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MINISTRY OF EDUCATION MINISTRY OF AGRICULTURE AND TRAINING AND RURAL DEVELOPMENT THUY LOI UNIVERSITY NGUYEN VAN HIEU THE STUDY TO IMPROVE THE QUALITY OF RAIN AND FLOOD FORECAST IN ORDER TO SERVICE THE OPERATION OF RESERVOIRS IN THE BA RIVER BASIN Specialization: Hydrology Code number: 44 02 24 SUMMARY OF DOCTORAL DISSERTATION HANOI, 2020 This scientific work has been accomplished at Thuy loi University Supervisor: Prof Dr Vu Minh Cat Reviewer No 1: Assoc Prof Dr Nguyen Tien Giang Reviewer No 2: Dr Nguyen Tien Thanh Reviewer No 3: Dr Vo Van Hoa This Doctoral dissertation will be defended at …………… ……… on date ………………………………………………………………… This dissertation is available at: - The National Library; - The Library of Thuyloi University INTRODUCTION Rationale of the research The Ba river system is one of major river systems in Vietnam Currently, there are large reservoirs to build in the river basin namely Ayun Ha, An Khe - Ka Nak, Krong HNang, Song Hinh and Ba Ha with a total flood control capacity of 260.5x106 m3 – a rather small volume in comparion with the largest total 5-day flood vulume of 2,507.3x 106 m3 occurred in Cung Son [1] Therefore, if accurate forecast of rainfall and flood flowing into these reservoirs with a longer expected time will create favorable conditions for the reservoir’s owners to operate proactively, avoiding the phenomenon of "shock release" to the downstream (increased discharge flow suddenly from m3 to the maximum flow value to the downstream, causing inundation and damage to people and properties downstream Therefore, the study to improve the quality of rain and flood forecast in order to service the operation of reservoirs in the Ba river basin" is necessary and urgent issues for that we can find scientific arguments and methods to improve the quality of medium-term forecast of rainfall and flood to actively operate reservoirs according to the inter-reservoir process in the Ba river basin Research objectives and new reserch contributions 2.1 Objectives of the study - Research to select and use appropriate numerical models for forecasting rainfall with an expected time of 72 to 120 hours - Research to select and us suitable hydrological models for forecast floods go into reservoirs in the Ba river basin for operating inter-lake systems under Decision 878/QD-TTG of Prime Minister dated in July 18, 2018 2.2 New research contributions - Through the research the meteorological models of IFS and WRFARW are selected to forecast quantitatively rainfall with extension of the expected time from the current 48 hours to from 72 hours to 120 hours with magnetic resolution of (14 x14) km to (5x5) km and MIKE-NAM and HEC-HMS hydrological models to forecast the flow goes into the reservoirs from 03 to 05 days in advance for Ba river basin - the successful application of the numerical rainfall models as well as the hydrological models forecast flood goes into 04 reservoirs in the basin, serving the reservoir operation in the Ba river basin Scope and subject of the study 3.1 Research scope In space: forecast flood flows goes into 04 reservoirs with a capacity of over 100 million m3 in the Ba River basin including Song Hinh, Krong H’nang, Ayun Ha and An Khe reservoirs In time: Increase the expected time of the forecast from the current 24 - 48 hours to 72 to 120 hours 3.2 Research subjects The factors that cause to create rainfall and flood flow go into 04 reservoirs in the Ba river basin are subjected to this study Scientific and practical significances 4.1 Scientific significance The selection, testing and verifying and application of IFS and WRFARW meteorological numerical models to forecast rainfall with reducing a resolution of (14 x14) km to (5x5) km in the Ba River basin, for that it can be improving the accuracy of the forecast results and extending the expected time from the current 48 hours to 72 hours to 120 hours The set of the hydrological models of MIKE-NAM and HEC-HMS has been applied for testing, combining with analysis and adjustment of affected factors such as topography, geology, soil, and surface covers in order to quantitatively forecast the inflows to the reservoirs for serving the operation of reservoirs and prevent and mitigate flooding for the downstream Ba river