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Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach

doi: 10.1007/s00376-016-5213-0

  • In order to reduce the uncertainty of offline land surface model (LSM) simulations of land evapotranspiration (ET), we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS (Institute of Tibetan Plateau Research, Chinese Academy of Sciences), Qian] and four LSMs (BATS, VIC, CLM3.0 and CLM3.5), to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over mainland China during 1982-2007. The results showed that various simulations of each member and their arithmetic mean (Ens_Mean) could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates (Obs_MTE). For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs_MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens_Mean was closer to Obs_MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs_MTE and Ens_Mean exhibited a significant increasing trend during 1982-98; whereas after 1998, when the last big EI Niño event occurred, the Ens_Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs_MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.
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  • Adler R.F., Coruthors, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeor., 4, 1147- 1167.10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;228942e6d-8b3e-4278-b81c-e1bdaf53e50fd567cc68010323a20c6188f2b04817b5 Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.517 latitude 17 2.517 longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the premicrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.
    Bonan G. B., 2008: Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science, 320( 5882), 1444- 1449.10.1126/ world's forests influence climate through physical, chemical, and biological processes that affect planetary energetics, the hydrologic cycle, and atmospheric composition. These complex and nonlinear forest-atmosphere interactions can dampen or amplify anthropogenic climate change. Tropical, temperate, and boreal reforestation and afforestation attenuate global warming through carbon sequestration. Biogeophysical feedbacks can enhance or diminish this negative climate forcing. Tropical forests mitigate warming through evaporative cooling, but the low albedo of boreal forests is a positive climate forcing. The evaporative effect of temperate forests is unclear. The net climate forcing from these and ot
    Chen J., B. Z. Chen, T. A. Black, J. L. Innes, G. Y. Wang, G. Kiely, T. Hirano, and G. Wohlfahrt, 2013: Comparison of terrestrial evapotranspiration estimates using the mass transfer and Penman-Monteith equations in land surface models. J. Geophys. Res.,118, 1715-1731, doi: 10.1002/2013JG002446.10.1002/ MT equation performs less robust than the PM equation in LSMs ET estimated by the MT equation has a large uncertainty in warm and wet seasons ET estimated by the PM equation is closer to the EC measurements on average The MT equation performs less robust than the PM equation in LSMs ET estimated by the MT equation has a large uncertainty in warm and wet seasons ET estimated by the PM equation is closer to the EC measurements on average
    Chen M., P. Xie, J. E. Janowiak, and P. A. Arkin, 2002: Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeor., 3, 249-;2&amp;link_type=DOI
    Chen Y., Coruthors, 2014: Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China. Remote Sensing of Environment,140, 279-293, doi: 10.1016/j.rse.2013. (ET) is a key component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. Several satellite-based ET models have been developed for extrapolating local observations to regional and global scales, but recent studies have shown large model uncertainties in ET simulations. In this study, we compared eight ET models, including five empirical and three process-based models, with the objective of providing a reference for choosing and improving methods. The results showed that the eight models explained between 61 and 80% of the variability in ET at 23 eddy covariance towers in China and adjacent regions. The mean annual ET for all of China varied from 535 to 85202mm02yr 61021 among the models. The interannual variability of yearly ET varied significantly between models during 1982–2009 because of different model structures and the dominant environmental factors employed. Our evaluation results showed that the parameters of the empirical methods may have different combination because the environmental factors of ET are not independent. Although the three process-based models showed high model performance across the validation sites, there were substantial differences among them in the temporal and spatial patterns of ET, the dominant environment factors and the energy partitioning schemes. The disagreement among current ET models highlights the need for further improvements and validation, which can be achieved by investigating model structures and examining the ET component estimates and the critical model parameters.
    Chen Y. Y., K. Yang, J. He, J. Qin, J. C. Shi, J. Y. Du, and Q. He, 2011: Improving land surface temperature modeling for dry land of China. J. Geophys. Res., 116,D20104, doi: 10.1029/ 2011JD015921.10.1029/
    Dickinson R. E., A. Henderson-Sellers, and P. J. Kennedy, 1993: Biosphere-Atmosphere Transfer Scheme (BATS) Version 1e as coupled to the NCAR Community Climate Model. NCAR Tech. Note NCAR/TN387+STR,77 pp.
    Dirmeyer P. A., 1994: Vegetation stress as a feedback mechanism in midlatitude drought. J.Climate, 7, 1463-
    Dirmeyer P. A., A. J. Dolman, and N. Sato, 1999: The pilot phase of the global soil wetness project. Bull. Amer. Meteor. Soc., 80( 5), 851- 878.10.1175/1520-0477(1999)080<0851:TPPOTG>2.0.CO; Global Soil Wetness Project (GSWP) is an ongoing land surface modeling activity of the International Satellite Land-Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment. The pilot phase of GSWP deals with the production of a two-year global dataset of soil moisture, temperature, runoff, and surface fluxes by integrating uncoupled land surface schemes (LSSs) using externally specified surface forcings from observations and standardized soil and vegetation distributions. Approximately one dozen participating LSS groups in five nations have taken the common ISLSCP forcing data to drive their state-of-the-art models over the 1987-88 period to generate global datasets. Many of the LSS groups have performed specific sensitivity studies, which are intended to evaluate the impact of uncertainties in model parameters and forcing fields on simulation of the surface water and energy balances. A validation effort exists to compare the global products to other forms of estimation and measurement, either directly (by comparison to field studies or soil moisture measuring networks) or indirectly (e.g., use of modeled runoff to drive river routing schemes for comparison to streamflow data). The soil wetness data produced are also being tested within general circulation models to evaluate their quality and their impact on seasonal to interannual climate simulations. An Inter-Comparison Center has also been established for evaluating and comparing data from the different LSSs. Comparison among the model results is used to assess the uncertainty in estimates of surface components of the moisture and energy balances at large scales and as a quality check on the model products themselves.
    Dirmeyer P. A., X. Gao, M. Zhao, Z. Guo, T. Oki, and N. Hanasaki., 2006: GSWP-2: multi-model analysis and implications for our perception of the land surface. Bull. Amer. Meteor. Soc., 87, 1381- 1397.10.1175/ The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.
    Fu J. L., W. H. Qian, X. Lin, and D. L. Chen, 2008: Trends of graded precipitation days in China from 1961 to 2000. Adv. Atmos. Sci.,25(2), 267-278, doi: 10.1007/s00376-008-0267-2.10.1007/ precipitation rates observed at 576 stations in China from 1961 to 2000 were classified into six grades of intensity,including trace(no amount),slight(1 mm d-1),small,large,heavy,and very heavy.The last four grades together constitute the so called effective precipitation(>1 mm d-1).The spatial distribution and temporal trend of the graded precipitation days are examined.A decreasing trend in trace precipitation days is observed for the whole of China,except at several sites in the south of the middle section of the Yangtze River,while a decreasing trend in slight precipitation days only appears in eastern China.The decreasing trend and interannual variability of trace precipitation days is consistent with the warming trend and corresponding temperature variability in China for the same period,indicating a possible role played by increased surface air temperature in cloud formation processes.For the effective precipitation days,a decreasing trend is observed along the Yellow River valley and for the middle reaches of the Yangtze River and Southwest China,while an increasing trend is found for Xinjiang,the eastern Tibetan Plateau,Northeast China and Southeast China.The decreasing trend of effective precipitation days for the middle-lower Yellow River valley and the increasing trend for the lower Yangtze River valley are most likely linked to anomalous monsoon circulation in East China.The most important contributor to the trend in effective precipitation depends upon the region concerned.
    Gao G., D. L. Chen, C. Y. Xu, and E. Simelton, 2007: Trend of estimated actual evapotranspiration over China during 1960-2002. J. Geophys. Res., 112,D11120, doi: 10.1029/2006JD 008010.10.1029/[1]In this study,the water balance methodology introduced by Thornthwaite and Mather (1955) is modified to estimate monthly actual evapotranspiration for 686 stations over China during 1960-2002.The modification is done by replacing the Thomthwaite potential evapotranspiration estimation with the Penman-Monteith method.Temporal trend and spatial distribution of the estimated annual actual evapotranspiration during the past 43 years are analyzed.The results show that(1) the actual evapotranspiration had a decreasing trend in most areas east of 100E,and there was an increasing trend in the west and the north parts of northeast China;(2) the spatial distribution of the trend for the actual evapotranspiration is similar to that of the potential evapotranspiration in south China,while the trends are opposite in north China;(3) for most parts of China,the change in precipitation played a key role for the change of estimated actual evapotranspiration, while in southeast China,the change of potential evapotranspiration appeared to be the major factor;and(4) in general,the hydrological cycle was intensified in western China, whereas it was weakened from the Yellow River basin northward.
