scholarly journals Dynamic and synergistic influences of air temperature and rainfall on general flowering in a Bornean lowland tropical forest

2019 ◽  
Author(s):  
Masayuki Ushio ◽  
Yutaka Osada ◽  
Tomo’omi Kumagai ◽  
Tomonori Kume ◽  
Runi anak Sylvester Pungga ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Tropical Forests ◽  
Air Temperature ◽  
National Park ◽  
Molecular Techniques ◽  
Meteorological Variables ◽  
Cool Temperature ◽  
Large Spatial Scale ◽  
Theoretical Approaches ◽  
General Flowering

AbstractSupra-annually synchronized flowering events occurring in tropical forests in Southeast Asia, known as general flowering (GF), are “spectacular and mysterious” forest events. Recently, studies that combined novel molecular techniques and model-based theoretical approaches suggested that cool temperature and drought synergistically drove GF. Although the novel approaches advanced our understanding of GF, it is usually difficult to know whether the mathematical formulation reasonably well represents the complex and dynamic processes involved in GF. In the present study, we collected a 17-year set of community-wide phenology data during 1993 to 2011 from Lambir Hills National Park in Borneo, Malaysia, and analyzed it using a model-free approach, empirical dynamic modeling (EDM), that does not rely on specific assumptions about the underlying mechanisms, to overcome and complement the previous limitations. By using EDM, we found that GF in the forest in Lambir Hills National Park is synergistically driven by cool air temperature and drought, which is consistent with the previous studies. Also, we found that cumulative meteorological variables, rather than instantaneous values, drive GF with delayed effects, which is also consistent with the previous studies. Interestingly, the present study showed that effects of cumulative meteorological variables on GF changed through time, which implies that the relationship between GF and meteorological variables may be influenced by other factors such as plant/soil nutrient resource dynamics. Future studies integrating novel mathematical/statistical frameworks, long-term and large spatial scale ecosystem monitoring and molecular phenology data are promising for better understanding and fore-casting of GF events in tropical forests in Southeast Asia.

scholarly journals Drought and cool temperature cue general flowering synergistically in the aseasonal tropical forests of Southeast Asia

2019 ◽  
Vol 34 (1) ◽  
pp. 40-49 ◽  
Author(s):  
Akiko Satake ◽  
Yu‐Yun Chen ◽  
Christine Fletcher ◽  
Yoshiko Kosugi
Keyword(s):  
Southeast Asia ◽  
Tropical Forests ◽  
Cool Temperature ◽  
General Flowering

scholarly journals Inter-comparison and assessment of gridded climate products over tropical forests during the 2015/2016 El Niño

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170406 ◽  
Author(s):  
C. Burton ◽  
S. Rifai ◽  
Y. Malhi
Keyword(s):  
Southeast Asia ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Air Temperature ◽  
El Niño ◽  
El Nino ◽  
Congo Basin ◽  
Wide Range ◽  
Spatio Temporal ◽  
The Impact

To understand the impacts of extreme climate events, it is first necessary to understand the spatio-temporal characteristics of the event. Gridded climate products are frequently used to describe climate patterns but have been shown to perform poorly over data-sparse regions such as tropical forests. Often, they are uncritically employed in a wide range of studies linking tropical forest processes to large-scale climate variability. Here, we conduct an inter-comparison and assessment of near-surface air temperature fields supplied by four state-of-the-art reanalysis products, along with precipitation estimates supplied by four merged satellite-gauge rainfall products. Firstly, spatio-temporal patterns of temperature and precipitation anomalies during the 2015–2016 El Niño are shown for each product to characterize the impact of the El Niño on the tropical forest biomes of Equatorial Africa, Southeast Asia and South America. Using meteorological station data, a two-stage assessment is then conducted to determine which products most reliably model tropical climates during the 2015–2016 El Niño, and which perform best over the longer-term satellite observation period (1980–2016). Results suggest that eastern Amazonia, parts of the Congo Basin and mainland Southeast Asia all experienced significant monthly mean temperature anomalies during the El Niño, while northeastern Amazonia, eastern Borneo and southern New Guinea experienced significant precipitation deficits. Our results suggest ERA-Interim and MERRA2 are the most reliable air temperature datasets, while TRMM 3B42 V7 and CHIRPS v2.0 are the best-performing rainfall datasets. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

scholarly journals Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea de Almeida Brito ◽  
Heráclio Alves de Araújo ◽  
Gilney Figueira Zebende
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Meteorological Variables ◽  
Hourly Data ◽  
Cross Correlations

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

scholarly journals The Smart Forest Conundrum: Contextualizing Pitfalls of Sensors and AI in Conservation Science for Tropical Forests

