生态与农业气象

生态与农业气象研究进展

Progress in Ecological and Agricultural Meteorology Research

1 生态气象

1 Ecological meteorology

1.1 Stomatal limitations to photosynthesis and their critical water conditions in different growth stages of maize under water stress

Stomata are the channels by which plants exchange water vapor and carbon dioxide with the environment.Clarifying the change from stomatal limitations (SL) to non-stomatal limitations (NSL) of photosynthesis and their critical conditions is vital for accurately recognizing the degree of crop drought and formulating countermeasures.A field experiment was carried out from 2013 to 2015 to study the critical water conditions when maize photosynthesis changed from being limited by SL to NSL in different leaf positions under different degrees of water stress in different growth stages (3rd leaf stage,7th leaf stage and jointing stage).Our results indicated that photosynthesis of maize leaves at different positions changed from being determined by SL to NSL under different water stress levels at different growth stages; moreover,maize photosynthesis changed from being directed by SL to NSL in the first fully expanded leaf at the top before the changes occurred in the third leaf.The effect of water stress during different growth stages on the maize leaf water content (LWC)at which photo-synthesis changed from being limited by SL to NSL was not distinct.The changing point of SL at different leaf positions was closely related to the LWC,and the LWC at the changing point of SL was different at different leaf positions,which indicated that the change in SL is mainly determined by the leaf position and LWC,and its occurrence showed a decreasing trend from plant top to bottom.The LWC at which the SL transformation point occurred in the first fully expanded leaf at the top (75.5% ± 1.5%–75.7% ± 1.3%)was higher than that at which the change occurred in the third leaf at the top (73.2% ± 1.1%–73.4% ± 1.6%).The phenomenon of photosynthesis changing from being limited by SL to NSL occurred first in the first fully expanded leaf at the top; additionally,the LWC of the first fully expanded leaf at the top was the best indicator in maize under water stress and could be used as the critical condition marking the transformation of maize damage from water stress to damage from plant physiological and ecological stress.These results could provide a basis for the identification of crop drought disasters and their classification and provide a methodological reference for the identification and monitoring of drought in other crops.(Zhou Guangsheng )

1.2 Triggers of widespread dieback and mortality of poplar (Populus spp.) plantations across northern China

Drought-induced mortality has been reported in many forest biomes around the world.In recent years,large-scale forest mortality has been observed across the northern China,with forests exhibiting widespread crown die-back symptoms.Previous reports have attributed dieback and mortality to drought stress.In this study,a field survey was undertaken along two transects in the northern China.Our aims were to clarify the underlying mechanisms of the widespread dieback and mortality of poplar (Populusspp.) plantations under various environ-mental stresses.Under these conditions,we observed narrower tree-ring width,and decreased soil water content,indicating that forest growth increased drought stress,and drought stress played a prominent role in triggering dieback and mortality.Bacterial canker disease and low soil nutrient were also linked to dieback and mortality.We observed xylem damage,showing that latent bacterial canker disease was present in many poplar tree stands across the northern China,which may increase their susceptibility to drought stress.The results showed that widespread dieback and mortality of poplar forests were related to the interaction of drought stress,bacterial canker and low soil nutrient.The results contributed to understanding the causes for poplar plantation deaths,and could help in the prevention of large-scale death of poplar plantations.(Ji Yuhe,Zhou Guangsheng )

1.3 Prominent vegetation greening and its correlation with climatic variables in northern China

Global vegetation has been reported to be turning greener,especially in China and India.The Yellow River Basin is one of the most prominent greening areas in China.While some studies have attributed vegetation greening to large-scale ecological restoration efforts,our study focuses on the role of climate change in vegetation greening.We selected a time series of annual vegetation net primary productivity (NPP)and vegetation coverage from satellite data to quantify the vegetation greening trend.Annual temperature and precipitation were selected to examine the climate trend from 2000 to 2019.The results showed that the Yellow River Basin experienced a rapid increase in temperature and precipitation during this period.Annual temperature increased with an average speed of 0.905 °C per decade,approximately 4.5 times larger than that of global warming.Annual precipitation increased by 82.8%,with an average speed of 9.17 mm per year.There was widespread vegetation greening in the Yellow River Basin during 2000-2019.This was demonstrated by an increase in vegetation NPP and vegetation coverage in the Yellow River Basin.The increase of annual NPP and coverage from 2000 to 1019 was 26.6% and 30.8%,respectively.Even while considering the effects of conservation and restoration efforts,the rapid increases in temperature and precipitation allowed vegetation to flourish,as evidenced by significant positive correlations between climate variables and vegetation variables.Therefore,climate change played an important positive role in vegetation greening,rather than an undesirable disturbance.(Ji Yuhe,Zhou Guangsheng )

1.4 Variation of net primary productivity and its drivers in China’s forests during 2000-2018

Net primary productivity (NPP) in forests plays an important role in the global carbon cycle.However,it is not well known about the increase rate of China’s forest NPP,and there are different opinions about the key factors controlling the variability of forest NPP.This paper established a statistics-based multiple regression model to estimate forest NPP,using the observed NPP,meteorological and remote sensing data in five major forest ecosystems.The fluctuation values of NPP and environment variables were extracted to identify the key variables influencing the variation of forest NPP by correlation analysis.The long-term trends and annual fluctuations of forest NPP between 2000 and 2018 were examined.The results showed a significant increase in forest NPP for all five forest ecosystems,with an average rise of 5.2 g m−2per year over China.Over 90% of the forest area had an increasing NPP range of 0–161 g m−2per year.Forest NPP had an interannual fluctuation of 50–269 g m−2per year for the five major forest ecosystems.The evergreen broadleaf forest had the largest fluctuation.The variability in forest NPP was caused mainly by variations in precipitation,then by temperature fluctuations.All five forest ecosystems in China exhibited a significant increasing NPP along with annual fluctuations evidently during 2000–2018.The variations in China’s forest NPP were controlled mainly by changes in precipitation.(Ji Yuhe,Zhou Guangsheng )

1.5 Evaluation of restoration approaches on the Inner Mongolian Steppe based on criteria of the Society for Ecological Restoration

Ecological restoration is becoming an increasingly common management tool world-wide.However,a challenge still exists on how to effectively monitor restoration out-comes and evaluate restoration success for ecological restoration managers.In this review,the goal is to evaluate whether the research in a degraded area has been sufficient for fostering efficient restoration measures and follow-up of restoration success based on the Society for Ecological Restoration (SER) criteria.We selected the Inner Mongolian Steppe (IMS) in China as a model system.This area has been the subject of substantial research over the most recent years to understand degradation processes and restoration outcomes.We put together the variables used to assess degradation and restoration needs in the IMS and analyzed restoration results based on SER’s nine criteria for evaluating restoration success.We found that the accomplished research in the IMS only partially supplied the data needed for evaluation of restoration success.The available results were sufficient for a proper evaluation of species composition and tentatively supported assessments of another seven criteria but not self-sustainability.Grazing exclusion led to the fastest and most successful recovery of degraded steppe,but landscape-scale processes during restoration in the IMS are still incompletely known.Our review supports large-scale restoration of the IMS and emphasizes the need for long-time monitoring for a more complete evaluation of the outcome of the IMS restoration following all SER’s criteria.(Zhou Guangsheng )

1.6 Climatic causes of maize production loss under global warming in Northeast China

Maize (Zea maysL.) is one of the most important staple crops in Northeast China,and yield losses are mainly induced by climate anomalies,plant diseases and pests.To understand how maize yield loss is affected by global warming,daily precipitation and temperatures,together with provincial agricultural data sets,were analyzed.The results showed that the accumulated temperature,an important factor in agricultural productivity,increased by 5% in 1991–2017,compared to 1961–1990,and that the frequency of low temperatures decreased by 14.8% over the same time period.An increase in drought by 21.6% was observed from 1961–1990 to 1991–2017,caused by decreased growing-season precipitation by 4 mm/decade.In addition,days with heavy rain in August and September increased slightly in Northeast China.In general,maize growth responded positively to the increased thermal conditions; in 1961–1990,22.7% of observed maize yield-loss cases were due to low temperatures,but only 10% in 1991–2017.However,during the same time,the number of drought-induced yield loss cases increased from 27.3% to 46.7%.Moreover,yield loss cases caused by heavy rainstorms increased from 4.5% to 13.3%,indicating that heavy rainstorms have become an increasing threat to agriculture in Northeast China over the last three decades.In total,at least 70% of cases of provincial yield losses in Northeast China over the last three decades could be attributed to climatic factors.The frequency of climate hazards has changed under global warming,resulting in new challenges for agriculture.While drought and low temperatures were the primary causes for climate-induced yield losses before the 1990s,negative impacts from extreme events,mainly drought but also heavy precipitation,have increased in the last three decades,associated with global change.Farmers,agricultural scientists,and government policy makers could use these results when planning for adaptation to climate change.(Zhou Guangsheng )

1.7 Environmental explanation of maize specific leaf area under varying water stress regimes

Specific leaf area (SLA) is an essential functional trait and indicator for estimating plant strategies in response to environmental changes.Intraspecific and interspecific variations of SLA have been extensively studied,whereas the combined effects of soil water content and heat conditions on SLA dynamics have received little attention.Field experiments with different irrigation regimes for maize (Zea maysL.) were designed and conducted across two growing seasons during 2013–2014 in North China,which usually have onset of severe drought conditions.Our results showed that maize plant SLA decreased with effective accumulated temperature under both rain-fed and drought conditions.Different intensities of water stress beginning at the three-leaf stage (2014) and the seven-leaf stage (2013) resulted in significant decreases of leaf area and leaf dry mass,however,with no significant differences in SLA.It is the occurrence time rather than the intensity of water stress that significantly affected the scaling of SLA.Moreover,the total effect of soil moisture on SLA was much higher in 2014 than that in 2013,but it was the opposite for heat conditions.The results imply that there is a trade-off mechanism between soil water and heat conditions in shaping the dynamic characteristics of SLA.The results may give insights into the accurate simulation of SLA dynamic characteristic and guidance for the production and management of maize.(Zhou Guangsheng )

1.8 Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem.

