scholarly journals An opportunity of application of excess factor in hydrology

2012 ◽  
Vol 9 (12) ◽  
pp. 13635-13649 ◽  
Author(s):  
V. Kovalenko ◽  
E. Gaidukova ◽  
A. Kachalova
Keyword(s):  
Stochastic Model ◽  
River Flow ◽  
Large Error ◽  
Hydrological Processes ◽  
Multiplicative Noises ◽  
Flow Formation ◽  
Thick Tails ◽  
Engineering Hydrology

Abstract. In last few years in hydrology an interest to excess factor has appeared as a reaction to unsuccessful attempts to simulate and predict evolving hydrological processes, which attributive property is statistical instability. The article shows, that the latter has a place at strong relative multiplicative noises of probabilistic stochastic model of a river flow formation, phenomenological display of which are "the thick tails" and polymodality, for which the excess factor "answers", by being ignored by a modern hydrology in connection to the large error of its calculation because of insufficient duration of lines of observation over a flow. However, it is found out, that the duration of observation of several decades practically stabilizes variability of the excess factor, the error of which definition appears commensurable with an error of other calculated characteristics used in engineering hydrology.

scholarly journals A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

2019 ◽  
Vol 23 (1) ◽  
pp. 73-91 ◽  
Author(s):  
Theano Iliopoulou ◽  
Cristina Aguilar ◽  
Berit Arheimer ◽  
María Bermúdez ◽  
Nejc Bezak ◽  
...  
Keyword(s):  
River Flow ◽  
Frequency Estimation ◽  
Low Flow ◽  
Time Lags ◽  
Low Flows ◽  
Flow Formation ◽  
Gaussian Probability Distribution ◽  
High Flows ◽  
Seasonal Correlation ◽  
Seasonal Streamflow

Abstract. The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes.

scholarly journals Impacts of Regional Groundwater Flow and River Fluctuation on Floodplain Wetlands in the Middle Reach of the Yellow River

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1922
Author(s):  
Yunmei Fu ◽  
Yanhui Dong ◽  
Yueqing Xie ◽  
Zhifang Xu ◽  
Liheng Wang
Keyword(s):  
Remote Sensing ◽  
Groundwater Flow ◽  
River Flow ◽  
Yellow River ◽  
Hydrological Processes ◽  
The Yellow River ◽  
Floodplain Wetlands ◽  
Middle Reach ◽  
Groundwater Levels ◽  
Regional Groundwater

Floodplain wetlands are of great importance in the entire river and floodplain ecosystems. Understanding the hydrological processes of floodplain wetlands is fundamental to study the changes in wetlands caused by climate change and human activities. In this study, floodplain wetlands along the middle reach of the Yellow River were selected as a study area. The hydrological processes and the interactions between the river and the underlying aquifer were investigated by combining remote sensing, hydraulic monitoring, and numerical modeling. Wetland areas from 2014 to 2019 were extracted from Landsat 8 remote sensing images, and their correlation with the river runoff was analyzed. The results indicate that the river flow had a limited impact on the wetland size and so did groundwater levels, due to the strong reliance of wetland vegetation on water levels. Based on hydrological and hydrogeological conditions, a surface water–groundwater coupled numerical model was established. The comparison and correlation analysis between the monitored groundwater head and the simulated river stage also show that river flow did not play a first-order role in controlling the groundwater levels of wetlands in the study area. The simulation results also suggest that it is the regional groundwater flow that mainly sustains shallow groundwater of floodplain wetlands in the study area. The floodplain wetland of the study area was dynamic zones between the regional groundwater and river, the contrasting pattern of hydrological regimes on both banks of the Yellow River was due to a combination of regional groundwater flow and topography.

Integral assessment of climate impact on the transboundary Mesta River flow formation in Bulgaria

2006 ◽  
Vol 127 (1-3) ◽  
pp. 383-388 ◽  
Author(s):  
Ivan K. Diadovski ◽  
Maya P. Atanassova ◽  
Ivan S. Ivanov
Keyword(s):  
River Flow ◽  
Climate Impact ◽  
Flow Formation ◽  
Integral Assessment

scholarly journals Stochastic model of 6-jet kinematic dynamo

2019 ◽  
Vol 127 ◽  
pp. 02028
Author(s):  
Gleb Vodinchar ◽  
Liubov Feshchenko
Keyword(s):  
Magnetic Field ◽  
Stochastic Model ◽  
Coherent Structures ◽  
Large Scale ◽  
Large Scale Structure ◽  
Small Scale ◽  
Control Parameters ◽  
Multiplicative Noises ◽  
Kinematic Dynamo ◽  
Turbulent Field

The low-moded stochastic model of kinematic geodynamo is studied. The model is based on the indirect data about the large-scale structure of convection. The intensities of large-scale and turbulent field generators are affected by pulsed multiplicative noises. These random pulses are interpreted as the formation and destruction of coherent structures from small-scale modes of velocity and magnetic field. The perturbation of this control parameters by stochastic influence leads to switching between different dynamo regimes.

scholarly journals Modeling and Flight Experiments for Swarms of High Dynamic UAVs: A Stochastic Configuration Control System with Multiplicative Noises

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3278 ◽  
Author(s):  
Hongbo Zhao ◽  
Sentang Wu ◽  
Yongming Wen ◽  
Wenlei Liu ◽  
Xiongjun Wu
Keyword(s):  
Stochastic Model ◽  
Large Scale ◽  
Kinematic Model ◽  
Robustness Analysis ◽  
Dynamic Configuration ◽  
Analysis And Design ◽  
Multiplicative Noises ◽  
Uav Swarm ◽  
High Dynamic ◽  
Configuration Control

UAV Swarm with high dynamic configuration at a large scale requires a high-precision mathematical model to fully exploit its boundary performance. In order to instruct the engineering application with high confidence, uncertainties induced from either systematic measurement or the environment cannot be ignored. This paper investigates the I t o ^ stochastic model of the UAV Swarm system with multiplicative noises. By combining the cooperative kinematic model with a simplified individual dynamic model of fixed-wing-aircraft for the first time, the configuration control model is derived. Considering the uncertainties in actual flight, multiplicative noises are introduced to complete the I t o ^ stochastic model. Following that, the estimator and controller are designed to control the formation. The mean-square uniform boundedness condition of the proposed stochastic system is presented for the closed-loop system. In the simulation, the stochastic robustness analysis and design (SRAD) method is used to optimize the properties of the formation. More importantly, the effectiveness of the proposed model is also verified using real data of five unmanned aircrafts collected in outfield formation flight experiments.

Sign in / Sign up

Export Citation Format

Share Document