Compound drought‐heatwave (CDHW) events threaten ecosystem productivity and are often characterized by low soil moisture (SM) and high vapor pressure deficit (VPD). However, the relative roles of SM ...and VPD in constraining forest productivity during CDHWs remain controversial. In the summer of 2022, China experienced a record‐breaking CDHW event (DH2022). Here, we applied satellite remote‐sensing data and meteorological data, and machine‐learning techniques to quantify the individual contributions of SM and VPD to forest productivity variations and investigate their interactions during the development of DH2022. The results reveal that SM, rather than VPD, dominates the forest productivity decline during DH2022. We identified a possible critical tipping point of SM below which forest productivity would quickly decline with the decreasing SM. Furthermore, we illuminated the evolution of SM, VPD, evapotranspiration, forest productivity, and their interactions throughout DH2022. Our findings broaden the understanding of forest response to extreme CDHWs at the ecosystem scale.
Plain Language Summary
Low soil moisture (SM) and high vapor pressure deficit (VPD) are widely recognized as the dominant drivers of forest productivity decline during compound drought‐heatwave (CDHW) events. In the summer of 2022, a record‐breaking CDHW (DH2022) struck China. In this study, we decoupled the respective impacts of SM and VPD in determining forest productivity decline during DH2022. We found that during DH2022, SM, rather than VPD, is the dominant driver of forest productivity decline, and once SM decreases below a certain threshold, forest productivity would decline sharply. We illuminated the evolution of SM, VPD, evapotranspiration, forest productivity, and their interactions throughout DH2022. Our findings promote the understanding of forest response to extreme CDHWs at the ecosystem scale and thus potentially improve terrestrial ecosystem models' ability to evaluate and predict the impacts of CDHWs.
Key Points
Soil moisture (SM), rather than vapor pressure deficit, dominates the forest productivity decline in the 2022 China compound drought‐heatwave event
Forest productivity would decline sharply once SM drops below a certain threshold during extreme compound drought‐heatwave events
Evolution of the 2022 China compound drought‐heatwave event and its impacts on forests were illuminated
Extremely high temperatures represent one of the most severe abiotic stresses limiting crop productivity. However, understanding crop responses to heat stress is still limited considering the ...increases in both the frequency and severity of heat wave events under climate change. This limited understanding is partly due to the lack of studies or tools for the timely and accurate monitoring of crop responses to extreme heat over broad spatial scales. In this work, we use novel spaceborne data of sun‐induced chlorophyll fluorescence (SIF), which is a new proxy for photosynthetic activity, along with traditional vegetation indices (Normalized Difference Vegetation Index NDVI and Enhanced Vegetation Index EVI) to investigate the impacts of heat stress on winter wheat in northwestern India, one of the world's major wheat production areas. In 2010, an abrupt rise in temperature that began in March adversely affected the productivity of wheat and caused yield losses of 6% compared to previous year. The yield predicted by satellite observations of SIF decreased by approximately 13.9%, compared to the 1.2% and 0.4% changes in NDVI and EVI, respectively. During early stage of this heat wave event in early March 2010, the SIF observations showed a significant reduction and earlier response, while NDVI and EVI showed no changes and could not capture the heat stress until late March. The spatial patterns of SIF anomalies closely tracked the temporal evolution of the heat stress over the study area. Furthermore, our results show that SIF can provide large‐scale, physiology‐related wheat stress response as indicated by the larger reduction in fluorescence yield (SIFyield) than fraction of photosynthetically active radiation during the grain‐filling phase, which may have eventually led to the reduction in wheat yield in 2010. This study implies that satellite observations of SIF have great potential to detect heat stress conditions in wheat in a timely manner and assess their impacts on wheat yields at large scales.
While the response of crops to extreme high temperatures has been well documented at the site scale, the understanding is still limited over broad spatial scales due to lack of studies or tools timely and accurately monitor crop responses to extreme heat. In this work, we use the novel spaceborne data of sun‐induced chlorophyll fluorescence (SIF) to investigate the impacts of heat stress on winter wheat. An earlier and more pronounced response comparing to traditional vegetation indices was found suggesting the SIF have strong potentials to detect heat stress conditions in wheat in a timely manner.
Remote sensing of far-red sun-induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship ...between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top-of-canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (fesc) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near-infrared radiation. We combined rice, wheat and corn canopy-level in-situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R2 = 0.4–0.6) of fesc to the seasonal dynamics of the photosynthetic light use efficiency (LUEP), while the estimated physiological SIF yield was almost entirely uncorrelated to LUEP both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and fesc, explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half-hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for fesc, improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF-GPP relationship and establish a strong, mechanistic link between the near-infrared reflectance of vegetation (NIRV) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates.
