
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12346-8
10.1016/j.heliyon.2024.e36315
e36315
Research Article
Assessment of sediment yield and accumulation in reservoir: The case of Gibe One Reservoir, Southwestern Ethiopia
Kuma Hailu Gisha hailu.gishaK@wsu.edu.et
a⁎
Chinasho Ermias Mekonnen b1
Tolke Abrham Asha c1
a College of Engineering, Department of Hydraulic & Water Resources Engineering, Wolaita Sodo University, Sodo, 138, Ethiopia
b Collage of Engineering, Department of Hydraulic & Water Resources Engineering , Wolaita Sodo University, Sodo, 138, Ethiopia
c Collage of Natural & Computational Sciences, Department of Geology, Wolaita Sodo University, Sodo, 138, Ethiopia
⁎ Corresponding author. hailu.gishaK@wsu.edu.et
1 Co-authors: Ermias Mekonnen Chinasho, Abrham Asha Tolke.

17 8 2024
15 9 2024
17 8 2024
10 17 e363159 10 2023
31 7 2024
13 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Soil erosion and sediment buildup are the factors that speed up the decline in capacity and function of reservoirs, agricultural products, and water resources. In order to simulate sediment and runoff and map high sediment-yielding sub-basins in the Gibe Gojeb catchment in southwest Ethiopia, this study used the Soil and Water Assessment Tool (SWAT) model. Using data on sediment and river flow, calibration and validation were carried out. Between 2003 and 2016, the catchment produced an average annual sediment loading of 62.5 tons ha−1 yr−1, with loading fluctuations ranging from 0.2 to 108.4 tons ha−1 yr−1. The acceptable sediment yield threshold value ranges from 12.3 to 108.4 tons ha−1 yr−1 for 56 sub-basins, and from 0.2 to 10 tons ha−1 yr−1 for 5 sub-basins. The most significant sub-basins with very high to extremely severe sediment yields were sub-basins 1 to 30, 32 to 44, 47, 48, 50, 51, and 53 to 61. After thirteen years of operation, the yearly amount of 58,802 tons of sediment transferred from the catchment and deposited into Gibe One reservoir has decreased the capacity by 5.7 %. The accumulation of sediment in a reservoir has an impact on its functionality, power production, and capacity, affecting the safety of dams and the environment. The study's findings enhanced our comprehension of sediment accumulation in reservoirs and furnished us with the necessary information regarding reservoir safety, integrated soil, and water management.

Keywords

Gibe Gojeb catchment
Reservoir sedimentation
Sediment yield
SWAT model
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pmc1 Introduction

Reservoirs constructed for the purposes of hydroelectric power, water supply, flood control, and irrigation face challenges due to the accumulation of sediment loads from the catchments. There are issues with sediment buildup in reservoirs that affect energy production [1]. Sedimentation issues impede reservoirs' ability to perform their intended roles and lifespan. One of the main causes of irrigation reservoirs' shortened useful lives is sediment accumulation [2]. According to their assessment, the yearly volume decline of the reservoir was 1.25 percent, and the mean rate of sedimentation was 49,500 m3/yr (60,390 tons/yr). Similarly, reservoir storage capacity is thought to be decreasing globally at a rate of 0.5 %–1.0 % every year [3]. Similarly, Zarfl and Lucia [4] reported that sediment entrapment is a neglected element in reservoir planning and environmental assessment. As a result, reservoirs lose 1 % of their capacity annually.

Sediment accumulation in the reservoirs reduced their capacity, resulting in difficulties in irrigation, hydropower generation, water supply, and flood control. For instance, Behonegn and Aweke [5] reported that in 2014, hydropower production was decreased due to a 17 million cubic meter annual fall in the Koka dam reservoir. In addition, sediment buildup caused the Awash Melkasa irrigation reservoir to shut off [6].

Significant threats to reservoirs and lakes arise from anthropogenic activities, including land cover changes, which are intended to satisfy immediate human needs. In the study catchment, numerous studies on the Gojeb River, Shenkolla, Dedo, and Abelti basins revealed a decrease in grass and grazing grounds and a raise in agricultural areas at the expenditure of forest and shrub lands [[7], [8], [9], [10]]. Studies confirm such conditions. For instance, according to Curtis and Flint [11], human activity has reduced the amount of water that can be stored in the Laguna Reservoir by speeding up soil erosion and sediment transport. Sediment output is a serious issue that censoriously concerns agricultural areas and the sustainability of hydropower, according to a study by Oybitet et al. [12].

