Assessing Flood Risk of the Chao Phraya River Basin Based on Statistical Rainfall Analysis

Paper: Assessing Flood Risk of the Chao Phraya River Basin Based on Statistical Rainfall Analysis

Shakti P. C.∗1,†, Mamoru Miyamoto∗2, Ryohei Misumi∗1,YousukeNakamura∗3, Anurak Sriariyawat∗4, Supattra Visessri∗4,∗5, and Daiki Kakinuma∗2

∗1National Research Institute for Earth Science and Disaster Resilience (NIED) 3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan †Corresponding author, E-mail: [email protected] ∗2International Centre for Water Hazard and Risk Management under the auspices of UNESCO (ICHARM), Public Works Research Institute (PWRI), Ibaraki, Japan ∗3Mitsui Consultants Co., Ltd., Tokyo, Japan ∗4Department of Water Resources Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand ∗5Disaster and Risk Management Information Systems Research Group, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand [Received June 25, 2020; accepted September 7, 2020]

The Chao Phraya River Basin is one of the largest in water-related ones most commonly cause severe damage Asia and is highly vulnerable to water-related disas- to industries, property, and infrastructure, as well as loss ters. Based on rainfall gauge data over 36 years (1981– of lives almost on an annual basis worldwide [2]. Hence, 2016), a frequency analysis was performed for this numerous projects have focused on studying water-related basin to understand and evaluate its overall flood risk; disasters globally to address such issues. One of the most daily rainfall measurements of 119 rain gauge stations significant contributions of the private sector to disaster within the basin were considered. Four common prob- risk management is the Business Continuity Plan (BCP) ability distributions, i.e., Log-Normal (LOG), Gum- and Business Continuity Management (BCM) system, bel type-I (GUM), Pearson type-III (PE3), and Log- which were standardized as ISO22301 and disseminated Pearson type-III (LP3) distributions, were used to cal- across numerous business enterprises worldwide [3]. Re- culate the return period of rainfall at each station and cently, a major project on Regional Resilience Enhance- at the basin-scale level. Results of each distribution ment was launched by establishing the Area-BCM at in- were compared with the graphical Gringorten method dustrial complexes across Thailand, aiming to enhance re- to analyze their performance; GUM was found to be gional resilience by visualizing disaster risks through col- the best-fitted distribution among the four. Thereafter, laboration of industry, government, and academia. The design hyetographs were developed by integrating the project is mainly focused on Thailand and is under the return period of rainfall based on three adopted meth- Science and Technology Research Partnership for Sus- ods at basin and subbasin scales; each method had its tainable Development (SATREPS), a Japanese govern- pros and cons for hydrological applications. Finally, ment program that promotes international joint research. utilizing a Rainfall-Runoff-Inundation (RRI) model, The program is structured as a collaboration between the we estimated the possible flood inundation extent and Japan Science and Technology Agency (JST) [4]. Sev- depth, which was outlined over the Chao Phraya River eral components are to be integrated to build a resilient re- Basin using the design hyetographs with different regional community against disasters by visualizing disaster turn periods. This study can help enhance disaster risks and introduction of the Area-BCM. A hydrological resilience at industrial complexes in Thailand for sus- investigation dealing with disaster-risk is a key compo- tainable growth. nent of the project to highlight possible hydrological risk in the industrialized urban areas of Thailand. A basic approach in assessing hydrological risk and sci- Keywords: probability distribution, return period of rain- entifically evaluating a river basin is the collection and fall, design hyetograph, flood inundation, Chao Phraya analysis of hydrometeorological data. Precipitation is Basin a major component, which includes rain, snow, drizzle, and sleet. In the warm climate region, precipitation in the form of rain is common. Precipitation studies can 1. Introduction play a vital role in dealing with hydrological cycles in nature; the practice has been adopted globally. For in- Natural disasters have threatened human life and instance, analyzing rainfall data over a long-term could be a frastructure globally, and urbanized regions are espe- good reference for various hydrological analyses and wa- cially vulnerable [1]. Among the several natural disasters, ter resource-based planning, which has been highlighted

