Habtamu Assaye

Jan Nyssen

| Jean Poesen

Hanibal Lemma

| Derege

Tsegaye Meshesha

Alemayehu Wassie

| Enyew Adgo

| Deribew Fentie

| Amaury Frankl

Event-based runoff and sediment yield dynamics and controls in the subhumid headwaters of the Blue Nile, Ethiopia

Keywords: Badlands, rock fragment cover, soil erosion, soil organic matter, stone bunds

Soil erosion is a major environmental problem in the humid highlands of Ethiopia. Here, we studied the effect of land use types and management on the runoff and erosion response of micro-catchments. We emphasised

| INTRODUCTION

Soils provide a wide range of ecosystem services, such as the provision of food and fibre, carbon sequestration, and flood regulation (FAO, 2015). Soil erosion by water (i.e. water erosion) has been identified as the most important soil degradation process globally, threatening food security and sustainable development in many regions, especially in the sub-humid tropics characterised by a steep mountainous topography, erosive rains, and poor land use and management practices [START_REF] Ebabu | Analyzing the variability of sediment yield : A case study from paired watersheds in the Upper Blue Nile basin , Ethiopia Geomorphology Analyzing the variability of sediment yield : A case study from paired watersheds in the Upper Blue Nile basin[END_REF][START_REF] Haregeweyn | Soil erosion and conservation in Ethiopia: A review[END_REF][START_REF] Lemma | Identifying erosion hotspots in Lake Tana Basin from a multisite Soil and Water Assessment Tool validation: Opportunity for land managers[END_REF][START_REF] Nyssen | Land degradation in the Ethiopian Highlands[END_REF][START_REF] Wuepper | Countries and the global rate of soil erosion[END_REF]. The off-site effects of soil erosion are considerable, causing flash floods, pollution of water bodies, and rapid sedimentation of lakes and reservoirs [START_REF] Lemma | Identifying erosion hotspots in Lake Tana Basin from a multisite Soil and Water Assessment Tool validation: Opportunity for land managers[END_REF].

Most studies on water erosion are based on measurements from runoff plots or from large river catchments [START_REF] Poesen | Soil erosion in the Anthropocene: Research needs[END_REF]. Much less information is available on small catchments in headwater areas for which topography, soil, land use, cover, and management are rather homogeneous and, therefore, correspond to land units relevant for tailored land restoration initiatives. A recent study showed that land management interventions such as stone bunds and vegetation rehabilitation on steep slopes have significantly reduced catchment runoff [START_REF] Assaye | Curve number calibration for measuring impacts of land management in sub-humid Ethiopia[END_REF]. Besides the effect of land use and cover on water erosion dynamics, of particular interest is the effect of land management practices in cropland on soil characteristics (e.g. organic matter content or rock fragment cover; RFC) and their consequent effects on water erosion dynamics. For example, soil organic matter (SOM) content has been shown to have an important impact on catchment hydrology, as it enhances soil structural porosity and hence infiltration capacity [START_REF] Yimer | Effects of different land use types on infiltration capacity in a catchment in the highlands of Ethiopia[END_REF]. The relationship between RFC and sediment yield (SY) can generally be expressed by an exponential decay function [START_REF] Nyssen | Dynamics of soil erosion rates and controlling factors in the Northern Ethiopian highlands -towards a sediment budget[END_REF][START_REF] Poesen | Rock fragments in top soils : significance and processes[END_REF]Poesen et al., 1994).

However, few studies have analysed the effects of these factors on soil erosion in sub-humid tropical environments.

In northwest Ethiopia, as in many other subtropical areas, poor land use and management practices have severely accelerated soil erosion [START_REF] Borrelli | An assessment of the global impact of 21st century land use change on soil erosion[END_REF]. Growing demands driven by a fast population growth [START_REF] Bruin | Tackling Complexity: Understanding the Food-Energy-Environment Nexus in Ethiopia's Lake Tana Sub-basin[END_REF][START_REF] Minale | Population and environment interaction : the case of gilgel abbay catchment , northwestern Ethiopia[END_REF] have been largely fulfilled in the past by expanding cropland areas at the expense of forests, shrublands, or grazing lands, even on steep slopes [START_REF] Zeleke | Implications of Land Use and Land Cover Dynamics for Mountain Resource Degradation in the Northwestern Ethiopian Highlands Implications of Land Use and Land Cover Dynamics for Mountain Resource Degradation in the Northwestern Ethiopian Highlands[END_REF]. Such conversions have exposed the top soils to erosive rains, resulting in high erosion rates [START_REF] Wassie | Natural resource degradation tendencies in Ethiopia: a review[END_REF]. Consequently, most of the areas affected by severe soil erosion are associated with very high population densities [START_REF] Haregeweyn | Comprehensive assessment of soil erosion risk for better land use planning in river basins: Case study of the Upper Blue Nile River[END_REF].

The loss of fertile topsoil has gradually led to increased soil compaction, and thereby, reduced soil infiltration capacity [START_REF] Yimer | Effects of different land use types on infiltration capacity in a catchment in the highlands of Ethiopia[END_REF] and an increase in runoff volume. The runoff erosivity further aggravates the rates of soil erosion [START_REF] Rodríguez-Blanco | Réponse pluie-débit et coefficients de ruissellement événementiels dans une région humide (nordouest de l'Espagne)[END_REF]. Ethiopia accounts for half of the total soil erosion in East Africa, although it covers only 18% of the region's area [START_REF] Fenta | Land susceptibility to water and wind erosion risks in the East Africa region[END_REF].

