Anthropogenic activities exacerbated by population growth, demanding land for food production and settlements, have led degradation of the Bontanga watershed. The aim of this study was to assess the impact of land use and land cover (LULC) change on the surface runoff in the Bontanga watershed from 1997 to 2022. LULC change maps for the years 1997, 2002, 2013, and 2022 were generated from Landsat images using ArcGIS, achieving overall accuracies of 92, 91.89, 95.27, and 83.64%, respectively. Surface runoff was estimated using the SCS–CN model. Correlation analysis was employed to identify predominant LULC change classes that impact surface runoff. The agricultural land and built-up area increased by 20.81% and 27.20% causing an increase in the surface runoff by 13.92 and 14.49% from 1997 to 2022. Due to anthropogenic activities, dense forest, grassland, mixed forest and shrub land, mixed shrub and grassland decreased by 20.31, 29.97, 22.51, and 25.58% causing an increase in surface runoff by 14.98, 14.06, 15.78, and 15.05%, respectively. Correlation analysis showed that changes in surface runoff were positively correlated with agricultural land, and mixed forest and shrub and negatively correlated with mixed shrub and grassland, and grassland.

  • Due to anthropogenic activities, the Bontanga watershed has experienced substantial degradation from 1997 to 2022.

  • Surface runoff was positively correlated with agricultural land, and mixed forest and shrub and negatively correlated with mixed shrub and grassland, and grassland.

  • The study employed the SCS–CN model, remote sensing data, and GIS to comprehend the influence of LULC change on surface runoff.

Land use and land cover (LULC) change exerts a profound influence on the surface runoff, thereby altering the movement of water over the surface of the earth (Tannor et al. 2012; Tumsa 2023). Hu et al. (2020a) reported that the transformation of natural landscapes into agricultural land or urban areas induces changes in LULC, consequently resulting in the conversion of soil layers into impervious layers. This alteration leads to a reduction in the soil's water infiltration capacity, and groundwater recharge thereby impacting the hydrological processes within the watershed (Sertel et al. 2019). The changes in LULC as a result of human-induced activities precipitate modifications in both the magnitude and temporal distribution of surface runoff, thereby engendering shifts in water resource availability, heightened flood risk, and exacerbated soil erosion (Tufa et al. 2014; Abdulkareem et al. 2017; Nut et al. 2021). The absence of adequate soil vegetation cover results in escalated surface water runoff, thereby increasing the likelihood of erosion and flooding (Tufa et al. 2014; Meshesha et al. 2016).

Adongo et al. (2020) conducted a study to evaluate reservoir sedimentation in the Bontanga irrigation dam. The researcher utilized the grab sampling method for collecting surface runoff inflow in the reservoir, and conducted a bathymetric survey integrated with ArcGIS 10.4 to assess reservoir sedimentation. The result showed that the storage capacity of the dam has declined by 10.80%. This decrease was attributed to sedimentation, as the results of LULC changes driven by anthropogenic activities within the watershed. This study does not evaluate the long-term dynamics of LULC within the watershed, and its potential influence on surface runoff. Therefore, there is a need for this study to understand the dynamics of hydrological change in the watershed.

