Virtual Station Research Articles

Evaluation of Sentinel-3 SRAL SAR altimetry over Chinese rivers

Satellite radar altimetry observations of water surface elevation (WSE) have become an important data source to supplement river gauge records. Sentinel-3 is the first radar altimetry mission operating with a synthetic aperture radar (SAR) altimeter at global scale and with a new on-board tracking system (i.e. open-loop), which has great potential in terms of delivering reliable observations of inland water bodies for the next two decades (several future missions include an open-loop tracking mode). In this context, it is very important to investigate the data quality at an early stage. In this study, a comprehensive evaluation of Sentinel-3A (S3A) is conducted at 50 virtual stations (VS) located on a wide range of rivers in China.The evaluation of Level 1 data shows that, over mountain rivers, a good prior surface elevation estimate on-board is vital to deliver useful datasets using the S3A open-loop tracking system. The Open-Loop Tracking Command version 5 (OLTC V5) has significantly improved the placement of the range window, which was misplaced and resulted in lack of data over many mountain rivers prior to OLTC V5 (March 2019). However, application of S3A over mountain rivers still requires careful evaluation, especially before March 2019.Four retrackers are evaluated including a physical SAR Altimetry Mode Studies and Applications retracker (SAMOSA+), a traditional Offset Center Of Gravity (OCOG), a Primary Peak Center Of Gravity (PPCOG), and a modified Multiple Waveform Persistent Peak (MWaPP+) retracker. For 26 VSs in plain areas, retracked WSE data achieved a root mean square error (RMSE) ranging from 0.12 m to 0.9 m. The comparison of retracking methods reveals that SAMOSA+, OCOG, and PPCOG are unable to handle multi-peak waveforms. But the MWaPP+ can significantly improve the accuracy of the estimated WSE over large rivers, especially when the waveforms are contaminated.Moreover, our result shows no considerable difference between medium (ca. 300 m wide) and large (wider than 500 m) rivers. Instead, surrounding topography and homogeneity of surroundings are very important factors influencing the shape of a waveform. For rivers surrounded by lakes, man-made channels etc., special care must be taken when processing altimetry data. Dedicated retracking methods, such as MWaPP+, and sophisticated methods for outlier detection are needed to improve precision over such rivers, as demonstrated here for the Yangtze River.

Daily River Discharge Estimation Using Multi-Mission Radar Altimetry Data and Ensemble Learning Regression in the Lower Mekong River Basin

Estimating river discharge (Q) is critical for ecosystems and water resource management. Traditionally, estimating Q has depended on a single rating curve or the Manning equation. In contrast to the single rating curve, several rating curves at different locations have been linearly combined in an ensemble learning regression method to estimate Q (ELQ) at the Brazzaville gauge station in the central Congo River in a previous study. In this study, we further tested the proposed ELQ and apply it to the Lower Mekong River Basin (LMRB) with three locations: Stung Treng, Kratie, and Tan Chau. Two major advancements for estimating Q with ELQ are presented. First, ELQ successfully estimated Q at Tan Chau, downstream of Kratie, where hydrodynamic complexities exist. Since the hydrologic characteristics downstream of Kratie are extremely diverse and complex in time and space, most previous studies have estimated Q only upstream from Kratie with hydrologic models and statistical methods. Second, we estimated Q over the LMRB using ELQ with water levels (H) obtained from two radar altimetry missions, Envisat and Jason-2, which made it possible to estimate Q seamlessly from 2003 to 2016. Owing to ELQ with multi-mission radar altimetry data, we have overcome the problems of a single rating curve: Locations for estimating Q have to be close to virtual stations, e.g., a few tens of kilometers, because the performance of the single rating curve degrades as the distance between the location of Q estimation and a virtual station increases. Therefore, most previous studies had not used Jason-2 data whose cross-track interval is about 315 km at the equator. On the contrary, several H obtained from Jason-2 altimetry were used in this study regardless of distances from in-situ Q stations since the ELQ method compensates for degradation in the performance for Q estimation due to the poor rating curve with virtual stations away from in-situ Q stations. In general, the ELQ-estimated Q ( Q ^ E L Q ) showed more accurate results compared to those obtained from a single rating curve. In the case of Tan Chau, the root mean square error (RMSE) of Q ^ E L Q decreased by 1504/1338 m3/s using Envisat-derived H for the training/validation datasets. We successfully applied ELQ to the LMRB, which is one of the most complex basins to estimate Q with multi-mission radar altimetry data. Furthermore, our method can be used to obtain finer temporal resolution and enhance the performance of Q estimation with the current altimetry missions, such as Sentinel-3A/B and Jason-3.

