Research on the relationship between rainfall and reference crop evapotranspiration with multi-time scales

Zhang, Jinping; Ding, Zhihong; Yuan, Wenlin; Zuo, Qiting

doi:10.1007/s10333-012-0338-y

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…Many studies report that the ET increases/decreases with the rainfall increase/decrease (Reynolds et al, ; Zhang et al, ; Yang et al, ; Kundu et al, ). This relation mainly depends on the teleconnections, air‐sea interactions and solar activity (Zhang et al, ). But, however, during the rainy season, higher relative humidity levels suppress the evaporative ability from land to atmosphere than in other seasons.…”

Section: Resultsmentioning

confidence: 99%

“…The negative and poor positive correlations among these data sets need to be critically examined by considering the site specific conditions. Many studies report that the ET increases/decreases with the rainfall increase/decrease (Reynolds et al, 2000;Zhang et al, 2013;Yang et al, 2016;Kundu et al, 2017). This relation mainly depends on the teleconnections, air-sea interactions and solar activity (Zhang et al, 2013).…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region

Purnadurga

Kumar

Rao

et al. 2019

Intl Journal of Climatology

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In this study, we have computed the evapotranspiration (ET) from the input variables of India Meteorological Department (IMD) for different stations in Monsoon Core Region (MCR) of India and Indian Peninsular Region (IPR) and compared with the ERA Interim (ERA‐I) and CRU ET data sets. While studying the discrepancies among the data sets, rainfall (source: IMD gridded), relative humidity (source: ERA Interim gridded), air temperature (source: IMD gridded) and soil moisture (source: TRMM/LPRM/TMI‐Model) were made use to illustrate the ET variations. When compared with IMD ET, our results show the CRU ET is underestimated but maintained the close pattern over MCR and IPR during South West (SW) monsoon (June–September) and North East (NE) monsoon (October–December) period, respectively. ERA‐I ET bounded to have mixed response over MCR and are higher than the IMD ET over IPR. Daily comparison of the IMD and ERA‐I ET data sets shows a large bias during the beginning of SW monsoon (June month) compared to other months. Site wise correlations show the substantial positive correlations between IMD and CRU ET over MCR than IPR. Overall analysis shows the monsoon features were better explained by the variations in IMD ET compared to CRU and ERA‐I ET data sets. The reported disparities among the data sets play an important role in the choice of selection for different applications such as water resource assessments, crop water requirements, monitoring of droughts etc.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region

Purnadurga

Kumar

Rao

et al. 2019

Intl Journal of Climatology

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“…In other words, potential evapotranspiration is directly related to the rainfall a place receives. Jinping Zhang et al have used the Fourier analysis to prove that there exists a complex relationship between the crop evapotranspiration and rainfall which may be related to El Nino, air-sea intersection and solar activity [22].…”

Section: Methodsmentioning

confidence: 99%

Artificial Neural Network Models for Rainfall Prediction in Pondicherry

Dubey¹

2015

IJCA

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Rainfall forecasting plays an important role in catchment management applications, the flood warning system being one of them. Rainfall forecasting is one of the most difficult tasks given the variability of space, time and other given conditions change rapidly. Over the years, with the evolution of the intelligent computing methods, many rainfall prediction methods have been proposed, Artificial Neural Network being one of the most prominent. Since the last decade, many researchers have proposed different artificial neural network models in order to create accurate rainfall prediction models. In this paper, different artificial neural networks have been created for the rainfall prediction of Pondicherry, a coastal region in India. These ANN models were created using three different training algorithms namely, feed-forward back propagation algorithm, layer recurrent algorithm and feedforward distributed time delay algorithm. The number of neurons for all the models was kept at 20. The mean squared error was measured for each model and the best accuracy was obtained by feed-forward distributed time delay algorithm with MSE value as low as .0083.

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“…To overcome the problems of non-stationary of the time series, Engle and Granger (1987) provides the cointegration theory. The cointegration theory is mainly applied in econometrics in the past few decades (Han et al 2004;Seung-Hoon 2007), because it can describe the short-term dynamic fluctuations and long-term balance relationship simultaneously, some researches started to introduce this theory into the hydrological field and also found many valuable results (Zhang et al 2006a(Zhang et al , b, 2008(Zhang et al , 2013(Zhang et al , 2014a, but it is relative rare in the relationship for runoff and sediment load.…”

Section: Introductionmentioning

confidence: 98%

Multi-Resolution Cointegration Prediction for Runoff and Sediment Load

Zhang

Zhao

Xiao

2015

Water Resour Manage

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With the wavelet transform method, the multi-resolution analysis of runoff and sediment load at Huaxian hydrological station located in the lower reaches of the Weihe River in China is presented to find the varying quasic-periodic waveforms existing in different decomposed time scales. The cointegration theory is introduced to reveal the long-term balance relationship and short-term fluctuations of the original and decomposed runoff and sediment load time series. Thus, considering the decomposed sediment load components in different time scales as the input data series, and the corresponding decomposed runoff component acting as the output data series, the multi-resolution cointegration method is produced. The results show that the multi-resolution cointegration method has the higher prediction accuracy, and the prediction errors are almost less than 5 %.

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Research on the relationship between rainfall and reference crop evapotranspiration with multi-time scales

Cited by 12 publications

References 13 publications

Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region

Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region

Artificial Neural Network Models for Rainfall Prediction in Pondicherry

Multi-Resolution Cointegration Prediction for Runoff and Sediment Load

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