IJERD – International Journal of Environmental and Rural Development (2017) 8-1

Research article
erd

Event-based Rainfall-runoff Simulations using GETFLOWSfor Kourtimalei Catchment in Djibouti
FADOUMO A. MALOW*
Graduate School of Agriculture, Tokyo University of Agriculture, Tokyo, Japan
Email: fatoumaalimalow@gmail.com

SAWAHIKO SHIMADA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email: shima123@nodai.ac.jp

AURELIEN HAZART
Geosphere Environmental Technology Corp., Tokyo, Japan
Email: auhazart@getc.co.jp
Received 1 August 2017

Accepted 27 June 2017(*Corresponding Author)

Abstract This work was focused on the simulation of a surface terrestrial water flow processmodel in the area of Kourtimalei in Djibouti using GIS, RS and GETFLOWS, a physics-based
surface and subsurface fully coupled fluids flow code. A trial and error were used to calibrate
the model using observed surface water level of the pond. The manual calibration wasperformed until the surface water level of the pond RMSE to be 0.40 m with K>0.8.
Furthermore kappa coefficient was used to evaluate the agreement between the pond areas
extents derived from available LANDSAT-8 images during the simulation period with the pond
area extents results of GETFLOWS simulation. The analysis showed that GETFLOWS
successfully simulated the surface water flow process. We conclude that the use satellite
derived datasets can help calibrate and evaluate GEFTLOWS hydrologic model for an
ungauged watershed like in the present case..
Keywords Djibouti, GETFLOWS, LANDSAT-8, simulation, surface runoff

INTRODUCTION
Djibouti is a typical arid country located in the horn of Africa with fluctuating and low rainfall (lessthan 200 mm yr-1) and high temperature in summer (June to August). However, intensive rainfall has
sometimes caused abrupt runoff on bare land surface, resulting in heavy flood damages along thealluvial fan. When such rainfall occurs in the closed watershed, rainfall water accumulates at the lowest
point in the watershed and the ponding area can stay up for several months sometimes. Usually thatponding water is lost through evaporation and seepage in a three to six months period. Kourtimalei
catchment at Grand Bara desert is a good example of a closed watershed where ponding water appears
during intensive rainfall. For a period of two years (May 2012-August 2014) the local government put
in place a pilot farm project in the area investigating suitable irrigation techniques and cultivationtechniques. In this project, source of the irrigation water was set to be a pond collected from surface
runoff of the catchment water.
The purpose of this work is to characterize the runoff process of the watershed by applying
observed precipitation (and other meteorological variables), at scale of hourly interval. Therefore wecreated a 3D model simulating the surface flow process on the basis of spatial data (DEM, land cover,Ⓒ ISERD169

IJERD – International Journal of Environmental and Rural Development (2017) 8-1geology) along with hydrogeological and hydrological parameters and integrating into the flow
computer code GETFLOWS (General purpose terrestrial fluid-flow simulator) (Tosaka et al., 2000).
METHODOLOGY
Study Area
Located near the Grand Bara desert in the southern part of Djibouti, Kourtimalei watershed records anannual precipitation that ranges approximately from 150 to 200 mm yr-1. The land cover distributioncovers mostly bare land with sparse shrubs (Acacias), rocks, and alluvial fan surfaces on the steep hillarea. The geology of the area is comprised of alluvium sediment group along the alluvial fan or in thePetit-Grand Bara desert and of basaltic rocks like Dalha basalts (3.4-9 Myr) Stratoid basalts (1.5-3.4Myr) (Jalludin et al., 2003) (Fig. 1). The alluvium was found to be comprised of loam, sandy loam andgravel deposit which has various grain sizes. The topography influences best explains the observabledifferences of the soil texture and in the infiltration capacity seen tin the catchment (Toyoda et al.,2015).

