Environmental effects on measured structural frequencies-model prediction of short-term shift during heavy rainfall and comparison with full-scale observations

2009 ◽  
Vol 16 (4) ◽  
pp. 406-424 ◽  
Author(s):  
Maria I. Todorovska ◽  
Yousef Al Rjoub
Keyword(s):  
Environmental Effects ◽  
Model Prediction ◽  
Heavy Rainfall ◽  
Full Scale ◽  
Short Term

Effects of strategic tillage on short-term erosion, nutrient loss in runoff and greenhouse gas emissions

Soil Research ◽  
2017 ◽  
Vol 55 (3) ◽  
pp. 201 ◽  
Author(s):  
A. R. Melland ◽  
D. L. Antille ◽  
Y. P. Dang
Keyword(s):  
Nitrous Oxide ◽  
Greenhouse Gas ◽  
Heavy Rainfall ◽  
Ghg Emissions ◽  
Nutrient Loss ◽  
Nitrous Oxide Emissions ◽  
Nutrient Losses ◽  
Short Term ◽  
Trade Offs ◽  
Carbon Dioxide Co2

Occasional strategic tillage (ST) of long-term no-tillage (NT) soil to help control weeds may increase the risk of water, erosion and nutrient losses in runoff and of greenhouse gas (GHG) emissions compared with NT soil. The present study examined the short-term effect of ST on runoff and GHG emissions in NT soils under controlled-traffic farming regimes. A rainfall simulator was used to generate runoff from heavy rainfall (70mmh–1) on small plots of NT and ST on a Vertosol, Dermosol and Sodosol. Nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4) fluxes from the Vertosol and Sodosol were measured before and after the rain using passive chambers. On the Sodosol and Dermosol there was 30% and 70% more runoff, respectively, from ST plots than from NT plots, however, volumes were similar between tillage treatments on the Vertosol. Erosion was highest after ST on the Sodosol (8.3tha–1 suspended sediment) and there were no treatment differences on the other soils. Total nitrogen (N) loads in runoff followed a similar pattern, with 10.2kgha–1 in runoff from the ST treatment on the Sodosol. Total phosphorus loads were higher after ST than NT on both the Sodosol (3.1 and 0.9kgha–1, respectively) and the Dermosol (1.0 and 0.3kgha–1, respectively). Dissolved nutrient forms comprised less than 13% of total losses. Nitrous oxide emissions were low from both NT and ST in these low-input systems. However, ST decreased CH4 absorption from both soils and almost doubled CO2 emissions from the Sodosol. Strategic tillage may increase the susceptibility of Sodosols and Dermosols to water, sediment and nutrient losses in runoff after heavy rainfall. The trade-offs between weed control, erosion and GHG emissions should be considered as part of any tillage strategy.

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Heavy Rainfall ◽  
Predictive Performance ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Training Data ◽  
Short Term ◽  
Data Set ◽  
Radar Images

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>

scholarly journals FULL-SCALE MODEL TEST ON THE APPLICABILITY OF L-SHAPED GEOSYNTHETIC DRAIN SYSTEM TO TERRE ARMÉE WALL AGAINST HEAVY RAINFALL

2013 ◽  
Vol 28 (0) ◽  
pp. 353-360
Author(s):  
Je-Min BAEK ◽  
Satoru SHIBUYA ◽  
Jin-Suk HUR ◽  
Takefumi OGATA ◽  
Byeong-Su KIM ◽  
...  
Keyword(s):  
Model Test ◽  
Heavy Rainfall ◽  
Full Scale ◽  
Scale Model

Short-term risk forecasts of heavy rainfall

2002 ◽  
Vol 45 (2) ◽  
pp. 121-125 ◽  
Author(s):  
W. Schmid ◽  
S. Mecklenburg ◽  
J. Joss
Keyword(s):  
Probability Distribution ◽  
Density Function ◽  
Heavy Rainfall ◽  
Heavy Precipitation ◽  
Severe Weather ◽  
Short Term ◽  
Radar Images ◽  
Error Density ◽  
Estimated Error ◽  
Near Future

