scholarly journals Remote sensing applications for reservoir water level monitoring, sustainable water surface management, and environmental risks in Quang Nam province, Vietnam

2021 ◽  
Author(s):  
Dinh Nhat Quang ◽  
Nguyen Khanh Linh ◽  
Ho Sy Tam ◽  
Nguyen Trung Viet
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Surface Water ◽  
Water Level ◽  
Water Resource Management ◽  
Measurement Data ◽  
Water Levels ◽  
Sensing Applications ◽  
Volume Curve

Abstract Monitoring surface water provides vital information in water management; however, limited data is a fundamental challenge for most developing countries, such as Vietnam. Based on advanced remote sensing technologies, the authors proposed a methodology to process satellite images and use their outcomes to extract surface water in water resource management of Quang Nam province. Results of the proposed study show good agreement with in situ measurement data when the obtained Overall Accuracy and Kappa Coefficient were greater than 90% and 0.99, respectively. Three potential applications based on the surface water results are selected to discuss sustainable water management in Quang Nam province. Firstly, reservoir operating processes can be examined, enhanced, and even developed through long-term extracted water levels, which are the interpolation results between the extracted surface water area and the water level–area–volume curve. Secondly, the long-term morphological change for the Truong Giang river case between 1990 and 2019 can also be detected from the Water Frequency Index performance and provided additional information regarding permanent and seasonal water changes. Lastly, the flood inundation extent was extracted and separated from permanent water to assess the damage of the Mirinae typhoon on 2 November 2009 in terms of population and crop aspects.

scholarly journals Spatiotemporal change analysis of long time series inland water in Sri Lanka based on remote sensing cloud computing

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Jianfeng Li ◽  
Jiawei Wang ◽  
Liangyan Yang ◽  
Huping Ye
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Surface Water ◽  
Water Resources ◽  
Sri Lanka ◽  
Water Resource Management ◽  
Google Earth ◽  
Water Extraction ◽  
Inland Lakes ◽  
Lakes And Reservoirs

AbstractSri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.

Sedimentary Cladocera as indicators of past water-level changes in shallow northern lakes

2011 ◽  
Vol 75 (3) ◽  
pp. 430-437 ◽  
Author(s):  
Liisa Nevalainen ◽  
Kaarina Sarmaja-Korjonen ◽  
Tomi P. Luoto
Keyword(s):  
Water Level ◽  
Lake Level ◽  
Water Levels ◽  
Sediment Record ◽  
Inference Model ◽  
Water Level Changes ◽  
Long Term Changes ◽  
Lower Water Level ◽  
The Relationship

AbstractThe usability of subfossil Cladocera assemblages in reconstructing long-term changes in lake level was examined by testing the relationship between Cladocera-based planktonic/littoral (P/L) ratio and water-level inference model in a surface-sediment dataset and in a 2000-yr sediment record in Finland. The relationships between measured and inferred water levels and P/L ratios were significant in the dataset, implying that littoral taxa are primarily deposited in shallow littoral areas, while planktonic cladocerans accumulate abundantly mainly in deepwater locations. The 2000-yr water-level reconstructions based on the water-level inference model and P/L ratio corresponded closely with each other and with a previously available midge-inferred water-level reconstruction from the same core, showing a period of lower water level around AD 300–1000 and suggesting that the methods are valid for paleolimnological and -climatological use.

Long-Term Tidal Water Level Measurements in Abu Dhabi Emirate

2008 ◽  
Author(s):  
Khaled A. Mohamed
Keyword(s):  
Water Level ◽  
Water Levels ◽  
Abu Dhabi ◽  
Tidal Analysis ◽  
Tidal Motion ◽  
Tidal Water ◽  
Abu Dhabi Emirate ◽  
Tidal Constituents ◽  
Water Level Measurements

