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Background: In flood prone areas, understanding flood causing factors, assessing the flood induced risks and adopting landscape based mitigation strategies can increase the knowledge, awareness and individual initiatives to protect themselves and their properties using appropriate flood management measures before and during flood events. Dire Dawa city is located in the foothill of southern mountains from where rivers crossing the city are originated. The multidimensional causes of flood hazard and limited landscape based mitigation strategies in the study area have worsen the impacts of flooding. This study was conducted in the Dire Dawa city watershed with the aims of assessing flood causing factors and to propose landscape based flood mitigation strategies. Results: To meet the intended objectives, the study employed the collection of both primary and secondary data. The primary data were collected from 110 households located in flood vulnerable villages. Moreover, the secondary data were collected from the Ethiopian Meteorology Agency, land use map of Dire Dawa city administration and government reports. Rainfall index method and descriptive statistics were used for analysis of primary data. The former was used to check the effect of intense rainfall and flood risk in terms of different duration yearly, monthly, daily and hourly basis, while the latter was used for identification of various factors precipitating flood risks of the study area. The analysis of secondary data employed morphometric analysis so as to identify flood susceptible sub- watersheds. Findings of the study indicated that flood risk in the study area has resulted from multiple factors such as intense rainfall, topography, encroachment to the river banks, institutional problems and aggravating factors resulted from power interruption during heavy rain and regime changes. More importantly, flood risk of the study area was found to be sensitive to hourly variation of rainfall distributions and varies on the location of the sub-watersheds. Following that, flood susceptibility of sub-watershed was ranked based on linear and shape morphometric parameters where higher values of linear and lower values of shape parameters were attributed to high flooding risk. Based on the prioritization of sub-watersheds’ susceptibility to flood risk through morphometric analysis, sub-watershed 5 and 18 were identified as the most flood risk susceptible watersheds demanding urgent landscape-based conservation measures. To this end suitable sites and sustainable water conservation structures are identified across the watersheds. Conclusion: Check dams, terracing, nala bunds, percolation tanks and storage tanks were proposed for different locations across the watershed as effective landscape-based flood risk mitigation strategies. The overall results of the study shows that managing the root causes of flooding at the upper catchments and adopting recommended proposed water conservation structures at proposed site helps to sustainably curb flood induced risks of Dire Dawa city. Keywords: Flood causes, Landscape, Morphometry, Mitigation strategies * Correspondence: firstname.lastname@example.org School of Geography and Environmental Studies, Haramaya University, Dire Dawa, Ethiopia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 2 of 19 Background mitigation strategies. To this end, different researchers Flood is one of the leading natural hazards worldwide identified various factors triggering urban flooding. For both in terms of the frequency of occurrence and the example, Youssef et al. (2016) and Dawod et al. (2012) resulting damages to human lives, the environment, and carried out a detailed analysis on causes of flash flood in economic assets (Jonkman 2005; Doocy et al. 2013). The Jeddah city and identified various factors such as heavy occurrence of flood depends on meteorology, topog- rainfall, topography, effect of climate change and charac- raphy, land use, soil type, and antecedent moisture con- teristics of the drainage networks of the city. Similar ditions (Funk 2006; Youssef et al. 2016; Agbola et al. study conducted in Nigeria (Ologunorisa and Adejumo 2012). The multi-dimensional causes of flood made it 2005; Agbola et al. 2012) revealed that flood was caused less predictable and aggravated its impacts worldwide. In by encroaching to riverbanks, poor housing, lack of early the coming decades, the effect of climate change and warning information, dam breaking, heavy rainfall, urbanization is expected to exacerbate flood induced dumping solid waste in drainage channels and land risks (Nijland 2005; International Strategy for Disaster use changes. Many of the previous studies conducted Reduction (ISDR) 2008). Compared to rural areas that on causes of flood risk and mitigation strategies yield 25% of surface runoff, urban watersheds lose about confirmed that flood triggering factors are mainly 90% of the storm rainfall to runoff (Shang and Wilson derived from meteorological, hydrological and an- 2009). This shows that urban flood incidents are thropogenic effects (Funk 2006; Agbola et al. 2012; expected to increase as a result of high rate of urban Youssef et al. 2011). growth (Lenderink and van Meijgaard 2008; Miller and Dire Dawa city is located at foothills of the mountains Hutchins 2017). located in the southern part (Fig. 1). The flooding in As flood risk and its impacts are increasing from time Dire Dawa occurs as a result of its geographical location, to time, emphasis was given by flood research scholars topography, and rainfall pattern of the city watershed. to understand the root causes of flood risk and its This shows that the flood risk studies of Dire Dawa city Fig. 1 Map of Dire Dawa city watershed Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 3 of 19 demands watershed level analysis and planning strategies guidelines that employ slope, soil, permeability, land use that can be done across the political boundaries. land cover and stream orders of the watershed (Prinz Flood risk has been historically affecting Dire Dawa and Singh 2000;FAO 2003; Panhalkar and Gowtham city since the establishment of the city. Even though, 2011). The objectives of this study are (i) to explore the adequate records on urban floods of Dire Dawa city causes of flood risk in Dawa city; (ii) to identify flood were patchy and incomplete, historical information re- susceptible sub-watersheds in the study area; and (iii) to vealed that the first damaging flood risk was recorded in propose landscape based suitable and sustainable water 1945. Since then flood has been affecting the city at detention and retention structures. different times. Different authors have investigated the causes of flood in Dire Dawa city with respect to Methods geomorphology, hydrology and land use changes within Study area the city boundary (Yonas 2015; Girma and Bhole 2015; The study area, Dire Dawa City, is located in the great 0 0 Alemayehu 2007). The municipality has also been con- East African rift valley between 9 25’Nand 9 45’Nlatitude 0 0 structing flood protection walls around the banks of and 41 40′Eand 42 10′E longitude. It is bordered in the Dechatu and Goro Rivers to protect