Identifying Flood Events over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations, Hydrological Models and In Situ Data

Zhou, Hao; Luo, Zhicai; Tangdamrongsub, Natthachet; Zhou, Zebing; He, Ling‐Yun; Xu, Chuang; Li, Qiong; Wu, Yun

doi:10.3390/rs10050713

Cited by 21 publications

(12 citation statements)

References 49 publications

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“…Each PM2.5 component was significantly anti-correlated with precipitation in most areas of the YRB, primarily due to wet scavenging [26][27][28][29][30][31][32][33][34][35]. However, a positive relationship was observed in the SYR, where dust particle was the main air pollutant (see Section 3.2) and precipitation did not occur frequently [46][47][48]. Even if there was a small amount of precipitation over the SYR, it may have only increased the relative humidity, resulting in the growth of hygroscopic particles [27].…”

Section: Sensitivity Of Pm25 Components To Precipitationmentioning

confidence: 97%

Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence

Lin

Chen

et al. 2019

Remote Sensing

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A good understanding of how meteorological conditions exacerbate or mitigate air pollution is critical for developing robust emission reduction policies. Thus, based on a multiple linear regression (MLR) model in this study, the quantified impacts of six meteorological variables on PM2.5 (i.e., particle matter with diameter of 2.5 µm or less) and its major components were estimated over the Yangtze River Basin (YRB). The 38-year (1980–2017) daily PM2.5 and meteorological data were derived from the newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) products. The MERRA-2 PM2.5 was underestimated compared with ground measurements, partly due to the bias in the MERRA-2 Aerosol Optical Depth (AOD) assimilation. An over-increasing trend in each PM2.5 component occurred for the whole study period; however, this has been curbed since 2007. The MLR model suggested that meteorological variability could explain up to 67% of the PM2.5 changes. PM2.5 was robustly anti-correlated with surface wind speed, precipitation and boundary layer height (BLH), but was positively correlated with temperature throughout the YRB. The relationship of relative humidity (RH) and total cloud cover with PM2.5 showed regional dependencies, with negative correlation in the Yangtze River Delta (YRD) and positive correlation in the other areas. In particular, PM2.5 was most sensitive to surface wind speed, and the sensitivity was approximately −2.42 µg m−3 m−1 s. This study highlighted the impact of meteorological conditions on PM2.5 growth, although it was much smaller than the anthropogenic emissions impact.

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Section: Sensitivity Of Pm25 Components To Precipitationmentioning

confidence: 97%

Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence

Lin

Chen

et al. 2019

Remote Sensing

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“…Models developed by different approaches and processing strategies are also provided by other agencies, for example, the Astronomical Institute of the University of Bern (Meyer et al, ), the Delft Institute of Earth Observation and Space Systems (Farahani et al, ), the Groupe de Recherche de Geodesie Spatiale (Bruinsma et al, ), the Huazhong University of Science and Technology (HUST; Zhou, Luo, Zhou et al, ), the Institute of Geodesy and Geophysics, Chinese Academy of Sciences (Wang et al, ), the Institute of Theoretical Geodesy and Satellite Geodesy at the Graz University of Technology (ITSG; Klinger & Mayer‐Gürr, ), the Tongji University (Tongji; Chen et al, ), and the Wuhan University (Guo et al, ). These temporal gravity field models have been widely used in hydrology, oceanology, glaciology, meteorology, and seismology (e.g., Chao et al, ; Humphrey et al, ; Long et al, ; Pan et al, ; Tangdamrongsub et al, ; Tregoning et al, ; Zhou, Luo, Tangdamrongsub, et al, ; Zhou, Luo, Tangdamrongsub, et al, ).…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Processing Strategy to Improve Temporal Gravity Field Solution