Capability of Tokai Strainmeter Network to Detect and Locate a Slow Slip: First Results

The Tokai Strainmeter Network (TSN), a dense network deployed in the Tokai region, which is the easternmost region of the Nankai trough, has been designed to monitor slow slips that reflect changes in the coupling state of the plate boundary. It is important to evaluate the current capability of TSN to detect and locate slow slips. For this purpose, the probability-based magnitude of completeness developed for seismic networks was modified to be applicable to the evaluation of TSN’s performance. Using 35 slow slips having moment magnitudes M5.1–5.8 recorded by TSN in 2012–2016, this study shows that the probability that TSN detected and located a M5-class slow slip is high (> 0.9) when considering a region in and around the TSN. The probability has been found to depend on the slip duration, especially for M5.5 or larger, namely the longer the duration, the lower the probability. A possible use of this method to assess the network’s performance for cases where virtual stations are added to the existing network was explored. The use of this application when devising a strategic plan of the TSN to extend its coverage westwards is proposed. This extension that allows TSN to cover the entire eastern half of the Nankai trough is important, because the historical records show that the eastern half of this trough tends to rupture first.

DEMENTIA IMMERSION SIMULATION EXPERIENCE (DISE): ELICITING EMPATHY, IMPROVING COMMUNICATION SKILLS IN DEMENTIA

Dementia awareness training alone does not improve care or outcomes for patients living with dementia. Effective dementia education programs for family caregivers and healthcare providers can lead to improved care practices and patient outcomes. The Dementia Immersion Simulation Experience (DISE) is a face-to-face 2-hour educational program that includes simulation, videos, a virtual reality station, group debriefing, and a didactic session delivered by faculty with dementia caregiving expertise. The purpose of this project was to evaluate the effectiveness of DISE in a group of 48 interdisciplinary healthcare providers, trainees and administrative staff. A program evaluation and pre and post knowledge questionnaires were administered. Prior to the activity, the mean score of all participants was 8.85. After the activity, the mean score was 10.1 (p<0.0001). 35.4% of all participants were well informed on dementia before DISE and 70.8% were well informed after the activity (p <0.0005). Qualitative analysis of the comments section of the program evaluation showed that 95% of the participants mentioned empathy for those living with dementia. Participants rated DISE on a scale of 1 (Strongly Disagree) to 5 (Strongly Agree) across ten categories, covering objectives, relevance, effectiveness, and value of the learning experience. Over 95% of respondents agreed or better (score = 4 or 5) with each evaluation statement and at least 85% strongly agreed with each statement. The evaluation scores are further evidence of an effective program. DISE is an effective tool to teach and support family caregivers, healthcare workers, and healthcare professionals and trainees.

Performance Evaluation of a Dense MEMS-Based Seismic Sensor Array Deployed in the Sichuan-Yunnan Border Region for Earthquake Early Warning.

