Total Basin Discharge From GRACE and Water Balance Method for the Yarlung Tsangpo River Basin, Southwestern China

Xie, Jingkai; Xu, Yue‐Ping; Gao, Chao; Xuan, Weidong; Bai, Zhixu

doi:10.1029/2018jd030025

Cited by 35 publications

(20 citation statements)

References 75 publications

(112 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparing with single decision trees, the RF and XGB could reduce overfitting and improve accuracy because of their ensemble (bagging and boosting) techniques. And comparing with the ANN-based model that was used for estimating total basin discharge time series in recent publication (Xie et al, 2019), the models presented in this study are able to reconstruct the spatial patterns of the TWSA instead of one-dimensional time series by introducing geolocations as variables and training the models at grids scale. In addition, variable importance can be quantified in the RF and XBG model, providing an insight into the variables' significance to the TWSA estimations.…”

Section: Discussionmentioning

confidence: 99%

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Jing

Zhao

Yao

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

Freshwater stored on land is an extremely vital resource for all the terrestrial life on Earth. But our ability to record the change of land water storage is weak despite its importance. In this study, we attempt to establish a data‐driven model for simulating terrestrial water storage dynamics by relating climate forcings with terrestrial water storage anomalies (TWSAs) from the Gravity Recovery and Climate Experiment (GRACE) satellites. In the case study in Pearl River basin, China, the relationships were learned by using two ensemble learning algorithms, the Random Forest (RF) and eXtreme Gradient Boost (XGB), respectively. The TWSA in the basin was reconstructed back to past decades and compared with the TWSA derived from global land surface models. As a result, the RF and XGB algorithms both perform well and could nicely reproduce the spatial pattern and value range of GRACE observations, outperforming the land surface models. Temporal behaviors of the reconstructed TWSA time series well capture those of both GRACE and land surface models time series. A multiscale GRACE‐based drought index was proposed, and the index matches the Standardized Precipitation‐Evapotranspiration Index time series at different time scales. The case analysis for years of 1963 and 1998 indicates the ability of the reconstructed TWSA for identifying past drought and flood extremes. The importance of different input variables to the TWSA estimation model was quantified, and the precipitation of the prior 2 months is the most important variable for simulating the TWSA of the current month in the model. Results of this study highlight the great potentials for estimating terrestrial water storage dynamics from climate forcing data by using machine learning to achieve comparable results than complex physical models.

show abstract

Section: Discussionmentioning

confidence: 99%

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Jing

Zhao

Yao

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

show abstract

“…Effective discharge monitoring is important for water management and utilization, as well as the scientific development of dispatch plans [26][27][28]. However, due to the variation in regional precipitation frequency and intensity, the estimation of discharge remains challenging.…”

Section: Introductionmentioning

confidence: 99%

Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

et al. 2021

View full text Add to dashboard Cite

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

show abstract

“…Dinh and Fllipe [38] found that water volume change evaluated from GRACE TWSA was highly correlated to river discharge with correlations ranging from 0.77 to 0.88 over the Amazon River Basin. Chen et al [39] and Xie et al [40] also declared that TWSA data from GRACE satellites were essential to estimate discharge at a monthly and seasonal time scale, by comparing with simulations and in situ measurements. Based on the fact that the above-mentioned studies merely focused on single data sources, the combination of Jason-2/3 altimetry satellites and the GRACE mission in this study will greatly improve the discharge estimation.…”

Section: Introductionmentioning

confidence: 99%

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

2021

View full text Add to dashboard Cite

Remotely sensing data have advantages in filling spatiotemporal gaps of in situ observation networks, showing potential application for monitoring floods in data-sparse regions. By using the water level retrievals of Jason-2/3 altimetry satellites, this study estimates discharge at a 10-day timescale for the virtual station (VS) 012 and 077 across the midstream Yangtze River Basin during 2009–2016 based on the developed Manning formula. Moreover, we calibrate a hybrid model combined with Gravity Recovery and Climate Experiment (GRACE) data, by coupling the GR6J hydrological model with a machine learning model to simulate discharge. To physically capture the flood processes, the random forest (RF) model is employed to downscale the 10-day discharge into a daily scale. The results show that: (1) discharge estimates from the developed Manning formula show good accuracy for the VS012 and VS077 based on the improved Multi-subwaveform Multi-weight Threshold Retracker; (2) the combination of the GR6J and the LSTM models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RF-downscaled daily discharge demonstrates a general consistency with in situ data, where NSE/KGE between them are as high as 0.69/0.83. Our approach, based on multi-source remotely sensing data and machine learning techniques, may benefit flood monitoring in poorly gauged areas.

show abstract

Total Basin Discharge From GRACE and Water Balance Method for the Yarlung Tsangpo River Basin, Southwestern China

Cited by 35 publications

References 75 publications

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

Contact Info

Product

Resources

About