Arctic River Water Temperatures and Thermal Regimes

Yang, Daqing; Park, Ho-Teak; Peterson, A.; Liu, Baozhong

doi:10.1007/978-3-030-50930-9_10

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Climate data were summarised as the long‐term average (LTA; 1970–2000) maximum August air temperature, as a measure of summer high temperatures, and the LTA annual precipitation coefficient of variation (CV), as a standardised measure of variability in precipitation (from WorldClim Version 2; http://worldclim.org/version2; Fick & Hijmans, 2017). Air temperature was used as a proxy for water temperature as recommended by Yang et al., (2021), as water temperature data were generally not available. Permafrost extent was summarised from the Circum‐Arctic Map of Permafrost and Ground‐Ice Conditions (Version 2; https://nsidc.org/data/ggd318; Brown et al., 2002).…”

Section: Methodsmentioning

confidence: 99%

Temperature and spatial connectivity drive patterns in freshwater macroinvertebrate diversity across the Arctic

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Temperature and spatial connectivity drive patterns in freshwater macroinvertebrate diversity across the Arctic

et al. 2021

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“…For example, it was demonstrated that T w can be used as an important predictor for aquatic invasive species (Walsh et al, 2020), and it should be taken into account that T w should be considered in all studies and knowledge related to the habitat of river ecosystems (Morid et al, 2020). Although T w is one of the simplest measures of water quality, available information is still limited in terms of temporal frequency and representativeness (Yang et al, 2021). Addressing the considerable lack of direct T w measured data in a large number of countries and the difficulties in procuring instantaneous data, there is clearly an interest in an effective use of models for T w retrieval, and predicting river T w is becoming a fundamental challenge.…”

Section: Introductionmentioning

confidence: 99%

Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling

Heddam

Kim

Mehr

et al. 2022

Geographical Journal

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Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat-ELM) is demonstrated for river water temperature (T w ) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature (T a ) as input for predicting T w , and (2) using T a and the periodicity (i.e., day, month and year number). River T w calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat-ELM accurately predicts the T w and surpassed all other models with coefficient of correlation (R) values ranging within the limits of 0.973 to 0.981, and the Nash-Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat-ELM using the periodicity by enhancing its ability to estimate river T w .

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“…Further, increased water temperature can result in marked changes in species composition and the functioning of aquatic ecosystems (Yang et al 2021). Several researchers have reported that water temperature is considered to be a key variable in determining the overall health of fish and other aquatic organisms (Risley 1997;Karvonen et al 2010;Piccolroaz et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Long Memory, Time Trends, and the Degree of Persistence in Water Temperatures of Five European Rivers and Lakes

Gil-Alana,

González-Blanch,

Lafuente

et al. 2023

JWMM

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This paper uses long memory and fractional integration techniques to analyze the presence of time trends in the water temperatures of three large European rivers (the Rhine at Lobith, the Danube at Wienna, the Meuse at Eijsden) and two lakes (Saimaa in Finland, and Võrtsjärv in Estonia). Long memory is a feature frequently observed in hydrological data, and it is important to consider it to appropriately estimate the potential trends in the data. The results indicate the existence of significant positive trends in all the five series examined, possibly as a consequence of global warming. Interestingly, once the time trends are taken into consideration, the degree of persistence substantially decreases in all cases and the long memory property in the data disappears.

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Arctic River Water Temperatures and Thermal Regimes

Cited by 7 publications

References 27 publications

Temperature and spatial connectivity drive patterns in freshwater macroinvertebrate diversity across the Arctic

Temperature and spatial connectivity drive patterns in freshwater macroinvertebrate diversity across the Arctic

Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling

Long Memory, Time Trends, and the Degree of Persistence in Water Temperatures of Five European Rivers and Lakes

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