scholarly journals Quantifying the Impacts of Climate Change and Vegetation Variation on Actual Evapotranspiration Based on the Budyko Hypothesis in North and South Panjiang Basin, China

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 508 ◽  
Author(s):  
Tiansheng Li ◽  
Jun Xia ◽  
Dunxian She ◽  
Lei Cheng ◽  
Lei Zou ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climatic Factors ◽  
Actual Evapotranspiration ◽  
Maximum Temperature ◽  
Linear Regression Method ◽  
North And South ◽  
Average Contribution ◽  
Vegetation Variation ◽  
Impacts Of Climate Change

Actual evapotranspiration (Ea) plays a key role in the global water and energy cycles. The accurate quantification of the impacts of different factors on Ea change can help us better understand the driving mechanisms of Ea in a changing environment. Climate change and vegetation variations are well known as two main factors that have significant impacts on Ea change. Our study used three differential Budyko-type equations to quantify the contributions of climate change and vegetation variations to Ea change. First, in order to establish the relationship between the parameter n, which usually presents the land surface characteristics in the Budyko-type equations, with basic climatic variables and the Normalized Difference Vegetation Index (NDVI), the stepwise linear regression method has been used. Then, elasticity and contribution analyses were performed to quantify the contributions of different numbers of climatic factors and NDVI to Ea change. The North and South Panjiang basin in China was selected to investigate the efficiency of the modified Budyko-type equations and quantify the impacts of climate change and vegetation variations on Ea change. The empirical formal of the parameter n established in this study can be used to simulate the parameter n and Ea for the study area. The results of the elasticity and contribution analyses suggest that climate change contributed (whose average contribution is 149.6%) more to Ea change than vegetation variation (whose average contribution is −49.4%). Precipitation, NDVI and the maximum temperature are the major drivers of Ea change, while the minimum temperature and wind speed contribute the least to Ea change.

scholarly journals Direct Impacts of Climate Change and Indirect Impacts of Non-Climate Change on Land Surface Phenology Variation across Northern China

2018 ◽  
Vol 7 (11) ◽  
pp. 451 ◽  
Author(s):  
Zhaohui Luo ◽  
Qingmei Song ◽  
Tao Wang ◽  
Huanmu Zeng ◽  
Tao He ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Northern China ◽  
Ecosystem Dynamics ◽  
Land Surface Phenology ◽  
Mean Values ◽  
Combined Impact ◽  
Impacts Of Climate Change

Land surface phenology (LSP) is a sensitive indicator of climate change. Understanding the variation in LSP under various impacts can improve our knowledge on ecosystem dynamics and biosphere-atmosphere interactions. Over recent decades, LSP derived from remote sensing data and climate change-related variation of LSP have been widely reported at the regional and global scales. However, the smoothing methods of the vegetation index (i.e., NDVI) are diverse, and discrepancies among methods may result in different results. Additionally, LSP is affected by climate change and non-climate change simultaneously. However, few studies have focused on the isolated impacts of climate change and the impacts of non-climate change on LSP variation. In this study, four methods were applied to reconstruct the MODIS enhanced vegetation index (EVI) dataset to choose the best smoothing result to estimate LSP. Subsequently, the variation in the start of season (SOS) and end of season (EOS) under isolated impacts of climate change were analyzed. Furthermore, the indirect effects of isolated impacts of non-climate change were conducted based on the differences between the combined impact (the impacts of both climate change and non-climate change) and isolated impacts of climate change. Our results indicated that the Savitzky-Golay method is the best method of the four for smoothing EVI in Northern China. Additionally, SOS displayed an advanced trend under the impacts of both climate change and non-climate change (hereafter called the combined impact), isolated impacts of climate change, and isolated impacts of non-climate change, with mean values of −0.26, −0.07, and −0.17 days per year, respectively. Moreover, the trend of SOS continued after 2000, but the magnitudes of changes in SOS after 2000 were lower than those that were estimated over the last two decades of the twentieth century (previous studies). EOS showed a delayed trend under the combined impact and isolated impacts of non-climate change, with mean values of 0.41 and 0.43 days per year, respectively. However, EOS advanced with a mean value of −0.16 days per year under the isolated impacts of climate change. Furthermore, the absolute mean values of SOS and EOS trends under the isolated impacts of non-climate change were larger than that of the isolated impacts of climate change, indicating that the effect of non-climate change on LSP variation was larger than that of climate change. With regard to the relative contribution of climatic factors to the variation in SOS and EOS, the proportion of solar radiation was the largest for both SOS and EOS, followed by precipitation and temperature.

