生態(tài)與農(nóng)業(yè)氣象研究進(jìn)展

Progress in Ecological and Agricultural Meteorology Research

1 生態(tài)氣象

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(xiāo),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 拔節(jié)期干旱和復(fù)水對(duì)春玉米物候的影響及其生理生態(tài)機(jī)制

物候不僅是氣候變化的指示指標(biāo)，也是作物模型的關(guān)鍵參數(shù)?，F(xiàn)有研究主要關(guān)注物候變化與氣候環(huán)境因子的關(guān)系，關(guān)于植物物候變化的生理生態(tài)機(jī)制研究很少?；诖河衩装喂?jié)期干旱與不同時(shí)間（抽雄期和吐絲期）復(fù)水的田間模擬試驗(yàn)分析表明：（1）不同時(shí)間復(fù)水均使灌漿期延長(zhǎng)，乳熟期推遲（9 d），表明物候?qū)η捌谒置{迫存在記憶。（2）干旱條件下葉片凈光合速率（Pn）、蒸騰速率（Tr）、氣孔導(dǎo)度（Gs）和相對(duì)葉綠素含量（SPAD）均隨物候進(jìn)程呈先降后升再降趨勢(shì)，且均在抽雄期達(dá)到極小值；不同時(shí)間復(fù)水均使Pn、Tr和Gs在吐絲期達(dá)到極大值，而 SPAD 則在灌漿期達(dá)到極大值；葉水勢(shì)（LWP）隨干旱進(jìn)程整體呈下降趨勢(shì)，不同時(shí)間復(fù)水均只是減緩了其下降速度，表明 LWP可用于描述物候?qū)η捌谒置{迫的記憶。（3）通徑分析和決策系數(shù)分析表明，Pn是最主要的物候影響因子，而影響LWP的土壤相對(duì)濕度（RSWC）則是物候的主要控制因子，物候的變化是由Pn的累積變化引起，表明存在Pn的物候觸發(fā)閾值。研究結(jié)果為春玉米物候變化的準(zhǔn)確預(yù)測(cè)提供了依據(jù)。（周廣勝）

1.26 夏玉米不同生育期葉片和冠層含水量的遙感反演

高光譜遙感技術(shù)監(jiān)測(cè)作物含水量是了解作物生長(zhǎng)狀況的重要技術(shù)。為實(shí)現(xiàn)夏玉米不同生育期葉片和冠層含水量的快速、精細(xì)化、無(wú)損監(jiān)測(cè)，本文基于2014年和2015年的6—10月華北夏玉米不同生育期不同灌水量干旱模擬試驗(yàn)數(shù)據(jù)構(gòu)建了植被水分指數(shù)（W1，MSI，GVMI）復(fù)比指數(shù)（WNV和WCG）和紅邊反射率曲線面積（Darea）的夏玉米冠層等效水厚度（EWTC）和葉片可燃物含水量（FMC）的反演模型。結(jié)果表明：6個(gè)指標(biāo)反演夏玉米三葉期的EWTC模型均未達(dá)到0.05顯著性水平，三葉期后各指標(biāo)反演EWTC模型均達(dá)到0.01的顯著性水平，且總體而言模型精度從高到低為抽雄期、拔節(jié)期、灌漿期、成熟期和七葉期。6個(gè)指標(biāo)反演七葉期和拔節(jié)期的FMC均達(dá)到0.01顯著性水平。因此，同一光譜指標(biāo)反演夏玉米不同生育期葉片和冠層含水量的精度差異較大。光譜指標(biāo)反演夏玉米葉片和冠層含水量指標(biāo)的精度與夏玉米生育期有很大關(guān)系，進(jìn)而提出了夏玉米不同生育期含水量反演模型。研究結(jié)果可為準(zhǔn)確模擬夏玉米不同生育期含水量提供技術(shù)支撐。（周廣勝）

1.27 臺(tái)灣青棗在福建主產(chǎn)區(qū)的氣候適宜度

臺(tái)灣青棗屬新興果樹(shù)品種，經(jīng)濟(jì)效益顯著。為實(shí)現(xiàn)臺(tái)灣青棗在福建的優(yōu)質(zhì)高產(chǎn)，本研究基于產(chǎn)量和氣象數(shù)據(jù)，結(jié)合文獻(xiàn)和物候觀測(cè)資料以及農(nóng)業(yè)氣候適宜度模型，給出了臺(tái)灣青棗在福建主產(chǎn)區(qū)的氣候適宜度模型參數(shù)，分析了主產(chǎn)區(qū)氣候適宜度特征及其變化趨勢(shì)。結(jié)果表明： 基于等權(quán)重求和法構(gòu)建的模型可靠性最高；臺(tái)灣青棗在福建主產(chǎn)區(qū)的氣候適宜度較高，多數(shù)年份為適宜或較適宜；1996—2013年，氣候條件對(duì)臺(tái)灣青棗生長(zhǎng)的影響總體呈趨好態(tài)勢(shì)，有利于發(fā)展青棗生產(chǎn)；主產(chǎn)區(qū)全生育期溫度適宜度 綜合氣候適宜度日照適宜度 降水適宜度，9—10月是水分管理的關(guān)鍵期。研究結(jié)果對(duì)福建省臺(tái)灣青棗的生產(chǎn)管理和長(zhǎng)期規(guī)劃具有指導(dǎo)意義。（周廣勝）

1.28 基于閾值指標(biāo)分類(lèi)法的玉米營(yíng)養(yǎng)生長(zhǎng)階段受旱程度分級(jí)

基于田間小區(qū)試驗(yàn)，就玉米對(duì)不同強(qiáng)度及持續(xù)時(shí)間的干旱響應(yīng)進(jìn)行研究。玉米播種前進(jìn)行底墑?wù){(diào)控，使各小區(qū)土壤底墑基本一致。三葉期開(kāi)始，按照研究區(qū)7月多年平均降水量的 100%、80%、60%、40%、20%和7%分別進(jìn)行一次性灌水，此后不再進(jìn)行灌溉，全生育期利用大型電動(dòng)遮雨棚遮擋自然降水，隨生育時(shí)間推移形成6個(gè)不同初始土壤水分梯度的持續(xù)干旱過(guò)程。分析不同處理玉米營(yíng)養(yǎng)生長(zhǎng)階段（三葉期—拔節(jié)期）的形態(tài)（株高、葉面積）和生物量（莖干重、葉干重、總干重）指標(biāo)對(duì)干旱程度的響應(yīng)規(guī)律，采用閾值指標(biāo)分類(lèi)法（TITAN）確定各生長(zhǎng)指標(biāo)對(duì)干旱程度響應(yīng)規(guī)律發(fā)生明顯改變的臨界點(diǎn)，并基于不同指標(biāo)響應(yīng)干旱程度臨界點(diǎn)的同步性確定玉米植株水平響應(yīng)干旱程度（D）的臨界點(diǎn)，從而將玉米的受旱等級(jí)劃分為4個(gè)等級(jí)。結(jié)果表明：當(dāng)0D≤0.07 時(shí)，玉米受到輕旱影響，其形態(tài)和生物量指標(biāo)的平均降幅僅為1.2%～3.0%；當(dāng)0.07D≤0.47 時(shí)，玉米受到中旱影響，葉面積和株高的平均降幅分別為15.9%和8.6%，莖、葉干重及總干重的平均降幅分別為18.8%、15.4%和12.4%；當(dāng)0.47D≤0.73時(shí)，玉米受到重旱影響，葉面積的平均降幅為37.8%，株高的平均降幅為 16.9%，莖、葉干重及總干重的平均降幅分別為 43.3%、45.2%和 28.9%；當(dāng)0.73D≤1 時(shí)，玉米受到特旱影響，葉面積和株高的平均降幅分別為83.6%和 53.3%，葉干重和莖干重的降幅均高達(dá)90.0%以上，總干重的平均降幅達(dá)87.0%。研究結(jié)果可為作物干旱受災(zāi)程度的定量分級(jí)與評(píng)價(jià)提供方法和依據(jù)。（周廣勝）

