































GROUNDWATER FLOW AND CONTAMINANT TRANSPORT MODELING


Global Sustainability Research                                                                                                                      ISSN: 2833-986X 

https://doi.org/10.56556/gssr.v2i3.515         

                                                                  

 

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RESEARCH ARTICLE  

Spatial analysis of flash flood and drought impact from climate change in Phongsaly 

district, Phongsaly province, by using Geo-Informatics technology and Modelling 

Sanxay Boutsamaly1, Chankhachone Sonemanivong2, Soulyphan Kannitha2, Phoummixay Siharath1*, 

Somchay Vilaychaleun1, Khampasith Thammathevo1, Amphayvanh Oudomdeth3, Tavanh Kittiphone3 

1Environmental Engineering Department, Faculty of Engineering, National University of Laos 
2Civil Engineering Department, Faculty of Engineering, National University of Laos 
3Department of Climate Change, Ministry of Natural Resources and Environment 

 

Corresponding Author: Sanxay Boutsamaly : J3ew.bsml@gmail.com 

Received: 28 May, 2023, Accepted: 16 June, 2023, Published: 22 June, 2023 

 

Abstract 

A method for predicting the water resource in the region in the future to be used as a basis for mitigating the consequences 

is to study how climate change affects hydrology. The purpose of this study is to i). choose a global climate model that is 

suitable for the area, ii). rainfall run-off modelling, iii). drought and flood hazard index map. The SSP-126, SSP-245, and 

SSP-585 scenarios were chosen as the most appropriate global climate model among the four institutes, with efficiency 

criteria using the coefficient of Nash-Sutcliffe and Kling-Gupta and then calibrate the data with the Bias Correction Linear 

Scaling method which divides the analysis period into 2 periods for Near-Future and Far-Future from analyzing Rainfall 

Run-off Modeling from Rainfall Concentration 1-hours, 3-hours and 6-hours. It was found that the SSP-585 scenario in 

the Rainfall Concentration 1-hours model has the most dangerous area for very high risk until the end of the 21st century. 

For the analysis of drought indices SPI_1, SPI_3 and SPI_6 in Near-Future, it was found that the frequency of droughts 

is increasing according to the worst scenario, the scenario with the most drought is SSP-585 and in the Far-Future, the 

frequency of drought is decreasing according to the worst scenario, the scenario with the most drought is SSP-126. 

 

Keywords: Drought; Flood; Flash flood; Model; Climate change; IPCC 

Introduction 

In late 2021, the IPCC released its Sixth Assessment 

Report (AR6), detailing the rise in global temperatures to 

1.5 C̊ Inevitably above pre-industrial levels, Floods and 

landslides are quite likely across Asia, and the continent's 

temperatures will rise. by proposing a new method for 

projecting greenhouse gases by bringing Shared 

Socioeconomic Pathway (SSP) applying a prediction to 

the end of the 21st century in 5 scenarios: (IPCC, Climate 

Change 2021: The Physical Science Basis, the Working 

Group I contribution to the Sixth Assessment Report, 

2021). 

- SSP1-1.9: A 1.5 °C temperature rise by 2050. 

- SSP1-2.6: A 1.8 °C temperature rise by 2100. 

- SSP2-4.5: A 2.7 °C temperature rise by 2100. 

- SSP3-7.0: A 3.6 °C temperature rise by 2100. 

- SSP5-8.5: A 4.4 °C temperature rise by 2100. 

 

Figure 1. The graph shows the temperature increase in 

each scenario. 

According to the Lao Green Climate Fund (GCF) Work 

Plan, Lao PDR has emitted 52,790Gg CO2eq of 

greenhouse gases into the atmosphere, of which 82% of 

total emissions come from land use change. In addition, 

after 2009, Lao PDR is prone to floods and droughts, 

mailto:J3ew.bsml@gmail.com


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especially in the northern region, which often 

experiences flash floods. (GCF, 2021) 

Lao PDR is a landlocked country. but has much more 

natural resources than many Asian nations, particularly 

in the areas of water, forests, and minerals. In 1940, the 

Lao PDR had 70% of its land covered in forests, but by 

2002, that number had decreased to 41.5% (UNDP, 

2012), making Lao PDR the 42nd most vulnerable 

country to climate change. 

