Google Trends (GT) is an online tool designed for searching for changes over time. We assessed its use for evaluating changes in the timing of cherry flowering phenology, which is of intense interest ...to Japanese people. We examined the relationship between time-series of relative search volume (RSV: relative change in search requests over time obtained from the GT access engine) and cherry flowering information published on websites (as ground truth) in relation to three famous ancient cherry trees. The time-series of RSV showed an annual bell-shaped seasonal variability, and the dates of the maximum RSV tended to correspond to the dates of full bloom. Our results suggest that GT allows monitoring of multiple famous cherry flowering sites where we cannot obtain long-term flowering data to evaluate the spatiotemporal variability of cherry flowering phenology.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Drought has a deep impact in Inner Mongolia, which relies on livestock and farming. The temporal and spatial variability of drought conditions in Inner Mongolia were analyzed with a drought severity ...index (DSI), which is derived from the ratio of evapotranspiration to potential evapotranspiration and the normalized difference vegetation index (NDVI), as measured by remote sensing. Soil moisture as an important drought measure has not been used in the calculation of DSI. Using monthly DSI data during the growing season (May–September) in 2001–2010, the relationship between DSI and soil moisture was investigated with correlation coefficient and regression analysis. Different seasonal and spatial patterns of drought occurrence were found in the notable drought years of 2001, 2007, and 2009. The largest correlations between DSI and soil moisture were attributed to the links between evapotranspiration and soil moisture. The spatial distribution of the correlation coefficients between DSI and soil moisture varied seasonally, tracking closely with the movement of rainfall belt. DSI could not reflect the variation of soil moisture in woodland. In grassland, DSI correlated with surface soil moisture in the east in the beginning of the growing season. As the rainfall belt expands to the west in the second half of the growing season, DSI reflected apparently deeper soil moisture conditions because of the spatial difference of soil properties, i.e., the water-holding capacity becomes larger from west to east.
The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial carbon dioxide fluxes in Asia. In this study, we developed such a ...standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem carbon dioxide exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated carbon dioxide fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r (exp 2) =0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r (exp 2)=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land carbon dioxide fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land carbon dioxide fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land carbon dioxide fluxes. These data-driven estimates can provide a new opportunity to assess carbon dioxide fluxes in Asia and evaluate and constrain terrestrial ecosystem models.
•A canopy-scale daily-mean CH4 flux over a Cajander larch forest was net emission.•CH4 flux was nearly the same as the upper boundary of the limit of flux detection.•Daytime CH4 flux showed ...dependencies on both temperature and soil moisture.
Canopy-Scale methane (CH4) flux measurement over a larch forest in eastern Siberia was conducted by eddy covariance method using an open-path CH4 gas analyzer. Though the uncorrected flux showed strong CH4 uptake in the daytime, this changed to CH4 emission after density and spectroscopic effects were corrected. Random flux errors calculated from cross-covariance functions suggested that CH4 flux was nearly the same as the upper boundary of the limit of flux detection at the 95th percentile, being barely resolved by the measurement system; and that most of the daytime CH4 flux remained positive even after uncertainties due to random flux errors were taken into consideration. CH4 flux showed clear diurnal variation, representing emission in the daytime and near-zero in the nighttime, irrespective of wind direction. The daytime CH4 flux was dependent on both air temperature and volumetric soil water content. The CH4 flux from May 29, to June 12, was calculated as net emissions of 4.9–13.8 nmol m−2 s−1 in daily average, ranging between the forest floor and a mesotrophic fen near this site measured by static chambers in a previous study.
