基于临近台站气象数据的参考作物蒸散量估算方法
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摘要
参考作物蒸散量(ET_0)是评估区域植被耗水进而指导水资源优化管理所需的主要参数之一,但我国大部分地区标准化气象台站稀疏、部分研究点的气象资料通常难以获取,给ET_0的计算带来了很大困难。以地处内陆旱区的内蒙古河套灌区为例,利用该区4个标准气象站1981-2006年的气象资料,讨论了研究点在没有历史气象数据且现有气象数据不完备的情况下,采用临近台站气象数据估算ET_0的可靠性。估算方法分别为估算未知气象数据的FAO56Penman-Monteith方程(PM56)、基于临近台站气象数据校正的经验公式以及利用临近台站气象数据训练的人工神经网络模型。结果表明:(1)在完全没有气象数据的条件下,可采用临近站点的气象数据估算研究点的ET_0,平均绝对误差(MAE)为0.43～0.52 mm d~(-1),均方根误差(RMSE)为0.56～0.63 mm d~(-1);估算精度与台站间的距离有关,利用维度信息校正太阳辐射值可提高估算精度。(2)仅有最高和最低气温数据时,估算气象数据的PM56方程计算误差较大,且站点之间表现不稳定,人工神经网络模型的估算精度最高,MAE和RMSE分别为0.14～0.22mm d~(-1)和0.17～0.29 mm d~(-1);校正后的Hargreaves公式的估算效果次之,MAE和RMSE分别为0.23～0.26 mm d~(-1)和0.30～0.31 mm d~(-1)。(3)在已知温度和辐射数据时,利用临近台站气象数据训练的人工神经网络模型依然表现最好,MAE和RMSE分别为0.13～0.19 mm d~(-1)和0.17～0.25 mm d~(-1),其他两种方法误差较大。在内陆干旱条件下,利用研究点的气温数据结合临近台站的历史气象信息可有效估算参考作物蒸散发。
Potential evapotranspiration(ET_0) is one of the most critical parameters that are essential for evaluating regional vegetation water use and managing water and soil resources.However, accurate estimation of it is so difficult for many parts of China due to a limited number of weather stations. Weather data from nearby stations are available for most sites, but past relevant works mainly focused on ET_0 predicting methods with local weather data and less work was done to investigate the approaches for estimating ET0 with data from other stations.Therefore, this study was conducted to test the reliability of estimating ET_0 with weather information from nearby stations. Whether data of four weather stations located in Hetao Irrigation District of western Inner Mongolia were collected. The study area has an arid climate with annual rainfall and pan evaporation of 130-215 and 2100-2300 mm respectively. Three commonly used approaches, namely, FAO56 Penman-Monteith equation(PM56) with estimated weather data, the empirical formula corrected with meteorological data of nearby stations, and the artificial neural network model(ANN) developed using meteorological data from nearby stations, were compared for predicting ET0 when data are limited in this work. The results showed that:(1) When all of the necessary parameters were not measured at the study sites, weather data from nearby stations could be used directly, the average absolute error(MAE) of the ET_0 calculation was 0.43-0.52 mm d~(-1), and the root mean square error(RMSE)was 0.56-0.63 mm d~(-1), and the error could be narrowed by correcting the radiation data using the latitude information of the stations;(2) When the maximum and minimum air temperature data were available, PM56 with estimated weather data performed the worst, and the performance of the ANN model is the best with the MAE and RMSE ranging between 0.14-0.22 mm d~(-1) and 0.17-0.29 mm d~(-1), respectively; and the results of the calibrated Hargreaves formula are intermediate with the MAE and RMSE values of 0.23-0.26 mm d~(-1) and 0.30-0.31 mm d~(-1), respectively;(3) When knowing the temperature and radiation data, the ANN model trained with meteorological data from nearby stations is still the best, with MAE and RMSE values of 0.13-0.19 mm d~(-1) and 0.17-0.25 mm d~(-1), respectively, and the other two methods have larger error and the performance is inconsistent among different stations. The results of this study demonstrated that we can estimated ET_0 accurately using air temperature data in combination with weather data of nearby stations under arid conditions, and the reliability of the methods still need to be validated under other climatic conditions.
