陕甘宁地区退耕还林还草的气候和农业效应模拟研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
植被覆盖状况的改变不仅直接影响陆地生态系统的形成和演变,而且还可能引起不同尺度的大气环流和气候变化。同时,气候变化也直接影响陆面植被状况的改变。因此,植被大气相互作用及其反馈机制已成为当前全球变化研究中的重要科学问题之一。我国西部干旱半干旱地区气候条件恶劣、生态环境脆弱且经济落后。1999年以来,该区逐步实施了退耕还林还草生态建设工程。这一土地利用方式的巨大转变可能引起当地气候的变化,并进而影响农业产生。如何客观评估退耕还林还草的气候和农业效应,是涉及当地社会经济和生态环境可持续发展的重大问题。
     本研究以陕甘宁降水较少,林木立地条件差,退耕还林还草为主的地区为例,首先根据观测数据分析当地植被变化与区域气候间的相互影响关系,然后探讨区域气候模式与作物/牧草生长模式的耦合方法及耦合效果,最后结合退耕还林还草的实际进展情况,利用耦合模式长年代多样本的数值模拟试验,重点研究土地利用渐变条件下的气候-植被相互作用,探讨单纯由退耕还林还草所产生的气候和农业效应。主要研究结论有:
     1.观测研究表明,自2000年退耕还林还草以来陕甘宁地区的主要土地利用类型分布发生了显著性的变化,冬小麦种植面积由于退耕而每年缩小约1568 km~2,草地面积由于转换为半沙漠、冬小麦种植区或林地等原因而每年减少2624 km~2,沙漠面积由于防沙治沙工程的实施而每年减少1776 km~2,落叶阔叶林由于植树造林而每年增加3312 km~2,半沙漠由于草地退化、治沙、退耕地还未发展成草地或林地而每年增加2300 km~2;与研究区北部非退耕区相比,中南部典型退耕区夏季高温随时间的增速趋缓而降水量随时间的增速变快;夏季温度变化与落叶阔叶林、半沙漠、沙漠面积的变化显著相关;春季温度和降水量对冬小麦产量有明显影响,而秋季温度和年降水量影响着牧草产草量。这些观测事实是开展退耕还林还草气候和农业效应模拟研究的前提。
     2.模式校准和性能分析表明,作物生长模型WOFOST中加入干物质再运转过程提高了模型在单点上的模拟性能,对冬小麦参数的分区实现了模型的区域应用。牧草生长模型LINGRA中加入茎秆生长过程和钝化水分胁迫系数后能够较好地适应对天然牧草的模拟。网格(水平分辨率为20km)和次网格(水平分辨率为4km)尺度下的模拟检验表明,区域气候模式RegCM3能够合理再现陕甘宁地区气候的季节变化和区域分布特征,但模拟温度稍偏低,模拟降水量偏高。三个模式的校准和性能分析为应用研究奠定了基础。
     3.模式耦合设计和试验结果表明,RegCM3与RegWOFOST\LINGRA可以在日和次网格尺度上实现时空匹配。RegCM3向RegWOFOST\LINGRA输出最高、最低温度、降水量、辐射、水汽压和风速等气象数据,RegWOFOST\LINGRA以叶干重与比叶面积之积的方法计算植被LAI,再反馈回RegCM3而实现两类模式的耦合。耦合模式RegCM3\WOFOST\LINGRA在一定程度上改善了RegCM3模拟降水量偏高、温度偏低的现象,提升了RegCM3的模拟性能,并实现了气候、作物和牧草的同时模拟,为气候和农业效应研究备好了工具。
     4.退耕过程中(1999-2008年)RegCM3\WOFOST\LINGRA的数值模拟结果表明,单纯的退耕还林还草导致研究区多数地区最高、最低和平均温度明显下降,太阳总辐射略有下降,而降水量、蒸散以及土壤湿度增加的区域均较减小的范围更广,数量更大,地表径流降水比变化较小,增减范围大小相当。从时间变化看,单纯的退耕还林还草对研究区范围的日最高温度持续升高有一定的抑制趋势,对南部典型退耕区的抑制趋势更明显,对降水量的增加也有微弱促进作用,并由此导致冬小麦成熟期延迟,贮存器官干重等产量要素明显增加。其中的降温主要与耕地或草地转换为林地相对应。
     5.退耕前后RegCM3\WOFOST\LINGRA的长期(1990-2015年)数值模拟表明,单纯的退耕还林还草造成陕甘宁全区域平均的最高温度有所降低,降幅在0.05-0.27℃之间,最低温度降低在0.1-0.3℃之间,降水量增加在25mm以内,太阳总辐射减少在80mj m~(-2)以内,由此导致冬小麦成熟期提前2天左右,开花期LAI增加在0.2以下,成熟期贮存器官干重增加在614 kg hm~(-2)以下。其中,平均气温和降水量的变化占总的气候变化的比重在3%~7%之间,对其它气候和农业要素的影响则更大一些。表明目前的退耕还林还草所产生的气候效应是探讨区域范围内气候变化不可忽略的部分,但它尚不能逆转全球气候变化带来的影响。
     本研究在揭示观测事实、开展区域气候模式与作物/牧草生长模型耦合、量化单纯有退耕还林还草引起的气候和农业效应、考虑实际土地利用变化状况、以及在较高空间分辨率进行长时间数值积分和多样本模拟试验等方面具有一定创新。本研究针对气候与植被之间相互作用及其反馈机制的科学问题,在研究的方法论上进行了有益的尝试。本研究既促进气候和农业学科交叉研究的发展,同时又具有一定社会效益。下一步还可在模式性能改善、耦合的切入点、更大研究区域、空间气候变量效应分析等方面继续开展研究。
Changes of vegetation cover not only directly affect the formation and evolution of terrestrial ecosystems, but also may give rise to different scales of atmospheric circulation and climate change. At the same time, climate change has a direct impact on changes of land surface vegetation. Thus the interaction and feedback mechanism between vegetation and atmosphere becomes one of the important scientific issues in current global change research. There