With the last decades of development, earthquake early warning (EEW) has proven to be one of the potential means for disaster mitigation. Usually, the density of the EEW network determines the performance of the EEW system. For reducing the cost of sensors and building a dense EEW network, an upgraded low-cost Micro Electro Mechanical System (MEMS)-based sensor named GL-P2B was developed in this research. This device uses a new high-performance CPU board and is built on a custom-tailored Linux 3.6.9 operating system integrating with seismological processing. Approximately 170 GL-P2Bs were installed and tested in the Sichuan-Yunnan border region from January 2017 to December 2018. We evaluated its performance on noise-level, dynamic range (DR), useful resolution (NU), collocated recording comparison, and shake map generation. The results proved that GL-P2B can be classified as a type of Class-B sensor. The records obtained are consistent with the data obtained by the collocated traditional force-balanced accelerometers even for stations with an epicenter distance of more than 150 km, and most of the relative percentage difference of peak ground acceleration (PGA) values is smaller than 10%. In addition, with the current density of the GL-P2B seismic network, near-real-time refined shake maps without using values derived for virtual stations could be directly generated, which will significantly improve the capability for earthquake emergency response. Overall, this MEMS-based sensor can meet the requirements of dense EEW purpose and lower the total investment of the National System for Fast Seismic Intensity Report and Earthquake Early Warning project.

Estimating the parking demand of free-floating bike sharing: A journey-data-based study of Nanjing, China

Abstract In recent years, free-floating bike sharing (FFBS) has been rapidly promoted in China, attracting numerous users and consuming many resources. The extensive supply and inefficient repositioning of FFBS are challenges for sustainable development, and put forward higher requirements for parking planning. This paper estimates the parking demand of all three FFBS companies by combining the journey data of Mobike in Nanjing and the Nanjing FFBS bike survey. Three clustering methods were applied to determine the virtual stations of bike aggregation. The maximum number of bikes in a day is recognized as the parking demand in each virtual station. The results show that more than half of bikes are in low turnover rate, and the management of FFBS should be improved. The K-means method turns out to observe the best clustering result for determining virtual stations. The demand for parking spaces of all FFBS companies in Nanjing is estimated accordingly. In addition, the relations of bicycle supply, repositioning and parking are discussed, showing the impact of parking demand on greenhouse gas (GHG) emissions. The research could help to propose an appropriate plan for meeting FFBS parking demand, and have enlightening sight of emissions reduction and sustainable development in the FFBS services.

Validation and application of water levels derived from Sentinel-3A for the Brahmaputra River

For the first time ever, this study aims at applying Sentinel-3A to the Great Brahmaputra River (GBR) and validating water levels derived from this newly-launched altimetry satellite mission. The GBR is divided into three primary parts: (1) a large section of the Yarlung Zangbo River in China, also termed the Upper Brahmaputra River (UBR) in this study, featured by high elevation, complex terrain, narrow river widths (from less than 100 to 400 m), and limited in situ measurements; (2) the Middle Brahmaputra River (MBR) with widths varying from ~400 m to ~1 km; (3) the Lower Brahmaputra River (LBR), dominated by braided channels with wide river channels (up to several kilometers). For the altimetry data, both waveform retracking and hooking effect correction were applied to mitigate the influence caused by land contamination and to improve the accuracy of water level retrievals. Water levels were derived from 41 virtual stations (VSs) across the GBR and different retracking algorithms were compared with in situ data from two gauging stations in the UBR. Time series of altimetry-based water levels were categorized into three types based on the quality: no data (type 1), degraded (type 2), and good (type 3). Results showed that the VSs (type 1) only existed in the mountainous regions, accounting for ~ a half of the total in the UBR. Validation with the gauged water levels showed that the TIC algorithm performed best among all of the retrackers applied, followed by the Ice-1 algorithm. The standard deviation of the difference between the gauged and TIC-derived water levels ranged from 0.41 to 0.76 m among four different VSs (type 3). In addition, the quality of VSs in the LBR was best, followed by the MBR. Our study has demonstrated the capability of Sentinel-3A in monitoring water levels in the GBR, thereby paving the way for future applications such as discharge estimation and hydrologic/hydrodynamic applications.

