Integrated flood modeling for flood hazard assessment in Kigali City ...

Integrated flood modeling for flood hazard assessment in Kigali City, Rwanda Herve V. HABONIMANA1, Jean Pierre BIZIMANA2, Ernest UWAYEZU3, Joseph TUYISHIMIRE4, John MUGISHA5 12

University of Rwanda/College of Science and Technology/Geography Department; and affiliate at Centre for Geographic Information Systems (CGIS), Huye, Rwanda.

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University of Rwanda, College of Science and Technology, Centre for Geographic Information Systems (CGIS), Huye, Rwanda. [email protected]

I.

INTRODUCTION

Abstract—Due the rapid urbanization coupled with climate change, the drivers of urban floods are shifting and their impacts are accelerating. Recently, Kigali suffered from major floods taking lives and damaging infrastructures. Being a very recent phenomenon, no quantitative research has been done on flash floods in Kigali City. The main objective of this research was to bridge the gap in the existing flood literature on Rwanda by analysing floods in Kigali City based on rainfall events of different return periods. In addition, the research aimed to collect necessary information on processes believed to have influence on flood dynamics in Kigali City. Namely: soil, land cover, topography, and drainage systems. The OpenLISEM software was used to model the flood hazard by simulating the runoff and flash floods for a single rainfall event. Three scenarios have been simulated based on synthetized daily rainfall events of different return periods. Due to the lack of measured discharge and flood information, the results of the model were compared to the highest flood depth experienced by local community. The model was calibrated by reducing the saturated hydraulic conductivity and manning’s n to increase the correlation with the reported flood depth. OpenLISEM predicted floods in all the location survey and agreed with a reasonable correlation to the population. However, there is a need of comparison of the findings of this research with other researches using other techniques of flood models calibration. By displaying the flood extent, depth, discharges and velocity the results from this modelling approach are salient for the urban policy and decision makers aiming disaster risk reduction.

The process of urbanization is among the causes of the continuous increase of flood hazard events and the associated losses worldwide; mainly due to the increasing impervious surfaces and the exposure of people and their wealth (Muis et al., 2015, Barrow, 1995, Douglas et al., 2008). Urban impervious surfaces, houses, roads, and many more reduce the infiltration capacity of the former rural catchment (Hollis, 1975, Burns et al., 2005, Brody et al., 2008, Nirupama and Simonovic, 2007, Li et al., 2013). This results into the upsurge in the amount of water available for runoff and leading to flash floods. In addition to the increase of the impervious surfaces, there is the soil compaction. According to the research done by Gregory et al. (2006) and Olson et al. (2013), the infiltration capacity of the soils can reduce up to 70 to 99 percent due to the compaction induced by construction activities. Besides, some other unintended activities like moving trucks and humans also increase compaction (Randrup and Dralle, 1997). On the other hand, the urbanization process influences the positive trends in total losses associated with flood hazard. As reported by Bouwer (2010), Jonkman (2005), Roger (2012), the floodplains being the preferred places for urbanization, continuous increase of the population and their properties in those flood prone areas significantly increase the impacts of flood regardless of the change in the intensification of the frequency of hazardous flood events.

Keywords—Flash floods modelling, Kigali City, Local Community, OpenLISEM, Rainfall, Urbanization.

In Rwanda, mainly in Kigali City, heavy rainfall events cause rapid surges in the flow of rivers and drainage systems leading to floods downstream. Two types of floods are common: river and flash floods. The flash floods are