The second largest contributor to reservoir sedimentation is rainfall-runoff from watershed areas, followed by human activity. Land cover characteristics, soil erosion rate, and watershed rainfall-runoff are all intimately correlated with sediment load in reservoirs [13]. According to Hergarten et al. [14], the primary cause of sediment dissociation, transport, and deposition is surface runoff. Therefore, runoff is important when considering the processes of soil erosion and reservoir sedimentation.

For the purpose of estimating soil erosion and deposition at reservoirs and for various scenarios, sediment modeling is important [15]. Besides, sediment modeling offers environmentally significant evidence, primarily needed to understand the usual and human effects on water bodies. Sediment modeling and analysis were performed for various reasons throughout the world.

Limited modeling studies were performed in some catchments in Ethiopia to evaluate sediment accumulation in the reservoirs using SWAT and other models. Among these were the assessment of sediment inflow at Tendaho Dam [16]; bathymetry investigation for reservoir sedimentation evaluation at Shumburit Earth Dam [2]; sediment yield and sedimentation in Koga Reservoir [17]; identifying the Gilgel Gibe watershed's soil-erosion-prone zones [18]; and evaluation of Gibe One Reservoir's nutrient enrichment and siltation using water samples [19]. Using spatial and attribute data, the studies used the SWAT model to replicate sediment yield and hydrological processes in large watersheds. To simulate long-term hydrological responses of watersheds, the SWAT model needs spatial input data and meteorological stations. The SWAT model requires weather stations and spatial input data and is used to simulate long-term hydrological responses. However, detailed modeling of sediment accumulation in reservoirs was not done in the Gibe Gojeb catchment. Therefore, the study evaluates the relevance of the SWAT model for predicting sediment yield from the catchment and accumulation in the Gibe One reservoir. The findings of the study are important in focusing on actions by the Gibe Gojeb catchment managers.

2 Materials and methods

2.1 Study area

The area of the Gibe Gojeb catchment in Ethiopia spans 3,039,243 ha and is situated in the Oromia and Southern Nations Nationalities Regional (SNNPR) states (Fig. 1). The study catchment is positioned between 6°25' to 9°24' N latitude and 35°36' to 38°34' E longitude. The elevation in this catchment ranges from 790 to 3612 m above sea level. It is well-known for its irregular terrain and has suffered significant ecosystem degradation due to a decline in forest, more grazing, and inadequate land management. The adapted annual temperature and rainfall in the region are 19.2 °C and 1587 mm, respectively. Agriculture is the main driver of economic activity in the catchment.Fig. 1 Location map of study area.

Fig. 1

2.2 Data sources

2.2.1 Land use land cover map

Sentinel-2 imagery with a resolution of 10 m was used to download the land use and land cover map of study catchment in 2017 (https://www.arcgis.com/home/item.html?id=cfcb7609de5f478eb7666240902d4d3d). The images covering the catchment were mosaicked. The land use and cover classes were verified through the use of Google Earth. Water bodies (WATR), wet lands (WETL), urban and built-up areas (URBN), forest lands/scattered (FRST), plantations (FRSE), barren lands (BARR), range land (RNGE), crops (AGRL), and cultivated lands (AGRC) are the nine land use and cover types that are coded in accordance with the SWAT database. The land cover and use for 2017 are shown in Fig. 2a.Fig. 2 Land use/cover, DEM and soil map of Gibe Gojeb catchment.

Fig. 2

2.2.2 Digital elevation model (DEM)

The USGS (https://earthexplorer.usgs. Gov) provided the 30 m-resolution digital elevation model (DEM) for the research catchment. It was then utilized to assess the drainage patterns of the land surface terrains, slope classification and to draw the watershed's boundaries using SWAT. On the D-WGS 1984 spheroid, the DEM was estimated to UTM region 37 north (Fig. 2b).

