A model-aided satellite-altimetry-based flood forecasting system for the Mekong River

Chang, Chin‐Yu; Lee, Hyongki; Hossain, Faisal; Basnayake, Senaka; Jayasinghe, Susantha; Chishtie, Farrukh; Saah, David; Yu, Hanwen; Khem, Sothea; Bui, Duong Du

doi:10.1016/j.envsoft.2018.11.017

Cited by 30 publications

(13 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, several studies have been developed in order to predict hydrological behavior. In these studies, the learning machine techniques are the most used, mainly in works that have the objective of predicting river flow [2], [3] In [4], water availability was predicted by forecasting the water level with the application of the Variable Infiltration Capacity (VIC) hydrological model for the Mekong River in Asia. In [5], a Feed Forward Neural Network (FFNN) was applied and compared with Long Short-Term Memory (LSTM).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

et al. 2020

View full text Add to dashboard Cite

Hydrological models are essential tools to forecast daily water resources' availability, which are used to plan the short-term electrical systems' operation. However, there is a trade-off when choosing a given model. Complex models may provide good results depending on very complicated analytical and optimization procedures beyond sophisticated data, whereas simpler models offer reasonable results with much more amenable tuning approaches. To improve the quality of simpler models this paper proposes the coupling of the Soil Moisture Accounting Procedure (SMAP) hydrological model with a Deep Learning architecture based on Conv3D-LSTM. In the proposed methodology, the SMAP is first optimized to obtain general parameters of the hydrographic basin. This optimized model's output is used as input to the Conv3D-LSTM estimator to provide the final results. This gray estimator model can generate fast and accurate results. Studies whit the goal of forecast the natural flow seven days ahead are carried out for two large Brazilian hydroelectric plants to validate the method. The results obtained by the architecture are better than those obtained with decoupled techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Finally, the precipitation considered P (t) is obtained by multiplying P d(t) by the factor P cof , as shown in (4). P cof must be calibrated along with the other parameters, in order to guarantee the water balance.…”

Section: A the Smap Modelmentioning

confidence: 99%

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Hydrodynamic models can be used to interpolate spatial and temporal variations in the WSE between gauging stations. Integration of hydrodynamic models and observations has proved to be a powerful approach for augmenting our understanding of the process and is becoming increasingly important in streamflow forecasting, as demand for assessments of extreme climate events grows [3][4][5][6][7][8][9]. Nevertheless, a lack of spatially resolved observations can limit the skill of these models in terms of predicting WSE at a local scale.…”

Section: Introductionmentioning

confidence: 99%

The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling

Jiang

Bandini

Smith³

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Water level or water surface elevation (WSE) is an important state variable of rivers, lakes, and wetlands. Hydrodynamic models of rivers and streams simulate WSE and can benefit from spatially distributed WSE observations, to increase model reliability and predictive skill. This has been partially addressed by satellite radar altimetry, but satellite altimetry is unable to deliver useful data for small rivers. To overcome such limitations, we deployed a radar altimetry system on an unmanned aerial vehicle (UAV), to map spatially distributed WSE. We showed that UAV altimetry can provide observations of WSE with a very high spatial resolution (ca. 0.5 m) and accuracy (ca. 3 cm), in a time-saving and cost-effective way. Furthermore, we investigated the value of this dataset for the calibration and validation of hydrodynamic models. Specifically, we introduced spatially distributed roughness parameters in a hydrodynamic model and estimated these parameters, using the observed WSE profiles along the stream as input. A case study was conducted in the Åmose stream, Denmark. The results showed that UAV-borne WSE can identify significant variations of the Manning–Strickler coefficients, along this small and highly vegetated stream and over time. Moreover, the model performed extremely well using distributed roughness coefficients, but it could not reproduce WSE satisfactorily using uniform roughness. We concluded that distributed roughness coefficients should be considered, especially for small vegetated rivers, to improve model performance, both locally and globally. Spatially distributed parameterizations of the effective channel roughness could be constrained with UAV-borne WSE. This study demonstrated for the first time that UAV-borne WSE can help to understand the variations of hydraulic roughness, and can support efficient river management and maintenance.

show abstract

“…In addition, tidal intrusion can reach up to Phnom Penh [20,21]. Due to these hydraulic complexities in the LMRB, most previous studies derived Q from a statistical method or a hydrologic model, focusing only on relatively upstream locations of the LMRB.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

A model-aided satellite-altimetry-based flood forecasting system for the Mekong River

Cited by 30 publications

References 26 publications

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling

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

Contact Info

Product

Resources

About