Climate change severely impacts the grassland carbon cycling by altering rates of litter decomposition and soil respiration (Rs),especially in arid areas.However,little is known about theRsresponses to different warming magnitudes and watering pulses in situ in desert steppes.To examine their effects onRs,we conducted long-term mod-rate warming (4 years,about 3℃),short-term acute warming (1 year,about 4℃)and watering field experiments in a desert grassland of the northern China.While experimental warming significantly reduced averageRsby 32.5% and 40.8% under long-term moderate and short-term acute warming regimes,respectively,watering pulses (fully irrigating the soil to field capacity) stimulated it substantially.This indicates that climatic warming constrains soil carbon release,which is controlled mainly by decreased soil moisture,consequently influencing soil carbon dynamics.Warming did not change the exponential relationship betweenRsand soil temperature,whereas the relationship betweenRsand soil moisture was better fitted to a Sigmoid function.The belowground biomass,soil nutrition,and microbial biomass were not significantly affected by either long-term or short-term warming regimes,respectively.The results of this study highlight the great dependence of soil carbon emission on warming regimes of different durations and the important role of precipitation pulses during the growing season in assessing the terrestrial ecosystem carbon balance and cycle.(Zhou Guangsheng)

1.9 Nitrogen deposition magnifies the sensitivity of desert steppe plant communities to large changes in precipitation

Precipitation alteration and nitrogen (N) deposition caused by anthropogenic activities could profoundly affect the structure and functioning of plant communities in arid ecosystems.However,the plant community impacts conferred by large temporal changes in precipitation,especially with a concurrent increase in N deposition,remain unclear.To address this uncertainty,from 2016 to 2017,an in situ field experiment was conducted to examine the effects of five precipitation levels,two N levels and their interaction on the plant community function and composition in a desert steppe in the northern China.Above-ground net primary production (ANPP) and plant community weighted mean (CWM) height significantly increased with increasing precipitation,and both were well fitted with a positive linear model,but with a higher slope under N addition.The ANPP increase was primarily driven by the increase in Artemisia capillaris,a companion forb sensitive to precipitation variation.The plant community composition shifted with precipitation enhancement—from a community dominated by Stipa tianschanica,a perennial grass,to a community dominated by Artemisia capillaris.Synthesis.The findings imply that the ecosystem sensitivity to future changes in precipitation variability will be mediated by two potential mechanisms:concurrent N deposition and plant communitylevel change.It is suggested that we should consider the vegetation compositional shift and multiple resource colimitation in assessing the sensitivity of terrestrial ecosystems to climate change.(Zhou Guangsheng )

1.10 Responses of plant biomass and yield component in rice,wheat,and maize to climatic warming:A meta-analysis

The responses of crop yields to climatic warming have been extensively reported from experimental results,historical yield collections,and modeling research.However,an integrative report on the responses of plant biomass and yield components of three major crops to experimental warming is lacking.Here,a metaanalysis based on the most recent warming experiments was conducted to quantify the climatic warming responses of the biomass,grain yield (GY),and yield components of three staple crops.The results showed that the wheat total aboveground biomass (TAGB) increased by 6.0% with general warming,while the wheat GY did not significantly respond to warming; however,the responses shifted with increases in the mean growing season temperature (MGST).Negative effects on wheat TAGB and GY appeared when the MGSTs were above 15 °C and 13 °C,respectively.The wheat GY and the number of grains per panicle decreased by 8.4% and 7.5%,respectively,per 1 °C increase.Increases in temperature significantly reduced the rice TAGB and GY by 4.3% and 16.6%,respectively,but rice straw biomass increased with increasing temperature.However,the rice grain weight and the number of panicles decreased with continuous increasing temperature (ΔTa).The maize biomass,GY,and yield components all generally decreased with climatic warming.Finally,the crop responses to climatic warming were significantly influenced by warming time,warming treatment facility,and methods.Our findings can improve the assessment of crop responses to climatic warming and are useful for ensuring food security while combating future global climate change.(Zhou Guangsheng )

1.11 Temperature sensitivity increases with decreasing soil carbon quality in forest ecosystems across Northeast China

Soil respiration universally exhibits exponential temperature dependence (Respiration=R0eβTandQ10=e10β),and temperature sensitivity (Q10) and soil organic carbon quality (as expressed by basal respiration rate at 0°C,R0) are the key parameters.Despite their importance for predicting the responses of forest ecosystems to climate change and quantifying the magnitude of soil CO2efflux,the controlling factors of temperature sensitivity and soil carbon quality and their relationships among various forest types at a regional scale are yet unknown.Here,we present a comprehensive analysis ofQ10,R0,and their related variables by assembling 154 independent temperature–respiration functions under a common standard in forest ecosystems across Northeast China (41°51′−51°24′N,118°37′−129°48′E).TheR0values ranged from 0.1700 to 2.1194 μmol m−2s−1(mean=0.8357 μmol m−2s−1),and theQ10values from 1.29 to 5.42 (mean=2.72).The relationships betweenQ10andR0could be best expressed with exponential decay equations (R2=0.460–0.611,P<0.01).They indicated that the temperature sensitivity decreased with increasing the soil carbon quality,and then tended to level off when theR0values were larger than about 1 μmol m−2s−1.Soil carbon quality (R0) was closely related to the minimum soil temperature and its corresponding soil respiration rate during the growing season (R2=0.696–0.857,P<0.01).Such a synthesis is necessary to fully understand the spatial heterogeneity in the temperature sensitivity of soil respiration and to increase our ability to make robust predictions about the future carbon budget.(Zhou Guangsheng )

1.12 Quantifying the interannual litterfall variations in China’s forest ecosystems

Aims litterfall is a key parameter in forest biogeochemical cycle and fire risk prediction.However,considerable uncertainty remains regarding the litterfall variations with forest ages.Quantifying the interannual variation of forest litterfall is crucial for reducing uncertainties in large-scale litterfall prediction.Methods Based on the available dataset (N=318) with continuous multi-year (≥2 years) measurements of litterfall in Chinese planted and secondary forests,coefficient of variation (CV),variation percent (VP),and the ratio of next-year litterfall to current-year litterfall were used as the indexes to quantify the interannual variability in litterfall.Important findings in the interannual variations of litterfall showed a declining trend with increasing age from 1 to 90 years.The litterfall variations were the largest in 1–10 years (meanCV=23.51% and meanVP=−28.59% to 20.89%),which were mainly from tree growth (mean ratio of next-year to current-year = 1.20).In 11–40 years,the interannual variations of litterfall gradually decreased but still varied widely,meanCVwas about 18% and meanVPranged from −17.69% to 21.19%.In 41–90 years,the interannual variations minimized to 8.98% in meanCVand about 8% in meanVP.As a result,forest litterfall remained relatively low and constant when stand age was larger than 40 years.This result was different from the previous assumptions that forest litterfall reached relatively stable when stand age was larger than 30,20 or even 15 years.Our findings can improve the knowledge about forest litter ecology and provide the groundwork for carbon budget and biogeochemical cycle models at a large scale.(Zhou Guangsheng )

1.13 Predicting cloud condensation nuclei number concentration based on conventional measurements of aerosol properties in the North China Plain

Cloud condensation nuclei (CCN) play an important role in the formation and evolution of cloud droplets.However,the dataset of global CCN number concentration (NCCN) is still scarce due to the lack of direct CCN measurements,hindering an accurate evaluation of its climate effects.Alternative approaches to determine N CCN have thus been proposed to calculate NCCN based on measurements of other aerosol properties,such as particle number size distribution,bulk aerosol chemical composition and aerosol optical properties.To better understand the interaction between haze pollution and climate,we performed direct CCN measurements in the winter of 2018 at the Gucheng site,a typical polluted suburban site in the North China Plain (NCP).The results show that the average CCN concentrations were 3.81× 103cm−3,5.35× 103cm−3,9.74 × 103cm−3,1.27× 104cm−3,1.44× 104cm−3at measured supersaturation levels of 0.114%,0.148%,0.273%,0.492%and 0.864%,respectively.Based on these observational data,we have further investigated two methods of calculating NCCN from:(1) bulk aerosol chemical composition and particle number size distribution; (2) bulk aerosol chemical composition and aerosol optical properties.Our results showed that both methods could well reproduce the observed concentration (R2>0.88) and variability of NCCN with a 9% to 23% difference in the mean value.Further error analysis shows that the estimated NCCN tends to be underestimated by about 20%during the daytime while overestimated by <10% at night compared with the measured NCCN.These results provide quantitative instructions for the NCCN prediction based on conventional aerosol measurements in the NCP.(Zhou Guangsheng )

1.14 Dust-dominated coarse particles as a medium for rapid secondary organic and inorganic aerosol formation in highly polluted air

Secondary aerosol (SA) frequently drives severe haze formation on the North China Plain.However,previous studies mostly focused on submicron SA formation,thus our understanding of SA formation on supermicron particles remains poor.In this study,PM2.5chemical composition and PM10number size distribution measurements revealed that the SA formation occurred in very distinct size ranges.In particular,SA formation on dust-dominated supermicron particles was surprisingly high and increased with relative humidity (RH).SA formed on supermicron aerosols reached comparable levels with that on submicron particles during evolutionary stages of haze episodes.These results suggested that dust particles served as a medium for rapid secondary organic and inorganic aerosol formation under favorable photochemical and RH conditions in a highly polluted environment.Further analysis indicated that SA formation pathways differed among distinct size ranges.Overall,our study highlights the importance of dust in SA formation during nondust storm periods and the urgent need to perform size-resolved aerosol chemical and physical property measurements in future SA formation investigations that are extended to the coarse mode because the large amount of SA formed thereon might have significant impacts on ice nucleation,radiative forcing,and human health.(Zhou Guangsheng )

1.15 Multiphase chemistry experiment in fogs and aerosols in the North China Plain (McFAN):Integrated analysis and intensive winter campaign 2018

Fine-particle pollution associated with winter haze threatens the health of more than 400 million people in the North China Plain.The multiphase chemistry experiment in fogs and aerosols in the North China Plain (McFAN) investigated the physicochemical mechanisms leading to haze formation with a focus on the contributions of multiphase processes in aerosols and fogs.We integrated observations on multiple platforms with regional and box model simulations to identify and characterize the key oxidation processes producing sulfate,nitrate and secondary organic aerosols.An outdoor twin-chamber system was deployed to conduct kinetic experiments under real atmospheric conditions in comparison to literature kinetic data from laboratory studies.The experiments were spanning multiple years since 2017 and an intensive field campaign was performed in the winter of 2018.The location of the site minimizes fast transition between clean and polluted air masses,and regimes representative for the North China Plain were observed at the measurement location in Gucheng near Beijing.The consecutive multi-year experiments document recent trends of PM2.5pollution and corresponding changes of aerosol physical and chemical properties,enabling in-depth investigations of established and newly proposed chemical mechanisms of haze formation.This study is mainly focusing on the data obtained from the winter campaign 2018.To investigate multiphase chemistry,the results are presented and discussed by means of three characteristic cases:low humidity,high humidity and fog.We find a strong relative humidity dependence of aerosol chemical compositions,suggesting an important role of multiphase chemistry.Compared with the low humidity period,both PM1and PM2.5show higher mass fraction of secondary inorganic aerosols (SIA,mainly as nitrate,sulfate and ammonium) and secondary organic aerosols(SOA) during high humidity and fog episodes.The changes in aerosol composition further influence aerosol physical properties,e.g.,with higher aerosol hygroscopicity parameter and single scattering albedo SSA under high humidity and fog cases.The campaign-averaged aerosol pH is 5.1 ± 0.9,of which the variation is mainly driven by the aerosol water content (AWC) concentrations.Overall,the McFAN experiment provides new evidence of the key role of multiphase reactions in regulating aerosol chemical composition and physical properties in polluted regions.(Zhou Guangsheng )