2.2.3 Soil map

The FAO provided the soil of the study catchment, together with comprehensive data on its physical and chemical properties [20]. The organization’s categorization scheme was modified to meet the needs of the SWAT reproduction [21]. The ArcGIS 10.3 program was used to carry out the extraction. Nineteen different soil types were identified, as indicated by Fig. 2c. The most dominant soil type of the catchment is Pellic Vertisols, covering an area 42.24 % of the land. The least dominant soil type is Calcaric Fluvisols, covering an area of 0.04 % of the catchment. The other soil types including Eutric Cambisols, Dystric Nitissols, Orthic Acrisols, Dystric Fluvisols, Eutric Nitisos, Eutric Fluvisols, Chromic Vertisols, Orthic Luvisols, Lepthosols, Orthic Solonchacs, Dystric Gleysols, Calcic Fluvisols, and Gypsic Yemosols, Chromic Cambisols and Cambisols cover area of 15.68, 9.29, 7.13, 6.12, 5.15, 3.39, 3.68, 2.58, 1.29, 1.07, 0.91, 0.89, 0.28 %, 0.14, and 0.07 %, respectively.

2.2.4 Weather data

SWAT needs daily weather data, which is a measured data set. Observed daily rainfall, temperature, humidity, wind, and daily sunshine hours from 2003 to 2016 were collected from the National Meteorological Agency of Ethiopia. The data were arranged following the completeness and spatio-temporal consistency of the records of the representative location in the Gibe Gojeb catchment. The Jimma weather station was used for analyzing the weather data in the basin (Fig. 3). The station has all types of weather data significant for the SWAT input. The station was used as a weather-generating (synoptic) station for others. The station has missing data. The missing values of historical daily data were filled by using the weather generator in the SWAT model.Fig. 3 River networks, reservoir, meteorological and hydrological stations.

Fig. 3

2.2.5 Hydrological data

Gibe River flow records (2003–2016) at Abelti gauge location and sediment records at Asendabo were collected from the Ministry of Water and Energy of Ethiopia (Fig. 3). The records were employed to complete the standardization and justification of the SWAT model.

2.3 Reservoir schematization

In the study catchment, the river Gibe starts its flow at East Wolega Zone, Oromia Region State. The river Gibe (1) then flows to the south towards its meeting with the river Gilgel Gibe (3) (from the west side) and Wabe River (2) from the Gurage highlands. The convergence of the Gibe River over the Wabe, Gilgel Gibe, and Gojeb River (4) (from the southwest side), forms the river Omo Gibe (5) (Fig. 3). The river Omo Gibe flows to Lake Turkana in Ethiopia and Kenya. The drainage basin is mentioned as the Omo Gibe River Basin, which is one of twelve river basins in the country. On the Gilgel Gibe River (3) in Fig. 3, you can find the Gibe One reservoir, and was put into operation in 2004 with an active capacity of 717 MCM [22].

The reservoir was schematized in the SWAT model through watershed delineation with five successive steps: DEM display, stream characterization, outlet and inlet characterization, watershed outlet(s) selection and characterization, and computation of sub-basin parameters. Under the calculation of sub-basin parameters, there are two steps: calculation of sub-basin parameters and adding or deleting the reservoir. Lastly, the reservoir was added, and simulated outputs such as SED-IN (tons), SED-OUT (tons), and other hydrological components were reported in the output table of SWAT.

2.4 Hydrological modeling

A physically-based, semi-distributed SWAT model was established to simulate the influence of land use and land feature management practices on hydrological processes that are taking place in catchments [23]. The SWAT model, which runs on a continuous timescale, was utilized to replicate surface runoff, infiltration, percolation, channel routing, and shallow and deep aquifer flow [24]. The hydrologic routines replicated by the model are established by the water equilibrium equation:(1) SWt=SW0+∑i=1t(Pday−Qsur−Ea−Wseep−Qgw)

Where the measure of parameters on the ith day is in millimeters: SWt = final soil water, SWo = primary soil water, Pday = precipitation, Qsurf = surface runoff, Ea = evapotranspiration, Wseep = measure of water inflowing the vadose region from the soil face, Qgw = return flow or base flow, and t = period (days).

Erosion and sediment yield in SWAT are simulated with the Modified Universal Soil Loss Equation (MUSLE). The USLE uses rainwater as a marker of main force; MUSLE uses the quantity of overflow to simulate soil removal and sediment buildup [24].(2) Sed=11.8(Qsurf*qpeak*AHRU)0.45*KUSLE*CUSLE*PUSLE*LSUSLE*CFRG

That is: Sed = sediment produced on assumed diurnal (metric tons), Qsurf = overland runoff from the watershed (mm/ha), qpeak = highest runoff amount (m3/s), AHRU = extent of HRU, KUSLE = USLE soil erodibility influence, CUSLE = USLE land cover and management influence, PUSLE = USLE support practice influence, LSUSLE = USLE topographic influence and CFRG is the rough portion influence.