Zhou

Luo

2019

JGR Solid Earth

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The way to reach the prelaunch baseline accuracy is still one of crucial issues for Gravity Recovery and Climate Experiment (GRACE) temporal gravity field model determination. In this work, two hybrid processing strategies are developed to improve the accuracy of GRACE temporal solution. One is for kinematic empirical parameters, the other is for arc‐specific parameters. These two hybrid processing strategies are compared with two solely processing strategies including filter predetermined strategy (FPS) and pure predetermined strategy (PPS). Assessing with a closed‐loop simulation study, the results demonstrate the outperformance of our hybrid processing strategies. Using GRACE Level 1B data, the GRACE monthly gravity field models HUST‐FPS‐PPS and HUST‐Grace2019 are, respectively, determined with the hybrid processing strategies, and they are compared with the solely solutions HUST‐FPS and HUST‐PPS. The comparison results demonstrate that (1) as for the kinematic empirical parameter, the hybrid processing strategy can retain the temporal signal but also reduce the noise level. For instance, the annual amplitudes over Amazon River Basin are 16.0 cm for HUST‐PPS, 19.1 cm for HUST‐FPS, and 19.4 cm for HUST‐FPS‐PPS. In contrast, the cumulative geoid difference of HUST‐FPS‐PPS reduces by 11% and 26%, when compared to HUST‐PPS and HUST‐FPS. (2) As for the arc‐specific parameters, the hybrid processing strategy also significantly improves the recovered monthly solution. Compare with Center for Space Research RL05 and HUST‐FPS‐PPS, the cumulative geoid difference of HUST‐Grace2019 reduces by 19% and 12%. The comparison results in spatial domain also support our conclusions. The hybrid processing strategy of this work is likely applicable for GRACE Follow‐On data.

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“…Fourth, the ∆TWS or ∆r is computed from the filtered SHC variations (see also Section 3.1), and the Glacial Isostatic Adjustment correction provided by Caron et al [43] is applied. Finally, a signal restoration approach [36,44] is performed to restore the damped signal caused by the applied filters. It is noteworthy that the signal restoration used in this study is applied to the TWS (not to displacements directly).…”

Section: Grace Datamentioning

confidence: 99%

The Assessment of Hydrologic- and Flood-Induced Land Deformation in Data-Sparse Regions Using GRACE/GRACE-FO Data Assimilation

Tangdamrongsub

Šprlák

2021

Remote Sensing

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The vertical motion of the Earth’s surface is dominated by the hydrologic cycle on a seasonal scale. Accurate land deformation measurements can provide constructive insight into the regional geophysical process. Although the Global Positioning System (GPS) delivers relatively accurate measurements, GPS networks are not uniformly distributed across the globe, posing a challenge to obtaining accurate deformation information in data-sparse regions, e.g., Central South-East Asia (CSEA). Model simulations and gravity data (from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)) have been successfully used to improve the spatial coverage. While combining model estimates and GRACE/GRACE-FO data via the GRACE/GRACE-FO data assimilation (DA) framework can potentially improve the accuracy and resolution of deformation estimates, the approach has rarely been considered or investigated thus far. This study assesses the performance of vertical displacement estimates from GRACE/GRACE-FO, the PCRaster Global Water Balance (PCR-GLOBWB) hydrology model, and the GRACE/GRACE-FO DA approach (assimilating GRACE/GRACE-FO into PCR-GLOBWB) in CSEA, where measurements from six GPS sites are available for validation. The results show that GRACE/GRACE-FO, PCR-GLOBWB, and GRACE/GRACE-FO DA accurately capture regional-scale hydrologic- and flood-induced vertical displacements, with the correlation value and RMS reduction relative to GPS measurements up to 0.89 and 53%, respectively. The analyses also confirm the GRACE/GRACE-FO DA’s effectiveness in providing vertical displacement estimates consistent with GRACE/GRACE-FO data while maintaining high-spatial details of the PCR-GLOBWB model, highlighting the benefits of GRACE/GRACE-FO DA in data-sparse regions.

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Identifying Flood Events over the Poyang Lake Basin Using Multiple Satellite Remote Sensing Observations, Hydrological Models and In Situ Data

Cited by 21 publications

References 49 publications

Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence

Assessment of MERRA-2 Surface PM2.5 over the Yangtze River Basin: Ground-based Verification, Spatiotemporal Distribution and Meteorological Dependence

A New Hybrid Processing Strategy to Improve Temporal Gravity Field Solution

The Assessment of Hydrologic- and Flood-Induced Land Deformation in Data-Sparse Regions Using GRACE/GRACE-FO Data Assimilation

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