ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT AZOJETE December 2021. Vol. 17(4):439-452 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 439 ORIGINAL RESEARCH ARTICLE RAINFALL-RUNOFF MODELING FOR CHALLAWA AND JAKARA CATCHMENT AREAS OF KANO CITY, NIGERIA A. Mohammed1, S. Dan’Azumi1, A. A. Modibbo2, A. A. Adamu3 and Y. Ibrahim4 1Department of Civil Engineering, Bayero University, Kano, Nigeria 2Department of Civil Engineering, University of Maiduguri, Borno, Nigeria 3Department of Civil Engineering, Kaduna Polytechnic, Kaduna, Nigeria 4Department of Geology, University of Maiduguri, Maiduguri, Nigeria *Corresponding author’s email address: abdulrasheeddoko@gmail.com 1.0 Introduction Recently, the use of hydrological models to estimate runoff of a catchment that are ungauged have gained considerable attentions from hydrologists and students of hydrology all over the world. Many researches conducted in urban flooding have resulted in the development of many mathematical modeling tools both free and commercial (Rangari et al., 2018). For example, with the advent of Storm Water Management Model (SWMM), HEC-HMS, HEC-RAS, MIKE LOOD modelling of the urban flood became easy and outputs of great understanding have been generated by these models. ArcGIS has made the work simpler due to its ability to extract data for directs inputs to the model (Hasyman et al., 2015). Generally, hydrological models are primarily classified as statistical and deterministic models. The statistical models produce outputs that have partial randomness while deterministic models do not give randomness. Furthermore, deterministic hydrologic models are further classified into three major categories; lumped model, semi-distributed model and distributed model. Lumped model assesses the catchment response simply at the outlet without obviously ARTICLE INFORMATION ABSTRACT For the past decade, the magnitude and frequency of rainfall that resulted into flooding of Kano city have been relatively high and there is need to manage the flood in order to safe life and properties. This research aimed at generating Challawa and Jakara catchment areas runoff for hydrological applications. The two catchments were first delineated and their basic physical properties were extracted from processed 30 m x 30 m Digital Elevation Models (DEMs) of the respective watershed. Basin models of the watersheds were imported from ArcGIS 10.7 to Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS) environment, the meteorological models were developed in HEC-HMS with rainfall data and control specifications assigned which defined the period and time step of the simulation run. SCS-Curve Number, Unit Hydrograph and Muskingum methods were used for estimation of loss, runoff and flow routing respectively. The rainfall-runoff simulations were carried out for 31 years of maximum daily rainfall record (1988 – 2018). The simulated flow rate obtained can be used for further hydrological analysis such as flood frequency analysis and in design of safe and economical drainage and flood control facilities or in maintenance of culverts, sewers, bridges and roads for Jakara and Challawa catchment areas. It is recommended that; the catchment areas be gauged while further research should be conducted on the quality of the simulated flow obtained. © 2021 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved. Submitted 16 March, 2021 Revised 8 July, 2021 Accepted 20 July, 2021 Keywords: Challawa basin DEM HEC-HMS Jakara basin Rainfall-runoff Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 440 counting for an individual sub-basins response. The semi-distributed model is partly permitted to change in space with a division of the catchment into a number of sub-basins. Whereas, the distributed model, permits its parameters to change in place at a resolution normally chosen by the client (Tassew et al., 2019). Good storm water design practices help to maintain drainage systems, minimize disturbance to existing drainage patterns, control flooding of property, structures, and