Land Use/Land Cover Change Impact on Hydrological Process in the Upper Baro Basin, Ethiopia

Getu Engida, Tewodros; Nigussie, Tewodros Assefa; Aneseyee, Abreham Berta; Barnabas, John

doi:https://doi.org/10.1155/2021/6617541

Applied and Environmental Soil Science

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Abstract Introduction Materials and Methods Results and Discussion Conclusions Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2021 | Article ID 6617541 | https://doi.org/10.1155/2021/6617541

Land Use/Land Cover Change Impact on Hydrological Process in the Upper Baro Basin, Ethiopia

Tewodros Getu Engida,¹Tewodros Assefa Nigussie,²Abreham Berta Aneseyee,³and John Barnabas⁴

Academic Editor: Evgeny Abakumov

Received17 Dec 2020

Revised30 Jun 2021

Accepted17 Jul 2021

Published29 Jul 2021

Abstract

Understanding the hydrological process associated with Land Use/Land Cover (LU/LC) change is vital for decision-makers in improving human wellbeing. LU/LC change significantly affects the hydrology of the landscape, caused by anthropogenic activities. The scope of this study is to investigate the impact of LU/LC change on the hydrological process of Upper Baro Basin for the years 1987, 2002, and 2017. The Soil Water Assessment Tool (SWAT) model was used for the simulation of the streamflow. The required data for the SWAT model are soils obtained from the Food and Agriculture Organization; Digital Elevation Model (DEM) and LU/LC were obtained from the United States Geological Survey (USGS). The meteorological data such as Rainfall, Temperature, Sunshine, Humidity, and Wind Speeds were obtained from the Ethiopian National Meteorological Agency. Data on discharge were obtained from Ministry of Water, Irrigation and Electricity. Ecosystems are deemed vital. Landsat images were used to classify the LU/LC pattern using ERDAS Imagine 2014 software and the LU/LC were classified using the Maximum Likelihood Algorithm of Supervised Classification. The Sequential Uncertainty Fitting (SUFI-2) global sensitivity method within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) was used to identify the most sensitive streamflow parameters. The calibration was carried out using observed streamflow data from 01 January 1990 to 31 December 2002 and a validation period from 01 January 2003 to 31 December 2009. LU/LC analysis shows that there was a drastic decrease of grassland by 15.64% and shrubland by 9.56% while an increase of agricultural land and settlement by 18.01% and 13.01%, respectively, for 30 years. The evaluation of the SWAT model presented that the annual surface runoff increased by 43.53 mm, groundwater flow declined by 27.58 mm, and lateral flow declined by 5.63 mm. The model results showed that the streamflow characteristics changed due to the LU/LC change during the study periods 1987–2017 such as change of flood frequency, increased peak flows, base flow, soil erosion, and annual mean discharge. Curve number, an available water capacity of the soil layer, and soil evaporation composition factor were the most sensitive parameters identified for the streamflow. Both the calibration and validation results disclosed a good agreement between measured and simulated streamflow. The performance of the model statistical test shows the coefficient of determination (R²) and Nash–Sutcliffe (NS) efficiency values 0.87 and 0.81 for calibration periods of 1990–2002 and 0.84 and 0.76 for the validation period of 2003 to 2009, respectively. Overall, LU/LC significantly affected the hydrological condition of the watershed. Therefore, different conservation strategies to maintain the stability and resilience of the ecosystem are vital.

1. Introduction

The response of hydrologic circulation is closely related to land use planning and management [1, 2]. Land Use/Land Cover (LU/LC) change is one of the factors that directly affect the watershed hydrological cycle [3–5]. It has been widely accepted that human activities cause LU/LC change and have imposed a great impact on the hydrological processes and water resources of the watershed [6] For example, a study [1] showed that the water provision and the hydrological process decreased as consequence LU/LC change that was aggravated by significant increasing population pressure and development in Pennar Basin in India. This can be either by increasing the water yield or by eliminating the flow in some circumstances, thereby increasing the sediment load and decreasing the groundwater [7, 8].

LU/LC changes and population growth are the most common problems in developing countries such as Ethiopia since their economic development mainly depends on agriculture [9]. In the past 40 years, the increases in human activities resulted in the expansion of agricultural land, extraction of timber, and urbanization, thereby resulting in deforestation in southwest Ethiopia [10, 11], while these changes in land cover affect the local watershed hydrological cycle and flood vulnerability of various subwatersheds [12]. The modifications/conversions of natural vegetation and physical soil conditions are usually the principal cause of changes in rainfall-runoff characteristics of the local catchment, which consequently change the river flow regimes [13, 14]. Several studies show that the changes in vegetation cover, i.e., deforestation, lead to an increase in water yield and sedimentation [15, 16].

