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Article

Impacts of Land-Use Change, Associated Land-Use Area and Runoff on Watershed Sediment Yield: Implications from the Kaduna Watershed

by
Japheth Daramola
1,2,
Ekundayo J. Adepehin
3,*,
Toriman M. Ekhwan
1,*,
Lam K. Choy
1,
Jaafar Mokhtar
1 and
Tabiti S. Tabiti
2
1
Geography Program, Social, Environmental, Development, Sustainability Research Centre (SEEDS), Faculty of Social Sciences and Humanities, National University of Malaysia, Bangi 43600, SGR, Malaysia
2
Urban and Regional Planning Department, Federal Polytechnic Bida, Bida 912101, Nigeria
3
Department of Earth and Environmental Sciences, Faculty of Science and Technology, National University of Malaysia, Bangi 43600, SGR, Malaysia
*
Authors to whom correspondence should be addressed.
Water 2022, 14(3), 325; https://doi.org/10.3390/w14030325
Submission received: 22 October 2021 / Revised: 10 January 2022 / Accepted: 12 January 2022 / Published: 22 January 2022
(This article belongs to the Special Issue Inland Surface Water and Deep Learning)
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Versions Notes

Abstract

:
An uncontrolled sediment influx from the watershed upstream is a known threat to dam stability, while the pattern and amount of sediment yield are influenced by the predominant upstream land-use and land cover (LULC) types, precipitation amount, and intensity. Hence, the need to monitor sediment yield accumulation and its controlling factors in dam operation becomes crucial. In this paper, the Soil and Water Assessment Tool (SWAT) was used to assess the roles of land-use change, land cover area, and runoff on watershed’s sediment yield based on change detection analysis between 1975 and 2013 in the Kaduna Watershed (Nigeria), Western Africa. The SWAT standard procedures for the simulation of hydrological characteristics and sediment yields prediction were adopted. The datasets were calibrated for a period of 46 years and validated using 2015–2017 measured flow data, and suspended sediments concentration (SSC) acquired between March and October 2018. The model function was statistically determined using the Nash-Sutcliffe (NS), the coefficient of determination (r2) and the percentage of observed data (p-factor). The evaluation results of the SWAT model yielded NS, r2 and p-factor of 0.71, 0.80, and 0.86, respectively. These data suggest that the model performed satisfactorily for streamflow and sediment yield predictions. Findings suggest that the extinction of evergreen forests and a significant change in land-use from range grasses and forest to agriculture generic and residential types between 1975 and 2013, which resulted in surface runoff, sediment yield, and flow alteration. Evapotranspiration increased by 22.40% between 1975 and 2013. These changes have negatively impacted the watershed runoff by 56.00% and model sediment yield by 68.00% at the end of 2013. Thus, these variations can influence various human activities in the watershed, such as food security, livestock, energy production and water supply. It is hypothesized from the presented data that land use types exact a more dominant control on runoff and sediment yield than land cover area, although climatic influence may not be ruled out.

1. Introduction

Land-use land cover (LULC) information is an essential component in watershed modelling with regards to hydrology, sediment yield, and water quality within the basin area, because changes in LULC may result in significant modification of sediment yield pattern within the watershed. Additionally, it could lead to variation in the hydrological response of watersheds, thus impacting river flows [1,2]. Given constant hydrological variables, change in sediment yield can be attributed to the corresponding alteration in land use in the upstream catchment, as it gave rise to detrimental sedimentation in the Ethiopian Haramaya Lake [3].
The process of urbanization as an anthropogenic factor is a major cause of global LULC change, which often leaves irremediable consequences on the environment [4]. Hence, adequate knowledge of the processes involved in the alteration of LULC and careful operational management are of utmost importance. The geographical landmass of Nigeria plays residence to the largest population of black people globally, with an estimated population of ca. 200 million people [5]. For livelihood sustainability, the country has taken advantage of its well-drained and close river/stream networks to build several dams for different purposes (Figure 1). The Kainji (760 MW generating capacity) and Jebba (540 MW generating capacity) dams are situated on the River Niger, while the Ikere Gorge dam (690 million m³ reservoir capacity) and the Oyan dam (270 million m3 reservoir capacity) are situated on the Ogun River and Oyan River, respectively. Situated on the Kaduna River are the Shiroro (600 MW generating capacity) and Zungeru (700 MW generating capacity) dams.
Notwithstanding the abundance of qualitative and quantitative studies on dam management globally [1,3,7,8,9], there exist a paucity of reliable research data on dams in West Africa, especially in Nigeria [10]. This challenge is exacerbated by the erratic and inconsistent routine maintenance, and low commitment to research, hence, the need for holistic and realistic research datasets on this subject become very important. Morris and Fan defined reservoir sedimentation as the process through which stream sediments are transported and subsequently deposited in the reservoir upstream of the dam [7]. Stream sedimentation problem results due to increased delivery of uncontrolled upstream sediments into the basin [7]. As the dam on the river course accrues sediments into the reservoir, water volume steadily reduces [11]. Under extreme conditions, the water behind the dam exceeds the crest leading to an overflow, and by implication causing potential flooding downstream.
Although sediment yield modelling has gained wider attention in Earth and environmental sciences research, the scarcity of reliable data, difficulty in erosion measurement due to the largeness of watersheds, inadequate human and capital resources, land use types, and reservoir sedimentation are still common problems associated with watershed management in many developing nations [10,12,13,14,15,16]. The application of mathematically based geospatial techniques, which generally require lesser resources and saves time, without compromising accuracy can offer lasting and adequate solutions to some of these challenges, especially when verified by ground truthing [17,18]. Hence, the use of empirical models has gained general acceptance [17,18,19,20,21]. Of the empirical models that are based on geomorphologic parameters established for watershed soil erosion and sediment yield evaluation, the SWAT model is widely used due to its user-friendliness, accuracy, and precision [19,22,23,24,25,26].
The Kaduna watershed is of great importance to hydroelectric power generation in Nigeria (Figure 1). An estimated 70.00% of water influx into the 8 × 109 cubic meter reservoir capacity of the Shiroro hydropower dam is sourced from the Kaduna River alone, in addition to the inflows from its four major and eight minor tributaries [27]. The Shiroro dam at its optimal capacity is projected to supply energy to ca. 404,000 households in north-central Nigeria [16]. Annual sediment influx into River Kaduna is ca. 96 t/km2 (66.6 × 106) tons [28]. This connotes high sediments’ input from the dam’s upstream into the reservoir [29]. Mobilisation of muddy and sandy sediments from the upstream principally contributed to the 1999 and 2004 failures of the Shiroro dam [29,30]. A future failure of the dam will undoubtedly exacerbate the erratic power supply in the region. A detailed spatial and temporal scale analysis of LULC and current understanding of the hydrological processes that impact sedimentation upstream of the Shiroro reservoir, therefore, become necessary as a guide to provide expert advice to policymakers and concerned government agencies to prevent future failure. Hence, the objective of this paper is to determine the roles of land-use change, land-use area, and runoff on sediment yield into the Kaduna Watershed, North Central Nigeria, and to also identify the LULC types that generate the highest runoff and sediment load within the watershed using the Soil and Water Assessment Tool (SWAT). Consequently, this contributes to knowledge by making available dependable datasets that could be of help in managing dam sedimentation in sub-Saharan Africa.