basin 4.2 Practical significance Establish a flood forecasting tools with high accuracy and extended forecasting time from the existing 48 hours to 120 hours, which can be used practically to improve the forecasting accuracy and extend the time expected from the current 48 hours up to 72 hours to 120 hours and increased resolution domain in rain forecasts as well as making the reservoir operation more flexibly and also it hepls to have more options to achieve the highest efficiency, ensuring reservoir safety, minimizing floods and inundation for downstream areas The medium-term rainfall and flood forecasting methods in the Ba River basin can be used as a reference’s materials for students and postgraduate students The structure of the doctoral thesis In addition to the introduction, conclusion, list of references, appendices, the thesis is structured into 03 chapters: Chapter 1: Reserch Overview on medium-term rainfall and flood forecasting Chapter 2: Building scientific basis for medium-tearm rainfall and flood forecast in Ba river basin Chapter 3: results and discussions CHAPTER OVERVIEW OF RESEARCH ON MEDIUM-TERM RAINFALL AND FLOOD FORECASTING 1.1 Research Overview on rainfall and flood in the world Currently, medium-term forecasting is an integral part of all major forecasting centers in the world Most approaches to medium-term forecasting are based on a combination of predictive methods in order to capture the sources of uncertainty caused by the original field In 1992, the European Center for Medium-term Forecasting, EPS also used the SV (Singular Vector) method to create initial disturbances (Palmer et al., 1992) [4] This EPS now has 51 component forecasts, makes daily forecasts and provides results to countries in the European Communities that are members of the ECMWF This EPS system is called VAREPS (Variable Resolution EPS) for 15 days forecast, in which the first days the system runs with TL399L62 resolution (about 50 km, 62 layers) and for the last days with TL255L62 resolution (approximately 80 km, 62 layers) This is the medium-term model with highest resolution EPS currently in the world At NCEP, the GFS model is run times a day with every 06 hours with selected resolution options of 0.25x0.25 degrees, 0.5x0.5 degrees and 1.0x1.0 longitude and latitude The forecast duration of the model is up to 384 hours (15 days), that can completely meet the requirement for getting forecast rainfall that is served as an input for the current hydrological models In a study by F Pappenberger et al., in 2008, the LISFLOOD model was also used to study the 2007 flood event in Romania, using the input of rainfall forecast from well known centres in the world like ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM The results from these centers could give alert of the flood with days in advance The results also show that the combined prediction based on the multi-model approach implemented by ECMWF and UKMO had the best average characteristic of, the simulation, especially at the tail of the distribution function, i.e the extreme value can be occured V Triemig et al (2015) used a combined flood forecasting system (AFFS) for medium to large sized river basins in Africa with a 15-day forecast duration The main component of the forecasting system is the LISFLOOD distributed hydrological model with GIS data and forecasted rainfall data from the ECMWF center The flood event in March 2003 at the Sabi River basin (Zimbabwe) was selected to simulate, where 36 monitoring points having observed data for cpmparison The AFFS verification process has achieved good results (estimated to reach over 70%) Especially, the system has good flood forecasting results with a longer than week) and basins with large areas (over 10,000 km2) It can be seen that the application of quantitatively forecast rainfall done by ECMWF for medium term flood forecast will give satisfactory results 1.2 Overview of research on of medium-term rain and flood forecasting in Vietnam and Ba river basin Currently, the meteorological HRM, COSMO and WRF models are being applicable to run professionally at the National Meteo-hydrological Forecast Center (VNNMHFC), while MM5 and WRF models have been used to forecast rainfall at the Institute of Science and Technology of Climate Change and RAMS model