Real Time Rainfall Monitoring for Pipeline Geohazards

Author(s):  
Gerry Ferris ◽  
Patrick Grover ◽  
Aron Zahradka

Abstract Oil and gas pipelines are subjected to multiple types of geohazards which cause pipeline failures (loss of containment); two of the most common types occur at watercourse crossings and at landslides. At watercourse crossings, the most common geohazard which causes pipeline failures is flooding during which excessive scour may result in the exposure of the buried pipeline and if the exposure results in a free spanning pipeline, then this may fail due to fatigue caused by cyclic loading from vortex-induced vibration. Fortunately the free span length and water velocity combinations that lead to failure can be defined and can be used to identify the flood discharge that should be monitored for in order to trigger actions to manage the hazard and avoid failure. Most watercourse crossings in a pipeline network are on ungauged watercourses and necessitate the use of a proxy gauged watercourse. The “proxy” gauged watercourse is used to infer whether flooding is occurring on the ungauged crossing, and the owner can take appropriate actions. Often the proxy gauged watercourse is too far away or the watercourse may not be representative of the crossing of concern (e.g. large difference in the drainage areas). Real-time rainfall data can be used in conjunction with streamflow monitoring to determine when extreme precipitation has occurred within the ungauged watercourses catchment which may result in flooding. Where pipelines cross landslide prone areas, large scale movements can be initiated, or slow on-going movement rates increased when extreme rainfall occurs. The definition of the extreme rainfall event for slope sites is the key component of providing a suitable warning of potentially dangerous conditions; shallow slides can be caused by short term events from sub-hourly to 3 day duration precipitation events whereas large deep seated (creeping) landslides can be driven by annual and intra-annual rainfall amounts. Monitoring of real time rainfall can be used to determine when extreme rainfall occurs at a landslide site. The density of in-situ weather stations collecting real-time rainfall data prevents the application along remote sections of pipeline routes and within large sections of Canada. Gridded real time rainfall from quantitative precipitation estimations which integrate a multiple data sources including in-situ, numerical weather prediction, satellite and weather radar, can be used to overcome this problem and provide warnings when pre-determined rainfall thresholds are exceeded on a site-specific basis.

2013 ◽  
Vol 184 (1-2) ◽  
pp. 165-170 ◽  
Author(s):  
Arpita Mandal ◽  
Anuradha Maharaj

Abstract Flash flooding, from extreme rainfall is one of the major natural disasters affecting Jamaica and other small island states of the Caribbean. Flooding in Jamaica is mainly riverine, coastal and depression with the major coastal towns being affected owing to their location on low lying areas. Such localization is driven by increase in urbanization and tourism along the coastal areas. The present work aims in a broad discussion of the flooding in Jamaica with special reference to riverine flooding of Port Maria, the capital of St Mary, one of the parishes lying in the high rain zone of the island and being affected by repeated events of flooding. Analysis of the extreme rainfall event of November 23rd–24th, 2006 shows that it exceeded the 30 yr annual rainfall of the area and the 100 yr return period as calculated from 30 yr annual rainfall data for the island. The Port Maria river lacks a gauging station to monitor flow data and flood discharge peaks. Several methods are used to calculate the run-off in such small ungauged catchments. In this study the Soil Conservation Systems Curve Number (CN) method was used to calculate the run-off from the measured rainfall data using empirical equations. Results show an unprecedented high of 13–14 inches affecting the buildings and other infrastructures, leading to the collapse of a newly constructed bridge over the river Port Maria. The town continues to get flooded from intense short duration rainfall continuing to affect life and property. Flood plain maps exist for the larger watersheds of the island but smaller yet flood prone ones have not been mapped so far. Hence this becomes very important to create a floodplain map showing the extent of the runoff from rainfall with respect to the buildings and other infrastructures of the area. The present work thus aims in creating a spatial distribution map of the runoff from the rainfall measurements aiding in developing a no build zone for this and for other low lying coastal areas of the island.