Watershed studies can provide direct evidence of LU/LC change impacts on runoff [17]. For these reasons, hydrological models are becoming important for studying LU/LC change effect on the hydrological cycle in a landscape [18, 19]. Several studies were carried out on the hydrology of the watershed by utilizing LU/LC data in different regions of Ethiopia using the Soil and Water Assessment Tool (SWAT). They indicated an increase in the wet seasonal flow, surface runoff, and water yield [20–23]. However, studies on the impact of the LU/LC dynamic on the hydrology regime of the Upper Baro Basin are limited.

The Upper Baro Basin is hurt from human-induced degradation mainly due to large-scale agricultural expansion, which leads to LU/LC dynamism. The impacts of such changes on local hydrology are poorly understood. Hence, systematic analyses and understanding the impacts of LU/LC changes on the watershed hydrology are important for planning water resources management and maintaining the sustainable flow of water to the Baro-Akobo River. This study also helps decision-makers know where to invest to conserve the hydrological services.

Therefore, the objectives of the study are as follows: (1) to assess the LU/LC change over the past 30 years on the hydrological response of Upper Baro Basin and (2) to check the applicability of SWAT model for the study area.

2. Materials and Methods

2.1. The Study Area

The Upper Baro Basin is found in southwestern Ethiopia located at 766 km away from Addis Ababa, the capital city of Ethiopia, between latitudes 7.44° and 9.41°N and longitudes 34.52° and 36.31°E, covering an area of 23462 km². The altitude ranged from 390 m to 3266 m above sea level (Figure 1).

The Upper Baro basin has a long rainy season in the months of May-September. The area indicates a monomodal, bimodal, and triple-modal rainfall pattern. The annual average rainfall is varying from about 1163.03 mm to over 2258.36 mm with a monthly maximum rainfall record of 302.79 mm in August, while relative mean monthly rainfall for dry seasons occurs between December and March (Figure 2).

The annual rainfalls show pronounced annual and seasonal fluctuations (Figure 3) and the maximum temperature of Upper Baro Basin ranges between 27.7°C (in the wet season) and 37.2°C (in the dry season) and occurs in June to August, while the minimum falls between 10.8°C and 13.7°C in the year.

The topography of the Upper Baro Basin is characterized by variation from flat to mountainous terrain and falls in the altitude range of 390 m to over 3000 m a.m.s.l. with 42% in between 1000 m and 2000 m a.m.s.l. The eastern two-thirds of the basin area lies between 1000 m and 2400 m a.m.s.l. and a gently sloping plain lies in the west between 380 m and 500 m a.m.s.l. Dystric nitisols and dystric gleysols are the dominant soil type in the study area.

2.2. SWAT Model Data Types and Analysis

2.2.1. Meteorological Data

The SWAT model requires daily meteorological (Weather) data such as daily precipitation, minimum and maximum air temperature, relative humidity, wind speed, dew point, and daily sunshine. The standard deviation of each parameter was calculated. Thiessen method is used to adjust for nonuniform meteorological station distribution by proportion to the area which is closer. Eleven meteorological stations were selected (Table 1) based on the quantity, quality, period, consistency, homogeneity, and their uniform distribution within and around the Upper Baro Basin. These data were obtained from the National Meteorological Agency of Ethiopia (NMAE) over the period (1987–2017). The missing values were estimated from other stations around the missed record station by using both arithmetic mean method and normal ratio method and also consistency test which shows double mass.

The SWAT weather generator model (WGEN) was used to match the access table and lookup table. The daily precipitation and temperature of all gauging stations were prepared in comma-delimited (.csv) format. Solar radiation, relative humidity, and wind speed data were available only for principal Gore and Masha stations (synoptic stations).

2.2.2. Soil Data

Seventy soil types have been identified at Upper Baro Basin (Table 2). The spatial soil data were obtained from the Ethiopian Ministry of Water, Irrigation, and Electricity (MWIE), which is prepared according to FAO soil classification. The most dominant soil in the basin is Dystric nitisols and Dystric gleysols.

SWAT model requires physical and chemical soil properties in the simulation of a hydrological component such as available water content, soil texture, hydraulic conductivity, bulk density, and organic carbon content for the different layers of each soil type where shown. The major soils of the Upper Baro Basin are as shown below table and a lookup table was prepared for each type of soil.

2.2.3. DEM

DEM with a resolution of 30 m was downloaded from United States Geological Survey https://earthexplorer.usgs.gov/. The DEM is one of the essential inputs required by SWAT to delineate Upper Baro Basin (Figure 4), define the stream network, and determine sub-basin parameters such as slope area and slope length.