the city from flood North, East, and West by Somali Regional State and in the induced damages. Despite all these efforts, flooding has South and South west by Oromia Regional State. The city continued to cause damage to human lives and proper- is located along the foothills of surrounding mountains ties of the city each year. This demands a detailed drained by tributaries of five major rivers, Dechatu, Goro, analysis of flood causing factors of the study area and Laga Hare, Butuji and Melka Jabdu. Dechatu, the largest proposing appropriate landscape-based mitigation strat- river, runs through the heart of the city. All these rivers egies in and outside of the city boundaries. are coming from the southern mountains originating from The study of flood and flood management strategies the districts of Oromia Regional States (Kombolcha, require the estimation of flow of water in river channels Haramaya, Kersa and Meta) Fig. 1. (Chandniha and Kansal 2017; Bouwer 2013). However, Dire Dawa Administration Council is composed of runoff estimation in ungauged stations is very challen- urban and rural areas. The combined area of urban and ging (Nyamathi and Kavitha 2013). The study of physical rural Dire Dawa covers an area of 1288 km . Dire Dawa behavior of the catchment helps to understand the city watershed comprises the surrounding districts of hydrologic and geomorphic conditions that causes flood- Oromia and Somali Regional States. The watershed ing and soil erosion (Eze and Efiong 2010). In this case covers an area of 698.5 km . The elevation of Dire Dawa morphometric analysis helps to better understand the ranges from 1000 to 1600 m above sea level. The eleva- hydrologic characteristics and watershed information of tion of mountains in the watershed reaches up to the basins (Horton 1945; Strahler 1964a, b; Krishna- 2400 m above sea level. Dangago is the highest moun- murthy et al. 1996). Many of the earlier studies on mor- tain located at the upper part of the watershed. Based on phometric analysis focused on drainage basins and their 2013 projected statistical report of Ethiopia, Dire Dawa geometric characteristics such as topology, texture pat- city population is estimated at about 466,000 with an- tern, shape, and relief characteristics used to predict flood nual growth rate of 2.7% (CSA 2013). By the virtue of peaks, sediment yields, and estimation of erosion rates being surrounded by various mountains and drained by (Abrahams 1984; Gardiner 1990). However, this study the tributaries of several major rivers, the city has always aims at combining morphometric analysis with watershed been subjected to periodic flooding. The recall of elders geomorphologic characteristics to estimate and rank levels and flood related reports of the city indicate that major of flood risk at different sub-watersheds (Kumar et al. floods have occurred in 1945, 1968, 1977, 1983, 2000; Youssef et al. 2011; Singh et al. 2014). 1985,1995, 2003, 2005, 2006, 2010, and 2016 due to flash Flood is mainly the result of increase in surface runoff. flood coming from surrounding mountains (Table 1). Reduction of surface runoff can be achieved by con- Among these, the 2006 flood that caused a death of 339 structing suitable structures such as ponds, percolation human deaths and 9027 displacements was the most tanks, storage tanks, and check dams (Kumar et al. 2016; catastrophic. Youssef et al. 2016; Agarwal et al. 2013; Chowdary et al. 2013; Jung et al. 2013) used to collect excess waters. Methods Effective way of water harvesting system heavily depends This study considered both primary and secondary data on identification of suitable sites (Ammar et al. 2016, to achieve the intended objectives of the study. To col- Al-Adamat et al. 2012; Panhalkar and Gowtham 2011). lect the primary data, a structured questionnaire were The identification of suitable water harvesting sites are designed and administered in the selected villages using made using the combined criterion of FAO and Inte- purposive and systematic random sampling method. The grated Mission for Sustainable Development (IMSD) selection of villages was determined purposely to address Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 4 of 19 Table 1 Summary of flood induced risks in Dire Dawa city watershed since 2006 Village /city Human loss Livestock loss Property loss Remarks Metokomia 2 Not stated Not stated 9027 People were displaced Biftu Geda 5 11 10 ha crops Dire Dawa city 339 Not stated 200 houses, roads and bridges Ija Anani 2 21 178 ha crops Harala Balina 4 27 80 ha crops and 40 Houses Lazaret 22 900 Not stated Shinile Not stated 200 3 houses Total 374 1100 268 ha crops, 243 houses Source: Compiled from Dire Dawa city flood assessment unpublished report (2006–2016) villages vulnerable to flood hazards. Accordingly, based When dealing with extremes, it is important to employ on Dire Dawa city flood prone village reports, 4 villages the probability of return period (eq. 2). If ‘P’ is the prob- (05, 06, 07 and 09) were purposely identified as flood ability that the event will be equaled or exceeded in par- vulnerable areas. Sample households from each village ticular year then the return period ‘T’ may be expressed. were then selected using systematic random sampling. T ¼ N þ 1=m ð2Þ Accordingly, from 1163 households that fall in flood vul- nerable villages, 10% (110) households were picked for Where, m is order or rank of the event household survey sampling. The final selection of sample N is total number of event in the data households of each of the four villages was done using T is return period random sampling method. In different methods, the probability of occurrence of Apart from primary data sources, flood causative a particular extreme rainfall is important and such infor- analysis employed the analysis of land use land cover mation is obtained by the frequency analysis of rainfall changes using different decade satellite imagery ana- data (eq. 3). lysis. Considering the relationship between regime change and land use policy of the country, four land use maps P ¼ 1=T ð3Þ from satellite remote sensing at about 10 years interval (1985, 1995, 2006 and 2016) were selected for land use Where T is return period land cover change analysis. This helped to know the P is probability decadal urban growth change and direction of urban ex- To meet the objective of landscape based flood mitiga- pansion. In addition to this, the precipitation based cause tion strategy, the analysis of morphometric parameters of flood hazard analysis employed time series analysis of was employed. Morphometric parameters were used to rainfall data obtained from Dire Dawa (1953–2014), Dan- identify highly erosion sensitive sub-watersheds that may gago (1986–2014) and Haramaya (1960–2013) meteoro- require highest priority of intervention for conservation logical stations to detect climate variability. The frequency activities. The watershed prioritization has been done distribution of hourly rainfall was assessed by identifying based on the linear and shape aspects of morphometric the number of days having maximum hourly rainfall ex- analysis. The linear parameters such as drainage density, ceeding 30 mm. The daily rainfall distribution of each stream