土壤湿度的模拟和估算研究
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摘要
土壤湿度作为全球能量与水循环中非常重要的水文变量,不仅关系到天气预报和气候预测的准确度,同时在水文、生态、农业等学科中也具有重要的研究意义。通过开展大量的针对土壤湿度的观测实验和模拟研究,人们对土壤湿度的认识水平有较大提高,特别是卫星遥感资料的出现,为提高土壤湿度的准确模拟创造了很好的条件。本论文主要围绕土壤湿度的模拟和估算展开了一系列的研究,为提高土壤湿度的模拟提供一些理论依据。
首先,由于陆面模式的出处和用途的不同,导致其种类繁多。为了理解陆面过程在天气和气候模式中的作用,定量的评价不同陆面模式的模拟性能,以促进陆面模式的继续发展,国内外已经展开了一系列的陆面模式比较计划,本论文继续关注陆面过程中的土壤湿度和通量的模拟。
本论文的第一部分工作检验了不同模式对土壤湿度的模拟差别,与前人工作不同的是考察土壤湿度方程的离散化方案以及土壤分层和土壤性质(均匀和非均匀性)对土壤湿度模拟的影响,同时为了防止上边界参数化方案(如蒸发和径流等)差别带来的影响,采用了三类理想上边界条件来对土壤湿度方程求解。结果表明：当土壤质地均匀时,土壤分层较多的情况下,不同的差分格式求解的不同土壤类型的湿度廓线相差不大,同时对比细网格(分层较多)和粗网格(分层较少)的湿度廓线发现：不同粗网格对土壤湿度,尤其是深层土壤湿度的模拟比较差。当土壤质地不均匀时,不同的差分格式对土壤湿度廓线求解差别比较大,主要表现在不同差分格式计算得到的土壤湿度的连续性不同,通过对比粗网格和细网格之间的均方差后发现,不同离散化方法均方差的变化亦不同。最后,为了比较不同的差分格式求解中离散化过程可能引起的误差,特引入了一种迭代方法对比,通过对比不同数值方法求解得到的土壤湿度廓线后,结果显示增量法和迭代法求解得到的土壤湿度廓线比较一致。本论文第二章中列出了不同模式和本文中使用的土壤湿度方程的详细离散化过程,第三章通过试验的设计,比较不同的离散化方法的差别。
本论文的第二部分工作为实际大气观测驱动下的模拟试验。使用中日合作项目“全球能量水循环之亚洲季风青藏高原试验研究"(GAME/Tibet)的观测数据和美国土壤湿度试验(SMEX03)的观测数据,分别对三个陆面模式(CoLM,CABLE和Noah LSM)展开模式比较工作,重点比较了对土壤温湿度和感潜热通量的模拟结果。结果显示：不同的陆面模式都能够模拟出土壤的温度和湿度的基本特征,表层土壤湿度对降水过程响应比较快,但是具体模拟的土壤温、湿度值却与实际观测存在较大的差别,此外,不同模式模拟得到的土壤温、湿度之间也存在较大的差别。该部分工作出现在本论文第三章中。
其次,来自不同源的土壤湿度的观测,尤其是微波遥感反演得到的表层(一般深度小于10 cm)土壤湿度可以和陆面模式融合在一起并借助数据同化技巧来估算土壤湿度廓线,特别是对植被根区土壤湿度的改进具有更明显的气候学意义。同时,大量的研究结果表明,多模式的集合预报已经被证明要好于单个模式的集合预报效果,而多模式集合预报中的数据同化算法的相关研究还相对较少。
本论文的最后一部分内容展开了估算土壤湿度廓线的陆面数据同化试验。数据同化方法使用了集合Kalman滤波,首先分别单独使用三个陆面模式来比较它们同化表层土壤湿度观测对深层土壤湿度、感热通量和潜热通量的改进效果,同时考察了不同误差源对同化效果的影响。该部分工作出现在第五章。然后,鉴于多模式的集合预报效果要好于单个模式的集合预报效果,展开多模式超集合卡曼滤波的陆面数据同化试验,继续采用同化表层湿度观测来估算土壤湿度廓线这-经典陆面数据同化框架。为此,提出了两种用来估算超集合背景场及其误差协方差的方法,文中称为“样本均值法”(SMA)和“加权平均法”(WAM),并比较两种方法用在多模式土壤湿度同化系统中的表现。试验结果显示：“样本均值法”对深层土壤湿度的改进较小,甚至有时会比单个模式的同化效果还差,而“加权平均法”却能取得比较好的同化效果,比单个模式估算的土壤湿度精度都高。该部分多模式同化工作将出现在第六章。
最后,第七章总结了博士论文的全部工作,指出存在的不足,并对后续工作进行了展望。
The role of soil moisture is widely recognised as a key paremeter in global energy and water cycle and numerous environmental studies, including not only in meteorology and climatology but also in hydrology, ecology and agriculture, et al. Lots of field and similation experiments approached to understand the importance of soil moisture, and the remote sensing data provided a great chance to improve soil moisture estimation. This thesis mainly addresses the simulation of soil moisture by using different land surface models (LSMs) and the estimation of soil moisture by using ensemble Kalman filter (EnKF) data assimilation scheme wih the background provided by each single-model or multimodel. The main contents are as follows:
Firstly, there are various kinds of Land surface models in different nations and institutions. To evaluate the porformance of different models, and ultimately to improve the parameterization of land models, many land model intercomparision projects have been arranged to this aim.
