Agricultural Meteorological Stations Research Articles

Control Mechanisms and Simulation of <i>Populus simonii</i> Leaf Unfolding

Populus simonii Carr., one of the main poplar tree species, is cultivated widely in Northeast and Northwest China in protection and timber forests. Plant phenology plays an important role in timber production by controlling the growing period (i.e., the period between the leaf unfolding and the leaf turning yellow). It is important to understand this control mechanism and to improve the accuracy of the simulation of leaf unfolding phenology for P. simonii in order to determine accurately the timber production of P. simonii plantations. In this study, based on phenological observation data from 10 agricultural meteorological stations in Heilongjiang Province, China, model simulation was employed to determine the control mechanism of leaf unfolding of P. simonii. Furthermore, the predicting effects of nine phenology-simulating models for P. simonii leaf unfolding were evaluated and the distribution characteristics of P. simonii leaf unfolding in China in 2015 were simulated. The results show that P. simonii leaf unfolding is sensitive to air temperature; consequently, climate warming could advance the P. simonii leaf unfolding process. The phenological model based on air temperature could be better suited for simulating P. simonii leaf unfolding, with 76.7% of the calibration data of absolute error being less than three days. The performance of the models based solely on forcing requirements was found superior to that of the models incorporating chilling. If it was imperative that the chilling threshold is reached, the south of the Yunnan, Guangdong, and Guangxi provinces would be unsuitable for planting P. simonii. In this regard, the phenology model based on the chilling threshold as necessary condition was indicated a more reasonable model for the distribution characteristics of P. simonii leaf unfolding.

Historical data provide new insights into response and adaptation of maize production systems to climate change/variability in China

Extensive studies had been conducted to investigate the impacts of climate change on maize growth and yield in recent decades; however, the dynamics of crop husbandry in response and adaptation to climate change were not taken into account. Based on field observations spanning from 1981 to 2009 at 167 agricultural meteorological stations across China, we found that solar radiation and temperature over the observed maize growth period had decreasing trends during 1981–2009, and maize yields were positively correlated with these climate variables in major production regions. The decreasing trends in solar radiation and temperature during maize growth period were mainly ascribed to the adoption of late maturity cultivars with longer reproductive growth period (RGP). The adoption of late maturing cultivars with longer RGP contributed substantially to grain yield increase during the last three decades. The climate trends during maize growth period varied among different production areas. During 1981–2009, decreases in mean temperature, precipitation and solar radiation over maize growth period jointly reduced yield most by 13.2–17.3% in southwestern China, by contrast in northwestern China increases in mean temperature, precipitation and solar radiation jointly increased yield most by 12.9–14.4%. Our findings highlight that the adaptations of maize production system to climate change through shifts of sowing date and genotypes are underway and should be taken into accounted when evaluating climate change impacts.

Heat stress impacts on wheat growth and yield were reduced in the Huang-Huai-Hai Plain of China in the past three decades

Heat stress impacts on crop growth and yield have been investigated by controlled-environment experiments, however little is known about the impacts under field conditions at large spatial and temporal scales, particularly in a setting with farmers’ autonomous adaptations. Here, using detailed experiment observations at 34 national agricultural meteorological stations spanning from 1981 to 2009 in the Huang-Huai-Hai Plain (HHHP) of China, we investigated the changes in climate and heat stress during wheat reproductive growing period (from heading to maturity) and the impacts of climate change and heat stress on reproductive growing duration (RGD) and yield in a setting with farmers' autonomous adaptations. We found that RGD and growing degree days above 0°C (GDD) from heading to maturity increased, which increased yield by ∼14.85%, although heat stress had negative impacts on RGD and yield. During 1981–2009, high temperature (>34°C) degree days (HDD) increased in the northern part, however decreased in the middle and southern parts of HHHP due to advances in heading and maturity dates. Change in HDD, together with increase in GDD and decrease in solar radiation (SRD), jointly increased wheat yield in the northern and middle parts but reduced it in the southern part of HHHP. During the study period, increase in GDD and decrease in SRD had larger impacts on yield than change in HDD. However, with climate warming of 2°C, damage of heat stress on yield may offset a large portion of the benefits from increases in RGD and GDD, and eventually result in net negative impacts on yield in the northern part of HHHP. Our study showed that shifts in cultivars and wheat production system dynamics in the past three decades reduced heat stress impacts in the HHHP. The insights into crop response and adaptation to climate change and climate extremes provide excellent evidences and basis for improving climate change impact study and designing adaptation measures for the future.