Potential evapotranspiration(ET_0) is one of the most critical parameters that are essential for evaluating regional vegetation water use and managing water and soil resources.However, accurate estimation of it is so difficult for many parts of China due to a limited number of weather stations. Weather data from nearby stations are available for most sites, but past relevant works mainly focused on ET_0 predicting methods with local weather data and less work was done to investigate the approaches for estimating ET0 with data from other stations.Therefore, this study was conducted to test the reliability of estimating ET_0 with weather information from nearby stations. Whether data of four weather stations located in Hetao Irrigation District of western Inner Mongolia were collected. The study area has an arid climate with annual rainfall and pan evaporation of 130-215 and 2100-2300 mm respectively. Three commonly used approaches, namely, FAO56 Penman-Monteith equation(PM56) with estimated weather data, the empirical formula corrected with meteorological data of nearby stations, and the artificial neural network model(ANN) developed using meteorological data from nearby stations, were compared for predicting ET0 when data are limited in this work. The results showed that:(1) When all of the necessary parameters were not measured at the study sites, weather data from nearby stations could be used directly, the average absolute error(MAE) of the ET_0 calculation was 0.43-0.52 mm d~(-1), and the root mean square error(RMSE)was 0.56-0.63 mm d~(-1), and the error could be narrowed by correcting the radiation data using the latitude information of the stations;(2) When the maximum and minimum air temperature data were available, PM56 with estimated weather data performed the worst, and the performance of the ANN model is the best with the MAE and RMSE ranging between 0.14-0.22 mm d~(-1) and 0.17-0.29 mm d~(-1), respectively; and the results of the calibrated Hargreaves formula are intermediate with the MAE and RMSE values of 0.23-0.26 mm d~(-1) and 0.30-0.31 mm d~(-1), respectively;(3) When knowing the temperature and radiation data, the ANN model trained with meteorological data from nearby stations is still the best, with MAE and RMSE values of 0.13-0.19 mm d~(-1) and 0.17-0.25 mm d~(-1), respectively, and the other two methods have larger error and the performance is inconsistent among different stations. The results of this study demonstrated that we can estimated ET_0 accurately using air temperature data in combination with weather data of nearby stations under arid conditions, and the reliability of the methods still need to be validated under other climatic conditions.
引文
[1]姜艳阳,王文,周正昊. MODIS MOD16蒸散发产品在中国流域的质量评估.自然资源学报, 2017, 32(3):517-528.[JIANG Y Y, WANG W, ZHOU Z H. Evaluation of MODIS MOD16 evapotranspiration product in Chinese river basins.Journal of Natural Resources, 2017, 32(3):517-528.]
[2]佟玲,康绍忠,杨秀英.西北旱区石羊河流域作物耗水点面尺度转化方法的研究.农业工程学报, 2006, 22(10):45-51.[TONG L, KANG S Z, YANG X Y. Transforming method from point to area of crop evapotranspiration in Shiyang River Basin in an arid region of Northwest China. Transactions of the CSAE, 2006, 22(10):45-51.]
[3]王自奎,吴普特,赵西宁,等.小麦/玉米套作田棵间土壤蒸发的数学模拟.农业工程学报, 2013, 29(21):72-81.[WANG Z K, WU P T, ZHAO X N, et al. Mathematical simulation of soil evaporation from wheat/maize intercropping field. Transactions of the CSAE, 2013, 29(21):72-81.]
[4]高思远,崔晨风,范玉平.基于岭估计的青海省东部农业区ET0遥感反演研究.自然资源学报, 2016, 31(4):693-702.[GAO S Y, CUI C F, FAN Y P. Remote sensing inversion of ET0in eastern agricultural area of Qinghai province based on ridge estimation. Journal of Natural Resources, 2016, 31(4):693-702.]
[5] WANG Z K, WU P T, ZHAO X N, et al. GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions. Theoreical and Applied Climatology, 2014, 116(3):481-489.
[6] ALLEN R G, PEREIRA L S, RAES D, et al. Crop Evapotranspiration:Guidelines for Computing Crop Requirements.FAO Irrigation and Drainage Paper No. 56, Rome, Italy, 1998.
[7]李志.参考作物蒸散简易估算方法在黄土高原的适用性.农业工程学报, 2012, 28(6):106-111.[LI Z. Applicability of simple estimating method for reference crop evapotranspiration in Loess Plateau. Transactions of the CSAE, 2012, 28(6):106-111.]
[8]闫浩芳,史海滨,薛铸,等.内蒙古河套灌区ET0不同计算方法的对比研究.农业工程学报, 2008, 24(4):103-106.[YAN H F, SHI H B, XUE Z, et al. Comparison of estimating ET0with different methods in Hetao Irrigation District in Inner Mongolia. Transactions of the CSAE, 2008, 24(4):103-106.]
