Near-Ground-Based Optical Plant Phenology Measurements at China Ecological Meteorology Sites

2021 ◽  
Author(s):  
Dasheng Yang ◽  
Shilin Cui ◽  
Benzhi Zhang ◽  
Dongli Wu ◽  
Yong Lei ◽  
...  
Keyword(s):  
Climate Change ◽  
Plant Community ◽  
Image Sensor ◽  
Plant Phenology ◽  
Observation Data ◽  
Meteorological Observation ◽  
Phenological Model ◽  
Near Surface ◽  
Great Ability ◽  
Observation Network

<p><strong>Abstract</strong> Climate change is a hot issue in the global scale. The some varieties of phenological phase of plants (trees, grasslands and crops et al.) can directly and objectively reflected climate change and Commonly, the response of plant phenology to climate change is sensitive, especially to climatic factors such as precipitation, temperature, soil characteristics in the growing environment, and sometimes can be considered as an indicator of climate change. Those meteorology and soil factors must be taken into account when we build phenological model so as to quantitatively study the relationship between climate change and plant phenology. Beside of those factors, the high frequency and multi-scale acquisition of phenological observation data is also the basis for phenological model researches. Since February of 2020, China Meteorological Administration (CMA) has established 25 vegetation ecological observation sites in Inner Mongolia autonomous region, Shaanxi, Hebei, Sichuan, Guangxi, Fujian and Anhui provinces. The automatic vegetation eco-meteorological observation instruments, whichi are composed of image sensor (digital camera), multispectral sensor, laser altimeter, point cloud laser radar and sound sensor, have been installed in the sites. They can provide so much products as image of plant community, normal difference vegetation index (NDVI), plant height, canopy height and animal sound at present. Of all these products, image data of plant community can be further retrieved to generate the greenness chromatic coordinate (Gcc) data, which can be widely applied into the phenological studies and the validations of satellite terrestrial vegetation products. After months of experimental operation, these equipments show the great ability to monitor the growth and development of terrestrial plants in China. This ability also lays a foundation for the establishment of the plant ecological observation network in China (China Vegetation Ecological Meteorological Observation Network).</p><p><strong>KEYWORDS</strong>：Plant phenology, near-surface-based measurement, observation network</p>

Environmental Sensor Network of NTT DOCOMO

2016 ◽  
Vol 11 (2) ◽  
pp. 334-339 ◽  
Author(s):  
Hisakazu Tsuboya ◽  
◽  
Ken Kumagai ◽  
Yasuko Furuta ◽  
Akiko Miyajima ◽  
...  
Keyword(s):  
Climate Change ◽  
Sensor Network ◽  
Water Levels ◽  
Trigger Factor ◽  
Observation Data ◽  
Disaster Prevention ◽  
Observation Network ◽  
Environmental Sensor ◽  
Weather Observation ◽  
Heavy Rains

Natural disasters due to local heavy rains and storms have been increasing over and under the ground owing to climate change. Since 2009, NTT DOCOMO, INC. has been working on a nationwide weather observation network, the Environmental Sensor Network, by installing weather observation devices in wireless stations nationwide. In addition, the company has assumed that weather observation data are the trigger factor that may cause disasters and promoted the provision of comprehensive observation data, including river water levels, directly related to disasters such as floods. This paper presents an outline of the environmental sensor network as well as its functions and applications to the disaster prevention field.

scholarly journals How did the characteristics of the growing season change during the past 100 years at a steep river basin in Japan?

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255078
Author(s):  
Nagai Shin ◽  
Taku M. Saitoh ◽  
Kenlo Nishida Nasahara
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Growing Season ◽  
River Basins ◽  
Plant Phenology ◽  
Effect Of Temperature ◽  
Phenological Model ◽  
Degree Day ◽  
Leaf Flush