    Gao G., C. Y. Xu, D. L. Chen, and V. P. Singh, 2012: Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China. Stochastic Environmental Research and Risk Assessment,26, 655-669, doi: 10.1007/ s00477-011-0525-1.10.1007/ Spatial and temporal characteristics of actual evapotranspiration over the Haihe River basin in China during 1960&ndash;2002 are estimated using the complementary relationship and the Thornthwaite water balance (WB) approaches. Firstly, the long-term water balance equation is used to validate and select the most suitable long-term average annual actual evapotranspiration equations for nine subbasins. Then, the most suitable method, the Pike equation, is used to calibrate parameters of the complementary relationship models and the WB model at each station. The results show that the advection aridity (AA) model more closely estimates actual evapotranspiration than does the Granger and Gray (GG) model especially considering the annual and summer evapotranspiration when compared with the WB model estimates. The results from the AA model and the WB model are then used to analyze spatial and temporal changing characteristics of the actual evapotranspiration over the basin. The analysis shows that the annual actual evapotranspirations during 1960&ndash;2002 exhibit similar decreasing trends in most parts of the Haihe River basin for the AA and WB models. Decreasing trends in annual precipitation and potential evapotranspiration, which directly affect water supply and the energy available for actual evapotranspiration respectively, jointly lead to the decrease in actual evapotranspiration in the basin. A weakening of the water cycle seems to have appeared, and as a consequence, the water supply capacity has been on the decrease, aggravating water shortage and restricting sustainable social and economic development in the region. KeywordsComplementary relationship&ndash;Thornthwaite water balance model&ndash;Actual evapotranspiration&ndash;Trend&ndash;Haihe River basin&ndash;China
    Giorgi F., R. Francisco, and J. S. Pal, 2003: Effects of a subgrid-scale topography and land use scheme on the simulation of surface climate and hydrology. Part I: Effects of temperature and water vapor disaggregation. Journal of Hydrometeorology, 4, 317- 333.10.1175/1525-7541(2003)42.0.CO; A mosaic-type parameterization of subgrid-scale topography and land use is implemented within the framework of a regional climate model, and its effects on a multiseasonal simulation over the European region are tested, with focus on the Alpine subregion. The parameterization adopts a regular finescale surface subgrid for each coarse model grid cell. Meteorological variables are disaggregated from the coarse grid to the fine grid, land surface calculations are then performed separately for each subgrid cell, and surface fluxes are reaggregated onto the coarse grid cell for input to the atmospheric model. The primary effects of the subgrid surface scheme are 1) an improvement of the finescale structure and overall simulation of surface air temperature over complex terrain, and 2) a more realistic simulation of snow as driven by the complex terrain features. The subgrid scheme also affects the warm season simulation through feedbacks between precipitation and the surface hydrology. The primary aspect of the scheme that has an impact on the model is the subgrid disaggregation of temperature and water vapor, which is based on the difference between the topographical elevation of the subgrid and corresponding coarse grid cells. The mosaic-type approach presented here with suitable meteorological disaggregation techniques and with the possible addition of a parameterization of subgrid-scale effects on precipitation can provide an effective tool to bridge the scaling gap between climate models and surface hydrological processes.
    Guo Z. C., P. A. Dirmeyer, X. Gao, and M. Zhao, 2007: Improving the quality of simulated soil moisture with a multi-model ensemble approach. Quart. J. Roy. Meteor. Soc., 133, 731- 747.10.1002/ Available
    He J., 2010: Development of a surface meteorological dataset of China with high temporal and spatial resolution. M.S. thesis, Institute of Tibetan Plateau Research , Chinese Academy of Sciences, Beijing, China.(in Chinese)
    Jaksa W. T., V. Sridhar, J. L. Huntington, and M. Khanal, 2013: Evaluation of the complementary relationship using Noah Land Surface Model and North American Regional Reanalysis (NARR) data to estimate evapotranspiration in semiarid ecosystems. Journal of Hydrometeorology, 14( 1), 345- 359.10.1175/ Available
    Jung M., M. Reichstein, and A. Bondeau, 2009: Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6, 2001- 2013.10.5194/ Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998&ndash;2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity. We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained). We demonstrate the substantially improved accuracy of MTE over individual model trees in particular for the monthly anomalies and for situations of extrapolation. We estimate that roughly one fifth of the domain is subject to extrapolation while MTE is still able to reproduce 73% of the LPJmL GPP variability here. This paper presents for the first time a benchmark for a global FLUXNET upscaling approach that will be employed in future studies. Although the real world FLUXNET upscaling is more complicated than for a noise free and reduced complexity biosphere model as presented here, our results show that an empirical upscaling from the current FLUXNET network with MTE is feasible and able to extract global patterns of carbon flux variability.
    Jung, M., Coruthors, 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951- 954.10.1038/ than half of the solar energy absorbed by land surfaces is currently used to evaporate water. Climate change is expected to intensify the hydrological cycle and to alter evapotranspiration, with implications for ecosystem services and feedback to regional and global climate. Evapotranspiration changes may already be under way, but direct observational constraints are lacking at the global scale. Until such evidence is available, changes in the water cycle on land61a key diagnostic criterion of the effects of climate change and variability61remain uncertain. Here we provide a data-driven estimate of global land evapotranspiration from 1982 to 2008, compiled using a global monitoring network, meteorological and remote-sensing observations, and a machine-learning algorithm. In addition, we have assessed evapotranspiration variations over the same time period using an ensemble of process-based land-surface models. Our results suggest that global annual evapotranspiration increased on average by 7.165±651.065millimetres per year per decade from 1982 to 1997. After that, coincident with the last major El Ni09o event in 1998, the global evapotranspiration increase seems to have ceased until 2008. This change was driven primarily by moisture limitation in the Southern Hemisphere, particularly Africa and Australia. In these regions, microwave satellite observations indicate that soil moisture decreased from 1998 to 2008. Hence, increasing soil-moisture limitations on evapotranspiration largely explain the recent decline of the global land-evapotranspiration trend. Whether the changing behaviour of evapotranspiration is representative of natural climate variability or reflects a more permanent reorganization of the land water cycle is a key question for earth system science.
    Kalnay E., Coruthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437-
    Li X.L., Coruthors, 2014: Estimation of evapotranspiration over the terrestrial ecosystems in China. Ecohydrology, 7, 139- 149.10.1002/ Quantifying regional evapotranspiration (ET) and environmental constraints are particularly important for understanding water and carbon cycles of terrestrial ecosystems. However, a large uncertainty in the regional estimation of ET still remains for the terrestrial ecosystems in China. This study used ET measurements of 34 eddy covariance sites within China and adjacent regions to examine the performance of the revised Remote Sensing-Penman Monteith (RS-PM) model over various ecosystem types including forests, grasslands, wetlands and croplands. No significant systematic error was found in the revised RS-PM model predictions, which explained 61% of the ET variations at all of the validation sites. Regional patterns of ET at a spatial resolution of 1065×651065km were quantified using a meteorology dataset from 753 meteorological stations, Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis products and satellite data such as the Advanced Very High Resolution Radiometer (AVHRR) leaf area index. ET decreased from the southeast of China toward the northwest. Relatively high ET values were found in the southern China such as Yunnan, Hainan, Fujian and Guangdong Provinces, whereas low ET values occurred in northwestern China such as in the Xinjiang autonomous region. On average, the annual ET presented an increasing trend during the 1982–2009, with relatively low ET in 1985, 1993, 1997, 2000 and 2009. We found that the mean annual ET was higher than world average, ranging spatially between 484 and 52165mm65yr 611 , with a mean value of 50065mm65yr 611 , which accounted for approximately 5·6–8·3% of the world's total land-surface ET. Copyright 08 2012 John Wiley & Sons, Ltd.
    Liang X., Z. H. Xie, 2001: A new surface runoff parameterization with subgrid-scale soil heterogeneity for land surface models. Advances in Water Resources, 24, 1173- 1193.10.1016/S0309-1708(01) heterogeneity plays an important role in determining surface runoff generation mechanisms. At the spatial scales represented by land surface models used in regional climate model and/or global general circulation models (GCMs) for numerical weather prediction and climate studies, both infiltration excess (Horton) and saturation excess (Dunne) runoff may be present within a studied area or a model grid cell. Proper modeling of surface runoff is essential to a reasonable representation of feedbacks in the land鈥揳tmosphere system. In this paper, a new surface runoff parameterization that dynamically represents both Horton and Dunne runoff generation mechanisms within a model grid cell is presented. The new parameterization takes into account of effects of soil heterogeneity on Horton and Dunne runoff. A series of numerical experiments are conducted to study the effects of soil heterogeneity on Horton and Dunne runoff and on soil moisture storage under different soil and precipitation conditions. The new parameterization is implemented into the current version of the hydrologically based variable infiltration capacity (VIC) land surface model and tested over three watersheds in Pennsylvania. Results show that the new parameterization plays a very important role in partitioning the water budget between surface runoff and soil moisture in the atmosphere鈥搇and coupling system. Significant underestimation of the surface runoff and overestimation of subsurface runoff and soil moisture could be resulted if the Horton runoff mechanism were not taken into account. Also, the results show that the Horton runoff mechanism should be considered within the context of subgrid-scale spatial variability of soil properties and precipitation. An assumption of time-invariant spatial distribution of potential infiltration rate may result in large errors in surface runoff and soil moisture. In addition, the total surface runoff from the new parameterization is less sensitive to the choice of the soil moisture shape parameter of the distribution.
    Liang X., E. F. Wood, and D. P. Lettenmaier, 1996: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification. Global and Planetary Change, 13, 195-
