In order to explore the appropriate irrigation schedule for summer maize,a field experiment was conducted in 2013 in Lubotan of Shaanxi Province.Soil water content,soil salinity,soil hydraulic parameters,crop growth p...In order to explore the appropriate irrigation schedule for summer maize,a field experiment was conducted in 2013 in Lubotan of Shaanxi Province.Soil water content,soil salinity,soil hydraulic parameters,crop growth parameters and summer maize yield were measured in the experiment.The SWAP model was calibrated based on field experiment observation data in 2013.The SWAP model was used to simulate and optimize irrigation schedule for summer maize after calibration.The results showed that model simulation results of soil water content,soil salinity and summer maize yield agreed well with the measured values.The Root Mean Square Error(RMSE)and Mean Relative Error(MRE)were within the allowable error ranges.The RMSE values were all lower than 0.05 cm3/cm3 and the MRE values were lower than 15%in soil water content calibration.The RMSE values were all lower than 0.1 mg/cm3 and the MRE values were lower than 20%in soil salinity calibration.The RMSE and MRE values were 1299.6 kg/hm2 and 15.26%in summer maize yield calibration.The model parameters suitable for the study area were obtained in calibration.The SWAP model could be used to simulate and optimize irrigation schedule for summer maize after calibration.The SWAP model was used to simulate soil water-salt balance,summer maize yield and water use efficiency under different irrigation schedules.The model simulation results for different irrigation schedules indicated that the optimal irrigation schedules of summer maize were three times each for jointing stage(July 5),heading stage(August 5)and grain filling stage(August 30)with irrigation amount of 128 mm,128 mm and 96 mm,respectively.The optimal irrigation quota was 352.0 mm for summer maize in the study area.展开更多
基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensembl...基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensemble smoother,IES),构建了适合旱区春小麦的SWAP-IES同化模拟系统,并利用2019—2020年田间观测试验数据,评估了同化叶面积指数(leaf area index,LAI)、土壤水分(soil water content,SW)及其组合在旱区春小麦生长模拟和估产中的作用。结果表明,相较于无同化情景,在吸收6次土壤水分观测数据后,模型对土壤水分模拟的R^(2)从0.48提升到0.87。同化LAI时,各水分胁迫处理下LAI的模拟精度均最高,R^(2)从无同化的0.35~0.62提升到0.76~0.96。同化LAI+SW时,各处理对生物量模拟的精度均最高,R^(2)从无同化的0.40~0.67提升到0.73~0.96。轻度水分胁迫处理(T4~T5)下,仅同化LAI即可达到较好的估产效果,相对误差为4.05%~9.17%,而在中度或重度水分胁迫处理(T1~T3)下,准确的产量估算需同时吸收LAI和SW,相对误差为3.87%~8.38%。开花期和拔节期的观测数据对提高SWAP-IES系统估产精度的作用最大,同时吸收开花期和拔节期LAI+SW观测数据时估产的R^(2)可从无同化的0.45提高到0.79。说明所构建的SWAP-IES同化模拟系统,在融入开花期和拔节期等关键生育期的观测数据后能有效模拟不同水分处理下春小麦生长和产量形成过程,可为田块尺度下旱区春小麦精准监测提供技术参考。展开更多
This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geomet...This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geometric Brownian motion, and the default barrier follows a continuous stochastic process. Using the Gaver-Stehfest algorithm and the non-arbitrage asset pricing theory, we give the default probability of the first passage time, and more, derive the price of the Credit Default Swap.展开更多
基金This research was financially supported by Jiangxi Educational Bureau Science-Technology Research Program(GJJ170981)National Natural Science Foundation of China(51709144)and Farmland Irrigation Research Institute,Chinese Academy of Agricultural Sciences(FIRI2017-22-01).
文摘In order to explore the appropriate irrigation schedule for summer maize,a field experiment was conducted in 2013 in Lubotan of Shaanxi Province.Soil water content,soil salinity,soil hydraulic parameters,crop growth parameters and summer maize yield were measured in the experiment.The SWAP model was calibrated based on field experiment observation data in 2013.The SWAP model was used to simulate and optimize irrigation schedule for summer maize after calibration.The results showed that model simulation results of soil water content,soil salinity and summer maize yield agreed well with the measured values.The Root Mean Square Error(RMSE)and Mean Relative Error(MRE)were within the allowable error ranges.The RMSE values were all lower than 0.05 cm3/cm3 and the MRE values were lower than 15%in soil water content calibration.The RMSE values were all lower than 0.1 mg/cm3 and the MRE values were lower than 20%in soil salinity calibration.The RMSE and MRE values were 1299.6 kg/hm2 and 15.26%in summer maize yield calibration.The model parameters suitable for the study area were obtained in calibration.The SWAP model could be used to simulate and optimize irrigation schedule for summer maize after calibration.The SWAP model was used to simulate soil water-salt balance,summer maize yield and water use efficiency under different irrigation schedules.The model simulation results for different irrigation schedules indicated that the optimal irrigation schedules of summer maize were three times each for jointing stage(July 5),heading stage(August 5)and grain filling stage(August 30)with irrigation amount of 128 mm,128 mm and 96 mm,respectively.The optimal irrigation quota was 352.0 mm for summer maize in the study area.
文摘基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensemble smoother,IES),构建了适合旱区春小麦的SWAP-IES同化模拟系统,并利用2019—2020年田间观测试验数据,评估了同化叶面积指数(leaf area index,LAI)、土壤水分(soil water content,SW)及其组合在旱区春小麦生长模拟和估产中的作用。结果表明,相较于无同化情景,在吸收6次土壤水分观测数据后,模型对土壤水分模拟的R^(2)从0.48提升到0.87。同化LAI时,各水分胁迫处理下LAI的模拟精度均最高,R^(2)从无同化的0.35~0.62提升到0.76~0.96。同化LAI+SW时,各处理对生物量模拟的精度均最高,R^(2)从无同化的0.40~0.67提升到0.73~0.96。轻度水分胁迫处理(T4~T5)下,仅同化LAI即可达到较好的估产效果,相对误差为4.05%~9.17%,而在中度或重度水分胁迫处理(T1~T3)下,准确的产量估算需同时吸收LAI和SW,相对误差为3.87%~8.38%。开花期和拔节期的观测数据对提高SWAP-IES系统估产精度的作用最大,同时吸收开花期和拔节期LAI+SW观测数据时估产的R^(2)可从无同化的0.45提高到0.79。说明所构建的SWAP-IES同化模拟系统,在融入开花期和拔节期等关键生育期的观测数据后能有效模拟不同水分处理下春小麦生长和产量形成过程,可为田块尺度下旱区春小麦精准监测提供技术参考。
基金Supported by The National Natural Science Foundation of China(71261015)Humanity and Social Science Youth Foundation of Education Ministry in China(10YJC630334)Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region
文摘This paper discusses the valuation of the Credit Default Swap based on a jump market, in which the asset price of a firm follows a double exponential jump diffusion process, the value of the debt is driven by a geometric Brownian motion, and the default barrier follows a continuous stochastic process. Using the Gaver-Stehfest algorithm and the non-arbitrage asset pricing theory, we give the default probability of the first passage time, and more, derive the price of the Credit Default Swap.
