SPECIAL COLLECTION ON REGIONAL AND GLOBAL LDASs

Special Issue on Development and Applications of Regional and Global Land Data Assimilation Systems (LDAS)

JMR-LDAS Call for Papers

Land Data Assimilation Systems (LDASs) have gone through almost two decades of research and development where numerous exciting and inspiring progresses have been witnessed. Since the initiation of the North American and Global LDAS (NLDAS and GLDAS) by scientists from the NASA, NOAA, Princeton University, University of Washington, as well as other universities in the beginning of 2000, various national and regional LDASs have been developed in Europe, South America, Canada, and China. These systems have also been extended from offline (uncoupled), semi-coupled, to fully coupled. With satellite products becoming widely and continuously available, LDASs have been largely improved with benefits of data assimilation. At the same time, as more and more in situ and satellite observations become available, the scientific understating of land surface processes and land surface models (LSM) have been greatly improved by addition of more realistic physical processes, optimized model parameters, new soil and vegetation datasets, and upgraded model structures. Improvements in LSM and assimilation of satellite data improved the quality and reliability of LDAS products such that they can be used to provide optimal initial conditions for coupled weather and climate modeling and to support drought monitoring, agricultural crop planning, and water resources management. Many LDAS systems have been operationally implemented at various national service centers to produce timely products to users. Two examples are the NLDAS at NCEP/NOAA and the China Meteorological Administration (CMA) LDAS system (CLDAS for short) at the National Meteorological Information Center (NMIC)/CMA.

In the past, CMA did not have an operational LDAS system. Users from both scientific community and service sectors have been utilizing NASA GLDAS products, as well as NOAA and ECMWF reanalysis products for their research and applications. Recently, CLDAS has seen a rapid development. CLDAS version 1.0 was operationally implemented in 2013, and version 2.0 in 2017. The CLDAS products have been released to the public. Its surface metrological forcing data, energy fluxes, water fluxes, and state variables need to be comprehensively evaluated against in situ observations, satellite retrievals, and reanalysis products. At the same time, many applications of these products are being carried out in both research institutions and service sectors. In addition, a regional LDAS system is being developed specially for the arid and semi-arid area in northwestern China, in an effort to better cope with the challenges of coarse/low-quality meteorological observations, as well as the lack of scientific understanding on land surface processes there. Furthermore, the NLDAS and GLDAS are moving forward to increasing spatial resolution, improving forcing data, using latest versions of land-surface models, adding data assimilation procedures, and using new soil and vegetation datasets. These new developments have facilitated the advancement of atmospheric, climatological, and hydrological sciences.

We invite contributions of original research and review articles that will facilitate various LDAS efforts in the science and application community. Potential topics include but are not limited to:

@ Development and progress of national, regional, and global LDAS systems

@ Improvement and assessment of surface meteorological forcing

@ Application of data assimilation techniques in LDAS

@ Comparison analysis of LDAS and reanalysis products

@ Evaluation of LDAS products against in situ observations and satellite retrievals

@ Application of LDAS products in regional and global coupled weather and climate models

@ Improvement of land surface/hydrological models including model physical processes, soil and vegetation datasets, model structure and parameters, etc.

@ Application of LDAS products in drought/flood monitoring and prediction, wild fire, agriculture and crop management, water resource management, etc.

@ Impacts of LDAS products on atmospheric data assimilation

We are especially interested in papers elaborating on improvement of CLDAS and its application in the arid and semi-arid area of Northwest China, as well as comparative investigations between CLDAS and other LDAS/reanalysis products. In support of the publication of this special issue, publication charges of innovative, well-written papers will be waived, pending on the scores and comments of the handling Editor/reviewers and the Responsible Editors Team of this special issue; and three best papers will be awarded with certificates and cash prizes. Contributions from both Chinese and overseas authors are well encouraged.

