Most anthropogenic activities have a profound effect on land cover that affects the water cycle and, ultimately, the availability of water in the watershed. The idea of this study was to evaluate the hydrological response to land use and land cover change in the Bilata watershed. To achieve this objective, supervised land use land cover classification was applied for the years 2000, 2010, and 2020 using ERDAS IMAGINE 2015. The watershed underwent significant land use and land cover changes from 2000 to 2020. There was an abrupt expansion of agricultural land and a reduction of forest. The SWAT model was applied to evaluate the impact of land use and land cover change on hydrological processes. The calibration and validation of the SWAT model showed that the model has performed well in simulating the hydrology of the watershed with a coefficient of determination, Nash–Sutcliffe efficiency, and per cent of bias greater than 0.6 for both calibration and validation. Due to intensive agricultural expansion and settlement, the surface runoff increased from 172 to 259.5 and 265.0 mm in 2000, 2010, and 2020, respectively. The average streamflow increased from 132 to 134 and 150 m3/s between 2000, 2010, and 2020.

  • Using ERDAS IMAGINE 2015 classify Landsat 7 and 8 images for land use land cover change assessment.

  • Change detection analysis.

  • Modeling the Bilata watershed using the SWAT model, through static land use (SLU), sensitivity, and model calibration was done.

  • Evaluating the response of the hydrological process in the watershed under land use land cover change.

LULC has pronounced effects on ecological, environmental, and hydrologic systems and processes (Liyew et al. 2019). Urbanization, agriculture expansion, mining, and other land uses have exponentially changed the earth's environment. Most of these anthropogenic changes have a profound impact on the water cycle and, finally, on the availability and quality of water (WoldeYohannes et al. 2018). To reduce these impacts, environmental protection, sustainable development, and a study of the effects of land use change are needed. It is necessary to forecast the effect of land use changes, agricultural practices, afforestation and deforestation, and related activities on hydrology (Liu et al. 2022).

LULC have pronounced effects on ecological, environmental, and hydrologic systems and processes (Ndulue et al. 2015). Land cover change, combined with the expansion of agriculture, cattle raising, and urbanization, could have a profound impact on the hydrological process of a watershed (Terefe et al. 2021). Land use/land cover changes occur in the country, Ethiopia, as a whole and in the study area in particular due to the increasing population, which has almost doubled (Mathewos et al. 2019; Sulamo et al. 2021). The effect of land use land cover, especially in the mountainous part of the catchment has a significant effect on the rainfall–runoff response of the catchment (WoldeYohannes et al. 2018; Sulamo et al. 2021; Tamire et al. 2022).

Even though today, the spatial and temporal change of LULC and its impacts on hydrological processes are given attention in the country (Choto & Fetene 2019; Engida et al. 2021; Negese 2021; Tavares et al. 2021; Gebre et al. 2022) still it needs further study, particularly in the study area to protect reservoirs, lakes, and rivers. Land use and land cover change are important characteristics of the runoff process that affect infiltration, interception, erosion, and evapotranspiration. These changes have caused severe stress on forested water resources due to rapid development in the catchment, which is subjected to land use and cover changes causing the area to form impervious surfaces. Hydrological modeling and water resource management are highly related to the processes of the hydrologic cycle. This cycle can be affected by land use and land cover change. This LU/LC change is mainly caused by anthropogenic activities. Thus, it is essential to analyze the possible impacts of these changes on hydrological characteristics for environmental protection.

To evaluate LULC change impacts on the river flow characterization of the rainfall–runoff relationships of the watershed need both spatial and temporal data to observe the effect, the chosen model should incorporate both temporal and spatial parameters. Therefore, the physically based, semi-distributed, computationally efficient and public domain model, SWAT was used. Many scholars in the country have tried to quantify the impact of land use change on the hydrological process using the SWAT model (Belihu et al. 2020; Aragaw et al. 2021; Kuma et al. 2021; Ayele et al. 2023). Therefore, this research sought to assess the changes in land use and land cover (LULC) effects on the hydrological characteristics of the Bilata watershed. A deep understanding of the LULC change is important for the conservation of previous LULC changes, predicting future changes for adequate and sustainable streamflow and decreased sediment depositions, and developing sustainable land resource management techniques intended to protect essential landscape functioning. This knowledge gives crucial information for producing evidence that helps local communities, actors functioning within a given environment, spatial planners, and decision-makers to design the best policies and strategies. An understanding of the extent of the hydrological process in the watershed and knowing the changes in streamflow are necessary for an effective reservoir and basin management program. Also, understanding the hydrological process associated with LULC change is vital for researchers for further study and for non-governmental organizations who are interested in developing any measurable structures to protect the fast-growing degradation of the land and in improving human wellbeing.

Location

Bilata River is one of the inland rivers of Ethiopia. Its coverage is about 5,252 km2 from the outlet, its upper and middle courses are found in the eastern fringes of western highlands and the lower course is within the great Ethiopian rift valley specifically in the lakes sub-basin. Its absolute location extends from 6° 36′00″ N to 8° 11′30″ N and 37°48′00″ E to 38°40′00″ E (Figure 1). The main river flows straight southwards to Lake Abaya. Most of the perennial tributaries come from the western side.
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Close modal

Bilata watershed is characterized by diverse topographic conditions. The upper part of the watershed is characterized by mountainous and highly separated terrain with steep slopes and the downstream part is a gentle slope with elevation ranges from 3,318 m a.m.s.l. in the mountains area to 1,116 m a.m.s.l. in the lowland. The general topography of the catchments is undulating hills and flats. It gradually decreases in elevation to the south (Kuma et al. 2021).