are poor climatic conditions, fragile ecological environment and backwardness economic in arid and semiarid climate regions in West China. The Ecological Reforestation Project had been implemented since 1999 in there. This land use change is bound to cause great changes of local climate and to affect agriculture. How to assess the possibility climatic and agricultural effects of returning farmland to forest and grassland is the major issue involved in local sustainable development of socio-economic and ecological environment.
     In the study, based on the region with less precipitation, poor forest site, converting farmland into forest or grass land in ShanGanNing, the relationship between local vegetation change and regional climate was firstly analyzed by using some observations. Then, it was investigated that coupling method and its effect between regional climate model and crop / pasture growth model. Finally, interactions between vegetation and climate were researched under the conditions of gradient changes of land use in the actual progress. Climate and agricultural effects produced by Alone Returning Farmland to Forest and Grassland (AReFFG) were discussed through many numerical simulations by using the coupling model. The main conclusions were as follows:
     1. Analysis of observational data showed that (1) Distribution of major land use types in ShanGanNing region had produced significant changes since 2000. Planting area of winter wheat reduced by about 1568 km~2 y~(-1) due to returning farmland to forest or grassland. As a result of degradation or converting to farm land or forest land, the area of grass land reduced by 1776 km~2 y~(-1). There were reductions of 1776 km~2 y~(-1) in desert area since the implementation of desertification control projects. Deciduous broad-leaved forest area increased by 3312 km~2 y~(-1) based on afforestation. Because of conversion from farmland or degradation of grassland, semi-desert / sparse grass area increased by 2300 km~2 y~(-1). (2) Increasment slowed down in July temperature while accelerated in precipitation with time in Typical region of Ecological Reforestation in Central and Southern study Area (TERCS region). (3) Summer temperature was significantly associated with changes of deciduous broad-leaved forest, semi-desert and desert area. (4) Spring temperature and precipitation had a significant effect on the yields of winter wheat, while autumn temperature and annual precipitation affected the amount of forage grass. These facts from observations were a prerequisite to carry out simulation studies on climate and agricultural effect of converting farmland into forest or grass land.