frequency, bifurcation ratio, texture ratio have a years and standardized hourly rainfall index was com- direct relationship with flooding. Higher value of linear puted and examined for Dire Dawa, Haramaya and parameters means high flood. Hence for the prioritization Dangago stations based on FAO (1998) using Eqs. 1, 2 and 3. of the sub-watershed the values with highest linear param- eter are ranked as 1. The next highest are ranked as 2 and the rank continues for all sub-watersheds. On the Z ¼ðÞ x −x = d ð1Þ contrary, shape parameters such as elongation ratio, com- pactness constant, circulatory ratio and form factor have If Z < -1, it would be below normal an inverse relationship with flooding. The lower values If -1 ≤Z ≤ 1, it would be normal means the more the flooding is. Hence for the Z > 1, it would be above normal prioritization of the watershed the values with lowest Where, Z = Standardized minimum rainfall anomaly shape value are ranked as 1. The next lower value is ∑d Standard deviation ranked as 2 and the same procedure was applied for all x Mean hourly rainfall records the sub- watersheds. Prioritization rating of all the sub- x Observed hourly rainfall watersheds was carried out by calculating the compound i Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 5 of 19 Table 2 Formula for computation of morphometric parameters No Morphometric Parameters Formula/ definitions/methods References Linear aspect 1 Stream order Hierarchical order Strahler 1964a, b 2 Stream Length (Lu) Length of the Stream (km) Horton 1945 3 Mean Stream Length (Lsm) Lsm = Lu / Nu, Where, Lu = Total Stream length of order ‘U’, Horton 1945 Nu = Stream length of next higher stream order. 4 Stream length Ratio(Rl) Rl = Lu/Lu-1; where, Lu = Total number of stream segment Horton 1945 of order ‘U’, Lu-1 = Stream length of next lower order 5 Bifurcation Ratio (Rb) Rb = Nu / Nu+ 1 Schumm 1956 Where, Nu = Total Number of stream segments of order ‘U’ Nu+ 1 = Number of segments of the next higher order. Areal aspect 6 Drainage Density (Dd) Dd = L/A Where, L = Total length of Stream, A = Area of the Watershed Horton 1945 7 Stream Frequency (Fs) Fs = N /A. Where, N = Total number of Stream, A = Area of the Watershed. Horton 1945 8 Texture ratio (T) T = N1/P. Where N1 = Total number of first order stream, p = perimeter of watershed Horton 1945 9 Form Factor (Rf) Rf = A / (Lb) 2. Where, A = Area of the Watershed, Lb = Maximum Basin length. Horton 1932 10 Circularity Ratio (Rc) Rc = 4ðA/ P2. Where, A = Area of the Watershed, P = Perimeter of the basin, Ð = 3.14 Strahler 1964a, b 11 Elongation Ratio (Re) Re = 2√ (A/ð) / Lb. Where, A = Area of the Watershed, Lb = Maximum Basin length, Ð = 3.14. Schumm 1956 parameter values. The sub-watershed with the lowest technologies. As shown in Table 3, water conservation site compound parameter value was given the highest pri- identification was conducted using soil, permeability, ority. Accordingly, sub-watersheds are broadly classi- slope, drainage order and land use (Prinz and Singh 2000; fied into five priority zones based on their compound FAO 2003). parameter value (Cp) as, extremely high (8.0–9.9), Very high (10.0–11.9), high (12.0–13.9), moderate Result and discussion (14.0–15.9) and low priority (16.0–17.9) (Farhan and Causes of flood hazards Anaba 2016;Ratnam et al. 2005). Sub- watersheds In Dire Dawa, flash floods are caused by a combination which consist of steep slopes, high drainage density; of natural and anthropogenic effects. The survey based high stream frequency, low form factor and low collected perceived flood causing factors in Dire Dawa elongation ratio could have less compound value and city are depicted in Table 4. are classified under very severe flooding susceptibility zone. Thus it needs immediate attention to take soil Intense rainfall conservation measures (Kanth and Hassan 2012). Rainfall is the major factor directly associated with flood Morphometric parameters used for this study were hazard of Dire Dawa city. Duration, magnitude and in- calculated based on the standard formula of morpho- tensity of rainfall determine the formation of flood. The metric parameters developed by different scholars survey result of (Table 4) shows that 92.7% of respon- (Table 2). dents agreed that heavy rainfall is the causes for Dawa Following identification of flood susceptible sub-water- city flooding. There are three meteorological stations sheds, FAO and IMSD guidelines were employed to iden- (Dire Dawa, Haramaya, and Dengago) that fall inside the tify suitable sites where to adopt water conservation watershed. The meteorological analysis of this study Table 3 Water conservation site identification criterion Structure type Soil Permeability LULC Slope Drainage Terracing Sandy clay, clay loam, sandy loam Medium, high Bush land and shrub land 5–30 1st, 2nd order rd Percolation Tank Silty loam/ Clay loam high Bare/shrub land < 10 2nd,/3 /4th order Pond Sandy loam low shrub < 5% 1st order Silty loam Check Dam Sandy clay loam low Bare/ shrub < 15% 5th and 6th order rd Nala bunds Silty loam low Bare/ shrub < 10 3 and 4th order Storage tank Silty loam, clay low Bare < 30 2nd, 1st and 2nd order Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 6 of 19 Table 4 Perceived causes of flood hazard in Dire Dawa city Flood causing factors Agree Disagree No response Count % Count % Count % Intense rainfall 102 92.7 0 0 8 7.3 Flood zone occupation 64 58 12 11 34 31 Land use change 83 75.5 21 19 6 5.5 Topography 83 75.5 15 13.4 12 10 Damping of solid waste in river channel 51 46.3 54 49.1 5 4.6 Aggravating factors 77 70 23 21 10 9 Institutional problem 15 13.5 70 63.3 25 22.7 Source: Field survey 2017 shows that (i) flood inducing rainfall is coming from the The standardized daily rainfall anomaly shows that surrounding highlands located out of Dire Dawa city ad- sometimes years with highest hourly rainfall could have ministration. The records of Dire Dawa meteorological a declined daily rainfall pattern below mean hourly rain- station shows that the station has experienced marked fall records. This confirms that the hourly variation of decline of rainfall trend. The slope of rainfall trend ana- rainfall distribution that determine the occurrence of lysis show that the rainfall graph for Dire Dawa station flood risk is working independent of hourly, monthly shows a negative trend (Y = − 0.001x + 60.22) and posi- and yearly rainfall distributions. The detailed monthly tive trend for Dangago (Y = 0.040x-24.69) and Haramaya rainfall analysis of Dire Dawa meteorological station (Y = 0.0257x-462.1) stations which are located in the showed that severe and heavy rainfall was recorded in upper catchments (Figs. 2, 3, 4). This shows that the ef- 1956, 1961, 1963, 1964, 1983, 1996 and 2010 at Dire fect of rainfall in Dire Dawa station is insignificant to Dawa meteorological stations. The comparison of these precipitate flood occurrence. Hence, flood causing rain- higher raining years with historically identified flood fall is coming from upper catchments of Dangago and event years shows that most of these events have no Haramaya stations located out of the political boundary causative relationship. However, the hourly distribution of Dire Dawa city. of extreme rainfall