Quality Control of Real-Time Water Level Data: The U.S. IOOS® QARTOD Project

2018 ◽  
Vol 52 (2) ◽  
pp. 13-17
Author(s):  
Mark Bushnell
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Sea Level Rise ◽  
Water Level ◽  
Real Time ◽  
Sea Level ◽  
Hampton Roads ◽  
Level Data ◽  
Water Level Data ◽  
The U.S

AbstractWithin the U.S. Integrated Ocean Observing System Program, the Quality Assurance/Quality Control of Real-Time Oceanographic Data (QARTOD) Project develops manuals that describe variable-specific quality control (QC) tests for operational use. The QARTOD's Manual for Real-Time Quality Control of Water Level Data: A Guide to Quality Control and Quality Assurance for Water Level Observations was created with broad support from entities engaged in operational observations of water levels. The process used to generate this manual and all other QARTOD manuals exemplifies the integration of “federal, state, and local government agencies as well as the private and nonprofit sectors” described by the Hampton Roads Sea Level Rise Preparedness and Resilience Intergovernmental Pilot Project.Another project that supports Hampton Roads, Virginia, sea level rise and utilizes multiple partners is the deployment of continuous global positioning system (cGPS) receivers directly on water level sensors. These cGPS installations enable the determination of absolute sea level rise and local land subsidence. Successful transition of cGPS to an operational status requires the application of real-time data QC.

Status and Near-Term Plans for the U.S. IOOS Quality Assurance / Quality Control of Real-time Oceanographic Data (QARTOD) Project

2018 ◽  
Author(s):  
Mark Bushnell ◽  
Robert Heitsenrether ◽  
Julie Thomas ◽  
Charlton Galvarino ◽  
Eugene Burger ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Real Time ◽  
Oceanographic Data ◽  
Near Term ◽  
The U.S

scholarly journals Sea level rise scenarios for Ho Chi Minh City in the context of the climate change

2019 ◽  
Vol 2 (5) ◽  
pp. 184-191
Author(s):  
Tuan Ngoc Le ◽  
Thinh Nam Ngo ◽  
Phung Ky Nguyen
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Ho Chi Minh City ◽  
Intergovernmental Panel ◽  
Rcp Scenarios ◽  
Level Data ◽  
Water Level Data ◽  
Local Water ◽  
Important Basis

This work aimed to develope sea level rise (SLR) scenarios in Ho Chi Minh City (HCMC) to 2100, corresponding to the scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP8.5 and the approach mentioned in the AR5 report of the Intergovernmental Panel on Climate Change (IPCC) through SIMCLIM software, and the local water level data (updated to 2015). The results showed that the SLR in the coastal area of HCMC increased gradually over the years as well as the increase in greenhouse gas scenarios. In the period of 2025-2030, SLR would increase relatively equally among RCP scenarios. SLR in 2030 would increase about 12cm as compared to sea level in the period of 1986-2005 in all RCP scenarios. By 2050, the average SLR for the scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP8.5 would be 21 cm, 21 cm, 22 cm, and 25 cm, respectively. The corresponding figures for 2100 would bee 43 cm, 52 cm, 54 cm, and 72 cm, respectively. The research results provide an important basis for calculations and assessments of impact and vulnerability due to the climate change to socio-economic development in HCMC.

scholarly journals Modeling the Impacts of Sea Level Rise on Storm Surge Inundation in Flood-Prone Urban Areas of Hampton Roads, Virginia

2018 ◽  
Vol 52 (2) ◽  
pp. 92-105 ◽  
Author(s):  
Luca Castrucci ◽  
Navid Tahvildari
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Storm Surge ◽  
Hydrodynamic Model ◽  
Urban Areas ◽  
The United States ◽  
Hampton Roads ◽  
Level Data ◽  
The Difference ◽  
Surge Flooding

AbstractHampton Roads is a populated area in the United States Mid-Atlantic region that is highly affected by sea level rise (SLR). The transportation infrastructure in the region is increasingly disrupted by storm surge and even minor flooding events. The purpose of this study is to improve our understanding of SLR impacts on storm surge flooding in the region. We develop a hydrodynamic model to study the vulnerability of several critical flood-prone neighborhoods to storm surge flooding under several SLR projections. The hydrodynamic model is validated for tide prediction, and its performance in storm surge simulation is validated with the water level data from Hurricane Irene (2011). The developed model is then applied to three urban flooding hotspots located in Norfolk, Chesapeake, and the Isle of Wight. The extent, intensity, and duration of storm surge inundation under different SLR scenarios are estimated. Furthermore, the difference between the extent of flooding as predicted by the hydrodynamic model and the “bathtub” approach is highlighted.

scholarly journals Real-time flood forecasting by employing artificial neural network based model with zoning matching approach

2011 ◽  
Vol 8 (5) ◽  
pp. 9357-9393 ◽  
Author(s):  
M. Sulaiman ◽  
A. El-Shafie ◽  
O. Karim ◽  
H. Basri
Keyword(s):  
Water Level ◽  
Real Time ◽  
High Volume ◽  
High Water ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Flood Events ◽  
High Water Level ◽  
Level Data ◽  
Water Level Data