Exploring the relationships between climatic variables and climate-induced yield of spring maize in Northeast China

Understanding regional relationships between climate change and crop yield will help with making the strategic decisions for food security in China under climate change. In this study, the contributions of climate change to spring maize yield over the past three decades in Northeast China were decoupled based on the daily climate variables gathered from 68 meteorological stations and detailed observed data of spring maize from 55 agricultural meteorological experimental stations for the period 1978–2010 in Northeast China, analyzed with a linear statistical model. Then, the key climatic factors limiting the climate-induced yield of spring maize were identified. The agro-climatic similarity theory was applied. Finally, the relationships between the climatic variables and the climate-induced yield of spring maize were further explored by provinces. The results show that: from 1978 to 2010, the observed yields of spring maize in Northeast China increased markedly, with inter-annual fluctuations. Compared with the methods of moving average and harmonic average, Logistic regression optimally decoupled the climate-induced yield of spring maize. The key meteorological factors limiting the climate-induced yield were temperature, precipitation and sunshine, varying in the different regions. In Heilongjiang Province, the climate-induced yields of spring maize were mainly affected by maximum temperatures in August and precipitation in June. In Jilin Province, climate-induced yield was closely related to precipitation during daily the average temperature stably passing 10°C (≥10°C). In Liaoning Province, when the maximum temperature was high and the sunshine was abundant in June, the climate-induced yield of spring maize significantly increased. Finally, the regression models between climatic variables and climate-induced yield of spring maize in 11 representative zones in Northeast China also established geographical differences.

Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China.

Abstract: Paddy rice is one of the most important crops in the world. Accurate estimation and monitoring of paddy rice phenology is necessary for management and yield prediction. Remotely sensed time-series data are essential for estimation of crop phenology stages across large areas. Here, the paddy rice phenological stages (i.e., transplanting, tillering, heading, and harvesting) were detected in Jiangxi Province, China. A comparison study was conducted using ground observation data from 10 agricultural meteorological stations, collected between 2006 and 2008. The phenological stages were detected using Moderate Resolution Imaging Spectroradiometer (MODIS) time-series enhanced vegetation index (EVI) data. Savitzky–Golay filter and wavelet transform were used to reduce the noise in the time-series EVI data and reconstruct the smoothed EVI time-series profile. Key phenological stages of double-cropping rice were detected using the characteristics of the smoothed EVI profile. The root mean square errors (RMSEs) for each stage were ±10 days around the ground observation data. The results suggest that Savitzky–Golay filter and wavelet transform are promising approaches for reconstructing high-quality EVI time-series data. Moreover, the phenological stages of double-cropping rice could be detected using time-series MODIS EVI data smoothed by Savitzky–Golay filter and wavelet transform. Keywords: remote sensing, phenology, paddy rice, time series MODIS EVI, growth monitoring, Savitzky-Golay filter, wavelet transform DOI: 10.3965/j.ijabe.20140706.005 Citation: Li S H, Xiao J T, Ni P, Zhang J, Wang H S, Wang J X. Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China. Int J Agric & Biol Eng, 2014; 7(6): 28－36.

