ANALYSIS OF CLIMATE CHANGE IMPACT
USING BIAS-CORRECTED PRECIPITATIONIN ST. PAUL RIVER BASIN, LIBERIA
Cynthia Wantee Davis*
MEE 18712

Supervisors: Assoc. Prof. Miho Ohara**
Prof. Kuniyoshi Takeuchi***Dr. Katsunori Tamakawa****Prof. Hitoshi Ieda*****
ABSTRACTThe coastal area of Liberia usually has heavy rainfall which frequently causes flood along the river. A reporton “National Policy and Response Strategy on Climate Change” in 2018 anticipated that average rainfall
during 2010–2050 will increase by 2.0–5.0% and temperature will increase by 0.4–1.3°C, However,interpretation of climate change projection needs further consideration of uncertainties for implementing
these results into practical flood risk management. This study mainly focuses on the climate change impactin St. Paul river basin, which is noted as one of the flood-prone areas in Liberia. Bias-correction method isfirst applied to the African country on CMIP5 Data Analysis System on Data Integrated and Analysis
System (DIAS) by newly adding reference rainfall data into the system. Then, by comparing bias-corrected
precipitation in the future under RCP 8.5 scenario with the past precipitation, the trend between the past andfuture is quantified. As a result, three Global Climate Models (GCMs) among the selected four suitablemodels in CMIP5 predicted the increase in monthly average precipitation in the future during 2080-2099 in
the St. Paul River Basin while one model predicted decreasing trend.
Keywords: Climate Change, Bias Correction, Precipitation, CMIP5, GCM
INTRODUCTION
Liberia is positioned within the west coast of Africa. It is located between the longitudes of 7030` and11030` west and latitudes 40 18` and 80 30` north. The population is 4.5m (the Republic of Liberia, 2004).
A report on “National Policy and Response Strategy on Climate Change” was published in Liberia in 2018.
The report anticipated that average rainfall during 2010–2050 will increase by 2.0–5.0% and temperature
will increase by 0.4–1.3°C. It indicates that Liberia faces challenge with the impact of climate change
coupled with many socio-economic problems like poverty, poor infrastructure, lack of information
technology and access to finance, and weak institutions and resource competition, among others challenge.
However, interpretation of climate change projection needs further consideration of uncertainties for
implementing these results into practical flood risk management.
Considering the above, primary aim of this study is to analyze the climate change impact using Global
Climate Models (GCMs) data in Couple Model Inter-comparison Project Phase 5 (CMIP5) on Data
Integrated and Analysis System (DIAS), which stores earth observation data to convert it into information__________________________________*Coordinator, Planning and Policy National Disaster Management Agency of Liberia**Adjunct Associate Professor, National Graduate Insititue for Policy Studies (GRIPS) and SeniorResearcher, International Centre for Water Hazard and Risk Management (ICHARM)***Lecturer, GRIPS****Research Specialist, ICHARM*****Professor, GRIPSuseful for disaster management and to make this information available within Japan and overseas. Biascorrection method is first applied to the African country on CMIP5 Data Analysis System on DIAS by
newly adding reference rainfall data into the system. By using the bias-corrected precipitation data, the trend
between the past and future precipitation is quantified. Through this analysis, it aims to recommend future
approaches to reduce flood risk under climate change.
The target area of the study is St. Paul River Basin, which locates in Monsterrado County, Liberia. Figure
1 shows the location of St. River Basin in Liberia The St.Paul River is 25 km upstream of Monrovia. Major
tributaries are Via, Wuni, and Tuma. It has the length of 495km. The total catchment area is 20, 500 km²(7,900 sq. miles), or of which 11,000 km² (4,450 sq. miles) are in Liberia and 9,000 km² (3, 450 sq. miles)
in Guinea. The River has a dam which is called the Mount Coffee Dam, it is a Hydroelectric Plant built in
1966. Most of the basins are dominated by heavy rainfall and the Monsterrado tableland has the largest
water flow during the rainy season from April to September. This gives a high-level raise ranging from 1
meter to 2 meters due to heavy precipitations. The St. Paul river basin has a serious history of flooding
which was reported in 2018 that over 30, 000 people have been affected. Therefore, for future projection,
there is a concern for mitigation. Figure 2 shows the elevation map of St Paul River Basin.