Responsible Editors for the Special Issue:

Lead Editor:

Youlong Xia, I.M. Systems Group at EMC\NCEP, College Park, Maryland, USA, xiay@imsg.com

PhD from Ludwig-Maximilians University of Munich, Germany in 1999. Serving as a Senior Research Scientist since 2006 at EMC/NCEP to coordinate and develop the North American Land Surface Data Assimilation System. His areas of interest include land surface modeling, model optimization and uncertainty estimate, drought/hydrologic monitoring and prediction, seasonal hydrological forecast system, data verification and evaluation, data assimilation, and so on.

Editors:

Chunxiang Shi, National Meteorological Information Center, China Meteorological Administration (CMA), Beijing, China, shicx@cma.gov.cn

PhD from Chinese Academy of Sciences in 2008. As a Chief Scientist in NMIC of CMA, she has led the research on data blending from multiple sources and its operational application. She has been building the first China real-time operational Land Data Assimilation System (CLDAS), and is now co-leading a research team to develop the CMA next generation 40-yr global atmosphere reanalysis project (CRA-40).

Ming Pan, Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA, mpan@princeton.edu

BE from Tsinghua University in 2000 and PhD from Princeton University in 2006. As a Research Scientist, he serves as PI and Co-I for a number of projects funded by U.S. institutions such as NASA. His areas of interest include hydrologic remote sensing, land surface modeling, hyper-resolution modeling, data assimilation/fusion/learning, hydrologic monitoring and short/long-term forecast.

Guest Editors:

Yaohui Li, Institute of Arid Meteorology, China Meteorological Administration, Lanzhou, China, liyh@iamcma.cn

PhD from Chinese Academy of Sciences in 2006. As a Senior Research Scientist and Director of his institute, he investigates arid climate change, drought formation and monitoring, regional arid climate modeling, and land-atmosphere interaction.

Xiwu Zhan, NOAA-NESDIS Center for Satellite Applications and Research, College Park, Maryland, USA, Xiwu.Zhan@noaa.gov

PhD from Cornell University. As a senior research scientist, he has been leading multiple research projects for NOAA and NASA. Main areas of his research team at NOAA include: development and application of operational satellite land surface data products, land data assimilation, drought monitoring, and so on.

Lifeng Luo, Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, USA, lluo@msu.edu

BS from Peking University in 1998, and PhD from Rutgers University in 2003. As an Associate Professor, he focuses on hydrology and climate sciences, including land-atmosphere interaction and its impact on the global climate and hydrological cycle, climate extremes, seasonal drought prediction, and climate change.

Aihui Wang, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, wangaihui@mail.iap.ac.cn

PhD from Chinese Academy of Sciences in 2007. As a research professor, she is interested in land surface/hydrology model improvement, drought reconstruction and predication, land surface data construction and validation, and so on.

Jifu Yin, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA, jifu.yin@noaa.gov

PhD from Nanjing University of Information Science & Technology (NUIST) in 2015. Post-doc at NOAA-NESDIS-STAR. Currently as an Assistant Research Scientist, he is interested in satellite remote sensing of land surface soil moisture and its assimilation, climate and hydrologic modeling, and drought monitoring and forecasting.

Xitian Cai, Lawrence Berkley National Laboratory, Berkeley, California, USA, xtcai@lbl.gov

PhD from University of Texas in 2015. Currently as a Postdoctoral Fellow, he focuses on investigating water, carbon, and nutrient cycles using land surface and e arth system models.

Baoqing Zhang, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China, baoqzhang@lzu.edu.cn

PhD from Utah State University and Northwest A&F University (China). As an Associate Professor, he works on development of physically based multiscalar drought indices, rainwater harvesting potential in arid and semi-arid regions, etc.

Bailing Li, Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA, bailing.li-1@nasa.gov

PhD from University of Arizona in 1998. Currently as a research scientist, she is working on land surface modeling, data assimilation of satellite estimated soil moisture and terrestrial water storage changes.

Zengcahao Hao, College of Water Sciences, Beijing Normal University, Beijing, China, haozc@bnu.edu.cn

PhD in 2012 from Texas A&M University. As an assistant professor now, his mainly works on drought monitoring and prediction, hydrological simulation, climate change and extremes.