Far-field tsunami data assimilation for the 2015 Illapel earthquake

SUMMARYThe 2015 Illapel earthquake (Mw 8.3) occurred off central Chile on September 16, and generated a tsunami that propagated across the Pacific Ocean. The tsunami was recorded on tide gauges and Deep-ocean Assessment and Reporting of Tsunami (DART) tsunameters in east Pacific. Near-field and far-field tsunami forecasts were issued based on the estimation of seismic source parameters. In this study, we retroactively evaluate the potentiality of forecasting this tsunami in the far field based solely on tsunami data assimilation from DART tsunameters. Since there are limited number of DART buoys, virtual stations are assumed by interpolation to construct a more complete tsunami wavefront for data assimilation. The comparison between forecasted and observed tsunami waveforms suggests that our method accurately forecasts the tsunami amplitudes and arrival time in the east Pacific. This approach could be a complementary method of current tsunami warning systems based on seismic observations.

Improving C1 and C3 empirical Green’s functions from ambient seismic noise in NW Iran using RMS ratio stacking method

The retrieval of stable and reliable empirical Green’s functions using ambient seismic noise plays a major role when studying the Earth’s structure at various scales. High-resolution noise correlation functions are obtained in the NW of Iran by processing techniques including dividing the continuously recorded raw data into short (i.e., 10 min) overlapping (i.e., 80%) time windows. We compare four stacking methods (i.e., linear, RMS, RMS ratio, and Nth-root stacking methods) to study robust and stable inter-station empirical Green’s functions. Our results indicate that the new RMS ratio method of stacking would be the optimal method to stack coherent signals. In other words, this method tackles problems including low signal-to-noise ratio (hereafter SNR) value, distortion of wave shape, and phase instability/unstable polarity treatment. In addition to noise correlation functions, we propose another strategy for the computation of the empirical Green’s functions. In this technique, the cross-correlation of scattered coda waves of the calculated noise correlation functions is performed individually. In addition to coda window length, we also investigate another effective parameter, the geometry of various virtual stations to obtain reliable empirical Green’s functions from the scattered coda waves of correlation functions with high SNR. The error of the velocities of Rayleigh wave empirical Green’s functions is on the order of approximately 0.6%, when compared to ambient seismic noise and scattered coda waves for a period band range of 3–10 s.

Optimizing the Location of Virtual Stations in Free-Floating Bike-Sharing Systems with the User Demand during Morning and Evening Rush Hours

In recent years, free-floating bike-sharing systems (FFBSSs) have been considerably developed in China. As there is no requirement to construct bike stations, this system can substantially reduce the cost when compared to the traditional bike-sharing systems. However, FFBSSs have also become a critical cause of parking disorder, especially during the morning and evening rush hours. To address this issue, the local governments stipulated that FFBSSs are required to deploy virtual stations near public transit stations and major establishments. Therefore, the location assignment of virtual stations is sufficiently considered in the FFBSSs, which is required to solve the parking disorder and satisfy the user demand, simultaneously. The purpose of this study is to optimize the location assignment of virtual stations that can meet the growing demand of users by analyzing the usage data of their shared bikes. This optimization problem is generally formulated as a mixed-integer linear programming (MILP) model to maximize the user demand. As an alternative solution, this article proposes a clustering algorithm, which can solve this problem in real time. The experimental results demonstrate that the MILP model and the proposed method are superior to the K-means method. Our method not only provides a solution for maximizing the user demand but also gives an optimized design scheme of the FFBSSs that represents the characteristics of virtual stations.

Tsunami Data Assimilation Without a Dense Observation Network

AbstractThe tsunami data assimilation method enables tsunami forecasting directly from observations, without the need of estimating tsunami sources. However, it requires a dense observation network to produce desirable results. Here we propose a modified method of tsunami data assimilation for regions with a sparse observation network. The method utilizes interpolated waveforms at virtual stations. The tsunami waveforms at the virtual stations between two existing observation stations are estimated by shifting arrival times with the linear interpolation of observed arrival times and by correcting the amplitudes for their water depths. In our new data assimilation approach, we employ the Optimal Interpolation algorithm to both the real observations and virtual stations, in order to construct a complete wavefront of tsunami propagation. The application to the 2004 Sumatra‐Andaman earthquake and the 2009 Dusky Sound, New Zealand, earthquake reveals that addition of virtual stations greatly helps improve the tsunami forecasting accuracy.