Aridity Changes and Related Climatic Drivers in the Drylands of China during 1960–2019

2021 ◽  
Vol 60 (4) ◽  
pp. 607-617
Author(s):  
Jinqin Xu ◽  
Yan Zeng ◽  
Xinfa Qiu ◽  
Yongjian He ◽  
Guoping Shi ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Land Surface ◽  
Arid Regions ◽  
Climatic Factors ◽  
Sunshine Duration ◽  
Aridity Index ◽  
Maximum Temperature ◽  
The Impact ◽  
Precipitation And Temperature

AbstractDrylands cover about one-half of the land surface in China and are highly sensitive to climate change. Understanding climate change and its impact drivers on dryland is essential for supporting dryland planning and sustainable development. Using meteorological observations for 1960–2019, the aridity changes in drylands of China were evaluated using aridity index (AI), and the impact of various climatic factors [i.e., precipitation P; sunshine duration (SSD); relative humidity (RH); maximum temperature (Tmax); minimum temperature (Tmin); wind speed (WS)] on the aridity changes was decomposed and quantified. Results of trend analysis based on Sen’s slope estimator and Mann–Kendall test indicated that the aridity trends were very weak when averaged over the whole drylands in China during 1960–2019 but exhibited a significant wetting trend in hyperarid and arid regions of drylands. The AI was most sensitive to changes in water factors (i.e., P and RH), followed by SSD, Tmax, and WS, but the sensitivity of AI to Tmin was very small and negligible. Interestingly, the dominant climatic driver to AI change varied in the four dryland subtypes. The significantly increased P dominated the increase in AI in the hyperarid and arid regions. The significantly reduced WS and the significantly increased Tmax contributed more to AI changes than the P in the semiarid and dry subhumid regions of drylands. Previous studies emphasized the impact of precipitation and temperature on the global or regional dry–wet changes; however, the findings of this study suggest that, beyond precipitation and temperature, the impact of wind speed on aridity changes of drylands in China should be given equal attention.

scholarly journals Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peixin Ren ◽  
Zelin Liu ◽  
Xiaolu Zhou ◽  
Changhui Peng ◽  
Jingfeng Xiao ◽  
...  
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Time Lag ◽  
Evergreen Forest ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Study Region ◽  
Cumulative Precipitation

Abstract Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

scholarly journals Impact of Climate Change on Water Availability and Food Security of Nepal

2012 ◽  
pp. 59-63
Author(s):  
Roshan Kumar Mehta ◽  
Shree Chandra Shah
Keyword(s):  
Climate Change ◽  
Crop Production ◽  
Climatic Factors ◽  
Monsoon Season ◽  
Groundwater Table ◽  
Maximum Temperature ◽  
Aquatic Life ◽  
Temperature And Precipitation ◽  
Average Maximum ◽  
Water Scarce

The increase in the concentration of greenhouse gases (GHGs) in the atmosphere is widely believed to be causing climate change. It affects agriculture, forestry, human health, biodiversity, and snow cover and aquatic life. Changes in climatic factors like temperature, solar radiation and precipitation have potential to influence agrobiodiversity and its production. An average of 0.04°C/ year and 0.82 mm/year rise in annual average maximum temperature and precipitation respectively from 1975 to 2006 has been recorded in Nepal. Frequent droughts, rise in temperature, shortening of the monsoon season with high intensity rainfall, severe floods, landslides and mixed effects on agricultural biodiversity have been experienced in Nepal due to climatic changes. A survey done in the Chitwan District reveals that lowering of the groundwater table decreases production and that farmers are attracted to grow less water consuming crops during water scarce season. The groundwater table in the study area has lowered nearly one meter from that of 15 years ago as experienced by the farmers. Traditional varieties of rice have been replaced in the last 10 years by modern varieties, and by agricultural crops which demand more water for cultivation. The application of groundwater for irrigation has increased the cost of production and caused severe negative impacts on marginal crop production and agro-biodiversity. It is timely that suitable adaptive measures are identified in order to make Nepalese agriculture more resistant to the adverse impacts of climate change, especially those caused by erratic weather patterns such as the ones experienced recently.DOI: http://dx.doi.org/10.3126/hn.v11i1.7206 Hydro Nepal Special Issue: Conference Proceedings 2012 pp.59-63

scholarly journals An Analysis of Land Surface Temperature Trends in the Central Himalayan Region Based on MODIS Products

2019 ◽  
Vol 11 (8) ◽  
pp. 900 ◽  
Author(s):  
Wei Zhao ◽  
Juelin He ◽  
Yanhong Wu ◽  
Donghong Xiong ◽  
Fengping Wen ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Environment ◽  
Himalayan Region ◽  
Maximum Temperature ◽  
Temperature Cycle ◽  
Increasing Trend ◽  
Nighttime Temperatures