One of these is Phongsaly Province in the north of Lao 

PDR, which regularly experiences flash floods and 

droughts. Lao PDR is thought to be the country most 

susceptible to drought. because rivers and streams are 

where most people reside. The primary industry is 

agriculture. (ADB, 2012) 

According to the Mekong River Commission's (MRC) 

Measuring Station's observational monitoring data for 

the years 2020–2022, the Phongsaly District experienced 

40 droughts in total, including 9 severe, 5 extreme, and 1 

catastrophic drought, the most of which took place 

between the July to December. (MRC, 2019) 

Additionally, one of the Sustainable Development Goals 

(SDGs), the 13th goal with a plan to address it, includes 

addressing climate change (Climate Action). 

In the scenario, it serves as a guideline for planning, 

mitigating, and adapting to climate change, as well as for 

resolving issues and creating a development plan for the 

Lao PDR to lessen the effects of potential disasters like 

floods and droughts brought on by the worsening severity 

of climate change. Additionally, it tries to adjust 

development strategies in accordance with the scenario 

and increase awareness of the severity of the issue. 

Therefore, we are interested in using geographic 

information systems and models to analyze the places in 

Phongsaly District and Phongsaly Province that have 

experienced floods and droughts because of climate 

change. 

 

Literature review 

Several studies have looked at global climate change 

issues, but in Lao PDR, there are still few regional 

studies. Phrakonkham, Khasama, et al. (2020), integrated 

and mapped hazards using the Analytical Hierarchy 

Process (AHP) across Laos. From the results of the study, 

it was found that hazard areas are distributed in the 

northern and southern regions of Lao PDR. Rasanak, 

Sanxay et al. (2021) studied the impact of climate change 

by using the CMIP5 global climate model and rainfall 

runoff model to map flood hazard areas in the Xedon 

basin in the southern part of Lao PDR. From the results, 

the most high-hazard area found in the far future, the 

RCP8.5 scenario, covers an area of 483.9 km2. Maisor et 

al. (2022) used the CMIP6 global climate model by 

making a flood hazard map in the Xechamphone Basin in 

the central region of Lao PDR. From the results of the 

study, it was found that in the near future, the SSP126 

scenario has the most area of very high hazard, and in the 

near future, the SSP585 scenario has the most area of 

very high hazard. from earlier studies Natural disasters 

and climate change continue to have an adverse impact 

on many regions. There is still no regional climate change 

analysis in the northern part of Lao PDR, which is 

essential for planning mitigation strategies. 

Methodology 

Study area 

The capital of Phongsaly Province, Phongsaly District, 

has a total area of 2,855 km2 and is located at latitude and 

longitude 208962 and 2419580, respectively, between 

(Zone) UTM 47 and 48N. It has a main river named Nam 

Ou and is the highest city in the Lao PDR; its northern 

border is with China, its southern border is with Xay 

District in Oudomxay Province, and its eastern border is 

with Thailand. Additionally, Phongsaly District has a 

favorable climate for agriculture and produces 

agricultural goods.  

Phongsaly District, Phongsaly Province, is reportedly 

one of the regions that has suffered disasters every year 

for the previous ten years or more, according to reports 

from various organizations. together with the issue of 

climate change that the world is currently facing. As a 

result, we decided to research this issue's consequences 

to develop guidelines for avoiding and lessening its 

negative effects. 



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Figure 2. Map showing study area, elevation, and topography of Phongsaly Province. 

For this study, we divided the analysis period into two 

time periods: the near future from 2018 to 2050 and the 

far future from 2051 to 2100, and daily rainfall data for a 

21-year period (1997-2017) was collected from four 

stations in the Nam Ou catchment area, namely 

Phongsaly, Muong Ngoy, Dien Bien and Oudomxay. 

Refer to the data from the Mekong River Commission 

(MRC) rain gauge stations as shown in the table below. 

This historical data will be interpolated in an area with a 

resolution of 12.5 x 12.5 m by using the Inverse Distance 

Weight (IDW) technique. Global climate models by 

using downscaling techniques and adjusting data 

predictions to be as close to reality as possible. The most 

appropriate Global climate models were then selected for 

use in the analytical study. 

 

Table 1. Rain gauge station used in this study. 

