scholarly journals River ice flux and water velocities along a 600 km long reach of Lena River, Siberia, from satellite stereo

2013 ◽  
Vol 10 (7) ◽  
pp. 9967-9997 ◽  
Author(s):  
A. Kääb ◽  
M. Lamare ◽  
M. Abrams
Keyword(s):  
River Flow ◽  
Thermal Emission ◽  
Time Lag ◽  
Open Water ◽  
Image Resolution ◽  
Surface Velocity ◽  
River Ice ◽  
Data Set ◽  
Hazard Management ◽  
Lena River

Abstract. Knowledge of water-surface velocities in rivers is useful for understanding a range of river processes. In cold regions, river-ice break up and the related downstream transport of ice debris is often the most important hydrological event of the year, leading to flood levels that typically exceed those for the open-water period and to strong consequences for river infrastructure and ecology. Accurate and complete surface-velocity fields on rivers have rarely been produced. Here, we track river ice debris over a time period of about one minute, which is the typical time lag between the two or more images that form a stereo data set in spaceborne, along-track optical stereo-mapping. Using a series of 9 stereo scenes from the US/Japanese Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard the NASA Terra spacecraft with 15 m image resolution, we measure the ice and water velocity field over a 620 km long reach of the lower Lena River, Siberia, just above its entry into the Lena delta. Careful analysis and correction of higher-order image and sensor errors enables an accuracy of ice-debris velocities of up to 0.04 m s−1 from the ASTER data. Maximum ice or water speeds, respectively, reach up to 2.5 m s−1 at the time of data acquisition, 27 May 2011 (03:30 UTC). Speeds show clear along-stream undulations with a wavelength of about 21 km that agree well with variations in channel width and with the location of sand bars along the river reach studied. The methodology and results of this study could be valuable to a number of disciplines requiring detailed information about river flow, such as hydraulics, hydrology, river ecology and natural-hazard management.

scholarly journals River ice flux and water velocities along a 600 km-long reach of Lena River, Siberia, from satellite stereo

2013 ◽  
Vol 17 (11) ◽  
pp. 4671-4683 ◽  
Author(s):  
A. Kääb ◽  
M. Lamare ◽  
M. Abrams
Keyword(s):  
River Flow ◽  
Thermal Emission ◽  
Time Lag ◽  
Open Water ◽  
Image Resolution ◽  
Surface Velocity ◽  
River Ice ◽  
Data Set ◽  
Hazard Management ◽  
Lena River

Abstract. Knowledge of water-surface velocities in rivers is useful for understanding a range of river processes. In cold regions, river-ice break up and the related downstream transport of ice debris is often the most important hydrological event of the year, leading to flood levels that typically exceed those for the open-water period and to strong consequences for river infrastructure and ecology. Accurate and complete surface-velocity fields on rivers have rarely been produced. Here, we track river ice debris over a time period of about one minute, which is the typical time lag between the two or more images that form a stereo data set in spaceborne, along-track optical stereo mapping. Using a series of nine stereo scenes from the US/Japanese Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard the NASA Terra spacecraft with 15 m image resolution, we measure the ice and water velocity field over a 620 km-long reach of the lower Lena River, Siberia, just above its entry into the Lena delta. Careful analysis and correction of higher-order image and sensor errors enables an accuracy of ice-debris velocities of up to 0.04 m s−1 from the ASTER data. Maximum ice or water speeds, respectively, reach up to 2.5 m s−1 at the time of data acquisition, 27 May 2011 (03:30 UTC). Speeds show clear along-stream undulations with a wavelength of about 21 km that agree well with variations in channel width and with the location of sand bars along the river reach studied. The methodology and results of this study could be valuable to a number of disciplines requiring detailed information about river flow, such as hydraulics, hydrology, river ecology and natural-hazard management.

scholarly journals River-ice and water velocities using the Planet optical cubesat constellation

2019 ◽  
Vol 23 (10) ◽  
pp. 4233-4247 ◽  
Author(s):  
Andreas Kääb ◽  
Bas Altena ◽  
Joseph Mascaro
Keyword(s):  
Land Surface ◽  
Time Lag ◽  
Image Resolution ◽  
River Ice ◽  
Earth Surface ◽  
Spatial Image ◽  
The Amur River ◽  
Ice Floes ◽  
Image Pairs ◽  
Short Time