2.2.4. Slope Classes in Subwatershed

To develop the hydrological response unit (HRU) in the SWAT model, the slope is essential. It generates from the resolution of 30 m ∗ 30 m DEM for the study area. Slope classification may be single class or multiclass. For this study, the slope option (an option for considering different slope classes for HRU definition) was selected. Therefore, the slope class in this study was classified into four classes. According to [22], slope classification was used to account for lower range in hydrological modelling. Depending on the slope of 0–5%, 5–10%, 10–15%, 15–20%, and >20% were selected for HRU creation of the study Upper Baro Basin. Finally, HRU definition analysis in SWAT helps to load LULC and soil type projects.

2.2.5. Land Use/Land Cover (LU/LC) Analysis

Landsat Satellite images of the years 1987 (Landsat 5), 2002 (Landsat 7) and 2017 (Landsat 8) were obtained from United States Geological Survey (USGS) (https://www.glovis.USGS.gov) (Table 3). The multidate multisensor satellite imagery was collected for successive cropping for each study year. Dataset selections were in dry season fixed with the lowest percent or zero monthly cloud cover. Therefore, all images are cloud-free (0%) in the study area.

The images were already terrain corrected projected to the Universal Transverse Mercator (UTM) of WGS84 zone 37 N. They were radiometrically calibrated to reflectance value and the Quick Atmospheric Correction algorithm was applied in ERDAS software. Radiometric and geometric corrections were applied to the removal of sensor noise, haze, correction data loss, and missing lines due to a solar position, satellite calibration [24, 25]. A supervised image classification method was employed using Maximum Likelihood Algorithm classification. For supervised classification, the Region of Interest (ROI) was made a signature for different LU/LC categories. The reference data collected using a GPS receiver were first converted to a vector file, then the region of interest (ROI) was made in ERDAS software. Using the ROI, the spectral signature of each LU/LC type has been extracted. Further investigations were carried out by taking these sample points in the land use types using a handheld GPS receiver. Based on this analysis and the existing land use distribution information, the area was categorized into eight broad LU/LC classes: cultivated land, woodland, forest land, grazing land, bare land, shrubland, water bodies, and built-up (Table 3).

Overall accuracy and Kappa coefficient, the classify maps will check classification accuracy. According to [26, 27], a good overall classification map accuracy is accepted if and only it possesses an accuracy of at least 85%. The LU/LC was an input of the SWAT model to describe the HRU of the subwatershed. The SWAT Model has predetermined four-letter codes for each LU/LC category (Table 4). These codes were used to link the LU/LC map of the study area with the SWAT land database. To make it compatible, LU/LC with the SWAT model lookup table was prepared for each type of LU/LC.

2.3. SWAT Model Description

SWAT is a physically based semidistributed continuous time-scale hydrological model, which works on a daily time step. This model can simulate runoff, sediment, nutrients, pesticide, and bacterial transport from agricultural watersheds [28]. It simulates the hydrological cycle parameters based on the water balance represented in equation (1) within the watershed [29]. More details on the information about the SWAT model can be found in the SWAT user manual [29].

The hydrologic cycle simulated by the SWAT is based on the water balance equation [29] as follows:where SW_t and are the final and initial soil water content, respectively, (mm), Q_i = daily surface runoff (mm), R_i = daily rainfall (mm), ET_i = daily evapotranspiration (mm), R_i = daily lateral flow (mm), and P_i = daily percolation (mm).

2.4. Peak Runoff Rate

The peak runoff rate is a result of the erosive power of a storm and is used to predict sediment loss. SWAT calculates the peak runoff rate with a modified rational method for each HRU using the following equation:where Q peak is peak runoff rate (m³/s), the fraction of daily rainfall that occurs during the time of concentration, Q_surf is the surface runoff (mm), A is the sub-basin area (km²), and t_conc is the time of concentration (hr).

2.5. Surface Runoff Generation

The CN method was initially developed for small agricultural watersheds and the CN varies nonlinearly with the moisture content of the soil. It drops to zero as the soil approaches the wilting point and increases to be near 100 as the soil approaches saturation, with higher CNs associated with higher runoff potential watershed. This method is widely used [28].

According to USDA, 1985 [30], this can be mathematically expressed using the following equation:where Q_s is the accumulated runoff (mm), Ra is the rainfall depth for the day (mm), andwhere CN is the curve number for the day and its value is the function of land use practice, soil permeability and soil hydrologic group. Conceptual framework showing the components and relationships have been used as a framework for the analysis of hydrology in research (Figure 5).