Asymptotic Analysis of Sum Rate of CL-DAS Based on Expected Large Scale Fading

Under the assumption of large scale antenna, we analyze the sum rate of circular layout distributed antenna systems (CL-DAS). First, we propose a concept of virtual base station and an expected large scale fading (E-LSF) for an arbitrarily located user as a function of the locations of users and distributed antennas and a path loss exponent. Based on E-LSF, we derive an approximated sum rate of CL-DAS whose form is identical to the sum rate of centralized multiple-input multiple-output (C-MIMO) systems. These results make it possible that the sum rate of CL-DAS with arbitrary beamforming schemes or power allocation algorithms can be evaluated using the existing approaches for C-MIMO systems. Simulation results verify that our analyses with E-LSF are valid for practical systems.

Assessing Orinoco river sediment discharge trend using MODIS satellite images

The aim of this study was to derive the Surface Suspended Sediment Concentration (SSSC) and river sediment discharge (for the period 2000-2016) along the Orinoco River reaches using MODIS remote sensing data. A single SSC retrieval algorithm was calibrated using HYBAM long term monitoring program data (n = 183). The comparison of satellite and field measurements showed a RMSE of 32%, corresponding to a mean absolute error of 21 mg l−1. These results supported the use of MODIS sensors to monitor the SSSC at six virtual stations in the middle and lower reaches of the Orinoco River. The highest mean SSSC were found downstream of the Meta River (101 mg l−1) and of the Apure River (99 mg l−1), which provide the major sediment input to the Orinoco main stream. The lowest mean SSSC was found upstream of the Meta River (42 mg l−1). In the lower reach, at Ciudad Bolivar station we assessed a mean SSSC of 88 mg l−1.MODIS-derived and ground-derived assessments of the annual sediment discharge show very fine agreement, within −2%. Coupling satellite-derived SSSC estimates and river discharge monitoring data, we calculated the Orinoco River sediment discharge at Ciudad Bolivar station between 2001 and 2016. For a mean Orinoco River discharge of 33,320 m3.s−1, the sediment loads varied from 75 to 103 Mt.yr−1, with a mean value of 89 Mt.yr−1 (RSD of 9%) corresponding to a sediment yield of 91–125 t.km−2yr−1 with no positive or negative trends over the observed period.

A decomposition method for assembly/disassembly systems with blocking and general distributions

A modelling methodology is presented for assembly/disassembly systems with general processing time distributions and finite buffers. The approach combines the distributions discretization and a decomposition technique to analyze large manufacturing systems in a reasonable computational time and with good accuracy. In the decomposition technique, the system is decomposed into two station subsystems and the processing time distributions of the virtual stations are iteratively modified to approximate the impact of the rest of the network, adding estimations of the blocking and starving distributions. To analyze each subsystem, the general processing time distributions are discretized by aggregation of the probability masses, and the subsystem is then analytically modeled using a discrete Markov chain. We first show that this approach allows an accurate estimation of the subsystems cycle time distributions, which is crucial in the decomposition technique. Using computational experiments, we show that our decomposition method leads to accurate performance evaluation for large manufacturing systems (relative error on the order of 1%) and that the fine distribution estimation indeed seems to bring an improvement. Furthermore, we show on examples that, using decomposition, the cycle time distributions can be approximated reliably for large systems.

Variations of Surface and Subsurface Water Storage in the Lower Mekong Basin (Vietnam and Cambodia) from Multisatellite Observations

