Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China

Wang, Fei; Chen, Yaning; Li, Zhi; Fang, Gonghuan; Li, Yupeng; Wang, Xuanxuan; Zhang, Xueqi; Kayumba, Patient Mindje

doi:10.3390/rs13050889

Cited by 24 publications

(7 citation statements)

References 80 publications

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“…The significantly decreased TWSA in the TRB has also been confirmed by previous studies (Wang et al, 2021;Zuo et al, 2021). Increased AET is also the main factor influencing changes in the TWSA.…”

Section: Comparison With Previous Study Resultssupporting

confidence: 84%

Trends of terrestrial water storage and actual evapotranspiration in Chinese inland basins and their main affecting factors

Liu

Wei

2022

Front. Environ. Sci.

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The Chinese inland basins (CIBs) are vulnerable to global warming and human activities due to low precipitation and high potential evaporation. Terrestrial water storage (TWS) is an important component of the hydrological cycle and essential for evaluating the water resource security of the CIBs. Although some studies have focused on water storage trends in sub-basins of the CIBs, only few studies have analyzed water storage trends in the CIBs as a whole. In this study, trends and magnitudes of precipitation, TWS, and actual evapotranspiration (AET) were detected by the rank-based non-parametric Mann–Kendall test and trend magnitude method. Based on the hydrological budget of the closed inland basin, the monthly series of AET were simulated and the main factors affecting TWS changes in the CIBs and each closed basin were identified. Results showed that both precipitation and AET significantly increased in the CIBs. Precipitation decreased from the northwest and southeast regions to the central region in the CIBs. Moreover, the annual TWS in the CIBs significantly decreased mainly due to the increased AET. Approximately 60% AET increase was attributed to increased irrigation diversions. At the basin scale, similar to the CIBs, changes in AET were the predominant factor influencing changes in TWS in the Tarim basin (TRB), Turpan basin (TPB), and Hexi Corridor basin (HCB). Qiangtang Plateau basin (QPB) Qaidam basin (QDB) the increase in precipitation contributed more than 60% increase in TWS glacier melting and irrigation diversion.

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Section: Comparison With Previous Study Resultssupporting

confidence: 84%

Trends of terrestrial water storage and actual evapotranspiration in Chinese inland basins and their main affecting factors

Liu

Wei

2022

Front. Environ. Sci.

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“…Recent studies have used LSTM models [29][30][31][32][33][34][35], ML models [36][37][38][39] and ANN models [40,41] to forecast groundwater level (GWL). Earlier investigations have forecasted the GWL within the time series of observed data while others have forecasted for three months [36].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Modeling of Groundwater Storage Change

Haq¹,

Jilani²,

Prabu³

2022

Computers, Materials &Amp; Continua

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The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 2003-2025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 2003-2020 with a rate ranging from −5.88 ± 1.2 mm/year to −14.12 ± 1.2 mm/year and −3.5 ± 1.5 to −10.7 ± 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from −7.78 ± 1.2 to −15.6 ± 1.2 for TWSC and −4.97 ± 1.5 to −12.21 ± 1.5 for GWSC from 2020-2025. An interesting observation was a minor increase in rainfall during the study period for three basins.

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“…River discharge forecasting is considered a pivotal task in various fields of water resource management i.e., flood control, irrigation planning, and hydropower production [1][2][3][4][5][6][7]. River discharge has a significant impact on the physical, chemical, and biological activities in the river contributing high correlation to the fluvial ecosystem [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Mehedi¹,

Khosravi²,

Yazdan³

et al. 2022

Preprint

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River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

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Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China

Cited by 24 publications

References 80 publications

Trends of terrestrial water storage and actual evapotranspiration in Chinese inland basins and their main affecting factors

Trends of terrestrial water storage and actual evapotranspiration in Chinese inland basins and their main affecting factors

Deep Learning Based Modeling of Groundwater Storage Change

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

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