Multiscale gravity measurements to characterize 2020 flood events and their spatio-temporal evolution in Yangtze river of China

Wang, Linsong; Peng, Zhenran; Ma, Xian; Zheng, Yuhao; Chen, Chao

doi:10.1016/j.jhydrol.2021.127176

Cited by 12 publications

(5 citation statements)

References 50 publications

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“…The variability of turbidity in the MYR is significant. In August 2020, turbidity in th MYR increased significantly as a result of flooding [39]. According to the search resul (Figure 6), the average value of turbidity in the middle reaches of the Yangtze River wa more than 100 NTU.…”

Section: Variability Of Turbidity and Tp Concentration In The Myrmentioning

confidence: 98%

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

Yang,

Feng,

Zhou

et al. 2024

Remote Sensing

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Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP retrieval model using Sentinel-2 observations and field-measured daily-scale water quality. The model was successfully applied to estimate the temporal and spatial variations of TP concentration in the middle reaches of the Yangtze River (MYR) from 2020 to 2023. Our results show: (1) the model accuracy of TP concentration retrieval with turbidity is significantly higher (R2 = 0.71, MAPE = 15.78%) than that for the model without turbidity (R2 = 0.62, MAPE = 16.38%); (2) the turbidity and TP concentration in the MYR is higher in summer and autumn than in winter and spring; and (3) the turbidity and total phosphorus (TP) concentration of the Yangtze River showed a significant increase after passing through Dongting Lake (p < 0.05).

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Section: Variability Of Turbidity and Tp Concentration In The Myrmentioning

confidence: 98%

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

Yang,

Feng,

Zhou

et al. 2024

Remote Sensing

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“…Given the abovementioned flood-water-observation-oriented optimum sensor selection problem, this study proposes the OCEM based on the analysis of flood-event-related studies [34][35][36]. Figure 1 shows the framework of the OCEM, which takes a flood disaster event as input and outputs a ranking of available satellite sensors by quantitatively evaluating their observation capabilities while accounting for the flood spatiotemporal characteristics of the flood.…”

Section: Ocem Formalization 221 Ocem Frameworkmentioning

confidence: 99%

“…studies [34][35][36]. Figure 1 shows the framework of the OCEM, which takes a flood disaster event as input and outputs a ranking of available satellite sensors by quantitatively evaluating their observation capabilities while accounting for the flood spatiotemporal characteristics of the flood.…”

Section: Ocem Formalization 221 Ocem Frameworkmentioning

confidence: 99%

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Duan,

Zhang,

Liu

et al. 2023

Applied Sciences

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Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.

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“…Therefore, GRACE TWSC data have an incomparable advantage in detecting large-scale floods [22]. Several scholars have applied GRACE TWSC to detect floods in multiple regions, for example, Europe [23], the Yangtze River basin [24,25], the Nile River basin [26], the Niger River basin [27], and the Liao River basin [28]. However, the GRACE and GRACE-FO missions have an 11-month data gap period, which led to the interruption of observations and bring great inconvenience to flood research [29,30].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evaluation of the Flood Potential Index and Its Driving Factors across the Volga River Basin Based on Combined Satellite Gravity Observations

Zou,

Li,

Cui

et al. 2023

Remote Sensing

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Floods have always threatened the survival and development of human beings. To reduce the adverse effects of floods, it is very important to understand the influencing factors of floods and their formation mechanisms. In our study, we integrated the Gravity Recovery and Climate Experiment and its Follow-On and Swarm solutions to estimate an uninterrupted 19-year flood potential index (FPI) time series, discussed the spatiotemporal distribution characteristics of the FPI and monitored major floods in the Volga River basin (VRB) from 2003 to 2021. Finally, we analyzed the relationship between the FPI and hydrometeorological factors to comprehend the flood formation mechanism. The results show that data fusion has reduced the uncertainty of terrestrial water storage change (TWSC), and the TWSC from the combined satellite gravity observations has a good consistency with that from the Global Land Data Assimilation System model (correlation coefficient = 0.92). During the study period, two major floods (June 2005 and May 2018) occurred in the VRB. The FPI has a significant seasonal change characteristic, and shows a high flood risk in spring and a low one in autumn. With regards to spatial distribution, the flood risk is increasing in the north (increasing rate = 0.1) and decreasing in the south (decreasing rate = 0.39). Snow water equivalent (SWE, correlation coefficient = 0.75) has a stronger correlation with the FPI than precipitation (PPT, correlation coefficient = 0.46), which is attributed to the recharge of SWE on water resources greater than that of PPT. The rising surface temperature (ST) speeds up snow melt, resulting in excessive groundwater and soil moisture, and the flood risk greatly increases at this time. The process lasts about three months. Therefore, except for PPT, ST is also a climatic factor leading to the floods in the VRB. Our study provides a reference for flood research in high-latitude regions.

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Multiscale gravity measurements to characterize 2020 flood events and their spatio-temporal evolution in Yangtze river of China

Cited by 12 publications

References 50 publications

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection

Spatiotemporal Evaluation of the Flood Potential Index and Its Driving Factors across the Volga River Basin Based on Combined Satellite Gravity Observations

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