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in several studies [5–7]. If looked into greater detail, a fall as the input for the hydrological model. frequency analysis of historical extreme rainfall events For a comprehensive hydrological analysis of rainfall can help calculate their frequency, as well as in predicting over a river basin focusing on historical data, we selected floods. Therefore, policy makers, engineers, and planners the Chao Phraya River Basin, one of the larger basins would be interested in understanding the probabilities of in Asia, which is considered an important river basin in occurrence of future events for various return periods. Thailand for several reasons [13]. Flooding is quite com- In general, long-term historical data sets have been an- mon in this basin, which always causes significant eco- alyzed using various statistical methods for this purpose. nomic losses. The rapid urbanization, industrialization, However, availability of such data may vary from place to and intensification of agricultural practices are quite com- place because of several factors such as the physiographic mon around the Chao Phraya River. The severe flood- condition, setting up of rain gauges, and climate; hence, ing that occurred during the 2011 monsoon season in- rainfall data over 30 years are believed to be reliable for undated large parts of Bangkok, causing 815 deaths and a frequency analysis of any river basin. Spatial distribu- over $45 billion in economic damages [14]. The Thai tion of rainfall pattern may vary greatly at any location. economy reportedly contracted by 9.0% in 2011 mainly Therefore, data from a dense network of rain gauges over owing to this severe flooding [13], which clearly indi- any basin is desirable to obtain reasonable estimates of the cated the risks of business disruption and further impact intensity and frequency of rare events; however, no strict on national, regional, and global economies through sup- rules exist for having a specific number of rain gauges. If ply chains when disasters occur anywhere [3]. Although a river basin has only a few rain gauges with data avail- several studies related to specific flooding events over the able over a long period, the results might be unreliable at a Chao Phraya River Basin exist [3, 13–20], a comprehen- basin scale; hence, a frequency analysis of rainfall based sive hydrological analysis considering historical rainfall on the most available number of rain gauges are useful data from several rain gauge stations is insufficient, es- and have various hydrological applications. In most cases, pecially for such a large river basin. Hence, a frequency a frequency analysis of the average rainfall of a basin has analysis of historical rainfall over the Chao Phraya River been performed for various return periods; in general, for Basin could certainly contribute to a flood risk analysis return periods of 50, 100, 200, and 500 years. Various and to the Area-BCM by providing insights on flood risk. methods and practices have been adopted to determine the Such an analysis could also be useful to planners, engi- probability of occurrence of events [6, 8–11]. Note that neers, and the general public within the considered river no fixed method exists for calculating the return period basin. of rainfall; hence, numerous discussions exist in selecting appropriate methods. Various theoretical and analytical distributions have 2. Data and Methods been used to calculate the return period of rainfall at a gauge station or at the basin scale; each distribution might Daily rainfall data from 1981–2016 from all 119 rain give slightly different values because each is based on gauge stations were used in this study. Fig. 1 shows the a different principle. Several studies show that the best geographical location of the Chao Phraya River Basin and distribution could vary for each region or river basin [6– the distribution of the rain gauge stations within. The dis- 8, 10, 11]. Hence, selecting an appropriate distribution to tribution of a dense rain gauge network appears in the cen- obtain a reliable return period of extreme rainfall for any tral part of the basin. The Royal Irrigation Department river basin is one of the major engineering challenges. (RID) and the Thai Meteorological Department (TMD) Conversely, the calculated return period of rainfall needs are responsible for regularly monitoring rainfall data at proper utilization in a hydrological analysis for mitigating these stations. Daily rainfall data were missing from sta- possible water-related disaster risks. Therefore, a design tions for some years and for some time intervals; to ad- hyetograph by combining various return periods of rain- dress this and recover missing data in good format is be- fall is considered in such cases. Although it is a crucial yond the scope of this study. Before processing the daily aspect in terms of application, various approaches exist rainfall data, we confirmed the total number of years with that can be adopted to a design hyetograph [9]. In some available data for rain gauges within the basin (Fig. 2). cases, complex nondimensional design hyetographs are It is clear that most gauge stations have rainfall data for used [12]. almost over 30 years. Hence, a statistical analysis of rain- A design hyetograph along with the return period of fall considering all these stations could possibly provide rainfall can provide vital information for identifying pos- reliable results. sible flood risks for various periods. Here, we aimed to Figure 3 shows the step-by-step procedure for the hy- find the probability distributions with the best fit among drological analysis. First, daily rainfall data at each sta- the most common distributions to calculate the return tion were extrapolated to a monthly and annual basis. period of rainfall for various years and design rainfall Then, the return period of rainfall at each station was cal- hyetographs based on various approaches. Thereafter, a culated using historical rainfall data based on probabilistic profile of the flood inundation depth and extent areas of and analytical methods. Similarly, average basin rainfall a river basin was generated based on the purposed design was calculated considering each gauge station. The return hyetographs by combining various return periods of rain- period of rainfall based on average basin rainfall was also

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Fig. 2. Total number of years with available daily rainfall data at each station over 36 years (1981–2016). Fig. 1. Topographic map showing the location of rain gauge stations within the Chao Phraya River Basin, Thailand. 2.1. Probability Distributions Emphasis was placed on the total rainfall from April to calculated. To find a suitable distribution that could pro- December as the rainy season mainly occurs from May to vide accurate estimates of extreme rainfall, it was neces- December in the Chao Phraya River Basin. Various prob- sary to evaluate available distributions. After comparing ability distributions have been used across numerous stud- the frequency analyses, the best probability distribution ies [6–8, 10, 11, 21]; we used four types including Log- was considered for further analysis. Normal (LOG), Gumbel type-I (GUM), Pearson type-III Thereafter, a design rainfall hyetograph was processed (PE3), and Log-Pearson type-III (LP3) distributions to based on available rainfall data to simulate inundation. calculate the return period of rainfall. A detailed math- Here, we considered various design hyetographs with dif- ematical explanation of each distribution is presented in fering return periods of rainfall as shown in Fig. 3.The various research papers [6, 8–11]. Note that the best dis- first hyetograph was generated based on the maximum an- tribution could vary from region to region or basin to basin nual rainfall over 36 years. The second was generated depending on the type of rainfall distribution. Hence, based on the 36-year mean of average basin rainfall. The we aimed to understand the distribution that best fit the third was based on rainfall at each station independently; Chao Phraya River Basin. Therefore, we also used an an- there are 119 stations with average rainfall trends annu- alytical method, i.e., Gringorten method, World Meteoro- ally. Then, return periods of rainfall were considered for logical Organization (WMO), 1981 [22], also known as each of the three generated hyetographs, with all rainfall the Graphical Method (GM); it has been widely accepted trends being used separately in the hydrological simula- and used for rainfall frequency analyses. After compar- tion. Hence, we obtained different outputs for the flood ing the results of each probability distribution with that of inundation depth and extent areas within the basin. More- the graphical method, the best theoretical distribution was over, the difference between outputs was noteworthy. used for further analysis.

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Fig. 3. Flow chart of the hydrological analysis.

