Wavelet-Aided Analysis to Estimate Seasonal Variability and Dominant Periodicities in Temperature, Precipitation, and Streamflow in the Midwestern United States

Pathak, P.; Kalra, Ajay; Ahmad, Sajjad; Bernardez, M.

doi:10.1007/s11269-016-1445-0

Cited by 58 publications

(30 citation statements)

References 49 publications

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“…In this study, the trends in the WBCs were detected using the nonparametric MK test (Mann, 1945;Kendall, 1975) because it exhibits a better performance than the parametric test (Nalley et al, 2012) for identifying trends in hydrological variables, such as rainfall (Shahid, 2010), temperature (Kamruzzaman et al, 2016a), P ET (Kumar et al, 2016), soil moisture (Tabari and Talaee, 2013), runoff (Pathak et al, 2016), groundwater level , and water quality (Lutz et al, 2016). The MK test cannot be used to accurately calculate the test statistic (Z) if there exists a significant serial correlation at lag 1 in the time series data (Yue et al, 2002) because the variance is underestimated (Hamed and Rao, 1998).…”

Section: Trend Testmentioning

confidence: 99%

“…Wavelet analysis has been used in different parts of the world to identify the periodicity in hydroclimatic time series data (Smith et al, 1998;Azad et al, 2015;Nalley et al, 2012;Araghi et al, 2015;Pathak et al, 2016). WT, a multiresolution analytical approach, can be applied to analyze time series data because it offers flexible window functions that can be changed over time (Nievergelt, 2001;Percival and Walden, 2000).…”

Section: Wavelet Transform (Wt) and Periodicitymentioning

confidence: 99%

“…WT, a multiresolution analytical approach, can be applied to analyze time series data because it offers flexible window functions that can be changed over time (Nievergelt, 2001;Percival and Walden, 2000). WT can be applied to detect the periodicity in hydroclimatic time series data (Smith et al, 1998;Pišoft et al, 2004;Sang, 2012;Torrence and Compo, 1998;Araghi et al, 2015;Pathak et al, 2016) and exhibits better a performance than traditional approaches (Sang, 2013). There exist two main types of WT, namely continuous WT (CWT) and DWT.…”

Section: Wavelet Transform (Wt) and Periodicitymentioning

confidence: 99%

“…Data-based forecasting models and statistical models are suitable alternatives for overcoming these limitations. The most common statistical methods for hydrological forecasting are the ARIMA model and multiple linear regression (Young, 1999; Adamowski,icity using wavelet-transformed details and using the approximation components of the hydrometeorological time series data can provide insight regarding the effects of the time period on the data trend (Nalley et al, 2013;Araghi et al, 2015;Pathak et al, 2016). As a result, detecting the periodicity through the wavelet transformation of hydrometeorological time series data has gained popularity in recent years (Partal and Küçük, 2006;Partal, 2009;Nalley et al, 2013;Araghi et al, 2015;Pathak et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The most common statistical methods for hydrological forecasting are the ARIMA model and multiple linear regression (Young, 1999; Adamowski,icity using wavelet-transformed details and using the approximation components of the hydrometeorological time series data can provide insight regarding the effects of the time period on the data trend (Nalley et al, 2013;Araghi et al, 2015;Pathak et al, 2016). As a result, detecting the periodicity through the wavelet transformation of hydrometeorological time series data has gained popularity in recent years (Partal and Küçük, 2006;Partal, 2009;Nalley et al, 2013;Araghi et al, 2015;Pathak et al, 2016). Studies have been conducted on the spatiotemporal characteristics of hydrometeorological variables, such as rainfall (Shahid and Khairulmaini, 2009;Ahasan et al, 2010;Kamruzzaman et al, 2016a;Rahman and Lateh, 2016;Syed and Al Amin, 2016), temperature (Shahid, 2010;Nasher and Uddin, 2013;Syed and Al Amin, 2016;Kamruzzaman et al, 2016a), and P ET Acharjee, 2017), in Bangladesh.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Rahman