•A mechanistic decomposition of canopy SIF for three in situ crop datasets.•The canopy structure and radiation factor outperforms SIF for GPP estimation.•Canopy escape fraction of SIF correlates with photosynthetic light use efficiency.•Correcting SIF for canopy scattering improves the correlation to APAR but not GPP.•Estimates of physiological SIF yield show no clear seasonal patterns.
The shuttling behavior and sluggish conversion kinetics of the intermediate lithium polysulfides (LiPSs) represent the main obstructions to the practical application of lithium–sulfur (Li–S) ...batteries. Herein, an anion‐deficient design of antimony selenide (Sb2Se3−x) is developed to establish a multifunctional LiPS barrier toward the inhibition of polysulfide shuttling and enhancement of battery performance. The defect chemistry in the as‐developed Sb2Se3−x promotes the intrinsic conductivity, strengthens the chemical affinity to LiPSs, and catalyzes the sulfur electrochemical conversion, which are verified by a series of computational and experimental results. Attributed to these unique superiorities, the obtained LiPS barrier efficiently promotes and stabilizes the sulfur electrochemistry, thus enabling excellent Li–S battery performance, e.g., outstanding cyclability over 500 cycles at 1.0 C with a minimum capacity fading rate of 0.027% per cycle, a superb rate capability up to 8.0 C, and a high areal capacity of 7.46 mAh cm−2 under raised sulfur loading. This work offers a defect engineering strategy toward fast and durable sulfur electrochemistry, holding great promise in developing practically viable Li–S batteries as well as enlightening the material design of related energy storage and conversion systems.
Hierarchical defect‐rich antimony selenide composite microspheres are developed for the establishment of a highly conductive, adsorptive, and catalytic barrier toward improved lithium–sulfur battery performance, i.e., excellent cyclability over 500 cycles with a minimum capacity fading of 0.027% per cycle, superb rate capability up to 8.0 C, and high areal capacity of 7.46 mAh cm−2 under raised sulfur loading.
•Machine learning algorithms have been applied to estimate wheat yield for Australia.•Combining climate and satellite data achieves high performance for yield prediction.•The unique and overlapping ...information of climate and satellite data is quantified.•Optimal prediction performance can be achieved two-month lead time before maturity.
Wheat is the most important staple crop grown in Australia, and Australia is one of the top wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is important for regional and global food security. Prior studies use either climate data, or satellite data, or a combination of these two to build empirical models to predict crop yield. However, though the performance of yield prediction using empirical methods is improved by combining the use of climate and satellite data, the contributions from different data sources are still not clear. In addition, how the regression-based methods compare with various machine-learning based methods in their performance in yield prediction is also not well understood and needs in-depth investigation. This work integrated various sources of data to predict wheat yield across Australia from 2000 to 2014 at the statistical division (SD) level. We adopted a well-known regression method (LASSO, as a benchmark) and three mainstream machine learning methods (support vector machine, random forest, and neural network) to build various empirical models for yield prediction. For satellite data, we used the enhanced vegetation index (EVI) from MODIS and solar-induced chlorophyll fluorescence (SIF) from GOME-2 and SCIAMACHY as metrics to approximate crop productivity. The machine-learning based methods outperform the regression method in modeling crop yield. Our results confirm that combining climate and satellite data can achieve high performance of yield prediction at the SD level (R2 ˜ 0.75). The satellite data track crop growth condition and gradually capture the variability of yield evolving with the growing season, and their contributions to yield prediction usually saturate at the peak of the growing season. Climate data provide extra and unique information beyond what the satellite data have offered for yield prediction, and our empirical modeling work shows the added values of climate variables exist across the whole season, not only at some certain stages. We also find that using EVI as an input can achieve better performance in yield prediction than SIF, primarily due to the large noise in the satellite-based SIF data (i.e. coarse resolution in both space and time). In addition, we also explored the potential for timely wheat yield prediction in Australia, and we can achieve the optimal prediction performance with approximately two-month lead time before wheat maturity. The proposed methodology in this paper can be extended to different crops and different regions for crop yield prediction.
Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In ...this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, Vcmax at a reference temperature of 25 °C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, Vcmax, in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007–2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to Vcmax at the seasonal scale (R2 = 0.70 for C4 and R2 = 0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking Vcmax with SIF that was used to estimate spatially and temporally varying crop Vcmax for the US Corn Belt region. The resulting Vcmax maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite-based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal Vcmax from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional Vcmax using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.
•Far-red SIF shows strong link to Vcmax at the seasonal scale than VIs.•Spatially-explicit maps of Vcmax from SIF were developed for crops.•The resulting Vcmax maps improve the regional GPP modeling.