2.6. SWAT Model Simulation, Sensitivity Analysis, Calibration, and Validation

The simulation result cannot be directly used for further analysis. Instead, the ability of the model to sufficiently predict the constituent streamflow should be evaluated through sensitivity analysis, model calibration, and model validation [31].

2.6.1. Sensitivity Analysis

Fifteen hydrological parameters related to streamflow were selected for sensitivity analysis in the study area. The sensitivity of each parameter was identified using the T test and values. values were used to determine the significance of the sensitivity. A value close to zero has more significance. T test provides a measure of sensitivity and the most sensitive parameters usually appear with values less than the alpha level of 0.05 (larger absolute values are more sensitive) [32].

2.6.2. Model Calibration and Validation

The SUFI-2 algorithm was applied and frequently used in this study of calibration for the SWAT model at a large scale due to its easy application and the reduced number of model runs needed to achieve a decent prediction [33, 34]. In this study, automatic calibration was made every month from January 1, 1990, to December 31, 2002, until the average simulated value came closer to the measured value. Automatic calibration makes use of a numerical algorithm to increase the performance of the model and to optimize the numerical objective functions. After the simulation result for the calibration, the period had fulfilled the above statistical criteria, validation was performed for an independent period of records from January 1, 2003, to December 31, 2009, and the results were compared against an independent set of measured Upper Baro Basin.

2.6.3. Model Performance Evaluation

Model performance was carried out to verify the robustness of the model to simulate hydrological processes. A model performance framework proposed by [35] was used in this study.

Nash and Sutcliffe simulation efficiency (NSE) indicates the degree of fitness of observed and simulated data, and it is calculated using the following equation:where N = number of compared values, Q_obs is the observed data, is the observed mean, and Q_sim is the simulated data.

The NSE indicates how well the plot of observed versus simulated value fits the 1 : 1 line. The closer the model efficiency is to 1, the more accurate the model, and if it is found between 0 and 1, it indicates deviations between measured and predicted values. If NSE is negative, predictions are poor, and the average value of output is a better estimate than the model prediction [36].

Coefficient of determination (R²) measures the ability of a model to predict or explain an outcome in the linear regression setting (Equation (6)). R² ranges from 0 to 1, with higher values indicating less error variance, and typically values greater than 0.5 are considered acceptable [35, 37, 38].where is measured value, is average measured value, is simulated value, and is the average simulated value.

Percent bias (PBIAS) measures the average difference between the simulated and measured values for a given quantity over a specified period (usually the entire calibration or validation period) and it is calculated using the following equation [39, 40]:where is measured value and is simulated value.

Model evaluation classifies as follows: (i)0.75 < NSE < 1.00, PBIAS < ±10—better predictor(ii)0.65 < NSE ≤ 0.75, ±10 < PBIAS < ±15—good(iii)0.50 < NSE ≤ 0.65, ±15 < PBIAS < ±25—satisfactory(iv)(NSE ≤ 0.50, PBIAS >±25)—unsatisfactory

3. Results and Discussion

3.1. LU/LC Change

The spatial distribution and percentage of the total area of the eight LU/LC types between 1987–2002, 2002–2017, and 1987–2017 are shown in Figure 6 (Table 5).

The results obtained for 1987–2002 reveal that forest land covers 36.74% and followed by grassland, 19.77% and shrubland and woodland also cover 19.76% and 11.32%, respectively. Between 2002 and 2017, the distribution of forest cover demonstrates highest in the catchment, which covered 33.33%, followed by agricultural land and shrubland, with the value of 15.6%, 15.1%, respectively. Forest land and shrubland decreased after 1987–2002, with −3.41% and −4.65%, respectively (Figure 7). The area of forest land decreased due to of change of land use (conversion) to the expansion of agricultural land in the southwestern part of the catchment. The urban area gradually expanded from 2.01% to 15.51% over the entire study period. These results indicate that government policy does not have a strong impact on land use.

In the last 30 years, the study displays an increase in agriculture, bare land, urban, and shrubland, whereas the forest, grassland, water, and wetland were noted declined. Agricultural land increased by 18.01% during the study period. This is due to the increase of population growth that causes the increase in demand for cultivation land for different agricultural products like cereal crops, field crops, industrial crops, whereas the forest area was diminished. This might be due to deforestation activities that have taken place for agriculture and urban expansion.

Consequently, agricultural investment policies encouraged further deforestation that leads to land conversion [41] and various types of natural forests and woodlands in the Western part of Ethiopia, being cleared and replaced by commercial agriculture [42, 43]. In the study basin, cultivated land has gained/increased by 18.01%, mainly converted from the grazing land by 15.64%. Forest and shrubland conversion also contributed to the increasing cultivated land.

