The FluViSat project: Measuring streamflow from space with very high resolution Planet satellite video
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The measurement of streamflow in the world’s rivers is critical to the management of water as a resource and to predicting and managing the impacts of potentially damaging hydrological events such as major floods. The European Space Agency sponsored FluViSat (Fluvial Video from Satellite) project has successfully demonstrated the potential of very high resolution satellite imagery for the determination of water flow speeds, and hence streamflow rates, using established surface velocimetry techniques.Video imagery kindly provided by Planet Labs PBC from the 21 satellites in their SkySat constellation was pre-processed to stabilise and georectify it, and then analysed using Space Time Imaging Velocimetry (STIV) techniques to provide water speed vectors across the river’s surface. The method was successfully demonstrated on rivers in Australia, the UK and Africa, with field based validation undertaken where possible. Additionally, a series of six videos was obtained and analysed to provide near a sequence of observations of flood flows on the Indus River in Pakistan during the devastating flooding of 2022.Benefits of the FluViSat innovation include the ability to observe water flow rates almost anywhere on the planet, the potential for multiple daily repeat observations and largely eliminating the need for locally based people, equipment and infrastructure.This presentation presents results from the research, explains the methods employed to derive and validate flow speeds, and explores opportunities to further enhance the FluViSat methodology.Cite
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Statistic
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Flood forecasting
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This work aims to study the streamflow statistic patterns in the Sapucaí River watershed, state of Minas Gerais, Brazil. This study embraces the streamflow probabilistic modeling to determine the reference streamflow and, later, the streamflow regionalization to improve the water resources management. A 26-year-data series (1989 - 2014) of maximum, average, and minimum streamflow were used. Probability density functions were applied to the maximum and minimum daily streamflow to determine the recurrence periods. Long-term average annual and monthly streamflow were also calculated. Linear and non-linear regressions were adjusted for the streamflow regionalization. The drainage area and the streamflow equivalent to the total rainfall (with and without abstractions) were used as predictor variables. The probability density functions that best adjusted the maximum streamflow data set were the Generalized Extreme Values, and for the minimum streamflow was the normal distribution. Linear and non-linear regressions were efficient (R²> 0.90 and d Willmott> 0.97) in the regionalization process regardless of the predictor variables. However, a small statistical advantage was found for the adjustment of non-linear regressions that used the predictor variables drainage area and the streamflow equivalent to the total rainfall (without abstractions).
Statistic
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This study investigates the capability of improving the distributed hydrological model performance by assimilating the streamflow observations. Incorrectly estimated model states will lead to discrepancies between the observed and estimated streamflow. Consequently, streamflow observations can be used to update the model states, and the improved model states will eventually benefit the streamflow predictions. This study tests this concept in upper Huai River basin. We assimilate the streamflow observations sequentially into the Soil and Water Assessment Tool (SWAT) using the ensemble Kalman filter (EnKF) to update the model states. Both synthetic experiments and real data application are used to demonstrate the benefit of this data assimilation scheme. The experiment shows that assimilating the streamflow observations at interior sites significantly improves the streamflow predictions for the whole basin. Assimilating the catchment outlet streamflow improves the streamflow predictions near the catchment outlet. In real data case, the estimated streamflow at the catchment outlet is significantly improved by assimilating the in situ streamflow measurements at interior gauges. Assimilating the in situ catchment outlet streamflow also improves the streamflow prediction of one interior location on the main reach. This may demonstrate that updating model states using streamflow observations can constrain the flux estimates in distributed hydrological modeling.