1.29 2000—2010年中國(guó)典型陸地生態(tài)系統(tǒng)實(shí)際蒸散量和水分利用效率數(shù)據(jù)集

蒸散是陸地生態(tài)系統(tǒng)水分循環(huán)和能量平衡的關(guān)鍵過(guò)程，水分利用效率是反映生態(tài)系統(tǒng)碳水循環(huán)間耦合關(guān)系的重要指標(biāo)，二者在生態(tài)學(xué)、農(nóng)學(xué)、水文學(xué)、氣候?qū)W等多個(gè)學(xué)科中均具有重要的應(yīng)用價(jià)值。渦度相關(guān)法被認(rèn)為是現(xiàn)今唯一能直接測(cè)量生物圈與大氣間物質(zhì)與能量交換通量的標(biāo)準(zhǔn)方法，已成為生態(tài)系統(tǒng)尺度碳水交換通量觀測(cè)的主要方法。本文通過(guò)整合中國(guó)陸地生態(tài)系統(tǒng)通量觀測(cè)聯(lián)盟（ChinaFLUX）的長(zhǎng)期觀測(cè)數(shù)據(jù)和中國(guó)區(qū)域其他觀測(cè)站點(diǎn)基于渦度相關(guān)法發(fā)表的文獻(xiàn)數(shù)據(jù)，構(gòu)建了一套中國(guó)典型陸地生態(tài)系統(tǒng)實(shí)際蒸散量和水分利用效率數(shù)據(jù)集。本數(shù)據(jù)集共有實(shí)際蒸散量數(shù)據(jù)記錄143條、水分利用效率數(shù)據(jù)記錄96條，涉及5種生態(tài)系統(tǒng)類(lèi)型45個(gè)生態(tài)系統(tǒng)，時(shí)間跨度為2000—2010年。本數(shù)據(jù)集可以為陸地生態(tài)系統(tǒng)碳水循環(huán)、生態(tài)系統(tǒng)管理和評(píng)估、全球變化等相關(guān)領(lǐng)域的研究提供數(shù)據(jù)支持。（周廣勝）

1.30 2002—2010年中國(guó)典型生態(tài)系統(tǒng)輻射及光能利用效率數(shù)據(jù)集

輻射是陸地生態(tài)系統(tǒng)能量的主要來(lái)源，其利用效率表現(xiàn)為光能利用率，反映了生態(tài)系統(tǒng)轉(zhuǎn)化光能、生成有機(jī)物質(zhì)的能力。揭示典型生態(tài)系統(tǒng)的輻射及光能利用效率可以為評(píng)估區(qū)域光能資源及其利用效率提供參考，也為評(píng)估區(qū)域有機(jī)物質(zhì)固定能力及碳吸收能力提供依據(jù)?；谥袊?guó)陸地生態(tài)系統(tǒng)通量觀測(cè)研究聯(lián)盟（ChinaFLUX）的長(zhǎng)期觀測(cè)結(jié)果及已發(fā)表文獻(xiàn)的公開(kāi)數(shù)據(jù)，構(gòu)建了2002—2010年中國(guó)典型生態(tài)系統(tǒng)輻射及光能利用效率數(shù)據(jù)集，包含51個(gè)生態(tài)系統(tǒng)126個(gè)站點(diǎn)年輻射、光能利用效率及吸收光能利用效率的觀測(cè)記錄。另外，本數(shù)據(jù)集還包含生態(tài)系統(tǒng)代碼、年份、經(jīng)度、緯度、海拔、生態(tài)系統(tǒng)類(lèi)型、年均氣溫、年總降水量、年均CO2質(zhì)量濃度、年均葉面積指數(shù)、最大葉面積指數(shù)等生物氣候信息。本數(shù)據(jù)集可以為評(píng)估生態(tài)系統(tǒng)生產(chǎn)能力、應(yīng)對(duì)氣候變化等方面的研究提供數(shù)據(jù)支持。（周廣勝）

1.31 中國(guó)毛葡萄和刺葡萄分布的氣候適宜性

毛葡萄和剌葡萄是起源于中國(guó)且用于葡萄酒釀造的兩大野生葡萄品種。本研究基于巳有中國(guó)毛葡萄和刺葡萄的氣候影響因子研究成果，利用最大嫡原理從充分性與必要性方面確定了影響中國(guó)毛葡萄和刺葡萄種植分布的主導(dǎo)氣候因子，并基于這些因子綜合作用反映的毛葡萄和剌葡萄種植分布的存在概率分析了中國(guó)毛葡萄和剌葡萄分布區(qū)的氣侯適宜性。結(jié)果表明：影響中國(guó)毛葡萄、刺葡萄分布的主導(dǎo)氣候因子是年日照時(shí)數(shù)、開(kāi)花期5月降水量、年極端最低氣溫、最冷月平均氣溫。中國(guó)毛葡萄、刺葡萄氣候高適宜區(qū)分布在湖南西部和南部、廣西中北部、貴州東南部、重慶中部。氣候高適宜區(qū)、適宜區(qū)、低適宜區(qū)面積分別為研究區(qū)域總面積的2%、14%和16%。毛葡萄、刺荷萄氣候適宜及以上區(qū)域的年日照時(shí)數(shù)閾值為1200～1800 h，年極端最低氣溫為?8 ℃以上，最冷月平均氣溫閭值為2～13 ℃，5月降水量為110～320 mm。（周廣勝）

1.32 基于文獻(xiàn)整合的中國(guó)不同下墊面植被覆蓋度遙感估算模型數(shù)據(jù)集

植被覆蓋度是衡量植被群落覆蓋地表狀況的一個(gè)綜合量化指標(biāo)，是研究生態(tài)環(huán)境、水土保持和氣候變化等方面的重要基礎(chǔ)數(shù)據(jù)。科學(xué)定量地反演植被覆蓋度對(duì)實(shí)現(xiàn)生態(tài)環(huán)境治理與生態(tài)建設(shè)服務(wù)具有重要的指導(dǎo)意義。本文收集了1980—2016年中國(guó)區(qū)域發(fā)表的不同下墊面植被覆蓋度遙感估算模型資料，構(gòu)建了中國(guó)不同下墊面植被覆蓋度遙感估算模型數(shù)據(jù)集。研究區(qū)范圍覆蓋 24.49°～51.42°N，80.23°～128.95°E，涉及的地區(qū)包括西北地區(qū)（內(nèi)蒙古、新疆、青海、西藏、甘肅），華北地區(qū)（北京、河北、山東、河南），西南和南方地區(qū)（云南、廣西、江西）。本模型數(shù)據(jù)集涵蓋的主要下墊面類(lèi)型包括林地、灌叢、草地、濕地、沙漠化草地、農(nóng)田、城鎮(zhèn)和石漠化區(qū)。本數(shù)據(jù)集的建立與共享，可為生態(tài)、水保、土壤、水利、植物等領(lǐng)域的定量研究提供模型數(shù)據(jù)基礎(chǔ)，并可為生態(tài)效益評(píng)估、區(qū)域生態(tài)安全保護(hù)以及生態(tài)保護(hù)紅線提供模型數(shù)據(jù)支撐。（周廣勝）