Understanding the impact of LULC change on surface runoff is significant for formulating effective mitigation measures that can safeguard against excessive surface runoff, thereby enhancing sustainable watershed management. When evaluating the consequences of LULC changes, various hydrological models, such as SWAT (Soil and Water Assessment Tool), HEC-HMS (The Hydrologic Engineering Center's-Hydrologic Modeling System), the Soil Conservation Service–Curve Number (SCS–CN) of the United States Department of Agriculture, MIKE System Hydrological European (Mike SHE), and Storm Water Management Model (SWMM), have been extensively utilized to assess the effects of LULC changes on surface runoff (Cho & Engel 2018; Hu et al. 2020a). For example, Shrestha et al. (2021) employed GIS and SCN–CN methods to assess the impact of land use change on surface runoff in Xiamen City due to urbanization. Also, Satheeshkumar et al. (2017) utilized integrated GIS and SCN–CN models to estimate rainfall-runoff in the Pappiredipatti watershed. In this study, integrated GIS, remote sensing data, and SCS–CN model were utilized to assess the influence of land use change, and their spatial-temporal variation in surface runoff in the Bontanga watershed. Trend analysis of LULC change was performed by using the Mann–Kendall (MK) trend test and the Sen slope estimator. Several methods for trend assessment are available in the literature. Although the non-parametric MK trend test and the Sen slope estimator are widely used for trend detections in hydro-meteorological data (Ali et al. 2019). Researchers prefer employing these non-parametric methods due to their advantages in handling missing data within the time series datasets, requiring few assumptions, and being independent of data distribution (Kisi 2015; Öztopal & Şen 2017). However, the disadvantage of MK trend test and the Sen slope estimator it that it is affected by autocorrelation in the datasets (Hu et al. 2020b). This method has been applied widely in different studies globally. For example, Gedefaw et al. (2023) utilized the MK test and Sen's slope estimator to evaluate the impact of LULC change on water resources in the Nile River basin, Ethiopia. The results revealed that the increasing trend in precipitation and temperature within the basin was associated with changes in LULC. Furthermore, Aswad et al. (2020) examined the trends in annual and monthly variability of rainfall data over the past 70 years spanning from 1940 to 2010 in Iraq, using the MK test and Sen's slope estimator. Their findings indicated an annual decrease in precipitation, with an observed increasing trend in the months of October and April, while a decreasing trend was observed for the remaining months.

The main objectives of the study were to: (a) examine LULC change characteristics in the watershed from 1997 to 2022, (b) assess the impact of land use change classes on surface runoff, and (c) identify the predominant land use change classes impacting surface runoff in the watershed. Understanding the impact of rapid urbanization, agricultural land expansion, deforestation, and other land use changes in the watershed is crucial for studying the hydrological response of the watershed. Investigating the effects of these changes on surface runoff is essential for developing effective land management strategies, mitigating potential risks such as increased flooding or decreased water quality, and promoting sustainable land use practices.

Study area

Bontanga irrigation dam is located in the Kumbungu District of the Northern Region of Ghana. The district lies between latitude 9° 17′ and 10° 6′ N and longitude 1° 2′ and 1° 19′ W (Zakaria et al. 2014). The Bontanga dam watershed lies approximately between latitudes 9° 24′ and 9° 35′ N and longitudes 0° 56′ and 1° 4′ W (Figure 1).
Figure 1

Location map of the Bontanga watershed.

Figure 1

Location map of the Bontanga watershed.

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The Bontanga irrigation dam stands as the largest earth-fill dam within Ghana's Northern Region. Originally designed with dual objectives, the dam serves as a critical water source for both crop irrigation, covering an area of up to 490 hectares primarily dedicated to paddy cultivation, as well as for aquaculture (Alhassan et al. 2014). The communities situated in the upstream catchment area of the Bontanga dam, including Kumbungu, Kpalsogu, Zangbalung, Sakuba, Dalun, Tibung, and Wuba, are primarily engaged in fishing and subsistence farming. Climatic conditions within the region exhibit a mean annual rainfall of 1,100 mm, relative humidity at 75%, and a temperature range spanning from 15 to 42 °C, with an average annual temperature of 28.3 °C. The rainy season typically commences in April or May and extends through to September or October, with peak rainfall occurring between July and August. The dry season begins from November to March (Alhassan et al. 2014).

Data acquisition and processing

The data used in this study were acquired from four different sources:

  • To conduct this study, a 30 m spatial resolution digital elevation model (DEM), satellite imageries (Landsat 5 (TM), Landsat 7 (ETM+), Landsat 8 (OLI), and Landsat 9 (OLI-2)) with different bands downloaded during the dry season (November to December) were acquired from the link (https://earthexplorer.usgs.gov/) as shown in Table 1. These images were used for LULC change detection in the study area. ArcGIS 10.0 was utilized for satellite image processing and mapping LULC change.

  • Daily rainfall data corresponding to 1997, 2002, 2013 and 2022 was collected from SARI (Savanna Agricultural Research Institute) weather Station – Tamale. Microsoft Excel version 2019 was utilized for the analysis of rainfall data and the creation of various figures pertaining to LULC change and surface runoff.

  • Soil maps for the Bontanga watershed were obtained from https://www.fao.org/soils-portal.

