Natural and human impacts on the landscape evolution and hydrography of the Chico River basin (Argentinean Patagonia)

Scordo, Facundo; Seitz, Carina; Melo, Walter; Perillo, Gerardo M. E.

doi:10.1016/j.catena.2020.104783

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…These streams play a crucial role in their landscape's environmental and hydrological connectivity [5][6][7]. The transition between wet and dry states in IRES is an influential factor in promoting the peak biodiversity of riparian vegetation [8], controlling the kinetics of biogeochemical cycles [9], and channel geomorphology [10]. Additionally, IRES offer beneficial ecosystem services like forage, nesting sites, and transportation routes for both aquatic and terrestrial wildlife [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas

Forghanparast

Mohammadi

2022

Water

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Predicting streamflow in intermittent rivers and ephemeral streams (IRES), particularly those in climate hotspots such as the headwaters of the Colorado River in Texas, is a necessity for all planning and management endeavors associated with these ubiquitous and valuable surface water resources. In this study, the performance of three deep learning algorithms, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Self-Attention LSTM models, were evaluated and compared against a baseline Extreme Learning Machine (ELM) model for monthly streamflow prediction in the headwaters of the Texas Colorado River. The predictive performance of the models was assessed over the entire range of flow as well as for capturing the extreme hydrologic events (no-flow events and extreme floods) using a suite of model evaluation metrics. According to the results, the deep learning algorithms, especially the LSTM-based models, outperformed the ELM with respect to all evaluation metrics and offered overall higher accuracy and better stability (more robustness against overfitting). Unlike its deep learning counterparts, the simpler ELM model struggled to capture important components of the IRES flow time-series and failed to offer accurate estimates of the hydrologic extremes. The LSTM model (K.G.E. > 0.7, R2 > 0.75, and r > 0.85), with better evaluation metrics than the ELM and CNN algorithm, and competitive performance to the SA–LSTM model, was identified as an appropriate, effective, and parsimonious streamflow prediction tool for the headwaters of the Colorado River in Texas.

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