23 research outputs found
Operational reservoir inflow forecasting with radar altimetry: The Zambezi case study
River basin management can greatly benefit from short-term river discharge
predictions. In order to improve model produced discharge forecasts, data
assimilation allows for the integration of current observations of the
hydrological system to produce improved forecasts and reduce prediction
uncertainty. Data assimilation is widely used in operational applications to
update hydrological models with in situ discharge or level measurements. In
areas where timely access to in situ data is not possible, remote sensing
data products can be used in assimilation schemes.
<br><br>
While river discharge itself cannot be measured from space, radar altimetry
can track surface water level variations at crossing locations between the
satellite ground track and the river system called virtual stations (VS).
Use of radar altimetry versus traditional monitoring in operational settings
is complicated by the low temporal resolution of the data (between 10 and 35
days revisit time at a VS depending on the satellite) as well as the fact
that the location of the measurements is not necessarily at the point of
interest. However, combining radar altimetry from multiple VS with
hydrological models can help overcome these limitations.
<br><br>
In this study, a rainfall runoff model of the Zambezi River basin is built
using remote sensing data sets and used to drive a routing scheme coupled to
a simple floodplain model. The extended Kalman filter is used to update the
states in the routing model with data from 9 Envisat VS. Model fit was
improved through assimilation with the NashâSutcliffe model efficiencies
increasing from 0.19 to 0.62 and from 0.82 to 0.88 at the outlets of two
distinct watersheds, the initial NSE (NashâSutcliffe efficiency) being low at one outlet due to large
errors in the precipitation data set. However, model reliability was poor in
one watershed with only 58 and 44% of observations falling in the
90% confidence bounds, for the open loop and assimilation runs
respectively, pointing to problems with the simple approach used to
represent model error
River monitoring from satellite radar altimetry in the Zambezi River basin
Satellite radar altimetry can be used to monitor surface water levels from space. While current and past altimetry missions were designed to study oceans, retracking the waveforms returned over land allows data to be retrieved for smaller water bodies or narrow rivers. The objective of this study is the assessment of the potential for river monitoring from radar altimetry in terms of water level and discharge in the Zambezi River basin. Retracked Envisat altimetry data were extracted over the Zambezi River basin using a detailed river mask based on Landsat imagery. This allowed for stage measurements to be obtained for rivers down to 80m wide with an RMSE relative to in situ levels of 0.32 to 0.72m at different locations. The altimetric levels were then converted to discharge using three different methods adapted to different data-availability scenarios: first with an in situ rating curve available, secondly with one simultaneous field measurement of cross-section and discharge, and finally with only historical discharge data available. For the two locations at which all three methods could be applied, the accuracies of the different methods were found to be comparable, with RMSE values ranging from 4.1 to 6.5% of the mean annual in situ gauged amplitude for the first method and from 6.9 to 13.8% for the second and third methods. The precision obtained with the different methods was analyzed by running Monte Carlo simulations and also showed comparable values for the three approaches with standard deviations found between 5.7 and 7.2% of the mean annual in situ gauged amplitude for the first method and from 8.7 to 13.0% for the second and third methods
Operational river discharge forecasting in poorly gauged basins: the Kavango River basin case study
Operational probabilistic forecasts of river discharge are essential for
effective water resources management. Many studies have addressed this topic
using different approaches ranging from purely statistical black-box
approaches to physically based and distributed modeling schemes employing
data assimilation techniques. However, few studies have attempted to develop
operational probabilistic forecasting approaches for large and poorly gauged
river basins. The objective of this study is to develop open-source software
tools to support hydrologic forecasting and integrated water resources
management in Africa. We present an operational probabilistic forecasting
approach which uses public-domain climate forcing data and a
hydrologicâhydrodynamic model which is entirely based on open-source
software. Data assimilation techniques are used to inform the forecasts with
the latest available observations. Forecasts are produced in real time for
lead times of 0â7 days. The operational probabilistic forecasts are
evaluated using a selection of performance statistics and indicators and the
performance is compared to persistence and climatology benchmarks. The
forecasting system delivers useful forecasts for the Kavango River, which
are reliable and sharp. Results indicate that the value of the forecasts is
greatest for intermediate lead times between 4 and 7 days
Operational river discharge forecasting in poorly gauged basins: the Kavango River Basin case study
Operational probabilistic forecasts of river discharge are essential for
effective water resources management. Many studies have addressed this topic
using different approaches ranging from purely statistical black-box
approaches to physically based and distributed modeling schemes employing
data assimilation techniques. However, few studies have attempted to develop
operational probabilistic forecasting approaches for large and poorly gauged
river basins. The objective of this study is to develop open-source software
tools to support hydrologic forecasting and integrated water resources
management in Africa. We present an operational probabilistic forecasting
approach which uses public-domain climate forcing data and a
hydrologicâhydrodynamic model which is entirely based on open-source
software. Data assimilation techniques are used to inform the forecasts with
the latest available observations. Forecasts are produced in real time for
lead times of 0â7 days. The operational probabilistic forecasts are
evaluated using a selection of performance statistics and indicators and the
performance is compared to persistence and climatology benchmarks. The
forecasting system delivers useful forecasts for the Kavango River, which
are reliable and sharp. Results indicate that the value of the forecasts is
greatest for intermediate lead times between 4 and 7 days
An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope
The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ââremote sensingââ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results