2021 ◽  
Vol 14 ◽  
pp. 194008292110147
Author(s):  
Dipto Sarkar ◽  
Colin A. Chapman
Keyword(s):  
Tropical Forests ◽  
National Park ◽  
Critical Discussion ◽  
Research Field ◽  
Kibale National Park ◽  
Conservation Science ◽  
Field Station ◽  
Data Production ◽  
Global Corporations ◽  
The One

The term ‘smart forest’ is not yet common, but the proliferation of sensors, algorithms, and technocentric thinking in conservation, as in most other aspects of our lives, suggests we are at the brink of this evolution. While there has been some critical discussion about the value of using smart technology in conservation, a holistic discussion about the broader technological, social, and economic interactions involved with using big data, sensors, artificial intelligence, and global corporations is largely missing. Here, we explore the pitfalls that are useful to consider as forests are gradually converted to technological sites of data production for optimized biodiversity conservation and are consequently incorporated in the digital economy. We consider who are the enablers of the technologically enhanced forests and how the gradual operationalization of smart forests will impact the traditional stakeholders of conservation. We also look at the implications of carpeting forests with sensors and the type of questions that will be encouraged. To contextualize our arguments, we provide examples from our work in Kibale National Park, Uganda which hosts the one of the longest continuously running research field station in Africa.

Sucrose accumulation in sugarcane is influenced by temperature and genotype through the carbon source - sink balance

2010 ◽  
Vol 61 (2) ◽  
pp. 111 ◽  
Author(s):  
N. G. Inman-Bamber ◽  
G. D. Bonnett ◽  
M. F. Spillman ◽  
M. H. Hewitt ◽  
D. Glassop
Keyword(s):  
Water Stress ◽  
Molecular Techniques ◽  
Clonal Variation ◽  
Sink Strength ◽  
Extension Rate ◽  
Cool Temperature ◽  
Sucrose Accumulation ◽  
Temperature Regimes ◽  
New Research ◽  
Source Sink

While substantial effort has been expended on molecular techniques in an attempt to break through the apparent ceiling for sucrose content (SC) in sugarcane stalks, molecular processes and genetics limiting sucrose accumulation remain unclear. Our own studies indicate that limiting expansive growth with water stress will enhance sucrose accumulation in both low- and high-sucrose clones. Sucrose accumulation was largely explained (72%) by an equation with terms for photosynthesis, plant extension rate (PER), and plant number. New research was conducted to determine if this simple model stands when using temperature rather than water stress to perturb the source–sink balance. We also applied a thinning treatment to test the proposal implicit in this equation that SC will increase if competition between plants for photo-assimilate is reduced. Four clones from a segregating population representing extremes in SC were planted in pots and subjected to warm and cool temperature regimes in a glasshouse facility. A thinning treatment was imposed on half the pots by removing all but 6 shoots per pot. Temperature as a means of reducing sink strength seemed initially to be more successful than water regime because PER was 43% lower in the cool than in the hot regime while photosynthesis was only 14% less. PER was a good indicator of dry matter allocation to expansive growth, limited by water stress but not by temperature, because stalks tended to thicken in low temperature. Thinning had little effect on any of the attributes measured. Nevertheless the clonal variation in plant numbers and the response of PER to temperature helped to explain at least 69% of the variation in sucrose accumulation observed in this experiment. Thus the earlier model for sucrose accumulation appeared to be valid for the effect on sucrose accumulation of both temperature and water stress on the source–sink balance. The next step is to include internodes in models of assimilate partitioning to help understand the limiting steps in sucrose accumulation from the basics of source–sink dynamics.

scholarly journals Phylogenetic diversity correlated with above‐ground biomass production during forest succession: Evidence from tropical forests in Southeast Asia

2018 ◽  
Vol 107 (3) ◽  
pp. 1419-1432 ◽  
Author(s):  
Manichanh Satdichanh ◽  
Huaixia Ma ◽  
Kai Yan ◽  
Gbadamassi G.O. Dossa ◽  
Leigh Winowiecki ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Tropical Forests ◽  
Biomass Production ◽  
Phylogenetic Diversity ◽  
Forest Succession ◽  
Above Ground Biomass ◽  
Ground Biomass

scholarly journals A Note on the Flowering of Ajuga orientalis L. in Relation to Air Temperature in Mount Aenos (Cephalonia, Greece)

2018 ◽  
Vol 6 (3) ◽  
pp. 261-267 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Athanasios Kamoutsis ◽  
Aikaterini Chronopoulou-Sereli
Keyword(s):  
Plant Species ◽  
Air Temperature ◽  
Landscape Design ◽  
Point Of View ◽  
Meteorological Variables ◽  
Phenological Stage ◽  
Aesthetic Value ◽  
Beginning Of Flowering ◽  
Start Dates