Abu Dhabi Emirate, United Arab Emirates has a unique tidal system. Understanding the tidal hydrodynamics in Abu Dhabi waters is very important for the design of the hydraulic structures and in the marine environmental studies. The objective of this study is to investigate the tidal water levels and tidal motion in Abu Dhabi, making use of the long-term water levels available. To achieve the aim of the study, the National Energy and Water Research Center (NEWRC) of Abu Dhabi Water and Electricity Authority installed tidal gauges at different locations in Abu Dhabi waters to obtain long-term water level measurements. At present, long-term water level measurements for at least 3 years period are available at different locations in Abu Dhabi waters. Tidal analysis was carried out on the available data to determine the characteristics of the tidal wave in Abu Dhabi Emirate and to get the main tidal constituents affecting the tidal motion. The obtained tidal constituents are used in updating and improving the boundary conditions of the numerical hydrodynamic models simulating the flow pattern in Abu Dhabi waters. The set up of the water level measurement program in Abu Dhabi waters and the results of the tidal analysis are presented and discussed in the paper.

scholarly journals Long-Term Discharge Estimation for the Lower Mississippi River Using Satellite Altimetry and Remote Sensing Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2693
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering ◽  
Florian Seitz
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Cross Sections ◽  
Mississippi River ◽  
Satellite Altimetry ◽  
Altimetry Data ◽  
Virtual Station ◽  
Lower Mississippi River ◽  
Satellite Altimetry Data

Despite increasing interest in monitoring the global water cycle, the availability of in situ gauging and discharge time series is decreasing. However, this lack of ground data can partly be compensated for by using remote sensing techniques to observe river stages and discharge. In this paper, a new approach for estimating discharge by combining water levels from multi-mission satellite altimetry and surface area extents from optical imagery with physical flow equations at a single cross-section is presented and tested at the Lower Mississippi River. The datasets are combined by fitting a hypsometric curve, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is derived from the differences between virtual station elevations, which are computed in a least square adjustment from the height differences of all multi-mission satellite altimetry data that are close in time. Using the virtual station elevations, satellite altimetry data from multiple virtual stations and missions are combined to one long-term water level time series. All required parameters are estimated purely based on remote sensing data, without using any ground data or calibration. The validation at three gauging stations of the Lower Mississippi River shows large deviations primarily caused by the below average width of the predefined cross-sections. At 13 additional cross-sections situated in wide, uniform, and straight river sections nearby the gauges the Normalized Root Mean Square Error (NRMSE) varies between 10.95% and 28.43%. The Nash-Sutcliffe Efficiency (NSE) for these targets is in a range from 0.658 to 0.946.

Analysis of the Use of Remote Sensing Measurements for Developing Wind Power Projects

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Gustavo S. Böhme ◽  
Eliane A. Fadigas ◽  
Julio R. Martinez ◽  
Carlos E. M. Tassinari
Keyword(s):  
Remote Sensing ◽  
Wind Power ◽  
Power Plants ◽  
Measurement Data ◽  
Flow Modeling ◽  
Short Term ◽  
Wind Power Plants ◽  
Reduced Data ◽  
Average Uncertainty

Micrositing wind flow modeling presents one of the most relevant uncertainties in the project of wind power plants. Studies in the area indicate that the average uncertainty related to this item varies between 2.4% and 8% of the annual energy production (AEP). The most efficient form to mitigate this uncertainty is to obtain additional measurements from the site. This can be achieved by installing met masts and by applying short-term remote sensing campaigns (LIDAR and SODAR). Ideally, measurement campaigns should have at least one complete year of data to capture seasonal changes in the local wind behavior and to increase the long-term representation of the sample. However, remote sensing is frequently performed in reduced periods of measurement, coming down to months or even weeks of campaign. The main contribution of this paper is to analyze whether short-term remote sensing measurements contribute to the development of wind power projects, given the associated uncertainties due to low representativeness of the reduced data sample. This study was performed using over 60 years of wind measurement data. Its main findings indicate that the contribution of short-term remote sensing campaigns vary depending on the complexity of the local terrain, and the respective uncertainty related to horizontal and vertical extrapolation of micrositing models. The results showed that in only 30% of the cases, a 3 month measurement campaign reduced the projects overall uncertainty. This number increases to 50% for a 6 month campaign and 90% for a 10 month campaign.

scholarly journals What Caused Record Water Level Rise in the Great Lakes?