According to the Ethiopian agro-climatic zone classification, the climate of the study area ranges from hot temperate (Kola) to cool temperate (Wurch) in the mountains and escarpment.

Rainfall distribution over the area is Bimodal, characterized by a short rainy season (Belg) that occurs between March and May and a long rainy season (kiremt) that occurs from June to July, with a dry season from December to February. The watershed area has a tropical climate with pronounced aridity in the southern part and a warm-temperate rainy climate in the central and northern highlands. It receives about 854–1,039 mm of rainfall annually (Sulamo et al. 2021). The other factor which characterizes the watershed is the temperature. The average value of the temperature in the watershed has been recorded as 20.6 °C in the summer season and 12.57 °C has been recorded in winter (dry season). The mean annual maximum and minimum temperatures are between 22.8 and 30.4 °C and between 10.7 and 16.8 °C (Kuma et al. 2022).

Data collection and analysis

Materials and software used

For this study, GIS version 10.4 and SWAT 2012 version were used. The image classification was performed by the Earth Resources Data Analysis System (ERDAS) Imagine 2015 software package to generate the land use and land cover maps so as to detect the change in different categories of land use and land cover. Other software and materials like SWAT-CUP, Google Earth and GPS were used for model calibration and validation, homogeneity test, hydro-meteorological data analysis, ground verification, and collection ground control point's (GCP's), respectively.

Data collected

Different data were collected from various sources and field observations. It includes primary data such as ground control points and secondary data including recorded meteorological data, hydrological data, digital elevation model (DEM), soil map, and land use land cover. After collecting the required data, the quality of the data was checked mainly through preliminary checking, plotting and removal of errors in order to ensure the quality of the data for further investigation.

Hydrological data
The daily streamflow of Bilata River stations was collected from the Ministry of Water and Energy (MoWE) (Figure 2).
Figure 2

Mean annual streamflow at Bilata Tena gauge station.

Figure 2

Mean annual streamflow at Bilata Tena gauge station.

Close modal
Meteorological data
The meteorological data include daily data of precipitation, maximum and minimum temperature, relative humidity, wind speed, and solar radiation/sunshine hour and they were collected from the National Meteorological Institute of Ethiopia (NMIE) from 1987 to 2022 for different gauge stations (Figure 3).
Figure 3

Annual precipitation.

Figure 3

Annual precipitation.

Close modal
Spatial data

The data which are considered as spatial data include DEM, land use land cover, and soil map. A 20 m × 20 m DEM were obtained from the USGS website, https://earthexplorer.usgs.gov.

Soil properties are one of the major input data required by the SWAT model of the watershed. The soil data covering the study area were obtained from the Digital Soil Map of the World (DSMW) on the FAO website, in Environmental System Research Institute (ESRI) shape file format.

Land use is the most important factors that affect runoff, evapotranspiration, and surface erosion in a watershed. These data are required by the SWAT model to describe the hydrological response units (HRUs) of the watershed together with slope and soil map. The land use land cover map of the study area for different years was obtained from the website.

Land use land cover analysis

Image processing

Landsat imageries were used for change detection of land use and land cover in the Bilata watershed from 2000 to 2020. For this period, three Landsat images from 2000, 2010, and 2020 were downloaded from the United States Geological Survey (USGS) website (earthexplorer.usgs.gov) GeoTIFF file format.

Landsat 7 Enhanced thematic mappers (ETM), Landsat 8 operational land imager (OLI), and thermal infrared sensor (TIRS) were used for data acquisition. To avoid a seasonal variation in vegetation pattern and distribution throughout a year, the selection of dates of the acquired data was made in the same season of the acquired years. The images used in this study area were geo-referenced to a Universal Transverse Mercator (UTM) projection using datum WGS (World Geodetic System) 84 zone 37 N prior to image classification. So as to view and distinguish the surface features clearly, all the input satellite images were composed using the RGB color composition.

In order to use remote sensing data for land use land cover analysis, it needs to be preprocessed. This study utilized both radiometric and geometric processing techniques. Radiometric processing is necessary to minimize the effects that hinder the measurement of surface reflectance through remote sensing. Geometric correction, on the other hand, involves distorting an image to fit a specific map projection. However, this process results in a resampling of the image pixels which can lead to some degradation of the original data.

Land use land cover classification

An image classification process is required for classifying all pixels in the form of digital images into several land cover classes, or ‘themes’. Image classification is typically carried out using ERDAS Imagine software. ERDAS Imagine is a software package designed for image processing that enables users to process geospatial and other types of imagery, as well as vector data. It can handle hyperspectral imagery and LiDAR data from various sensors, and includes a 3D viewing module (Virtual GIS) and a vector module for modeling. The native programming language used in ERDAS is called EML (ERDAS Macro Language). ERDAS is integrated with other applications such as Google Earth and remote sensing applications, and the imagery storage format can be read in many other applications. In this study, land use land cover was classified using a supervised classification system in ERDAS Imagine software. In supervised image classification a set of training points is developed as a pre-processing step for speeding up image retrieval in large databases and improving accuracy, or for performing automatic image annotation. The integration of Google Earth and ERDAS Imagine software was used, and the results were verified with survey data obtained from the field.