Dagang Wang, Department of Water Resources and Environment, Sun Yat-Sen University, Guangzhou, China, wangdag@mail.sysu.edu.cn

BE from Dalian University of Technology in 1997 and PhD from University of Connecticut in 2007. Now as an Associate Professor, he works on land surface modeling, urbanization on climate, and hydrometeorological forecast.

Tongren Xu, Faculty of Geographical Science, Beijing Normal University, Beijing, China, xutr@bnu.edu.cn

PhD from Beijing Normal University in 2011. Now as an Associate Professor, he focuses on developing evapotranspiration data assimilation and uncertainties analysis, eco-hydrology, and so on.

Xing Yuan, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, yuanxing@tea.ac.cn

PhD from Chinese Academy of Sciences in 2008. Awarded “Thousand Talents Program for Distinguished Young Scholars” in 2015. Now as a research professor, he is interested in hydroclimatology, including hydrological modeling and forecasting, and so on.

Important Dates

Submission open: January 15, 2018

Submission deadline: June 30, 2019

Publication time: As soon as the paper is accepted and edited. The Special Issue in virtual format will be compiled online and the Special Issue in print is available upon request.

Style and format instructions available at http://www.cmsjournal.net:8080/Jweb_jmr/EN/column/column23.shtml

Submission gateway: https://mc03.manuscriptcentral.com/acta-e

Journal of Meteorological Research (JMR), formerly Acta Meteorologica Sinica, is published internationally by the Chinese Meteorological Society and Springer Nature. JMR intends to promote the exchange of scientific and technical innovation and thoughts between Chinese and foreign meteorologists. It covers all fields of meteorology, including observational, modeling, and theoretical research and applications in weather forecasting and climate prediction, as well as related topics in geosciences and environmental sciences.

JMR contains academic papers, research/field program highlights, conference reports, and comprehensive discussions on meteorological research and operation undertaken both in China and worldwide.

For more information about JMR, visit

http://www.springer.com/journal/13351, or

http://www.cmsjournal.net/qxxb_en

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

Youlong XIA, Zengchao HAO, Chunxiang SHI, Yaohui LI, Jesse MENG, Tongren XU, Xinying WU, Baoqing ZHANG

2019, 33(2): 159-189. DOI: 10.1007/s13351-019-8172-4

Abstract Full Text PDF More Citation

Abstract：Since the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS) were established in 2004, significant progress has been made in development of regional and global LDASs. National, regional, project-based, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications (from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure, parameters, and forcing are discussed.More+

Evaluation of Routed-Runoff from Land Surface Models and Reanalyses Using Observed Streamflow in Chinese River Basins

Yue MIAO, Aihui WANG

2020, 34(1): 73-87. DOI: 10.1007/s13351-020-9120-z

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Abstract：Previous studies have demonstrated that offline land surface models (LSMs) and global hydrological models (GHMs) can reasonably reproduce streamflow in large river basins. Global reanalyses supply fine spatiotemporal runoff estimates, but they are not fully intercompared and evaluated in China. This study assesses the routed-runoff from five offline LSM/GHM runs (VIC-CN05.1, CLM-CFSR, CLM-ERAI, CLM-MERRA, and CLM-NCEP) and three reanalysis datasets (ERAI/Land, JRA55, and MERRA-2) against the gauged streamflow (26 stations) in major Chinese river basins during 1980–2008. The Catchment-based Macro-scale Floodplain model (CaMa-Flood) is employed to route those runoff datasets to the hydrological stations. Four statistical quantities, including the correlation coefficient (R), standard deviation (STD), Nash–Sutcliffe efficiency coefficient (NSE), and relative error (RE), along with a ranking method, are used to quantify the quality of those products. The results show that the spatial patterns of both modeled and observed streamflow in summer are similar, but their magnitudes are different. Except for MERRA-2, the other products can reproduce well the interannual variability of streamflow in both the Yangtze and Yellow River basins. All products generally underestimate the magnitude and variance of monthly streamflow, while VIC-CN05.1 and JRA55 are closer to observations compared to other products. The correlation coefficients for all products are overall larger than 0.61, with the highest value (0.85) from VIC-CN05.1. In addition to CLM-MERRA, MERRA-2, and CLM-NCEP with relatively small precipitation, other products can simulate peak flow well with positive NSEs up to 0.41 (ERAI/Land). Considerable uncertainties exist among the eight products at the Yellow River outlet, which might be because the LSMs ignore frequent human activities. Based on the above statistics, performances of the eight runoff products are ranked in descending order as follows: VIC-CN05.1, ERAI/Land, JRA55, CLM-CFSR, CLM-ERAI, MERRA-2, CLM-MERRA, and CLM-NCEP, which provides a reference for flood/hydrological drought warning and hydroclimatic research in the future.More+