Attribution of maize yield increase in China to climate change and technological advancement between 1980 and 2010

Crop yields are affected by climate change and technological advancement. Objectively and quantitatively evaluating the attribution of crop yield change to climate change and technological advancement will ensure sustainable development of agriculture under climate change. In this study, daily climate variables obtained from 553 meteorological stations in China for the period 1961–2010, detailed observations of maize from 653 agricultural meteorological stations for the period 1981–2010, and results using an Agro-Ecological Zones (AEZ) model, are used to explore the attribution of maize (Zea mays L.) yield change to climate change and technological advancement. In the AEZ model, the climatic potential productivity is examined through three step-by-step levels: photosynthetic potential productivity, photosynthetic thermal potential productivity, and climatic potential productivity. The relative impacts of different climate variables on climatic potential productivity of maize from 1961 to 2010 in China are then evaluated. Combined with the observations of maize, the contributions of climate change and technological advancement to maize yield from 1981 to 2010 in China are separated. The results show that, from 1961 to 2010, climate change had a significant adverse impact on the climatic potential productivity of maize in China. Decreased radiation and increased temperature were the main factors leading to the decrease of climatic potential productivity. However, changes in precipitation had only a small effect. The maize yields of the 14 main planting provinces in China increased obviously over the past 30 years, which was opposite to the decreasing trends of climatic potential productivity. This suggests that technological advancement has offset the negative effects of climate change on maize yield. Technological advancement contributed to maize yield increases by 99.6%–141.6%, while climate change contribution was from −41.4% to 0.4%. In particular, the actual maize yields in Shandong, Henan, Jilin, and Inner Mongolia increased by 98.4, 90.4, 98.7, and 121.5 kg hm−2 yr−1 over the past 30 years, respectively. Correspondingly, the maize yields affected by technological advancement increased by 113.7, 97.9, 111.5, and 124.8 kg hm−2 yr−1, respectively. On the contrary, maize yields reduced markedly under climate change, with an average reduction of −9.0 kg hm−2 yr−1. Our findings highlight that agronomic technological advancement has contributed dominantly to maize yield increases in China in the past three decades.

Responses of wheat growth and yield to climate change in different climate zones of China, 1981–2009

The experiment observations at 120 agricultural meteorological stations spanning from 1981 to 2009 across China were used to accelerate understandings of the response of wheat growth and productivity to climate change in different climate zones, with panel regression models. We found climate during wheat growth period had changed significantly during 1981–2009, and the change had caused measurable impacts on wheat growth and yield in most of the zones. Wheat anthesis date and maturity date advanced significantly, and the lengths of growth period before anthesis and whole growth period were significantly shortened, however the length of reproductive growth period was significantly prolonged despite of the negative impacts of temperature increase. The increasing adoption of cultivars with longer reproductive growth period offset the negative impacts of climate change and increased yield. Changes in temperature, precipitation and solar radiation in the past three decades jointly increased wheat yield in northern China by 0.9–12.9%, however reduced wheat yield in southern China by 1.2–10.2%, with a large spatial difference. Our studies better represented crop system dynamics using detailed phenological records, consequently better accounted for adaptations such as shifts in sowing date and crop cultivars photo-thermal traits when quantifying climate impacts on wheat yield. Our findings suggest the response of wheat growth and yield to climate change is underway in China. The changes in crop system dynamics and cultivars traits have to be sufficiently taken into account to improve the prediction of climate impacts and to plan adaptations for future.

Response of maize phenology to climate warming in Northeast China between 1990 and 2012

Investigating the temporal changes in crop phenology is essential for understanding crop response and adaption to climate change. Using observed climatic and maize phenological data from 53 agricultural meteorological stations in Northeast China between 1990 and 2012, this study analyzed the spatiotemporal changes in maize phenology, temperatures and their correlations in major maize-growing areas (latitudes 39–48°N) of Northeast China. During the investigation period, seedling and heading dates advanced significantly at 22 out of the 53 stations; maturity dates delayed significantly at 23 stations, and the growing period (GP, from seedling to maturity), the vegetative growing period (VGP, from seedling to heading) and the reproductive growing period (RGP, from heading to maturity) increased significantly at 30 % of the investigated stations. GP length was positively correlated with T mean at 40 stations and significantly at 10 stations (P < 0.01). Both negative and positive correlations were found between VGP and T mean, while RGP length was significantly and positively correlated with T mean. The results indicated that agronomic factors contribute substantially to the shift in maize phenology and that most farmers had adopted longer season cultivars because the increase in temperature provided better conditions for maize germination, emergence and grain filling. The findings on the various changes to maize phenology can help climate change impact studies and will enable regional maize production to cope with ongoing climate change.