The scientific community has widely reported the impacts of climate change on the Central Himalaya. To qualify and quantify these effects, long-term land surface temperature observations in both the daytime and nighttime, acquired by the Moderate Resolution Imaging Spectroradiometer from 2000 to 2017, were used in this study to investigate the spatiotemporal variations and their changing mechanism. Two periodic parameters, the mean annual surface temperature (MAST) and the annual maximum temperature (MAXT), were derived based on an annual temperature cycle model to reduce the influences from the cloud cover and were used to analyze their trend during the period. The general thermal environment represented by the average MAST indicated a significant spatial distribution pattern along with the elevation gradient. Behind the clear differences in the daytime and nighttime temperatures at different physiographical regions, the trend test conducted with the Mann-Kendall (MK) method showed that most of the areas with significant changes showed an increasing trend, and the nighttime temperatures exhibited a more significant increasing trend than the daytime temperatures, for both the MAST and MAXT, according to the changing areas. The nighttime changing areas were more widely distributed (more than 28%) than the daytime changing areas (around 10%). The average change rates of the MAST and MAXT in the daytime are 0.102 °C/yr and 0.190 °C/yr, and they are generally faster than those in the nighttime (0.048 °C/yr and 0.091 °C/yr, respectively). The driving force analysis suggested that urban expansion, shifts in the courses of lowland rivers, and the retreat of both the snow and glacier cover presented strong effects on the local thermal environment, in addition to the climatic warming effect. Moreover, the strong topographic gradient greatly influenced the change rate and evidenced a significant elevation-dependent warming effect, especially for the nighttime LST. Generally, this study suggested that the nighttime temperature was more sensitive to climate change than the daytime temperature, and this general warming trend clearly observed in the central Himalayan region could have important influences on local geophysical, hydrological, and ecological processes.

Evaluation of the Impacts of Climate Change on Land Suitability for Jatropha Cultivation

2014 ◽  
Vol 955-959 ◽  
pp. 3777-3782 ◽  
Author(s):  
Xiao Feng Zhao ◽  
Bin Le Lin
Keyword(s):  
Climate Change ◽  
Rank Order ◽  
Global Scale ◽  
Land Suitability ◽  
Climate Change Scenarios ◽  
Climate Scenarios ◽  
The West ◽  
The North ◽  
North And South ◽  
Impacts Of Climate Change

We evaluated land suitability for Jatropha cultivation at a global scale under current and future climate scenarios. Areas that are suitable for Jatropha cultivation include southern South America, the west and southeast coasts of Africa, the north of South Asia, and the north and south coasts of Australia. In the predicted climate change scenarios, areas near the equator become less suitable for Jatropha cultivation, and areas further from the equator become more suitable. Our analyses suggest that the rank order of the six climate change scenarios, from the smallest to the largest effects on Jatropha cultivation, was as follows: B1, A1T/B2, A1B, A2, and A1FI.

scholarly journals Integrating Statistical and System Dynamics Modelling to Analyse the Impacts of Climate Change on Rice Production in West Nusa Tenggara, Indonesia

2021 ◽  
Author(s):  
◽  
Muhamad Bahri
Keyword(s):  
Climate Change ◽  
System Dynamics ◽  
Statistical Models ◽  
Rice Yield ◽  
Rice Production ◽  
Maximum Temperature ◽  
Dynamics Model ◽  
Rice Varieties ◽  
Changing Climate ◽  
Impacts Of Climate Change