[9]黄会平,曹明明,宋进喜,等. 1957-2012年中国参考作物蒸散量时空变化及其影响因子分析.自然资源学报, 2015,30(2):315-326.[HUANG H P, CAO M M, SONG J X, et al. Temporal and spatial changes of potential evapotranspiration and its influencing factors in china from 1957 to 2012. Journal of Natural Resources, 2015, 30(2):315-326.]
[10]赵璐,梁川,崔宁博,等.不同ET0计算方法在川中丘陵地区的比较及改进.农业工程学报, 2012, 28(24):92-98.[ZHAO L, LIANG C, CUI N B, et al. Comparison and improvement of different calculation methods for ET0in hilly area of central Sichuan Basin. Transactions of the CSAE, 2012, 28(24):92-98.]
[11]刘钰, PEREIRA L S.气象数据缺测条件下参照腾发量的计算方法.水利学报, 2001, 32(3):11-17.[LIU Y, PEREIRA L. Calculation methods for reference evapotranspiration with limited weather data. Journal of Hydraulic Engineering,2001, 32(3):11-17.]
[12] KUMAR M, RAGHUWANSHI N S, SINGH R, et al. Estimating evapotranspiration using artificial neural network.Journal of Irrigation and Drainage Engineering, 2002, 128(4):224-233.
[13] SUDHEER K P, GOSAIN A K, RAMASASTRI K S. Estimating actual evapotranspiration from limited climatic data using neural computing technique. Journal of irrigation and drainage engineering, 2003, 129(3):214-218.
[14] RAHIMIKHOOB A. Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theoretical and Applied Climatology, 2010, 101(1-2):83-91.
[15] JAIN S K, NAYAK P C, SUDHEER K P. Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes, 2008, 22(13):2225-2234.
[16] KISI O, SANIKHANI H, ZOUNEMAT-KERMANI M, et al. Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture, 2015, 115:66-77.
[17]郑景云,尹云鹤,李炳元.中国气候区划新方案.地理学报, 2010, 65(1):3-12.[ZHENG J Y, YIN Y H, LI B Y. A new scheme for climate regionalization in China. Acta Geographica Sinica, 2010, 65(1):3-12.]
[18] HARGREAVES G H, SAMANI Z A. Reference crop evapotranspiration from temperature. Applied Engineering in Agricuture, 1985, 1(2):96-99.
[19]范文波,吴普特,韩志全,等.玛纳斯河流域ET0影响因子分析及对Hargreaves法的修正.农业工程学报, 2012, 28(8):19-24.[FAN W B, WU P T, HAN Z Q, et al. Influencing factors analysis of reference crop evapotranspiration and modification of Hargreaves method in Manas Rriver Basin. Transactions of the CSAE, 2012, 28(8):19-24.]
[20] RITCHIE J T, PORTER C H, JUDGE J, et al. Extension of an existing model for soil water evaporation and redistribution under high water content conditions. Soil Science Society of America Journal, 2009, 73(3):792-801.
[21] JONES J W, HOOGENBOOM G, BATCHELOR W D, et al. The DSSAT cropping system model. European Journal of Agronomy, 2003, 18(3/4):235-265.
[22]蒋任飞,阮本清,韩宇平,等.基于BP神经网络的参照腾发量预测模型.中国水利水电科学研究院学报, 2005, 3(4):308-311.[JIANG R F, RUAN B Q, HAN Y P, et al. Model for estimating reference evapo-transpiration based on BP neural network. Journal of China Institute of Water Resources and Hydropower Research, 2005, 3(4):308-311.]
[23] KUAMR M, RAGHUWANSHI N S, SIGHN R. Development and validation of GANN model for evapotranspiration estimation. Journal of Hydrologic Engineering, 2009, 14(2):131-140.
[24] CHARZITHOMAS C D, ALEXANDRIS S G. Solar radiation and relative humidity based, empirical method, to estimate hourly reference evapotranspiration. Agricultural Water Management, 2015, 152:188-197.
[25] GOCIC M, MOTAMEDI S, SHAMSHIRBAND S, et al. Soft computing approaches for forecasting reference evapotranspiration. Computers and Electronics in Agriculture, 2015, 113(C):164-173.
[26]郭映,董阳,周振方,等.半干旱区玉米茎流规律及其对气象因子的响应.干旱区资源与环境, 2014, 28(9):94-99.[GUO Y, DONG Y, ZHOU Z F, et al. The sap flow of corn during the growth period and its response to meteorological factors in semi-arid area. Journal of Arid Land Resources and Environment, 2014, 28(9):94-99.]
