黑龙江省气象因子插值优化及与落叶松NPP相关性分析
|
摘要
【目的】通过比较不同插值方法模拟黑龙江省气象因子,利用最佳插值结果,探寻落叶松样地气象因子与森林植被净初级生产力(NPP)的关系,为黑龙江省落叶松林的生产经营和管理提供科学参考。【方法】以黑龙江省2010年生长季(5—9月)气象因子(日平均气温、日降水量)及1 521块落叶松林固定样地数据为数据源,分别使用反距离加权(IDW)、普通克里金(OK)、多元线性回归(MLR)和混合插值法(包括回归反距离加权(RIDW)和回归克里金(RK))5种插值方法对生长季月均气温和月均降水量进行插值及比较,以最佳插值方法得到黑龙江省2010年生长季月均气温和月均降水量空间分布。根据东北地区树种生物量异速模型估算落叶松样地单位面积森林地上生物量(AGB)和净初级生产力,并与样地气象因子进行相关性分析。【结果】5种插值方法中RK的生长季月均气温和月均降水量均方根误差(RMSE)分别为0.420和10.110,均优于其他插值方法。生长季月均气温由南至北降低的同时落叶松NPP随之降低,月均降水量自西向东增大,落叶松NPP随之升高。生长季月均气温、月均降水与NPP的Pearson相关系数分别为0.221和0.241,二者P值都小于0.01,呈极显著相关。【结论】考虑地形因子和多元回归模型结果残差的RK方法可以更好地模拟黑龙江省生长季月均气温和月均降水量。生长季落叶松NPP在经、纬度方向上分布趋势与气温、降水量相同,且落叶松NPP与生长季月均气温和月均降水量均有一定相关性,其中与降水量相关性更为明显。
【Objective】In this paper,we compared different interpolation methods to estimate meteorological factors in Heilongjiang Province and investigated the relationship between meteorological factors and net primary productivity( NPP) of larch using optimized interpolation result. It provided a scientific reference for the production and management of larch in Heilongjiang Province. 【Method 】Based on meteorological factors( average daily temperature and precipitation) during growing season( from May to September) in 2010 in Heilongjiang Province and 1 521 fixed plots data of larch forests,this study applied inverse distance weighed( IDW),ordinary Kriging( OK),multiple linear regression( MLR) and mixed interpolation( including regression inverse distance weighed( RIDW) and regression Kriging( RK)) to interpolate and explored monthly average temperature and precipitation of growing season,and obtained spatial distribution maps of average monthly temperature and precipitation during growing season in 2010 by using the best interpolation method. Then,biomass allometric models of tree species of Northeastern China were applied to calculate aboveground biomass( AGB) and NPP of larch forest and the correlation with meteorological factors was analyzed.【Results】The RMSE of average monthly temperature and precipitation of the RK during growing season were 0. 420 and10. 110 respectively,which were better than the other interpolation methods. During the growing season,the NPP decreased as average monthly temperature decreased from south to north; the NPP increased as average monthly precipitati-on increased from west to east. The coefficient of Pearson correlation between the average temperature and NPP,and the average precipitation and NPP during the growing season was 0. 221 and 0. 241,respectively. Both P values were lower than 0.01,which means that they were significantly correlated. 【Conclusion】The RK method that considered terrain factors and residuals of multiple regression model could estimate average monthly temperature and precipitation during growing season in Heilongjiang Province better than the other methods did. During growing season,the NPP of larch had the same pattern as temperature and precipitation in latitude and longitude directions. The NPP of larch had some correlation with both average monthly temperature and average monthly precipitation,in which the correlation with precipitation was more obvious than the other factor.
