Water Level Estimation Research Articles

Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys

Due to recent technological advancements in the field of cloud-based satellite remote sensing, the barriers to global analysis ready dataset access and processing have lowered drastically increasing the opportunity for large-scale long-term satellite-derived shoreline monitoring solutions. However, validation studies employing synchronous and independent in-situ instantaneous shorelines at scale are limited. Thanks to a unique dataset of 47 synchronous UAV photogrammetric surveys in 15 coastal locations in Victoria, Australia, over 3 years, we validated Sentinel-2 instantaneous subpixel shorelines extracted using the most common water indices, CoastSat and a tidal-balanced convolutional neural network (Unet+++). We assessed the accuracy of the water level estimation using 9 wave run-up models and wave data sourced from a nearshore wave buoy network, a regional hindcast model and a global altimeter wave dataset. This paper also presents the first validation of Deep Learning-derived shorelines using synchronous independent in-situ data. We found the best overall performance and least threshold-sensitive shoreline extraction approach to be the WI index, whilst NDWI had the poorest performances despite being the most utilized across the literature. We found that the thresholdless Unet+++ solution coupled with an ensemble wave run-up model and the altimeter wave data is an adequate trade-off in terms of accuracy, adaptability and wave data access for global scale shoreline studies.Moreover, we highlighted the very high spatial variability of beach-scale shoreline extraction performances and found that generally, the best SDS extractor only performs best along 30–40% of a coastline. Our results suggest that future satellite-derived shoreline approaches should investigate the relationships between scene-dependent variables and shoreline accuracies in order to better understand the causes of beach-scale performance variations and increase the accuracy of satellite-derived shorelines.

Water Level Change Monitoring Based on a New Denoising Algorithm Using Data from Landsat and ICESat-2: A Case Study of Miyun Reservoir in Beijing

Inland lakes or reservoirs are the main surface water resources, related to people’s survival and social development. On-site water level data are critical for water resource management, while satellite laser and remote sensing data are a significant tool for long-term change monitoring. In this study, we took the Miyun Reservoir, the most important source of drinking water in Beijing, as the research area. We used Landsat images and the Ice, Cloud, and Land Elevation Satellite (ICESat-2) for small-scale water level change monitoring and inversion across unrecorded periods. First, we proposed a new two-step denoising method based on local statistics to denoise the ATL03 single photon data from 2018 to 2020, and extracted the effective elevation information of Miyun Reservoir. Second, based on Landsat images from 1984 to 2020, the MNDWI index was used to extract the water area and generated a percentage map of water occurrence. Finally, the water and land boundary elevation were selected from the effective photon elevation information according to the boundary position under each percentage of the water generation percentage graph, the area, and the water level elevation model (A–E model) was established, and the water level values of unrecorded periods were inverted. The denoising results showed that the denoising accuracy of all data is higher than 95%. The area of Miyun Reservoir has changed greatly in the past 37 years, especially in the northeast corner and northern region. The inverted water level elevation had a strong correlation with the measured value, the correlation coefficient reached 0.97, the root mean square error (RMSE) was 0.553 m, and the estimated water level value was highly consistent with the recorded value and fluctuated dramatically from 1984 to 2020. This method complements the traditional water level mapping method, provides a new idea for the monitoring of water level changes in lakes or reservoirs without field records, and also contributes to the timely and effective monitoring and management of global lake and reservoir water level and time changes.

Generating reliable estimates of tropical-cyclone-induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks

Abstract. Deriving reliable estimates of design water levels and wave conditions resulting from tropical cyclones is a challenging problem of high relevance for, among other things, coastal and offshore engineering projects and risk assessment studies. Tropical cyclone geometry and wind speeds have been recorded for the past few decades only, thus resulting in poorly reliable estimates of the extremes, especially in regions characterized by a low number of past tropical cyclone events. In this paper, this challenge is overcome by using synthetic tropical cyclone tracks and wind fields generated by the open-source tool TCWiSE (Tropical Cyclone Wind Statistical Estimation Tool) to create thousands of realizations representative of 1000 years of tropical cyclone activity for the Bay of Bengal. Each of these realizations is used to force coupled storm surge and wave simulations by means of the processed-based Delft3D Flexible Mesh Suite. It is shown that the use of synthetic tracks provides reliable estimates of the statistics of the first-order hazard (i.e., wind speed) compared to the statistics derived for historical tropical cyclones. Based on estimated wind fields, second-order hazards (i.e., storm surge and waves) are computed that are generated by the first-order hazard of wind. The estimates of the extreme values derived for wind speed, wave height and storm surge are shown to converge within the 1000 years of simulated cyclone tracks. Comparing second-order hazard estimates based on historical and synthetic tracks shows that, for this case study, the use of historical tracks (a deterministic approach) leads to an underestimation of the mean computed storm surge of up to −30 %. Differences between the use of synthetic versus historical tracks are characterized by a large spatial variability along the Bay of Bengal, where regions with a lower probability of occurrence of tropical cyclones show the largest difference in predicted storm surge and wave heights. In addition, the use of historical tracks leads to much larger uncertainty bands in the estimation of both storm surges and wave heights, with confidence intervals being +80 % larger compared to those estimated by using synthetic tracks (probabilistic approach). Based on the same tropical cyclone realizations, the effect that changes in tropical cyclone frequency and intensity, possibly resulting from climate change, may have on modeled storm surge and wave heights was computed. As a proof of concept, an increase in tropical cyclone frequency of +25.6 % and wind intensity of +1.6 %, based on literature values and without accounting for uncertainties in future climate projection, was estimated to possibly result in an increase in storm surge and wave heights of +11 % and +9 %, respectively. This suggests that climate change could increase tropical-cyclone-induced coastal hazards more than just the actual increase in maximum wind speeds.

Correcting GEDI Water Level Estimates for Inland Waterbodies Using Machine Learning

The Global Ecosystem Dynamics Investigation (GEDI) LiDAR on the International Space Station has acquired more than 35 billion shots globally in the period between April 2019 and August 2021. The acquired shots could offer a significant database for the measure and monitoring of inland water levels over the Earth’s surface. Nonetheless, previous and current studies have shown that the provided GEDI elevation estimates are significantly less accurate than any available radar or LiDAR altimeter. Indeed, our analysis of GEDI’s altimetric capabilities to retrieve water levels over the five North American Great Lakes presented estimates with a bias that ranged between 0.26 and 0.35 m and a root mean squared error (RMSE) ranging between 0.54 and 0.68 m. Therefore, our objective in this study is to post-process the original GEDI water level estimates from an error model taking instrumental, atmospheric, and lakes surface state factors as proxies, which affect the physical shape of the waveforms, hence introducing uncertainties on the elevation estimates. The first tested model, namely a random forest regressor (RFICW) with the instrumental, atmospheric, and water surface state factors as inputs, was validated temporally (trained on a given year and validated on another) and spatially (trained on a given lake and validated on the remaining four). The results showed a significant decrease in elevation estimation errors both temporally and spatially. The temporally validated models showed an RMSE on the corrected elevation estimates of 0.18 m. Concerning the spatially validated model, the results varied based on the lake data used for training. Indeed, the most accurate spatially validated model showed an RMSE of 0.17 m, while the least accurate model showed an RMSE of 0.26 m. Finally, given that an elevation correction model using all the factors (instrumental, atmospheric, and water surface state factors) presents a best-case scenario, as water surface state factors are only available over a selected number of lakes globally, three additional models based on random forest were tested. The first, RFI, uses only instrumental factors as correction factors, RFIC uses both instrumental and atmospheric factors, while the third, RFIW, uses instrumental and water surface state factors. The temporal validation of these models showed that the model using instrumental factors, while less accurate than the remaining two models, was capable of correcting the original GEDI elevation estimates by a factor of two across the five lakes. On the other hand, the RFIC model was the most accurate between the three, with a slight degradation in comparison to the full model. Indeed, the RFIC model showed an RMSE on the estimation of water levels of 0.21 m.