Flood forecasting
SWAT model
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Streamflow characteristics as of 1984 for the Missouri River Basin, Wyoming, based on data from 204 streamflow-gaging stations are summarized. The streamflow characteristics reported include mean monthly and mean annual streamflow; duration of daily mean flow; and magnitude and probability of instantaneous peak flow, annual low flow, and annual high flow. Recurrence intervals of 2, 5, 10, 20, or 50, and 100 yr are determined for the peak-flow, low-flow, and high-flow characteristics. Annual low-flow and high-flow characteristics are also listed for various numbers of consecutive days. A station description, tables of streamflow characteristics, and graphs of mean monthly streamflow and duration of daily mean streamflow are presented for each station. Streamflow characteristics for periods before and after dam construction or transbasin diversion are presented for six stations. (USGS)
Mean flow
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First posted April 5, 2016 For additional information, contact: Director, Wyoming-Montana Water Science CenterU.S. Geological Survey3162 Bozeman AveHelena, MT 59601 http://wy-mt.water.usgs.gov/ Chapter E of this Scientific Investigations Report documents results from a study by the U.S. Geological Survey, in cooperation with the Montana Department of Environmental Quality and the Montana Department of Natural Resources and Conservation, to provide an update of statewide streamflow characteristics based on data through water year 2009 for streamflow-gaging stations in or near Montana. Streamflow characteristics are presented for 408 streamflow-gaging stations in Montana and adjacent areas having 10 or more years of record. Data include the magnitude and probability of annual low and high streamflow, the magnitude and probability of low streamflow for three seasons (March–June, July–October, and November–February), streamflow duration statistics for monthly and annual periods, and mean streamflows for monthly and annual periods. Streamflow is considered to be regulated at streamflow-gaging stations where dams or other large-scale human modifications affect 20 percent or more of the contributing drainage basin. Separate streamflow characteristics are presented for the unregulated and regulated periods of record for streamflow-gaging stations with sufficient data.
Geological survey
Water year
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Streamflow data have been collected for the Narraguagus River from 1948 to the present (2000) at the U.S. Geological Survey (USGS) streamgaging station at Cherryfield, Maine. This report describes a study done by the USGS to determine streamflow statistics using the streamflow record at the Narraguagus River station for use in total water use management plans implemented by State and Federal agencies. Because the effect of changes in irrigation practices from 1993 to the present on streamflow in the Narraguagus basin is unknown and potentially significant, streamflow data after December 1992 were not used in the determination of the streamflow statistics. For the period 1948- 92, monthly median streamflows range from 93.0 ft3/s (August) to 1,000 ft3/s (April). The median streamflow for the selected period of record for all days (1948-92) is 302 ft3/s.
Water year
Geological survey
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Streamflow measurement techniques of the Mississippi River at St. Louis have changed through time (1866–present). In addition to different methods used for discrete streamflow measurements, the density and range of discrete measurements used to define the rating curve (stage versus streamflow) have also changed. Several authors have utilized published water surface elevation (stage) and streamflow data to assess changes in the rating curve, which may be attributed to be caused by flood control and/or navigation structures. The purpose of this paper is to provide a thorough review of the available flow measurement data and techniques and to assess how a strict awareness of the limitations of the data may affect previous analyses. It is concluded that the pre-1930s discrete streamflow measurement data are not of sufficient accuracy to be compared with modern streamflow values in establishing long-term trends of river behavior.
Rating curve
Flood forecasting
Elevation (ballistics)
Flood control
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Simulating streamflow and the effects of projected climate change on the Savage River, Maryland, USA
The Savage River in western Maryland and its associated reservoir and watershed serves many purposes including recreation, drinking water supply, and auxiliary water supply for Washington DC. Streamflow on the Savage River was modeled using a simple hydrologic model and validated with historical streamflow observations. Future projected climate data were used to drive the model to assess the impact of temperature and precipitation changes on future streamflow. Winter streamflow is projected to increase, while spring, summer, and fall streamflow are projected to decrease. Annual streamflow totals show a slight negative trend over the coming century. Future changes in precipitation are more influential on future streamflow during the winter while temperature may be more important during the summer and fall. On an annual basis, by the year 2098, the impacts of temperature and precipitation will essentially cancel each other out resulting in only a small negative trend in annual streamflow. Increased streamflow during the winter months may not be able to compensate for decreased flow during the remainder of the year which raises concerns about the ability of the reservoir to supply water during future droughts.
Flood forecasting
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