roadways, and minimize environmental impacts of storm water runoff. Urbanization tends to increase downstream peak flows, runoff volumes, and runoff velocities. These changes can cause the capacity of adequately designed drainage systems to be exceeded and disrupt natural waterways (Antigha et al., 2014). In Kano Metropolis, urban growth has, over the years, resulted in land use changes and modifications, which significantly increased area of imperviousness. As urbanization continues, there will be increase in population density and more areas will be devoted to infrastructural development which will in turn increase impervious surfaces. With urbanization and increase in impervious surfaces threat of flooding is bound to increase during any major storm event. Rainfall-runoff models are often used as a tool for a wide range of applications such as the modeling of flood events, the monitoring of water levels during different water conditions or the prediction of floods (Tassew et al., 2019). Flow rate data are required for flood modelling and in flood risk assessment. Hence, this research is aimed at obtaining flow data through rainfall-runoff modeling for ungauged Challawa and Jakara catchment areas in Kano city. A number of case studies carried out through researches have shown high efficiency in the use of HEC-HMS to simulate runoff from rainfall data for a given watersheds Tassew et al., 2019; Rangari et al., 2018; Chang et al., 2015). Therefore, this study employed HEC-HMS instead of using traditional methods to simulate runoff data using rainfall data and other watershed characteristics for the catchments of concerned. This research would consider two basins (Jakara and Challawa basins). Challawa comprised Gwale, Kano Municipal Council, Tarauni and Kumbotso Local Government Areas consisted of 32 wards (districts) while, Jakara basin comprised Dala, Fagge, Ungogo and some part of Nasarawa Local Government Areas consisted 62 wards (districts). 2. MATERIALS AND METHODS 2.1 Description of Study Area Kano State is located in the Northwestern Nigeria on latitude 12°N and longitude 8.30°E within the semi-arid Sudan savannah zone of West Africa. Kano has a mean height of about 481 m above sea level (Mustapha et al., 2014). The state was created on May 27, 1967 from part of the Northern region, Kano State borders Katsina State to the north-west, Jigawa State to the north-east, Bauchi State to the south-east and Kaduna State to the south-west. Kano Metropolitan Area consists of eight Local Government Areas which include Fagge, Dala, Gwale, Kano Municipal, Tarauni, Nassarawa, Kumbotso and Ungogo LGAs with total area of 499 km2 and a total population of 2,828,86l people (NPC, 2006). The latest official estimate (for 2016) is 3,931,300 inhabitants and it does suffice to say that the city is densely populated. Kano City is the second largest industrial center after Lagos State in Nigeria and the largest in the Northern Nigeria with textile, tanning, footwear, cosmetics, plastics, pharmaceuticals, ceramics, furniture, food and beverages and other industries (Mustapha et al., 2014). The city’s infrastructure both Government owned and Private Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 440 owned cannot be overemphasized. These infrastructures include schools (elementary and higher institutions), health centers, communication centers, religion centers and other social amenities. The temperature of Kano usually ranges between a maximum of 33°C and a minimum of 15.8°C although sometimes during the harmattan it falls down to as low as 10°C. Kano has two seasonal periods, which consist of four to five months of wet season and a long dry season lasting from October to April. The movement of the South West maritime air masses originating from Atlantic Ocean, influences the wet season which starts from May to September. The mean annual rainfall is about 800 mm around metropolitan Kano. Great temporal variation occurs in the amount of rainfall received and no two consecutive years record the same amount. The