In this study, we estimate monthly variations of surface-water storage (SWS) and subsurface water storage (SSWS, including groundwater and soil moisture) within the Lower Mekong Basin located in Vietnam and Cambodia during the 2003–2009 period. The approach is based on the combination of multisatellite observations using surface-water extent from MODIS atmospherically corrected land-surface imagery, and water-level variations from 45 virtual stations (VS) derived from ENVISAT altimetry measurements. Surface-water extent ranges from ∼6500 to ∼40,000 km 2 during low and high water stages, respectively. Across the study area, seasonal variations of water stages range from 8 m in the upstream parts to 1 m in the downstream regions. Annual variation of SWS is ∼40 km 3 for the 2003–2009 period that contributes to 40–45% of total water-storage (TWS) variations derived from Gravity Recovery And Climate Experiment (GRACE) data. By removing the variations of SWS from GRACE-derived TWS, we can isolate the monthly variations of SSWS, and estimate its mean annual variations of ∼50 km 3 (55–60% of the TWS). This study highlights the ability to combine multisatellite observations to monitor land-water storage and the variations of its different components at regional scale. The results of this study represent important information to improve the overall quality of regional hydrological models and to assess the impacts of human activities on the hydrological cycles.

GENERATION AND PERFORMANCE ANALYSIS OF GPS AND GLONASS VIRTUAL DATA FOR POSITIONING UNDER DIFFERENT IONOSPHERIC CONDITIONS

Several approaches concerning the use in positioning of GNSS (Global Navigation Satellite Systems) can be considered: systems, applied methods and errors that can affect the signals. Following the GLONASS (GLObal NAvigation Satellite System) constellation reestablishment (2011), there was renewed interest in its use with GPS (Global Positioning System). Different possibilities are available concerning applied methods, such as the virtual reference station (VRS) concept (it is possible to obtain data for a virtual station that does not physically exist, using data from a network). One of the main sources of error related to the GNSS signal, is the ionosphere. Many studies have been developed aiming to evaluate GPS positioning quality and influences that can affect it, but there are still several investigation possibilities concerning GLONASS. In this context, this research is intended to assess the GPS/GLONASS virtual data positioning performance considering regions and periods with different ionospheric behavior. A high correlation between the results from virtual and real data (Pearson’s correlation coefficients around 0.8) was noticed. GPS/GLONASS data performance presented better mean squared error results compared to GPS alone (average 3D improvement was 45 cm - 49%). In addition, it was possible to verify ionosphere influence in the positioning error, taking into account station region and period of the year.

Network-based RTK Performance for Drone Navigation

The Real-time Kinematic (RTK) was predominantly used for the positioning of the ground applications including moving vehicles. Due to the limited baseline lengths, the Network-based RTK (NRTK) was introduced since late-1990s, and successfully applied to the high precision surveying purposes for more than a decade. We conducted the experiments to analyze the performance of the single-baseline RTK (SRTK) as well as the NRTK using one of the operational NRTK systems of Korea, which is the Virtual Reference Stations (VRS) technique. A total of 46 Continuously Operating Reference Stations (CORS) are used for both SRTK and NRTK to secure the redundancy of the baseline lengths. Since the CORS are quite evenly located at an average of 50-60 km, the analysis of error behavior is restricted in terms of seamless variation of baseline length. Therefore, on top of the existing CORS, we simulated the GNSS RINEX data of the rover (that is, the drone position) at a specific location to get the uniform distribution to the reference stations. Furthermore, the rover was generated for different altitude to evaluate the performance of vertical component of the NRTK system. The CORS GNSS data were obtained at 1 Hz in RINEX format, and the low-cost receiver (ublox) was used for the rover with same interval, single-frequency capability. The correction information from the NRTK server was simultaneously logged for post-processing using the open source software RTKLIB. The percentage of ambiguity resolution was quite reasonable up to 50 km, and drastically drops down as the baseline gets longer. The generated VRS was located with an offset of a few meters in a certain direction, and the horizontal errors also showed a similar pattern. The vertical error behavior due to the tropospheric model deficiency should also be analyzed further.