     2. Calibration and validation of models showed that (1) the performance of WOFOST in local scale was improved by adding re-operation process of the dry matter. Regional application of WOFOST was achieved based on regionalization of winter wheat parameters. (2) LINGRA could not simulate the growth of natural grass until including stem growth process and making the water stress coefficient insensitive. (3) RegCM3 could reasonably reproduce the seasonal changes and regional distribution of climate in ShanGanNing region under both grid and semi-grid scale. However, there were slightly lower in simulated temperature and higher in simulated precipitation than observations.
     3. Coupling design and test of models showed that (1) RegCM3 and RegWOFOST\LINGRA could match in days and semi-grid scale. Both models were coupled by means of that RegCM3 outputted the maximum, minimum temperature, precipitation, radiation, vapor pressure and wind speed to RegWOFOST\LINGRA, and vegetation LAI, which was calculated by using leaf dry weight and specific leaf area in the latter, was then fed back the former. (2) RegCM3\WOFOST\LINGRA slightly improved the situation of high in precipitation and low in temperature simulated by RegCM3 and achieved to simulate simultaneously the climate, crops and pasture.
     4. Numerical simulation by using RegCM3\WOFOST\LINGRA during 1999 to 2008 showed that (1) As a result of AReFFG, there were a decrease in the maximum, minimum and average temperature, a slight decrease in global solar radiation in most parts of the study area, the region of increase in precipitation, evapotranspiration and soil moisture was wider than the one of decrease, and the ratio of surface runoff and precipitation had a little change. (2) AReFFG had inhibitory effect on continuous increase of the maximum temperature in study area and promoted effect on the increase of precipitation in TERCS region. These situations led to noticeable delay in winter wheat maturity and increase of dry weight of storage organ (WSO). Temperature change was mainly corresponding with conversion of farmland or grassland into forest land.
     5. Numerical simulation results by using RegCM3\WOFOST\LINGRA during 1990 to 2015 showed that (1) As a result of AReFFG, there were a decrease of 0.05℃-0.27℃in the maximum temperature, a decrease of 0.1-0.3℃in the minimum temperature, a reduction of less than 80mj m~(-2) in the global solar radiation, a increase of less than 25mm in precipitation, a advancement of about 2 days in winter wheat maturity, a increase of less than 0.2 in LAI in anthesis, an enhance of less than 614 kg hm~(-2) in WSO in maturity in average of study area. (2) The proportion of average temperature and precipitation changes caused by AReFFG were 3%-7% in the total climate changes, while other climate and agricultural elements account for the larger proportion. Climate and agricultural effect of AReFFG could not be neglected in probing regional climate change. However, it still could not reverse the large background of climate change.
     In this paper, there were innovation in revealing the observed facts, coupling regional climate model and crop / pasture growth model, trying to separate the effects of AReFFG, considering the actual change situation of land use, higher spatial resolution and long-time numerical integration and many numerical simulation test as well. We had a useful attempt in study methodology against scientific issues of the interaction and feedback mechanism between vegetation and climate. This research not only promotes the interdisciplinary study of climate and agricultural, but also has social benefits. It can be further investigated to improve model performance, add more entry point of models coupling, and consider the situation of larger area and effects of space weather variables in the next step.