movement of the tropical maritime air masses from the southwest to the North determines the weather of Kano State during the wet season (Mustapha et al., 2014). The hydrogeology of Kano is distinguished by three large watersheds; the outfall to River Challawa, the outfall to River Jakara and the other is to River Wateri. These basins are considered as the main receptacles of runoff from the city. Figure 1 shows the map of Kano metropolis. The first source of the River Jakara is traced into the Kano city (i.e. the Jakara/Rafin Mallam) and the second source is the Bompai Industrial Estate (the Getsi). The River and its tributaries have most of their length within the Metropolis. Along the stretch of the banks are rapid encroachment of settlements which increases their risk and vulnerability to flooding. For this, the quality of the water is very low and not suitable for many uses. While Challawa River takes its source from Kaduna and Katsina States in the Katsina Highlands around Malumfashi, which also serves as one of the headwaters of the Challawa River System which creates dendritic networks moving eastward. Some of its major tributaries include Koganya, Kwakwa, Jare and Marashi all of which join the Challawa River. The last three tributaries meet at Damagari in Tudun-Kaya, Karaye Local Government. Koganya flows into Challawa at the upstream of the Gorge Dam in Rogo Local Government area. Other Rivers from this axis that joined the Challawa are Kumanda, Kariya, Bagwari and Bargi Mohammed et al: Rainfall-Runoff Modeling for Challawa and Jakara Catchment Areas of Kano City, Nigeria. AZOJETE, 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 441 Figure 1: Map of the study area 2.2 Input Data The data required for running rainfall-runoff modelling included rainfall, digital elevation model, land use/land cover 30 m x 30 m digital elevation model of the Challawa and Jakara basins were obtained from United States Geological Survey (USGS) website and used to extract basic physiographic characteristics of the catchment areas. ArcGIS 10.7, ArcHydro tool and HEC- GeoHMS were used for terrain pre-processing, basin processing and model development to generate the characteristics of basin and input files for HEC-HMS. Other required data included Basin storage (S), Initial abstractions (Ia), Time of concentrations (Tc), Curve numbers (CN), Lag time and Muskingum parameters k and x. These parameters were obtained using the following Equations; Basin storage (S) (1) Time of concentration Tc = (2) Initial abstractions (Ia) Ia = 0.2S (3) Lag time L= 0.6Tc (4) Muskingum constant (k) K = N∆t (5) Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 442 2.3 Land-use/land-cover (LULC) Classification of Challawa and Jakara Basins The methods of LULC classification followed by Ahmad (2020), Haruna et al., (2019), Alfa et al., and (2018) were adopted for this research. LULC is required for the estimation of catchment area Curve Numbers (CN) and the CN is in turn required for rainfall- Runoff modelling using HEC- HMS. The LULC classifications were carried out for the periods of 1988 - 2018. Figure 2 shows the flowchart methodology for LULC classification. 2.4 Watershed analysis of Challawa and Jakara basins in Kano city For this study, Challawa and Jakara basins in Kano city were considered. This study was conducted using ArcHydro tools, HEC-GeoHMS 10.7 and ArcGIS 10.7. To accomplish the task of watershed analysis, three major processes were carried out which included terrain pre-processing, watershed delineation and terrain processing and basin model development. Figure 3 shows the flowchart of watershed analyses methodology. Figure 2: Land-use/Land-cover classification methodology flow chart Landsat Imagery Layer Stacking Image Sub setting Color Composite Image Classification  Supervised  Maximum Likelihood Field Survey Extraction by Mask Land use/Land cover Map Reclassification Land use/Land cover Map Mohammed et al: Rainfall-Runoff Modeling for Challawa and Jakara Catchment Areas of Kano City, Nigeria. AZOJETE, 