Using SWAT modeling, the predictive volume of sediment for the Gibe One reservoir and reaches were simulated. The maximum sediment transported from a reach was calculated by the Bagnold default equation in the SWAT model. This method determines the highest convey volume as a purpose of waterway slope and velocity [25]. The equation was acknowledged by Williams [26] based on stream power as defined by Bagnold [27]. The maximum conveyance capacity of a reach section is estimated by Equation:(3) concsed,ch,mx=csp·vch,pkSPEXP

Where concsed,ch,mx is the maximum sediment concentration transported by water (ton/m3), csp(SPCON) is the user defined sediment coefficient, vch,pk is the peak channel velocity (m/s), and SPEXP is the user defined coefficient in the exponential term.

Sediment buildup is categorized into classes: low (0–5), moderate (5–10), high (10–15), very high (15–30), severe (30–50), and very severe (>50) t ha−1yr−1 [28]. Sediment yield from watersheds and its buildup in reservoirs is usually high, indicating the importance of using sediment management.

2.5 Watershed delineation

Watershed delineation based on 30 m resolution DEM was used to delineate the sub-basins and analyze the drainage patterns of the land surface terrain. Delineation of basin boundary, sub-basins, sub-basin size and distribution, and channel network were performed by using the DEM in Arc SWAT. To make for more detailed drainage networks and a smaller number of HRUs, a smaller threshold area of 25,000 ha was provided and an outlet defined.

2.6 Hydrologic Response Units (HUR) and sub-basin analysis

The sub-basin discretization focused on the 3,039,243 ha of Gibe Gojeb catchment. The HRU analysis tool in Arc SWAT helped to load land use, soil layers, and slope. Using their lookup tables, the anticipated land use and soil map were entered into SWAT. After choosing the multiple slope option, the slope was defined into four ranges. The various options take into consideration combinations of 15 % slope threshold, 15 % soil, and 15 % land usage. Reclassification and overlaying of the land use, soil, and slope occurred. Defining HRU: a single HRU that takes into account each sub-basin's main land use, soil composition, and slope. The watershed was divided into 61 sub-basins and 559 Hydrologic Response Units (HRUs).

2.7 Sensitivity analysis, calibration, validation and model performance evaluation

The most reactive parameters that significantly persuade stream flow and sediment buildup were found separately using the universal sensitivity analysis [29,30]. The parameters were ranked using the iterations of t-stat and p-value. The higher absolute value of the t-stat implies greater sensitivity, while a p-value of zero shows greater significance.

The adjustment of model parameters within the endorsed arrays is to advance the replicated yield so it matches the observed data performed during model calibration. Examining the calibrated parameters along with an independent set of observed data with no extra changes to the parameters was executed during validation. The SUFI-2 algorithm set in SWAT-CUP 2012 was used through calibration and validation [31]. Statistical measurements for example, the coefficient of determination (R2) of 0.64–1.0 and Nash-Sutcliffe simulation efficiency (NSE) of 0.5–1.0 were used to assess the SWAT model performance [32,33]. Percent bias (PBIAS) measures the average tendency of the simulated data to be larger or smaller than their observed correspondences. Moriasi et al. [34] recommend percent bias (PBIAS) for stream flow: PBIAS< ±10 is very good, ±10 ⩽ PBIAS ⩽ ±15 is good, ±15 ⩽ PBIAS ⩽ ±25 is satisfactory, and PBIAS ⩾ ±25 is unsatisfactory. Similarly, PBIAS for sediment: PBIAS < ±15 is very good, ±15 ⩽ PBIAS ⩽ ±30 is good, ±30 ⩽ PBIAS ⩽ ±55 is satisfactory, and PBIAS ⩾ ±55 is unsatisfactory.

2.8 Sediment rating curve

A sediment rating curve was developed using the corresponding stream flow data because the suspended sediment data obtained from the Ministry of Water and Energy [35] is not continuous. According to Ref. [36], the sediment bearing curve is an association among the stream flow and the sediment buildup. The sediment data measured from the Gilgel Gibe River at Asendabo was used to derive a sediment assessment curve and time-series data for modification and justification.

To obtain consistent time step sediment data, the graph was established by relating daily stream flow (m3/s) and suspended sediment measures (mg/l) using the equation:(4) S=0.0864*Q*C

Here S is sediment buildup (ton/day), Q is stream flow (m3/s), C is sediment concentration (mg/l), and a value 0.0864 is a unit less coefficient for unit change.