Eos ◽  
2016 ◽  
Vol 97 ◽  
Author(s):  
Terri Cook
Keyword(s):  
Great Lakes ◽  
Water Level ◽  
Water Levels ◽  
Climate Trends ◽  
Modeling Framework ◽  
Water Level Rise ◽  
Short And Long Term ◽  
Insight Into

A new modeling framework offers insight into how specific lakes' water levels respond to short- and long-term climate trends.

scholarly journals Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera

2021 ◽  
Vol 11 (20) ◽  
pp. 9691
Author(s):  
Nur Atirah Muhadi ◽  
Ahmad Fikri Abdullah ◽  
Siti Khairunniza Bejo ◽  
Muhammad Razif Mahadi ◽  
Ana Mijic
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
River Water ◽  
Correlation Coefficient ◽  
Rank Order ◽  
Measurement Data ◽  
Strong Relationship ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Surveillance Systems

The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities.

scholarly journals Geoinformation Modeling of Flooded Areas in Settlements (in the Example of Lutsk)

2018 ◽  
Author(s):  
Anna Shostak ◽  
Volodymyr Voloshyn ◽  
Oleksandr Melnyk ◽  
Pavlo Manko
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Practical Significance ◽  
Natural Phenomenon ◽  
Short Term ◽  
Sensing Data ◽  
Geoinformation Modeling ◽  
Flooded Areas

Object. Flooding in Ukraine is a common natural phenomenon that repeats periodically and in some cases it becomes disastrous. In an average year floods on the rivers of Volyn region take place from one to three times which extend beyond the limits of the floodplain. The floodplain of Styr river is located in the historical center of Lutsk city, that`s why issues of research and forecasting of floods are very important for a given city. Methodology. Using modern technologies of geodesy and remote sensing allows to quickly determine and predict the floodplain area of settlements. Based on the statistical data of the Volyn Regional Center for Hydrometeorology during the 7 year period 2011-2017 about water levels of the river Styr. We conducted mathematical modeling of fluctuations of water levels within the territory of Lutsk, based on creating a partial Fourier series for discrete values of middle-ten-day water levels values. The post hydrological measurements of Styr river water levels in the territory of Lutsk located on the Shevchenko Street comply with an altitude 172.87 meters. Based on the data of short-term flood forecasting in February and March, and relief data from the Department of Architecture and Urban Development of Volyn State Administration, we conducted visualization of the results using geographic information system QGIS. Results. The results of mathematical processing were the basis for geoinformation simulation of flooded areas using remote sensing data that are publicly available. Use of statistical and geospatial data in this article has great potential for further application in modeling the processes of natural and technogenic origin. Scientific novelty. The mathematical model of short-term forecasting of water levels during the flood period on the river Styr with implementation of geoinformation modeling of flooded areas using remote sensing data is proposed. Practical significance. The research results of water level changes on the Styr River and flood zones within the limits of Lutsk is proposed. The spring flood in February-March 2018, with the maximum water level 5.33 m, corresponds to an absolute mark of 178.20 m, which is forecasted in this article.

scholarly journals An Integrative Approach for Environmental Assessment and Water Resources Management Using Direct Current Resistivity (DC), Geographic Information System (GIS), Remote Sensing, and Gain and Loss Method

2021 ◽  
Author(s):  
Dina Ragab Desouki Abdelmoneim
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Resource ◽  
Water Resource Management ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Dc Resistivity ◽  
Seepage Rate ◽  
Loss Method ◽  
Shallow Groundwater Table