1.33 氣候變化背景下麥田溝金針蟲(chóng)爆發(fā)性發(fā)生為害

近年華北地區(qū)大面積推行保護(hù)性耕作措施和作物秸稈粉碎還田，冬小麥與夏玉米一年兩熟連續(xù)輪作種植，為溝金針蟲(chóng)創(chuàng)造了有利的取食和棲息環(huán)境。地處華北北部的中國(guó)氣象局固城農(nóng)業(yè)氣象野外科學(xué)試驗(yàn)基地2018—2019年秋季、冬季、春季氣溫出現(xiàn)了冷暖交替，尤其最低氣溫顯著偏高，誘發(fā)麥田溝金針蟲(chóng)爆發(fā)性發(fā)生為害。據(jù)春季麥田挖土調(diào)查，蟲(chóng)口密度最高達(dá)144頭/m2，蟲(chóng)口重量最重達(dá)18.764 g/m2。58個(gè)調(diào)查點(diǎn)達(dá)防治指標(biāo)5頭/m2占98.27%。拔節(jié)—收獲期調(diào)查蟲(chóng)口密度孕穗期最高，拔節(jié)期次之，收獲期最低。冬小麥與夏玉米禾本科作物連作種植田間蟲(chóng)口密度達(dá)35.3～40.4頭/m2，顯著高于前茬大豆、玉米、冬小麥休閑地，且花生地、春玉米地比大豆地蟲(chóng)口密度高5倍多，蟲(chóng)口重量高10倍以上。成熟期蟲(chóng)害麥田測(cè)產(chǎn)，籽粒減產(chǎn)36.8%；蟲(chóng)口密度增加10頭/m2，籽粒減產(chǎn)率增加4.824%；蟲(chóng)口重量增加1 g/m2，籽粒減產(chǎn)率增加3.871%；植株蟲(chóng)害率增加10%，籽粒減產(chǎn)率增加11.587%。（任三學(xué)）

1.34 印巴兩國(guó)邊境沙漠蝗群對(duì)當(dāng)?shù)刂脖坏挠绊懠捌湮磥?lái)可能發(fā)展趨勢(shì)

2020年初非洲東北和印巴邊境沙漠蝗群席卷多個(gè)國(guó)家，大面積農(nóng)田及自然植被被啃食，是什么氣候條件促成了此次沙漠蝗災(zāi)？距離中國(guó)最近的印巴邊境蝗群成為研究以及中國(guó)媒體關(guān)注的熱點(diǎn)，蝗災(zāi)對(duì)當(dāng)?shù)刂脖坏挠绊懭绾?？其發(fā)展趨勢(shì)如何？從氣候?qū)W上分析，歷史上是否曾經(jīng)或者未來(lái)蝗群是否可向印度東邊遷飛而進(jìn)入中國(guó)呢？這些成為社會(huì)關(guān)注的焦點(diǎn)。本研究利用長(zhǎng)時(shí)間序列的衛(wèi)星遙感數(shù)據(jù)和氣象氣候觀測(cè)數(shù)據(jù)，對(duì)沙漠蝗群的可能擴(kuò)展趨勢(shì)及其是否可能進(jìn)入中國(guó)進(jìn)行了分析。研究結(jié)果表明：（1）由于沙漠蝗群的啃食，2020年1月和2月，在蝗群分布區(qū)大面積植被區(qū)的歸一化植被指數(shù)較常年大幅度下降，2月（2月3日數(shù)據(jù)）的啃食面積較1月明顯擴(kuò)大；（2）發(fā)生在2018年5和10月兩次印度洋颶風(fēng)和2019年12月強(qiáng)熱帶風(fēng)暴等幾個(gè)罕見(jiàn)氣旋給非洲和阿拉伯半島帶來(lái)的強(qiáng)降水，是本次非洲—西亞蝗災(zāi)的形成重要原因；（3）從影響沙漠蝗群的起飛的氣溫和沙漠蝗蟲(chóng)適合的降水條件來(lái)看，歷史上或未來(lái)沙漠蝗群遷徙到印度東邊的機(jī)會(huì)很少，進(jìn)入中國(guó)境內(nèi)的概率幾乎為零。（房世波）

1.35 作物耕作節(jié)律與多時(shí)相遙感結(jié)合的山地耕地信息提取方法探索

由于山地地貌區(qū)的耕地分布破碎度大，僅應(yīng)用中分辨率遙感影像難以獲得高精度的山地耕地分布信息，如何提高應(yīng)用中分辨率遙感影像提取山地耕地信息的精度是亟需研究的問(wèn)題。本研究在分析區(qū)域主要作物及其生育期隨季節(jié)變化的基礎(chǔ)上，根據(jù)耕地與其他地物在植被覆蓋時(shí)間序列變化上的區(qū)別，基于多時(shí)相遙感影像，提出了一種將作物生長(zhǎng)和耕作節(jié)律與多時(shí)相遙感結(jié)合的耕地信息遙感提取方法。應(yīng)用該方法準(zhǔn)確的提取了典型山地區(qū)四川省會(huì)理縣的耕地分布信息。這種方法提取速度快，結(jié)果具有一定的實(shí)時(shí)性和較高的準(zhǔn)確性，可以滿足耕地利用及管理中對(duì)耕地信息適時(shí)獲取的要求，也可以應(yīng)用于對(duì)歷史耕地矢量數(shù)據(jù)中地塊錯(cuò)分進(jìn)行修正、更新漏分問(wèn)題等。（房世波）

1.36 2000—2016年三江源區(qū)植被生長(zhǎng)季NDVI變化及其對(duì)氣候因子的響應(yīng)

目前使用較為廣泛的植被指數(shù)與氣候因子的相關(guān)性分析方法有2種：NDVI與生長(zhǎng)季內(nèi)和生長(zhǎng)季間氣候因子的相關(guān)性分析，前者通常計(jì)算多個(gè)生長(zhǎng)季內(nèi)每個(gè)月的NDVI與氣溫、降水等氣候因子的關(guān)系，后者通常計(jì)算多年同一月份的NDVI與相同月份氣溫、降水等氣候因子的關(guān)系。這兩種方法分析的結(jié)果有什么差異，差異的原因是什么？哪種方法更適合NDVI與氣候因子之間的關(guān)系分析？本文以三江源區(qū)NDVI與生長(zhǎng)季內(nèi)和生長(zhǎng)季間氣候因子的關(guān)系分析為例，探析這個(gè)問(wèn)題?；?000—2016年三江源區(qū)MODIS13A1C6歸一化植被指數(shù)數(shù)據(jù)，結(jié)合研究區(qū)植被類(lèi)型圖、氣溫與降水等氣象數(shù)據(jù)，采用上述兩種方法，對(duì)三江源區(qū)2000—2016年NDVI與氣候因子的相關(guān)性進(jìn)行了分析。結(jié)果表明：（1）生長(zhǎng)季內(nèi)植被NDVI與同期氣溫和降水的相關(guān)性隨著時(shí)間尺度的增加而增大。生長(zhǎng)季間NDVI與同期氣溫、同期降水的相關(guān)性較小且不一致。（2）生長(zhǎng)季間植被NDVI在整個(gè)生長(zhǎng)季內(nèi)對(duì)于氣溫的時(shí)滯響應(yīng)程度并不明顯，前推月份的降水對(duì)高寒草甸生長(zhǎng)季后期能夠產(chǎn)生一定的積極作用。（3）兩種方法所得出的結(jié)論有一定差異性，從統(tǒng)計(jì)學(xué)角度分析，相關(guān)系數(shù)會(huì)隨著樣本數(shù)量的增加而變大，但這種相關(guān)是一種由樣本數(shù)量累加造成的偽相關(guān)，不一定能真實(shí)反映植被NDVI與氣溫、降水等因素的關(guān)系，而生長(zhǎng)季間植被NDVI與氣候因子的關(guān)系在相關(guān)性分析不存在這樣的問(wèn)題，更能真實(shí)反映氣候因子年際變化對(duì)植被的影響。（徐嘉昕，房世波）