1.16 Prominent vegetation greening and its correlation with climatic variables in northern China

Global vegetation has been reported to be turning greener,especially in China and India.The Yellow River Basin is one of the most prominent greening areas in China.While some studies have attributed vegetation greening to large-scale ecological restoration efforts,our study focuses on the role of climate change in vegetation greening.We selected a time series of annual vegetation net primary productivity (NPP)and vegetation coverage from satellite data to quantify the vegetation greening trend.Annual temperature and precipitation were selected to examine the climate trend from 2000 to 2019.The results showed that the Yellow River Basin experienced a rapid increase in temperature and precipitation during this period.Annual temperature increased with an average speed of 0.905 °C per decade,approximately 4.5 times larger than that of global warming.Annual precipitation increased by 82.8%,with an average speed of 9.17 mm per year.There was widespread vegetation greening in the Yellow River Basin during 2000-2019.This was demonstrated by an increase in vegetation NPP and vegetation coverage in the Yellow River Basin.The increase of annual NPP and coverage from 2000 to 2019 was 26.6% and 30.8%,respectively.Even while considering the effects of conservation and restoration efforts,the rapid increases in temperature and precipitation allowed vegetation to flourish,as evidenced by significant positive correlations between climate variables and vegetation variables.Therefore,climate change played an important positive role in vegetation greening,rather than an undesirable disturbance.(Ji Yuhe)

1.17 Contrasting yield responses of winter and spring wheat to temperature rise in China

Wheat growth,development,and grain yield are affected by global climate warming.The general consensus is that global warming shortens the overall length of wheat growing period and reduces global wheat yield.Here,focusing on China,the largest wheat producer in the world,we show that warming increases wheat yield in most winter wheat growing regions in China.We collated data from field experiments under stressfree conditions and artificial warming from 12 locations over China to assess the impact of warming on wheat yield.The data cover 14 wheat cultivars,27 site-years,and a range of growing season temperatures from 7.5 ℃to 17.2 ℃.Our results indicate that warming up to +3 ℃ increased winter wheat yield by 5.8% per ℃ (change rate of yield/average of yield),while reduced spring wheat yield by 16.1% per ℃.Although artificial warming reduced the total growth duration,warming induced longer early developmental phases and grain filling duration,and subsequently more and larger grains contributed to the yield increase of winter wheat.The yield decline of spring wheat was due to the opposite changes of those key processes in response to temperature rise.(Fang Shibo)

1.18 Could vegetation index be derive from synthetic aperture radar?—The linear relationship between interferometric coherence and NDVI

Due to many factors in the physical properties of the ground surface,the corresponding interferometric coherence values change dynamically over time.Among these factors,the roles of the vegetation and its temporal variation have not yet been revealed so far.In this paper,synthetic aperture radar (Sentinel-1) data and optical remote sensing (Landsat TM) images over four whole seasons are employed to reveal the relationship between the interferometric coherence and the normalized difference vegetation index (NDVI) at five sites that have ground deformation due to mining in Henan Province,China.The result showed:(1) As for the village area with few vegetation cover,the related coherence values are significantly higher than those in the farm land area with high densities of vegetation in the spring and summer,which indicates that the subsidence by mining in few vegetation cover area is easier to be monitored.(2) Linear regression coefficients between the interfereometric coherence values and the NDVI values is 0.62,which indicate the interferometric coherence values and the NDVI values change reversely in both farm land and village areas over the year.It suggests months between November and March with lower NDVI value are more suitable for deformation detecting.Therefore,the interfereometric coherence values can be used to detect the density of vegetation,while NDVI values can be reference for elucidating when the traditional differential interferometric synthetic aperture radar(DInSAR) could be effectively used.(Fang Shibo)

1.19 Risk analysis of maize yield losses in mainland China at the county level

Food security in China is under additional stress due to climate change.The risk analysis of maize yield losses is crucial for sustainable agricultural production and climate change impact assessment.It is difficult to quantify this risk because of the constraints on the high-resolution data available.Moreover,the current results lack spatial comparability due to the area effect.These challenges were addressed by using long-term countylevel maize yield and planting area data from 1981 to 2010.We analyzed the spatial distribution of maize yield loss risks in mainland China.A new comprehensive yield loss risk index was established by combining the reduction rate,coefficient of variation,and probability of yield reduction after removing the area effect.A total of 823 counties were divided into areas of lowest,low,moderate,high,and highest risk.High risk in maize production occurred in Heilongjiang and Jilin provinces,the eastern part of Inner Mongolia,the eastern part of GansuXinjiang,west of the Loess Plateau,and the western part of the Xinjiang Uygur Autonomous Region.Most counties in Northeast China were at high risk,while the Loess Plateau,middle and lower reaches of the Yangtze River and the Gansu-Xinjiang region were at low risk.(Fang Shibo)

1.20 Monitoring maize growth on the North China Plain using a hybrid genetic algorithm-based back-propagation neural network model

Crop growth and early yield information is crucial for the establishment and adjustment of agricultural management plans.Timely,precise and regional assessments of crop growth conditions and production greatly benefit the national economy and agriculture.In this study,the remotely sensed leaf area index (LAI) and vegetation temperature condition index (VTCI) data retrieved from the global land surface satellite (GLASS)and moderate-resolution imaging spectroradiometer (MODIS) data were selected as the key indicators of maize growth and a hybrid genetic algorithm (GA)-based back-propagation neural network (BPNN) (GABPNN) model was applied to provide complementary information on maize grow that the main growth stage.GA-BPNN models with different architectures were established,and an architecture with 10,9 and 1 nodes in the input,hidden and output layers,respectively,achieved the best training and testing performance.The root mean square error (RMSE) values of the training and testing samples were 588.2 kg hm−2and 663.4 kg hm−2,respectively.Thus,the hybrid model with the best architecture (10-9-1) was selected to calculate the values of the integrated growth monitoring index (G) at the regional scale with a 1 km spatial resolution in the study area from 2010 to 2018.The results showed that the monitored maize growth well reflected the actual situation and the correlations betweenGvalues and sites’ measured variables,such as the maize yield and soil relative humidity,were higher than those of a pure BPNN model.The linear relationship between the GA-BPNNbasedGvalues and maize yields was analyzed to estimate the maize yield in the North China Plain.Most of the RMSE and mean absolute percentage error (MAPE) values between the estimated and actual maize yields were less than 700.0 kg hm−2and 10.0%,respectively.Considering that the estimation errors of most statistical samples were small and there were no obvious differences between the estimated maize yields in the adjacent regions of the northern,central and southern plain,the GA-BPNN-based yield estimation model provided reliable and spatially continuous estimates of maize yield.(Fang Shibo)

1.21 Using Fengyun-3C VSM data and multivariate models to estimate land surface soil moisture

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research.The Fengyun-3C microwave radiation imager (FY-3C/MWRI) collects various earth geophysical parameters,and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regionalscale surface SM contents.The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE),unbiased RMSE (ubRMSE),mean absolute error (MAE),and correlation coefficient (R) values between the retrieved and measured SM.A lower accuracy in July(RMSE=0.164 cm3cm−3,ubRMSE=0.130 cm3cm−3,and MAE=0.120 cm3cm−3) than in the other months was found due to the impacts of vegetation and climate variations.To show a detailed relationship between SM and multiple factors,including vegetation coverage,location,and elevation,quantile regression (QR) models were used to calculate the correlations at different quantiles.Except for the elevation at the 0.9 quantile,the QR models of the measured SM with the FY-3C VSM,MODIS NDVI,latitude,and longitude at each quantile all passed the significance test at the 0.005 level.Thus,the MODIS NDVI,latitude,and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM.Multivariate linear regression(MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results.The MBPNN model with three inputs (MBPNN-3)achieved the highestR(0.871) and lowest RMSE (0.034 cm3cm−3),MAE (0.026 cm3cm−3),and mean relative error (MRE) (20.7%) values,which were better than those of the MLR models with one,two,or three independent variables (MLR-1,-2,-3) and those of the MBPNN models with one or two inputs (MBPNN-1,-2).Then,the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019.The estimated SM images were more consistent with the measured SM than the FY-3C VSM.This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results.(Fang Shibo)

1.22 A new agricultural drought index for monitoring the water stress of winter wheat

Timely and effectively monitoring agricultural droughts for winter wheat production is crucial for water resource management,drought mitigation and even national food security.With soil moisture and actual evapotranspiration (ET) products from 2001 to 2018 supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) and moderate resolution imaging spectroradiometer (MODIS) data,respectively,two agricultural drought indices,i.e.,the univariate soil moisture and evapotranspiration index (USMEI) and bivariate soil moisture and evapotranspiration index (BSMEI),were developed to reflect water stress for winter wheat.Our case study on the North China Plain (NCP) indicated that the USMEI could effectively monitor agricultural drought,especially in autumn and winter from October to January.Furthermore,compared with the evaporative stress index (ESI) and soil moisture anomaly percentage index (SMAPI),the correlations between the USMEI and climatic yields were acceptable at the county level or site scale.However,for the rest of the winter wheat growing season,the ESI and SMAPI performed better than the USMEI.In addition,the BSMEI was not suitable for monitoring droughts for winter wheat because this index overestimated the drought intensity.(Wu Dong,Li Zhenhong,Zhu Yongchao,Li Xuan,Wu Yingjie,Fang Shibo)

1.23 Indication of the two linear correlation methods between vegetation index and climatic factors:An example in the three river-headwater region of China during 2000-2016

The within-growing-season correlations (WGSC) and the inter-growing-season correlations (IGSC)are widely used linear correlation analysis methods between vegetation index and climatic factors (such as temperature,precipitation,and so on).The WGSC method usually calculates the linear correlation coefficient between vegetation index and climatic factors of each month in all the growing seasons,for instance,whether vegetation index or temperature had data of 204 months (12 months×17 years) during 2000-2016 to get the WGSC.The IGSC calculates the linear correlation coefficient between the vegetation index and climatic factors in the same month of each growing season among all the years,for example,only 17 couples’ data of vegetation index and temperature during 2000-2016 were used to get the linear correlation of IGSC.What is the difference between the results of the two methods and why do the results show that difference? Which is the more suitable method for the analysis of the relationship between the vegetation index and climatic conditions? To clarify the difference of the two methods and to explore more about the relationship between the vegetation index and climatic factors,we collected the data of 2000–2016 moderate resolution imaging spectroradiometer (MODIS)13A1 normalized difference vegetation index (NDVI) and the meteorological datatemperature and precipitation,then calculated WGSC and IGSC between NDVI and the climatic factor in three river-headwater regions of China.The results showed that:(1) as for WGSC,the more of the years included,the higher the correlation coefficient between NDVI and the temperature/precipitation.The correlation coefficient of WGSC is dependent on how many years’ the data were included,and it was increased with the more year’s data included,while the correlation coefficients of IGSC are relatively independent on the amount of the data; (2) the WGSC showed a pseudo linear correlation between NDVI and climatic conditions caused by the accumulation of data amount,while the IGSC can more accurately indicate the impact of climatic factors on vegetation since it did not rely on the data amount.(Fang Shibo)