Underestimation of the Warming Trend over the Tibetan Plateau during 1998–2013 by Global Land Data Assimilation Systems and Atmospheric Reanalyses

Peng JI, Xing YUAN

2020, 34(1): 88-100. DOI: 10.1007/s13351-020-9100-3

Abstract Full Text PDF More Citation

Abstract：Accurate surface air temperature (T_2m) data are key to investigating eco-hydrological responses to global warming. Because of sparse in-situ observations, T_2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system (LDAS) are widely used in research over alpine regions such as the Tibetan Plateau (TP). It has been found that the warming rate of T_2m over the TP accelerates during the global warming slowdown period of 1998–2013, which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature. By evaluating two global LDASs, five global atmospheric reanalysis datasets, and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis, we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86% during 1998–2013. This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP, respectively. Although GLDAS2.0, ERA5, and MERRA2 reduce biases of T_2m simulation from their previous versions by 12%–94%, they do not show significant improvements in capturing the warming trend. The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement, as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP. Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period, and the WRF dynamical downscaling dataset provides more accurate T_2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.More+

Quality Control and Evaluation of the Observed Daily Data in the North American Soil Moisture Database

Weilin LIAO, Dagang WANG, Guiling WANG, Youlong XIA, Xiaoping LIU

2019, 33(3): 501-518. DOI: 10.1007/s13351-019-8121-2

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Abstract：The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m³ m^–3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.More+

Assessing the Performance of Separate Bias Kalman Filter in Correcting the Model Bias for Estimation of Soil Moisture Profiles

Bangjun CAO, Fuping MAO, Shuwen ZHANG, Shaoying LI, Tian WANG

2019, 33(3): 519-527. DOI: 10.1007/s13351-019-8057-6

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Abstract：The performance of separate bias Kalman filter (SepKF) in correcting the model bias for the improvement of soil moisture profiles is evaluated by assimilating the near-surface soil moisture observations into a land surface model (LSM). First, an observing system simulation experiment (OSSE) is carried out, where the true soil moisture is known, two types of model bias (i.e., constant and sinusoidal) are specified, and the bias error covariance matrix is assumed to be proportional to the model forecast error covariance matrix with a ratio λ. Second, a real assimilation experiment is carried out with measurements at a site over Northwest China. In the OSSE, the soil moisture estimation with the SepKF is improved compared with ensemble Kalman filter (EnKF) without the bias filter, because SepKF can properly correct the model bias, especially in the situation with a large model bias. However, the performance of SepKF becomes slightly worse if the constant model bias increases or temporal variability of the sinusoidal model bias becomes large. It is suggested that the ratio λ should be increased (decreased) in order to improve the soil moisture estimation if temporal variability of the sinusoidal model bias becomes high (low). Finally, the assimilation experiment with real observations also shows that SepKF can further improve the estimation of soil moisture profiles compared with EnKF without the bias correction.More+

Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model

Xiaolei FU, Zhongbo YU, Ying TANG, Yongjian DING, Haishen LYU, Baoqing ZHANG, Xiaolei JIANG, Qin JU