  • The ground truth data was collected using Geographical Positioning System (GPS) for accuracy assessment of the satellite imagery.

Table 1

Satellite imagery data

YearSpace craft and sensor IDSensor IDPath/rowAcquisition dateCloud coverImage resolution (m)
1997 Landsat 5 TM 194/53 10/12/1997 <5% 30 
2002 Landsat 7 ETM + 194/53 17/12/2002 <5% 30 
2013 Landsat 8 (OLI) TIRS 194/53 05/11/2013 <5% 30 
2022 Landsat 9 (OLI-2) TIRS-2 194/53 22/11/2022 <5% 30 
YearSpace craft and sensor IDSensor IDPath/rowAcquisition dateCloud coverImage resolution (m)
1997 Landsat 5 TM 194/53 10/12/1997 <5% 30 
2002 Landsat 7 ETM + 194/53 17/12/2002 <5% 30 
2013 Landsat 8 (OLI) TIRS 194/53 05/11/2013 <5% 30 
2022 Landsat 9 (OLI-2) TIRS-2 194/53 22/11/2022 <5% 30 

TM, Thematic Mapper; ETM + , Enhanced Thematic Mapper Plus; OLI, Operational Land Imager; OLI-2, Operational Land Imager 2; TIRS, Thermal Infrared Sensor; TIRS-2, Thermal Infrared Sensor 2.

LULC change classification

The study area was categorized into seven classes of land use: mixed shrub and grassland, dense forest, agricultural land, mixed forest and shrub, grassland, water bodies and build-up area as shown in Table 2. ArcGIS software version 10.0 was used for LULC change classification of different multispectral landsat satellite imagery of 4 years (1997, 2002, 2013, and 2022).

Table 2

LULC classification

S. No.LULC classDescription
Dense forest Forest montane, broadleaf, which includes evergreen forest land with no clearly visible indications of human activities. 
Agricultural land Areas used for crops production by irrigation or rainfed agriculture. 
Mixed forest and shrub Land area dominated with low density tress, with bushes and vegetation's forming open habitat with plenty of sunlight and limited shade. 
Mixed shrub and grassland Land areas dominated with bushes, mixed with grasses. 
Grassland Land areas dominated with grasses. 
Water bodies Reservoir, charco dams and farm ponds. 
Built-up Infrastructures made by human being such as roads, buildings, and settlement. 
S. No.LULC classDescription
Dense forest Forest montane, broadleaf, which includes evergreen forest land with no clearly visible indications of human activities. 
Agricultural land Areas used for crops production by irrigation or rainfed agriculture. 
Mixed forest and shrub Land area dominated with low density tress, with bushes and vegetation's forming open habitat with plenty of sunlight and limited shade. 
Mixed shrub and grassland Land areas dominated with bushes, mixed with grasses. 
Grassland Land areas dominated with grasses. 
Water bodies Reservoir, charco dams and farm ponds. 
Built-up Infrastructures made by human being such as roads, buildings, and settlement. 

