新疆天山北坡是我国西北干旱区重要的农业生产基地,近年来受到气候变化和水肥管理措施影响强烈。该地区气候整体上呈现出“暖湿化”的特征,同时水肥管理措施不断优化,使得农田生态系统生产力发生明显变化。在此背景下,深入揭示气候变化...新疆天山北坡是我国西北干旱区重要的农业生产基地,近年来受到气候变化和水肥管理措施影响强烈。该地区气候整体上呈现出“暖湿化”的特征,同时水肥管理措施不断优化,使得农田生态系统生产力发生明显变化。在此背景下,深入揭示气候变化与水肥管理措施对农田生态系统的耦合作用机制,并确定科学的水肥管理措施以适应气候变化给干旱区农田带来的影响,对保障区域粮食安全与发挥农业大国的优势具有重要意义。基于Biome-BGC MuSo模型,将天山北坡主要农作物生理生态参数、管理模块参数本地化,揭示1979—2018年天山北坡农田生态系统NPP的时空分布特征,量化气候变化与水肥管理措施对NPP的相对贡献,探究NPP对不同灌溉、施肥措施的响应。结果表明:(1)1979—2018年天山北坡农田生态系统NPP以2011年为分界点先增后减,多年平均值为0.409 kg C m^(-2)a^(-1)。NPP高值区主要位于奇台县南部以及温泉县等,以玉米、小麦种植为主;低值区较为分散,在乌苏市北部、沙湾县、玛纳斯县等均有分布,以棉花种植为主。(2)气候变化和管理措施对天山北坡农田生态系统NPP的相对贡献率分别为38.75%、61.25%,并且科学的管理措施能够有效放大气候变化对NPP的正向作用、缓解气候变化的负面影响。(3)玉米、小麦、棉花NPP随灌溉量和施肥量的增加先上升后趋于不变,灌溉量475 mm和施肥量236 kg/hm^(2)(基准量的1.3倍)构成水肥管理最优组合,超出此阈值农作物NPP增幅明显减弱。研究结果可为干旱区农田生态系统应对气候变化、维持区域可持续发展提供理论参考。展开更多
Background:Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations.Although t...Background:Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations.Although the Biome-Biogeochemical Cycles(Biome-BGC)model is broadly applied in simulating forest carbon and water fluxes,its single-layer soil module cannot represent vertical variations in soil moisture.This study introduces the Biome-BGC MuSo model,which is composed of a multi-layer soil module and new modules pertaining to phenology and management for simulations of carbon and water fluxes.Although this model considers soil processes among active layers,estimates of soil-related variables might be biased,leading to inaccurate estimates of carbon and water fluxes.Methods:To improve the estimations of soil-related processes in Biome-BGC MuSo,this study assimilates ground-measured multi-layer daily soil temperature and moisture at the Changbai Mountains forest flux site by using the Ensemble Kalman Filter algorithm.The modeled estimates of water and carbon fluxes were evaluated with measurements using determination coefficient(R2)and root mean square error(RMSE).The differences in the RMSEs from Biome-BGC MuSo and the assimilated Biome-BGC MuSo were calculated(ΔRMSE),and the relationships betweenΔRMSE and the climatic and biophysical factors were analyzed.Results:Compared with the original Biome-BGC model,Biome-BGC MuSo improved the simulations of ecosystem respiration(ER),net ecosystem exchange(NEE)and evapotranspiration(ET).Data assimilation of the soil-related variables into Biome-BGC MuSo in real time improved the accuracies of the simulated carbon and water fluxes(ET:R^2=0.81,RMSE=0.70 mm·d^-1;ER:R^2=0.85,RMSE=1.97 gC·m^-2·d^-1;NEE:R^2=0.70,RMSE=1.16 gC·m^-2·d^-1).Conclusions:This study proved that seasonal simulation of carbon and water fluxes are more accurate when using Biome-BGC MuSo with a multi-layer soil module than using Biome-BGC with a single-layer soil module.Moreover,assimilating the observed soil temperature and moisture data into Biome-BGC MuSo improved the modeled estimates of water and carbon fluxes via calibrated soil-related simulations.The assimilation strategy is applicable to various climatic and biophysical conditions,particularly densely forested areas,and for local or regional simulation.展开更多