2. Study Location

The Kaduna Watershed has an estimated 32,124 square kilometres of area, and it is situated in the upper section of the Niger River Basin in Nigeria (Figure 1). The entire Niger Basin covers an estimated area of 584,193 km2 accounting for approximately 63.00% of the 923,763 km2 landmass of the country. Alongside its main tributaries (the Benue, Sokoto and Kaduna Rivers), the Niger River is the major river network within the country. The drainage pattern of these rivers exhibits three hydrological characteristics: (1) the short and relatively swift rivers, which are found in the coastal region and drain into the Atlantic Ocean directly. They are associated with double peak flow and flood regimes, connected with the two peaks of rainfall associated with the region (Coastal area); (2) the long rivers, associated mainly with plateau. Rivers such as River Kaduna, Sokoto-Rima, Sarkinpawa, Dinya, Gutalu etc., characterized by one peak flow and flow pattern belong to this category; (3) the third category is in the catchment of the Niger and Benue rivers. They are very long rivers having several tributaries with complex flow pattern and depend on rainfall from neighbouring west Africa countries. The Niger River plays host to the Kainji Dam, which is the largest dam and the major source of hydroelectric power generation. Three other River Basins exist within the Nigeria landmass: the Lake Chad Basin, West Central Coast Basin, and the West Coast Basin. These three play significant roles in maintaining the hydrological balance of the entire country. Except for the Lake Chad Drainage System, all other drainage systems discharge into the Atlantic Ocean, thus forming a network of deltaic channels (Figure 1A). The vegetation of the watershed is predominated by tall grasses and scattered trees that are typical of the guinea savannah, although its contiguous northern region is characterised by Sudan savanna vegetation owing to its moderate rainfall regime [31]. The mean annual rainfall is generally higher in the south (1200 mm) than in the northern part (1000 mm) of the study area, and maximum rainfall in the region occurs for ca. 6 months, usually between April and September.
Geologically, the Kaduna Watershed is underlain by sedimentary and Precambrian basement rocks (Figure 1B). The northern and north-western reaches of the watershed are dominated by basement rocks, while the southernmost part flanks the Anambra Basin and the Central Benue Aulacogen [32,33]. The basement rocks are typified by igneous rocks such as granite, migmatite and metasedimentary materials (quartzite, schist, and marble) that are occasionally folded and broken up into mountain ranges [31]. Post-Santonian detritus of the intra-cratonic Bida Basin in north-western Nigeria constitute the geology of the watershed in the southwestern reach. A more detailed review of the geology of the study area was presented by Daramola et al. [34].

3. Materials and Methods

3.1. Data Collection

The research data includes a 30 m resolution digital elevation model (DEM) of the study area that was downloaded from Shuttle Radar Topography Mission (SRTM). Land-use and land cover data obtained from the conducted reconnaissance survey in the watershed were integrated with a 2 km resolution land cover classification map of Western Africa [35] and an extracted soil map of Nigeria with 1 km resolution from the FAO Soil database 2012. Meteorological datasets of precipitation (Table A1), relative humidity, wind, and solar radiation, as well as minimum and maximum temperature datasets obtained from the Shiroro dam metrological station (1990–2018) and the Nigeria Metrological Station (NIMET), Kaduna, Nigeria (1987–2018) also formed part of the input parameters. Additionally, streamflow data (2015–2017) of four major tributaries of the watershed obtained from the African Flood and Drought Monitor [36] were also used (Table A2). Suspended sediments were sampled using USDH-2A sampler in the tributaries of the Kaduna River for 8 months. Parameters such as total suspended solid (TSS), sediment concentration (SSC), total dissolved solid (TDS) and turbidity were determined from the suspended sediment using standard laboratory procedures. The streamflow data for 2015–2017 and the laboratory analysis result of SSC were used for the calibration and validation analysis.

3.2. Methodology

The SWAT model requires input data (Table 1) on the terrain, land use, soil and climate, and its setup involves two components: (1) a GIS system for storage and display of maps, performance of terrain analysis to delineate watersheds and identify associated sub-basins, and (2) a component that can generate all the files needed by SWAT, partly from the input maps and analyses, and partly by manual editing. SWAT model simulation of the hydrologic cycle today is centred on water balance equation [29,32]. The stream power equation embedded in the current version of the SWAT model was used for sediment routing in the derived channel [20,37,38,39,40,41,42]. All spatial data used were projected to the Universal Transverse Mercator Zone 32 Northern Hemisphere (UTM Zone 32N) that corresponds to the study location, and all input files were in metres. Automatic watershed delineation (AWD), hydrologic response unit creation, and SWAT input tables were generated following standard modelling procedures [24,38,39,43].