Abstract. Flood forecasting models are a necessity, as they help in planning for flood events, and thus help prevent loss of lives and minimize damage. At present, artificial neural networks (ANN) have been successfully applied in river flow and water level forecasting studies. ANN requires historical data to develop a forecasting model. However, long-term historical water level data, such as hourly data, poses two crucial problems in data training. First is that the high volume of data slows the computation process. Second is that data training reaches its optimal performance within a few cycles of data training, due to there being a high volume of normal water level data in the data training, while the forecasting performance for high water level events is still poor. In this study, the zoning matching approach (ZMA) is used in ANN to accurately monitor flood events in real time by focusing the development of the forecasting model on high water level zones. ZMA is a trial and error approach, where several training datasets using high water level data are tested to find the best training dataset for forecasting high water level events. The advantage of ZMA is that relevant knowledge of water level patterns in historical records is used. Importantly, the forecasting model developed based on ZMA successfully achieves high accuracy forecasting results at 1 to 3 h ahead and satisfactory performance results at 6 h. Seven performance measures are adopted in this study to describe the accuracy and reliability of the forecasting model developed.

scholarly journals Sistem Monitoring Ketersediaan Air pada Perangkat Cuci Tangan Portable berbasis IoT

2021 ◽  
Vol 9 (2) ◽  
pp. 455
Author(s):  
DADAN NUR RAMADAN ◽  
SUGONDO HADIYOSO ◽  
INDRARINI DYAH IRAWATI
Keyword(s):  
Internet Of Things ◽  
Water Level ◽  
Real Time ◽  
Junior High School ◽  
Sensor Reading ◽  
Web Information System ◽  
Level Data ◽  
Water Level Data ◽  
Cloud Database ◽  
Monitoring Devices

ABSTRAKPada studi ini diimplementasikan sebuah sistem untuk memantau ketinggian air di dalam drum secara online real-time menggunakan platform Internet of Things (IoT). Sistem ini terdiri dari sensor ultrasonik untuk estimasi ketinggian air, kemudian data tersebut dikirim ke firebase cloud database, untuk diakses oleh perangkat monitoring atau mengakses halaman website. Level air yang tersisa direpresentasikan dalam nilai persen (%). Rata-rata kesalahan pembacaan sensor adalah tidak lebih dari 2%. Delay pengiriman yang digenerate adalah 39,06 ms, sesuai dengan rekomendasi ITU-T untuk komunikasi real-time. Sistem informasi web dapat menampilkan data ketinggian air dalam bentuk numerik dan grafik. Sistem ini telah diterapkan di sekolah menengah pertama Al-Azhar kota Bandung dan diharapkan dapat diperluas penerapannya.Kata kunci: drum, ketinggian air, real-time, IoT ABSTRACTIn this study, a real-time online monitoring of the water level in the drum was implemented using the internet of things (IoT) platform. This system consists of ultrasonic sensors to estimate the water level, then the data is sent to the Firebase cloud database, to be accessed by monitoring devices or accessing a website page. Water level is represented as a percent (%). The average sensor reading error is not more than 2%. The generated delivery delay is 39.06 ms, according to ITU-T recommendations for real-time communication. The web information system can display water level data in numerical and graphic form. This system has been implemented in Al-Azhar junior high school in Bandung and it is hoped that its application can be expanded.Keywords: drums, water level, real-time, IoT

scholarly journals Real-time monitoring of water level and storage dynamics of irrigation tank using IoT

2020 ◽  
Vol 3 (1) ◽  
pp. 392-400
Author(s):  
Muthiah Krishnaveni ◽  
S. K. Praveen Kumar ◽  
E. Arul Muthusamy ◽  
J. Kowshick ◽  
K. G. Arunya
Keyword(s):  
Water Level ◽  
Real Time ◽  
Web Application ◽  
Low Cost ◽  
The Internet ◽  
Volume Curve ◽  
Level Data ◽  
Water Level Data ◽  
Irrigation Tank ◽  
Tank Water

Abstract The internet of things (IoT), an emerging technological marvel, consists of a group of physical objects such as vehicles, machines and sensors to monitor and transfer data over the internet with much less human to machine interaction. It relies on a host of technologies like application programming interfaces (API), which in turn, help the devices to get connected with the internet. Efficient irrigation tank management requires a strong database on continuous water level dynamics for irrigation decision-making. Real-time tank water level monitoring is possible through an IoT device by integrating sensors and microcontroller that can send the water level data to the cloud. Google sheet is used to store the water level data that can be viewed using a web application as well as a mobile application. The contour map of the study tank is used to develop the stage (water level) vs volume curve. The volume of water present in the tank at any time can be arrived at for any tank water level using the above curve. The developed device can provide real-time continuous water level data with low cost and simple infrastructure, thus aiding tank water management.

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