In this study, the accuracy assessment was performed using the 340 referenced points collected through stratified sampling. Overall accuracies obtained were 92.14%, 94.63%, and 95.93% for the 1987, 2002, and 2017 images, respectively (Table 6). The overall accuracy was computed by dividing the correct classification by the total number of reference pixels in the error matrix.

3.2. Stream Flow Modeling Using SWAT

3.2.1. Sensitivity Analysis

Sensitivity analysis was carried out to identify which model parameter was the most important or sensitive. Flow sensitivity analysis was carried out for 13 years, the calibration period (from January 1, 1990, to December 31, 2002). The default and optimal parameters and their ranking in terms of the 10 highest relative sensitivity values used to develop, calibrate, and validate the model (Figure 8).

3.2.2. SWAT Model Calibration and Validation

The simulated monthly flows demonstrated a good agreement with the monthly observed discharge of Upper Baro Basin with a coefficient of determination (R² = 0.86), Nash–Sutcliffe efficiency (NSE = 0.80), and percent bias (PBIAS = −17%) (Figure 9). However, the model overestimated the peak monthly flow for some years in the calibration period; it followed the trend of observed monthly Upper Baro basin discharge and presented a good response to extreme rainfall events, which resulted in high runoff volume.

SWAT model also successfully validated streamflow for an independent period (2003–2010) with R² = 0.84, NS = 0.76, and PBIAS = −4.5% during the validation period; the values fulfilled the statistical model performance criteria R² > 0.6 and NSE > 0.5 recommended by SWAT developer [37]. The percent bias (PBAIS) values (−17% and 4.5%) during calibration and validation periods, respectively, also fulfilled the statistical model performance criteria PBAIS> ±15 [35] (Table 7).

The percentage of observations bracketed by the 95 PPU was 75% for calibration and 72% for validation. The r-factor, which is the average thickness of the 95PPU band divided by the standard deviation of the measured data, was 1.08 for calibration and 0.84 for validation. According to [32], a p-factor greater than 0.70 and r-factor less than 1.5 display that the model is accurate in simulating streamflow. Thus, the results displayed that the model accurately simulated the biophysical processes influencing flow in the study area.

Generally, the above information showed that the performance of the model was better during the calibration period more than the validation period (Table 8) and also the calibration validation period (Figures 9–12) shows there was good relation with observed and simulated streamflow.

3.3. Land Use/Land Cover Change Response to Hydrological Process

The simulated results of the Upper Baro Basin under different land use/land cover scenarios are shown in Table 9.

3.4. Mean Monthly Wet and Dry Month Streamflow Simulation and Their Variability

Months July, August, and September were considered as wet periods and Jan, Feb, and March were considered dry months; these seasonal variabilities of streamflow were evaluated.

3.5. Spatial Variability of Surface Runoff over Sub-Basin

Spatial variability of surface runoff shows that surface runoff is highly sensitive to the change of LU/LC over subwatersheds in the period of 1987, 2002, and 2017 (Figure 13).

3.6. Comparison of Mean Monthly Surface Runoff and Groundwater

Decreases in base flow had a potential effect on the change in annual river flow (Figure 14). Results from LU/LC change scenarios, the mean monthly (wet and dry) flow has increased from 82.9 mm and decreased 20.54 mm concerning the change of LU/LC from 1987 to 2017 (Figure 15).

The results (Table 10) demonstrate that the mean dry monthly flow for 1987–2017 land cover decreased by −20.54 (m³/s) when compared to the land cover in 1987–2017. Changes in land use occurred in all parts of the Basin but mainly in the southwestern part. Vegetation cover within the catchment had changed considerably due to human activities, mainly through the conversion of natural vegetation to agriculture, Urban, and largescale investment. These changes have altered water resources through biophysical and biogeochemical processes in the soil. Through these land-cover changes, the biophysical and morphological characteristics of the vegetation have been modified, and therefore, the changes have affected the exchange of water between the atmosphere and land surface. Similarly, the results indicate an increase in surface runoff increased from 773 mm/yr in 1987 to 793.05 mm/yr in 2002 and 816.53 mm/yr in 2017 due to a drastic increase in agriculture and urban activity at the expense of other land covers while groundwater was decreased from 283.21 mm to 257.13 mm in 1987 as compared to 2017 due to LULC change. Similarly, [44, 45] reported that an increase in annual surface runoff increased when forest lands were converted to the other classes especially cultivated land. The expansion of agricultural land by displacing forest and shrubland results in an increase in surface erosion generation following rainfall events and causes a reduction in soil moisture conditions and groundwater recharge to shallow aquifers. Similarly, studies suggested that grassland and lateral flow are positively correlated [46, 47].

