中国青冈的地理分布与气候的关系

在广泛收集青冈（Ｃｙｃｌｏｇｂａｌａｎｏｐｓｉｓｇｌａｕｃａ（Ｔｕｎｂ．）Ｏｅｒｓｔ．）地理分布资料的基础上,利用目前国际上较流行的研究植被与气候相互关系的指标和方法,包括Ｋｉｒａ的水热指标、Ｐｅｎｍａｎ的公式、Ｔｈｏｒｎｔｈｗａｉｔｅ的指标和气候分类、Ｈｏｌｄｒｉｄｇｅ的生命地带分类系统指标,以及年平均气温、１月均温、７月均温、极端最高气温、极端最低气温≥１０℃积温和年降水量,研究了青冈在中国的     

腾格里沙漠沙坡头地区人工植被蒸散耗水与水量平衡的研究

运用大型称重式电子蒸散系统研究沙坡头地区人工植被蒸散耗水量与水分平衡,结果表明∶固沙造林初期,年平均降水量１８６．６ ｍ ｍ 能够满足植物正常生长需要,是保证植物固沙成功的基本条件． 正常降水年份,植物（密度为７５ 株／１００ ｍ ２）蒸散耗水量大于降水量． 油蒿（Ａｒｔｅｍ ｉｓｉａ ｏｒｄｏｓｉｃａ Ｋｒａｓｃｈｅｎ．）和柠条（Ｃａｒａｇａｎａ ｋｏｒｓｈｉｎｓｋｉｉＫｏｍ ．）的蒸散量分别占同期降水量的１３６．６％ 和１３１．１％ ,两种植物的蒸腾量分别占其蒸散量的４５．６％ 和４３．４％ ． 在１００～２００ ｍ ｍ 降水范围内,沙地物理蒸发量与降水量的比值随降水量的减少而增大;降水量１００ｍ ｍ 时,是人工固沙植被所需降水的最低下限． 降水的时空分布和地表结皮的形成对人工植被水分平衡与水分利用产生较大的影响     

岷江上游黑水河流域不同景观结构小流域径流系数的比较

在GIS技术支持下,结合岷江上游黑水河流域1988-2002年多年平均降水量和蒸散量空间分布,对不同景观结构的小流域多年平均径流系数进行比较.结果表明:不同土地覆盖类型组合流域多年平均径流系数大小关系依次为林灌流域《林灌草复合流域《森林流域《林草流域,这与流域土地利用类型、流域海拔分布和迎风坡向分布特征有关;对于森林流域和灌木流域,其多年平均径流系数主要受多年平均降水量、多年平均蒸散量和平均坡度共同影响;对于林草流域和林灌草复合流域,其多年平均径流系数主要受流域的多年平均降水量、多年平均蒸散量和多林草面积比共同影响.     

福州鼓山季风常绿阔叶林及其林缘几种植物叶热值和营养成分

研究了福州鼓山次生的南亚热带季风常绿阔叶要及其林缘不同层次１３种植物叶热值、灰分含是及营养成分。结果表明：群落的不同层次平均灰分含量从低到高依次是乔木层（６．８％）,草本层（８．５％）、灌木层（１０％）;平均含灰分热值（ＧＣＶ）从低到高低次是草本层（１６．８９ＫＪ／ｇ）、灌木层（１７．７５ＫＪ／ｇ）、乔木层（１９．７９ＫＪ／ｇ）;平均不含灰分热值（ＡＦＣＶ）从低到高依次是草本层（１８．５５ＫＪ／ｇ     

