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.

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

2011 ◽  
Vol 15 (1) ◽  
pp. 241-254 ◽  
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.

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

2013 ◽  
Vol 10 (3) ◽  
pp. 2879-2925 ◽  
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 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.

Responses of river discharge and sediment load to climate change in the transboundary Mekong River Basin

2019 ◽  
Vol 34 (S1) ◽  
pp. 367-380
Author(s):  
Dao Nguyen Khoi ◽  
Van Thinh Nguyen ◽  
Truong Thao Sam ◽  
Nguyen Ky Phung ◽  
Nguyen Thi Bay
Keyword(s):  
Climate Change ◽  
River Basin ◽  
River Discharge ◽  
Sediment Load ◽  
Mekong River ◽  
Mekong River Basin

scholarly journals Upstream GPS Vertical Displacement and its Standardization for Mekong River Basin Surface Runoff Reconstruction and Estimation

2019 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Hok Sum Fok ◽  
Linghao Zhou ◽  
Yongxin Liu ◽  
Zhongtian Ma ◽  
Yutong Chen
Keyword(s):  
Time Series ◽  
River Water ◽  
River Basin ◽  
Relative Error ◽  
Surface Runoff ◽  
Water Discharge ◽  
Vertical Displacement ◽  
Mekong River ◽  
Mekong River Basin

Surface runoff (R), which is another expression for river water discharge of a river basin, is a critical measurement for regional water cycles. Over the past two decades, river water discharge has been widely investigated, which is based on remotely sensed hydraulic and hydrological variables as well as indices. This study aims to demonstrate the potential of upstream global positioning system (GPS) vertical displacement (VD) and its standardization to statistically derive R time series, which has not been reported in recent literature. The correlation between the in situ R at estuaries and averaged GPS-VD and its standardization in the river basin upstream on a monthly temporal scale of the Mekong River Basin (MRB) is examined. It was found that the reconstructed R time series from the latter agrees with and yields a similar performance to that from the terrestrial water storage based on gravimetric satellite (i.e., Gravity Recovery and Climate Experiment (GRACE)) and traditional remote sensing data. The reconstructed R time series from the standardized GPS-VD was found to have a 2–7% accuracy increase against those without standardization. On the other hand, it is comparable to data that are obtained by the Palmer drought severity index (PDSI). Similar accuracies are exhibited by the estimated R when externally validated through another station location with in situ time series. The comparison of the estimated R at the entrance of river delta against that at the estuaries indicates a 1–3% relative error induced by the residual ocean tidal effect at the estuary. The reconstructed R from the standardized GPS-VD yields the lowest total relative error of less than 9% when accounting for the main upstream area of the MRB. The remaining errors may be the result of the combined effect of the proposed methodology, remaining environmental signals in the data time series, and potential time lag (less than a month) between the upstream MRB and estuary.

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

2013 ◽  
Vol 45 (1) ◽  
pp. 148-164 ◽  
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.

scholarly journals Water levels of the Mekong River Basin based on CryoSat-2 SAR data classification

2017 ◽  
Author(s):  
Eva Boergens ◽  
Karina Nielsen ◽  
Ole B. Andersen ◽  
Denise Dettmering ◽  
Florian Seitz
Keyword(s):  
River Basin ◽  
Target Identification ◽  
Unsupervised Classification ◽  
Radar Data ◽  
Water Levels ◽  
Mekong River ◽  
Mekong River Basin ◽  
Synthetic Aperture Radar Data ◽  
Sar Data ◽  
Land Water