The records of discharges transformed into sediment load after the rating curve has been established based on the following equation [17].(5) S=aQb

Here S is sediment buildup (ton/day) and Q is river flow (m3/s), a and b are regression constants. The sediment rating curve with an equation of 14.653Q1.211 and an R2 value of 0.8608 is shown in Fig. 4.Fig. 4 Sediment rating curve of Gilgel Gibe River at Asendabo.

Fig. 4

3 Results and discussions

3.1 Flow simulation

SWAT run, calibration, and validation were performed from 2003 to 2016, 2006 to 2011, and 2012 to 2016, respectively. Model sensitivity analysis was executed using SWAT-CUP through the SUFI-2 algorithm to detect the most sensitive flow parameters for calibration. The sensitivity analysis is carried out considering ten parameters, which were selected for evaluating their effects on stream flow. From the SWAT literature the parameters were established to have a considerable effect on streamflow with their optimum values [21,52].

The sensitive parameters and their values within the allowable ranges until a promise between observed and simulated flow were found. The Nash-Sutcliffe objective function was set during iteration. Sensitive parameters were selected and measured using the t-stat and p-values, based on the larger absolute t-stat and p-values closer to zero. The parameters considered for sensitivity analysis for stream flow modeling for the Gibe Gojeb catchment, their calibrating ranges, best fitted values, t-stat and p-values are indicated in Table 1.Table 1 Calibrated flow parameters.

Table 1Si. No	Parameters	Fitted value	Minimum	Maximum	t-Stat	p-value	
1	R_GW_REVAP.gw	0.37	0	1	6.95	0	
2	R_CN2.mgt	0.0164	−0.1	0.35	4.79	0	
3	V_GW_DELAY.gw	4.53	0	12	4.75	0	
4	R_SOl_K.sol	0.3145	−0.05	2	8.81	0	
5	R_SOl_AWc.sol	0.59	0	1	6.73	0	
6	R_ESCO.bsn	0.05	0.02	0.2	8.58	0	
7	R_CH_K2.rte	101.72	5	130	11.72	0	
8	R_SURLAG.bsn	6.62	0	12	7.93	0	
9	V_ALPHA-BF.gw	0.432	0	1	9.76	0	
10	R_CANMX.hru	80.37	0	100	14.74	0	

Besides, the parameters have the suffixes (.gw, .mgt, .sol, .bsn, .rte, .hru and prefixes (R_, V_). The suffixes indicate which group the parameters fit in to, i.e., (.gw, .mgt, .sol, .bsn, .rte, .hru signify groundwater, management, soil, basin and channel character, respectively. The prefixes R_ and V_ signify relative and replace, respectively. R_prefixed parameter, the primarily set parameter is to be multiplied by (1+ best-fitted value) and V_ prefixed parameter, their optimum values are to be replaced by a value from the given parameter range. The details of the suffixes, prefixes and their optimum values referred from literature [[53], [54], [55]] and SWAT web site (https://swat.tamu.edu/).

The hydrograph was derived for calibration and validation to see the simulated and observed flow values (Fig. 5). Calibration and validation display that SWAT has achieved relatively good agreement between observations and simulations.Fig. 5 Calibration (2006–2011) and validation (2012–2016) of observed and simulated flow.

Fig. 5

The SWAT model presented acceptable consistency between replicated and observed monthly river flows, resulting in NSE, R2, and PBIAS values of 0.70, 0.71, and 9.8 % for calibration and 0.72, 0.74, and 11.02 % for validation, respectively.

3.2 Sediment yield simulation

Eight parameters recognized as the most sensitive to changing sediment yields were soil erodibility, average slope steepness, support practice factor, channel sediment routing, cover and management factor, linear factor for channel sediment routing, initial residue cover, and channel erodibility factor (Table 2).Table 2 Calibrated sediment parameters.