1.24 Comparative analysis of drought indicated by the SPI and SPEI at various timescales in Inner Mongolia,China

The global climate is noticeably warming,and drought occurs frequently.Therefore,choosing a suitable index for drought monitoring is particularly important.The standardized precipitation index (SPI)and the standardized precipitation evapotranspiration index (SPEI) are commonly used indicators in drought monitoring.The SPEI takes temperature into account,but the SPI does not.In the context of global warming,what are the differences and applicability in regional drought monitoring? In this study,after calculating the SPI and SPEI at 1-,3-,6-,and 12-month timescales at 102 meteorological stations in Inner Mongolia from 1981 to 2018,we compared and analyzed the performances of the SPI and SPEI in drought monitoring from temporal and spatial variations,and the consistency and applicability of the SPI and SPEI were also discussed.The results showed that (1) with increasing timescale,the temporal variations in the SPI and SPEI were increasingly consistent,but there were still slight differences in the fluctuation value and continuity; (2) due to the difference in time series,the drought characteristics identified by the SPI and SPEI were quite different in space at various time scales,and with the increase in timescale,the spatial distributions of the drought trends in Inner Mongolia were basically consistent,except in Alxa; (3) at the shortest time scale,the difference between the SPI and SPEI was the largest,and the drought reflected by the SPI and SPEI may be consistent at longtime scales; and (4) compared with typical drought events and vegetation indexes,the SPEI may be more suitable than the SPI for drought monitoring in Inner Mongolia.It should be noted that the adaptability of the SPI and SPEI may be different in different periods and regions,which remains to be analyzed in the future.(Fang Shibo)

1.25 拔节期干旱和复水对春玉米物候的影响及其生理生态机制

物候不仅是气候变化的指示指标，也是作物模型的关键参数。现有研究主要关注物候变化与气候环境因子的关系，关于植物物候变化的生理生态机制研究很少。基于春玉米拔节期干旱与不同时间（抽雄期和吐丝期）复水的田间模拟试验分析表明：（1）不同时间复水均使灌浆期延长，乳熟期推迟（9 d），表明物候对前期水分胁迫存在记忆。（2）干旱条件下叶片净光合速率（Pn）、蒸腾速率（Tr）、气孔导度（Gs）和相对叶绿素含量（SPAD）均随物候进程呈先降后升再降趋势，且均在抽雄期达到极小值；不同时间复水均使Pn、Tr和Gs在吐丝期达到极大值，而 SPAD 则在灌浆期达到极大值；叶水势（LWP）随干旱进程整体呈下降趋势，不同时间复水均只是减缓了其下降速度，表明 LWP可用于描述物候对前期水分胁迫的记忆。（3）通径分析和决策系数分析表明，Pn是最主要的物候影响因子，而影响LWP的土壤相对湿度（RSWC）则是物候的主要控制因子，物候的变化是由Pn的累积变化引起，表明存在Pn的物候触发阈值。研究结果为春玉米物候变化的准确预测提供了依据。（周广胜）

1.26 夏玉米不同生育期叶片和冠层含水量的遥感反演

高光谱遥感技术监测作物含水量是了解作物生长状况的重要技术。为实现夏玉米不同生育期叶片和冠层含水量的快速、精细化、无损监测，本文基于2014年和2015年的6—10月华北夏玉米不同生育期不同灌水量干旱模拟试验数据构建了植被水分指数（W1，MSI，GVMI）复比指数（WNV和WCG）和红边反射率曲线面积（Darea）的夏玉米冠层等效水厚度（EWTC）和叶片可燃物含水量（FMC）的反演模型。结果表明：6个指标反演夏玉米三叶期的EWTC模型均未达到0.05显著性水平，三叶期后各指标反演EWTC模型均达到0.01的显著性水平，且总体而言模型精度从高到低为抽雄期、拔节期、灌浆期、成熟期和七叶期。6个指标反演七叶期和拔节期的FMC均达到0.01显著性水平。因此，同一光谱指标反演夏玉米不同生育期叶片和冠层含水量的精度差异较大。光谱指标反演夏玉米叶片和冠层含水量指标的精度与夏玉米生育期有很大关系，进而提出了夏玉米不同生育期含水量反演模型。研究结果可为准确模拟夏玉米不同生育期含水量提供技术支撑。（周广胜）

1.27 台湾青枣在福建主产区的气候适宜度

台湾青枣属新兴果树品种，经济效益显著。为实现台湾青枣在福建的优质高产，本研究基于产量和气象数据，结合文献和物候观测资料以及农业气候适宜度模型，给出了台湾青枣在福建主产区的气候适宜度模型参数，分析了主产区气候适宜度特征及其变化趋势。结果表明： 基于等权重求和法构建的模型可靠性最高；台湾青枣在福建主产区的气候适宜度较高，多数年份为适宜或较适宜；1996—2013年，气候条件对台湾青枣生长的影响总体呈趋好态势，有利于发展青枣生产；主产区全生育期温度适宜度 综合气候适宜度日照适宜度 降水适宜度，9—10月是水分管理的关键期。研究结果对福建省台湾青枣的生产管理和长期规划具有指导意义。（周广胜）

1.28 基于阈值指标分类法的玉米营养生长阶段受旱程度分级

基于田间小区试验，就玉米对不同强度及持续时间的干旱响应进行研究。玉米播种前进行底墒调控，使各小区土壤底墒基本一致。三叶期开始，按照研究区7月多年平均降水量的 100%、80%、60%、40%、20%和7%分别进行一次性灌水，此后不再进行灌溉，全生育期利用大型电动遮雨棚遮挡自然降水，随生育时间推移形成6个不同初始土壤水分梯度的持续干旱过程。分析不同处理玉米营养生长阶段（三叶期—拔节期）的形态（株高、叶面积）和生物量（茎干重、叶干重、总干重）指标对干旱程度的响应规律，采用阈值指标分类法（TITAN）确定各生长指标对干旱程度响应规律发生明显改变的临界点，并基于不同指标响应干旱程度临界点的同步性确定玉米植株水平响应干旱程度（D）的临界点，从而将玉米的受旱等级划分为4个等级。结果表明：当0D≤0.07 时，玉米受到轻旱影响，其形态和生物量指标的平均降幅仅为1.2%～3.0%；当0.07D≤0.47 时，玉米受到中旱影响，叶面积和株高的平均降幅分别为15.9%和8.6%，茎、叶干重及总干重的平均降幅分别为18.8%、15.4%和12.4%；当0.47D≤0.73时，玉米受到重旱影响，叶面积的平均降幅为37.8%，株高的平均降幅为 16.9%，茎、叶干重及总干重的平均降幅分别为 43.3%、45.2%和 28.9%；当0.73D≤1 时，玉米受到特旱影响，叶面积和株高的平均降幅分别为83.6%和 53.3%，叶干重和茎干重的降幅均高达90.0%以上，总干重的平均降幅达87.0%。研究结果可为作物干旱受灾程度的定量分级与评价提供方法和依据。（周广胜）

1.29 2000—2010年中国典型陆地生态系统实际蒸散量和水分利用效率数据集

蒸散是陆地生态系统水分循环和能量平衡的关键过程，水分利用效率是反映生态系统碳水循环间耦合关系的重要指标，二者在生态学、农学、水文学、气候学等多个学科中均具有重要的应用价值。涡度相关法被认为是现今唯一能直接测量生物圈与大气间物质与能量交换通量的标准方法，已成为生态系统尺度碳水交换通量观测的主要方法。本文通过整合中国陆地生态系统通量观测联盟（ChinaFLUX）的长期观测数据和中国区域其他观测站点基于涡度相关法发表的文献数据，构建了一套中国典型陆地生态系统实际蒸散量和水分利用效率数据集。本数据集共有实际蒸散量数据记录143条、水分利用效率数据记录96条，涉及5种生态系统类型45个生态系统，时间跨度为2000—2010年。本数据集可以为陆地生态系统碳水循环、生态系统管理和评估、全球变化等相关领域的研究提供数据支持。（周广胜）

1.30 2002—2010年中国典型生态系统辐射及光能利用效率数据集

辐射是陆地生态系统能量的主要来源，其利用效率表现为光能利用率，反映了生态系统转化光能、生成有机物质的能力。揭示典型生态系统的辐射及光能利用效率可以为评估区域光能资源及其利用效率提供参考，也为评估区域有机物质固定能力及碳吸收能力提供依据。基于中国陆地生态系统通量观测研究联盟（ChinaFLUX）的长期观测结果及已发表文献的公开数据，构建了2002—2010年中国典型生态系统辐射及光能利用效率数据集，包含51个生态系统126个站点年辐射、光能利用效率及吸收光能利用效率的观测记录。另外，本数据集还包含生态系统代码、年份、经度、纬度、海拔、生态系统类型、年均气温、年总降水量、年均CO2质量浓度、年均叶面积指数、最大叶面积指数等生物气候信息。本数据集可以为评估生态系统生产能力、应对气候变化等方面的研究提供数据支持。（周广胜）

1.31 中国毛葡萄和刺葡萄分布的气候适宜性

毛葡萄和剌葡萄是起源于中国且用于葡萄酒酿造的两大野生葡萄品种。本研究基于巳有中国毛葡萄和刺葡萄的气候影响因子研究成果，利用最大嫡原理从充分性与必要性方面确定了影响中国毛葡萄和刺葡萄种植分布的主导气候因子，并基于这些因子综合作用反映的毛葡萄和剌葡萄种植分布的存在概率分析了中国毛葡萄和剌葡萄分布区的气侯适宜性。结果表明：影响中国毛葡萄、刺葡萄分布的主导气候因子是年日照时数、开花期5月降水量、年极端最低气温、最冷月平均气温。中国毛葡萄、刺葡萄气候高适宜区分布在湖南西部和南部、广西中北部、贵州东南部、重庆中部。气候高适宜区、适宜区、低适宜区面积分别为研究区域总面积的2%、14%和16%。毛葡萄、刺荷萄气候适宜及以上区域的年日照时数阈值为1200～1800 h，年极端最低气温为−8 ℃以上，最冷月平均气温闾值为2～13 ℃，5月降水量为110～320 mm。（周广胜）

1.32 基于文献整合的中国不同下垫面植被覆盖度遥感估算模型数据集

植被覆盖度是衡量植被群落覆盖地表状况的一个综合量化指标，是研究生态环境、水土保持和气候变化等方面的重要基础数据。科学定量地反演植被覆盖度对实现生态环境治理与生态建设服务具有重要的指导意义。本文收集了1980—2016年中国区域发表的不同下垫面植被覆盖度遥感估算模型资料，构建了中国不同下垫面植被覆盖度遥感估算模型数据集。研究区范围覆盖 24.49°～51.42°N，80.23°～128.95°E，涉及的地区包括西北地区（内蒙古、新疆、青海、西藏、甘肃），华北地区（北京、河北、山东、河南），西南和南方地区（云南、广西、江西）。本模型数据集涵盖的主要下垫面类型包括林地、灌丛、草地、湿地、沙漠化草地、农田、城镇和石漠化区。本数据集的建立与共享，可为生态、水保、土壤、水利、植物等领域的定量研究提供模型数据基础，并可为生态效益评估、区域生态安全保护以及生态保护红线提供模型数据支撑。（周广胜）

