Skip to main content

Quantitative evaluation of watershed attributes for water resources management in the Rift Valley Lakes Basin, Ethiopia: a case from Tikur Wuha river watershed


Characterization of watershed hydrological process is vital for sustainable water resource management. The principal goal of this study was to investigate the inference of drainage attributes on basic hydrological processes using spatial-based morphometric analysis on Tikur Wuha river watershed. The result obtained indicated that the area was characterized with fifth-order stream. Drainage area with higher stream order has lower infiltration capacity, and the shorter stream lengths were associated with the steepness of the area which affects water flow. Based on Nu value, sub-watersheds were categorized in the active erosion stage (SW7) and matured topography development (SW6). The interpretation from watershed geometry identified circular areas most susceptible to rapid hydrological response (SW11). Hydrological process and underlying materials are mainly correlated with the drainage texture parameter, and the lower the values indicated less rocky terrain and very high infiltration capacity which contributes toward less erosion (SW11). Relief parameters such as Rr value indicate the rate of stream flow and are well used in sediment yield estimation. The findings of this investigation will provide core information for water resource planning and further studies like identification of groundwater potential zones; flood risk assessment; erosion-prone area prioritization; and to select suitable sites for the construction of water harvesting structures.


Morphometric analysis of a drainage basin is a quantitative investigation of a basin attributes, and it is an important aspect to know the linear, aerial and relief character of a basin (Strahler 1964; Ayele et al. 2017; Tesfaye and Wondimu 2014). Mathematical analysis of drainage basin features provides useful inputs in order to characterize and understand various hydrological processes for planning and management (Farrukh et al. 2013; Tesfaye and Wondimu 2014; Yangchan et al. 2015). Since long ago, Horton (1945) and Strahler (1964) tried to indicate the association between morphometric parameters and basin hydrology. As many scholars said, the implementation of watershed management is fundamental to achieve sustainable uses of land and water resources to mitigate the increasing demand (Javed et al. 2009; Rai et al. 2017, 2018; Prakash et al. 2019).

Morphometric characterization assists decision makers in watershed management as it is considered to be the most acceptable method for good watershed management (Geena and Ballukraya 2011; Markose et al. 2014). Morphometric method has been used as a tool to know the area–height correlation and its implication on watershed hydrology for water resource planning (Kanth 2012; Bharadwaj et al. 2014). With the advent of remote sensing (RS) and geospatial information technology (GIS), as several studies confirmed, GIS-based morphometric analysis is most effective, time saving and accurate technique for watershed characterization, planning and management (Benukantha et al. 2019).

The Rift Valley Lakes Basin of Ethiopia has undergone a major land use change in the past, and such alterations remain vivid nowadays too, affecting the hydrology and ecology of the basin (WWDSE 2001). The inflow of runoff with heavy silt to Lake Hawassa causes rapid storage loss and an increase in the level of the lake (Mulugeta 2013; World Meteorological Organization 2003; Halcrow Group Limited and Generation Integrated Rural Development (GIRD) Consultants 2008; Yacob 2010). Tikur Wuha river is perennial river that flows to Lake Hawassa, and its drainage area covers approximately half of Lake Hawassa watershed and has been deforested over the past few decades (Yacob 2010; Wolka et al. 2014) causing hydrological imbalance on the lake. The main objective of this study was to implement GIS-assisted morphometric characterization on Tikur Wuha river watershed to reveal its impact on basic hydrological process.

Materials and methods

Study area description

Tikur Wuha river is the major tributary of Lake Hawassa, Ethiopia, draining from the eastern part of the lake watershed (Fig. 1). Tikur Wuha river watershed is found in the northeast shore of Lake Hawassa geographic location of 6048’00’’–7010’00’’N latitude and 38°26’30’’–38°43’00’’E longitude. Its altitude ranges between 1643 m and 2976 m above mean sea level. Agroecologically, the study area was classified into midland and highland. Land use in the watershed is dominated by agriculture/intensive cultivation (Yacob 2010).

Fig. 1

Location map of Tikur Wuha river watershed (satellite map)

Datasets used

Drainage aspects of Tikur Wuha river watershed were mathematically measured using hydrological tools in ArcGIS 10.7.1 setup. To carry out the analysis, digital elevation model (DEM) from with 20 m by 20 m resolution was acquired as a data source. Prior to morphometric analysis, delineation of watershed boundary and digitization of all existing stream including its tributaries were done digitally in ArcGIS package (Kotei et al. 2015). Sink fill and flow direction was computed to understand which way the water is flowing by comparing each cell with its neighboring using the eight-direction pour-point method (ESRI 2011). The accumulated number of cells that are draining to any particular cell was also identified using the flow direction grid to define the stream network and determine where streams turn into river. Then, watershed subdivision was carried out after stream segmentation (Gajul et al. 2016; Panhalkar et al. 2012). In this study, the mathematical expression (Table 1) of morphometric parameters assembled into linear aspect, watershed geometry, drainage texture and relief parameters was adopted from different sources. Further, the geological map of the study area was also used to correlate the underlying conditions with the morphometric parameters.