Ajuga orientalis L. is a widespread plant species in many countries, such as Greece, Italy and Turkey, with promising aesthetic value in the field and in landscape design, but nothing is known about its phenology, from a detailed, quantitatively, point of view, in relation to meteorological variables. Thus, under the aforementioned context, the purpose of our work is the elucidation of part of the phenology of this plant, especially concerning its flowering. To achieve this, the phenological stage ‘Beginning of flowering’, in terms of its start dates (julian days), was investigated in relation to average air temperature (T) of March in two areas, Roudi and Kaboulieri at north-northwest and south-southeast slopes, respectively, of Mount Aenos, Cephalonia, Greece, for three successive years (2014-2016). From the analysis of the T of March, it was confirmed that Kaboulieri area was significantly warmer (P<0.05) than Roudi area by 0.8 oC both in 2014 and 2015, with a significantly earlier appearance (P<0.05) of ‘Beginning of flowering’ of A. orientalis in Kaboulieri, ranging from 9.1 (2015) to 10.9 (2014) julian days. The findings of our study could be used for the planning of an efficient preservation program process of the aforementioned plant species in a vulnerable mountainous environment, such as the Mount Aenos environment, as well as for its further exploitation as a decorative plant.

scholarly journals Meteorological factors responsible for the growth and development of sugarcane at two locations in Rio Grande do Sul, Brazil

2021 ◽  
Vol 51 (10) ◽  
Author(s):  
Fábio Miguel Knapp ◽  
Jaqueline Sgarbossa ◽  
Claiton Nardini ◽  
Denise Schmidt ◽  
Liliane Bárbara Tibolla ◽  
...  
Keyword(s):  
Air Temperature ◽  
Rio Grande ◽  
Morphological Characters ◽  
Temperature Tolerance ◽  
Stem Diameter ◽  
Meteorological Variables ◽  
Rio Grande Do Sul ◽  
Stem Height ◽  
Thermal Sum ◽  
Minimum Air Temperature

ABSTRACT: This study determined the meteorological variable that most contribute to the productivity of sugarcane stalks in the northwest and central regions of Rio Grande do Sul. The following sugarcane genotypes were used: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, and IAC87-3396. The UFSM cultivars originate from a mutation process in the breeding program conducted at the Federal University of Santa Maria, Frederico Westphalen campus, and have low temperature tolerance. The productivity-associated morphological characters included in the models were average stem diameter, average stem number per meter of furrow, and average stem height. The following meteorological variables were used: minimum air temperature, precipitation, incident solar radiation, and accumulated thermal sum. Pearson’s correlation, canonical correlations, and Stepwise regression were performed between morphological characters and meteorological variables: minimum air temperature had the greatest influence on sugarcane productivity in the studied regions, and accumulated thermal sum showed the highest correlation and contributed most to stem diameter and average stem height. Thus, the models indicated that the growth of sugarcane is positively associated with the accumulated thermal sum, and sugarcane can be cultivated at the studied regions.

Effect of input variables on rainfall-runoff modeling using a deep learning method

2021 ◽  
Author(s):  
Kazuki yokoo ◽  
Kei ishida ◽  
Takeyoshi nagasato ◽  
Ali Ercan
Keyword(s):  
Deep Learning ◽  
Air Temperature ◽  
Meteorological Variables ◽  
Learning Method ◽  
Rainfall Runoff ◽  
Model Accuracy ◽  
Runoff Modeling ◽  
Input Variables ◽  
Rainfall Runoff Model ◽  
Runoff Model

&lt;p&gt;In recent years, deep learning has been applied to various issues in natural science, including hydrology. These application results show its high applicability. There are some studies that performed rainfall-runoff modeling by means of a deep learning method, LSTM (Long Short-Term Memory). LSTM is a kind of RNN (Recurrent Neural Networks) that is suitable for modeling time series data with long-term dependence. These studies showed the capability of LSTM for rainfall-runoff modeling. However, there are few studies that investigate the effects of input variables on the estimation accuracy. Therefore, this study, investigated the effects of the selection of input variables on the accuracy of a rainfall-runoff model by means of LSTM. As the study watershed, this study selected a snow-dominated watershed, the Ishikari River basin, which is in the Hokkaido region of Japan. The flow discharge was obtained at a gauging station near the outlet of the river as the target data. For the input data to the model, Meteorological variables were obtained from an atmospheric reanalysis dataset, ERA5, in addition to the gridded precipitation dataset. The selected meteorological variables were air temperature, evaporation, longwave radiation, shortwave radiation, and mean sea level pressure. Then, the rainfall-runoff model was trained with several combinations of the input variables. After the training, the model accuracy was compared among the combinations. The use of meteorological variables in addition to precipitation and air temperature as input improved the model accuracy. In some cases, however, the model accuracy was worsened by using more variables as input. The results indicate the importance to select adequate variables as input for rainfall-runoff modeling by LSTM.&lt;/p&gt;

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