2 GeoTechRwanda 2015 – Kigali, 18-20 November 2015 observed mainly in urbanized areas where the rapid urbanization of hill slopes has dramatically increased the runoff water (REMA, 2011, REMA, 2010). In addition, inadequate drainage systems and constructions in floodprone zones have made many neighbourhoods of Kigali City highly susceptible to flooding. The flood impacts are accelerated by the physical structure of the area (mainly hilly topography) coupled with demographic pressure on scarce land resources (Bizimana and Schilling, 2010). Although Flooding has been experienced since 1960s in Kigali City, but its frequency has significantly increased since 2000s, and its impacts have been great on human development, properties, infrastructures as well as environment (Nsengiyumva, 2012). In 2006, 27 per cent of buildings in Kigali City were in flood-prone zones within the Nyabugogo River floodplain where vulnerable populations, infrastructures, and various economic activities were exposed to flash floods. That was why in the past few years, numerous flood events resulted into the destruction of houses, shops, roads, carrying away cars and even causing loss of lives (Bizimana and Schilling, 2010, REMA, 2013).This happened mainly in the long rainy season from April to June and September to December where rainfall events may extend from several hours to days although the most hazardous rainfall events w mostly of short duration, around two hours, with very high intensities. Considering the magnitude of flood impacts, the Kigali City Municipality decided to relocate many homes, shops and Kiruhura market from the flood prone areas, and one factory was closed. The relocation was mainly based on the study done by Bizimana and Schilling (2010) who analysed floods based on the local expertise. While flood hazard study focuses on severity and probability, the study conducted by Bizimana and Schilling (2010) was limited only to the severity of the hazard. The hazard zonation map was obtained by elevating a certain height over a digital elevation model (DEM). However, a comprehensive analysis of flood requires a probabilistic analysis of flood’s main dynamics and Flood Risk Assessment explore the flood process sequence from precipitation, runoff generation and accumulation in the catchment, flood routing in the river system, probable failure of flood protection measures, inundation to economic damage (Apel et al., 2006, Apel et al., 2004). There has been a lack of holistic study of floods in Kigali mainly because of the lack of data and expertise. The main objective of the research therefore was to bridge the gap in the existing flood literature on Rwanda by analysing floods in Kigali City based on rainfall events of different return periods. In addition, the research aimed to collect necessary information on processes believed to have influence on flood dynamics in Kigali City. Namely: soil, land cover, topography, and drainage systems. The outcomes of this research are useful for future planning initiatives of different institutions operating in the country and will serve as a baseline for flood disaster risk reduction in Kigali City.

II.

STUDY AREA

The study area of this research is located in Kigali city, the capital of Rwanda, which is a large region of hills and valleys, referred to as the Central Plateau. More specifically, the study area is part of Nyabugogo catchment with two main streams of channels taking water to Nyabugogo River; Mpazi and Rwezangoro. As shown in Figure 1-1, the catchment boundary of the study area is located in the administrative sectors of Nyamirambo, Rwezamenyo, Nyakabanda, Gitega, Nyarugenge and Muhima of Nyarugenge District; Gikondo, Kicukiro, Kigarama and Gatenga of Kicukiro District; Gisozi, Kacyiru and Kimihurura of Gasabo District in Kigali City between 30°2´13"E and 30°25´56"E and between 1°54´37"S and 1°59´51". With more than one million people (representing 10.8 per cent of the country’s total population) living in around 483912 houses (NISR, 2012), Kigali has the highest population density of the country (REMA, 2013, MIDIMAR, 2015). Kigali keeps growing by leaps and bounds over the hilly terrain surrounding the centre (REMA, 2013). The landscape of Kigali City is developed in quartzitic substratum. The elevation ranges between 1372 and 1866 meters from the mean sea level. The slope varies between 10-45% for the largest part of the zone. Within the study area boundary, most of the thalwegs are generally middle, large and narrow, with both intermittent and permanent drainage system. They are almost dry, except the part of the wetland located in the southeast of the study area, which is flooded during the great rain season. The main rivers found in that zone are Mpazi, Katabaro, Nyakabanda, Rwezangoro, Rugunga, Rwampara, Rugenge, Karuruma and Rwintare. The landscape is a result of the successive folding and erosion of the earth strata. Kigali City is characterized by a humid tropical climate. The seasons are marked by an alternate succession of rains and droughts. According to three levels of drought (atmospheric dryness, pedological dryness and geological/hydrological dryness), the site experiences four months of long dry season (mid-May to mid-September) followed by a short rainy season (Mid-September, October, November and mid-December), another short dry season (mid-December, January and mid- February) and finally a long rainy season that extend from Mid-February, to midMay (REMA, 2013). Due to climate change or variability, the four seasons are sometimes irregular, and one cannot precisely fix the temporal limits of each season. The rainy season may extend for some weeks into the dry season and vice versa. In general, the average precipitations range from 65 mm to 200 mm per month. III.

METHODS

The analysis of flood hazard in Kigali was done using openLISEM. OpenLISEM is a data hungry model that requires detailed data on topography, land cover, soil’s hydraulic properties, and the drainage systems. It is well

3 GeoTechRwanda 2015 – Kigali, 18-20 November 2015 known that every model needs calibration and validation data. For this research, most of the baseline data required for flood hazard analysis were mostly missing or presented gaps, and therefore not ready to be used. This would have affected the outcome of the study or make it impossible. However, below we explain in details how we overcame the challenges related to data gaps and unavailability for openLISEM’s input requirements.

temporal details. The model is designed to simulate the effects of detailed land use changes on runoff, flooding and erosion during a single rainfall event. OpenLISEM was chosen because it is a model designed to be used in disaster risk management. Figure 1-2: Simplified flowchart of OpenLISEM and input data

Figure 1-1: Study area location and topography.