1982-2016年东北黑土区植被NDVI动态及其对气候变化的响应

植被是陆地生态系统的重要组成部分,在调节气候、水土保持等方面具有重要作用,因此,监测植被生长变化并探讨其与气候变化之间的关系,在全球变化研究中具有重要意义。基于MODIS NDVI和GIMMS NDVI数据集,并通过一致性检验,在区域和像元两个空间尺度上,利用一元线性回归模型,研究东北黑土区1982-2016年植被生长动态,分析植被生长对气温和降水量的响应程度。结果表明：区域尺度上,1982-2016年东北黑土区植被生长季NDVI变化分为3个阶段（先增加继而减少最后再增加）,区域植被的生长在气温、降水量的共同作用下,呈现出明显季节差异;像元尺度上,1982-2016年东北黑土区NDVI总体趋势为改善状态,主要改善植被类型为草原、森林和农业植被,鹤岗市、绥化市和长春市改善面积较大;多年平均NDVI值与同期气温和降水量具有一定的相关关系,平原地区植被NDVI与气温主要呈显著正相关关系,植被类型主要为耕地;平原地区边缘和山地地区的植被NDVI与降水量以显著正相关关系为主,主要植被类型为森林和草地。     

中国木荷及木荷林的地理分布与气候的关系

在广泛收集木荷（ＳｃｈｉｍａｓｕｐｅｒｂａＧａｒｄｎ．ｅｔＣｈａｍｐ．）及木荷林地理分布资料的基础上,利用目前国际上比较流行的研究植被－气候相互关系的指标和方法,包括Ｋｉｒａ的水热指标,Ｐｅｎｍａｎ的公式,Ｔｈｏｒｎｔｈｗａｉｔｅ的指标和气候分类,Ｈｏｌｄｒｉｄｇｅ的生命地带分类系统指标,以及单一气象因子年平均气温、年降水量等,分析研究了木荷及木荷林在中国的分布与气候的关系,讨论了木荷分布上限、下限以及北界的Ｋｉｒａ热量指标状况,并利用Ｈｏｌｄｒｉｄｇｅ生命地带分类系统指标预测了未来气候变化条件下中国木荷及木荷林分布区的可能变化     

若尔盖地区25万年以来的植被与气候

本文根据ＲＭ孔上部６０ｍ２１０个样品的花粉分析结果,探讨了若尔盖地区２５万年以来的植被演替和气候变化。研究结果表明,若尔盖地区过去２５万年的植被演替和气候变化可划分为７个阶段。在约２５０－１９５ＫａＢ．Ｐ．（７阶段）,植被以亚高山莎草草甸为主,气温和降水与现在接近。在１９５－１２８ＫａＢ．Ｐ．（６阶段）和３２－１１ＫａＢ．Ｐ．（２阶段）,植被主要是荒漠草原,反映寒冷干旱的冰期气候,年平均气温比现在低５－６℃,年降水量仅为现在的６０－８０％左右。１２８－７１ＫａＢ．Ｐ．（５阶段）是过去２５万年中水热条件最好的时期,植被主要是亚高山暗针叶林和亚高山草甸,大部分时间的气温比现在高１℃左右,降水量稍高于现在;但在此阶段的气温和降水仍有较大的波动,５ｅ、５ｃ和５ａ阶段的气温和降水高于５ｂ和５ｄ阶段。７１－５８ＫａＢ．Ｐ．（４阶段）的植被主要是高山草甸为主,年平均气温比今低３－５℃,年降水量约为现在的８０％,在５８－３２ＫａＢ．Ｐ．（３阶段）,早期植被以亚高山暗针叶林和草甸为主,晚期主要是亚高山禾草草甸,气温和降水均明显高于４阶段,在晚期气温呈非线性逐渐下降的趋势。     