【Objective】In this paper,we compared different interpolation methods to estimate meteorological factors in Heilongjiang Province and investigated the relationship between meteorological factors and net primary productivity( NPP) of larch using optimized interpolation result. It provided a scientific reference for the production and management of larch in Heilongjiang Province. 【Method 】Based on meteorological factors( average daily temperature and precipitation) during growing season( from May to September) in 2010 in Heilongjiang Province and 1 521 fixed plots data of larch forests,this study applied inverse distance weighed( IDW),ordinary Kriging( OK),multiple linear regression( MLR) and mixed interpolation( including regression inverse distance weighed( RIDW) and regression Kriging( RK)) to interpolate and explored monthly average temperature and precipitation of growing season,and obtained spatial distribution maps of average monthly temperature and precipitation during growing season in 2010 by using the best interpolation method. Then,biomass allometric models of tree species of Northeastern China were applied to calculate aboveground biomass( AGB) and NPP of larch forest and the correlation with meteorological factors was analyzed.【Results】The RMSE of average monthly temperature and precipitation of the RK during growing season were 0. 420 and10. 110 respectively,which were better than the other interpolation methods. During the growing season,the NPP decreased as average monthly temperature decreased from south to north; the NPP increased as average monthly precipitati-on increased from west to east. The coefficient of Pearson correlation between the average temperature and NPP,and the average precipitation and NPP during the growing season was 0. 221 and 0. 241,respectively. Both P values were lower than 0.01,which means that they were significantly correlated. 【Conclusion】The RK method that considered terrain factors and residuals of multiple regression model could estimate average monthly temperature and precipitation during growing season in Heilongjiang Province better than the other methods did. During growing season,the NPP of larch had the same pattern as temperature and precipitation in latitude and longitude directions. The NPP of larch had some correlation with both average monthly temperature and average monthly precipitation,in which the correlation with precipitation was more obvious than the other factor.
引文
[1]DIXON R K,SOLOMON A M,BROW N S,et al.Carbon pools and flux of global forest ecosystems[J].Science,1994,263(5144):185.
[2]FIELD C B,BEHRENFELD M J,RANDERSON J T,et al.Primary production of the biosphere:integrating terrestrial and oceanic components[J].Science,1998,281(5374):237-240.
[3]王智,吴友均,梁凤超,等.新疆地区年降水量的空间插值方法研究[J].中国农业气象,2011,32(3):331-337.WANG Z,WU Y J,LIANG F C,et al.Study on spatial interpolation method of annual precipitation in Xinjiang[J].Chinese Journal of Agrometeorology,2011,32(3):331-337.
[4]刘琰琰.气象要素插值的空间化精度提高方法研究[J].气象科学,2017,37(2):278-282.LIU Y Y.Analysis of spatial interpolation methods for meteorological elements anomaly[J].Journal of the Meteorological Sciences,2017,37(2):278-282.
[5]NALDER I A,WEIN R W.Spatial interpolation of climatic Normals:test of a new method in the Canadian boreal forest[J].Agricultural&Forest Meteorology,1998,92(4):211-225.
[6]HUTCHINSON M F.Interpolating mean rainfall using thin plate smoothing splines[J].International Journal of Geographical Information Science,1995,9(4):385-403.
[7]DALY C.Guidelines for assessing the suitability of spatial climate data sets[J].International Journal of Climatology,2006,26(6):707-721.
[8]VICENTE-SERRANO S M,SAZ-SACHEZ M A,CUADRAT J M.Comparative analysis of interpolation methods in the middle Ebro Valley(Spain):application to annual precipitation and temperature[J].Climate Research,2003,24:161-180.
[9]YUN H S,UM M J,CHO W C,et al.Orographic precipitation analysis with regional frequency analysis and multiple linear regression[J].Journal of Korea Water Resources Association,2009,42(6):465-480.
[10]张燕卿,刘勤,严昌荣,等.黄河流域积温数据栅格化方法优选[J].生态学报,2009,29(10):5580-5585.ZHANG Y Q,LIU Q,YAN C R,et al.Methodology for rasterizing accumulated temperature data in the Yellow River Basion[J].Acta Ecologica Sinica,2009,29(10):5580-5585.
