Priyambodoho et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-021-00425-8(2021) 8:34

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Flood inundation simulations based on
GSMaP satellite rainfall data in Jakarta,
Indonesia
Bambang Adhi Priyambodoho1, Shuichi Kure2* , Ryuusei Yagi1 and Nurul Fajar Januriyadi3

Abstract
Jakarta is the capital of Indonesia and is considered one of the most vulnerable cities to climate-related disasters,
including flooding, sea-level rise, and storm surges. Therefore, the development of a flood-forecasting system for
Jakarta is crucial. However, the accurate prediction of flooding in Jakarta is challenging because of the short flood
concentration time in highly urbanized basins and the shortage of rainfall data in poorly gauged areas. The aim of
this study is to simulate recent flood inundation using global satellite mapping of precipitation (GSMaP) products.
The GSMaP products (NRT and Gauge V7) were evaluated and compared with hourly observation data from fiveground stations in the Ciliwung River Basin. In addition, a rainfall-runoff and flood inundation model was applied tothe target basin. The results of the analysis showed that the GSMaP Gauge data were more accurate than the
GSMaP NRT data. However, the GSMaP Gauge cannot be used to provide real-time rainfall data and is, therefore,
inadequate for real-time flood forecasting. We conclude that the GSMaP Gauge is suitable for replicating past flood
events, but it is challenging to use the GSMaP NRT for real-time flood forecasting in Jakarta.
Keywords: GSMaP, Flood inundation simulation, Rainfall-runoff simulation, Jakarta, Indonesia, Chiliwung River

1 Introduction
Jakarta is considered to be one of the most vulnerable
cities in the world to climate-related disasters, such as
flooding, sea-level rise, and storm surges (Firman et al.
2011). Jakarta has experienced several flood disasters in
the past, including in 1996, 2002, 2007, 2013, and 2020.
These floods not only led to severe economic damage
but also human casualties (Kure et al. 2014).
Several studies related to flood problems in Jakarta
have been conducted. Moe et al. (2016) applied arainfall-runoff and flood inundation model to the 2013
flood event and concluded that a shortage of capacity inthe lower Ciliwung and other rivers accounted for 79.6%of the total flood inundation volume in Jakarta, withurbanization and land subsidence accounting for the* Correspondence: kure@pu-toyama.ac.jp
2
Department of Environmental and Civil Engineering, Toyama Prefectural
University, 5180 Kurokawa Imizu, Toyama 939-0398, Japan
Full list of author information is available at the end of the article

remaining 20.4%. According to Bricker et al. (2014), thereduced capacity of the drainage system generated bytrash-clogging flood gates is a factor that causes flooding. Budiyono et al. (2016) and Januriyadi et al. (2018)
reported that climate change in the future would increase flood risk in Jakarta.
In these flood-prone situations, several countermeasures have been implemented in Jakarta to mitigate flood
damages, such as dredging and diversion tunnels. However, flood risk in Jakarta is still high, and more than 60
people were killed in Jakarta during the most recent
flood event that occurred in January 2020 (Berlinger andYee 2020). Thus, a flood-forecasting system is requiredin Jakarta to ensure early evacuation and prevent traffic
jams during flood disasters. Nevertheless, the development of a flood-forecasting system in Jakarta is a challenging task because the rapid flooding of rivers andcanals will not provide sufficient lead time for a prediction based on the water levels in the upstream regions© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

(Miyamoto et al. 2012). In addition, the shortage of rainfall data, such as radar rainfall and ground gauge data,
results in uncertainty and low accuracy in predicting
flood hydrographs and inundations based on hydrologic
models owing to insufficient rainfall input accuracy,model calibrations, and data assimilation opportunities(Kure et al. 2013).
In this study, we analyzed whether satellite rainfall
data can be used as an input for real-time flood forecasting in Jakarta because of the discontinuation of rainfall
radars in 2013 owing to high maintenance costs. Various
satellite rainfall products can be accessed and downloaded freely, and the data are provided in near realtime worldwide. We used global satellite mapping of
precipitation (GSMaP) products as the satellite rainfall
data in this study because of their high spatial and temporal resolutions suitable for flood simulations in highly
urbanized areas. GSMaP products have been evaluatedand verified through comparison with observational datafrom several previous studies.
Based on a verification study of hourly GSMaP rainfall
conducted by Setiawati and Miura (2016), GSMaP-MVK
data can potentially be used to replace rain gauge data,
particularly for lowland areas in the Kyusyu region,
Japan, if inconsistencies and errors are resolved. However, without bias correction, significant underestimation
or overestimation of heavy rainfall events will be observed. Moreover, the former algorithm of the GSMaP
microwave radiometer does not consider topographicaleffects (Setiawati and Miura 2016). Other researchers
have also reported an underestimation via the GSMaP(Fu et al. 2011; Admojo et al. 2018; Pakoksung and
Takagi 2016). Fu et al. (2011) evaluated the accuracy of
the GSMaP using a gauge station in a basin in China
and found that GSMaP products generally underestimated the precipitation amount. Additionally, GSMaP
rainfall data are less accurate when used for mountainous regions than flat areas owing to the occurrence of
topographical rainfall (Fu et al. 2011). Conversely, Tianet al. (2010) reported that satellite products (e.g.,
GSMaP) overestimate rainfall in the summer based on
the estimations over the contiguous United States.
Hence, GSMaP rainfall products provide less accurate
results compared with gauge-based rainfall networks orradar rainfall information systems. Nevertheless, GSMaP
rainfall products are often used as inputs for hydrological models in simulating flood events. Admojo et al.
(2018) and Pakoksung and Takagi (2016) statisticallyevaluated satellite rainfall products, including the
GSMaP, and applied hydrological simulations to a large
river basin in Thailand using satellite data. They showed
acceptable model results (high correlation coefficient:0.85) to simulate the observed discharge in a river basin,
but underestimations (NSE = 0.37) of simulated runoff