The rate of soil erosion in Ethiopia shows a wide range of spatial variability and is among the highest in the world [START_REF] Haregeweyn | Soil erosion and conservation in Ethiopia: A review[END_REF][START_REF] Haregeweyn | Comprehensive assessment of soil erosion risk for better land use planning in river basins: Case study of the Upper Blue Nile River[END_REF]. In a pioneering study, [START_REF] Hurni | Soil Erosion and Soil Formation in Agricultural Ecosystems : Ethiopia and Northern Thailand: Mountain Research and Development[END_REF] estimated the annual soil loss by water erosion at approximately 1.5 billion Mg, with an average of 42 Mg ha -1 yr -1 on croplands. Several other studies reported annual soil loss in the range of 6-200 Mg ha -1 yr -1 [START_REF] Taye | Effects of land use, slope gradient, and soil and water conservation structures on runoff and soil loss in semi-arid Northern Ethiopia[END_REF][START_REF] Adugna | Soil erosion assessment and control in Northeast Wollega[END_REF]Adimasu et al., 2014;[START_REF] Bewket | Assessment of soil erosion hazard and prioritization for treatment at the watershed level: case study in the Chemoga Watershedm Blue Nile Basin, Ethiopia. Land. Land Degrad[END_REF], which is much higher than the tolerance limit range of 5-11 Mg ha -1 yr -1 [START_REF] Hurni | Soil Erosion and Soil Formation in Agricultural Ecosystems : Ethiopia and Northern Thailand: Mountain Research and Development[END_REF]). The highest rates of soil erosion (up to 79 Mg ha -1 yr -1 ) were recorded for the subhumid environments [START_REF] Bewket | Assessment of soil erosion in cultivated fields using a survey methodology for rills in the Chemoga watershed, Ethiopia. Agriculture[END_REF][START_REF] Steenhuis | Predicting discharge and sediment for the Abay (Blue Nile) with a simple model[END_REF]. Most studies, however, focus on agricultural land, whereas soil erosion in other land use types and with different land management practices is underrepresented. For instance, physical erosion control structures such as stone bunds, terraces, diversion channels, and grassed waterways have been implemented extensively over the last few decades [START_REF] Abera | Characterizing and evaluating the impacts of national land restoration initiatives on ecosystem services in Ethiopia[END_REF][START_REF] Wolka | Effects of soil and water conservation techniques on crop yield, runoff and soil loss in Sub-Saharan Africa: A review[END_REF]. However, subsequent impact studies were lacking and, hence, actual achievements were not sufficiently recognised and reliable conclusions could not be drawn [START_REF] Haregeweyn | Soil erosion and conservation in Ethiopia: A review[END_REF][START_REF] Tamene | Soil structural degradation and nutrient limitations across land use categories and climatic zones in Southern Africa[END_REF].

Therefore, in this study, we investigated the effects of land use type and land management practices on water erosion in the headwater areas of the Lake Tana Basin in sub-humid NW Ethiopia. We do this by considering zero-order catchments, that is, catchments which have no channel and only sporadically produce runoff and SY following rainfall events, but have been overlooked in most watershed and hydrology studies [START_REF] Storey | Effects of Development on Zero-order Streams in the Waikato Region[END_REF][START_REF] Tsuboyama | Estimation of temporal variation in splash detachment in two Japanese cypress plantations of contrasting age[END_REF]. The specific objectives are: (i) to analyse event-based runoff and SY for zero-order catchments for the major land use types in NW Ethiopia (i.e., natural forests, plantations, croplands, grazing lands, and badlands) and (ii) to identify factors controlling the erosion rates.

| MATERIALS AND METHODS

| Study area

This study was conducted in the Enkulal catchment (Figure 1; 11°37-11°39' N, 35°46'-37°49' E), representative of the headwaters of the eastern Lake Tana Basin in terms of topography, climate, soil, land use, and management. The catchment drains to the Gumara River, which is a major sediment contributor to Lake Tana [START_REF] Lemma | Identifying erosion hotspots in Lake Tana Basin from a multisite Soil and Water Assessment Tool validation: Opportunity for land managers[END_REF][START_REF] Lemma | Consolidated sediment budget of Lake Tana, Ethiopia (2012-2016)[END_REF]. It covers an area of 10.4 km 2 with an elevation ranging from 2229 m to 2561 m above sea level (a.s.l.). The average annual precipitation is 1250 mm, and the daily average air temperature ranges from 18 °C to 25 °C [START_REF] Adem | Predicting Reference Evaporation for the Ethiopian Highlands[END_REF][START_REF] Temesgen | Population dynamics and land use/land cover changes in Dera District, Ethiopia[END_REF]. The geological composition of the catchment consists mainly of basalt, with minor deposits of volcanic ash (ignimbrite) [START_REF] Poppe | Geomorphology of the Lake Tana Basin, Ethiopia[END_REF].

Young soils prevail, with Luvisols and Leptosols being the major soil types [START_REF] Colot | Soil-landscape relation at regional scale in Lake Tana Basin (Ethiopia)[END_REF]. In terms of land use, rainfed cropland is dominant, and the main crops cultivated in this area are teff (Eragrostis teff), maize (Zea mays), barley (Hordeum vulgare), wheat (Triticum aestivum), pearl millet (Pennisetum glaucum), and potato (Solanum tuberosum) [START_REF] Abera | Agriculture in the Lake Tana Sub-basin of Ethiopia[END_REF]. Grazing lands are found only in small, fragmented parcels, some used for free grazing, and some in a cut-and-carry management system. Over the past few decades, eucalyptus trees (mostly Eucalyptus globulus) have been widely planted by farmers on fresh or degraded croplands. Land degradation in the area is caused by deforestation, overgrazing, and poor management of the agricultural land [START_REF] Dessie | Effects of the floodplain on river discharge into Lake Tana (Ethiopia)[END_REF][START_REF] Frankl | Can woody vegetation in valley bottoms protect from gully erosion? Insights using remote sensing data (1938-2016) from subhumid NW Ethiopia[END_REF]. A wide range of interventions on stone bunds, check dams, trenches, and tree planting have been undertaken recently [START_REF] Mofed | Ethiopian Ministry of Finance and Economic Development (MoFED)[END_REF][START_REF] Npc | Growth and transformation plan II (GTP II): 2015-2020[END_REF].