1.33 气候变化背景下麦田沟金针虫爆发性发生为害

近年华北地区大面积推行保护性耕作措施和作物秸秆粉碎还田，冬小麦与夏玉米一年两熟连续轮作种植，为沟金针虫创造了有利的取食和栖息环境。地处华北北部的中国气象局固城农业气象野外科学试验基地2018—2019年秋季、冬季、春季气温出现了冷暖交替，尤其最低气温显著偏高，诱发麦田沟金针虫爆发性发生为害。据春季麦田挖土调查，虫口密度最高达144头/m2，虫口重量最重达18.764 g/m2。58个调查点达防治指标5头/m2占98.27%。拔节—收获期调查虫口密度孕穗期最高，拔节期次之，收获期最低。冬小麦与夏玉米禾本科作物连作种植田间虫口密度达35.3～40.4头/m2，显著高于前茬大豆、玉米、冬小麦休闲地，且花生地、春玉米地比大豆地虫口密度高5倍多，虫口重量高10倍以上。成熟期虫害麦田测产，籽粒减产36.8%；虫口密度增加10头/m2，籽粒减产率增加4.824%；虫口重量增加1 g/m2，籽粒减产率增加3.871%；植株虫害率增加10%，籽粒减产率增加11.587%。（任三学）

1.34 印巴两国边境沙漠蝗群对当地植被的影响及其未来可能发展趋势

2020年初非洲东北和印巴边境沙漠蝗群席卷多个国家，大面积农田及自然植被被啃食，是什么气候条件促成了此次沙漠蝗灾？距离中国最近的印巴边境蝗群成为研究以及中国媒体关注的热点，蝗灾对当地植被的影响如何？其发展趋势如何？从气候学上分析，历史上是否曾经或者未来蝗群是否可向印度东边迁飞而进入中国呢？这些成为社会关注的焦点。本研究利用长时间序列的卫星遥感数据和气象气候观测数据，对沙漠蝗群的可能扩展趋势及其是否可能进入中国进行了分析。研究结果表明：（1）由于沙漠蝗群的啃食，2020年1月和2月，在蝗群分布区大面积植被区的归一化植被指数较常年大幅度下降，2月（2月3日数据）的啃食面积较1月明显扩大；（2）发生在2018年5和10月两次印度洋飓风和2019年12月强热带风暴等几个罕见气旋给非洲和阿拉伯半岛带来的强降水，是本次非洲—西亚蝗灾的形成重要原因；（3）从影响沙漠蝗群的起飞的气温和沙漠蝗虫适合的降水条件来看，历史上或未来沙漠蝗群迁徙到印度东边的机会很少，进入中国境内的概率几乎为零。（房世波）

1.35 作物耕作节律与多时相遥感结合的山地耕地信息提取方法探索

由于山地地貌区的耕地分布破碎度大，仅应用中分辨率遥感影像难以获得高精度的山地耕地分布信息，如何提高应用中分辨率遥感影像提取山地耕地信息的精度是亟需研究的问题。本研究在分析区域主要作物及其生育期随季节变化的基础上，根据耕地与其他地物在植被覆盖时间序列变化上的区别，基于多时相遥感影像，提出了一种将作物生长和耕作节律与多时相遥感结合的耕地信息遥感提取方法。应用该方法准确的提取了典型山地区四川省会理县的耕地分布信息。这种方法提取速度快，结果具有一定的实时性和较高的准确性，可以满足耕地利用及管理中对耕地信息适时获取的要求，也可以应用于对历史耕地矢量数据中地块错分进行修正、更新漏分问题等。（房世波）

1.36 2000—2016年三江源区植被生长季NDVI变化及其对气候因子的响应

目前使用较为广泛的植被指数与气候因子的相关性分析方法有2种：NDVI与生长季内和生长季间气候因子的相关性分析，前者通常计算多个生长季内每个月的NDVI与气温、降水等气候因子的关系，后者通常计算多年同一月份的NDVI与相同月份气温、降水等气候因子的关系。这两种方法分析的结果有什么差异，差异的原因是什么？哪种方法更适合NDVI与气候因子之间的关系分析？本文以三江源区NDVI与生长季内和生长季间气候因子的关系分析为例，探析这个问题。基于2000—2016年三江源区MODIS13A1C6归一化植被指数数据，结合研究区植被类型图、气温与降水等气象数据，采用上述两种方法，对三江源区2000—2016年NDVI与气候因子的相关性进行了分析。结果表明：（1）生长季内植被NDVI与同期气温和降水的相关性随着时间尺度的增加而增大。生长季间NDVI与同期气温、同期降水的相关性较小且不一致。（2）生长季间植被NDVI在整个生长季内对于气温的时滞响应程度并不明显，前推月份的降水对高寒草甸生长季后期能够产生一定的积极作用。（3）两种方法所得出的结论有一定差异性，从统计学角度分析，相关系数会随着样本数量的增加而变大，但这种相关是一种由样本数量累加造成的伪相关，不一定能真实反映植被NDVI与气温、降水等因素的关系，而生长季间植被NDVI与气候因子的关系在相关性分析不存在这样的问题，更能真实反映气候因子年际变化对植被的影响。（徐嘉昕，房世波）

1.37 基于 Faster R-CNN的野外环境中蝗虫快速识别

蝗虫是常见的害虫之一，对农作物和生态系统具有很大的危害，采用常规的方法对蝗虫进行监测存在一定局限性，为了有效应用海量野外影像数据实现对蝗虫实时监测，本文建立了一种基于深度学习网络的蝗虫自动识别模型，利用手机模拟摄像头获取的内蒙古锡林浩特附近草原的280 张蝗虫的RGB 图像，采用深度学习算法中的 Faster R-CNN(Faster Region-based Convolutional Neural Network)网络结构建立了蝗虫识别模型。经验证该模型的精确度为0.756，可以较准确地将蝗虫从野外复杂环境中识别出来，与以往同类研究相比，在识别结果和实用性方面均有较大的进步，该模型是建立蝗虫实时监测系统的基础，可以为蝗虫的防治提供辅助信息，该网络结构还可以应用于其他害虫的识别，具有较强的推广性，拓宽了深度学习算法的应用领域。（武英洁，房世波）

2 农业气象

2 Agricultural meteorology

2.1 Summer maize growth under different precipitation years in the Huang-Huai-Hai Plain of China

Maize (Zea maysL.) is one of the important crops for meeting the high food demand for both humans and animals in the world.Promptly monitoring and accurately assessing growth of summer maize,a major crop in the Huang-Huai-Hai Plain (the HHH Plain) in China,is regularly conducted for estimating national yield and assessing food security.In this study,the process-based Remote-Sensing-Photosynthesis-Yield Estimation for Crops (RS-P-YEC) model,driven by remote sensing products,meteorological observations,phenophases of summer maize,and some auxiliary parameters,was used to simulate daily net primary productivity (NPP) of summer maize in the HHH Plain from June to September in 2000–2017.Summer maize growth at the county scale level (characterized by NPP) under different precipitation years was evaluated along West-East,North-South,and Northwest-Southeast transects in the HHH Plain.Results showed that with increasing accumulative precipitation during the summer maize growing season,maize growth exhibited the characteristics of a downward opening parabolic curve for a dataset including all site-years and for single year datasets.The best simulated maize growth and actual observed yield generally occurred when accumulative precipitation during the summer maize growing season was between 300 and 500 mm.Furthermore,summer maize growth was reduced in years with growing season accumulative precipitation less than 300 mm or greater than 500 mm as seen in the analysis of data from several stations under different precipitation levels along the three transects.This study confirmed that NPP simulated with the RS-P-YEC model,driven by remote sensing products and ground-based meteorological observations,is a good indicator for monitoring and evaluating summer maize growth under different precipitation levels in the HHH Plain.As such,the evaluation results will be helpful for forecasting yield across broad geographical areas,and for assessing national food security.(Wang Peijuan)

2.2 Optimizing parameters of a non-linear accumulated temperature model and method to calculate linear accumulated temperature for spring maize in Northeast China

Accumulated temperature is an important factor for modeling crop growth.It is stable in theory,but in practice,the accumulated temperature needed for crops during different growth stages differs markedly among different years,regions,and varieties,so the stability is relative and the instability is absolute.Therefore,it is useful to establish a general model to calculate accumulated temperature that is relatively stable and applicable to different maize varieties.In this study,we analyzed the stability of accumulated temperature and the parameters of the non-linear accumulated temperaturemodel (NLM).The NLM was optimized to improve its application range.A linear accumulated temperature model (LM) was also optimized based on the most important factor affecting the stability of accumulated temperature.We compared different methods for calculating accumulated temperature.We found that the accumulated temperature needed for crops during different growth stages differed markedly among different years,regions,and varieties.The main reason for the instability of calculated accumulated temperature values was temperature strength for a certain variety.Therefore,the calculation method was revised by adding a quadratic function,generating the temperature revision model after revision (TRM).The parameterQof the NLM is a thermal-sensitive parameter.There were strong correlations betweenQand mean active accumulated temperature or effective accumulated temperature for different varieties during emergence to maturity,indicating thatQwas related to the maturity type.Consequently,we proposed two general accumulated temperature models,AARM and EARM,in which the parameters of NLM were denoted by the active accumulated temperature or effective accumulated temperature.Comparing the different models,the TRM generated minimal bias but AARM and EARM had a wider application range for many varieties on a large scale.AARM had better simulation effect,while EARM was more stable.The applicability of the optimized models was improved.The results provide a new approach for optimization of agrometeorological indexes and upscaling of accumulated temperature models for other crops.(Guo Jianping)

2.3 Possible impact of climate change on apple yield in Northwest China

Apples (Malus pumilaMill.) are widely cultivated in 95 countries and regions around the globe.China is the world’s largest producer of apples.Prediction of apple yield in the context of climate change has become an important topic of research.The study sites in this investigation include 28 apple-producing base counties located in the Shaanxi Province of the northwest Loess Plateau.In this study,grey relational analysis was used to examine 88 climatic factors and to extract those factors that significantly influence the meteorological yield (MY) of apples.A support vector machine (SVM) was used to make a quantitative prediction of changes in MY in the apple-producing areas of Shaanxi Province from the years 2000-2099 under 2 climate change scenarios,RCP 4.5 and RCP 8.5.In addition,fuzzy information granulation was used to analyze the variation trends and variation spaces of MY from 2020 to 2049 and 2050 to 2099,compared with the 1990-2019 reference period.The results showed that for the 10-day and monthly climatic factors affecting the MY of apples,climate resource factors are more influential than meteorological disaster factors and spring factors are significantly more influential than other seasonal factors.Overall,there are more and broader climate resource factors affecting MY,and spring climatic conditions are more important for it.In the RCP 4.5 scenario,9 base counties showed slight decreases,2 counties showed significant decreases,15 counties maintained or had slightly increased,and 2 counties showed significant increases.The variation of per-unit yield was −1.44–1.85 t hm−2.In the RCP 8.5 scenario,10 base counties showed slight decreases,2 counties showed significant decreases,12 counties maintained or had slightly increased,and 4 counties showed significant increases.The variation of per-unit yield was −2.43–2.78 t hm−2.For both future climate change scenarios,the uncertainty of MY increased with time.(Guo Jianping)