Adaptability of APSIM Maize Model in Northeast China

The APSIM (Agricultural Production Systems Simulator) model was introduced to simulate maize growth, development and yields in Northeast China using the field experimental data and climate data collected from six typical agricultural meteorological stations in the studied area. APSIM was calibrated using the first part of data to determine the varietal parameters, then simulated the growing periods, leaf area index (LAI), total above-ground biomass and yields using the second part of data at each site. The results showed that there was a good agreement between the simulated and measured values in growing periods. The difference of growing periods from sowing to emergence, from sowing to flowering and from sowing to maturity between simulated and measured data was 0–2.0, 0.7–2.0, and 0.7–2.3 d, respectively. The Normalized Root Mean Square Error (NRMSE) values for measured and simulated LAI and total above-ground biomass in Harbin station were 33% and 11%, respectively. NRMSE values for measured and simulated yield in Harbin, Hailun, Tailai, Huadian, Tonghua, and Benxi station were 18%, 13%, 4%, 4%, 5%, and 2%, respectively. These re-sults indicated that APSIM model has good ability to simulate the growing periods, dynamic process of LAI, dynamic process of above-ground biomass and yield of maize in Northeast China. This research supports the model application in Northeast China, such as simulating maize potential yield, or prescribing yield limiting factors.

中国北方春小麦生育期变化的区域差异性与气候适应性

PDF HTML阅读 XML下载 导出引用 引用提醒 中国北方春小麦生育期变化的区域差异性与气候适应性 DOI: 10.5846/stxb201306091491 作者: 作者单位: 中国气象科学研究院,中国气象科学研究院,中国气象科学研究院,中国气象科学研究院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展计划资助项目(2010CB951302);国家自然科学基金资助项目(41275115) The regional diversity of changes in growing duration of spring wheat and its correlation with climatic adaptation in Northern China Author: Affiliation: Chinese Academy of Meteorological Sciences,Chinese Academy of Meteorological Sciences,Chinese Academy of Meteorological Sciences,Chinese Academy of Meteorological Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用北方18个农业气象观测站春小麦主要发育期和气象观测等资料,通过相关性分析等方法,研究了北方春小麦生育期间气候和发育期变化特点及发育期变化区域差异性形成原因。结果表明,我国北方春小麦生长季普遍增温,大部分观测站春小麦生育期间和灌浆期的平均气温显著升高,有效积温显著增加,生育期显著缩短。然而,稳定通过0 ℃初日没有显著提前,表明增温主要发生在生长季后期。春小麦主要发育期和生育期与不同生育阶段的平均气温和有效积温的相关性分析表明,后期增温并没有完全显著提前成熟期,春小麦生育期缩短是播种期推迟和成熟期提前共同作用的结果。春小麦生育期间的平均气温与生育期的相关性比有效积温与生育期的相关性更高,能更好地定量刻画北方春小麦生长发育客观规律。春小麦品种改良变换、播种期调整以及其它适应性措施的实施以及措施实施程度在区域上的差异性是春小麦生育期变化区域差异的主要原因。北方春小麦生长发育的区域性差异是各自适应气候变化的结果。 Abstract:Changes in climate have had a significant impact on crop growth and development across China. However, the responses in crop phenology and growing duration to climate change were regionally diverse, especially in northern China where spring wheat is planted as the staple crop. This study explored the time trends and characteristics of the changes in climate variables as well as the phenology and growing duration of spring wheat. The correlations between these variables have also been examined to find the reasons for the regional diversity of spring wheat growth and development in response to climate change, based on the spring wheat phenology data set and meteorology derived from 18 agricultural meteorological observation stations and the use of correlation analysis methods. The results show that the annual average air temperatures during February to August, which represent the thermal resource availability for the spring wheat growing season, have a significant warming trend since 1980, with the largest increase in temperature measured as 0.78 ℃/decade. However, the initial dates of more than zero Celsius degree have not become significantly earlier. In response to these characteristics of changes in climate, the planting and seedling dates have not become significantly earlier, showing some significant and slight delay in some stations and slight advancement in others. The annual average temperatures and growing degree days, from planting to maturity and grain filling stages have increased significantly, indicating that regional warming in northern China mainly occurs during the reproductive growth stage rather than in vegetative growth stage. Due to the delay and advancement in planting dates in some stations, the growing duration during the growing period has decreased significantly in 10 stations and slightly decreased in the other stations. However, the results of correlation analyses show that the correlation coefficients between planting dates and growing durations are noticeably higher than that between the maturity dates and growing duration, suggesting a greater influence from changes in planting date on the growing duration when compared to maturity date. There are stronger correlations between growing duration and annual average temperature than between growing duration and growing degree days, indicating that the relationship between the annual average temperature and growing duration can better describe the principal process of spring wheat growth and development than the relationship between growing degree days and growing duration. Along with global warming, the measures implemented for climate adaptation, such as cultivar improvement, adjustment of planting dates and others, as well as the regional differences in performance, have caused the regional diversity in the changes to spring wheat growth and development. The regional diversity of changes in growth and development has been the result of adaptation to climate change over the past decades. 参考文献 相似文献 引证文献

