Agricultural and Forest Meteorology最新文献

Changes in the dates of autumn foliar senescence (DFS) have significant impacts on regional carbon uptake, while current approaches for the estimation of DFS are still lacking. The most important issue is that there are complicated factors that affect the DFS, among which drought effects probably have contributed the most. Using long-term DFS observations derived from the third-generation normalized difference vegetation index dataset (NDVI3g), we found a wider spread of earlier DFS trends over the Northern Hemisphere from 1999 to 2015, three times larger than that from 1982 to 1998. The five multivariate analysis of variance approaches consistently suggest the key role of drought in regulating these changes. We therefore derived a new DFS algorithm with the standardized precipitation evapotranspiration index (SPEI) to characterize these drought effects, and validations from both NDVI3g and MODIS data demonstrated that our new algorithm provided significantly improved estimates of DFS for all plant functional types, with higher accuracy for water-limited ecosystems. We further applied this new algorithm to predict DFS under various shared socioeconomic pathways (SSPs) by the end of this century, and we found overall earlier DFS than the current expectations. Our results therefore highlight the importance of drought in the modeling of plant phenology using remote sensing observations and thus are highly important for understanding the relationships between land carbon sinks and climate change, especially given that droughts are projected to be more severe and frequent in the future.

Wildfires are a natural part of the ecosystem in the U.S.. It is vital to classify wildfires using a comprehensive approach that simultaneously considers wildfire activity (the number of wildfires) and burned area. On this basis, the influence of hydrometeorological variables on wildfires can be further analyzed. Therefore, this study first classified wildfire types using a wildfire bivariate probability framework. Then, by considering six hydrometeorological variables, the dominant hydrometeorological variables for different wildfire types in 17 ecoregions of the United States were quantified. In addition, based on the results of this hydrometeorology-wildfire relationship analysis, we obtained new clusters that simultaneously considered wildfire characteristics and the impact of hydrometeorology on wildfires. In particular, the results were as follows: (1) Through the probability of wildfire bivariate statistical characteristics, wildfires could be classified into five types in this paper: WT-1 (mega-wildfire), WT-2 (joint wildfire-1), WT-3 (joint extremes), WT-4 (joint wildfire-2), and WT-5 (super frequent wildfires); (2) The dominant hydrometeorological variables under different wildfire types were discussed in 17 ecoregions of the United States; and (3) In the four new cluster regions, intensifying droughts are a concern in clusters 1 and 4, while there are multiple concerns in cluster 3, namely, stronger winds, higher temperatures, and more drought.

Accurate and unbiased simulation of crop phenology under various climate conditions is a necessary feature of phenology models. Nine models were evaluated for simulating the vegetative growth period (VGP) and the reproductive growth period (RGP) of maize (Zea mays L.) under historical climate variation. Seven models were based on a constant thermal/photothermal assumption (MAIS, SIMCOY, EPIC, MCWLA, WOFOST, Beta, CERES), and two models were based on a non-constant thermal/photothermal assumption (coupling response and adaptation model, RAM; average number of growing days, NGD). Phenology observations from 150 agrometeorological observation sites across China (1981–2021) were collected to evaluate model performance. Results showed that: (1) Most models simulated flowering and maturity dates well. Average RMSE of VGP was lower than that of RGP. Generally, models based on non-constant thermal/photothermal assumptions had lower RMSE than models based on constant thermal/photothermal assumptions; (2) Models having a fairly high development rate when temperature was slightly higher than base temperature (RAM, Beta, CERES, NGD, MAIS) had the lowest RMSE during RGP; (3) Simulations by some models had systematic biases. First, during VGP, standard deviations of flowering date simulations obtained from models with flexible temperature response curves across sites and years (EPIC, MCWLA, WOFOST, Beta, CERES, RAM) increased more slowly than the standard deviations of observations, while those of other models increased faster. Second, during RGP, unlike RMSE values from other models, those RMSE values obtained from RAM and NGD showed no significant correlation with the average growth period temperature. Our results suggest the importance of further investigating the impact of low temperatures on development rate during RGP in order to reduce systematic bias of models when applied under climate change conditions. Research efforts should be devoted to developing models that have flexible phenology response to temperature curves across sites and years.