is used by the faculty for Meteo-hydrology and Oceanography, Hanoi Natural Sciences University In my dissertation, the WRF model that is being run professionally at VNNMHFC is selected as the following reasons: + NWP is regional and hydrostatic model that is used to run professionally with 5km resolution + WRF has a 3DVAR data synchronization system integrated coupling into the model and it can assimilate a variety of remote sensing data such as satellites and radars, … + WRF has been evaluated and verified by many scientific studies and has been assessed as good rainfall forecast model for the central region of Vietnam (Bui Minh Tang et al., 2014) The analytical and prediction field of ICMWF's global NWP model IFS of 14km resolution was chosen to be as the initial condition and boundary conditions depending on the time of WRF model with 5km resolution IFS model was chosen as this is the best-rated global NWP model in the world at present and also the highest resolution global model currently (Vo Van Hoa et al., 2017) Figure 1.1 below shows the diagram and briefly introduces the methods and models used in the study for each specific objective The Ba River basin is a large river in the Central part and Highlands of Viet Nam The density of the monitoring network is sparse, so the rainfall forecasting technology used must be quantitative, objective and highly resolution, coupled with modern flood forecat tools In other words, the research direction applies highly-resolution NWP models, hydrostatic-type Fig 1.1: The diagram of research approach with horizontal resolution of about km to be able to capture small and medium-scale convection processes well Therefore, the application of the NWP models is the most suitable option for heavy rainfall forecasting in the Ba river basin in which the horizontal resolution ranges within 5km Therefore, in this study, IFS and WRF-ARW models will be chosen to forecast rainfall for Ba river basin CHAPTER SCIENTIFIC BASIS FOR MEDIUM-TERM RAINFALL AND FLOOD FORECAST IN BA RIVER BASIN 2.1 Overview of the Ba river basin The Ba River basin is one of the largest basins in the Central region Vietnam, with a total catchment area of 13,417 km2 The length of the main river is of 374 km, originating from the Ngoc Ro mountain range (Kon Tum province) at an elevation of 1,549 m, flowing through the territory of Kon Tum and Gia Lai provinces in the North-South direction, turning to the Northwest Southeast from Krong Pa district (Gia Lai province) and West-East from the territory of Phu Yen province and finally going to the East Sea at the Da Rang estuary in Tuy Hoa city In this river basin with high potential water resources in combination with steep slope, so the potential of hydropower on the mainstream and its tributaries is huge According to the hydropower planning, there are expected 12 hydropower projects Fig 2.1: Map of the Ba River basin namely An Khe-Ka Nak, DakSong, Ba Thuong River, Ayun Thuong 1, 2, HChan, HMun, Ayun Ha, Ea KRong Hnang, Ba Ba Ha, Hinh will be built with a total installed capacity of 700 MW and an annual electricity of 2.6 billion KWh 2.2 Establish rainfall forecast method in Ba river basin 2.2.1 Rainfall forecast numerical models for Ba River basin The WRFARW model is used to forecast rainfall for the entire area (12ºN15ºN; 107ºE-110ºE) with a surface resolution of 5km corresponding to 90 x 90 grid nodes on the ground and 50 vertical levels This model is run in a hydrostatic form with a time step of 20 seconds as selected resolution of less than km The input data of WRFARW model of 5km resolution is taken from IFS model with 14km resolution The running procedure of WRFARW model for a single forecast is as follows: Step Download the IFS data including the analytical and forecast fields (every hours) until the 3-day term forecast Step Decode the appropriate data domain to get enough input data Step Interpolation of IFS data from 14km resolution to 5km resolution Step Vertical interpolation from 26 pressure levels of IFS data to 50 vertical levels of WRFARW model Step Set running duration and the route of input data for WRFARW model Step Run WRFARW model Step Decode the forecast rainfall data from WRFARW model and interpolate the forecast rainfall data to the area with the observation stations according