Analysis of Environmental and Atmospheric Influences in the Use of SAR and Optical Imagery from Sentinel-1, Landsat-8, and Sentinel-2 in the Operational Monitoring of Reservoir Water Level

In this work, we aim to evaluate the feasibility and operational limitations of using Sentinel-1 synthetic aperture radar (SAR) data to monitor water levels in the Poço da Cruz reservoir from September 2016–September 2020, in the semi-arid region of northeast Brazil. To segment water/non-water features, SAR backscattering thresholding was carried out via the graphical interpretation of backscatter coefficient histograms. In addition, surrounding environmental effects on SAR polarization thresholds were investigated by applying wavelet analysis, and the Landsat-8 and Sentinel-2 normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to compare and discuss the SAR results. The assessment of the observed and estimated water levels showed that (i) SAR accuracy was equivalent to that of NDWI/Landsat-8; (ii) optical image accuracy outperformed SAR image accuracy in inlet branches, where the complexity of water features is higher; and (iii) VV polarization outperformed VH polarization. The results confirm that SAR images can be suitable for operational reservoir monitoring, offering a similar accuracy to that of multispectral indices. SAR threshold variations were strongly correlated to the normalized difference vegetation index (NDVI), the soil moisture variations in the reservoir depletion zone, and the prior precipitation quantities, which can be used as a proxy to predict cross-polarization (VH) and co-polarization (VV) thresholds. Our findings may improve the accuracy of the algorithms designed to automate the extraction of water levels using SAR data, either in isolation or combined with multispectral images.

Utility of geospatial techniques in estimating dam water levels: insights from the Katrivier Dam

To achieve informed integrated water resource management and sustainability, an understanding of the quantity of water available for use within a spatial and temporal context is needed. This study was consequently focused on the estimation of water levels with the use of geospatial techniques. The availability of water data is a significant challenge, especially for smaller dams used by farmers. The lack of consistent water data in turn poses a problem by limiting the estimation of the overall water availability in water strategy models. This challenge is attributed to the cost of modeling all available water resources and the lack of complete records of all available water resources, as some small dams are not officially registered. This paper provides a simple protocol that can be implemented to reliably derive water levels for dams that are yet to be registered or accounted for, using the Katrivier Dam as a case study. Three main datasets were used which enabled the calculation of water levels – a 12.5 m digital elevation model, Sentinel-2 optical images, and water data from the Department of Water and Sanitation (DWS), as in-situ data. The resulting water level values were derived using a proposed model that includes two correction factors, k and s. The results obtained showed that the estimated water levels from the model proposed in this paper are analogous with those observed by the DWS. Therefore, the proposed method can serve as an additional cost-effective method in water accounting procedures as it requires less expensive equipment than alternatives such as bathymetric methods.

Forecasting the Water Level of the Euphrates River in Western Iraq Using Artificial Neural Networks (ANN)

Forecasting water levels of rivers downstream major dams are essential for agricultural and industrial purposes as well as for efficient water management. Haditha Dam is one of the major projects on the Euphrates River in Iraq that is used for flood control and water management. The area downstream of the dam contains many strategic agricultural and industrial projects that are highly affected by the variation in the river water level. In this study, a neural network model (ANN) was created to forecast the levels of the Euphrates downstream of Haditha Dam. The model was trained in MATLAB with four inputs representing water levels at present and previous times. The data was utilized for training a daily model for 496 days and a monthly model for 241 months. The results indicated that ANN can estimate water level (t+1) with a high degree of accuracy. Furthermore, the results provide that the ANN is an effective technique to predict daily and monthly water levels and that the empirical equation can be used to compute daily and monthly levels with a regression coefficient greater than 92 percent for (training, validation, testing, and all data) for the daily model and greater than 84 percent for the monthly model. The ANN model could be simplified into a practical and straightforward formula from which the water level for the two models could be calculated.

