Rainfall-runoff-inundation (rri) model application in bago river basin
The Bago River basin is a flood prone area in Myanmar. Thousands of people are affected due to the perennial flooding. The Bago River flows from the Bago Yoma mountainous region to the Ayeyarwaddy River through Yangon River. The Bago River is about 300 km long and its catchment area is 5348 km2 that receives an annual precipitation of 3300 mm.
This basin is selected as a study area for the flood simulation with 2D rainfall-runoff-inundation model (RRI model).
The objective of the study is to assess the RRI model performance in the Bago River basin. This study analyzed rainfall-runoff and flood inundation area in the basin for the period of serious flood events in 2015.
The model calibration process was used to calibrate the model parameters using data in 2015. Data from 2016 to 2017 were used to validate the model using these model parameters. The statistical measure of the Nash-Sutcliffe efficiency (NSE) was used to evaluate the performance of the model.
The simulation flood depth for August 2015 is calibrate with vector data which is derived from Radarsat-2 image acquired on 09 August 2015 and analyzed by by United Nations Institute for Training and Research (UNITAR) Operational Satellite Applications Programme (UNOSAT). Although the simulated results showed a good agreements with the observed one, the simulated discharge showed some differences because of the uncertainties involved in the observed discharge.
Keywords: Bago River basin, 2D rainfall-runoff-inundation model, flood inundation area
Floods are one kinds of disaster around the world, affecting to human lives and make economic losses approximately about 66% of water related disasters in the world are floods .
Nowadays, impacts of floods have been increased because of population growth, decreasing of floods plain, and climate change.
Flood mitigations have two guidelines, structural and non-structural, are selected by social and also investment. The major tools firstly used for planning and developing structural and non-structural flood mitigation and management approaches, which is hydrologic and hydraulic modeling used for flood simulation on decision and design.
Understanding floods behavior used for simulation modelling of magnitude and flow direction, is the challenges of hydrological community. For actual of flood behavior in magnitude, the best input dataset are ground truth observation data, rain gauge, topographical and land cover data. The flow direction of the actual flood mechanism is spatial heterogeneity to represent on quadrate grid system.
The flood modeling to conform to real situation has two components, input dataset and flow distribution algorithm. This mechanism of flow distribution can be modeled by using distributed hydrological modelling, to require the spatial input data. The ground truth dataset, rainfall, elevation and land use, is normally observed based on point data, are specific in some convenient area.
Nevertheless, we should be aware of how well the inundation area on a satellite image corresponds to actual flood-affected area. Therefore, hydrological modelling is expected to be used to complement remote sensing. If models can be used to identify the extent of flooded areas on a near real-time basis, the information can be useful for disaster managers to estimate the severity of the damage and to prioritize regions for effective rescue work.
In this study, we applied 2D rainfall-runoff-inundation model (RRI model) (Sayama et al. 2010) to simulate the flood in the Bago River basin, Myanmar. The model performance was investigated compared with the satellite product flood map and simulated flood inundation area during 2015 when the serious floods were occurred in the study area.
The objectives of this study are to investigate the rainfall-runoff-inundation model ability over the Bago River basin and to set the model parameters of the study basin for future flood prediction.
The Bago River Basin covering 91% of the Bago district with an area of 5348 km2. The basin area lies within latitudes 16° 43? 15?N and 18° 26? 17? N, and longitudes 95° 53? 30? E and 96° 43? 30? E. Bago River originates from the middle mountainous region named Bago Yoma and the large portion of the river itself is within the Bago township of Bago Region.
The main river is about 331.5 km long.The Bago River is one of the most important and useful river basin in lower Myanmar for hydropower generation, irrigation use, fisheries and navigation use (citation). The location map of the Bago River Basin is shown in Figure 1. The upper part of the basin is hilly region and lower part is nearly flat plain.
Figure 1. Location of Study area
Please Rainfall-Runoff-Inundation (RRI) model is developed by International Center for Water Hazard and Risk Management (ICHRM) and distributed hydrological model. This model is a two-dimensional model capable of simulating rainfall-runoff and flood inundation simultaneously (Sayama et al. 2010).
The model deals with slopes and river channels separately. At a grid cell in which a river channel is located, the model assumes that both slope and river are positioned within the same grid cell. The channel is discretized as a single line along its centerline of the overlying slope grid cell.
The flow on the slope grid cells is calculated with the 2D diffusive wave model, while the channel flow is calculated with the 1D diffusive wave model. For better representations of rainfall-runoff-inundation processes, the RRI model simulates also lateral subsurface flow, vertical infiltration flow and surface flow.
The lateral subsurface flow, which is typically more important in mountainous regions, is treated in terms of the discharge-hydraulic gradient relationship, which takes into account both saturated subsurface and surface flows. On the other hand, the vertical infiltration flow is estimated by using the Green-Ampt model.
The flow interaction between the river channel and slope is estimated based on different overflowing formulae, depending on water-level and levee-height conditions (Sayama et al. 2012). Figure 2 shows the schematic diagram of RRI model.
The model simulation was conducted from June to September in 2015, when the serious flood events were occurred in the lower part of the Bago River basin.
As the model was being set up, Digital Elevation Model (DEM) in Figure 1, flow direction in Figure 3(a) and flow accumulation in Figure 3(b) were delineated from HydroSHEDS 15 s (about 450 m) to be upscaled to 30 s (about 900 m) resolution.
To set the target river basin and channel, the river outlet was located in the most downstream of the Bago River. In this study, river channel locations and river width and depth were confirmed by checking with the some measured cross-sections of the river.
