Reconstruction of GRACE terrestrial water storage anomalies using Multi-Layer Perceptrons for South Indian River basins

Kumar, K. Satish; AnandRaj, Pallakury; Sreelatha, Koppala; Sridhar, Venkataramana

doi:10.1016/j.scitotenv.2022.159289

Cited by 10 publications

(8 citation statements)

References 62 publications

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“…The Seasonal and Trend decomposition using Loess (STL) method is a statistical approach for analyzing time series data. In this method, a robust loess weighted regression is used as a smoother to fit a weighted polynomial regression to the observed data over time 25 . The weights assigned to each data point decrease as the distance from the point to the observation window increases.…”

Section: Methodsmentioning

confidence: 99%

Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes

Yin,

Xia,

Liu

et al. 2024

Sci Rep

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The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010–2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions.

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Section: Methodsmentioning

confidence: 99%

Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes

Yin,

Xia,

Liu

et al. 2024

Sci Rep

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“…Several studies have proposed various models (e.g., linear model, random forest, and neural networks) to reconstruct long-term TWS changes data before GRACE era by learning their empirical relationship with different driving factors (e.g., Humphrey & Gudmundsson, 2019;Satish Kumar et al, 2023;Wang et al, 2023;Yin et al, 2023). runoff, where precipitation is the key recharge source for TWS and runoff changes, whereas temperature plays a significant role in influencing evapotranspiration (Chen et al, 2020).…”

Section: Selection Of Explanatory Variablesmentioning

confidence: 99%

“…Both anthropogenic activities and climate variability influence changes in TWS derived from GRACE. Despite distinct predictors having been used in previous studies to build their empirical relationship with GRACE data (e.g., Humphrey & Gudmundsson, 2019;Satish Kumar et al, 2023;Wang et al, 2023), they dominantly present the climatic drivers, as human-induced changes in TWS are not adequately observed and cannot be incorporated into these models. For instance, Humphrey et al (2016) and Humphrey et al (2017) indicated that changes in TWS are tightly related to fluctuations in precipitation and temperature and thus can be statistically reconstructed from them.…”

Section: Introductionmentioning

confidence: 99%

Reconstructed Global Total Water Storage Products (1923-2022): Insights and Challenges in Humid and Arid Regions

Wang,

Shen,

Awange

et al. 2024

Preprint

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“…Backpropagation techniques are used to solve unknowns in Equations ( 5) and ( 6) during the training period in order to acquire the appropriate weights in each layer. For further information regarding multilayer perceptron's, readers are encouraged to read the following publication [56,[59][60][61].…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…As a result, a significant amount of water is used for irrigation throughout this season. As the rainfall is unevenly distributed throughout the country, most irrigation is carried out using groundwater [15,60]. Therefore, a reduction in groundwater levels can be observed during the monsoon season.…”

Section: Spatial Analysis Of Seasonal Groundwater Levels From 2003 To...mentioning

confidence: 99%

Bridging the Data Gap between the GRACE Missions and Assessment of Groundwater Storage Variations for Telangana State, India

Kumar

Sridhar

Varaprasad

et al. 2022

Water

Self Cite

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Because of changing climatic conditions, uneven distribution of rainfall occurs throughout India. As a result, dependence on groundwater for irrigation has increased tremendously for industrial and domestic purposes. In India approximately 89% of agricultural demands are met through groundwater. Due to increases in population, demand for groundwater and lack of effective utilization have resulted in rapid depletion of groundwater in most parts of the country. Therefore, quantifying groundwater resources is a serious concern in populated states of India, because it is now difficult to supply enough water to every citizen, and will remain so in the future. Because of difficulties in accessing observation data, researchers have begun to depend on satellite-based remote sensing information to deal with groundwater variations. The present study deals with filling the data gap between Gravity Recovery And Climate Experiment (GRACE) and GRACE Follow On (GRACE FO) missions using multilayer perceptron’s (MLPs) during 2017–2018 to obtain a continuous terrestrial water storage anomaly (TWSA) series from 2003 to 2020 for Telangana state, India. The MLP model performed well in predicting the TWSA, with a correlation coefficient of r = 0.96 between modeled TWSA and GRACE TWSA during the test period. Telangana state observed negative TWSAs (annual) in the years 2003, 2004, 2005, 2009, 2012, 2015, and 2016–19. This TWSA series (2003–2020) was then used to evaluate regional groundwater storage anomalies (GWSAs) in Telangana state, which is considered to be one of the water stress regions in India. The TWSAs were converted to GWSAs using Global Land Data Assimilation System (GLDAS) parameters. The Telangana state experienced decreasing GWSA in the years 2005, 2009, and 2012, and from 2015 to 2019, leading to severe droughts. Groundwater well measurements were obtained from the Central Groundwater Board (CGWB) and converted to GWSA at a seasonal scale. The GWSAs obtained from GRACE (GWSAGRACE) were converted to seasonal values and compared with GWSAs obtained from observation well data (GWSAobs). The performance metrics of r = 0.74, RMSE = 5.3, and NSE = were obtained between (GWSAGRACE) and (GWSAobs), representing a good correlation among them. Over the past decade, Telangana state has significantly relied on groundwater resources for irrigation, domestic, and industrial purposes. As a result, evaluating groundwater storage variations at a regional scale may help policy makers and water resource researchers in the sustainable utilization and management of groundwater resources.

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Reconstruction of GRACE terrestrial water storage anomalies using Multi-Layer Perceptrons for South Indian River basins

Cited by 10 publications

References 62 publications

Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes

Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes

Reconstructed Global Total Water Storage Products (1923-2022): Insights and Challenges in Humid and Arid Regions

Bridging the Data Gap between the GRACE Missions and Assessment of Groundwater Storage Variations for Telangana State, India

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