Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data

Araghi, Alireza; Baygi, Mohammad Mousavi; Adamowski, Jan; Malard, Julien; Nalley, D.; Hasheminia, Seyed Majid

doi:10.1016/j.atmosres.2014.11.016

Cited by 121 publications

(84 citation statements)

References 61 publications

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“…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%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

show abstract

“…Observable negative effects of global climate change on the environment (e.g., ecosystems and biodiversity), water availability and quality, energy consumption, crop productivity , the magnitude and frequency of natural disasters, and the spread of disease are well documented 2012a, b;ADAMOWSKI, PROKOPH 2013;ARAGHI et al 2015;BELAYNEH et al 2014;CAMPISI et al 2012;DANESHMAND et al 2014;HAIDARY et al 2013;NALLEY et al 2012;NAMDAR et al 2014;PIN-GALE et al 2014;SAADAT et al 2014;TIWARI, ADA-MOWSKI 2013]. According to the Intergovernmental Panel on Climate Change [IPCC 2007a;, the magnitude of climate change effects on individual DOI: 10.1515DOI: 10.…”

Section: Introductionmentioning

confidence: 99%

Community based adaptation options for climate change impacts on water resources: The case of Jordan

Hammouri

Al-Qinna

Salahat

et al. 2015

Journal of Water and Land Development

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A strategic vision to ensure an adequate, safe and secure drinking water supply presents a challenge, particularly for such a small country as Jordan, faced with a critical supply-demand imbalance and a high risk of water quality deterioration. In order to provide sustainable and equitable long-term water management plans for the future, current and future demands, along with available adaptation options should be assessed through community engagement. An analysis of available water resources, existing demands and use per sector served to assess the nation's historic water status. Taking into account the effect of both population growth and rainfall reduction, future per sector demands were predicted by linear temporal trend analysis. Water sector vulnerability and adaptation options were assessed by engaging thirty five stakeholders. A set of weighed-criterions were selected, adopted, modified, and then framed into comprehensive guidelines. A quantitative ratio-level approach was used to quantify the magnitude and likelihood of risks and opportunities associated with each proposed adaptation measure using the level of effectiveness and severity status. Prioritization indicated that public awareness and training programs were the most feasible and effective adaptation measures, while building new infrastructure was of low priority. Associated barriers were related to a lack of financial resources, institutional arrangements, and data collection, sharing, availability, consistency and transparency, as well as willingness to adapt. Independent community-based watershed-vulnerability analyses to address water integrity at watershed scale are recommended.

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“…Given the advent of climate change, these problems will likely become more acute in the future [SAADAT et al 2011;ARAGHI et al 2015]. Accurate forecasting of short-term water demand can contribute to the efficient operation and management of urban water supply systems, resulting in demand being met efficiently and sustainably CAMPISI-PINTO et al 2012;TIWARI, ADAMOWSKI 2014].…”

Section: Introductionmentioning

confidence: 99%

Water demand forecasting using extreme learning machines

Tiwari¹,

Adamowski²,

Adamowski³

2016

Journal of Water and Land Development

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The capacity of recently-developed extreme learning machine (ELM) modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W) or bootstrap (B) methods (i.e., ELM, ELM W , ELM B ), was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANN W , ANN B ). The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELM B and ANN B models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANN W and ELM W models provided greater accuracy, with the ELM W model outperforming the ANN W model. Significant improvement in peak urban water demand prediction was only achieved with the ELM W model. The superiority of the ELM W model over both the ANN W or ANN B models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

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Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data

Cited by 121 publications

References 61 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

Community based adaptation options for climate change impacts on water resources: The case of Jordan

Water demand forecasting using extreme learning machines

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