OpenLISEM This research used openLISEM model developed by Jetten (2013) and De Roo et al. (1996). OpenLISEM is an open source model that that simulates the surface water and sediment balance for every pixel of a raster grid. The simulations are event based and have very high spatial and

The estimations made by OpenLISEM are either no spatial or spatial. As summarized in Figure 2-2, most of nonspatial hydrological processes related to the water balance are calculated for the pixel itself in 1D. Spatial processes are the hydraulic processes of runoff, channel flow, and flooding from the channels. The rainfall is received by bare soil surface, roads, vegetation and buildings. From the net received rainfall, the interception and infiltration are subtracted in 1D for every pixel by taking into consideration the vegetation cover and the buildings. The infiltration is subtracted from net rainfall to derive total amount of water available for runoff. The available overland flow is calculated by subtracting surface storage. Using a flow network, local drainage network (ldd), the water moves down slope with a 1D kinematic wave and added to channel

4 GeoTechRwanda 2015 – Kigali, 18-20 November 2015 cell. Once the channel water becomes higher than the channel depth, the water is spread to the surrounding area with a 2D flow over the DEM. The addition of flooding makes openLISEM into a combined 1D/2D model. One of the key characteristic about the model is the ability to handle high spatio-temporal resolution and sub-pixel surface properties. A pixel can comprehend a bare soil, crusted/compacted soil, vegetated surface, a road, a house and a channel. These surface characteristics are provided in distinct layers as fractions: the base layer is made by the soil surface with its hydrological characteristics and the user supplies additional maps that cause additional hydrological processes in the model. For instance, the presence of vegetation results in interception on a part of the pixel, presence of a house in interception and a partly impervious surface, and a road has no infiltration…etc. Model input

OpenLISEM uses maps generated by a GIS package, PC Raster, form five main layers: Rainfall, DEM, soil physical properties, land cover, and infrastructures (building foot prints, roads, channels, bridges, and culverts). All the types of data were generated from the existing dataset or collected as explained below:

DEM The DEM of the catchment was extracted from the DEM of 10m resolution provided by Rwanda Natural Resources Authority (RNRA) (Figure 1-1). The DEM was useful in the calculation of local drainage direction (ldd), slope, slope gradient of the drainage system, and the identification of the main outlet. Drainage data Characterization of the entire drainage system is essential for accurate model of the flood dynamics. It is worthwhile to mention that OpenLISEM simulates floods when the height of water in the channel is larger than the channel’s depth. Likewise, the drainage systems cannot deal with the voluminous amount of runoff water and lead to localized floods (Douglas et al., 2008)as it is the case in Kigali City. The measurements of top width, bottom width, and depth of the main drainage channels( in Figure 1-4)that were needed as input in the simulation were taken using measuring tapes. In total 97 measurements have been made for the main channels taking water to Mpazi or Rwezangoro. As shown in Figure 1-4, the highest channel depth measured was 5m, almost 100% of the drainage channels had a depth below 3 m.

Figure 2-3 : Drainage channel size characteristics used as input in the simulation

Figure 1-4: from left to right : the digital elevation model of the study area, land cover of 2013, information on soil texture.

The widest channel on the top was 14m while the bottom was 12m. Beside, very few outliers, almost the 100% of channels has a top width of around 7m or below with 50% being below 2m. On the other hand, 100% of the bottom width measurements were around 4m and below while around 75% are below 2.5m. In general, most of the channels are wider at the top and became narrow at the bottom. Land cover The landcover within the catchment of the study area was generated through a supervised classification of a highresolution worldview image (50 cm pixel size) acquired in 2013. The classification followed the methodology proposed by (Thanapura et al., 2007). The classification was done in three steps; the first step was to classify the image into classes based on NDVI techniques. The second step was to subtract the building footprint from non-vegetation class. The third step was to extract road network from none vegetation class. The important land cover classes identified in the image were the vegetation cover (forest and glass land), built up areas, road network, bare lands, and water bodies.

