scholarly journals Variations in the key hydrological elements of the Yarlung Zangbo River basin

2018 ◽  
Vol 19 (4) ◽  
pp. 1088-1096 ◽  
Author(s):  
Dian Li ◽  
Jia Li ◽  
Linglei Zhang ◽  
Yun Deng ◽  
Yaowen Zhang
Keyword(s):  
Time Series ◽  
River Basin ◽  
Periodic Oscillation ◽  
Monthly Variation ◽  
Middle Section ◽  
Periodic Oscillations ◽  
Yarlung Zangbo River ◽  
Wavelet Variance ◽  
High Level ◽  
Different Time Scales

Abstract Based on the monthly water level, runoff, precipitation and evaporation data from the four main hydrometric stations in the middle section of the Yarlung Zangbo River basin from 1956 to 2000, the periodic oscillations, trends and transformation characteristics at different time-scales are investigated via wavelet analysis. Moreover, the main periods of each time-series are identified by estimating the wavelet variance. The results show that the transformation scales of the monthly variation of the key hydrological elements over the last 44 years were 80–120, 40–70 and 16–24 months and that a high level of consistency was maintained at 16–24 months, where the periodic oscillation was the most significant. In addition, the first and second main periods of all hydrological elements were 18 and 9 months, respectively.

scholarly journals Climate Changes and Associated Multiscale Impacts on Watershed Discharge over the Upper Reach of Yarlung Zangbo River Basin, China

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jinping Liu ◽  
Wanchang Zhang
Keyword(s):  
River Basin ◽  
Time Scales ◽  
Climate Changes ◽  
Ensemble Empirical Mode Decomposition ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Alpine Regions ◽  
Yarlung Zangbo River ◽  
Different Time Scales

Watershed discharge (WD) in the alpine regions, such as the upper reach of Yarlung Zangbo River Basin (YZRB), China, could have changed severely in response to climate changes. Yet, how hydrometeorological variables varied at different time scales and how WD varied in response to hydrometeorological variables in the alpine regions remained questions to be answered. The ensemble empirical mode decomposition (EEMD) method was employed in this study to investigate the nonlinear climate change trends (averaged and extreme states) and the associated multiscale impacts on WD variations over the upper reach of the YZRB during 1961–2009. All investigated hydroclimatic variables, i.e., precipitation, temperature, and WD, were found to be varied nonlinearly with clear multiscale oscillations characterizing great differences in the oscillation periods, corresponding significance levels, and variance contribution rates, among which precipitation posed a weak impact on WD variations, while temperature played a significant role in WD fluctuations. Furthermore, among all temperature extremes, the dominant index affecting WD variations was TXm (annual mean of the daily maximum temperature) but not TXx (annual maximum of the daily maximum temperature) at both interannual and interdecadal scales, which might be caused by that TXx increased evapotranspiration and reduced WD. A significant correlation between temperature (both averaged and partial extreme states) and annual WD at both interannual and interdecadal scales indicated that a synchronous change existed between them. The present study provided first insight into how hydrometeorological variables varied at different time scales and how WD fluctuated in response to hydrometeorological variables over the upper reach of the YZRB, China.

scholarly journals Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine

2021 ◽  
Vol 13 (23) ◽  
pp. 4785
Author(s):  
Hao Fu ◽  
Wei Zhao ◽  
Qiqi Zhan ◽  
Mengjiao Yang ◽  
Donghong Xiong ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Thematic Mapper ◽  
Temporal Information ◽  
Middle Section ◽  
Landsat Images ◽  
Planting Time ◽  
Yarlung Zangbo River ◽  
Ndvi Time Series ◽  
Google Earth Engine

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control.

River-Basin Planning for Control of Nitrate Pollution

1982 ◽  
Vol 14 (4-5) ◽  
pp. 245-252 ◽  
Author(s):  
C S Sinnott ◽  
D G Jamieson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Water Resource ◽  
Potable Water ◽  
Potential Problem ◽  
Nitrate Pollution ◽  
Optimal Mix ◽  
Regional Basis ◽  
The Impact

The combination of increasing nitrate concentrations in the River Thames and the recent EEC Directive on the acceptable level in potable water is posing a potential problem. In assessing the impact of nitrates on water-resource systems, extensive use has been made of time-series analysis and simulation. These techniques are being used to define the optimal mix of alternatives for overcoming the problem on a regional basis.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

Runoff Fractal Dimension of Songhua River Basin in Harbin Station Based on Db4 Wavelet

2012 ◽  
Vol 550-553 ◽  
pp. 2537-2540
Author(s):  
Hai Yan Gu ◽  
Yong Wang ◽  
Lei Yu
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
River Basin ◽  
Fractal Theory ◽  
Measurement Methods ◽  
Self Similarity ◽  
Songhua River ◽  
Songhua River Basin ◽  
The Songhua River Basin ◽  
The Songhua River

The wavelet analysis and fractal theory into the analysis of hydrological time series, fluctuations in hydrological runoff sequence given the complexity of the measurement methods--- fractal dimension. The real monthly runoffs of 28 years from Songhua River basin in Harbin station are selected as research target. Wavelet transform combined with spectrum method is used to calculate the fractal dimension of runoff. Moreover, the result demonstrates that the runoff in Songhua River basin has the characteristic of self-similarity, and the complexity of runoff in the Songhua River basin in Harbin station is described quantificationally.

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