Page 2 of 17

were reported in a previous study (Admojo et al. 2018).
To improve the flood simulation results based on satellitedata, bias correction (Sayama et al. 2012) of satellite rainfall products, and ensemble flood simulation methods(Jiang et al. 2014) have been successfully used for flood
simulations in large-scale basins. Sayama et al. (2012) applied a hydrological model with a bias-corrected GSMaP
for flood inundation simulation in Pakistan to provide
additional information for flood relief operations. The
simulated flood inundation area reasonably matched well(the fit index: 0.61, peak discharge ratio: 1.0, and inundated area ratio: 1.0) with the actual area, even though thesatellite rainfall products were used as the input for thesimulation.
These literature reviews indicate that the accuracy of
GSMaP data should be verified for several cities and regions before being used in practice. In several studies,hydrological models were applied with satellite rainfall
data to large basins, where the flood travel time is relatively slow (Sayama et al. 2012). However, GSMaP evaluation investigations of highly urbanized cities prone to
rapid flooding in rivers and subjected to local convective
rainfall owing to urban heat environments or humid
tropical climates have not been conducted in detail.
The main objective of this study is to investigate asatellite-based rainfall product for flood inundation
modeling of a flood event in Jakarta, a mega Asian city
located in a humid tropical region. Satellite-based rainfall
can be used to reconstruct historical flood events. A problem faced by developing countries is the evaluation ofhistorical flood events with insufficient survey and hydrological observation data. Thus, GSMaP data were also evaluated in this study as input rainfall data to simulate thehistorical flood events in Jakarta, including the most recent
large-flood event that occurred in January 2020.

2 Study area
Jakarta is the capital of Indonesia and is located on thenorthwest coast of Java island. Jakarta is the largest
metropolitan city in Indonesia, and its development is
progressing rapidly.
The rainy season in Jakarta begins in November andends in March, and peak rainfall intensity often occurs
in January and February. Thirteen main rivers flow
through the region, with the Ciliwung River being thelongest. The area selected for this study included Jakarta
and the surrounding river basins, which cover a total of1346.6 km2 (Fig. 1). It should be emphasized that Jakarta
is a highly urbanized area with complex urban systemsof river channels and canals, buildings, and roads. Thus,
the flood concentration time is relatively short (approximately 12–16 h), which creates problems regarding theuse of warning systems, evacuation, and the preventionof traffic congestion.

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Page 3 of 17

Fig. 1 Study area

Almost every year, Jakarta experiences flooding inJanuary, February, or both, owing to high rainfall withinsufficient capacity flows in the drainage system. The
details of the floods and damage are listed in Table 1. In
Table 1, the damage cost and main damages were obtained from several sources, such as web online news
and several reports, and the values of the rainfall, waterlevel, and flooded area were obtained from the observed
data. In the 2013 flood event, the failure of an embankment on the west drainage canal at Laturharharioccurred, and the city’s financial core, including thepresident’s palace, was inundated, which led to the deathof 41 people. In 2020, at least 67 people were killed, and60,000 were displaced in the worst flooding that has
occurred in the area since 2007.

3 Methods
3.1 Dataset

For the rainfall-runoff and flood inundation simulation,
the following data were used. A digital elevation model(2021) 8:34

Priyambodoho et al. Progress in Earth and Planetary Science

Page 4 of 17

Table 1 Summary of historical flood eventsYear

Averaged rainfall(mm)

Maximum water level (cm)at Manggarai

Flood area
(km2)

Death person

Main damage

Damage Cost
(IDR)

1996

421

970

-

10

529 houses were highly damaged

6.4 Trillion

2002

464

1050

160

32

Electrical System Shutdown

9.9 Trillion

2007

340

1060

397

80

Electrical System Shutdown

8.8 Trillion

2013

168

1020

132

41

Embankment failure

1.5 Trillion

2014

581

830

201

26

134,662 persons were affected

5 Trillion

2015

310

890

196

5

Electrical System Shutdown

1.5 Trillion

2016

275

580

152

2

-

3 Trillion

2017

322

700

139

6

1,178 houses were inundated

147 Billion

2018

346

775

79

1

42 houses were highly damaged

150 Billion

2019

154

890

84

2

-

100 Billion

2020

196

965

150

67

Electical System Shutdown

1 Trillion

data set of the Shuttle Radar Topography Mission having
a 30 m resolution was used in this study. The rivercross-sectional and drainage system data for the Ciliwung
River recorded in 2011 were obtained from the project authority of the Japan International Cooperation Agency.
Water level and river flow discharge data for the Ciliwung
River and a flood inundation map of Jakarta were provided
by Badan Penanganan Bencana Daerah (Jakarta Disaster
Management Agency).
3.2 Satellite rainfall products

The GSMaP project was implemented in 2002 to develop retrieval algorithms for rainfall rates and to produce high-resolution global precipitation maps based onsatellite data (Ushio et al. 2009; Aonashi and Liu 2000).
GSMaP products are distributed by the Japan Aerospace
Exploration Agency (JAXA) Global Rainfall Watch.
GSMaP Now, GSMaP NRT, and GSMaP MVK are provided by JAXA.
GSMaP Now only allows the extrapolation of rainfall maps every 30 min; therefore, the accuracy of thedata may be relatively low. Moreover, it only provides
data for 2017, 2018, and 2019. GSMaP MVK is a reanalysis version of GSMaP NRT and has a resolutionof 0.1°/h with a domain coverage from 60° N to 60°
S. It was available from March 2000 until December
2010. GSMaP NRT uses the same algorithm as
GSMaP MVK, and it has been available since October
2008. GSMaP NRT is released every hour (4 h latency), and a Kalman filter algorithm is applied to thedata (Ushio et al. 2009). GSMaP Gauge V7 is a calibrated version based on the ground gauge data andyields high accuracy. Its data have been available
since March 2014.
In this study, we evaluated the data using GSMaP
NRT and GSMaP Gauge V7. GSMaP NRT can be usedas the input for a real-time flood-forecasting system, and

the GSMaP Gauge can be used for reconstructing past
flood events. The simulated data were compared withground observation data.
3.3 Ground observation rainfall data