2021 ◽  
Author(s):  
Luísa Vieira Lucchese ◽  
Guilherme Garcia de Oliveira ◽  
Olavo Correa Pedrollo

<p>Rainfall-induced landslides have caused destruction and deaths in South America. Accessing its triggers can help researchers and policymakers to understand the nature of the events and to develop more effective warning systems. In this research, triggering rainfall for rainfall-induced landslides is evaluated. The soil moisture effect is indirectly represented by the antecedent rainfall, which is an input of the ANN model. The area of the Rolante river basin, in Rio Grande do Sul state, Brazil, is chosen for our analysis. On January 5<sup>th</sup>, 2017, an extreme rainfall event caused a series of landslides and debris flows in this basin. The landslide scars were mapped using satellite imagery. To calculate the rainfall that triggered the landslides, it was necessary to compute the antecedent rainfall that occurred within the given area. The use of satellite rainfall data is a useful tool, even more so if no gauges are available for the location and time of the rainfall event, which is the case. Remote sensing products that merge the data from in situ stations with satellite rainfall data are increasingly popular. For this research, we employ the data from MERGE (Rozante et al., 2010), that is one of these products, and is focused specifically on Brazilian gauges and territory. For each 12.5x12.5m raster pixel, the rainfall is interpolated to the points and the rainfall volume from the last 24h before the event is accumulated. This is added as training data in our Artificial Neural Network (ANN), along with 11 terrain attributes based on ALOS PALSAR (ASF DAAC, 2015) elevation data and generated by using SAGA GIS. These attributes were presented and analyzed in Lucchese et al. (2020). Sampling follows the procedure suggested in Lucchese et al. (2021, in press). The ANN model is a feedforward neural network with one hidden layer consisting of 20 neurons. The ANN is trained by backpropagation method and cross-validation is used to ensure the correct adjustment of the weights. Metrics are calculated on a separate sample, called verification sample, to avoid bias. After training, and provided with relevant information, the ANN model can estimate the 24h-rainfall thresholds in the region, based on the 2017 event only. The result is a discretized map of rainfall thresholds defined by the execution of the trained ANN. Each pixel of the resulting map should represent the volume of rainfall in 24h necessary to trigger a landslide in that point. As expected, lower thresholds (30 - 60 mm) are located in scarped slopes and the regions where the landslides occurred. However, lowlands and the plateau, which are areas known not to be prone to landslides, show higher rainfall thresholds, although not as high as expected (75 - 95 mm). Mean absolute error for this model is 16.18 mm. The inclusion of more variables and events to the ANN training should favor achieving more reliable outcomes, although, our results are able to show that this methodology has potential to be used for landslide monitoring and prediction.</p>


2011 ◽  
Vol 11 (2) ◽  
pp. 421-437 ◽  
Author(s):  
S. Wang ◽  
L. W. O'Neill ◽  
Q. Jiang ◽  
S. P. de Szoeke ◽  
X. Hong ◽  
...  

Abstract. This paper presents an evaluation and validation of the Naval Research Laboratory's COAMPS® real-time forecasts during the VOCALS-REx over the area off the west coast of Chile/Peru in the Southeast Pacific during October and November 2008. The analyses focus on the marine boundary layer (MBL) structure. These forecasts are compared with lower troposphere soundings, in situ surface measurements, and satellite observations. The predicted mean MBL cloud and surface wind spatial distributions are in good agreement with the satellite observations. The large-scale longitudinal variation of the MBL structure along 20° S is captured by the forecasts. That is, the MBL height increases westward toward the open ocean, the moisture just above the inversion decreases, and the MBL structure becomes more decoupled offshore. The observed strong wind shear across the cloud-top inversion near 20° S was correctly predicted by the model. The model's cloud spatial and temporal distribution in the 15 km grid mesh is sporadic compared to satellite observations. Our results suggest that this is caused by grid-scale convection likely due to a lack of a shallow cumulus convection parameterization in the model. Both observations and model forecasts show wind speed maxima near the top of MBL along 20° S, which is consistent with the westward upslope of the MBL heights based on the thermal wind relationship. The forecasts produced well-defined diurnal variations in the spatially-averaged MBL structure, although the overall signal is weaker than those derived from the in situ measurements and satellite data. The MBL heights are generally underpredicted in the nearshore area. An analysis of the sensitivity of the MBL height to horizontal and vertical grid resolution suggests that the underprediction is likely associated with overprediction of the mesoscale downward motion and cold advection near the coast.