    Liu J. G., Z. H. Xie, 2013: Improving simulation of soil moisture in China using a multiple meteorological forcing ensemble approach. Hydrology and Earth System Sciences ,17, 3355-3369, doi:10.5194/hess-17-3355-2013.10.5194/ quality of soil moisture simulation using land surface models depends largely on the accuracy of the meteorological forcing data. The present study investigated how to reduce the uncertainty arising from meteorological forcings in a simulation by adopting a multiple meteorological forcing ensemble approach. Simulations by the Community Land Model version 3.5 (CLM3.5) over mainland China were conducted using four different meteorological forcings, and the four sets of soil moisture data related to the simulations were then merged using simple arithmetical averaging and Bayesian model averaging (BMA) ensemble approaches. Compared to in situ observations, the four simulations captured the spatial and seasonal variations of soil moisture in most cases with some mean bias. They performed differently when simulating the seasonal phases in the annual cycle, the interannual variation and the magnitude of observed soil moisture over different subregions of mainland China, but no individual meteorological forcing performed best for all subregions. The simple arithmetical average ensemble product outperformed most, but not all, individual members over most of the subregions. The BMA ensemble product performed better than simple arithmetical averaging, and performed best for all fields over most of the subregions. The BMA ensemble approach applied to the ensemble simulation reproduced anomalies and seasonal variations in observed soil moisture values, and simulated the mean soil moisture. It is presented here as a promising way for reproducing long-term, high-resolution spatial and temporal soil moisture data.
    Mao, J. F., Coruthors, 2015: Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends. Environmental Research Letters, 10(9),094008, doi: 10.1088/1748-9326/10/9/094008.10.1088/1748-9326/10/9/ examined natural and anthropogenic controls on terrestrial evapotranspiration (ET) changes from 1982 to 2010 using multiple estimates from remote sensing-based datasets and process-oriented land surface models. A significant increasing trend of ET in each hemisphere was consistently revealed by observationally-constrained data and multi-model ensembles that considered historic natural and anthropogenic drivers. The climate impacts were simulated to determine the spatiotemporal variations in ET. Globally, rising COranked second in these models after the predominant climatic influences, and yielded decreasing trends in canopy transpiration and ET, especially for tropical forests and high-latitude shrub land. Increasing nitrogen deposition slightly amplified global ET via enhanced plant growth. Land-use-induced ET responses, albeit with substantial uncertainties across the factorial analysis, were minor globally, but pronounced locally, particularly over regions with intensive land-cover changes. Our study highlights the importance of employing multi-stream ET and ET-component estimates to quantify the strengthening anthropogenic fingerprint in the global hydrologic cycle.
    Matthews E., 1983: Global vegetation and land use: New high resolution data bases for climate studies. J. Clim. Appl. Meteor., 22, 474-
    Mitchell K.E., Coruthors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple CIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109,D07S90, doi: 10.1029/ - Scientific documents that cite the following paper: 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system
    Niu, G. Y., Coruthors, 2007: Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data. J. Geophys. Res.-Atmos.,112, doi: 7110.01029/02006jd007522.10.1029/[1] Groundwater interacts with soil moisture through the exchanges of water between the unsaturated soil and its underlying aquifer under gravity and capillary forces. Despite its importance, groundwater is not explicitly represented in climate models. This paper developed a simple groundwater model (SIMGM) by representing recharge and discharge processes of the water storage in an unconfined aquifer, which is added as a single integration element below the soil of a land surface model. We evaluated the model against the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage change ( S ) data. The modeled total water storage (including unsaturated soil water and groundwater) change agrees fairly well with GRACE estimates. The anomaly of the modeled groundwater storage explains most of the GRACE S anomaly in most river basins where the water storage is not affected by snow water or frozen soil. For this reason, the anomaly of the modeled water table depth agrees well with that converted from the GRACE S in most of the river basins. We also investigated the impacts of groundwater dynamics on soil moisture and evapotranspiration through the comparison of SIMGM to an additional model run using gravitational free drainage (FD) as the model's lower boundary condition. SIMGM produced much wetter soil profiles globally and up to 16% more annual evapotranspiration than FD, most obviously in arid-to-wet transition regions.
    New M., M. Hulme, and P. Jones, 1999: Representing twentiethcentury space-time climate variability. Part I: Development of a 1961-90 mean monthly terrestrial climatology. J.Climate, 12, 829-
    Oki T., S. Kanae, 2006: Global hydrological cycles and world water resources. Science ,313, 1068-1072, doi:10.1126/science.1128845.10.1126/ is a naturally circulating resource that is constantly recharged. Therefore, even though the stocks of water in natural and artificial reservoirs are helpful to increase the available water resources for society, the flow of water should be the main focus in water resources assessments. The climate system puts an upper limit on the rate of available renewable freshwater resources (RFWR). Although current global withdrawals are well below the upper limit, more than two billion people live in highly water-stressed areas because of the uneven distribution of RFWR in time and space. Climate change is expected to accelerate water cycles and thereby increase the available RFWR. This would slow down the increase of people living under water stress; however, changes in seasonal patterns and increasing probability of extreme events may offset this effect. Reducing current vulnerability will be the first step to prepare for such anticipated changes.
    Oleson, K. W., Coruthors, 2004: Technical description of the community land model (CLM). Tech. Note NCAR/TN-461+STR, Natl. Cent. Atmos. Res., Boulder CO, 174 pp.
    Oleson, K. W., Coruthors, 2007: CLM 3.5 documentation. 34 pp. [Available online at]
    Pinker R. T., I. Laszlo, 1992: Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteor.,31, 194-211, doi: 10.1175/1520-0450(1992)031<0194: MSSIFS>2.0.CO;2.10.1175/1520-0450(1992)031<0194:MSSIFS>2.0.CO; During the last few years, the feasibility of deriving surface radiation budget (SRB) components from satellite observations has been demonstrated and a better understanding of the need for SRB information in climate research was formulated. Much attention has been given to the scales at which such information is needed and to the accuracies required at different spatial and temporal scales. Recently, global acts of satellite observations became available, allowing implementation of satellite models for SRB on a global scale, and international frameworks were established for validating such models. To respond to these developments, we modified and expanded an early version of a physical model to derive surface solar irradiance from satellite observations. The model is based on radiative transfer theory, and can produce both direct and diffuse spectral components in the 0.2-4.0m interval. Attention is given to the absorption and scattering processes in the atmosphere and the interaction of radiation with the surface. The bidirectional nature of the exiting radiation at the top of the atmosphere is also accounted for. In this paper the emphasis will be on describing the current status of the model and its implementation on a global scale with the International Satellite Cloud Climatology Project (ISCCP) C1 data.
    Qian T. T., A. G. Dai, K. E. Trenberth, and K. W. Oleson, 2006: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations. Journal of Hydrometeorology, 7, 953- 975.10.1175/ Available
    Rodell M., Coruthors, 2004: The global land data assimilation system. Bull. Amer. Meteor. Soc., 85, 381- 394.10.1175/ A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The ongoing GLDAS archive (started in 2001) of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.
    Sheffield J., G. Goteti, and E. F. Wood, 2006: Development of a 50-year high- resolution global dataset of meteorological forcings for land surface modeling. J. Climate,19, 3088-3111, doi: 10.1175/JCLI3790.1.10.1175/ Understanding the variability of the terrestrial hydrologic cycle is central to determining the potential for extreme events and susceptibility to future change. In the absence of long-term, large-scale observations of the components of the hydrologic cycle, modeling can provide consistent fields of land surface fluxes and states. This paper describes the creation of a global, 50-yr, 3-hourly, 1.0 dataset of meteorological forcings that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the National Centers for Environmental Predictionational Center for Atmospheric Research (NCEP-CAR) reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature, and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation, which have been found to exhibit a spurious wavelike pattern in high-latitude wintertime. Wind-induced undercatch of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0 by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3 hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave radiation, specific humidity, surface air pressure, and wind speed) are downscaled in space while accounting for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project (GSWP2). The final product provides a long-term, globally consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and interannual variability and for the evaluation of coupled models and other land surface prediction schemes.