Abstract
The fate of a boreal forest may depend on the trend in its normalized difference vegetation index (NDVI), such as whether the NDVI has been increasing significantly over the past few ...decades. In this study, we analyzed the responses of two Siberian larch forests at Spasskaya Pad and Elgeeii in eastern Siberia to various waterlogging-induced disturbances, using satellite-based NDVI and meteorological data for the 2000–2019 period. The forest at Spasskaya Pad experienced waterlogging (i.e. flooding events caused by abnormal precipitation) during 2005–2008 that damaged canopy-forming larch trees and increased the abundance of water-resistant understory vegetation. By contrast, the forest at Elgeeii did not experience any remarkable disturbance, such as tree dieback or changes in the vegetation community. Significant increasing NDVI trends were found in May and June–August at Elgeeii (
p
< 0.05), whereas no significant trends were found at Spasskaya Pad (
p
> 0.05). NDVI anomalies in May and June–August at Elgeeii were significantly associated with precipitation or temperature depending on the season (
p
< 0.05), whereas no significant relationships were found at Spasskaya Pad (
p
> 0.05). Thus, the 20 year NDVI trend and NDVI–temperature–precipitation relationship differed between the two larch forests, although no significant trends in temperature or precipitation were observed. These findings indicate that nonsignificant NDVI trends for Siberian larch forests may reflect waterlogging-induced dieback of larch trees, with a concomitant increase in water-resistant understory vegetation.
•Unusual perennial excess soil water enhanced growth of understory vegetation of larch forest.•Through wet years and following drying years understory GPP and ER increased, while no trend was found ...in ecosystem fluxes.•Declined overstory larch led to preferable light condition and enhanced turbulent mixing for understory fluxes.•The understory growth and remaining deeper soil water compensated for the declined larch contribution to all ecosystem fluxes.
This study investigated the CO2 exchange over a 10-year period (2005–2014) inside and above a larch-dominant forest in the central Lena river basin, eastern Siberia. A wet-soil condition, such as that found in the active layer (seasonally thawed soil layer of upper permafrost), containing unusually high soil water close to saturation and partial surface waterlogging, was prolonged during the warm season of 2005–2009. In later years, the soil layer closer to the ground surface became dry (∼10% volumetric water content), although the deeper part remained relatively wet (∼30%). We quantitatively compared the whole forest and the understory CO2 exchanges to detect the separate effects of excessive soil waters on the overstory and understory vegetation. The conventional light and temperature response functions for half-hourly CO2 fluxes, that is, the net ecosystem exchange of daytime and night-time, respectively, were applicable to the understory observations. Comparison of the fitting parameters of the light response function at two levels revealed a smaller maximum net ecosystem exchange (NEE) under light saturation with a steep response under weak light conditions for the understory. The CO2 exchanges at the understory increased from the wet-soil period to the drying soil period by 46% (1.3 g C m−2 d−1) of gross primary production (GPP) and 29% (1.2 g C m−2 d−1) of ecosystem respiration (ER), while no trend was found in the ecosystem scale fluxes. These increases were due to an increasing understory biomass, changes in plentiful light and soil water in the inside-canopy environments, and enhanced turbulent mixing. The decline in the larch contribution could be compensated for by the understory growth and the remaining wetness of the active layer, which indicated that the interactions between the larch and the understory supported the stability of carbon cycles in this forest ecosystem.
Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides ...dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous thermokarst landscape in eastern Siberia. A combination of random forest and conditional generative adversarial networks (pix2pix) machine learning (ML) methods were applied to data fusion between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2, with the addition of ancillary hydrometeorological information. The results show that our algorithm successfully filled in observational gaps in the MODIS data caused by cloud interference, thereby improving MODIS data availability from 30.3% to almost 100%. The water fraction estimated by our algorithm was consistent with that derived from the reference MODIS data (relative mean bias: −2.43%; relative root mean squared error: 14.7%), and effectively rendered the seasonality and heterogeneous distribution of the Lena River and the thermokarst lakes. Practical knowledge of the application of ML to surface water monitoring also resulted from the preliminary experiments involving the random forest method, including timing of the water-index thresholding and selection of the input features for ML training.
