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

Geophysical Research Letters

et al. 2021

Terrestrial water storage (TWS), which includes ice and snow, lakes, rivers, soil water and groundwater, is a key parameter to close the water balance at global river basins (e.g., Liu et al., 2016). Moreover, the changes in TWS (∆S) and its components present considerable spatiotemporal heterogeneity at the global scale (Frappart & Ramillien, 2018). The traditional methods of estimating ∆S, including in situ observations and hydrological modeling, have their own limitations in different aspects. First, in situ observations are limited to the point scale and it is difficult to obtain ΔS observations in terrain with complicated topography (A. Wang & Zeng, 2012). Second, uncertainties in models and the lack of observation data in some areas will lead to large variations in the accuracy of model simulation results. Although microwave remote sensing could retrieve the TWS components, it is restricted to obtaining information about changes in soil moisture content only at depths up to 5 cm from the surface. The Gravity Recovery and Climate Experiment (GRACE) twin satellite mission, jointly implemented by the National Aeronautics and Space Administration (NASA), and German Aerospace Center, is primarily aimed at obtaining temporal variations in the Earth's gravity field with unprecedented accuracy (e.g., Save et al., 2016). GRACE not only effectively avoids the shortcomings of traditional methods, but also provides a new tool for directly monitoring large-scale ΔS. However, GRACE has its own limitations. In addition to the coarse spatial and temporal resolution of the raw data, it is difficult to trace

Section: Accepted Articlesupporting

confidence: 87%

“…thus, TWS in CLSM is the sum of groundwater, soil moisture, snow water equivalent, and canopy water [Xia et al, 2017;Xia et al, 2019]. More details about evaluations/applications of GLDAS products can also be found at literatures [e.g., Chen Y et al, 2013;Qi et al, 2015;Qi et al, 2016;Qi et al, 2018;Wang et al, 2011;Wang et al, 2016].…”

Section: Accepted Articlementioning

confidence: 99%

Divergent Changes in Terrestrial Water Storage Across Global Arid and Humid Basins

Geophysical Research Letters

et al. 2021

“…They found that the latest version consistently led to improvements relative to the earlier version. In a more recent study, two versions of the Global Land Data Assimilation System (models' family name is hereafter referred to as NOAA (National Oceanic and Atmospheric Administration)) were also compared by Xia et al [19], and it was found that the most recent version showed closer soil moisture anomalies compared to ground observations. While these studies have been useful in highlighting the improvements in the newer soil moisture products, their comparisons with in situ datasets were largely restricted to datasets from Europe, Australia, and the US, and did not include China as a whole.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Soil Moisture Anomalies from Global Model-Based Datasets over the People’s Republic of China

Hagan

Parinussa

et al. 2019

Water

Soil moisture is an important factor in land-atmosphere interactions and other land processes. Improved estimates from climate models have, in the last two decades, become an important alternate source of information. In this study, we extend the evaluation of soil moisture anomalies of different generations of three families of model datasets (the European Center for Medium-Range Weather Forecasts’ (ECMWF) reanalysis, the Modern Era Retrospective Analysis for Research and Applications of NASA, and the Global Land Data Assimilation System of theNational Oceanic and Atmospheric Administration (NOAA)) in recent studies to the People’s Republic of China. Two validation techniques, namely, root-mean-square error (RMSE) from triple collocation analysis (TCA) and correlations (R) with ground observations, were used. The study confirmed the results of previous studies that focused on other regions and showed that the newer generations of each modeling family generally had better skill than the older generations with higher correlations and lower RMSEs. A cross-validation of the results from the two techniques for the newer products showed that the higher correlations and lower RMSEs from the TCA were found over regions with moderate vegetation cover, while regions with less vegetation cover had lower correlations and larger RMSEs (ECMWF (R: −0.93), NASA (R: −0.73), and NOAA (R: −0.61)), indicating that these two techniques complement each other to fairly validate the products.

“…The three satellite and reanalysis data products GLDAS, MERRA2, and TRMM were acquired from the National Aeronautics and Space Administration (NASA) website (https://disc.gsfc.nasa.gov/, last access: 10 July 2020 ). GLDAS ingests satellite-and ground-based observational data products and applies advanced land surface modeling and data assimilation techniques (Rodell et al, 2004;Zaitchik et al, 2010;Xia et al, 2019); it has been widely used for river discharge simulations, groundwater monitoring, and many other fields (Wang et al, 2011;Chen et al, 2013;Qi et al, 2018;Verma and Katpatal, 2019). MERRA2 is the first long-term global reanalysis dataset to assimilate spacebased observations of aerosols and represent their interactions alongside other physical processes in the climate system (Marquardt Collow et al, 2016;Reichle et al, 2017a, b), and TRMM is a joint mission between NASA and the Japan Aerospace Exploration Agency (JAXA) to study rainfall for weather and climate research (Xu et al, 2017;Ali et al, 2019;.…”

Section: Datasetsmentioning

confidence: 99%

An integration of gauge, satellite, and reanalysis precipitation datasets for the largest river basin of the Tibetan Plateau

et al. 2020

Earth Syst. Sci. Data

Abstract. As the largest river basin of the Tibetan Plateau, the upper Brahmaputra River basin (also called “Yarlung Zangbo” in Chinese) has profound impacts on the water security of local and downstream inhabitants. Precipitation in the basin is mainly controlled by the Indian summer monsoon and westerly and is the key to understanding the water resources available in the basin; however, due to sparse observational data constrained by a harsh environment and complex topography, there remains a lack of reliable information on basin-wide precipitation (there are only nine national meteorological stations with continuous observations). To improve the accuracy of basin-wide precipitation data, we integrate various gauge, satellite, and reanalysis precipitation datasets, including GLDAS, ITP-Forcing, MERRA2, TRMM, and CMA datasets, to develop a new precipitation product for the 1981–2016 period over the upper Brahmaputra River basin, at 3 h and 5 km resolution. The new product has been rigorously validated at different temporal scales (e.g., extreme events, daily to monthly variability, and long-term trends) and spatial scales (point and basin scale) with gauge precipitation observations, showing much improved accuracies compared to previous products. An improved hydrological simulation has been achieved (low relative bias: −5.94 %; highest Nash–Sutcliffe coefficient of efficiency (NSE): 0.643) with the new precipitation inputs, showing reliability and potential for multidisciplinary studies. This new precipitation product is openly accessible at https://doi.org/10.5281/zenodo.3711155 (Wang et al., 2020) and additionally at the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn, last access: 10 July 2020, login required).