查看更多>>摘要：? 2022Understanding spatial heterogeneity of grassland phenology responses to climate change is of crucial importance for revealing regional and species differences in ecosystem processes. In this study, three spring phenology models, namely, growing-degree-day model (GDD), temperature-precipitation parallel model (TPP) and temperature-precipitation sequential model (TPS) were employed to simulate the growing season start (GSS) retrieved from remote sensing data during 1981–2014 in mid-latitude (30°N-55°N) grasslands of the Northern Hemisphere. Results show that the average accuracies of predicted GSS based on TPP (root mean square errors (RMSE) = 9.9 days) and TPS (RMSE = 9.7 days) models are slightly higher than that based on GDD model (RMSE = 10.1 days) overall. Meanwhile, TPP/TPS model also exhibits a stronger capacity to simulate interannual variation of GSS (correlation coefficient (R) = 0.38/0.41 on average) than the GDD model (R = 0.3 on average). A revised Akaike information criterion (AICc_R) by including correlation coefficient between predicted and retrieved GSS was designed for the optimal model selection. The optimal models based on AICc_R present a stronger power in capturing the temporal pattern of spring phenology. GDD, TPP, and TPS model account for 32.7%, 20.7%, and 46.6% of the whole study region, respectively. Sub-regionally, TPS model dominates the temperate grasslands (66.1%), whereas GDD model dominates the cool semidesert grasslands (58.1%). Regions occupied by GDD model as the optimal model are generally cooler and wetter during February to May than those taken up by TPP and TPS model. Further analysis indicates higher heat and water requirements are needed in the most of warmer places. Overall, this study emphasizes the important role of thermal-moisture background in controlling the spatial pattern of spring phenology of northern grasslands in response to climate change. Precipitation is very important for triggering spring phenology in the temperate grasslands but not in the cool semidesert grasslands.
查看更多>>摘要：? 2022 Elsevier B.V.Transpiration is a fundamental biophysical process, directly measured in plants by dividing sap flow by total leaf area. Under non-limiting conditions, transpiration and reference evapotranspiration (ETo) are hypothesized to be equal when ETo is normalized by the leaf area index of the reference crop, i.e., LAI = 2.88. Known as the E2.88 model, it has only been tested with ETo derived from Penman-Monteith FAO56. Phenological influences on canopy conductance potentially decouple transpiration from atmospheric evaporative demand and lower the accuracy of E2.88. This study tested the accuracy of 20 E2.88 models in predicting apple (Malus domestica (Suckow) Borkh. var. Granny Smith) and pear (Pyrus communis L. var. Beurre Bosc Pear) transpiration over the 2020-2021 austral growing season. For apple, the Penman-Monteith ASCE-EWRI model had the highest predictive power with 7% error and r2 = 0.89; whereas for pear the Valiantzas (2018, Eq. (7)) showed 2% error and r2 = 0.96 evaluated via linear regression. Generally, models that included a humidity parameter had stronger predictive power than models excluding humidity. Yet, the predictive power of the E2.88 models decreased considering the phenological phases for each crop. For apple, early and late season E2.88 models underestimated transpiration by at least 27%. For pear, late season error increased to 7% as the E2.88 models overestimated transpiration. Canopy conductance and the atmospheric decoupling factor were significantly greater in early and late season for apple and significantly lower in late season for pear. Therefore, phenology decreased the predictive power of the E2.88 model in early and late season by decoupling physiological processes from atmospheric evaporative demand.
查看更多>>摘要：? 2022 Elsevier B.V.Snow is considered a vital component in the northern climate system, however, our understanding of the impacts of snowmelt on spring phenological shifts is still limited for high-latitude ecosystems. In this study, we analyzed the start of growing season (SOS) and its response to the process of snowmelt over 2000–2019 using multiple ground observations, satellite data products, and climate datasets in Alaska and part of Canada Yukon among five ecoregions. We found that the earlier snowmelt resulting from warming spring dominates the advanced spring vegetation phenology in the region. Spring vegetation greenup follows the start of snowmelt (SOM). During 2000–2019, the SOM and SOS advanced in a similar spatial pattern across the ecoregions, and SOS experienced further advancement than SOM. On average, SOS advanced about 0.49 days per day of SOM advancement. The impacts of snowmelt on vegetation phenology are largely driven by the snowmelt-induced synergistic changes in soil temperature and soil water content. As snowmelt starts, the infiltration of snowmelt water and insulation of the remaining snow cover stimulate the vegetation activities below the snow. The responses of SOS to the snowmelt phenology varied among the different ecoregions are associated with the varied climate backgrounds and vegetation types. Greater SOS and SOM advancements were found in the taiga-tundra ecotone, while lower changes were detected over the tundra. This study suggests that the snow cover and snowmelt phenology should be considered to better understand the vegetation seasonality under climate change in the high latitudes.
查看更多>>摘要：? 2022Yearly climate fluctuations introduce variability in forest productivity that impacts final yield. Over the decade-to-century timescale, climate change is likely to have similar effects. Therefore, yield forecasts based on average climatic values are expected to provide a biased snapshot of potential carbon fixation when using process-based models. To bridge this gap, we propose a spatially explicit framework to estimate the potential net primary productivity (NPPpot) as well as its expected uncertainty. We relied on the Physiological Principles in Predicting Growth (3-PG) model that was modified to include a stochastic climate generator based on observed climatic values. We called this framework the Stochastic Spatial 3-PG (3-PGS2) model. The 3-PGS2 model is a set of functions programmed in the R software, tailored to estimate the mean, standard deviation, and confidence intervals of NPPpot. This framework is able to identify and quantify areas more susceptible to climate uncertainty as well as allowing for sensitivity analysis. For illustration, we estimate the NPPpot for loblolly pine in the southeastern U.S. Our predictions for average NPPpot coincide with average values published in the literature. Most importantly, we provide a spatial assessment of the areas with the largest NPPpot uncertainty, showing that areas with lower productivity tend to have higher relative variation and those areas with higher productivity have lower relative variation.