近20年来东北三省春玉米物候期变化趋势及其对温度的时空响应

Investigating the historical processes behind crop phenology is essential for understanding crop response and adaption for climate change. Based on the 1990—2009 crop phenophase records from 46 agricultural meteorological stations in three provinces of northeast China,the maize phenophases( including the stages of seeding,maturity and length of the growth period) were extracted. The related annual slope change rates( θ) were then calculated and used to analyze the responses to temperature changes in the maize growing seasons of Northeast China during 1990—2009. The results showed that( 1) over the past 20 years,positive trends of average temperature in May( T5) and September( T9),as well as an extended temperature-allowing period,were found in most areas of the three provinces.( 2) With this background,various changes and responses had occurred in maize phenophases. Temporal trends of advanced seeding stage( 0. 02θ 0. 15 d /a),postponed harvesting stage( 0. 18θ 0. 38 d / a) and extended length of the growth period( 0. 22 θ 0. 44 d / a)were observed. It can be inferred the adaptive action by adjusting the early / middle maturing types to middle / late maturing types has been implemented to fully utilize the prolonged growth period under climate warming.( 3) In response to the rising trend of T5,advancing of the maize seedling stage occurred,which was most significant in the north of Songnen Plain,the middle and the east of Jilin and the middle of Liaoning. Corresponding to the rising trend of T9,the maize maturity stage showed a postponement trend,which was more significant in the middle and east of Jilin. In response to the extending trend of the temperature-allowing period,the maize growth period showed an overall significant extending trend.Generally,the temperature changes during the crop growth period in Northeast China resulted in better temperature conditions for maize growth-especially for early-planting,late-harvesting varieties with a longer growth period. This may benefit future maize production,especially in northern areas. The findings provided implications for improving maize responses and adaptation studies,for researchers wishing to breed higher yielding maize cultivars and for enabling maize production to cope with ongoing climate change.