文摘新疆天山北坡是我国西北干旱区重要的农业生产基地,近年来受到气候变化和水肥管理措施影响强烈。该地区气候整体上呈现出“暖湿化”的特征,同时水肥管理措施不断优化,使得农田生态系统生产力发生明显变化。在此背景下,深入揭示气候变化与水肥管理措施对农田生态系统的耦合作用机制,并确定科学的水肥管理措施以适应气候变化给干旱区农田带来的影响,对保障区域粮食安全与发挥农业大国的优势具有重要意义。基于Biome-BGC MuSo模型,将天山北坡主要农作物生理生态参数、管理模块参数本地化,揭示1979—2018年天山北坡农田生态系统NPP的时空分布特征,量化气候变化与水肥管理措施对NPP的相对贡献,探究NPP对不同灌溉、施肥措施的响应。结果表明:(1)1979—2018年天山北坡农田生态系统NPP以2011年为分界点先增后减,多年平均值为0.409 kg C m^(-2)a^(-1)。NPP高值区主要位于奇台县南部以及温泉县等,以玉米、小麦种植为主;低值区较为分散,在乌苏市北部、沙湾县、玛纳斯县等均有分布,以棉花种植为主。(2)气候变化和管理措施对天山北坡农田生态系统NPP的相对贡献率分别为38.75%、61.25%,并且科学的管理措施能够有效放大气候变化对NPP的正向作用、缓解气候变化的负面影响。(3)玉米、小麦、棉花NPP随灌溉量和施肥量的增加先上升后趋于不变,灌溉量475 mm和施肥量236 kg/hm^(2)(基准量的1.3倍)构成水肥管理最优组合,超出此阈值农作物NPP增幅明显减弱。研究结果可为干旱区农田生态系统应对气候变化、维持区域可持续发展提供理论参考。
基金supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF under grant CAFYBB2017QC005General Financial Grant from the China Postdoctoral Science Foundation(2017M611036)+1 种基金National Natural Science Foundation of China(Grant No.41771392)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19030302)
文摘Background:Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations.Although the Biome-Biogeochemical Cycles(Biome-BGC)model is broadly applied in simulating forest carbon and water fluxes,its single-layer soil module cannot represent vertical variations in soil moisture.This study introduces the Biome-BGC MuSo model,which is composed of a multi-layer soil module and new modules pertaining to phenology and management for simulations of carbon and water fluxes.Although this model considers soil processes among active layers,estimates of soil-related variables might be biased,leading to inaccurate estimates of carbon and water fluxes.Methods:To improve the estimations of soil-related processes in Biome-BGC MuSo,this study assimilates ground-measured multi-layer daily soil temperature and moisture at the Changbai Mountains forest flux site by using the Ensemble Kalman Filter algorithm.The modeled estimates of water and carbon fluxes were evaluated with measurements using determination coefficient(R2)and root mean square error(RMSE).The differences in the RMSEs from Biome-BGC MuSo and the assimilated Biome-BGC MuSo were calculated(ΔRMSE),and the relationships betweenΔRMSE and the climatic and biophysical factors were analyzed.Results:Compared with the original Biome-BGC model,Biome-BGC MuSo improved the simulations of ecosystem respiration(ER),net ecosystem exchange(NEE)and evapotranspiration(ET).Data assimilation of the soil-related variables into Biome-BGC MuSo in real time improved the accuracies of the simulated carbon and water fluxes(ET:R^2=0.81,RMSE=0.70 mm·d^-1;ER:R^2=0.85,RMSE=1.97 gC·m^-2·d^-1;NEE:R^2=0.70,RMSE=1.16 gC·m^-2·d^-1).Conclusions:This study proved that seasonal simulation of carbon and water fluxes are more accurate when using Biome-BGC MuSo with a multi-layer soil module than using Biome-BGC with a single-layer soil module.Moreover,assimilating the observed soil temperature and moisture data into Biome-BGC MuSo improved the modeled estimates of water and carbon fluxes via calibrated soil-related simulations.The assimilation strategy is applicable to various climatic and biophysical conditions,particularly densely forested areas,and for local or regional simulation.