查看更多>>摘要：? 2022 Elsevier B.V.Increasing drought is considered one of the major threats associated with climate change in central Europe. To provide an objective, quantitative tool that represents current drought conditions, the Czech Drought Monitor System (CzechDM) was established in 2012. Like other drought monitoring systems worldwide, the CzechDM uses several approaches to provide drought data. However, the CzechDM is unique internationally due to its utilization of a network of voluntary reporters (farmers) who complete a weekly online questionnaire to provide information about soil water content and the impacts of drought on crop yield. In this study, the results from the questionnaires from individual farms were aggregated by district. Reporters’ data were compared and validated with the outputs of the SoilClim model (a core tool of the CzechDM) and with other drought monitoring tools, such as the water balance model, the soil water index and the evaporative stress index. The soil water content estimated by the reporters was significantly correlated (on average r = 0.8) with the outputs of the SoilClim model. Conversely, the correlation between the drought impacts on yield estimated by the reporters and the SoilClim outputs was lower (on average r = 0.4), suggesting that in situ observations by farmers provide additional insights into the occurrence of drought impacts. Importantly, it was found that farmers reported significant drought impacts on yield earlier in the season than any other methods (models or remote sensing). The main findings of this study are that the drought monitoring provided by reporters is a useful and reliable component of the CzechDM. We conclude that weekly reports by farmers represent a significant enhancement to drought monitoring and have potential for use in developing automated approaches that combine in situ, modeling and remote sensing data within a data fusion or machine learning framework.
查看更多>>摘要：? 2022 Elsevier B.V.Accurate monitoring of vegetation phenology (e.g., the start and end of the growing season, SOS and EOS) is helpful for understanding the impacts of climate change on vegetation and the terrestrial carbon cycle. The remote sensing-based vegetation index (e.g., enhanced vegetation index, EVI) and remote sensing-based phenology index (e.g., normalized difference greenness index, NDGI) are the major data sources for phenology monitoring at regional and global scales. However, these remote sensing-based indices are vulnerable to the influences of backgrounds and their variations. As a result, it is difficult to obtain high-precision vegetation phenology by using only the remote sensing-based indices, especially for the EOS. In this study, we developed a background-free phenology index (BFPI) by coupling the remote sensing-based index and the meteorological factor-based normalized growing season index (calculated by the normalized daily minimum temperature, vapor pressure deficit, and photoperiod). The BFPIs (BFPIEVI and BFPINDGI) were constructed as products of the EVI/NDGI and normalized growing season index. The performances of the BFPIs in phenology monitoring were evaluated by using the gross primary production data from 64 carbon flux towers and green chromatic coordinate data from 57 PhenoCam sites in forests and grasslands in the Northern Hemisphere. The results showed that the BFPIs performed better than the remote sensing-based indices in phenology monitoring for both forests and grasslands. The BFPINDGI performed better than the BFPIEVI for SOS monitoring, and the two BFPIs had nearly equal performances for monitoring the EOS of grasslands. For forests, the BFPIEVI performed better than the BFPINDGI in phenology monitoring. Although the performances of the BFPIs were limited for EOS monitoring, the phenology monitoring accuracy based on BFPIs were still obviously higher than that based on the remote sensing indices. Overall, the newly-developed BFPI that integrates biological and meteorological factors not only improved the precision of phenology monitoring, but also provided a new perspective for multisource data-based phenology monitoring.
查看更多>>摘要：? 2022 Elsevier B.V.The heterogeneous responses of mixed forests to climate change are integral to the promotion of the resilience and resistance of forest ecosystems, yet the hydraulic and growth sensitivity of mixed tree species to environmental drivers remain largely unexplored. In this study, stem radial variations in larch (Larix principis-rupprechtii) and spruce (Picea meyeri) were monitored using high-resolution point dendrometers over six years (2014–2019) in a mixed coniferous stand in semi-humid China. The extracted hourly tree water deficit-induced stem shrinkage (TWD) and stem radial increment (SRI) were compared between tree species and among years, and the species-specific responses of the daily maximum TWD and cumulative SRI to environmental variables were fitted by linear mixed models. Averagely, larch had a lower TWD and greater SRI rate than spruce in all six growing seasons. The interspecific differences in the TWD (length, magnitude, and intensity) were significant in dry years, while significant differences in the SRI rates were in wet years. The SRI rate of larch showed a more rapid decrease than that of spruce with increasing intensity of TWD. Furthermore, the TWD of both species was mainly regulated by vapor pressure deficit (VPD) and soil water content (SWC). The TWD of larch had a stronger and earlier response to VPD, while TWD of spruce was more affected by SWC (10 cm depth). The daily SRI of larch was more significantly related to all the measured environmental variables, indicating a more sensitive response to environmental drivers than that of spruce. We identified a shift in competitive advantage from sensitive L. principis-rupprechtii to conservative P. meyeri under extreme drought conditions, providing deep insights into species‐specific water and carbon dynamics in mixed forests.
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