    Sheffield J., E. F. Wood, 2007: Characteristics of global and regional drought, 1950-2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. J. Geophys. Res., 112,D17115, doi: 10.1029/2006JD008288.10.1029/ occurrence is analyzed over global land areas for 1950-2000 using soil moisture data from a simulation of the terrestrial water cycle with the Variable Infiltration Capacity (VIC) land surface model, which is forced by an observation based meteorological data set. A monthly drought index based on percentile soil moisture values relative to the 50-year climatology is analyzed in terms of duration, intensity and severity at global and regional scales. Short-term droughts (<= 6 months) are prevalent in the Tropics and midlatitudes, where inter-annual climate variability is highest. Medium term droughts (7-12 months) are more frequent in mid- to high-latitudes. Long term (12+ months) droughts are generally restricted to sub-Saharan Africa and higher northern latitudes. The Sahel region stands out for having experienced long-term and severe drought conditions. Severe regional drought events are systematically identified in terms of spatial coverage, based on different thresholds of duration and intensity. For example, in northern Europe, 1996 and 1975 were the years of most extensive 3- and 12-month duration drought, respectively. In northern Asia, severe drought events are characterized by persistent soil moisture anomalies over the wintertime. The drought index identifies several well-known events, including the 1988 US, 1982/83 Australian, 1983/4 Sahel and 1965/66 Indian droughts which are generally ranked as the severest and most spatially extensive in the record. Comparison with the PDSI shows general agreement at global scales and for these major events but they diverge considerably in cooler regions and seasons, and especially in latter years when the PDSI shows a larger drying trend.
    Shi X. Y., J. F. Mao, P. E. Thornton, and M. Y. Huang, 2013: Spatiotemporal patterns of evapotranspiration in response to multiple environmental factors simulated by the Community Land Model, Environ. Environmental Research Letters, 8,024012, doi: 10.1088/1748-9326/8/2/024012.10.1088/1748-9326/8/2/ patterns of evapotranspiration (ET) over the period from 1982 to 2008 are investigated and attributed to multiple environmental factors using the Community Land Model version 4 (CLM4). Our results show that CLM4 captures the spatial distribution and interannual variability of ET well when compared to observation-based estimates. We find that climate dominates the predicted variability in ET. Elevated atmospheric CO2 concentration also plays an important role in modulating the trend of predicted ET over most land areas, and replaces climate to function as the dominant factor controlling ET changes over the North America, South America and Asia regions. Compared to the effect of climate and CO2 concentration, the roles of other factors such as nitrogen deposition, land use change and aerosol deposition are less pronounced and regionally dependent. The aerosol deposition contribution is the third most important factor for trends of ET over Europe, while it has the smallest impact over other regions. As ET is a dominant component of the terrestrial water cycle, our results suggest that environmental factors like elevated CO2, nitrogen and aerosol depositions, and land use change, in addition to climate, could have significant impact on future projections of water resources and water cycle dynamics at global and regional scales.
    Tian X. J., Z. H. Xie, A. G. Dai, B. H. Jia, and C. X. Shi, 2010: A microwave land data assimilation system: Scheme and preliminary evaluation over China. J. Geophys. Res., 115,D21113, doi: 10.1029/2010JD014370.10.1029/[1] To make use of satellite microwave observations for estimating soil moisture, a dual-pass land data assimilation system (DLDAS) is developed in this paper by incorporating a dual-pass assimilation framework into the Community Land Model version 3 (CLM3). In the DLDAS, the model state (volumetric soil moisture content) and model parameters are jointly optimized using the gridded Advanced Microwave Scanning Radiometer&ndash;EOS (AMSR-E) satellite brightness temperature (T b ) data through a radiative transfer model (RTM), which acts as an observation operator to provide a link between the model states and the observational variable (i.e., Tb). The DLDAS embeds a state assimilation pass and a parameter calibration pass. In the assimilation pass, the whole soil moisture profiles are assimilated from the T b data using an ensemble-based four-dimensional variational assimilation method (En4DVar). Simultaneously, several key parameters in the RTM are also optimized using the ensemble Proper Orthogonal Decomposition-based parameter calibration approach (EnPOD_P) in the parameter optimization pass to account for their high variability or uncertainty. To quantify the impacts of the T b assimilation on CLM3-calculated soil moisture, the original CLM3 (Sim) and the DLDAS (Ass) were run separately over China on a 0.5 grid forced with identical, observation-based atmospheric forcing from 2004 to 2008. Soil moisture data from 226 stations over China are averaged over seven different climate divisions and compared with the soil moisture from the Sim and Ass runs. It is found that the assimilation of the AMSR-E T b data through the DLDAS greatly improves the soil moisture content within the top 10 cm with reduced mean biases and enhanced correlations with the station data in all divisions except for southwest China, where the current satellite sensors may have difficulties in measuring soil moisture due to the dense vegetation and complex terrain over this region. The results suggest that the AMSR-E T b data can be used to improve soil moisture simulations over many regions and the DLDAS is a promising new tool for estimating soil moisture content from satellite T b data.
    Vinukollu R. K., J. Sheffield, E. F. Wood, M. G. Bosilovich, and D. Mocko, 2012: Multimodel analysis of energy and water fluxes: Intercomparisons between operational analyses, a land surface model, and remote sensing. Journal of Hydrometeorology, 13( 1), 3-
    Wang A. H., X. B. Zeng, 2011: Sensitivities of terrestrial water cycle simulations to the variations of precipitation and air temperature in China. J. Geophys. Res., 116,D02107, doi: 10.1029/2010JD014659.10.1523/ Sexually transmitted infections (STIs) are a major public health concern. While testing has been proven important in addressing STIs, there is little systematic information on how macro-level messages about testing are processed on the interpersonal level. This research fills this gap by examining interpersonal communication about STIs and testing, including what sexual partners talk about, when, and why. Data for this research comes from an online questionnaire conducted April-May 2012. Mixed method data was analyzed from 181 participants, (79.6% women and 20.4% men, mean age of 26). The majority identified as white (82.3%) and heterosexual (79.6%). While 52 participants reported talking to their partners about STIs, 32.7% of participants did not before having sex. In terms of content, 50% clarified types of STIs they had been tested for, 59.6% clarified about sexual exclusivity, and 32.7% asked about testing chronology. Only 9.6% asked about IV drug use history. The majority (85.5%) did not ask for proof of testing and had never asked a partner to go to a STI clinic together (76.1%). A small but notable percentage (4.4%) reported lying about STI status and 34.6% reported telling a partner they did not have an STI even though they had not been tested since last sexual activity. Public health educators need to promote not only STI communication, but how to have effective and informed conversations about STIs. Campaigns may need to target what to discuss with partners about STIs in order to make subsequent sexual health decisions.
    Wang A. H., D. P. Lettenmaier, and J. Sheffield, 2011: Soil moisture drought in China, 1950-2006. J.Climate, 24, 3257- 3271.10.1175/ Four physically based land surface hydrology models driven by a common observation-based 3-hourly meteorological dataset were used to simulate soil moisture over China for the period 1950–2006. Monthly values of total column soil moisture from the simulations were converted to percentiles and an ensemble method was applied to combine all model simulations into a multimodel ensemble from which agricultural drought severities and durations were estimated. A cluster analysis method and severity–area–duration (SAD) algorithm were applied to the soil moisture data to characterize drought spatial and temporal variability. For drought areas greater than 150 000 km 2 and durations longer than 3 months, a total of 76 droughts were identified during the 1950–2006 period. The duration of 50 of these droughts was less than 6 months. The five most prominent droughts, in terms of spatial extent and then duration, were identified. Of these, the drought of 1997–2003 was the most severe, accounting for the majority of the severity–area–duration envelope of events with areas smaller than 5 million km 2 . The 1997–2003 drought was also pervasive in terms of both severity and spatial extent. It was also found that soil moisture in north central and northeastern China had significant downward trends, whereas most of Xinjiang, the Tibetan Plateau, and small areas of Yunnan province had significant upward trends. Regions with downward trends were larger than those with upward trends (37% versus 26% of the land area), implying that over the period of analysis, the country has become slightly drier in terms of soil moisture. Trends in drought severity, duration, and frequency suggest that soil moisture droughts have become more severe, prolonged, and frequent during the past 57 yr, especially for northeastern and central China, suggesting an increasing susceptibility to agricultural drought.
    Wang K. C., R. E. Dickinson, M. Wild, and S. L. Liang, 2010: Evidence for decadal variation in global terrestrial ET between 1982 and 2002: 2.Results. J. Geophys. Res. , 115,D20113, doi: 10.1029/2010JD013847.
    Wang K. C., R. E. Dickinson, 2012: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 50,RG2005, doi: 10.1029/2011RG000373.10.1029/[1] This review surveys the basic theories, observational methods, satellite algorithms, and land surface models for terrestrial evapotranspiration, E (or 位E , i.e., latent heat flux), including a long-term variability and trends perspective. The basic theories used to estimate E are the Monin-Obukhov similarity theory (MOST), the Bowen ratio method, and the Penman-Monteith equation. The latter two theoretical expressions combine MOST with surface energy balance. Estimates of E can differ substantially between these three approaches because of their use of different input data. Surface and satellite-based measurement systems can provide accurate estimates of diurnal, daily, and annual variability of E . But their estimation of longer time variability is largely not established. A reasonable estimate of E as a global mean can be obtained from a surface water budget method, but its regional distribution is still rather uncertain. Current land surface models provide widely different ratios of the transpiration by vegetation to total E . This source of uncertainty therefore limits the capability of models to provide the sensitivities of E to precipitation deficits and land cover change.