Ahmed

Adnan

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The objectives of the present study were to explore the changes in the water balance components (WBCs) by co-utilizing the discrete wavelet transform (DWT) and different forms of the Mann-Kendall (MK) test and develop a wavelet denoise autoregressive integrated moving average (WD-ARIMA) model for forecasting the WBCs. The results revealed that most of the potential evapotranspiration (P ET ) trends (approximately 73 %) had a decreasing tendency from 1981-1982 to 2012-2013 in the western part of Bangladesh. However, most of the trends (approximately 82 %) were not statistically significant at a 5 % significance level. The actual evapotranspiration (A ET ), annual deficit, and annual surplus also exhibited a similar tendency. The rainfall and temperature exhibited increasing trends. However, the WBCs exhibited an inverse trend, which suggested that the P ET changes associated with temperature changes could not explain the change in the WBCs. Moreover, the 8-year (D3) and 16-year (D4) periodic components were generally responsible for the trends found in the original WBC data for the study area. The actual data was affected by noise, which resulted in the ARIMA model exhibiting an unsatisfactory performance. Therefore, wavelet denoising of the WBC time series was conducted to improve the performance of the ARIMA model. The quality of the denoising time series data was ensured using relevant statistical analysis. The performance of the WD-ARIMA model was assessed using the Nash-Sutcliffe efficiency (NSE) coefficient and coefficient of determination (R 2 ). The WD-ARIMA model exhibited very good performance, which clearly demonstrated the advantages of denoising the time series data for forecasting the WBCs. The validation results of the model revealed that the forecasted values were very close to actual values, with an acceptable mean percentage error. The residuals also followed a normal distribution. The performance and validation results indicated that models can be used for the short-term forecasting of WBCs. Further studies on different combinations of wavelet analysis are required to develop a superior model for the hydrological forecasting in the context of climate change. The findings of this study can be used to improve water resource management in the highly irrigated western part of Bangladesh.

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Section: Trend Testmentioning

confidence: 99%

Section: Wavelet Transform (Wt) and Periodicitymentioning

confidence: 99%

Section: Wavelet Transform (Wt) and Periodicitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Rahman

Ahmed

Adnan

et al. 2018

Hydrol. Earth Syst. Sci.

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Climatic variability of the Pacific and Atlantic Oceans and western US snowpack

Pathak¹,

Kalra

Lamb

et al. 2017

Intl Journal of Climatology

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This study examines the changing characteristics of snow water equivalent (SWE) in western United States and their teleconnections with large‐scale variability of the Pacific and Atlantic Oceans. This study evaluated the relationship among sea surface temperatures (SST), 500‐mbar geopotential height (Z500), 500‐mbar east–west wind (U500), and western United States SWE (1 March, 1 April, and 1 May). The averages of SST, Z500, and U500 were computed to create a lead time of 2–10 months. Three different 6‐month series were computed – January–June, April–September, and July–December – and were correlated with SWE stations in the western United States. The study utilized singular‐value decomposition (SVD) for a period of 1961–2016 to correlate the SWE and climate indices. SVD was applied between the standardized SST and SWE to obtain their correlation and to evaluate connection with Z500 and U500 within the same SVD relationship. Specifically, SVD was not applied independently on each variable prior to this study. The correlations were observed to increase as the lead time decreased. First mode SVD showed a pronounced non‐El Niño‐Southern Oscillation short‐wave train for the July–December climate in the Z500 data; this terminated over the Great Lakes region, and was strongly correlated with the warming of the SST near the east coast of Japan, resulting in decreased SWE in the study area. Second mode SVD showed a negative correlation between SWE stations in Colorado and Utah and SST near the Asian and Australian continents. The associations between climate indices and SWE that were identified during this study could be used to improve long‐term forecasts, resulting in better management of water resources in the region.

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Analysis of Low-Flow Indices in the Era of Climate Change: An Application of CanESM2 Model

Goodarzi

Faraji

2022

Springer Climate

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Wavelet-Aided Analysis to Estimate Seasonal Variability and Dominant Periodicities in Temperature, Precipitation, and Streamflow in the Midwestern United States

Cited by 58 publications

References 49 publications

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Climatic variability of the Pacific and Atlantic Oceans and western US snowpack

Analysis of Low-Flow Indices in the Era of Climate Change: An Application of CanESM2 Model

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