Data for designing discharge monitors in Surma River

Published: 31 August 2023| Version 1 | DOI: 10.17632/972vtvnnx7.1


Data (attached) focuses on a case study of designing and evaluating the discharge monitoring station of the Surma River using the entropy-based method. In the first phage, a 1-D model has been developed for the Surma River to extract the time series of discharge data. Afterward, two entropy contents (Joint entropy and total Correlation) were used to design and evaluate the optimal number and placement of the monitoring stations in the Surma River. Non-dominated sorting genetic algorithm II (NSGA-II by Dev et al., 2002) and Greedy algorithm (Alfonso et al., 2013; Banik et al., 2017a) have optimized the monitoring network. 1. MIKE-II model data: Data containing this folder had been used to build the 1-D hydrodynamic model. The model was built for two datasets (2015-2019 and 2021-2022) 2. Calibration data (.csv file ) 3. Extracted time series discharge data (2015-2019 and 2021-22) for valuating the optimal number and placement of the monitoring stations in the Surma River (.csv file)


Steps to reproduce

A 1D-hydrodynamic model is necessary to design and evaluate discharge monitoring networks. Therefore, the upper part of the Surma River (Kanaighat to Sunamganj) has been modeled on the MIKE11. Rainfall events have not been included in this model. The analysis of the River under the response to rainfall events is beyond the objectives of this study. However, this developed model can be updated and complemented for other uses. The information on water levels and discharges of river stations, river network, and bathymetry is obtained from the Institute of Water Modelling (IWM), Bangladesh, to develop the 1D- hydrodynamic model of the Surma River between Kanaighat and Sunamganj. The data used are described in detail as follows. • Water level and discharge Two model scenarios were considered, one from January 2015 to December 2019 and the other from June 2021 to June 2022. The latter is the extreme event where the maximum discharge was found due to the worst flood event in 122 years (Iqbal, 2022), with a higher average yearly discharge. • Boundary condition The boundaries of the model are the discharge data of Kanaighat (upstream) and the water level data of Sunamganj (downstream). The time series of the four tributaries (Muslimpur, Islampur, Jaflong, and Sarighat) were included as point sources. • River network, bathymetry, and cross-sections The model of the Surma River has been developed using a network point every 500 meters. Thus, the Surma River contains 301 points with chainage ranging from 0 km at Kanaighat (upstream) to 150 km at Sunamganj (downstream). The bathymetry and cross-section information of all those 301 points were collected from the Department of Surveying of IWM, Bangladesh. Model data extraction for optimal monitor placement In order to prepare time-series data that can be used for information theory analysis, a 1D-hydrodynamic model was developed using MIKE11. The model includes 150 km of Surma River with points placed approximately every 500 m. The hydrological data from January 2015 to December 2019 and July 2021 to June 2022 were used with the complete data records at the tributaries and hydrologic stations. Water level data of 2019 obtained from the Sylhet station and the Chhatak station were used to calibrate the model. Afterward, the validation of the model was done using July 2021 to June 2022 data. Once the model was calibrated and validated, two sets of the discharge data (2015-19 and 2021-22) was extracted from the result file.


Shahjalal University of Science and Technology


Water Resource Management


Shahjalal University of Science and Technology