Calibration and Validation

The major input considered in this study include the Digital Elevation Model (DEM) and land-use and soil data (for watershed delineation), daily weather data (e.g., precipitation, minimum and maximum temperature, relative humidity, wind, and solar radiation), and streamflow and suspended sediment concentration (for validation and calibration using SWAT CUP). The best possible criteria for site selection and water sampling within the watershed area was followed [44]. Collected water samples were thereafter subjected to standard laboratory procedures to determine SSC (Table 2). To minimize the difference between the observed and simulated values, calibration and validation were done. The former involved model parameterisation and sensitivity analysis. Model parameterisation was actualised by using the Soil and Water Assessment Tool Calibration/Uncertainty or sensitivity program (SWAT-CUP SUFI 2). The DEM was used to derive the streamflow network, the land use, soil, and slope definition were used to create the HRU, while the laboratory SSC results was used for the calibration and validation of SWAT suitability and applicability alongside land-use change detection analysis using geostatistical parameters (e.g., coefficient of determination (r2) and Nash-Sutcliffe Efficiency (NS)) to ascertain the performance evaluation of the model. The 95% Prediction Uncertainty (95PPU) (p-factor) and r-factor (Ratio of Average thickness of the 95PPU) were used to evaluate the goodness of calibration and uncertainty analysis. Thereafter, the validation performance evaluation result was then used to rewrite the SWAT model for final model simulation of sediment and hydrological impacts such as evapotranspiration, water yield, surface runoff, streamflow, lateral flow, and groundwater flow.
The streamflow and sediment yield performance evaluations were executed using the spatial calibration and validation method. Land-use scenarios were not mimicked, but the ArcSWAT model was used for simulation after inputting the DEM, LULC, soil and metrological datasets. Water sampling from the four reaches (Sarkinpawa, Dinya, Kaduna and Gutalu basin) of the Shiroro reservoir was carried out for eight months (March–October 2018) (Figure 1C). Collected samples were analysed for suspended sediment concentration (SSC) and turbidity (Table A3). In the 2015 to 2018 streamflow data, SSC measurements formed the inputs data for the model’s validation. Results from the sensitivity analysis were adopted as a base for the streamflow calibration and validation using observed monthly streamflow hydrological data of the four tributaries. The results of the streamflow calibration and validation were used as a base for the calibration and validation of the sediment concentration using the SSC results obtained from the laboratory [45]. The validated results were used to rerun the model for 46 years, with three years’ warm-up period independently using the three land-use land-cover maps of 1975, 2000 and 2013 while keeping the other SWAT inputs constant.
For the spatial calibration and validation, the observed streamflow of sub-basins Sarkinpawa (69) and Dinya (83) between 2015 and 2017 were used for calibration of streamflow, and validation was carried out using the observed streamflow of Kaduna (62) and Gutalu (79). Sediment concentration of sub-basins Sarkinpawa (69) and Dinya (83) for March–October 2018 was used for the calibration, and validation was done using the observed sediment concentrations of sub-basins Kaduna (62) and Gutalu (79) for March–October to 2018 because of their similarity in climatic, soil, and land-use data. The model performance was evaluated using the Nash-Sutcliffe (NS), Coefficient of determination (r2) and percentage of observed data (p-factor) (Table 3) [9,25,39,46,47,48,49,50]. The validated results were used to re-write the SWAT file input tables, and the SWAT executable file was re-run to enable the model to obtain realistic information on the current state of the watershed.

4. Results and Discussion

4.1. Relationship between Land-Use Change and Land-Use Area Size

The variation in land-use between 1975 and 2013 is summarised in Figure 2. Analysis across the years 1975, 2000 and 2013 identified the conversion of evergreen forests, range grasses and wetlands mixed with mostly agriculture, urban development, and other forms of land-use types as the major anthropogenic activities that impacted the watershed area (Table 4). An estimated 0.03% of the watershed land area preoccupied with evergreen forests land use was lost predominantly to urbanisation and agriculture between 1975 and 2000 (Table 5). This observation aligns with earlier scholars who had investigated land use in other watersheds globally [51,52,53,54,55]. The period between 1975 and 2013 (38 years) witnessed 23.92% range grasses’ loss. This loss was mostly gained by agricultural land use, which increased by 15.51% between 1975 and 2000, and 12.30% between 2000 and 2013 (Table 4 and Table 5).
Similarly, wetlands-mixed gained 0.26% of watershed land area between 1975 and 2000, whereas it lost an estimated 0.08% watershed land area between 2000 and 2013, amounting to a cumulative gain of 0.18% at the end of 2013 (Table 5). An increase in land area submerged by water is indicated according to the recorded 0.33% and 0.20% areas gained from 1975 to 2000, and 2000 to 2013, respectively. Within the studied period, barren land and urban/residential land-uses both recorded gains of 0.02% and 0.46%, respectively. Agricultural land-use loss accounts for the documented urban/residential land use gain in the study area, thus agreeing with earlier studies that urbanisation encroaches and replaces arable farmlands [56,57,58,59]. Forest mixed and wetland forests also experienced cumulative watershed area losses within the 38 years (Table 5).
The period between 1975 and 2000 witnessed the extinction of evergreen forests land-use in the study area (Figure 2). This disappearance of evergreen forests is of concern, given the somewhat unregulated deforestation, and it partly corroborates Aruofor’s [60] postulation that 50.00% of forest land area in Nigeria would have been removed by the year 2015. Forest land losses have been attributed to subsistence agriculture, logging, and the collection of fuelwoods [61]. Increasing deforestation is a major contributor to atmospheric CO2, which is partly responsible for global warming, oceanic acidification, and depletion of the ozone layer [62,63,64]. According to Boucher et al., [65], tropical deforestation alone accounts for ca. 15.00% of the world’s global warming, and thus, poses more hazards than the total effect produced from automobiles, aircraft, boats, and ships on globally. Although the menace of deforestation has been documented in different countries and its potential hazards to the environment are also well known [66], the situation in Nigeria (evidenced by the data in this study) is alarming (Figure 2). FAO rated deforestation in Nigeria as the highest in the whole World between 2000 and 2005 [61]. This somewhat justifies the loss of forest land in the studied region since the year 2000 (Figure 2; Table 5). The extinction of evergreen forests and reduction in other land use types makes the soil susceptible to soil erosion, due to the direct impact of precipitation-induced runoff. Principally, agricultural land and residential area are the major beneficiaries of range grasses, evergreen forests, forest mixed, and wetlands forested watershed area losses (Table 5). These losses imply changes from the natural land cover to artificial land-use types through clearing and degradation of the natural landscape. Besides, the recorded loss of agricultural land to urban land use in the study area is supported by the report of Saleh et al., [67] that deep encroachment of urbanization in the Kaduna metropolis constitutes threats to future agricultural activities (Figure 2; Table 5). This is partly substantiated by the proximity of the nation’s Federal Capital Territory (Abuja) to Kaduna City.