引文
1.戴永久.陆面过程模式及其与GCM藕合模拟研究.中国科学院大气物理研究所博士论文. 1995. pp288
    2.丁一汇,李巧萍,董文杰.植被变化对中国区域气候影响的数值模拟研究.气象学报, 2005, 63(5): 613-621
    3.高亮之.农业模型学基础.香港:天马图书有限公司. 2004. pp320
    4.高亮之,金之庆,黄耀,等.水稻栽培计算机模拟优化决策系统.北京:中国农业出版社. 1992. pp152
    5.高亮之,金之庆,郑国清,冯利平,张立中,石春林,葛道阔.小麦栽培模拟优化决策系统(WCSODS).江苏农业学报, 2000, 16(2): 65-72
    6.国家气象局.农业气象观测规范(上卷).北京:气象出版社. 1993. 11-11
    7.范广洲,吕世华,罗四维.西北地区绿化对该区及东亚、南亚区域气候影响的数值模拟.高原气象, 1998, 17(3): 300-309
    8.冯利平,高亮之,金之庆,马新明.小麦发育期动态模拟模型研究.作物学报. 1997. 23(4): 418-424
    9.符淙斌,王淑瑜,熊哲,冯锦明.亚洲区域气候模式比较计划的进展.气候与环境研究, 2004, 9(2): 225-239
    10.符淙斌,魏和林,郑维忠等.中尺度模式对中国大陆地表覆盖类型的敏感性试验[C].全球变化与我国未来的生存环境.北京:气象出版社. 1996. pp286.
    11.侯琼,王英舜,师桂花,杨泽龙.锡林郭勒典型草原牧草生长特性与主要生态因子分析.中国农学通报, 2010, (14): 1-7
    12.金之庆,方娟,葛道阔等.全球气候变化影响我国冬小麦生产之前瞻.作物学报, 1994, 20(2): 186~197
    13.金之庆,葛道阔,石春林,高亮之.东北平原适应全球气候变化的若干粮食生产对策的模拟研究.作物学报, 2002, 28(1): 24-31
    14.李维京,陈丽娟.动力延伸预报产品释用方法的研究.气象学报, 1999, 57(3): 338-345
    15.梁玲,吕世华,尚伦宇.黄土高原植被改变对夏季当地气候影响的数值模拟.高原气象, 2008, (2): 293-300
    16.梁伟,白翠霞,孙保平,等.黄土丘陵区退耕地土壤水分有效性及蓄水性能—以陕西省吴旗县柴沟流域为例[J ].水土保持通报, 2006, 26 (4): 38-40.
    17.刘布春,王石立,马玉平.国外作物模型区域应用研究的进展.气象科技. 2002, 30: 193-2033
    18.刘建栋,王馥棠,于强,王建林,毕建杰,樊广华.华北地区农业干旱预测模型及其应用研究.应用气象学报, 2003, 14(5): 593-604
    19.刘贤赵,宿庆.黄土高原水土流失区生态退耕对粮食安全的影响.山地学报, 2006, 24(1): 7-12
    20.陆其峰,潘晓玲,钟科,李永东.区域气候模式研究进展.南京气象学院学报, 2003, 26(4): 557-565
    21.罗勇,王绍武,党鸿雁,赵宗慈.近20年来气候模式的发展与模式比较计划.地球科学进展, 2002, 17(3): 372-377
    22.吕建华,季劲钧.青藏高原大气-植被相互作用的模拟试验I物理通量和参数.大气科学, 2002, 26(1): 111-126
    23.吕世华,陈玉春.西北植被覆盖对我国区域气候变化影响的数值模拟.高原气象, 1999, 18(3): 416-424
    24.季劲钧,胡玉春.一个植物冠层物理传输和生理生长过程的多层模式.气候与环境研究, 1999, 4(2): 152-164
    25.马玉平,王石立,张黎,庄立伟.基于升尺度方法的华北冬小麦区域生长模型初步研究-- I.潜在生产水平.作物学报, 2005, 31(6): 697-705
    26.毛嘉富,王斌,戴永久,等. 2008.一个动态植被模型在欧洲森林碳水循环模拟中的适应性评估研究.大气科学, 32 (6) : 1379-1391
    27.潘学标,韩湘玲,石元春. COTDGROW:棉花生长发育模拟模型.棉花学报. 1996. 8(4): 180-188
    28.任洪玉,温仲明,杨勤科.黄土沟壑区植被恢复及其物种多样性的变化—以吴起县植被恢复为例[ J ].干早地区农业研究, 2003, 21 (2): 154-158.