shows that the result is high for the (ii) Flood of the study area is the result of hourly periods identified as flood event years. This confirms rainfall variability. The trend analysis of daily rainfall that the hourly variation of rainfall distribution of Dire distribution shows that the daily variation of rainfall Dawa station is precipitating the cause of flash flood in is not pronounced over the study periods for all and around city boundaries. This can be seen by consid- stations. More importantly, variations arise from ering the hourly rainfall distribution of 2006 which was hourly distribution of rainfall plays a significant role. known for its devastating flood hazards. For example, Naturally, rainstorms in the tropics are highly local- the 61 mm/hr. amount of rainfall recorded in 2006 con- ized, intense, and of short duration, covering less than firms that the figure represents the highest ever record 10 km and usually lasting anhourorless (Guptaand of hourly rainfall during the study period at all stations Ahmad 1999). The variation of hourly distribution (Fig. 5). This implies that the hourly variation of rainfall and its effects are depicted in Figs. 4 and 5 for all condition of this station is a potential cause for flood stations. hazards of the city. Fig. 2 Daily rainfall distribution of Dire Dawa (1953–2014) and Dangago (1986–2014) station Fig. 3 Daily rainfall distribution of Haramaya station (1960–2013) Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 7 of 19 different elevation points that range from 1000 to 2400 m above sea level. Dire Dawa city, where different rivers meet is the lowest point with an altitude of 1000 m above sea level. Dangago Mountain, with the peak altitude is located at South of Dire Dawa city in Oromia Regional State. In addition, there are other mountain chains located South of the city that surround the city in Southern direction. These include Kersa, Kulubi and Meta mountains located in the Oromia Re- gional State boundaries. The location of Dire Dawa at the foot of these mountain chains has exacerbated the flooding of the city complementing with other factors. Fig. 4 Standardized daily extreme rainfall anomalies of Dire Dawa, Haramaya and Dangago stations Encroachment to river banks In most cases, flood damage is the result of exposure. Similarly, the result of hourly variation of rainfall in Encroachments to flood river way is the primary expos- other stations complements the scenario of Dire Dawa ure causing flood risk in Dire Dawa city. Different types station that hourly variation of rainfall is causing flood of settlements belonging to individuals, organizations hazard of the study area. The daily trend analysis of rain- and government institutions have been located inside fall distribution shows that high rainfall were recorded the original flood path ways. Public institutions such as in 1966, 1967,1968,1983, 1987, 2008 and 2013 for Hara- Medehanalem primary and secondary schools, Addis maya station and 2001, 2009, 2010, 2012 and 2013 for Ketema secondary school and mosques are among some Dangago station (Figs. 2 and 4). At both stations the institutions located in flood prone areas. In the same high daily rainfall records have no strong relation with way, Coca Cola Company has reclaimed inundation identified flood event years. On the Contrary, the max- areas and filling up the flood zones with polluted wastes imum hourly rainfall distribution of both stations shows released from the company. The interviewed people that the hourly distribution result has a relationship with reported that the effect of flood is changing both in identified flood event years. magnitude and damaging capacity with increasing settle- Furthermore, the trend analysis result of hourly rain- ments encroaching to flood prone areas. They further fall data distribution shows that the likelihood of getting explained that the construction of settlements has aggra- more than 30 mm of hourly maximum rainfall happened vated flooding problem by reducing width of the river every five, four and 3 years cycle for Haramaya, Dire banks. The large informal encroachments of river banks Dawa and Dangago meteorological stations, respectively. are confined to inner city centers such as Kezera, The cumulative return period of the three station shows Dechatu, Ashewa and Bahire Tsige villages. On the other that rainfall affects the city at every 4 years interval. This hand, the flood plains of Koka area and Addis ketema forecast helps to develop an advance flood mitigation have been occupied by formally approved private and strategies at every forecasted years. government houses. Among the sampled households 58% of them (Table 4) agreed that the occupation of Topography flood hazard zone is precipitating the cause of flood haz- Dire Dawa is located on a low land area in the Great ard. Regarding this, one of the elder respondent reported East African Rift Valley. Its watershed comprises that water never miss its line. He expressed that people have been affected and potentially to be affected if they interfere with the line of the river. People encroach the flood zone for two reasons. Primarily they perceive flood zones as a vacant space and land leftover. Secondly, the rise of urban land price in planned areas pushed low economic classes to settle in hazard zones. As a result greater proportion of migrants prefers to reside in infor- mal way of settlements within the flood zones accom- panied by mushrooming of informal settlements. Waste management factor Fig. 5 Hourly rainfall distribution of Dire Dawa, Haramaya and Respondents accounting 46.3% (Table 4) reported that Dangago station damping of waste materials in rivers and drainage Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 8 of 19 Fig. 6 Solid waste dumped into Dechatu River across the flood protection wall channels is another factor that exacerbates the flooding drainage line which stretches from the edge of Tonny problem of Dire Dawa city. Due to the absence of river Agriculture to Gate No.2 of Dire Dawa University (Fig. 7) management policies properly and planned storm water is almost completely covered with invasive vegetation management and waste disposal systems, the city resi- and blocked by solid wastes and siltation. dents of Dire Dawa use river channels as main site of waste disposal areas. As a result, it is common to see the Land use change and urban expansion damped wastes in open spaces and river channels. Para- Dynamics of population growth and infrastructural de- doxically, the largest sizes (49.1%) of the sample popula- velopment are among major important forces that tion were unable to associate the causative and effect obliges the expansion of the city. As a result, the city is relationship of poor waste management and flood hazard forced to expand its boundary in all directions including problem. They disagreed that flood is not an immediate to mountainous and flood prone areas. The expansion of cause of damping of solid waste in to the river channels. urban area which is accompanied by infrastructural de- However, the existing fact indicated that the damped velopment would be an immediate cause for increase of waste material (Fig. 6) affected the size and the depth of impervious surface. To explore the land use change and the river channel and disturbing the water velocity. growth direction of Dire Dawa city, the analysis of four Complementing with other associated problems, the decade satellite image was conducted (Table 5). damped waste is decomposed and helped the growth of The overall land use land cover change of the study vegetation on the banks of the river. For example, area shows that the sizes of forest, bush and grass lands Fig. 7 Vegetated drainage line at Gate no.2 of Dire Dawa University Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 9 of 19 Table 5 Land use land cover changes of the watershed directions along the foot of the mountains (Fig. 8). Dur- ing this period the size of the city reached 27.6 km . Land use (Km ) 1985 1995 2006 2016 During the subsequent decade from (1995 to 2006) all Forest 63. 