The effects of climate change on plant phenological events such as flowering, leaf flush, and leaf fall may be greater in steep river basins than at the horizontal scale of countries and continents. This possibility is due to the effect of temperature on plant phenology and the difference between vertical and horizontal gradients in temperature sensitivities. We calculated the dates of the start (SGS) and end of the growing season (EGS) in a steep river basin located in a mountainous region of central Japan over a century timescale by using a degree-day phenological model based on long-term, continuous, in situ observations. We assessed the generality and representativeness of the modelled SGS and EGS dates by using phenological events, live camera images taken at multiple points in the basin, and satellite observations made at a fine spatial resolution. The sensitivity of the modelled SGS and EGS dates to elevation changed from 3.29 days (100 m)−1 (−5.48 days °C−1) and −2.89 days (100 m)−1 (4.81 days °C−1), respectively, in 1900 to 2.85 days (100 m)−1 (−4.75 days °C−1) and −2.84 day (100 m)−1 (4.73 day °C−1) in 2019. The long-term trend of the sensitivity of the modelled SGS date to elevation was −0.0037 day year−1 per 100 m, but the analogous trend in the case of the modelled EGS date was not significant. Despite the need for further studies to improve the generality and representativeness of the model, the development of degree-day phenology models in multiple, steep river basins will deepen our ecological understanding of the sensitivity of plant phenology to climate change.

scholarly journals Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 95
Author(s):  
Yuan Gong ◽  
Christina L. Staudhammer ◽  
Susanne Wiesner ◽  
Gregory Starr ◽  
Yinlong Zhang
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Prescribed Fire ◽  
Water Availability ◽  
Longleaf Pine ◽  
Growing Season ◽  
Soil Water Availability ◽  
Future Climate Change ◽  
Short Term ◽  
Phenological Model

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d ± 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region’s growing season and lead to a reduction in the longleaf pine ecosystem’s carbon sequestration capacity.

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

4D-Quantification of alpine permafrost degradation in a bedrock ridge using multiple inversion schemes and deformation measurements

2021 ◽  
Author(s):  
Maike Offer ◽  
Riccardo Scandroglio ◽  
Daniel Draebing ◽  
Michael Krautblatter
Keyword(s):  
Climate Change ◽  
Mechanical Stability ◽  
Regularization Parameter ◽  
Time Lapse ◽  
Permafrost Degradation ◽  
Thermal Dynamics ◽  
Near Surface ◽  
Data Error ◽  
Near Surface Geophysics ◽  
Thawing Rate

<p>Warming of permafrost in steep rock walls decreases their mechanical stability and could triggers rockfalls and rockslides. However, the direct link between climate change and permafrost degradation is seldom quantified with precise monitoring techniques and long-term time series. Where boreholes are not possible, laboratory-calibrated Electrical Resistivity Tomography (ERT) is presumably the most accurate quantitative permafrost monitoring technique providing a sensitive record for frozen vs. unfrozen bedrock. Recently, 4D inversions allow also quantification of frozen bedrock extension and of its changes with time (Scandroglio et al., in review).</p><p>In this study we (i) evaluate the influence of the inversion parameters on the volumes and (ii) connect the volumetric changes with measured mechanical consequences.</p><p>The ERT time-serie was recorded between 2006 and 2019 in steep bedrock at the permafrost affected Steintälli Ridge (3100 m asl). Accurately positioned 205 drilled-in steel electrodes in 5 parallel lines across the rock ridge have been repeatedly measured with similar hardware and are compared to laboratory temperature-resistivity (T–ρ) calibration of water-saturated samples from the field. Inversions were conducted using the open-source software BERT for the first time with the aim of estimating permafrost volumetric changes over a decade.</p><p>(i) Here we present a sensitivity analysis of the outcomes by testing various plausible inversion set-ups. Results are computed with different input data filters, data error model, regularization parameter (λ), model roughness reweighting and time-lapse constraints. The model with the largest permafrost degradation was obtained without any time-lapse constraints, whereas constraining each model with the prior measurement results in the smallest degradation. Important changes are also connected to the data error estimation, while other setting seems to have less influence on the frozen volume. All inversions confirmed a drastic permafrost degradation in the last 13 years with an average reduction of 3.900±600 m<sup>3</sup> (60±10% of the starting volume), well in agreement with the measured air temperatures increase.</p><p>(ii) Average bedrock thawing rate of ~300 m<sup>3</sup>/a is expected to significantly influence the stability of the ridge. Resistivity changes are especially evident on the south-west exposed side and in the core of the ridge and are here connected to deformations measured with tape extensometer, in order to precisely estimate the mechanical consequences of bedrock warming.</p><p>In summary, the strong degradation of permafrost in the last decade it’s here confirmed since inversion settings only have minor influence on volume quantification. Internal thermal dynamics need correlation with measured external deformation for a correct interpretation of stability consequences. These results are a fundamental benchmark for evaluating mountain permafrost degradation in relation to climate change and demonstrate the key role of temperature-calibrated 4D ERT.</p><p> </p><p>Reference:</p><p>Scandroglio, R. et al. (in review) ‘4D-Quantification of alpine permafrost degradation in steep rock walls using a laboratory-calibrated ERT approach’, <em>Near Surface Geophysics</em>.</p>

