# Spatial scale impact on daily surface water and sediment fluxes in Thukela river, South Africa

Macdex Mutema, Graham P. W. Jewitt, Pauline Chivenge, Samuel Kusangaya, Vincent Chaplot

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Macdex Mutema, Graham P. W. Jewitt, Pauline Chivenge, Samuel Kusangaya, Vincent Chaplot. Spatial scale impact on daily surface water and sediment fluxes in Thukela river, South Africa. Physics and Chemistry of the Earth. Parts A/B/C, 2016, 92, pp.34 - 43. 10.1016/j.pce.2015.10.001. hal-01497829

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Physics and Chemistry of the Earth xxx (2015) 1–10

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# Spatial scale impact on daily surface water and sediment fluxes in Thukela river, South Africa

Macdex Mutema<sup>a</sup>, Graham Jewitt<sup>a</sup>, Pauline Chivenge<sup>a, b</sup>, Samuel Kusangaya<sup>a</sup>, Vincent Chaplot<sup>a, c, *</sup>

## ARTICLE INFO

**Article history:**

Received 26 March 2015

Received in revised form

28 September 2015

Accepted 1 October 2015

Available online xxx

**Keywords:**

Erosion mechanisms

Multiple nested catchments

Sediment fluxes

Water quality

## ABSTRACT

The on- and off-site effects of soil erosion in many environments are well known, but there is still limited understanding of the fluxes in downstream direction due, among other factors, to scarce and poor quality. A four year study to (i) evaluate water and sediment fluxes at different spatio-temporal scales and (ii) interpret the results in terms of processes involved and the controlling factors, was conducted in Thukela basin, South Africa. Five hierarchically nested catchments; namely microcatchment (0.23 km²), subcatchment (1.20 km²), catchment (9.75 km²), sub-basin (253 km²) and basin (29,038 km²), were used in addition to fifteen (1 m²) microplots and ten (10 m²) plots on five locations within the microcatchment. The results showed 19% decrease of unit-area runoff (q) from 3.1 L m⁻² day⁻¹ at microplot to 2.5 L m⁻² day⁻¹ at plot scale followed by steeper (56%) decrease at microcatchment scale. The q decreased in downstream direction to very low level (q ≤ 0.26 L m⁻² day⁻¹). The changes in q were accompanied by initial 1% increase of soil loss (SL) from 18.8 g m⁻² day⁻¹ at microplot to 19.1 g m⁻² day⁻¹ at plot scale. The SL also decreased sharply (by 39 fold) to 0.50 g m⁻² day⁻¹ at microcatchment scale, followed by further decrease in downstream direction. The decrease of q with spatial scale was attributed to infiltration losses, while initial increase of SL signified greater competence of sheet than splash erosion. The decrease of SL beyond the plot scale was attributed to redistribution of the soil on the hillslope and deposition on the stream channel upstream of the microcatchment outlet. Therefore, erosion control strategies focussing on the recovery of vegetation on the slope and stabilization of gullies are recommended.

© 2015 Published by Elsevier Ltd.

## 1. Introduction

Soil erosion is a natural process primarily driven by lateral movement of water on landscapes and involves three main stages; soil particle detachment, transportation and sedimentation (Kinnell, 2008). Accelerated soil erosion has been a serious problem in different parts of the world for many years with well documented on- and off-site effects. On-site, soil loss impacts on long-term sustainability of agriculture due to loss of topsoil and reduction of soil depth for plant growth and nutrient storage. Off-site, soil

erosion affects water security and the associated ecosystem functions (Flugel et al., 2003), through pollution and siltation of reservoirs (Chihombori et al., 2013; Négrel et al., 2014). Water pollution has also contributed to escalation of water supply costs, due to expensive water treatment processes to make the water portable for domestic use in some environments. Soil erosion has other far reaching effects such as global warming because it exposes soil aggregate protected organic carbon to decomposition processes, thus accelerating green-house gas (e.g. CO₂) emissions (Lal, 2004).