Ground truth and accuracy assessment

Ground truth or field survey is done in order to observe and collect information about the actual condition on the ground at a site to determine the relationship between remotely sensed data and the object to be observed. It is recommended to have a ground truth at the same time of data acquisition, or at least within the time that the environmental condition does not change.

Accuracy assessment is an important step in the image classification process. The objective of this process is to quantitatively determine how effectively pixels were grouped into the correct feature classes in the area under investigation. It is a process used to estimate the accuracy of image classification by comparing the classified map with a reference map. The most widely used classification accuracy is in the form of an error matrix which can be used to derive a series of descriptive and analytical statistics. The columns of the matrix depict the number of pixels per class for the reference data, and the rows show the number of pixels per class for the classified image. From this error matrix, the number of accuracy measures such as overall accuracy, mapping accuracy, producer's accuracy, user's accuracy, and Kappa coefficient of the agreement are estimated. The overall accuracy is used to indicate the accuracy of the whole classification (i.e., the number of correctly classified pixels divided by the total number of pixels in the error matrix).

User's accuracy is regarded as the probability that a pixel classified on the map actually represents that class on the ground or reference data, whereas producer's accuracy tells us how well a certain area can be classified. It is obtained by dividing the number of correctly classified pixels in the category by the total number of pixels of the category in the reference data. The producer's accuracy is also known as an omission error, which is the probability of reference pixels being classified correctly. It gives only the proportion of correctly classified pixels. The accuracy assessment of the classified map was done through the comparison of the classified image and the sampling points from the field survey and existing land cover maps. In this study, a total of 80 testing sample points for each land use class were selected randomly for the years 2000, 2010, and 2020.

SWAT model input data

The SWAT model requires weather data, hydrological and spatial data like watershed characteristics such as topography, land use/land cover, and soil.

SWAT model setup

SWAT (Arnold et al. 2002) can simulate hydrological cycles, vegetation growth, and nutrient cycling with a daily time step by disaggregating a river basin into sub-basins and HRUs. SWAT uses the following water balance system to simulate the hydrologic cycle within a watershed (Equation (1)).
formula
(1)
where SWt is the final water content (mm H2O), SWo is the initial soil water content on the day i (mm H2O), t is time, days, Rday is the amount of precipitation on the day i (mm H2O), Qsurf is the amount of surface runoff on the day i (mm H2O), Ea is the actual evapotranspiration on the day i (mm H2O), Qseep is the amount of water entering the vadose (unsaturated) zone from the Soil profile on the day i (mm H2O), Qgw is the amount of return flow on the day i (mm H2O). The model reflects the difference in evapotranspiration for various land uses and soil types in the subdivision of the watershed. The runoff was predicted separated from each HRU and routed to obtain the total yield for the watershed. Hence, increases the accuracy and gives a better physical description of water balance.

Sensitivity analysis

SWAT input parameters are process-based and must be held within a realistic uncertainty range. The first step in the calibration and validation process in SWAT is the determination of the sensitivity parameters for a given watershed or sub-watershed. Sensitivity analysis is the way of determining the rate of change in model results concerning changes in model inputs (parameters) (Arnold et al. 2012).

Calibration and validation

The SWAT hydrological model was calibrated and validated using a static land use (SLU) (2000) setup. Calibration is an effort to better parameterize a model to a given set of local conditions, thereby reducing the prediction uncertainty (Abbaspour et al. 2017). The prediction of the uncertainty of SWAT model calibration and validation results will be analyzed by the SWAT calibration uncertainties program known as SWAT-CUP. It can integrate various calibration/uncertainty analysis procedures for SWAT in one user interface. It is a public domain program that links sequential uncertainty fitting (SUFI-2) (Abbaspour et al. 2007), particle swarm optimization (PSO) (Beven & Binley 1992), and parameter solution (ParaSol) (Beven & Binley 1992), and Marko Chain Monte Carlo (MCMC). For this study, SUFI-2 was used for the calibration of the model because its algorithms account for several sources of uncertainties.

Model validation is the process of describing that a given site-specific model is capable of making satisfactory simulations. It is the comparison of model results with an independent data set without further adjustment of the model parameters. Validation embraces running a model using parameters that were estimated during the calibration process and comparing the predictions to observed data not used in the calibration process.

Model performance evaluation

Herein the performance of the model has been checked by statistical tests that can be used to judge the SWAT model. According to Coffey et al. (2004), the statistical tests that are used to judge the SWAT model include R2, NSE, root mean square error (RMSE), nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. For this study, the most widely used tests of Nash–Sutcliffe efficiency (NSE), per cent bias (PBIAS), and coefficient of determination (R2) are used which is recommended by Moriasi et al. (2007) (Table 1).

Table 1

General performance ratings of simulated discharge (Moriasi et al. 2007)

Performance ratingNSEPBIASR2
Very good 0.75 < NSE < 1 PBIAS < ±10% 0.75 < R2 < 1 
Good 0.65 < NSE < 0.75 ±10% < PBIAS < ±15% 0.65 < R2 < 0.75 
Satisfactory 0.5 < NSE < 0.65 ±15%< PBIAS < ±25% 0.5 < R2 < 0.65 
Unsatisfactory NSE < 0.5 PBIAS > ±25% R2 < 0.5 
Performance ratingNSEPBIASR2
Very good 0.75 < NSE < 1 PBIAS < ±10% 0.75 < R2 < 1 
Good 0.65 < NSE < 0.75 ±10% < PBIAS < ±15% 0.65 < R2 < 0.75 
Satisfactory 0.5 < NSE < 0.65 ±15%< PBIAS < ±25% 0.5 < R2 < 0.65 
Unsatisfactory NSE < 0.5 PBIAS > ±25% R2 < 0.5 