FIGURE 1 here

| Measurement of rainfall and antecedent soil moisture

Five tipping-bucket rain gauges (HOBOware with data logger) were installed (Figure 1), and rainfall was measured automatically during 2018 and 2019. For every rainfall event, the total rainfall depth (P, mm) and the maximum 30-min rainfall intensity (I 30 , mm h -1 ) were derived from the continuous rainfall data series (5-min time step). Five-day cumulative rainfall was used as an indicator for antecedent moisture condition (AMC).

| Measurement of runoff and sediment yield

Fourteen catchments (324-1715 m 2 ) draining to first-order streams or gullies were selected for this study (Table 1) to represent the observed variability in land use and management. A minimum of two catchments were selected from each land use type to represent the differences in controlling factors. The outlet of each catchment was equipped with a V-notch weir, locally made from plastic jars as a less sensitive alternative to theft. During rainfall events, the runoff stage from the weir crest was recorded at 5-min intervals, and the runoff discharge was calculated using Eq. 1 [START_REF] Shen | Discharge characteristics of triangular-notch thin-plate weirs[END_REF]:

Q= 8 15 x C v x √2 g× tan ( θ 2 ) x h 5 2 , ( 1
)
where Q is the runoff discharge (m 3 s -1 ), C v is the discharge coefficient (0.593 for a 90° V-notch), θ = 90° is the notch angle, g is the acceleration due to gravity (m s -2 ), and h is the stage height (m).

Then, the event runoff volume (RO v , m 3 ) and runoff depth (RO d , mm) were determined using Eqs. 2 and 3, respectively.

RO v = ∑ i=1 n Q i * t i , ( 2
)
RO d =10 3 * RO v / A (3)
where Q i is discharge (m 3 s -1 ), t is time (s), which is 300 s for 5 min, A is the catchment area (m 2 ), and i is the specific 5-min time step.

The event average suspended sediment concentration (SSC, g l -1 ) was determined by taking a composite sample of one L taken during the flow rising, peak, and recession times in 2018.

However, it was not possible to develop a rating curve from the composite samples; hence, in 2019, 1 L sample was taken during each event at about the peak time. The runoff samples were filtered using Whatman filter paper 42, and SSC was determined following the standard laboratory method [START_REF] Gray | Comparability of Suspended-Sediment Concentration and Total Suspended Solids Data[END_REF]. Then, the SSC to Q rating curve was developed (Table 3), and the event sediment yield (SY, Mg) was determined using Eq. 4.

SY = ∑ i=1 n SSC i * Q i ¿ t i , (4)
Therefore, the event-weighted average SSC was determined using Eq. 5.

SSC=SY / RO v , (5)
Then, runoff depth (mm), runoff coefficient (RC in%, that is a fraction of rain depth that runs off), SSC, and SY were determined for each rain event. In addition, a rating curve was developed for the event rainfall depth and SY (Annex A) which was used to estimate the SY for night-time events and hence, annual SY.

FIGURE 2 here

| Physical characterisation

Each catchment was delineated, and its surface area was determined using a geographic positioning system (GARMIN handheld GPS) and ground surveying techniques (https://www.civilknowledges.com/chain-surveying/). The slope gradient was measured using a clinometer on a representative section of the catchment, mostly from bottom to top. RFC was determined by laying a 1 m × 1 m square grid [START_REF] Gong | Spatial distribution of caliche nodules in surface soil and their influencing factors in the Liudaogou catchment of the northern Loess Plateau, China[END_REF][START_REF] Poesen | Rock fragments in top soils : significance and processes[END_REF]) that was photographed perpendicularly. Subsequently, a square mesh (20 × 20 mesh with 5 cm × 5 cm units) was overlaid over the photo of the square grid, and rock fragments covered by mesh units (25 cm 2 ) were counted on a computer screen; the rock fragment cover (RFC, %) was calculated using Eq. 6.

RFC= Nms 400 x 100 % , (6)
where N ms is number of mesh units with rock fragments.

Mineral fragments with a minimum size of 2 mm were considered as rock fragments (Poesen et al., 1994), and small rock fragments that did not cover a mesh unit (25 cm 2 ) were subjectively added to make up a mesh unit. The RFC was also visually estimated in the field for crossvalidation of results.

A soil sample was collected from each catchment at a representative position to a depth of 30 cm. Then, SOM was determined using the [START_REF] Walkley | An examination of the degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method[END_REF] method and soil texture was analysed through the hydrometer method [START_REF] Beretta | Soil texture analyses using a hydrometer: modification of the Bouyoucos method[END_REF].

| Data analysis

Event runoff depth, RC, and SY were compared among the different land use types and seasons using one-way analysis of variance (ANOVA). Correlation coefficients and simple linear regression were used to identify the relationship between catchment characteristics, event runoff, and SY of micro-catchments. Principal component analysis (PCA) was used to identify the catchment characteristics associated with runoff and SY.