73 50. 01 48. 71 37. 09 expansions have taken place towards the East and along Settlements 17. 27 27. 60 30. 06 44. 66 river side areas which are flood prone zones and left for Bush and grass 345. 90 316. 91 282. 27 236. 24 river buffer. During this decade the size of the city in- Agriculture 139. 34 102. 73 63. 02 73. 64 2 creased to 30 km . From 2006 to 2016, the city has been Bare land 115. 61 171. 37 261. 29 308. 45 expanding in the western directions based on the pro- River side 20. 76 34. 37 17. 59 2. 83 posal of the city master plan. The settlement analysis of 2016 satellite imagery shows that the city covered an are continuously declining over all the study periods. On area of 44.7 Km . The overall Dire Dawa city expansion the contrary settlements and bare land are significantly shows that until 2006 the city has been expanding hap- increasing. This shows that land use land covers that are hazardly in all directions including to foots of mountain discouraging flood are consistently decreasing while land and flood inundation areas. After 2006 western direction use aggravating flood hazard are showing a significant is serving as the major site of expansions. This happened increase. because of various reasons. In the first place the 2006 Regarding urban expansion, the 1985 satellite image flood damage gave a lesson not to settle in flood analysis shows that Dire Dawa city was confined to the sensitive areas. Secondly, the possible expansions to old inner cities of Magala, Kezera and Taiwan villages mountain sides are already saturated and no free space established during Italian occupation and covers the area for more expansion. Thirdly, the western part is identi- of 17.28 Km (Table 5). Through time the expansion fied to be an expansion site by the municipality. Owing took place in all directions. From 1985 to 1995 the city to these facts, it is worth to say that the west ward ex- was expanded mainly to the eastern and southern pansions follows appropriate way of city planning and Fig. 8 Land use land cover change (1985, 1995, 2006, 2016) Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 10 of 19 landscape analysis while expansion to the southern and the forests were owned by landlord that hinders the eastern direction followed sprawl and haphazard expan- community to freely access the forest land. Following sion that result to the occupation of flood hazard zones the collapse of the Imperial rule, massive forests were and unsuitable places for settlement. colonized and destructed by communities. This was hap- pening partly in retaliation to the landlords. In the Institutional factor 1980s, the military government launched a large scale The lack of strong and trans-regional boundary institu- re-afforestation campaign on the upper catchments of tional setup is among the major factors contributed to Dire Dawa city including Dangago. This campaign had flood hazard of Dire Dawa city. Due to the geographical registered significant contribution to the rehabilitation location and topography, Dire Dawa city is serving as an of the degraded environment. However, upon the fall of outlet for floods coming from different districts of Oro- the military government in 1991, large scale deforest- mia Regional State. The sources of floods are outside of ation and human settlement took place within the reha- Dire Dawa administration. The trans-regional nature of bilitated area especially during transition period (1991– floods in Dire Dawa city demands the integration and 1995). According to key informants, people illegally set- collaboration of institutions from different regions across tled and secured farmlands on the hilly slopes during political boundaries. The institutional gap in flood man- this period. As a result, areas which have been a dense agement can be seen from different perspective. At the forest in the past become devoid of vegetation cover. first place, there was no institutional setup that incorpo- The information obtained from key informant, former rates institutions from different political boundaries. research document and satellite imagery analysis con- Secondly, there were no intuitions that specifically deal firms this fact. with flood issue neither at city nor at federal levels. Power interruption during heavy rain: Large number Thirdly, even the existing city level institutions are weak of respondents (70%) reported that the interruption of in capacity. Therefore, lack of development control exac- power during rainy season has exacerbated the degree of erbated problems related to unplanned settlements and risks and damages caused by flooding. They further indi- made the expansion of the city sporadic. The 2006 mas- cated that the condition is worst when the rain take ter plan of Dire Dawa city proposed a minimum river place during night time. The city is using a wooden elec- buffer which ranges from 30 to 50 m for rivers travers- tric pool that can be susceptible to damage with the pos- ing the inner city. Despite the existence of this docu- sibility of causing fire hazard during heavy and windy ment, there are various formal and informal ongoing rainy days. This threatened the Ethiopian Electric construction activities within the proposed river buffer Power Corporation (EEPCo) officials to interrupt the zones. The ongoing constructions, especially the for- power whenever heavy rainfalls take place. The lack of mally approved private, government and business com- light during night time makes the people not to identify panies confirm that weakness of existing institution is the volume and intensity of the flood. This made the contributing to flood problem of the city. flood to be less understood so that the mobility of the people to rescue their property at expense of their life Aggravating factors will occur. Beside the other factor directly causing flood risks, there are numerous factors that exacerbate the level of risks Morphometric analysis of the watershed and damages resulted from flooding. Regime change and Various morphometric parameters that are attributed to power interruption during intense rainfall were also re- determine the nature of flooding are employed to iden- ported as major factors aggravating flood induced risks. tify flood susceptible sub-watersheds. These are stream Regime change and related land use changes: Land use order, stream length, bifurcation ratio, drainage density, changes