砚瓦川流域河川基流变化规律及其驱动因素

基流是黄河径流的重要补给来源,目前大部分研究都集中在黄河流域径流变化规律上,而对维持河道基本流量和生态环境安全方面发挥着重要作用的基流却研究较少。因此,基于黄河中游砚瓦川流域1981-2016年的水文、气象及植被资料,选用9种数值模拟法对基流进行分割并分析其适用性,利用Mann-Kendall法和滑动t检验法对基流进行了趋势分析和突变点检验,并定性和定量的探讨了气候变化和植被变化对流域河川基流变化的影响。结果表明：（1）在各种基流分割方法中,Lyne-Hollick滤波法的计算结果精度较高,且分割结果比较符合实际日基流变化规律,因此更适用于研究区的基流估算;（2）流域多年平均河川基流量和基流指数BFI （基流量与河川流量的比值）分别为0.152 m³/s和0.58,两者在年际上均呈现极显著的减少趋势（P<0.01）,且分别于1993年和2006年附近发生了突变;（3）基流量与潜在蒸散发量相关性最强,流域降水量、潜在蒸散发量及NDVI的变化对基流量变化的贡献率分别为-99.1%、113.3%和85.8%,可见潜在蒸散发量和NDVI的增加是引起基流量减少的主要原因,而基流指数与NDVI相关性最强,且呈负相关关系,流域降水量、潜在蒸散发量及NDVI的变化对基流指数变化的贡献率分别为41.3%、-27.7%和86.5%,这说明流域NDVI的增加对流域基流指数的降低起到了主导作用。     

基于树轮记录的阿尔泰山中段1798年以来6-7月平均最低气温变化研究

通过分析新疆阿尔泰山中段西伯利亚落叶松树轮宽度年表与气候因子的相关关系证明6-7月平均最低气温是研究区树木径向生长的主控气候因子（r=0.649,P<0.001）。利用线性回归方法构建了阿尔泰山中段1798-2017年6-7月平均最低气温方程,通过计算重建方程方差解释量（41.1%）和利用逐一剔除法对重建方程各项参数进行了稳定性检验,证明了重建方程是稳定可靠的。分析重建的平均最低气温年际变化表明,研究区经历了4个暖期（1906-1919年、1922-1935年、1945-1960年和1997-2017年）和4个冷期（1816-1831年、1840-1867年、1869-1886年和1888-1905年）,并且与相邻地区平均气温重建序列冷暖变化阶段一致。基于多窗谱分析结果发现重建6-7月平均最低气温序列存在2a、2.4a、2.8-3a、11a、20-22a左右的变化周期,表明研究区气候变化主要受厄尔尼诺-南方涛动（ENSO）、太阳黑子活动和全球海温变化（SST）驱动。本研究为系统掌握阿尔泰山气候变化规律和科学预测未来气候变化提供了依据。     

1980—2005年松嫩平原土壤湿度对气候变化的响应

基于松嫩平原16个农业气象站1980—2005年作物生长季的旬土壤湿度、月气温和月降水量观测资料,采用统计分析方法,分析了研究区表层（0～30 cm）土壤湿度的时空变化特征及其对气温、降水量等气候变化的响应.结果表明：1980—2005年间,松嫩平原不同区域作物生长季表层土壤湿度均呈减小趋势,松嫩平原表层土壤存在干旱化趋向,其中,西部和南部地区尤为明显;20世纪90年代以前,研究区表层土壤处于比较湿润阶段,之后土壤湿度持续降低,并发生了偏干现象;研究期间,松嫩平原作物生长季平均气温呈周期性波动上升,每6 a一个周期,期间小幅波动,1992年开始明显上升;生长季降水量的年际变幅较大,存在一个4～5 a的波动周期;研究区作物生长季表层土壤湿度与气温呈极显著负相关关系,与降水量呈极显著正相关关系（P<0.01）.气温和降水量是影响松嫩平原作物生长季表层土壤湿度变化的主要气候因素.     

综合土地利用及空间异质性的土壤有机碳空间插值模型

土壤有机碳库是陆地碳库的重要组成部分,土壤有机碳库及其动态变化对陆地生态系统碳循环有着重要的影响.土壤有机碳密度(SOCD)是土壤碳储量的重要参数,也是评价农田土壤质量的重要指标,准确预测区域SOCD空间分布对发展精确农业有重要意义.本文使用江汉平原地区242个农田土壤样本数据,探究平原地区土地利用类型对SOCD空间分布的影响,以及当SOCD空间分布规律呈现空间异质性且存在空间异常值的情况下,虚拟变量回归克里格法(DV_RK)、均值中心化克里格法(MC_OK1)和中位数中心化克里格法(MC_OK2)这3种结合土地利用的克里格法在SOCD空间预测中的应用.结果表明: 土地利用方式的差异是研究区水田和水浇地SOCD存在空间异质性的原因之一,导致SOCD存在空间非平稳特征,降低了普通克里格法(OK)的预测精度;而DV_RK、MC_OK1和MC_OK2在消除了由土地利用引起的SOCD空间异质性对建模的影响后,模型的稳定性提升,其预测精度均高于OK,其中,MC_OK2的模型可靠程度、预测精度和对SOCD总方差的解释能力最优.因此,土地利用类型作为易获取的辅助变量,可以有效减弱空间异质性和空间异常值对SOCD空间插值的影响,提升模型精度,降低不确定性,并与MC_OK2结合生成更高精度的SOCD空间分布图,帮助揭示SOCD空间分异规律,指导农业生产.     