The soil loss tolerance limit for the study area was 6 Mg ha -1 yr -1 (derived from [START_REF] Hurni | Soil Erosion and Soil Formation in Agricultural Ecosystems : Ethiopia and Northern Thailand: Mountain Research and Development[END_REF]. This annual soil loss tolerance limit was distributed to each event by multiplying the ratio of the event rainfall with the annual rainfall as a weighting factor; hence, the corresponding event threshold SY was determined. Then, the SY of the observed event and its corresponding threshold were compared to identify events that exceeded the tolerance limit (threshold). Statistical analysis and graphics were performed using R software (R Core Team, 2020).

| RESULTS

| Event rainfall, runoff, and sediment yield

A total of 3458 rainfall events were measured in the two years in all catchments, which is equivalent to the average of 124 rainfall events per year per catchment. Hence, the runoff was measured for 618 rainfall events (during daytime and when a runoff was generated), which is equivalent to 22 rainfall-runoff events per year per catchment. A summary of the event means P, I 30 , and RO d is presented in Table 2. The average event rainfall varied between 6 mm and 14 mm, and the maximum 30 min intensity (I 30 , mm h -1 ) was from 13.2 to 56 in the different catchments and years.

Runoff responses varied significantly across land use types (p < 0.01) (Table 2). The average runoff coefficient (RC) in natural forests was the lowest, and that in the badland was the highest.

The RC of grazing lands was higher than that of cultivated and natural forests. The RC of plantation forests showed higher values than that of natural forests, but lower values than that of grazing land (Figure 4a, Annex B). The RC response in the different land use types was in the order of BDL > GR > CR > PL > NF (Table 1).

As presented in Figure 4b, the SSC of an average event was the highest in the badland and the lowest in the protected grazing land, natural forest, and plantation. The average event SSCs from grazing land, natural forest, and plantation forest were significantly smaller than the average event SSCs for the other land use types (Table 2). Because of the presence of a footpath that goes through the forest, a higher SSC was registered for the forest than grazing land.

The average event SY was the highest in the badlands (Figure 4c), whereas intermediate values were observed for croplands. However, the lowest average event SY was obtained for the natural forest. The variability for the event SY response was the highest in the badlands. Overall, the event average SY was higher in 2019 than in 2018 because of the higher rainfall in 2019. In addition, the high SY variation between the two years in the croplands could be due to variations in crop type and management (Annex C).

| Annual sediment yield for the different land use types

The average event SY was significantly different among land use types (p < 0.01, Annex B). The highest SY was observed for badlands, which was significantly higher than that of all other land use types, and the lowest was recorded for the natural forest.

FIGURE 5 here

The annual SY for the croplands varied between 9.4 and 24.7 Mg ha -1 , which is significantly lower than SY for the badlands and higher than SY for the grazing lands, natural forests, and plantations. Significant variations were also observed in the different cropland catchments (Figure 5). There was no significant difference among the forests, plantations, and protected grazing lands. The annual SY was generally higher in 2019 than in 2018, although the difference was not statistically significant (p = 0.76).

FIGURE 6 here

The cumulative events P, RO d , and SY for the different land use types are presented in Figure 6.

The steepest gradient (grey shaded) indicates the period during which there were high runoff and erosion, observed from mid-July to mid-August. In addition, the cumulative SY and runoff were the highest in the badlands and the lowest in the forests. In grazing lands and plantations, the cumulative runoff continues to rise with cumulative rainfall, while the cumulative SY remains nearly constant, particularly after the end of August. However, in croplands, the cumulative SY continues to rise with rainfall (CR3 and CR4 in particular), indicating that even under a high crop cover, runoff in croplands still causes soil erosion as compared to grazing lands and plantations.

| Factors determining the runoff and erosion processes

| Factors related to the rainfall erosivity agent

The frequency of occurrence of rainfall, SY, and I 30 in the different rainfall classes are presented in Figure 7. The contributions (in %) of the total rainfall in rainfall classes 2 and 3 were comparable. However, the contribution of SY in rainfall class 3 was higher because I 30 was also higher. Rainfall class 6, which contributed only 2% of the total rainfall, had ~13% of the total SY, as the highest I 30 was observed in this class. In contrast, about half of the rainfall events had a rainfall depth (P, mm) of less than 5 mm, but contributed less than 5 % of the total SY (Figure 7, Table 4).

FIGURE 7 here

The correlations between rainfall parameters, antecedent moisture, and runoff are presented in Figure 8. The results show that event RO d was positively correlated with the corresponding P and I 30 . However, the correlation between the antecedent moisture conditions and I 30 was weak.

FIGURE 8 here

In Figure 9, event SY was presented in relation to event P which indicated that in the badlands and croplands, sediment export occurred even during small rainfall events. However, in the forests, grasslands, and plantations, the SY response was negligible, and it was only evident during high rainfall events (> 20 mm). The solid line in Figure 9 is the event soil loss tolerance threshold, which is equivalent to the fraction of the annual soil loss tolerance limit weighted by the proportion of the event rainfall to the annual rainfall. Up to 95% of the events in the badlands were above the threshold level, while in the forests, no event was found to have SY above the threshold limit (Table 4).

FIGURE 9 here

TABLE 4 here

| Factors related to catchment properties

The PCA results indicated that clay, RO d , and RC were strongly and positively associated, while silt and SOM were strongly but negatively associated according to the first PCA (Dim 1, Figure 10). The SY was moderately and positively associated with both the first and second PCAs. This indicates that the runoff parameters are strongly influenced by soil properties, texture, and SOM.