Table 1 Adopted mathematical equations to compute the morphometric parameters

Analysis of morphometric attributes

Interrelation between parameters

To understand how the different morphometric parameters interact and influence each other, the relationship between variables was tested using graphical regression equation and tabular correlation matrix (Ali and Hassan, 2018).

Results and discussion

The hydrological response of a catchment was largely controlled by its morphometric parameters (Diakakis 2010). Analyzing morphometric variables of Tikur Wuha river watershed plays an important role to understand various hydrological processes. The result obtained from the morphometric analysis and its implication on the hydrology of the river watershed was discussed henceforward:

Linear aspect

Stream order (u)

SW5 specifically and the study watershed in general accounted for fifth-order stream (Fig. 2) with dendritic type of drainage pattern. It is considered as the trunk stream through which all discharge of water and sediment passes (Strahler1957). However, SW4, SW5, SW7, SW12 and SW13 were assigned as fourth order and SW1 was the only one assigned as second order. The remaining eight SWs were categorized as third-order stream hierarchic. Stream order is inversely correlated with infiltration capacity; the higher the u value, the lesser in terms of permeability and infiltration (Gajbhiye et al. 2015). Hence from water resource management aspect, construction of water harvesting structures and the implementation of artificial recharge should be encouraged based on the biophysical condition.

Fig. 2

SW with drainage network (left); flow direction and flow accumulation map (right)

Stream number (Nu)

The NuT per each SW is tabulated in Table 2, 13.5% of the total Nu was situated in SW7, and SW6 accounts the smallest of all, that is, 1.8%. Across the watershed, 273 NuT were counted. In one way or the other, Nu indicates the erosional status and development stage of a given watershed (Gutema et al. 2017). Hence, relatively large Nu as in the case of SW7 indicates the topography is still undergoing erosion and the lesser the value (SW6) implies matured topography. It is observed (Table 1) that the maximum frequency is in case of first-order streams (Nu = 198) and decreases as the stream order increases (Nu = 1). This study also confirmed Horton’s low of Nu (1945); there is an inverse geometric correlation between u and the logarithm scale of Nu across the area of interest (Fig. 3a and 4). It was believed that the variation observed in the respective parameters was governed by the physiographic difference and structural condition of the watershed (Nikhil Raj and Azeez 2012; Praveen et al. 2018; Biswas 2016). The consistent decrease in Nu against u revealed the presence of erosional landform throughout the watershed (Avijit 2019).

Table 2 Stream number, stream length, mean stream and stream length ratio of individual SWs
Fig. 3

The relationship between Nu (a) and Lu (b) with u for Tikur Wuha river watershed

Fig. 4

Regression of Nu against u for each SW

Stream length (Lu)

Lu and slope of any watershed are highly correlated to each other; streams with smaller Lu indicate the area is characterized as steep slope and vice versa (Withanage et al. 2014; Dubey et al. 2015; Christopher et al. 2010). Hence, Lu is considered as a useful hydrological feature to understand surface runoff characteristics as it is dependent on slope. The LuT of Tikur Wuha river watershed was accounted for 646.7 km with fifth order of 4.15 km and first order of 317.42 km (Table 1). In general, the plot of the logarithm of Lu versus u (Fig. 3b) showed some deviation from its general behavior (linear pattern) indicates that the terrain is characterized by variation in lithology and topography (Nageswara et al. 2010). The logarithm scale of LuT is inversely correlated with u for each SW (Fig. 5). More deviation (r2 < 0.5) was observed in six SWs (SW4–8, 11) subjected to varied rock (parent) material, and the remaining nine SWs were characterized with homogeneous rock material (Nageswara et al. 2010).

Fig. 5

Regression of Lu on u for each SW

Mean stream length (Lum)

Lum of the study area was ranged from 0.15 (SW11) to 18 km (SW10). It is directly proportional to the size and topography of a drainage basin (Mangesh 2015). The mean stream length ratio (Lurm) was found between 1 (SW2) and 57 (SW11) (Table 1). Variation was also observed in stream length ratio (Lur), among successive stream orders. This variation revealed that the watershed was characterized by varying topography and slope according to Strahler (1964) and Gutema et al. (2017). Hence, it has an important control on discharge and erosional stage of the watershed (Sreedevi et al. 2004).