No 
Rain Gauge 

Station 
Station ID Time Intervals 

Coordinate (UTM) 

Source 

X Y 

1 Phongsaly 210201 1922-2019 210505 2406007 

Mekong River 

Commission 

(MRC) 

2 Dien Bien 210301 1979-2022 293236 2363995 

3 Muong Ngoy 200201 1996-2017 259051 2290796 

4 Oudomxay 200204 1984-2019 810792 2289916 

 



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Figure 3. Map showing location of rain gauge station. 

For simulated data collection, we rely on global climate 

models (GCMs) from four institutes. The 4 models are: 

the Institute Pierre Simon Laplace, France (IPSL), the 

Max Planck Institute for Meteorology, Germany (MPI), 

the Meteorological Institute, Norway (NorESM), the 

National Institute for Environmental Studies, and the 

Japan Agency for Marine-Earth Science and Technology, 

Japan (MIROC). which has forecast the rainfall data from 

now until the end of the 21st century, which is detailed in 

the table below: 

Table 2. Global Climate Models are used in this study. 

No GCMs Institute Frequency Resolution 

1 IPSL-CM6A-LR Institute Pierre Simon Laplace, France day 2.5 ̊ x 1.3  ̊

2 MPI-ESM1-2-LR Max Planck Institute for Meteorology, Germany day 1.9 ̊ x 1.9  ̊

3 NorESM2-LM Meteorological Institute, Norway day 2.5 ̊ x 1.9  ̊

4 MIROC6 
National Institute for Environmental Studies, and Japan 

Agency for Marine-Earth Science and Technology, Japan 
day 1.4 ̊ x 1.4  ̊



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Extract time series 

The four institutes' climate models have different data 

sets. Therefore, we write a coding algorithm to retrieve 

data from specific global climate models using Python. 

So, we merged the files into one file first. and then 

retrieve information in the area we study. 

Performance indicator 

Nash-Sutcliffe efficiency (NSE) 

Nash-Sutcliffe efficiency (NSE) (J.E. Nash, 1970) It is a 

popular index used to determine model accuracy or 

model performance. To estimate the desired value with 

the following formula: 

NSE = 1 −
∑ (Yi − Yî)

2n
i=1

∑ (Yi − Y̅)2n
i=1

 

Where  

YI: The observed value of I when I is between 1 and 

n. 

YÎ: Prediction value Yi 

Y̅: Mean value of Yi 

NSE have a value between -∞ to 1 Which can interpret 

the meaning of the NSE value as shown in the table 

below: 

Table 3. Meaning of NSE value. 

NSE Value Model Accuracy 

1 Perfect Fit 

>0 - <1 Arithmetic Mean 

0 

The model can predict the values 

as accurately as the prediction 

using the mean. 

<0 
The model can predict with less 

accuracy than the mean estimate. 

≥0.75 
Good prediction (Yangqing 

Lian, 2007) 

0.36 – 0.75 
Satisfactory prediction 

(Yangqing Lian, 2007) 

Kling-Gupta efficiency (KGE) 

This measure of fit was created by (Hoshin V. Gupta, 

2009) to aid Nash-Sutcliff performance categorization 

analysis. This makes it easier to evaluate the relative 

significance of the variables (correlation, bias, and 

variance) within the context of a hydrological model 

(Harald Kling, 2012), whose equations are as follows: 

KGE = 1 − √(CC − 1)2 + (
cd

rd
− 1)2 + (

cm

rm
− 1)2 

Where   

CC:  Pearson Coefficient 

rm: Average of Observed values 

cm: Average of Forecast values 

rd: Standard Deviation of Observation values 

cd: Standard Deviation of Forecast values 

The Pearson coefficient can be obtained from the 

equation below: 

r =
∑ (Oi − O̅)(Pi − P̅)n
i=1

√∑ (Oi − O̅)2n
i=1 √∑ (Pi − P̅)2n

i=1

 

Where 

Oi: Observation value 

Pi: Prediction value 

O̅: Mean value of Oi 

P̅: Mean value of Pi 

Calibration 

Large geographic resolution data are available from 

global climate models for precipitation forecasts. The 

data must be adjusted using Linear Scaling Bias 

Correction (LS) before using [9]. LS is a simple statistical 

estimating approach utilized in numerous investigations. 