Upscaling leaf area index in an Arctic landscape through multiscale observations

Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site‐specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub‐Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine‐scale harvest LAI–NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI–NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI–NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of ～0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground‐based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI–LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio‐temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.     

基于C值和>5℃积温的内蒙古自治区植被-气候分类

C值是崔启武(1981)提出的一个水热联系方程的参数,代表了一个地区的干湿状况。本研究根据150个气象站点资料,计算了内蒙古自治区1961—1990年30年平均C值和>5℃积温,并生成空间分布图。结合内蒙古自治区植被地带分布图,绘制了内蒙古10个植被地带的C值和>5℃积温的散点图,较好地表现了内蒙古各植被带与气候指标的关系,并确定了内蒙古各植被带的C值和>5℃积温的界限。根据确定的各个植被带的C值和>5℃积温的界限,模拟了内蒙古自治区植被带的空间分布。通过Kappa一致性检验结果表明,C值与>5℃积温结合可以较好地反映内蒙古植被地带的空间分布状况。作为一个综合气候指标,C值为模拟内蒙古植被带分布提供了重要信息。     

人类群体遗传空间结构的"克立格"模型

通过将“克立格”技术应用于人类群体遗传学领域,构建了人类群体遗传空间结构的“克立格”模型,并论述了其原理和计算方法。以HLA-A基因座为例,应用“克立格”模型,定量分析了中国人群HLA-A基因座的空间遗传异质性;对HLA-A基因频率的空间数据矩阵进行了主成分分析,进而定义了人类群体遗传结构的综合遗传测度(SPC),绘制了综合遗传测度和主成分(PC)的“克立格”地图,分析了其群体遗传空间结构特性。与其他空间插值或平滑方法相比,人类群体遗传空间结构的“克立格”模型具有明显优点：1)“克立格”估计以空间遗传变异函数模型为基础,在绘制空间遗传结构地图之前,可利用变异函数模型定量分析所研究基因座(或多基因座)的空间遗传异质性;2)“克立格”插值方法是真正意义上的无偏估计模型,它利用待估区域周围的已知群体遗传调查点数据,并充分考虑调查点的空间影响范围,给出待估区域的最优估计值;3)“克立格”模型允许估计插值误差,这种插值误差既可用于评价空间估计效果,又可通过绘制误差地图指导在误差过高的地点增加新的群体遗传调查样本点,以优化估计效果。然而,人类群体遗传空间结构的“克立格”模型也存在一定缺点：1)若不能用任何理论遗传变异函数模型拟合观察遗传变异函数值,则不能建立“克立格”模型;2)若理论遗传变异函数的拟合优度很低,则据此建立的“克立格”模型的估计标准差在整个空间范围内会很大,此时“克立格”模型不适用于估计群体遗传空间结构。出现上述两种情形时,应选用不考虑空间相关性的空间随机插值方法绘制群体遗传结构地图,如基因绘图软件中的Cavalli-Sforza方法,反向距离加权法和条样函数插值法等。     