2.4 Use of a plastic temperature response function reduces simulation error of crop maturity date by half

Understanding how crop development rate responds to the environment provides the basis for evaluating the impact of climate change on crop yield.In most crop simulation models,temperature response functions of development rate during the reproductive growth period (RGP) are assumed to vary only with temperature and not with other environmental factors.However,studies have indicated that the response functions may be plastic with other factors.Until now,little attention has been paid to this type of response.Here,using extensively collected field observations DOYRand data from intentionally designed interval planting experiments with winter wheat (Triticum aestivumL.),rice (Oryza sativaL.),and spring maize (Zea maysL.),we show that temperature response functions during RGP are plastic with day of year of flowering/heading(DOYR).Coefficients of determination between DOYRand development rate were significant for 69% sites.Partial correlation coefficients between development rate,temperature,and DOYRsuggest that DOYRexplains almost the same variability in maturity date as temperature.The plastic model was developed by coupling DOYRwith a linear temperature response function.The model can improve the fitting efficiency by 112%,while dependency between DOYRand temperature explains less than 25% of this improvement.The average RMSEs of simulated maturity date estimated by the plastic model in the three crops were 2.1,2.5,and 3.7 days,respectively,while the corresponding values given by widely applied traditional models were 3.1,6.5,and 7.4 days,respectively.Therefore,the plastic model can reduce simulation error by half.Moreover,simulation errors resulting from the plastic model have less systematic bias than traditional models.The plastic model simply and effectively provides accurate estimates of crop maturity and reduces the system deviation of the estimates.Coupling the plastic model of crop development with crop simulation models will likely decrease uncertainties in simulated yield under warming conditions.Additionally,results of this study will encourage future studies of other phenotype plasticity considered in current crop simulation models.(Wu Dingrong)

2.5 Plastic temperature response function accurately simulates crop flowering or heading date

Although crop phenology is responsive and adaptable to cultural and climatic conditions,many phenology models are too sensitive to variable climatic conditions.This paper developed a plastic temperature response function by assuming that development rate was linearly related to temperature,and that the linearity was linearly responsive to day of year (DOYV) of the starting date of the vegetative growth period (VGP).Phenology observations and weather data were acquired for winter wheat (Triticum aestivumL.),rice (Oryza sativaL.),maize (Zea maysL.),and soybean (Glycine maxL.Merrill) at twelve locations over 15 to 26 years.Additional data were observed for maize grown in an interval planting experiment.For 78.6% of the sites,the crop development rate during the VGP was positively affected by DOYV.Partial correlation analysis(controlling for temperature) indicated that DOYVwas independent of temperature.When averaged over all crops and sites,the root mean square error (RMSE) for a plastic phenology model based on both response and adaptation mechanisms was lower (RMSE=2.81 days) than models (RMSE=3.39 days) based only on response mechanism (p0.01).Furthermore,simulations produced by the plastic model showed less bias to days,temperature,and year.The plastic function provided a simple and effective method for achieving better phenology simulation accuracy.According to the plastic function,growing season under warming conditions will not be reduced by as much as simulated by models based only on response mechanism,so yield loss due to warming is likely to be overestimated.(Wu Dingrong)

2.6 Improvement of the CERES-Rice model using controlled experiments and a meta-analysis

Extreme heat has occurred more frequently in recent years and will intensify in the future,and this change has serious impacts on rice (Oryza sativaL.) yields.Thus,it was crucial to evaluate its influence on rice yield reductions.Recent papers have shown that a lack of experimental data makes it difficult for most crop models to capture the impacts of heat stress.Therefore,this paper explored how to improve the performance of crop models under extreme heat stress based on the decision support system for agrotechnology transfer(DSSAT) CERES-Rice model.This study primarily focused on:(i) quantifying spikelet fertility based on daily temperature and durations derived from controlled experiments,(ii) improving the performance of the CERESRice model under extreme heat stress,and (iii) simulating historical and future rice yields using the improved model.Specifically,a meta-analysis method was utilized to build a new heat stress function between spikelet fertility and temperature and heat day duration with high realization.Subsequently,independent artificial controlled experiments at two sites were proposed to calibrate and validate the CERES-Rice model.The results showed a higherR2( 0.739) and a lower RMSE that was reduced by 38%−68% after incorporating the new heat stress function in the CERES-Rice model compared to that of the original model.Furthermore,a historical simulation (1980−2010) demonstrated that an improved CERES-Rice model could better capture rice yields in response to extreme heat.Using an ensemble of five climate model datasets and four representative concentration pathways (RCPs),the analysis of the projected future (2020−2099) rice yields showed that the rice yield reduction caused by high temperature was considerable; however,the rice yields were overestimated by 34% and 18%,respectively,at the two sites.Some regions rarely affected by heat are likely to experience yield reductions in the future due to climate change.(Sun Qing )

2.7 Hot weather event-based characteristics of double-early rice heat risk:A study of Jiangxi Province,South China

Frequent occurrences of extreme hot weather create severe rice heat disasters.Precisely assess rice heat risk based on the identification of the particular period severely hit by hot weather events is of great merit to improve public planning to minimize the deleterious impact of rice heat.In this study,maximum temperature,disaster and phenophase data on rice in Jiangxi Province (typical planting area for double early rice in South China) were integrated to represent the historical heat of early double-cropping rice,facilitating the identification of particular period severely hit by historical rice heat and construction of hot weather eventbased evaluation level of rice heat.Afterwards,a rice heat index (RHI) was constructed and calculated based on hot weather events and the exact rice growth stage (days before/after flowering,DF).The results showed that (1) Heat disasters occur approximately 15 days before flowering and the DF −5−0 was determined to have the highest possibility of rice heat,followed by the DF −10 to −5,with 29.41% and 22.06% of heat disasters starting in each period,respectively.(2) The probability of moderate and light heat damage was more than 80% when 3−5 days of hot weather occurred in the DF −5−5,while the probability of moderate and severe heat damage increased to 100% when > 5 d of hot weather occurred in this period.More than 80% of 8 d rice heat started in DF −15 to 0,with severe rice heat accounting for approximately 90% in such a period.(3) Severe,moderate and light rice heat for 3−5 days was identified at DF −6−3,4−5 and 6−9,respectively.Similarly,severe,moderate and light rice heat lasting for 6−8 days and 8 days started at DF −6−1,2−5,6−18 and−7−−5,−4−4,5−14,respectively.(4) A high RHI was mainly found in the middle and northeastern part of the study area from 1981 to 2015,with the RHI in most stations being greater than 0.25.Increasing trends of a high RHI occurred in the same areas of the RHI belt.Most stations in such areas exhibited slopes 0.15 per 10 years.The results can provide technical and theoretical support for targeted rice heat assessment,and it can also be universally applied in relative researches on rice heat.(Yang Jianying)

2.8 Potential dynamic of irrigation water requirement for rice across Northeast China

Sufficient water is essential for maintaining rice production yields,but precipitation and ground water generally do not meet the requirements for rice growth.Irrigation is therefore necessary and the quantity of irrigation water requirement (IWR) is also highly dependent on climatic alterations.We utilized an ensemble of 20 fine-resolution downscaled global climate models to characterize the future dynamics of IWR across Northeast China,under two representative concentration pathway scenarios (RCP4.5 and RCP8.5).Crop evapotranspiration was a critical factor in IWR determinations and was estimated through the Hargreaves model.The model was recalibrated to optimize its performance and this resulted in normalized root mean squared errors of < 10%.Based on reliable crop evapotranspiration and effective precipitation data in baseline (1976–2005) and future periods (2036–2065 and 2070–2099),IWR decreased from the southwestern Heilongjiang and western Jilin to the southeastern and northeastern areas.The IWR displayed a general increasing trend but overall the tendency decreased from west to east.The western areas were exposed to higher magnitudes of IWR increases,indicating that the water deficit for rice would be more severe in these regions.IWR levels increased with time slice under RCP8.5 relative to RCP4.5.The predicted IWR changes in future periods were the greatest for Heilongjiang,followed by Jilin and Liaoning.In addition,Heilongjiang was predicted to have the most stable IWR in the future.These predictions of IWR dynamics highlight sensitive areas prone to water deficits and can serve as guides for specific irrigation schedules in the different rice growing regions across Northeast China.(Huo Zhiguo)

2.9 Potential dry/wet dynamic in China under RCP scenarios

Dry and wet division is one of the most basic contents in climate classifications.In order to explore regional potential features,20 global climate models (GCMs) were statistically downscaled to reproduce temperature and precipitation at a resolution of 0.25° × 0.25° across China,which illustrated agreeable performance in comparison with observation.Taking temperatures as inputs,the Hargreaves model was implemented to estimate potential evapotranspiration (PET).This model was typically recalibrated to keep accuracy in further usage,which resulted in normalized root mean squared error being less than 5%.The indicator defined by the ratio of annual precipitation to annual PET,namely dry/wet index (IDW),was projected in potential dynamic of dry/wet division.IDW was consistently expected in an increasing trend under RCP4.5 relative to the baseline period of 1986–2005.Regards RCP8.5,IDW was diagnosed in a decreasing trend in the northern Xinjiang and most central-southern regions,but an increasing trend in most northern regions,implying dry tendency in current wet condition in southern parts while wet tendency in dry condition in northern parts.The possible contribution of precipitation and PET could unveil regional differential change in IDW.It highlighted that the area exposed to extreme dry and dry was likely to decrease,but the exposure to wet and extreme wet tended to increase in the future.These can provide a better knowledge of potential change of climate and water resource,supporting adaptive strategies in response to climate change.(Huo Zhiguo)

2.10 土壤水分对冬小麦叶片光合速率影响模型构建

植物叶片光合速率是表征植物光合能力的重要参数，对土壤水分反应敏感，建立不同土壤水分对冬小麦叶片光合速率影响模型，有助于准确理解冬小麦的光合作用和产量形成。该文收集整理了1996—2017年我国冬小麦主产区11个试验地点、17个冬小麦品种的干旱和渍水试验数据共64组310个样本，分别构建干旱和渍水对冬小麦叶片光合速率影响的分段式和指数型模型，形成土壤水分对冬小麦叶片光合速率影响模型（SMEP）。结果表明：随着土壤相对湿度增加，冬小麦叶片光合速率系数呈稳定低值—线性增加—稳定高值—缓慢下降的特点；随着渍水时间延长，冬小麦叶片光合速率系数呈缓慢下降—快速下降的特点。对SMEP模型进行回代检验、外推检验、单点验证、单发育期验证发现，模型模拟结果与文献数据有较好的一致性，回归系数在1.0附近，且均达到0.01显著性水平。SMEP模型将嵌入中国农业气象模式（CAMM1.0），为CAMM不断完善提供科技支撑。（王培娟）