    29.施伟来,王汉杰.中国西部退耕还林(草)与沙漠绿化的区域性气候效应[C].见:西部开发与生态建设.北京:中国林业出版社, 2001, 592.
    30.施伟来,王汉杰.中国西部绿化对东亚季风气候影响的数值模拟.解放军理工大学学报(自然科学版), 2003, 4(3): 76-81
    31.孙智辉,曹雪梅,李新亚,雷延鹏,刘志超.气候变化和人类活动对吴起土壤侵蚀的影响.水土保持研究, 2009, (6): 30-39
    32.王石立,马玉平.作物生长模拟模型在我国农业气象业务中的应用研究进展及思考.气象. 2008, 34(6): 3-9
    33.吴礼军,刘青,李栎,赵萱,赵铁珍.全国退耕还林工程进展成效综述.林业经济. 2009, (9): 21-37
    34.谢云, Kiniry J R.国外作物生长模型发展综述.作物学报, 2002, 28(2): 190-195
    35.熊伟,杨婕,林而达,许吟隆.未来不同气候变化情景下我国玉米产量的初步预测.地球科学进展, 2008, 23(10): 1092-1101
    36.杨光,丁国栋,赵廷宁,等.黄土丘陵沟壑区退耕还林的水土保持效益研究—以陕西省吴旗县为例[J ].内蒙古农业大学学报, 2005, 6 (2): 20-23.
    37.杨光,孙保平,赵廷宁,等.黄土丘陵沟壑区退耕还林工程植被恢复效益初步研究[J ].干旱区资源与环境, 2006, 20 (2): 165-169.
    38.姚德良,李家春,杜岳,李新荣,张景光.沙坡头人工植被区陆气耦合模式及生物结皮与植被演变的机理研究.生态学报, 2002, 22(4): 452-460
    39.张宇,王馥棠.气候变暖对中国水稻生产可能影响的研究.气象学报, 1998, 56(3): 369-376
    40.郑益群,钱永甫,苗曼倩,于革,孔玉寿,章东华.植被变化对中国区域气候的影响Ⅰ:初步模拟结果.气象学报, 2002a, 60(1): 1-16
    41.郑益群,钱永甫,苗曼倩,于革,孔玉寿,章东华.植被变化对中国区域气候的影响Ⅱ:机理分析.气象学报, 2002b, 60(1): 17-29.
    42.中国农林作物气候区划协作组.中国农林作物气候区划.北京:气象出版社. 1987, pp4-35
    43.周立华,马永欢,马绍休.中国北方农牧交错带风水蚀复合区的粮食与退耕还林(草)问题.中国沙漠. 2007, 27(4): 552-557
    44. Bonan GB, Levis S, Sitch S, et al1. A dynamic global vegetation model for use wit h climate models: Concepts and description of simulated vegetation dynamics. Global Change Biology, 2003, 9: 1543~1566
    45. Bouman, B. A. M., H. van Keulen, H. H. van Laar, and R. Rabbinge. The School of de Wit crop growth simulation models: a pedigree and historical overview. Agricultural Systems, 1996, 52(2-3): 171-198
    46. Carlos A N, P J Sellers, J Shukla. Amazonian deforestation and regional climate change. Journal of Climate, 1991, 4: 957-988
    47. Charney J G. Dynamics of deserts and drought in the Sahel. Quart J R Meteor Soc, 1975, 101(428): 193-202.
    48. Chase T N, Pielke R A, Kittel TGF, Nemani R, Running SW. Sensitivity of a general circulation model to global changes in leaf area index. J Geophys Res, 1996, 101(D3): 7393-7408.