Hourly rainfall data were obtained for the target areafrom Badan Meteorologi, Klimatologi dan Geofisika(Indonesian Agency for Meteorology, Climatology, andGeophysics). We evaluated the uncertainties in thehourly satellite rainfall data from the Citeko, Darmaga,
Pondok Betung, Kemayoran, and Tanjung Priok stations.
The locations of the stations are shown in Fig. 1. Table 1
shows the total rainfall values at the stations during
flood events. Then, the satellite product rainfall data for
January and February of 2015–2020 were obtained fromboth GSMaP NRT and GSMaP Gauge data.
3.4 Flood inundation model

As mentioned earlier, we modeled the flood inundationin Jakarta based on rainfall-runoff (Kure and Yamada
2004; Kure et al. 2008) and flood inundation modeling(Moe et al. 2017). The flood inundation model comprised a rainfall-runoff module for each sub-basin, ahydrodynamic module for the rivers and canal networks,
and a flood inundation module for the flood plains.
For the rainfall-runoff simulation, the lumped rainfallrunoff model used by Kure and Yamada (2004) and Kureet al. (2008) was applied to each sub-basin because thismodel can be used to simulate the Horton overland flow
in urban areas, as well as subsurface and saturation overland flow in mountainous areas. These flows depend on
the relationship between the soil and geological characteristics and the intensity of rainfall on hill slopes.
Saint-Venant equations for the conservation of continuity and momentum were applied to connect runoffs
from each sub-basin and conduct river flood routing as
the distributed model in the river channels and drainage

Event
2018

Event
2017

Event
2016

497

258

143

213

213

188

Darmaga

PondokBetungKemayoran

Tanjung Priok

Basin
Averaged

228

Basin
Averaged

Citeko233

543

Tanjung Priok

111

Kemayoran

146

172

Basin
Averaged

PondokBetung256

Tanjung Priok

Darmaga178

Kemayoran

361

104

PondokBetungCiteko176

126

Darmaga

283

Basin
Averaged

Citeko

505

425

156

PondokBetungTanjung Priok

206

Darmaga

Kemayoran

160

CitekoEvent
2015

Observed
Rainfall(mm)

Location/coveragearea

Period

2
-49

-28

-53

-39

-73

-84

14

-53

-9

121

-100

-24

83

37

94

90

31

8

-46

-54

-61

24

-52

-34

96

154

100

87

69

80

260

253

212

246

282

273

316

350

346

198

165

189

150

196

196

193

98

107

-0.03

-0.04

0.18

0.02

-0.04

-0.01

-0.02

0.04

0.09

0.00

-0.03

0.02

0.16

0.20

0.35

-0.02

-0.01

0.01

0.53

0.45

0.47

-0.03

0.05

0.07

coefficients
(CCs)

2

3

1

3

4

6

3

6

3

3

4

4

3

5

5

3

6

3

4

9

9

10

5

4

-0.02

0.61

0.30

0.07

-0.37

0.09

-0.37

0.19

-0.08

0.04

-0.17

-0.34

0.49

0.84

0.81

0.15

-0.30

-0.12

0.69

0.82

0.92

-0.28

-0.45

0.04

(mm) coefficients
(CCs)

15

11

7

12

28

60

28

45

20

20

34

34

30

22

28

29

36

22

34

74

66

60

42

24

74

17

19

101

71

-46

41

-46

27

203

129

-6

59

-3

53

180

114

52

12

-41

-44

127

17

81

(mm) (%)

(mm)

325

250

253

290

440

268

322

292

295

336

333

338

275

248

273

292

270

268

310

250

282

353

240

290

(mm)

0.07

-0.04

0.21

0.00

0.05

0.00

0.005

0.01

0.17

-0.02

-0.03

-0.06

0.15

0.39

0.43

-0.05

-0.04

0.01

0.55

0.40

0.44

-0.03

-0.08

0.10

coefficients
(CCs)

Total
Hourly Rainfall
RainfallCorrelation

(%)

RMSE

VolumeBias

RMSE Correlation

GSMaP Gauge

Total
Hourly Rainfall
RainfallCorrelation

VolumeBias

Daily Rainfall

GSMaP NRT
Daily Rainfall

2

3

2

3

4

6

3

6

3

3

4

4

2

4

4

4

6

3

4

9

8

11

5

5

0.35

0.29

0.53

0.07

0.17

0.29

-0.21

-0.32

0.27

0.03

-0.25

-0.19

0.47

0.96

0.43

-0.01

-0.34

-0.25

0.79

0.90

0.91

-0.18

-0.34

0.28

(mm) coefficients(CCs)

RMSE Correlation

23

15

19

19

41

53

33

52

19

27

31

32

24

14

33

41

43

31

28

63

55

84

58

49

(mm)

RMSE

(2021) 8:34

4

50

190

920

2

4

50

190

920

2

4

50

190

920

2

4

50

190

920

Altitude(m)

Table 2 Evaluation index of rainfall comparisonsPriyambodoho et al. Progress in Earth and Planetary Science
Page 5 of 17

Event
2020

563

230

343

445

424

310

Darmaga

PondokBetungKemayoran

Tanjung Priok

Basin
Averaged

215

Basin
Averaged

Citeko151

186

Tanjung Priok

21

PondokBetungKemayoran

1141

174

Darmaga

Event
2019

Observed
Rainfall(mm)