2021 ◽  
Vol 906 (1) ◽  
pp. 012058
Author(s):  
Jan Douša ◽  
Pavel Václavovic ◽  
Petr Bezdĕka ◽  
Guergana Guerova

Abstract Near real-time GNSS double-difference network processing is a traditional method still used within the EUMETNET EIG GNSS Water Vapour Programme (E-GVAP) for the atmosphere water vapour content monitoring in support of Numerical Weather Prediction. The standard production relies on estimating zenith tropospheric path delays (ZTDs) for GNSS ground stations with a 1-hour time resolution and a latency of 90 minutes. The Precise Point Positioning (PPP) method in real-time mode has reached the reliability and the accuracy comparable to the near real-time solution. The effectiveness of the PPP method relies on exploiting undifferenced observations from individual receivers, thus optimal use of all tracked systems, observations and signal bands, possible in-situ processing, high temporal resolution of estimated parameters and almost without any latency. The solution may implicitly include horizontal tropospheric gradients and slant tropospheric path delays for enabling the monitoring of a local asymmetry of the troposphere around each individual site. We have been estimating ZTD and gradients in real-time continuously since 2015 with a limited number of stations. Recently, the solution has been extended to a pan-European and global production consisting of approximately 200 stations. The real-time product has been assessed cross-comparing ZTDs and horizontal gradients at 11 collocated stations and by validating real-time ZTDs with respect to the final post-processing products.


Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1030 ◽  
Author(s):  
Amanda García-Marín ◽  
Javier Estévez ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
José Ayuso-Muñoz ◽  
...  

Testing the homogeneity in extreme rainfall data series is an important step to be performed before applying the frequency analysis method to obtain quantile values. In this work, six homogeneity tests were applied in order to check the existence of break points in extreme annual 24-h rainfall data at eight stations located in the Umbria region (Central Italy). Two are parametric tests (the standard normal homogeneity test and Buishand test) whereas the other four are non-parametric (the Pettitt, Sequential Mann–Kendal, Mann–Whitney U, and Cumulative Sum tests). No break points were detected at four of the stations analyzed. Where inhomogeneities were found, the multifractal approach was applied in order to check if they were real or not by comparing the split and whole data series. The generalized fractal dimension functions Dq and the multifractal spectra f(α) were obtained, and their main parameters were used to decide whether or not a break point existed.


Author(s):  
Dongkyun Kim ◽  
Christian Onof

<p>We introduce a stochastic model reproducing various rainfall characteristics at timescales between 5 minutes and one decade. The model is composed of three moduels as follow: First, the model generates the fine-scale rainfall data based on a type of Bartlett-Lewis rectangular pulse model; Second, sequence of the generated rainstorms are shuffled so that their correlation structure can be preserved; Third, the time series is rearranged at the monthly timescale to reflect the coarse scale correlation structure. The method was tested based on the 69 years of 5-minute rainfall data of Bochum, Germany. The mean, variance, covariance, skewness, and proportion of wet/dry periods were well reproduced at the timescales from 5 minutes to a decade. The extreme values were also successfully reproduced at the timescales between 5 minutes and 3 days. The antecedent moisture condition before an extreme rainfall event was reproduced well too.</p>


1986 ◽  
Vol 19 (9) ◽  
pp. 239-244 ◽  
Author(s):  
R A E Sargeant

An expert system is a computer software technique which is best (but not necessarily) implemented using languages and hardware systems from the artificial intelligence stable. The software technique offers the potential to encapsulate the experience and knowledge from many human experts and to effectively communicate it to other experts. The knowledge is mostly expressed in simple rules from which the expert system makes inferences that lead (to other rules and ultimately) to solutions to problems. The feasibility of building real time expert systems for applications in control rooms of process plants has been proven. Companies with sharp forward plans are investing in such systems now in order to obtain early benefits. The benefits can manifest themselves in improved security of production which is frequently directly quantifiable as cost savings. In 1984 the author led the team which provided one of the first practical demonstrations in the UK of a real time expert system for process control room applications. Here he reflects on practical issues of the pioneering exercise and some of the experience obtained whilst evaluating feasibility of large scale applications with European oil and gas companies. (Fig 1)


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