Land Response to Atmosphere at Different Resolutions in the Common Land Model over East Asia

Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.

Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects

Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.

GLDAS和CMIP5产品的中国土壤湿度-降水耦合分析及变化趋势

利用GLDAS同化产品和12个CMIP5模式的输出结果,从土壤湿度对降水影响的两个中间环节出发,通过分析陆面耦合指数ILH、潜热通量—抬升凝结高度耦合指数ILCL以及抬升凝结高度ZLCL间接研究中国区域土壤湿度与降水间耦合特征,并对1958～2013年及RCP4.5辐射强迫情景下50年（2006～2055年）的4个代表性区域夏季耦合强度的年代际变化特征进行分析。研究发现：1958～2013年期间,内蒙古阴山山脉附近、新疆和青海的部分地区为夏季中国土壤湿度与降水耦合的最强区域;陆面耦合指数ILH变化幅度从高到低依次出现在华北、华南、内蒙古中部和西北地区,并在20世纪70年代中到80年代中发生转折。2006～2055年的平均而言,预估内蒙古阴山山脉附近仍为耦合最强区;与历史时期（1958～2005年）比较,新疆中部和内蒙古阴山山脉附近的耦合指数ILH增大,而广西和广东地区的则减小;对于耦合指数ILH的年代际变化（2006～2055年）,2026～2035年间华北最大而华南最小,西北地区变化不大,而内蒙古中部地区的耦合强度逐渐增大。

基于GLDAS的中国区地表能量平衡数值试验

利用全球陆面数据同化系统(GLDAS)的软件平台—陆面信息系统(LIS)模拟了中国区域2003年能量平衡方程的各个分量,在此基础上进行了残差分析和地表温度的对比验证。残差的分布具有一定的时空分布特征,残差的时间分布特征表明LIS对春秋两季的模拟效果要好于其它季节,残差的空间分布特征表明LIS对纬度较高和海拔较高地区的模拟效果要逊于其他地区。对比了模拟的GLDAS地表温度与MODIS地表温度产品,结果显示两者之间的差值介于-5～5K之间,两者的散点图和标准离差显示模拟的夜间地表温度要比模拟的白天地表温度精确2～3K。

GLDAS Noah模型水文产品与中国地面观测及卫星观测数据的对比

全球陆面数据同化系统(GLDAS)是全球水循环研究的重要数据源,基于重力卫星(GRACE)观测数据、中国的地面降水与径流观测数据,从模拟数据与观测数据之间的偏差、相关性及时空分布一致性角度,对GLDAS两个版本(GLDAS-1及GLDAS-2)的Noah模型模拟水文产品进行了评估。结果表明,GLDAS与GRACE TWS陆地水蓄量(TWS)变化情况在中国大部分地区一致性较差,且GLDAS-1与GLDAS-2均不能充分反映GRACE TWS数据的季节性变化规律;GLDAS-1与GLDAS-2模拟的中国外流区的径流明显低于观测径流,同时长江流域与松花江流域GLDAS-2模拟径流的精度远比GLDAS-1模拟径流的精度高;GLDAS-1与GLDAS-2在中国的外流流域的蒸散发均呈高估现象,且GLDAS-1的高估程度大于GLDAS-2。

利用GRACE卫星分析安徽省地下水储量的时空变化

文章利用重力恢复与气候实验卫星(Gravity Recovery and Climate Experiment,GRACE)时变重力场球谐系数文件,联合全球陆面数据同化系统(Global Land Data Assimilation System,GLDAS)水文模型反演安徽省2003—2016年地下水储量的时空变化。通过奇异谱分析(Singular Spectrum Analysis,SSA)地下水时间序列,结合热带降雨测量任务(Tropical Rainfall Measuring Mission,TRMM)降雨数据对地下水储量变化规律进行分析。结果表明,安徽省地下水储量在2011年和2014年前后发生较大变化,在2003—2011年的变化率为0.37 cm/a,2011—2014年的下降速率为-0.2 cm/a,2014—2016年的增长速率为1.9 cm/a;进一步与降雨数据关联,发现降雨量是影响安徽省地下水储量年际变化和季节性变化的主要因素。在空间上,安徽省呈现自东北向西南逐渐缓和的趋势,最大亏损出现在皖北地区,为-7.52 mm/a,在西南地区的最大盈余达到8.38 mm/a。

Water storage changes and balances in Africa observed by GRACE and hydrologic models