The SOM was negatively associated with both the RO d and SY. A linear regression on the logtransformed data indicated that small improvements in SOM would result in a significant reduction in runoff and SY (Figure 10). A dramatic reduction in runoff and SY was particularly observed for SOM content < 4%, in which the runoff was reduced by approximately 50% and SY by ~5 times. Stone Bund, Feses, and RFC (variables with high values in croplands where SY was also high) were positively associated with SY in the second PCA. Slope gradient (SlopeG) seems to be less associated with both PCAs. However, the total variance explained by the two PCAs was only ~57%, and the effect of the slope gradient might show better association in the third component, which explains only ~16% of the variance (Annex G). Slope length (SlopeL) showed a strong negative association with the second PCA.

The SY also showed a declining trend with RFC, which was expressed as an exponential decay function (Figure 12). A decrease in the RFC from 50% to 40% caused an 11% increase in SY.

However, a decrease in the RFC from 10% to 1% caused a 186% increase in annual SY. The effect of RFC was more pronounced on annual SY than on RO d . Peak runoff and SY periods were observed between mid-July and mid-August. This was due to frequent rainfall with the steepest slope of the cumulative rainfall (Figure 6). In addition, the AMC was high, triggering the immediate generation of runoff. In addition, July and August are periods of intensive agricultural activities such as ploughing, weeding, and planting (Annex E) which favour detachment and transport of sediment.

FIGURE 10 here

Among the human-induced factors that affect runoff and SY, land use change and management play a key role [START_REF] Poesen | Soil erosion in the Anthropocene: Research needs[END_REF]. In this study, a wide range in variations was observed in event runoff and SY among the different land use types. The natural forests produced the least amount of runoff and SY (Figures 4 and5), as was the case in other studies [START_REF] Descheemaeker | Runoff curve numbers for steep hillslopes with natural vegetation in semi-arid tropical highlands , northern Ethiopia[END_REF][START_REF] Kassa | Sediment yield at southwest Ethiopia's forest frontier[END_REF]. This is due to the effect of forest interception and a thick litter layer that shields the soil substrate from any form of impact caused by raindrops. Otherwise, concentrated droplets, if falling from big leaves from higher canopy, would in some situations cause higher splash [START_REF] Tsuboyama | Estimation of temporal variation in splash detachment in two Japanese cypress plantations of contrasting age[END_REF]. In addition, extensive root systems in forests also facilitate rapid rates of infiltration, percolation, and subsurface flow [START_REF] Farrick | Infiltration and soil water dynamics in a tropical dry forest: It may be dry but definitely not arid[END_REF]. The runoffs and SYs observed from the natural forests in this study are primarily due to the presence of a narrow footpath (approximately 0.5 m × 20 m); otherwise, both runoff and SY are close to nil.

Forest ecosystems are vital in regulating water balance, reducing surface runoff [START_REF] Alaoui | Preferential Flow Effects on Infiltration and Runoff in Grassland and Forest Soils[END_REF], and distributing rainwater in different storages [START_REF] Archer | Rainfall infiltration and soil hydrological characteristics below ancient forest, planted forest and grassland in a temperate northern climate[END_REF]. Leaf litter alone reduces runoff by approximately 30% and SY by over 80% [START_REF] Li | The effect of leaf litter cover on surface runoff and Soil Erosion in Northern China[END_REF]. In another study, a 50% plant cover reduced RC to below 2% and soil erosion to nil [START_REF] Sajikumar | Impact of land cover and land use change on runoff characteristics[END_REF].

In contrast to forests, badlands produced the highest rate of event RC and SY (Figures 4, 5, and 6), which was supported by additional measurements using exposed tree roots of known age.

From 70 exposed tree root depth measurements (tree age ranging from 4 years to 30 years, average 10.1 years; root exposure depth range from 5 cm to 75 cm, average 18.3 cm), the average annual soil erosion rate corresponds to a soil depth of 1.83 cm, which is equivalent to ca.

230 Mg ha -1 yr -1 (Figure 13), assuming a mean soil bulk density of 1.5 Mg m -3 . Badlands are, therefore, hotspots of soil erosion but they have not gained sufficient scientific attention in the Lake Tana Basin. High soil erosion rates (200 Mg ha -1 yr -1 ) in badlands of the Upper Blue Nile basin [START_REF] Haregeweyn | Comprehensive assessment of soil erosion risk for better land use planning in river basins: Case study of the Upper Blue Nile River[END_REF] as well as in the Mediterranean region (i.e. ca. 250 Mg ha -1 yr -1

for catchments with similar size as those investigated in this study; Nadal [START_REF] Nadal-Romero | Corrigendum to "Scale-dependency of sediment yield from badland areas in Mediterranean environments[END_REF] have also been reported. The lack of vegetation cover and direct exposure of the soil substrate to raindrop impact causes compaction and surface sealing which is exacerbated by soil detachment due to animal trampling. Hence, the rate of infiltration is reduced, causing an increase in runoff and erosion. While the runoff mechanism in the study area is generally driven by excess in saturation [START_REF] Tilahun | Distributed discharge and sediment concentration predictions in the sub-humid Ethiopian highlands: The Debre Mawi watershed[END_REF], Hortonian flow can be dominant in the badlands. Low SOM content and surface roughness would also favour high-flow shear stresses during runoff events.

Finally, as badlands are intensely dissected and severely degraded areas [START_REF] Nadal-Romero | Corrigendum to "Scale-dependency of sediment yield from badland areas in Mediterranean environments[END_REF], the average local slope is higher than the overall landscape slope.