普通克立格法在昆虫生态学中的应用

地统计学是以区域化变量为基础,以变差函数为主要工龄,分析空间相关变量结构的统计方法。在对波动较大的实验变差函数进行拟合时,虽无法获得最优拟合,但运用人机对话的拟合方法来灵活选取参数,可以得到较理想的变差函数模型的参数。本文运用加权多项式回归法以及人机对话的方法,得到了较理想的1级与2级球状模型拟合结果,同时利用直线函数对实验变差函数进行了拟合,最后利用普通Kriging法,对待估计点进行各理论模型的最优、线性、无偏内插估计,得出克立格内插权重。将此方法应用于广东省四会市大沙镇富溪乡试验田稻飞观测数据,由待估点周围若干观测点的数据,有效地估计出待估点的昆虫分布密度,并讨论比较了不同理论模型的拟合效果以及估计误差。结果表明,2级球状模型的拟合最好,一级球状模型次之,直线函数的拟合最差,但直线函数计算最为简便。     

基于模型和GIS技术的中国稻田甲烷排放估计

将一个比较成熟的稻田甲烷排放模型CH4MOD和GIS空间化数据库结合,模拟估计了中国大陆2000年水稻生长季稻田甲烷的排放。模型的空间输入参数包括:逐日气温、耕层土壤砂粒含量、外源有机质施用量、稻田水分管理模式、水稻移栽期与收获期、水稻种植面积与单产,空间分辨率为10km×10km。模拟结果表明:2000年稻田甲烷排放量为6.02Tg,其中:早稻生长季排放1.63Tg、晚稻1.46Tg、单季稻2.93Tg。提高区域稻田甲烷排放估计精度的进一步目标应放在减小输入参数误差和提高空间数据精度上,在现有数据库基础和模型———GIS技术下探讨我国稻田甲烷排放估计的不确定性范围是必要的。     

分布式模型在流域蒸散模拟中的应用与验证

根据四川杂谷脑河流域上游地区1989～2000年气象站常规观测数据,应用分布式模型方法,考虑流域的空间异质性及时空变异性,选择离散单元格尺度为500 m,时间步长为1 d,采用Penman-Monteith公式的改进形式,估算流域多年平均潜在蒸散量的时空分布;结合流域下垫面特点,估算逐日实际蒸散量的时空分布;并将模型模拟的多年平均值与研究区同期水量平衡法计算结果相比,相对误差为+3.47%且时空分布合理.为流域分布式降雨-径流模型提供了可靠的实际蒸散量模拟方法.     

Summer Rains and Dry Seasons in the Upper Blue Nile Basin: The Predictability of Half a Century of Past and Future Spatiotemporal Patterns

During the last 100 years the Ethiopian upper Blue Nile Basin (BNB) has undergone major changes in land use, and is now potentially facing changes in climate. Rainfall over BNB supplies over two-thirds of the water to the Nile and supports a large local population living mainly on subsistence agriculture. Regional food security is sensitive to both the amount and timing of rain and is already an important political challenge that will be further complicated if scenarios of climate change are realized. In this study a simple spatial model of the timing and duration of summer rains (Kiremt) and dry season (Bega), and annual rain over the upper BNB was established from observed data between 1952 and 2004. The model was used to explore potential impacts of climate change on these rains, using a down-scaled ECHAM5/MP1-OM scenario between 2050 and 2100. Over the observed period the amount, onset and duration of Kiremt rains and rain-free Bega days have exhibited a consistent spatial pattern. The spatially averaged annual rainfall was 1490 mm of which 93% was Kiremt rain. The average Kiremt rain and number of rainy days was higher in the southwest (322 days) and decreased towards the north (136 days). Under the 2050–2100 scenario, the annual mean rainfall is predicted to increase by 6% and maintain the same spatial pattern as in the past. A larger change in annual rainfall is expected in the southwest (ca. +130 mm) with a gradually smaller change towards the north (ca. +70 mm). Results highlight the need to account for the characteristic spatiotemporal zonation when planning water management and climate adaptation within the upper BNB. The presented simple spatial resolved models of the presence of Kiremt and annual total rainfall could be used as a baseline for such long-term planning.     

