7,649 research outputs found
Drought events and their effects on vegetation productivity in China
Many parts of the world have experienced frequent and severe droughts during the last few decades. Most previous studies examined the effects of specific drought events on vegetation productivity. In this study, we characterized the drought events in China from 1982 to 2012 and assessed their effects on vegetation productivity inferred from satellite data. We first assessed the occurrence, spatial extent, frequency, and severity of drought using the Palmer Drought Severity Index (PDSI). We then examined the impacts of droughts on China\u27s terrestrial ecosystems using the Normalized Difference Vegetation Index (NDVI). During the period 1982β2012, China\u27s land area (%) experiencing drought showed an insignificant trend. However, the drought conditions had been more severe over most regions in northern parts of China since the end of the 1990s, indicating that droughts hit these regions more frequently due to the drier climate. The severe droughts substantially reduced annual and seasonal NDVI. The magnitude and direction of the detrended NDVI under drought stress varied with season and vegetation type. The inconsistency between the regional means of PDSI and detrended NDVI could be attributed to different responses of vegetation to drought and the timing, duration, severity, and lag effects of droughts. The negative effects of droughts on vegetation productivity were partly offset by the enhancement of plant growth resulting from factors such as lower cloudiness, warming climate, and human activities (e.g., afforestation, improved agricultural management practices)
Contributions of natural and human factors to increases in vegetation productivity in China
Increasing trends in vegetation productivity have been identified for the last three decades for many regions in the northern hemisphere including China. Multiple natural and human factors are possibly responsible for the increases in vegetation productivity, while their relative contributions remain unclear. Here we analyzed the long-term trends in vegetation productivity in China using the satellite-derived normalized difference vegetation index (NDVI) and assessed the relationships of NDVI with a suite of natural (air temperature, precipitation, photosynthetically active radiation (PAR), atmospheric carbon dioxide (CO2) concentrations, and nitrogen (N) deposition) and human (afforestation and improved agricultural management practices) factors. Overall, China exhibited an increasing trend in vegetation productivity with an increase of 2.7%. At the provincial scale, eleven provinces exhibited significant increases in vegetation productivity, and the majority of these provinces are located within the northern half of the country. At the national scale, annual air temperature was most closely related to NDVI and explained 36.8% of the variance in NDVI, followed by afforestation (25.5%) and crop yield (15.8%). Altogether, temperature, total forest plantation area, and crop yield explained 78.1% of the variance in vegetation productivity at the national scale, while precipitation, PAR, atmospheric CO2 concentrations, and N deposition made no significant contribution to the increases in vegetation productivity. At the provincial scale, each factor explained a part of the variance in NDVI for some provinces, and the increases in NDVI for many provinces could be attributed to the combined effects of multiple factors. Crop yield and PAR were correlated with NDVI for more provinces than were other factors, indicating that both elevated crop yield resulting from improved agricultural management practices and increasing diffuse radiation were more important than other factors in increasing vegetation productivity at the provincial scale. The relative effects of the natural and human factors on vegetation productivity varied with spatial scale. The true contributions of multiple factors can be obscured by the correlation among these variables, and it is essential to examine the contribution of each factor while controlling for other factors. Future changes in climate and human activities will likely have larger influences on vegetation productivity in China
Phenological response of vegetation to upstream river flow in the Heihe Rive basin by time series analysis of MODIS data
Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe River basin in northwestern China. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remotely sensed NDVI data acquired by Terra-MODIS are used to monitor the vegetation dynamic for a seven years period between 2000 and 2006. Due to cloud-contamination, atmospheric influence and different solar and viewing angles, however, the quality and consistence of time series of remotely sensed NDVI data are degraded. A Fourier Transform method β the Harmonic Analysis of Time Series (HANTS) algorithm β is used to reconstruct cloud- and noise-free NDVI time series data from the Terra-MODIS NDVI dataset. Modification is made on HANTS by adding additional parameters to deal with large data gaps in yearly time series in combination with a Temporal-Similarity-Statistics (TSS) method developed in this study to seek for initial values for the large gap periods. Secondly, the same Fourier Transform method is used to model time series of the vegetation phenology. The reconstructed cloud-free NDVI time series data are used to study the relationship between the water availability (i.e. the local precipitation and upstream water yield) and the evolution of vegetation conditions in Ejina Oasis from 2000 to 2006. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in the spring monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis. Warmer climate expressed by the degree-days showed positive influence on the vegetation phenology in particular during drier years. The time of maximum green-up is uniform throughout the oasis during wetter years, but showed a clear S-N gradient (downstream) during drier years
Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield
There has been growing evidence that vegetation greenness has been increasing in many parts of the northern middle and high latitudes including China during the last three to four decades. However, the effects of increasing vegetation greenness particularly afforestation on the hydrological cycle have been controversial. We used a process-based ecosystem model and a satellite-derived leaf area index (LAI) dataset to examine how the changes in vegetation greenness affected annual evapotranspiration (ET) and water yield for China over the period from 2000 to 2014. Significant trends in vegetation greenness were observed in 26.1% of China\u27s land area. We used two model simulations driven with original and detrended LAI, respectively, to assess the effects of vegetation \u27greening\u27 and \u27browning\u27 on terrestrial ET and water yield. On a per-pixel basis, vegetation greening increased annual ET and decreased water yield, while vegetation browning reduced ET and increased water yield. At the large river basin and national scales, the greening trends also had positive effects on annual ET and had negative effects on water yield. Our results showed that the effects of the changes in vegetation greenness on the hydrological cycle varied with spatial scale. Afforestation efforts perhaps should focus on southern China with larger water supply given the water crisis in northern China and the negative effects of vegetation greening on water yield. Future studies on the effects of the greenness changes on the hydrological cycle are needed to account for the feedbacks to the climate
Desertification
IPCC SPECIAL REPORT ON CLIMATE CHANGE AND LAND (SRCCL)
Chapter 3: Climate Change and Land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystem
Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River
Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management
Responses of seasonal indicators to extreme droughts in southwest China
Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region
Quantifying the Impact of Climate Change and Human Activities on Streamflow in a Semi-Arid Watershed with the Budyko Equation Incorporating Dynamic Vegetation Information
Understanding hydrological responses to climate change and land use and land cover change (LULCC) is important for water resource planning and management, especially for water-limited areas. The annual streamflow of the Wuding River Watershed (WRW), the largest sediment source of the Yellow River in China, has decreased significantly over the past 50 years at a rate of 5.2 mm/decade. Using the Budyko equation, this study investigated this decrease with the contributions from climate change and LULCC caused by human activities, which have intensified since 1999 due to Chinaβs Grain for Green Project (GFGP). The Budyko parameter that represents watershed characteristics was more reasonably configured and derived to improve the performance of the Budyko equation. Vegetation changes were included in the Budyko equation to further improve its simulations, and these changes showed a significant upward trend due to the GFGP based on satellite data. An improved decomposition method based on the Budyko equation was used to quantitatively separate the impact of climate change from that of LULCC on the streamflow in the WRW. Our results show that climate change generated a dominant effect on the streamflow and decreased it by 72.4% in the WRW. This climatic effect can be further explained with the drying trend of the Palmer Severity Drought Index, which was calculated based only on climate change information for the WRW. In the meantime, although human activities in this watershed have been very intense, especially since 1999, vegetation cover increase contributed a 27.6% decline to the streamflow, which played a secondary role in affecting hydrological processes in the WRW
The 2010 spring drought reduced primary productivity in southwestern China
Many parts of the world experience frequent and severe droughts. Summer drought can significantly reduce primary productivity and carbon sequestration capacity. The impacts of spring droughts, however, have received much less attention. A severe and sustained spring drought occurred in southwestern China in 2010. Here we examine the influence of this spring drought on the primary productivity of terrestrial ecosystems using data on climate, vegetation greenness and productivity. We first assess the spatial extent, duration and severity of the drought using precipitation data and the Palmer drought severity index. We then examine the impacts of the drought on terrestrial ecosystems using satellite data for the period 2000β2010. Our results show that the spring drought substantially reduced the enhanced vegetation index (EVI) and gross primary productivity (GPP) during spring 2010 (MarchβMay). Both EVI and GPP also substantially declined in the summer and did not fully recover from the drought stress until August. The drought reduced regional annual GPP and net primary productivity (NPP) in 2010 by 65 and 46 Tg C yrβ1, respectively. Both annual GPP and NPP in 2010 were the lowest over the period 2000β2010. The negative effects of the drought on annual primary productivity were partly offset by the remarkably high productivity in August and September caused by the exceptionally wet conditions in late summer and early fall and the farming practices adopted to mitigate drought effects. Our results show that, like summer droughts, spring droughts can also have significant impacts on vegetation productivity and terrestrial carbon cycling
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