Abstract. The PlanetScope constellation consists of ∼150 optical cubesats that are evenly distributed like strings of pearls on two orbital planes, scanning the Earth's land surface once per day with an approximate spatial image resolution of 3 m. Subsequent cubesats on each of the orbital planes image the Earth surface with a nominal time lag of approximately 90 s between them, which produces near-simultaneous image pairs over the across-track overlaps of the cubesat swaths. We exploit this short time lag between subsequent Planet cubesat images to track river ice floes on northern rivers as indicators of water surface velocities. The method is demonstrated for a 60 km long reach of the Amur River in Siberia, and a 200 km long reach of the Yukon River in Alaska. The accuracy of the estimated horizontal surface velocities is of the order of ±0.01 m s−1. The application of our approach is complicated by cloud cover and low sun angles at high latitudes during the periods where rivers typically carry ice floes, and by the fact that the near-simultaneous swath overlaps, by design, do not cover the complete Earth surface. Still, the approach enables direct remote sensing of river surface velocities for numerous cold-region rivers at a number of locations and occasionally several times per year – which is much more frequent and over much larger areas than currently feasible. We find that freeze-up conditions seem to offer ice floes that are generally more suitable for tracking, and over longer time periods, compared with typical ice break-up conditions. The coverage of river velocities obtained could be particularly useful in combination with satellite measurements of river area, and river surface height and slope.

scholarly journals A Canadian River Ice Database from the National Hydrometric Program Archives

2020 ◽  
Vol 12 (3) ◽  
pp. 1835-1860 ◽  
Author(s):  
Laurent de Rham ◽  
Yonas Dibike ◽  
Spyros Beltaos ◽  
Daniel Peters ◽  
Barrie Bonsal ◽  
...  
Keyword(s):  
Water Level ◽  
Hydrological Cycle ◽  
Open Water ◽  
Cold Climate ◽  
River Ice ◽  
Ice Thickness ◽  
Original Research ◽  
Data Set ◽  
Spring Break ◽  
Break Up

Abstract. River ice, like open-water conditions, is an integral component of the cold-climate hydrological cycle. The annual succession of river ice formation, growth, decay and clearance can include low flows and ice jams, as well as midwinter and spring break-up events. Reports and associated data of river ice occurrence are often limited to single locations or regional assessments, are season-specific, and use readily available data. Within Canada, the National Hydrometric Program (NHP) operates a network of gauging stations with water level as the primary measured variable to derive discharge. In the late 1990s, the Water Science and Technology Directorate of Environment and Climate Change Canada initiated a long-term effort to compile, archive and extract river-ice-related information from NHP hydrometric records. This data article describes the original research data set produced by this near 20-year effort: the Canadian River Ice Database (CRID). The CRID holds almost 73 000 recorded variables from a subset of 196 NHP stations throughout Canada that were in operation within the period 1894 to 2015. Over 100 000 paper and digital files were reviewed, representing 10 378 station years of active operation. The task of compiling this database involved manual extraction and input of more than 460 000 data entries on water level, discharge, ice thickness, date, time and data quality rating. Guidelines on the data extraction, rating procedure and challenges are provided. At each location, time series of up to 15 variables specific to the occurrence of freeze-up and winter-low events, midwinter break-up, ice thickness, spring break-up, and maximum open-water level were compiled. This database follows up on several earlier efforts to compile information on river ice, which are summarized herein, and expands the scope and detail for use in Canadian river ice research and applications. Following the Government of Canada Open Data initiative, this original river ice data set is available at https://doi.org/10.18164/c21e1852-ba8e-44af-bc13-48eeedfcf2f4 (de Rham et al., 2020).

Tidal modulation of river-flood deposits: How low can you go?

Geology ◽  
2020 ◽  
Vol 48 (7) ◽  
pp. 663-667 ◽  
Author(s):  
Vittorio Maselli ◽  
Alexandre Normandeau ◽  
Michael Nones ◽  
Tommaso Tesi ◽  
Leonardo Langone ◽  
...  
Keyword(s):  
River Discharge ◽  
River Flow ◽  
Surface Profile ◽  
Open Water ◽  
Flow Strength ◽  
Po River ◽  
Data Set ◽  
Storm Waves ◽  
Before And After ◽  
Storm Wave

Abstract Quantification of the interaction between river discharge and tides is vital to characterize fluvio-deltaic systems, to identify diagnostic elements of tidal signatures in the rock record, and to reconstruct paleogeographies. In modern systems, even microtides can significantly influence delta morphodynamics; yet, many fundamental processes, particularly in prodeltaic settings, remain elusive. Here, by combining a unique process-product data set acquired during a flood event of the Po River (Italy) with numerical modeling, we show that tidal signatures are recorded in the open-water prodelta zone of a microtidal system. Based on the analyses of box-cores collected before and after a flood off the main distributary channel, we interpreted storm beds, tide-modulated flood strata of alternating normal and inverse graded beds, and rapid bioturbation. Modeling of the river discharge indicates that, at the peak of the flood, the steepening of the water-surface profile forced by 0.15 m lowering of sea level during low tides generated an 8% increase in river flow velocity. The alternation of profile steepness and associated cyclicity in flow strength during consecutive tidal cycles controlled the sediment load of the plume and, consequently, led to the deposition of tidal-modulated strata. Formation of microtidal signals appears to be enhanced in fluvio-deltaic successions characterized by multiple distributaries and in basins where river floods are out of phase with storm-wave activity. Bioturbation of sediment, which can start during the waning stage of the flow, and erosion by storm waves hamper the preservation of tidal signals, unless rapid burial occurs. The recognition of tidal-modulated strata in river-dominated settings may facilitate the characterization of mudstone reservoirs and reconstruction of paleogeographic conditions during deposition.

