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 ◽  
...  

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.

2021 ◽  
Vol 13 (22) ◽  
pp. 4707
Author(s):  
Hui Ping Tsai ◽  
Geng-Gui Wang ◽  
Zhong-Han Zhuang

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.


Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.


Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 489
Author(s):  
Jinxiu Liu ◽  
Weihao Shen ◽  
Yaqian He

India has experienced extensive land cover and land use change (LCLUC). However, there is still limited empirical research regarding the impact of LCLUC on climate extremes in India. Here, we applied statistical methods to assess how cropland expansion has influenced temperature extremes in India from 1982 to 2015 using a new land cover and land use dataset and ECMWF Reanalysis V5 (ERA5) climate data. Our results show that during the last 34 years, croplands in western India increased by ~33.7 percentage points. This cropland expansion shows a significantly negative impact on the maxima of daily maximum temperature (TXx), while its impacts on the maxima of daily minimum temperature and the minima of daily maximum and minimum temperature are limited. It is estimated that if cropland expansion had not taken place in western India over the 1982 to 2015 period, TXx would likely have increased by 0.74 (±0.64) °C. The negative impact of croplands on reducing the TXx extreme is likely due to evaporative cooling from intensified evapotranspiration associated with croplands, resulting in increased latent heat flux and decreased sensible heat flux. This study underscores the important influences of cropland expansion on temperature extremes and can be applicable to other geographic regions experiencing LCLUC.


2005 ◽  
Vol 18 (23) ◽  
pp. 5011-5023 ◽  
Author(s):  
L. A. Vincent ◽  
T. C. Peterson ◽  
V. R. Barros ◽  
M. B. Marino ◽  
M. Rusticucci ◽  
...  

Abstract A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America.


2021 ◽  
Author(s):  
Mastawesha Misganaw Engdaw ◽  
Andrew Ballinger ◽  
Gabriele Hegerl ◽  
Andrea Steiner

&lt;p&gt;In this study, we aim at quantifying the contribution of different forcings to changes in temperature extremes over 1981&amp;#8211;2020 using CMIP6 climate model simulations. We first assess the changes in extreme hot and cold temperatures defined as days below 10% and above 90% of daily minimum temperature (TN10 and TN90) and daily maximum temperature (TX10 and TX90). We compute the change in percentage of extreme days per season for October-March (ONDJFM) and April-September (AMJJAS). Spatial and temporal trends are quantified using multi-model mean of all-forcings simulations. The same indices will be computed from aerosols-, greenhouse gases- and natural-only forcing simulations. The trends estimated from all-forcings simulations are then attributed to different forcings (aerosols-, greenhouse gases-, and natural-only) by considering uncertainties not only in amplitude but also in response patterns of climate models. The new statistical approach to climate change detection and attribution method by Ribes et al. (2017) is used to quantify the contribution of human-induced climate change. Preliminary results of the attribution analysis show that anthropogenic climate change has the largest contribution to the changes in temperature extremes in different regions of the world.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; climate change, temperature, extreme events, attribution, CMIP6&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgement:&lt;/strong&gt; This work was funded by the Austrian Science Fund (FWF) under Research Grant W1256 (Doctoral Programme Climate Change: Uncertainties, Thresholds and Coping Strategies)&lt;/p&gt;


Author(s):  
Kanawut Chattrairat ◽  
Waranyu Wongseree ◽  
Adisorn Leelasantitham

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.


Author(s):  
Roshan Kumar Mehta ◽  
Shree Chandra Shah

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


2018 ◽  
Vol 50 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Lei Chen ◽  
Jianxia Chang ◽  
Yimin Wang ◽  
Yuelu Zhu

Abstract An accurate grasp of the influence of precipitation and temperature changes on the variation in both the magnitude and temporal patterns of runoff is crucial to the prevention of floods and droughts. However, there is a general lack of understanding of the ways in which runoff sensitivities to precipitation and temperature changes are associated with the CMIP5 scenarios. This paper investigates the hydrological response to future climate change under CMIP5 RCP scenarios by using the Variable Infiltration Capacity (VIC) model and then quantitatively assesses runoff sensitivities to precipitation and temperature changes under different scenarios by using a set of simulations with the control variable method. The source region of the Yellow River (SRYR) is an ideal area to study this problem. The results demonstrated that the precipitation effect was the dominant element influencing runoff change (the degree of influence approaching 23%), followed by maximum temperature (approaching 12%). The weakest element was minimum temperature (approaching 3%), despite the fact that the increases in minimum temperature were higher than the increases in maximum temperature. The results also indicated that the degree of runoff sensitivity to precipitation and temperature changes was subject to changing external climatic conditions.