4. Conclusions

In the present study, the Upper Baro Basin has been simulated using the SWAT hydrological model for assessing the impact of LULC change on its hydrological regime successfully. Initially, the change in LULC between the years 1987, 2002, and 2017 has been assessed. It is evident that land cover changes had occurred between 1987 and 2017. Forest cover had decreased from 36.74% to 31.21%, attributable to logging for timber, firewood, and clearing for agricultural purposes. In contrast, the agricultural area increased over the years from 8.81% to 26.81%. Similarly, grasslands and bushes have also been converted to agricultural areas. Sensitivity analysis of the SWAT parameters indicates that runoff is most sensitive to curve number, an available water capacity of the soil layer, soil evaporation composition factor, manning’s “n” value for the main channel, and saturated hydraulic conductivity. Seasonal streamflow variability due to the LU/LC was assessed and comparisons were made on surface runoff, lateral flow, and groundwater flow contributions to streamflow based on the three simulation outputs. Surface runoff was increased from 713.85 mm to 760.49 mm, while groundwater was decreased from 283.21 mm to 257.13 mm in the years 1987 and 2017. Also, surface runoff was increased from 737.67 mm to 760.49 mm, while groundwater was decreased from 266.32 mm to 257.13 mm in the year 2002 and 2017. Land use/land cover change over the last 30 years also affect the soil and soil properties in the study area significantly. For example, forest land declined and agricultural land is increased. This dynamism would maximize soil erosion, which leads to the loss of important soil nutrients. Therefore, different soil and water conservation measures are required to reverse the challenges.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