scholarly journals Impacts of Climate and Supraglacial Lakes on the Surface Velocity of Baltoro Glacier from 1992 to 2017

2018 ◽  
Vol 10 (11) ◽  
pp. 1681 ◽  
Author(s):  
Anna Wendleder ◽  
Peter Friedl ◽  
Christoph Mayer
Keyword(s):  
Thermal Emission ◽  
Standardized Precipitation Index ◽  
Environmental Parameters ◽  
Heavy Precipitation ◽  
Surface Velocity ◽  
Mountain Range ◽  
Glacier Surface ◽  
Hazard Management ◽  
Supraglacial Lakes ◽  
Velocity Variations

The Baltoro Glacier is one of the largest glaciers in the Karakoram mountain range. Long-term monitoring of glacier dynamics provides key information on glacier evolution in a changing climate, which is essential for regional water resource and natural hazard management. On large glaciers, detailed field based mass balance is not feasible. Ice dynamic variations quantify changes in mass transport and possibly the influence of environmental parameters on the evolution of the glacier. Although velocity variations of Baltoro Glacier during winter and summer are linked to seasonally enhanced basal sliding, little is known about differences in timing and magnitude of (intra-)seasonal velocity variations and their determining mechanisms. We present time series of annual, seasonal, and intra-seasonal glacier surface velocities by means of intensity offset tracking applied on multi-mission Synthetic Aperture Radar (SAR) data for a period of 25 years from 1992 to 2017. Supraglacial lakes forming on the downstream glacier surface in summer were mapped from 1991 to 2017 based on the Normalized Difference Water Index (NDWI), calculated from multi-spectral Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery. Additionally, precipitation data of the Tropical Rainfall Measurement Mission (TRMM) and temperature data of ERA-Interim were used to derive the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) from 1998 to 2017. Linking surface velocities to the SPI confirmed a strong correlation between heavy precipitation events in winter and the magnitude and the timing of glacier acceleration in summer. Downstream extensions of summer acceleration that have been found since 2015 may be explained by additional water draining from an increased number of supraglacial lakes through crevasses that have been formed in consequence of higher initial velocities, evoked by strong winter precipitation. The warmer melt seasons observed in the years 2015 to 2017 additionally affects the formation of a supraglacial lake, so stronger summer acceleration events in recent years may be indirectly related to global warming.

Information technology investment and innovation performance: does investment paradox exist?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Peinan Ji ◽  
Xiangbin Yan ◽  
Yan Shi
Keyword(s):  
Information Technology ◽  
Time Lag ◽  
Innovation Performance ◽  
Test Model ◽  
It Investment ◽  
Firm Innovation ◽  
Data Set ◽  
Content Type ◽  
Lag Effect ◽  
Enterprise Innovation

Purpose The purpose of this study is to deepen the understanding of the effects of information technology (IT) investment on firm innovation performance and examining the investment paradox effect in China. Design/methodology/approach Using a sample of China’ public firms IT investment data between 2010 and 2016, the authors establish a test model of IT investment and innovation performance. Findings The result indicates that IT investment in firms have no effect on innovation performance in the investment period. However, in the full sample and manufacturing sample, the IT investment has a significant positive effect on innovation performance in the post-investment years. In addition, this study finds that large companies and low-age companies may contribute more to innovation when firm investment in IT. Research limitations/implications There are several limitations in this research. First, the authors are failed to obtain a larger sample about the IT investment information data set in China, so this study was compelled to use limited sample data from China, hence, this could lead to errors of too early generalization. Second, the authors use the number of invention patent applications to represent the performance of enterprise innovation, which may not show enterprise innovation effectively. Third, the firms in the sample are all in China Listed Companies, so this may not accurately reflect the entire environment of firm innovation performance, and could possibly. Practical implications The research confirms that there is a paradox and time lag effect in IT investment, which enterprises should pay attention to. Originality/value Existing research confirms that corporate IT investments can bring new products or services. However, the authors still do not know whether IT investment has improved the company’s ability of innovation. This study will fill this gap and the industry effect and time lag effect of the influence of IT investment on innovative performance are also examined.

