河套灌区玉米和向日葵ET的S-I估算模型关键参数分析
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摘要
农田作物蒸散的快速评估对于灌区水资源最优调配和灌溉实时管理至关重要。简化S-I模型综合考虑作物冠层温度、田间气象参数和作物特征参数,可以进行实时农田作物ET的精确估算。该文利用河套灌区解放闸灌域2015—2016年2 a田间试验观测资料,对主要农作物玉米和向日葵的S-I模型中2个特征参数分别进行了率定和验证,并分析了模型蒸散估算的相关影响因素。结果表明:1)利用S-I简化模型可以对玉米和向日葵田间进行作物日蒸散量(daily evapotranspiration,ET_d)的估算,在该地区以13:00时率定和验证结果最优。在玉米主要生育期(6—8月),利用S-I模型估算ET_d可以达到较高的精度;而在7—8月,采用模型估算向日葵地ET_d也可以达到很高的精度;2)S-I模型中特征参数值受风速、地表覆盖度、表面粗糙度等因素的影响,不同作物其值不同。13:00时玉米S-I模型中特征参数值皆为负值,而向日葵中特征参数值为1正1负,进而影响模型估算精度。叶面积指数变化对特征参数值大小的影响在玉米和向日葵田块呈相反的趋势,而风速的影响则为一致。推荐13:00时率定的参数值可以在河套灌区玉米和向日葵作物需水量估算时直接应用。
It is very important to estimate field crop evapotranspiration rapidly when water resources distribution and irrigation management would be implemented well-off in irrigation district. The simplified S-I model takes into consideration of crop canopy temperature and field micro-meteorology factors and it has been widely used for evapotranspiration estimation. In this study, 2 key crop parameters in S-I model were calibrated for the use of Hetao Irrigation District situated along the Yellow River in Inner Mongolia, the second-largest irrigation project in China. Two-year field experiments were carried out in Jiefangzha irrigation area of Hetao Irrigation District in 2015—2016 in maize and sunflowers fields(the major crop in Hetao Irrigation District). Measurements and system tests were conducted in an experimental field located at the Shahaoqu Experimental Station in the middle of the Jiefangzha Irrigation Area(40?55?8?N, 107?8?16?E, 1 036 m elevation). Continuous field measurements were taken from mid-May to September in 2015 and 2016 when the maize and sunflowers were in their primary growing periods. The experimental sites were equipped with a Canopy Temperature and Meteorology Monitoring System(CTMS) mounted on a stainless steel stand column. The slope and intercept of the S-I model related the difference of canopy temperature and air temperature and the difference of daily evapotranspiration(ETd) and solar net radiation represented two characteristic parameters of model. The parameters were calibrated based on the 2 year experimental results and then validated by using the other year results, respectively. And the related influencing factors of the parameters were also analyzed. The results showed that: 1) The S-I model could be utilized to estimate ETd in maize and sunflower field, and the best calibration and validation was at 13:00 in Hetao Irrigation District based on 2-year results. High accuracy of the S-I model occurred in crop vigorous growth period, for maize in June, July and August, for sunflower in July and August, respectively. 2) The characteristic parameters of S-I model were impacted by wind speed, surface coverage, and surface roughness, but the impacts varied for different crops. At the best performance time of daily 13:00, the characteristic parameters of S-I model were both negative for maize field, but one was positive and the other was negative for sunflower. These could affect the estimating values and its accuracy. Leaf area index(LAI) impacted the model slope of maize in a different way with that on sunflower, but wind speed had the similar influence on these 2 crops. 3) The calibrated values of characteristic parameters of S-I model could be utilized to estimate crop daily evapotranspiration in maize and sunflower fields in Hetao Irrigation District. The estimation based on data at 13:00 was the best for both sunflower and maize. The recommended values of intercept and slope of the S-I model were-0.251 and-0.209 for maize, and 0.655 and-0.358 for sunflower, respectively. These parameters values could be used as a recommendation to ETd estimation in the other areas when the experimental data were lack. However, if the high accuracy of evapotranspiration estimation are required, we suggested to calibrate the parameters based on local field experiments and measurements.