Continental water storage plays a major role in Earth's climate system.However,temporal and spatial variations of continental water are poorly known,particularly in Africa.Gravity Recovery and Climate Experiment(GRACE)satellite mission provides an opportunity to estimate terrestrial water storage(TWS)variations at both continental and river-basin scales.In this paper,seasonal and secular variations of TWS within Africa for the period from January 2003 to July 2013 are assessed using monthly GRACE coefficients from three processing centers(Centre for Space Research,the German Research Centre for Geo-sciences,and NASA's Jet Propulsion Laboratory).Monthly grids from Global Land Data Assimilation System(GLDAS)-I and from the Tropical Rainfall Measuring Mission(TRMM)-3B43 models are also used in order to understand the reasons of increasing or decreasing water storage.Results from GRACE processing centers show similar TWS estimates at seasonal timescales with some differences concerning inter-annual trend variations.The largest annual signals of GRACE TWS are observed in Zambezi and Okavango River basins and in Volta River Basin.An increasing trend of 11.60 mm/a is found in Zambezi River Basin and of 9 mm/a in Volta River Basin.A phase shift is found between rainfall and GRACE TWS GRACE TWS is preceded by rainfall by 2-3 months in parts of south central Africa.Comparing GLDAS rainfall with TRMM model,it is found that GLDAS has a dry bias from TRMM model.

A 10-Yr Global Land Surface Reanalysis Interim Dataset(CRA-Interim/Land):Implementation and Preliminary Evaluation

A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.

Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data

The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.

黄河流域陆地水储量时空变化

针对黄河流域干旱、水资源短缺,陆地水储量较难监测的问题,本文利用GRACE重力卫星和GLDAS水文模型,分析了黄河流域陆地水储量的空间和季节变化特征;通过M-K趋势分析、滞后性分析及快速傅里叶变换等,探讨黄河流域水储量变化与降水、径流、气温等影响因子间的响应关系。结果表明:GRACE时变重力场能较精确反演黄河流域水储量变化;黄河流域陆地水储量整体呈现下降趋势,陆地水储量变化受同期径流的影响最直接;陆地水储量变化对降水、气温及径流等主要水文气象因素的响应存在一定滞后性;对各影响要素作滞后相关分析,影响陆地水储量变化的主要水文气象因素是降水和气温,径流影响程度较低。

基于GRACE和GRACE-FO的黄河流域陆地水储量及影响因素分析

基于GRACE和GRACE-FO卫星陆地水储量遥感数据,采用长短期记忆(LSTM)神经网络模型,结合水量平衡方程和全球陆地数据同化系统(GLDAS)重建GRACE与GRACE-FO间的陆地水储量变化量,分析黄河流域2002年4月至2020年3月陆地水储量变化特征,探究影响陆地水储量变化的环境因子。结果表明:LSTM模型可以有效填补GRACE与GRACE-FO间的陆地水储量变化量;黄河流域陆地水储量呈明显下降趋势,上、中、下游下降趋势依次增大,陆地水储量与地下水储量的变化特征高度相关;黄河流域上、中、下游年陆地水储量变化量与年降水量和年干燥度指数呈极显著相关关系,表明黄河流域陆地水储量变化受到降水和蒸散发的影响。

基于全球陆面数据同化系统蒸散量的GSAC模型率定

在识别缺资料流域水文模型参数时,目前常采用的区域化方法存在相似流域间降雨径流关系差别较大、模型参数与流域属性间的相关性不明显、在大范围缺资料地区难于选取参考流域等问题。本文从全球陆面数据同化系统(GLDAS)获取流域蒸散量数据,提出利用GLDAS蒸散量率定GSAC模型的方法。首先,通过合并网格建立GSAC模型模拟的蒸散量与GLDAS蒸散量在时间和空间方面的对应关系;其次,基于纳什效率系数的定义构建了一个模型率定指标,以评价GSAC模型模拟的蒸散量对GLDAS蒸散量的拟合效果;最后,依据GLDAS蒸散量与GSAC模型模拟蒸散量之间的拟合关系率定GSAC模型。呼兰河流域应用结果表明,GLDAS提供的蒸散量能够较好反映流域实际蒸散量的变化情况,为率定GSAC模型提供了一种有效的输入数据;在率定期与验证期,利用GLDAS蒸散量率定的GSAC模型对流量模拟的纳什效率系数分别为0.81和0.77,与利用流量数据率定的GSAC模型模拟结果相近。

基于GRACE重力数据反演黄河流域陆地水储量变化

基于GRACE重力卫星数据反演黄河流域2002-2013年陆地水储量变化,并通过GLDAS验证GRACE反演结果。在此基础上采用皮尔逊相关系数法进一步探究陆地水储量与降水、气温、蒸散和植被NDVI的关系。结果表明:基于GRACE数据能够较好地反演陆地水储量;2002-2013年间黄河流域的水储量以0.56 cm/a的速度减少,且具有明显的季节特征,水储量在1-6月呈亏损状态,7-12月呈盈余状态;对黄河流域水储量变化与降水、气温、蒸散和NDVI进行滞后性分析,表明其与降水、蒸散、NDVI有两个月的滞后效应,与气温为3个月。