In spite of advances in knowledge on erosion mechanisms, there is no consensus among researchers on the scale effect on soil erosion fluxes on landscapes and stream channels. Several studies have reported on decreasing unit-area soil loss (SL) with landscape area or slope length (e.g. Van de Giesen et al., 2000; De Vente and Poesen, 2005; de Vente et al., 2007; Mayor et al., 2011). However,

<footnote><sup>a</sup> CWRR, SAEES, University of KwaZulu-Natal, PB X01, Scottsville 3209, Pietermaritzburg, South Africa</footnote>

<footnote><sup>b</sup> International Crops Research Institute for the Semi-Arid Tropics, P O Box 776, Bulawayo, Zimbabwe</footnote>

<footnote><sup>c</sup> Institut de Recherche pour le Développement (IRD), Laboratoire d'Océanographie et du Climat (LOCEAN), Université Pierre et Marie Curie, 4, place Jussieu
75252 Paris Cedex 05, France</footnote>

<footnote>* Corresponding author. Institut de Recherche pour le Développement (IRD), Laboratoire d'Océanographie et du Climat (LOCEAN), Université Pierre et Marie Curie, 4, place Jussieu, 75252 Paris Cedex 05, France.</footnote>

<footnote>E-mail addresses: chaplot@ird.fr, vincent.chaplot@gmail.com (V. Chaplot).</footnote>
other studies (e.g. Le Bissonnais et al., 1998; Parsons et al., 2006) have reported on an initial increase of SL followed by a decrease with further increase of slope length. For example, Parsons et al. (2006) observed increasing SL up to a maximum at a slope length of 7 m and a decrease thereafter. Erosion studies have also reported on preferential removal and transport of certain soil particle size classes depending on spatio-temporal scale; however no consensus on particle sizes to be eroded at a particular scale exists. For example, Smith and Dragovich (2007) reported on selectivity for

fine particles at small spatial areas while Shi et al. (2012) reported dominance by coarse materials. Soil erosion itself is a complex process dependent on interactions amongst many promoters like hydrological regimes (e.g. rainfall and surface flow), anthropogenic activities (e.g. land use and management), biotic (e.g. fauna and flora) and abiotic (e.g. soil properties and other non-living parameters) factors. General surface hydrology highlights a progression of soil erosion mechanisms from splash effect at point scale, lateral sheet and interrill at field level, to linear rill and gully mechanisms

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Combo map showing the location of KwaZulu-Natal province within South Africa, the Thukela Basin in KwaZulu-Natal as well as the sub-basin in where Potshini catchment is located. Also shown are the annual rainfall (mm yr⁻¹) and altitude (masl) distribution maps of the Thukela Basin.</figcaption></figure>
at larger landscapes. This transition of mechanisms follows prog-
ress in channelization of surface flow and increasing competence to
transport detached materials.

A four year (2010–2014) study was performed in Thukela Basin,
South Africa, to (i) evaluate water and sediment fluxes at different
spatio-temporal scales, and (ii) and interpret the results in terms of
main processes involved and factors of control. Such a study is
essential because river basin management aiming to reduce land
degradation, improve water security and safeguard ecosystem
health requires better knowledge of upstream–downstream
changes in water and sediment fluxes.

**2. Materials and methods**

2.1. Study basin and experimental set-up

The study was carried out in Thukela Basin (28.97–31.43° E, 27.42–29.40° S), which is the largest river basin of KwaZulu-Natal province, South Africa, with approximate area of 30,000 km² (Fig. 1). The main river flows 502 km in an easterly direction from Drakensburg Mountains to the Indian Ocean. The basin climate varies from being largely subtropical in the high altitude zone to semi-arid in the valley region. Annual rainfall varies widely across the basin and from one year to the other; however most of the rain falls during summer months between September and April.

Progressively nested catchments; namely microcatchment (size 0.23 km²), subcatchment (1.20 km²), catchment (9.75 km²), sub-basin (253 km²) and basin (29,038 km²), were used in this study. The microcatchment and subcatchment outlet were located in Potshini, a rural community 10 km west of Bergville town. The climate of Potshini was classified as sub-tropical with long-term annual rainfall, temperature and potential evaporation of 684 mm, 13 °C and 1600 mm, respectively (Schulze, 1997). The catchment and sub-basin outlets were located in a commercial farming zone. The basin outlet for the study was near Mandini (31.39°E, 29.14°S), about 1 km² upstream of Thukela mouth. In addition, fifteen runoff microplots (size 1 m²) and 10 plots (10 m²) replicated three and two times respectively on five hillslope positions within the microcatchment were used. The slope positions represented different levels of overgrazing in terms of vegetation cover (the microcatchment was used for communal livestock grazing), topography, soil types, geology and soil surface characteristics (Dlamini et al., 2011; Oakes et al., 2012; Orchard et al. 2013). The hillslope is very steep (50–70% gradient) with a relatively flat plateau. The main soil types here were reddish dolerites (of good drainage) extending from the plateau to a terrace

downslope followed by *Acrisols* which were shallow at the mid-
slope and deep (~2 m) at the bottom (Deckers et al., 1998). The
topo-sequence on this slope suggested a full complement of
recharge, interflow and responsive soils (Van Tol et al., 2013).