Satellite imagery classification and accuracy assessment

A supervised classification method employing maximum likelihood was applied to classify four Landsat satellite images captured in the years 1997, 2002, 2013, and 2022, utilizing ArcGIS 10.0. Seven LULC change classes, namely mixed shrub and grassland, dense forest, agricultural land, mixed forest and shrub, grassland, water bodies, and built-up, were identified within the study area. A composite band was created through the combination of various landsat satellite image bands, which were then used to generate training samples for each LULC class. Ground truth data for the seven LULC change classes were collected using a handheld GPS and were utilized for the accuracy assessment of the landsat images. A confusion matrix was employed to compare the classified LULC change classes with the actual ground truth data. The confusion matrix, incorporating key metrics such as the producer's accuracy, User accuracy, and the Kappa coefficient (K), was estimated for the accuracy assessment of the LULC change map. The confusion matrix is structured as a table, with rows representing the combined classification of LULC categories, and ground truth data derived from composite pixel analysis of remote sensing data. The columns of the matrix signify the total observed data or ground truth data. Mekuriaw (2019) pointed out that the overall accuracy of the classified imagery serves as a vital indicator, representing the percentage of pixels that have been accurately classified. This overarching accuracy is calculated as the ratio of the total number of correctly classified pixels (found on the diagonal of the confusion matrix) to the total number of pixels within the matrix. Producer accuracy, on the other hand, is determined by calculating the ratio of correctly classified pixels within a specific class to the total number of pixels derived from that particular class. Similarly, user accuracy is evaluated by comparing the number of correctly classified pixels within each class to the total number of ground truth pixels obtained from composite remote sensing data. The Kappa coefficient (K) is another essential method for assessing the accuracy of satellite imagery. It serves as a quantifiable measure of agreement between the model's predictions (LULC) and the actual reality represented by the composite remote sensing data. The Kappa coefficient was calculated using Equation (1), a method suggested by Mewded et al. (2021); Mekuriaw (2019); Mutayoba et al. (2018). Kappa coefficient values can range from 0 to 1, with the following categories: < 0 indicates no agreement or complete randomness, 0–0.2 signifies slight agreement, 0.21–0.41 indicates poor agreement, 0.41–0.6 reflects moderate agreement, 0.61–0.8 denotes significant agreement, and 0.81–1.0 signifies almost perfect agreement, in accordance with the classification scheme described by Mfwango et al. (2022).
formula
(1)
where N refers to the total number of observations (pixels); rrefers to the number of rows and columns in error matrix; Xii refers to the number of correct classified pixels in row i and column i; Xi+ refers to the marginal total of row i, and X+i refers to the marginal total of column i.
The rate of change of different LULCs was estimated using the following formula (Kashaigili & Majaliwa 2010; Mutayoba et al. 2018)
formula
(2)
formula
(3)
formula
(4)
where Area year i refers to the area of land cover i at the first year, Areayear i+1 refers to the area of land cover i at the second year, refers to the total land cover area for i = 1 to n years, tyears refers to the time period between the first and second year.

Hydrological soil classification

The hydrological soil group serves as a vital classification framework employed to categorize soils according to their inherent hydrological characteristics. This classification system plays an essential role in comprehending the dynamics of surface runoff within a specific location. The level of surface runoff is primarily determined by the soil's physical properties, such as texture, structure, and permeability (Zewide 2021). Different soil types have varying capacities to absorb and retain water. For example, sandy soil has a higher infiltration rate and lower surface runoff than clay soil. The hydrological soil group classification segregates soils into four distinct groups, as comprehensively elucidated by Maidment & Mays (1988).

  • Group A soils: These soils have a high infiltration rate and low runoff potential.

  • Soils are characterized by good permeability, low compaction, and a well-developed root system, e.g. sand, sandy loam, or loamy sand.

  • Group B soils: These soils have an intermediate infiltration rate and runoff potential.

  • Soils are usually found in areas where there is a mix of good permeability and moderate compaction, or where there are areas of compacted soil alternating with areas of good permeability, e.g. loam, silt loam, or silt.

  • Group C soils: These soils have a low infiltration rate and high runoff potential.

  • Soils are characterized by high compaction, low permeability, and limited vegetation cover, e.g. sandy clay loam.

  • Group D soils: These soils have a very low infiltration rate and high runoff potential and

  • consist mainly of clay soil. Group D soils are characterized by high compaction, very low permeability, and swelling potential, e.g. clay loam, silty clay loam, sandy clay, silty clay, or clay.

In the Bontanga watershed, the soil classification was categorized into two primary groups: loamy sandy, which predominates, encompassing 90% of the watershed area, and sandy clay loam, which constitutes the remaining 10% of the watershed. These soil group classifications were sourced from data provided by the Ghana Irrigation Development Authority (GIDA) in Tamale as shown in Supplementary material, Appendix 1, Figure S1, and were subsequently employed to ascertain the hydrological soil group characteristics of the watershed.