1.37 基于 Faster R-CNN的野外環(huán)境中蝗蟲(chóng)快速識(shí)別

蝗蟲(chóng)是常見(jiàn)的害蟲(chóng)之一，對(duì)農(nóng)作物和生態(tài)系統(tǒng)具有很大的危害，采用常規(guī)的方法對(duì)蝗蟲(chóng)進(jìn)行監(jiān)測(cè)存在一定局限性，為了有效應(yīng)用海量野外影像數(shù)據(jù)實(shí)現(xiàn)對(duì)蝗蟲(chóng)實(shí)時(shí)監(jiān)測(cè)，本文建立了一種基于深度學(xué)習(xí)網(wǎng)絡(luò)的蝗蟲(chóng)自動(dòng)識(shí)別模型，利用手機(jī)模擬攝像頭獲取的內(nèi)蒙古錫林浩特附近草原的280 張蝗蟲(chóng)的RGB 圖像，采用深度學(xué)習(xí)算法中的 Faster R-CNN(Faster Region-based Convolutional Neural Network)網(wǎng)絡(luò)結(jié)構(gòu)建立了蝗蟲(chóng)識(shí)別模型。經(jīng)驗(yàn)證該模型的精確度為0.756，可以較準(zhǔn)確地將蝗蟲(chóng)從野外復(fù)雜環(huán)境中識(shí)別出來(lái)，與以往同類(lèi)研究相比，在識(shí)別結(jié)果和實(shí)用性方面均有較大的進(jìn)步，該模型是建立蝗蟲(chóng)實(shí)時(shí)監(jiān)測(cè)系統(tǒng)的基礎(chǔ)，可以為蝗蟲(chóng)的防治提供輔助信息，該網(wǎng)絡(luò)結(jié)構(gòu)還可以應(yīng)用于其他害蟲(chóng)的識(shí)別，具有較強(qiáng)的推廣性，拓寬了深度學(xué)習(xí)算法的應(yīng)用領(lǐng)域。（武英潔，房世波）

2 農(nóng)業(yè)氣象

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 土壤水分對(duì)冬小麥葉片光合速率影響模型構(gòu)建

植物葉片光合速率是表征植物光合能力的重要參數(shù)，對(duì)土壤水分反應(yīng)敏感，建立不同土壤水分對(duì)冬小麥葉片光合速率影響模型，有助于準(zhǔn)確理解冬小麥的光合作用和產(chǎn)量形成。該文收集整理了1996—2017年我國(guó)冬小麥主產(chǎn)區(qū)11個(gè)試驗(yàn)地點(diǎn)、17個(gè)冬小麥品種的干旱和漬水試驗(yàn)數(shù)據(jù)共64組310個(gè)樣本，分別構(gòu)建干旱和漬水對(duì)冬小麥葉片光合速率影響的分段式和指數(shù)型模型，形成土壤水分對(duì)冬小麥葉片光合速率影響模型（SMEP）。結(jié)果表明：隨著土壤相對(duì)濕度增加，冬小麥葉片光合速率系數(shù)呈穩(wěn)定低值—線性增加—穩(wěn)定高值—緩慢下降的特點(diǎn)；隨著漬水時(shí)間延長(zhǎng)，冬小麥葉片光合速率系數(shù)呈緩慢下降—快速下降的特點(diǎn)。對(duì)SMEP模型進(jìn)行回代檢驗(yàn)、外推檢驗(yàn)、單點(diǎn)驗(yàn)證、單發(fā)育期驗(yàn)證發(fā)現(xiàn)，模型模擬結(jié)果與文獻(xiàn)數(shù)據(jù)有較好的一致性，回歸系數(shù)在1.0附近，且均達(dá)到0.01顯著性水平。SMEP模型將嵌入中國(guó)農(nóng)業(yè)氣象模式（CAMM1.0），為CAMM不斷完善提供科技支撐。（王培娟）

2.11 基于線性生長(zhǎng)假設(shè)的作物積溫模型穩(wěn)定性比較

利用山西省2個(gè)冬小麥觀測(cè)站、3個(gè)春玉米觀測(cè)站和3個(gè)夏玉米觀測(cè)站長(zhǎng)時(shí)間序列的作物生育期觀測(cè)資料和地面氣象觀測(cè)資料，基于4種作物生長(zhǎng)發(fā)育速率線性假設(shè)，建立了作物不同生育階段的活動(dòng)積溫（Aa）和4種有效積溫模型，并對(duì)各積溫模型的穩(wěn)定性進(jìn)行統(tǒng)計(jì)分析與檢驗(yàn)。結(jié)果表明：以變異系數(shù)為指標(biāo)檢驗(yàn)各模型穩(wěn)定性時(shí)，活動(dòng)積溫模型最穩(wěn)定，考慮作物三基點(diǎn)溫度的有效積溫模型（Ae4）次之，僅考慮作物下限溫度的有效積溫模型（Ae1）及考慮作物上、下限溫度的有效積溫模型（Ae2和Ae3）最不穩(wěn)定。以生育期模擬偏差和生育期模擬準(zhǔn)確率為指標(biāo)檢驗(yàn)各模型穩(wěn)定性時(shí)，Aa 模型對(duì)作物生育期的模擬效果最好，穩(wěn)定性最高；4種有效積溫模型中，Ae1、Ae2 和Ae3 模型模擬效果無(wú)顯著差異，準(zhǔn)確率和穩(wěn)定性高于Ae4模型。各積溫模型在春玉米和夏玉米出苗—抽雄期和抽雄—成熟期的穩(wěn)定性表現(xiàn)一致，出苗—抽雄期各積溫模型的穩(wěn)定性高于抽雄—成熟期；冬小麥在出苗—抽穗期和抽穗—成熟期各積溫模型的穩(wěn)定性表現(xiàn)因地區(qū)不同而有所差異。因此，在實(shí)際應(yīng)用中，還需根據(jù)作物種植區(qū)域、品種類(lèi)型以及生育期選取合適的基點(diǎn)溫度，綜合分析多種積溫模型穩(wěn)定性，選取穩(wěn)定性更高的積溫模型。(郭建平)

2.12 葫蘆島玉米生育期氣候資源及限制因子

明確農(nóng)作物生長(zhǎng)發(fā)育的主要?dú)夂蛳拗埔蜃蛹跋拗瞥潭龋蔀檗r(nóng)業(yè)應(yīng)對(duì)氣候變化和高效利用氣候資源提供科學(xué)依據(jù)?；谶|寧省葫蘆島市玉米主栽區(qū)綏中縣和建昌縣1980—2018年逐日氣象觀測(cè)數(shù)據(jù)和農(nóng)業(yè)氣象觀測(cè)數(shù)據(jù)，采用生態(tài)氣候適宜度方法，分析了玉米出苗—拔節(jié)、拔節(jié)—抽雄、抽雄—成熟3個(gè)階段的主要限制因子及限制程度。結(jié)果表明：效能模型可以用于明晰玉米不同生育階段的環(huán)境限制因子并定量評(píng)估限制程度；研究區(qū)氣候平均限制程度達(dá)30%以上，拔節(jié)—抽雄期氣候限制程度最大，出苗—拔節(jié)期氣候限制程度隨年份逐漸下降；降水因子對(duì)葫蘆島市氣候資源有效性限制程度最大，為27%～61%，其次是日照因子，溫度因子限制程度最??；玉米產(chǎn)量與氣候限制程度有密切關(guān)系，氣候的劇烈波動(dòng)是導(dǎo)致雨養(yǎng)玉米產(chǎn)量不穩(wěn)的重要環(huán)境因素，因此提高氣候資源利用率是保障玉米高產(chǎn)穩(wěn)產(chǎn)的重要舉措。(郭建平)