2.2. RRI Model 3. Results Various hydrological models are used to calculate the output of river basins globally. Hydrological models As discussed earlier, daily rainfall was converted to an- could be selected based on specific interests. Several im- nual rainfall for all 119 rain gauge stations within the proved models have been used for the hydrological sim- Chao Phraya River Basin. We followed the procedure ulation of river basins [16, 23–26], most of them being shown in Fig. 3. Hence, first, basin and sub-basin aver- freely available for research purposes. However, each age rainfall were prepared. Then, selected distributions model could have specific advantages and disadvantages. were fitted and evaluated for both basin average rainfall Here, one of our aims was to examine the spatial distri- and gauged rainfall. Considering both rainfall data and bution of the profile of the maximum inundation depth the best-fitted distribution, hyetographs were designed for over the basin. The Rainfall-Runoff-Inundation (RRI) different return periods based on three approaches ex- model [26] has been previously used for the Chao Phraya plained in the next section. Finally, inundation profile of River Basin [7, 19, 20, 27] and for various river basins in the basin was estimated and evaluated based on the differ- other regions [25–28]. Therefore, we used this model as ent design hyetographs of different return periods as the it was suitable for the Chao Phraya River Basin, which input to hydrological simulation. includes mountainous areas and floodplains. The model As presenting all results could be lengthy, we present helps simultaneously consider runoff and flood inunda- those for a single data type as an example, and the fi- tion; it is applicable for a rainfall-runoff analysis and in- nal results are summarized. Note that rainfall data were undation profiling at each grid of a river basin. Topo- missing for a station, which was left uncorrected; this graphic and meteorological data are the main inputs for may slightly affect the total rainfall measurements. As an RRI model. The effect of dam reservoirs could be rep- we used extreme values (considering our target) and eval- resented in the model; hence, parameters such as outflow uated maximum flood depths under different scenarios, it discharge and maximum storage of the Bhumibol and could be a good reference for disaster prevention and the Sirikit dams (Fig. 1) were considered. River discharge, Area-BCM. water level, and inundation depth were simulated. A de- A frequency analysis of annual rainfall was undertaken tailed description and mathematical explanation of the at each station. Fig. 4 shows the calculated return period RRI model have been reported in several studies [19, 25– of annual rainfall for different time intervals at a station 27]. close to Bangkok. Probability distributions (LOG, GUM, PE3, and LP3) were used to calculate the return period of

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Fig. 4. Fitted frequency distributions for annual rainfall at a rain gauge station near Bangkok. Black dots indicate rain gauge data.

Fig. 5. Best-ﬁt probability distributions with their percentage ratio at each gauge station based on PAE (left panel), PBIAS (middle- panel), and RMSE (right-panel).

rainfall for different time intervals. Similarly, the GM was the distribution with the best-fit [6]. Here, we used a sim- used to analyze annual rainfall at the station. The return ple approach by comparing probability distributions with period of rainfall was different for each distribution, es- the GM; three statistical tools were used, i.e., Percentage pecially for higher return periods; similar scenarios might Absolute Error (PAE), Percent Biases (PBIAS), and Root be observable at other stations. The GUM and PE3 dis- Mean Square Error (RMSE). Mathematical descriptions tributions (trends) were considerably close to each other of these tools are reported in several studies, e.g., P. C. for the station (Fig. 4). Note that all considered probabil- et al. [29]. Fig. 5 shows distributions with the best-fit ity distributions were basic fits for extreme events; hence, based on each of these statistical tools. The GUM clearly fitted distributions might not be realistic for considerably had the best-fit among all 119 gauge stations. We further short return periods. Fitted probability distributions were summarized the total percentage ratio of the best-fit prob- also prepared for the other 118 stations. Then, the distri- ability distribution for each selected station. bution with the best-fit was selected among them. Among the 119 gauge stations, GUM had the best fit for 67 (82%), 65 (77%), and 66 (78%) stations using PAE, 3.1. Comparison of Probability Distributions PBIAS, and RMSE, respectively (Fig. 5). PE3 had the best-fit for about 11 (13%), 12 (15%), and 13 (16%) sta- Fitting for all four theoretical distributions was under- tions using PAE, PBIAS, and RMSE, respectively. The taken for each rain gauge station. Similarly, the GM was remaining two distributions (LOG and LP3) did not have also applied for each station. Various tests could help find

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The Chao Phraya River Basin received a maximum annual rainfall of about 1485 mm in 2011. Three subbasins, i.e., Ping, Yom, and Nan, received a maximum annual rainfall of about 1395 mm, 1660 mm, and 1743 mm, respectively in 2011, but the Tha Chin and Pasak subbasins received a maximum annual rainfall of 1431 mm and 1431 mm in 1999 and 2006, respectively (Fig. 7). The maximum annual average rainfall was clearly not the same for all subbasins. Interestingly, each subbasin did not receive maximum rainfall during a particular year. We calculated the average basin rainfall to understand its variability. Thereafter, the return period of the average basin rainfall was calculated using the same method as above. The same procedure was repeated for the subbasins of the Chao Phraya River Basin. The return period of rainfall at each river basin could undoubtedly vary. Hence, we had two choices, i.e., using the average basin rainfall of the Greater Chao Phraya River Basin, and using that of the subdivisions of the Greater River basin, thereafter integrating their values for the hydrological application.