Table 2Parameter	Parameter description	Range	Fitted value	t-Stat	p-value	
R_USLE_K.sol	Soil erodibility factor	0–0.65	0.28	3.76	0	
R_SLSUBBSN.hru	Average slope steepness	10–150	51.10	4.72	0	
R_USLE_P.mgt	USLE support practice factor	0–2	1.02	5.84	0	
R_SPEXP.bsn	Channel sediment routing	1–1.5	0.97	4.65	0	
R_USLE_C.plant.dat	USLE cover and management factor	0.001–0.5	0.07	5.61	0	
R_SPCON.bsn	linear factor for channel sediment routing	0.0001–0.01	0.001	5.32	0	
R_RSDIN.hru	Initial residue cover [kg/ha]	0–10000	46	8.12	0	
R_CH_COV1.rte	Channel erodibility factor	−0.05–0.6	0.34	8.06	0	

The hydrograph was derived to see the replicated and observed sediment yield values for calibration and validation (Fig. 6). The SWAT model has achieved acceptable agreement through calibration and validation for simulated and observed sediment yield.Fig. 6 Calibration (2006–2011) and validation (2012–2016) of simulated and observed sediment yield.

Fig. 6

Subsequent adjustments of the parameters were performed during iterations. The simulated and observed monthly sediment loads resulted in NSE, R2, and PBIAS values of 0.69, 0.70, and 8.2 % for calibration and 0.70, 0.73, and 9.2 % for validation, respectively.

3.3 Sub-basins contribution to sediment yield and runoff

Fig. 7 displays the sediment yield supply over 61 sub-basins in the Gibe Gojeb catchment. The intensity of sediment yield ranged from 0.2 to 106.1 tons ha−1yr−1. Sub-basins 2, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 37, 40, 44, 48, 53, 54, 57, 58, 59, 60, and 61 are falling under very severe; 1, 9, 38, 42, and 55 are falling under severe; 3, 6, 20, 36, 39, 41, 43, 47, and 56 are falling under very high; sub-basin 51 is falling under high; sub-basin 45 is falling under moderate; while sub-basins 31, 46, 49 and 52 are falling under low sediment yield classes. This shows how much the sub-basins are susceptible to erosion as a result of more intensive agricultural activity and a decline in land cover feature changes, generally less managed catchment. As a result, the condition enhances the sedimentation of Gibe One reservoir. This result agrees with Abban et al. [37] and Papanicolaou et al. [38] who reported that sediment yield and transport from hillslope erosion in intensively managed landscapes are developed and tested under different rainfall and land cover conditions, and they conveyed that there were differences in flow rate and sediment concentration during simulation.Fig. 7 SWAT simulated annual sediment yield of sub-basins.

Fig. 7

It is necessary to implement proper and best management practices in areas that are prone to sediment yield. The Gibe Gojeb catchment has an average yearly sediment output of 62.5 tons ha−1.

In the Gibe Gojeb catchment, the average yearly surface runoff amounts to around 318 mm yr−1. The SWAT model was used to assess each sub-basin's runoff supply in order to identify the primary sources of the catchment's overall runoff (Fig. 8). The following sub-basins contribute to the high surface runoff (from 500 to 594.3 mm yr−1): 5, 6, 8, 9, 10, 12, 13, 15, 18, 19, 23, 24, 26, 27, 28, 30, 32, 33, 35, 40, 48, 54, and 60.170–449 mm yr−1 is accounted for by the remaining sub-basins.Fig. 8 SWAT simulated annual surface runoff of sub-basins.

Fig. 8

Generally, surface runoff is the source of soil erosion. The velocity of surface runoff increases with movement, which exacerbates soil erosion. That is to say, the amount of sediment grows together with the scraping and dislodging of soil particles. In the Gibe Gojeb catchment, surface runoff and sediment loads are correlated. For example, the SWAT model simulation for the sub-basins 8, 27, 35, 40, 54, and 60 revealed incredibly high values for both sediment load and surface runoff.

The locations of erosion hotspots and the sources of sediment loading were identified using the SWAT model analysis throughout the 61 contributing sub-basins (Fig. 9). As an illustration, sub-basins 31, 45, 46, 49, 50, 51, and 52 are responsible for 9.3, 7.7, 1.4, 2.2, 1.6, 12.3, 2, and 0.2 tons ha−1 yr−1, in that order.Fig. 9 Sediment/erosion prone sub-basins.

Fig. 9

Sub-basins 1 to 30, 32 to 44, 47, 48, and 53 to 61 are the remaining ones, and they are responsible for sediment loads above 18 tons ha−1yr−1. Accordingly, soil regeneration measures depend on acceptable soil loss and sediment yield threshold levels. Based on different studies, the threshold levels vary for ecosystem services [39]; ecological zones in Ethiopia range between 2 and 18 tons ha−1yr−1 [40]. The acceptable soil erosion threshold value is 10 tons ha−1 yr−1 [41]. According to Tsegaye and Bharti [28], the tolerable sediment yield value of 10–15 tons ha−1 yr−1 is from low to high, and greater than 15 tons ha−1 yr−1 is from very high to very severe. The majorities of the Gibe Gojeb catchment’s sub-basins require water and soil conservation actions to lessen reservoir sedimentation, based on a variety of studies and assertions. Soil regeneration, water and soil protection actions must be taken to keep up crop production and to sustain the reservoirs' lives and livelihoods for a growing population.