## 2.2. Equipment and measurements of water and sediment fluxes

2.2.1. Runoff microplots and plots

Each runoff microplot and plot was demarcated by galvanised
metal sheets inserted 10 cm into the ground and leaving another
10 cm above ground to eliminate run-on water during rain events.
Surface water and sediments generated collected into a protected
gutter through openings in a downslope side metal sheet. The
gutter was fitted with a delivery pipe connected to a reservoir about
1.5 m downslope. All runoff and sediments from microplots were
collected into reservoirs. However, fractions of runoff and sedi-
ments had to be from plots to avoid frequent overtopping of res-
ervoirs. One plot at each of the five slope positions was equipped
with a water divisor to split flow into five parts and only one part
was collected into the reservoir. The other plot was equipped with a
tipping-bucket in addition to the divisor; hence one tenth of flow
(and sediments) was collected into the reservoir. Accordingly, total
flow and sediments from plots were obtained by multiplying
measured quantities by five or ten.

2.2.2. Outlets on the stream channel

The equipment setup at microcatchment and subcatchment
outlets was described in detail by Kongo et al. (2010). At each of
the two outlets was an H-flume equipped with a differential
pressure transducer (for continuous monitoring of stream stage)
and automatic sampler (ISCO Model 2900). Both the pressure
transducer and sampler were coupled to datalogger (CR200). The
pressure transducer automatically converted stream stage to
discharge (Q, L s⁻¹). The sampler was calibrated to collect water
samples more frequently during high than low flows. The catch-
ment outlet was a double-opening bridge culvert equipped with a
pressure transducer. Another bridge culvert with eight holes
marked the sub-basin outlet and flow measurements were done
manually during weekly visits. Flow data at the basin outlet was
obtained from the Department of Water Affairs online database.
Water sampling at catchment, sub-basin and basin outlet was by
hand from streamflow during visits. In addition to automatic
samples, grab samples were collected during visits at micro- and
subcatchment outlets.

A portable turbid-meter (TSS Portable HACH) was used to
measure sediment concentration (SC, g L⁻¹) in water samples after

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Variability of rainfall (mm day⁻¹) and cumulative annual rainfalls for each of the years (mm yr⁻¹) during the study period as recorded by a tipping-bucket rain gauge at Potshini, South Africa.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Box plots showing medians, 25–75% and non-outlier ranges of (a) q: unit-area runoff (L m⁻² day⁻¹), (b) SC: sediment concentrations (g L⁻¹), and (c) SL: soil losses (g m⁻² day⁻¹) at different spatial scales (microplot: 1 m², plot: 10 m², microcatchment: 0.23 km², subcatchment: 1.20 km², catchment: 9.75 km², sub-basin: 253 km², and basin: 29,038 km²) in Thukela basin during the study.</figcaption></figure>
thorough stirring to obtain homogeneous water-sediment mixtures. Rainfall was measured by means of a tipping-bucket rain gauge located at Potshini. The rain gauge was connected to a data logger (CR200) and data collected was used to characterize the rain events in terms of total daily rainfall amount (Rainfall, mm day⁻¹), storm event duration (Dur, mins), average storm intensity (I, mm h⁻¹) and maximum 6-min rainfall intensity (Max₆minI, mm h⁻¹). Antecedent 3-day rainfall (PreRain-3, mm) and cumulative rainfall since the onset of the main rain season (RainC, mm) were also computed from the data.

## 2.3. Evaluation of daily water and sediment fluxes

Average runoff volume per day for the 15 microplots and 10 plots on the hillslope were divided by respective scale size to obtain a unit-area runoff flux (q, L m⁻² day⁻¹). Discharge (Q, L s⁻¹) at stream outlets were added together to get daily values which were then divided by respective scale size (in m²) to obtain unit-area runoff fluxes (q, L m⁻² day⁻¹). The assumption here was that each m² within a scale size contributed uniformly to measured (or observed) runoff or surface flow. Simple linear interpolation was used to estimate daily q for day with no data (due to equipment failure or lack of measurement e.g. at sub-basin level). Average SC (g L⁻¹) for each day at the different scales was computed. Linear interpolation was also used to estimate SC for days without observed data. Soil loss fluxes (SL), the amount of sediment discharged per unit area per day, were calculated using the following equation (1).

$$SL = q \times SC \quad (1)$$

Where SL = soil loss flux (g m⁻² day⁻¹), q = unit-area surface runoff flux (L m⁻² day⁻¹) and SC = average sediment concentration (g L⁻¹).