3.3. Design Hyetographs with Return Period of Rainfall An appropriate design rainfall hyetograph is critical for the hydrological analysis of extreme events. Design hyetographs were integrated with the return periods of rainfall; updated hyetographs were then used as the primary input for the hydrological analysis. The hyetograph Fig. 6. Subdivisions of the Chao Phraya River Basin. trends could possibly depend on the rainfall data sets. Here, we used three approaches for design hyetographs with various return periods; hence, the resulting hydrological models could differ. the best-fit, relatively, for the selected stations (Fig. 5). Different scenarios could be considered to calculate the 3.3.1. First Approach: Maximum Average Basin return period of rainfall from each applied distribution. Rainfall Here, we considered three statistical tools to select the best probability distribution. GUM produced a reasonable This included a design hyetograph using a classical probability distribution for the Chao Phraya River Basin; approach, which has been used in several studies, e.g., hence, it was considered for further analysis. Shrestha et al. [20]. Here, we considered the maximum annual average basin rainfall across all available data periods. Fig. 8 shows the average daily basin rainfall from 3.2. Frequency Analysis of Average Basin Rainfall May to December over the Chao Phraya River Basin Average basin rainfall can be calculated based on avail- over 36 years. Note that such a trend can be obtained able rain gauge data for a given river basin, and is widely for all 365 days, but the rainy season in Thailand is usu- used in hydrological applications. Its accuracy is nor- ally from May to October. The distribution of rainfall ev- mally based on the rain gauge network data. The spa- ery year was clearly somewhat different; annual rainfall tial distribution of a rainfall system and basin size are trends were not the same over 36 years. Hence, select- also important factors [29]. Note that the total area ing the periods with the maximum total rainfall across all of the Chao Phraya River Basin is considerably large available periods was the first approach. With regard to (∼160,000 km2); hence, considering only one basin av- the Chao Phraya River basin, total rainfall in 2011 was the erage would be unrealistic. Therefore, we divided the maximum across the 36 years. Hence, as a first option, the basin into five subbasins (Fig. 6) and calculated the av- trend of average daily basin rainfall was considered for the erage rainfall for the entire river basin as well as the five hydrological model. subbasins. We used the Thiessen polygon method, the Based on the average basin rainfall of 2011, most commonly used method in hydrology for determin- hyetographs for different return periods and their cu- ing average rainfall distribution over a basin. Fig. 7 shows mulative trends were designed using the calculated re- the average annual rainfall for each subbasin of the Chao turn periods of average basin rainfall. Fig. 9 shows the Phraya River. hyetographs with different return periods for the entire

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Fig. 7. Average annual rainfall over the Chao Phraya River Basin and its subbasins during 1981–2016.

Fig. 8. Average daily basin rainfall over the Chao Phraya River Basin over 36 years (1981–2016).

Chao Phraya and Pasak River Basins. Total cumulative 3.3.2. Second Approach: Mean of Basin Average average basin rainfall found as 1266 mm and 1204 mm Rainfall for the Chao Phraya and Pasak River Basin, respectively. In general, the gap between the total accumulated It considers a wide-range, which is based on the mean hyetographs for the longest return periods was not consid- of the average basin rainfall over 36 years. Fig. 10 shows erably high. For example, the cumulative rainfall for 100- a trend of the mean basin rainfall over 36 years for the year, 200-year, and 500-year return periods for the basin Chao Phraya River Basin. Undoubtedly, the design hyeto- was found to be about 1400 mm, 1467 mm, and 1554 mm, graph in this case seems smoother. However, uncer- respectively, suggesting that the difference becomes grad- tainties could exist, especially for extreme trends due to ual with increasing return periods of rainfall. Such values smoothening of each hyetograph trend. For example, the were comparatively less with regard to the Pasak River standard deviation for such a trend appears higher, es- Basin (Fig. 9). Such trends could persist due to precipita- pecially around the rainy seasons; while its upper-limit tion distribution over the basin. and lower-limit are generally unknown. Total cumulative average basin rainfall found as 1018 mm and 1006 mm for the Chao Phraya and Pasak River Basin, respectively.

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Fig. 9. Design hyetographs with different return periods based on the ﬁrst approach for the Chao Phraya (upper-panel) and Pasak (lower-panel) River Basins.

Fig. 10. Design hyetographs with different return periods based on the second approach for the Chao Phraya (upper-panel) and Pasak (lower-panel) River Basins.

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Fig. 11. Spatial distribution of average daily rainfall (left-panel) with 50-year and 200-year return periods (middle and right-panel) over the Chao Phraya River Basin.