to the nearest interpolation method 2.2.2 Corrective method to determine forecast rainfall values The research approach is determined by the following steps: - Step 1: Assess the rainfall forecast results - Step 2: Set the corrective coefficient and adjust the forecasted rainfall - Step 3: Set the adjusted data as input to the hydrological modeling system Based on the forecast rainfall and cumulative observed rainfall at monitoring stations, a regression equation between them for each forecast period must be developed With the forecast period from to 240 hours, the 40 cumulative rainfall periods of hours are corrected Thus, for each prediction period, linear regression equations are constructed separately Based on comparison between simulation flow and observed measurement at the station to adjust and find the optimal parameter set on each sub-basin to ensure sufficient reliability CONCLUSION OF CHAPTER In order to forecast medium-term flood, the study will conduct adjustment and testing separately for each sub-basin with two models, namely MIKE NAM model and HEC-HMS model, in order to identify the optimal parameters and ensure to ensure good simulation of the process of precipitation flow in each sub-basin The basic input for the flood forecasting model is the projected rain value on the grid cells The study considers each grid cell to be a part of the catchment area that rainfall is controlling, thereby calculating the weight for each grid cell and finally, the average rainfall of the basin by the weighted average method The flood forecast results from the forecast rain data will be assessed for errors, verification criteria, forecast quality and practical application recommendations CHAPTER 3: RESULTS AND DISCUSSION 3.1 Rainfall forecast results in Ba river basin 3.1.1 Forecasting results from the model The forecast rainfall of the two models is selevted from historical flood events occurred at the river basin and shown in Table 3.2 With rain_wrfarw data, the grid size (5x5km) is equivalent to (90 x 90) cells, while rain_ifs data with the size (14x14km) km are (39x39) cells covers the entire Ba river basin Tab 3.2 Estimated rainfall time using IFS and WRFARW models Year Time period 2017 01-06/12 02/12/17 2016 1-5/11 15-18/12 2015 7-12/10 11 2014 27/11-1/12 2013 3-8/11 2012 3-7/10 Rainfall forecast results for the Ba River basin of the two models will cover the entire basin with integral areas of 12ºN-15ºN and 107ºE-110ºE and total forcast time for IFS model is 120 hours and it is only 72 hours for WRFARW model Format of these data is shown in figure 3.9-3.10 Fig 3.9 Format of forecasted rain Fig 3.10 Format of forecasted rain data data from IFS (39x39) grid pattern from WRFARW (90x90) grid pattern 3.1.2 Assess the forecasting skills of the model To evaluate the rainfall forecasting skill for WRFARW model in the central region, heavy rains from 2013 to 2017 are selected A total of more than 50 samples were collected for evaluated purposes On average, each heavy rain lasts for days with the average total rainfall of about 250-300mm To assess whether the dynamic downscaling approach based on the WRFARW model is really effective, the following assessments are also made for rainfall forecasts from the IFS model Rainfall forecast values from IFS and WRFARW models are interpolated to the location of the monitoring station in the study area according to the nearest interpolation method (the rain forecast value at the station will be the projected valuem, showing rainfall at the nearest grid point to the station) In the study basin, a total of 12 rainfall observed stations and rainfall results are compared with these stations The accumulated 24h rainfall and threshold values of 50mm in 24h and 100mm in 24h rain were used to evaluate forecasting skills between IFS and WRF models The 24, 48, and 72 h forecast durations are put into evaluation, in which the accumulated rainfall from - 24 h is called the first day of rain 12 forecast, from 24 - 48 h is called the second day and 48 - 72 h is called the 3rd day, Specifically, to evaluate rainfall prediction skills, the average errors (ME), absolute errors (MAE) and the square errors (RMSE) were used to assess To assess the skills to forecast heavy and very large rainfall, indicators of BIAS, POD, FAR and ETS