Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets

Remote Sensing of Environment, Journal Year: 2020, Volume and Issue: 240, P. 111697 - 111697

Published: Feb. 14, 2020

Language: Английский

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Glacial change and hydrological implications in the Himalaya and Karakoram

Nature Reviews Earth & Environment, Journal Year: 2021, Volume and Issue: 2(2), P. 91 - 106

Published: Feb. 2, 2021

Language: Английский

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Our Skill in Modeling Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks

Bulletin of the American Meteorological Society, Journal Year: 2019, Volume and Issue: 100(12), P. 2473 - 2490

Published: July 30, 2019

Abstract In mountain terrain, well-configured high-resolution atmospheric models are able to simulate total annual rain and snowfall better than spatial estimates derived from in situ observational networks of precipitation gauges, significantly radar or satellite-derived estimates. This conclusion is primarily based on comparisons with streamflow snow basins across the western United States Iceland, Europe, Asia. Even though they outperform gridded datasets gauge networks, still disagree each other average often more their representation individual storms. Research address these difficulties must make use a wide range observations (snow, streamflow, ecology, radar, satellite) bring together scientists different disciplines communities.

Language: Английский

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Projecting climate change impacts on hydrological processes on the Tibetan Plateau with model calibration against the glacier inventory data and observed streamflow

Journal of Hydrology, Journal Year: 2019, Volume and Issue: 573, P. 60 - 81

Published: March 15, 2019

Language: Английский

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Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets

Water Resources Research, Journal Year: 2020, Volume and Issue: 56(1)

Published: Jan. 1, 2020

Abstract Hydrological model calibration combining Earth observations and in situ measurements is a promising solution to overcome the limitations of traditional streamflow‐only calibration. However, multiple data sources requires meaningful integration sets, which should harness their most reliable contents avoid accumulation uncertainties mislead parameter estimation procedure. This study analyzes improvement selection by using only spatial patterns satellite remote sensing data, thereby ignoring absolute values. Although products are characterized uncertainties, key feature representation patterns, unique relevant source information for distributed hydrological models. We propose novel multivariate framework exploiting simultaneously incorporating streamflow three (i.e., Global Land Evaporation Amsterdam Model [GLEAM] evaporation, European Space Agency Climate Change Initiative [ESA CCI] soil moisture, Gravity Recovery Experiment [GRACE] terrestrial water storage). The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature set used evaluation. A bias‐insensitive multicomponent pattern matching metric developed formulate multiobjective function. proposed tested with mesoscale Hydrologic (mHM) applied poorly gauged Volta River basin located predominantly semiarid climate West Africa. Results show that decrease performance (−7%) storage (−6%) counterbalanced an increase moisture (+105%) evaporation (+26%). These results demonstrate there benefits when suitably integrated robust parametrization scheme.

Language: Английский

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Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 255, P. 112301 - 112301

Published: Jan. 22, 2021

Language: Английский

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Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Water Resources Research, Journal Year: 2020, Volume and Issue: 56(4)

Published: March 20, 2020

Abstract Launched in May 2018, the Gravity Recovery and Climate Experiment Follow‐On mission (GRACE‐FO)—the successor of erstwhile GRACE mission—monitors changes total water storage, which is a critical state variable regional global hydrologic cycles. However, gap between data two missions breaking continuity observations limiting its further application. In this study, we used three learning‐based models, that is, deep neural network, multiple linear regression (MLR), seasonal autoregressive integrated moving average with exogenous variables, six solutions (i.e., Jet Propulsion Laboratory spherical harmonics (JPL‐SH), Center for Space Research SH (CSR‐SH), GeoforschungsZentrum Potsdam (GFZ‐SH), JPL mass concentration blocks (mascons) (JPL‐M), CSR mascons (CSR‐M), Goddard Flight (GSFC‐M)) to reconstruct missing monthly at grid cell scale. Evaluation showed models were reliable reconstruction areas humid no/low human interventions. The network slightly outperformed variables significantly most 60 basins studied. mascon sets performed better than basin similar performance, but varied markedly some basins. Results study are expected provide reference bridging gaps GRACE‐FO satellites selecting suitable studies.

Language: Английский

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Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States

Journal of Hydrology, Journal Year: 2019, Volume and Issue: 578, P. 124105 - 124105

Published: Sept. 5, 2019

Language: Английский

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Groundwater Storage Changes in China from Satellite Gravity: An Overview

Remote Sensing, Journal Year: 2018, Volume and Issue: 10(5), P. 674 - 674

Published: April 26, 2018

Groundwater plays a critical role in the global water cycle and is drinking source for almost half of world’s population. However, exact quantification its storage change remains elusive due primarily to limited ground observations space time. The Gravity Recovery Climate Experiment (GRACE) twin-satellite data have provided variations at monthly sampling over decade half, enable estimate changes groundwater (GWS) after removing other components using auxiliary datasets models. In this paper, we present an overview GWS three main aquifers within China GRACE data, conduct comprehensive accuracy assessment situ well hydrological detects significant depletion rate 7.2 ± 1.1 km3/yr North Plain (NCP) during 2002–2014, consistent with model predictions. Liaohe River Basin (LRB) experienced pronounced decline 2005–2009, 5.0 1.2 km3/yr. Since 2010, GRACE-based reveal slow recovery LRB, excellent agreement observations. For whole study period no long-term found LRB nor Tarim Basin. A case Inner Tibetan Plateau highlights there still exist large uncertainties estimates.

Language: Английский

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Impacts of climate change and human activities on the flow regime of the dammed Lancang River in Southwest China

Journal of Hydrology, Journal Year: 2019, Volume and Issue: 570, P. 96 - 105

Published: Jan. 11, 2019

Language: Английский

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