4.2. Interplay of Land Use Change, Land Use Area Size, Runoff and Sediment Yield

The bivariate plots of percentage watershed area of land types and sediment yield in 1975, 2000 and 2013 are presented in Figure 3 to better constrain the relationship of the duos. These plots show approximated correlation coefficients of 71.00%, 48.10%, and 65.60% for the years 1975, 2000 and 2013, respectively (Figure 3). These coefficients indicate the total percentage of sediments generated across the different land-use types within the watershed explainable by the total watershed land area size. The documented correlation coefficients of 71.00% and 65.60% in 1975 and 2013, respectively, are suggestive of a good relationship between sediment yield and watershed land area size. Contrastingly, the documented r2 of ~48.10% in 2000 indicates a weak relationship between the watershed land area size and cumulative sediment yield across the different land-use types (Figure 3). The 29.00% (1975), 51.90% (2000) and 34.35% (2013) sediment inputs that were unaccounted for by the watershed area size are interpreted to be sourced from other anthropogenic activities, such as mining and industrial waste discharge that are known to take place within the area, although the SWAT model could not account for such.
These datasets show that land-use percentage in the watershed area has a somewhat significant impact on runoff and sediment yield, although the contributions of other factors to sediment yield may not be ruled out (Figure 3). Critical investigations, however, revealed that rangeland use constituted a stronger control on sedimentation than the size of the land-use area. The documented decrease in SSC with increasing basin land area corroborates the assertions of Griffiths et al., [68], Oyebande and Daramola et al. [69,70] (Table 6). This kind of relationship may have resulted from the interplay of steeper slopes and stream gradients (in smaller basins) [71], topography [72], and sediment sourced from hillslope and bank erosion [73]. However, Ichim [74], attributed such an inverse relationship in small basins to relief, higher precipitation and increase in erodible rock.
The rangeland use class consists of the steppe, i.e., an open intermittent herbaceous ground cover, occasionally mixed with shrubs and trees with inadequate cover to withstand severe anthropogenic activities. Our data identified rangeland and agriculture as the major contributors of sediments into the entire watershed, whereas “water”, which connotes stagnated “water bodies” in the model had no contribution to the influx of sediment into the watershed (see Figure 3 and Table 6). The consistent high rates of sediment yield in range grasses land across the three years can be attributed largely to the presence of little or no vegetal cover. These characteristics made the area susceptible to erosion activities. Though with a land area equivalent to only half of the rangeland, agricultural land use yielded significant sediment influx into the basin in 1975 (Table 6). This observation conforms with previously reported data by other authors [75,76,77].
Some studies have associated the loss of forest land with an increase in runoff and sediment yield and postulated that forest cover loss results in albedo changes, decrease in smooth roughness, a decrease of leafy areas and rooting depth [26,78]. Similarly, a decrease in evapotranspiration, which also affects streamflow has been associated with reduction of forest land [79]. Since streamflow is the principal sediment conveyor in the watershed, a direct variation between the runoff and sediment influx is construable [80,81,82]. Conversely, inverse relationships between agricultural land surface area, and each of runoff and sediment yield have also been suggested by others [37,83]. However, a careful correlation of the land surface area, runoffs and sediment yields across the three years (1975, 2000 and 2013) indicated that the area extent of land use is not a dominant influencer of runoff and sediment yield in the study area (Table 6). This is substantiated by the increasing influx of sediments contributed by rangeland, notwithstanding the noticeable decrease in rangeland area within the studied period (Table 6). Although the region witnessed an increase in agricultural land use between 1975 (113,790.10 km2) and 2013 (229,492.70 km2), the sediment yields generally reduced with increasing agricultural land-use area extent (Table 6).
Plots of surface runoff and sediment yield for the years under study were constructed to understand the relationship of the duos between 1975 and 2013 (Figure 4). The bivariate plots yielded correlation coefficients of 0.81, 0.76 and 0.67 for the years 1975, 2000 and 2013, respectively. Notwithstanding the strong correlations between sediment yield, and each of runoff (r2 = 0.81) and % watershed area (r2 = 0.71) in 1975 (Figure 3A and Figure 4A), the documented higher correlation coefficient in Figure 4A indicates that runoff has a stronger influence on sediment yield within the watershed than percentage land-use area (Figure 3 and Figure 4). This assertion is supported by the presented range grassland use/range grass (RNGE) data, having the highest runoff volume (12,022.2 mm), and the corresponding highest sediment yield (393.0 t/ha) as tabulated in Table 6. The year 2000′s result shows that sediment yield in RNGE reduced by 36.10% compared with the year 1975 (Table 6). This is interpreted to have resulted from the 21.30% reduction in runoff volume and 13.90% loss of watershed area to other land-use types.
Although the percentage of agricultural land use area increased by 15.51%, a downturn of 42.10% sediment yield was recorded, as runoff reduced by 10.00% (Table 6). These parameters validated the strong (r2 of 0.76) and moderate (r2 = 0.48) correlations between sediment yield and each runoff and percentage land-use area, respectively (Figure 4), thus further upholding the fact that an increase in the percentage of the watershed land area does not automatically translate to an increase in sediment input, and that the latter is highly dependent on runoff. This is better appreciated by the 58.3% reduction in sediment yield between 2000 and 2013 (Table 6). Although this could have been partly caused by the 10.00% loss of RNGE to other land-use types within the watershed, it is reasonable to conclude that the documented 86.90% reduction in runoff is conspicuously the major cause of the sharp downturn in sediment accumulation within the basin (Table 6). This analysis suggests the importance of the runoff factor over land area as regards sediment yield generation in the watershed.
Sediment yield was reduced in all the land-use types except “urban land” in the year 2013 (Table 6). A critical examination of the analysis results of land-use types and their corresponding impacts on hydrology shows that forested land-use were lost to other forms of land-use types within the studied period (see Table 5 and Figure 4). This loss of forested land use contributes to the global warming menace, and thus affects the entire watershed hydrology and sediment yield generation [84,85]. The hydrology and sediment inputs into the Kaduna Watershed is shown to be strongly controlled by land-use types and to a lesser extent their sizes within the period under study (Table 7). The simulated average annual sediment yield models for the entire simulation period did not account for the inlet sediment which is ideally an indicator of point source water pollution (Figure 5). These models show that average annual maximum and mean upland sediment yield (Mg/ha) into the Kaduna watershed consistently reduced across the three years, with values of 12.50 and 2.54, 9.07 and 1.27 and 8.32 and 1.04 for 1975, 2000 and 2013, respectively (Figure 5).
These data confirmed that land-use change and cover have a profound impact on the quality and quantity of water resources, as well as increased variability of hydrological components [77,86,87,88,89]. Additionally, the likelihood of flood, changes in runoff patterns, and alteration of streamflow regime that can alter the hydrological processes, such as groundwater recharge, infiltration, base flow, and runoff, have also been arrogated to land-use change [77]. In agreement with previous research [87,90] , our datasets show a consistent decrease in precipitation from 1975 to 2013, with a corresponding increase in evapotranspiration (ET) (Table 7), thus indicating high temperature within the watershed and leading to a reduction in the available total water yield, which is the differential balance between precipitation and evapotranspiration values. Water yield declined consistently from 1975 (603.67 mm) to 2013 (387.27 mm) (Table 7). These trends have negatively impacted the water resources in the watershed by reducing surface runoff which in turn affects groundwater recharge and sediment yield within the watershed, thereby giving way for climate change [85,87,91].
These modifications have impacts on the hydrological operations and the responses of the forest land and range grasses and their environs [92]. The average annual surface runoff values for the simulation period decreased progressively from 86.21 to 60.31 mm, leading to a 30% reduction as of the year 2000, while a percentage decrease amounting to 26.00% was documented between the years 2000 and 2013 (Table 7; Figure 5). This dataset corroborates the assertion of Roudier et al., [82], of a strong correlation between rainfall and runoff in West Africa. Gabiri, [93] also documented a reduction in annual water yield in Uganda, Eastern Africa, which led to a decrease in sediment yield from 2.54 t/ha as of 1975 to 1.27 t/ha in 2000 and 1.04 t/ha in 2013 about 68.00% reduction.
The groundwater shallow aquifer reduced from 489.86 mm in 1975 to 321.71 mm in 2000 and appreciates a little to 341.04 mm in 2013, whereas the groundwater deep aquifer dropped from 25.79 mm in 1975 to 16.82 mm in 2000 and to 0.00 mm in the year 2013 (Table 7). The regression analysis showed a strong correlation between runoff intensity, that is the surplus of rainfall intensity over infiltration capacity, and sediment yield for the three selected years of study, consequently alluding to the fact that runoff is the major determinant of sediment yield [81,94,95,96]. However, the correlation indicated a decline from 1975 (~81.30%) to 2013 (~67.20%), thereby upholding that runoff alone is not enough as a factor to determine sediment yield generation within the watershed. The fact that runoff was more pronounced in range grasses land-use type than any other land-use type made the sediment for range grasses higher than any other land use except wetlands forested in the year 2000 only (Table 6).

