Incorporation of the Flow Duration Curve Method Within Digital Filtering Algorithms to Estimate the Base Flow Contribution to Total Runoff

2014 ◽  
Vol 28 (15) ◽  
pp. 5477-5489 ◽  
Author(s):  
Furat A. M. Al-Faraj ◽  
Miklas Scholz
Keyword(s):  
Digital Filtering ◽  
Base Flow ◽  
Flow Duration Curve ◽  
Filtering Algorithms ◽  
Flow Duration ◽  
Total Runoff ◽  
Curve Method

Comparing Spatial Interpolation Schemes for Constructing a Flow Duration Curve in an Ungauged Basin

2015 ◽  
Vol 29 (7) ◽  
pp. 2249-2265 ◽  
Author(s):  
Muhammad Waseem ◽  
Ji-yae Shin ◽  
Tae-Woong Kim
Keyword(s):  
Spatial Interpolation ◽  
Flow Duration Curve ◽  
Flow Duration ◽  
Ungauged Basin

Implementation of digital filtering algorithms using pipelined vector processors

1987 ◽  
Vol 75 (9) ◽  
pp. 1293-1303 ◽  
Author(s):  
Wonyong Sung ◽  
S.K. Mitra
Keyword(s):  
Digital Filtering ◽  
Filtering Algorithms ◽  
Vector Processors

scholarly journals Exploring the physical controls of regional patterns of flow duration curves – Part 1: Insights from statistical analyses

2012 ◽  
Vol 16 (11) ◽  
pp. 4435-4446 ◽  
Author(s):  
L. Cheng ◽  
M. Yaeger ◽  
A. Viglione ◽  
E. Coopersmith ◽  
S. Ye ◽  
...  
Keyword(s):  
Gamma Distribution ◽  
Classical Method ◽  
Regional Patterns ◽  
Flow Duration ◽  
Total Runoff ◽  
Time Symmetry ◽  
Physical Controls ◽  
Flow Duration Curves ◽  
Predictions In Ungauged Basins ◽  
Fast Flow

Abstract. The flow duration curve (FDC) is a classical method used to graphically represent the relationship between the frequency and magnitude of streamflow. In this sense it represents a compact signature of temporal runoff variability that can also be used to diagnose catchment rainfall-runoff responses, including similarity and differences between catchments. This paper is aimed at extracting regional patterns of the FDCs from observed daily flow data and elucidating the physical controls underlying these patterns, as a way to aid towards their regionalization and predictions in ungauged basins. The FDCs of total runoff (TFDC) using multi-decadal streamflow records for 197 catchments across the continental United States are separated into the FDCs of two runoff components, i.e., fast flow (FFDC) and slow flow (SFDC). In order to compactly display these regional patterns, the 3-parameter mixed gamma distribution is employed to characterize the shapes of the normalized FDCs (i.e., TFDC, FFDC and SFDC) over the entire data record. This is repeated to also characterize the between-year variability of "annual" FDCs for 8 representative catchments chosen across a climate gradient. Results show that the mixed gamma distribution can adequately capture the shapes of the FDCs and their variation between catchments and also between years. Comparison between the between-catchment and between-year variability of the FDCs revealed significant space-time symmetry. Possible relationships between the parameters of the fitted mixed gamma distribution and catchment climatic and physiographic characteristics are explored in order to decipher and point to the underlying physical controls. The baseflow index (a surrogate for the collective impact of geology, soils, topography and vegetation, as well as climate) is found to be the dominant control on the shapes of the normalized TFDC and SFDC, whereas the product of maximum daily precipitation and the fraction of non-rainy days was found to control the shape of the FFDC. These relationships, arising from the separation of total runoff into its two components, provide a potential physical basis for regionalization of FDCs, as well as providing a conceptual framework for developing deeper process-based understanding of the FDCs.

scholarly journals Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing

2018 ◽  
Vol 41 (8) ◽  
pp. 2338-2351 ◽  
Author(s):  
Anna Swider ◽  
Eilif Pedersen
Keyword(s):  
Signal Processing ◽  
Fourier Transformation ◽  
Filter Design ◽  
Real Life ◽  
Digital Filtering ◽  
Data Preprocessing ◽  
Sensor Data ◽  
Discrete Fourier Transformation ◽  
Filtering Algorithms ◽  
Multiple Sensors

In the phase of industry digitalization, data are collected from many sensors and signal processing techniques play a crucial role. Data preprocessing is a fundamental step in the analysis of measurements, and a first step before applying machine learning. To reduce the influence of distortions from signals, selective digital filtering is applied to minimize or remove unwanted components. Standard software and hardware digital filtering algorithms introduce a delay, which has to be compensated for to avoid destroying signal associations. The delay from filtering becomes more crucial when the analysis involves measurements from multiple sensors, therefore in this paper we provide an overview and comparison of existing digital filtering methods with an application based on real-life marine examples. In addition, the design of special-purpose filters is a complex process and for preprocessing data from many sources, the application of digital filtering in the time domain can have a high numerical cost. For this reason we describe discrete Fourier transformation digital filtering as a tool for efficient sensor data preprocessing, which does not introduce a time delay and has low numerical cost. The discrete Fourier transformation digital filtering has a simpler implementation and does not require expert-level filter design knowledge, which is beneficial for practitioners from various disciplines. Finally, we exemplify and show the application of the methods on real signals from marine systems.

Estimation of Flow Duration Curve at Ungauged Locations in Taiwan

2017 ◽  
Vol 22 (8) ◽  
pp. 05017009 ◽  
Author(s):  
Nien-Sheng Hsu ◽  
Chi-Jen Huang
Keyword(s):  
Flow Duration Curve ◽  
Flow Duration

Flow Duration Curve for Downstream of Sungai Perak Using Minimum and Maximum Flow

2019 ◽  
pp. 125-132
Author(s):  
Nurshahira Mohd Noh ◽  
Lariyah Mohd Sidek ◽  
Azwin Zailti Abd Razad ◽  
Hidayah Basri ◽  
Ahmad Asri Harun ◽  
...  
Keyword(s):  
Maximum Flow ◽  
Flow Duration Curve ◽  
Flow Duration
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