2.13 晉東南潞黨參生態(tài)氣候適生種植區(qū)劃

基于晉東南地區(qū)16個(gè)國(guó)家氣象觀測(cè)站1981—2018年的氣候資料，分析了氣候因子與潞黨參產(chǎn)量的相關(guān)性。選取全生育期≥10 ℃積溫和降水量、根生長(zhǎng)期平均氣溫以及苗期降水量等主要影響因子作為生態(tài)氣候區(qū)劃指標(biāo)，選取DEM 和土壤質(zhì)地作為地理環(huán)境影響指標(biāo)，分別建立各指標(biāo)的空間分析模型，按照90×90 m的細(xì)網(wǎng)格進(jìn)行推算，采用隸屬函數(shù)計(jì)算得到的各指標(biāo)評(píng)判值以及熵權(quán)法確定的權(quán)重系數(shù)，構(gòu)建了潞黨參生態(tài)氣候適生綜合評(píng)判指標(biāo)，對(duì)晉東南地區(qū)潞黨參生態(tài)氣候適生種植區(qū)進(jìn)行了區(qū)劃。結(jié)果表明:晉東南地區(qū)潞黨參生態(tài)氣候適宜區(qū)主要分布在東部太行山區(qū)、西部太岳山區(qū)以及晉城西南部的太岳山和中條山的交界處，區(qū)域內(nèi)光溫水資源匹配較好，適宜潞黨參生長(zhǎng)發(fā)育; 較適宜區(qū)主要分布在太行山和太岳山向中部上黨盆地和晉城盆地過(guò)渡的淺山丘陵區(qū)，該區(qū)域熱量條件較好，降水資源相對(duì)短缺; 不適宜區(qū)主要分布在上黨盆地和晉城盆地的中心區(qū)域，區(qū)域海拔較低，夏季高溫，不適宜潞黨參種植。本研究結(jié)果可為晉東南地區(qū)潞黨參優(yōu)化生態(tài)布局，科學(xué)、合理利用氣候資源提供參考。(郭建平)

2.14 玉米葉面積指數(shù)估算通用模型

基于2018年黑龍江哈爾濱、吉林榆樹(shù)、遼寧錦州、新疆烏蘭烏蘇、甘肅西峰、河北固城6個(gè)農(nóng)業(yè)氣象試驗(yàn)站不同屬性品種玉米的分期播種試驗(yàn)資料，以當(dāng)?shù)爻Ｄ甏筇飳?shí)際播種期為界，提前10 d播種為第1播期，正常播種為第2播期，比正常晚10 d播種為第3播期，晚20 d為第4播期，以第1播期、第3播期和第4播期實(shí)測(cè)值計(jì)算的有效積溫相對(duì)值為自變量，采用修正的Logistic方程，構(gòu)建了通用的玉米葉面積指數(shù)估算模型，進(jìn)一步利用有效積溫相對(duì)值對(duì)模型在三葉期和七葉期的殘差進(jìn)行訂正，并用2018年6個(gè)農(nóng)業(yè)氣象試驗(yàn)站及2019年吉林榆樹(shù)、甘肅西峰和山東泰安3個(gè)農(nóng)業(yè)氣象試驗(yàn)站，8個(gè)不同品種玉米的分期播種試驗(yàn)資料對(duì)模型進(jìn)行檢驗(yàn)。結(jié)果顯示：以多屬性品種玉米有效積溫相對(duì)值為自變量的RLAI擬合曲線完全符合修正的Logistic方程變化規(guī)律，模型擬合優(yōu)度（R2）達(dá)到0.93，通過(guò)了0.01水平的顯著性檢驗(yàn)，具有較高的精度。玉米全生育期不同品種模擬RLAI與實(shí)測(cè)計(jì)算RLAI的相關(guān)性較高，通過(guò)了0.01水平的顯著性檢驗(yàn)，相關(guān)系數(shù)均超過(guò)0.9，平均相對(duì)誤差介于13.8%～27.6%。不同生育期模擬RLAI 與實(shí)測(cè)計(jì)算RLAI 的平均相對(duì)誤差介于9.4%～30.7%，七葉期最高，乳熟期最低。說(shuō)明以不同屬性玉米品種、土壤性質(zhì)、管理措施、種植密度下的試驗(yàn)資料為基礎(chǔ)構(gòu)建的LAI 估算模型，較以往基于單站、單品種、單播期或單站多品種LAI估算模型更具普適性，適用于大多數(shù)屬性品種玉米的LAI模擬。(郭建平)

2.15 晉北農(nóng)牧交錯(cuò)帶農(nóng)業(yè)旱災(zāi)脆弱性評(píng)價(jià)

晉北地區(qū)是北方農(nóng)牧交錯(cuò)帶的重要組成部分，是半干旱區(qū)與半濕潤(rùn)區(qū)的過(guò)渡帶，干旱是影響該區(qū)農(nóng)業(yè)生產(chǎn)最主要的氣象災(zāi)害，正確評(píng)價(jià)農(nóng)業(yè)旱災(zāi)脆弱性是科學(xué)應(yīng)對(duì)干旱的基礎(chǔ)和前提。選取晉北地區(qū)作物因素、環(huán)境因素和人為因素的11項(xiàng)指標(biāo)，運(yùn)用熵權(quán)法和層次分析法相結(jié)合的組合賦權(quán)法確定各指標(biāo)的權(quán)重，通過(guò)綜合加權(quán)建立農(nóng)業(yè)旱災(zāi)脆弱性評(píng)估模型，并基于GIS的Iso非監(jiān)督聚類(lèi)方法進(jìn)行分區(qū)。結(jié)果表明:晉北農(nóng)牧交錯(cuò)帶旱災(zāi)脆弱性分布特征是，東北和西南地區(qū)較重，中部和東南部地區(qū)較輕。重度和中度脆弱區(qū)占晉北地區(qū)總面積的58.2%，輕度脆弱區(qū)占27.0%，晉北農(nóng)牧交錯(cuò)帶農(nóng)業(yè)旱災(zāi)脆弱性整體來(lái)說(shuō)較重。在各影響指標(biāo)中，有效灌溉面積和機(jī)井?dāng)?shù)影響最大，其次是鄉(xiāng)村人均收入和降水量，說(shuō)明氣候和人為抗旱能力對(duì)農(nóng)業(yè)旱災(zāi)脆弱性有很大影響，因此，應(yīng)適當(dāng)增加農(nóng)業(yè)投入，提高天然降水的利用率和土壤蓄水保水能力，大力推廣農(nóng)業(yè)節(jié)水技術(shù)，提高農(nóng)業(yè)防旱抗旱能力。(郭建平)

2.16 冬小麥氨基酸品質(zhì)與氣候生態(tài)因子關(guān)系研究

選用南北方冬小麥品種作試驗(yàn)材料，通過(guò)地理分期播種試驗(yàn)，采用方差分析、主成分分析、聚類(lèi)分析等方法對(duì)冬小麥籽粒氨基酸品質(zhì)等進(jìn)行評(píng)價(jià)，利用典型相關(guān)和回歸分析等方法分析冬小麥氨基酸品質(zhì)與氣候生態(tài)因子的相關(guān)程度，并選擇相關(guān)性顯著的氣候生態(tài)因子構(gòu)建冬小麥氨基酸含量預(yù)測(cè)模型。結(jié)果表明，各冬小麥氨基酸成分中，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環(huán)境適應(yīng)性相對(duì)較強(qiáng)，而半必需氨基酸在進(jìn)行優(yōu)質(zhì)育種時(shí)選擇潛勢(shì)較大；冬小麥氨基酸成分含量呈現(xiàn)出北方品種高于南方品種的區(qū)域分布特征，蘇氨酸、苯丙氨酸、精氨酸、天冬氨酸、谷氨酸和甘氨酸含量地域性差異顯著。氨基酸品質(zhì)可由累計(jì)貢獻(xiàn)率達(dá)97.796%的3個(gè)主成分解釋?zhuān)渚C合品質(zhì)評(píng)價(jià)為固城郯麥98表現(xiàn)最優(yōu)，泰安山農(nóng)18表現(xiàn)較好，而荊州鄭麥9023、宿州皖麥52、徐州徐麥33表現(xiàn)較差；聚類(lèi)分析中類(lèi)群排列與冬小麥氨基酸成分含量及其地域分布關(guān)系密切，Ⅰ、Ⅱ、Ⅲ、Ⅳ類(lèi)依次為華北麥區(qū)郯麥98、黃淮北部麥區(qū)山農(nóng)18、黃淮南部麥區(qū)皖麥52、黃淮南部麥區(qū)徐麥33和江淮麥區(qū)鄭麥9023，與主成分分析中的綜合評(píng)價(jià)排序有一致性；冬小麥氨基酸含量與氣候生態(tài)因子相關(guān)密切，其中必需氨基酸含量與溫濕條件相關(guān)最顯著，大部分氨基酸成分均可以通過(guò)調(diào)節(jié)小氣候環(huán)境或土壤濕度的方式提高其含量品質(zhì)。(郭建平)