4.3. Sensitivity Analysis, Calibration, and Validation Datasets

The streamflow of reach Sarkinpawa is very well simulated as indicated by the NS of 0.71, r2 of 0.80, p-factor of 0.86, and r-factor (i.e., the ratio of the average thickness of the 95% prediction uncertainties—95PPU) of 5.50 (Table 3). These values, according to Moriasis et al., [97], are satisfactory since the NS exceeded 0.5, thus implying a good sediment prediction, although the documented 5.50 value for the r-factor is indicative of relatively large uncertainty that could not be captured by the model (Table 3). Basin Dinya on the other hand captured the river flow dynamics with NS (3.26), r2 (0.43), p-factor (0.61) and r-factor (0.77) values that suggest incongruities mostly in timing [9]. These could have resulted due to the diversity of water management and uses in the basin with 17 different HRUs. Notwithstanding the documented flow parameters’ inconsistencies in the Dinya sub-basin, the results are apposite for sediment simulation taking clues from the model guidelines of Abbaspour et al., [9].
The performance evaluation for the calibration of sediments concentration shows Nash-Sutcliff (NS) 0.01, Coefficient of Determination (r2) 0.53 and p-factor (0.88) for Sarkinpawa Basin (Table 3), which could be regarded as good data, although the low NS may have resulted from excessive sediments load, orchestrated by local mining activities that could not be adequately captured by the SWAT model [25]. Adeogun et al., [98], earlier reported a similar scenario at the riverbank of the River Kontagora from western Africa. However, the statistical summary of the model’s performance evaluation in the Dinya reach was highly satisfactory with NS of 0.91, r2 of 0.93 and p-factor of 1.00 (Table 3) [26,47]. The sediments validation results show statistical summary for the Kaduna sub-basin with NS (0.47), r2 (0.82), p-factor (0.63) and r2 (1.49) values that are suitable for modelling (Table 3) [47], and somewhat contrasting to the non-satisfactory dataset of the Gutalu sub-basin with NS of −0.11, r2 of 0.06, p-factor of 0.88, and r-factor of 2.96. The documented high r-factor of the flow and sediments calibration/validation resulted from model uncertainties, which may be in the form of (i) conceptual uncertainties (i.e., portioning method of SCS curve number), (ii) processes occurring within the watershed that were not included in the model (wetland, erosion mining, etc.) or (iii) processes included in the program that are unknown to the model users or cannot be accounted for due to data limitation [9,25].

4.4. Surface Runoff and Sediment Yield

The driving force behind SWAT processes is water balance because it influences sediment movement, plant growth, nutrients, pathogen, and pesticides within the watershed [39]. The quantification of important elements of water balance, such as precipitation, evapotranspiration, surface runoff, and lateral and base flow was carried out upstream of the Shiroro using the SWAT model. Evaporation and transpiration (Sum of ETmm) recorded the highest values of 812.9 mm (68.00%) of the water balance, while Lateral flow (Sum of Lat_Qmm) has 9.76 (1.00%) of the total rainfall, total aquifers recharge (Sum of GW_Qmm) 340.15, (28%), and runoff (Sum of SUR_Qmm) is 40.91 mm (3.00%) of total rainfall (Figure 6). These hydrological results have an impact on the sediment yield loading of the study area with a total average upland sediment loading of 0.98 t/ha.
The predicted highest sediment yields were in basins 67, 71 and 62 with sediment values of 79.40, 75.10 and 73.80 t/ha, respectively. The dominant LULC types in this basin are range grasses and agriculture. These LULC types make the basin susceptible to erosion. The lowest sediment yield values were recorded in basins 28, 63 and 68 with values of 16.3, 16.1 and 13.9 t/ha, respectively, with dominant agricultural LULC types. The percentage land area of agricultural land-use in the watershed in 1975 was 28.89%, and it increased to 56.70% by 2013. This might be responsible for the reduction in sediment yield in the watershed, because of the agricultural practice system of the area. Most of the farmers have now changed from extensive (nomadic herding and shifting cultivation) to specific and permanent systems such as terrace and compounds farming system [99]. The foremost advantage of terraced farming is the conservation of soil and water; it decreases water velocity and flow amount across the soil surface, which greatly reduces soil erosion [100]. A total sediment yield of 2438.358 t/ha was produced in all the basins during the 32 years simulation periods with 3 years warm-up period, and basin average sediment yield of about 0.98 t/ha/yr. The annual sediment yield production in the watershed is 84.1 t/ha/yr which translates to ~2.7 × 109 tons of sediment between 1990 and 2018 (Figure 7).