Phis(d)
∗ = Phis(d) ∙ [μm{Pobs(d)/{μmPhis(d)}] 

Psim(d)
∗ = Psim(d) ∙ [μm{Pobs(d)}/{μmPhis(d)}] 

Where   

Pobs: Observation data 

Phis: Historical data from GCMs 

Phis
∗: Historical data from GCMs after Bias correction 

Psim
∗: Precipitation data from GCMs after Bias 

correction 

 



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Psim: Precipitation data from GCMs 

d: Precipitation data 

μm: Correction factor 

Return period 

To estimate the probabilities of total monthly rainfall and 

to estimate rainfall around the recurrence of total 

monthly rainfall in this research, we used a method 

(Weibull, 1939) to obtain a frequency of exceedance that 

is closest to the mean value of the distribution, with the 

following formula: 

P =
N

(m + 1)
 

Where 

P: Probability of exceedance 

N: Total number of years record 

m: Rank of observed rainfall value 

T: Return period (mm) given by T =
1

P
 

Standardized precipitation index (SPI) 

(Thomas B. McKee, 1993) developed the SPI method to 

determine and monitor climate drought conditions, which 

is now used by various organizations. It is widely used to 

analyze the SPI index from monthly rainfall. The SPI will 

be analyzed over 3 periods, namely 1 month, 3 months, 

and 6 months, which this research will define as SPI_1, 

SPI_6, and SPI_12, respectively. 

SPI =
Xij − Xim

σ
 

Where 

Xij: Precipitation for the station i month j (mm) 

Xim: Mean precipitation (mm) 

σ: Standardized deviation 

The criteria used by the SPI to categorize drought levels 

are listed in the table below: 

Table 4. Drought Classification based on SPI (McKee et 

al., 1993) 

Drought classes SPI 

≥ 2 Extremely wet (EW) 

1.50 to 1.99 Very wet (SW) 

1.00 to 1.49 Moderately wet (MW) 

-0.99 to 0.99 Near normal (N) 

-1.00 to -1.49 Severely dry (MD) 

-1.50 to -1.99 Severely dry (SD) 

≤ -2 Extremely dry (ED) 

Flood hazard index classification 

By considering the flood depth of each square in the flood 

map and converting it to a value is a disaster index, we 

proposed the hazard level with a flood disaster index 

adjusted for the relationship between flood depth and 

velocity (Sally J. Priest, 2009), as shown in the table and 

figure below: 

Table 5. Flood depth-hazard index relationship. 

Flood depth (m) Hazard index 

Small hazard < 0.3 0.00 – 0.25 

Medium hazard < 0.6 0.25 – 0.50 

High risk < 2 0.50 – 0.75 

Very high risk > 2 0.70 – 1.00 

 



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Figure 4. Flood depth and hazard index relationship curve. 

 

Results and discussions 

Performance indicator 

Using observed rainfall data from five stations in the 

watershed area and historical data from four GCMs, a 

performance indicator was developed using Nash-

Sutcliffe and Kling-Gupta. From the result of the 

distribution of monthly rainfall with observed and 

simulated data, in most cases the simulated data is much 

less than the actual observed data, but in most cases the 

sample is close to the actual rainfall data. 

 

 

Figure 5. Scatter plot of observed and simulated data: (a) Phongsaly-IPSL-CM6A-LR, (b) DienBien-IPSL-CM6A-LR, 

(c) Oudomxay-IPSL-CM6A-LR and (d) MuongNgoy-IPSL-CM6A-LR. 

The IPSL-CM6A-LR, which has an average coefficient 

primarily near 1, is the model that is most suitable 

according to Nash-Sutcliffe and Kling-Gupta 

performance indicator, which also places the MPI-

ESM1-2-LR, MIROC6, and NorESM2-M next. 

 

(a) (b) 

(c) (d) 



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Table 6. Efficiency value of Nash-Sutcliffe and Kling-Gupta. 