[11]郭婧.基于AMMRR插值法的中国草地综合顺序分类图的研制[D].兰州:甘肃农业大学,2011.GUO J.Production of Chinese grassland classification map based on integrated orderly classification system and AMMRR interpolation method[D].Lanzhou:Gansu Agricultural University,2011.
[12]LI J,HEAP A D.A review of spatial interpolation methods for environmental scientists[M].Australia:Geoscience Australia,2008.
[13]STEWART S B,NITSCHKE C R.Improving temperature interpolation using MODIS LST and local topography:a comparison of methods in south east Australia[J].International Journal of Climatology,2016,37(7):3098-3110.
[14]JOSEPH V R,KANG L L.Regression-based inverse distance weighting with applications to computer experiments[J].Technometrics,2011,53(3):254-265.
[15]张莉莉,陶忠良,张京红.基于DEM的海南岛平均气温空间插值研究[J].热带作物学报,2012,33(4):699-703.ZHANG L L,TAO Z L,ZHANG J H.Spatial interpolation of the average air temperature in Hainan Island based on DEM[J].Chinese Journal of Tropical Crops,2012,33(4):699-703.
[16]范敏锐.北京山区森林生态系统净初级生产力对气候变化的响应[D].北京:北京林业大学,2011.FAN M R.The response of net primary productivity on climate change in forest ecosystem of Beijing Mountain Area[D].Beijing:Beijing Forestry University,2011.
[17]王云霓,熊伟,王彦辉,等.宁夏六盘山三种针叶林初级净生产力年际变化及其气象因子响应[J].生态学报,2013,33(13):4002-4010.WANG Y X,XIONG W,WANG Y H,et al.The interannual variation of net primary productivity of three coniferous forests in Liupan Mountains of Ningxia and its responses to climatic factors[J].Acta Ecologica Sinica,2013,33(13):4002-4010.
[18]DUBRULE O.Cross validation of Kriging in a unique neighborhood[J].Mathematical Geosciences,1983,15(6):687-699.
[19]DONG L,ZHANG L,LI F.A compatible system of biomass equations for three conifer species in Northeast,China[J].Forest Ecology&Management,2014(329):306-317.
[20]王斌,刘某承,张彪.基于森林资源清查资料的森林植被净生产量及其动态变化研究[J].林业资源管理,2009(1):35-43.WANG B,LIU M C,ZHANG B.Dynamics of net production of Chinese forest vegetation based on forest inventory data[J].Forest Resources Management,2009(1):35-43.
[21]张海平,李凤日,董利虎,等.基于气象因子的白桦天然林单木直径生长模型[J].应用生态学报,2017,28(6):1851-1859.ZHANG H P,LI F R,DONG L H,et al.Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors[J].Chinese Journal of Applied Ecology,2017,28(6):1851-1859.
[2]FIELD C B,BEHRENFELD M J,RANDERSON J T,et al.Primary production of the biosphere:integrating terrestrial and oceanic components[J].Science,1998,281(5374):237-240.
[3]王智,吴友均,梁凤超,等.新疆地区年降水量的空间插值方法研究[J].中国农业气象,2011,32(3):331-337.WANG Z,WU Y J,LIANG F C,et al.Study on spatial interpolation method of annual precipitation in Xinjiang[J].Chinese Journal of Agrometeorology,2011,32(3):331-337.
[4]刘琰琰.气象要素插值的空间化精度提高方法研究[J].气象科学,2017,37(2):278-282.LIU Y Y.Analysis of spatial interpolation methods for meteorological elements anomaly[J].Journal of the Meteorological Sciences,2017,37(2):278-282.
[5]NALDER I A,WEIN R W.Spatial interpolation of climatic Normals:test of a new method in the Canadian boreal forest[J].Agricultural&Forest Meteorology,1998,92(4):211-225.