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 443 Figure 3 Flowchart of watershed analyses methodology 2.5 Rainfall-runoff Modeling in HEC-HMS Rainfall-Runoff Modelling in HEC-HMS required four major components; Basin Models, Meteorological Models, Control Specifications and Time-Series Data. The methods followed by Rangari et al., (2018), Rathod et al., (2015), Choudhari et al., (2014), Martin et al., (2012), Output Delineated watershed and processed terrain Inputs STRM DEM Stream Network Terrain Pre-processing of Challawa Basin (ArcGIS 10.7 and ArcHydro tool) -DEM Reconditioning - Stream segmentation -Fill sinks - Catchment grid delineation -Flow direction - Catchment polygon processing -Flow accumulation - Drainage line processing -Stream Defination - Adjoint catchment processing Output Rasters Vectors -DEM -catchment -fill map -drainageline -fdr map -adjointcatchment -fac -str -strlnk -cat Watershed Delineation and Terrain Processing (ArcGIS 10.7 and ArcHydro tool) i. Project Setup iii. Basin characteristics - Start new project - River length - Define project point - River slope - Generate project - Basin slope ii. River Profile - Basin centroid - Basin centroid elevation - Centroid longest flow path Basin Model Development (ArcGIS 10.7 and HEC-GeoHMS) i. HMS inputs/parameters v. Creating input files for HEC-HMS ii. Selecting HMS process - Map to HMS units iii. River auto name - Check data iv. Basin auto name - HMS schematic - Add coordinates - Prepare data for model export - Background shape file Output Basin model Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 444 Merwade, (2012) and HEC-HMS Technical Reference Manual guidelines were adopted in this study. Two catchment areas were considered for this research (Challawa and Jakara). Rainfall-runoff simulations were ran for thirty one years of maximum daily rainfall events (1988- 2018) for Challawa and Jakara basins in Kano metropolis. 3. RESULTS AND DISCUSSIONS 3.1 LULC for Challawa and Jakara Catchment Areas of Kano City The results of LULC characterization for Challawa and Jakara catchment areas for year 2018 are presented in Figure 4 and Figure 5 respectively. The vegetation and Water body in both catchment areas are low as it can be seen, vegetation covered 18.05% land use, water body covered 1.06% for Challawa catchment area and 16.61% vegetation, 0.45% water body for Jakara catchment area in year 2018. In comparison of the two catchments, it could be observed that Jakara catchment area with built area of 64.26% is more urbanized than Challawa catchment area with built up area of 60.87%. The land use land cover classifications were used alongside with soil data for estimation of curve numbers for both catchment areas. Figure 4: Challawa land use land cover classification map for year 2018 Mohammed et al: Rainfall-Runoff Modeling for Challawa and Jakara Catchment Areas of Kano City, Nigeria. AZOJETE, 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 445 Figure 5: Jakara land use land cover classification map for year 2018 Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 446 3.2 Rainfall-Runoff Simulations Using HEC-HMS 4. 2 3.2.1 Challawa basin model Figure 6 shows the Challawa basin model. From Figure 6, it can be seen that Challawa basin has 15 subbasins, 7 junctions, 7 reaches and one outlet. . . Figure 6: Challawa Basin Model 3.2.2 Computed HEC-HMS input parameters for Challawa basin Table 1 shows input parameters for Challawa basin. From Table 1, it can be seen that, subbasin “W160” has the biggest basin area of 11.92 km2 with longest flow path, basin slope, curve number, storage, initial abstraction, time of concentration and lag time of 4651 m, 0.0049, 82, 2.1951, 0.4390, 101 minutes and 60.60 minutes respectively. While subbasin “W190” has minimum basin area of 0.40 km2 with longest flow path, basin slope, curve number, storage, initial abstraction, time of concentration and lag time of 1707 m, 0.0216, 78, 2.82, 0.56, 26 minutes and 15.78 minutes respectively. Muskingum parameters k and x values were estimated to be 1 for