A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations

Jing, Wenlong; Di, Liping; Zhao, Xiaodan; Yao, Ling; Xia, Xiaolin; Liu, Yangxiaoyue; Yang, Ji; Li, Yong; Zhou, Chenghu

doi:10.1016/j.advwatres.2020.103683

Cited by 22 publications

(7 citation statements)

References 71 publications

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“…It is also interesting to note that the beginning of the increase shown in the daily TWSA is earlier than that of streamflow. This is partly because high antecedent soil moisture, which is an important component of TWSA, has been identified as an important driver of flood events for regions (Fatolazadeh and Goïta, 2022;Jing et al, 2020;Reager et al, 2014;Wasko and Natthan, 2019). Meanwhile, this result indicates that the daily TWSA can be potentially useful in building early flood warning systems since it may identify the extreme flood events much more earlier than streamflow.…”

Section: Relation Between Daily Twsa and Streamflow During Flood Eventsmentioning

confidence: 97%

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Xie

Xu²,

Yu³

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellite provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor flood in various regions at monthly intervals. However, the coarse temporal resolution of GRACE and GRACE-FO satellite data has been limiting their applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then temporally downscaled into daily values based on three different learning-based models, namely a multi-layer perceptron (MLP) model, a long-short term memory (LSTM) model and a multiple linear regression (MLR) model. Furthermore, a new index incorporating temporally downscaled TWSA estimates combined with daily average precipitation anomalies is proposed to monitor the severe flood events at sub-monthly timescales for the Yangtze River basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing the monthly TWSA with root mean square error (RMSE) = 10.9 mm per month and Nash–Sutcliffe efficiency (NSE) = 0.89 during the validation period; (2) the MLP model can be useful in temporally downscaling monthly TWSA estimates into daily values; (3) the proposed normalized daily flood potential index (NDFPI) facilitates robust and reliable characterization of severe flood events at sub-monthly timescales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual subbasins. All these findings can provide new opportunities for applying GRACE and GRACE-FO satellite data to investigations of sub-monthly signals and have important implications for flood hazard prevention and mitigation in the study region.

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Section: Relation Between Daily Twsa and Streamflow During Flood Eventsmentioning

confidence: 97%

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Xie

Xu²,

Yu³

et al. 2022

Hydrol. Earth Syst. Sci.

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“…Total water storage anomalies (TWSA), monitored by NASA's GRACE satellites via satellite gravimetry, are currently used for retrieving the exclusive data of TWSC in hydrological and climatic applications (Landerer and Swenson, 2012;Long et al, 2014;Jing et al, 2020a). Notably, the GRACE TWSA observation data only covers the period 2002-2017 (Jing et al, 2020b).…”

Section: Precipitation and Grace Datasetsmentioning

confidence: 99%

Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe

Wang

Tong

et al. 2023

Front. Environ. Sci.

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The wide application of the evapotranspiration (ET) products has deepened our understanding of the water, energy and carbon cycles, driving increased interest in regional and global assessments of their performance. However, evaluating ET products at a global scale with varying levels of dryness and vegetation greenness poses challenges due to a relative lack of reference data and potential water imbalance. Here, we evaluated the performance of eight state-of-the-art ET products derived from remote sensing, Land Surface Models, and machine learning methods. Specifically, we assessed their ability to capture ET magnitude, variability, and trend, using 1,381 global watershed water balance ET as a baseline. Furthermore, we created aridity and vegetation categories to investigate performance differences among products under varying environmental conditions. Our results demonstrate that the spatial and temporal performances of the ET products were strongly affected by aridity and vegetation greenness. The poorer performances, such as underestimation of interannual variability and misjudged trend, tend to occur in abundant humidity and vegetation. Our findings emphasize the significance of considering aridity and vegetation greenness into ET product generation, especially in the context of ongoing global warming and greening. Which hopefully will contribute to the directional optimizations and effective applications of ET simulations.

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“…The ARIMA model was selected to explore another statistical-based approach (deterministic statistic) to fill the missing record between GRACE and GRACE-FO. Currently, several ongoing research efforts are implementing either complex modeling tools [92], different explanatory variables from satellite or LSMs (i.e., precipitation, and temperature) [31,93], or independent geophysical datasets (i.e., Swarm satellite data) [94] to fill the one-year gap between GRACE and GRACE-FO. Herein, we present an alternative/easyto-apply approach to fill the one-year gap without any exogenous/independent variables/data.…”

Section: Arima Modelmentioning

confidence: 99%

“…Several research studies have implemented various statistical tools to extend the GRACE-TWS to before-GRACE-era, i.e., [31,92,[101][102][103][104]. Fewer studies, however, have provided GRACE-like TWS in the NRB during the past century, i.e., [31].…”

Section: Hydrological Extremes In the Nrbmentioning

confidence: 99%

Using GRACE/GRACE-FO, Data-Driven, and Modeling to Assess the Twentieth and Twenty-first Century Water Storage Changes in the Nile River Basin

Hasan¹,

Tarhule²,

Kirstetter³

2021

Preprint

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This research assesses the changes in the total water storage (TWS) during the twentieth century and their future projections in the Nile River Basin (NRB) via TWSA (TWS anomalies) records from GRACE (Gravity Recovery and Climate Experiment), GRACE-FO (Follow-On), data-driven-reanalysis TWSA and land surface model (LSM), in association with precipitation, temperature records, and standard drought indicators. The analytical approach incorporates the development of 100+ yearlong TWSA records using a probabilistic conditional distribution fitting approach by the GAMLSS (Generalized Additive Model for Location, Scale, and Shape) model. The drought and flooding severity, duration, magnitude, frequencies, and recurrence were assessed during the studied period. The results showed, 1- The NRB between 2002 to 2020 has transited to substantial wetter conditions. 2- The TWSA reanalysis records between 1901 to 2002 revealed that the NRB had experienced a positive increase in TWS during the wet and dry seasons. 3- The projected TWSA between 2021 to 2050 indicated slight positive changes in TWSA during the rainy seasons. The analysis of drought and flooding frequencies between 1901 to 2050 indicated the NRB has ~64 dry-years compared to ~86 wet-years. The 100+ yearlong TWSA records assured that the NRB transited to wetter conditions relative to few dry spells. These TWSA trajectories call for further water resources planning in the region especially during flood seasons. This research contributes to the ongoing efforts to improve the TWSA assessment and its associated dynamics for transboundary river basins. It also demonstrates how an extended TWSA record provides unique insights for water resources management in the NRB and similar regions.

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A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations

Cited by 22 publications

References 71 publications

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe

Using GRACE/GRACE-FO, Data-Driven, and Modeling to Assess the Twentieth and Twenty-first Century Water Storage Changes in the Nile River Basin

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