The FLUXNET network with numerous eddy covariance stations distributed worldwide is an important backbone for the study of ecosystem-atmosphere interactions. In order to provide reliable data for a variety of related research fields all parts of the ecosystem-atmosphere interactions need to be fully captured. Energy balance closure can be an indicator that all fluxes are fully recorded. However, in an investigation of the FLUXNET data set over 20 years ago, a systematic imbalance of around 20 % was observed in the surface energy balance. By improving measurement instruments and arrangements as well as data post-processing, the imbalance was reduced to about 15 % within the following ten years. We show that the remaining imbalance has hardly changed to this day. In the meantime, it has become clear that the energy transport through mesoscale secondary circulations, which by definition cannot be captured with single-tower eddy covariance measurements, accounts for a large proportion of the remaining imbalance and leads to an underestimation of atmospheric energy fluxes. Storage changes, which have so far only been partially recorded, were also found to strongly contribute to the imbalance. In addition to recommendations for improving storage change measurements, we therefore present various energy balance closure approaches. These can be used to complement FLUXNET measurements by accounting for those flux contributions that cannot be captured by single-tower measurements or to parameterize the transport by secondary circulations in numerical weather and climate models. Another important finding in energy balance closure research is that secondary circulations contribute not only to energy transport but also to the transport of CO₂ and other substances, but more research is needed in this area. We conclude that research into energy balance closure problem has made great progress in recent years, which is crucial for investigating the role of ecosystems in the Earth system.

Global nitrogen (N) deposition substantially enhances ecosystem carbon cycling but usually results in minor carbon sequestration. The mechanisms underlying the minor stimulation of N deposition on carbon sequestration are not fully understood. Here, we used 22 sets of observations from a gradient N addition experiment with rates at 0, 2, 4, 8, 16, to 32 g N·m^-2·year^-1 in an alpine meadow ecosystem to constrain parameterization of the process-oriented Grassland ECOsystem (GECO) model. Our results indicate that the parameters related to plant N uptake and photosynthetic N use efficiency are proportionally downregulated with the rate of N addition. This is, the higher the rate of N addition, the larger the downward adjustment is in plant N uptake and use efficiency. GECO with parameter values not being adjusted to N treatments simulated higher annual GPP by 16.7 ± 7.1 %, 20.7 ± 6.7 %, 25.2 ± 8.2 %, 23.1 ± 7.0 %, and 49.5 ± 9.1 % under addition rates of 2, 4, 8, 16, and 32 g N·m^-2·year^-1, respectively, in comparison to these with parameter adjustment. Similarly, the ecosystem C storage simulated by GECO model without parameter adjustment was higher by 4.4 ± 2.5 % to 12.0 ± 3.0 % under these with parameter adjustment. Without adjustment of ecosystem physiological processes, such as the plant N uptake rate and use efficiency, Earth system models (ESMs) generally overestimate C uptake and storage under N deposition. Therefore, it is essential to incorporate these adjustments into ESMs to realistically predict global C dynamics under future N enrichment and its feedback to climate change.

