scholarly journals A Revisit of Rainfall Simulator as a Potential Tool for Hydrological Research

2020 ◽  
Author(s):  
Felix Gemlack Ngasoh ◽  
Constantine Crown Mbajiorgu ◽  
Matthew Boniface Kamai ◽  
Gideon Onyekachi Okoro

Different means of hydrological data collection have developed and used. However, they are constraint in one way or other. This paper therefore revisited the rainfall simulator as potential tool for hydrological research. The research disclosed that there are three different types of rainfall simulators; drop former simulator, pressure nozzle simulator and hybrid simulator. It can further be classified as indoor model and outdoor. The research also showed that precipitation is the driving force in hydrological studies. Consequently, in the design of rainfall simulator, the following should be taken into consideration: nozzle spacing, pump size, nozzle size, nozzle type, nozzle spacing, plot size and pressure. Meanwhile, intensity, distribution uniformity, kinetic energy, rainfall drop size and rainfall terminal velocity should be noted in its evaluation. Factoring-in the aforementioned design considerations, data collection is made easy without necessarily waiting for the natural rainfall. Since the rainfall can be controlled, the erratic and unpredictable changeability of natural rainfall is eliminated. Emanating from the findings, pressurized rainfall simulator produces rainfall characteristics similar to natural rainfall, which is therefore recommended for laboratory use if natural rainfall-like characteristics is the main target.

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 152
Author(s):  
Juan Naves ◽  
Jose Anta ◽  
Joaquín Suárez ◽  
Jerónimo Puertas

Rainfall simulators are useful tools for controlling the main variables that govern natural rainfall. In this study, a new drop-forming rainfall simulator, which consists of pressure-compensating dripper grids above a horizontal mesh that breaks and distributes raindrops, was developed to be applied in wash-off experiments in a large-scale physical model of 36 m2. The mesh typology and size, and its distance to drippers, were established through a calibration where rain uniformity and distributions of raindrop sizes and velocities were compared with local natural rainfall. Finally, the rain properties of the final solution were measured for the three rain intensities that the rainfall simulator is able to generate (30, 50 and 80 mm/h), obtaining almost uniform rainfalls with uniformity coefficients of 81%, 89% and 91%, respectively. This, together with the very suitable raindrop size distribution obtained, and the raindrop velocities of around 87.5% of the terminal velocity for the mean raindrop diameter, makes the proposed solution optimal for wash-off studies, where rain properties are key in the detachment of particles. In addition, the flexibility seen in controlling rain characteristics increases the value of the proposed design in that it is adaptable to a wide range of studies.


1997 ◽  
Vol 77 (4) ◽  
pp. 669-676 ◽  
Author(s):  
S. C. Nolan ◽  
L. J. P. van Vliet ◽  
T. W. Goddard ◽  
T. K. Flesch

Interpreting soil loss from rainfall simulators is complicated by the uncertain relationship between simulated and natural rainstorms. Our objective was to develop and test a method for estimating soil loss from natural rainfall using a portable rainfall simulator (1 m2 plot size). Soil loss from 12 rainstorms was measured on 144-m2 plots with barley residue in conventional tillage (CT), reduced tillage (RT) and zero tillage (ZT) conditions. A corresponding "simulated" soil loss was calculated by matching the simulator erosivity to each storm's erosivity. High (140 mm h−1) and low (60 mm h−1) simulation intensities were examined. The best agreement between simulated and natural soil loss occurred using the low intensity, after making three adjustments. The first was to compensate for the 38% lower kinetic energy of the simulator compared with natural rain. The second was for the smaller slope length of the simulator plot. The third was to begin calculating simulator erosivity only after runoff began. After these adjustments, the simulated soil loss over all storms was 99% of the natural soil loss for CT, 112% for RT and 95% for ZT. Our results show that rainfall simulators can successfully estimate soil loss from natural rainfall events. Key words: Natural rainfall events, simulated rainfall, erosivity, tillage


