Socio-economic impacts of changes in water resources due to global warming

Arnell, Nigel W.

doi:10.1007/978-3-642-79830-6_16

Cited by 1 publication

(1 citation statement)

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“…For example, for snow‐fed rivers, the flow discharge will be prominent covariate since snowmelt flow dominates RWT predictions, with more the snow‐fed discharges less the RWT. Radiation and evaporation will be important covariates in the case of semiarid climate rivers as the sun heats the water much faster as the water depth is lower due to frequent low flow events under evaporation (Arnell, 1996).…”

Section: Discussionmentioning

confidence: 99%

Assessment of Impacts of Climate Change on Indian Riverine Thermal Regimes Using Hybrid Deep Learning Methods

Rehana

Rajesh

2023

Water Resources Research

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Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for seven major polluted river catchments of India. The hybrid kNN‐LSTM has effectively predicted RWT at monthly time scales under data limitations and outperformed the standalone LSTM, WT‐LSTM, and hybrid three‐parameter version air2stream models. The RWT increase for Tunga‐Bhadra, Musi, Ganga, and Narmada basins is predicted as 3.0, 4.0, 4.6, and 4.7°C, respectively, for 2071–2100.

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Section: Discussionmentioning

confidence: 99%