### 2.3.1. Statistical analysis

Basic descriptive statistics of unit-area runoff (q), sediment concentrations (SC) and soil losses (SL) such as Min: minimum, Max: maximum, Median, Mean, Q1: quartile 1, Q2: quartile 2, Stdev: standard deviation, SE: standard error of mean, Skew:

skewness, Kurt: Kurtosis and CV%: coefficient of variation were computed. Spearman rank correlation analysis was also performed to understand the one-on-one relationships between the fluxes and rainfall characteristics. Spearman coefficients were used because the Skewness and Kurtosis values (from the general statistics) indicated that the datasets were not normally distributed. In all analyses, differences and correlations were considered to be significant at *p* < 0.05, unless stated.

## 3. Results

### 3.1. Daily and annual rainfall during the study

Daily and annual rainfalls as recorded by the tipping-bucket rain gauge at Potshini during the study are presented in Fig. 2. The results show great variability of rainfall, from one year to the other. The highest annual rainfall (1056 mm yr⁻¹) was recorded in the first year of study and this was extremely wet in comparison with the long-term mean of nearby Bergville (684 mm yr⁻¹). However, this was followed by a dry year with annual rainfall of 617 mm yr⁻¹. The two remaining years were wetter than the long-term mean. Overall, the study period was wetter than average with a four-year mean annual rainfall of 807 mm yr⁻¹.

### 3.2. Variability of daily water and sediment fluxes with spatial scale

The box-plot results shown in Fig. 3 highlight the overall variability of unit-area runoff (Fig. 3a), sediment concentration (Fig. 3b) and soil losses (Fig. 3c) with spatial scale. The results in Fig. 3a show a decline of unit-area runoff (q) from microplot to plot scale followed by steeper decrease on the stream channel. On average, q decreased by 19% from 5.67 L m⁻² day⁻¹ at microplot to 4.59 L m⁻² day⁻¹ at plot scale (Table 1). This was followed by a 76% decrease from plot to 1.11 L m⁻² day⁻¹ at microcatchment scale. The q decreased further in a downstream direction, by 77% from microcatchment to subcatchment scale, and to very low values at catchment, sub-basin and basin scales. The decrease of q from microplot to plot scale was accompanied by increasing sediment concentration (SC) and unit-area soil losses (SL) (Fig. 3b–c). On

<caption>**Table 1**</caption>

General statistics of daily fluxes (q: unit-area runoff, SC: sediment concentration, and SL: soil loss) at different spatial scales (microplot: 1 m², plot: 10 m², microcatchment: 0.23 km², subcatchment: 1.20 km², catchment: 9.75 km², sub-basin: 253 km², and basin: 29,038 km²) in Thukela basin during the study.