The first part of work in this thesis is to simulate soil moisture by using different numerical discretization schemes. The 1-D Richards equation for soil moisture is highly nonlinear and it is impossible to find out an analytical solution with general initial and boundary condition. Therefore, numerical approximations are typically used to solve the equation. To prevent the influence of the parameterization schemes of upper boundary(eg. evaporation and run-off), three kinds of ideal upper boundary conditions (BCs) are adopted, including fixed evaporation rate, fixed infiltration rate and fixed near-surface soil moisture. The main purpose is to evaluate the influence of the numbers of soil layers and the heterogeneity of soil on the prediction of soil moisture. The results show that if the soil along depth is homogeneous (i.e. one soil type with fixed percentages of sand, clay and loam) and the large number of soil layers is used, the different schemes will produce almost similar results. By comparing the soil moisture profiles produced with more layers and less layers, we found that the soil moisture predicton becomes worse with less layers than with more layers at deep soil layers. The results also shows that if the soil along the depth is heterogeneous (i.e. still one soil type but with different percentages of sand, clay and loam in each layer), the different schemes will produce very different solutions, and the main difference is the continuity of the soil moisture profile. The rms difference of the soil moisture predictions by the fine grids (i.e., more soil layers) and coarse grids (i.e., less soil layers) is not consistent among differet LSMs. In order to quantitatively evaluate the impact of linearization on the modeling of soil moisture, an interative scheme with no linearization is introduced and its results are used to compare with those from the different LSMs, showing that the profile with the scheme of CoLM is similar with the interative scheme. The second chapter lists the different numerical discretization schemes. The third chapter gives the result of the experiments.
The Second part of work in this thesis is to simulate the soil moisture, soil temperature and surface heat fluxes by the forcings of the meteorological observations. The dataset is from the Soil Moisture Experiments in 2003 (SMEX03) and the GEWEX Asia Monsoon Experiment over the Tibetan Plateau (GAME/Tibet) and the three LSMs are CoLM, CABLE and Noah. The result shows the models can not correctly simulate the soil temperature and soil moisture at near-surface layer, however, they all do a fairly good job in reproducing the soil moisture anomalies. The soil temperature and soil moisture simulated by different models have large differences at near-surface layer, but are very similar at deep soil layers. For latent and sensible heat fluxes, The fluxes from CoLM are relatively larger and those from Noah LSM are smaller than the observations, however, the fluxes from CABLE are close to the measurements. This content appears in the fourth chapter.
Secondly, Soil moisture observation, especially remote sensing techniques, can provide additional information about near-surface soil moisture (generally less than 10cm) at large scales. Land data assimilation scheme can be designed to estimate soil moisture profile and fluxes by assimilating different data sources. Multi-model ensembles have been found to perform significantly better than a single-model system in weather and seasonal climate forecasts, however, data assimilation rarely used in multi-model forecasts.
The last part of work in this thesis is estimating the soil moisture profile by the land data assimilation schemes. In this part of work, the ensemble Kalman filter (EnKF) is used in land data assimilation system. The soil moisture and fluxes are estimated with three single models by assimilating near-surface soil moisture observations. Random errors are added in initial soil moisture profile, forcing datasets and soil parameters are used to evaluate the influence of different sources of errors. This work appears in chapter five.
Then, we carry out a multi-model EnKF data assimilation experiments by assimilating surface soil moisture observation for the retrieval of soil moisture profile. Two algorithms, i.e., the simple model average (SMA) and the weighted average method (WAM), are investigated for estimating the multi-model background superensemble mean and the corresponding multi-model background superensemble error covariance matrix. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically generated and actual near-surface soil moisture measurements into the multi-model. The results from the experiment show the SMA does not help to improve the estimates of soil moisture at the deep layers, even worse than with single model. On the contrary, the results from the WAM are better than those from any single model and SMA. This part of wok appears in chapter six.