It is very important to estimate field crop evapotranspiration rapidly when water resources distribution and irrigation management would be implemented well-off in irrigation district. The simplified S-I model takes into consideration of crop canopy temperature and field micro-meteorology factors and it has been widely used for evapotranspiration estimation. In this study, 2 key crop parameters in S-I model were calibrated for the use of Hetao Irrigation District situated along the Yellow River in Inner Mongolia, the second-largest irrigation project in China. Two-year field experiments were carried out in Jiefangzha irrigation area of Hetao Irrigation District in 2015—2016 in maize and sunflowers fields(the major crop in Hetao Irrigation District). Measurements and system tests were conducted in an experimental field located at the Shahaoqu Experimental Station in the middle of the Jiefangzha Irrigation Area(40?55?8?N, 107?8?16?E, 1 036 m elevation). Continuous field measurements were taken from mid-May to September in 2015 and 2016 when the maize and sunflowers were in their primary growing periods. The experimental sites were equipped with a Canopy Temperature and Meteorology Monitoring System(CTMS) mounted on a stainless steel stand column. The slope and intercept of the S-I model related the difference of canopy temperature and air temperature and the difference of daily evapotranspiration(ETd) and solar net radiation represented two characteristic parameters of model. The parameters were calibrated based on the 2 year experimental results and then validated by using the other year results, respectively. And the related influencing factors of the parameters were also analyzed. The results showed that: 1) The S-I model could be utilized to estimate ETd in maize and sunflower field, and the best calibration and validation was at 13:00 in Hetao Irrigation District based on 2-year results. High accuracy of the S-I model occurred in crop vigorous growth period, for maize in June, July and August, for sunflower in July and August, respectively. 2) The characteristic parameters of S-I model were impacted by wind speed, surface coverage, and surface roughness, but the impacts varied for different crops. At the best performance time of daily 13:00, the characteristic parameters of S-I model were both negative for maize field, but one was positive and the other was negative for sunflower. These could affect the estimating values and its accuracy. Leaf area index(LAI) impacted the model slope of maize in a different way with that on sunflower, but wind speed had the similar influence on these 2 crops. 3) The calibrated values of characteristic parameters of S-I model could be utilized to estimate crop daily evapotranspiration in maize and sunflower fields in Hetao Irrigation District. The estimation based on data at 13:00 was the best for both sunflower and maize. The recommended values of intercept and slope of the S-I model were-0.251 and-0.209 for maize, and 0.655 and-0.358 for sunflower, respectively. These parameters values could be used as a recommendation to ETd estimation in the other areas when the experimental data were lack. However, if the high accuracy of evapotranspiration estimation are required, we suggested to calibrate the parameters based on local field experiments and measurements.
引文
[1]Jackson R D,Idso S B,Reginato R J,et al.Canopy temperature as a crop water stress indicator[J].Water Resources Research,1981,17(4):1133-1138.
[2]Hiler E A,Howel T A,Lewis R B,et al.Irrigation timing by the stress day index method[J].Transactions of the ASAE,1974,17(3):393-398.
[3]Jackson R D,Reginato R J,Idso S B.Wheat canopy temperature:A practical tool for evaluating water requirements[J].Water Resources Research,1977,13(3):651-656.
[4]Clawson K L,Jackson R D,Pinter P J.Evaluating plant water stress with canopy temperature differences[J].Agronomy Journal,1988,88(6):858-863.
[5]杨文攀,李长春,杨浩,等.基于无人机热红外与数码影像的玉米冠层温度监测[J].农业工程学报,2018,34(17):68-75.Yang Wenpan,Li Changchun,Yang Hao,et al.Monitoring of canopy temperature of maize based on UAV thermal infrared imagery and digital imagery[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(17):68-75.(in Chinese with English abstract)
[6]Webber H,Ewert F,Kimball B A,et al.Simulating canopy temperature for modelling heat stress in cereals[J].Environmental Modelling&Software,2016,77:143-155.
[7]Webber H,Martreb P,Assengc S,et al.Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment:A multi-model comparison[J/OL].Field Crops Research,2015,http://dx.doi.org/10.1016/j.fcr.2015.10.009.
[8]“10000个科学难题”农业科学编委会.10000个科学难题-农业科学卷[M].北京:科学出版社,2011.9.
[9]Kustas W P,Norman J M.Advances in thermal infrared remote sensing for land surface modeling[J].Agricultural and Forest Meteorology,2009,149:2071-2081.
[10]Norman J M,Kustas W P,Humes K S.Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature[J].Agricultural and Forest Meteorology,1995,77:263-293.
[11]Qiu G Y,Ben-Asher J,Yano T,et al.Estimation of soil evaporation using the differential temperature method[J].Soil Science Society of America Journal,1999,63:1608-1614.
[12]Qiu G Y,Momii K,Yano T.Estimation of plant transpiration by imitation leaf temperature.I.Theoretical consideration and field verification[J].Transactions of the Japanese Society of Irrigation,Drainage and Reclamation Engineering,1996,64:401-410.
[13]Qiu G Y,Yano T,Momii K.An improved methodology to measure evaporation from bare soil based on comparison of surface temperature with a dry soil surface[J].Journal of Hydrology,1998,210:93-105.
[14]Seguin B,Itier B.Using midday surface temperature to estimate daily evaporation from satellite thermal IR data[J].International Journal of Remote Sensing,1983,4(2):371-383.
[15]Lagouarde J P.Use of NOAA AVHRR data combined with an agrometeorological model for evaporation mapping[J].International Journal of Remote Sensing,1991,12(9):1853-1864.