Abstract. In this study we use CryoSat-2 SAR (Delay-Doppler Synthetic Aperture Radar) data over the Mekong River Basin to estimate water levels. Smaller inland waters can be observed with CryoSat-2 data with a higher accuracy compared to the classical radar altimeters due to the increased along track resolution of SAR and the smaller footprint. However, even with this SAR data the estimation of water levels over smaller (width less than 500 m) is still challenging as only very few consecutive observations over the water body are present. The usage of land-water-masks for target identification tends to fail as the river becomes smaller. Therefore, we developed a classification to divide the observations into water and land observations based solely on the observations. The classification is done with an unsupervised classification algorithm, and it is based on features derived from the SAR and RIP (Range Integrated Power) waveforms. After the classification, classes representing water and land are identified. The measurements classified as water are used in a next step to estimate water levels for each crossing over the Mekong River. The resulting water levels are validated and compared to gauge data, Envisat data and CryoSat-2 water levels derived with a land-water mask. The CryoSat-2 classified water levels perform better than results based on the land-water-mask and Envisat. Especially, in the smaller upstream regions the improvements of the classification approach for CryoSat-2 are evident.

scholarly journals Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 56 ◽  
Author(s):  
Chelsea Dandridge ◽  
Bin Fang ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
River Basin ◽  
River Basins ◽  
Large River ◽  
Mekong River ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Large River Basins

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

scholarly journals Uncertainty in climate change projections of discharge for the Mekong River Basin

2010 ◽  
Vol 7 (4) ◽  
pp. 5991-6024 ◽  
Author(s):  
D. G. Kingston ◽  
J. R. Thompson ◽  
G. Kite
Keyword(s):  
Climate Change ◽  
River Basin ◽  
River Discharge ◽  
River Flow ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Mekong River ◽  
Electricity Production ◽  
Mekong River Basin ◽  
Snow Storage

Abstract. The Mekong River Basin comprises a key regional resource in Southeast Asia for sectors that include agriculture, fisheries and electricity production. Here we explore the potential impacts of climate change on freshwater resources within the river basin. We quantify uncertainty in these projections associated with GCM structure and climate sensitivity, as well as from hydrological model parameter specification. This is achieved by running pattern-scaled GCM output through a semi-distributed hydrological model (SLURP) of the basin. These pattern-scaled GCM outputs allow investigation of specific thresholds of global climate change including the postulated 2 ºC threshold of "dangerous" climate change as simulated using outputs from seven different GCMs. Detailed analysis of results based on HadCM3 climate scenarios reveals a relatively small but non-linear response of annual river discharge to increasing global mean temperature, ranging from a 5.4% decrease to 4.5% increase. Intra-annual (monthly) changes in river discharge are greater (from −16% to +55%, with greatest decreases in July and August, greatest increases in May and June) and result from complex and contrasting intra-basin changes in precipitation, evaporation and snow storage/melt. Whilst overall results are highly GCM dependent (in both direction and magnitude), this uncertainty is primarily driven by differences in GCM projections of future precipitation. In contrast, there is strong consistency between GCMs in terms of both increased potential evapotranspiration and a shift to an earlier and less substantial snowmelt season. Indeed, in the upper Mekong (Lancang sub-basin), the temperature-related signal in discharge is strong enough to overwhelm the precipitation-related uncertainty in the direction of change in discharge, with scenarios from all GCMs leading to increased river flow from April–June, and decreased flow from July–August.

scholarly journals River monitoring from satellite radar altimetry in the Zambezi River Basin

2012 ◽  
Vol 9 (3) ◽  
pp. 3203-3235 ◽  
Author(s):  
C. I. Michailovsky ◽  
S. McEnnis ◽  
P. A. M. Berry ◽  
R. Smith ◽  
P. Bauer-Gottwein
Keyword(s):  
River Basin ◽  
High Flow ◽  
Water Levels ◽  
Data Availability ◽  
Radar Altimetry ◽  
Satellite Radar ◽  
Satellite Radar Altimetry ◽  
Zambezi River Basin ◽  
Zambezi River

Abstract. 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. In this study, retracked Envisat altimetry data was 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 80 m wide with an RMSE relative to in situ levels of 0.32 to 0.72 m 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 5.5 to 7.4 % terms of high flow estimation relative to in situ gauge measurements. 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 8.2 and 25.8 % of the high flow estimates.

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