Bifurcation ratio (Rb)

The mean bifurcation ratio (Rbm) of the study watershed was ranged between 2 and 12. The higher the value of Rb implies there is strong structural control on the drainage pattern (Nag and Chakraborty 2003). Referring Table 3, SW10 with high value of Rbm was developed over easily erodible steeply dipping that also indicates high overland flow and discharge (Biswas 2016; Gajbhiye et al. 2015; Rawat et al. 2012). High Rb indicates early hydrograph peak with a potential for flash flooding during the storm events which results in degradation of top soil (Ajaykumar et al. 2019). The rest of the Tikur Wuha SWs (2 < Rbm < 5) were less affected by the underlying geological disturbance and relatively characterized by high permeability and low erodibility (Pankaj and Kumar 2009). However, soil permeability may also be affected by other watershed parameters like shallow soil depth and less structural control on the catchment development (Asiamah et al. 2000; Kotei et al. 2015). The value of Rb is also indicative of the shape of the basin. Elongated basin, which is likely to have higher Rb, yields a low but extended peak flow, while circular basins with low Rb procure sharp peak (Gajbhiye et al. 2015; Harinath and Raghu 2013).

Table 3 Bifurcation ratio, mean bifurcation ratio and Rho coefficient

Rho coefficient (ρ)

ρ is an important parameter which assists the evaluation of storage capacity of drainage network and, hence, a determinant of ultimate degree of drainage development in a given watershed (Horton 1945). Climatic, geologic, biologic, geomorphological and anthropogenic factors determine the changes in ρ (Chaitanya and Kanak 2015). ρ values of the study watershed are ranging between 0.3 and 15 for SW2 and SW15, respectively (Table 3). This implies higher hydrological storage during floods and attenuation of effects of erosion during elevated discharge.

Watershed geometry features

Drainage area (A) and perimeter (P)

The size of storm hydrograph and runoff magnitude are directly affected by the generated basin area (Strahler 1957); hence hydrologically, it is an important parameter. The SW’s A value is ranged between 11.14 and 89.25 km2 with perimeter (it is the length of the SW boundary) measured from 22.8 to 60.35 km, respectively, for SW1 and SW15 (Tables 4 and 5).

Table 4 Watershed geometry parameters
Table 5 Drainage parameters of the study area

Shape factor ratio (Bs)

Bs is the qualitative representation of a watershed that affects the stream discharge pattern. And it was expressed as the inverse of Rf. The computed value of Bs is ranging between 3.7 and 13.5 for SW11 and SW9, respectively. The greater the circular character of the basin is, the greater is the rapid response of the watershed after a storm event (Tucker and Bras 1997). Therefore, in terms of Bs, SW9 has the longest basin lag time and SW11 the shortest.

Form factor ratio (Rf)

Basin with high Rf is characterized by peak flow of shorter duration, whereas elongated watershed with low Rf has lower peak flow with longer duration (Gajbhiye et al. 2015). In this study, maximum Rf was 0.27 for SW11 and minimum value 0.07 for SW9. The result shows that SW9 relatively more elongated the other SWs.

Elongation ratio (Re)

The Re values of near 1 indicate that there are less geomorphological controls on river basin (Strahler 1964). A circular basin is more efficient in runoff discharge than an elongated basin (Singh and Singh 1997). The Re values of Tikur Wuha SW were varied from 0.31 to 0.59. The result indicated that SW9 and SW10 are more elongate than others implying low peak discharge, whereas SW2, SW3 and SW11 were relatively circular in nature (Avijit 2019).

Circularity ration (Rc)

The Rc is an important tool to understand the geomorphological development stage of tributary basins. It varies depending on the lithological characteristics and other morphometric parameters of specific basin. Rc value of 0.42 (maximum) was calculated for SW3 indicative of circular shape, moderate to high relief and permeable surface relatively with old development stage. A minimum Rc value of 0.15 was assigned for SW10 that indicates elongated, low relief and impermeable surface with a young stage of geomorphological adjustment (Ajaykumar et al. 2019).

Compactness coefficient (Cc)

The Cc value of a basin is inversely related with Re (Waikar and Aditya 2014; Zakaria et al. 2016). In this study, the Cc varies from 1.55 to 2.6 (Table 3). The lower value of Cc in the case of SW3 was the indication of more elongation and less erosion, while the reverse was observed in SW10, that is, the higher the Cc value, the lower the elongation and the higher the erosion occurrence probability. The Cc directly corresponds to its infiltration capacity (Ajaykumar et al. 2019).