4.5. Implications for Land Use Policy and Dam Management

The data presented in this study has shown that catchment area land use types have unprecedented control on sediment yield, and thus constitute a potential threat to dam failure. Sedimentation adversely affects dam safety, and significantly diminishes energy production, storage capacity, discharge volume, as well as flood mitigation competencies [101]. It also increases the loads on the dam and gates, damages mechanical equipment and generates a host of environmental impacts. Thus, in addition to topography, site geology and foundation conditions that are conventionally taken into consideration when siting a dam, relevant government agencies, and private investors must consider the dominant land-use types in the watershed area. Land-use policies that regulate the land use types in the upstream catchment area of the watershed are also advocated. This is because different land-use types respond inevitably to upland erosion of the catchment area due to runoff.
An area with a preponderance of WETL—wetland mixed, WATR—water, BARR—Barren land, URBN—urban/residential, FRST—forest mixed or WETF—wetland forested would ideally generate a smaller influx of upland sediments into the dam, whereas catchment area predominated by RNGE and AGRL (agriculture land) would yield significant sediment into the dam, based on the findings of this research. Thus, it can be construed that the documented sediment yield of 84.10 t/ha/yr reported by Daramola et al., [16], into the Kaduna Watershed is mainly supplied by RNGE and AGRL. According to Lukman et al. sedimentation of suspended muddy and sandy materials significantly contributed to the two episodic failures of the Shiroro dam in 1999 and 2004. Similarly, the failure of the IVEX Dam, Chagrin River, Chagrin Falls, OH, USA in 1994 had also been partly linked to sediment impacts [102]. To mitigate future failures of dams, and thus the sustainability of resources, consistent and effective dam management activities are recommended. This may include significant efforts to (i) minimise dam-bound sedimentation through erosion control and upstream trapping of sediments [103], (ii) streamflow management during sediment yield upsurge to minimize sediment trapping within dam reservoirs [104], and (iii) excavation of trapped reservoir sediments [105].

5. Conclusions

The impacts of land-use change, associated land-use area size, and runoff on sediment yield were assessed based on data from the Kaduna Watershed in northcentral Nigeria. The study shows that forested land use was lost to other forms of land-use type between the years 1979 and 2013 within the watershed. Anthropogenic activities were identified as the major converter of evergreen forests, range grasses, and wetlands mixed mostly with agriculture, urban development, and other forms of land-use type. The maximum and average upland sediment yield values in the Kaduna Watershed consistently reduced by 12.50 Mg/ha and 2.54 Mg/ha, 9.07 Mg/ha and 1.27 Mg/ha, and 8.32 Mg/ha and 1.04 Mg/ha for the years 1975, 2000, and 2013, respectively. The conversion of range grasses to agricultural land does not amount to a rise in basinal sediment input, and runoff was identified as a major control on sediment yield. Data from this study also stressed that land-use area size does not contribute to notable increase in basin sedimentation. Conversely, increased basin sedimentation is strongly tied with land-use types. Principally, the research demonstrated that the range grasses are mostly prone to runoff, and by implication generate higher sediment yield than other land use types identified in the Kaduna Watershed. We recommend the conversion of the upland range grass area in the watershed to other low sediment-generating land types, thus reducing the quantity of dam-bound sediments and mitigating further failures. The simulated sediment yield models across the study period do not account for the inlet/sources sediment which is an indicator of point source water pollution. This contribution revealed that land erodibility, runoff intensity, and sediment downstream accumulations are more strongly dependent on land use types than on the size of the land use. The parameters driving the decrease in sediment yield in the watershed are precipitation, runoff and land use. Finally, the consistent decline in precipitation, vis-à-vis its accompanying of soaring ET values, suggest higher temperature and climate variation.

Author Contributions

Conceptualization, J.D., E.J.A. and T.M.E.; methodology, J.D. and E.J.A.; software, J.D., T.M.E., L.K.C. and J.M.; validation, J.D. and T.S.T.; resources, J.D., L.K.C. and T.M.E.; data curation, J.D. and E.J.A.; writing—E.J.A.; writing—review and editing, E.J.A. and J.D.; visualization, J.D. and E.J.A.; supervision, T.M.E., J.M. and L.K.C. All authors have read and agreed to the published version of the manuscript.

Funding

The work detailed here did not benefit from any sponsorship.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable. Relevant data used for this research are shown in the appendices. Further enquiries may be directed to the correponding auhors.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Rainfall records of Shiroro and Kaduna Meteorological stations 1990 to 2018.
Table A1. Rainfall records of Shiroro and Kaduna Meteorological stations 1990 to 2018.
YearPrecipitation ShiroroPrecipitation Kaduna
19901747.41036.1
19911368.9811371.063
19921419.71096.1
19931352.51244.5
1994749.6731066.9
19951662.7871151.6
19961151.3091217.2
19971209.9841293.6
1998899.5421109.4
1999841.5981286.1
20001150.321232.8
20011341.281188.2
20021169.61315.4
20031344.721418.05
20041014.811379.3
20051080.2461011.3
20061536.8898.7
20071403.1865
20081371.2827.9
20091365.91217.9
20101215.91276.3
20111236.51096.4
20121659.811491.57
20131039.11169.94
20141450.91246.24
20151219.62966.942
20161487.31352.004
20171176.91220.293
20181579.721266.539
Grand Total40,553.1037,722.16
Table
Table A2. Streamflow data of Rivers Kaduna, Sarkinpawa, Gutalu and Dinya in the years 2015–2017.
Table A2. Streamflow data of Rivers Kaduna, Sarkinpawa, Gutalu and Dinya in the years 2015–2017.
YearMonthR. Kaduna StreamflowR. Sarkinpawa StreamflowR. Gutalu StreamflowR. Dinya Streamflow
2015January1.2610.1740.1470.147
2015February1.1410.1450.1550.155
2015March10.0091.7542.5432.543
2015April2.3320.3280.8810.881
2015May46.2494.3435.2765.276
2015June127.2527.778.5878.587
2015July226.24814.33712.78612.786
2015August260.27531.85428.73828.738
2015September583.37732.65428.40928.409
2015October173.0737.8887.5237.523
2015November8.0910.5170.5830.583
2015December3.1880.3140.2010.201
2016January2.0490.2130.1490.149
2016February1.4140.160.1110.111
2016March19.8272.8123.3143.314
2016April22.5197.66815.69815.698
2016May25.8358.0917.8547.854
2016June23.95616.17418.24518.245
2016July29.2621.98416.94716.947
2016August32.21332.31130.15330.153
2016September35.77824.99325.95625.956
2016October38.525.7068.7358.735
2016November12.2912.6121.6521.652
2016December1.7640.3110.2320.232
2017January1.2780.2230.1580.158
2017February0.9680.1690.1270.127
2017March1.8580.1930.1140.114
2017April23.0612.833.4043.404
2017May196.36313.96814.68914.689
2017June269.64921.01625.29925.299
2017July176.0310.24512.20512.205
2017August485.27529.38733.05333.053
2017September421.12228.47131.81831.818
2017October124.4413.694.2054.205
2017November2.9760.3950.4670.467
2017December1.9010.2890.2230.223
Table
Table A3. Temporal variation of suspended sediment concentration (mg/L) of Kaduna watershed.
Table A3. Temporal variation of suspended sediment concentration (mg/L) of Kaduna watershed.
MonthSarkinpawaKadunaDinyaGutalu
March17.514.553.513.5
April15.513.56411.5
May42765852
June116136178125
July283268368386
August1374.513661452.51196
September590515350370
October405500255340
Total384438893781.53444