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•Bimodal porous carbon with high specific surface area was synthesized.•BPC is beneficial to improve the electrochemical performance of Li-S battery.•The BPC/S cathode exhibits ...outstanding cyclic stability and rate performance.
With the advantages of excellent theoretical specific capacity and specific energy, lithium-sulfur (Li-S) battery is regarded as one of promising energy storage systems. However, poor conductivity and shuttle effect of intermediate electrochemical reaction products limit its application. As good sulfur carriers, porous carbon materials can effectively remit these shortcomings. In this paper, a combination of a hydrothermal KOH activation and successive pyrolysis of biomass reed flowers is proposed to prepare a bimodal porous carbon (BPC) material with high specific surface area (1712.6 m2 g−1). The as-obtained low-cost BPC/S cathodes exhibit excellent cycling performance (908 mAh g−1 at 0.1 C after 100 cycles), good rate capability and cyclability (663 mAh g−1 at 1 C after 1000 cycles), as well as a high areal capacity (6.6 mAh cm−2 at 0.1 C after 50 cycles with a sulfur loading of 8.3 mg cm−2). Such excellent electrochemical performance was mainly ascribed to a specific bimodal porous structure with high specific surface area and plenty spaces for sulfur impregnating, which significantly reduces the escape of polysulfides during cycling and guarantees a good cycling stability. Moreover, the secondary class pores (mesopores and micropores) of the material offer plenty of small channels to improve the electronic and ionic transfer rate and, consequently, to enhance the rate capability. The as-synthesized BPC material presents a great potential as a sulfur carrier material for Li-S battery applications. In this work, we also demonstrate a simple route to develop low-cost carbon materials derived from renewable biomass which may expand and promote their use in energy storage applications.
Transport Control Protocol (TCP) incast congestion happens in high-bandwidth and low-latency networks when multiple synchronized servers send data to the same receiver in parallel. For many important ...data-center applications such as MapReduce and Search, this many-to-one traffic pattern is common. Hence TCP incast congestion may severely degrade their performances, e.g., by increasing response time. In this paper, we study TCP incast in detail by focusing on the relationships between TCP throughput, round-trip time (RTT), and receive window. Unlike previous approaches, which mitigate the impact of TCP incast congestion by using a fine-grained timeout value, our idea is to design an Incast congestion Control for TCP (ICTCP) scheme on the receiver side. In particular, our method adjusts the TCP receive window proactively before packet loss occurs. The implementation and experiments in our testbed demonstrate that we achieve almost zero timeouts and high goodput for TCP incast.
Remote sensing of sun-induced chlorophyll fluorescence (SIF) is a novel optical tool for the assessment of terrestrial photosynthesis or gross primary production (GPP). Several recent studies have ...demonstrated the strong link between GPP and space-borne retrievals of SIF at broad scales. However, critical gaps remain between short-term small-scale mechanistic understanding and seasonal global observations. Here, we present a model-based analysis of the relationship between SIF and GPP across scales for diverse vegetation types and a range of meteorological conditions, with the ultimate focus on reproducing the environmental conditions during remote sensing measurements. The coupled fluorescence-photosynthesis model SCOPE is used to simulate GPP and SIF at the both leaf and canopy levels for 13 flux sites. Analyses were conducted to investigate the effects of temporal scaling, canopy structure, overpass time, and spectral domain on the relationship between SIF and GPP. The simulated SIF is highly non-linear with GPP at the leaf level and instantaneous time scale and tends to linearize when scaling to the canopy level and daily to seasonal. These relationships are consistent across a wide range of vegetation types. The relationship between SIF and GPP is primarily driven by absorbed photosynthetically active radiation (APAR), especially at the seasonal scale, although the photosynthetic efficiency also contributes to strengthen the link between them. The linearization of their relationship from leaf to canopy and averaging over time is because the overall conditions of the canopy fall within the range of the linear responses of GPP and SIF to light and the photosynthetic capacity. Our results further show that the top-of-canopy relationships between simulated SIF and GPP have similar linearity regardless of whether we used the morning or midday satellite overpass times. Field measurements confirmed these findings. In addition, the simulated red SIF at 685nm has a similar relationship with GPP as that of far-red SIF at 740nm at the canopy level. These findings provide model-based evidence to interpret remotely sensed SIF data and their relationship with GPP.
•SIF tend to linearize with GPP from leaf to canopy and short-term to seasonal scale.•SIF-GPP relationships are similar between morning and midday satellite overpass.•The simulated SIF685 has a similar relationship with GPP as SIF740 at canopy level.
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