    Wei J. F., P. A. Dirmeyer, and Z. C. Guo, 2008: Sensitivities of soil wetness simulation to uncertainties in precipitation and radiation. Geophys. Res. Lett., 35,L15703, doi: 10.1029/2008 GL034494.10.1029/ analyzing the data from the Second Global Soil Wetness Project (GSWP-2) sensitivity experiments, this study quantifies the sensitivities of soil wetness simulation to uncertainties in precipitation and radiation forcings and estimates the actual influence of these uncertainties. The sensitivity of soil moisture to uncertainties in radiation has a large seasonal cycle because of its temperature dependence. Sensitivity to uncertainties in precipitation is less temporally variable and is large in semiarid regions. Precipitation sensitivity is generally dominant over radiation sensitivity in cold climate, while in warm climate radiation sensitivity is dominant or both of them are high. However, actual uncertainties in precipitation are typically much larger than for net radiation in warm climate, so the practical impact is dominated by our lack of accurate precipitation estimates. Precipitation uncertainties account for about 2/3 of the total soil wetness uncertainties from all forcing data, while radiation uncertainties only account for about 1/6.
    Xu C. Y., L. B. Gong, T. Jiang, D. L. Chen, and V. P. Singh, 2006: Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. J. Hydrol., 327( 1-2), 81- 93.10.1016/ In this study the Penman&ndash;Monteith reference evapotranspiration, pan evaporation measured by a 20 cm pan, and pan coefficient, i.e., the ratio of Penman&ndash;Monteith evapotranspiration to pan evaporation, at 150 meteorological stations during 1960&ndash;2000 in the Changjiang (Yangtze River) catchment in China are calculated, compared and regionally mapped. Their spatial distributions and temporal variations are examined and the causes for the variations are discussed. The spatial distributions of temporal trends in the reference evapotranspiration as well as in the meteorological variables that determine evapotranspiration are analyzed. The contributions of various meteorological variables to the temporal trend detected in the reference evapotranspiration and pan evaporation are then determined. The results show that: (1) the spatial distributions of reference evapotranspiration and pan evaporation are roughly similar. Spatial correlation coefficients between the reference evapotranspiration and the pan evaporation are high for both the seasonal and annual values. The temporal correlation between the two estimates is higher in the lower (humid) region than in the upper (semi-arid) region. The spatial distribution pattern of the pan coefficient is significantly influenced by wind speed and relative humidity in the region. Higher values of the pan coefficient were found in the central area of the catchment with a relatively high humidity (as compared with the upper area) and a very low wind speed (as compared with other areas); (2) for the whole catchment, there is a significant decreasing trend in both the reference evapotranspiration and the pan evaporation, which is mainly caused by a significant decrease in the net total radiation and to a lesser extent by a significant decrease in the wind speed over the catchment. No temporal trend is detected for the pan coefficient; (3) sensitivity analysis shows that the reference evapotranspiration is most sensitive to the net total radiation, followed by relative humidity, air temperature and wind speed.
    Yang K., T. Koike, and B. S. Ye, 2006: Improving estimation of hourly, daily, and monthly solar radiation by importing global data sets. Agricultural and Forest Meteorology, 137, 43- 55.10.1016/ solar radiation is an important parameter in hydrological models and crop yield models. This study developed a model to estimate solar radiation from sunshine duration. The model is more accurate and more general than traditional 03ngstr02m–Prescott models. It can explicitly account for radiative extinction processes in the atmosphere. Moreover, global data sets that describe the spatial and temporal distribution of ozone thickness and 03ngstr02m turbidity were introduced in the model to enhance its universal reliability and applicability. The model was calibrated in lowland and humid sites and validated at a number of sites in various climate and elevation regions. The new model shows overall better performances than three 03ngstr02m–Prescott models. Because this model follows the simple form of the 03ngstr02m–Prescott model, and its inputs (sunshine duration, air temperature, and relative humidity) are accessible from routine surface meteorological observations, it can be easily applied to hydrological and agricultural studies. The source code and the auxiliary data of the model are available from the authors upon request.
    Zhang Y.-C., W. B. Rossow, A. A. Lacis, V. Oinas, and M. I. Mishchenko, 2004: Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J. Geophys. Res., 109,D19105, doi: 10.1029/2003 JD004457.10.1029/ We continue reconstructing Earth's radiation budget from global observations in as much detail as possible to allow diagnosis of the effects of cloud (and surface and other atmospheric constituents) variations on it. This new study was undertaken to reduce the most noticeable systematic errors in our previous results (flux data set calculated mainly using International Satellite Cloud Climatology Project-C1 input data (ISCCP-FC)) by exploiting the availability of a more advanced NASA Goddard Institute for Space Studies (GISS) radiative transfer model and improved ISCCP cloud climatology and ancillary data sets. The most important changes are the introduction of a better treatment of ice clouds, revision of the aerosol climatology, accounting for diurnal variations of surface skin/air temperatures and the cloud-radiative effects on them, revision of the water vapor profiles used, and refinement of the land surface albedos and emissivities. We also extend our previous flux results, limited to the top of atmosphere (TOA) and surface (SRF), to also include three levels within the atmosphere, forming one integrated vertical atmospheric flux profile from SRF to TOA, inclusive, by combining a new climatology of cloud vertical structure with the ISCCP cloud product. Using the new radiative transfer model and new input data sets, we have produced an 18-year at 3-hour time steps, global at 280-km intervals, radiative flux profile data set (called ISCCP-FD) that provides full- and clear-sky, shortwave and longwave, upwelling and downwelling fluxes at five levels (SRF, 680 mbar, 440 mbar, 100 mbar, and TOA). Evaluation is still only possible for TOA and SRF fluxes: Comparisons of monthly, regional mean values from FD with Earth Radiation Budget Experiment, Clouds and the Earth's Radiant Energy System and Baseline Surface Radiation Network values suggest that we have been able to reduce the overall uncertainties from 10-15 to 5-10 W/m2 at TOA and from 20-25 to 10-15 W/m2 at SRF. Annual mean pressure-latitude cross sections of the cloud effects on atmospheric net radiative fluxes show that clouds shift the longwave cooling downward in the Intertropical Convergence Zone, acting to stabilize the tropical atmosphere while increasing the horizontal heating gradient forcing the Hadley circulation, and shift the longwave cooling upward in the midlatitude storm zones, acting to destabilize the baroclinic zones while decreasing the horizontal heating gradient there.
  • [1] Yuejian ZHU, 2005: Ensemble Forecast: A New Approach to Uncertainty and Predictability, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 781-788.  doi: 10.1007/BF02918678
    [2] Jianguo LIU, Zong-Liang YANG, Binghao JIA, Longhuan WANG, Ping WANG, Zhenghui XIE, Chunxiang SHI, 2023: Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 235-250.  doi: 10.1007/s00376-022-2091-5
    [3] SONG Xiang and ZENG Xiaodong*, , 2014: Investigation of Uncertainties of Establishment Schemes in Dynamic Global Vegetation Models, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 85-94.  doi: 10.1007/s00376-013-3031-1
    [4] Chenxi WANG, Zhihua ZENG, Ming YING, 2020: Uncertainty in Tropical Cyclone Intensity Predictions due to Uncertainty in Initial Conditions, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 278-290.  doi: 10.1007/s00376-019-9126-6
    [5] Guoxiong WU, Bian HE, Anmin DUAN, Yimin LIU, Wei YU, 2017: Formation and Variation of the Atmospheric Heat Source over the Tibetan Plateau and Its Climate Effects, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 1169-1184.  doi: 10.1007/s00376-017-7014-5
    [6] HU Shujuan, CHOU Jifan, 2004: Uncertainty of the Numerical Solution of a Nonlinear System's Long-term Behavior and Global Convergence of the Numerical Pattern, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 767-774.  doi: 10.1007/BF02916373
    [7] Deliang CHEN, Christine ACHBERGER, Jouni R¨AIS¨ANEN, Cecilia HELLSTR¨OM, 2006: Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 54-60.  doi: 10.1007/s00376-006-0006-5
    [8] Chengjun XIE, Tongwen WU, Jie ZHANG, Kalli FURTADO, Yumeng ZHOU, Yanwu ZHANG, Fanghua WU, Weihua JIE, He ZHAO, Mengzhe ZHENG, 2023: Spatial Inhomogeneity of Atmospheric CO2 Concentration and Its Uncertainty in CMIP6 Earth System Models, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 2108-2126.  doi: 10.1007/s00376-023-2294-4
    [9] ZHANG Zongjie, QIAN Weihong, 2011: Identifying Regional Prolonged Low Temperature Events in China, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 338-351.  doi: 10.1007/s00376-010-0048-6
    [10] Lin WANG, Gang HUANG, Wen ZHOU, Wen CHEN, 2016: Historical Change and Future Scenarios of Sea Level Rise in Macau and Adjacent Waters, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 462-475.  doi: 10.1007/s00376-015-5047-1
    [11] TIAN Di, GUO Yan*, DONG Wenjie, 2015: Future Changes and Uncertainties in Temperature and Precipitation over China Based on CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 487-496.  doi: 10.1007/s00376-014-4102-7
    [12] ZHOU Mengzi, WANG Huijun, 2015: Potential Impact of Future Climate Change on Crop Yield in Northeastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 889-897.  doi: 10.1007/s00376-014-4161-9
    [13] Xiaoxin WANG, Dabang JIANG, Xianmei LANG, 2018: Climate Change of 4°C Global Warming above Pre-industrial Levels, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 757-770.  doi: 10.1007/s00376-018-7160-4
    [14] JIANG Dabang, 2008: Projected Potential Vegetation Change in China under the SRES A2 and B2 Scenarios, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 126-138.  doi: 10.1007/s00376-008-0126-1
    [15] GUO Yanjun, DING Yihui, 2011: Impacts of Reference Time Series on the Homogenization of Radiosonde Temperature, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1011-1022.  doi: 10.1007/s00376-010-9211-3
    [16] Zhizhen XU, Jing CHEN, Zheng JIN, Hongqi LI, Fajing CHEN, 2020: Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 328-346.  doi: 10.1007/s00376-020-9171-1
    [17] Zhizhen XU, Jing CHEN, Mu MU, Guokun DAI, Yanan MA, 2022: A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1432-1450.  doi: 10.1007/s00376-022-1341-x
    [18] Lu WANG, Xueshun SHEN, Juanjuan LIU, Bin WANG, 2020: Model Uncertainty Representation for a Convection-Allowing Ensemble Prediction System Based on CNOP-P, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 817-831.  doi: 10.1007/s00376-020-9262-z
    [19] Youlong XIA, Zong-Liang YANG, Paul L. STOFFA, Mrinal K. SEN, 2005: Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 142-157.  doi: 10.1007/BF02930878
    [20] LI Fang, LIN Zhongda, 2015: Improving Multi-model Ensemble Probabilistic Prediction of Yangtze River Valley Summer Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 497-504.  doi: 10.1007/s00376-014-4073-8