我国春玉米潜在种植分布区的气候适宜性

PDF HTML阅读 XML下载 导出引用 引用提醒 我国春玉米潜在种植分布区的气候适宜性 DOI: 10.5846/stxb201111161744 作者: 作者单位: 中国气象科学研究院;南京信息工程大学大气物理学院,中国气象科学研究院;中国科学院植物研究所植被与环境变化国家重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点基础研究发展计划项目(2010CB951303);公益性行业(农业)科研专项经费(200903003) Climatic suitability of potential spring maize cultivation distribution in China Author: Affiliation: Chinese Academy of Meteorological Sciences;School of Atmospheric Physics, Nanjing University of Information Science and Technology,Chinese Academy of Meteorological Sciences;State Key Laboratory of Vegetation and Environmental Change，Institute of Botany, Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:根据中国气象局216个春玉米农业气象观测站资料与1971-2000年10 km×10 km空间分辨率的气候资料,基于全国区域和年尺度筛选出了影响我国玉米种植分布的潜在气候指标,利用最大熵(Maximum Entropy,MaxEnt)模型和ArcGIS空间分析技术,构建了春玉米潜在种植分布与气候因子关系模型,研究了影响我国春玉米潜在种植分布区的主导气候因子及其气候适宜性。结果表明,影响我国春玉米潜在种植分布的主导气候因子有:≥10 ℃积温、≥10 ℃的天数、最热月平均温度、年平均温度、年降水、湿润指数和气温年较差;结合春玉米存在概率,将我国春玉米潜在种植分布区划分成4个等级:气候不适宜区、次适宜区、适宜区和最适宜区,给出了各气候适宜区的气候特征。选取作物在待预测地区的存在概率这一综合反映各主导气候因子影响的指标来划分作物潜在种植分布区,有助于更加准确地进行气候区划,从而可为制定玉米应对气候变化措施提供科学依据。 Abstract:Based on the spring maize cultivation geographical information from 216 agricultural meteorological observation stations of China Meteorological Administration, climate data with 10km×10km spatial resolution from 1971 to 2000 in China, and the potential climate indices at national and annual scales influencing maize cultivation distribution from the references, together with the maximum entropy (MaxEnt) model as well as ArcGIS spatial analysis technique, the relationship between potential spring maize cultivation distribution and climate and the climatic suitability regionalization of potential spring maize cultivation in China were studied in this paper. The results show that the MaxEnt model is able to develop the relationship between potential spring maize cultivation distribution and climate. The key climatic factors affecting spring maize cultivation distribution include ≥10℃ accumulated temperature, duration days of ≥10℃, the warmest month average temperature, annual average temperature, annual precipitation, humidity index and annual temperature range. The climatic suitability regionalization of potential spring maize cultivation in China was given by the existence probability from the relationship between potential spring maize cultivation distribution and climate. According to the statistical principles and the description of probability from the Fourth Assessment Report of the International Panel on Climate Change (IPCC), the suitability class of spring maize cultivation distribution was given by the existence probability (P): P<0.05 (unsuitable area), 0.05≤P<0.33 (less suitable area), 0.33≤P<0.66 (suitable area), and P≥0.66 (optimum area). The optimum area of spring maize was approx. 5% of the total land area in China, included midwest and northeast parts of Heilongjiang province; midwest part of Jilin province; Liaoning province; parts of Hebei, Shanxi, Shannxi provinces, Inner Mongolia Autonomous Region (Inner Mongolia for short) and Xinjiang Uygur Autonomous Region (Xinjiang for short). The suitable area of spring maize was approx. 30%, which included south of Heihe city in Heilongjiang province; Jilin province except its east part; most parts of Hebei, Tianjin, Shanxi, Shannxi, Shandong, Hubei, Jiangsu, Guizhou, Chongqing provinces and Ningxia Hui Auonomous Region (Ningxia for short); Tongliao, Chifeng, Hohhot and Erdos in Inner Mongolia; middle and south Gansu province; Sichuan basin; small parts of Henan, Anhui, Hunan, Yunnan provinces and Guangxi Zhuang Autonomous Region (Guangxi for short); Shannan district of Xizang (Tibet) Autonomous Region (Xizang for short). The less suitable area was approx. 34%, mainly included south Huma county in Heilongjiang province; west Inner Mongolia; north Gansu; most parts of the South Yangtze River Regions and South China; most parts of Xinjiang and Yunnan provinces. The unsuitable area of spring maize was approx. 31% of the total land area in China, which included north part of 52.6°N; east Inner Mongolia; east Jilin province; Qinghai-Tibet Plateau and high latitude areas of Xijiang. The north boundary of spring maize cultivation distribution given by P≥0.05 was around 52.6°N, and it is very close to the actual cultivation boundary at present. Furthermore, the climatic characteristics of different climatic suitability zones of potential spring maize cultivation were discussed. The actual distribution of spring maize cultivation in China depends not only on climate, socio-economic conditions, and local production technologies, but also on soil type, geographic characteristics, crop varieties, human activity and so on, especially in relation to its yield and economic value. This research provides scientific support for planning spring maize production and designing the countermeasures against the effects of climate change on spring maize. 参考文献 相似文献 引证文献