Surface runoff estimation

In order to comprehensively assess the impacts of LULC changes on surface runoff, we employed the SCS–CN model to calculate surface runoff within the Bontanga catchment. The SCS–CN model is an empirical hydrological model that estimates runoff from rainfall events, taking into account soil type and land use factors. In the SCS–CN model, the Curve Number (CN) is a pivotal parameter that is used to compute the potential maximum runoff retention of a watershed. The CN is influenced by land use type, soil hydrologic groups, and antecedent moisture conditions (Hu et al. 2020a). Higher CN values indicate a greater potential for runoff. The SCS–CN model stands as one of the most widely utilized methods for computing the direct volume of surface runoff resulting from various land use types, including agricultural land, forests, and built-up areas (Ahmadi-sani et al. 2022). The SCS–CN model's appeal lies in its simplicity, user-friendliness, and applicability to ungauged watersheds. The derivation of the SCS–CN model is based on the water balance equation, as shown in Equation (6), and two fundamental assumptions described in Equations (5) and (7) (Maidment & Mays 1988). The methodologies adopted for the estimation of the surface runoff using the SCS–CN method are illustrated in Supplementary material, Appendix 1, Figure S2.
formula
(5)
Water Balance Equation can be written as;
formula
(6)
By combining Equations (5) and (6) to solve Q gives
formula
(7)
which is the equation for calculating the depth of excess rainfall or direct runoff from rainfall using the SCS method. From the experimental study of various watersheds, Equation (7) is valid for PIa. For Ia = 0.2S, then, the Equation (8) can be written as:
formula
(8)
formula
where Q is the direct surface runoff depth (mm), P is the daily rainfall depth (mm), Ia is the initial abstraction of the rainfall (mm), F is the cumulative infiltration excluding Ia, S is the potential maximum retention.
The value of potential maximum retention (S) was computed using Equation (9).
formula
(9)
where S is the potential maximum retention, and CN is the curve number, depending on soil hydrologic group and land use type
As illustrated in Equation (8), both precipitation data (P) and curve number data (CN) assume a significant role in the estimation of surface runoff (Q)mm. The surface runoff was estimated for different study periods (i.e., 1997, 2002, 2013, and 2022), and CN values for a specific LULC change type were computed for each respective study period. To calculate a composite CN value, an area-averaged method was employed, as described in Equation (10).
formula
(10)
where CN refers to the curve number of areas i, Ai refers to the area of each LULC for the area i and n refers to the number of LULC classes.
In this study, the antecedent soil moisture condition (AMCII) - normal condition as described in Table 3 was used. The CN for AMCI – dry condition and AMCIII- wet condition was computed using the following equations;
formula
(11)
formula
(12)
Table 3

Antecedent moisture classes (AMCs) for the SCS–CN method of seasonal rainfall limits

5-Day antecedent rainfall (mm)
AMC groupDormant seasonGrowing seasonAverage
<13 <36 <23 
II 13–28 36–53 23–40 
III >28 >53 >40 
5-Day antecedent rainfall (mm)
AMC groupDormant seasonGrowing seasonAverage
<13 <36 <23 
II 13–28 36–53 23–40 
III >28 >53 >40 

Source: Soil Conservation Service 1972.

Trend analysis of the relationship between LULC and surface runoff

The MK trend test and Sens's slope estimator were utilized to analyze the trend, magnitude and direction of LULC change in the watershed. MK trend test a non-parametric test is used to detect statistically significant increases or decreases in trends in the time series data set (Mfwango et al. 2022; Jiqin et al. 2023). The MK trend test has two hypotheses: (a) H0: There is no trend in the series and (b) H1: There is a trend in the series. Meena (2020), reported that at a 5% significance level, if the P-value is less than or equal to α = 0.05, the H1 (alternative hypothesis) is accepted; otherwise, Ho (null hypothesis) is accepted. The Sen's slope estimator is used to quantify the magnitude and direction of trends of data sets over time (Agarwal et al. 2021). According to Mondal et al. (2012), a positive (+) Sen's slope signifies an upward trend, whereas a negative (-) Sen's slope indicates a downward trend. Detailed statistical equations for the MK trend test and Sen's slope estimator trend test are well elaborated by Yousif & Ibrahim (2020) and Ahmed et al. (2014).

Correlation analysis between land use change and surface runoff change

The Pearson correlation was used to measure the strength of the linear relationship between land use change and runoff change (Akoglu 2018). The strength of the correlation coefficient (r) ranges between 0 and 1 as follows: 0.00–0.10 indicates no agreement, 0.1–0.39 as weak, 0.40–0.69 as moderate, 0.70–0.89 as strong, and 0.90–1.0 as almost perfect agreement (Schober & Schwarte 2018).