(a)(b)(c)Fig. 1 Location of the study area; (a) pilot farm near Kourtimalei pond;(b) Kourtimalei pond; (c) pumping engine
As in Djibouti, precipitation is sometimes intense and causes floods therefore the government ofDjibouti has decided to promote the active mobilization of surface water for agriculture. Therefore,around Kourtimalei, a pond reservoir was created (volume capacity 5,400 m3) (Fig. 1.b) and irrigatedcrops have started on a small scale (pilot farm of 0.65 ha) (Fig. 1.a) with a small pumping engine (Fig.
1.c). The major problem of this source of irrigation remains the instability of the reservoir volumes.
Most of the water is lost either through evaporation or seepage (JICA, 2014).
Data Collection
The key datasets enabling the implementation and testing of the numerical model are based on thewidely published information but also site investigation data in the Kourtimalei basin. It includes thefollowings:Ⓒ ISERD170

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1) In this work, we used 4 LANDSAT-8 images (July 28, September 14 and 30, and December 03)
for the pond extent mapping and the area measurements. The images represent also a crucial partof the validation phase of the simulation. It offers a good combination of two months coveragewith an acceptable spatial resolution (30 m pixels).
2) ASTER GDEM data (30 m resolution DEM) published by NASA and The Ministry of Economy,
Trade, and Industry (METI) of Japan was used as elevation data for the watershed area. The
vertical accuracy was improved by modifying with point elevation data from ground survey in thereservoir area.
3) Observed precipitation records at different location in the watershed indicating an average of less
100 mm month-1. Although the small scale of the area, it was found that the amount of rainfall isn’t
homogenously distributed (Toyoda et al., 2015). Evapotranspiration was estimated from potential
evapotranspiration and represents 84% of the annual average precipitation.
4) Hourly record of the water level of pond collected from sensors within Kourtimalei reservoir.
5) The surface geology is from the national geology map (ORSTOM, 1986)
6) The land cover map with 3 classes (rock, soil and vegetation) was derived from LANDSAT-8
images by unsupervised classification (Fig. 1).
7) The fluid properties of water and air are represented by the typical value in the standard conditions.
The soil is mainly comprised of loamy and sandy loam. The loam soil hydraulic conductivity
ranges from 50 to 100 mm h-1, while for the sandy loam soil texture, larger ranges (160-190 mm h1) were recorded (Toyoda et al., 2015). The permeability of basaltic rock, weathered rock on thesurface was estimated by pumping test and considered to be small as shown in Table 1 (Jalludinand Razack, 1994)
Table 1 Hydraulic parameters usedGeology
Loamy soil
Alluvium deposit
Weathered rockBasalts

Intrinsic
Permeability (cm/s)
5.63 x 10-2
1.00 x 10-2
1.00 x 10-6
1.00 x 10-5

Porosity (%)
4040
1010

GETFLOWS Modeling and Verifications
The General purpose terrestrial fluid-flow simulator (GETFLOWS), developed by Tosaka et al. (2000),
was used to simulate rainfall runoff events at the study site. It is a physically based model that solves
both the surface and subsurface systems simultaneously by employing an integral finite difference
method approach for solving the governing equations. The natural system is discretized into small cells
in order to obtain exact solutions (Magnus et al., 2011). The governing equation for air/water twophase fluid flow in GETFLOWS is expressed by the following equation of mass conservation (Eq. 1).
𝜕−∇ (𝜌𝑝 𝑢𝑝 ) − 𝜌𝑝 𝑞𝑝 = 𝜕 (𝜌𝑝 ∅𝑆𝑝 )𝑡(𝑝 = 𝑤𝑎𝑡𝑒𝑟, 𝑎𝑖𝑟)

(1)

Where 𝑢𝑝 is the fluid flow velocity (m s-1), 𝜌𝑝 is the fluid density (kg m-3), 𝑞𝑝 is the production
and/or injection rate (m3 m-3 s-1), ∅ is effective porosity (-), 𝑆𝑝 is saturation degree (m3 m-3) and p is asubscripts that indicate quantities on each fluids phases. In the surface GETFLOWS solves theManning’s equation for the horizontal surface water flow by using the linearized-diffusion-wave
approximation equations (Tosaka et al., 2000). The flow in the subsurface and the vertical flow in thesurface are expressed with the Darcy’s equation .GETFLOWS solves the saturated, unsaturatedⒸ ISERD

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subsurface flow and surface flow in a continuum approach within a single matrix. Nonlinear
interactions between all components of the system are simulated without a priori specification of thecoupling between surface and subsurface flow. Therefore streams formation are purely based onhydrodynamic principles governed by recharge, topography, hydraulic conductivity and flow
parameters (when water is ponded due to surfaces flux exceeding the infiltration capacity of the soil ordue to excess from subsurface soil saturation).
The study area was discretized spatially in 2D plane by grid blocks of approximately 30 m. The
secondary data (i.e. elevation, geology. land cover) were assigned into the corner-point coordinate of
the 2D grid blocks. Then the 2D plane grid-block system was enlarged into depth direction anddiscretized again along Z axis to generate the 3D grid-block system. The total number of grid cells
recorded to be 100,890 cells (Fig. 2).