are also used The BIAS indicates whether the bias is high (BIAS> 1) or low (BIAS 50mm /24h) IFS Model (14x14) km WRF Model (5x5)km Rainfall forecast BIAS POD FAR ETS BIAS POD FAR ETS Day 1st Day 2nd 0.33 0.15 0.42 0.28 0.32 0.49 0.21 0.10 0.65 0.38 0.75 0.56 0.25 0.33 0.34 0.28 Day 3th -0.08 0.13 0.62 0.09 0.24 0.34 0.42 0.19 Tab 3.6 Results of evaluation and comparison of rain forecast quality for IFS model (14km) and WRF model (5km) with very heavy rain threshold (> 100mm /24h) Rainfall IFS Model (14x14) km WRF Model (5x5)km forecast BIAS POD FAR ETS BIAS POD FAR ETS Day 1st Day 2nd 0.21 -0.09 0.36 0.22 0.46 0.59 0.13 0.08 0.46 0.30 0.45 0.38 0.22 0.41 0.28 0.20 Day 3th -0.24 0.09 0.72 0.02 0.21 0.20 0.48 0.15 3.1.3 Correct and compute the forecasted rainfall value The steps for making corrections as well as methods for re-calculating the forecasted rainfall values are presented in chapter Based on the forecasted results and cumulative rainfall at monitoring stations, the thesis has developed a regression equation for each forecast period With the forecasted time from to 240 hours, equivalent to 40 periods of cumulative rainfall of hours is adjusted Thus, for each prediction period, linear regression equations are constructed separately 3.1.3.1 Assessing rainfall forecast results that has not yet adjusted the statistics In Figure 3.3 presents the calculated results of ME (mm) between forecast rainfall and actual observation data at some stations in the study area It is found that the forecast rainfall value from models has a tendency to lower than that in observed cases with the popular ME index from -4.85mm to about 0mm In particular, the forecast error is most obvious at An Khe and 14 Buon Ho stations, with the popular ME index less than -2mm, especially at An Khe station In contrast, the predicted error is rather small at Kon Tum and Pleiku stations, with the common ME index ranging from -1mm to less than 1mm In addition, the forecast results also show that the forecast error is larger in the short prediction period and for the longer the forecast period, it seems the better quality Fig 3.17 ME index of accumulated rainfall forecast for hours with observed data (mm) Fig 3.18 Correlation coefficient (FA) between forecast rainfall and observed data In Figure 3.18, correlation coefficients between rainfall forecast and observed data are presented The results showed that the correlation coefficients were positive in most cases This shows that the forecast results partly indicate the actual trend of rainfall However, the correlation coefficient between forecast and observed rainfall is quite low In particular, the highest correlation coefficient for the forecast period of less than and equal to 78 hours is about value of 0.2 In contrast, when forecast time of greater than 78h, the correlation coefficient is very low, around value of 0.1 From the above analysis results, it can be found that the forecast models get fit in term of time series with observed ones, but having large difference in peak value of rainfall when forecast duration is less than 78 hours Conversely, the model does not forecast well when forecast time is increased with greter than 78 hrs (from 78 to 240 hours) in term of time series, but lower errors in term of peak value 3.1.3.2 Assessment of forecast quality after statistical adjustment Based on the univariate linear regression method, the thesis has adjusted rainfall for each forecasting period Rainfall forecast value is adjusted based on the coefficients "a" and "b" of the regression equation 15 Corrected rainfall forecast results get rather low errors, with the common ME index ranging from -1mm to about mm In general, the adjusted rainfall forecast results tend to be lower than the actual observed data In which the forecast error is largest at stations An Khe and Buon Ho However, in comparison with uncorrected forecast rainfall, the revised forecast results have significantly lower errors The value of the correlation coefficient between forecast rainfall after being corrected with observed data shows that after adjustment, rainfall forecast is positively correlated with observed data in all forecasting cases It means that the forecast rainfall after adjustment reflect the good trend in comparison with actual observed data