Nash-Sutcliffe Efficiency 

Rainfall Station 

Predicted Rainfall 

IPSL-CM6A-LR MIROC6 MPI-ESM1-2-LR NorESM2-M 

SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 

Phongsaly 0.553 0.558 0.564 0.367 0.343 0.380 0.406 0.416 0.404 0.259 0.250 0.261 

DienBien 0.552 0.552 0.554 0.379 0.354 0.381 0.605 0.630 0.624 0.293 0.285 0.281 

Oudomxay 0.478 0.477 0.452 0.332 0.277 0.358 0.457 0.487 0.487 0.207 0.191 0.201 

Muong Ngoy 0.493 0.513 0.527 0.361 0.324 0.375 0.627 0.646 0.642 0.203 0.194 0.189 

Average 

scenario 0.519 0.525 0.524 0.360 0.324 0.374 0.524 0.545 0.539 0.241 0.230 0.233 

Average GCMs 0.52 0.35 0.54 0.23 

Kling-Gupta Efficiency 

Rainfall Station 

Predicted Rainfall 

IPSL-CM6A-LR MIROC6 MPI-ESM1-2-LR NorESM2-M 

SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 

Phongsaly 

     

0.74  

     

0.74  

    

0.747  

    

0.66  

    

0.64  

    

0.65  

    

0.56  

    

0.56  

    

0.56  

    

0.63  

    

0.62  

    

0.63  

DienBien 

     

0.65  

     

0.65  

      

0.65  

    

0.58  

    

0.57  

    

0.58  

    

0.66  

    

0.69  

    

0.68  

    

0.49  

    

0.48  

    

0.48  

Oudomxay 

     

0.67  

     

0.66  

      

0.65  

    

0.62  

    

0.60  

    

0.62  

    

0.62  

    

0.63  

    

0.63  

    

0.60  

    

0.59  

    

0.59  

Muong Ngoy 

     

0.63  

     

0.64  

      

0.65  

    

0.59  

    

0.57  

    

0.59  

    

0.66  

    

0.66  

    

0.66  

    

0.50  

    

0.50  

    

0.50  

Average 

Scenario 

    

0.675  

    

0.674  

     

0.673  

     

0.61  

     

0.59  

     

0.61  

     

0.62  

     

0.63  

     

0.63  

     

0.55  

     

0.55  

     

0.55  

Average GCMs 0.67 0.60 0.63 0.55 

Average 

Efficiency 0.60 0.48 0.58 0.39 

 

 

Model calibration 

The observed precipitation data over a 21-year period 

was used in a bias correction process with historical data 

from the IPSL-CM6A-LR based on scenarios SSP-126, 

SSP-245, and SSP-585. The analysis found that the 

historical data was lower than the observed rainfall. But 

in some scenarios, it's the opposite. which is shown in the 

cumulative distribution curve as follows: 

  



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Figure 6. Cumulative distribution function of rainfall data at rainfall stations (a) Phongsaly, (b) DienBien, (c) Oudomxay, 

and (d) Muong Ngoy based on each scenario. 

Flood hazard 

1 Hour concentration interval 

In the near future, the area prone to very high-risk 

flooding under the SSP-126 scenario is 87.13 km2, the 

SSP-245 scenario has an area of 160.73 km2, an increased 

from the SSP-126 scenario of 84.46%, and the SSP585 

scenario has an area of 180.20 km2, an increased from the 

scenario in SSP-245 by 12.11%. According to the 

analysis, the scenario at SSP-585 was the worst-case 

scenario. 

In the far future, the area prone to very high-risk flooding 

under the SSP-126 scenario is 170.95 km2, the SSP-245 

scenario has an area of 168.90 km2, a decreased from the 

SSP-126 scenario of -1.20%, and the SSP585 scenario 

has an area of 171.08 km2, an increased from the scenario 

in SSP-245 by 1.29%. According to the analysis, the 

scenario at SSP-585 was the worst-case scenario. 

(a) 

(b) 

(c) 

(d) 



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Figure 7. Flood hazard map in 1 hour’s concentration of rainfall for each scenario. 

 

 

 

Table 7. Flood hazard area in 1 hour’s concentration of rainfall for each scenario. 

1 Hours Concentration Interval Flood Hazard Area (km2) 

No Hazard Zones 

Near Future Far Future 

SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 

1 Low hazard 2627.25 2630.79 2613.99 2622.64 2624.57 2622.53 

2 Medium hazard 36.49 9.83 9.87 9.96 9.88 9.98 

3 High risk 99.97 49.50 46.78 47.29 47.50 47.25 

4 Very high risk 87.13 160.73 180.20 170.95 168.90 171.08 

 

 

Flood hazard in SSP126 

scenario in near future 

Flood hazard in SSP245 

scenario in near future 

Flood hazard in SSP585 

scenario in near future 

Flood hazard in SSP126 

scenario in far future 

Flood hazard in SSP245 

scenario in far future 

Flood hazard in SSP585 

scenario in far future 



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Figure 8. Graph showing comparison of the area in 1 hour’s concentration of rainfall for each scenario. 