k and 0.2 for x and used for all the sub-basins. Mohammed et al: Rainfall-Runoff Modeling for Challawa and Jakara Catchment Areas of Kano City, Nigeria. AZOJETE, 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 447 Table 1: HEC-HMS input parameters for year 2018 Challawa basin simulation Sub- basin Drain- ID Basin Area (km2) Longest flow path (m) Basin slope (m/m) CN S Ia Tc (Mins) Lag time (Mins) W160 17.0000 11.9232 4651.0010 0.0049 82.0000 2.1951 0.4390 101.0030 60.6018 W170 18.0000 5.9316 6167.9422 0.0099 85.0000 1.7647 0.3529 95.4172 57.2503 W180 16.0000 4.6315 6087.7806 0.0049 81.0000 2.3457 0.4691 124.1885 74.5131 W190 19.0000 0.4046 1707.1707 0.0216 78.0000 2.8205 0.5641 26.2925 15.7755 W200 20.0000 9.8054 5857.7530 0.0022 88.0000 1.3636 0.2727 162.6705 97.6023 W210 21.0000 12.6894 8187.2330 0.0032 91.0000 0.9890 0.1978 182.2641 109.3584 W220 22.0000 11.0967 5607.9437 0.0024 88.0000 1.3636 0.2727 153.9108 92.3465 W230 25.0000 1.7218 5179.1608 0.0041 85.0000 1.7647 0.3529 116.6048 69.9629 W240 23.0000 5.7248 2753.6001 0.0069 84.0000 1.9048 0.3810 58.9601 35.3760 W250 24.0000 6.8009 6936.0208 0.0041 85.0000 1.7647 0.3529 146.1082 87.6649 W260 26.0000 2.0230 2212.8179 0.0091 85.0000 1.7647 0.3529 44.8304 26.8982 W270 29.0000 5.0964 4974.3777 0.0068 88.0000 1.3636 0.2727 93.6560 56.1936 W280 27.0000 13.5416 4010.8410 0.0028 87.0000 1.4943 0.2989 111.6392 66.9835 W290 28.0000 6.2328 6936.0210 0.0022 91.0000 0.9890 0.1978 185.4966 111.2980 W300 30.0000 7.2916 5398.4978 0.0005 88.0000 1.3636 0.2727 270.0820 162.0492 CN = Curve number, S = Storage, Ia = Initial abstraction and Tc = Time of concentration 3.2.3 HEC-HMS output results for Challawa basin The main purpose of running HEC-HMS was to get flow data for Challawa catchment of Kano city. Following the standard procedures and guidelines of running HEC-HMS model as described in the HEC-HMS technical manual Table 2 shows the results for year 2018 simulation. From Table 2, it can be seen that, Challawa basin has a total drainage area of 104.9154 km2 with a peak discharge of 188.00 m3/s and discharge volume of 36.99 mm. Subbasin “W200” has highest peak discharge of 83.2 m3/s with a discharge volume of 36.11 mm, followed by Subbasin “W220” which have 72.6 m3/s peak discharge with a discharge volume of 34.92 mm while Subbasin “W190 recorded lowest peak discharge of 2.7 m3/s with a discharge volume of 39.98 mm. Table 2: Result for year 2018 simulation Subbasin Drainage Area (km2) Peak Discharge (m3/s) Volume (MM) W160 11.9232 32.9 39.98 W170 5.9316 25.4 44.40 W180 4.6315 24.6 38.63 W190 0.4046 2.7 39.98 W200 9.8054 83.2 36.11 W210 12.6894 54.0 36.11 W220 11.0967 72.6 34.92 W230 1.7218 8.4 36.11 W240 5.7248 25.7 39.98 W250 6.8009 29.5 44.40 W260 2.0230 12.2 56.56 W270 5.0964 45.8 28.72 W280 13.5416 56.6 31.64 W290 6.2328 36.7 36.11 W300 7.2916 36.8 32.69 Outlet 104.9154 188.0 36.99 Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 448 3.2.5 Jakara basin model Figure 7 shows the Jakara basin model . From Figure 7, it is observed that, Jakara basin has 17 subbasins, 8 junctions, 8 reaches and one outlet. . Figure 7: Jakara Basin Model Mohammed et al: Rainfall-Runoff Modeling for Challawa and Jakara Catchment Areas of Kano City, Nigeria. AZOJETE, 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 449 3.2.5 Computed HEC-HMS input parameters for Jakara basin Table 3 shows computed HEC-HMS input parameters for year 2018 Jakara basin simulation. It can be observed from Table 3 that, subbasin “W280” has the biggest basin area of 20.27 km2 with longest flow path, basin slope, curve number, storage, initial abstraction, time of concentration and lag time of 9223.77 m, 0.0065, 85, 1.76, 0.35, 153.20 minutes and 91.92 minutes respectively. While subbasin “W300” has minimum basin area of 0.69 km2 with longest flow path, basin slope, curve number, storage, initial abstraction, time of concentration and lag time of 1444.74 m, 0.0040, 87, 1.49, 0.30, 136.79 minutes and 82.07 minutes respectively. Muskingum parameters k and x values were estimated to be 1 for k