scholarly journals Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study

2013 ◽  
Vol 10 (7) ◽  
pp. 9615-9644 ◽  
Author(s):  
C. I. Michailovsky ◽  
P. Bauer-Gottwein
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
River Basin ◽  
River Discharge ◽  
Remote Sensing Data ◽  
River System ◽  
River Basin Management ◽  
Hydrological Models ◽  
Radar Altimetry

Abstract. 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 optimal 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. 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 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. Combining radar altimetry from multiple VS with hydrological models could overcome these limitations. In this study, a rainfall runoff model of the Zambezi River Basin is built using remote sensing datasets 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 Nash-Sutcliffe model efficiencies increasing from 0.21 to 0.63 and from 0.82 to 0.87 at the outlets of two distinct watersheds. However, model reliability was poor in one watershed with only 54% and 55% of observations falling in the 90% confidence bounds, for the deterministic and assimilation runs respectively, pointing to problems with the simple approach used to represent model error.

scholarly journals Operational reservoir inflow forecasting with radar altimetry: the Zambezi case study

2014 ◽  
Vol 18 (3) ◽  
pp. 997-1007 ◽  
Author(s):  
C. I. Michailovsky ◽  
P. Bauer-Gottwein
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
River Basin ◽  
River Discharge ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Data Set ◽  
Radar Altimetry ◽  
Sensing Data

Abstract. 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. 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. 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 Nash–Sutcliffe 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 (Nash–Sutcliffe 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.

scholarly journals Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4

2020 ◽  
Vol 13 (3) ◽  
pp. 1267-1284 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Y. Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Lake Ecosystem ◽  
Sensing Data ◽  
Localization Scheme ◽  
Spatiotemporal Scales

Abstract. The understanding of physical dynamics is crucial to provide scientifically credible information on lake ecosystem management. We show how the combination of in situ observations, remote sensing data, and three-dimensional hydrodynamic (3D) numerical simulations is capable of resolving various spatiotemporal scales involved in lake dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we develop a flexible framework by incorporating DA into 3D hydrodynamic lake models. Using an ensemble Kalman filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in situ and satellite remote sensing temperature data into a 3D hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatiotemporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed with the goal of near-real-time operational systems (e.g., integration into meteolakes.ch).

scholarly journals Assimilation of Snowmelt Runoff Model (SRM) Using Satellite Remote Sensing Data in Budhi Gandaki River Basin, Nepal

2020 ◽  
Vol 12 (12) ◽  
pp. 1951 ◽  
Author(s):  
Til Prasad Pangali Sharma ◽  
Jiahua Zhang ◽  
Narendra Raj Khanal ◽  
Foyez Ahmed Prodhan ◽  
Basanta Paudel ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
River Basin ◽  
Satellite Remote Sensing ◽  
River Discharge ◽  
Remote Sensing Data ◽  
Himalayan Region ◽  
Snowmelt Runoff ◽  
Sensing Data ◽  
Runoff Model

The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R2) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.

Using high-resolution groundwater data for the validation of a global hydrological model: evaluating WaterGAP and calibration/data assimilation (C/DA) performance over France

2021 ◽  
Author(s):  
Kuei-Hua Hsu ◽  
Laurent Longuevergne ◽  
Annette Eicker ◽  
Mehedi Hasan ◽  
Andreas Güntner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Calibration Data ◽  
Data Set ◽  
Freshwater Resources ◽  
Sensing Data

<p>The dynamic global water cycle is of ecological and societal importance as it affects the availability of freshwater resources and influences extreme events such as floods and droughts. This work is set in the frame of the GlobalCDA Research Unit, which has the goal of developing a calibration/data assimilation approach (C/DA) to improve the quantification of freshwater resources by combining the global hydrological model WaterGAP with geodetic (GRACE, altimetry) and remote sensing data. This presentation focuses on the validation of the C/DA results using an independent in-situ groundwater data set based on ~1500 monitoring boreholes in France.</p><p>The resulting validation data set is applied to independently assess the output of several C/DA experiments: data assimilation using different combinations of the available geodetic and remote sensing data sets and different methods of model calibration, based on either an ensemble Kalman filter approach or a Pareto-optimal calibration algorithm.</p><p>To further understand in-situ groundwater and WaterGAP data set, we subtract the coherent signals using Empirical orthogonal function (EOF).  Over 85% variances can be explained by the first 3 EOFs for both data sets.</p>

Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

2020 ◽  
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Sensing Data

<p>Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.</p><p>In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM’s “Database of Hydrological Time series of Inland Waters” (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.</p><p>Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.</p>

scholarly journals Data assimilation of in-situ and satellite remote sensing data to 3D hydrodynamic lake models

2019 ◽  
Author(s):  
Theo Baracchini ◽  
Philip Yifei Chu ◽  
Jonas Šukys ◽  
Gian Lieberherr ◽  
Stefan Wunderle ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Three Dimensional ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Temporal Scales ◽  
Localization Scheme ◽  
Spatio Temporal

Abstract. The understanding of lakes physical dynamics is crucial to provide scientifically credible information for ecosystem management. We show how the combination of in-situ data, remote sensing observations and three-dimensional hydrodynamic numerical simulations is capable of delivering various spatio-temporal scales involved in lakes dynamics. This combination is achieved through data assimilation (DA) and uncertainty quantification. In this study, we present a flexible framework for DA into lakes three-dimensional hydrodynamic models. Using an Ensemble Kalman Filter, our approach accounts for model and observational uncertainties. We demonstrate the framework by assimilating in-situ and satellite remote sensing temperature data into a three-dimensional hydrodynamic model of Lake Geneva. Results show that DA effectively improves model performance over a broad range of spatio-temporal scales and physical processes. Overall, temperature errors have been reduced by 54 %. With a localization scheme, an ensemble size of 20 members is found to be sufficient to derive covariance matrices leading to satisfactory results. The entire framework has been developed for the constraints of operational systems and near real-time operations (e.g. integration into http://meteolakes.ch).

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

2019 ◽  
Vol 11 (22) ◽  
pp. 2684 ◽  
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.

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

2010 ◽  
Vol 7 (5) ◽  
pp. 8347-8385 ◽  
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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