The NSE (Nash & Sutcliffe 1970) indicates how well the plot of observed versus simulated data fits and it is commonly referred to as the coefficient of determination in the hydrological model but it is slightly different. It indicates how well the model expresses the variance in the observation. It generally ranges from − to 1 the optimum value is unity and it shows a good explanation of the observed versus simulated data fits on a one-to-one line. NSE is computed as shown by the following equation:
formula
(2)
where NSE is the Nash–Sutcliffe efficiency, Pi is the simulated flow, Oi is the observed flow, is the mean of observed data, and N is the total number of observations.
Per cent bias measures the average tendency of the simulated data to be higher or smaller than their observed values. The optimal value of PBIAS is 0.0, with less magnitude values indicating exact model simulation. The negative value of PBIAS indicates the models overestimated the simulated and the positive shows the model underestimates the simulated flow (Moriasi et al. 2007). PBIAS is computed by the following equation:
formula
(3)
Coefficient of determination (R2) (Equation (4)) described the proportion of the variance in the measured data explained by the model. R2 ranges from 0 to 1, with higher values indicating less error variance, and typically values greater than 0.5 are considered acceptable (Moriasi et al. 2007).
formula
(4)
where is the predicted flow and the remaining variable is stated above.

Land use land cover classification accuracy assessment

The accuracy assessment was conducted for all classified imageries using a standard method. The user's, producer's, overall accuracy, and the kappa coefficients were computed. In general, all the classifications met the recommended minimum 85% accuracy (Anderson et al. 1976). The result of the classification reveals that the overall accuracy was 90.97% and the Kappa coefficient of 88.94 for 2,000 land use/land cover classification, which indicates that the land cover classes were correctly classified.

Similarly, the overall accuracy and kappa coefficient for the years 2010 and 2020 are 91.8 and 86.8%, 90.52 and 86.21%, respectively (Table 2). A similar study by Mathewos et al. (2019) on one of the Bilata sub-watersheds on land use land cover dynamics shows that the overall accuracy and Kappa coefficient are greater than 85% which is almost identical to this study. The spectral property similarities among other land cover classes cause the class accuracy values the lowest which indicates misclassification.

Table 2

LULC classes and accuracy assessment of classified images of Bilata watershed

Classes2000
2010
2020
Producer (%)Users (%)Producer (%)Users (%)Producer (%)Users (%)
Settlement 83.33 100 100 90 57.14 80 
water bodies 100 100 100 100 100 100 
Grass land 72.72 80 93.3 82.35 71.42 100 
Forest 90 90 80 80 88.89 80 
Bare land 85.71 88.89 100 90 91.67 88 
Shrub land 84.21 94.12 57.14 80 64.28 81.81 
Regularly flooded 100 100 69.23 100 100 90 
Crop land 100 88 97.4 94.12 96 92.3 
Overall accuracy 90.97 91.8 90.52 
Kappa coefficient  88.94  86.8  86.21 
Classes2000
2010
2020
Producer (%)Users (%)Producer (%)Users (%)Producer (%)Users (%)
Settlement 83.33 100 100 90 57.14 80 
water bodies 100 100 100 100 100 100 
Grass land 72.72 80 93.3 82.35 71.42 100 
Forest 90 90 80 80 88.89 80 
Bare land 85.71 88.89 100 90 91.67 88 
Shrub land 84.21 94.12 57.14 80 64.28 81.81 
Regularly flooded 100 100 69.23 100 100 90 
Crop land 100 88 97.4 94.12 96 92.3 
Overall accuracy 90.97 91.8 90.52 
Kappa coefficient  88.94  86.8  86.21 

Classified land use/land cover map

Land use land cover maps were made from the data that have been generated from Landsat imageries of 2000, 2010 and 2020 years to describe land use land cover change patterns with time series and analyze its impacts on runoff and sediment yield of the Bilata watershed.

The result of the classified land use/land cover map is shown in Figure 4. As can be seen from the classification, the LULC of the study area includes eight major classes, such as settlement, shrubland, forest, bare land, water bodies, regularly flooded/vegetative aquatic areas, grassland, and agriculture. It is visible that the Bilata watershed has been subjected to significant land use/cover change from 2000 to 2020. The 2000's LULC of Bilata watershed was majorly classified as settlement 0.556% (3,131.21 ha), water bodies 0.13% (724.32 ha), regularly flooded 0.16% (927.28 ha), crop land 50.39% (283,492.29 ha), grass land 2.51% (14,096.99 ha), forest 1.90% (10,712.34 ha), shrub land 16.78% (94,360.50 ha), and bare land 27.56% (155,046.43). From 2000 to 2010, there was an abundant LULC change due to different anthropogenic and natural causes.
Figure 4

Land use land cover map.

Figure 4

Land use land cover map.

Close modal

The satellite image of 2010 was classified similar with 2000 LULC class which includes settlement 0.631% (3,547.91 ha), water bodies 0.125% (701.13 ha), regularly flooded 0.1% (563.28 ha), crop land 58.73% (330,383.33 ha), grass land 11.40% (64,124.783 ha), forest 1.11% (6,268.63 ha), shrub land 6.5% (36,585.58 ha), and bare land 21.39% (120,314.97 ha) (Figure 4).