[6]HUTCHINSON M F.Interpolating mean rainfall using thin plate smoothing splines[J].International Journal of Geographical Information Science,1995,9(4):385-403.
[7]DALY C.Guidelines for assessing the suitability of spatial climate data sets[J].International Journal of Climatology,2006,26(6):707-721.
[8]VICENTE-SERRANO S M,SAZ-SACHEZ M A,CUADRAT J M.Comparative analysis of interpolation methods in the middle Ebro Valley(Spain):application to annual precipitation and temperature[J].Climate Research,2003,24:161-180.
[9]YUN H S,UM M J,CHO W C,et al.Orographic precipitation analysis with regional frequency analysis and multiple linear regression[J].Journal of Korea Water Resources Association,2009,42(6):465-480.
[10]张燕卿,刘勤,严昌荣,等.黄河流域积温数据栅格化方法优选[J].生态学报,2009,29(10):5580-5585.ZHANG Y Q,LIU Q,YAN C R,et al.Methodology for rasterizing accumulated temperature data in the Yellow River Basion[J].Acta Ecologica Sinica,2009,29(10):5580-5585.
[11]郭婧.基于AMMRR插值法的中国草地综合顺序分类图的研制[D].兰州:甘肃农业大学,2011.GUO J.Production of Chinese grassland classification map based on integrated orderly classification system and AMMRR interpolation method[D].Lanzhou:Gansu Agricultural University,2011.
[12]LI J,HEAP A D.A review of spatial interpolation methods for environmental scientists[M].Australia:Geoscience Australia,2008.
[13]STEWART S B,NITSCHKE C R.Improving temperature interpolation using MODIS LST and local topography:a comparison of methods in south east Australia[J].International Journal of Climatology,2016,37(7):3098-3110.
[14]JOSEPH V R,KANG L L.Regression-based inverse distance weighting with applications to computer experiments[J].Technometrics,2011,53(3):254-265.
[15]张莉莉,陶忠良,张京红.基于DEM的海南岛平均气温空间插值研究[J].热带作物学报,2012,33(4):699-703.ZHANG L L,TAO Z L,ZHANG J H.Spatial interpolation of the average air temperature in Hainan Island based on DEM[J].Chinese Journal of Tropical Crops,2012,33(4):699-703.
[16]范敏锐.北京山区森林生态系统净初级生产力对气候变化的响应[D].北京:北京林业大学,2011.FAN M R.The response of net primary productivity on climate change in forest ecosystem of Beijing Mountain Area[D].Beijing:Beijing Forestry University,2011.
[17]王云霓,熊伟,王彦辉,等.宁夏六盘山三种针叶林初级净生产力年际变化及其气象因子响应[J].生态学报,2013,33(13):4002-4010.WANG Y X,XIONG W,WANG Y H,et al.The interannual variation of net primary productivity of three coniferous forests in Liupan Mountains of Ningxia and its responses to climatic factors[J].Acta Ecologica Sinica,2013,33(13):4002-4010.
[18]DUBRULE O.Cross validation of Kriging in a unique neighborhood[J].Mathematical Geosciences,1983,15(6):687-699.
[19]DONG L,ZHANG L,LI F.A compatible system of biomass equations for three conifer species in Northeast,China[J].Forest Ecology&Management,2014(329):306-317.
[20]王斌,刘某承,张彪.基于森林资源清查资料的森林植被净生产量及其动态变化研究[J].林业资源管理,2009(1):35-43.WANG B,LIU M C,ZHANG B.Dynamics of net production of Chinese forest vegetation based on forest inventory data[J].Forest Resources Management,2009(1):35-43.
[21]张海平,李凤日,董利虎,等.基于气象因子的白桦天然林单木直径生长模型[J].应用生态学报,2017,28(6):1851-1859.ZHANG H P,LI F R,DONG L H,et al.Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors[J].Chinese Journal of Applied Ecology,2017,28(6):1851-1859.