气候变化领域集成服务门户(CCPortal): Self-Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements

CCPortal

DOI	10.1029/2020GL089258
	Self-Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements
	Irrgang C.; Saynisch-Wagner J.; Dill R.; Boergens E.; Thomas M.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:17
英文摘要	Quantifying and monitoring terrestrial water storage (TWS) is an essential task for understanding the Earth's hydrosphere cycle, its susceptibility to climate change, and concurrent impacts for ecosystems, agriculture, and water management. Changes in TWS manifest as anomalies in the Earth's gravity field, which are routinely observed from space. However, the complex underlying distribution of water masses in rivers, lakes, or groundwater basins remains elusive. We combine machine learning, numerical modeling, and satellite altimetry to build a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. A novel constrained training is introduced, allowing the neural network to validate its training progress with independent satellite altimetry records. We show that the neural network can accurately derive the TWS in 2019 after being trained over the years 2003 to 2018. Further, we demonstrate that the constrained neural network can outperform the numerical model in validated regions. ©2020. The Authors.
英文关键词	Agricultural robots; Aneroid altimeters; Climate change; Earth (planet); Groundwater; Groundwater resources; Neural networks; Numerical models; Satellites; Water management; Down-scaling; Earth's gravity fields; Groundwater basins; Satellite altimetry; Space-borne; Terrestrial water storage; Underlying distribution; Deep learning; artificial neural network; downscaling; gravity flow; hydrosphere; machine learning; numerical model; satellite altimetry; terrestrial ecosystem; water storage
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169839
作者单位	Section 1.3: Earth System Modelling, Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Potsdam, Germany; Institute of Meteorology, Freie Universität Berlin, Berlin, Germany
推荐引用方式 GB/T 7714	Irrgang C.,Saynisch-Wagner J.,Dill R.,et al. Self-Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements[J],2020,47(17).
APA	Irrgang C.,Saynisch-Wagner J.,Dill R.,Boergens E.,&Thomas M..(2020).Self-Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements.Geophysical Research Letters,47(17).
MLA	Irrgang C.,et al."Self-Validating Deep Learning for Recovering Terrestrial Water Storage From Gravity and Altimetry Measurements".Geophysical Research Letters 47.17(2020).