and 0.2 for x and used for all the sub-basins. Table 3: HEC-HMS input parameters for year 2018 Jakara basin simulation Sub- basin Drain- ID Basin Area (km2) Longest flow path (m) Basin slope (m/m) CN S Ia Tc (Mins) Lag time (Mins) W180 18.0000 8.6002 6113.5908 0.0050 85.0000 1.7647 0.3529 123.5141 74.1084 W190 19.0000 0.7059 1497.7251 0.0523 87.0000 1.4943 0.2989 16.9040 10.1424 W200 20.0000 2.6601 3143.9508 0.0104 87.0000 1.4943 0.2989 55.7902 33.4741 W210 21.0000 8.2386 5902.2136 0.0037 82.0000 2.1951 0.4390 134.1988 80.5193 W220 22.0000 2.4019 2721.7620 0.0061 82.0000 2.1951 0.4390 61.0568 36.6341 W230 23.0000 4.3647 5144.0259 0.0017 83.0000 2.0482 0.4096 163.6706 98.2024 W240 24.0000 4.6143 4001.5161 0.0057 82.0000 2.1951 0.4390 84.8289 50.8973 W250 25.0000 9.5558 6330.9961 0.0039 82.0000 2.1951 0.4390 138.8014 83.2808 W260 26.0000 0.9556 1569.3613 0.0028 82.0000 2.1951 0.4390 53.8258 32.2955 W270 27.0000 8.8068 6798.7766 0.0040 85.0000 1.7647 0.3529 146.0918 87.6551 W280 28.0000 20.2737 9223.7715 0.0065 85.0000 1.7647 0.3529 153.1997 91.9198 W290 29.0000 6.8182 6327.6992 0.0041 87.0000 1.4943 0.2989 136.7884 82.0731 W300 30.0000 0.6887 1444.7393 0.0040 87.0000 1.4943 0.2989 44.3466 26.6080 W310 31.0000 13.3522 6971.1559 0.0038 88.0000 1.3636 0.2727 151.0514 90.6309 W320 32.0000 4.2011 4241.4344 0.0054 88.0000 1.3636 0.2727 90.1367 54.0820 W330 33.0000 8.9015 6152.0230 0.0043 88.0000 1.3636 0.2727 130.7820 78.4692 W340 34.0000 4.6401 4564.8113 0.0043 88.0000 1.3636 0.2727 103.9898 62.3939 CN = Curve number, S = Storage, Ia = Initial abstraction and Tc = Time of concentration 3.2.5 HEC-HMS output results for Jakara basin Table 4 shows Jakara basin global summary result for year 2018 simulation, . From Table 4, it is observed that, Jakara basin has a total drainage area of 109.7759 km2 with a peak discharge of 207.70 m3/s and discharge volume of 36.56 mm. The highest peak discharge of 82.7 m3/s with a discharge volume of 36.11 mm was recorded at subbasin “W280”. While subbasin “W300 recorded lowest peak discharge of 6.3 m3/s with a discharge volume of 38.63 mm. Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 450 Table 4: Result for year 2018 Jakara simulation Subbasin Drainage Area (km2) Peak Discharge (m3/s) Volume (MM) W180 8.60 41.0 36.11 W190 0.71 8.30 38.63 W200 2.66 20.40 38.63 W210 8.24 33.90 32.70 W220 2.41 14.90 32.69 W230 4.36 15.50 33.78 W240 4.61 26.80 32.69 W250 9.56 38.40 32.69 W260 0.96 6.40 32.69 W270 8.81 37.50 36.11 W280 20.27 82.70 36.11 W290 6.82 32.60 38.63 W300 0.68 6.30 38.63 W310 13.35 61.10 39.98 W320 4.20 28.60 39.98 W330 8.90 45.40 39.98 W340 4.64 27.70 39.98 Outlet 109.78 208.7 36.56 3.2.6 Simulated flow data for Challawa and Jakara basins Consequently, the simulated flow data obtained for Challawa and Jakara basins are presented in Table 5 and can be used for further hydrological analysis. From Table 5, it could be seen that, the flow rate of Jakara basin is higher than that of Challawa basin across the period of consideration (1988-2018). This is because, Jakara basin is more urbanized than Challawa basin as proven by the LULC characterization result. Additionally, Mukherjee, (2016) revealed that urbanization greatly influenced the runoff generation. Furthermore, the drainage area of Jakara basin is higher than that of Challawa basin which is one of the parameters that influence runoff generation in catchment area (Kavian and Mohammadi, 2012). Mohammed et al: Rainfall-Runoff Modeling for Challawa and Jakara Catchment Areas of Kano City, Nigeria. AZOJETE, 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 451 Table 5: Simulated flow data for Challawa and Jakara catchment areas 4. Conclusion and Recommendations The flow rate data for ungauged Jakara and Challawa catchment areas of Kano city have been successfully simulated through rainfall-runoff modelling using HEC-HMS 4.2. The results can safely be used for further hydrological analysis. It is strongly recommended that, the catchment areas should be gauged, and further research be conducted on the quality of the