Such an approach could be applied to other subbasins to terns. generate hyetographs with different return periods of rain- To design a hyetograph for each station, we used the fall. average daily rainfall across 36 years; hence, there were Once hyetographs are designed using this approach, 119 rainfall hyetographs with different return periods. the different return periods of rainfall can be integrated Thereafter, return periods of rainfall for these hyetographs into the design hyetographs (Fig. 10). The return pe- were also generated for each station. Each hyetograph riods of rainfall were the same for the first and second was then interpolated to generate a spatial distribution for approaches. Therefore, the cumulative return periods of each day of a particular year. Fig. 11 shows the spatial the hyetographs were the same for the first and second distribution of the hyetographs, and for different return approaches. A difference was only found in the tempo- periods (50-year and 200-year) for the 249th Julian day. ral trend of the hyetograph. These statistical analyses The spatial distribution of the hyetographs varies greatly. are based on historical data, which help make forecasts. We believe that this could be a better approach in terms of These two approaches might be useful for a hydrologi- spatial distribution of rainfall. This demonstrates that the cal analysis; however, uncertainties could be greater as all spatial distribution of rainfall is significantly important for the information for such a large river basin is averaged. hydrological applications [29]. Therefore, the same procedure was also applied to each subbasin of the Chao Phraya River Basin. 3.4. Inundation Analysis We found different hyetograph trends for each subbasin; the calculated return periods were also different for We considered each design hyetograph with different each case. Note that the areas of the subbasins are large; return periods for the hydrological simulation using the hence efforts in reducing any uncertainties, especially for RRI model for the Chao Phraya River Basin. Although extreme cases, might be insufficient. outputs of the RRI model include river discharge, water level, and inundation depth for each grid of the basin, we analyzed only inundation depth. The spatial resolution 3.3.3. Third Approach: Using Return Periods for of the output was ∼1 km (grid size) for the entire basin. Each Station We aimed to find the potential flood inundation depth and We considered the third approach to overcome uncer- flood extent area over the Chao Phraya River Basin. Such tainties of the first and second approaches, which was an inundation profile could possibly help understand and slightly challenging but more realistic; such an approach mitigate possible flood risk in the basin. could possibly minimize any uncertainties to prepare a Flood inundation profile was generated by hydrolog- design hyetograph with different return periods. This ap- ical simulation. Note that the RRI model has been al- proach was based on the return periods of rainfall for each ready tested and evaluated over the Chao Phraya River station, which were calculated as in the previous section. Basin [19]. Therefore, the modelled results were not eval- Each station had a return period of rainfall different from uated here. First, peak flood inundation depths were gen- the others but also with differing temporal rainfall pat- erated separately using the three hyetographs with their

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Fig. 12. Spatial distribution of simulated inundation proﬁle over the Chao Phraya River Basin using 50-year design hyetographs from the ﬁrst, second, and third approaches.

Fig. 13. Spatial distribution of simulated inundation proﬁle over the Chao Phraya River Basin using 100-year, 200-year, and 500-year design hyetographs from the third approach.

return periods. Fig. 12 shows the estimated inundation depth profile was obtained from the input hyetograph of profile over the Chao Phraya River Basin using gener- the third approach as compared to that of the others. Un- ated hyetographs with a 50-year return period based on derstanding the possibilities of the maximum inundation all three approaches. Maximum inundation depths of distribution profile within a basin is critical, especially for 5.55 m, 4.74 m, and 7.44 m were found from the first, understanding future flood risk. Hence, we believe that second, and third approaches, respectively. The impact the results from the third approach will have greater ap- of the hyetographs on the inundation depth from the first plicability. Fig. 13 shows the estimated inundation pro- and second approaches was noticeably less as compared file over the Chao Phraya River Basin using hyetographs to that of the third approach. They were similarly eval- of different return periods (100-year, 200-year, and 500- uated for their return periods; the maximum inundation year) from the third approach. Possibilities of flood inun-

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dation depths are distinctly visible and differ from each provided the best-fit to annual precipitation in Japan [31]. other based on the return periods of rainfall. Here, GUM had the best-fit among the selected distribu- The maximum inundation depths for different return tions for the Chao Phraya River Basin, followed by PE3. periods clearly shows a difference in inundation profiles Hence, our statistical assessment could prove that GUM is over the basin, especially in its downstream area, which suitable for extreme events in the river basins of Thailand; includes Bangkok as well as Rojana Industrial Park; these although such scenarios might not be the same as those in are the primary target areas of the Area-BCM in the the analysis of daily or monthly extreme rain events. Each SATREPS Project. Note that such profiles were also gen- distribution provides different values for given return pe- erated using the first and second approach; although, as riods, and in some cases, they have closer values. Hence, their hyetographs had relatively flatter trends and singular the selection of average or median values among all fitted return periods (at basin-scale), the maximum inundation distributions could be of use in hydrological applications. profiles were lower than that of the third approach. The selection of an appropriate distribution depends mainly on evaluations that may yield very different conclusions than those of previous researches. It should be 4. Discussion noted that such analysis is based on the statistical analysis of historical available data. Statistical theory for extremes As the Chao Phraya River Basin is a large-scale river is used to express that the frequency of such events is de- basin in Asia, we carefully performed a rainfall frequency pendent on the numbers of historical data set. For longer analysis. Then, hyetographs with different return periods data series, Peak Over Threshold (POT) method can be were generated based on three approaches, which were used to select the rainfall values, which is especially im- finally used in inundation analyses of the Chao Phraya portant for extreme value analysis [34]. Other important River Basin. In our view, return period of rainfall and factors such as location, climate, topographic features of design of hyetographs are the two important issues to be river basins, as well as data availability may be the reasons discussed here. for such different best-fitted distributions in different regions of the world. In most cases, large amount of precip- 4.1. Return Period of Rainfall itation occurs during summer season with different trends The daily rainfall data of 36 years were examined for in different regions. For example, in Japan, summer rain- each station. Data gaps at some stations for short time fall is mainly due to tropical cyclones, Baiu frontal ac- periods were common, especially in the older years com- tivity, and local convective system in Japan; however, in pared to recent years. However, the rainfall frequency Thailand, the southwest monsoon, the inter tropical con- analysis was not greatly sensitive to data gaps. Because vergence zone, and tropical cyclones are the main sources we focused on extreme values, it can be assumed that for large amount of rainfall in summer. These differences there was no significant impact while fitting the proba- may reflect the different rainfall patterns. Hence, best- bility distributions. Such data gaps could be significant fitted distribution is different in these two countries. if one wants to analyze them especially for trends of low rainfall (drought). 4.2. Selection of Hyetograph for the Hydrological There are several discussions on the best-fit of probability distributions that have been tested globally [6, 8– Application 11, 30–33]. Therefore, we tried to fit the four most com- Designing a hyetograph by integrating return periods mon probability distributions, which were compared with of rainfall is challenging, especially in the hydrologi- the return periods of rainfall obtained from the GM to ana- cal analyses of extreme events; uncertainties in design lyze their performance. Several goodness-of-fit tests were hyetographs always persist, which is a common issue in used to find the best-fit distribution [6]. The PAE, PBIAS, hydrological communities. Therefore, we attempted a dif- and RMSE statistical tools were used to find the best-fit ferent approach to design the hyetograph. As the Chao probability distributions. We contend that these tools are Phraya River Basin is considerably large, designing a sufficient to judge the best-fit distribution. hyetograph for such a single basin may be insufficient, The choice of a suitable probability distribution is one considering the possibilities for potential flood inundation of the major concerns in engineering practice [30] due to depth and flood extent areas. By dividing the basin into the lack of a concrete guideline. Therefore, applications several subbasins, we found a distinct difference in the to- of probability distributions to rainfall data have been in- tal rainfall as well the average rainfall trends of each sub- vestigated by several researchers from different regions of basin. Hence, if one wants to obtain a hyetograph based the world. Best-fitted distributions are different at each re- on average basin rainfall for a large river basin, dividing gion for instance Generalized Extreme Value (GEV), LP3, it into smaller subbasins and generating hyetographs for GUM showed the best-fit results for extreme annual val- those subbasins could be useful for a hydrological anal- ues at several gauged stations in Bangladesh [6]; LP3 was ysis of extreme events. However, it depends on a good found to be the best-fit probability distribution of the an- network of available data in a basin. Similar situations nual rainfall gauging stations in Northern Pakistan [10]; were also found in calculating the return period of rainfall GEV provided the best fit for annual and seasonal precip- at basin-scale. itation in river basins of Northwest China [11]; and LP3 Moreover, a possibly fluctuating trend within a hyeto-