Accuracy assessment of Landsat images

The accuracy assessment for classified landsat images of the years 1997, 2002, 2013, and 2022 corresponding to six different LULC change classes are presented in Supplementary material, Appendix 1, Tables S1–S4. The results revealed that the overall accuracy of classified images for the years 1997, 2002, 2013, and 2022 were 92, 91.89, 95.27, and 83.64% respectively. Also, the respective Kappa coefficient was 90, 90, 94, and 82%. In the study area, agricultural land had an average producer's accuracy of 100%, followed by water bodies (98.72%), dense forest (93.52%), mixed forest and shrub (82.98%), grassland (82.25%), and mixed shrub and grassland (80.96%). A higher value of Kappa coefficient greater than 80% signifies almost perfect classification of images according to Foody (2020). The generated LULC maps were used for dynamic analysis of the hydrological response of surface runoff in the watershed.

LULC change classification in the study area

From 1997 to 2022, the Bontanga catchment area experienced significant environmental degradation, potentially attributed to the increasing population within the watershed and a decline in community awareness regarding environmental protection among the community residing in the watershed area (Kundu & Olang 2011). Land use classification maps for the years 1997, 2002, 2013, and 2022, as shown in Figures 25 were used to generate LULC change classes, as depicted in Figure 6. Detailed results of the statistical analysis conducted on the various LULC change classes are comprehensively presented in Supplementary material, Appendix 2, Table S5. Analysis revealed that, on average, agricultural land dominated the Bontanga watershed for about 34.93% of the total watershed area. Dense forest area increased from 45.87 km2 in 1997 to 62.39 km2 in 2002, these increase could be due to reforestation efforts or natural regrowth of the forest (Chazdon et al. 2020). On the other hand, the decrease in dense forest area from 13.12 km2 in 2013 to 10.53 km2 in 2022 could be the result of deforestation, either for agricultural, fuel wood, charcoal burning or urban expansion or because of natural disasters such as wildfires and drought (D'Almeida et al. 2007). Agricultural land exhibited an increase in area from 25.35 km2 in 1997 to 47.88 km2 in 2002. This increase is likely driven by the growing population within the watershed, which has led to increased demand for food resources to sustain their livelihoods (Mekuria 2018). The decrease in agricultural area to 17.70 km2 in 2013 within the catchment area can be attributed to the effects of climate change, which compelled farmers to abandon their farmland due to crop failure resulting from insufficient rainfall, which limited their crops from reaching maturity (Habib-ur-Rahman et al. 2022). The increase in agricultural land from 17.70 km2 in 2013 to 141.00 km2 in 2022 can be attributed to several factors. These include; (1) improved transportation infrastructure to facilitate the movement of agricultural machinery within the catchment, enabling more efficient farming over larger areas in shorter time frames, (2) a growing population demands increased land for food production, and (3) favorable climatic conditions which motivate farmers to cultivate larger land areas (Opoku et al. 2019). Grassland area increased from 21.68 km2 in 1997, to 23.73 km2 in 2002 and 26.40 km2 in 2013, this increase could be due to a combination of several factors such as improvement of grazing management in the watershed, and changes in rainfall patterns. On the other hand, the decrease in grassland area in 2022 up to 0.03 km2 could be due to natural disasters such as wildfires and prolonged drought or changes in land use and management practices. Decrease in mixed forest and shrubland from 31.20 km2 in 1997, 21.26 km2 in 2002 and 2.21 km2 in 2013 could be due to the clearing of forests for agriculture, urbanization or resource extraction (Mfwango et al. 2022), changes in the climate which can affect the growth and distribution of mixed forests and shrub lands, and lack of environmental conservation efforts aimed at protecting these ecosystems (Keenan 2015). An increase in the area of mixed forests and shrublands in 2022 to 4.5 km2 might be due to reforestation efforts, or natural regrowth of the forest (Hor et al. 2014). The area covered by water bodies decreased from 5.79 km2 in 1997 to 4.45 km2 in 2002, this decrease can be due to changes in rainfall patterns, alteration of wetlands and swampy areas into bareland, an increase of built-up area in the watershed which can lead to over obstruction of downstream flow and human activities such as agriculture which can alter water flow patterns and then decrease the area covered by water bodies (Maru et al. 2023). On the other hand, the increased water bodies from 6.10 km2 in 2013 to 6.52 km2 in 2022, can be due to the conservation and retention of surface runoff through the construction of small farm ponds, dugouts and dams in the catchment area for small-scale irrigation and domestic consumption (Acheampong et al. 2018). Built-up area in the catchment increased from 0.04 km2 in 1997 to 0.38 km2 in 2022, this increase can be due to population growth, which demands housing and other infrastructure such as a road network for easy transportation of goods (James 2020). Also, social-economic development activities in the upstream area of the catchment have led to the growth of urban, as communities are attracted to invest more in the areas.
Figure 2