Fitness ratio (Ff)

The Ff value of the study is varied between 0.26 and 0.5 for SW11 and SW7, respectively. It is a measure of topographic fitness (Praveen et al. 2018; Melton 1957).

Length area relation (Lar)

In this study, the Lar is ranged from a maximum of 20.7 for SW15 to a minimum value of 5.9 for SW1. Lar is an important geometric parameter to understand the association between stream length and basin area (Hack 1957).

Drainage texture attributes

Drainage density (Dd)

Because of its wide ratio of variation, drainage density is a number of primary importance in landform scale analysis (Strahler 1957). According to Melton in 1957, high value of Dd indicates highly dissected catchment with a relatively rapid hydrological response to rainfall, while a low drainage density expresses a poorly drained basin with a slow hydrological response.

Figure 6 indicates the Dd distribution map generated based on Smith (1950) and Chandrashekar et al. (2015) classification range. Dd is one of the factors controlling the surface runoff and consequently influences the output of sediment and water from the basin (Chorley 1969). In general, as the Dd number increases, the size of individual drainage units, such as the first-order drainage basin, decreases proportionately (Strahler 1957). The lower drainage density of any watershed indicates streams are widely spaced and low erosion occurrence is expected (Ajaykumar et al. 2019).

Fig. 6

Drainage density distribution map

Stream frequency (Fs)

Fs is directly correlated with Dd; the lesser the Fs indicates less Dd resulted in low runoff value, and hence, flooding is less likely to occur (Thomas et al. 2010). Fs depends on the rainfall, relief, as well as drainage density of the basin. In the study, SW’s Fs value (Tables 4 and 5) is ranged from 0.2 to 0.85 numbers per km2. Low Fs indicates that the watershed has less rocky terrain and very high infiltration capacity which contributes toward less erosion (Ajaykumar et al. 2019; Avijit 2019). The higher Fs value of 0.85 numbers per km2 in SW7 indicated that the area was dominated by high slope and low permeability. Low value of Fs was observed in SW6 and SW11 that reveals the sub-watersheds were characterized by less relief and low slope ranges.

Drainage texture (Dt)

Dt is an important fluvial parameter which denotes the relative spacing of drainage network of any basin (Avijit 2019). Dt is affected by the amount of rainfall, density of vegetation, soil types, infiltration capacity, stages of geomorphic development and relief (Horton 1945; Smith 1950). Based on Smith (1950) classification range, the Dt less than 2 indicates very coarse, between 2 and 4 is related to coarse, between 4 and 6 is moderate, between 6 and 8 is fine and greater than 8 is very fine drainage texture. The Dt of the entire area was below 2 indicating very coarse texture range (Tables 4 and 5), which implies higher permeability and enhanced groundwater (Albaroot et al. 2018). Regions of low infiltration capacity will give rise to higher Dt and thus will lead to more erosion (Ajaykumar et al. 2019). In this study, minimum value was accounted for SW6 that revealed the SW is plain with lower degree of slopes, while maximum Dt value indicates the SW was associated with high degree of slope (SW7 for instance) as noted by Gajbhiye et al. (2015).

Length of overland flow (Lg)

Lg is one of the most important independent variables affecting both the hydrological and physiographic development of drainage basins. Referring Tables 4 and 5, a higher value of Lg (SW15) represents low relief, whereas a low value of Lg (SW7 and SW13) is an indicative of high relief (Biswas 2016). Lg is directly related to average slope of drainage channel; hence, the greater the value, more is the erosion severity in the case of SW15 (Ajaykumar et al. 2019).

Constant of channel maintenance (C)

The lower value of C indicates higher flood potentiality and young geomorphological adjustment. Steep slope area generally has low C values due to lower infiltration of bare soil. The calculated C value is varied between minimum of 0.41 for SW13 and maximum of 1.74 for SW15 (Tables 4 and 5). Low C value for SW13 revealed that the SW is associated with very low-resistance and low infiltration capacity of bare soils; sparse vegetation and mountainous (steep slope) terrain resulted in high overland flow and high flood potentiality (Farrukh et al. 2013; Avijit 2019).

Relief aspect

Basin relief (H)

H indicates the erosional stage of a river basin, and it varies relaying upon the underlain geology, geomorphology and drainage characteristics of particular region. Mountain–plain front river basin has higher basin relief than plateau–plain front river basin (Thomas et al. 2010). The elevation of Tikur Wuha river basin ranged from 1643 m (SW5, the outlet to Lake Hawassa) in the northwestern part to 2976 m (SW15) in the southern part of the study area (Fig. 1). In the present study, the maximum H value is 988 m for SW13 indication of susceptibility for erosional activity and the minimum was 95 m for SW6.