CitekoLocation/coveragearea

Period

2

4

50

190

920

2

4

50

190

920

Altitude
(m)

-81

-19

-29

-46

-23

-26

-68

-41

-56

-43

508

28

217

244

301

240

265

170

181

128

82

86

128

222

-0.03

0.35

0.48

0.23

0.10

0.12

0.22

0.15

0.06

0.18

-0.01

-0.09

coefficients
(CCs)

6

7

7

9

4

10

2

4

2

1

5

12

0.91

0.79

0.91

0.98

0.91

0.52

-0.05

0.41

0.50

0.29

-0.14

-0.10

(mm) coefficients
(CCs)

27

59

61

23

16

80

15

22

13

12

26

122

-36

-63

-62

-41

-20

-68

-29

-32

-13

520

25

-82

(mm) (%)

(mm)

196

156

169

203

183

181

154

126

132

130

216

204

(mm)

0.11

0.51

0.50

0.03

-0.01

0.13

0.46

0.29

0.08

0.08

0.00

-0.10

coefficients
(CCs)

Total
Hourly Rainfall
RainfallCorrelation

(%)

RMSE

VolumeBias

RMSE Correlation

GSMaP Gauge

Total
Hourly Rainfall
RainfallCorrelation

VolumeBias

Daily Rainfall

GSMaP NRT

Table 2 Evaluation index of rainfall comparisons (Continued)
Daily Rainfall

6

7

7

9

4

10

2

3

2

1

4

12

0.99

0.92

0.99

0.98

0.90

0.60

0.65

0.44

0.50

0.14

-0.29

0.46

(mm) coefficients(CCs)

RMSE Correlation

RMSE

24

69

68

43

16

78

13

20

16

12

26

116

(mm)

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34
Page 6 of 17

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Fig. 2 Comparisons of hourly rainfall between GSMaP products (NRT & Gauge V7) and observation for flood event of 2015

Fig. 3 Comparisons of daily rainfall between GSMaP products (NRT & Gauge V7) and observation for flood event in 2015

Page 7 of 17

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Fig. 4 Comparisons of hourly rainfall between GSMaP products (NRT & gauge.v7) and observation for flood event of 2020

Fig. 5 Comparisons of daily rainfall between the GSMaP products (NRT & Gauge V7) and observation for flood event of 2020

Page 8 of 17

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

systems. Unsteady two-dimensional flow equations, i.e.,the continuity and momentum equations, were numerically solved for the flood inundation simulation of theflood plain.
The rainfall-runoff and flood inundation model wereapplied to the 2013 flood event based on the radar rainfall data and were validated against the observations (seeMoe et al. 2016, 2017) for the calibrated parameters anddetails of the simulations.
In this study, flood observation data and flood inundation simulations modeled using the GSMaP rainfall input were compared.
3.5 Target flood events

Six yearly largest flood events from 2015 to 2020 wereselected as the target in this study because both GSMaP
NRT and GSMaP Gauge data are available for theseflood events. These events produced the highest waterlevels at the stations in the Chiliwung river during theseyears. The event periods lasted for approximately a week
in January or February.
3.6 Evaluation index

We compared the agreement of the GSMaP products
and examined the rainfall data of the study area.

Fig. 6 Hydrograph comparisons for flood event of 2015

Page 9 of 17

Statistical validation methods, such as the root meansquare error (RMSE), correlation coefficients (CCs), andvolume bias, were used as evaluation indexes; these were
employed to evaluate the relationship between the
GSMaP and observed rainfall data. The RMSE was used
to compare the magnitude of the error between the
GSMaP and observation data sets. CC represented thecorrelation between the data sets; its value ranged between zero and one. The volume bias (%) is the difference in the percentages of the total rainfall volume
between the GSMaP and ground rainfall observation. It
is calculated using the following equation: (100 ×((GSMaP − Observation)/Observation)). For the floodhydrograph comparisons, the Nash–Sutcliffe efficiency
index (NSE) was computed. The NSE values ranged
from minus infinity to 1, and an efficiency index of 1 indicated a perfect match.

4 Results
4.1 Rainfall comparison

Good performances on a daily scale with respect to theCCs and volume bias were observed for the 2015, 2019,and 2020 events, as listed in Table 2. Figures 2 and 3
show the hourly and daily comparisons, respectively, between the basin-averaged GSMaP and rainfall

Fig. 7 Hydrograph comparisons for flood event of 2016

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

observation data for the 2015 flood event. In this case,we confirmed relatively strong correlations for the dailybasin-averaged gauge rainfall, particularly for the GSMaP
Gauge and observation data (0.79 for the daily basinaveraged gauge rainfall). Figures 4 and 5 show the hourly
and daily comparisons, respectively, between the averagebasin observation and GSMaP data for the 2020 flood
event. It is noted that the observed rainfall in 2020 only
provides the data at 3 h intervals, so that hourly data
was made from these 3 h intervals assuming uniform
time distributions at each hour. In this case, the best
correlation existed (0.99, for the daily basin-averaged
rainfall), but weak correlations could be confirmed onan hourly time scale. Table 2 summarizes the evaluationindex data. The values for the other years are listed inTable 2. The agreement of the GSMaP data was not very
high (Table 2). Only weak correlations were found forthe flood events from 2016 to 2018, both hourly anddaily. Overestimation of the GSMaP was observed forthe 2016, 2017, and 2018 events except for the GSMaP
NRT in 2018, whereas slight underestimation was observed for the 2019 and 2020 events. It should be notedthat the volume bias in the Pondok Betung station in2019 showed an extremely large overestimation for bothhourly and daily scales. This is because the observed

Fig. 8 Hydrograph comparisons for flood event of 2017

Page 10 of 17

rainfall in the Pondok Betung station in the 2019 event
was 21 mm, which is too small compared with the values
at other stations. The data quality of the rainfall stations
during heavy rainfall events should be checked in
Jakarta.