Soil moisture retrieval from satellite images and its application to heavy rainfall simulation in eastern China

The soil water index (SWI) from satellite remote sensing and the observational soil moisture from agricultural meteorological stations in eastern China are used to retrieve soil moisture. The analysis of correlation coefficient (CORR), root-mean-square-error (RMSE) and bias (BIAS) shows that the retrieved soil moisture is convincible and close to the observation. The method can overcome the difficulties in soil moisture observation on a large scale and the retrieved soil moisture may reflect the distribution of the real soil moisture objectively. The retrieved soil moisture is used as an initial scheme to replace initial conditions of soil moisture (NCEP) in the model MM5V3 to simulate the heavy rainfall in 1998. Three heavy rainfall processes during 13–14 June, 18–22 June, and 21–26 July 1998 in the Yangtze River valley are analyzed. The first two processes show that the intensity and location of simulated precipitation from SWI are better than those from NCEP and closer to the observed values. The simulated heavy rainfall for 21–26 July shows that the update of soil moisture initial conditions can improve the model’s performance. The relationship between soil moisture and rainfall may explain that the stronger rainfall intensity for SWI in the Yangtze River valley is the result of the greater simulated soil moisture from SWI prior to the heavy rainfall date than that from NCEP, and leads to the decline of temperature in the corresponding area in the heavy rainfall days. Detailed analysis of the heavy rainfall on 13–14 June shows that both land-atmosphere interactions and atmospheric circulation were responsible for the heavy rainfall, and it shows how the SWI simulation improves the simulation. The development of mesoscale systems plays an important role in the simulation regarding the change of initial soil moisture for SWI.

Intra‐geosystem, inter‐geosystem and regional variability of soil moisture for Kursk model area

The scale of spatial and temporal variability of soil moisture reserves was examined in the very heterogeneous landscapes of the central forest‐steppe zone of the Russian plain. Three main scales of variability were determined in connection with intra‐geosystem, inter‐geosystem and regional (basin) differences in soil and vegetation cover and the groundwater. The spatial‐temporal variability of the soil moisture revealed in elementary geosystem during a full annual period in 1988 and a partial annual period in 1991. Intra‐annual year dynamic features of the soil moisture reserves were observed for representative elementary geosystem types. Intraseasonal changes in soil moisture at different agricultural meteorological stations (agrometeostations) were found to be highly correlated during years of normal and high precipitation.

An Estimation of Distribution of Solar Radiation Using Data from 17 Agricultural Meteorological Stations in Hyogo Prefecture

In order to observe solar radiation, we set up Agricultural Meteorological Stations (AMS) at 17 sites in Hyogo Prefecture in 1994 and 1995. An AMS was equipped with a thermometer, a rain gauge, a pyranometer and a data-logger. Using the observed solar radiation data at the AMS sites, daily solar radiation (DSR) in each non-AMS site in both July and August 1995 was calculated.The estimating method was as follows: in the first step, DSR of an average year was calculated by the harmonic method from monthly solar radiation as standardized by the National Institute Agro-Environmental Science of Japan. In the second step, the estimated DSR in the non-AMS site was caluculated from: DSR e, t=DSR a, t+Σ[(DSR e, i-DSR a, i)/Li]/Σ(1/Li), where DSR e, t and DSR a, t are respectively the estimated and an average year's DSR of the target non-AMS site, DSR e, i and DSR a, i are respectively the estimated and an average year's DSR of i-th selected AMS site, and Li is the distance between the target non-AMS site and i-th selected AMS site. Errors between estimated DSR and observed DSR at each AMS site were shown by the Root Mean Square Error. Daily RMSEs in July and August ranged from 1.0 to 3.1MJ/m2/day, and this estimation method is useful in obtaining accurate solar radiation for all non-AMS sites.