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graph is another challenge if we consider the mean of the applied for each gauge station. Besides, a common GM average basin rainfall for each year. Maximum fluctuating was applied for each gauged station to calculate the re- values can be used, especially for the rainy season, while turn period of rainfall. We compared the results ob- in other cases, a minimum trend can be used, which can be tained from the probability distributions with the analyti- another alternative for hydrological applications. We also cal method and found that the GUM distribution fit well believe that the return period at a basin-scale always has among all theoretical distributions. Therefore, we adopted more symmetric values. Here, we demonstrated several the GUM distribution for a further frequency analysis over options to generate individual hyetographs with different the basin. return periods by dividing the Chao Phraya River Basin The Chao Phraya River Basin is considerably large as into five subbasins. However, we believe that these di- compared to its watershed area; hence, the entire basin vided areas are still large. Dividing the entire basin into was divided into five subbasins. Then, a similar process smaller subbasins and designing hyetographs based on av- carried out over the entire basin was applied to those sub- erage subbasin rainfall could be considered in future stud- basins. Thereafter, we calculated the average rainfall of ies. However, to address this, we suggested a third ap- the main basin as well as of each subbasin. The trends proach; we contend that this approach can help minimize of average basin rainfall were different for each subbasin, uncertainties related to the first and second approach. suggesting that the spatial distribution of rainfall varied The upper part of the Chao Phraya River Basin is hilly greatly over the Chao Phraya River Basin. and mountainous (Fig. 1). Hence, in these areas, flood in- To perform an inundation analysis over the entire river undation profile is almost negligible except river course. basin, we designed hyetographs by integrating the vari- In the downstream of the basin, flood inundation profile ous return periods of rainfall separately. We suggested appears in most of the area. One of the reasons is due three methods to design hyetographs focusing on their to the flat area in that region. Spatial distribution of the pros and cons. These design hyetographs were considered simulated inundation depth profile over the Chao Phraya in an RRI model to calculate the maximum inundation River Basin varies clearly with increasing return periods, depth profile over the basin for different return periods. especially for the depth profile (Fig. 13). Rainfall values We found that the design hyetograph with different return do not follow an exponential trend with increasing return periods from the first and second approaches yielded a periods. Therefore, drastic changes on estimated inun- lower peak of flood inundation depth and flood extent ar- dation profile for higher return periods does not appear eas compared to that generated from the third approach. within the basin with the model input of the higher return The third approach covered the spatial distribution of the period rainfall hyetograph. hyetographs as well as the return periods of rainfall rela- The spatial distribution of rainfall measurements over tively well. Hence, we suggest that an inundation analy- the basin is another critical issue. High spatial resolution sis could be more realistic, considering our proposed third of rainfall measurements became recently available [35]; approach, especially focusing on extreme events in the fu- such high-resolution gridded data is being made available ture. We found that the maximum flood water depth could for hydrological applications [23–25, 29, 36]. We used reach up to about 8.31 m, 9.15 m, and 10.27 m over the point data for this analysis which were interpolated by the Chao Phraya River Basin within the next 100, 200, and Thiessen polygon method to cover only the spatial distri- 500 years, respectively. bution of rainfall. However, various advanced interpola- The study can be a good reference for risk analysis and tion techniques are available, which could help interpolate evaluation over the Chao Phraya River Basin, which is the point data to achieve a reliable distribution of rainfall an integral part of the SATREPS Area-BCM project on which was also a limitation here. Regional Resilience Enhancement through Establishment of the Area-BCM at Industry Complexes in Thailand. It is expected that outcomes of this study would help enhance 5. Conclusions disaster resilience at industry complexes in Thailand for sustainable growth. A comprehensive hydrological analysis of a river basin based on historical rainfall is critical for disaster mitigation; hence policy makers, decision makers, engineers, Acknowledgements and other related stakeholders are always interested in This research was supported by Science and Technology Re- such issues. For such an analysis, availability of long- search Partnership for Sustainable Development (SATREPS) in term and reliable rainfall data is advantageous. Here, we collaboration between Japan Science and Technology Agency considered the Chao Phraya River Basin, the largest in (JST, JPMJSA1708) and Japan International Cooperation Agency Asia, which is often vulnerable to water-related disasters. (JICA). 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Name: Name: Shakti P. C. Ryohei Misumi