Figure 1: Location of St. Paul River BasinFigure 2: Elevation map of St Paul River Basin

METHODOLOGY
In this study, climate change impact is evaluated byanalyzing the trends of past and future climate using biascorrected precipitation of CMIP5 Data which isaccumulated on DIAS. Figure 3 describes a flowchart ofthe methodology. DIAS has a set of tools that provide theeasy display and analysis of data from the CMIP5, which
has a wide-ranging spatiotemporal resolution. All the 44
models data in CMIP5 data can be downloaded fromDIAS. Usually, each GCM has 7 climate components
such as Precipitation, Air Temperature, Outgoing
Longwave Radiation, Sea Level Pressure, Zonal Wind,
Meridional Wind, and Sea Surface Temperature.
At first, 44 models data in CMIP5 are downloadedrom Figure 3: Methodological approach of the study(DIAS). In order to find the suitable models for the target
area, Spatial Correlation (S_Corr) and Root Means Square Error (RMSE) between each GCMs in CMIP5
data and climate reanalysis data (JRA55) are calculated for each climate components for 6 months (April-September) in Liberia area. The most suitable GCMs for Liberia are selected by considering the values ofS_Corr and RMSE.
For detail analysis of climate change impact, bias correction of the GCM data is essential. Although theCMIP5, DIAS provides a function of bias correction, this function didn’t cover the African country because
the reference data to be used with bias correction only covers Asian countries. Therefore, in this study, thereference data for African country is newly selected and added on CMIP5 Data Analysis System. Amongthe various data, daily precipitation data of CPC Gauge-based analysis is selected as it has been constructed
over the global land areas. In this analysis, gauge reports from over 30,000 stations are collected frommultiple sources including GTS, COOP, and other national and international agencies. Figure 5 shows CPC
historical rainfall data from 1980 to 2005 at the location of four stations inside or near the St. Paul River
Basin, whose locations are plotted in Figure 4. On CMIP5 Data Analysis System on DIAS, the bias
correction of the selected GCMs data is conducted by using the CPC data in Figure 5 and climate change
impact is quantified in the target river basin. Finally, based on the results, future approaches to reduce floodrisk under climate change are recommended.

Figure 4: The locatoion of 4 stations insideand near the target river basin

Figure 5: CPC historical rainfall data from 1980 to2005 at the location of 4 stations

RESULTS AND DISCUSSIONS
Model Selection
Model selection is important to identify the different strengths and weaknesses of climate models; in somecases, especially over tropical region. As the first step, the most suitable GCMs for Liberia were selectedusing CMIP5 Data Analysis System, by comparing the 7 climate components of the models with climate
reanalysis data (JRA55). As a result, four models listed below were selected as the suitable models for thetarget area. Figure 6 shows the spatial distribution of monthly average precipitation in September for 20years during 1980 -1999 based on the reanalysis data, while Figure 7 illustrates that by the selected models.
A: Institute of Atmospheric Physics (LASG) and Centre for Earth System Science (CESS) FGOALS_g2.
B: Community Climate System Model, CCSM version 4. (CCSM4).
C: Community Earth System Model version 1 (CESM1) with CAM5.2 as its atmospheric component.
(CESM1 CAM5).
D: A New Global Climate Model of the Meteorological Research Institute.

A: FGOAL_g2

C: CESM1 CAM5

B: CCSM4

D: MRI-CGCM3

Figure 6: Monthly average
Precipitation during
1980 -1999 based on
the reanalysis data

Figure 7 Selected Climate Models

Result of Bias Correction
With the change of the system on DIAS, the bias correction of the selected GCM data (Figure 7) wasconducted and climate change impact was quantified in the target river basin. Figure 8 shows the monthly
precipitation before and after the bias correction. The bias-corrected monthly precipitation in the past
indicates that it increase in June. Precipitation of the models maintain consistency along with the historical
data (insitu data) for July onward and up to September, gradually decrease in October.