V. Garg, B. R. Nikam, P. K. Thakur, S. P. Aggarwal, P. K. Gupta, and S. K. Srivastav, “Human-induced land use land cover change and its impact on hydrology,” HydroResearch, vol. 1, pp. 48–56, 2019.
View at: Publisher Site | Google Scholar
Y. Li, J. Chang, L. Luo et al., “Spatiotemporal impacts of land use land cover changes on hydrology from the mechanism perspective using SWAT model with time-varying parameters,” Hydrology Research, vol. 50, no. 1, pp. 244–261, 2019.
View at: Publisher Site | Google Scholar
H. Brook, M. Argaw, H. Sulaiman, and T. A. Abiye, “The impact of land use/land cover change on hydrological components due to resettlement activity: SWAT model approach,” International Journal of Ecology and Environmental Sciences, vol. 37, pp. 49–60, 2015.
View at: Google Scholar
R. E. Schulze, “Modelling hydrological responses to land use and climate change: a southern african perspective,” AMBIO: A Journal of the Human Environment, vol. 29, no. 1, pp. 12–22, 2000.
View at: Publisher Site | Google Scholar
T. J. Stohlgren, T. N. Chase, R. A. Pielke, T. G. F. Kittel, and J. S. Baron, “Evidence that local land use practices influence regional climate, vegetation, and stream flow patterns in adjacent natural areas,” Global Change Biology, vol. 4, pp. 495–504, 1998.
View at: Publisher Site | Google Scholar
N. Marie Mireille, H. M. Mwangi, J. K. Mwangi, and J. Mwangi Gathenya, “Analysis of land use change and its impact on the hydrology of kakia and esamburmbur sub-watersheds of Narok county, Kenya,” Hydrology, vol. 6, no. 4, p. 86, 2019.
View at: Publisher Site | Google Scholar
S. Babar and H. Ramesh, “Streamflow response to land use-land cover change over the nethravathi river Basin, India,” Journal of Hydrologic Engineering, vol. 20, no. 10, 2015.
View at: Publisher Site | Google Scholar
Food and Agriculture Organization, “World reference base for soil resources 2014, international soil classification system for naming soils and creating legends for soil maps,” Tech. Rep., Food and Agriculture Organization, Rome, Italy, 2014, World Soil Resources Report 106.
View at: Google Scholar
D. F. Tufa, Y. Abbulu, and G. Srinivasarao, “Watershed hydrological response to changes in land use/land covers patterns of river basin: a review,” International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development (IJCSEIERD), vol. 4, pp. 157–170, 2014.
View at: Google Scholar
A. B. Dixon, “The hydrological impacts and sustainability of wetland drainage cultivation in Illubabor, Ethiopia,” Land Degradation & Development, vol. 13, no. 1, pp. 17–31, 2002.
View at: Publisher Site | Google Scholar
T. Gashaw, T. Tulu, M. Argaw, and A. W. Worqlul, “Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia,” Science of the Total Environment, vol. 619-620, pp. 1394–1408, 2018.
View at: Publisher Site | Google Scholar
V. Garg, S. P. Aggarwal, P. K. Gupta et al., “Assessment of land use land cover change impact on hydrological regime of a basin,” Environmental Earth Sciences, vol. 76, no. 18, p. 635, 2017.
View at: Publisher Site | Google Scholar
G. Shao, Y. Guan, D. Zhang, B. Yu, and J. Zhu, “The impacts of climate variability and land use change on streamflow in the Hailiutu River Basin,” Water, vol. 10, no. 6, p. 814, 2018.
View at: Publisher Site | Google Scholar
L. Zhang, R. Karthikeyan, Z. Bai, and R. Srinivasan, “Analysis of streamflow responses to climate variability and land use change in the Loess Plateau region of China,” Catena, vol. 154, pp. 1–11, 2017.
View at: Publisher Site | Google Scholar
P. Sun, Y. Wu, X. Wei et al., “Quantifying the contributions of climate variation, land use change, and engineering measures for dramatic reduction in streamflow and sediment in a typical loess watershed, China,” Ecological Engineering, vol. 142, Article ID 105611, 2020.
View at: Publisher Site | Google Scholar
A. A. Vaighan, N. Talebbeydokhti, and A. M. Bavani, “Assessing the impacts of climate and land use change on streamflow, water quality and suspended sediment in the Kor River Basin, southwest of Iran,” Environmental Earth Sciences, vol. 76, p. 543, 2017.
View at: Publisher Site | Google Scholar
Y. S. Getahun and H. Van Lanen, “Assessing the impacts of land use-cover change on hydrology of Melka Kuntrie subbasin in Ethiopia, using a conceptual hydrological model,” Hydrology: Current Research, vol. 6, no. 3, pp. 1–11, 2015.
View at: Publisher Site | Google Scholar
T. Karvonen, H. Koivusalo, M. Jauhiainen, J. Palko, and K. Weppling, “A hydrological model for predicting runoff from different land use areas,” Journal of Hydrology, vol. 217, no. 3-4, pp. 253–265, 1999.
View at: Publisher Site | Google Scholar
M. Wegehenkel, “Estimating of the impact of land use changes using the conceptual hydrological model THESEUS––a case study,” Physics and Chemistry of the Earth, Parts A/B/C, vol. 27, pp. 631–640, 2002.
View at: Publisher Site | Google Scholar
H. E. Getachew and A. M. Melesse, “The impact of land use change on the hydrology of the Angereb watershed, Ethiopia,” International Journal of Water Sciences, vol. 1, 2012.
View at: Publisher Site | Google Scholar
D. Legesse, T. Abiye, C. Vallet-Coulomb, and H. Abate, “Streamflow sensitivity to climate and land cover changes: Meki River, Ethiopia,” Hydrology and Earth System Sciences, vol. 14, no. 11, 2010.
View at: Publisher Site | Google Scholar
S. G. Setegn, R. Srinivasan, and B. Dargahi, “Hydrological modelling in the lake Tana Basin, Ethiopia using SWAT model,” The Open Hydrology Journal, vol. 2, no. 1, pp. 49–62, 2008.
View at: Publisher Site | Google Scholar
G. Zeleke and H. Hurni, “Implications of land use and land cover dynamics for mountain resource degradation in the Northwestern Ethiopian highlands,” Mountain Research and Development, vol. 21, pp. 184–191, 2001.