2.11 基于线性生长假设的作物积温模型稳定性比较

利用山西省2个冬小麦观测站、3个春玉米观测站和3个夏玉米观测站长时间序列的作物生育期观测资料和地面气象观测资料，基于4种作物生长发育速率线性假设，建立了作物不同生育阶段的活动积温（Aa）和4种有效积温模型，并对各积温模型的稳定性进行统计分析与检验。结果表明：以变异系数为指标检验各模型稳定性时，活动积温模型最稳定，考虑作物三基点温度的有效积温模型（Ae4）次之，仅考虑作物下限温度的有效积温模型（Ae1）及考虑作物上、下限温度的有效积温模型（Ae2和Ae3）最不稳定。以生育期模拟偏差和生育期模拟准确率为指标检验各模型稳定性时，Aa 模型对作物生育期的模拟效果最好，稳定性最高；4种有效积温模型中，Ae1、Ae2 和Ae3 模型模拟效果无显著差异，准确率和稳定性高于Ae4模型。各积温模型在春玉米和夏玉米出苗—抽雄期和抽雄—成熟期的稳定性表现一致，出苗—抽雄期各积温模型的稳定性高于抽雄—成熟期；冬小麦在出苗—抽穗期和抽穗—成熟期各积温模型的稳定性表现因地区不同而有所差异。因此，在实际应用中，还需根据作物种植区域、品种类型以及生育期选取合适的基点温度，综合分析多种积温模型稳定性，选取稳定性更高的积温模型。(郭建平)

2.12 葫芦岛玉米生育期气候资源及限制因子

明确农作物生长发育的主要气候限制因子及限制程度，可为农业应对气候变化和高效利用气候资源提供科学依据。基于辽宁省葫芦岛市玉米主栽区绥中县和建昌县1980—2018年逐日气象观测数据和农业气象观测数据，采用生态气候适宜度方法，分析了玉米出苗—拔节、拔节—抽雄、抽雄—成熟3个阶段的主要限制因子及限制程度。结果表明：效能模型可以用于明晰玉米不同生育阶段的环境限制因子并定量评估限制程度；研究区气候平均限制程度达30%以上，拔节—抽雄期气候限制程度最大，出苗—拔节期气候限制程度随年份逐渐下降；降水因子对葫芦岛市气候资源有效性限制程度最大，为27%～61%，其次是日照因子，温度因子限制程度最小；玉米产量与气候限制程度有密切关系，气候的剧烈波动是导致雨养玉米产量不稳的重要环境因素，因此提高气候资源利用率是保障玉米高产稳产的重要举措。(郭建平)

2.13 晋东南潞党参生态气候适生种植区划

基于晋东南地区16个国家气象观测站1981—2018年的气候资料，分析了气候因子与潞党参产量的相关性。选取全生育期≥10 ℃积温和降水量、根生长期平均气温以及苗期降水量等主要影响因子作为生态气候区划指标，选取DEM 和土壤质地作为地理环境影响指标，分别建立各指标的空间分析模型，按照90×90 m的细网格进行推算，采用隶属函数计算得到的各指标评判值以及熵权法确定的权重系数，构建了潞党参生态气候适生综合评判指标，对晋东南地区潞党参生态气候适生种植区进行了区划。结果表明:晋东南地区潞党参生态气候适宜区主要分布在东部太行山区、西部太岳山区以及晋城西南部的太岳山和中条山的交界处，区域内光温水资源匹配较好，适宜潞党参生长发育; 较适宜区主要分布在太行山和太岳山向中部上党盆地和晋城盆地过渡的浅山丘陵区，该区域热量条件较好，降水资源相对短缺; 不适宜区主要分布在上党盆地和晋城盆地的中心区域，区域海拔较低，夏季高温，不适宜潞党参种植。本研究结果可为晋东南地区潞党参优化生态布局，科学、合理利用气候资源提供参考。(郭建平)

2.14 玉米叶面积指数估算通用模型

基于2018年黑龙江哈尔滨、吉林榆树、辽宁锦州、新疆乌兰乌苏、甘肃西峰、河北固城6个农业气象试验站不同属性品种玉米的分期播种试验资料，以当地常年大田实际播种期为界，提前10 d播种为第1播期，正常播种为第2播期，比正常晚10 d播种为第3播期，晚20 d为第4播期，以第1播期、第3播期和第4播期实测值计算的有效积温相对值为自变量，采用修正的Logistic方程，构建了通用的玉米叶面积指数估算模型，进一步利用有效积温相对值对模型在三叶期和七叶期的残差进行订正，并用2018年6个农业气象试验站及2019年吉林榆树、甘肃西峰和山东泰安3个农业气象试验站，8个不同品种玉米的分期播种试验资料对模型进行检验。结果显示：以多属性品种玉米有效积温相对值为自变量的RLAI拟合曲线完全符合修正的Logistic方程变化规律，模型拟合优度（R2）达到0.93，通过了0.01水平的显著性检验，具有较高的精度。玉米全生育期不同品种模拟RLAI与实测计算RLAI的相关性较高，通过了0.01水平的显著性检验，相关系数均超过0.9，平均相对误差介于13.8%～27.6%。不同生育期模拟RLAI 与实测计算RLAI 的平均相对误差介于9.4%～30.7%，七叶期最高，乳熟期最低。说明以不同属性玉米品种、土壤性质、管理措施、种植密度下的试验资料为基础构建的LAI 估算模型，较以往基于单站、单品种、单播期或单站多品种LAI估算模型更具普适性，适用于大多数属性品种玉米的LAI模拟。(郭建平)

2.15 晋北农牧交错带农业旱灾脆弱性评价

晋北地区是北方农牧交错带的重要组成部分，是半干旱区与半湿润区的过渡带，干旱是影响该区农业生产最主要的气象灾害，正确评价农业旱灾脆弱性是科学应对干旱的基础和前提。选取晋北地区作物因素、环境因素和人为因素的11项指标，运用熵权法和层次分析法相结合的组合赋权法确定各指标的权重，通过综合加权建立农业旱灾脆弱性评估模型，并基于GIS的Iso非监督聚类方法进行分区。结果表明:晋北农牧交错带旱灾脆弱性分布特征是，东北和西南地区较重，中部和东南部地区较轻。重度和中度脆弱区占晋北地区总面积的58.2%，轻度脆弱区占27.0%，晋北农牧交错带农业旱灾脆弱性整体来说较重。在各影响指标中，有效灌溉面积和机井数影响最大，其次是乡村人均收入和降水量，说明气候和人为抗旱能力对农业旱灾脆弱性有很大影响，因此，应适当增加农业投入，提高天然降水的利用率和土壤蓄水保水能力，大力推广农业节水技术，提高农业防旱抗旱能力。(郭建平)

2.16 冬小麦氨基酸品质与气候生态因子关系研究

选用南北方冬小麦品种作试验材料，通过地理分期播种试验，采用方差分析、主成分分析、聚类分析等方法对冬小麦籽粒氨基酸品质等进行评价，利用典型相关和回归分析等方法分析冬小麦氨基酸品质与气候生态因子的相关程度，并选择相关性显著的气候生态因子构建冬小麦氨基酸含量预测模型。结果表明，各冬小麦氨基酸成分中，非必需氨基酸中的谷氨酸平均含量最高，必需氨基酸中的蛋氨酸平均含量最低；必需氨基酸和非必需氨基酸环境适应性相对较强，而半必需氨基酸在进行优质育种时选择潜势较大；冬小麦氨基酸成分含量呈现出北方品种高于南方品种的区域分布特征，苏氨酸、苯丙氨酸、精氨酸、天冬氨酸、谷氨酸和甘氨酸含量地域性差异显著。氨基酸品质可由累计贡献率达97.796%的3个主成分解释，其综合品质评价为固城郯麦98表现最优，泰安山农18表现较好，而荆州郑麦9023、宿州皖麦52、徐州徐麦33表现较差；聚类分析中类群排列与冬小麦氨基酸成分含量及其地域分布关系密切，Ⅰ、Ⅱ、Ⅲ、Ⅳ类依次为华北麦区郯麦98、黄淮北部麦区山农18、黄淮南部麦区皖麦52、黄淮南部麦区徐麦33和江淮麦区郑麦9023，与主成分分析中的综合评价排序有一致性；冬小麦氨基酸含量与气候生态因子相关密切，其中必需氨基酸含量与温湿条件相关最显著，大部分氨基酸成分均可以通过调节小气候环境或土壤湿度的方式提高其含量品质。(郭建平)

2.17 玉米冠层对降水的截留模型构建

降水资源是农作物的主要水分来源，农作物通过吸收土壤中的水分维持正常的生长发育，但由于未考虑农作物冠层对降水截留作用，在水资源评估和农田水分平衡研究中往往高估降水作用。该文通过2018年玉米生长季在辽宁锦州农业气象试验站开展的降水模拟试验系统分析了玉米冠层对降水的截留效应，结果表明：在降水量一定条件下，玉米冠层截留量与叶面积指数的二次多项式拟合相关最佳；在叶面积指数一定条件下，玉米冠层截留量与降水量的幂函数拟合相关最佳。综合叶面积指数和降水量分析表明：玉米冠层截留量与叶面积指数平方及降水量对数函数拟合呈正相关。根据我国玉米传统种植方式，高产玉米的叶面积指数最大一般为5～6，因此，对一次降水的最大截留量通常约为1.5～2.3 mm，当叶面积指数小于1时，对降水的截留可忽略不计。(郭建平)

2.18 植物对降水截留的研究进展

降水资源是植物生长发育和产量形成的主要水分来源，植物通过吸收土壤中的水分维持正常生长发育，降水不仅影响自然植物物种分布，也影响植物生产力。由于未考虑植物冠层对降水的截留作用，在水资源评估和农田水分平衡研究中往往高估降水作用，因此，讨论降水截留在水文生态学和农业气象学中均有重要意义。该文系统介绍降水截留的观测方法，包括间接测量法中各分量测定方法、直接测量法详细过程及应用各种方法需注意的问题；系统回顾有关森林和农作物对降水截留的研究成果；探讨在植物对降水截留研究中存在的主要问题：对截留概念的理解不同导致截留测定结果差异显著，没有完善的方法导致测定结果准确性不足，植物种植密度不同导致截留差异，降水强度不同导致截留差异，风速、植物形态结构、叶片表面特性等因素也会影响降水截留的大小。降水过程中植物叶面蒸发问题、降雪的截留问题、风的影响、研究尺度、研究方法以及综合模拟模型将是未来研究的重点和难点。(郭建平)

2.19 华北平原农田CO2浓度变化特征

旨在了解农田CO2浓度长期动态变化特征、趋势、浓度增量分布模式等，收集了2007—2018年中国气象局固城生态与农业气象试验站开路式涡相关CO2浓度观测数据。研究了华北平原农田CO2浓度的年际、年内、昼夜和CO2通量等动态变化特征，对比分析了华北平原农田CO2浓度与城市站和大气本底站CO2浓度变化趋势及差异。结果表明，近十多年来华北平原农田CO2年平均浓度显著升高31.0 μmol/mol（r=0.263，P0.01），年均增幅（2.58 μmol/mol）与全球和瓦里关本底站大气CO2浓度增幅接近，但农田CO2浓度年际和年内季节变化波动巨大，日平均浓度和逐时平均浓度标准差分别为33.7、33.5 μmol/mol。夜间CO2平均浓度为395.8 μmol/mol，比白天高36.2 μmol/mol（10.1%），8月最高差值达到74.4 μmol/mol（20.6%）。在作物生长季节，5月和8—9月白天CO2浓度出现的两个谷值准确地对应了CO2通量动态变化的两个峰值，表明4—9月昼间CO2浓度和通量动态变化很好地反映了华北平原冬小麦和夏玉米生长过程、农事活动和农田碳交换的关系。农田CO2浓度动态变化与城市、湿地和大气本底站的变化特征不同，表明其动态变化的形成机制有差异。农田CO2浓度昼夜及季节变化特征为研究和评估CO2浓度升高影响作物生长和产量提供指导依据。(俄有浩)