    49. Chipanshi A C, E A Ripley, R G Lawford. Large-scale simulation of wheat yield in semi-arid environments using a crop-growth model. Agricultural System, 1999, 59: 57-66
    50. Crame W, Bondeau A, Woodward F I, et al1. Global response of terrest rial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models. Global Change Biology, 2001, 7: 357~374
    51. Deardroff J W. Efficient prediction of ground surface temperature and moisture with inclusion of a layer of cegetation. J. Geophys. Res., 1978, 83: 1889-1903
    52. Dickinson, R. E., R. M. Errico, F. Giorgi, and G. T. Bates A regional climate model for the western United States, Climatic Change, 1989, 15, 383–422.
    53. Dickinson R E, Henderson-Sellers A. Modeling tropical deforestation: A study of GCM land-surface parameterization. Quart J R Meteor Soc, 1988, 114(480): 439-462.
    54. Dickinson, R. E., A. Henderson-Sellers, P. J. Kennedy, and M. F. Wilson. Biosphere Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model. NCAR Technical Note, NCAR, TN275+STR, 1986, 69 pp.
    55. Dickinson R E, Henderson-Sellers A, Kennedy P J. Biosphere-atmosphere transfer scheme (BATS) Version 1e as coupled to the NCAR Community Climate Model. NCAR Techn. Note-378+STR, 1993.
    56. Dickinson R E, Kennedy P. Impacts on regional climate of Amazon deforestation. Geophys Res Lett, 1992, 19(19): 1947-1950.
    57. Dirmeyer P A, Shukla J. The effect on regional and global climate of expansion of the world’s deserts. Quart J Roy Meteor Soc, 1996, 122(530): 451-482.
    58. Duncan W G, Loomis R S, Williams W A, etal. A model for simulating photosynthesis in plant communities. Hilgardia, 1967, 38: 181-205
    59. Farquhar G D, von Caemmerer S and Berry J A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 1980, 149: 78~90.
    60. Gerrit Hoogenboom. The State-of-the Art in Crop Modeling. In: (M. V. K. Sivakumar Ed.)Climate Prediction and Agriculture. Proceedings of the START/WMO International Workshop held in Geneva, Switzerland, 27-29 September 1999. Washington, DC, USA, International START Secretariat, 2000. 69-75
    61. Giorgi, F. Two-dimensional simulations of possible mesoscale effects of nuclear war fires, J. Geophys. Res., 1989, 94, 1127–1144.
    62. Giorgi, F., X. Q. Bi, and Y. Qian Indirect vs. direct effects of anthropogenic sulfate on the climate of east asia as simulated with a regional coupled climate-chemistry/ aerosol model, Climatic Change, 2003, 58, 345–37 6.
    63. Henderson-Sellers A, Dickinson R E, Durbidge T B, Kennedy P J, McGuffie K, Pitman A J. Tropical Deforestation: Modeling Local- to Regional scale climate change. J. Geophys Res, 1993, 98(D4): 7289-7315.
    64. Henderson-Sellers A, Gornitz V. Possible climate impacts of land cover transformations, with particular emphasis on tropical deforestation. Climate Change, 1984, 6(3-4): 231-258.
    65. Huang M, Ji J, Li K, et al. The ecosystem carbon accumulation after conversion of grasslands to pine plantations in sub-tropical red soil of South China. Tellus, 2007, 59B: 439-448
    66. James W. Hansen and James W. Jones. Scaling-up Crop Models for Climate Prediction Applications. In: (M. V. K. Sivakumar Ed.) Climate Prediction and Agriculture. Proceedings of the START/WMO International Workshop held in Geneva, Switzerland, 27-29 September 1999. Washington, DC, USA, International START Secretariat, 2000. 77-117
    67. Ji Jinjun, A climate-vegetation interaction model: simulating physical and biological processes at the surface. Journal of Biogeography, 1995, 22, 445-451
    68. Ji Jinjun and Hu Yuchun. A simple land surface process model for use in climate studies. Acta. Meteorologica sinica. 1989, (3): 342-351.
    69. Kropff, M. J., H. H. van Laar, and R. Matthews. ORAZA1, an Ecophysiological Model for Irrigation Rice Production. SARP Research Proceedings. DLO Research Institute for Agrobiology and Soil Fertility, Wageningen, the Netherlands, 1994. 110pp.