Soil Foursoilparametersareneededforsoilhydrologymodelling usingOpenLISEM (Jetten, 2013); the saturated hydraulic conductivity (Ksat in mm/h), porosity, depth and crusting. The Ksat was considered as equivalent to the stationary infiltration rate, and it was used to determine the infiltration curve. The porosity (cm3/cm3) determines the soil water storage capacity. The porosity and the depth of the helped to determine the short-term storage capacity and time until saturation excess flow. A part of the soil’s physical properties of the catchment were extracted from the dataset collected by the conventional national soil survey that started in 1981(Verdoodt and Ranst, 2006). The dataset contained the information of the soil’s texture, bulk density, and the depth. Other information related to porosity, saturated hydraulic conductivity where generated using the software developed by Saxton and Rawls (2009); Soil Water Characteristics: Hydraulic Properties Calculator. The software package estimates soil water tension, conductivity and water holding ability based on the soil texture, organic matter, gravel content, salinity, and compaction

Table 1-1: Input soil’s physical properties for different soil units of the catchment Soil texture

Sand 30 33 52 60

20 33 6 12

50 34 42 28

bulk density 1.36 1.4 1.48 1.51

65 20 7

25 60 46

10 20 47

1.46 1.37 1.24

% Clay Clay loam Sandy clay Sandy clay loam Sandy loam Silt loam Silty clay

Silt %

Clay %

Flood scenarios modelling and calibration Three scenarios were simulated depending on daily rainfall events of different return periods as presented in Table 1-2estimated by Nduta (2015) . However, as openLISEM requires very high temporal resolution rainfall data (ideally not exceeding 30min), the daily rainfall was synthetized using the online tool developed by USDANRCS (2003).The rainfall resolution was set to 10-minute time step proposed by Hessel (2005) to be the optimum timestep. The simulation time was set to 1380 minutes and a time step of 15 minutes while the pixel size was 15 meters. The results of the simulations model are statistics and maps of infiltration, runoff, flood depth and velocity, and the flood warning time. The results were calculated at the main outlet or at the catchment scale. However, the challenge was the calibration as there no calibration or validation data in terms of observed discharge, flood depth or extent. A questionnaire survey was conducted in the areas where people and infrastructures are believed to suffer from floods. The main information needed was flood depth, duration, and extent in the floodplain of the most extreme event

Ksat (mm/h) 0.78 4.56 0.84 7.84 50.34 12.19 3.81

Porosity (%) 54.67 53.33 44.15 41.06

Area (Km) 0.55 25.15 2.50 21.79

44.91 48.30 43.64

7.30 0.82 3.76

remembered. Table 1-2: Return period rainfalls of daily rainfall over Kigali City. Scenario 1 2 3

Return P 5 i d(Y 15 25

)

Rainfall(mm) 70.4 89.1 97.4

In order to minimize the inaccuracy in answers provided, the collection of information was carried out by taking information to closer point for later comparison. In addition, the information on the time expressed in years that the respondents have been living in the catchment was asked. The collected information was compared to the outcome of the model simulations and used as calibration data by taking into consideration the return period of a relative rainfall. IV.

FLOOD CHARACTERISTICS

OpenLISEM successfully simulated floods for different scenarios. In total four scenarios have been run. The results of the model are summarized in Table 2-1, and Figure 2-1. Kigali soils although compacted with the ongoing

6 GeoTechRwanda 2015 – Kigali, 18-20 November 2015 urbanization store a huge amount of rainfall water. As shown in Table 2-1, the infiltration took more than 70% of rainfall water in all scenarios. This is due probably to the long duration of rainfall with very low intensities. As explained by Huang et al. (2012) and Li and Shao (2006), soil infiltration is highly linked with rainfall intensity and rainfall duration. Note that, the simulations used daily rainfall subdivided into 15 minutes time step following the approach proposed by USDA et al. (2003). This might have decreased the rainfall intensities thus increasing the infiltration amount which is inversely proportional to rainfall intensities. In general, floodplains are characterised by very high

infiltrations. The rest of the catchment, excluding built up areas are characterized by zero infiltration, which his less than the half of the infiltration of the floodplain. Flood characteristics vary considerably from one location to another. As portrayed by Figure 2-1, floods can be shallow with few centimetres above the ground or very deep with more than one meter of flood depth. In total, around 524499 m2 are estimated to be covered by more than 1m of flood water with a return period of 25 years. The largest area predicted to be affected by flood is 2.53 km2 or 4% of the total area of the catchment in the fourth scenario.

Table 2-1: Simulated flash flood characteristics over Kigali for the three scenarios.