A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks

Climate change is driving worsening flood events worldwide. In this study, a hybrid approach based on a combination of the optimization of a hydrodynamic model and an error correction modeling that exploit different aspects of the physical system is proposed to improve the forecasting accuracy of flood water levels. In the parameter optimization procedure for the hydrodynamic model, Manning’s roughness coefficients were estimated by considering their spatial distribution and temporal variation in unsteady flow conditions. In the following error correction procedure, the systematic errors of the optimized hydrodynamic model were captured by combining the input variable selection method using partial mutual information (PMI) and artificial neural networks (ANNs), and therefore, complementary information provided by the data was achieved. The developed ANNs were used to analyze the potential non-linear relationships between the considered state variables and simulation errors to predict systematic errors. To assess the hybrid forecasting approach (hydrodynamic model with an ANN-based error correction model), performances of the hydrodynamic model, two ANN-based water-level forecasting models (ANN1 and ANN2), and the hybrid model were compared. Regarding input candidates, ANN1 considers the historical observations only, and ANN2 considers not only the historical observations that used in ANN1 but also the prescribed boundary conditions required for the hydrodynamic forecast model. As a result, the hybrid model significantly improved the forecasting accuracy of flood water levels compared to individual models, which indicates that the hybrid model is able to take advantage of complementary strengths of both the hydrodynamic model and the ANN model. The optimization of the hydrodynamic model allowing spatially and temporally variable parameters estimated water levels with acceptable accuracy. Furthermore, the use of PMI-based input variable selection and optimized ANNs as error correction models for different sites were proven to successfully predict simulation errors in the hydrodynamic model. Hence, the parameter optimization of the hydrodynamic model coupled with error correction modeling for water level forecasting can be used to provide accurate information for flood management.

An alternative approach to estimate river cross-sections using LIDAR-based digital elevation model

ABSTRACT Topographic LIDAR can be used to estimate elevation values for dry areas down to the river water level during the extraction of river cross-sections (XS). However, LIDAR cannot accurately predict the submerged topography, which causes uncertainty in river XS area estimation. This uncertainty affects the channel water level and flood inundation depth estimation in in situ sparse data. Therefore, an alternative approach is presented to estimate unknown submerged topography (UST) using topographic LIDAR. The one dimension/two dimension Hydrologic Engineering Center River Analysis System (1D/2D HEC-RAS) model is used to simulate the estimated river XS with the help of in situ river water level and flow data which is later validated using in situ data. The results show that the proposed approach accurately estimates water level (error >0.5 m), channel flow areas, and floodplain water depths. Notably, the extent of the estimated floodplain overflow by UST models was in 94% agreement with the real XS.

Impacts of Hurricane Matthew on adjacent developed and undeveloped barrier islands in southeastern North Carolina

High-magnitude storms such as hurricanes can cause significant and potentially long-lasting morphological coastal change, particularly along low-lying barrier islands. This study investigated the impacts of Hurricane Matthew (2016) on neighboring undeveloped Masonboro Island reserve and engineered/nourished Wrightsville Beach barrier islands, located in southeast North Carolina. Using a combination of high-resolution pre- and post-storm RTK–GPS beach surveys, coupled with direct observations, storm surge and wave runup calculations and aerial imagery, a range of contrasting storm-induced coastal changes and impact regimes were identified across the two adjacent barriers. Storm impacts were especially pronounced across low-lying undeveloped central/southern Masonboro Island, which was dominated by significant overwash processes, leading to landward directed barrier crest migration. In contrast, only short-lived and minor collision with the dune base was observed at Wrightsville Beach, were storm impacts were dominated by a swash storm regime resulting in significant beach erosion. Field- and aerial based observations match well with modeled extreme water level estimates. This study offers a real-time example of how geomorphologically different neighboring islands respond to specific storm events, and how storm impact regime type and duration helps explain differences in barrier responses. Similar storm impacts are likely at other locations with comparable barrier island settings and differing coastal management approaches.