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Water 14 00325 g001 550
Figure 1. Study location: (A) Annotated map of Nigeria showing the Kaduna watershed and other basins, (B) Geological map of Nigeria showing areas overlaid by sedimentary basins (SB) and basement rocks (BR)- Adapted from Ola and Adepehin 2017 [6], (C) Map of the Kaduna Watershed showing the Shiroro Dam, major tributaries, delineated reaches, sampling points, and gauge stations.
Figure 1. Study location: (A) Annotated map of Nigeria showing the Kaduna watershed and other basins, (B) Geological map of Nigeria showing areas overlaid by sedimentary basins (SB) and basement rocks (BR)- Adapted from Ola and Adepehin 2017 [6], (C) Map of the Kaduna Watershed showing the Shiroro Dam, major tributaries, delineated reaches, sampling points, and gauge stations.
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Figure 2. Comparative maps showing land use variation across 38 years. Note the relative increase in the area covered by agricultural land (AGRL), vis-à-vis the general decrease of range land (RNGE) and wetland forest (WETF) between 1975 and 2013 in the study area.
Figure 2. Comparative maps showing land use variation across 38 years. Note the relative increase in the area covered by agricultural land (AGRL), vis-à-vis the general decrease of range land (RNGE) and wetland forest (WETF) between 1975 and 2013 in the study area.
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Figure 3. Bivariate relationship between % watershed area and sediment yield. Note the differences in correlation coefficients (R2) in the three years. (A–C) represent the scenarios in 1975, 2000 and 2013, respectively.
Figure 3. Bivariate relationship between % watershed area and sediment yield. Note the differences in correlation coefficients (R2) in the three years. (A–C) represent the scenarios in 1975, 2000 and 2013, respectively.
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Figure 4. Bivariate relationship between runoff and sediment yield. Note the differences in correlation coefficient (R2) values in the three years. (A–C) represent the scenarios in 1975, 2000 and 2013, respectively.
Figure 4. Bivariate relationship between runoff and sediment yield. Note the differences in correlation coefficient (R2) values in the three years. (A–C) represent the scenarios in 1975, 2000 and 2013, respectively.
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Figure 5. Simulated sediment yield models for the Kaduna Watershed between 1975 and 2013. Note the consistent reduction in the upland sediment yield values from 1975 to 2013.
Figure 5. Simulated sediment yield models for the Kaduna Watershed between 1975 and 2013. Note the consistent reduction in the upland sediment yield values from 1975 to 2013.
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Figure 6. Pie-diagram showing the quantification of the basin hydrological processes (except precipitation). Note that evaporation and transpiration (Sum of ETmm) have the highest share of 812.9 mm (68%) of the water balance, while lateral flow (Sum of Lat_Qmm) has 9.76 (1%) of the total rainfall. Total aquifer recharge (Sum of GW_Qmm) is 340.15 (28%) and runoff (Sum of SUR_Qmm) constitute 40.91 mm (3%) of total rainfall.
Figure 6. Pie-diagram showing the quantification of the basin hydrological processes (except precipitation). Note that evaporation and transpiration (Sum of ETmm) have the highest share of 812.9 mm (68%) of the water balance, while lateral flow (Sum of Lat_Qmm) has 9.76 (1%) of the total rainfall. Total aquifer recharge (Sum of GW_Qmm) is 340.15 (28%) and runoff (Sum of SUR_Qmm) constitute 40.91 mm (3%) of total rainfall.
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Figure 7. Basins within Kaduna watershed showing sediment yield (t/ha). The map shows the classification of the basin into five categories based on sediment yield generation. Land area in green has the lowest sediment yield while places in red colour generate the highest sediment yield.
Figure 7. Basins within Kaduna watershed showing sediment yield (t/ha). The map shows the classification of the basin into five categories based on sediment yield generation. Land area in green has the lowest sediment yield while places in red colour generate the highest sediment yield.
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Table
Table 1. SWAT model input data.
Table 1. SWAT model input data.
Data TypeDescriptionResolutionSource
WeatherPrecipitation, Min. and Max. Temperature, Relative Humidity, Wind and Solar RadiationDailyShiroro Dam Meteorological station and NIMET Kaduna
TopographyDigital Elevation Model30 mShuttle Radar Topography Mission (SRTM)
Land Cover MapLand cover classification20 mThe European Space Agency (ESA) Sentinel-2 Satellite Observations
Land Cover MapLand cover classification2 kmU.S. Geological Survey Earth Resources Observation and Science (USGS EROS)
Soil MapSoil types and texture1 kmFAO Digital Soil database map of the World
StreamflowMonthly2015–2017African Flood and Drought Monitor
Table
Table 2. Geographic location of sample points/station for calibration.
Table 2. Geographic location of sample points/station for calibration.
S/nReach NameLatitudeLongitudeData Details
1.Kaduna10.1005096.883008Flow/Rainfall/SSC
2.Sarkinpawa10.0628516.934190Flow/Rainfall/SSC
3.Gutalu9.9027736.883824Flow/Rainfall/SSC
4.Dinya9.8930156.853076Flow/Rainfall/SSC
Table
Table 3. Calibration and validation results of the streamflow and sediment concentration across the tributaries of the River Kaduna.
Table 3. Calibration and validation results of the streamflow and sediment concentration across the tributaries of the River Kaduna.
Sampling PointsFlow CalibrationSampling PointsFlow Validation
NSr2p-Factorr-FactorNSr2p-Factorr-Factor
Kaduna (69)0.620.670.974.70Sarkinpawa (69)0.710.800.865.50
Gutalu (83)−0.300.400.610.73Dinya (83)−0.370.430.610.77
Sampling PointsSediment CalibrationSampling PointsSediment Validation