2014 ◽  
Vol 695 ◽  
pp. 380-383 ◽  
Author(s):  
Manal Osman ◽  
Suhaimi B. Hassan ◽  
Khamaruzaman B. Wan Yusof

The irrigation uniformity of sprinkler irrigation system depends on many design factors such as nozzle type, nozzle diameter, operating pressure and riser height. An experimental study was performed to investigate the effect of combination factors of operating pressure, nozzle diameter and riser height on sprinkler irrigation uniformity. Different operating pressures, nozzle diameters and riser heights have been used. The irrigation uniformity coefficients such as coefficient of uniformity (CU) and distribution uniformity of low quarter (DUlq) have been studied. This study concluded that, the irrigation uniformity of sprinkler irrigation system was more affected by the combination of operating pressure, nozzle diameter and riser height.


Atmosphere ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 113
Author(s):  
Bo Liu ◽  
Xiaolei Wang ◽  
Lihua Shi ◽  
Xichuan Liu ◽  
Zhaojing Kang ◽  
...  

CATENA ◽  
2016 ◽  
Vol 140 ◽  
pp. 77-89 ◽  
Author(s):  
Marco Lora ◽  
Matteo Camporese ◽  
Paolo Salandin

2015 ◽  
Vol 26 (6) ◽  
pp. 604-612 ◽  
Author(s):  
Tamás Lassu ◽  
Manuel Seeger ◽  
Piet Peters ◽  
Saskia D. Keesstra

2003 ◽  
Vol 28 (13) ◽  
pp. 1483-1490 ◽  
Author(s):  
Bofu Yu ◽  
Cyril A. A. Ciesiolka ◽  
Paul Langford

2021 ◽  
Author(s):  
Harris Ramli ◽  
Siti Aimi Nadia Mohd Yusoff ◽  
Mastura Azmi ◽  
Nuridah Sabtu ◽  
Muhd Azril Hezmi

Abstract. It is difficult to define the hydrologic and hydraulic characteristics of rain for research purposes, especially when trying to replicate natural rainfall using artificial rain on a small laboratory scale model. The aim of this paper was to use a drip-type rainfall simulator to design, build, calibrate, and run a simulated rainfall. Rainfall intensities of 40, 60 and 80 mm/h were used to represent heavy rainfall events of 1-hour duration. Flour pellet methods were used to obtain the drop size distribution of the simulated rainfall. The results show that the average drop size for all investigated rainfall intensities ranges from 3.0–3.4 mm. The median value of the drop size distribution or known as D50 of simulated rainfall for 40, 60 and 80 mm/h are 3.4, 3.6, and 3.7 mm, respectively. Due to the comparatively low drop height (1.5 m), the terminal velocities monitored were between 63–75 % (8.45–8.65 m/s), which is lower than the value for natural rainfall with more than 90 % for terminal velocities. This condition also reduces rainfall kinetic energy of 25.88–28.51 J/m2mm compared to natural rainfall. This phenomenon is relatively common in portable rainfall simulators, representing the best exchange between all relevant rainfall parameters obtained with the given simulator set-up. Since the rainfall can be controlled, the erratic and unpredictable changeability of natural rainfall is eliminated. Emanating from the findings, drip-types rainfall simulator produces rainfall characteristics almost similar to natural rainfall-like characteristic is the main target.


1993 ◽  
Vol 7 (4) ◽  
pp. 799-807 ◽  
Author(s):  
James E. Hanks ◽  
Chester G. McWhorter

Spray droplet size of water and paraffinic oil was affected by air pressure, nozzle type, and liquid flow rate when applied with an ultralow volume (ULV), air-assist sprayer. Volume median diameters of water were generally larger than oil at constant air pressure and liquid flow rate. Droplet size decreased as air pressure increased, but increased as liquid flow rate increased. Volume median diameters of water droplets ranged from 41 to 838μm and from 16 to 457μm with oil when atomized at air pressures ranging from 14 to 84 kPa. Relative spans ranged from 1.2 to 18.0 and 2.0 to 7.2 for water and oil, respectively.


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