such as inappropriate, misuse and informal oc- texture ratio, form factor, circularity ratio, elongation ra- cupation of protected areas were affected by political in- tio and compactness constant. stability. Political transition and regime change allow irresponsible peoples to colonize forests and other buffer Stream orders zones left for rehabilitation purposes. The continuous Dire Dawa city watershed is composed of 26 sub-water- forest cutting and colonization of agricultural land par- sheds. A total of 483 streams were present in the water- ticularly on the steep slopes of Dangago and other sur- shed of which 376 belong to 1st order, 87 to 2nd order, 18 rounding mountains was done as result of political to 3rd order and 2 are 4th order (Table 6). Higher stream vacuum created at different times. According to report order is associated with greater discharge, and higher vel- from elder respondents, the vegetation cover of the ocity (Costa 1987). The 4th order which possibly can gen- southern highlands of Dangago during the rule of the erate high discharge and velocity is located at Emperor (1954–1974) was modestly dense. At that time sub-watersheds where Dechatu River is located in the Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 11 of 19 Table 6 Stream morphometry analysis No of streams Stream length (km) Cumulative stream length (km) Watershed 1st 2nd 3rd 4th Nu 1st 2nd 3rd 4th 1st 2nd 3rd 4th 1 3 1 4 1.8 3.91 0.00 0.00 1.8 5.7 5.75 5.75 2 14 3 1 18 11.5 6.49 0.81 0.00 11.5 18.0 18.79 18.79 3 6 1 7 2.7 0.77 0.00 0.00 2.7 3.4 3.43 3.43 4 36 7 1 44 5.5 22.49 24.78 0.00 35.5 58.0 82.74 82.74 5 53 13 2 1 69 53.9 24.55 8.05 13.97 53.9 78.4 86.50 100.47 6 6 1 7 3.0 1.35 0.00 0.00 3.0 4.3 4.32 4.32 7 19 6 1 26 27.1 13.59 11.05 0.00 27.1 40.7 51.74 51.74 8 6 2 1 9 7.6 1.76 1.43 0.00 7.6 9.3 10.75 10.75 9 16 3 1 20 12.8 3.10 14.09 0.00 12.8 15.9 30.00 30.00 10 21 4 1 26 19.1 4.29 15.07 0.00 19.1 23.4 38.43 38.43 11 2 1 3 2.6 1.78 0.00 0.00 2.6 4.4 4.41 4.41 12 3 1 4 3.6 4.94 0.00 0.00 3.6 8.5 8.54 8.54 13 10 3 1 14 9.6 9.11 4.55 0.00 9.6 18.7 23.29 23.29 14 8 1 9 10.0 8.25 0.00 0.00 10.0 18.2 18.24 18.24 15 11 4 1 16 5.4 4.33 7.41 0.00 5.4 9.8 17.18 17.18 16 7 1 8 4.4 5.58 0.00 0.00 4.4 10.0 9.99 9.99 17 15 4 1 20 8.5 4.09 10.33 0.00 8.5 12.5 22.87 22.87 18 36 8 1 45 32.9 17.69 18.82 0.00 32.9 50.6 69.38 69.38 19 22 3 1 26 8.0 3.93 1.42 0.00 8.0 11.9 13.37 13.37 20 15 5 1 21 13.8 9.35 11.23 0.00 13.8 23.2 34.41 34.41 21 17 5 2 1 25 19.8 6.21 2.48 6.49 19.8 26.0 28.46 34.96 22 21 4 1 26 19.9 8.59 9.45 0.00 19.9 28.5 37.9 37.9 23 15 3 1 19 11.8 8.30 8.07 0.00 11.8 20.1 28.2 28.2 24 4 1 5 6.9 5.54 0.00 0.00 6.9 12.4 12.40 12.40 25 8 1 9 6.0 5.94 0.00 0.00 6.0 11.9 11.93 11.93 26 2 1 3 1.3 2.43 0.00 0.00 1.3 3.7 3.74 3.74 center of Dire Dawa city (Fig. 9). The distribution of the discharges traveling to downstream. Higher stream or- stream network over the watershed indicates that most of ders (3rd and 4th) are passing through the heart of the the 1st and 2nd order streams find their origin from sur- city carrying large volume of water collected from the rounding mountainous and hilly areas of Oromia Region upper catchment and made the risk of flooding very high (Haramaya, Kersa and Meta) districts where agricultural and challenging to handle. Therefore, it is important to activity encroaches steep slopes and fragile areas. The lo- start the management of flooding at upper catchments cation and distribution of 1st order streams confirms that before the streams join each other to form the high the upper catchment is the primary source of flash flood order. that affects the whole downstream of the watershed. Texture ratio Stream length Texture ratio (T) represents the total number of stream Based on the law of Horton (1945), the total stream segments of all orders per unit perimeter of the basin lengths have an inverse relation with stream order. The (Rudraiah et al. 2008; Ramaiah et al. 2012). It is governed law proposed that the total length decreases as stream by lithology, soil, relief, vegetation, infiltration-capacity order increases. The analysis of cumulative stream and climate (Farhan 2017). The lower texture ratio implies lengths helps to know the drainage characteristics and the coarser the drainage is while high texture ratio leads movements of the water at different basins (Altaf et al. to fine drainage texture (Ozdemir and Bird 2009). Based 2013). Furthermore, the analysis of stream length of in- on texture ratio, basins are classified in to different classes dividual stream order helps to estimate the amount of (Smith 1950), coarse (2–4), fine (5–8) and very fine (> 8) Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 12 of 19 Fig. 9 Stream order distribution of study area watershed texture ratios. The texture ratio of study area is less than 2 geological structure controls the basin (Verstappen over all the sub-watersheds which show that the whole 1995; Strahler 1964a, b; Chow 1964). The values of 2 Rb study area watershed is classified under very course tex- rarely happen and more theoretical. The mean Rb of the ture that imply high infiltration. watershed lies between 3 and 5 for sub-watersheds 1, 2, 5, 7, 9, 10, 12, 13, 15, 17, 20, 22, 23, and 24. This implies Bifurcation ratio that these watersheds have a developed drainage pat- Bifurcation ratio (Rb) is the ratio between the numbers terns free of geological influence. Higher Rb was com- of streams of any given order to the number of streams puted for sub-watershed 3, 4, 6, 14, 16, 18, 19 and 25. of next higher order. Bifurcation ratio is an important This indicates that these watershed experiences elon- parameter to estimate where flood can take place in the gated shape and mature topography implying lower watershed. Basins with higher value of bifurcation ratio water infiltration and possibility of flooding. On the con- are favorite to higher chance of flooding. In a river net- trary, lower Rb ratio was computed for sub-watersheds work with high bifurcation ratio, the outlet receives 11, 21 and 26 implying less effect of geological structure. water from large number of tributaries. The concentra- tion of the waters in to single point increases the volume Stream frequency and intensity of water at the specific river. The mean bi- Stream frequency is an expression of total number of furcation ratio of study area sub-watersheds ranges from streams of all orders to total unit area. Stream frequency 2 to 8. The value of bifurcation ratio tells about the geo- has direct relationship with flooding. The higher the logic nature and shape of the watershed (Verstappen value of stream frequency is the more erodible the basin. 