Finally, all of work in this doctoral thesis is summarized in chapter seven, and further research is also included.
首先,由于陆面模式的出处和用途的不同,导致其种类繁多。为了理解陆面过程在天气和气候模式中的作用,定量的评价不同陆面模式的模拟性能,以促进陆面模式的继续发展,国内外已经展开了一系列的陆面模式比较计划,本论文继续关注陆面过程中的土壤湿度和通量的模拟。
本论文的第一部分工作检验了不同模式对土壤湿度的模拟差别,与前人工作不同的是考察土壤湿度方程的离散化方案以及土壤分层和土壤性质(均匀和非均匀性)对土壤湿度模拟的影响,同时为了防止上边界参数化方案(如蒸发和径流等)差别带来的影响,采用了三类理想上边界条件来对土壤湿度方程求解。结果表明：当土壤质地均匀时,土壤分层较多的情况下,不同的差分格式求解的不同土壤类型的湿度廓线相差不大,同时对比细网格(分层较多)和粗网格(分层较少)的湿度廓线发现：不同粗网格对土壤湿度,尤其是深层土壤湿度的模拟比较差。当土壤质地不均匀时,不同的差分格式对土壤湿度廓线求解差别比较大,主要表现在不同差分格式计算得到的土壤湿度的连续性不同,通过对比粗网格和细网格之间的均方差后发现,不同离散化方法均方差的变化亦不同。最后,为了比较不同的差分格式求解中离散化过程可能引起的误差,特引入了一种迭代方法对比,通过对比不同数值方法求解得到的土壤湿度廓线后,结果显示增量法和迭代法求解得到的土壤湿度廓线比较一致。本论文第二章中列出了不同模式和本文中使用的土壤湿度方程的详细离散化过程,第三章通过试验的设计,比较不同的离散化方法的差别。
本论文的第二部分工作为实际大气观测驱动下的模拟试验。使用中日合作项目“全球能量水循环之亚洲季风青藏高原试验研究"(GAME/Tibet)的观测数据和美国土壤湿度试验(SMEX03)的观测数据,分别对三个陆面模式(CoLM,CABLE和Noah LSM)展开模式比较工作,重点比较了对土壤温湿度和感潜热通量的模拟结果。结果显示：不同的陆面模式都能够模拟出土壤的温度和湿度的基本特征,表层土壤湿度对降水过程响应比较快,但是具体模拟的土壤温、湿度值却与实际观测存在较大的差别,此外,不同模式模拟得到的土壤温、湿度之间也存在较大的差别。该部分工作出现在本论文第三章中。
其次,来自不同源的土壤湿度的观测,尤其是微波遥感反演得到的表层(一般深度小于10 cm)土壤湿度可以和陆面模式融合在一起并借助数据同化技巧来估算土壤湿度廓线,特别是对植被根区土壤湿度的改进具有更明显的气候学意义。同时,大量的研究结果表明,多模式的集合预报已经被证明要好于单个模式的集合预报效果,而多模式集合预报中的数据同化算法的相关研究还相对较少。
本论文的最后一部分内容展开了估算土壤湿度廓线的陆面数据同化试验。数据同化方法使用了集合Kalman滤波,首先分别单独使用三个陆面模式来比较它们同化表层土壤湿度观测对深层土壤湿度、感热通量和潜热通量的改进效果,同时考察了不同误差源对同化效果的影响。该部分工作出现在第五章。然后,鉴于多模式的集合预报效果要好于单个模式的集合预报效果,展开多模式超集合卡曼滤波的陆面数据同化试验,继续采用同化表层湿度观测来估算土壤湿度廓线这-经典陆面数据同化框架。为此,提出了两种用来估算超集合背景场及其误差协方差的方法,文中称为“样本均值法”(SMA)和“加权平均法”(WAM),并比较两种方法用在多模式土壤湿度同化系统中的表现。试验结果显示：“样本均值法”对深层土壤湿度的改进较小,甚至有时会比单个模式的同化效果还差,而“加权平均法”却能取得比较好的同化效果,比单个模式估算的土壤湿度精度都高。该部分多模式同化工作将出现在第六章。
最后,第七章总结了博士论文的全部工作,指出存在的不足,并对后续工作进行了展望。
The role of soil moisture is widely recognised as a key paremeter in global energy and water cycle and numerous environmental studies, including not only in meteorology and climatology but also in hydrology, ecology and agriculture, et al. Lots of field and similation experiments approached to understand the importance of soil moisture, and the remote sensing data provided a great chance to improve soil moisture estimation. This thesis mainly addresses the simulation of soil moisture by using different land surface models (LSMs) and the estimation of soil moisture by using ensemble Kalman filter (EnKF) data assimilation scheme wih the background provided by each single-model or multimodel. The main contents are as follows:
Firstly, there are various kinds of Land surface models in different nations and institutions. To evaluate the porformance of different models, and ultimately to improve the parameterization of land models, many land model intercomparision projects have been arranged to this aim.