at stations The most significant high correlation coefficient of above 0.4 is found when forecast time is less than 78 hours When forecast time is greater than 78 hours, the correlation coefficient is gradually reduced, getting value of about 0.2 So it can be said that statistical adjustment helps to improve forecast quality with rainfall series is more consistent with reality 3.1.3.3 Forecast rainfall field for serving calculation The forecast rainfall results after corrected by linear regression are significantly improved in term of peak errors and good fit in term of time series It means that the forecast error of corrected rainfall is quite low, with the common ME index from -1mm to 0mm With the forecast time less than 78h, the correlation coefficient of the forecast has been corrected with observation is quite good with value of bigger than 0.4, some cases up to 0.8 (at Pleiku station at the time of forecast 18-24h and 42-48h) However, with a longer forecast period (over 78 hours), although the correlation coefficient has improved, but the value is still quite low, common in 0.2 Rainfall data after adjustment are used for further flood simulation In the study, it shows that in case of forecast time of less than 78 hrs the forecast time series value is a better fit with the actual data For hydrological simulation, coefficients a and b have been spatially interpolated for the study sub-basins 16 Tab 3.7 The results of calculating the dependent coefficient (a) and freedom (b) in the regression equation for predictive products by IFS model Value coefficients a and b of linear regression equation Forecast An Khe Buon Ho Kon tum Pleiku period 00-06h 06-12h 12-18h 18-24h 24-30h 30-36h 36-42h 42-48h 48-54h 54-60h : 234240h a 1.02 1.89 1.26 1.13 2.05 1.09 0.53 1.24 2.64 : b 4.01 0.82 4.03 3.04 1.52 2.14 4.73 2.98 0.6 2.24 : 0.96 3.06 a 0.05 0.28 0.95 -0.11 0.27 0.25 0.5 0.31 0.16 -0.04 : 0.07 b 1.86 1.38 4.57 2.75 1.46 1.51 5.31 2.1 1.67 1.84 : a 1.65 0.49 0.01 0.23 0.14 1.09 0.43 0.15 0.1 0.1 : 0.06 b 1.18 1.9 2.01 0.12 1.94 1.08 1.29 0.19 1.91 2.12 : a 0.82 0.96 0.55 1.33 0.43 1.07 0.19 0.81 0.21 -0.13 : b 0.61 0.68 0.92 -0.29 1.11 0.39 0.79 -0.27 1.28 1.75 : 0.01 Tab 3.8 The results of calculating dependency coefficients (a) and freedom (b) in the regression equation for forecast products by WRF model Value coefficients a and b of linear regression equation Forecast An Khe An Khe period 00-06h 06-12h 12-18h 18-24h 24-30h 30-36h 36-42h 42-48h 54-60h a 2.08 1.55 0.01 0.52 0.7 0.28 1.87 1.11 0.98 b 0.4 -0.21 4.18 5.05 2.72 3.25 1.27 2.98 0.35 a 0.08 0.51 -0.12 -0.24 0.01 0.42 0.31 -0.21 1.91 2.58 6.14 3.89 2.73 3.02 5.99 3.15 2.84 17 a 0.47 0.85 0.31 -0.02 1.56 0.2 0.44 0.16 0.53 1.75 0.77 2.07 0.54 1.28 2.43 2.36 0.73 1.88 a 0.49 0.31 0.53 0.11 -0.11 0.07 0.27 0.11 0.28 0.61 0.94 0.92 0.87 2.44 1.84 1.29 1.16 1.19 Fig 3.19 The ME index of the accumulated rainfall forecast for hours has been corrected with observed data (mm) Fig 3.20 The correlation coefficient between rainfall forecast has been adjusted statistically with observed data Thus, based on the method of calculating, evaluating and adjusting the rainfall forecast value by two models of IFS and WRF, it can say that the forecast rainfall value after adjustment is fitted with observed value This predicted rainfall value will be used as an input in the hydrological forecasting models in the Ba River basin 3.2 Flow forecast results in Ba river basin 3.2.1 Calibrating and verifying flood forecasting models As presented in Chapter 2, the measured rainfall data in the past with different flood peak levels is selected to calibrate and verify and find the model's parameter set for both NAM MIKE and HEC-HMS models for each sub-basin (An Khe, Ayun Ha, Krong Hnang and Hinh river) Tab 3.9 Table of Results of Mike NAM and HEC-HMS model parameters for An Khe and Ayun Ha sub-basins 18 Tab 3.10 Results of MIKE NAM and HEC-HMS model parameters for Krong Hnang sub-basin and Hinh river 3.2.2 Flow forecast results to the reservoirs in the Ba River basin 3.3.2.1 Setting forecast rain data as input for the hydrological models Forecasted rainfall values according to IFS and WRF models were determined in content 3.2 The predicted rainfall value has also been adjusted to match the actual figures and ensure reliability