The memory timescale that characterizes root‐zone soil moisture remains the dominant measure in seasonal forecasts of land‐climate interactions. This memory is a quasi‐deterministic timescale ...associated with the losses (e.g., evapotranspiration) from the soil column and is often interpreted as persistence in soil moisture states. Persistence, however, represents a distribution of time periods where soil moisture resides above or below some prescribed threshold and is therefore inherently probabilistic. Using multiple soil moisture data sets collected at high resolution (subhourly) across different biomes and climates, this paper explores the differences, underlying dynamics, and relative importance of memory and persistence timescales in root‐zone soil moisture. A first‐order Markov process, commonly used to interpret soil moisture fluctuations derived from climate simulations, is also used as a reference model. Persistence durations of soil moisture below the plant water‐stress level (chosen as the threshold), and the temporal spectrum of upcrossings and downcrossings of this threshold, are compared to the memory timescale and spectrum of the full time series, respectively. The results indicate that despite the differences between meteorological drivers, the spectrum of threshold‐crossings is similar across sites, and follows a unique relation with that of the full soil moisture series. The distribution of persistence times exhibits an approximate stretched exponential type and reflects a likelihood of exceeding the memory at all sites. However, the rainfall counterpart of these distributions shows that persistence of dry atmospheric periods is less likely at sites with long soil moisture memory. The cluster exponent, a measure of the density of threshold‐crossings in a time frame, reveals that the clustering tendency in rainfall events (on‐off switches) does not translate directly to clustering in soil moisture. This is particularly the case in climates where rainfall and evapotranspiration are out of phase, resulting in less ordered (more independent) persistence in soil moisture than in rainfall.
Key Points:
Persistence and memory timescales in soil moisture are examined for several soil moisture data sets
Soil moisture persistence times exceed memory timescales in seasonal climates
Rainfall persistence of dry periods need not translate directly to soil moisture
•N transformation rates in ∼40 forest sites across Japan were investigated.•Cause–effect relationships were assessed by structural equation modeling.•Soil organic matter content mediated net N ...transformations through gross rates.•Indirect effect of Andosols on net rates was significant in Japanese forest soils.
Nitrogen (N) is the primary limiting nutrient for forest production. Therefore, understanding how environmental factors affect N transformation rates is essential for the provision of sustainable ecosystem services. Because these factors are interlinked, it is important to consider direct and indirect structural relationships to better understand the factors contributing to N transformations. In this study, we analyzed the structural cause–effect relationships surrounding N transformations by structural equation modeling using a database containing net and gross N transformation rates and related soil chemical properties from 38 sites across the Japanese archipelago. The average net N mineralization and nitrification rates in the Japanese forest soils were 0.62±0.68 and 0.59±0.65mgNkg−1d−1, respectively, and gross N mineralization and nitrification rates were 4.22±3.59 and 0.98±0.68mgNkg−1d−1, respectively. Compared with previous large scale studies, net and gross N transformation rates in Japanese forest soils were considerably diverse despite their relatively small land area and were representative of temperate forest ecosystems. Structural equation modeling analysis showed that net N transformations were directly affected by gross N transformations, which in turn were significantly and directly affected by soil organic matter contents. Soil organic matter was significantly affected by organic layer amount, tree species and soil group. The effect of soil group was the greatest among these factors, suggesting that soil organic matter contents in Japanese forest soils were mainly influenced by soil parent materials. This was especially evident for Andosols, which are derived from volcanic sediments and contain large amounts of soil organic matter leading to high N transformation rates in the Japanese forest soils. Among the factors related to organic layers and mineral soil layers, soil organic matter content and organic layer amount, which represent substrate availability, had significant effects on gross and net N transformation rates. However, by refining the scale of the dataset using soil groups/soil parent materials, the influence of substrate quality and soil chemical properties on N transformations was suggested. From the current dataset, it was indicated that soil parent materials were the most important factor controlling the pattern of N transformations in the soil of Japanese forest ecosystems. This conclusion should be repeatedly refined considering the spatial distribution of factors such as climatic conditions and forest types with additional site datasets obtained from future surveys.
Terrestrial forest ecosystems are crucial to the global carbon cycle and climate system; however, these ecosystems have experienced significant warming rates in recent decades, whose impact remains ...uncertain. This study investigated radial tree growth using the tree-ring width index (RWI) for forest ecosystems throughout the Northern Hemisphere to determine tree growth responses to autumn climate change, a season which remains considerably understudied compared to spring and summer, using response function and random forest machine learning methods. Results showed that autumn climate conditions significantly impact the RWI throughout the Northern Hemisphere. Spatial variations in the RWI response were influenced by geography (latitude, longitude, and elevation), climatology, and biology (tree genera); however, geographical and/or climatological characteristics explained more of the response compared to biological characteristics. Higher autumn temperatures tended to negatively impact tree radial growth south of 40° N in regions of western Asia, southern Europe, United State of America and Mexico, which was similar to the summer temperature response found in previous studies, which was attributed to temperature-induced water stress.
PDF
