Wavelet analyses of western US streamflow with ENSO and PDO

Tamaddun, Kazi Ali; Kalra, Ajay; Ahmad, Sajjad

doi:10.2166/wcc.2016.162

Cited by 59 publications

(28 citation statements)

References 63 publications

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“…Wavelet transform has been widely used in the fields of hydrology and geophysics [61][62][63][64]. In hydrology, wavelet transform was successfully applied for time series forecasting, and it was also used to identify the trends and periodicities in hydrological variables in different regions [61][62][63][64]. In this paper, we calculate the cross wavelet transform (XWT) and wavelet coherence (WTC) of air temperature or precipitation and large-scale indices by [65,66].…”

Section: Wavelet Transformmentioning

confidence: 99%

Hydro-Meteorological Trends in the Yarlung Zangbo River Basin and Possible Associations with Large-Scale Circulation

Zuo

Ban

2020

Water

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Climate change poses potential challenges to sensitive areas, such as high-elevation regions. The Yarlung Zangbo River (YLZR) basin is located in the southeast of the Qinghai-Tibetan Plateau. It contains large amounts of snow and numerous glaciers that are vulnerable to climate change. Based on daily observational data at 17 meteorological stations in and around the YLZR basin during 1957–2015, the variability of precipitation, air temperature, and streamflow were analyzed. The nonparametric Mann–Kendall test, Sen’s slope estimate method, cross wavelet transform (XWT), and wavelet coherence (WTC) were used to identify the annual seasonal trends. the abrupt changes of precipitation and air temperature, and their associations with large-scale circulation. The results showed that the YLZR basin experienced an overall rapid warming and wetting during the study period, with an average warming rate of 0.33 °C/10 a and wetting rate of 4.25 mm/10a, respectively. Abrupt change points in precipitation and air temperature occurred around the 1970s and 1990s, respectively. The abrupt change points of three hydrological stations occurred around the late 1960s and the late 1990s, respectively. The precipitation, annual average temperature, and the streamflow of the three hydrological stations were negatively correlated with the Pacific decadal oscillation (PDO) and the multivariate El Niño-Southern Oscillation (ENSO) index (MEI), reaching a significant level of 0.05.

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Section: Wavelet Transformmentioning

confidence: 99%

Hydro-Meteorological Trends in the Yarlung Zangbo River Basin and Possible Associations with Large-Scale Circulation

Zuo

Ban

2020

Water

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“…In revent decades, wavelet transform (WT) has become a reliable technique in analyzing periodicities and variations in time series analysis [32][33][34][35]. Wavelet analysis is expected to be a promising tool because of its multiresolution in frequency and time domains in modeling hydrological issues, which improves the accuracy of forecasting models by providing subseries of time series.…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

et al. 2020

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Soil phosphorus (P) is a vital but limited element which is usually leached from the soil via the drainage process. Soil phosphorus as a soluble substance can be delivered through agricultural fields by runoff or soil loss. It is one of the most essential nutrients that affect the sustainability of crops as well as the energy transfer for living organisms. Therefore, an accurate simulation of soil phosphorus, which is considered as a point source pollutant in elevated contents, must be performed. Considering a crucial issue for a sustainable soil and water management, an effective soil phosphorus assessment in the current research was conducted with the aim of examining the capability of five different wavelet-based data-driven models: gene expression programming (GEP), neural networks (NN), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM) in modeling soil phosphorus (P). In order to achieve this goal, several parameters, including soil pH, organic carbon (OC), clay content, and soil P data, were collected from different regions of the Neyshabur plain, Khorasan-e-Razavi Province (Northeast Iran). First, a discrete wavelet transform (DWT) was applied to the pH, OC, and clay as the inputs and their subcomponents were utilized in the applied data-driven techniques. Statistical Gamma test was also used for identifying which effective soil parameter is able to influence soil P. The applied methods were assessed through 10-fold cross-validation scenarios. Our results demonstrated that the wavelet–GEP (WGEP) model outperformed the other models with respect to various validations, such as correlation coefficient (R), scatter index (SI), and Nash–Sutcliffe coefficient (NS) criteria. The GEP model improved the accuracy of the MARS, RF, SVM, and NN models with respect to SI-NS (By comparing the SI values of the GEP model with other models namely MARS, RF, SVM, and NN, the outputs of GEP showed more accuracy by 35%, 30%, 40%, 50%, respectively. Similarly, the results of the GEP outperformed the other models by 3.1%, 2.3%, 4.3%, and 7.6%, comparing their NS values.) by 35%-3.1%, 30%-2.3%, 40%-4.3%, and 50%-7.6%, respectively.

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“…The PDO is determined by the SST in the North Pacific Ocean, and the positive or negative phase of the PDO shows a lower or upper SST, respectively, in the central Pacific region [11]. The PDO and ENSO, based on the variation of temperature and precipitation, can change the streamflow, and thus, accurate prediction of the streamflow, based on ENSO and PDO inputs, is considered a complex and important issue [12]. The predication of streamflow, based on the ENSO and PDO models, depends on finding the correlation value between the models and the streamflow and the indexes of ENSO and PDO.…”

Section: Introductionmentioning

confidence: 99%

“…The predication of streamflow, based on the ENSO and PDO models, depends on finding the correlation value between the models and the streamflow and the indexes of ENSO and PDO. Of course, finding the lag times of the used climate indexes with the streamflow for the different years or months is another important issue [5,12]. The predication models need a large amount of data for the streamflow simulation, and thus, artificial intelligence models, such as neural networks or fuzzy models, can be considered as good selections for the streamflow simulation.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

Ehteram

Afan

Dianatikhah

et al. 2019

Water

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The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.

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Wavelet analyses of western US streamflow with ENSO and PDO

Cited by 59 publications

References 63 publications

Hydro-Meteorological Trends in the Yarlung Zangbo River Basin and Possible Associations with Large-Scale Circulation

Hydro-Meteorological Trends in the Yarlung Zangbo River Basin and Possible Associations with Large-Scale Circulation

Estimating Soil Available Phosphorus Content through Coupled Wavelet–Data-Driven Models

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

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