FIGURE 13 around here

The soil erosion rate in the region is strongly affected by ongoing land use changes [START_REF] Temesgen | Population dynamics and land use/land cover changes in Dera District, Ethiopia[END_REF], similar to that in many countries around the world [START_REF] Msofe | Land use change trends and their driving forces in the Kilombero Valley Floodplain, Southeastern Tanzania[END_REF]. Forests have been converted to croplands in recent decades either through gradual expansion by frontier farmers [START_REF] Zeleke | Implications of Land Use and Land Cover Dynamics for Mountain Resource Degradation in the Northwestern Ethiopian Highlands Implications of Land Use and Land Cover Dynamics for Mountain Resource Degradation in the Northwestern Ethiopian Highlands[END_REF] or through large-scale private investments commissioned by the government (Teklemariam et al., 2016a). As can be observed from the results of this study (Figures 4,5,and 6), the event runoff and SY were two orders of magnitude higher in the croplands than in the forests. Therefore, the conversion of remaining forests to croplands will significantly aggravate soil erosion and lead to land degradation [START_REF] Teklemariam | How sustainable is transnational farmland acquisition in Ethiopia? Lessons learned from the Benishangul-Gumuz Region[END_REF].

In contrast, there is a concerted effort to rehabilitate degraded areas, particularly in the highlands [START_REF] Abera | Characterizing and evaluating the impacts of national land restoration initiatives on ecosystem services in Ethiopia[END_REF]. Such efforts will play a paramount role in the hydrologic and erosion processes of the area in the long term. In addition, there is a growing trend of converting marginally productive land to eucalyptus woodlots, with log prices increasing constantly [START_REF] Birhanu | Review on Expansion of Eucalyptus, its Economic Value and Related Environmental Issues in Ethiopia[END_REF]. In our study, the eucalyptus catchments had the second lowest SY, next only to natural forests. Therefore, disregarding other ecological effects, it can be concluded that expanding eucalyptus plantations, often in croplands, would reduce soil erosion. Other studies have also found a significant reduction in runoff in eucalyptus woodlots compared to croplands [START_REF] Mbilinyi | Effect of Eucalyptus expansion on surface runoff in the central highlands of Ethiopia Effect of Eucalyptus expansion on surface runoff in the central highlands of Ethiopia[END_REF]. However, as eucalyptus is a controversial tree species with high water consumption and allelopathic effects on other plants [START_REF] Chanie | Eco-hydrological impacts of Eucalyptus in the semi humid Ethiopian Highlands: the Lake Tana Plain[END_REF], warranting further studies to evaluate the water balance, nutrient balance, and long-term effects of expanding eucalyptus plantations on soil health and sustainability.

| Catchment characteristics that control runoff and erosion

RFC significantly reduced the runoff (Figure 12). The RFC affects the hydrological process in different ways. If rock fragments are well-embedded in the topsoil, they reduce the infiltration rate and cause an increase in the surface runoff. However, if rock fragments rest on the soil surface, they enhance infiltration and reduce the surface runoff by reducing flow velocity [START_REF] Poesen | Variation of rock fragment cover and size along semiarid hillslopes: a case-study from southeast Spain[END_REF]. Taye et al. (2014) found a declining trend in runoff along an increasing slope gradient, which was attributed to a corresponding increase in the RFC. Using simulated rainfall, [START_REF] Lv | Effects of rock fragment content, size and cover on soil erosion dynamics of spoil heaps through multiple rainfall events[END_REF] also found that rock fragments reduced surface runoff and erosion and increased the infiltration of spoil heaps. In addition, [START_REF] Chen | Spatial distribution of rock fragments on steep hillslopes in karst region of northwest[END_REF] also reported an increasing RFC with increasing topographic position.

In our study, the RFC was mostly high in croplands where the long-term ploughing action moved rock fragments by kinetic sieving to the soil surface [START_REF] Oostwoud Wijdenes | Chiselling effects on the vertical distribution of rock fragments in the tilled layer of a Mediterranean soil[END_REF]). In addition, the rock fragments in the grazing lands or plantations were often partly or fully embedded in the soil. This could be an additional reason why the RC was higher in grazing lands than in croplands (Figure 4). The RFC reduced event runoff and annual SY, and the trend was expressed by a negative exponential relation [START_REF] Poesen | Rock fragments in top soils : significance and processes[END_REF][START_REF] Smets | Impact of plot length on the effectiveness of different soil-surface covers in reducing runoff and soil loss by water[END_REF].

However, the effect of RFC on SY was more pronounced when the RFC was generally lower.

When making stone bunds by collecting rock fragments from the soil surface, it is important to evaluate whether it is better to make stone bunds or to leave rock fragments in the field. Based on our study, it could be inferred that when rock fragments exceed 50%, collecting 10% of rock fragments for making stone bunds would have a net positive effect, as a 10% decrease in the RFC will reduce the SY by less than 10%, and stone bunds reduce the SY by over 30% [START_REF] Tamene | Soil structural degradation and nutrient limitations across land use categories and climatic zones in Southern Africa[END_REF]. However, if the rock fragments cover is less than 10%, reducing RFC will increase SY by over 185%, in which case collecting rock fragments for the construction of stone bunds will result in a net increase in SY. Reducing the RFC from 20% to 10% increased the SY by 27%

which is comparable to the gain obtained from constructing stone bunds [START_REF] Adimassu | Effect of soil bunds on runoff , soil and nutrient losesm and cropyield in the central highlands of Ethiopia[END_REF].

Therefore, a 20% stone cover can be set as the lower limit for the trade-off between maintaining RFC and constructing stone bunds [START_REF] Nyssen | Removal of rock fragments and its effect on soil loss and crop yield, Tigray, Ethiopia[END_REF]. However, further investigation is required to obtain a solid and accurate threshold. Studies on the effect of the size and surface arrangement of the rock fragments on SY are additionally required.