4.2 Flood hydrographWe performed rainfall-runoff and flood inundation simulations at an hourly time step using the GSMaP rainfall
data as the input and compared the observed and simulated results.
Figures 6, 7, 8, 9, 10, and 11 show the hydrographs forthe observations at the Katulampa, Depok, and Manggarai stations and the corresponding simulations forthe 2015–2020 flood events. The locations of the stations are shown in Fig. 1. The simulated hydrographs
showed relatively good agreement with the observations
on an hourly time scale with respect to CCs (the average
CCs of the Gauge simulation is 0.53), but some underestimations and/or overestimations with respect to thepeak discharge bias (from 62 % to 134 % in the Gauge
simulations) occurred. For the Nash index, generally low
performance was confirmed, except for the 2019 and2020 events.

Fig. 9 Hydrograph comparisons for flood event of 2018

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Fig. 10 Hydrograph comparisons for flood event of 2019

For the 2015 flood event (Fig. 6), flood peak discharges
were captured through GSMaP Gauge simulations at the
Katulanmpa and Depok stations, but apparent peak timeand volume differences were observed. These differencesin the flood event resulted in the worst Nash index
values of all events, although the hourly rainfall correlations in Fig. 2 performed well, as discussed in the previous sentences. At the Manggarai station, the GSMaP
Gauge simulation was overestimated in the peak discharge compared with the observation. For 2016 and2017 flood events (Figs. 7 and 8), several high flood-flow
fluctuations were observed and simulated, but the simulated flood hydrograph occasionally overestimated andunderestimated the observations. The simulation resultsof the GSMaP Gauge simulations for the 2018 event
showed good correlations (Fig. 9) in CCs (0.64–0.73),
good peak discharge biases (− 39–35%), and relatively
acceptable Nash index values (0.38 and 0.39). The flood
peak timing and values of the 2018 flood event were
accurately simulated, but the GSMaP NRT simulations did not show any floods in the event. This isbecause significant underestimations of the rainfall of
the GSMaP NRT during the event were found at all
stations (Table 2 and Fig. 9), even though GSMaP

Page 11 of 17

Fig. 11 Hydrograph comparisons for flood event of 2020

NRT captured the rainfall well in the 2018 rainy season, except during this flood event. One possibility ofthis underestimation might be due to shallow orographic rainfall, because large rainfall observation
values at high elevations can clearly be observed inthis event, as shown in Table 2. This type of rainfall
may still be difficult to capture by the satellite after a
new orographic/non-orographic rainfall classification
scheme was installed (Kubota et al. 2020). This will
be discussed further in the discussion section.
For the 2019 flood event (Fig. 10), good CCs andNash index values were observed in the gauge simulation. The best Nash index values were confirmed forthe 2019 flood event. For the 2020 flood event (Fig.
11), CCs show more than 0.6 in all stations for boththe GSMaP NRT and gauge simulations. The relatively acceptable Nash index values (0.15–0.54) wereobserved, except at the Manggarai station. As such,the 2019 and 2020 events show good flood simulation
results because these events show good daily rainfall
correlations and volume bias of the GSMaP Gauge
data compared with the observations. The simulation
results for the hourly time scales are presented inTable 3. The peak bias (%), CCs, RMSE, and NSE

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Page 12 of 17

Table 3 Summary of discharge hydrograph comparison
PeriodWater
Level
station

GSMaP NRTGSMaP Gauge

Peak Discharge Correlation
Bias (%)

Event 2015 Katulampa -46

Nash Index RMSE

coefficients (CCs)

Peak Discharge Correlation(m3/s) Bias (%)

coefficients (CCs)Nash Index RMSE
(m3/s)

0.03

-0.36

3.15

0

0.52

-1.15

3.97

-41

0.50

0.20

10.02

13

0.63

-0.80

15.02

Manggarai -19

0.53

-0.31

17.99

134

0.72

-2.39

28.92

Depok

Event 2016 Katulampa -13
Depok

0.14

0.31

4.30

-23

0.03

0.44

3.86

-15

0.35

-0.36

11.74

-7

0.24

-0.40

11.92

Manggarai 122

0.49-10.39

30.31

104

0.52

-5.18

22.32

Event 2017 Katulampa 190

-0.03

0.59

4.52

128

0.42

0.67

4.04

Depok

0

0.16

-1.35

14.65

36

0.39

-0.44

11.48

Manggarai 33

0.38

-0.78

16.85

76

0.39

-0.94

17.61

Event 2018 Katulampa -80

0.13

-0.45

9.83

35

0.69

0.39

6.41

-80

0.21

-0.46

25.75

-39

0.64

0.38

16.75

Manggarai -42

0.20

-0.53

20.19

24

0.73

-0.18

17.76

Event 2019 Katulampa -19

0.35

0.57

4.71

-62

0.60

0.69

3.96

-30

0.25

0.19

12.48

-52

0.60

0.43

10.49

Manggarai -5

0.36

-0.08

9.75

-28

0.49

-0.06

9.67

Depok

Depok

Event 2020 Katulampa 80

0.60

0.51

5.65

2

0.68

0.54

4.37

22

0.64

0.32

17.22

-19

0.60

0.15

19.25

Manggarai -25

0.64

-0.43

21.77

-50

0.62

-0.61

23.11

Depok

were computed (Table 3). From Table 3, it can beobserved that the gauge simulations are better thanthe NRT simulation, especially for the 2018 event.
The gauge simulation results in Table 3 show thatstations at higher altitudes show good RMSE andNSE values because of the small catchment size andsub-basin numbers and few opportunities to have uncertainty (Moriasi et al. 2007). Several NRT simulations yielded negative NSE values, which signified thatthese simulations were not useful for flood prediction.
Based on the comparison, GSMaP Gauge simulations
were found to be significantly better than GSMaP NRTbased simulations. Significant underestimation of the
GSMaP NRT simulation occurred compared with theobservation. GSMaP NRT data were designed as input
for the flood-forecasting simulations because these data
sets provided near real-time rainfall data. However, interms of agreement, the GSMaP NRT data were unsuitable for the real-time forecasting of flooding in Jakarta,and significant bias corrections or modifications are required to obtain more accurate simulation results. Finally, it should be emphasized that GSMaP Gauge
simulations showed relatively good performance. These
results encourage us to use satellite-driven rainfall data
to reconstruct historical flood events in poorly gauged
basins and developing countries, even when the target
areas are highly urbanized.