    70. Lean, J., and D. A. Warrilow, Simulation of the regional climatic impact of Amazon deforestation, Nature, 1989, 342: 411-413
    71. Li Jianping, Guan Jingde, Han Yingjuan, Wang Shili, Ma Yuping. The vegetation classification of the return tarmland to pasture or forest region in Shaanxi--Gansu--Ningxia based on SPOT/VEGETATION data, Agricultural Science&Technology. 2009. 10(5): 179-183
    72. Manabe S. Climate and the ocean circulation: I The atmospheric circulation and the earth’s surface. Mon Wea Rev, 1969, 97(10): 739-774.
    73. Mc Cown, R. L., G. L. Hammer, and J. N. G. Hargreaves et al. APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agricultural Systems, 1996, 50: 255-271
    74. Mckinion J M, Baker D N. Application of the GOSSYM/COMAX system to cotton crop management. Agriculture Systems, 1989, 31: 31-35
    75. Moen T N, H M Kaiser, S J Riha. Regional yield estimation using a crop simulation model: concepts, methods, and validation. Agricultural System, 1994, 46: 79-92
    76. Niyogi D,Alapaty K,Raman S,et al. Development and evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM)for mesoscale weather forecasting applications. J Appl Meteor Climatol, 2009, 48:349-368
    77. Omer Lutfi Sen, Bin Wang, Yuqing Wang. Impacts of Re-greening the Desertified Lands in Northwestern China: Implications from a Regional Climate Model Experiment. Journal of the Meteorological Society of Japan, 2004, 82(6), 1679-1693
    78. Omer Lutfi Sen, Yuqing Wang, Bin Wang. Impact of Indochina deforestation on the East-Asian summer monsoon. Journal of climate, 2004, 17: 1366-1380
    79. Penning de Vries F W T, van Laar H H. Simulation of plant growth and crop production. In: Penning de Vries F W T, van Laar H H eds. Simulation Monographs. Wageningen, Netherlands: PUDOC, 1982. 114~136
    80. Ritchie J T. Model for predicting evaporation from a row crop with incomplete cover. Water Resources Research, 1972, 8: 1204-1213
    81. Rodriguez D., M. Van Oijen, AHMC. Schapendonk. LINGRA-CC: a sink–source model to simulate the impact of climate change and management on grassland productivity. New Phytologist. 1999, 144(2): 359–368
    82. Samul L., Gordon B. Bonan, Mariana Vertenstein, et al. The community land model’s dynamic global vegetation model (CLM-DGVM). Technical description and user’s guide. NCAT/TN-459-1A NCAR technical note. National center for atmospheric research, Boulder, Colorado USA. 2004, 1-50
    83. Schapendonk AHCM, Stol W, Van Kraalingen DWG, Bouman BAM. LINGRA, a sink/source model to simulate grassland productivity in Europe. European Journal of Agronomy, 1998, 9: 87-100.
    84. Sellers P J. Biophysical models of land surface processes. Trenberth K E. Climate System Modeling. Cambridge: Cambridge University Press, 1992: 451-490.
    85. Sellers, P. J., R. E. Dickinson, D. A. Randall, et al. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 1997, 275 ( 5299): 502-509
    86. Sellers, R. J., Y Minitz, Y C Sud, A Dalcher. A simple biosphere model (SiB) for use within general circulation models, J. Atmos. Sci., 43 ( 6), 505-531, 1986
    87. Shi Weilai, Wang Hanjie. The regional climate effects of replacing farmland and re-greening the desertification lands with forest or grass in West China. Advances in Atmosphere Sciences, 2003, 20(1): 45-54
    88. Shrikant S. Jagtap and James W. Jones. Scaling-up crop models for regional yield and production estimation: A case-study of soybean production in the State of Georgia, USA. In: Crop Monitoring and Prediction at Regional Scales. Proceedings of the NIAES-STA International Workshop 2001 held in Tsukuba, Japan, 19-21 February 2001. 171-186
    89. Shukla J, Nobre C, Sellers P J. Amazon Deforestation and climate change. Science, 1990, 247(4948): 1322-1325.
    90. Sitch S, Prentice IC, Smith B. LPJ - A coupled model of vegetation dynamics and the terrestrial carbon cycle. In: Sitch S, eds. The Role of Vegetation Dynamics in the Control of atmospheric CO2 Content. Ph. D. thesis. 2000. Lund, Sweden: Lund University.