 

Figure 9. Graph showing the percent change in area in 1 hour’s concentration of rainfall for each scenario. 

3 Hour concentration interval 

In the near future, the area prone to very high-risk 

flooding under the SSP-126 scenario is 160.69 km2, the 

SSP-245 scenario has an area of 158.12 km2, a decreased 

from the SSP-126 scenario of -1.60%, and the SSP585 

scenario has an area of 170.01 km2, an increased from the 

scenario in SSP-245 by 7.52%. According to the analysis, 

the scenario at SSP-585 was the worst-case scenario. 

In the far future, the area prone to very high-risk flooding 

under the SSP-126 scenario is 165.70 km2, the SSP-245 

scenario has an area of 164.56 km2, a decreased from the 

SSP-126 scenario of -0.69%, and the SSP585 scenario 

has an area of 165.74 km2, an increased from the scenario 

in SSP-245 by 0.72%. According to the analysis, the 

scenario at SSP-585 was the worst-case scenario. 



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Figure 10. Flood hazard map in 3 hour’s concentration of rainfall for each scenario. 

 

Table 8. Flood hazard area in 3 hour’s concentration of rainfall for each scenario. 

3 Hours Concentration Interval Flood Hazard Area (km2) 

No Hazard Zones 

Near Future Far Future 

SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 

1 Low hazard 2640.82 2643.48 2631.20 2635.61 2636.78 2635.50 

2 Medium hazard 9.35 9.36 9.29 9.27 9.37 9.30 

3 High risk 39.98 39.89 40.35 40.26 40.13 40.29 

4 Very high risk 160.69 158.12 170.01 165.70 164.56 165.74 

 

Flood hazard in SSP126 

scenario in near future 

Flood hazard in SSP245 

scenario in near future 

Flood hazard in SSP585 

scenario in near future 

Flood hazard in SSP126 

scenario in far future 

Flood hazard in SSP245 

scenario in far future 

Flood hazard in SSP585 

scenario in far future 



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Figure 11. Graph showing comparison of the area in 3 hour’s concentration of rainfall for each scenario. 

 

Figure 12. Graph showing the percent change in area in 3 hour’s concentration of rainfall for each scenario. 

 

6 Hour concentration interval 

 

In the near future, the area prone to very high-risk 

flooding under the SSP-126 scenario is 144.35 km2, the 

SSP-245 scenario has an area of 142.56 km2, a decreased 

from the SSP-126 scenario of -1.24%, and the SSP585 

scenario has an area of 151.16 km2, an increased from the 

scenario in SSP-245 by 6.03%. According to the analysis, 

the scenario at SSP-585 was the worst-case scenario. 

In the far future, the area prone to very high-risk flooding 

under the SSP-126 scenario is 148.81 km2, the SSP-245 

scenario has an area of 147.77 km2, a decreased from the 

SSP-126 scenario of -0.70%, and the SSP585 scenario 

has an area of 148.99 km2, an increased from the scenario 

in SSP-245 by 0.83%. According to the analysis, the 

scenario at SSP-585 was the worst-case scenario. 

 



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Figure 13. Flood hazard map in 6 hour’s concentration of rainfall for each scenario. 

 

Table 9. Flood hazard area in 6 hour’s concentration of rainfall for each scenario. 

6 Hours Concentration Interval Flood Hazard Area (km2) 

No Hazard Zones 

Near Future Far Future 

SSP126 SSP245 SSP585 SSP126 SSP245 SSP585 

1 Low hazard 2660.64 2662.51 2653.56 2656.20 2657.23 2659.28 

2 Medium hazard 8.84 8.87 8.94 8.79 8.80 8.82 

3 High risk 37.02 36.90 37.18 37.05 37.04 37.35 

4 Very high risk 144.35 142.56 151.16 148.81 147.77 148.99 

 

Flood hazard in SSP126 

scenario in near future 

Flood hazard in SSP245 

scenario in near future 

Flood hazard in SSP585 

scenario in near future 

Flood hazard in SSP126 

scenario in far future 

Flood hazard in SSP245 

scenario in far future 

Flood hazard in SSP585 

scenario in far future 



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Figure 14. Graph showing comparison of the area in 6 hour’s concentration of rainfall for each scenario. 