4.3 Flood inundation

The flood inundation conditions were also compared. Figures 12, 13, 14, 15, 16, and 17 show flood inundation maps
based on the simulation and observation data for 2015–2020 flood events. The observed flood inundation maps
were provided by the National Disaster Management
Agency. These observation maps were based on eyewitness reports of government officers during the flood
events and interviews with residents after the event. The
observation maps tended to overestimate the inundation
area because the entire district in the map was treated as
the entire inundation when the flooding of a part of a district was reported. The simulation of the flood inundation
results using the GSMaP Gauge showed relatively goodconsistency with the observations, particularly for 2017,2018, 2019, and 2020. A slight overestimation of the floodinundation in some districts can be confirmed in 2015
and 2016 due to overestimation of the flood hydrographat some stations. The GSMaP NRT captured theflood inundation for 2015, 2016, and 2020 events but
could not capture the inundation for 2018 due to significant underestimations of the rainfall during theevent. It is noted that these comparisons were madeby visual-graph comparisons because the observation
maps were not appropriate for use as a statistical test
because of the overestimation of the inundation area,
as explained in the above sentences.

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Page 13 of 17

Fig. 12 Flood Inundation comparisons for flood event of 2015

From these results, we concluded that the GSMaP
Gauge data could be used to reproduce previous floodinundation events in Jakarta. However, it is challenging
to use GSMaP NRT data as the input in a real-time
flood-forecasting system owing to its low accuracy.

5 Discussion
Based on the comparison between gauge-based observations and satellite-based GSMaP rainfall data, we foundthat the GSMaP NRT is not useful as an input for the
real-time flood-forecasting system in Jakarta. Several
previous studies have shown that the GSMaP products
can capture the overall rainfall pattern (Kubota et al.
2009; Pakoksung and Takagi 2016) and could be useful
as inputs to hydrologic models to reproduce past flood
events (Sayama et al. 2012). However, several studies (Fuet al. 2011; Admojo et al. 2018; Pakoksung and Takagi2016) have pointed out that the GSMaP products tended
to be underestimated, and this underestimation was due

Fig. 13 Flood inundation comparisons for flood event of 2016

to orographic effects (Kubota et al. 2009, 2020). Overcoastal mountain ranges, heavy rainfall can be caused byshallow orographic rainfall, which is inconsistent withthe assumption in the PMW algorithm that heavy rainfall results from deep clouds with significant ice (Kubota
et al. 2020). Therefore, a new orographic/non-orographic
rainfall classification scheme was installed in the PMW
algorithm in V6 for the TMI and V7 for all sensors(Kubota et al. 2020). From the analysis of the paper inJakarta, we observed both the underestimation and overestimation of the GSMaP rainfall, and only the 2018
rainfall event showed difficulty in capturing the rainfall
in the high-altitude zones. Therefore, it might be inferred that the orographic effects on the quality of the
GSMaP products in Jakarta are negligible. It should
be noted that it is difficult to capture the 2018 rainfall event that showed clear orographic effects evenafter the new scheme was installed. However, it is difficult to draw any conclusions from only one case,Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Page 14 of 17

Fig. 14 Flood inundation comparisons for flood event of 2017and other atmospheric conditions, such as wind fields,
should be analyzed to understand the orographic rainfall effects in Jakarta.
Many reasons are attributed to the general difficulties
in capturing heavy rainfall in Jakarta. First, Jakarta andits surroundings are highly urbanized areas, and convectional rainfall typically occurs in urban areas ofhumid tropical regions. Additionally, the heat island
phenomenon is significantly progressing in Jakarta, andthere exists the urban thermal influence on the background environment of convective rainfall (Sugawara
et al. 2018). The rain retrieval algorithms may have errors when applied to “warm rain” processes that are
typical of convectional rainfall in tropical regions(Chang et al. 2013). Thus, it might be difficult to predict and capture convectional rainfall in urban areasusing satellite information. This convection-type rainfall in an urban area may be a challenge for the accurate prediction of rainfall in Jakarta. Furthermore, theFig. 15 Flood inundation comparisons for flood event of 2018

timing of microwave observation from the satellite
might be related to the low quality of GSMaP because
the local heavy rains regularly fall for short periods in
Jakarta.
Second, the flood travel time in Jakarta is short. Jakarta
and its surrounding areas are highly urbanized, and theflood travel time in rivers and canals is approximately
12–16 h. Local heavy rainfall should be captured hourly
using the satellite to predict rapid floods. In previous
studies, the GSMaP was evaluated at large-scale basins
daily and monthly. This rapid flood response to rainfall
is another challenge for GSMaP prediction in Jakarta.
Some researchers applied a bias correction method
for rainfall simulation using the GSMaP algorithm toreduce the underestimation of rainfall intensity andamount (e.g. Sayama et al. 2012). However, for
Jakarta, the difficulties mentioned above complicate
the application of bias correction because GSMaP
simulations
occasionallyoverestimatedand/orPriyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Page 15 of 17