    91. Smith J B &Tirpak D (eds.). The potential effects of global climate change on the United States, US/EPA, 1989.
    92. Sud Y C, Fennessy M J. A study of the influence of surface albedo on July circulation in semi-arid regions using the GLAS GCM. J Climatol, 1982, 2(2): 05-125.
    93. Sud Y C, Molod A. AGCM simulation study of the influence of Saharan evapotranspiration and surface-albedo anomalies on July circulation and rainfall. Mon Wea Rev, 1988, 116, 2388-2400
    94. Supit I., A. A. Hooijper, C. A. van Diepen, etc. System description of the WOFOST6. 0 crop simulation model implemented in CGMS, volume 1: theory and algrorithms. The winand starting centre for integrated land, soil and water research (SC-DLO), Wagenningen, the Netherlands. 1994.
    95. Thornley J H M. Respiration, growth and maintenance in plants. Nature, 1970. 227: 304-305.
    96. Van Diepen, C. A., Wolf, J., Van Keulen, H., Rappoldt, C. WOFOST: a simulation model of crop production. Soil Use Manage. 1989, 5, 16–24.
    97. van Keulen H. Simulation of water use and herbage growth in arid regions. In: Penning de Vries F W T, van Laar HHeds. Simulation Monographs. Wageningen, Netherlands: PUDOC, 1982. 176
    98. Wit CT de. Photosynthesis of leaf canopies. Agricultural research report 663. (Pudoc), Wageningen, The Netherlands. 1965: 1-57
    99. Wit CT de. Dynamic concepts in biology. In: I. Setlik (Ed.): Prediction and Management of Photosynthetic Productivity. Proceedings International Biological Program Plant Production Technical Meeting. Wageningen, Netherlands: PUDOC, 1970: 17-23
    100. Xue, Y., P. J. Sellers, J. L. KinterⅢand Shukla, A simplified biosphere model for global climate studies, J. Climate, 1991, 4, 345-364
    101. Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate, PartⅠ: Desertification. J Climate, 1993, 6(12): 2232-2245.
    102. Xue Y, Shukla J. The influence of land-surface properties on Sahel Climate, PartⅡ: Deforestation. J Climate, 1996a, 9(12): 3260-3275.
    103. Xue Y. The impact of desertification in the Mongolian and the Inner Mongolian Grassland on the regional climate. J Climate, 1996b, 9(9): 2173-2189.
    104. Yin X Y, Chasalow S C, Dourleijn C J, et al. Coupling estimated effects of QTLs for physiological traits to a crop growth model: predicting yield variation among recombinant inbred lines in barley[J ]. Heredity, 2000, 85: 539~549
    105. Yuqing wang, L Ruby Leung, John L Mcgregor, Dong Kyou Lee, Wei Chyung Wang, Yihui Ding, Fujio Kimura. Regional climate modeling: progress, challenge, and prospect. Journal of the meteorological society of Japan, 2004, 82: 1599-1628
    106. Zhang H., Henderson-Sellers A, McGuffie K. Impacts of tropical deforestation, PartⅠ: Process analysis of local climatic change. J Climate, 1996a, 9(7): 1497-1517.
    107. Zhang H, Henderson-Sellers A, McGuffie K. Impacts of tropical deforestation, PartⅡ: The role of large-scale dynamics. J Climate, 1996b, 9(10): 2498-2521.
    108. Zhao M, A J Pitman, T N Chase The impact of land cover change on the atmospheric circulation. Climate Dynamics, 2001, 17(5/6): 467-477.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700