NSr2p-Factorr-FactorNSr2p-Factorr-Factor
Sarkinpawa (69)0.010.530.882.25Gutalu (79)−0.110.060.882.96
Dinya (83)0.910.931.007.57Kaduna (62)0.470.820.631.49
Note: Nash-Sutcliffe is NS, Coefficient of determination is r2, percentage of observed data is p-factor, while r-factor is the ratio of the average thickness of the 95% prediction uncertainties (95PPU).
Table
Table 4. Changes in land-use caused by anthropogenic activities between 1975–2013.
Table 4. Changes in land-use caused by anthropogenic activities between 1975–2013.
Land-Use TypesLand-Use CodeArea [ha] 1975% Watershed 1975Area [ha] 2000% Watershed 2000Area [ha] 2013% Watershed 2013
Forest—EvergreenFRSE999.450.030.000.000.000.00
Range—GrassesRNGE1,819,805.4956.650.0042.721,051,344.0032.73
Wetlands—MixedWETL26,519.360.8335,061.071.0932,286.051.01
Agricultural Land—GenericAGRL928,049.8628.890.0044.400.0056.70
WaterWATR8847.520.2819,708.460.6125,980.970.81
BarrenBARR19,069.080.5918,628.460.5819,593.870.61
ResidentialURBN16,950.010.5342,377.781.3257,211.341.78
Forest—MixedFRST144,708.674.5095,345.632.9778,382.102.44
Wetlands—ForestedWETF247,513.447.70202,678.846.31126,353.303.93
Table
Table 5. Percentage changes in the land-use area in the watershed between 1975 and 2013. Note the significant losses of evergreen forests, range grasses, forest mixed and wetland forest, and the converse gain in other land-use covers.
Table 5. Percentage changes in the land-use area in the watershed between 1975 and 2013. Note the significant losses of evergreen forests, range grasses, forest mixed and wetland forest, and the converse gain in other land-use covers.
Land Use Landcover1975–2000
% Watershed Land Area		2000–2013
% Watershed Land Area		Total
% Watershed Land Area		% Balance 2013Remark
TypesCodeLossGainLossGainGainLoss
Forest—EvergreenFRSE0.030.000.000.000.000.030.00Lost
Range—GrassesRNGE13.930.0010.000.000.0023.9232.73Lost
Wetlands–MixedWETL0.000.260.080.000.180.081.01Gain
Agricultural LandAGRL0.0015.510.0012.3027.810.0056.70Gain
WaterWATR0.000.330.000.200.530.000.81Gain
BarrenBARR0.010.000.000.030.020.010.61Gain
ResidentialURBN0.000.790.000.461.250.001.78Gain
Forest—MixedFRST1.530.000.530.000.002.062.44Lost
Wetlands—ForestedWETF1.390.002.380.000.003.773.93Lost
Table
Table 6. Relationship between land use type sizes and sediment yield in the study area. Note the consistent higher runoff (mm) and sediment yield (T/ha) values in rangeland (RNGE) compared to agriculture (AGRL) regardless of its smaller sizes across the three years. Note also that the tabulated values are average annual values for the simulation period.
Table 6. Relationship between land use type sizes and sediment yield in the study area. Note the consistent higher runoff (mm) and sediment yield (T/ha) values in rangeland (RNGE) compared to agriculture (AGRL) regardless of its smaller sizes across the three years. Note also that the tabulated values are average annual values for the simulation period.
Land Use1975 AREA (km2)2000 AREA (km2)2013 AREA (km2)1975 Runoff (mm)2000 Runoff (mm)2013 Runoff (mm)1975 SYLD (T/ha)2000 SYLD (T/ha)2013 SYLD (T/ha)
AGRL113,790.10179,463.80229,492.709271.208343.401106.50329.80190.7093.50
BARR1015.101053.01052.00611.30328.5094.5050.9020.609.50
URBN849.002925.303324.601281.301569.70963.707.802.606.70
FRST12,623.304983.804660.704642.502148.90347.1062.3017.9017.40
RNGE226,716.0172,722.40132,990.1012,022.209466.601244.60393.00251.30104.70
WATR152.70153.001086.300.000.000.000.000.000.00
WETF29,920.2023,680.2012,531.404409.805019.10845.70307.0278.9072.80
WETL428.90514.10357.90712.90546.3023.7022.3012.802.90
r2 81.00%76.00%67.00%
Note: RNGE—range grasses, WETL—wetland mixed, AGRL—agriculture land, WATR—water, BARR—Barren land, URBN—urban/residential, FRST—Forest mixed, WETF—wetland forested.
Table
Table 7. Annual summary and percentage differences of important hydrological parameters.
Table 7. Annual summary and percentage differences of important hydrological parameters.
Item1975200020131975–2000
% < or >		2000–2013
% < or >		Total %
< or >
Precipitation (mm)1225.201198.101174.40<2.20%<2.00%<4.20
Surface runoff q (mm)86.2160.3144.68<30.00%<26.00%<56.00%
Lateral soil q1.811.631.55<10.00%<5.00%<15.00%
Groundwater (shal aq) q (mm)489.86321.71341.04<34.00%>6.00%<30.00%
Groundwater (deep aq) q (mm)25.7916.820.00<35.00%<100.00%<100.00%
Revap (shal aq soil/plants) (mm)43.5128.6728.54<34.00%<0.50%<34.5%
Deep aq recharge (mm)25.7816.940.00<34.00%<100.00%<100.00
Total aq recharge (mm)515.61338.79341.01<34.00%>0.70%<34.70%
Total water yld (mm)603.67400.48387.27<34.00%<3.3%<37.30%
Percolation out of soil (mm)513.02351.43338.37<31.00%<3.70%<34.70
ET (mm)624.10785.40796.30>21.00%>1.40%>22.40%
PET (mm)2196.602933.302860.90>25.00%<2.50%>23.00%
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Daramola, J.; Adepehin, E.J.; Ekhwan, T.M.; Choy, L.K.; Mokhtar, J.; Tabiti, T.S. Impacts of Land-Use Change, Associated Land-Use Area and Runoff on Watershed Sediment Yield: Implications from the Kaduna Watershed. Water 2022, 14, 325. https://doi.org/10.3390/w14030325

AMA Style

Daramola J, Adepehin EJ, Ekhwan TM, Choy LK, Mokhtar J, Tabiti TS. Impacts of Land-Use Change, Associated Land-Use Area and Runoff on Watershed Sediment Yield: Implications from the Kaduna Watershed. Water. 2022; 14(3):325. https://doi.org/10.3390/w14030325

Chicago/Turabian Style

Daramola, Japheth, Ekundayo J. Adepehin, Toriman M. Ekhwan, Lam K. Choy, Jaafar Mokhtar, and Tabiti S. Tabiti. 2022. "Impacts of Land-Use Change, Associated Land-Use Area and Runoff on Watershed Sediment Yield: Implications from the Kaduna Watershed" Water 14, no. 3: 325. https://doi.org/10.3390/w14030325

APA Style

Daramola, J., Adepehin, E. J., Ekhwan, T. M., Choy, L. K., Mokhtar, J., & Tabiti, T. S. (2022). Impacts of Land-Use Change, Associated Land-Use Area and Runoff on Watershed Sediment Yield: Implications from the Kaduna Watershed. Water, 14(3), 325. https://doi.org/10.3390/w14030325

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