Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon

Abstract. In this work we introduce and evaluate a data assimilation framework for gauged and radar altimetry-based discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites and also transferring information to ungauged rivers reaches. Altimetry data assimilation improves results at a daily basis in terms of water levels and discharges with minor degree, even though radar altimetry data has a low temporal resolution. Sensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. The deterioration of model performance at some unmonitored reaches indicates the need for proper characterization of model errors and spatial localization techniques for hydrological applications. Finally, we evaluated stream flow forecasts for the Amazon basin based on initial conditions produced by the data assimilation scheme and using the ensemble stream flow prediction approach where the model is forced by past meteorological forcings. The resulting forecasts agreed well with the observations and maintained meaningful skill at large rivers even for long lead times, e.g. > 90 days at the Solimões/Amazon main stem. Results encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote sensing information.

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Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon

Hydrology and Earth System Sciences ◽

10.5194/hess-17-2929-2013 ◽

2013 ◽

Vol 17 (7) ◽

pp. 2929-2946 ◽

Cited By ~ 39

Author(s):

R. C. D. Paiva ◽

W. Collischonn ◽

M.-P. Bonnet ◽

L. G. G. de Gonçalves ◽

S. Calmant ◽

...

Keyword(s):

Data Assimilation ◽

Hydrodynamic Model ◽

Stream Flow ◽

Large Scale ◽

Water Levels ◽

Large Rivers ◽

Altimetry Data ◽

Radar Altimetry ◽

Prediction Approach

Abstract. In this work, we introduce and evaluate a data assimilation framework for gauged and radar altimetry-based discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites (change in root-mean-squared error Δrms = −49%) and also transferring information to ungauged rivers reaches (Δrms = −16%). Altimetry data assimilation improves results, in terms of water levels (Δrms = −44%) and discharges (Δrms = −15%) to a minor degree, mostly close to altimetry sites and at a daily basis, even though radar altimetry data has a low temporal resolution. Sensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. The deterioration of model performance at some unmonitored reaches indicates the need for proper characterisation of model errors and spatial localisation techniques for hydrological applications. Finally, we evaluated stream flow forecasts for the Amazon basin based on initial conditions produced by the data assimilation scheme and using the ensemble stream flow prediction approach where the model is forced by past meteorological forcings. The resulting forecasts agreed well with the observations and maintained meaningful skill at large rivers even for long lead times, e.g. >90 days at the Solimões/Amazon main stem. Results encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote-sensing information.

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Using radar altimetry to update a large-scale hydrological model of the Brahmaputra river basin

Hydrology Research ◽

10.2166/nh.2013.191 ◽

2013 ◽

Vol 45 (1) ◽

pp. 148-164 ◽

Cited By ~ 13

Author(s):

Flemming Finsen ◽

Christian Milzow ◽

Richard Smith ◽

Philippa Berry ◽

Peter Bauer-Gottwein

Keyword(s):

Real Time ◽

River Basin ◽

Large Scale ◽

Hydrological Model ◽

Water Levels ◽

Hydrological Models ◽

Altimetry Data ◽

Brahmaputra River ◽

Radar Altimetry ◽

Rating Curves

Measurements of river and lake water levels from space-borne radar altimeters (past missions include ERS, Envisat, Jason, Topex) are useful for calibration and validation of large-scale hydrological models in poorly gauged river basins. Altimetry data availability over the downstream reaches of the Brahmaputra is excellent (17 high-quality virtual stations from ERS-2, 6 from Topex and 10 from Envisat are available for the Brahmaputra). In this study, altimetry data are used to update a large-scale Budyko-type hydrological model of the Brahmaputra river basin in real time. Altimetry measurements are converted to discharge using rating curves of simulated discharge versus observed altimetry. This approach makes it possible to use altimetry data from river cross sections where both in-situ rating curves and accurate river cross section geometry are not available. Model updating based on radar altimetry improved model performance considerably. The Nash–Sutcliffe model efficiency increased from 0.77 to 0.83. Real-time river basin modelling using radar altimetry has the potential to improve the predictive capability of large-scale hydrological models elsewhere on the planet.

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

Remote Sensing ◽

10.3390/rs11222684 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2684 ◽

Cited By ~ 3

Author(s):

Kim ◽

Lee ◽

Chang ◽

Bui ◽

Jayasinghe ◽

...

Keyword(s):

River Basin ◽

Ensemble Learning ◽

River Discharge ◽

Water Levels ◽

Mekong River ◽

Altimetry Data ◽

Rating Curve ◽

Mekong River Basin ◽

Radar Altimetry

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 (QELQ) 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 QELQ 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.