Total rainfall (mm) Total discharge (mm) Total infiltration (mm) Total discharge (m3) Peak discharge (l/s) Peak time rainfall (min) Peak time discharge (min) Discharge/Rainfall (%) Flood volume (max level) (m3) Flood area (max level) (m2)

Scenario 1

Scenario 2

Scenario 3

70.40 13.12 55.34 162459.64 56106.30 690.33 736.67 18.63 898634.17 2007000

89.10 20.84 65.10 262550.91 97115.36 690.33 737.33 23.39 1301618.90 2174850

97.40 24.37 69.10 309685.26 107795.29 690.33 736.67 25.03 1456239.86 2238300

The 63 km2 of the catchment produce a big volume of water. 309685.26 m3 is the estimated total discharge at the main outlet for 25 years return period, a daily rainfall of 97.4mm the catchment can generate (…. a connection is missing between 97.4 mm and 1 million of cubic meters: check the phrasing ….) more than 1 million cubic meters of flood water after 25 hours of simulation (Table 2-1). The Mpazi section of the channel has the highest estimated peak discharge of 107.5 m3/s for 25 years return period compared to the rest of the catchment. This agrees more or less with the prediction done by (Hakizimana and Munyaneza, 2014) who estimated 118.9 m3/s to be a peak runoff discharge for 30 years. The high peak runoff discharge can be linked with the very densely urbanized steep slopes of the catchment and the types of construction materials of channels. Numerous deaths, infrastructure damages have been reported in Mpazi in the past years linked with very high runoff water velocity (MIDIMAR, 2015). The results of the simulations were compared with the results of the field survey conducted where the population living in floodplain who were asked about their flood experience. The information collected on flood depth was used. The preliminary simulation shown a poor correlation between the model and the population, as the model was not predicting floods in the surveyed locations. The model was

calibrated based on the first scenario of a rainfall of five years return period. The first scenario was selected because the average time the population have been living in the catchment’s floodplain was 4.8 years. The model was calibrated on Ksat and manning’s n (saturated hydraulic conductivity) with a multiplication value of 0.8. The objective of the calibration was to increase the correlation between the prediction of the model and the flood depth experienced by the population. After the calibration, openLISEM predicted floods in location surveyed and a correlation test was performed. The correlation between the population and the results of the model where 0.48, 0.5, and 0.52 for the three respective scenario. More calibration has been tried to increase the correlation, however openLISEM was predicting unrealistic flood depth for the catchment in general. The scatterplot in Figure 2-3 shows how the model agrees with the population’s experience. Many reasons can explain the low correlation between the model and the flood depth reported by the population. It can be either errors in the input data, the local elevation not captured by the DEM used, the error in the report from the population due a poor guess. The study was not done in the flood period it is possible that the population were starting to forget about the past flood events.

Figure 2-1: Simulated flood depth maps for the three scenarios

Figure 2-2: Hydrograph at the main outlet for the four scenarios.

Figure 2-3: Scatterplot of the result of the scenarios simulations in survey areas: Y axis show the population while X axis shows the predictions of the model.

V.

CONCLUSION

The integrated flood modelling for flood hazard assessment research in Kigali city covered a wide range of activities r aiming the understanding the extent of flood hazard. The study collected data on soil, drainages systems, rainfall, and analysed land cover of Kigali City. The flood hazard was analysed using openLISEM, an open source erosion and flood model. The model successfully simulated floods and different hydrology and hydraulic behaviours of the catchment have been evaluated. The amount of rainfall going into infiltration is still higher than the amount of water going into runoff. All scenarios combined, 4% of the catchment area is susceptible to be affected by a flooding events. The flood waters are characterized with very slow velocity and are shallow in the largest part of the flood zone. However, runoff water in drainage systems has very high velocity with very high peak discharges and have been associated with damages of roads, culvert, and bridge. openLISEM predicted floods in all locations and agreed with a reasonable correlation to the population’s experience. The result of this research are useful for the future physical planning initiatives in Kigali City and all activities related to flood disaster risk reduction and management. The research encountered various challenges related to lack or poor existing data on soil, rainfall and discharge data. Rainfall data presented gaps throughout the recording period and the research relied on daily rainfall data. This might have increased or decreased the rainfall intensities characterizing the catchment thus introducing errors in flood prediction. There is a need of long-term rainfall collection with a high spatio-temporal resolution. Additionally, water level monitoring systems in Kigali flood plains is highly recommended. The study focused only on flash floods however there are also river floods generated by Nyabugogo River. There is a need fora further study that can cover the whole Nyabugogo catchment to assess the effects of flood in the Kigali City. VI.

ACKNOWLEDGMENT

This study was partly funded by SERVIR Eastern & Southern African through the Regional Centre for Mapping of Resources for Development (RCMRD) of Nairobi Kenya. We are grateful to Rwanda Natural Resources Authority (RNRA) and Rwanda Meteo for the data shared. VII.

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