Ensemble learning regression for estimating river discharges using satellite altimetry data: Central Congo River as a Test-bed

Hydraulic variables obtained from remotely sensed data have been successfully used to estimate river discharge (Q). However, most studies have used a rating curve based on a single hydraulic variable or the Manning equation (multiplicative method). In this study, we developed a mathematically different approach to estimating Q by applying the ensemble learning regression method (here termed ELQ), which is one of the machine learning techniques that linearly combine several functions to reduce errors, over the Congo mainstem as a test-bed. Using the training dataset (November 2002 – November 2006) of water levels (H) derived from different Envisat altimetry observations, the ELQ-estimated Q at the Brazzaville in-situ station showed reduced root-mean-square error (RMSE) of 823 m3 s−1 (relative RMSE (RMSE normalized by the average in-situ Q, RRMSE) of 2.08%) compared to the Q obtained using a single rating curve. ELQ also showed improved performance for the validation dataset (December 2006 – September 2010). Based on the error analysis, we found the correlation coefficients between input variables affect the performance of ELQ. Thus, we introduced an index, termed the Degree of compensation (IDoC), which describes how ELQ performs compared to the classic hydraulic relation (e.g., H-Q rating curve). The performance of ELQ improves when IDoC increases because the additional information could be added in the ELQ process. Since ELQ can combine several variables obtained over different locations, it would be advantageous, particularly if there exist few virtual stations along a river reach. It is expected that ELQ can be also applied to the products of the Surface Water Ocean Topography (SWOT) mission, which will provide direct measurements of surface water slope (S), effective river width (We), and H, to be launched in 2021.

Using modelled discharge to develop satellite-based river gauging: a case study for the Amazon Basin

Abstract. River discharge measurements have proven invaluable to monitor the global water cycle, assess flood risk, and guide water resource management. However, there is a delay, and ongoing decline, in the availability of gauging data and stations are highly unevenly distributed globally. While not a substitute for river discharge measurement, remote sensing is a cost-effective technology to acquire information on river dynamics in situations where ground-based measurements are unavailable. The general approach has been to relate satellite observation to discharge measured in situ, which prevents its use for ungauged rivers. Alternatively, hydrological models are now available that can be used to estimate river discharge globally. While subject to greater errors and biases than measurements, model estimates of river discharge do expand the options for applying satellite-based discharge monitoring in ungauged rivers. Our aim was to test whether satellite gauging reaches (SGRs), similar to virtual stations in satellite altimetry, can be constructed based on Moderate Resolution Imaging Spectroradiometer (MODIS) optical or Global Flood Detection System (GFDS) passive microwave-derived surface water extent fraction and simulated discharge from the World-Wide Water (W3) model version 2. We designed and tested two methods to develop SGRs across the Amazon Basin and found that the optimal grid cell selection method performed best for relating MODIS and GFDS water extent to simulated discharge. The number of potential river reaches to develop SGRs increases from upstream to downstream reaches as rivers widen. MODIS SGRs are feasible for more river reaches than GFDS SGRs due to its higher spatial resolution. However, where they could be constructed, GFDS SGRs predicted discharge more accurately as observations were less affected by cloud and vegetation. We conclude that SGRs are suitable for automated large-scale application and offer a possibility to predict river discharge variations from satellite observations alone, for both gauged and ungauged rivers.

Use of non-polar orbiting satellite radar altimeters of the Jason series for estimation of river input to the Arctic Ocean

Due to the rapid decline of in situ observations on river discharge in Arctic regions, evaluation of the continental freshwater input to the Arctic Ocean has become problematic and necessitates the development of alternative approaches based on remote sensing. Radar altimetric satellites have demonstrated high potential for estimation of river water discharge. Compared to polar orbiting altimeters, non-polar orbit satellites have an advantage in temporal sampling. Their greatest drawback, however, is spatial coverage: observations do not cover the low reaches of most parts of Arctic rivers. In this study of the Lena River, we demonstrate a way to overcome this limitation by using a combination of in situ observations from tributaries and satellite observations in the middle river reaches. The water discharge as well as monthly and annual water flow were evaluated using three virtual stations. Direct combination of the water level from these virtual stations was not possible because of the difference in seasonal amplitude. However, the combination of altimetric discharge from the three independently processed tracks significantly improves the flow retrievals. The accuracy of the monthly water flow estimates at the river outlet is 23%. It increases with the integration time giving 7% for annual flow.