 

Figure 15. Graph showing the percent change in area in 6 hour’s concentration of rainfall for each scenario. 

 

Drought indices 

 

In the near future, SPI_1 under SSP-126 experienced 16 

droughts, SSP-245 experienced 19 of them, and SSP-585 

experienced 19 of them. SPI_3 under SSP-126 

experienced 10 droughts, and SSP-245 experienced 7, 

and SSP-585 appeared 17 times of them. In SPI_6 no 

drought was identified in any of the scenarios. Depending 

on how serious the scenario is, droughts happen more 

frequently. 

 
(a) 



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Figure 16. Comparison of SPI_1, SPI_3 and SPI_6 under (a) SSP-126, (b) SSP-245 and (c) SSP-585 in near future from 

year 2018-2050 at station 210201. 

Table 10. Summary statistics of indices in near future. 

Drought Index 

SSP-126 SSP-245 SSP-585 

SPI_1 SPI_3 SPI_6 SPI_1 SPI_3 SPI_6 SPI_1 SPI_3 SPI_6 

Highest drought index 4.06 2.71 0.00 2.78 2.29 0.00 2.07 2.03 0.00 

Lowest drought index -2.15 -1.65 0.00 -2.37 -1.65 0.00 -2.19 -1.95 0.00 

Total amont of drought (<-1, year) 16 10 0 19 7 0 19 17 0 

(b) 

(c) 



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In the far future, SPI_1 under SSP-126 experienced 32 

droughts, SSP-245 experienced 29, and SSP-585 

experienced 26 of them. SPI_3 under SSP-126 

experienced 13 droughts, SSP-245 experienced 18, and 

SSP-585 appeared 7 times of them. In SPI_6 no drought 

was identified in any of the scenarios. In extreme 

scenarios, droughts occur less frequently. 

 

 

 
Figure 17. Comparison of SPI_1, SPI_3 and SPI_6 under (a) SSP-126, (b) SSP-245 and (c) SSP-585 in far future from 

year 2051-2100 at station 210201. 

 

 

(a) 

(b) 

(c) 



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Table 11. Summary statistics of indices in far future. 

Drought Index 

SSP-126 SSP-245 SSP-585 

SPI_1 SPI_3 SPI_6 SPI_1 SPI_3 SPI_6 SPI_1 SPI_3 SPI_6 

Highest drought index 3.18 3.25 0.00 3.96 3.90 0.00 4.10 3.28 0.00 

Lowest drought index -2.33 -2.06 0.00 -1.83 -2.11 0.00 -2.44 -2.25 0.00 

Total amount of drought (<-1, year) 32 13 0 29 18 0 26 7 0 

Conclusion 

Among other natural disasters, drought and flooding are 

undoubtedly among the more destructive yet little 

predicted ones. Using drought indicators for drought 

monitoring is frequently a crucial foundation. A better 

outlook of the potential risk that can befall the region is 

provided by drought indices and rainfall runoff models 

calculated from predicted rainfall. In this paper, a method 

is offered to compare rainfall concentration and drought 

indices at various time scales for further research into the 

respective. 

Drought and flood are obviously one of the more 

damaging yet hardly determined natural disasters among 

others. Drought monitoring using drought indices often 

serves as an important base. Drought indices computed 

from forecasted rainfall gives a better outlook of 

potential risk that may be inflicted upon the region. In 

this study, a means is provided to compare among 

drought indices of different time scales for further study 

into respective drought types. 

Floods are likely to become more severe as the scenario 

worsens. From the results of this analysis, in both periods 

of the 1-hour rainfall concentration under the SSP-585 

scenario, there will be areas with the highest risk of 

flooding. In the near future, the frequency of droughts 

will increase as the scenario worsens, but in the far future, 

it will be the opposite. The frequency of droughts 

decreases as the scenario worsens. 

Acknowledgement 

The authors would like to acknowledge GEF, UNEP 

program for being very kind financial support which 

enabled us to gather relevant data for this study. 

Funding: None 

Conflict: The authors declare no conflict of interest  

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