Climate ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 165
Author(s):  
Prem B. Parajuli ◽  
Avay Risal

This study evaluated changes in climatic variable impacts on hydrology and water quality in Big Sunflower River Watershed (BSRW), Mississippi. Site-specific future time-series precipitation, temperature, and solar radiation data were generated using a stochastic weather generator LARS-WG model. For the generation of climate scenarios, Representative Concentration Pathways (RCPs), 4.5 and 8.5 of Global Circulation Models (GCMs): Hadley Center Global Environmental Model (HadGEM) and EC-EARTH, for three (2021–2040, 2041–2060 and 2061–2080) future climate periods. Analysis of future climate data based on six ground weather stations located within BSRW showed that the minimum temperature ranged from 11.9 °C to 15.9 °C and the maximum temperature ranged from 23.2 °C to 28.3 °C. Similarly, the average daily rainfall ranged from 3.6 mm to 4.3 mm. Analysis of changes in monthly average maximum/minimum temperature showed that January had the maximum increment and July/August had a minimum increment in monthly average temperature. Similarly, maximum increase in monthly average rainfall was observed during May and maximum decrease was observed during September. The average monthly streamflow, sediment, TN, and TP loads under different climate scenarios varied significantly. The change in average TN and TP loads due to climate change were observed to be very high compared to the change in streamflow and sediment load. The monthly average nutrient load under two different RCP scenarios varied greatly from as low as 63% to as high as 184%, compared to the current monthly nutrient load. The change in hydrology and water quality was mainly attributed to changes in surface temperature, precipitation, and stream flow. This study can be useful in the development and implementation of climate change smart management of agricultural watersheds.


Author(s):  
AWO Sourou Malikiyou ◽  
ALE Agbachi Georges ◽  
YABI Ibouraïma

La variabilité climatique dans les communes de Djidja et de Djougou engendre des conséquences aussi bien sur les niveaux de productivités, de production que sur les revenus des exploitants agricoles. L’objectif de cette recherche est d’étudier la vulnérabilité future des systèmes de productions agricoles face aux changements climatiques dans les Communes de Djidja et de Djougou.L’approche méthodologique utilisée comprend la collecte des données, leur traitement et l’analyse des résultats. Les enquêtes ont été faites dans les villages choisis sur la base de critères bien définis (la taille de la population agricole et son implication dans la production agricole). La méthode de D. Schwartz (1995, p. 94) a permis de constituer l’échantillon de 377 producteurs. Enfin, une projection climatique sur la période 2019-2050/2075 est faite au moyen du logiciel climatique « Climate explorer ».Il ressort des résultats de l’étude que, dans la commune de Djougou, la variation au niveau de la température minimale actuelle (RCP8.5) est comprise entre -1,62°C en 1992 et 2,29°C en 2075. La température maximale quant à elle varie entre -1,40°C en 1994 à 2,18°C en 2075. C’est à partir de 2071 que l’augmentation de la température minimale va dépasser les 2°C et si rien n’est fait cette hausse va s’accroître et devenir permanente. De même, dans la commune de Djidja, la température minimale la plus élevée est observée en 2075 avec des variations de 1 à 2°C pour les RCP4.5 et RCP8.5. Au niveau de la température maximale, l’année la moins chaude est 1992 (-1,33mm/jour) pour RCP8.5 et 1991 (-1,02mm/jour) pour RCP4.5. La même évolution s’observe au niveau des températures maximales. L’année 1992 reste la plus déficitaire avec une chute de -1,60°C et l’année la plus excédentaire sera l’année 2075 avec une hausse de 2,18 mm par jour, sur la période 1992-2080. La corrélation est observée en 2042 avec une valeur de 0,322 mm par jour. L’examen des résultats révèle que les valeurs des paramètres climatiques à savoir précipitations et évaporation sont à la hausse sur la période 1980-2080 dans la commune de Djidja. Suivant la trajectoire actuelle, RCP8.5, les années les plus arrosées sont 2037, 2070 et 2073 avec respectivement des variations égales à 0,17mm et 0,27mm de pluie par jour. Face à ces difficultés, les populations agricoles adoptent des mesures pour contrer les contraintes climatiques.ABSTRACTClimatic variability in the communes of Djidja and Djougou has consequences both on the levels of productivity and production and on the income of farmers. The objective of this research is to study the vulnerability of agricultural production systems to climate change in the Communes of Djidja and Djougou.The methodological approach used includes data collection, processing and analysis of the results. The surveys were carried out in the villages chosen on the basis of well-defined criteria (the size of the agricultural population and its involvement in agricultural production). The method of D. Schwartz (1995, p. 94) made it possible to constitute the sample of 377 producers. Finally, a climate projection over the period 2019-2050 / 2075 is made using the climate software "Climate explorer".The results of the study show that, in the municipality of Djougou, the variation in the current minimum temperature (RCP8.5) is between -1.62 ° C in 1992 and 2.29 ° C in 2075. The maximum temperature varies between -1.40 ° C in 1994 to 2.18 ° C in 2075. It is from 2071 that the increase in the minimum temperature will exceed 2 ° C and if nothing is In fact, this increase will increase and become permanent. Similarly, in the municipality of Djidja, the highest minimum temperature is observed in 2075 withvariations of 1 to 2 ° C for RCP4.5 and RCP8.5. At maximum temperature, the coolest year is 1992 (-1.33mm / day) for RCP8.5 and 1991 (-1.02mm / day) for RCP4.5. The same development can be observed at the level of maximum temperatures. The year 1992 remains the most in deficit with a fall of -1.60 ° C and the year the most in surplus will be the year 2075 with an increase of 2.18mm per day, over the period 1992-2080. The correlation is observed in 2042 with a value of 0.322 mm per day. Examination of the results reveals that the values of climatic parameters, namely precipitation and evaporation, are on the rise over the period 1980-2080 in the municipality of Djidja. Following the current trajectory, RCP8.5, the wettest years are 2037, 2070 and 2073 with respectively variations equal to 0.17mm and 0.27mm of rain per day. Faced with these difficulties, agricultural populations are adopting measures to counter climatic constraints. Keywords: Djidja, Djougou, vulnerability, production system, agriculture, climate change.


Sign in / Sign up

Export Citation Format

Share Document