[27]丁日升,康绍忠,张彦群,等.干旱内陆区玉米田水热通量特征及主控因子研究.水利学报. 2014, 45(3):312-319.[DING R S, KANG S Z, ZHANG Y Q, et al. Characteristics of water vapor and heat fluxes and the controlling factors over a maize field in the arid inland region. Journal of Hydraulic Engineering, 2014, 45(3):312-319.]
[28] ZHANG B Z, XU D, LIU Y, et al. Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in North China. Agricultural and Forest METeorology, 2016, 216:1-12.
[29] JENSEN M E, BURMAN R D, ALLEN R G. Evapotranspiration and irrigation water requirements:ASCE manuals and reports on engineering practice No. 70. New York:ASCE, 1990.
[30] CAI J B, LIU Y, LEI T W, et al. Estimating reference evapotranspiration with the FAO Penman Monteith equation using daily weather forecast messages. Agricultural and Forest Meteorology, 2007, 145(1-2):22-35.
[31] SINGH S K, VERMA C L, SHARMA D K. Prediction of evapotranspiration using artificial neural network model and compared with measured values. International Journal of Engineering Sciences&Research Technology, 2014, 1(3):287-292.
[2]佟玲,康绍忠,杨秀英.西北旱区石羊河流域作物耗水点面尺度转化方法的研究.农业工程学报, 2006, 22(10):45-51.[TONG L, KANG S Z, YANG X Y. Transforming method from point to area of crop evapotranspiration in Shiyang River Basin in an arid region of Northwest China. Transactions of the CSAE, 2006, 22(10):45-51.]
[3]王自奎,吴普特,赵西宁,等.小麦/玉米套作田棵间土壤蒸发的数学模拟.农业工程学报, 2013, 29(21):72-81.[WANG Z K, WU P T, ZHAO X N, et al. Mathematical simulation of soil evaporation from wheat/maize intercropping field. Transactions of the CSAE, 2013, 29(21):72-81.]
[4]高思远,崔晨风,范玉平.基于岭估计的青海省东部农业区ET0遥感反演研究.自然资源学报, 2016, 31(4):693-702.[GAO S Y, CUI C F, FAN Y P. Remote sensing inversion of ET0in eastern agricultural area of Qinghai province based on ridge estimation. Journal of Natural Resources, 2016, 31(4):693-702.]
[5] WANG Z K, WU P T, ZHAO X N, et al. GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions. Theoreical and Applied Climatology, 2014, 116(3):481-489.
[6] ALLEN R G, PEREIRA L S, RAES D, et al. Crop Evapotranspiration:Guidelines for Computing Crop Requirements.FAO Irrigation and Drainage Paper No. 56, Rome, Italy, 1998.
[7]李志.参考作物蒸散简易估算方法在黄土高原的适用性.农业工程学报, 2012, 28(6):106-111.[LI Z. Applicability of simple estimating method for reference crop evapotranspiration in Loess Plateau. Transactions of the CSAE, 2012, 28(6):106-111.]
[8]闫浩芳,史海滨,薛铸,等.内蒙古河套灌区ET0不同计算方法的对比研究.农业工程学报, 2008, 24(4):103-106.[YAN H F, SHI H B, XUE Z, et al. Comparison of estimating ET0with different methods in Hetao Irrigation District in Inner Mongolia. Transactions of the CSAE, 2008, 24(4):103-106.]
[9]黄会平,曹明明,宋进喜,等. 1957-2012年中国参考作物蒸散量时空变化及其影响因子分析.自然资源学报, 2015,30(2):315-326.[HUANG H P, CAO M M, SONG J X, et al. Temporal and spatial changes of potential evapotranspiration and its influencing factors in china from 1957 to 2012. Journal of Natural Resources, 2015, 30(2):315-326.]
[10]赵璐,梁川,崔宁博,等.不同ET0计算方法在川中丘陵地区的比较及改进.农业工程学报, 2012, 28(24):92-98.[ZHAO L, LIANG C, CUI N B, et al. Comparison and improvement of different calculation methods for ET0in hilly area of central Sichuan Basin. Transactions of the CSAE, 2012, 28(24):92-98.]
[11]刘钰, PEREIRA L S.气象数据缺测条件下参照腾发量的计算方法.水利学报, 2001, 32(3):11-17.[LIU Y, PEREIRA L. Calculation methods for reference evapotranspiration with limited weather data. Journal of Hydraulic Engineering,2001, 32(3):11-17.]
[12] KUMAR M, RAGHUWANSHI N S, SINGH R, et al. Estimating evapotranspiration using artificial neural network.Journal of Irrigation and Drainage Engineering, 2002, 128(4):224-233.
