from vegetation growth proxies, land surface temperature (LST), and surface energy balance factors (surface evapotranspiration (ET), albedo, and broadband emissivity (BBE)) to investigate the variations in timing (PPT) and productivity (PPmax) of seasonal peak photosynthesis and their time-lagged biophysical feedbacks to the post-season LST in Inner Mongolia (IM) during 2001-2020. We found that increased PPmax, rather than advanced PPT, exhibited a significant impact on LST, with divergent signs and magnitudes across diurnal periods and among different biomes. In the grassland biome, increased PPmax cooled both LST during daytime (LSTday) and nighttime (LSTnight) throughout the post-season period, with a more pronounced response during daytime and diminishing gradually from July to September. This cooling effect on LST was primarily attributed to enhanced ET, as evidenced by the greater effect of ET cooling than that of albedo warming and BBE cooling based on a structural equation model (SEM). In the forest biome, increased PPmax led to a symmetrical warming effect on LSTday and LSTnight, and none of the surface energy balance factors were identified as significant intermediate explanatory factors for the observed warming effect. Moreover, the responses of average LST (LSTmean) and diurnal temperature range of LST (LSTDTR) to variations in PPmax were consistent with those of LSTday at two biomes. The observations above elucidate the divergent feedback mechanisms of vegetation peak growth on LST among different biomes and diurnal cycles, which could facilitate the improvement of the realistic parameterization of surface processes in global climate models.

3.
Ying Yong Sheng Tai Xue Bao ; 32(7): 2534-2544, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34313072

RESUMO

Taking the Mongolian Plateau as the study area, the MODIS normalized difference vegetation index (NDVI) and the land surface temperature (LST) in the growing season from 2000 to 2019 were used to construct the NDVI-LST feature space, and based on which the temperature vege-tation dryness index (TVDI) of the Mongolian Plateau was calculated. We used Theil-Sen Median trend analysis, Mann-Kendall test, and Hurst index method to analyze the spatial and temporal varia-tions and future trends of TVDI on the Mongolian Plateau. Furthermore, we examined the relationship between meteorological factors and TVDI on the Mongolian Plateau using partial correlation analysis. The results showed that the TVDI of the Mongolian Plateau during 2000-2019 showed an increasing trend with a rate of 0.0001·a-1, indicating that the Mongolian Plateau's drought condition became heavier slightly in the last 20 years. The drought condition in meadow steppe and typical steppe gradually decreased, and that in desert steppe and alpine grassland was increased. The average Hurst index of TVDI in the growing season was 0.45, and the area with TVDI less than 0.5 accounted for 71.5% of the total area, which indicated that the TVDI during 2000-2019 in most areas turned opposite to the past. In the future, the drought condition in the central desert steppe area and the eastern meadow steppe area might increase, and that in most of the typical steppe and the desert steppe in Inner Mongolia tended to decrease. The drought change in the alpine grassland area was uncertain. There was a significant positive correlation between the TVDI and temperature in 33.6% area of the Mongolian Plateau and a significant negative correlation between the TVDI and precipitation in 34.8% of the area. Moreover, the meteorological factors heavily affected the typical steppe.


Assuntos
Secas , Conceitos Meteorológicos , China , Pradaria , Estações do Ano , Temperatura
4.
Sci Total Environ ; 668: 696-713, 2019 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-30856578

RESUMO

Remote sensing (RS)-based models play an important role in estimating and monitoring terrestrial ecosystem gross primary productivity (GPP). Several RS-based GPP models have been developed using different criteria, yet the sensitivities to environmental factors vary among models; thus, a comparison of model sensitivity is necessary for analyzing and interpreting results and for choosing suitable models. In this study, we globally evaluated and compared the sensitivities of 14 RS-based models (2 process-, 4 vegetation-index-, 5 light-use-efficiency, and 3 machine-learning-based models) and benchmarked them against GPP responses to climatic factors measured at flux sites and to elevated CO2 concentrations measured at free-air CO2 enrichment experiment sites. The results demonstrated that the models with relatively high sensitivity to increasing atmospheric CO2 concentrations showed a higher increasing GPP trend. The fundamental difference in the CO2 effect in the models' algorithm either considers the effect of CO2 through changes in greenness indices (nine models) or introduces the influences on photosynthesis (three models). The overall effects of temperature and radiation, in terms of both magnitude and sign, vary among the models, while the models respond relatively consistently to variations in precipitation. Spatially, larger differences among model sensitivity to climatic factors occur in the tropics; at high latitudes, models have a consistent and obvious positive response to variations in temperature and radiation, and precipitation significantly enhances the GPP in mid-latitudes. Compared with the results calculated by flux-site measurements, the model performance differed substantially among different sites. However, the sensitivities of most models are basically within the confidence interval of the flux-site results. In general, the comparison revealed that models differed substantially in the effect of environmental regulations, particularly CO2 fertilization and water stress, on GPP, and none of the models performed consistently better across the different ecosystems and under the various external conditions.

