Atmospheric Conditions, Landscape Characteristics, and Anthropogenic Factors Affecting Stream Water Temperature

Wu, Weijun; Yoshiyama, Kohei; Senge, Masateru

doi:10.7831/ras.3.46

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…(Kothandaraman 1971). The main factor that determines the thermal regime of rivers (Wu et al 2015) and lakes (Nõges and Nõges 2014;Schimd et al 2014;O'Reilly et al 2015;Piccolroaz et al 2021) is air temperature (Edinger et al 1968;Zabolotnia 2018;Kimura et al 2021). Some ______________________________________________________ authors (Langan et al 2001;Koycheva and Karney 2009) have calculated an increase of 0.3 to 0.9 o C in water temperature for every degree Celsius that air temperature increases.…”

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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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

View full text Add to dashboard Cite

show abstract

“…type of leave, twig, and branch structures in the space, different species induce different hydrological responses through canopy interception, evapotranspiration and percolation (Yavitt et al, 1995;Asner et al, 2004, Hisada et al, 2011, Wu et al, 2015. Therefore, the effects of land cover and vegetation management on the hydrological processes have been observed in various ways such as changing interception losses, evapotranspiration, soil moisture, peak discharge, discharge flow and total runoff (Sikka et al, 2003).…”

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confidence: 99%

Runoff Characteristcs of Forest Watersheds Under Different Forest Managements

Rahmat

Noda

Onishi

et al. 2018

RAS

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Investigating the Accuracy of Hybrid Models with Wavelet Transform in the Forecast of Watershed Runoff

Saravani,

Kashef,

Farmahini

et al. 2023

JWMM

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In the hydrological cycle, runoff precipitation is one of the most significant and complex phenomena. In order to develop and improve predictive models, different perspectives have been presented in its modeling. Hydrological processes can be confidently modeled with the help of artificial intelligence techniques. In this study, the runoff of the Leilanchai watershed was simulated using artificial neural networks (ANNs) and M5 model tree methods and their hybrid with wavelet transform. Seventy percent of the data used in the train state and thirty percent in the test state were collected in this watershed from 2000 to 2021. In addition to daily and monthly scales, simulated and observed results were compared within each scale. Initially, the rainfall and runoff time series were divided into multiple sub-series using the wavelet transform to combat instability. The resultant subheadings were then utilized as input for an ANN and M5 model tree. The results demonstrated that hybrid models with wavelet improved the ANN model's daily accuracy by 4% and its monthly accuracy by 26%. It also improved the M5 model tree's daily and monthly accuracy by 4% and 41%. The wavelet-M5 model's accuracy does not diminish to the same degree as the wavelet-ANN (WANN) model as the forecast horizon lengthens. Consequently, the Leilanchai watershed has a relatively stable behavior pattern. Finally, hybrid models, in conjunction with the wavelet transform, improve forecast accuracy.

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Atmospheric Conditions, Landscape Characteristics, and Anthropogenic Factors Affecting Stream Water Temperature

Cited by 3 publications

References 60 publications

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

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

Runoff Characteristcs of Forest Watersheds Under Different Forest Managements

Investigating the Accuracy of Hybrid Models with Wavelet Transform in the Forecast of Watershed Runoff

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