[13] SUDHEER K P, GOSAIN A K, RAMASASTRI K S. Estimating actual evapotranspiration from limited climatic data using neural computing technique. Journal of irrigation and drainage engineering, 2003, 129(3):214-218.
[14] RAHIMIKHOOB A. Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran. Theoretical and Applied Climatology, 2010, 101(1-2):83-91.
[15] JAIN S K, NAYAK P C, SUDHEER K P. Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes, 2008, 22(13):2225-2234.
[16] KISI O, SANIKHANI H, ZOUNEMAT-KERMANI M, et al. Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture, 2015, 115:66-77.
[17]郑景云,尹云鹤,李炳元.中国气候区划新方案.地理学报, 2010, 65(1):3-12.[ZHENG J Y, YIN Y H, LI B Y. A new scheme for climate regionalization in China. Acta Geographica Sinica, 2010, 65(1):3-12.]
[18] HARGREAVES G H, SAMANI Z A. Reference crop evapotranspiration from temperature. Applied Engineering in Agricuture, 1985, 1(2):96-99.
[19]范文波,吴普特,韩志全,等.玛纳斯河流域ET0影响因子分析及对Hargreaves法的修正.农业工程学报, 2012, 28(8):19-24.[FAN W B, WU P T, HAN Z Q, et al. Influencing factors analysis of reference crop evapotranspiration and modification of Hargreaves method in Manas Rriver Basin. Transactions of the CSAE, 2012, 28(8):19-24.]
[20] RITCHIE J T, PORTER C H, JUDGE J, et al. Extension of an existing model for soil water evaporation and redistribution under high water content conditions. Soil Science Society of America Journal, 2009, 73(3):792-801.
[21] JONES J W, HOOGENBOOM G, BATCHELOR W D, et al. The DSSAT cropping system model. European Journal of Agronomy, 2003, 18(3/4):235-265.
[22]蒋任飞,阮本清,韩宇平,等.基于BP神经网络的参照腾发量预测模型.中国水利水电科学研究院学报, 2005, 3(4):308-311.[JIANG R F, RUAN B Q, HAN Y P, et al. Model for estimating reference evapo-transpiration based on BP neural network. Journal of China Institute of Water Resources and Hydropower Research, 2005, 3(4):308-311.]
[23] KUAMR M, RAGHUWANSHI N S, SIGHN R. Development and validation of GANN model for evapotranspiration estimation. Journal of Hydrologic Engineering, 2009, 14(2):131-140.
[24] CHARZITHOMAS C D, ALEXANDRIS S G. Solar radiation and relative humidity based, empirical method, to estimate hourly reference evapotranspiration. Agricultural Water Management, 2015, 152:188-197.
[25] GOCIC M, MOTAMEDI S, SHAMSHIRBAND S, et al. Soft computing approaches for forecasting reference evapotranspiration. Computers and Electronics in Agriculture, 2015, 113(C):164-173.
[26]郭映,董阳,周振方,等.半干旱区玉米茎流规律及其对气象因子的响应.干旱区资源与环境, 2014, 28(9):94-99.[GUO Y, DONG Y, ZHOU Z F, et al. The sap flow of corn during the growth period and its response to meteorological factors in semi-arid area. Journal of Arid Land Resources and Environment, 2014, 28(9):94-99.]
[27]丁日升,康绍忠,张彦群,等.干旱内陆区玉米田水热通量特征及主控因子研究.水利学报. 2014, 45(3):312-319.[DING R S, KANG S Z, ZHANG Y Q, et al. Characteristics of water vapor and heat fluxes and the controlling factors over a maize field in the arid inland region. Journal of Hydraulic Engineering, 2014, 45(3):312-319.]
[28] ZHANG B Z, XU D, LIU Y, et al. Multi-scale evapotranspiration of summer maize and the controlling meteorological factors in North China. Agricultural and Forest METeorology, 2016, 216:1-12.
[29] JENSEN M E, BURMAN R D, ALLEN R G. Evapotranspiration and irrigation water requirements:ASCE manuals and reports on engineering practice No. 70. New York:ASCE, 1990.
[30] CAI J B, LIU Y, LEI T W, et al. Estimating reference evapotranspiration with the FAO Penman Monteith equation using daily weather forecast messages. Agricultural and Forest Meteorology, 2007, 145(1-2):22-35.
[31] SINGH S K, VERMA C L, SHARMA D K. Prediction of evapotranspiration using artificial neural network model and compared with measured values. International Journal of Engineering Sciences&Research Technology, 2014, 1(3):287-292.