3.4 Reservoir sedimentation

Gibe One is a power plant that has an lively capacity of 717 MCM and regulates a reservoir of 840 MCM. It was placed into operation in 2004 [22] and started generating power in the same year [42]. Gibe One reservoir is located on the main channel of the river Gilgel Gibe (Fig. 3) inside the catchment. Fig. 9 show that all the upstream sub-basins (36, 37, 38, 39, 41, 42, 43, 44, and 47) contribute flows and sediment loadings to the reservoir. The relative contributions from sub-basins 36, 37, 38, 39, 41, 42, 43, 44, and 47 are 24.7, 86.2, 32.7, 24.6, 18.1, 31.6, 22.9, 41.0, and 32.8 tons ha−1, respectively. These sub-basins' sediment flows into Gibe One reservoir, where it gathers. With the SWAT model, the reservoir volume from 2004 to 2016 was simulated.

The tons of sediment that the SWAT simulated entering and leaving the reservoir are displayed in Table 3. The reservoir volume was simulated by the SWAT model with 676 MCM, with a loss of 5.7 % after thirteen years of operation. The Gibe One reservoir's trap efficiency was assessed at 95.8 %. This finding is consistent with reports from White [3] and Yoon [43], who reported that the global decline in reservoir storage volume is estimated to be from 0.5 % to 1.0 % per year.Table 3 Simulated volume, and sediment in and out of the Gibe I reservoir.

Table 3Time series	Volume of Reservoir (MCM)	SED_IN (tons)	SED_OUT (tons)	
Jan	676	8797	454.9	
Feb	676	24930	623.6	
Mar	676	14380	593.1	
Apr	676	57730	1405	
May	676	115100	2528	
Jun	676	120200	2505	
Jul	676	91300	2683	
Aug	676	94020	3546	
Sep	676	65790	3985	
Oct	676	55810	4152	
Nov	676	36440	2019	
Dec	676	21130	1228	
SED_IN = sediment coming into the reservoir, SED_OUT = sediment leaving the reservoir, MCM = million cubic meters.

The SWAT model calculated a predicted average sediment load of 58,802 tons yr−1 for the Gibe One reservoir. Numerous studies confirm that sediment inflow to reservoirs and dams varies depending on their volumes and types. For example, the yearly sediment input into the Tarbela reservoir was approximately 200 million tons, whereas as of 2017, the yearly sediment input into the Besham Qila reservoir was estimated to be 147 million tons [44]. Reservoir sediment buildup is higher than other water forms because reservoirs retain a larger volume relative to the space of their catchment. Morris [45] also declared that sediment building would have eliminated more than thirty percent of the reservoir's volume by the center of the twenty-first century.

3.5 Discussions

The Gibe Gojeb catchment generates 62.5 tons of sediment ha−1year−1 on average, while the sediment load stored in Gibe One reservoir is 58,802 tons yr−1. This result is consistent with the findings of Ayele et al. [17], who found that the average sediment discharge entering Koga reservoir in the Nile basin in Northwestern Ethiopia was 58,010 tons yr−1. In a similar vein, Tsegaye and Bharti [28] reported that the 2254.5 tons of silt that are transported and dumped in the channels annually pose a threat to the Anjeb reservoir in the Anjeb watershed in Northwest Ethiopia's capacity to store water. Furthermore, 60,390 tons of sedimentation occur at the Shumburit dam in Northern Ethiopia on average each year, and the reservoir's yearly capacity drop is rated at 1.25 %, according to Endalew and Mulu [2]. They concluded that if the sediment deposition rate remains the same, the reservoir will not be functional for more than 15 years.

Water storage is hampered by reservoir sedimentation, especially in East Africa. All sectors and communities that rely on water storage for irrigation, electricity generation, and water supply have serious concerns about it. The condition of sediment buildup in reservoirs across Africa demonstrates that a significant proportion of reservoirs face significant sedimentation issues. Rising rates of erosion and sediment discharge were reducing the capacity of East African reservoirs to hold water and generate energy [46].

