Effect of channel network topology, basin segmentation and rainfall spatial distribution on the geomorphologic instantaneous unit hydrograph transfer function

2008 ◽  
Vol 22 (3) ◽  
pp. 395-419 ◽  
Author(s):  
Roger Moussa
Keyword(s):  
Spatial Distribution ◽  
Transfer Function ◽  
Network Topology ◽  
Unit Hydrograph ◽  
Channel Network ◽  
Instantaneous Unit Hydrograph ◽  
Rainfall Spatial Distribution

scholarly journals The geomorphological unit hydrograph – a critical review

1998 ◽  
Vol 2 (1) ◽  
pp. 1-8 ◽  
Author(s):  
A. Y. Shamseldin ◽  
J. E. Nash
Keyword(s):  
Critical Review ◽  
Drainage Network ◽  
Unit Hydrograph ◽  
Channel Network ◽  
Geomorphological Unit ◽  
Catchment Characteristics ◽  
Scale Factors ◽  
Instantaneous Unit Hydrograph ◽  
Plausible Hypothesis ◽  
Two Parameter

Abstract. The theory of the geomorphological unit hydrograph (GUH) is examined critically and it is shown that the inherent assumption that the operation of the drainage network may be modelled by a corresponding network of linear reservoirs so restricts the instantaneous unit hydrograph (IUH) shape that the effects of further restrictions, reflecting the constraints imposed by the geomorphological laws of the channel network, cannot easily be identified. Without such identification, the geomorphological unit hydrograph theory is untestable and must remain only a plausible hypothesis providing an indication of a two-parameter IUH whose shape and scale factors must still be related empirically to appropriate catchment characteristics.

Geomorphological Approach to the Skewed Shape of Instantaneous Unit Hydrograph

2015 ◽  
Vol 48 (2) ◽  
pp. 91-103
Author(s):  
Joo-Cheol Kim ◽  
◽  
Kwansue Jung ◽  
Dong Kug Jeong
Keyword(s):  
Unit Hydrograph ◽  
Instantaneous Unit Hydrograph

scholarly journals Sensitivity-informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2402
Author(s):  
David S. Ching ◽  
Cosmin Safta ◽  
Thomas A. Reichardt
Keyword(s):  
Bayesian Inference ◽  
Transfer Function ◽  
Network Topology ◽  
Random Search ◽  
Network Models ◽  
Training Data ◽  
Unknown Parameters ◽  
Network Parameter ◽  
Dimensional Parameter ◽  
Discrete Random Variables

Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.

scholarly journals A Comparison of Some Methods of Deriving the Instantaneous Unit Hydrograph

1985 ◽  
Vol 16 (1) ◽  
pp. 1-10 ◽  
Author(s):  
V. P. Singh ◽  
C. Corradini ◽  
F. Melone
Keyword(s):  
Peak Flow ◽  
Central Italy ◽  
Effective Rainfall ◽  
Unit Hydrograph ◽  
Time To Peak ◽  
Wide Range ◽  
Instantaneous Unit Hydrograph ◽  
Similar Accuracy ◽  
Two Parameters ◽  
Range Of Values

The geomorphological instantaneous unit hydrograph (IUH) proposed by Gupta et al. (1980) was compared with the IUH derived by commonly used time-area and Nash methods. This comparison was performed by analyzing the effective rainfall-direct runoff relationship for four large basins in Central Italy ranging in area from 934 to 4,147 km2. The Nash method was found to be the most accurate of the three methods. The geomorphological method, with only one parameter estimated in advance from the observed data, was found to be little less accurate than the Nash method which has two parameters determined from observations. Furthermore, if the geomorphological and Nash methods employed the same information represented by basin lag, then they produced similar accuracy provided the other Nash parameter, expressed by the product of peak flow and time to peak, was empirically assessed within a wide range of values. It was concluded that it was more appropriate to use the geomorphological method for ungaged basins and the Nash method for gaged basins.

scholarly journals A Hybrid MPI-OpenMP Parallel Algorithm for the Assessment of the Multifractal Spectrum of River Networks

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3122
Author(s):  
Leonardo Primavera ◽  
Emilia Florio
Keyword(s):  
River Basin ◽  
Message Passing Interface ◽  
Multifractal Spectrum ◽  
Computation Method ◽  
Processing Unit ◽  
Unit Hydrograph ◽  
Fast Evaluation ◽  
Correlation Integral ◽  
Central Processing ◽  
Instantaneous Unit Hydrograph

The possibility to create a flood wave in a river network depends on the geometric properties of the river basin. Among the models that try to forecast the Instantaneous Unit Hydrograph (IUH) of rainfall precipitation, the so-called Multifractal Instantaneous Unit Hydrograph (MIUH) by De Bartolo et al. (2003) rather successfully connects the multifractal properties of the river basin to the observed IUH. Such properties can be assessed through different types of analysis (fixed-size algorithm, correlation integral, fixed-mass algorithm, sandbox algorithm, and so on). The fixed-mass algorithm is the one that produces the most precise estimate of the properties of the multifractal spectrum that are relevant for the MIUH model. However, a disadvantage of this method is that it requires very long computational times to produce the best possible results. In a previous work, we proposed a parallel version of the fixed-mass algorithm, which drastically reduced the computational times almost proportionally to the number of Central Processing Unit (CPU) cores available on the computational machine by using the Message Passing Interface (MPI), which is a standard for distributed memory clusters. In the present work, we further improved the code in order to include the use of the Open Multi-Processing (OpenMP) paradigm to facilitate the execution and improve the computational speed-up on single processor, multi-core workstations, which are much more common than multi-node clusters. Moreover, the assessment of the multifractal spectrum has also been improved through a direct computation method. Currently, to the best of our knowledge, this code represents the state-of-the-art for a fast evaluation of the multifractal properties of a river basin, and it opens up a new scenario for an effective flood forecast in reasonable computational times.

Sign in / Sign up

Export Citation Format

Share Document