Afﬁliation: Afﬁliation: Associate Research Fellow, Storm, Flood, and Manger, Storm, Flood and Landslide Research Landslide Research Division, National Research Division, National Research Institute for Earth Institute for Earth Science and Disaster Re- Science and Disaster Resilience (NIED) silience (NIED)

Address: Address: 3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan 3-1 Tennodai, Tsukuba, Ibaraki 305-0006, Japan Brief Career: Brief Career: 2010-2013 Ph.D. (Geoenvironmental Science), University of Tsukuba 1992- NIED 2013- Research Fellow, National Research Institute for Earth Science and Selected Publications: Disaster Resilience (NIED) • R. Misumi, Y. Shoji, K. Saito, H. Seko, N. Seino, S. Suzuki, Y. Shusse, Selected Publications: K. Hirano, S. Bélair, V. Chandrasekar, D.-I. Lee, A. J. P. Filho, T. Nakatani, • S. P. C., M. Maki, S. Shimizu, T. Maesaka, D.-S. Kim, D.-I. Lee, and H. and M. Maki, “Results of the Tokyo metropolitan area convection study for Iida, “Correction of reflectivity in the presence of partial beam blockage extreme weather resilient cities (TOMACS),” Bulletin of the American over a mountainous region using X-band dual polarization radar,” J. of Meteorological Society, Vol.100, No.10, pp. 2027-2041, 2019. Hydrometeorology, Vol.14, No.4, pp. 744-764, 2013. • R. Misumi, Y. Uji, Y. Tobo, K. Miura, J. Uetake, Y. Iwamoto, T. Maesaka • S. P. C., “Quantitative precipitation estimation and hydrological and K. Iwanami, “Characteristics of droplet size distributions in low-level modeling in Japan,” J. of Japan Society of hydrology and Water Resources, stratiform clouds observed from Tokyo Skytree,” J. of the Meteorological Vol.30, No.1, pp. 6-17, 2017. Society of Japan Ser.II, Vol.96, No.4, pp. 405-413, 2018. • S. P. C., K. Hirano, and S. Iizuka, “Flood inundation mapping of the • R. Misumi, N. Sakurai, T. Maesaka, S. Suzuki, S. Shimizu, and Hitachi region in the Kuji River Basin, Japan, during the October 11-13, K. Iwanami, “Transition process from non-precipitating cumuli to 2019 extreme rain event,” J. Disaster Res., Vol.15, No.6, pp. 712-725, precipitating convective clouds over mountains: Observation by Ka-band 2020. Doppler radar and stereo photogrammetry,” J. of the Meteorological Academic Societies & Scientific Organizations: Society of Japan Ser.II, Vol.96A, pp. 51-66, 2018. • Society of Hydrologist and Meteorologist Nepal (SOHAM) Academic Societies & Scientific Organizations: • Japan Society of Hydrology and Water Resources (JSHWR) • American Meteorological Society (AMS) • Meteorological Society of Japan (MSJ) • Japan Society for Natural Disaster Science (JSNDS)

Name: Mamoru Miyamoto

Afﬁliation: International Centre for Water Hazard and Risk Management (ICHARM), Public Works Re- search Institute (PWRI)

Address: 1-6 Minamihara Tsukuba, Ibaraki 305-8516, Japan Brief Career: 2004- Researcher, Japan Science and Technology Agency (JST) 2007- Research Associate, College of Science and Technology, Nihon University 2010- Research Specialist, ICHARM, PWRI 2013- Researcher, ICHARM, PWRI Selected Publications: • M. Miyamoto, T. Ushiyama, Y. Iwami, and T. Koike, “Future change on inundation hazard considering duration in the Pampanga River Basin, Philippines,” J. of Japan Society of Civil Engineers, Ser. B1, Hydraulic Engineering, Vol.73, No.4, pp. I 277-I 282, 2017 (in Japanese). • M. Miyamoto and K. Matsumoto, “Usability assessment of rainfall data with different spatial resolution based on optimization of hydrological parameters,” J. of Japan Society of Civil Engineers, Ser. B1, Hydraulic Engineering, Vol.74, No.4, pp. I 1345-I 1350, 2018 (in Japanese). • M. Miyamoto and K. Matsumoto, “Influence of rainfall data with different spatial resolutions on flood forecasting reliability,” 13th Int. Conf. on Hydroinformatics, Vol.3, pp. 1406-1414, doi: 10.29007/74bp, 2018. Academic Societies & Scientific Organizations: • Japan Society of Civil Engineers (JSCE) • Japan Society of Hydrology and Water Resources (JSHWR)

1038 Journal of Disaster Research Vol.15 No.7, 2020 Assessing Flood Risk of the Chao Phraya River Basin Based on Statistical Rainfall Analysis