View at: Publisher Site | Google Scholar
T. Hilker, A. I. Lyapustin, C. J. Tucker, P. J. Sellers, F. G. Hall, and Y. Wang, “Remote sensing of tropical ecosystems: atmospheric correction and cloud masking matter,” Remote Sensing of Environment, vol. 127, pp. 370–384, 2012.
View at: Publisher Site | Google Scholar
A. I. Lyapustin, Y. Wang, I. Laszlo et al., “Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. atmospheric correction,” Remote Sensing of Environment, vol. 127, pp. 385–393, 2012.
View at: Publisher Site | Google Scholar
J. R. Anderson, A Land Use and Land Cover Classification System for Use with Remote Sensor Data, US Government Printing Office, Washington, DC, USA, 1976.
G. M. Foody, “Status of land cover classification accuracy assessment,” Remote Sensing of Environment, vol. 80, no. 1, pp. 185–201, 2002.
View at: Publisher Site | Google Scholar
J. G. Arnold, R. Srinivasan, R. S. Muttiah, and J. R. Williams, “Large area hydrologic modeling and assessment part I: model development,” Journal of the American Water Resources Association, vol. 34, no. 1, pp. 73–89, 1998.
View at: Publisher Site | Google Scholar
S. L. Neitsch, J. G. Arnold, J. R. Kiniry, and J. R. Williams, Soil and Water Assessment Tool Theoretical Documentation Version 2009, Texas Water Resources Institute, Austin, TX, USA, 2011.
“United State Department of Agriculture - Soil Conservation Service (USDA-SCS),” National Engineering Handbook, USDA, Washington, DC, USA, 1985.
View at: Google Scholar
K. L. White and I. Chaubey, “Sensitivity analysis, calibration, and validations for a multisite and multivariable SWAT model 1,” JAWRA Journal of the American Water Resources Association, vol. 41, pp. 1077–1089, 2005.
View at: Publisher Site | Google Scholar
K. C. Abbaspour, E. Rouholahnejad, S. Vaghefi, R. Srinivasan, H. Yang, and B. Kløve, “A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model,” Journal of Hydrology, vol. 524, pp. 733–752, 2015.
View at: Publisher Site | Google Scholar
K. C. Abbaspour, SWAT-CUP 2012. SWAT Calibration and Uncertainty Program—A User Manual, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland, 2013.
B. Narsimlu, A. K. Gosain, B. R. Chahar, S. K. Singh, and P. K. Srivastava, “SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting,” Environmental Processes, vol. 2, no. 1, pp. 79–95, 2015.
View at: Publisher Site | Google Scholar
D. N. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith, “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations,” Transactions of the ASABE, vol. 50, no. 3, pp. 885–900, 2007.
View at: Publisher Site | Google Scholar
J. E. Nash and J. V. Sutcliffe, “River flow forecasting through conceptual models part I—a discussion of principles,” Journal of Hydrology, vol. 10, no. 3, pp. 282–290, 1970.
View at: Publisher Site | Google Scholar
C. Santhi, J. G. Arnold, J. R. Williams, W. A. Dugas, R. Srinivasan, and L. M. Hauck, “Validation of the swat model on a large river Basin with point and nonpoint sources,” Journal of the American Water Resources Association, vol. 37, no. 5, pp. 1169–1188, 2001.
View at: Publisher Site | Google Scholar
M. Van Liew, J. Arnold, and J. Garbrecht, “Hydrologic simulation on agricultural watersheds: choosing between two models,” Transactions of the ASAE, vol. 46, p. 1539, 2003.
View at: Publisher Site | Google Scholar
H. V. Gupta, S. Sorooshian, and P. O. Yapo, “Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration,” Journal of Hydrologic Engineering, vol. 4, no. 2, pp. 135–143, 1999.
View at: Publisher Site | Google Scholar
P. Shi, C. Chen, R. Srinivasan et al., “Evaluating the SWAT model for hydrological modeling in the Xixian watershed and a comparison with the XAJ model,” Water Resources Management, vol. 25, no. 10, pp. 2595–2612, 2011.
View at: Publisher Site | Google Scholar
S. Gebreselassie, “Intensification of smallholder agriculture in Ethiopia: options and scenarios,” Future Agricultures Consortium Meeting at the Institute of Development Studies, pp. 20–22, 2006.
View at: Google Scholar
A. K. Gobena and T. Y. Gan, “Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system,” Journal of Hydrology, vol. 385, no. 1–4, pp. 336–352, 2010.
View at: Publisher Site | Google Scholar
E. N. Stebek, “Between “land grabs” and agricultural investment: land rent contracts with foreign investors and Ethiopia’s normative setting in focus,” Mizan Law Review, vol. 5, pp. 175–214, 2011.
View at: Google Scholar
L. A. Anaba, N. Banadda, N. Kiggundu, J. Wanyama, B. Engel, and D. Moriasi, “Application of SWAT to assess the effects of land use change in the murchison bay catchment in Uganda,” Computational Water, Energy, and Environmental Engineering, vol. 6, no. 1, pp. 24–40, 2017.
View at: Publisher Site | Google Scholar
T. S. Ngo, D. B. Nguyen, and P. S. Rajendra, “Effect of land use change on runoff and sediment yield in da river basin of Hoa Binh province, northwest Vietnam,” Journal of Mountain Science, vol. 12, no. 4, pp. 1051–1064, 2015.
View at: Publisher Site | Google Scholar
C. Gyamfi, J. Ndambuki, and R. Salim, “Hydrological responses to land use/cover changes in the Olifants Basin, South Africa,” Water, vol. 8, no. 12, p. 588, 2016.
View at: Publisher Site | Google Scholar
S. Shukla and S. Gedam, “Evaluating hydrological responses to urbanization in a tropical River Basin: a water resources management perspective,” Natural Resources Research, vol. 28, no. 2, pp. 327–347, 2019.
View at: Publisher Site | Google Scholar

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Copyright © 2021 Tewodros Getu Engida et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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