scholarly journals The Effects of Assimilating Conventional and ATOVS Data on Forecasted Near-Surface Wind with WRF-3DVAR

2015 ◽  
Vol 143 (1) ◽  
pp. 153-164 ◽  
Author(s):  
Feimin Zhang ◽  
Yi Yang ◽  
Chenghai Wang
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Lower Atmosphere ◽  
Observation Data ◽  
Initial Field ◽  
Infrared Observation ◽  
Forecast Performance ◽  
Near Surface ◽  
The Impact ◽  
Positive Effect

Abstract In this paper, the Weather Research and Forecasting (WRF) Model with the three-dimensional variational data assimilation (WRF-3DVAR) system is used to investigate the impact on the near-surface wind forecast of assimilating both conventional data and Advanced Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS) radiances compared with assimilating conventional data only. The results show that the quality of the initial field and the forecast performance of wind in the lower atmosphere are improved in both assimilation cases. Assimilation results capture the spatial distribution of the wind speed, and the observation data assimilation has a positive effect on near-surface wind forecasts. Although the impacts of assimilating ATOVS radiances on near-surface wind forecasts are limited, the fine structure of local weather systems illustrated by the WRF-3DVAR system suggests that assimilating ATOVS radiances has a positive effect on the near-surface wind forecast under conditions that ATOVS radiances in the initial condition are properly amplified. Assimilating conventional data is an effective approach for improving the forecast of the near-surface wind.

scholarly journals Plant Phenology and Its Anthropogenic and Natural Influencing Factors in Densely Populated Areas During the Economic Transition Period of China

2022 ◽  
Vol 9 ◽  
Author(s):  
Peijun Ju ◽  
Wenchao Yan ◽  
Jianliang Liu ◽  
Xinwei Liu ◽  
Liangfeng Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Economic Transition ◽  
Transition Period ◽  
Growing Season ◽  
Plant Phenology ◽  
Future Research ◽  
Urban Management ◽  
Climate Factors ◽  
Natural Ecosystems ◽  
The Impact

As a sensitive, observable, and comprehensive indicator of climate change, plant phenology has become a vital topic of global change. Studies about plant phenology and its responses to climate change in natural ecosystems have drawn attention to the effects of human activities on phenology in/around urban regions. The key factors and mechanisms of phenological and human factors in the process of urbanization are still unclear. In this study, we analyzed variations in xylophyta phenology in densely populated cities during the fast urbanization period of China (from 1963 to 1988). We assessed the length of the growing season affected by the temperature and precipitation. Temperature increased the length of the growing season in most regions, while precipitation had the opposite effect. Moreover, the plant-growing season is more sensitive to preseason climate factors than to annual average climate factors. The increased population reduced the length of the growing season, while the growing GDP increased the length of the growing season in most regions (8 out of 13). By analyzing the impact of the industry ratio, we found that the correlation between the urban management of emerging cities (e.g., Chongqing, Zhejiang, and Guizhou) and the growing season is more significant, and the impact is substantial. In contrast, urban management in most areas with vigorously developed heavy industry (e.g., Heilongjiang, Liaoning, and Beijing) has a weak and insignificant effect on plant phenology. These results indicate that different urban development patterns can influence urban plant phenology. Our results provide some support and new thoughts for future research on urban plant phenology.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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