Fig.2 Three dimensional model (topography)
Our aim is to analyze the runoff characteristic of the watershed that creates the Kourtimalei pond
during intense precipitation events. Thus the model was used for a short-term reproduction of therainfall events of August 01 to December 03, 2013. Recorded daily fluctuations of meteorologicalconditions were constrained onto the model. Then, GETFLOWS was calibrated using the available
daily pond water elevations observations for the period between August 01 and December 03, 2013 inKourtimalei. RMSE indicator is used to assess model accuracy in matching the model-simulated runoffwith observations of the differences in water level records of the pond. The criterion is used as an
objective functions for the calibration. Furthermore the Kappa coefficient is used to assess the accuracyof the model by comparing the pond area extent derived from LANDSAT-8 satellite images during thesimulated period with GETFLOWS simulation result at the 4 times when clear LANDSAT-8 images
were available (i.e., 10 am on August 29, September 14 and 30, and December 12, 2013) (Fig. 3).
RESULTS AND DISCUSSION
In this section, we present the results of the calibration process of GETFLOWS. The Root meansquared error (RMSE) has been used for comparison of simulated pond water levels with observed
water levels for the period of August to December.
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Fig. 3 LANDSAT-8 in false color (5:4:3) pond extents in August 29th,
September 14, September 30 and December 12th 2013
The trial and error method yielded for an optimized parameter combination that had a RMSE of0.40 m (Fig. 4). The Comparison of GETFLOWS-simulated spatial extent of the pond with satellitebased observations images (LANDSAT-8) (Fig3) was to provide a further evaluation of theGETFLOWS performance in simulating the spatiotemporal evolution of the pond extent during thecalibration period. Fig. 5 shows the statistical comparison between GETFLOWS and LANDSAT-8
which indicates that a rather good agreement between the obtained pond spatial extents ofGETFLOWS and LANDSAT-8.

Water Level (m)509508507506505504503502
2013 1-AugustObserved water level
Simulated water level2013 13-August

2013 26-AugustFig. 4 Comparison between the observed and simulated water levels
during the month of August, 2013

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Pond area simulated fromGETFLOWS (km2)

1.2
1.0
0.8
0.6
0.4
0.2
0.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2
Pond area from Landsat
imagery (km2)

Fig. 5 Comparison of the surface
pond area extent between
LANDSAT vs GETFLOWS

Fig. 6 Simulated water depths of Kourtimalei pondat four time step with pond profile on Dec 03
For further verification of GETFLOWS simulations results, the objective function selected toguide the agreement fit between satellite-based pond extent and GETFLOWS modeled extents was theKappa coefficient as shown in Table 2. Overall, GETFLOWS could recreate the rainfall-runoff process
successfully for each of the date. More 80% of the simulated pond extent matches the spatial extent
detected by LANDSAT-8 at the given date. Less than 20% of the simulated spatial extent of the pond
was either misclassified as flooded area (false) or either classified as non-flooded not all (missed).
Table 2 Comparison between GETFLOWS and LANDSAT simulated area vs derived area
Date ofevents
29/AUG
14/SEP
30/SEP
12/DECLANDSATdetected area
(km2)
1.00
0.86
0.77
0.22

Simulated area
(km2)
0.90
0.73
0.76
0.21

GETFLOWSCorrect
Missed
(%)
(%)
85.64
12.16
80.00
13.16
78.86
11.03
71.22
6.80

False(%)
2.20
6.83
10.10
21.99

Kappa
Statistic
K
0.88
0.84
0.83
0.82

CONCLUSION
In this work, we have run a rainfall-runoff simulation with GETFLOWS of the Kourtimalei watershed,and calibrated the model by using both water levels observations data, and then validated with satellite
derived datasets of the spatial extents of Kourtimalei pond. GETFLOWS model was able to reproduce
the rainfall-runoff process of the watershed fairly with RMSE of 0.40 m and statistics of Kappa largerⒸ ISERD

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than 0.8. This approach is in contrast with the conventional method of runoff modelling technique. The
proposed approach implements a distributed hydrologic model and further calibrates the model
parameters through satellite derived datasets that are freely available in the public domain. The impactof such a demonstrated technique is to provide a cost-effective tool in poorly gauged or ungauged
watershed like in this case study.
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