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Manuscript received: 07 October 2015
Manuscript revised: 25 December 2015
通讯作者: 陈斌,
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach

  • 1. High Performance Computing Center, School of Mathematics and Computational Science, Huaihua University, Huaihua, Hunan 418008
  • 2. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 3. National Meteorological Information Center, China Meteorological Administration, Beijing 100081

Abstract: In order to reduce the uncertainty of offline land surface model (LSM) simulations of land evapotranspiration (ET), we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS (Institute of Tibetan Plateau Research, Chinese Academy of Sciences), Qian] and four LSMs (BATS, VIC, CLM3.0 and CLM3.5), to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over mainland China during 1982-2007. The results showed that various simulations of each member and their arithmetic mean (Ens_Mean) could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates (Obs_MTE). For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs_MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens_Mean was closer to Obs_MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs_MTE and Ens_Mean exhibited a significant increasing trend during 1982-98; whereas after 1998, when the last big EI Niño event occurred, the Ens_Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs_MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.

1. Introduction
  • Land surface evapotranspiration (ET), i.e., the sum total of water transferred from the land surface to the atmosphere, plays vital roles in the global hydrological cycle, energy balance, and carbon cycle during land-atmosphere interactions within the climate system (i.e., latent heat flux) (Dirmeyer, 1994; Jung et al., 2010; Wang and Dickinson, 2012; Shi et al., 2013). Water exchange returns about 60% of the precipitation on the land surface to the atmosphere, which then affects the local climate (Oki and Kanae, 2006). Thus, it is very important to quantify the spatiotemporal patterns and changes in regional ET to understand the interactions between the land surface and atmosphere by monitoring the land surface conditions that force extreme events, such as drought and flood, as well as evaluating the potential impact on climate change (Xu et al., 2006; Gao et al., 2007; Bonan, 2008; Jung et al., 2010).

    Four methods are used to obtain ET estimates: ground-based observations, the water balance method (Gao et al., 2007, 2012), satellite retrieval (Chen et al., 2014; Li et al., 2014; Mao et al., 2015), and land surface modeling (Chen et al., 2013; Shi et al., 2013), where each has its own advantages and disadvantages (Wang and Dickinson, 2012). For example, ground-based observations can provide high quality ET records with high temporal resolution, but they have a short duration and sparse spatial coverage. Satellite retrieval can characterize the spatial variability of ET well, but these data cannot be used to establish the relatively long-term climate variability in ET. Previously, there have been few studies of long-term regional ET, and most have focused on the water balance method (Gao et al., 2007, 2012); however, it is difficult to derive a significant trend of ET from water balance because (1) there is large interannual variability of precipitation and inconsistencies between different data sources, and (2) the river discharge directly flowing into oceans is difficult to estimate (Wang and Dickinson, 2012).

    At present, land surface models (LSMs) are used widely to obtain ET estimates at global and continental scales because of their mechanism-based structure (Vinukollu et al., 2012; Chen et al., 2013; Jaksa et al., 2013; Shi et al., 2013; Mao et al., 2015); however, LSM simulations of ET still contain many errors (Dirmeyer et al., 2006; Wang and Dickinson, 2012). The main sources of error in offline LSM simulations of ET are the uncertainties in meteorological forcing and those in the land surface parameterizations. The accuracy of a simulation based on an LSM depends mainly on the quality of the meteorological forcing and the land surface parameterization scheme (Dirmeyer et al., 1999; Wang and Zeng, 2011; Chen et al., 2013). Multiple-LSM ensembles have been found to perform significantly better than a single LSM at land surface modeling, as well as reducing the uncertainties in the land surface parameterizations (Guo et al., 2007). Similar findings have been reported for meteorological forcing (Liu and Xie, 2013).

    However, previous studies of the spatiotemporal patterns and changes in regional ET have focused mainly on simulations by single LSMs (Shi et al., 2013) or multiple LSM simulations driven by a single meteorological dataset (Chen et al., 2013, Mao et al., 2015). Such work does not consider the uncertainties from the atmospheric forcing dataset and the land surface parameterizations, simultaneously. Therefore, in this study, we used three meteorological forcing datasets to drive four LSMs, to reduce both types of uncertainties in ET. The three meteorological forcing datasets were developed by (Sheffield et al., 2006) at Princeton University (hereafter, Princeton), (He, 2010) at the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (hereafter, ITPCAS), and (Qian et al., 2006) (hereafter, Qian). For the offline LSM simulation, four widely used LSMs were adopted in this study: (1) BATS (Dickinson et al., 1993; Giorgi et al., 2003); (2) VIC (Liang et al., 1996; Liang and Xie, 2001; Mitchell et al., 2004); (3) CLM3.0 (Oleson et al., 2004); and (4) CLM3.5 (Oleson et al., 2007). First, six simulations based on the four LSMs and driven by the three meteorological forcing datasets were conducted for mainland China. Next, the six sets of simulated ET data were merged by simply using the arithmetic average, and a comparison was conducted among the six simulations, ensemble simulations, and global land ET data, which were derived from the FLUXNET network of eddy covariance towers using the model tree ensembles (MTE) approach (Obs_MTE) (Jung et al., 2009, 2010). Furthermore, we analyzed the trends and spatiotemporal characteristics of ET over mainland China and the eight major river basins of China. Finally, we considered the spatiotemporal patterns in ET in response to climatic factors.

    The remainder of the paper is organized as follows: In section 2, we provide brief descriptions of the three types of meteorological forcing, as well as the four LSMs, the Obs_MTE data, and the experimental design. In section 3, we compare the Obs_MTE data with individual and ensemble simulations of ET, as well as illustrate the trends and spatiotemporal characteristics in ET, and the spatiotemporal patterns of ET in response to climatic factors over mainland China and the eight major river basins of China. We discuss the results in section 4 and finally provide a summary and give our conclusions in section 5.

2. Models, data and method
  • Offline LSM modeling requires external meteorological forcing data, and thus the accuracy of LSM modeling depends largely on the quality of meteorological forcing, where it is especially sensitive to precipitation, radiation, and temperature (Wei et al., 2008; Wang and Zeng, 2011). The external meteorological forcing data used to drive offline LSMs include the air temperature, wind speed, specific humidity, surface pressure, precipitation, and radiation. In this study, we used three sets of meteorological forcing data over mainland China, which were developed by different institutions to drive LSMs, as follows:

    (1) The Princeton meteorological forcing dataset, which combines the NCEP-NCAR reanalysis dataset (Kalnay et al., 1996) and a suite of global observation-based products. The observation-based products comprise the CRU TS2.0 product, the GPCP precipitation product, the TRMM precipitation product, and the NASA Langley Research Center SRB product. (Sheffield et al., 2006) described the details of this dataset, while (Sheffield and Wood, 2007) used this dataset to evaluate the global terrestrial water budget. In addition, (Wang et al., 2011) used this dataset to drive four different physical-based LSMs to explore soil moisture drought in China.

    (2) The ITPCAS forcing dataset, which is a hybrid of data obtained from the GLDAS dataset (Rodell et al., 2004) [the old version used the Princeton global meteorological forcing data as background data (Sheffield et al., 2006)] and a suite of observation-based products from China. Specifically, it combines the GLDAS dataset with observations from 740 operational stations of the China Meteorological Administration (CMA) to produce near-surface air temperature, pressure, wind speed, and specific humidity fields, as well as three merged precipitation datasets to determine the precipitation field. In addition, the corrected GEWEX-SRB (Pinker and Laszlo, 1992) shortwave radiation dataset, with reference to radiation estimates (Yang et al., 2006), is employed to ascertain the incident shortwave radiation fields. (Chen et al., 2011) used the old version to investigate the land surface temperature in dryland areas of China.