simulated flow obtained. References Ahmad, AA. 2020. Land Use Land Cover Change Analysis in Fagge Local Government, Kano State from 1991-2019. Research Square, DOI: https://doi.org/10.21203/rs.3.rs-33756/vi. Year Challawa Flow rate (m3/s) Jakara Flow rate (m3/s) 1988 207.70 342.1 1989 168.20 285.9 1990 145.30 252.4 1991 140.70 245.6 1992 181.00 304.3 1993 185.50 310.8 1994 178.90 301.3 1995 148.30 256.8 1996 255.00 407.2 1997 151.60 527.5 1998 262.20 417 1999 168.50 286.4 2000 251.50 402.5 2001 204.80 534.6 2002 172.30 291.8 2003 224.90 366 2004 170.00 260.2 2005 261.90 380.9 2006 295.10 423.3 2007 196.90 296.3 2008 166.40 255.5 2009 202.70 294.3 2010 177.50 260.7 2011 243.80 346.1 2012 210.20 522.3 2013 280.50 391.7 2014 226.30 598.3 2015 305.20 410.8 2016 316.80 401.5 2017 194.00 251.9 2018 188.00 208.7 Arid Zone Journal of Engineering, Technology and Environment, December, 2021; Vol. 17(4):439-452. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: abdulrasheeddoko@gmail.com 452 Alfa, MI., Ajibike, MA., Adie, DB. and Mudiare, OJ. 2018. Assessment of the Effect of Land Use/Land Cover Changes on Total Runoff from Ofu River Catchment in Nigeria. Journal of Degraded and Mining Land Management, 5(3): 1161-1169. Antigha, REE, Akor, AJ., Ayotamuno, MJ., Ologhodien, I. and Ogarekpe, NM. 2014. Rainfall Runoff Model for Calabar Metropolis Using Multiple Regression. Nigerian Journal of Technology, 33(4): 566 – 573. Chang, TJ., Wang, CH. and Chen, AS. 2015. A Novel Approach to Model Dynamics Flow Interactions between Storm Sewer System and Overland Surface for Different Land Covers in Urban Areas. Journal of hydrology, 52(4): 662-679. Choudhari, K., Panigrahi, B. and Paul, JC. 2014. Simulation of Rainfall-Runoff Process Using HEC- HMS Model for Balijore Nala Watershed, Odisha, India. International Journal of Geomatics and Geosciences, 5(2): 253-265. Haruna, M., Ibrahim, MK. and Shaibu, UM. 2019. Assessment of Land Use and Vegetative Cover in Kano Metropolis (from 1975-2015) Employing GIS and Remote Sensing Technology. Nigerian Journal of Basic and Applied Science, 27(2): 01-07. Hasyman, F., Khaleghi, MR. and Kamyar, M. 2015. Combination of HEC-HMS and HEC-RAS Models in GIS in Order to Simulate Flood. Journal of Water Resources and Recent Science, 4(8): 122-127. Hydrological Modelling System HEC-HMS. 2000. Technical Reference Manual. US Army Corps of Engineers, Hydrologic Engineering Center, St. Davis, California. Kavian, A. and Mohammadi, MA. 2012. Effect of Rainfall Type on Runoff and Sediment Yield in Plot Scale. Journal of Range and Watershed Management, 65(1): 117–130. Martin, O., Rugumayo, A. and Ovcharovichova, J. 2012. Application Of HEC-HMS/RAS and GIS Tools in Flood Modeling: A Case Study for River Sironko, Uganda. Global Journal of Engineering, Design & Technology, 1(2): 19-31. Merwade, V. 2012. Hydrologic Modeling using HEC-HMS. School of Civil Engineering, Purdue University, West Lafayette, Indiana. Mukherjee, D. 2016. Effect of urbanization on flood - a review with recent flood in Chennai (India). International Journal of Engineering Sciences & Research Technology, 5(7): 451-455. Mustapha, A., Yakudima, II., Alhaji, M., Nabegu, AB., Dakata, GAF., Umar, AY. and Musa, UB. 2014. Overview of the Physical and Human Setting of Kano Region, Nigeria. Journal of Geography, 1(5): 1-12. National Population Commission (NPC) 2006. Census, Abuja, Nigeria. Rangari, VA., Sridhar, V., Umamahesh, NV. and Patel, AK. 2018. Rainfall Runoff Modeling of Urban Area Using HEC-HMS: A case study of Hyderabad city, Conference Paper, Research gate. Rathod, P., Kalpesh, B., and Manekar, VL. 2015. Simulation of Rainfall - Runoff Process Using HEC- HMS (Case Study: Tapi River, India). HYDRO 2015 INTERNATION, 20th International Conference on Hydraulics, Water Resources and River Engineering. IIT Roorker, India. Tassew, BG., Belete, MA. and Miegel, K. 2019. Application of HEC-HMS Model for Flow Semulation in the Lake Tana Basin: A case of Gilgel Abay Catchment, Upper Blue Nile Basin, Ethiopia. Journal of Hydrology, 6(21): 1 – 17