[16]Dunkel Z,Szenyán,I G.Estimation of areal distribution of evapotranspiration using remotely sensed data during vegetation period in Hungary[J].Advances in Space Research,2000,26(7):1051-1054.
[17]郭家选,李玉中,严昌荣,等.华北平原冬小麦农田蒸散量[J].应用生态学报,2006,17(12):2357-2362.Guo Jiaxuan,Li Yuzhong,Yan Changrong,et al.Evapotranspiration of winter wheat field in North China Plain[J].Chinese Journal of Applied Ecology,2006,17(12):2357-2362.(in Chinese with English abstract)
[18]张建君.农田日蒸散量估算方法研究[D].北京:中国农业科学院,2009.Zhang Jianjun.Estimation of Cropland Daily Evapotranspiration[D].Beijing:Chinese Academy of Agricultural Science,2009.(in Chinese with English abstract)
[19]黄凌旭,蔡甲冰,白亮亮,等.利用作物冠气温差估算农田蒸散量[J].中国农村水利水电,2016(8):76-82.Huang Lingxu,Cai Jiabing,Bai Liangliang,et al.Estimation of cropland daily evapotranspiration[J].China Rural Water and Hydropower,2016(8):76-82(in Chinese with English abstract)
[20]巴彦淖尔统计局.巴彦淖尔统计年鉴[M].北京:中国统计出版社,2000-2015.
[21]Bai L L,Cai J B,Liu Y,et al.Responses of field evapotranspiration to the changes of cropping pattern and groundwater level in large irrigation district of Yellow River basin[J].Agricultural Water Management,2017,188:1-11.
[22]蔡甲冰,许迪,司南,等.基于冠层温度和土壤墒情的实时监测与灌溉决策系统[J].农业机械学报,2015,46(12):133-139.Cai Jiabing,Xu Di,Si Nan,et al.Real-time monitoring system of crop canopy temperature and soil moisture for irrigation decision-making[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(12):133-139.(in Chinese with English abstract)
[23]Allen R G,Pereira L S,Raes D,et al.Crop evapotranspiration:Guidelines for computing crop water requirements[M]//Irrigation and Drainage Paper 56.Rome,Italy:United Nations Food and Agriculture Organization,1998.
[24]闫浩芳.内蒙古河套灌区不同作物腾发量及作物系数的研究[D].呼和浩特:内蒙古农业大学,2008.Yan Haofang.The Study of Crop Evapotranspiration and Crop Coefficient in Hetao Irrigation District of Inner Mongolia[D].Huhhot:Inner Mongolia Agricultural University,2008.(in Chinese with English abstract)
[25]黄凌旭.利用作物冠层温度估算农田蒸散量[D].北京:中国水利水电科学研究院,2018.Huang Lingxu.Estimation of Evapotranspiration Using the Crop Canopy Temperature[D].Beijing:China Institute of Water Resources and Hydropower Research,2018.(in Chinese with English abstract)
[26]Willmott C J.Some comments on the evaluation of model performance[J].Bulletin of American Meteorological Society,1982,63(11):1309-1313.
[27]Carlson T N,Buffum M J.On estimating total daily evapotranspiration from remote surface temperature measurements[J].Remote Sensing of Environment,1989,29(2):197-207.
[28]Carlson T N,Capehart W J,Gillies R R.A new look at the simplified method for remote sensing of daily evapotranspiration[J].Remote Sensing of Environment,1995,54(2):161-167.
[2]Hiler E A,Howel T A,Lewis R B,et al.Irrigation timing by the stress day index method[J].Transactions of the ASAE,1974,17(3):393-398.
[3]Jackson R D,Reginato R J,Idso S B.Wheat canopy temperature:A practical tool for evaluating water requirements[J].Water Resources Research,1977,13(3):651-656.
[4]Clawson K L,Jackson R D,Pinter P J.Evaluating plant water stress with canopy temperature differences[J].Agronomy Journal,1988,88(6):858-863.
[5]杨文攀,李长春,杨浩,等.基于无人机热红外与数码影像的玉米冠层温度监测[J].农业工程学报,2018,34(17):68-75.Yang Wenpan,Li Changchun,Yang Hao,et al.Monitoring of canopy temperature of maize based on UAV thermal infrared imagery and digital imagery[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2018,34(17):68-75.(in Chinese with English abstract)
[6]Webber H,Ewert F,Kimball B A,et al.Simulating canopy temperature for modelling heat stress in cereals[J].Environmental Modelling&Software,2016,77:143-155.
[7]Webber H,Martreb P,Assengc S,et al.Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment:A multi-model comparison[J/OL].Field Crops Research,2015,http://dx.doi.org/10.1016/j.fcr.2015.10.009.