Methodologies for risk forecasts of severe weather hardly exist on the scale of nowcasting (0–3 hours). Here we discuss short-term risk forecasts of heavy precipitation associated with local thunderstorms. We use COTREC/RainCast: a procedure to extrapolate radar images into the near future. An error density function is defined using the estimated error of location of the extrapolated radar patterns. The radar forecast is folded (“smeared”) with the density function, leading to a probability distribution of radar intensities. An algorithm to convert the radar intensities into values of precipitation intensity provides the desired probability (or risk) of heavy rainfall at any position within the considered window in space and time. We discuss, as an example, a flood event from summer 2000.

Critical Pressure of Spherical Shell Acrylic Windows Under Short-Term Pressure Loading

1969 ◽  
Vol 91 (3) ◽  
pp. 573-584
Author(s):  
J. D. Stachiw
Keyword(s):  
Spherical Shell ◽  
Atmospheric Pressure ◽  
Convex Surface ◽  
Low Pressure ◽  
Full Scale ◽  
Pressure Loading ◽  
Short Term ◽  
Model Scale ◽  
Hydrostatic Loading ◽  
Sector Angle

Model and full scale acrylic windows in the form of spherical shell lenses with parallel convex and concave surfaces have been imploded by loading their convex surface hydro-statically at 650 psi/min rate while their concave surface was exposed to atmospheric pressure. The thickness of the model scale windows varied from 0.250 to 1.200 in. and of the full scale windows from 0.564 to 4.000 in., while the included spherical sector angle of the lens varied from 30 to 180 degrees in thirty degree increments. The low pressure face diameters of the model scale windows varied from 1.423 to 5.500 in., while those of the full scale windows varied from 6.200 to 35.868 in. In addition to critical pressures, displacement of the lens under hydrostatic pressure has been recorded and plotted as functions of pressure. The critical pressures of spherical acrylic windows have been found to be consistently higher than those of conical or flat disc acrylic windows of same thickness and low pressure face diameter subjected to short-term hydrostatic loading.

Instrumentation simultaneously measuring VCO2 and VO2 in humans using titration methods

1982 ◽  
Vol 52 (3) ◽  
pp. 786-791 ◽  
Author(s):  
C. M. Tsoi ◽  
D. B. Raemer ◽  
D. R. Westenskow
Keyword(s):  
Carbon Dioxide ◽  
Single Point ◽  
Open Circuit ◽  
Full Scale ◽  
Bench Test ◽  
Test Results ◽  
Short Term ◽  
Respiratory Flow ◽  
Analysis Technique ◽  
A New Technique

An instrument has been developed for the simultaneous measurement of carbon dioxide excretion (VCO2) and oxygen uptake (VO2). This instrument, the Nutrimeter, gives these breath-averaged measurements continuously without having to determine respiratory flow rate, perform timed spirometric gas collections, or determine absolute CO2 or O2 concentrations. It can be used on ventilated or nonventilated patients in long- and short-term studies. VO2 is determined via the replenishment technique. VCO2 is determined via a new technique, absorption-titration, described here. Bench test results of VCO2 measurements show a standard error of the estimate (SEE) +/- 0.591% of full scale (500 ml/min) and maximum single point error (MSPE) of +/- 3.54% over a 100--350 ml/min range. VO2 measurements show SEE +/- 0.518% of full scale (1,000 ml/min) and MSPE +/- 2.42% over a 100--450 ml/min range. In 31 human clinical trials the Nutrimeter was compared with the open-circuit spirometric collection and micro-Scholander analysis technique. VCO2 measurements show SEE +/- 2.208% and MSPE +/- 10.57% over 135--315 ml/min. VO2 measurements show SEE +/- 1.134% of full scale and MSPE +/- 9.54% over 170--360 ml/min. Response time is 60 s optimally for step changes in VO2 (0--90% of steady-state value), 90 s for VCO2.

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