<p>Climate change, manifested as temperature rise and rainfall change, will pose significant challenges to rice farmers, leading to a possible rice shortage under a changing climate. This research aims to understand the impacts of climate variability and change on rice production through the rest of this century using Representative Concentration Pathway (RCP) scenarios, and combination of statistical and system dynamic modelling. The area of study is West Nusa Tenggara, Indonesia. Wetland and dryland farming types are assessed separately because they have different rice varieties and different agricultural practices.  Overall, the research seeks to answer the question: How will climate change and climate variability affect rice production? Additional questions investigated are (1) What are the most significant supply uncertainties associated with a changing climate? and (2) What are possible solutions for reducing the impacts of climate change on rice production?. To answer these research questions, this study deals with three main research areas. First, based on observed data (1976-2011), this study developed regression-based statistical models in understanding the impacts of climate change on rice yield in West Nusa Tenggara. Statistical models find that the negative impacts of increased minimum temperature on rice yield are statistically significant.   By contrast, the effects of maximum temperature on rice yield are not statistically significant. A key reason for this is that the highest maximum temperature (32⁰C) in the observed period (1976-2011) was lower than 35⁰C, a rice threshold for maximum temperature. By 2090 (2077-2100), rice yield in wetland and dryland is projected to decrease by about 3% (RCP2.6 scenario), 4% (RCP4.5 scenario), 5% (RCP6.0 scenario) and 14% (RCP8.5 scenario).  Second, a system dynamics model was developed to assess the impacts of climate change on three issues including rice yield, harvested areas and rice production by 2090 (2077-2100). After embedding statistical models and estimating the impacts of maximum temperature on rice yield based on existing studies, the impacts of climate change on rice yield are projected. The system dynamics model is also equipped by August SOI to estimate the impacts of climate change on the timing of monsoon onset i.e the beginning of planting seasons. For assessing harvested areas under a changing climate, the system dynamics model is equipped by a mathematical relationship between seasonal rainfall and harvested areas.  Because the system dynamics model includes the impacts of high maximum temperature, the projected loss of rice yield in wetland and dryland is relatively higher compared to that in statistical models. It is projected that rice yield loss will be about 3% (RCP2.6 scenario), 6% (RCP4.5 scenario), 10% (RCP6.0 scenario) and 23% (RCP8.5 scenario) by 2090 (2077-2100). Likewise, rice production loss in wetland and dryland is projected to be about 1% (RCP2.6 scenario), 2% (RCP4.5 scenario), 7% (RCP6.0 scenario) and 19% (RCP8.5 scenario) by 2090 (2077-2100). The projected loss of rice production is relatively lower than rice yield loss as wetland harvested areas are projected to experience a slight increase about 3% by 2090 (2077-2100) under a changing climate. This also means that the ranking of the impacts of climate change from the most significant to the least significant is its impact on rice yield, rice production and harvested areas.   Third, policy options in overcoming the impacts of climate change on rice production are assessed. This study suggests that research on finding rice varieties with three main traits: heat tolerance, short growth duration and high yield is key to balance rice demand and rice supply in West Nusa Tenggara by 2090 (2077-2100). A failure to improve rice yield in such ways is likely to lead to significant reductions in rice supply in the face of climate change.  This study makes theoretical contributions, including the development of statistical models for understanding the impacts of climate change on rice yield and a causal system for investigating the impacts of climate change on rice yield, rice production and harvested areas. Again, the combination of statistical and system dynamics modelling simultaneously investigates the impacts of climate change on rice yield, rice production and harvested areas. This means that this study provides a more holistic view of the impacts of climate change compared to existing studies.  This study also offers practical contributions, advising that declining rice research should be avoided under a changing climate, and suggesting that farming intensification (more climate-resilient rice varieties) is more effective than farming extension (area expansion) in sustaining rice production under a changing climate. Again, research on developing more resilient-climate rice varieties is possible as projected rice yield in sustaining rice production by 2090 (2077-2100) is similar to rice’s yield potential.</p>

scholarly journals Vertical Differences in the Long-Term Trends and Breakpoints of NDVI and Climate Factors in Taiwan

2021 ◽  
Vol 13 (22) ◽  
pp. 4707
Author(s):  
Hui Ping Tsai ◽  
Geng-Gui Wang ◽  
Zhong-Han Zhuang
Keyword(s):  
Climate Change ◽  
Minimum Temperature ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Positive Effects ◽  
Central Regions ◽  
Long Term Trends

This study explored the long-term trends and breakpoints of vegetation, rainfall, and temperature in Taiwan from overall and regional perspectives in terms of vertical differences from 1982 to 2012. With time-series Advanced Very-High-Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data and Taiwan Climate Change Estimate and Information Platform (TCCIP) gridded monthly climatic data, their vertical dynamics were investigated by employing the Breaks for Additive Seasonal and Trend (BFAST) algorithm, Pearson’s correlation analysis, and the Durbin–Watson test. The vertical differences in NDVI values presented three breakpoints and a consistent trend from positive (1982 to 1989) to negative at varied rates, and then gradually increased after 2000. In addition, a positive rainfall trend was discovered. Average and maximum temperature had similar increasing trends, while minimum temperature showed variations, especially at higher altitudes. In terms of regional variations, the vegetation growth was stable in the north but worse in the central region. Higher elevations revealed larger variations in the NDVI and temperature datasets. NDVI, along with average and minimum temperature, showed their largest changes earlier in higher altitude areas. Specifically, the increasing minimum temperature direction was more prominent in the mid-to-high-altitude areas in the eastern and central regions. Seasonal variations were observed for each region. The difference between the dry and wet seasons is becoming larger, with the smallest difference in the northern region and the largest difference in the southern region. Taiwan’s NDVI and climatic factors have a significant negative correlation (p < 0.05), but the maximum and minimum temperatures have significant positive effects at low altitudes below 500 m. The northern and central regions reveal similar responses, while the south and east display different feedbacks. The results illuminate climate change evidence from assessment of the long-term dynamics of vegetation and climatic factors, providing valuable references for establishing correspondent climate-adaptive strategies in Taiwan.

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