2.17 玉米冠層對(duì)降水的截留模型構(gòu)建

降水資源是農(nóng)作物的主要水分來(lái)源，農(nóng)作物通過(guò)吸收土壤中的水分維持正常的生長(zhǎng)發(fā)育，但由于未考慮農(nóng)作物冠層對(duì)降水截留作用，在水資源評(píng)估和農(nóng)田水分平衡研究中往往高估降水作用。該文通過(guò)2018年玉米生長(zhǎng)季在遼寧錦州農(nóng)業(yè)氣象試驗(yàn)站開(kāi)展的降水模擬試驗(yàn)系統(tǒng)分析了玉米冠層對(duì)降水的截留效應(yīng)，結(jié)果表明：在降水量一定條件下，玉米冠層截留量與葉面積指數(shù)的二次多項(xiàng)式擬合相關(guān)最佳；在葉面積指數(shù)一定條件下，玉米冠層截留量與降水量的冪函數(shù)擬合相關(guān)最佳。綜合葉面積指數(shù)和降水量分析表明：玉米冠層截留量與葉面積指數(shù)平方及降水量對(duì)數(shù)函數(shù)擬合呈正相關(guān)。根據(jù)我國(guó)玉米傳統(tǒng)種植方式，高產(chǎn)玉米的葉面積指數(shù)最大一般為5～6，因此，對(duì)一次降水的最大截留量通常約為1.5～2.3 mm，當(dāng)葉面積指數(shù)小于1時(shí)，對(duì)降水的截留可忽略不計(jì)。(郭建平)

2.18 植物對(duì)降水截留的研究進(jìn)展

降水資源是植物生長(zhǎng)發(fā)育和產(chǎn)量形成的主要水分來(lái)源，植物通過(guò)吸收土壤中的水分維持正常生長(zhǎng)發(fā)育，降水不僅影響自然植物物種分布，也影響植物生產(chǎn)力。由于未考慮植物冠層對(duì)降水的截留作用，在水資源評(píng)估和農(nóng)田水分平衡研究中往往高估降水作用，因此，討論降水截留在水文生態(tài)學(xué)和農(nóng)業(yè)氣象學(xué)中均有重要意義。該文系統(tǒng)介紹降水截留的觀測(cè)方法，包括間接測(cè)量法中各分量測(cè)定方法、直接測(cè)量法詳細(xì)過(guò)程及應(yīng)用各種方法需注意的問(wèn)題；系統(tǒng)回顧有關(guān)森林和農(nóng)作物對(duì)降水截留的研究成果；探討在植物對(duì)降水截留研究中存在的主要問(wèn)題：對(duì)截留概念的理解不同導(dǎo)致截留測(cè)定結(jié)果差異顯著，沒(méi)有完善的方法導(dǎo)致測(cè)定結(jié)果準(zhǔn)確性不足，植物種植密度不同導(dǎo)致截留差異，降水強(qiáng)度不同導(dǎo)致截留差異，風(fēng)速、植物形態(tài)結(jié)構(gòu)、葉片表面特性等因素也會(huì)影響降水截留的大小。降水過(guò)程中植物葉面蒸發(fā)問(wèn)題、降雪的截留問(wèn)題、風(fēng)的影響、研究尺度、研究方法以及綜合模擬模型將是未來(lái)研究的重點(diǎn)和難點(diǎn)。(郭建平)

2.19 華北平原農(nóng)田CO2濃度變化特征

旨在了解農(nóng)田CO2濃度長(zhǎng)期動(dòng)態(tài)變化特征、趨勢(shì)、濃度增量分布模式等，收集了2007—2018年中國(guó)氣象局固城生態(tài)與農(nóng)業(yè)氣象試驗(yàn)站開(kāi)路式渦相關(guān)CO2濃度觀測(cè)數(shù)據(jù)。研究了華北平原農(nóng)田CO2濃度的年際、年內(nèi)、晝夜和CO2通量等動(dòng)態(tài)變化特征，對(duì)比分析了華北平原農(nóng)田CO2濃度與城市站和大氣本底站CO2濃度變化趨勢(shì)及差異。結(jié)果表明，近十多年來(lái)華北平原農(nóng)田CO2年平均濃度顯著升高31.0 μmol/mol（r=0.263，P0.01），年均增幅（2.58 μmol/mol）與全球和瓦里關(guān)本底站大氣CO2濃度增幅接近，但農(nóng)田CO2濃度年際和年內(nèi)季節(jié)變化波動(dòng)巨大，日平均濃度和逐時(shí)平均濃度標(biāo)準(zhǔn)差分別為33.7、33.5 μmol/mol。夜間CO2平均濃度為395.8 μmol/mol，比白天高36.2 μmol/mol（10.1%），8月最高差值達(dá)到74.4 μmol/mol（20.6%）。在作物生長(zhǎng)季節(jié)，5月和8—9月白天CO2濃度出現(xiàn)的兩個(gè)谷值準(zhǔn)確地對(duì)應(yīng)了CO2通量動(dòng)態(tài)變化的兩個(gè)峰值，表明4—9月晝間CO2濃度和通量動(dòng)態(tài)變化很好地反映了華北平原冬小麥和夏玉米生長(zhǎng)過(guò)程、農(nóng)事活動(dòng)和農(nóng)田碳交換的關(guān)系。農(nóng)田CO2濃度動(dòng)態(tài)變化與城市、濕地和大氣本底站的變化特征不同，表明其動(dòng)態(tài)變化的形成機(jī)制有差異。農(nóng)田CO2濃度晝夜及季節(jié)變化特征為研究和評(píng)估CO2濃度升高影響作物生長(zhǎng)和產(chǎn)量提供指導(dǎo)依據(jù)。(俄有浩)

2.20 江西早稻高溫?zé)岷Πl(fā)生時(shí)間分布特征

以江西早稻為例，利用氣象資料、早稻高溫?zé)岷?zāi)情史料和生育期資料，構(gòu)建歷史早稻高溫?zé)岷颖炯?，在Kolmogorov-Smirnov(K-S)分布擬合檢驗(yàn)的基礎(chǔ)上，采用信息擴(kuò)散方法計(jì)算得到早稻高溫?zé)岷倶颖竞筒煌掷m(xù)日數(shù)（3～5 d、6～8 d和8 d以上）不同等級(jí)（輕、中、重）熱害在早稻抽穗期前后的發(fā)生概率。結(jié)果表明：早稻高溫?zé)岷ζ鹗加诔樗肭? d至抽穗后20 d，抽穗揚(yáng)花期發(fā)生概率最高，隨著早稻進(jìn)入乳熟期高溫?zé)岷Πl(fā)生概率逐漸降低。早稻抽穗揚(yáng)花期持續(xù)3～5 d早稻高溫?zé)岷σ暂p、中度為主，5 d以上中、重度高溫?zé)岷Πl(fā)生概率為100%；隨著早稻進(jìn)入乳熟期，高溫?zé)岷σ灾卸群洼p度為主，重度熱害概率顯著降低。早稻輕度高溫?zé)岷Φ闹饕聻?zāi)時(shí)段為抽穗至灌漿中期，中度熱害的主要致災(zāi)時(shí)段為抽穗至灌漿中前期，而重度熱害的主要致災(zāi)時(shí)段為孕穗期至灌漿初期。(楊建瑩)