Fig. 16 Flood inundation comparisons for flood event of 2019

underestimated the flood-flow discharge of the 2016
and 2017 flood events. Moreover, the GSMaP NRT
simulations did not show any flood responses for the2018 event. In these situations, it is difficult to apply
bias correction to the GSMaP NRT data. If we could
find any clear underestimation or overestimation
trend of the GSMaP against the ground observation, abias correction method would work well to improve
the simulation results (Saber and Yilmaz 2018).
Multi-ensemble forecasting using several satellite
rainfall products has been performed in previous
studies (Jiang et al. 2014). Other satellite rainfall
products such as TRMM(3B42RT) might be used as
the input for flood modeling in Jakarta as a multiensemble forecasting. However, the temporal andspatial resolutions of other satellite products are inadequate for capturing local heavy rainfall in Jakarta.
Therefore, it is currently challenging to use GSMaP
as the input for real-time forecasting systems. Radar
information adjusted with ground gauge-based

Fig. 17 Flood inundation comparisons for flood event of 2020

rainfall data is a more viable option for forecasting systems. It should be noted that the five rain gauge stations
might be insufficient for capturing rainfall fields in
Jakarta. Hence, radar observation systems should be installed and operated properly to predict rainfall and flood
events in Jakarta in real time, and denser rain gauge station networks would be required to calibrate and assimilate the radar rainfall values based on the ground true
observation rainfall data.
GSMaP Gauge data might be useful for reconstructing and simulating historical flood events to evaluate
and compare past floods in poorly gauged basins.
This is because the GSMaP Gauge can be used to observe the heavy rainfall that occurred in the past.

6 Conclusions
This study was conducted to examine the possibility ofusing GSMaP rainfall data as the input for real-time
flood forecasting in Jakarta, Indonesia. The NRT and
Gauge V7 products of the GSMaP were compared with

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

ground observation rainfall data at several stations andbasin averages formed around Jakarta. The results indicated that the GSMaP Gauge data showed acceptableagreement in simulating the flood hydrograph and inundation of Jakarta. However, the gauge data were unavailable in real time and thus could not be used for realtime forecasting. The gauge data are suitable for replicating historical flood events that occur even in highly
urbanized areas.
The GSMaP NRT product, which provided near realtime rainfall data, was suitable for real-time flood forecasting. However, it is necessary to develop a significant
bias correction method or change the algorithm of the
NRT data set adjusted for urban areas to improve theaccuracy of the simulation results.
Abbreviations
GSMaP: Global satellite mapping of precipitation; RMSE: Root mean squareerror; CCs: Correlation coefficients; NSE: Nash–Sutcliffe efficiency index
Acknowledgements
We appreciate the S-14 project, particularly to Prof. So Kazama at Tohoku
University, for guiding the research project in Indonesia. The hourly observed
rainfall data in Jakarta was provided by BMKG Indonesia under the support
from ITB. We appreciate both BMKG and ITB.
Authors’ contributions
BP analyzed the data and conducted numerical experiments. SK designed
the study and leads the project. RY supported the numerical experiments. NJ
supported the fieldwork and data collections. All authors read and approved
the final manuscript.
Funding
This research was performed by the Environment Research and Technology
Development Fund (JPMEERF15S11415) of the Environmental Restoration
and Conservation Agency of Japan.
Availability of data and materials
The datasets supporting the conclusions of this article are currently notavailable in any repository. But the authors are willing to share the data
based on the requests. Please contact the corresponding author for data
requests.

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
Environmental and Civil Engineering, Graduate School of Engineering,
Toyama Prefectural University, 5180 Kurokawa Imizu, Toyama 939-0398,
Japan. 2Department of Environmental and Civil Engineering, Toyama
Prefectural University, 5180 Kurokawa Imizu, Toyama 939-0398, Japan.
3
Department of Civil Engineering, Pertamina University, Jalan Teuku Nyak
Arief, Simprug, Kebayoran Lama, Jakarta 12220, Indonesia.
Received: 30 September 2020 Accepted: 14 April 2021

References
Admojo DD, Tebakari T, Miyamoto M (2018) Evaluation of a satellite-based rainfall
product for a runoff simulation of flood event: a case study. J Japan Soc Civil
Eng Ser B1 74(4):I_73–I_78
Aonashi K, Liu G (2000) Passive microwave precipitation retrievals using TMI
during the Baiu period of 1998. Part I: algorithm description and validation. J
Appl Meteorol 39(12):2024–2037