LULC classification – 1997.

Figure 2

LULC classification – 1997.

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Figure 3

LULC classification – 2002.

Figure 3

LULC classification – 2002.

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Figure 4

LULC classification – 2013.

Figure 4

LULC classification – 2013.

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Figure 5

LULC classification – 2022.

Figure 5

LULC classification – 2022.

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Figure 6

LULC change in the Bontanga catchment.

Figure 6

LULC change in the Bontanga catchment.

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Impact of LULC change on surface runoff

The influence of land use change on surface runoff was investigated by evaluating the contributions of land use changes to surface runoff downstream of the watershed. Land cover change has significant effects on surface runoff, which can lead to altering the hydrological balance in the watershed. Land cover change can lead to reduced vegetation cover, which, in turn, diminishes the ability of the land to absorb and retain water. As a result, surface runoff increases, contributing to higher levels of surface runoff water downstream of the watershed (Manderso 2019). Rainfall datasets were simulated by using the SCN–CN model, based on the analysis, land use change categories that exhibited higher surface runoff levels were identified as having a greater impact on surface runoff dynamics in the watershed. The results illustrating the impact of LULC change classes on surface runoff are presented in Supplementary material, Appendix 4, Table S7, and Figure 7. The increase or decrease of surface runoff was closely related to LULC changes in the watershed. It can be seen from the results that decreasing the area covered by dense forest, grassland, mixed forest and shrub land, and mixed shrub and grassland by 20.31, 29.97, 22.51, and 25.58% from 1997 to 2022 caused an increase in surface runoff by 14.98, 14.06, 15.78, and 15.05%, respectively. (Luo et al. 2020) reported that forests and grasslands have a crucial role in intercepting rainfall and slowing down the water movement on the ground. Therefore, when these protective covers decrease, rainfall hits the soil directly, which in turn, increases the potential for surface runoff. Also, it was observed that increasing agricultural land and built-up area by 20.81 and 27.20% from 1997 to 2022 caused an increase in surface runoff of 13.92 and 14.49% respectively. This increase could be due to natural vegetation cover being replaced with impervious surfaces such as roads, buildings, and agricultural activities (Perry & Nawaz 2008; Samie et al. 2019; Hu et al. 2020b), which in turn, cause compaction of soil layers and reduce the volume of water that infiltrates into the soil, and increase the volume of surface runoff (Sugianto et al. 2022).
Figure 7

Impact of different LULC changes on the surface runoff.

Figure 7

Impact of different LULC changes on the surface runoff.

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Relationship between land use change and surface runoff change

The Pearson correlation analysis was used to analyse the linear relationship between surface runoff change ΔQ(mm) and land use change. The correlation coefficients between land use change classes and the corresponding surface runoff changes are presented in Supplementary material, Appendix 5, Table S8. Results reveal significant correlations between changes in surface runoff and land use change classes. Specifically, surface runoff exhibited positive correlations with changes in agricultural land, mixed forest and shrub, and dense forest, while showing negative correlations with changes in mixed shrub and grassland, built-up and grassland. It can be observed from the results that, the transformation of mixed forest and shrub to agricultural land was associated with an increase in surface runoff, whereas an increase in mixed shrub and grassland, and grassland resulted in a decrease in surface runoff within the watershed. Between 1997 and 2022, the degree of correlation between surface runoff changes and land use change classes was as follows: dense forest (r = 0.4361), agricultural land (r = 0.9998), mixed shrub and grassland (r = −0.8838), mixed forest and shrub (r = 0.9496), grassland (r = −0.9481), and built-up (r = −0.2508). Statistical analysis revealed that agricultural land, and mixed forest and shrub were the predominant land use change classes positively contributing to increased surface runoff, while grassland and mixed shrub and grassland were negatively impacted surface runoff within the watershed. Shukla et al. (2023) reported that the conversion of forest land to agricultural land, urban areas, or grassland resulted in an increase in runoff in the watershed by 43, 14, and 4%, respectively.