Figure 8: Monthly precipitation before and after the bias correction
corrected rainfall

Climate Change Impact on Monthly Average Precipitation
Figure 9 indicates the monthly average bias-corrected precipitation in 2025-2039, 2040-2059, 2060-2079
amd 2080-2099 in the future, which are predicted by the selected four models.The black line indicates thetrend of historical data. In 2040-2059, three models show increase with one model slightly decreasing, while
three models remain at increase upward December. In Mid-century, from 2060 to 2079, rhe figure indicates
all four model shows increase at the beginning of the rainly seaseon at the end of the rainly season. At theend of the century, from 2080 to 2099, the figure displays a high increase in in all models. In August, one
model show drastically decrease.

2025-2039

2040-2059

2060-2079

2080-2099

Figure 9: Monthly corrected precipitation

Climate Change Impact on Precipitation with Different Return period
Figure 10 indicates the precipipitation with different return period in 2025-2039, 2040-2059, 2060-2079
amd 2080-2099 in the future The black line indicates the trend of historical data. During the rainy season,
frequently precipitation are anticipated to happen while some increasingly extreme dry spell occasions areprobably going to happen with return period somewhere in the range of 5 and 20 in years. Within the timelineof the return period rainfall shows the quantity of to be expanded under this projected climate changesituation.

2025-2039

2040-2059

2060-2079

2080-2099

Figure 10: Precipitation with different return period in the future
Climate Change Impact Considering Trends in Population GrowthThe population of Liberia was 3,476,608 persons in 2008 (LIGIS, 2008). By examining the age structure, it
was found that the population is young and youthful and will continue to fuel further population growth formany years to come. By 2100 the population is expected to be 10,000,000. Therefore the impact of floods
will be worst because the unplanned settlement of people, living in river basin and with houses that are notin accordance with building code and household regulation. In additional to lack of disaster prevention

knowledge with limited climate change education, it is most likely that the effect of disaster on thepopulation will be severe.

CONCLUSIONS AND RECOMMENDATIONSThis study has established a fact about Liberia climate condition by analyzing future and past bias-corrected
precipitation of CMIP5 Data on DIAS. Considering the result, it has arrived at the point which states thatthe St. Paul River Basin in Liberia will experience heavy rainfall which will result in flood and drought in
the Dry season. The wet month becomes wetter and the dry month begin drier. Moreover, different
approaches from other cooperation need to be developed in order to realize the significant amount of output.
The studies give rise to further studies that will cover the entire country river management and flood
propagation system. Therefore, it is with an urgent need for policies development and immediate
implementation. With effective policies implementation, there are many consequences to a disaster that canbe addressed. Considering the outcome of this study, the country poses at the position where all effort should
be diverted to Sustainable Development and mitigating the effect of Disaster. Therefore the below-listed
recommendations should be implemented in the soonest possible time. a) The National Disaster
Management Policy needs Amendment b) Development and understanding of Hazard Map, c) Capacity
Building and Community awareness.

ACKNOWLEDGEMENT
In this study, 25 years historical data of the CPC Unified Gauge-Based Analysis of Global Daily
Precipitation was used. In addition, with the support by Dr. Yamamoto in the University of Tokyo, thesedata wad added to DIAS to conduct the bias-correction of the precipitation. Thank you for your effort.
REFERENCES
IPCC (2007), IPCC Fourth Assessment Report-Climate Change (2014).
Environmental Protection Agency (EPA) (2006), Liberian National Adaptation Programme of Action(NAPA). Liberia, Monrovia.
National Policy and Response Strategy on Climate Change, Liberia (2018)
Climatedataguide.ucar.edu/climate-data/cpc-unified-gauge-based-analysis-global-daily-precipitation
Kjellsson J., Döös K., Laliberté F. B., Zika J. D. (1998), The atmospheric general circulation inthermodynamical coordinates. Journal of the Atmospheric Sciences. 2014 Mar;71(3):916-28.
nunes Correia, Francisco, et al. (1998), "Flood hazard assessment and management: interface with thepublic." Water Resources Management 12.3 (1998): 209-227.