青藏高原中东部地区土壤湿度模拟性能评估以及误差分析

土壤湿度不仅是地表水循环的重要组成部分,而且对天气和气候也有重要影响,它的模拟误差严重阻碍了人们对水循环的认知.本文首先评估了1°×1°水平分辨率的全球陆面数据同化产品(Global Land Data Assimilation System,GLDAS)对青藏高原中部那曲地区和东部玛曲地区土壤湿度的模拟性能;鉴于GLDAS较粗的分辨率无法精细描述分析区域土壤湿度空间分布特征,于是我们基于通用陆面过程模式(Community Land Surface Model,Version 4.5),开展了高分辨率0.1°×0.1°的模拟,并对高分辨率模拟土壤湿度误差的原因进行了深入分析.结果表明:(1)GLDAS陆面数据同化产品和高分辨率陆面模式模拟结果都可以反映出土壤湿度的季节变化特征,但在非冻结期均存在不同程度的干偏差,尤其是在玛曲地区;(2)对比观测和模拟的土壤湿度发现,观测数据表现出强烈的空间异质性,而模拟结果呈现出的是空间均一性.按照模拟误差进行归类分组,对比模拟性能优劣的两组站点发现,模式物理过程不是模拟性能差异的主要因素,而两组站点间地表特征参数中的土壤质地和地形参数,以及驱动数据均没有体现出空间异质性,这可能是土壤湿度模拟结果没有表现出空间异质性的原因.

京津冀陆地水储量变化与区域降水相关性分析

针对京津冀地区水资源灾害频发、水储量监测手段存在局限性的问题,本文利用该地区2015年1月至2017年6月的重力场恢复与气候实验(GRACE)月重力场模型数据反演陆地水储量变化信息,结合区域降水、全球陆地资料同化系统(GLDAS)模型数据,对研究区域内水储量变化时空特征进行相关性分析。结果表明:GRACE监测的陆地水储量变化滞后降水2个月达到强相关性,监测到2016年春旱灾害的发生,扣除季节项影响后,GRACE&GLDAS相关系数为0.88,研究区陆地水储量上升速率为0.118 mm/a。研究结果可为该区域水储量时空调配、旱涝灾害防治提供理论依据。

利用GRACE反演昆士兰州地下水储量变化

昆士兰州作为澳大利亚的第二大州,开展针对该地区地下水储量变化的监测与分析对当地生态环境与用水管理政策制定具有重要现实意义.利用德克萨斯大学空间研究中心(CSR)发布的重力反演与气候实验卫星(GRACE)时变重力场模型与全球陆地数据同化系统(GL-DAS)地表同化模型,对昆士兰州地下水储量时空变化进行监测分析,并与实测水井数据和全球降水气候学项目(GPCP)降雨资料进行验证分析.研究结果表明:时间上昆士兰州地下水在2003—2015年约以1.3±0.09cm/a的速率递增,空间上呈现东增西减的显著空间差异;与GPCP降雨资料对比发现,降雨是影响地下水储量变化的主要因素;与水井实测数据对比发现地下反演结果与水井水位变化趋势基本一致.

基于GRACE卫星和GLDAS系统的地下水水位估算模型——以和田地区克里雅河流域为例

地球重力场的变化是导致陆地水储量变化的重要因素之一,利用GRACE(Gravity Recovery and Climate Experiment)重力场恢复与气候实验重力卫星数据,结合GLDAS(Global Land Data Assimilation Systems)全球陆面数据同化系统和实测地下水位数据,反演和田地区克里雅河流域11年间四季和田地区的陆地水储量动态变化,模拟计算地下水等效水高变化趋势,构建了地下水水位估算模型。研究结果表明:和田地区春、夏两季的陆地水储量呈现出增加趋势,而秋、冬两季出现亏损状态;GRACE地球重力卫星所反演的陆地水储量比GLDAS同化系统所模拟的水资源变化更为剧烈,但2类数据的动态变化拟合度很高;GLDAS水资源等效水高二阶微分、GLDAS水资源变化倒数一阶微分、GRACE陆地水储量变化倒数变化、地下水储量变化一阶微分的敏感程度最高,构建的多元逐步回归模型明显优于线性函数,且水位深度越浅,该估算模型的适用性越高。

水文气象因素对中国北疆流动重力变化的影响

水文气象因素引起的重力变化是影响地震重力变化成果解释的重要因素。以中国北疆地区为研究区域,借助全球陆地数据同化系统(global land data assimilation systems,GLDAS)全球水文模型数据、大气模型数据,计算2016-01—2017-12时段内水文气象因素对研究区域的重力影响。计算结果表明,陆地水影响的年变化为1.3μGal,两期陆地水影响空间分布的差异低于1μGal;大气影响的年变化为8μGal,两期大气影响空间分布的差异达到6μGal。利用2016-04、2016-08和2017-06三期流动重力测量数据,对比扣除水文气象因素前后的重力变化,可以看出,在中国北疆流动重力数据处理中,大尺度水文因素可以不予考虑,气象因素应予考虑。同时,为更好分析流动重力变化,建议流动重力测量过程中同时开展测点附近的土壤湿度、大气气压等观测。