Estimation of Monthly Mean Maximum and Minimum Soil Temperatures from Profiles Taken at 9:00 A.M.

At many agricultural meteorological stations, soil temperatures are observed once a day at 9:00 A.M. In the upper soil layers, however, the temperature fluctuates widely during the course of a day, and the vertical profile taken at a time of day is not sufficient for an understanding of the soil temperature regime. Thus, it is desired to evolve a method for utilizing the observations made at 9:00 A.M. as fully as possible.We constructed a simple numerical method for the purpose of estimating the monthly mean maximum and minimum temperatures in the upper soil layers from the monthly mean profile taken at 9:00 A.M., and examined the accuracy of the estimation, using the data obtained in the bare observation fields of Shimane University (clay) and Sand Dune Research Institute, Tottori University (sand).In the present method, monthly mean curves of diurnal temperature variations at all depths are approximated by pure sinusoidal functions of time around a mean which is constant throughout the layers. The approximation becomes better with increasing depth. The function has four parameters, that is, the daily mean, the amplitude at the surface, the damping depth and the instant at which the surface temperature is at its mean and increasing. The last one averaged over a month can be estimated within half an hour from the time of sunrise; the instant comes about 2 hours and 15 minutes after sunrise in bare clay fields and 15 to 30 minutes further behind it in bare sand fields. The other three parameters are estimated numerically from the instant and the monthly mean temperatures observed at three depths at 9:00 A.M.; then the maximum and minimum temperatures are calculated by using the estimates of the parameters.Results obtained by the analyses of the two sets of data above are similar in trend. The estimated values of monthly mean maximum and minimum temperatures at a depth of 5cm are about 1 to 3°C lower than the observed ones in the spring and summer months, but are in fair agreement with the observations in the autumn months. The accuracy of the estimated values at 20cm depth is about ±1°C for the maximum and about ±0.3°C for the minimum. The accuracy is quite satisfactory in view of the approximated character of the present method and the fact that only the time of sunrise and the soil temperature profile taken at 9:00 A.M. are needed to utilize the method.

Forecasting the Incidence of Aphids Using Weather Data1

Aphids have been trapped continuously at several heights at Aberystwyth (United Kingdom) since 1969. Relationships have been found between prior meteorological parameters (normally recorded at Agricultural Meteorological Stations) and numbers of aphids flying during both summer and autumn peaks, and with the first occurrence in the trap each year. Numbers of cereal aphids caught in the autumn are associated with previous winter and spring temperatures and particularly with summer rainfall. Cereal aphid numbers are also associated with the yields of their summer grass hosts which in turn are related to summer rainfall, thus providing a causal relationship. The value and accuracy of using meteorological parameters to forecast the incidence of aphids of agricultural importance is discussed.

EVAPORATION IN EAST AFRICA

Abstract Following a decision to estimate potential evaporation from meteorological observations rather than from evaporation pans, 53 agricultural meteorological stations were established over East Africa with the co-operation of several departments of three governments. Supplementary information from an additional 123 less well equipped stations was incorporated into the preparation of maps of monthly potential open water evaporation, using the Penman estimate. Annual values of potential evaporation range from more than 2,600 mm to less than 1,400 mm with 70% of the area of East Africa having potential evaporation rates, of between 1,800 and 2,200 mm per annum. There are marked seasonal variations in evaporation rates, mainly dependent on patterns of cloud cover, but average monthly rates rarely fall below 90 mm even in highland areas. These high rates of water demand have serious implications for the management of water catchments, land planning, crop breeding, tillage techniques, ground water recharge...