[8]“10000个科学难题”农业科学编委会.10000个科学难题-农业科学卷[M].北京:科学出版社,2011.9.
[9]Kustas W P,Norman J M.Advances in thermal infrared remote sensing for land surface modeling[J].Agricultural and Forest Meteorology,2009,149:2071-2081.
[10]Norman J M,Kustas W P,Humes K S.Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature[J].Agricultural and Forest Meteorology,1995,77:263-293.
[11]Qiu G Y,Ben-Asher J,Yano T,et al.Estimation of soil evaporation using the differential temperature method[J].Soil Science Society of America Journal,1999,63:1608-1614.
[12]Qiu G Y,Momii K,Yano T.Estimation of plant transpiration by imitation leaf temperature.I.Theoretical consideration and field verification[J].Transactions of the Japanese Society of Irrigation,Drainage and Reclamation Engineering,1996,64:401-410.
[13]Qiu G Y,Yano T,Momii K.An improved methodology to measure evaporation from bare soil based on comparison of surface temperature with a dry soil surface[J].Journal of Hydrology,1998,210:93-105.
[14]Seguin B,Itier B.Using midday surface temperature to estimate daily evaporation from satellite thermal IR data[J].International Journal of Remote Sensing,1983,4(2):371-383.
[15]Lagouarde J P.Use of NOAA AVHRR data combined with an agrometeorological model for evaporation mapping[J].International Journal of Remote Sensing,1991,12(9):1853-1864.
[16]Dunkel Z,Szenyán,I G.Estimation of areal distribution of evapotranspiration using remotely sensed data during vegetation period in Hungary[J].Advances in Space Research,2000,26(7):1051-1054.
[17]郭家选,李玉中,严昌荣,等.华北平原冬小麦农田蒸散量[J].应用生态学报,2006,17(12):2357-2362.Guo Jiaxuan,Li Yuzhong,Yan Changrong,et al.Evapotranspiration of winter wheat field in North China Plain[J].Chinese Journal of Applied Ecology,2006,17(12):2357-2362.(in Chinese with English abstract)
[18]张建君.农田日蒸散量估算方法研究[D].北京:中国农业科学院,2009.Zhang Jianjun.Estimation of Cropland Daily Evapotranspiration[D].Beijing:Chinese Academy of Agricultural Science,2009.(in Chinese with English abstract)
[19]黄凌旭,蔡甲冰,白亮亮,等.利用作物冠气温差估算农田蒸散量[J].中国农村水利水电,2016(8):76-82.Huang Lingxu,Cai Jiabing,Bai Liangliang,et al.Estimation of cropland daily evapotranspiration[J].China Rural Water and Hydropower,2016(8):76-82(in Chinese with English abstract)
[20]巴彦淖尔统计局.巴彦淖尔统计年鉴[M].北京:中国统计出版社,2000-2015.
[21]Bai L L,Cai J B,Liu Y,et al.Responses of field evapotranspiration to the changes of cropping pattern and groundwater level in large irrigation district of Yellow River basin[J].Agricultural Water Management,2017,188:1-11.
[22]蔡甲冰,许迪,司南,等.基于冠层温度和土壤墒情的实时监测与灌溉决策系统[J].农业机械学报,2015,46(12):133-139.Cai Jiabing,Xu Di,Si Nan,et al.Real-time monitoring system of crop canopy temperature and soil moisture for irrigation decision-making[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(12):133-139.(in Chinese with English abstract)
[23]Allen R G,Pereira L S,Raes D,et al.Crop evapotranspiration:Guidelines for computing crop water requirements[M]//Irrigation and Drainage Paper 56.Rome,Italy:United Nations Food and Agriculture Organization,1998.
[24]闫浩芳.内蒙古河套灌区不同作物腾发量及作物系数的研究[D].呼和浩特:内蒙古农业大学,2008.Yan Haofang.The Study of Crop Evapotranspiration and Crop Coefficient in Hetao Irrigation District of Inner Mongolia[D].Huhhot:Inner Mongolia Agricultural University,2008.(in Chinese with English abstract)
[25]黄凌旭.利用作物冠层温度估算农田蒸散量[D].北京:中国水利水电科学研究院,2018.Huang Lingxu.Estimation of Evapotranspiration Using the Crop Canopy Temperature[D].Beijing:China Institute of Water Resources and Hydropower Research,2018.(in Chinese with English abstract)
[26]Willmott C J.Some comments on the evaluation of model performance[J].Bulletin of American Meteorological Society,1982,63(11):1309-1313.
[27]Carlson T N,Buffum M J.On estimating total daily evapotranspiration from remote surface temperature measurements[J].Remote Sensing of Environment,1989,29(2):197-207.
[28]Carlson T N,Capehart W J,Gillies R R.A new look at the simplified method for remote sensing of daily evapotranspiration[J].Remote Sensing of Environment,1995,54(2):161-167.