From 2020 Landsat image, LULC of Bilata watershed was classified as settlement 0.71% (4,004.49 ha), water bodies 0.11% (616.43 ha), regularly flooded 0.11% (637.01 ha), crop land 69.89% (393,110.9 ha), grass land 4.86% (27,311.83 ha), forest 1.08% (6,084.53 ha) shrub land 6.7% (37,702.65 ha), and bare land 16.54% (93,015.62 ha). In this time period, the grassland was decreased by approximately 50% from 2010 (Table 3), mainly it may be converted to arable land and settlements. A study on land use land cover change and their effects in the Abaya-Chamo landscape including the Bilata watershed by WoldeYohannes et al. (2018) reveals that between 1985 and 2010, natural grassland, shrubland, and heterogeneous agricultural areas were mainly converted to arable land.

Table 3

Individual class areas and change statistics

Year
Land use/land cover class2000
2010
2020
Area (ha)% of coverageArea (ha)% of coverageArea (ha)% of coverage
Settlement 3,131.20799 0.556 3,547.91 0.631 4,004.49 0.71 
Water bodies 724.323343 0.129 701.13 0.125 616.43 0.11 
Regularly flooded 927.279816 0.16 563.28 0.10 637.01 0.11 
Crop land 283,492.29 50.39 330,383.33 58.73 393,110.9 69.89 
Grass land 14,096.99 2.51 64,124.783 11.40 27,311.83 4.86 
Forest 10,712.34 1.90 6,268.63 1.11 6,084.53 1.08 
Bare land 155,046.43 27.56 120,314.97 21.39 93,015.62 16.54 
Shrub land 94,360.50 16.78 36,585.58 6.5 37,702.65 6.7 
Year
Land use/land cover class2000
2010
2020
Area (ha)% of coverageArea (ha)% of coverageArea (ha)% of coverage
Settlement 3,131.20799 0.556 3,547.91 0.631 4,004.49 0.71 
Water bodies 724.323343 0.129 701.13 0.125 616.43 0.11 
Regularly flooded 927.279816 0.16 563.28 0.10 637.01 0.11 
Crop land 283,492.29 50.39 330,383.33 58.73 393,110.9 69.89 
Grass land 14,096.99 2.51 64,124.783 11.40 27,311.83 4.86 
Forest 10,712.34 1.90 6,268.63 1.11 6,084.53 1.08 
Bare land 155,046.43 27.56 120,314.97 21.39 93,015.62 16.54 
Shrub land 94,360.50 16.78 36,585.58 6.5 37,702.65 6.7 

The individual class areas and change statistics for the three periods are summarized in Table 3. A similar study in the previous period shows that the agricultural land settlement increased and the forest land was decreased. Recently, a study by Sulamo et al. (2021) in the Bilata watershed reports that the settlement and cropland was increased by 14.38 and 11.73% from 1989 to 2015, respectively. On the contrary, the forest land decreased by 4.3% from 1989 to 2015.

Change detection analysis

Evaluation of change detection for this study was done between two intervals from 2000 up to 2020. The first change detection analysis was done between the years 2000 and 2010. As shown in Figure 5, it reveals that the maximum change was observed in shrubland, grassland, and cropland class. The shrub and grassland decreased by 57,774.93 and 50,027.79 ha from the year 2000 to 2010. However, crop land increases by 46,891.04 ha from 2000 up to 2010. This is due to the increase in population growth that causes the increase in demand for cultivation land for different agricultural production. This might be because of the deforestation activities that have taken place for the purpose of agriculture and urban expansion. The remaining class type bare land, forest, water bodies and regularly flooded decreased by 34,731.45, 4,443.71 , 23.19, and 363.99 ha, respectively. On the contrary, the settlement increased by 416.7 ha between 2000 and 2010.
Figure 5

Change detection analysis between 2000 and 2010.

Figure 5

Change detection analysis between 2000 and 2010.

Close modal
The watershed has undergone significant land use land cover change from period of 2010 to 2020. As shown in Figure 6, it shows that an increase in cropland, shrubland, regularly flooded and settlement land there was a decrease in the forest, grassland, water bodies, and bare land during the period from 2010 to 2020. Cropland, shrub land, regularly flooded and settlement increased by 62,727.64, 1,117.07, 73.73, and 456.58 ha, respectively. On the other hand, forest, grassland, water bodies, and bare land decreased by 184.1, 36,812.95, 84.71, and 27,299.36 ha, respectively.
Figure 6

Change detection analysis between 2010 and 2020.

Figure 6

Change detection analysis between 2010 and 2020.

Close modal

The results reported here are in line with the recent work of Kenea et al. (2021) in Fincha watershed to evaluate the response of hydrological components under LULC change. The result reports that there is a significant reduction of forest land (−32.3%) and shrubland (−9.6%) between 1994 and 2018, mostly due to the creation of new agricultural zones (+27.2%) and settlement (+3.7%) to answer to an increasing population that needs new means of subsistence.

SWAT model analysis

Model sensitivity analysis for streamflow

The sensitivity of the model parameter for the given watershed was estimated based on the t-stat value and p-value. We can say the model parameter is sensitive when the t-stat value of the parameter is the higher and minimum value of the p-value.