According to an assessment of the sediment deposits in reservoirs, about 25 % of all reservoirs have lost between 10 % and 30 % of their initial capacity for storage [47]. For example, the annual sediment buildup into Chimhanda reservoir, in Mazowe Catchmen, Zimbabwe, was likely to be 330 tons and reduced its capacity by 39 % from 2003 to 2015 [48]. Murera reservoir in Kenya has a sediment buildup volume of 117,683.39 m3, which indicates that the reservoir lost 14 % of its actual storage space [49]. It was anticipated that the Tuli-Makwe reservoir's gross volume from sediment buildup over a 47-year period (1966–2013) would be 3.371 Mm3, or a 40.8 % reduction in gross capacity [50].

Sediment accumulation in reservoirs has implications such as reducing reservoir capacity to store water to meet hydropower production, damaging a facility's mechanical components, affecting the ease of dams and the environment. Production and accumulation of sediment should be a fundamental pace when choosing the position and management approach for dams and the environment. Therefore, decreasing sediment yield in the course of erosion control and upstream sediment trapping could be the best sediment management approach.

Sediment transport dynamics to reservoirs are not given enough consideration in East African dam spatial planning, which is primarily driven by political and economic considerations [46]. A variety of studies should give relevant information to east African governments and dam designers about the development, implementation, and assessment of mitigation strategies aimed at addressing reservoir sedimentation problems.

3.6 Limitation of study

The study found certain limitations. The study catchment lacked time-series sediment data, thus the data were derived using the sediment rating curve. There's a chance the sediment output is underestimated by SWAT.

One potential constraint of the model is the potential underestimation of the concentrations of immediate suspended sediment from river channels. Another limitation is that the model may underestimate concentrations of instantaneous suspended sediment from river channels. Moreover, the model may have a modest association between forecasted and observed sediment produce, indicating that it may not accurately simulate sediment yields. Even so, it has been demonstrated that the SWAT model is useful for simulating sediment yields in various catchments, including the Finchaa Catchment [51], the Koka Reservoir [5], and the Koga Reservoir [17] in Ethiopia's Basins.

4 Conclusions

The study was conducted in a catchment characterized by highly populated and intense agricultural practices, resulting in the generation and transport of runoff and sediment loads. Assessing sediment deposition in the Gibe One reservoir, determining the high-yielding sub-basins in the Gibe Gojeb catchment, and analyzing the spatial distribution of sediment yield were the objectives of this study. We employed the SWAT model to analyze the spatial variability of sediment output and reservoir sedimentation and identify sediment-yielding hotspot sub-basins for future watershed management. For both river flow and sediment simulations, the model statistical performance evaluation fell within acceptable limits. There was good agreement between the measured and simulated data groups.

Potential sediment-yielding sub-basins were identified from SWAT model outputs. Annually, about 58,802 tons per year of sediment yield were transported from the basin and accumulated at Gibe One reservoir. Consistently, SWAT simulation indicated that Gibe One reservoir volume decline was rated at 5.7 % after thirteen years of operation. Sediment yields from the sub-basins cause significant problems such as a decline in reservoir storage volumes, dam safety, power production, and aquatic life. Based on the results, it is suggested that alleviation and intervention actions be arranged for reservoir sedimentation problems. The differences in sediment yield risk among sub-basins help concerned bodies identify and prioritize catchment areas that need urgent water and soil management actions. The study's findings highlighted how crucial it is to manage soil and water in order to satisfy sediment load plans. Findings would benefit the community and policymakers regarding water and soil administration options and serve as a typical model for other catchments where soil and water management actions may be implemented.

Funding statement

This research received no external funding.

Data availability statement

The authors declare that data associated with our study has been deposited into a publicly available repository since data was used for the research described in the article.

Additional information

No additional information is available for this paper.

Ethics approval and consent to participate

Not applicable.

CRediT authorship contribution statement

Hailu Gisha Kuma: Writing – review & editing, Writing – original draft, Validation, Software, Methodology. Ermias Mekonnen Chinasho: Writing – review & editing, Supervision, Resources, Project administration, Conceptualization. Abrham Asha Tolke: Resources, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare no conflict of interest.

Acknowledgments

We would like to thank Wolaita Sodo University for providing materials and resource supports for the study. The authors are pleased to thank the Ministry of Water and Energy for the provision of hydro-meteorological data.
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