Stage estimation of Himalayan rivers using passive microwave radiometer and altimeter

ABSTRACT Hydrological variables such as surface water extent and soil moisture are continuously being monitored from satellites with good accuracy. However, orbital measurement of river stage and discharge remains a major challenge even today. Although altimeters provide valuable information on water level variations with a precision of few centimetres, they suffer from poor revisit time and limited gauging sites. Microwave radiometers that provide brightness temperature (BT) can be used to fill the gaps between altimeter passes with a revisit time of 1–2 days, although with limited accuracy. This paper presents an approach for estimation of river stage based on passive microwave radiometer using BT of river/measurement target (M) and upstream calibration target (C). We have used altimeter data from Jason-2/3 and BT data from Advanced Microwave Scanning Radiometer (AMSR2) at 36.5 GHz (descending orbit and horizontal polarization) for the period January 2013 to August 2020 to estimate river stage at 12 virtual gauging sites along Indus, Ganga, and Brahmaputra rivers. A new approach for selection of calibration targets is presented in this study which results in higher correlation between altimeter observed river stage and AMSR2 derived C/M ratio when compared to existing methods. Coefficient of determination (R 2) for C/M ratio with water level for the period 2013–2017 ranged from 0.45 to 0.89. Model functions were generated between C/M ratio and altimeter observed water level for all the gauging sites. Estimated water levels were validated against altimeter and ground observed values for the period January 2018–August 2020 with RMSE ranging from 0.35 to 1.45 m, which is 7–15% of total stage variability.

Wave Impact Analysis on a Beach-Dune System to Support Coastal Management and Nourishment Works: The Showcase of Mira, Portugal

At coasts, sandy beach-dune systems act as natural barriers to environmental forcing, preventing coastal flooding and protecting coastal communities. In the context of coastal studies, it is fundamental to identify beach-dune sectors exposed to wave impact to support coastal management and suggest soft engineering interventions. This work examines the morphodynamics of the Mira beach-dune system on the northern-central littoral of Portugal over the winter 2016-2017. Coupling topographic data with the estimation of wave runup and total water level (TWL) timeseries, we proposed an operational framework to spot the beach-dune system sectors most vulnerable to dune collision and overwash. The highest topographic variations occurred in the northern sector of the study area, where overwash events occurred due to low dune crest (DC) elevation. The dune toe (DT) was frequently collided by waves in the central sector, where the upper beach profile was the most variable during winter. Overall, wave collision and overwash events were registered not only during storms, but also during spring tides and mild wave conditions. Results highlight the importance of uploading the intertidal beach slope when computing the total water levels on wave-dominated mesotidal sandy beaches, and therefore, the need for a consistent monitoring program of the coastal zones. Findings of this work also supported the coastal management of beach-dune systems, where regional authorities need to restore dune ridges to prevent further overwash events and erosion. The presented framework can be applied to build a beach-dune system vulnerability map in response to wave forecast and predicted sea-level rise.

SUPPLEMENTARY TOOLS IN BASELINE INFORMATION BUILDING FOR WATER RESOURCES PROJECTS UNDER THE NEW NORMAL

Research and industry-related engineering projects are affected in many ways by the COVID-19 pandemic. For example, data gathering becomes more challenging in recent times. The main objective of this paper is to demonstrate alternative ways to gather baseline data related to water resources projects. There are supplemental open-source tools available for baseline information building that are used in the past years that are expected to play more significant roles now. The data gathering phase of some projects were used as examples. Firstly, the importance and basic know-hows of Google Earth Pro ™ in the context of rapid reconnaissance and appraisal (RRA) are portrayed. Google Earth Pro’s Street View and available historical satellite images enable spatially and temporally varied virtual tour of study areas, which can help in site characterization and identification of important study and design parameters. Secondly, the use of hydrologic modeling to obtain estimates of flow data is illustrated. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) is shown to be capable of generating approximate flood hydrographs for ungaged rivers and flow duration curves for water supply studies. Lastly, the capability of hydraulic models to estimate water levels is presented. Instead of getting anecdotal water levels in the field, hydraulic modeling packages such as Hydrologic Engineering Center – River Analysis System (HEC-RAS) and SOBEK-River are technically feasible to get water levels for water resources projects. Although the results are subjected to some limitations, as in the case of uncalibrated models, these provide opportunities to obtain valuable information amidst the pandemic.

