scholarly journals Is It Optimal to Use the Entirety of the Available Flow Records in the Range of Variability Approach?

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3280
Author(s):  
Yuanyuan Sun ◽  
Cailing Liu ◽  
Yanwei Zhao ◽  
Xianqiang Mao ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Numerical Simulations ◽  
Flow Regime ◽  
Biodiversity Conservation ◽  
Basic Principle ◽  
Flow Time ◽  
Range Of Variability Approach ◽  
Post Impact

Reducing the degree of flow regime alteration is a basic principle for biodiversity conservation in rivers. The range of variability approach (RVA) is the most widely used method to assess flow regime alteration. Generally, researchers tend to put all of the available pre-impact and post-impact flow records into the RVA. However, no research has tested whether it is optimal to use the entirety of the available flow records from the perspective of calculation accuracy for the degree of flow regime alteration. In this research, a series of numerical simulations is conducted, demonstrating that the greatest accuracy for flow regime alteration degree assessed by the RVA is achieved when the length of both the pre- and post-impact flow time series is set equal to multiples of periodicity length, and that, when attempting to put the whole available flow record into the RVA, calculation accuracy may be reduced. On the basis of these findings, we further propose revising the traditional RVA procedure by assessing the periodicity of the pre- and post-impact flow time series in advance. If the periodicity of the pre- or post-impact flows is detected, the length of the time series should be set equal to its periodicity.

Application of Support Vector Regression for Modeling Low Flow Time Series

2018 ◽  
Vol 23 (2) ◽  
pp. 923-934 ◽  
Author(s):  
Bibhuti Bhusan Sahoo ◽  
Ramakar Jha ◽  
Anshuman Singh ◽  
Deepak Kumar
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Flow Time ◽  
Low Flow ◽  
Support Vector

scholarly journals An improved framework to predict river flow time series data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7183 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Ishfaq Ahmad ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Indus Basin ◽  
Series Data ◽  
Percentage Error ◽  
Empirical Bayesian ◽  
Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

Wavelet neural network model for river flow time series

2012 ◽  
Vol 165 (8) ◽  
pp. 425-439 ◽  
Author(s):  
Budu Krishna ◽  
Yellamelli Ramji Satyaji Rao ◽  
Purna Chandra Nayak
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
River Flow ◽  
Flow Time ◽  
Wavelet Neural Network

scholarly journals The dynamics of miscible viscous fingering from onset to shutdown

2018 ◽  
Vol 837 ◽  
pp. 520-545 ◽  
Author(s):  
Japinder S. Nijjer ◽  
Duncan R. Hewitt ◽  
Jerome A. Neufeld
Keyword(s):  
Numerical Simulations ◽  
Flow Regime ◽  
Peclet Number ◽  
Viscous Fingering ◽  
Linear Stability Theory ◽  
Late Time ◽  
Exchange Flow ◽  
Mixing Rate ◽  
Péclet Number ◽  
Single Finger

We examine the full ‘life cycle’ of miscible viscous fingering from onset to shutdown with the aid of high-resolution numerical simulations. We study the injection of one fluid into a planar two-dimensional porous medium containing another, more viscous fluid. We find that the dynamics are distinguished by three regimes: an early-time linearly unstable regime, an intermediate-time nonlinear regime and a late-time single-finger exchange-flow regime. In the first regime, the flow can be linearly unstable to perturbations that grow exponentially. We identify, using linear stability theory and numerical simulations, a critical Péclet number below which the flow remains stable for all times. In the second regime, the flow is dominated by the nonlinear coalescence of fingers which form a mixing zone in which we observe that the convective mixing rate, characterized by a convective Nusselt number, exhibits power-law growth. In this second regime we derive a model for the transversely averaged concentration which shows good agreement with our numerical experiments and extends previous empirical models. Finally, we identify a new final exchange-flow regime in which a pair of counter-propagating diffusive fingers slow exponentially. We derive an analytic solution for this single-finger state which agrees well with numerical simulations. We demonstrate that the flow always evolves to this regime, irrespective of the viscosity ratio and Péclet number, in contrast to previous suggestions.

scholarly journals Anthropogenic and Climate Effects on a Free Dam Tropical River: Measuring the Contributions on Flow Regime

2020 ◽  
Vol 12 (23) ◽  
pp. 10030
Author(s):  
Verônica Léo ◽  
Hersília Santos ◽  
Letícia Pereira ◽  
Lilia Oliveira
Keyword(s):  
Climate Change ◽  
Flow Regime ◽  
River Flow ◽  
Human Impacts ◽  
Global Climate ◽  
Rate Of Change ◽  
Hydrologic Alteration ◽  
Tropical River ◽  
Range Of Variability Approach ◽  
Flow Components

The demand for freshwater resources and climate change pose a simultaneous threat to rivers. Those impacts are often analyzed separately, and some human impacts are widely evaluated in river dynamics—especially in downstream areas rather than the consequences of land cover changes in headwater reaches. The distinction between anthropogenic and climate on the components of the flow regime is proposed here for an upstream free dam reach whose watershed is responsible for the water supply in Rio de Janeiro. Indicators of hydrologic alteration (IHA) and the range of variability approach (RVA) combined with statistical analyses of anthropogenic and climate parameters indicated that (1) four river flow components (magnitude, frequency, duration, and rate of change) were greatly altered from the previous period (1947 to 1967) and the actual (1994 to 2014); (2) shifts in the sea surface temperature of the Atlantic correlated with flow magnitude; (3) the cattle activity effects on the flow regime of the studied area decreased 42.6% of superficial discharge; global climate change led to a 10.8% reduction in the same river component. This research indicated that climate change will impact the intensification of human actions on rivers in the southeast Brazilian headwaters.

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