Accurate forecasting of forest fuel moisture is critical for decision making for bushfire risk and prescribed burning. In-situ dead fuel moisture content (DFMC) monitoring (fuelsticks) has improved significantly, along with improvements in weather forecasting and spatial representation of forest density. Machine learning (ML) models have also out-performed traditional fuel moisture estimation approaches on open sites, however, these models are yet to be tested on a diverse range of below-canopy conditions using above-canopy weather observations. Even with significant advancements, forecasting DFMC has shown little improvement, as there are notable spatial and temporal problems associated with DFMC forecasting below forest. This research develops and validates a below canopy, 7-day-ahead forecasting system of daily minimum forest fuel dryness (10-h DFMC) that integrates an automated fuel sensor network, gridded weather forecasts, landscape attributes and a ML model (Gradient boosting algorithm; LightGBM). The study area was established across a diverse range of 28 sites in south-eastern Australia, producing the largest below canopy validation of its kind. Fuel moisture was measured half-hourly using 10-hour automated fuelsticks, with five years of observations. The model performance was evaluated on its capacity to predict minimum daily DFMC, and when DFMC conditions were within the burnable (9% – 16% DFMC) and high risk (<9% DFMC) ranges. Long-term sites were validated on a years’ worth of observations, assessing seasonal variability. The complete network of sites showing best performance in the first day of forecast (for both datasets mean R² of 0.88 and 0.87; RMSE of 6.06% and 6.07%), with degraded performance to day seven (mean R² of 0.63 and 0.52; RMSE of 11.84% and 13.33%). The results demonstrate that accurate DFMC forecasts can be achieved by the newly developed forecasting framework. The proposed system has the potential to be applied in any wildland fire setting where weather forecasts are available.

Flash droughts and their ecological impacts on terrestrial ecosystems have recently garnered increased attention due to their rapid intensification. However, research on the response and recovery of ecosystems to flash droughts, particularly regarding different types of flash droughts and their determinants, remains relatively limited. Here we classified flash droughts into meteorological, evaporative, and soil types based on the differences in primary drivers, and identified them in the middle and lower reaches of Yangtze River Basin (MLRYRB) from 2000 to 2022. We assessed the response and recovery time of ecosystems to different flash droughts based on solar-induced chlorophyll fluorescence (SIF), analyzed the factors affecting response and recovery times using random forest models, and identified the spatial patterns of dominant factors through partial correlation analysis. Our results revealed distinct characteristics among different flash droughts, with soil flash droughts exhibiting the highest frequency and longest duration. The average response time and recovery times ranged from 15.7 to 19.2 days and from 59.6 to 69.2 days, respectively, for different flash droughts, with soil flash droughts presenting the longest response time and shortest recovery time. Among all vegetations, mixed forests exhibited the longest response time to meteorological and soil flash droughts, while woody savannas presented significantly longer recovery time from evaporative and soil flash droughts. Analysis of primary drivers indicated that precipitation predominantly determined the response time to meteorological and evaporative flash droughts, while surface soil moisture played a primary role in soil flash drought. Furthermore, surface soil moisture was found to determine the recovery time from all flash droughts in over 57 % of pixels. Our findings could offer valuable insights into quantifying the ecological impacts and drivers of different flash droughts on ecosystems, deepening our understanding of ecosystem responses to flash droughts.

Climate change and extremes are increasingly threatening food security, especially in the Global South. Here, we examine how croplands and wheatlands of the southern Mediterranean region could be affected by projected changes in agrometeorological extremes over the 21st century. We use 17 bias-corrected climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to identify potential trends and assess the time of emergence of significant changes in agrometeorological extremes under the Shared Socioeconomic Pathway (SSP3–7.0). We note that simulated historical trends in agrometeorological extremes closely match observed trends, here derived from ERA5land, over croplands. Our analysis of CMIP6 projected scenarios reveals a consistent rise in heat intensity, drought intensity, and the frequency of compound dry and hot (D5/H95) days. While a reduction in frost intensity, combined with fewer wet and cold (W95/C5) and dry and cold (D5/C5) events offer some mitigation potential, concerns about water scarcity due to heightened heat and drought stresses may overshadow these benefits. These changes in agrometeorological extremes are projected to emerge in the near- and mid-term future (by 2030 and 2050). We also note that the projected decreases in cold extremes affect smaller agricultural regions than the increases in extreme heat. We find higher likelihoods of negative agrometeorological impacts over croplands and wheatlands throughout the 21st century, which could significantly challenge crop yields and agricultural sustainability. Without proactive adaptation and mitigation strategies, food security could come increasingly under threat in a changing climate in the southern Mediterranean region.