Relief ratio (Rr)

The Rr indicates the overall slope of the watershed surface (Strahler 1964) and also tells us the average drop in elevation per unit length of the stream. According to Ajaykumar et al. (2019), high Rr values indicate high rate of stream flow and erosion vulnerability due to high sediment loss. As confirmed by Schumm in 1954, Rr is useful in estimating sediment yield. The value obtained here varied from 0.01 (for SW5 and SW6) to 0.07 for SW3.

Ruggedness number (Rn)

The Rn value implies the susceptibility of watershed to soil erosion (Gutema et al. 2017). Ruggedness values range between zero and one, values nearer to 0 refer to relatively smoother situation, and those nearer to 1 show more rugged terrain characteristics. The higher the values of Dd and H are, the higher the value of Rn will become. As a result, the slope is not only steep but long. Referring to Table 6, SW13 with higher value of Dd and H scores the maximum Rn value 2.44 and SW6 with the minimum amount, that is, 0.11.

Table 6 Relief aspects of Tikur Wuha river basin

Slope and aspect of the watershed

The slope gradient directly influences the formation of drainage networks, and it enables the assessment of runoff generation, direction and amount (Zavoiance 1985). The runoff speed from a particular basin is dependent on the associated slope range, thereby affecting the required time for rainwater to enter in the stream beds (Mesa 2006). The slope of the study watershed varies from 0° to 73° and reclassified (Fig. 7). There is a high slope gradient in the eastern parts (northeast and southeast) because of the presence of high relief causing rapid runoff, increasing erosion rates, and low recharge potential. As slope determines the infiltration vs runoff relation, it is important to understand the nature of slope in any region. Infiltration capacity is inversely related to the slope (Avijit 2019).

Fig. 7

Slope distribution (a) and aspect (b) and geological (c) map

The result obtained from the correlation index (Table 7) indicated that relatively there is a strong correlation between drainage texture parameters. The two watershed geometry attributes: Cc and Rc, are inversely correlated to each other. Strong positive correlation was also observed between Rc and Rf; Lg and Dd were correlated inversely to each other. It is well correlated with Lg in opposite manner, and Dt was highly correlated with Fs. Direct relationship was identified between Rn and If. Dis is more strongly correlated with the Rhp of the watershed.

Table 7 Correlation matrix among different morphometric parameters

Geological feature

Based on the data from Geological Survey of Ethiopia, majority of the underlying area (Fig. 7c) is dominated by Pleistocene–Holocene, undifferentiated followed by lacustrine sediments where commonly created as a result of precipitation of salts dissolved in the lake water (Gilbert 1978) and a river or stream channel has carried sediment into the basin. Alkaline and peralkaline silicics are also found. Ignimbrites are of low density and high porosity, are very weak to weak in compression and have low tensile (Vicki 1993).


In this study, hydrological perspectives of morphometric parameters were assessed on Tikur Wuha river watershed. The result obtained indicated that there are spatial variability and correlation among the morphometric attributes, and hence, the study addressed the hydrological characteristics relying on the different attributes. The study area was characterized with low infiltration capacity as it has higher u value. In general sense, the watershed is in the state of undergoing/active erosion and matured topography development. Some SWs were characterized circular in nature and are most susceptible to rapid hydrological response due to the shortest time of concentration. Hydrological process and underlying materials are mainly correlated with Dd and Fs, and the lower the values indicated less rocky terrain and very high infiltration capacity which contributes toward less erosion (for example: SW11). With the aim of water resource planning, the findings of this investigation will provide core ideas to identify groundwater potential zones; to study flood risk assessment; and to select suitable sites for the construction of water harvesting structures.


  1. Ajaykumar KK, Tasadoq HJ, Sanjay SK, Bhavana NU, Rabindranath NS (2019) Identification of erosion-prone areas using modified morphometric prioritization method and sediment production rate: a remote sensing and GIS approach. Geom Nat Hazards Risk 10(1):986–1006.