The result of the global sensitivity analysis reveals that, from 20 model parameters, 16 parameters (Table 4) were found to be sensitive under the category of high to low sensitivity based on the value of p-value and t-stat. The higher the absolute value of the t-stat and minimum p-value, the more the sensitivity of the model parameter. From the 16 parameters, curve number (R_CN2), available water capacity of the soil layer (R_SOL_AWC), depth from soil surface to bottom of layer are the top five sensitive parameters (R__SOL_Z), baseflow alpha factor (days) (V__ALPHA_BF.gw), and threshold depth of water in the shallow aquifer (mm) (V__REVAPMN.gw) are the top five sensitive parameters. The result of the sensitivity analysis matches with the study by Beza et al. (2023) in the Awash basin at Jewuha watershed to model and assess the surface water potential using a combined SWAT model and spatial proximity regionalization technique. The result of the study reveals that curve number (R_CN2), saturated hydraulic conductivity (R_SOL_K), groundwater delay (days) (V_GW_DELAY), Manning's ‘n’ values for overland flow (R_OV_N), and available water capacity of the soil layer (R_SOL_AWC) are the most five sensitive parameters.

Table 4

Sensitive model parameters for streamflow

Parameter nameMin_valueMax_valueFitted Valet-statp-valueRank
R__CN2.mgt −0.2 0.2 0.07 19.35 0.00 
R__SOL_AWC(..).sol 0.5 0.66 −2.77 0.007 
R__SOL_Z(..).sol 2.5 2.16 2.15 0.034 
V__ALPHA_BF.gw 0.2 0.03 1.87 0.065 
V__REVAPMN.gw 35 150 102.27 −1.63 0.105 
V__GWQMN.gw 3,500 5,000 3,867.5 −1.26 0.208 
R__SLSUBBSN.hru 5.26 −1.26 0.209 
R__HRU_SLP.hru 0.5 0.38 −0.90 0.367 
V__GW_DELAY.gw 32 172 71.9 0.68 0.493 
R__EPCO.hru 0.3 0.6 0.55 −0.68 0.495 10 
R__ESCO.hru 0.4 0.7 0.43 −0.62 0.533 11 
V__CH_K2.rte 100 27.5 −0.52 0.602 12 
V__GW_REVAP.gw 0.1 0.2 0.14 −0.51 0.609 13 
R__SURLAG.bsn 12 7.9 −0.45 0.647 14 
V__RCHRG_DP.gw 0.001 0.00085 −0.436 0.663 15 
R__SOL_K(..).sol 0.3 0.27 0.10 0.919 16 
Parameter nameMin_valueMax_valueFitted Valet-statp-valueRank
R__CN2.mgt −0.2 0.2 0.07 19.35 0.00 
R__SOL_AWC(..).sol 0.5 0.66 −2.77 0.007 
R__SOL_Z(..).sol 2.5 2.16 2.15 0.034 
V__ALPHA_BF.gw 0.2 0.03 1.87 0.065 
V__REVAPMN.gw 35 150 102.27 −1.63 0.105 
V__GWQMN.gw 3,500 5,000 3,867.5 −1.26 0.208 
R__SLSUBBSN.hru 5.26 −1.26 0.209 
R__HRU_SLP.hru 0.5 0.38 −0.90 0.367 
V__GW_DELAY.gw 32 172 71.9 0.68 0.493 
R__EPCO.hru 0.3 0.6 0.55 −0.68 0.495 10 
R__ESCO.hru 0.4 0.7 0.43 −0.62 0.533 11 
V__CH_K2.rte 100 27.5 −0.52 0.602 12 
V__GW_REVAP.gw 0.1 0.2 0.14 −0.51 0.609 13 
R__SURLAG.bsn 12 7.9 −0.45 0.647 14 
V__RCHRG_DP.gw 0.001 0.00085 −0.436 0.663 15 
R__SOL_K(..).sol 0.3 0.27 0.10 0.919 16 

R indicates the existing parameter is replaced by (one plus the given value multiplied by the existing value), V indicates that the existing value is simply replaced by the given value.

Another research conducted by Setegn et al. (2008) studied hydrological modeling in the Lake Tana basin watershed, Ethiopia tested the performance and feasibility of the SWAT model for the prediction of streamflow. During sensitivity analysis ESCO, CN2, ALPHA_BF [days], REVAPMN.gw [mm H2O], [days], SOL_AWC[mm of H2O], GW_REVAP, CH_K [mm/h], and GWQMN.gw [mm H2O] are the most sensitive parameters which are similar to our study due to the physical similarity of the watershed except ESCO and ALPHA_BF due to the SWAT model performing better to simulate the groundwater in Lake Tana basin watershed than in Bilata watershed and also the study considers the impact of sub-basin discretization which resulted in a better representation of the hydrological processes and produced streamflow yield which had a better model efficiency in comparison to those who are not considered in basin discretization.

Model calibration and validation for streamflow

The SWAT (Soil and Water Assessment Tool) model is a widely used hydrological model that simulates the impacts of and management practices on water resources at the watershed scale. Calibration of the SWAT model is a crucial step in ensuring the model accurately represents the hydrological processes and provides reliable predictions. It involves adjusting the model parameters to minimize the difference between observed and simulated values of various hydrological variables, such as streamflow, nutrient concentrations, sediment yield, and groundwater levels (Abbaspour et al. 2017). The goal is to find the optimal set of parameter values that best represent the observed data (Abbaspour 2005; Beza et al. 2023).