Each grid cell of the studied sub-basins has been identified for both (5x5) km and (14 x 14) km types as shown in Figures 3.45 - 3.46 and Tables 3.19 - 3.20 Fig 3.46 Location of grid cells with the size of 14x14km Fig 3.45 Locations of grid cells of size (5x5) km The amount of precipitation at the specific grid cells for each 04 basins of reservoirs will be detailed and converted to calculate the forecasted rainfall values as tables 23 and 3.24 19 Tab 3.19 Grid diagram of resolution (5x5) km of An Khe sub-basin Tab 3.20 Grid diagram resolution (5x5) km Ayun Ha basin + Weight of grid cells: Tab 3.23 Weighted values for grid cells (5x5) km in An Khe sub-basin Cell M10 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 W 0.004 0.007 0.012 0.012 0.017 0.020 0.012 0.004 0.013 0.027 0.008 Tab 3.24 Weighted values for grid cells (14x14) km in An Khe sub-basin Cell E1 E3 G4 G3 E4 E2 F1 F3 F5 F4 F2 W 0.007 0.035 0.161 0.063 0.052 0.039 0.007 0.244 0.001 0.252 0.139 3.3.2.2 Flood forecast results for the Ba River basin The expected rainfall with an estimated time of hours, used to forecast floods in the sub-basins are: 13/11/2013 06:00 to 22/11/2013 12:00; 27 / XI / 2014 06:00 to 05/11/2014 06:00; 07 / X / 2015 06:00 to 19 / X / 2015 00:00; 30 / X / 2016 06:00 to 11/11/2016 06:00 Calculated results by Mike-NAM model for 2013, 2015, 2015 and 2016 based on rainfall forecast data with grid cell value of (5x5) km and (14x14) km Fig 3.48 forecast flood process to An Khe Lake XI / 2014 Fig 3.47 forecast flood process to An Khe Lake XI / 2013 20 Fig 3.49 forecast flood process to An Khe Lake X / 2015 Fig 3.50 forecast flood process to An Khe Lake X / 2016 Results calculated by HEC-HMS model for 2013, 2015, 2015 and 2016 according to rainfall forecast data (5x5) km and (14x14) km Fig 3.51 forecast flood process to An Khe Lake XI / 2013 Fig 3.52 forecast flood process to An Khe Lake XI / 2014 Fig 3.53 forecast flood process to An Khe Lake X / 2015 Fig 3.54 forecast flood process to An Khe Lake X / 2016 + Evaluation of flood forecast errors for the two models MIKE NAM and HEC-HMS is shown in Table 3.31 and Table 3.32 21 Tab 3.31 Criteria for evaluating flood forecasting quality according to MIKE NAM model for sub-basins on Ba river Tab 3.32 Criteria for evaluating flood forecast quality according to HECHMS model for sub-basins on Ba river The medium term flood forecast by MIKE NAM model based on the rainfall forecast in grid size of (5x5) km and (14x14) km has good results with satisfactory level and small difference Similarly, the results of medium-term flood forecasting by HEC-HMS model based on rainfall forecasted in grid size of (5x5) km and (14x14) km have good results Although the guarantee level is not met, the index is also approximately satisfactory level and small error 22 CONCLUSION OF CHAPTER The rainfall forecast results, corrected by linear regression, have significantly improved errors and fitted in time series values compared to uncalibrated results In particular, the error of corrected forecast rainfall is quite low, with the common ME index from -1mm to mm With the forecast time less than 78h, the correlation coefficient between simulated and observed data is quite good, commonly on 0.4, some cases up to 0.8 However, when forecast period is greater than 78 hours, although the correlation coefficient has improved, but the value is still quite low, common around 0.2 Corrected rainfall data will be used for further flood simulation In particular, the forecast results with the time less than 78h have a better fit on the trend of the actual data For hydrological calculations, the coefficients a and b have been spatially interpolated for the stations namely An Khe, Yaun Ha, Krong H’Nang and Hinh river sub-basins On the basis of the optimal parameter set, combined with the forecasted rain correction results under the two grid size options of 5km and 14km The results show that the ability to apply both MIKE NAM and HEC-HMS models to forecast moderate rainfall in grid cell sizes is quite good according to Nash criteria and flood peak error Proposal to use flood forecasting model as follows: Using rain forecast model with grid size (5x5) km as input data for flood forecasting model will give better results than rain forecast data grid size (14x14) km