    Article  Google Scholar 

  2. Albaroot M, Nabil MA, Hamdi SA, Mohammed A, Saleh AG (2018) Quantification of morphometric analysis using remote sensing and GIS techniques in the Qa’ Jahran Basin, Thamar Province, Yemen. Int J New Technol Res (IJNTR) 4(8):12–22

    Google Scholar 

  3. Ali H, Hassan K (2018) Morphometric, statistical, and hazard analyses using ASTER data and GIS technique of WADI El-Mathula watershed. Qena, Egypt

    Google Scholar 

  4. Asiamah RD, Adjei-Gyapong T, Yeboah E, Fening JO, Ampontuah EO, Gaisie E (2000) Soil characterization and evaluation of four primary cassava multiplication sites (Mampong, Wenchi, Asuansi and Kpeve) in Ghana. SRI Technical Report No. 200, Kumasi

  5. Avijit M (2019) The significance of morphometric analysis to understand the hydrological and morphological characteristics in two different morpho-climatic settings. Appl Water Sci 10:33.

    Article  Google Scholar 

  6. Ayele AF, Hiroshi Y, Katsuyuki Sh, Nigussie H, Kifle W (2017) Quantitative analysis and implications of drainage morphometry of the Agula watershed in the semi-arid northern Ethiopia. Appl Water Sci 7:3825–3840.

    Article  Google Scholar 

  7. Benukantha D, Nagaraju MSS, Sahu N, Nasre RA, Mohekar DS, Srivastava R, Singh SK (2019) Morphometric analysis for planning soil and water conservation measures using geospatial technique. Int J Curr Microbiol Appl Sci 8(01):2719–2728.

    Article  Google Scholar 

  8. Bharadwaj AK, Pradeep C, Thirumalaivasan D, Shankar CP, Madhavan N (2014) Morphometric analysis of Adyar watershed. IOSR J Mech Civ Eng (IOSR-JMCE). e- ISSN: 2278-1684, p-ISSN: 2320–334X.

  9. Biswas SS (2016) Analysis of GIS based morphometric parameters and hydrological changes in Parbati River Basin, Himachal Pradesh, India. J Geogr Nat Disast 6:175.

    Article  Google Scholar 

  10. Chaitanya BP, Kanak M (2015) GIS based quantitative morphometric analysis and its consequences: a case study from Shanur River Basin, Maharashtra India. Appl Water Sci 7:861–871.

    Article  Google Scholar 

  11. Chandrashekar H, Lokesh KV, Sameena M, Roopa J, Ranganna G (2015) In: Dwarakish GS (Elsevier Procedia) (eds) Proceedings of the International Conference on Water Resources, Coastal and Ocean Engineering (Mangalore) vol 4. Pp 1345–1353

  12. Chorley RJ (1969) Introduction to physical hydrology. Methuen and Co. Ltd., Suffolk, p 211

  13. Christopher O, Idowu A, Olugbenga A (2010) Hydrological analysis of Onitsha North East drainage Basin using geoinformatic techniques. World Appl Sci J 11(10):1297–1302

    Google Scholar 

  14. Diakakis M (2010) A method for flood hazard mapping based on basin morphometry: application in two catchment in Greece. Nat Hazards 56(3):803–814.

    Article  Google Scholar 

  15. Dubey SK, Sharma D, Mundetia N (2015) Morphometric analysis of the Banas River Basin using geographical information system, Rajasthan, India. Hydrology 3(5):47–57.

    Article  Google Scholar 

  16. ESRI (2011) Arc Hydro Tools—Tutorial Version 2.0, United States of America

  17. Faniran A (1968) The index of drainage intensity-a provisional new drainage factor. Austral J Sci 31:328–330

    Google Scholar 

  18. Farrukh A, Gowhar M, Romshoo SA (2013) Morphometric analysis to infer hydrological behaviour of lidder watershed, Western Himalaya, India. Hindawi Publishing Corporation, Geography Journal, London.

    Book  Google Scholar 

  19. Gajbhiye S, Mishra SK, Pandey A (2015) Prioritizing erosion-prone area through morphometric analysis: an RS and GIS perspective. Appl Water Sci 4:51–61.

    Article  Google Scholar 

  20. Gajul MD, Mujawar KC, Unhale PL, Prabhakar P (2016) Prioritization of Balatira Watershed by morphometric and landuse landcover analysis, Atpadi Taluka, Sangli District, Maharashtra. J Appl Geol Geophys 4(3)

  21. Geena GB, Ballukraya PN (2011) Morphometric analysis of Korattalaiyar River basin, Tamil Nadu, India: A GIS approach. Int J Geom Geosci 2(2). ISSN 0976-380

  22. Gilbert R (1978) Lacustrine sedimentation. In: Sedimentology. Encyclopedia of Earth Science. Springer, Berlin, Heidelberg

  23. Gravelius H (1914) Flusskunde. Goschen Verlagshan dlung Berlin. In: Zavoianu I (ed) Morphometry of drainage basins. Elsevier, Amsterdam