1995; Strahler 1964a, b). When the value of Rb ranges The analysis of stream frequency reflects permeability, between 3.0 and 5.0 the rock type is homogeneous, infiltration capacity, and relief of watersheds and runoff when it is more than 5, the basin is elongated and process (Youssef et al. 2009; Langbein 1947). Stream Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 13 of 19 frequency is inversely proportional to infiltration cap- et al. 1998; Altaf et al. 2013). The higher the value of Dd acity (Altaf et al. 2013). Basins with high stream fre- is the higher the risk of flooding and vice versa. Con- quency are characterized by low infiltration capacities. versely, basins with low and moderate Dd are composed The stream frequency analysis of study area shows that of permeable surface, good vegetation cover, and low re- the value of stream frequency is relatively higher for lief that results to more infiltration capacity and good sub-watershed 19, 3 and 26, respectively (Table 7). The site for ground water recharge (Altaf et al. 2013). In the result of this study shows that, the highest stream fre- study area sub-watershed 19 has maximum Dd (2.44) quency occurs at the final outlet of the watersheds lo- while sub-watershed 1 has a minimum Dd (0.54). The cated at the extremely downstream of Dire Dawa city overall Dd of study area ranges between 0.54 and 2.44 where flood problem is more severe. indicating that the study area watershed is characterized by low drainage density. This shows that even though Drainage density other factors are inflicting flooding, the study area Drainage density (Dd) is the total length of all streams watershed is favorable to establish water recharge sites per unit area. It expresses the closeness of spacing of that help to mitigate the causes of flooding. streams and drainage efficiency of the watershed (Hor- ton 1932). Like other linear parameters drainage density Elongation ratio has a direct relationship with flooding. It is governed by Elongation ratio (Re) is used to assess the shape of the various factors affecting surface runoff such as climate, basin which can tell about the flooding condition of the vegetation cover, soil, rock properties and so on (Moglen basin. Basins with higher elongation ratio are tending to Table 7 Watershed parameter analysis 2 2 Watershed Area (km ) Perimeter(km) Basin length (km) Drainage density (Km/km ) stream frequency 1 10.7 22.6 5.04 0.54 0.37 2 17.9 38.8 6.75 1.05 1.01 3 3.6 16.4 2.72 0.95 1.94 4 77.8 92.5 15.56 1.06 0.57 5 95.7 97.3 17.50 1.05 0.72 6 7.7 18.8 4.18 0.56 0.91 7 44.6 53.3 11.35 1.16 0.58 8 10.5 22.6 5.00 1.02 0.85 9 29.6 56.5 8.98 1.01 0.68 10 39.3 51.8 10.60 0.98 0.66 11 3.7 17.9 2.76 1.19 0.81 12 6.6 24.0 3.84 1.29 0.60 13 21.1 32.8 7.42 1.10 0.66 14 17.8 30.9 6.72 1.03 0.51 15 20.4 31.9 7.28 0.84 0.78 16 10.4 21.4 4.97 0.96 0.77 17 25.7 39.5 8.30 0.89 0.78 18 78.1 69.9 15.59 0.89 0.58 19 5.5 15.1 3.45 2.44 4.74 20 39.2 42.1 10.55 0.88 0.54 21 33.8 39.5 9.69 1.03 0.74 22 38.3 47.5 10.41 0.99 0.68 23 30.4 39.1 9.12 0.93 0.63 24 13.3 27.0 8.54 0.93 0.38 25 14.3 24.6 5.94 0.84 0.63 26 2.4 13.8 2.14 1.58 1.27 Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 14 of 19 have high infiltration capacity and low runoff. Under Circulatory ratio (Rc) varieties of climate condition and geologic type, the Circularity ratio expresses basin shape, rate of infiltra- value of elongation ratio lies between 0.6 to 1 (Dar et al. tion and the time needed for excess water to reach the 2013). A basin with elongation values close to 1 are ba- basin outlet. R is expressed as the area of the basin to sins with very low relief and termed as a circular basin, the area of a circle having the same circumference as the whereas basins with elongation ratio of 0.6 to 0.8 are perimeter of the basin (Miller 1953). Low, medium, and classified as high relief and steep slopes with oval high values of R denote young, mature, and old stages shape Strahler (1964a, b).Basins with elongation ratio of the geomorphic cycle of the catchment, respectively less than 0.6 are called elongated with high relief and (Farhan and Ayed 2017). Higher R shows that the basin ground slopes (Ali and Ahmad 2014). A circular basin could have delayed time to peak flow while the lower produces less discharge and runoff compared to the value shows shorter time to peak. Circulatory ratio of oval and elongated basin (Singh and Singh 1997). In the study area (Table 8) varies from 0.1 (sub-watershed the study area the elongation ratio ranges from 0.6 to 4) to 0.3 (sub-watershed 19). The result of R shows that 0.810 except for sub-watershed 24 with Re of 0.48 all sub-watersheds are elongated in shape indicating that (Table 8). This indicates that the whole watershed is the basins are characterized by elongated shape with low elongated in shape implying low infiltration capacity infiltration and high relief that can cause flood risks. As that leads to high soil erosion which induces flash mitigation strategy, elongated watershed gives a chance flooding. to reduce the water velocity through construction of Table 8 Morphometric parameter analysis Bifurcation ratio (Rb) Watershed Rc Re Rf T Cc 1 to 2 2 to 3 3 to 4 Mean Rb 1 0.26 0.73 0.42 0.18 1.94 3.0 3.0 2 0.15 0.71 0.39 0.46 2.59 4.7 3 3.8 3 0.17 0.79 0.49 0.43 2.43 6.0 6.0 4 0.11 0.64 0.32 0.48 2.96 5.1 7 6.1 5 0.13 0.63 0.31 0.71 2.81 4.1 6.5 2 4.2 6 0.27 0.75 0.44 0.37 1.92 6.0 6.0 7 0.20 0.66 0.35 0.49 2.25 3.2 6 4.6 8 0.26 0.73 0.42 0.40 1.96 3.0 2 2.5 9 0.12 0.68 0.37 0.35 2.93 5.3 3 4.2 10 0.18 0.67 0.35 0.50 2.33 5.3 4 4.6 11 0.15 0.79 0.49 0.17 2.62 2.0 2.0 12 0.14 0.76 0.45 0.17 2.63 3.0 3.0 13 0.25 0.70 0.38 0.43 2.01 3.3 3 3.2 14 0.23 0.71 0.39 0.29 2.07 8.0 8.0 15 0.25 0.70 0.39 0.50 1.99 2.8 4 3.4 16 0.29 0.73 0.42 0.37 1.87 7.0 7.0 17 0.21 0.69 0.37 0.51 2.20 3.8 4 3.9 18 0.20 0.64 0.32 0.64 2.23 4.5 8 6.3 19 0.30 0.77 0.46 1.72 1.82 7.3 3 5.2 20 0.28 0.67 0.35 0.50 1.90 3.0 5 4.0 21 0.27 0.68 0.36 0.63 1.92 3.4 2.5 2 2.6 22 0.21 0.67 0.35 0.55 2.17 5.3 4 4.6 23 0.25 0.68 0.37 0.49 2.00 5.0 3 4.0 24 0.23 0.48 0.18 0.18 2.09 4.0 4.0 25 0.30 0.72 0.40 0.37 1.84 8.0 8.0 26 0.16 0.81 0.52 0.22 2.53 2.0 2.0 Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 15 of 19 water harvesting structures such as dams and surface Table 9 Prioritization of watershed based morphometric analysis results reservoirs. Watershed Rbm Dd Fs T Rf Rc Cc Re CP Rank 1 21 18 26 24 1 20.0 7 18 16.875 23 Form factor (Rf) 2 18 14 4 13 24 6.0 21 15 14.375 18 Form factor is used to forecast the flow intensity of a given basin (Horton 1945). The value of form factor par- 3 6 25 2 14 25 8.0 19 25 15.5 20 ameter is directly proportional to flooding. Basins with 4 5 3 22 12 10 1.0 26 4 10.375 3 high form factors tend to face low flood risks while ba- 5 12 1 12 2 22 3.0 24 2 9.75 2 sins with low form factors are highly susceptible to 6 6 21 5 18 20 22.0 5 21 14.75 19 flooding. The computed value of form factor of the 7 11 4 20 10 7 10.0 17 5 10.5 4 study area lies between 0.3 and 0.52 (Table 8). This artic- 8 24 19 6 16 16 19.0 8 19 15.875 22 ulates that all sub-watersheds are elongated in shape and are susceptible to flooding. 9 13 10 14 20 13 2.0 25 11 13.5 14 10 9 5 16 8 14 9.0 18 6 10.625 6 Compactness constant (cc) 11 25 24 7 25 6 5.0 22 24 17.25 24 A perfectly circular shape watershed could have high 12 21 22 19 26 3 4.0 23 22 17.5 25 compactness constant that implies that the basin experi- 13 20 12 15 15 9 16.0 11 13 13.875 17 ences high infiltration. Watershed with less compactness 14 1 15 24 21 2 15.0 12 16 13.25 13 constant is susceptible to flooding. The computed value 15 19 13 8 7 17 18.0 9 14 13.125 12 of Cc of the study area shows that sub-watershed 4 has 16 3 20 10 17 12 24.0 3 20 13.625 15 a maximum value 2.96 while sub-watershed 19 has a minimum value 1.82. This shows that most of the 17 17 11 9 6 21 12.0 15 12 12.875 11 sub-watersheds are not circular in shape and hence sus- 18 4 2 21 3 11 11.0 16 3 8.875 1 ceptible to flooding. 