Name: Name: Yousuke Nakamura Supattra Visessri

Afﬁliation: Afﬁliation: Engineer, River and Sabo Division, Mitsui Con- Assistant Professor, Department of Water Re- sultants Co., Ltd. sources Engineering, Faculty of Engineering, Chulalongkorn University

Address: Address: 1-11-1 Osaki, Shinagawa, Tokyo 141-0032, Japan Phayathai Road, Patumwan, Bangkok 10330, Thailand Brief Career: Brief Career: 2006- Engineer, Mitsui Consultants Co., Ltd. 2015-2018 Lecturer, Department of Water Resources Engineering, Faculty 2017- Exchange Researcher, International Centre for Water Hazard and of Engineering, Chulalongkorn University Risk Management (ICHARM), Public Works Research Institute (PWRI) 2018- Assistant Professor, Department of Water Resources Engineering, 2020- Chief Engineer, Mitsui Consultants Co., Ltd. Faculty of Engineering, Chulalongkorn University Selected Publications: Selected Publications: • Y. Nakamura, K. Ikeuchi, S. Abe, T. Koike, and S. Egashira, “Real-time • S. Visessri and N. McIntyre, “Regionalisation of hydrological responses flood forecasting and prediction errors of water level in mountainous rivers under land-use change and variable data quality,” Hydrological Sciences J., – the case of heavy rain in the northern Kyushu on July 2017,” J. of Japan Vol.61, No.2, pp. 302-320, 2016. Society of Civil Engineers, Ser. B1, Hydraulic Engineering, Vol.74, No.4, • S. Visessri and N. McIntyre, “Uncertainty in flow time series predictions pp. I 1177-I 1182, 2018 (in Japanese). in a tropical monsoon-dominated catchment in northern Thailand,” J. of • Y. Nakamura, T. Koike, S. Abe, K. Nakamura, T. Sayama, and K. Hydrologic Engineering, Vol.21, No.10, 2016. Ikeuchi, “Development of real-time flood prediction utilizing the RRI • S. Visessri and C. Ekkawatpanit, “Flood management in the context of model with a particle filter,” J. of Japan Society of Civil Engineers, Ser. climate and land-use changes and adaptation within the Chao Phraya River B1, Hydraulic Engineering, Vol.74, No.5, pp. I 1381-I 1386, 2018 (in basin,” J. Disaster Res., Vol.15, No.5, pp. 579-587, 2020. Japanese). Academic Societies & Scientific Organizations: • Y. Nakamura, K. Ikeuchi, T. Koike, S. Egashira, H. Ito, and S. Abe, • Council of Engineers Thailand (COET) “Sequential estimation of river water level and riverbed fluctuation using a • British Hydrological Society (BHS) particle filter,” J. of Japan Society of Civil Engineers, Ser. B1, Hydraulic • International Association of Hydrological Sciences (IAHS) Engineering, Vol.75, No.2, pp. I 205-I 210, 2019 (in Japanese). • European Geosciences Union (EGU) Academic Societies & Scientific Organizations: • Japan Society of Civil Engineers (JSCE) • Japan Society of Hydrology and Water Resources (JSHWR) • Meteorological Society of Japan (MSJ) Name: Daiki Kakinuma

Affiliation: Name: Research Specialist, International Centre for Wa- Anurak Sriariyawat ter Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI) Affiliation: Assistant Professor, Department of Water Re- sources Engineering, Faculty of Engineering, Chulalongkorn University Address: 1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan Brief Career: 2019- Research Specialist, ICHARM, PWRI Address: Selected Publications: Phayathai Road, Patumwan, Bangkok 10330, Thailand • D. Kakinuma and T. Yamada, “A proposed method of water quality Brief Career: countermeasure and investigation of pollution mechanism by water and 2002-2015 Lecturer, Department of Water Resources Engineering, Faculty sediment quality survey in closed water area,” Advances in River of Engineering, Chulalongkorn University Engineering, Vol.25, pp. 435-440, 2019 (in Japanese). 2016- Assistant Professor, Department of Water Resources Engineering, • D. Harada, S. Egashira, D. Kakinuma, N. Nagumo, and H. Ito, Faculty of Engineering, Chulalongkorn University “Characteristics of flood flow with a large amount of sediment in the Selected Publications: Gofukuya River in the Typhoon No.19, 2019,” Advances in River • N. Dodd, A. M.Stoker, D. Calvete, and A. Sriariyawat, “On beach cusp Engineering, Vol.26, pp. 199-204, 2020 (in Japanese). formation,” J. of Fluid Mechanics, Vol.597, pp. 145-169, 2008. • D. Kakinuma, Y. Nakamura, H. Ito, and K. Ikeuchi, “A study on • A. Sriariyawat, K. Pakoksung, T. Sayama, S. Tanaka, and S. optimization method of RRI model parameters for flood forecasting by Koontanakulvong, “Approach to estimate the flood damage in Sukhothai combining multiple flood events,” Advances in River Engineering, Vol.26, Province using flood simulation,” J. Disaster Res., Vol.8, No.3, pp. 609-614, 2020 (in Japanese). pp. 406-414, 2013. Academic Societies & Scientific Organizations: • C. Kalakan, A. Sriariyawat, S. Naksuksakul, and T. Rasmeemasmuang, • Japan Society of Civil Engineers (JSCE) “Sensitivity analysis of coastal flooding to geographical factors: Numerical • Japan Society of Hydrology and Water Resources (JSHWR) model study on idealized beaches,” Engineering J., Vol.20, No.1, pp. 1-15, 2016. Academic Societies & Scientific Organizations: • Engineering Institute of Thailand (EIT)

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