An estimation of water levels associated with a storm along the coast of Bangladesh using a non-central difference method of lines

In this paper, depth-averaged shallow water equations were solved using a non-central difference method of lines together with the classical RK(4,4) method to estimate water levels associated with a cyclone along the coast of Bangladesh. A 4-point backward finite difference method was adopted in discretizing spatial derivatives retaining time derivatives as continuous. Thus, the shallow water equations together with the boundary conditions were transformed into a system of initial value problems, which was solved using the RK(4,4) method. A nesting approach was employed to incorporate the land-sea interface and bottom topography effectively with economic cost. A stable tidal oscillation was generated in the domain applying the M2 tidal constituent along the southern boundary of the parent model and the surge model was then run to get water levels owing to dynamic tide-surge interaction. Numerical experiments were performed to simulate water levels due to tide, surge, and nonlinear tide-surge interactions associated with the cyclonic storm, AILA. The obtained results were in good agreement with those attained in some previous studies and observations and the WLs due to the nonlinear tide-surge interaction were in a reasonable agreement with respect to the root mean square error values and predictive skills.

Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

Spaceborne LiDAR altimetry has been demonstrated to be an essential source of data for the estimation and monitoring of inland water level variations. In this study, water level estimates from the Global Ecosystem Dynamics Investigation (GEDI) were validated against in situ gauge station records over Lake Geneva for the period between April 2019 and September 2020. The performances of the first and second releases (V1 and V2, respectively) of the GEDI data products were compared, and the effects on the accuracy of the instrumental and environmental factors were analyzed in order to discern the most accurate GEDI acquisitions. The respective influences of five parameters were analyzed in this study: (1) the signal-over-noise ratio (SNR); (2) the width of the water surface peak within the waveform (gwidth); (3) the amplitude of the water surface peak within the waveform (A); (4) the viewing angle of GEDI (VA); and (5) the acquiring beam. Results indicated that all these factors, except the acquiring beam, had an effect on the accuracy of GEDI elevations. Nonetheless, using VA as a filtering criterion was demonstrated to be the best compromise between retained shot count and water level estimation accuracy. Indeed, by choosing the shots with a VA ≤ 3.5°, 74.6% of the shots (after an initial filter) were retained with accuracies similar to choosing A > 400 (46.2% retained shots), SNR > 15 dB (63.3% retained shots), or gwidth < 10 bins (46.5% of retained shots). Finally, the comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant, but higher, bias and fewer deviations to the in situ data than V1. Indeed, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r = 0.66) and 42.8 cm with V1 (r = 0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA ≤ 3.5°). Regarding the bias, a higher value was observed with V2, but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number and strong dependence on GEDI dates.

Sensitivity Analysis of C and Ku-Band Scatterometers for River Water Level Estimation

Water-level monitoring is essential for analyzing hydrological extremes, environmental flows, sustainable development and management of water resources. Satellite altimeters have operationally been used to estimate river water levels (e.g., SARAL-Altika, Jason-2/3) with poor revisit times (10&#x2013;35 days). In this current study, we propose a method to estimate river water levels using scatterometer data with 2-day temporal resolution. Sensitivity analysis of radar frequency (Ku- and C-bands) for river water-level estimation was carried out. The concept of change in radar backscatter with an increase in soil wetness is used to develop a model function between basin water index (BWI) and observed river stage. High-resolution OceanSat Scatterometer (OSCAT) (Ku-band) and advanced scatterometer (ASCAT) (C-band) scatterometer image reconstruction (SIR), digital elevation model, and observed water stage datasets have been used in this study. We observed that C-band scatterometer shows better results than Ku-band in terms of Pearson correlation coefficient (<inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>), Nash&#x2013;Sutcliffe Efficiency (NSE), and root mean square error (RMSE), which can be attributed to higher soil wetness sensitivity of the C-band compared with Ku-band scatterometers. <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> ranged between 0.93 and 0.97 for the C-band and between 0.89 and 0.95 for the Ku-band data. Similarly, NSE ranged from 0.84 to 0.95 for the C-band and between 0.56 and 0.95 for the Ku-band data over selected study sites in the Brahmaputra river basin. Model accuracy was found to be adequate for monitoring of river stage from a remote sensing platform. This study shows that scatterometer observations can be used for operational water-level estimations, which can be useful for water resource management.

Estimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learning

Estimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learning

Extraction of Absolute Water Level Using TanDEM-X Bistatic Observations With a Large Perpendicular Baseline

The application of Wetland synthetic aperture radar interferometry (InSAR) has often been restricted in practical hydrological monitoring because it is based on relative estimates of water level changes between two synthetic aperture radar acquisitions, as opposed to absolute water levels obtained by ground measurements. TanDEM-X bistatic observations can provide absolute water level estimates using simultaneous phase measurements by a two-satellite constellation with TerraSAR-X. We evaluated two datasets of TanDEM-X bistatic observations acquired during an experimental science phase on August 26 and 31, 2015, with a very large baseline configuration to extract absolute water levels of Everglades wetland in southern Florida, USA. The perpendicular baselines are 1.43 and 1.36 km, and the ambiguities of height were calculated as 3.61 and 3.90 m in each interferometric pair, respectively. Hourly water level measurements provided by the Everglades depth estimation network (EDEN) were used to verify the estimated absolute water levels. Several stage stations located in densely vegetated areas that showed incoherence were excluded from the verification as outliers. The verification results show an excellent agreement (degree of determination > 0.95) between the InSAR derived absolute water levels and the stage station measurements. The root mean square error (RMSE) between the TanDEM-X results and stage records was 0.77 and 0.66 m, respectively. Severe volume decorrelations over the vegetated area, owing to the large perpendicular baselines, were detected, despite near zero temporal baseline of the bistatic observations. The absolute water levels can be used as excellent constraints for wetland surface flow models.

Monitoring Lake Levels From Space: Preliminary Analysis With SWOT

Lakes are an essential component of biogeochemical processes, and variations in lake level are regarded as indicators of climate change. For more than a decade, satellite altimetry has successfully monitored variation in water levels over inland seas, lakes, rivers, and wetlands. Through altimetry, the surface water levels are measured at varying temporal scales depending on the orbit cycle of the satellite. The futuristic mission of Surface Water and Ocean Topography (SWOT) scheduled to be launched in year 2022 shall offer the spatial coverage and resolution suitable for water level estimation and volume calculation in small water bodies like lakes worldwide. With a radar interferometer in Ka-band, SWOT proposes to provide two-dimensional maps of water heights 21 days repeat orbit configuration. Cycle average SWOT datasets for land will be developed with higher temporal resolution, with temporal resolution varying geographically. This work assesses the potential of SWOT for monitoring water volumes over a case study lake by analyzing SWOT like synthetic data produced using the SWOT simulator developed by the Centre National d'Etudes Spatiales (CNES). With SWOT relying on a novel technology, the initial 90 days of this mission after launch shall focus on an extensive calibration and validation. Firsthand results of SWOT-simulated water levels and volumes are presented over a case study region in the tropical band, namely, Pookode Lake, in the ecologically fragile district of Wayanad, Kerala, India. It is the second-largest freshwater lake in Kerala that is being affected by anthropogenic activities, causing huge depletion in lake water storage in the last four decades. Our analysis indicated that the lake region is subjected to a rise in temperature of 0.018°C per year. We further assess the potential of remote sensing and SWOT data to monitor water storage of Pookode Lake, which is undergoing a rapid change. Results show that the proxy water surface elevations have immense potential in scientific studies pertaining to lake monitoring across the world. Overall, the study shows the potential of SWOT for monitoring the variability of water levels and volumes in this region.

Estimation of reactor core water level using gamma distribution measured at multiple axial points outside reactor in severe accident scenarios employing interacting multiple model

The water level inside the core provides information about the reactor’s safe operation. In severe accidents (SA), the water level information inside the reactor core may not be available as the conventional reactor devices for water level measurement are out of range. The gamma radiation collected from the detectors placed outside the reactor vessel is axially affected by the water level and fuel distribution present inside the core. This study predicts the water level in the core from the radiation distribution during events arising after the SA. An interacting multiple model (IMM) algorithm is used to determine the core water level by estimating the time-varying radiation distribution at multiple axial points. Numerical experiments using MELCOR code simulate the gamma radiation distribution in different SA scenarios. The results show that water level is estimated with reasonable accuracy using the proposed IMM method and helps diagnose and develop SA conditions measures.