5.
Sci Total Environ ; 654: 850-862, 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-30448674

RESUMO

With intensification of climate change and human activities, warming and drying trend has brought severe challenges to pastoral areas in arid and semi-arid regions. Consequently, it becomes imperative to explore non-stationarity features of drought in such regions. In this research, the SPEIbase v2.4 datasets with a 0.5 degree spatial resolution was employed to extract Standardized Precipitation Evapotranspiration Index (SPEI) in Inner Mongolia, China. We explored non-stationarity characteristics of drought using Breaks For Additive Seasonal and Trend (BFAST) method, investigated the variation characteristics of drought intensity in each time interval using intensity analysis method, and finally assessed the spatial and temporal gathering characteristics of drought with Empirical Orthogonal Function (EOF). The results showed that trend of regional drought had a tendency towards drought conditions, which is particularly significant from the year 1945 onwards in the overall Inner Mongolia. We have explored a long behavior of drought in semiarid and central regions of cold semihumid climate zone throughout the whole study period, and detected a drying trend in northeastern regions of Inner Mongolia at the latter decades. The overall drought intensity displayed an increasing trend first, which was followed by a decreasing trend, among which the extreme drought was dominant in period of 1960-1970. EOF mode1 showed that variation intensity of drought showed a not significantly increasing trend in the entire region, and the drought with high amplitude was likely to occur in the central region. EOF mode2 showed that variation intensity of drought displayed the opposite phases between the eastern and the western regions. The northeastern regions were prone to display a high amplitude of drought.

6.
Environ Res ; 158: 245-254, 2017 10.
Artigo em Inglês | MEDLINE | ID: mdl-28662450

RESUMO

The extensive use of a geographic information system (GIS) and remote sensing in ecological risk assessment from a spatiotemporal perspective complements ecological environment management. Chromophoric dissolved organic matter (CDOM), which is a complex mixture of organic matter that can be estimated via remote sensing, carries and produces carcinogenic disinfection by-products and organic pollutants in various aquatic environments. This paper reports the first ecological risk assessment, which was conducted in 2016, of CDOM in the Yinma River watershed including riverine waters, reservoir waters, and urban waters. Referring to the risk formation theory of natural disaster, the entropy evaluation method and DPSIR (driving force-pressure-state-impact-response) framework were coupled to establish a hazard and vulnerability index with multisource data, i.e., meteorological, remote sensing, experimental, and socioeconomic data, of this watershed. This ecological vulnerability assessment indicator system contains 23 indicators with respect to ecological sensitivity, ecological pressure, and self-resilience. The characteristics of CDOM absorption parameters from different waters showed higher aromatic content and molecular weights in May because of increased terrestrial inputs. The assessment results indicated that the overall ecosystem risk in the study area was focused in the extremely, heavily, and moderately vulnerable regions. The ecological risk assessment results objectively reflect the regional ecological environment and demonstrate the potential of ecological risk assessment of pollutants over traditional chemical measurements.


Assuntos
Monitoramento Ambiental , Substâncias Húmicas/análise , Lagos/química , Rios/química , China , Cidades , Medição de Risco
7.
Int J Environ Res Public Health ; 12(2): 1703-25, 2015 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-25654772

RESUMO

Desertification is a typical disaster risk event in which human settlements and living environments are destroyed. Desertification Disaster Risk Assessment can control and prevent the occurrence and development of desertification disasters and reduce their adverse influence on human society. This study presents the methodology and procedure for risk assessment and zoning of desertification disasters in Horqin Sand Land. Based on natural disaster risk theory and the desertification disaster formation mechanism, the Desertification Disaster Risk Index (DDRI) combined hazard, exposure, vulnerability and restorability factors and was developed mainly by using multi-source data and the fuzzy comprehensive evaluation method. The results showed that high risk and middle risk areas account for 28% and 23% of the study area, respectively. They are distributed with an "S" type in the study area. Low risk and very low risk areas account for 21% and 10% of the study area, respectively. They are distributed in the west-central and southwestern parts. Very high risk areas account for 18% of the study area and are distributed in the northeastern parts. The results can be used to know the desertification disaster risk level. It has important theoretical and practical significance to prevention and control of desertification in Horqin Sand Land and even in Northern China.


Assuntos
Conservação dos Recursos Naturais , Desastres , Monitoramento Ambiental/métodos , Solo , China , Entropia , Política Ambiental , Sistemas de Informação Geográfica , Humanos , Modelos Teóricos , Medição de Risco
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