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Real-time remote sensing driven river basin modelling using radar altimetry

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-8347-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 8347-8385 ◽

Cited By ~ 2

Author(s):

S. J. Pereira-Cardenal ◽

N. D. Riegels ◽

P. A. M. Berry ◽

R. G. Smith ◽

A. Yakovlev ◽

...

Keyword(s):

Real Time ◽

River Basin ◽

Reanalysis Data ◽

Water Levels ◽

Hydrological Models ◽

Altimetry Data ◽

Reservoir Water ◽

Radar Altimetry ◽

Modelling Approach

Abstract. Many river basins have a weak in-situ hydrometeorological monitoring infrastructure. However, water resources practitioners depend on reliable hydrological models for management purposes. Remote sensing (RS) data have been recognized as an alternative to in-situ hydrometeorological data in remote and poorly monitored areas and are increasingly used to force, calibrate, and update hydrological models. In this study, we evaluate the potential of informing a river basin model with real-time radar altimetry measurements over reservoirs. We present a lumped, conceptual, river basin water balance modelling approach based entirely on RS and reanalysis data: precipitation was obtained from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), temperature from the European Centre for Medium-Range Weather Forecast's (ECMWF) Operational Surface Analysis dataset and reference evapotranspiration was derived from temperature data. The Ensemble Kalman Filter was used to assimilate radar altimetry (ERS2 and Envisat) measurements of reservoir water levels. The modelling approach was applied to the Syr Darya River Basin, a snowmelt-dominated basin with large topographical variability, several large reservoirs and scarce hydrometeorological data that is shared between 4 countries with conflicting water management interests. The modelling approach was tested over a historical period for which in-situ reservoir water levels were available. Assimilation of radar altimetry data significantly improved the performance of the hydrological model. Without assimilation of radar altimetry data, model performance was limited, probably because of the size and complexity of the model domain, simplifications inherent in model design, and the uncertainty of RS and reanalysis data. Altimetry data assimilation reduced the mean error of the simulated reservoir water levels from 4.7 to 1.9 m, and overall model RMSE from 10.3 m to 6.7 m. Because of its easy accessibility and immediate availability, radar altimetry lends itself to being used in real-time hydrological applications. As an impartial source of information about the hydrological system that can be updated in real time, the modelling approach described here can provide useful medium-term hydrological forecasts to be used in water resources management.

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Real-time remote sensing driven river basin modeling using radar altimetry

Hydrology and Earth System Sciences ◽

10.5194/hess-15-241-2011 ◽

2011 ◽

Vol 15 (1) ◽

pp. 241-254 ◽

Cited By ~ 46

Author(s):

S. J. Pereira-Cardenal ◽

N. D. Riegels ◽

P. A. M. Berry ◽

R. G. Smith ◽

A. Yakovlev ◽

...

Keyword(s):

Real Time ◽

River Basin ◽

Model Performance ◽

Reanalysis Data ◽

Water Levels ◽

Altimetry Data ◽

Reservoir Water ◽

Radar Altimetry ◽

Modeling Approach

Abstract. Many river basins have a weak in-situ hydrometeorological monitoring infrastructure. However, water resources practitioners depend on reliable hydrological models for management purposes. Remote sensing (RS) data have been recognized as an alternative to in-situ hydrometeorological data in remote and poorly monitored areas and are increasingly used to force, calibrate, and update hydrological models. In this study, we evaluate the potential of informing a river basin model with real-time radar altimetry measurements over reservoirs. We present a lumped, conceptual, river basin water balance modeling approach based entirely on RS and reanalysis data: precipitation was obtained from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), temperature from the European Centre for Medium-Range Weather Forecast's (ECMWF) Operational Surface Analysis dataset and reference evapotranspiration was derived from temperature data. The Ensemble Kalman Filter was used to assimilate radar altimetry (ERS2 and Envisat) measurements of reservoir water levels. The modeling approach was applied to the Syr Darya River Basin, a snowmelt-dominated basin with large topographical variability, several large reservoirs and scarce hydrometeorological data that is located in Central Asia and shared between 4 countries with conflicting water management interests. The modeling approach was tested over a historical period for which in-situ reservoir water levels were available. Assimilation of radar altimetry data significantly improved the performance of the hydrological model. Without assimilation of radar altimetry data, model performance was limited, probably because of the size and complexity of the model domain, simplifications inherent in model design, and the uncertainty of RS and reanalysis data. Altimetry data assimilation reduced the mean absolute error of the simulated reservoir water levels from 4.7 to 1.9 m, and overall model RMSE from 10.3 m to 6.7 m. Model performance was variable for the different reservoirs in the system. The RMSE ranged from 10% to 76% of the mean seasonal reservoir level variation. Because of its easy accessibility and immediate availability, radar altimetry lends itself to being used in real-time hydrological applications. As an impartial source of information about the hydrological system that can be updated in real time, the modeling approach described here can provide useful medium-term hydrological forecasts to be used in water resources management.

