Introduction

Climate change has been defined as a change in the state of the climate that can be identified (e.g., using statistical tests) by changes in the mean and /or the variability of its properties, and that persists for an extended period typically decades or longer (IPCC 2007). Climate change arising from Global warming; a term used to describe the gradual increase in the average temperature of the earth’s atmosphere and its oceans, a change that is permanently changing earth’s climate forever. In fact, global warming has already begun, as there is a scientific consensus that the average earth’s temperature has risen between 0.4 and 0.8 °C in the last 100 years. Nigeria is one of the world’s most densely populated countries with a population of about 180 million people, half of which are considered to be in abject poverty; therefore, Nigeria is recognized as being vulnerable to climate change (Osinowo et al. 2015).

The flood occurrence of Ala River has been discovered as being disastrous as the river flow cuts across some part of the semi-urban and urban areas of Akure city (Ibitoye et al. 2019; Olalekan and Fadesola 2017). This can be attributed to a relative increase in the amount of rainfall which arises as a result of the climate change experienced round the world as well as sediment deposit on rivers channel. Das (2019) reported that there was an abrupt reduction in sediment deposit and flow of Subarnarekha River downstream after the construction of a dam along the river channel when four decades of water flow and sediment records were analyzed. In the study area, whenever there is a heavy downpour of rain, little or no assistance comes from the government; however, in recent times flooding has been a recurrent disaster because of heavy rain and stormwater that overflows the river bank. Due to the location of the study area and absence of infrastructure to handle stormwater effectively, the consequences of excessive rainfall has always been havocs on lives and properties, also, this damage(s) has been aggravated by unplanned physical development around the settlement with the absence of drainage and stormwater facilities. With regard to climate change, the need to mimic the effect of possible climate scenarios on the streamflow of Ala cannot be overemphasized. Increasingly, for environmental planning, due to the development of regulatory and planning tools, such as the river basin master plan, there is need for such simulations for flood forecasting and river management (Al-Khafaji and Al-Chalabi 2020; Pirnia et al. 2019; Pohle 2019; Opere et al. 2019).

Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows us to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices (Kadam 2011). The HEC-HMS model was designed to simulate the rainfall-runoff of dendritic watershed systems. Its detail algorithm has been discussed in previous studies (Gyawali and Watkins 2013; Kabiri et al. 2015; Yilmaz et al. 2012). Several studies have adopted the HEC-HMS software model under various soil condition and climatic condition and have come to various conclusions as due (Din et al. 2019; Joshi et al. 2019; Tassew 2019; Teng 2018; Nyaupane 2018; Bhuiyan 2017; Khoi 2016; Munyaneza et al. 2014; Yener et al. 2007; Chu and Steinman 2009).

Review of literature reveals that not much work has been done on the impact of climate change on the rainfall-runoff process in Ala River, Akure. Hence, the objective of this study is to simulate the effect of climate change on the stream-flow using HEC-HMS for the hydrological modeling of the Ala River watershed, Akure, Nigeria. It is on this premises that this paper is structured to provide necessary information and data on the extent to which change in the climatic condition of the area would affect the stream-flow of Ala River.

Materials and methods

Description of the study area

Akure, where the studied river is sited is the capital city of Ondo state, a microcosm of the Nigeria nation situated in the high forest zone (or rainforest) in the south-western region (Akinseye et al. 2016). It is located between latitude 7o 15′ N and 7o 28′ N of the equator and longitude 5° 6′E and 5o21′E of the Greenwich meridian, the total land area is approximately 41.2 km2. Ala River basin lies within the Akure metropolis. Ala River basin extent is between 5°9′E, 7° 17′ N and 5° 17′ E, 7° 16′ N. Ala River transverse between Akure South and Akure North Local Government Area. River Ala and its tributaries are one of the main tributaries of River Ogbese, Southwestern, Nigeria. River Ala has a total length of about 57 km with about 14.8 km within Akure Township (Fig. 1). It took its source from the northwestern part of Akure town and flow towards Southeastern part of the town. Akure Township dominates the upstream of River Ala while rural towns such as Ilado, Ehinala, Ajegunle, Owode, Aiyetoro and Araromi are located in the downstream where the water is being used for drinking water and other domestic purposes (Ayeni et al. 2011). The river is located on a gently undulating terrain surrounded by isolated hills and inselbergs. Topographic elevations of the area vary between 260 and 470 m above sea level (Owoyemi 1996). The climate of the area consists of two peaks rainy season with short and long dry season. The rainy season begins from March to mid-July and late-August to Mid-November. Little/short dry season occurs between Mid-July and early-August, the long dry season begins from late-November to March (Akinseye 2010).