Estimates of methane emissions from Chinese rice paddies by linking a model to GIS database

Methane is one of the principal greenhouse gases. Irrigated rice paddies are recognized as contributing to atmospheric methane concentration. Methane emissions from rice paddies are among the most uncertain estimates in rice-growing countries. Efforts have been made over the last decade to estimate CH₄ emissions from Chinese rice paddies via the model method. However, these estimates are very vague due to different models and upscaling methods. A reduction in these uncertainties may be achieved by coupling field-scale models with regional databases. The objective of this article is to develop a methodology of coupling a CH₄ emission model with regional databases by which CH₄ emissions from Chinese rice paddies can then be estimated. CH4MOD, a model for simulating CH₄ emissions from rice paddies with minimal input by using commonly available parameters, is of great potential in terms of upscaling as it has provided a realistic estimate of the observed results from various soils, climates and agricultural practices. By linking spatial databases to CH4MOD, CH₄ emissions from Chinese rice paddies in the 2000 rice-growing season were simulated on a day-by-day basis. The spatial databases were created by GIS with a spatial resolution of 10km10km, including soil sand percentage, amounts of crop straw and roots from the previous season and farm manure, the water management pattern, dates of rice transplanting and harvesting, acreage of rice planted, rice grain yield and daily air temperature. ARCGIS software was used to meet all GIS needs, including data access, projection definition, overlaying of different vector layers, creation of grids (a raster format of ARCGIS software) by converting vector data, and the data conversion between grids and ASCII formats. Methane emissions from rice paddies in mainland China in the 2000 rice-growing season were estimated to be 6.02 Tg (1 Tg = 10⁹ kg). Of the total, approximately 49% (2.93Tg) is emitted during the single rice-growing season, and 27% (1.63Tg) and 24% (1.46Tg) are from the early and late rice-growing seasons respectively. It was concluded that regional CH4 emissions from rice paddies could be estimated by coupling CH4MOD with regional databases with a high spatial resolution. A further effort should be made to improve the quality of the spatial databases, especially in terms of the amount of added organic matter and the water regime. It is also necessary to evaluate the uncertainties of the present estimates in order to improve the overall accuracy.     

Estimates of methane emissions from Chinese rice paddies by linking a model to GIS database

Methane is one of the principal greenhouse gases. Irrigated rice paddies are recognized as contributing to atmospheric methane concentration. Methane emissions from rice paddies are among the most uncertain estimates in rice-growing countries. Efforts have been made over the last decade to estimate CH₄ emissions from Chinese rice paddies via the model method. However, these estimates are very vague due to different models and upscaling methods. A reduction in these uncertainties may be achieved by coupling field-scale models with regional databases. The objective of this article is to develop a methodology of coupling a CH₄ emission model with regional databases by which CH₄ emissions from Chinese rice paddies can then be estimated. CH4MOD, a model for simulating CH₄ emissions from rice paddies with minimal input by using commonly available parameters, is of great potential in terms of upscaling as it has provided a realistic estimate of the observed results from various soils, climates and agricultural practices. By linking spatial databases to CH4MOD, CH₄ emissions from Chinese rice paddies in the 2000 rice-growing season were simulated on a day-by-day basis. The spatial databases were created by GIS with a spatial resolution of 10km×10km, including soil sand percentage, amounts of crop straw and roots from the previous season and farm manure, the water management pattern, dates of rice transplanting and harvesting, acreage of rice planted, rice grain yield and daily air temperature. ARCGIS software was used to meet all GIS needs, including data access, projection definition, overlaying of different vector layers, creation of grids (a raster format of ARCGIS software) by converting vector data, and the data conversion between grids and ASCII formats. Methane emissions from rice paddies in mainland China in the 2000 rice-growing season were estimated to be 6.02 Tg (1 Tg = 10⁹ kg). Of the total, approximately 49% (2.93Tg) is emitted during the single rice-growing season, and 27% (1.63Tg) and 24% (1.46Tg) are from the early and late rice-growing seasons respectively. It was concluded that regional CH4 emissions from rice paddies could be estimated by coupling CH4MOD with regional databases with a high spatial resolution. A further effort should be made to improve the quality of the spatial databases, especially in terms of the amount of added organic matter and the water regime. It is also necessary to evaluate the uncertainties of the present estimates in order to improve the overall accuracy.