2.21 江西早稻高溫?zé)岷Φ燃?jí)動(dòng)態(tài)判識(shí)及時(shí)空變化特征

構(gòu)建考慮高溫天氣過(guò)程發(fā)生時(shí)間、持續(xù)日數(shù)的水稻高溫?zé)岷χ笜?biāo)，實(shí)現(xiàn)水稻高溫?zé)岷Φ燃?jí)的動(dòng)態(tài)判識(shí)，對(duì)精準(zhǔn)監(jiān)測(cè)、預(yù)警與評(píng)估水稻高溫?zé)岷σ饬x重大。以江西早稻為研究對(duì)象，利用氣象資料、早稻高溫?zé)岷?zāi)情史料和生育期資料，在歷史早稻高溫?zé)岷Ψ囱莸幕A(chǔ)上，采用K-S分布擬合檢驗(yàn)和置信區(qū)間方法，構(gòu)建基于高溫天氣過(guò)程的早稻高溫?zé)岷?dòng)態(tài)指標(biāo)，并采用預(yù)留獨(dú)立早稻高溫?zé)岷颖具M(jìn)行檢驗(yàn)驗(yàn)證。在此基礎(chǔ)上，計(jì)算江西各站點(diǎn)早稻高溫?zé)岷χ笖?shù)（M）。結(jié)果表明：高溫天氣過(guò)程起始時(shí)間、持續(xù)日數(shù)是影響早稻高溫?zé)岷Πl(fā)生程度的關(guān)鍵因子，其中，起始時(shí)間的影響大于持續(xù)日數(shù)。3～5 d早稻輕、中、重度高溫?zé)岷Φ钠鹗紩r(shí)間閾值分別為抽穗后第10～12 d、5～9 d、2～4 d；6～8 d早稻輕、中、重度高溫?zé)岷Φ钠鹗紩r(shí)間閾值為抽穗后第11～18 d、8～10 d和1～7 d； 8 d早稻輕、中、重度高溫?zé)岷Φ钠鹗紩r(shí)間閾值為抽穗后第12～18 d、8～11 d和0～7 d。指標(biāo)驗(yàn)證完全一致的吻合率為73.7%，完全一致及相差1級(jí)的吻合率為89.5%。1981—2015年，早稻高溫?zé)岷χ笖?shù)的線性?xún)A向率為0.04/a，1999年左右發(fā)生由低到高突變；M高值區(qū)域主要位于江西中部和東北部，M0.18；江西中部、東北部和南部地區(qū)M值呈顯著增加趨勢(shì)，線性?xún)A向率均大于0.04/a。總體來(lái)說(shuō)，本文構(gòu)建的指標(biāo)實(shí)現(xiàn)了基于高溫天氣過(guò)程的早稻高溫?zé)岷?dòng)態(tài)判識(shí)，江西中部和東北部是早稻高溫?zé)岷Φ母唢L(fēng)險(xiǎn)區(qū)域。(楊建瑩)

2.22 北方蘋(píng)果干旱觸發(fā)判識(shí)方法

基于小樣本歷史災(zāi)害數(shù)據(jù)和長(zhǎng)序列氣象、林果生長(zhǎng)數(shù)據(jù)的林果災(zāi)害判識(shí)，對(duì)目前歷史災(zāi)害數(shù)據(jù)匱乏的林果等經(jīng)濟(jì)作物氣象災(zāi)害研究具有重要意義。該研究以中國(guó)陜西省富士系蘋(píng)果干旱災(zāi)害為例，利用氣象資料、蘋(píng)果干旱災(zāi)情史料和富士系蘋(píng)果發(fā)育期資料，充分考慮蘋(píng)果不同發(fā)育階段的水分需求和降水供給情況，以及前期水分盈虧狀況對(duì)當(dāng)前發(fā)育階段蘋(píng)果生長(zhǎng)的影響，在水分盈虧指數(shù)計(jì)算的基礎(chǔ)上，構(gòu)建蘋(píng)果干旱指數(shù)。通過(guò)概率分析、K-Means聚類(lèi)、歐式距離等方法，厘定陜西省富士系蘋(píng)果的干旱觸發(fā)閾值。采用致災(zāi)因子序列對(duì)比分析、預(yù)留樣本驗(yàn)證相結(jié)合的方法，驗(yàn)證蘋(píng)果干旱觸發(fā)閾值有效性。結(jié)果表明：（1）蘋(píng)果干旱觸發(fā)閾值分別為：蘋(píng)果果樹(shù)萌動(dòng)—萌芽期0.87，萌芽—盛花期0.84，盛花—成熟期0.73；（2）基于閾值提取的蘋(píng)果干旱年份的干旱指數(shù)序列與歷史災(zāi)害樣本干旱指數(shù)序列具有同一性；預(yù)留獨(dú)立樣本指標(biāo)判識(shí)準(zhǔn)確率為85.58%；典型站點(diǎn)長(zhǎng)時(shí)間序列檢驗(yàn)判識(shí)結(jié)果準(zhǔn)確率為80.95%。研究結(jié)果可為林果災(zāi)害指標(biāo)研究提供技術(shù)支撐。(楊建瑩)

2.23 山西省參考作物蒸散量的時(shí)空變化特征

基于山西省境內(nèi)70個(gè)地面氣象觀測(cè)站1960—2019年的逐日降水量、氣溫、日照時(shí)數(shù)、相對(duì)濕度、風(fēng)速、水汽壓等氣象資料，應(yīng)用Penman-Monteith公式計(jì)算參考作物蒸散量（ET0），對(duì)山西省ET0的時(shí)空變化特征及不同氣候帶和海拔的蒸散特征進(jìn)行定量分析。結(jié)果表明：1960—2019年，研究區(qū)年均ET0在空間上呈現(xiàn)由西向東逐漸遞減的趨勢(shì)；以1982年為拐點(diǎn)，前后兩個(gè)時(shí)段均呈逐年增加趨勢(shì)，月際、旬際波動(dòng)為單峰變化曲線。不同氣候帶ET0的差異性表現(xiàn)為：溫帶半干旱氣候區(qū)的年、春、夏、秋季ET0高于暖溫帶半濕潤(rùn)氣候區(qū)和暖溫帶半干旱氣候區(qū)；冬季，暖溫帶半濕潤(rùn)氣候區(qū)ET0最高。不同海拔ET0的差異性表現(xiàn)為： 660 m海拔區(qū)的年、夏、秋、冬季ET0高于其他海拔區(qū)域。（霍治國(guó)）