Figure 2. Schematic diagram of Rainfall-Runoff-Inundation (RRI) Model (Sayama et al. 2012)
(a) (b)
Figure 3. Flow direction and flow accumulation of Bago River basin
Ground gauged rainfall records provided by the Department of Meteorology and Hydrology (DMH) were used for the simulation. Daily gauged rainfall records at 14 locations were applied to cover the whole basin area. In the model, Thiessen polygon method is employed to convert point rainfall (station rainfall) into two-dimensional rainfall distribution.
The important model parameters of RRI model are roughness coefficient in river basin(ns-slope), roughness coefficient of river channel(nr-river), properties on vertical and lateral infiltrations (ksv,ka), depth of soil layer (soil depth) and depth and width of river. According to the suggestions of previous model application studies, ns-slope value (0.3 and 0.1 m-1/3 s) and ns-river value (0.02 to 0.04 m-1/3 s) were estimated in this study.
The model parameters for surface and subsurface were set by considering the vertical infiltration and overland flow due to excess of infiltration for the relatively flat regions in the downstream of the basin and the saturated subsurface flow and overland flow due to excess of saturation for the mountainous regions in upstream of the basin.
To simplify the land use data, land use data collected from GLCC (Global Land Cover Characterization) was grouped into six categories, such as forest, shrub, crop land. bare land, urban area and water body. Two different soil parameters were set for the model simulation. Figure 4 (a) and (b) shows the land use and soil type of the study basin.
The RRI model simulation was conducted for the period of 1 July to 31 August in 2015. The model was calibrated discharge and inundation areas by using the observed data from DMH and the UNOSAT satellite product.
Model validation is the process of testing the model ability to simulate observed data, other than those used for the calibration, within acceptable accuracy. During this process, calibrated model parameter values are kept constant. The quantitative measure of the match is again the degree of variation between computed and observed hydrographs. The model are validated for two different flood events in 2016 and 2017.
Figure 4.Land use and soil type of Bago River basin
In this study, during both calibration and validation periods, the goodness-of fit between the simulated and measured runoff was evaluated using the Nash-Sutcliffe coefficient efficiency (NSE).
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Where NSE= Nash and Sutcliffe efficiency, Qobs,i = observed discharge; Qsim,i = simulated discharge; Qobs =mean of the observed discharge; and n = number of observed or simulated data points (Zin et al., 2015).
During the rainy season, flood disaster also often occurs almost every year in the Bago River basin. Big scale of flood happened on 2011 and 2015 in this river basin. During these events, almost all the rivers in Myanmar flooded and massive property losses and a few human fatalities were caused.
RRI Model calibration is the adjustment of the model parameter within the recommended ranges. It optimizes the agreement between the measured data and the simulated results from the model .
Table 1 shows the calibrated parameters of the RRI model for Bago River basin and Figure 5 shows the observed and simulated hydrographs after the calibration process for 2015 flood event at Bago DMH station, although NSE was 0.94, indicates that there was closely related between observed and simulated discharge.
The simulation flood area for August 2015 was validated with flood area vector data which is derived from Radarsat-2 image acquired on 09 August 2015 and analyzed by UNITAR/UNOSAT. In this validation, the inundation area simulated by the model and peak flood area by satellite product for the 2015 flood event are 149 km2 and 114 km2 respectively shown in Table 2.
Table SEQ Table * ARABIC 1. Calibrated parameters of the RRI
Parameters Mountains Plains ns-slope (m-1/3 s) 0.3 0.1 ns-river (m-1/3 s) 0.025 0.025 ka(ms-1 ) 0.01 – ksv(ms-1 ) – 1.67×10-7 Sf(m) 0.208 0.3163
Figure 5. Comparison of observed and simulated discharges for calibration process in 2015
Table 2. Comparision of simulated inundation flood area for 2015 August flood event
Simulated area by the model Flood area observed by Unosat analysis Overestimated
Flood area above 2m (sqkm) 149.04 114.86 34.18
In this study, RRI model was validated by using 2016 and 2017 flood events. Figure 6 and 7 show the comparison of observed and simulated discharge for those years using data from the Bago DMH station. The NSE for these events were 0.95 and 0.94 respectively.
Figure 6. Validation result for 2016 flood event at Bago station
Figure 7. Validation result for 2017 flood event at Bago station
In 2016 and 2017 flood events, the observed peak discharges were less than the simulated peak discharges. It meant that the model simulated results were overestimated but NSE vale of 0.95 in 2016 flood and 0.94 in 2017 flood showed a good relation between the observed and simulated discharges.
In this study, a RRI Model was applied to simulate the rainfall-runoff and the inundation area during the serious flood event. The main focus was that how well the simulated flood peak agrees with the observed one. The results showed reliable estimates of flow with high Nash-Sutcliffe model efficiency (NSE) during both the calibration and validation periods.
As a result of this study, the RRI model performed well in predicting flood for the study area and the results were acceptable. So the model parameters can be initially set up in the river basin that is similar to the Bago River basin. RRI model is recommended to practice flood forecasting which predicts when and how much water level rise up for the target areas.
For the further study, the simulation should be improved if the operation of flood control facilities such as dam and weir are considered in the study basin and the actual river channel cross-section data should be applied to get the good model performance. Well distribution of rainfall stations and high-resolution elevation data may help the improvement of the RRI model simulation.
This research was supported by the Japan Science and Technology Agency (JST)/Japan International Cooperation Agency (JICA), and the Science and Technology Research Partnership for Sustainable Development Program (SATREPS).
I would like to express our gratitude to the Irrigation and Water Utilization Department (IWUMD) and the Department of Meteorology and Hydrology (DMH) for providing us the relevant data.
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