Applying NAM MIKE model to forecast flood will be best for Yaun Ha and An Khe sub-basins CONCLUSIONS The main contents conducted in the study: Overview on the study situation of rainfall and flood forecasting in the world as well as in Vietnam Correction and construction of quantitative forecast rain field after model Establishing a high-resolution quantitatively rainfall forecast problem in the Ba river basin by using the IFS and WRFARW meteorological models with 23 the resolution from (14 x14) km to the resolution (5x5) km to be expanded Estimated time from the current 48 hours up to 120 hours Integrated high-resolution quantitatively rainfall forecasts by meteorological IFS and WRFARW models with MIKE-NAM and HEC-HMS hydrological models to forecast medium term flow with analysis and adjustment of local factors such as topography, geology, soil and mulching to quantify the forecast of the flow to the reservoirs to serve the operation of reservoirs, preventing and mitigating flooding for downstream basins Ba river New research contributions - Studying and building a scientific basis for selecting IFS and WRFARW meteorological models to forecast rainfall with expected duration of the current 48 hours up to 72 hours to 120 hours with (14 x14) km to (5x5) km resolution in Ba River basin and MIKE-NAM and HEC-HMS hydrogeological models to forecast the flow to the reservoirs in advance from 03 to 05 days - Successfully integrating the meteorological rainfall and hydrological modeling set to test flood forecasts for 04 reservoirs in the basin, serving the management of reservoirs in the Ba river basin Future study of the doctoral thesis In this study, the usage models have grid cells from (14x14) km and (5x5) km, while the actual terrain is quite fragmented, so the averaged level both climatic and ground surface conditions is quite too big, reflecting poor real surface conditions, causing large errors, leading to unpredictable results To solve this problem, NCS proposed to increase the cell resolution to (1x1) km, which will better reflect the ground surface condition and will certainly give better results This proposal also means that the computing activities has increased quite a lot, but nowadays, the calculation tools are becoming more and more modern, the calculation speed will increase a lot and technological barriers are not a big problem in computtaion With this approach, forecasting the flow to the reservoirs will increase the expected time and facilitate active and better operation 24 LIST OF PUBLISHED ARTICLES RELATED Nguyen Van Hieu, Can Thu Van and Vu Minh Cat, 2018: Application of Hydrological Model to Simulate Rainfall-Runoff into An Khe Reservoir in the Ba River Basin, Vietnam, Journal of Environmental Science and Engineering, A7, pg 101-107 Nguyen Van Hieu, 2018: Study to establish a network of rainfall stations in the basin by the method of Kriging on the Ba river basin, Vietnam Journal of Hydrometeorology, 687, pg 42-52 Nguyen Van Hieu, Bui Minh Tang, Bui Duc Long, Vu Duc Long, 2014: Use high-resolution quantitative forecasts to improve the flood forecasting quality in the Central and Central Highlands regions, Vietnam Journal of Hydrometeorology, 644, pg 32-38 Dang Thanh Mai, Vu Duc Long, Nguyen Van Hieu, 2013: Integrating hydrological, hydraulic models, regulating reservoirs in flood and flood forecasting for Ba river system, Vietnam Journal of Hydrometeorology, 629, pg 37-43 Nguyen Van Hieu, Du Duc Tien, Nguyen Manh Linh, 2018: The study evaluated the possibility of forecasting heavy rainfall of WRF model combined with assimilation of 3DVAR data in the central region of Vietnam, Annual Science Conference Thuy Loi University, 608, pg 130133 ... basin CHAPTER SCIENTIFIC BASIS FOR MEDIUM-TERM RAINFALL AND FLOOD FORECAST IN BA RIVER BASIN 2.1 Overview of the Ba river basin The Ba River basin is one of the largest basins in the Central region... hydropower projects Fig 2.1: Map of the Ba River basin namely An Khe-Ka Nak, DakSong, Ba Thuong River, Ayun Thuong 1, 2, HChan, HMun, Ayun Ha, Ea KRong Hnang, Ba Ba Ha, Hinh will be built with a total... for sub-basins on Ba river Tab 3.32 Criteria for evaluating flood forecast quality according to HECHMS model for sub-basins on Ba river The medium term flood forecast by MIKE NAM model based on