The effects of flooding can be very serious, especially in developing countries, where rapid urbanization and socio-economic development increases risk. Reliable information is crucial to support decision-makers develop appropriate strategies to reduce flood risk. This article aims to develop a framework for assessing flood risk and adaptive capacity, based on a bottom-up approach, in Vietnam’s Gianh River watershed. Flood risk was computed by combining hazard, exposure, and vulnerability using hydrodynamic modeling and the Analytic Hierarchy Process method. The adaptive capacity of the population was assessed via interviews with 298 inhabitants. The results show that flood risk is high in areas with high population and construction density. Both the ability to access resources and communities' flood-risk perceptions are important factors in improving the capacity to adapt. This study can provide an important theoretical framework complementing the existing literature and supporting studies related to flood-risk management in the context of climate change and urbanization in other regions. The study also fills a gap in the knowledge of the negative effects of flooding. From a methodological standpoint, this study underlines the importance of using hydraulic models and socio-economic surveys in flood-risk management. 

Bottom-up approach for flood-risk management in developing countries: a case study in the Gianh River watershed of Vietnam

The present study aims to identify potential locations for small‐scale hydroelectric power (HEP) stations in hilly regions for the purpose of generating renewable energy. A rainfall‐runoff (R‐R) model of the Beas River catchment was established using the MIKE 11 NAM to estimate the available discharge. The model was calibrated and validated over the period of June‐2015–May‐2018 and June‐2018–May‐2020, respectively, using daily observed discharge data at the Pandoh Dam site. The model exhibited good performance with a coefficient of determination (R2) of 0.82 during calibration and 0.70 during validation and a water balance of −0.01% and −18%, respectively. However, Lmax, CK1, CK2 and CQOF are found most sensitive parameters during the calibration. Further, thirteen major streams of order five or higher were selected for the assessment of hydropower potential, resulting in the identification of 131 potential run‐of‐river (ROR) hydropower sites. The hydropower potential at two proposed sites, Bhang SHEP (9 MW) and Raison SHEP (18 MW), was estimated to be 11 and 15 MW, respectively, using 90% dependable flow. The results demonstrate the effectiveness of using Digital Elevation Model (DEM) and Geographic Information System (GIS) techniques for determining hydropower potential in ungauged basins in the Himalayas.

Quantitative analysis of hydropower potential in the upper Beas basin using geographical information system and MIKE 11 Nedbor Afrstromnings Model (NAM)


 
 This study presents a sediment rating curve (SRC), multiple regression (MR), and long short-term memory (LSTM) model for estimating daily suspended sediment concentration (SSC). The data of daily SSC at Yen Thuong and daily flow at five locations in the Ca River Basin, Vietnam are used to demonstrate multiple approaches. Using the daily flow and SSC data in the period from 2009 to 2019, appropriate coefficients in each method are identified carefully using five popular criteria. The results showed that SRC and MR approaches reproduced acceptably the observed values, with the values of RMSE, MAE, and ME of daily SSC being less than 5% of daily SSC magnitude observed at the station, while NSE ranges from 0.47 to 0.63 and r coefficient varies between 0.69 and 0.80. The LSTM model represented the observed values of daily SSC very well. The values of two dimensionless criteria are greater than 0.94 and its values of three-dimensional criteria are smaller than 2.0% of the observed magnitude of daily SSC in both training and validation steps. The LSTM model is found to be the best among the three investigated approaches. Then, the model is applied to estimate daily SSC values for the period from 1969 to 2008 and the year 2020.

Estimation of daily suspended sediment concentration in the Ca River Basin using a sediment rating curve, multiple regression, and long short-term memory model

Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam


 This paper presents the long short-term memory (LSTM), rating curve (RC), and rainfall–runoff model that can be used for estimating the daily flow at the outlet of river basins. The Hieu river basin in Vietnam is selected as an example for demonstrating the ability of multiple approaches. Hydro-meteorological data at the Quy Chau station are collected over a long period from 1/1/1991 to 31/12/2020. Multiple approaches mentioned above are implemented and used for calculating the daily flow in the studied river basin. The coefficients and modeling parameters in each approach are then carefully determined based on five statistical error indexes. The results revealed that the RC using either one or two segments and the LSTM model using water elevation as input data represented the observed daily flow very well, with the values of dimensional errors (i.e. mean error, mean absolute error and root mean square error) equal only to about of 1% of the observed magnitude of the flow in the studied river basin, while Nash–Sutcliffe efficiency and correlation coefficients are greater than 0.95. Impacts of different types of input datasets on estimated values of the daily flow are also presented when the LSTM model is applied.

Estimation of the daily flow in river basins using the data-driven model and traditional approaches: an application in the Hieu river basin, Vietnam

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Estimation of the daily flow in river basins using the data-driven model and traditional approaches: an application in the Hieu river basin, Vietnam