The calibration was done using 15 and 10 years of river discharge data for validation, which is obtained from the Minister of Water and Energy (MoWE), the SWAT model was calibrated at a monthly time scale from 1990 to 2005 and validated from 2006 to 2015. A model is considered calibrated upon propagation of parameter uncertainties of the 95% prediction uncertainties (95PPU) between the 2.5th and 97.5th percentiles covers more than X% of the measured data (i.e., 100 – X%) of the data is treated as outliers. Also, the average distance between 2.5th and 97.5th prediction percentiles is less than the standard deviation of the measured data (Figure 7).
Figure 7

Model calibration and validation for streamflow.

Figure 7

Model calibration and validation for streamflow.

Close modal

Performance evaluation of the model for streamflow

The result of the model performance evaluation for the Bilata watershed at Bilata Tena gauge station showed good results of p-factor 0.9 and 0.6, and r-factor 1.31 and 1.5 for streamflow and sediment calibration, respectively (Table 5). Similarly, in the case of validation, the p-factor was 0.85 and 0.55 and r-factor was 0.74 and 1.5, respectively. The p-factor of 0.9, which means the model captured 90% of observed streamflow in the 95PPU band. A smaller p-factor and a larger r-factor could be acceptable range (Beza et al. 2023; Dinku & Kebede 2023).

Table 5

Performance evaluation of the model for streamflow

Objective functionStreamflow
CalibrationValidation
R2 0.9 0.8 
NSE 0.7 0.6 
PBIAS −10.8 −7.4 
RSRa 0.54 0.16 
p-factor 0.9 0.85 
R-factor 1.31 0.74 
Objective functionStreamflow
CalibrationValidation
R2 0.9 0.8 
NSE 0.7 0.6 
PBIAS −10.8 −7.4 
RSRa 0.54 0.16 
p-factor 0.9 0.85 
R-factor 1.31 0.74 

aRoot mean square error observations standard deviation ratio.

Response of water balance under LULC change

The effect of land use land cover on the hydrology of the watershed was analyzed by keeping the input parameters constant and changing LULC data. The change in the annual average surface runoff, evaporation and transpiration, lateral flow, return flow, percolation to the shallow aquifer, and recharge to the deep aquifer was presented in Table 6. As shown in Table 6, the reduction in return flow and percolation to a shallow aquifer between 2000 and 2010 could be due to factors such as changes in land use, increased urbanization, or alterations in precipitation patterns leading to less water reaching the aquifer. On the other hand, the increase between 2010 and 2020 might be attributed to improved water management practices, increased recharge efforts, or changes in climate patterns resulting in more water infiltrating into the aquifer during that period.

Table 6

Change of water balance components

Water balance components200020102020Change
2000–20102010–2020
Surface runoff (mm) 220 259.33 265.05 39.33 5.72 
Evaporation and transpiration (mm) 174.4 185.1 199.3 10.7 14.2 
Lateral flow (mm) 38.41 22.4 17.43 13.99 −34.97 
Return flow (mm) 560.8 531.99 576.34 −28.81 44.35 
Percolation to shallow aquifer (mm) 729.06 607.08 651.72 −121.98 44.64 
Recharge to deep aquifer (mm) 36.45 34.08 30.78 −2.37 −3.3 
Water balance components200020102020Change
2000–20102010–2020
Surface runoff (mm) 220 259.33 265.05 39.33 5.72 
Evaporation and transpiration (mm) 174.4 185.1 199.3 10.7 14.2 
Lateral flow (mm) 38.41 22.4 17.43 13.99 −34.97 
Return flow (mm) 560.8 531.99 576.34 −28.81 44.35 
Percolation to shallow aquifer (mm) 729.06 607.08 651.72 −121.98 44.64 
Recharge to deep aquifer (mm) 36.45 34.08 30.78 −2.37 −3.3 

Due to intensive agricultural expansion and settlement, the surface runoff increases from 172, 259.55, and 265.05 mm in 2000, 2010, and 2020, respectively. Similarly, evaporation and transpiration increased from 174.4 mm in 2000 to 185.1 mm in 2010, and 199.3 mm in 2020. Lateral flow has reduced from 38.41 mm in 2000 to 22.4 mm in 2010, and to 17.43 mm in 2020. Including these and the remaining water balance component, the changes are shown in Figure 8. A study by Teshome et al. (2023) on watershed hydrological responses to land cover changes in the Muger watershed, Upper Blue Nile Basin, Ethiopia reveals that the main land cover changes that affected hydrological parameters in the Muger watershed are changes in agricultural land, forest land, and settlement. The expansion of agricultural land and deforestation reduces surface runoff and a reduction in groundwater between 1986 and 2003. Between 2003 and 2020, surface runoff decreased by 3.71% due to the effect of land landscape management practices.
Figure 8

Change of water balance components, where SR is the surface runoff; ET is evaporation and transpiration; LF is the lateral flow; RF is the return flow; PSA is the percolation to shallow aquifer and RDA is recharge to deep aquifer.

Figure 8

Change of water balance components, where SR is the surface runoff; ET is evaporation and transpiration; LF is the lateral flow; RF is the return flow; PSA is the percolation to shallow aquifer and RDA is recharge to deep aquifer.

Close modal

Response of streamflow under LULC change

One of the capabilities of the SWAT model is it can quantify the surface water using different parameters. Using LULC 2000, 2010 and 2020 scenarios, the outputs of the SWAT model for the Bilata watershed were compared based on some parameters. Alterations in land cover can impact surface runoff patterns and infiltration capacity of the soil, leading to changes in streamflow regimes. The average streamflow increased from 132 to 134 and 150 m3/s between 2000, 2010, and 2020 (Figure 9). This increment of flow is due to the large change of the area into agricultural and settlement areas. In a similar study by Maru et al. (2023) in the Akaki catchment, the average surface runoff increased from 236.01 to 272.39 mm between 1993 and 2016 due to the expansion of agricultural land and bare lands.
Figure 9

Change in streamflow.