2.24 中國(guó)富士系蘋(píng)果主產(chǎn)區(qū)花期模擬與分布

在中國(guó)富士系蘋(píng)果的5個(gè)主產(chǎn)區(qū)，分別選取花期資料系列較長(zhǎng)的山東福山（環(huán)渤海灣產(chǎn)區(qū)）、河南三門(mén)峽（黃河故道產(chǎn)區(qū)）、甘肅西峰（黃土高原產(chǎn)區(qū)）、云南昭通（西南冷涼高地產(chǎn)區(qū)）和新疆阿克蘇（新疆產(chǎn)區(qū)）作為代表站，利用SPSS統(tǒng)計(jì)軟件，分析和篩選影響蘋(píng)果花期的氣象要素，構(gòu)建富士系蘋(píng)果的花期模擬模型；采用平均絕對(duì)誤差（MAE）和分級(jí)加權(quán)滿分率計(jì)分評(píng)判法對(duì)模型進(jìn)行檢驗(yàn)，并用代表站周邊12個(gè)站點(diǎn)的物候觀測(cè)資料對(duì)模型進(jìn)行外延檢驗(yàn)；在此基礎(chǔ)上，逐站逐年模擬中國(guó)蘋(píng)果主產(chǎn)區(qū)416個(gè)氣象站1981—2018年富士系蘋(píng)果始花期和末花期。結(jié)果表明：代表站蘋(píng)果花期模擬模型單站檢驗(yàn)滿分率66.7%～100.0%，平均絕對(duì)誤差0.4～3.4 d，外延檢驗(yàn)平均絕對(duì)誤差1.2～5.1 d。1981—2018年中國(guó)不同產(chǎn)區(qū)富士系蘋(píng)果花期時(shí)間差異大，并呈提前變化的趨勢(shì)，提前變化分界點(diǎn)在1997年前后；代表站平均始花期最早與最晚相差27.0 d，平均末花期最早與最晚相差18.0 d；始花期提前變化幅度1.6～4.5 d/10a，末花期提前變化幅度1.2～3.8 d/10a。中國(guó)富士系蘋(píng)果花期空間分布特征表現(xiàn)為由南向北逐漸推遲，平均始花期從西南冷涼高地的3月中旬向北逐漸推遲至環(huán)渤海灣產(chǎn)區(qū)北部的4月下旬，平均末花期從西南冷涼高地的4月上旬向北逐漸推遲至環(huán)渤海灣產(chǎn)區(qū)北部的5月上旬。（霍治國(guó)）

2.25 北方地區(qū)小麥蚜蟲(chóng)氣象適宜度預(yù)報(bào)模型構(gòu)建

根據(jù)1958—2015年我國(guó)北方地區(qū)8個(gè)主產(chǎn)?。ㄊ校┬←溠料x(chóng)分省發(fā)生面積和發(fā)生程度資料、1958—2015年601個(gè)氣象站點(diǎn)相應(yīng)逐日氣象資料和農(nóng)業(yè)氣象站小麥發(fā)育期資料，采用相關(guān)分析、主成分分析和逐步回歸等方法，并利用相關(guān)系數(shù)法進(jìn)行因子普查，結(jié)合小麥蚜蟲(chóng)適宜生理氣象指標(biāo)和華北、黃淮小麥生育期規(guī)律，篩選影響小麥蚜蟲(chóng)年發(fā)生程度的關(guān)鍵氣象因子，構(gòu)建分區(qū)域的小麥蚜蟲(chóng)氣象適宜度預(yù)報(bào)模型，并將氣象適宜度指數(shù)劃分為非常適宜、適宜、較適宜、不適宜4個(gè)等級(jí)，以反映氣象條件對(duì)小麥蚜蟲(chóng)發(fā)生發(fā)展的適宜程度。結(jié)果表明：影響華北小麥蚜蟲(chóng)年發(fā)生程度的有8個(gè)關(guān)鍵氣象因子，影響黃淮小麥蚜蟲(chóng)年發(fā)生程度的有6個(gè)關(guān)鍵氣象因子。建立的華北、黃淮模型回代檢驗(yàn)等級(jí)準(zhǔn)確率分別為91.2%，93.1%，2016—2018年3年外推預(yù)報(bào)平均準(zhǔn)確率均在75%以上；利用黃淮模型反演蘇皖兩省2016—2018年小麥蚜蟲(chóng)發(fā)生等級(jí)、異地檢驗(yàn)3年預(yù)報(bào)效果均較理想。模型適用于從氣象角度對(duì)華北、黃淮及江淮地區(qū)小麥蚜蟲(chóng)發(fā)生等級(jí)進(jìn)行監(jiān)測(cè)和預(yù)報(bào)。（霍治國(guó)）

2.26 晉南地區(qū)不同海拔高度典型木本植物物候特征及其對(duì)氣候變化的響應(yīng)

基于1983—2016年臨汾市不同海拔高度上3個(gè)農(nóng)業(yè)氣象觀測(cè)站（堯都區(qū)、隰縣、安澤縣）的6種典型木本植物物候期和溫度的觀測(cè)資料，統(tǒng)計(jì)分析其變化特征及相互影響。結(jié)果表明：（1）研究區(qū)年及四季氣溫整體呈上升趨勢(shì)，堯都區(qū)增溫幅度最大，春季增溫極顯著；月平均氣溫除隰縣個(gè)別月份略有下降外，大多以增溫趨勢(shì)為主，其中3月增溫極顯著（P0.01），是年平均氣溫升高的主要因素。（2）研究區(qū)內(nèi)木本植物物候期最早和最晚平均相差1～2個(gè)月，物候期變化呈現(xiàn)較強(qiáng)的區(qū)域性特征，堯都區(qū)和安澤的木本植物春季物候期提前，秋季物候期推遲，植物生長(zhǎng)季延長(zhǎng)；隰縣木本植物春季物候期推遲，秋季物候期提前，植物生長(zhǎng)季呈縮短趨勢(shì)。（3）木本植物展葉始期對(duì)年、春季及展葉前1～2個(gè)月的平均氣溫響應(yīng)顯著，隨著氣溫升高，木本植物展葉始期表現(xiàn)為一致提前趨勢(shì)；木本植物落葉末期在堯都區(qū)和安澤縣隨著氣溫的升高表現(xiàn)為明顯的推遲趨勢(shì)，受年、秋季及落葉前1個(gè)月的平均氣溫影響顯著，在隰縣隨氣溫的升高表現(xiàn)為普遍提前趨勢(shì)，受年均氣溫變化影響顯著；隨著氣溫升高堯都區(qū)和安澤縣的木本植物生長(zhǎng)季延長(zhǎng)，隰縣木本植物生長(zhǎng)季變化不明顯。說(shuō)明晉南地區(qū)不同海拔高度的氣溫及其變化趨勢(shì)存在較大差異；各代表站典型木本植物的物候期和生長(zhǎng)季對(duì)氣候變化的響應(yīng)不同。（霍治國(guó)）

2.27 干旱對(duì)夏玉米根冠及產(chǎn)量影響試驗(yàn)

為揭示干旱對(duì)夏玉米根冠生長(zhǎng)及產(chǎn)量形成的影響，2013—2015年在山東夏津、山西運(yùn)城和河北固城開(kāi)展夏玉米水分脅迫控制試驗(yàn)，研究不同干旱條件下玉米根冠及產(chǎn)量的變化，厘定干旱敏感時(shí)段及臨界閾值。結(jié)果表明：同一干旱程度，影響玉米地上干物重、產(chǎn)量的關(guān)鍵時(shí)段為拔節(jié)—抽雄期，抽雄期最敏感，影響根系、根冠比的關(guān)鍵時(shí)段為出苗—拔節(jié)期，拔節(jié)期最敏感。不同干旱程度，在快速失墑階段，不同生育時(shí)段的地上干物重、根干重、根冠比均呈下降趨勢(shì)，分別較對(duì)照減少11.7%～67.8%，35.2%～85.8%和15%～62%；干旱維持階段與快速失墑階段相比，地上干物重呈持續(xù)下降趨勢(shì)，較對(duì)照減少24.3%～89.7%，根干重、根冠比呈上升趨勢(shì)或無(wú)明顯差異，分別較對(duì)照減少9.7%～80.8%，9.6%～62%。出苗—拔節(jié)期，土壤相對(duì)濕度60%～62%為玉米地上部生長(zhǎng)及形成合理根冠比的臨界閾值；出苗—七葉期，土壤相對(duì)濕度51%～60%利于根系生長(zhǎng)。土壤相對(duì)濕度62%為影響玉米產(chǎn)量的臨界閾值，土壤相對(duì)濕度31%～40%，出現(xiàn)在拔節(jié)、抽雄等敏感期，玉米減產(chǎn)七成以上。土壤相對(duì)濕度50%～60%持續(xù)時(shí)間少于8 d，復(fù)水后根冠可迅速恢復(fù)生長(zhǎng)，但對(duì)產(chǎn)量仍有一定程度的影響，減產(chǎn)1.4%～6.6%。（霍治國(guó)）