Fig. 1
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Map of the study area

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Data acquisition and analysis

A good understanding of the topographical, hydrological and climatic condition of the study area and proper set of data defining them are very important for analyzing and replicating the actual hydrologic and hydraulic situation. Further, the quality of data used for modeling directly affects the output, so the collected data was screened and processed before using them.

Software used for simulation and analysis

The following software was used to achieve the objectives of this research:

Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS)

Aeronautical Reconnaissance Coverage Geographic Information System (ARCGIS)

Statistical Downscaling Model Software (SDSM).

Data acquisition for precipitation projection

Observed daily Rainfall data for 17 years (1997–2014) was obtained from the West African Science Service Center on Climate Change and Adapted Land Use (WASSCAL), Federal University of Technology, Akure (FUTA). While large-scale climate variables (predictors) NCEP and CanESM2 for past and future precipitation based on different climate scenarios were obtained from the Canadian Climate Data and Scenarios website (https://climate-scenarios.canada.ca). Statistical downscaling method was used for the projection of future climate change under different Representative Concentration Pathway (RCP 4.5 and RCP 8.0).

Statistical downscaling model

A widely used approach for generating local-scale climate change impact is a statistical model, which relates large-scale climate variables (or predictors) to regional/local-scale climate/hydrologic variables in other to derive the regional information about the climate/hydrologic variable (Shashikanth and Sukumar 2017). Statistical downscaling uses statistical relationships to predict local climate variables (the predictands) from large‐scale variables (the predictors) (Benestad et al. 2008). Such statistical relationships between the regional‐ and large‐scale climate variables must be calibrated before they are applied to make predictions for the research region (Fan et al. 2013).

The precipitation projection was done by establishing an empirical relationship between historical large-scale atmospheric and local climate characteristics. Once the relationship has been determined and validated, future large-scale atmospheric conditions projected by GCMs are used to predict future local climate characteristics. A schematic of the downscaling process adopted is as given in the flow chart in Fig. 2:

Fig. 2
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Schematics for future climate change projection using statistical downscaling model

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Data for hydrological modeling: HEC-HMS

Data required for hydrologic modeling (HEC-HMS) are:

Digital Elevation Model (DEM):

Land use

Climate data (precipitation)

Digital elevation model

DEM represent the topographic feature of the study area. The DEM of the Ala River watershed was downloaded from www.earthexplorer.usgs.gov and the DEM acquired for the study area was refined using the HEC-GeoHMS tool in ARCGIS.

Land use

The Gis layer for land use of the Ala watershed was produced using ARCGIS, and as shown in Fig. 3, the land use was classified into six categories as Built-Up (Developed area), bare surface, light vegetation, dense vegetation, Wetland (Water Body), and Rock Outcrop. Built-Up area is the dominant land cover type whereas the water body is the least present in the river catchment. The Landsat data analyzed for the generation of the land use of the study area has a resolution of 30 m, and according to Kappa coefficient, the land use data ascertain an accuracy value of 96.4%.

Fig. 3
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Land use classification of Ala River basin

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From the land use classification and soil condition, the Curve Number adopted for the Ala watershed is given in Table 1.

Table 1 Curve number adopted for Ala River
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Hydrologic modeling: HECHMS

Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows us to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices (Kadam 2011). A schematic of the Hydrologic model simulation adopted is as given in Fig. 4:

Fig. 4
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Hydrologic modeling approach

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The selected methods adopted for the HMS process is given in Table 2:

Table 2 Selected methods for HMS
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In addition, Table 3 summarizes the hydrologic parameters for the HMS process.

Table 3 Summary of hydrological parameters for HMS process
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For simulating the hydrologic response of the river basin effectively, the entire watershed was divided into 9 different sub-basins each with different drainage area. Figure 5 shows the basin model map of Ala River basin.

Fig. 5
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HEC-HMS basin model of Ala River

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Results and discussions

Statistical downscaling model analysis

Data quality control

The quality of the locally observed data used for the projection of future climate change (precipitation) was first checked for any missing data and the summary of the result is shown in the result screen in Fig. 6.

Fig. 6
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Results screen for quality control check for the observed data

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There are 6570 values in the precipitation data of 1997–2014 with no missing data (that is, no missing values codes of − 999 were detected). The data ranges from 0 to 55.14 mm/day and have a mean of 4.20128 mm. Quality control test is necessary to validate the quality of the precipitation data obtained for the analysis.

Performance of SDSM compared to the observed daily precipitation

Statistical method was used to evaluate the performance of the calibrated model in reproducing the observed data and projecting future precipitation. Some of the statistical parameters include mean, variance, Sum, and so on.

The results in Fig. 7 depict a high level of accuracy of the model in reproducing the observed precipitation and in projecting future precipitation; hence, the statistical relationship between the predictand and predictor variables is strong.