Page 16 of 17

Berlinger J, Yee I (2020) 66 people now killed by flooding in Jakarta, and morerain appears to be on the way. CNN World News https://edition.cnn.com/202
0/01/06/asia/jakarta-floods-intl-hnk/index.html. Accessed 1 Apr 2021.
Bricker JD, Tsubaki R, Muhari A, Kure S (2014) Causes of the January 2013 canalembankment failure and urban flood in Jakarta, Indonesia. J Japan Soc Civil
Eng Ser B1 (Hydraulic Engineering) 70:91–96
Budiyono Y, Aerts JCJH, Tollenaar D, Ward PJ (2016) River flood risk in Jakarta
under scenarios of future change. Nat Hazards Earth Syst Sci 16(3):757–774.
https://doi.org/10.5194/nhess-16-757-2016
Chang L, Cheung K, Mcaneney J (2013) Case study of TRMM satellite rainfall
estimation for landfalling tropical cyclones: issues and challenges. Trop
Cyclone Res Rev 2(2):109
Firman T, Surbakti IM, Idroes IC, Simarmata HA (2011) Potential climate-change
related vulnerabilities in Jakarta: challenges and current status. Habitat Int
35(2):372–378. https://doi.org/10.1016/j.habitatint.2010.11.011
Fu Q, Ruan R, Liu Y (2011) Accuracy assessment of Global Satellite Mapping ofPrecipitation (GSMaP) product over Poyang Lake Basin, China. Procedia
Environ Sci 10:2265–2271. https://doi.org/10.1016/j.proenv.2011.09.354
Januriyadi NF, Kazama S, Moe IR, Kure S (2018) Evaluation of future flood risk in
Asian megacities: a case study of Jakarta. Hydrological Res Lett 12(3):14–22.
https://doi.org/10.3178/hrl.12.14
Jiang S, Ren L, Hong Y, Yang X, Ma M, Zhang Y, Yuan F (2014) Improvement ofmulti-satellite real-time precipitation products for ensemble streamflow
simulation in a middle latitude basin in South China. Water Resour Manag28(8):2259–2278. https://doi.org/10.1007/s11269-014-0612-4
Kubota T, Aonashi K, Ushio T, Shige S, Takayabu YN, Kachi M, Arai Y, Tashima T,
Masaki T, Kawamoto N, Mega T, Yamamoto MK, Hamada A, Yamaji M, Liu G,
Oki R (2020) Global satellite mapping of precipitation (GSMaP) products in
the GPM era. In: Levizzani V, Kidd C, Kirschbaum D, Kummerow C, Nakamura
K, Turk F (eds) Satellite precipitation measurement. Advances in globalchange research, vol 67. Springer, Cham. https://doi.org/10.1007/978-3-03
0-24568-9_20
Kubota T, Ushio T, Shige S, Kida S, Kachi M, Okamoto K (2009) Verification ofhigh-resolution satellite-based rainfall estimates around Japan using a gaugecalibrated ground-radar dataset. J Meteorol Soc Jpn 87A:203–222. https://doi.
org/10.2151/jmsj.87A.203
Kure S, Farid M, Fukutani Y, Muhari A, Bricker JD, Udo K, Mano A (2014) Several
social factors contributing to floods and characteristics of the January 2013
flood in Jakarta, Indonesia. J Japan Soc Civil Eng Ser G 70(5):I211–I217 (inJapanese with English abstract)
Kure S, Jang S, Ohara N, Kavvas ML, Chen ZQ (2013) WEHY-HCM for Modelinginteractive atmospheric-hydrologic processes at watershed scale: II. Modelapplication to ungauged and sparsely-gauged watersheds. J Hydrol Eng
18(10):1272–1281. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000701
Kure S, Watanabe A, Akabane Y, Yamada T (2008) Field observations of discharge andrunoff characteristics in urban catchments area. In: Proceedings of the 11th
International Conference on Urban Drainage, Edinburgh, Scotland, 1-5 Sept 2008
Kure S, Yamada T (2004) A study the nonlinearity of runoff phenomena andestimation of effective rainfall. In: Proceedings of the 2nd Asia Pacific
Association of Hydrology and Water Resources Conference, 2:76-85,
Singapore, 5-8 July 2004
Miyamoto M, Sugiura A, Okazumi T, Tanaka S, Nabesaka S, Fukami K (2012)
Suggestion for an advanced early warning system based on flood
forecasting in Bengawan Solo River basin, Indonesia. In: Proceedings of 10th
International Conference on Hydroinformatics, IWA IAHR, No.394
Moe IR, Kure S, Farid M, Udo K, Kazama S, Koshimura S (2016) Evaluation of floodinundation in Jakarta using flood inundation model calibrated by radar rainfall. J
Japan Soc Civil Eng Ser B1 (Hydraulic Engineering) 72(4):I_1243–I_1248
Moe IR, Kure S, Januriyadi NF, Farid M, Udo K, Kazama S, Koshimura S (2017)
Future projection of flood inundation considering land use change and land
subsidence in Jakarta, Indonesia. Hydrological Res Lett 11(2):99–105
Moriasi D, Arnold J, Van Liew MW, Bingner R, Harmel RD, Veith TL (2007) Modelevaluation guidelines for systematic quantification of accuracy in watershedsimulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153
Pakoksung K, Takagi M (2016) Effect of satellite based rainfall products on river
basin responses of runoff simulation on flood event. Model Earth Syst
Environ 2(3):143. https://doi.org/10.1007/s40808-016-0200-0
Saber M, Yilmaz KK (2018) Evaluation and bias correction of satellite-based rainfall
estimates for modelling flash floods over the Mediterranean region:
application to Karpuz River basin, Turkey. Water 10(5):657. https://doi.org/1
0.3390/w10050657

Priyambodoho et al. Progress in Earth and Planetary Science(2021) 8:34

Sayama T, Ozawa G, Kawakami T, Nabesaka S, Fukami K (2012) Rainfall-runoffinundation analysis of the 2010 Pakistan flood in the Kabul River basin.
Hydrol Sci J 57(2):298–312. https://doi.org/10.1080/02626667.2011.644245
Setiawati MD, Miura F (2016) Evaluation of GSMaP daily rainfall satellite data forflood monitoring: case study–Kyushu Japan. J Geosci Environ Prot 4(12):101–
117. https://doi.org/10.4236/gep.2016.412008
Sugawara H, Oda R, Seino N (2018) Urban thermal influence on the backgroundenvironment of convective precipitation. J Meteorol Soc Jpn 96A:67–76.
https://doi.org/10.2151/jmsj.2018-010
Tian Y, Peters-Lidard CD, Adler RF, Kubota T, Ushio T (2010) Evaluation of GSMaP
precipitation estimates over the contiguous United States. J Hydrometeorol
11(2):566–574. https://doi.org/10.1175/2009JHM1190.1
Ushio T, Sasashige K, Kubota T, Shige S, Okamoto KI, Aonashi K, Inoue T,
Takahashi N, Iguchi T, Kachi M, Oki R, Morimoto T, Kawasaki ZI (2009) A
Kalman filter approach to the global satellite mapping of precipitation(GSMaP) from combined passive microwave and infrared radiometric data.
J Meteorological Soc Japan Ser II 87A:137–151. https://doi.org/10.2151/jmsj.
87A.137

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Page 17 of 17