19 8 23 1 1 26 26.0 1 23 13.625 15 20 14 6 23 9 4 23.0 4 7 11.25 7 Prioritization of sub-watersheds for conservation activities 21 23 8 11 4 19 21.0 6 9 12.625 9 Conservation activity demands resource, manpower and 22 9 7 13 5 15 13.0 14 8 10.5 4 time. Taking into consideration these factors, it is not 23 14 9 18 11 8 17.0 10 10 12.125 8 possible to take the conservation of the whole area at a 24 14 17 25 23 18 14.0 13 1 15.625 21 time. At the same time, all sub-watersheds are not equally exposed to flooding. Hence prioritizing the 25 1 16 17 19 5 25.0 2 17 12.75 10 sub-watershed is the precondition to implement conser- 26 25 26 3 22 23 7.0 20 26 19 26 vation activities. The prioritization of the sub-watershed was determined based on linear and shape parameters of morphometric analysis. The morphometric result based Water conservation site identification rank of sub-watershed was depicted in Table 9 and The result of previous study shows that morphometric Fig. 10. parameters of the study area are elongated in shape and Based on compound parameter result of morphomet- less controlled by structural condition Girma and Bhole ric analysis, the study area sub-watersheds (SW) are (2015). This shows that landscape based strategy is more classified in to five categories. The sub-watersheds with important to fully the curb problem of flooding in the the least CP are ranked as the most flood vulnerable study area. To minimize the effect of flooding, different area that demands urgent intervention for conservation. sites were identified for different water conservation The next prioritization also follows the same trend. Ac- mechanisms using soil, permeability, slope, land use and cordingly, SW-5 and SWS-18 fall in the highest priority, stream conditions of sub-watersheds. Accordingly, SW-4, SW-7, SW-10, SW-20 and SW-22 fall in very 6257 ha were proposed for terracing activities. In the high priority and SW-9, SW-13, SW-14, SW-15, SW-16, same way 36 sites were identified for construction of SW-17, SW-19, SW-21, SW-23 and SW-25 fall in the ponds, 19 for percolation tanks, 9 for storage tanks, 8 high priority category. These sub-watersheds demand for nala bunds and 5 sites for check dams. These sites conservation measures on the basis of their priority cat- were identified using soil, permeability, slope, land use egory. The SW-2, SW-3, SW-6, SW-8 and SW-24 fall in land cover and stream orders and by combining these the moderate priority. Sub-watershed 1, 11, 12, and 26 overlays with flood sensitive sub-watershed generated fall in the low priority category and classified as sites free from morphometric parameters. As shown in Fig. 11, of flood threat (Fig. 10). most of the locations proposed for water and soil Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 16 of 19 Fig. 10 Map of Prioritized sub-watersheds conservation activities are located at upper catch- of the sources are coming from upper catchments. ments out of the city boundary. For example, many AS major areas of SW-5 are located in urban bound- percolation tanks are proposed at one of the most aries less percolation tanks were proposed at dawn flood vulnerable site (SW-18) of upper catchments, streams of this sub-watershed. In general the location while least is proposed for the SW-5 which is located of conservation measures confirms that effective within the urban boundary. This is to show that most utilization of proposed structures at proposed site can Fig. 11 Proposed map of water conservation sites Erena and Worku Geoenvironmental Disasters (2018) 5:16 Page 17 of 19 significantly minimize the amount of flood water en- Acknowledgements We are pleased to thank DAAD in country/ in Region scholarship for East tering the city boundaries. Africa and thematic area research fund (Addis Ababa University) for funding this study. Conclusion Funding This study was conducted to assess the causes of flood The data collection and field work cost was funded by thematic area risk and to propose landscape-based flood mitigation research obtained from Addis Ababa university. strategies. The causes of flood were analyzed from me- Availability of data and materials teorological, institutional, anthropogenic and physical The data sets supporting the analysis of this article are included in the body factors perspectives. Questionnaire based data, meteoro- of the paper. logical record, satellite image and GIS based morphom- Authors’ contributions etry analysis were used to assess the causes of flood risk The corresponding author, SHE is a senior lecturer in the School of in the study area. The findings of the study shows that Geography and Environmental studies, Haramaya University. He collected flood risk in Dire Dawa city is caused by multiple factors data and drafted the manuscript. HW, is a Professor of Urban Environmental planning at Ethiopian Institute of Architecture Building Construction and City such as rainfall variability, intense rainfall at the sur- Development (EiABC), Addis Ababa University. He supervised the study and rounding mountains, solid waste damping in the river reviewed the whole content. Both authors contributed for the preparation of channels, encroachment of settlements to the river the manuscript and deserve to be an author of the study. Both authors read and approved the final manuscript. banks, institutional problem, land use land cover change and topography. These causes are fuelled by aggravating Competing interests factors such as regime change and power interruption. The authors declare that they have no competing interests. Most importantly, the study area flooding was attributed to hourly rainfall variability. Following the comprehen- Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in sive assessment of causes of flood risk, sub-watersheds’ published maps and institutional affiliations. susceptibility to flood risk was analyzed. Accordingly, the watershed was divided in to 26 mini sub-watersheds. Author details School of Geography and Environmental Studies, Haramaya University, Dire The quantification of each sub-watershed was carried Dawa, Ethiopia. Ethiopian Institute of Architecture Building Construction and out using morphometric parameters such as linear as- City Development, Addis Ababa University, Addis Ababa, Ethiopia. pects including Stream order (Nu), Bifurcation ratio Received: 1 May 2018 Accepted: 9 October 2018 (Rb), drainage density (Dd), stream frequency (Fs),tex- ture ratio (T) and shape aspects such as, form factor (Rf), circulatory ratio (Rc), and elongation ratio (Re) and References Abrahams, A.D. 1984. Channel networks: A geomorphological perspective. Water compactness constant (Cc). Higher values of linear par- Resource Research 20: 161–168. ameter and lower values of shape parameters were at- Agarwal, R., P.K. Garg, and R.D. Garg. 2013. Remote sensing and GIS based tributed to high flood risk. The results of these approach for identification of artificial recharge sites. 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Geoenvironmental Disasters – Springer Journals
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