Fig. 7
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Graph of the performance of SDSM for a monthly mean, b monthly sum

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Rainfall projection under the representative concentration pathways

The time-series representation of the projected rainfall for the years under consideration is shown in Fig. 8;

Fig. 8
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Time series graph for projected precipitation for year a 2020, b 2025, c 2030, d 2045, e 2070, f 2100

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The projection of future precipitation under climate scenario RCP 4.5 gave a maximum of 123.3 mm, 128.2 mm, 136.1 mm, 147.1 mm, 164.3 mm, 167.1 mm rainfall depth for the year 2020, 2025, 2030, 2045, 2070, and 2100, respectively. While the precipitation projection under climate scenario RCP 8.0 gave a maximum of 133.0 mm, 147.9 mm, 155.3 mm, 168.2 mm, 190.6 mm, 237.9 mm rainfall depth for the year 2020, 2025, 2030, 2045, 2070 and 2100, respectively, and this indicates that there is an increase in the amount of precipitation as year progress.

Simulation results for climate scenario RCP 4.5

This section presents the hydrologic model results after considering medium–low climate scenario RCP 4.5 projections. Table 4 summarizes the peak discharges of the flow that occurs in the river channel as a result of the amount of rainfall, while Table 5 summarizes the Peak discharges and the direct runoff volume for RCP 4.5 at the Ala River Catchment Outlet.

Table 4 Summary of the peak discharges under RCP 4.5 Climate Scenario projections of Ala River catchment
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Table 5 Summary of the peak discharges and direct runoff volume for RCP 4.5 Climate Scenario projections of Ala River catchment outlet
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It is observed that the peak discharges increases as the depth of rainfall increases with year. The highest discharge for this scenario projection are 275.0 m3/s, 289.1 m3/s, 302.8 m3/s, 329.6 m3/s, 368.9 m3/s, 373.6 m3/s, for the year 2020, 2025, 2030, 2045, 2070 and 2100, respectively.

Simulation results for climate scenario RCP 8.0

This section presents the hydrologic model results after considering medium–low climate scenario RCP 8.0 projections. Table 6 summarizes the peak discharges of the flow that occurs in the river channel as a result of the amount of rainfall, while Table 7 summarizes the peak discharges and the direct runoff volume for RCP 8.0 at the Ala River Catchment Outlet.

Table 6 Summary of the peak discharges RCP 8.0 Climate Scenario projections of Ala River catchment
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Table 7 Summary of the peak discharges and direct runoff volume for RCP 8.0 Climate Scenario projections of Ala River catchment outlet
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It is observed that the peak discharges increases as the depth of rainfall increases with year. The highest discharge for this scenario projection are 298.8 m3/s, 332.3 m3/s, 350.2 m3/s, 374.3 m3/s, 430.7 m3/s, 538.1 m3/s for the year 2020, 2025, 2030, 2045, 2070, and 2100, respectively. Under the climate scenario RCP 8.0 the river will experience a very higher discharge compared to the RCP 4.5 scenario as the year progresses also, the percentage change of the peak discharges between the climate scenarios RCP4.5 and RCP8.0 for the years under consideration is as shown in Table 8.

Table 8 Comparison of the Peak Discharges of Ala River between Climate Scenario RCP 4.5 and RCP 8.0
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From the year 2020–2100 there is an increase in the peak discharge of Ala River from 8.7%—44% between the two scenarios under consideration.

Conclusion

In this study, future climate change of Ala, Akure was projected under two different climate scenarios RCP 8.0 and RCP 4.5 using the statistical downscaling model to downscale General Circulation Model taking into consideration local observed data. Hydrologic model was simulated using HEC-HMS to study the effect of projected climate change on surface runoff and peak discharges. It was observed and can be concluded that climate change increases as a result of the increase in the amount of precipitation projected with respect to historic rainfall. The SDSM model gave an output of approximately 123.3 mm, 128.2 mm, 136.1 mm, 147.1 mm, 164.3 mm, 167.1 mm for RCP 4.5 and 133.0 mm, 147.9 mm, 155.3 mm, 168.2 mm, 190.6 mm, 237.9 mm maximum rainfall depth for RCP 8.0 for the year 2020, 2025, 2030, 2045, 2070 and 2100, respectively. A peak discharge of 275.0 m3/s, 289.1 m3/s, 302.8 m3/s, 329.6 m3/s, 368.9 m3/s, 373.6 m3/s for RCP 4.5 and 298.8 m3/s, 332.3 m3/s, 350.2 m3/s, 374.3 m3/s, 430.7 m3/s, 538.1 m3/s for RCP 8.0 were simulated for the year 2020, 2025, 2030, 2045, 2070 and 2100, respectively.