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Assimilation of near real-time radar altimetry data into a hydrodynamic model for streamflow and water level forecasting

10.5194/egusphere-egu2020-15219 ◽

2020 ◽

Author(s):

Liguang Jiang ◽

Youjiang Shen ◽

Dedi Liu ◽

Henrik Madsen ◽

Peter Bauer-Gottwein

Keyword(s):

Data Acquisition ◽

Water Level ◽

Real Time ◽

Hydrodynamic Model ◽

Water Levels ◽

Observation Error ◽

Altimetry Data ◽

Radar Altimetry ◽

Virtual Station ◽

Time Critical

Satellite radar altimetry has been widely used in hydrological studies, such as monitoring of lakes and reservoirs, retrieving water level and discharge of rivers, calibration of river models, etc. Sentinel-3 SAR altimeter delivers data at three levels of latency, i.e. near real-time (less than 3 hours after data acquisition), slow time critical (within 48 hours after data acquisition), and non-time critical (typically one month after data acquisition). However, most studies use final products, i.e. non-time critical products of altimetry data for inland water monitoring or hydrological simulations. So far, to the best of our knowledge, no study has been exploiting the value of near-real time satellite altimetry data for hydrological research.In this study, we first investigate data quality of Sentinel-3 near real-time data against non-time critical product and in-situ data over the Han River in China. Then, we assimilate these data into a 1-D hydrodynamic model, i.e. MIKE Hydro River, to exploit the near-real time altimetry dataset for hydrological forecasting. Specifically, we use the Ensemble Kalman Filter to assimilate altimetry-derived water surface elevation data into MIKE Hydro River model. The model state variable that is updated is the water level defined on the numerical grid of the 1D hydrodynamic model. Observation error estimates are generated from the standard deviations of water levels at each virtual station. Applying this operational forecasting system retrospectively over historical periods, the effect of updating water level at multiple virtual stations on forecast performance is investigated.Through this study, we gain new knowledge about near real-time altimetry products for hydrological studies. This will be informative for both the hydrology community and satellite data providers.

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Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

10.5194/hess-2020-359 ◽

2020 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Rich Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Large Scale ◽

Pedotransfer Functions ◽

Surface Model ◽

Climate Projections ◽

Land Surface Models ◽

Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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In Situ and In Transit Computing for Large Scale Geoscientific Simulation

10.5194/egusphere-egu2020-21390 ◽

2020 ◽

Author(s):

Sebastian Friedemann ◽

Bruno Raffin ◽

Basile Hector ◽

Jean-Martial Cohard

Keyword(s):

Data Assimilation ◽

High Performance ◽

Large Scale ◽

Transfer Model ◽

Simulation Data ◽

Computing Platform ◽

Continental Scale ◽

Physically Based ◽

In Transit

In situ and in transit computing is an effective way to place postprocessing and preprocessing tasks for large scale simulations on the high performance computing platform. The resulting proximity between the execution of preprocessing, simulation and postprocessing permits to lower I/O by bypassing slow and energy inefficient persistent storages. This permits to scale workflows consisting of heterogeneous components such as simulation, data analysis and visualization, to modern massively parallel high performance platforms. Reordering the workflow components gives a manifold of new advanced data processing possibilities for research. Thus in situ and in transit computing are vital for advances in the domain of geoscientific simulation which relies on the increasing amount of sensor and simulation data available.In this talk, different in situ and in transit workflows, especially those that are useful in the field of geoscientific simulation, are discussed. Furthermore our experiences augmenting ParFlow-CLM, a physically based, state-of-the-art, fully coupled water transfer model for the critical zone, with FlowVR, an in situ framework with a strict component paradigm, are presented. This allows shadowed in situ file writing, in situ online steering and in situ visualization.In situ frameworks further can be coupled to data assimilation tools. In the on going EoCoE-II we propose to embed data assimilation codes into an in transit computing environment. This is expected to enable ensemble based data assimilation on continental scale hydrological simulations with multiple thousands of ensemble members.

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