Sustainable water resource management is a crucial national and global issue (Currell et al., 2012). In arid areas, groundwater is often the major source of water or at least a crucial supplement to other freshwater resources for agriculture, industry and domestic consumption (Vrba and Renaud, 2016). The complexity associated with groundwater-surface water interactions creates uncertainty about water resource sustainability in semi-arid environments, especially with urbanization and population growth. Flood irrigation in the early 1900s increased the shallow groundwater table in the Treasure Valley (TV), but with increasing irrigation efficiencies, they have been declining since the 1960s with a mean decline rate of about 2.9-3.9x10^-9 (m/s) (Contor et al., 2011). Quantifying how much surface water is being exchanged with the shallow groundwater table through canals in the TV is necessary for gaining a better understanding of groundwater-surface water interactions in this heavily managed system. This knowledge would help evaluate alternative management options for achieving sustainable management of existing water resources. The key objectives of this project are to determine the seepage rate through some canal reaches in the TV, evaluate the integration of the gain and loss method, remote sensing, GIS, hydrogeophysical simulation, and direct current (DC) resistivity geophysical methods for water resource management. We hypothesize that the underlying lithology and size of canals affect the magnitude of the seepage rate. Flow measurements were collected weekly between July and August 2020 in canal reaches representing different sizes and lithological units to determine the seepage rate using the reach gain/loss method. Canal variability and measurement uncertainty were included in seepage estimation for the entire TV using 3 alternative scaling approaches. DC resistivity was used as a complementary method to monitor the seepage effect on the shallow GW aquifer over 2 months. This research evaluates to what extent canal size and its underlying lithology affects the seepage rate, and how the integration of methods may provide additional insight into groundwater exchange-surface water.

scholarly journals Wave Climate Associated With Changing Water Level and Ice Cover in Lake Michigan

2021 ◽  
Vol 8 ◽  
Author(s):  
Chenfu Huang ◽  
Longhuan Zhu ◽  
Gangfeng Ma ◽  
Guy A. Meadows ◽  
Pengfei Xue
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Lake Michigan ◽  
Linear Trend ◽  
Ice Cover ◽  
Wave Climate ◽  
Water Levels ◽  
Regional Warming ◽  
Model Simulations

Detailed knowledge of wave climate change is essential for understanding coastal geomorphological processes, ecosystem resilience, the design of offshore and coastal engineering structures and aquaculture systems. In Lake Michigan, the in-situ wave observations suitable for long-term analysis are limited to two offshore MetOcean buoys. Since this distribution is inadequate to fully represent spatial patterns of wave climate across the lake, a series of high-resolution SWAN model simulations were performed for the analysis of long-term wave climate change for the entirety of Lake Michigan from 1979 to 2020. Model results were validated against observations from two offshore buoys and 16 coastal buoys. Linear regression analysis of significant wave height (Hs) (mean, 90th percentile, and 99th percentile) across the entire lake using this 42-year simulation suggests that there is no simple linear trend of long-term changes of Hs for the majority (>90%) of the lake. To address the inadequacy of linear trend analysis used in previous studies, a 10-year trailing moving mean was applied to the Hs statistics to remove seasonal and annual variability, focusing on identifying long-term wave climate change. Model results reveal the regime shifts of Hs that correspond to long-term lake water level changes. Specifically, downward trends of Hs were found in the decade of 1990–2000; low Hs during 2000–2010 coincident with low lake levels; and upward trends of Hs were found during 2010–2020 along with rising water levels. The coherent pattern between the wave climate and the water level was hypothesized to result from changing storm frequency and intensity crossing the lake basin, which influences both waves (instantly through increased wind stress on the surface) and water levels (following, with a lag through precipitation and runoff). Hence, recent water level increases and wave growth were likely associated with increased storminess observed in the Great Lakes. With regional warming, the decrease in ice cover in Lake Michigan (particularly in the northernmost region of the lake) favored the wave growth in the winter due to increased surface wind stress, wind fetch, and wave transmission. Model simulations suggest that the basin-wide Hs can increase significantly during the winter season with projected regional warming and associated decreases in winter ice cover. The recent increases in wave height and water level, along with warming climate and ice reduction, may yield increasing coastal damages such as accelerating coastal erosion.

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