2.20 江西早稻高温热害发生时间分布特征

以江西早稻为例，利用气象资料、早稻高温热害灾情史料和生育期资料，构建历史早稻高温热害样本集合，在Kolmogorov-Smirnov(K-S)分布拟合检验的基础上，采用信息扩散方法计算得到早稻高温热害总样本和不同持续日数（3～5 d、6～8 d和8 d以上）不同等级（轻、中、重）热害在早稻抽穗期前后的发生概率。结果表明：早稻高温热害起始于抽穗前6 d至抽穗后20 d，抽穗扬花期发生概率最高，随着早稻进入乳熟期高温热害发生概率逐渐降低。早稻抽穗扬花期持续3～5 d早稻高温热害以轻、中度为主，5 d以上中、重度高温热害发生概率为100%；随着早稻进入乳熟期，高温热害以中度和轻度为主，重度热害概率显著降低。早稻轻度高温热害的主要致灾时段为抽穗至灌浆中期，中度热害的主要致灾时段为抽穗至灌浆中前期，而重度热害的主要致灾时段为孕穗期至灌浆初期。(杨建莹)

2.21 江西早稻高温热害等级动态判识及时空变化特征

构建考虑高温天气过程发生时间、持续日数的水稻高温热害指标，实现水稻高温热害等级的动态判识，对精准监测、预警与评估水稻高温热害意义重大。以江西早稻为研究对象，利用气象资料、早稻高温热害灾情史料和生育期资料，在历史早稻高温热害反演的基础上，采用K-S分布拟合检验和置信区间方法，构建基于高温天气过程的早稻高温热害动态指标，并采用预留独立早稻高温热害样本进行检验验证。在此基础上，计算江西各站点早稻高温热害指数（M）。结果表明：高温天气过程起始时间、持续日数是影响早稻高温热害发生程度的关键因子，其中，起始时间的影响大于持续日数。3～5 d早稻轻、中、重度高温热害的起始时间阈值分别为抽穗后第10～12 d、5～9 d、2～4 d；6～8 d早稻轻、中、重度高温热害的起始时间阈值为抽穗后第11～18 d、8～10 d和1～7 d； 8 d早稻轻、中、重度高温热害的起始时间阈值为抽穗后第12～18 d、8～11 d和0～7 d。指标验证完全一致的吻合率为73.7%，完全一致及相差1级的吻合率为89.5%。1981—2015年，早稻高温热害指数的线性倾向率为0.04/a，1999年左右发生由低到高突变；M高值区域主要位于江西中部和东北部，M0.18；江西中部、东北部和南部地区M值呈显著增加趋势，线性倾向率均大于0.04/a。总体来说，本文构建的指标实现了基于高温天气过程的早稻高温热害动态判识，江西中部和东北部是早稻高温热害的高风险区域。(杨建莹)

2.22 北方苹果干旱触发判识方法

基于小样本历史灾害数据和长序列气象、林果生长数据的林果灾害判识，对目前历史灾害数据匮乏的林果等经济作物气象灾害研究具有重要意义。该研究以中国陕西省富士系苹果干旱灾害为例，利用气象资料、苹果干旱灾情史料和富士系苹果发育期资料，充分考虑苹果不同发育阶段的水分需求和降水供给情况，以及前期水分盈亏状况对当前发育阶段苹果生长的影响，在水分盈亏指数计算的基础上，构建苹果干旱指数。通过概率分析、K-Means聚类、欧式距离等方法，厘定陕西省富士系苹果的干旱触发阈值。采用致灾因子序列对比分析、预留样本验证相结合的方法，验证苹果干旱触发阈值有效性。结果表明：（1）苹果干旱触发阈值分别为：苹果果树萌动—萌芽期0.87，萌芽—盛花期0.84，盛花—成熟期0.73；（2）基于阈值提取的苹果干旱年份的干旱指数序列与历史灾害样本干旱指数序列具有同一性；预留独立样本指标判识准确率为85.58%；典型站点长时间序列检验判识结果准确率为80.95%。研究结果可为林果灾害指标研究提供技术支撑。(杨建莹)

2.23 山西省参考作物蒸散量的时空变化特征

基于山西省境内70个地面气象观测站1960—2019年的逐日降水量、气温、日照时数、相对湿度、风速、水汽压等气象资料，应用Penman-Monteith公式计算参考作物蒸散量（ET0），对山西省ET0的时空变化特征及不同气候带和海拔的蒸散特征进行定量分析。结果表明：1960—2019年，研究区年均ET0在空间上呈现由西向东逐渐递减的趋势；以1982年为拐点，前后两个时段均呈逐年增加趋势，月际、旬际波动为单峰变化曲线。不同气候带ET0的差异性表现为：温带半干旱气候区的年、春、夏、秋季ET0高于暖温带半湿润气候区和暖温带半干旱气候区；冬季，暖温带半湿润气候区ET0最高。不同海拔ET0的差异性表现为： 660 m海拔区的年、夏、秋、冬季ET0高于其他海拔区域。（霍治国）

2.24 中国富士系苹果主产区花期模拟与分布

在中国富士系苹果的5个主产区，分别选取花期资料系列较长的山东福山（环渤海湾产区）、河南三门峡（黄河故道产区）、甘肃西峰（黄土高原产区）、云南昭通（西南冷凉高地产区）和新疆阿克苏（新疆产区）作为代表站，利用SPSS统计软件，分析和筛选影响苹果花期的气象要素，构建富士系苹果的花期模拟模型；采用平均绝对误差（MAE）和分级加权满分率计分评判法对模型进行检验，并用代表站周边12个站点的物候观测资料对模型进行外延检验；在此基础上，逐站逐年模拟中国苹果主产区416个气象站1981—2018年富士系苹果始花期和末花期。结果表明：代表站苹果花期模拟模型单站检验满分率66.7%～100.0%，平均绝对误差0.4～3.4 d，外延检验平均绝对误差1.2～5.1 d。1981—2018年中国不同产区富士系苹果花期时间差异大，并呈提前变化的趋势，提前变化分界点在1997年前后；代表站平均始花期最早与最晚相差27.0 d，平均末花期最早与最晚相差18.0 d；始花期提前变化幅度1.6～4.5 d/10a，末花期提前变化幅度1.2～3.8 d/10a。中国富士系苹果花期空间分布特征表现为由南向北逐渐推迟，平均始花期从西南冷凉高地的3月中旬向北逐渐推迟至环渤海湾产区北部的4月下旬，平均末花期从西南冷凉高地的4月上旬向北逐渐推迟至环渤海湾产区北部的5月上旬。（霍治国）

2.25 北方地区小麦蚜虫气象适宜度预报模型构建

根据1958—2015年我国北方地区8个主产省（市）小麦蚜虫分省发生面积和发生程度资料、1958—2015年601个气象站点相应逐日气象资料和农业气象站小麦发育期资料，采用相关分析、主成分分析和逐步回归等方法，并利用相关系数法进行因子普查，结合小麦蚜虫适宜生理气象指标和华北、黄淮小麦生育期规律，筛选影响小麦蚜虫年发生程度的关键气象因子，构建分区域的小麦蚜虫气象适宜度预报模型，并将气象适宜度指数划分为非常适宜、适宜、较适宜、不适宜4个等级，以反映气象条件对小麦蚜虫发生发展的适宜程度。结果表明：影响华北小麦蚜虫年发生程度的有8个关键气象因子，影响黄淮小麦蚜虫年发生程度的有6个关键气象因子。建立的华北、黄淮模型回代检验等级准确率分别为91.2%，93.1%，2016—2018年3年外推预报平均准确率均在75%以上；利用黄淮模型反演苏皖两省2016—2018年小麦蚜虫发生等级、异地检验3年预报效果均较理想。模型适用于从气象角度对华北、黄淮及江淮地区小麦蚜虫发生等级进行监测和预报。（霍治国）

2.26 晋南地区不同海拔高度典型木本植物物候特征及其对气候变化的响应

基于1983—2016年临汾市不同海拔高度上3个农业气象观测站（尧都区、隰县、安泽县）的6种典型木本植物物候期和温度的观测资料，统计分析其变化特征及相互影响。结果表明：（1）研究区年及四季气温整体呈上升趋势，尧都区增温幅度最大，春季增温极显著；月平均气温除隰县个别月份略有下降外，大多以增温趋势为主，其中3月增温极显著（P0.01），是年平均气温升高的主要因素。（2）研究区内木本植物物候期最早和最晚平均相差1～2个月，物候期变化呈现较强的区域性特征，尧都区和安泽的木本植物春季物候期提前，秋季物候期推迟，植物生长季延长；隰县木本植物春季物候期推迟，秋季物候期提前，植物生长季呈缩短趋势。（3）木本植物展叶始期对年、春季及展叶前1～2个月的平均气温响应显著，随着气温升高，木本植物展叶始期表现为一致提前趋势；木本植物落叶末期在尧都区和安泽县随着气温的升高表现为明显的推迟趋势，受年、秋季及落叶前1个月的平均气温影响显著，在隰县随气温的升高表现为普遍提前趋势，受年均气温变化影响显著；随着气温升高尧都区和安泽县的木本植物生长季延长，隰县木本植物生长季变化不明显。说明晋南地区不同海拔高度的气温及其变化趋势存在较大差异；各代表站典型木本植物的物候期和生长季对气候变化的响应不同。（霍治国）

2.27 干旱对夏玉米根冠及产量影响试验

为揭示干旱对夏玉米根冠生长及产量形成的影响，2013—2015年在山东夏津、山西运城和河北固城开展夏玉米水分胁迫控制试验，研究不同干旱条件下玉米根冠及产量的变化，厘定干旱敏感时段及临界阈值。结果表明：同一干旱程度，影响玉米地上干物重、产量的关键时段为拔节—抽雄期，抽雄期最敏感，影响根系、根冠比的关键时段为出苗—拔节期，拔节期最敏感。不同干旱程度，在快速失墒阶段，不同生育时段的地上干物重、根干重、根冠比均呈下降趋势，分别较对照减少11.7%～67.8%，35.2%～85.8%和15%～62%；干旱维持阶段与快速失墒阶段相比，地上干物重呈持续下降趋势，较对照减少24.3%～89.7%，根干重、根冠比呈上升趋势或无明显差异，分别较对照减少9.7%～80.8%，9.6%～62%。出苗—拔节期，土壤相对湿度60%～62%为玉米地上部生长及形成合理根冠比的临界阈值；出苗—七叶期，土壤相对湿度51%～60%利于根系生长。土壤相对湿度62%为影响玉米产量的临界阈值，土壤相对湿度31%～40%，出现在拔节、抽雄等敏感期，玉米减产七成以上。土壤相对湿度50%～60%持续时间少于8 d，复水后根冠可迅速恢复生长，但对产量仍有一定程度的影响，减产1.4%～6.6%。（霍治国）