Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short‐Term Memory (LSTM) Networks

Xie, Jialin; Liu, Xiaomang; Tian, Wei; Wang, Kaiwen; Bai, Peng; Liu, Changming

doi:10.1029/2021wr031663

Cited by 12 publications

(3 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the distributions of drainage area do not appear bimodal for the different set of sites (Figure S6 in Supporting Information ); nor do we observe any spatial clustering of the sites. Given the strong linkage between catchment geomorphology and streamflow signature (Addor et al., 2018; Xie et al., 2022), it is rare for the I B distribution to be bimodal. Second, the contribution of baseflow to total flow has been estimated by different methods, such as process‐based and data‐driven hydrological modeling (Ghimire et al., 2023; Xie et al., 2022), remote sensing observations (Mohanasundaram et al., 2021), and water balance framework (Cheng et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Given the strong linkage between catchment geomorphology and streamflow signature (Addor et al., 2018; Xie et al., 2022), it is rare for the I B distribution to be bimodal. Second, the contribution of baseflow to total flow has been estimated by different methods, such as process‐based and data‐driven hydrological modeling (Ghimire et al., 2023; Xie et al., 2022), remote sensing observations (Mohanasundaram et al., 2021), and water balance framework (Cheng et al., 2021). These different estimation approaches consistently indicate a general spatial distribution of baseflow contribution similar to the one revealed by our analysis for CONUS.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Baseflow Separation Through Chemical Mass Balance: Comparing the Usages of Two Tracers, Two Concentration Estimation Methods, and Four Baseflow Filters

Mei,

Wang,

Zhu

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

Optimizing empirical baseflow filters using environmental tracers (e.g., specific electrical conductance (SEC), turbidity) is an effective and efficient way to quantify the contribution of baseflow to total flow. To execute this baseflow separation, three key components are needed: The tracer, the method to estimate tracer concentration in different flow components, and the empirical baseflow filter. However, a comprehensive evaluation of the various combinations of these components, especially with a large sample of catchments, is currently lacking in the literature. Therefore, our study assembles 16 hybrid baseflow filters from two tracers, two concentration estimation methods, and four empirical baseflow filters, and evaluated their performance in baseflow separation and producing two long‐term baseflow signatures for 1,100 catchments in the Contiguous United States. Our results suggest that SEC is a superior tracer to turbidity for baseflow separation. Additionally, using monthly maximum and minimum values to represent tracer concentration in flow components produces better separation than using a power function relationship between flow rate and concentration. The four empirical baseflow filters offer a similar level of performance, regardless of the other options used. Yet, some of these filters produce inconsistent results in calculating the baseflow signatures for the catchments. Our analysis shed light on the optimization of hybrid baseflow filters for the accurate quantification of baseflow contribution.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Optimal Baseflow Separation Through Chemical Mass Balance: Comparing the Usages of Two Tracers, Two Concentration Estimation Methods, and Four Baseflow Filters

Mei,

Wang,

Zhu

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…The RNN is an artificial neural network that can handle time-series prediction by piling up contextual information in the hidden state and using it as input for the next time step in the feedforward channel. This property of the RNN can cause problems with disappearing and exploding gradients, especially when solving RNN training for long sequence data [60]. The LSTM is a variation of the RNN, which controls input, memory, output and other information through a gated memory unit, and replaces neurons in the RNN with the gated memory units with control memory function, to solve the problem of RNN gradient disappearance and explosion [61].…”

Section: Long Short-term Memorymentioning

confidence: 99%

A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River

Zhou

Kang

2023

Water

View full text Add to dashboard Cite

Obtaining more accurate flood information downstream of a reservoir is crucial for guiding reservoir regulation and reducing the occurrence of flood disasters. In this paper, six popular ML models, including the support vector regression (SVR), Gaussian process regression (GPR), random forest regression (RFR), multilayer perceptron (MLP), long short-term memory (LSTM) and gated recurrent unit (GRU) models, were selected and compared for their effectiveness in flood routing of two complicated reaches located at the upper and middle main stream of the Yangtze River. The results suggested that the performance of the MLP, LSTM and GRU models all gradually improved and then slightly decreased as the time lag increased. Furthermore, the MLP, LSTM and GRU models outperformed the SVR, GPR and RFR models, and the GRU model demonstrated superior performance across a range of efficiency criteria, including mean absolute percentage error (MAPE), root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), Taylor skill score (TSS) and Kling–Gupta efficiency (KGE). Specifically, the GRU model achieved reductions in MAPE and RMSE of at least 7.66% and 3.80% in the first case study and reductions of 19.51% and 11.76% in the second case study. The paper indicated that the GRU model was the most appropriate choice for flood routing in the Yangtze River.

show abstract

Assessing Efficacy of Baseflow Separation Techniques in a Himalayan River Basin, Northern India

Bhardwaj,

Jha,

Uniyal

2024

Environ. Process.

View full text Add to dashboard Cite

Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short‐Term Memory (LSTM) Networks

Cited by 12 publications

References 94 publications

Optimal Baseflow Separation Through Chemical Mass Balance: Comparing the Usages of Two Tracers, Two Concentration Estimation Methods, and Four Baseflow Filters

Optimal Baseflow Separation Through Chemical Mass Balance: Comparing the Usages of Two Tracers, Two Concentration Estimation Methods, and Four Baseflow Filters

A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River

Assessing Efficacy of Baseflow Separation Techniques in a Himalayan River Basin, Northern India

Contact Info

Product

Resources

About