The first part of work in this thesis is to simulate soil moisture by using different numerical discretization schemes. The 1-D Richards equation for soil moisture is highly nonlinear and it is impossible to find out an analytical solution with general initial and boundary condition. Therefore, numerical approximations are typically used to solve the equation. To prevent the influence of the parameterization schemes of upper boundary(eg. evaporation and run-off), three kinds of ideal upper boundary conditions (BCs) are adopted, including fixed evaporation rate, fixed infiltration rate and fixed near-surface soil moisture. The main purpose is to evaluate the influence of the numbers of soil layers and the heterogeneity of soil on the prediction of soil moisture. The results show that if the soil along depth is homogeneous (i.e. one soil type with fixed percentages of sand, clay and loam) and the large number of soil layers is used, the different schemes will produce almost similar results. By comparing the soil moisture profiles produced with more layers and less layers, we found that the soil moisture predicton becomes worse with less layers than with more layers at deep soil layers. The results also shows that if the soil along the depth is heterogeneous (i.e. still one soil type but with different percentages of sand, clay and loam in each layer), the different schemes will produce very different solutions, and the main difference is the continuity of the soil moisture profile. The rms difference of the soil moisture predictions by the fine grids (i.e., more soil layers) and coarse grids (i.e., less soil layers) is not consistent among differet LSMs. In order to quantitatively evaluate the impact of linearization on the modeling of soil moisture, an interative scheme with no linearization is introduced and its results are used to compare with those from the different LSMs, showing that the profile with the scheme of CoLM is similar with the interative scheme. The second chapter lists the different numerical discretization schemes. The third chapter gives the result of the experiments.
The Second part of work in this thesis is to simulate the soil moisture, soil temperature and surface heat fluxes by the forcings of the meteorological observations. The dataset is from the Soil Moisture Experiments in 2003 (SMEX03) and the GEWEX Asia Monsoon Experiment over the Tibetan Plateau (GAME/Tibet) and the three LSMs are CoLM, CABLE and Noah. The result shows the models can not correctly simulate the soil temperature and soil moisture at near-surface layer, however, they all do a fairly good job in reproducing the soil moisture anomalies. The soil temperature and soil moisture simulated by different models have large differences at near-surface layer, but are very similar at deep soil layers. For latent and sensible heat fluxes, The fluxes from CoLM are relatively larger and those from Noah LSM are smaller than the observations, however, the fluxes from CABLE are close to the measurements. This content appears in the fourth chapter.
Secondly, Soil moisture observation, especially remote sensing techniques, can provide additional information about near-surface soil moisture (generally less than 10cm) at large scales. Land data assimilation scheme can be designed to estimate soil moisture profile and fluxes by assimilating different data sources. Multi-model ensembles have been found to perform significantly better than a single-model system in weather and seasonal climate forecasts, however, data assimilation rarely used in multi-model forecasts.
The last part of work in this thesis is estimating the soil moisture profile by the land data assimilation schemes. In this part of work, the ensemble Kalman filter (EnKF) is used in land data assimilation system. The soil moisture and fluxes are estimated with three single models by assimilating near-surface soil moisture observations. Random errors are added in initial soil moisture profile, forcing datasets and soil parameters are used to evaluate the influence of different sources of errors. This work appears in chapter five.
Then, we carry out a multi-model EnKF data assimilation experiments by assimilating surface soil moisture observation for the retrieval of soil moisture profile. Two algorithms, i.e., the simple model average (SMA) and the weighted average method (WAM), are investigated for estimating the multi-model background superensemble mean and the corresponding multi-model background superensemble error covariance matrix. The two algorithms are tested and compared in terms of their abilities to retrieve the true soil moisture profile by respectively assimilating both synthetically generated and actual near-surface soil moisture measurements into the multi-model. The results from the experiment show the SMA does not help to improve the estimates of soil moisture at the deep layers, even worse than with single model. On the contrary, the results from the WAM are better than those from any single model and SMA. This part of wok appears in chapter six.
Finally, all of work in this doctoral thesis is summarized in chapter seven, and further research is also included.
引文
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