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Projection of extreme river level in the slums of San Salvador, El Salvador, underclimate change conditions, using a large ensemble climate simulation dataset〇Mariana Beatriz AVALOS CABRERA, Masafumi YAMADA, Tomoharu HORI
Introduction

Caribbean region and because of this, climate change

In El Salvador, 88.7% of the territory is considered

impact projections are inconclusive in this zone. Inat risk, and 95.4% of the population lives there. The

general, Latin American research is skewed towards theMetropolitan Area of San Salvador (MASS), theAmazon River. In addition, they identified that small

country’s capital, is home to 792 slums in which 32%

tropical basins (<1,000 km2) are rarely studied,of the population is settled. They occupy 45% of its

constituting only 4% of the analysis. This study focusesurban land; unfortunately, 26% percent of them are inon filling these gaps.

identified risk sectors, such as inside river channel

Methodology

slopes. This study addresses the necessity of analyzing(1) d4PDF

the impact of climate change on these slums. The target

Hourly precipitation extracted from the HFB 4K

research is a catchment of 87 km2, where four slum(future) scenario (90 ensembles) and the HPB (past)

locations inside river channels were selected as casescenario (100 ensembles) was first bias corrected using

studies: 1) El Tanque, 2) La Cuchilla, 3) Diez dea non-parametric method and local gauged rainfall as aSeptiembre, and 4) Los Angeles B. The main objective

reference. After that, rainfall events from both datasets

is to assess the impacts of climate change on thewere extracted using the Minimum Interval TimeAcelhuate River and tributary's flow level and water(MIT), which equals 9 hours for this basin. After that,discharge and the consequent implications for theextreme rainfall events were defined as the top threeslums affected by the 2020 tropical storms in the MASS

yearly rainfall events with the maximum cumulativein a 4K (RCP8.5) warming scenario, using the database

precipitation in all the Catchment Response Timefor policy decision-making for future climate changes(CRT) windows. 36,055 and 31,708 extreme rainfall(d4PDF). This study answers the following research

events were extracted from the HPB and HFBquestions: 1) How will the inundation probability of thedatabases, respectively.

selected slums change in the future? 2) How fast does(2) RRI Model

the water level rise at the slum’s location before a

The RRI Model is a two-dimensional, distributed

flooding event? 3) How will the flashiness of thehydraulicselected slums’ location in the river change in thesimultaneously simulating rainfall-runoff and flood

future?

inundation4). The model was built using topography

Research Novelty

data from a locally sourced DEM, and more than 100Eccles R. et al1) and Jahandideh-Tehrani, M. et al.2)
did a quantitative analysis on the climate change impact
on riverine flooding and river streamflow in the tropicaland subtropical regions and pointed out how there is noresearch on this topic in Central America or theandhydrologicalmodel

capableofarbitrary river cross-sections were integrated into theRRI model. The model’s manual suggested parametersthat fit the soil texture of the target basin were tested
using local data, and the best-performing set waschosen to run the model. The Kling-Gupta efficiency

(KGE), the Nash-Sutcliffe efficiency (NSE), and theshows how the basin has a medium-to-low generalrelative Peak Error (PE) were used to evaluate theflashiness but increases to medium-to-high when it

model performance. The results can be seen in Table 1.

comes to extreme events.

The chosen parameter set is sandy loam and clay for theTable 4. Change in RBI flashiness.

mountain and city land uses, respectively.
Table 1. RRI Model Performance.

Results and discussion

Conclusions

In general terms, future river levels and waterThis research found an overall increase in extreme

discharge at the four slum locations are expected to beriver discharge and flow level in all slums, increasing

higher than the extreme levels of the past.

inundation probability in three out of four slums, and(1) Research Question 1

increasing ΔWL in all slums, leading to increasingThis study considers a slum floods whenever thewater speed in three out of the four slums. In addition,

river flow level reaches the slum level. The change inthe flashiness of the basin has a significant variance ininundation probability of each slum can be seen in

extreme rainfall events. Even though the study area isTable 2.

small, slum locations will face different impacts ofTable 2. Change in Inundation Probability.

climate change. In the future, it is crucial to identify
these local characteristics of the rest of the slums in thecity for the accurate formulation of relocation plans anddisaster risk management.

(2) Research Question 2
The Delta Water Levels (ΔWL) three hours before
the flood and at the time of flooding can be seen in
Table 3. Slums 1, 2, and 4 have increased future ΔWL,
whereas in slum 2, they remain the same. Extreme
rainfall events show a significant difference compared
to average values.
Table 3. Change in ΔWL.

References
1) Eccles, R., Zhang, H., & Hamilton, D. (2019). A review of theeffects of climate change on riverine flooding in subtropical andtropical regions. Journal of Water and Climate Change 10(4),687-707.
2) Jahandideh-Tehrani, M., Zhang, H., Helfer, F., & Yu, Y. (2019).
Review of climate change impacts on predicted river streamflowin tropical rivers. Environmental monitoring and assessment 191,1-23.
3) Sayama, T., Ozawa, G., Kawakami, T., Nabesaka, S., &Fukami, K. (2012). Rainfall–runoff inundation analysis of the2010 Pakistan flood in the Kabul River basin. . Hydrological

Regarding the maximum water levels, the slums will
have future flood water rising speeds of 0.67 m/h,0.09m/h, 0.64m/h, and 0.83 m/h, respectively.
(3) Research Question 3
The Richards-Baker Index (RBI)5) was used tomeasure the flashiness of the studied locations. Table 4

Sciences Journal, 57, 298–312.
4) Baker, D. B., Richards, R. P., Loftus, T. T., & Kramer, J. W.
(2004). A New

Flashiness Index:Characteristics

andApplications to Midwestern Rivers and Streams. JAWRA
Journal of the American Water Resources Association, 40(2),503-522.