2019, 33(2): 190-205. DOI: 10.1007/s13351-019-8138-6

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Abstract：Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter (EnKF) and Simple Biosphere Model (SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation, and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the dee-per water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.More+

A Soil Moisture Data Assimilation System for Pakistan Using PODEn4DVar and CLM4.5

Tariq MAHMOOD, Zhenghui XIE, Binghao JIA, Ammara HABIB, Rashid MAHMOOD

2019, 33(6): 1182-1193. DOI: 10.1007/s13351-019-9020-2

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Abstract：Soil moisture is an important state variable for land–atmosphere interactions. It is a vital land surface variable for research on hydrology, agriculture, climate, and drought monitoring. In current study, a soil moisture data assimilation framework has been developed by using the Community Land Model version 4.5 (CLM4.5) and the proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation (PODEn4DVar) algorithm. Assimilation experiments were conducted at four agricultural sites in Pakistan by assimilating in-situ soil moisture observations. The results showed that it was a reliable system. To quantify further the feasibility of the data assimilation (DA) system, soil moisture observations from the top four soil-depths (0–5, 5–10, 10–20, and 20–30 cm) were assimilated. The evaluation results indicated that the DA system improved soil moisture estimation. In addition, updating the soil moisture in the upper soil layers of CLM4.5 could improve soil moisture estimation in deeper soil layers [layer 7 (L7, 62.0 cm) and layer 8 (L8, 103.8 cm)]. To further evaluate the DA system, observing system simulation experiments (OSSEs) were designed for Pakistan by assimilating daily observations. These idealized experiments produced statistical results that had higher correlation coefficients, reduced root mean square errors, and lower biases for assimilation, which showed that the DA system is able to produce and improve soil moisture estimation in Pakistan.More+

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

Jianguo LIU, Chunxiang SHI, Shuai SUN, Jingjing LIANG, Zong-Liang YANG

2019, 33(6): 1194-1206. DOI: 10.1007/s13351-019-9067-0

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Abstract：The accuracy of land surface hydrological simulations using an offline land surface model (LSM) depends largely on the quality of the atmospheric forcing data. In this study, Global Land Data Assimilation System (GLDAS) forcing data and the newly developed China Meteorological Administration Land Data Assimilation System (CLDAS) forcing data are used to drive the Noah LSM with multiple parameterizations (Noah-MP) and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over mainland China. The monthly soil moisture (SM) and evapotranspiration (ET) simulations are then compared and evaluated against observations. The results show that the Noah-MP driven by the CLDAS forcing data (referred to as CLDAS_Noah-MP) significantly improves the simulations in most cases over mainland China and its eight river basins. CLDAS_Noah-MP increases the correlation coefficient (R) values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in mainland China, especially in the eastern monsoon area such as the Huang–Huai–Hai Plain, the southern Yangtze River basin, and the Zhujiang River basin. Moreover, the root-mean-square error is reduced from 0.078 to 0.068 m³ m⁻³ for the SM simulations, and from 12.9 to 11.4 mm month⁻¹ for the ET simulations over mainland China, especially in the southern Yangtze River basin and Zhujiang River basin. This study demonstrates that, by merging more in situ and remote sensing observations in regional atmospheric forcing data, offline LSM simulations can better simulate regional-scale land surface hydrological processes.More+

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Shuai SUN, Chunxiang SHI, Yang PAN, Lei BAI, Bin XU, Tao ZHANG, Shuai HAN, Lipeng JIANG

2020, 34(4): 879-892. DOI: 10.1007/s13351-020-9101-2

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Abstract：Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS), we blended the Climate Prediction Center (CPC) morphing technique (CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and tempo-ral downscaling to produce a multi-source precipitation fusion dataset for China (CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2 (GLDAS-V2.1) precipitation, and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement (GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrologi-cal observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.More+

SPECIAL COLLECTION ON REGIONAL AND GLOBAL LDASs

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SPECIAL COLLECTION ON REGIONAL AND GLOBAL LDASs

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