    (3) The Qian forcing dataset, which combines NCEP-NCAR reanalysis data and observation-based analyses of monthly precipitation, surface air temperature, and surface downward solar radiation. Details of the dataset, which was initially available for the period 1948-2004, are described in (Qian et al., 2006). Later, (Tian et al., 2010) extended this dataset up to 2010 by using ERA-Interim data along with the precipitation and temperature records from 740 CMA operational stations. They then employed the dataset to develop a land data assimilation system.

    Table 1 summarizes the primary features and the differences between the forcing data.

  • In this study, we employed four widely used LSMs (including a macroscale hydrological model)——BATS, VIC, CLM3.0 and CLM3.5——to model land ET over mainland China. These LSMs have been used in previous offline land surface modeling experiments, and their performances evaluated. In these LSMs, ET is parameterized as the sum of vegetation evaporation, vegetation transpiration, and soil evaporation. Spatial land surface heterogeneity in the four LSMs is represented as a subgrid hierarchy in which grid cells are composed of multiple snow/soil columns, plant function types, or other surface types. Biogeophysical progresses are simulated at the subgrid scale, and each subgrid unit maintains its own prognostic variables (e.g., ET). However, the same forcing is used to force all subgrid units within a grid cell. Finally, the grid-averaged ET is obtained by averaging the subgrid values weighted by their fraction areas. Thus, the vegetation and soil parameters should be provided in each LSM and taken from the LSM's standard setup.

    Table 2 summarizes the primary features and the differences between these LSMs, as well as the sources of the vegetation and soil parameters.

  • Due to the lack of direct observations of ET, we used Obs_MTE data to compare and evaluate our seven ET simulations (i.e., six simple simulations and their ensemble simulation). The point-wise ET observations from the FLUXNET measurement sites were up-scaled with geospatial information obtained from satellite remote sensing and surface meteorological data using the MTE algorithm, which yielded the monthly ET at a spatial resolution of 0.5° during 1982-2010 (Jung et al., 2009, 2010), which we designated as Obs_MTE in this study. (Jung et al., 2010) used this dataset to explore the recent decline in the global land ET and its possible explanations. (Shi et al., 2013) also used this dataset to evaluate the ET simulated by CLM4, and to further investigate the spatiotemporal patterns in ET in response to multiple environmental factors.

    Figure 1.  Locations of the eight major river basins in China: I, Yangtze River basin; II, Haihe River basin; III, Heihe River basin; IV, Huaihe River basin; V, Yellow River basin; VI, Songhuajiang River basin; VII, Tarim River basin; and VIII, Zhujiang River basin.

  • The six ET simulations were determined by four different LSMs (described in Table 2) driven by three different types of forcing (described in Table 1), which we coded as: (1) VIC_Prin (for the VIC model driven by Princeton forcing); (2) BATS_Prin (for the BATS model driven by Princeton forcing); (3) CLM3_Prin (for the CLM3.0 model driven by Princeton forcing); (4) CLM3.5_Prin (for the CLM3.5 model driven by Princeton forcing); (5) CLM3.5_Qian (for the CLM3.5 model driven by Qian forcing); and (6) CLM3.5_ITP (for the CLM3.5 model driven by ITPCAS forcing). In order to achieve an equilibrium state in the LSMs, the Princeton forcing was first used to drive CLM3.5 from 1948 to 2008, and the first files from 1 January 2009 were then saved and used to initialize all six simulations at the beginning of each of the six modeling processes. These simulations were all run at a resolution of 0.5°× 0.5°, and the forcing data were also interpolated to 0.5°. The six sets of modeled ET data were then merged using a simple arithmetical averaging ensemble method and designated as Ens_Mean.

  • The evaluation method comprised the following steps:

    (1) The Obs_MTE data were used to compare and evaluate the six simulations of ET and their ensemble simulation at a spatial resolution of 0.5° during 1982-2007 in China and the eight major basins of China. Figure 1 shows a map indicating the locations of the eight major basins in China.

    (2) The linear regression method (e.g., Fu et al., 2008; Gao et al., 2012) was used to compute the temporal trends in ET in China and the eight major basins of China.

    (3) The correlation coefficients (R) between ET and climate variables (temperature, precipitation, radiance, wind speed etc.) were calculated to explore the sensitivity of ET to climate change.

    Figure 2.  Comparison of the average ET density (mm yr$^-1$) estimated for the eight major river basins of China, and all of China, based on multiple LSM simulations, Ens_Mean and Obs_MTE, during 1982-2007.

3. Results
  • In order to ensure the credibility of the ensemble simulation of ET, we first compared the six simulations and their ensemble simulation of the mean annual ET with the Obs_MTE data from 1982-2007 in China and the eight major basins of China (Fig. 2). Figure 2 shows that BATS_Prin overestimated the mean annual ET over most of the basins and all of China, whereas VIC_Prin obtained underestimates. The ensemble simulation Ens_Mean produced a closer simulation to the mean annual ET obtained with Obs_MTE data. The annual mean ET during the study period, over all of China, was simulated by Ens_Mean as 383 mm yr-1, whereas the Obs_MTE estimate was 436 mm yr-1.

    Figure 3 shows the spatial distribution of the land annual-mean ET in China according to Obs_MTE, Ens_Mean, and the difference between them. Figure 3 demonstrates that the Obs_MTE data exhibited strong regional variations and there was an obvious southeast-northwest latitude gradient from high to low. In general, the lowest annual ET values were located in the arid/semiarid regions of Northeast China and Northwest China, such as the provinces of Xinjiang, Gansu, Inner Mongolia, and Ningxia (<400 mm yr-1), whereas the annual ET values in South China were relatively high, such as in the provinces of Hainan, Taiwan, Yunnan, Guangxi, and Guangdong (>700 mm yr-1) (Fig. 3a). The ensemble simulation Ens_Mean captured the spatial distribution of ET very well in China (Figs. 3a and b), but the Ens_Mean simulated ET was lower than that of the Obs_MTE over Southeast China (Fig. 3c), where the major contributor was the slightly lower Ens_Mean value estimated over all of China compared with the Obs_MTE data (Fig. 2).

    From the perspective of hydrological basins, relatively high annual ET values were found for the Yangtze River, Zhujiang River, and Huaihe River basins, where the estimated ET ranged from 641 to 858 mm yr-1 (Fig. 2). In these humid subtropical basins, the temperature and moisture were sufficient to satisfy the vegetation ET and soil ET. Relatively low annual ET values were found in the Heihe River and Tarim River basins, where the estimates of ET ranged from 164 to 189 mm yr-1 (Fig. 2). In these cold and arid basins, moisture or temperature was the limiting factor. Intermediate annual ET values were found in the Huanghe River, Haihe River, and Songhuajiang River basins, where the estimates of ET ranged from 368 to 507 mm yr-1 (Fig. 2). In these temperate semihumid basins, the moisture and temperature levels were intermediate.

  • 3.2.1. Seasonal patterns of ET

    Figure 4 shows the multi-year seasonal pattern of land ET in China during 1982-2007 according to the Obs_MTE, Ens_Mean, and the difference between them. It can be seen that the Obs_MTE data exhibited obvious seasonal patterns, where the spatial variability was controlled by the climatic conditions. The highest ET values occurred in summer (June-August), where the most easterly and southern parts of China had the most striking high ET values, while there was an obvious spatial variation pattern where ET decreased from southeast to northwest. Relatively high ET values occurred in spring (March-May), whereas the lowest ET values occurred in winter (first column in Fig. 4). Thus, the moisture and the temperature were sufficient to satisfy ET over the southeast of China in the summer.

    Figure 3.  Spatial distribution of the annual ET (mm yr$^-1$) during 1982-2007 (a) from Obs_MTE, (b) from Ens_Mean, and (c) the difference between Obs_MTE and Ens_Mean (i.e., Obs_MTE minus Ens_Mean). The white area in Northwest China represents the default Obs_MTE values.

    Figure 4.  Seasonal distribution of land ET (mm month$^-1$) in China during 1982-2007 obtained from Obs_MTE (first column), Obs_MTE (second column), and the difference between Obs_MTE and Ens_Mean (i.e., Obs_MTE minus Ens_Mean; third column). The white area in Northwest China represents the default Obs_MTE values.

    Figure 5.  Time series for land ET (mm month$^-1$) obtained from the Obs_MTE reported by Jung et al. (2009, 2010) and the Ens_Mean, for the eight major river basins of China.

    The ensemble simulation captured the seasonal pattern of ET in China very well (second column in Fig. 4), but the Ens_Mean simulated ET was lower than that of Obs_MTE over Southeast China (Fig. 4c), which was the major contributor to the slightly lower value of the Ens_Mean estimated for all of China compared with the Obs_MTE data.

    3.2.2. Time series

    In order to examine the performance of the ensemble-simulated ET quantitatively, we compared the Ens_Mean and Obs_MTE time series averaged over the eight major basins of China for 1991-2002 (Fig. 5). Figure 5 shows that the Ens_Mean captured the seasonal cycle and temporal evolution of Obs_MTE very well, but there were errors in some basins, such as those of the Heihe River, Zhujiang River, and Tarim River basins. The errors in the Heihe River basin may have been due to the greater human activity levels, such as land use, agricultural water use, and water use for environmental conservation. The errors in the Zhujiang River basin may have been due to complex surfaces, such as dense vegetation and complex terrain. There were large errors in the Tarim River basin (Obs_MTE higher than Ens_Mean), which were probably caused mainly by using the default Obs_MTE values in some areas of this basin because most of this area is desert and the ET values were very low.