Figure 9

Change in streamflow.

Close modal

The streamflow was increased in the wet season, the expansion of agricultural area throughout the study period had a direct influence on streamflow and a decrease of forest/bush land to some extent increased a generating runoff during rainfall. As shown in Figure 9, the mean monthly streamflow increased by 4 and 42 m3/s in the wet season and the mean monthly streamflow decreased by 1 and 20 m3/s in the dry season in the period between 2000 and 2010, 2010, and 2020, respectively.

Seasonal variation of flow under LULC change

LULC change variation of the Bilata watershed showed flow variations between the four seasons. In addition to the change in LULC between the years 2000, 2010, and 2020, the precipitation and temperature variations across the seasons make a change in streamflow from the four seasons. Winters (Bega) are dry in the watershed, so the streamflow deficit is high. The streamflow is increased in all the three seasons, i.e., Tseday (Autumn), Belg (spring), and Kiremit (Summer). In most parts of the Bilata Watershed, Kiremt (summer) is the main rainy season. Hence, as shown in Figure 10, the streamflow is high in the 3 years during Kiremit (summer) compared to the other seasons. The highest average streamflow is observed, more than 200 m3/s in the summer season. The water availability during this season is crucial for rainfed agriculture in the watershed.
Figure 10

Seasonal variation of streamflow. *SON: September, October, November; DJF: December, January, February; MAM: March, April, May; JJA: June, July, August.

Figure 10

Seasonal variation of streamflow. *SON: September, October, November; DJF: December, January, February; MAM: March, April, May; JJA: June, July, August.

Close modal

LULC change has substantial impacts on the hydrological process within a watershed. This study evaluates the effect of LULC change on the hydrological process of Bilata watershed. Alterations in land cover, significantly influence the hydrological processes. Landsat 7 and 8 in the years 2000, 2010, and 2020 are used for land use land cover classification assessment using a supervised classification system. The overall accuracy of the classified image was found to be above the recommended minimum of 85% for all years. The kappa coefficient was also high, indicating a good agreement between the classified image and reference data. The result of the classified land use/land cover map showed that the LULC of the study area includes eight major classes such as settlement, shrubland, forest, bare land, water bodies, regularly flooded/vegetative aquatic areas, grassland, and agriculture. It is visible that the Bilata watershed has been subjected to significant land use/cover change from 2000 to 2020. It reveals that the maximum change was observed in shrubland, grassland, and cropland class. The shrub and grassland decreased by 57,774.93 and 50,027.79 ha from the year 2000 to 2010. However, crop land increases by 46,891.04 ha from 2000 up to 2010. This is due to the increase in population growth that causes the increase in demand for cultivation land for different agricultural production. This might be because of the deforestation activities that have taken place for the purpose of agriculture and urban expansion. The remaining class type bare land, forest, water bodies and regularly flooded decreased by 34,731.45, 4,443.71, 23.19, and 363.99 ha, respectively. On the contrary, the settlement increased by 416.7 ha between 2000 and 2010. The watershed was modeling using the SWAT model. Analysis of sensitive model parameters reveals that, from 20 model parameters, 16 parameters were found to be sensitive under the category of high to low sensitivity. The calibration and validation of the model was held between 1990 to 2005 and 2006 to 2015 for streamflow. The performance of the model shows very good results which have R2 0.9, NSE 0.7, and PBIAS −10.8 for calibration and R2 0.8, NSE 0.6, and PBIAS −7.4 during validation. Alterations in land cover can impact surface runoff patterns and infiltration capacity of the soil, leading to changes in streamflow regimes. Due to intensive agricultural expansion and settlement, the surface runoff increases from 172 to 259.55 and 265.05 mm in 2000, 2010 and 2020, respectively. Similarly, evaporation and transpiration increased from 174.4 mm in 2000 to 185.1 mm in 2010, and 199.3 mm in 2020. Lateral flow has reduced from 38.41 mm in 2000 to 22.4 mm in 2010, and to 17.43 mm in 2020. Also, the average streamflow increased from 132, 134, and 150 m3/s between 2000, 2010, and 2020. The rapid expansion of agricultural land, deforestation and high population growth in the area resulted in a high rate of hydrological change in the watershed therefore a landscape intervention is needed to prevent the rate of hydrological change that risks the capacity and quality of Lake Abaya. Therefore, implementing conservation practices, such as terracing, contour ploughing, or reforestation, can help mitigate the impacts of LULC change on streamflow.

This work was not supported financially by any institute or organization.

A.M., M.B., E.T., and M.C. conceptualized the whole article; A.M. and M.B. developed the methodology; M.B. arranged the software; M.B. validated the data; A.M. and M.B. rendered support in formal analysis; A.M. M.B., E.T., and M.C. investigated the data; A.M. M.B., E.T., and M.C. arranged the resources; A.M. and M.B. rendered support in data curation; M.B. rendered support in writing the original draft; A.M., E.T., and M.C. wrote the review and edited the article; and A.M., M.B., E.T., and M.C. visualized the data. All authors read and approved the final manuscript.

The submitted work is original and has not been published elsewhere in any form or language (partially or in full).

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

The authors declare there is no conflict.

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