Assessment of 25 years trend of LULC change on surface runoff using MK analysis and Sen's slope

Sen's slope estimator a linear regression model was used to identify the linear trend of the data series (Yousif & Ibrahim 2020). The LULC change classes, shown in Supplementary material, Appendix 2, Table S5, were utilized to assess the 25-year time series trends of the datasets. Supplementary material, Appendix 6, Table S9 and Figure 8 illustrate the statistical results derived from the MK trend test and Sen's slope estimator for various LULC change. The analysis of the results revealed a significant positive increase in the trend for agricultural land area (km2), built-up area (km2), and water bodies (km2) within the study area. Conversely, the analysis also highlighted a decline in the trend for dense forest area (km2), grassland area (km2), mixed forest and shrub land area (km2) and mixed shrub and grassland area (km2).
Figure 8

Trend analysis of different LULC change classes and surface runoff for past 25 years.

Figure 8

Trend analysis of different LULC change classes and surface runoff for past 25 years.

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Observations from Figure 8 indicate a substantial positive Sen's slope of 30.54, signifying an increase in agricultural land. On the other hand, Sen's slopes of 0.06 for built-up areas and 0.33 for water bodies depict little or insignificant trends within the watershed. The negative Sen's slopes of −14.08 for dense forest, −2.58 for grassland, −9.45 for mixed forest and shrubland, and −6.16 for mixed shrub and grassland illustrate a considerable decrease in trend for these datasets over the same temporal span in the watershed.

The watershed's hydrological response has been significantly altered by anthropogenic activities driven by population growth and urban expansion, resulting in substantial changes in surface runoff patterns. This paper employed the SCS–CN model to simulate the runoff change, GIS, and remote sensing datasets, to assess the impact of land use change on the surface runoff in the Bontanga watershed. Agricultural land, water bodies, and built-up area show an increase in trend, while dense forest, grassland, mixed forest and shrub land, and mixed shrub and grassland show a decrease in trend. Additionally, it was observed that the conversion of dense forest, mixed forest and shrub land, and mixed shrub and grassland for other land use classes exacerbate an increase in runoff.

The following conclusions are drawn:

  • From 1997 to 2022, agricultural land, water bodies, and built-up area increased by 38.55 km2 (20.81%), 0.24 km2 (1.95%), and 0.11 km2 (27.20%), respectively.

  • Due to human activities within the catchment area, dense forest, grassland, mixed forest and shrub land, mixed shrub, and grassland decreased by 11.78 km2 (20.31%), 7.22 km2 (29.97%), 8.95 km2 (22.51%), and 10.96 km2 (25.58%) respectively.

  • Increases in agricultural land and built-up area by 20.81 and 27.20% from 1997 to 2022 caused an increase in surface runoff of 13.92 and 14.49%, respectively.

  • The decrease of dense forest, grassland, mixed forest and shrub land, and mixed shrub and grassland by 20.31 29.97, 22.51, and 25.58% from 1997 to 2022 caused an increase in surface runoff by 14.98, 14.06, 15.78, and 15.05%, respectively.

  • An increase in surface runoff was positively correlated with the changes in agricultural land, and mixed forest and shrub, but negatively correlated with the changes in mixed shrub and grassland, and grassland.

The study recommends land use planning at the community level and watershed conservation practices to ameliorate the current situation. Developing multidisciplinary approaches for integrated management is key to the sustainability of the Bontanga Irrigation Dam.

The authors would like to thank SARI (Savanna Agricultural Research Institute) for providing updated meteorological data for this research, USGS for the providing Landsat image, the University for Development Studies – Nyankpala Campus, and West Africa Center for Water, Irrigation and Sustainable Agriculture for material support.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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