Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model

Lee, Taesam; Shin, Ju Young; Kim, Jong‐Suk; Singh, Vijay P.

doi:10.1016/j.jhydrol.2019.124540

Cited by 51 publications

(24 citation statements)

References 40 publications

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“…Zhang et al (2018) found the LSTM to be a valuable tool for water table depth prediction in regions where it was difficult to obtain hydrogeological data, or the known hydrogeological characteristics were particularly complex. Lee et al (2020) found the long‐term variability and correlation structure of streamflow systems well preserved by the LSTM over annual timescales. Kratzert et al (2018) demonstrated that the LSTM was better at determining long‐term dependencies (runoff in spring based on snowfall in winter) than the RNN.…”

Section: Discussionmentioning

confidence: 99%

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Modern Strategies for Time Series Regression

Clark

Hyndman

Pagendam

et al. 2020

Int Statistical Rev

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This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.

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Section: Discussionmentioning

confidence: 99%

“…This method builds data sets from blocks of Figure 3. Observed daily rainfall and evaporation at station 32042 (Tully, QLD, 1957-2020. All available rainfall data (63 years) are shown on the top plot, and 2 years of seasonal patterns in rainfall and evaporation are evident on the lower plots.…”

Section: Climate Datamentioning

confidence: 99%

Modern Strategies for Time Series Regression

Clark

Hyndman

Pagendam

et al. 2020

Int Statistical Rev

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“…While many hydrologic models have been developed over the past 50 years, the challenge of providing streamflow forecasts accurately, efficiently and everywhere at all times remains. Several studies have applied deep learning in water resources fields, including surface water quality (Hu et al, 2019;Zhou, 2020), streamflow forecasting (Feng et al, 2020;Li et al, 2020;Qian et al, 2020;Sarkar et al, 2020;Van et al, 2020;Yue et al, 2020), soil moisture (Fang and Shen, 2020), groundwater (Wang et al, 2020;Yu et al, 2020), hydrometeorology (Chen et al, 2020;Lee et al, 2020), and water management (Liu et al, 2019). Recent studies (Chang et al, 2015;Granata et al, 2016;Faruk, 2010;Sit and Demir, 2019) have shown that many machine learning and deep learning models could be valuable in streamflow forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Regional Semi-Distributed Streamflow Model Using Deep Learning

Xiang¹,

Demir²,

Mantilla³

et al. 2021

Preprint

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Recent studies have shown that deep learning models in hydrological applications significantly improved streamflow predictions at multiple stations compared to traditional machine learning approaches. However, most studies lack generalization; i.e. researchers are training separate models for each location. The spatial and temporal generalization ability of deep learning models in hydrology that can be gained by training a single model for multiple stations is evaluated in this study. We developed a generalized model with a multi-site structure for hourly streamflow hindcasts on 125 USGS gauges. Considering watershed-scale features including drainage area, time of concentration, slope, and soil types, the proposed models have acceptable performance and slightly higher median NSE value than training individual models for each USGS station. Furthermore, we showed that the trained generalized model can be applied to any new gauge in the state of Iowa that was not used in the training set with acceptable accuracy. This study demonstrates the potential of deep learning studies in hydrology where more domain knowledge and physical features can support further generalization.

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“…Nowadays, because of the simple structure and free-assumptions, it is a new way to adopt ANNs models in indistinct hydrological simulation and prediction [24][25][26]. Lee et al [27] found that the long short-term memory (LSTM) model could be used to stochastically simulate hydrological and climatic variables as it better reproduces the variability and related structure at large time scales, as well as the core statistics of the original time domain. Compared with other NNs, the LSTM is better at using time-varying characteristics of time series data [23].…”

Section: Introductionmentioning

confidence: 99%

Improving Runoff Simulation and Forecasting with Segmenting Delay of Baseflow from Fast Surface Flow in Montane High-Vegetation-Covered Catchments

Wang

Liu

et al. 2021

Water

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Due to the complicated terrain conditions in montane catchments, runoff formation is fast and complicated, making accurate simulation and forecasting a significant hydrological challenge. In this study, the spatiotemporal variable source mixed runoff generation module (SVSMRG) was integrated with the long short-term memory (LSTM) method, to develop a semi-distributed model (SVSMRG)-based surface flow and baseflow segmentation (SVSMRG-SBS). Herein, the baseflow was treated as a black box and forecasted using LSTM, while the surface flow was simulated using the SVSMRG module based on hydrological response units (HRUs) constructed using eco-geomorphological units. In the case study, four typical montane catchments with different climatic conditions and high vegetation coverage, located in the topographically varying mountains of the eastern Tibetan Plateau, were selected for runoff and flood process simulations using the proposed SVSMRG-SBS model. The results showed that this model had good performance in hourly runoff and flood process simulations for montane catchments. Regarding runoff simulations, the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (R2) reached 0.8241 and 0.9097, respectively. Meanwhile, for the flood simulations, the NSE ranged from 0.5923 to 0.7467, and R2 ranged from 0.6669 to 0.8092. For the 1-, 3-, and 5-h baseflow forecasting with the LSTM method, it was found that model performances declined when simulating the runoff processes, wherein the NSE and R2 between the measured and modeled runoff decreased from 0.8216 to 0.8087 and from 0.9095 to 0.8871, respectively. Similar results were found in the flood simulations, the NSE and R2 values declined from 0.7414–0.5885 to 0.7429–0.5716 and from 0.8042–0.6547 to 0.7936–0.6067, respectively. This means that this new model achieved perfect performance in montane catchment runoff and flood simulation and forecasting with 1-, 3-, 5-h steps. Therefore, as it considers vegetation regulation, the SVSMRG-SBS model is expected to improve runoff and flood simulation accuracy in montane high-vegetation-covered catchments.

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Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model

Cited by 51 publications

References 40 publications

Modern Strategies for Time Series Regression

Modern Strategies for Time Series Regression

A Regional Semi-Distributed Streamflow Model Using Deep Learning

Improving Runoff Simulation and Forecasting with Segmenting Delay of Baseflow from Fast Surface Flow in Montane High-Vegetation-Covered Catchments

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