  24. Gutema D, Kassa T, Sifan AK (2017) Morphometric analysis to identify erosion Prone areas on the upper blue Nile using Gis (Case Study of Didessa and Jema Sub-Basin, Ethiopia). Int Res J Eng Technol (IRJET) 04(08). e-ISSN: 2395-0056

  25. Hack (1957) Studies in longitudinal stream profiles in Virginia and Maryland. US Geological Survey Professional Paper 249-B, pp 45–97

  26. Halcrow Group Limited and Generation Integrated Rural Development (GRID) Consultants (2008) Rift Valley Lakes Basin Integrated Resources Development Master Plan Study Project. Phase II. Institutions of the Federal Government of Ethiopia and Ministry of Water Resource

  27. Harinath V, Raghu V (2013) Morphometric analysis using arc GIS techniques A Case Study of Dharurvagu, South Eastern Part of Kurnool District, Andhra Pradesh, India. International Journal of Science and Research (IJSR), India Online ISSN: 2319–7064, vol 2(1)

  28. Horton RE (1932) Drainage basin characteristics. Trans Am Geophys Union 13:350–361

    Article  Google Scholar 

  29. Horton RE (1945) Erosional Development of Streams and Their Drainage Basins; hydrophysical approach to quantitative morphology”. Bull Geol Soc Am 56:275–370

    Article  Google Scholar 

  30. Javed A, Khanday MY, Ahmed R (2009) Prioritization of sub-watersheds based on morphometric and land use analysis using remote sensing and GIS techniques. J Indian Soc Remote Sens 37:261.

    Article  Google Scholar 

  31. Kanth TA (2012) Morphometric analysis and prioritization of watersheds for soil and water resource management in Wular catchment using Geo-spatial tools”. Int J Geol Earth Environ Sci 2(1):30–41

    Google Scholar 

  32. Kotei R, Agyare WA, Kyei-Baffour N, Atakora ET (2015) Morphometric analysis of the Sumanpa River Catchmentat Mampong-Ashanti in Ghana. ARPN J Earth Sci 4(2). ISSN 2305-493X

  33. Markose VJ, Dinesh A, Jayappa K (2014) Quantitative analysis of morphometric parameters of Kali River basin, southern India, using bearing azimuth and drainage (bAd) calculator and GIS. Environ Earth Sci 1–17

  34. Melton MA (1957) An analysis of the relations among elements of climate, surface properties, and geomorphology project NR 389-042, Tech. Rept. 11. Columbia University, New York

  35. Mesa LM (2006) Morphometric analysis of Subtropical Andean Basin (Tucuman, Argentina). Environ Geol 50(8):1235–1242.

    Article  Google Scholar 

  36. Mulugeta D (2013) The impact of sedimentation and climate variability on the hydrological status of Lake Hawassa, South Ethiopia

  37. Nag SK, Chakraborty S (2003) Influence of rock type and structure development of drainage network in hard rock terrain. J Indian Soc Remote Sens 31(1):25–35

    Article  Google Scholar 

  38. Nageswara RK, Swarna LP, Arun KP, Hari KM (2010) Morphometric analysis of Gostani River Basin in Andhara Pradesh State, India using spatial information technology. Int J Geom Geosci 1(2):179–187

    Google Scholar 

  39. Nikhil Raj PP, Azeez PA (2012) Morphometric analysis of a tropical medium river system: a case from Bharathapuzha River Southern India. Open J Mod Hydrol 2:91–98.

    Article  Google Scholar 

  40. Pankaj A, Kumar P (2009) GIS-based morphometric analysis of five major sub-watersheds of Song River, Dehradun District, Uttarakhand with special reference to landslide incidences. J Indian Soc Remote Sens 37:157–166.

    Article  Google Scholar 

  41. Prakash K, Rawat D, Singh S (2019) Morphometric analysis using SRTM and GIS in synergy with depiction: a case study of the Karmanasa River basin, North central India. Appl Water Sci 9:13.

    Article  Google Scholar 

  42. Rai PK, Mishra VN, Mohan K (2017) A study of morphometric evaluation of the son basin. India Using Geospatial Approach Remote Sens Appl: Society and Environ 7:9–20.

    Article  Google Scholar 

  43. Rai PK, Singh RC, Mishra VN, Singh P (2018) Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data. Appl Water Sci 8:15.

    Article  Google Scholar 

  44. Rawat KS, Mishra AK, Tripathi VK (2012) Hydro-morphometrical analyses of sub-Himalyan region in relation to small hydro-electric power. DOI, Arab J Geosci.