    3.2.3. Interannual variation and temporal trend of ET

    Figure 6 shows the changes in Chinese annual land ET anomalies for both the Obs_MTE and Ens_Mean data from 1982 to 2007, which demonstrate that both the Obs_MTE and Ens_Mean exhibited significant interannual variability during this period. Relatively low values occurred in 1984, 1985, 2001 and 2003, whereas the highest values occurred in 1998. During 1982-98, the ensemble-simulated ET Ens_Mean values were generally consistent with the Obs_MTE data (R=0.92, P<0.05). After 1998, the Ens_Mean data exhibited more significant interannual variability than Obs_MTE, and they were not consistent.

    On average, the Obs_MTE and Ens_Mean data exhibited a significant positive trend during 1982-98 (Obs_MTE: y=x-10.15, P<0.05; Ens_Mean: y=0.89x-3.79, P<0.05). During 1998-2007, the Ens_Mean data exhibited a significant negative trend (y=-2.19x+3.24, P<0.10) and the Obs_MTE data also exhibited a negative trend (y=-0.53x+0.49), but it was not significant. After 1998, when the last big EI Niño event occurred, the increasing trend in the land ET disappeared, and the subsequent declining trend was consistent with the Ens_Mean. This conclusion is consistent with the analysis of Obs_MTE for global ET data by (Jung et al., 2010), but slightly different from the results based on eight satellite-based ET models (Chen et al., 2014), in which four of the eight models showed significant increases in ET, while the other models presented relatively constant long-term values, or slightly decreasing ET, over terrestrial ecosystems in China from 1982 to 2009. The differences in satellite-based ET model structure and their dominant variables were the major causes (Chen et al., 2014).

    We also considered the regional-scale interannual variability in the land ET over the eight major basins of China. Figure 7 shows the temporal evolution of the annual land ET anomalies over the eight major basins of China, in terms of both the Obs_MTE and Ens_Mean, from 1982 to 2007. The results show that the Ens_Mean captured the basin-scale international variability in ET over the eight major basins in China, especially during 1982-1998, but the Ens_Mean exhibited more significant interannual variability compared with the MTE product. In 1998-2007, the Obs_MTE and Ens_ Mean exhibited differences in their interannual variability over most of the basins of China, and this conclusion is consistent with that over all of China.

    Figure 6.  Comparison of the differences in Chinese annual land ET anomalies between the Obs_MTE reported by Jung et al. (2009, 2010) and the Ens_Mean: (a) linear trend during 1982-2007;(b) linear trend during 1982-1998 and 1999-2007.

    Figure 7.  Comparison of the differences in the annual land ET anomalies for the eight major basins between the Obs_MTE reported by Jung et al. (2009, 2010) and the Ens_Mean.

    Figure 8.  Spatial distributions of the correlation coefficients for the relationships between annual ET and climate variables: (a) WIND, wind speed; (b) $T_a$, air temperature; (c) $R_n$, shortwave radiation; (d) Prec, precipitation. The white area in Northwest China represents the default Obs_MTE values.

  • In order to explore the sensitivity of ET to climate change, we studied the relationship between ET and climate variability. Figure 8 shows the correlation coefficients for the relationships between ET and climate variability in China, i.e., air temperature (T a), shortwave radiation (R n), precipitation, and wind speed. In general, the results demonstrate T a was the driver of ET over moist areas, with a significant positive correlation, e.g., in most parts of South China and some areas of Northeast China, where the correlation coefficients were lower in drier areas and negative over arid areas (Fig. 8b). We found a similar relationship between R n and ET (Fig. 8c), but the positive correlation was more significant between T a and ET. Precipitation controlled ET over dry areas, with a significant positive correlation, e.g., in most parts of Northwest China, where the correlation coefficients were lower in moister areas and zero or even negative for humid areas (Fig. 8d). There were few significant correlations between wind speed and ET in most parts of China, except the northwest of China (Fig. 8a). Thus, we found that the annual T a and precipitation were important climate variables that affected the variation in ET, and there was a significant spatial pattern in the ET response to T a and precipitation over mainland China during 1982-2007.

    We further investigated the regional-scale relationship between ET and the important climate variables for the eight major basins of China. Table 3 shows the correlation coefficients for the relationships between ET and the climate variables (T a and precipitation). These results demonstrate that ET had a significant positive correlation with T a for most of the major basins in China, except the Tarim River basin and Heihe River basin, which are located in arid areas, and the correlation was higher for humid areas (e.g., the Yangtze River basin, Huaihe River basin, and Zhujiang River basin). Our conclusions were similar for the correlation between ET and R n, but ET only had a significant positive correlation in humid areas (Yangtze River basin, Huaihe River basin, and Zhujiang River basin; not shown in Table 3), where T a or R n mainly controlled the long-term variation in ET in humid areas, and these conclusions agree with those of (Wang et al., 2010) and (Li et al., 2014). ET and precipitation were positively correlated in arid and semi-arid areas (Heihe River basin, Tarim River basin, Haihe River basin, Yellow River basin, and Songhuajiang River basin), where the water availability in the root zone or shallow surface controlled the ET process. It should be noted that ET had the strongest positive correlation with precipitation in the Tarim River basin (most of this area is desert), whereas the correlation coefficient between ET and precipitation in the Tarim River basin was the lowest in the arid and semi-arid areas [Heihe River basin (R=0.62), Tarim River basin (R=0.26), Haihe basin (R=0.34), Yellow river basin (R=0.36), and Songhuajiang River basin (R=0.4)]. This could largely be explained by the use of default values for the Obs_MTE data in some areas of the Tarim River basin.

4. Discussion
  • Land ET is a very important variable for the global hydrological cycle and energy cycle because of land-atmosphere interactions within the climate system. Due to the lack of actual long-term ET observation data, quantifying the spatiotemporal patterns and changes in regional ET are major challenges for the hydrology and climate research community. Thus, in this study, we employed multiple types of forcing and multiple models in an ensemble method to reduce the uncertainties during simulations of the actual ET using land surface modeling, which is a promising method for reproducing high resolution long-term spatial and temporal ET data.

    It should be noted that this study had some limitations. First, the use of multiple types of forcing and multiple models in ensemble simulations can reduce some uncertainties, and the ensemble-simulated ET obtained using a simple arithmetical averaging ensemble method (Ens_Mean) captured the spatial patterns and temporal variations very well in China; however, some errors were still present, such as underestimated ET in South China (Figs. 3-6). The simple arithmetical averaging ensemble method is an effective strategy for reducing the uncertainty of individual ensemble members, but it is still inferior to the best individual ensemble member in most cases (Guo et al., 2007).

    Therefore, it may be necessary to employ a more advanced ensemble method, such as the Bayesian model averaging (BMA) method (Liu and Xie, 2013). The BMA ensemble method can be applied to multiple types of forcing and multiple models to obtain the ensemble-simulated ET and further reduce the uncertainties, thereby obtaining more accurate actual ET estimates.

    Second, in order to evaluate and compare the ensemble-simulated actual ET, we used Obs_MTE data as the ET observations. In fact, the Obs_MTE data comprise estimates of the local ET and meteorological records based on FLUXNET tower and satellite observations. Thus, these data contain errors, e.g., in cold and dry deserts these data do not consider the non-vegetated areas (Jung et al., 2009, 2010). We did not consider the uncertainty of Obs_MTE data in the present study.

5. Summary and conclusion
  • In this study, we conducted comparisons of six simulations and the Ens_Mean with Obs_MTE over mainland China and the eight major river basins of China during 1982-2007, where we explored the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climatic factors.

    In general, the six simulations and Ens_Mean showed similar broad spatial patterns of ET, with a decrease from southeast to northeast, and then to the northwest, and the lowest annual ET values located in the Tarim River and Heihe River basins of Northwest China. However, there were considerable differences in magnitude among the four LSMs. For example, BATS driven by the Princeton dataset overestimated the mean annual ET for most of the basins, whereas VIC obtained underestimates using the same forcing dataset. Compared with the Obs_MTE product (436 mm yr-1), the Ens_Mean underestimated the annual mean ET in China by 18% (383 mm yr-1); however, the simulated ET exhibited more significant overall variation. In terms of the temporal trend in ET, both the Obs_MTE and Ens_Mean in mainland China exhibited significant increasing trends during 1982-98, whereas the Ens_Mean exhibited a significant decreasing trend during 1998-2007 and the Obs_MTE had a non-significant decreasing trend.

    The sensitivity of ET to climate change in this study showed that air temperature was the main factor that controlled the long-term variation in ET over humid areas (e.g., the Yangtze River basin, Huaihe River basin and Zhujiang River basin), whereas precipitation made more of a contribution in arid and semi-arid areas (e.g., the Heihe River basin, Tarim River basin, Haihe River basin, Yellow River basin, and Songhuajiang River basin).

    These results suggest that climatic factors such as precipitation and air temperature can have a significant impact on future projections of water cycle dynamics at basin and regional scales.




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