The SOM increases infiltration and soil water-holding capacity, reduces soil erodibility, and amends soil structure [START_REF] Olubanjo | Evaluation of the Effect of Soil Physico-Chemical Properties on Erodibility and Infiltration[END_REF]. As also observed in our study, a declining SOM in croplands to below 2% would dramatically increase SY (Figure 12). For soils with < 4% SOM, small improvements would result in a rapid reduction in runoff and SY. Therefore, crop residue management and the integration of other vegetative soil and water conservation activities can play a vital role in reducing erosion in the area.

| CONCLUSION

This study provides deeper insights into event runoff and SY under different land use types in sub-humid tropical highlands. Forests produced the least amount of runoff and the lowest SY, whereas badlands were found to be the hotspots of soil erosion. The annual SY from croplands shows a large variation (9.8-25.6 Mg ha -1 yr -1 ), which is still higher than the soil loss tolerance limit estimated for the area (6 Mg ha -1 yr -1 ). However, the estimated mean SY from croplands (14.8 Mg ha -1 yr -1 ) is generally lower than that in previous studies which could be attributed to the soil and water conservation efforts that have been widely practiced in the area. The year-toyear variations in runoff and erosion responses were not significant. SOM was found to be one of the most important parameters that had a profound effect on both event runoff and SY.

Anthropogenic activities that would reduce SOM would therefore worsen the runoff erosion process. Therefore, particular attention is needed in land management activities that contribute to enhancing SOM. The relationship between the RFC and SY was expressed with a negative exponential function which highlighted the need to balance the trade-off between the role of RFC when left in the field or collected for bund construction. A 20% RFC has been suggested as a threshold below which it is not worth collecting rock fragments from the soil surface to construct bunds. As badlands are found to be hotspots of soil erosion by water, further study is needed on their distribution, causes, and possible remedial measures. They should also be given priority when treating catchments with soil and water conservation measures to reduce the overall catchment SY. the dash line represents the soil loss tolerance limit for the highlands of Ethiopia for equivalent elevation of our study area (6 ton ha -1 year -1 ) adopted from [START_REF] Hurni | Soil Erosion and Soil Formation in Agricultural Ecosystems : Ethiopia and Northern Thailand: Mountain Research and Development[END_REF] FIGURE 6 Cumulative event rainfall (Pcum, mm), cumulative event runoff (ROcum, mm), cumulative event sediment yield (SY, Mg ha -1 ) for the different catchments (ID) of the 2019 rainy season (for more details see Table 1). Grey-shaded period indicates the steepest gradient in the curves

List of tables

FIGURE 7

The percentage of the number of events (n), rainfall depth (P, mm), sediment yield (SY, Mg ha -1 yr -1 ) and average event maximum 30 minute intensity (I 30 , mm h -1 ) in the different event rainfall classes

  FIGURE 12 here

FIGURE 3 FIGURE 4

 34 FIGURE 3 Illustration of the land use in some of the studied micro-catchments; PL1 is Eucalyptus plantation, GR2 is grazing land (cut-and-carry), BDL1 is badland completely bare, BDL2 is rehabilitating badland, CR5 is cropland

FIGURE 8

 8 FIGURE 8Correlation coefficient between the event rainfall (P, mm), runoff (RO d , mm), antecedent moisture condition (AMC, mm), 30-minute maximum intensity (I 30 , mm h -1 ) variables; the darker the tone of the color and the bigger the size of the circle both indicate strong correlation, while the blue color is for positive correlation, the red color is for negative correlation. Besides, any significant correlation with p-value < 0.01 is indicated with the circular and when it is not significant it remains blank

FIGURE 10 FIGURE 12 FIGURE 13

 101213 FIGURE 10 Principal component analysis (PCA) biplot for catchment characteristics

TABLE 1 here FIGURE 3 here

 1

TABLE 2 here TABLE 3 here FIGURE 4 here

 2

TABLE 1

 1 Physical characteristics of the studied catchments. RFC is rock fragment cover, SOM is soil organic matter content, 'Feses' is a seasonal drainage system that farmers prepare with deep furrowing to facilitate smooth runoff and reduce potential sheet erosion, BDL is badlands, CR is croplands, GR is grazing, NF is natural forest, and PL is plantation

		Land use types	Area	Mean	Slope	Stone	Feses	RFC	SOM	Soil		Texture (%)
	ID		(m 2 )	slope gradient	length (m)	bund density	density (m ha -1 )	(%)	(%)	depth (cm)	Sand	Silt	Clay
				(%)		(m ha -1 )
	BDL1	Badland	520	22	34	0	0	3	1.8	50	59	17	24
	BDL2	Badland	546	26	28	0	0	2	3.7	50	44	20	36
	CR4	Cropland	1715	13	30	196	1389	20	2.2	50	45	18	37
	CR5	Cropland	716	28	32	336	489	40	4.4	15	74	12	14
	CR1	Cropland	1205	12	11.5	896	855	20	3.4	40	52	22	26
	CR2	Cropland	1354	10	15.5	625	591	70	3.7	50	48	28	24
	CR3	Cropland	578	25	28	554	1211	35	4.5	50	46	22	32
	GR1	Grazing land	534	23	26	0	0	30	3.7	40	40	12	48
	GR2	Grazing land	634	20	32	0	0	2	3.3	>100	52	20	28
	NF1	Natural forest	682	22	31	0	0	3	10.5	50	66	24	10
	NF2	Natural forest	520	19	27	0	0	4	10.4	50	60	26	14
	NF3	Natural forest	760	9	28	0	0	4	5.0	40	64	22	14
	PL1	Plantation	324	11	33	0	0	5	4.0	>100	44	18	38
		(Eucalypt) 8canopy
	PL2	cover) Plantation	566	10	37	0	0	6	3.7	>100	46	20	34
		(Eucalypt)