    Book  Google Scholar 

  45. Schumm SA (1956) The evolution of drainage systems and slopes in Badlands at Perth Amboy, New Jersey. Geological Society of America Bulletins 67:597–646

    Article  Google Scholar 

  46. Singh S, Dubey A (1994) Geo-environmental planning of watershed in India. Chugh Publications, Allahabad, pp 28–69

    Google Scholar 

  47. Singh S, Singh MC (1997) Morphometric analysis of Kanhar river basin. Nat Geogr J India 43(1):31–43

    Google Scholar 

  48. Smith KG (1950) Standards for grading textures of erosional topography. Am J Sci 248:655–668

    Article  Google Scholar 

  49. Sreedevi PD, Subrahmanyam K, Ahmed S (2004) The significance of morphometric analysis for obtaining groundwater potential zones in a structurally controlled terrain. Environ Geol 47:412–420

    Article  Google Scholar 

  50. Strahler AN (1957) Quantitative analysis of watershed geomorphology. Trans Am Geophys Union 38:913–920

    Article  Google Scholar 

  51. Strahler AN (1964) Quantitative geomorphology of drainage basins and channel networks. In: Ven Te Chow (ed) Handbook of applied hydrology, pp 439–476

  52. Tesfaye M, Wondimu M (2014) Morphometric analysis of Didessa River catchment in Blue Nile Basin, Western Ethiopia. Sci Technol Arts Res J.

  53. Thomas J, Joseph S, Thrivikramaji KP (2010) Morphometric aspects of a small tropical mountain river system, the southern Western Ghats, India. Int J Digit Earth 3(2):135–156

    Article  Google Scholar 

  54. Tucker GE, Bras RL (1997) The role of rainfall variability in drainage basin evaluation: implication of stochostic model. EOS Trans AGU 78:283

    Google Scholar 

  55. Vicki GM (1993) Geotechnical characteristics of ignimbrite: a soft pyroclastic rock type. Eng Geol.

    Article  Google Scholar 

  56. Waikar ML, Aditya PN (2014) Morphometric analysis of a drainage basin using geographical information system: a case study. Int J Multidiscip Curr Res 2:179184

    Google Scholar 

  57. Withanage NS, Dayawansa NDK, De Silva RP (2014) Morphometric analysis of the Gal Oya River Basin using spatial data derived from GIS. Trop Agric Res 26(1):175–188

    Article  Google Scholar 

  58. Wolka K, Mengistu T, Taddese H, Tolera A (2014) Impact of land cover change on water quality and stream flow in Lake Hawassa Watershed of Ethiopia. Agric Sci 5:647–659

    Google Scholar 

  59. World Meteorological Organization (2003) The associated program on Integrated Flood Management: Case of Ethiopia: Integrated Flood Management, Technical Supprt Init, Kefyalew Achamyeleh (ed).

  60. WWDSE (2001) The study of Lake Awassa level, lake Awassa study and design project. Water Works Design and Supervision Enterprise, Addis Ababa

    Google Scholar 

  61. Yacob E (2010) Evaluating the impact of land use land cover change on soil erosion and runoff using SWAT model at Tikur Wuha watershed. Unpublished master’s thesis. Addis Ababa, Ethiopia

  62. Yangchan J, Jain AK, Tiwari AK, Sood A (2015) Morphometric analysis of drainage basin through GIS: a case study of Sukhna Lake Watershed in Lower Shiwalik, India. Int J Sci Eng Res 6(2)

  63. Zakaria M, Abul Kashem M, Majadur R (2016) Morphometric analysis of Reju Khal drainage basin using geographic information system (GIS) and SRTM data. Int J Sci Eng Res 7(6). ISSN 2229-5518

  64. Zavoiance I (1985) Morphometry of drainage basins (development in water science). Elsevier Science, New York

    Google Scholar 

Download references


This study was supported by Hawassa University Office of Vice President for Research and Technology Transfer, and we would like to acknowledge the office. The authors are also thankful to the academic and financial staffs from Wondo Genet College of Forestry and Natural Resources, Hawassa University.


For this study, funding was provided by Hawassa University Office of Vice President for Research and Technology Transfer.

Author information



Corresponding author

Correspondence to Rediet Girma.

Ethics declarations

Conflict of interest

The authors declared that there is no conflict of interest in this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Girma, R., Abraham, T. & Muluneh, A. Quantitative evaluation of watershed attributes for water resources management in the Rift Valley Lakes Basin, Ethiopia: a case from Tikur Wuha river watershed. Appl Water Sci 10, 196 (2020).

Download citation


  • Water resource
  • Hydrology
  • Morphometric analysis
  • Tikur Wuha