scholarly journals Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R1-R10 ◽  
Author(s):  
Zhendong Zhang ◽  
Tariq Alkhalifah ◽  
Zedong Wu ◽  
Yike Liu ◽  
Bin He ◽  
...  
Keyword(s):  
Field Data ◽  
Extreme Values ◽  
Time Lag ◽  
Waveform Inversion ◽  
Optimal Time ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Velocity Models ◽  
Full Waveform ◽  
The Earth

Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.

scholarly journals Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey

2007 ◽  
Vol 4 (3) ◽  
pp. 1369-1406 ◽  
Author(s):  
M. Firat
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
River Flow ◽  
Fuzzy Inference ◽  
Flow Data ◽  
Data Set ◽  
Inference System ◽  
Forecasting Models ◽  
River Flow Forecasting ◽  
Best Fit

Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), for forecasting of daily river flow is investigated and the Seyhan catchment, located in the south of Turkey, is chosen as a case study. Totally, 5114 daily river flow data are obtained from river flow gauges station of Üçtepe (1818) on Seyhan River between the years 1986 and 2000. The data set are divided into three subgroups, training, testing and verification. The training and testing data set include totally 5114 daily river flow data and the number of verification data points is 731. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN methods. The results of ANFIS, GRNN and FFNN models for both training and testing are evaluated and the best fit forecasting model structure and method is determined according to criteria of performance evaluation. The best fit model is also trained and tested by traditional statistical methods and the performances of all models are compared in order to get more effective evaluation. Moreover ANFIS, GRNN and FFNN models are also verified by verification data set including 731 daily river flow data at the time period 1998–2000 and the results of models are compared. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily River flow forecasting.

Refraction wavefield migration

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. Q27-Q37
Author(s):  
Yang Shen ◽  
Jie Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Surface Velocity ◽  
Data Set ◽  
Near Surface ◽  
Depth Migration ◽  
Reflection Data ◽  
Imaging Tool ◽  
Imaging Results ◽  
Migration Method ◽  
Single Method

Refraction methods are often applied to model and image near-surface velocity structures. However, near-surface imaging is very challenging, and no single method can resolve all of the land seismic problems across the world. In addition, deep interfaces are difficult to image from land reflection data due to the associated low signal-to-noise ratio. Following previous research, we have developed a refraction wavefield migration method for imaging shallow and deep interfaces via interferometry. Our method includes two steps: converting refractions into virtual reflection gathers and then applying a prestack depth migration method to produce interface images from the virtual reflection gathers. With a regular recording offset of approximately 3 km, this approach produces an image of a shallow interface within the top 1 km. If the recording offset is very long, the refractions may follow a deep path, and the result may reveal a deep interface. We determine several factors that affect the imaging results using synthetics. We also apply the novel method to one data set with regular recording offsets and another with far offsets; both cases produce sharp images, which are further verified by conventional reflection imaging. This method can be applied as a promising imaging tool when handling practical cases involving data with excessively weak or missing reflections but available refractions.

scholarly journals A Long–Term Response-Based Rainfall-Runoff Hydrologic Model: Case Study of The Upper Blue Nile

Hydrology ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 69 ◽  
Author(s):  
Eatemad Keshta ◽  
Mohamed A. Gad ◽  
Doaa Amin
Keyword(s):  
River Flow ◽  
Hydrologic Model ◽  
Initial Assessment ◽  
Rainfall Runoff ◽  
Time Step ◽  
Data Set ◽  
Blue Nile ◽  
Catchment Areas ◽  
Surface And Groundwater

This study develops a response-based hydrologic model for long-term (continuous) rainfall-runoff simulations over the catchment areas of big rivers. The model overcomes the typical difficulties in estimating infiltration and evapotranspiration parameters using a modified version of the Soil Conservation Service curve number SCS-CN method. In addition, the model simulates the surface and groundwater hydrograph components using the response unit-hydrograph approach instead of using a linear reservoir routing approach for routing surface and groundwater to the basin outlet. The unit-responses are Geographic Information Systems (GIS)-pre-calculated on a semi-distributed short-term basis and applied in the simulation in every time step. The unit responses are based on the time-area technique that can better simulate the real routing behavior of the basin. The model is less sensitive to groundwater infiltration parameters since groundwater is actually controlled by the surface component and not the opposite. For that reason, the model is called the SCHydro model (Surface Controlled Hydrologic model). The model is tested on the upper Blue Nile catchment area using 28 years daily river flow data set for calibration and validation. The results show that SCHydro model can simulate the long-term transforming behavior of the upper Blue Nile basin. Our initial assessment of the model indicates that the model is a promising tool for long-term river flow simulations, especially for long-term forecasting purposes due to its stability in performing the water balance.

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