TABLE 2

 2 Event summary of results: mean event rainfall (P), mean event suspended sediment concentration (SSC), mean event sediment yield (SY), mean event runoff depth (RO d ), number of measured rainfall-runoff events (n) for the different catchments (ID). Values in parentheses are

	standard deviation
	ID	n		P (mm)		I 30		SSC (g l -1 )		SY (kg ha -1 )		RO d (mm)
		2018	2019	2018	2019	2018	2019	2018	2019	2018	2019	2018	2019
	BDL1	20	24	5.9(1.1)	8.5(1.3)	37.2	56.4	5.8(1)	7.1(0.4)	202(67)	429(96)	3.5(0.9)	5.1(0.9)
	BDL2	14	23	7.7(1.4)	6.9(1.6)	37.2	56.4	6.1(1.6)	3.5(0.7)	301(101)	337(222)	4.6(0.7)	3.8(1.4)
	CR4	18	24	6.1(0.8)	8.1(1.4)	37.2	56.4	3.8(0.6)	5.2(0.6)	131(38)	164(60)	3.1(0.6)	2.1(0.6)
	CR5	20	28	10.0(1.3)	12.2(1.5)	44.4	43.6	2.8(0.5)	4.1(0.3)	99 (36)	152(48)	2.6(0.5)	2.7(0.6)
	CR1	23	27	12.2(2.2)	13.6(1.6)	58.4	49.2	2.7(0.4)	2.4(0.3)	151(34)	134(45)	7.1(1.7)	3.1(0.7)
	CR2	20	32	11.8(2.0)	12.7(1.4)	13.2	42.0	1.9(0.3)	4.6(0.3)	120(36)	365(119)	6.0(1.3)	5.5(1.1)
	CR3	26	41	7.2(1.0)	8.5(1.3)	37.2	56.4	4.3(0.6)	4.0(0.2)	106(48)	192(69)	1.8(0.7)	3.4(0.8)
	GR1	19	34	7.3(1.0)	10.2(1.4)	37.2	56.4	4.0(0.7)	1.4(0.1)	133(25)	81(19)	3.6(0.5)	4.7(0.8)
	GR2	12	33	10.1(1.8)	12.0(1.4)	44.4	43.6	0.3(0.1)	0.4(0.0)	14(2.4)	40(11)	4.7(0.9)	6.4(1.2)
	NF1	6	24	12.5(3.1)	7.8(1.6)	37.2	56.4	2.5(1.0)	0.7(0.2)	4.8(2.1)	6.4(3.6)	0.3(0.1)	0.3(0.2)
	NF2	14	20	8.0(1.7)	8.8(1.9)	37.2	56.4	2.0(0.2)	0.5(0.2)	5.5(2)	2.0(1.0)	0.4(0.2)	0.1(0.1)
	NF3	21	34	11.8(2.1)	10.6(1.2)	58.4	49.2	2.4(0.3)	1.8(0.0)	22.0(6)	13.7(2.5)	0.9(0.2)	0.7(0.1)
	PL1	9	16	12.0(2.6)	11.5(2.7)	58.4	49.2	0.7(0.3)	1.8(0.0)	8.6(5.3)	69(30)	1.1(0.5)	3.3(0.1)
	PL2	4	32	9.0(2.0)	11.3(1.4)	44.4	43.6	2.2(0.3)	0.3(0.0)	55(21)	21(4.7)	2.2(0.6)	4.7(0.1)

Note: For details of the catchments ID see Table

1

TABLE 3

 3 Rating curve parameters for ( SSC i [g l -1 ] = aQ i [m 3 S -1 ] + b), n = number of runoff events for catchments (ID)

	ID	a	b	R 2	n	p
	BDL1	707.300	5.235	0.43	48	<0.01
	BDL2	1028.879	1.625	0.75	15	<0.01
	CR4	526.753	4.599	0.70	31	<0.01
	CR5	978.402	2.595	0.61	25	<0.01
	CR1	868.331	0.560	0.46	30	<0.01
	CR2	515.974	2.771	0.20	35	<0.01
	CR3	452.310	3.530	0.11	51	0.016
	GR1	203.462	1.088	0.45	47	<0.01
	GR2	45.775	0.251	0.20	37	<0.01
	NF1	469.753	1.615	0.55	16	<0.01
	NF2	22624.819	1.965	0.62	5	0.11
	NF3	469.753	1.615	0.55	16	<0.01
	PL1	293.032	0.195	0.73	17	<0.01
	PL2	56.253	0.224	0.40	39	<0.01

Note: For details of the catchments ID see Table

1

TABLE 4

 4 Frequency of events above the event tolerance limit threshold for the different

	catchments (ID)
	ID	Number of	Number of events over	Number of events	% events above
		events	the threshold	below the	the threshold
				threshold
	BDL1	44	42	2	95
	BDL2	37	33	4	89
	CR1	50	23	27	46
	CR2	52	13	39	25
	CR3	67	54	13	81
	CR4	42	32	10	76
	CR5	48	38	10	79
	GR1	53	21	32	40
	GR2	45	0	45	0
	PL1	25	0	25	0
	PL2	36	2	34	6
	NF1	30	0	30	0
	NF2	34	0	34	0
	NF3	55	0	55	0
	Note: For ID details see

Table FIGURE 2

 FIGURE V-notch weir cut from a plastic jar allowing one to measure runoff and sediment yield in micro-catchments at low cost and low risk (at CR2).

Table 1 )

 1 FIGURE 5Annual sediment yield (SY, Mg ha -1 ) of the different catchments (ID, see Table1);

ACKNOWLEDGEMENTS

The authors acknowledge the financial support provided by the VLIR-UOS through the Institutional University Cooperation (IUC) project of Bahir Dar University.

List of figures