<table><thead><tr><th>Scale</th><th>Min</th><th>Max</th><th>Median</th><th>Mean</th><th>Q1</th><th>Q3</th><th>Stdev</th><th>SE</th><th>Skew</th><th>Kurt</th><th>CV%</th></tr></thead><tbody><tr><td>q (L m<sup>-2</sup> day<sup>-1</sup>)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1m<sup>2</sup></td><td>0.05</td><td>40.55</td><td>4.14</td><td>5.67</td><td>1.45</td><td>7.51</td><td>6.17</td><td>0.38</td><td>2.3</td><td>7.3</td><td>109</td></tr><tr><td>10m<sup>2</sup></td><td>0.01</td><td>36.31</td><td>3.25</td><td>4.59</td><td>1.00</td><td>6.02</td><td>5.19</td><td>0.32</td><td>2.4</td><td>8.4</td><td>113</td></tr><tr><td>0.23 km<sup>2</sup></td><td>8 &times; 10<sup>-5</sup></td><td>18.87</td><td>0.26</td><td>1.11</td><td>0.01</td><td>1.16</td><td>2.11</td><td>0.06</td><td>3.7</td><td>18.3</td><td>191</td></tr><tr><td>1.20 km<sup>2</sup></td><td>1 &times; 10<sup>-7</sup></td><td>4.69</td><td>0.01</td><td>0.26</td><td>6 &times; 10<sup>-5</sup></td><td>0.14</td><td>0.61</td><td>0.02</td><td>3.8</td><td>17.4</td><td>230</td></tr><tr><td>9.75 km<sup>2</sup></td><td>2 &times; 10<sup>-6</sup></td><td>3 &times; 10<sup>-5</sup></td><td>9 &times; 10<sup>-5</sup></td><td>1 &times; 10<sup>-4</sup></td><td>6 &times; 10<sup>-5</sup></td><td>2 &times; 10<sup>-5</sup></td><td>6 &times; 10<sup>-5</sup></td><td>2 &times; 10<sup>-6</sup></td><td>0.4</td><td>-0.3</td><td>65</td></tr><tr><td>253 km<sup>2</sup></td><td>1 &times; 10<sup>-4</sup></td><td>3 &times; 10<sup>-3</sup></td><td>2 &times; 10<sup>-4</sup></td><td>2 &times; 10<sup>-4</sup></td><td>9 &times; 10<sup>-5</sup></td><td>3 &times; 10<sup>-4</sup></td><td>1 &times; 10<sup>-4</sup></td><td>4 &times; 10<sup>-5</sup></td><td>-0.3</td><td>-1.3</td><td>50</td></tr><tr><td>29,038 km<sup>2</sup></td><td>1 &times; 10<sup>-4</sup></td><td>2 &times; 10<sup>-5</sup></td><td>2 &times; 10<sup>-5</sup></td><td>2 &times; 10<sup>-5</sup></td><td>1 &times; 10<sup>-5</sup></td><td>2 &times; 10<sup>-6</sup></td><td>1 &times; 10<sup>-5</sup></td><td>4 &times; 10<sup>-7</sup></td><td>-0.2</td><td>-1.2</td><td>9</td></tr><tr><td>SC (g L<sup>-1</sup>)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1m<sup>2</sup></td><td>0.04</td><td>19.19</td><td>1.33</td><td>2.84</td><td>0.56</td><td>2.74</td><td>3.79</td><td>0.23</td><td>1.9</td><td>2.7</td><td>133</td></tr><tr><td>10m<sup>2</sup></td><td>0.09</td><td>24.35</td><td>2.45</td><td>3.77</td><td>1.28</td><td>3.60</td><td>4.23</td><td>0.26</td><td>1.9</td><td>3.2</td><td>112</td></tr><tr><td>0.23 km<sup>2</sup></td><td>0.01</td><td>7.94</td><td>0.01</td><td>0.14</td><td>0.01</td><td>0.01</td><td>0.64</td><td>0.02</td><td>7.1</td><td>61.5</td><td>450</td></tr><tr><td>1.20 km<sup>2</sup></td><td>0.01</td><td>4.93</td><td>0.01</td><td>0.08</td><td>0.01</td><td>0.01</td><td>0.38</td><td>0.01</td><td>8.1</td><td>73.6</td><td>487</td></tr><tr><td>9.75 km<sup>2</sup></td><td>0.01</td><td>0.53</td><td>0.01</td><td>0.02</td><td>0.01</td><td>0.01</td><td>0.04</td><td>1 &times; 10<sup>-4</sup></td><td>7.6</td><td>66.3</td><td>229</td></tr><tr><td>253 km<sup>2</sup></td><td>0.01</td><td>3.19</td><td>0.01</td><td>0.03</td><td>0.01</td><td>0.01</td><td>0.17</td><td>4 &times; 10<sup>-4</sup></td><td>10.4</td><td>132.4</td><td>531</td></tr><tr><td>SL (g m<sup>-2</sup> day<sup>-1</sup>)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1m<sup>2</sup></td><td>5 &times; 10<sup>-3</sup></td><td>778.03</td><td>4.97</td><td>34.19</td><td>1.12</td><td>14.14</td><td>83.46</td><td>5.17</td><td>4.4</td><td>27.1</td><td>244</td></tr><tr><td>10m<sup>2</sup></td><td>1 &times; 10<sup>-3</sup></td><td>884.07</td><td>6.57</td><td>34.70</td><td>1.53</td><td>15.65</td><td>85.91</td><td>5.32</td><td>5.4</td><td>41.0</td><td>248</td></tr><tr><td>0.23 km<sup>2</sup></td><td>8 &times; 10<sup>-7</sup></td><td>65.29</td><td>3 &times; 10<sup>-4</sup></td><td>0.50</td><td>1 &times; 10<sup>-5</sup></td><td>0.01</td><td>2.98</td><td>0.08</td><td>11.4</td><td>186.3</td><td>597</td></tr><tr><td>1.20 km<sup>2</sup></td><td>1 &times; 10<sup>-9</sup></td><td>17.22</td><td>2 &times; 10<sup>-5</sup></td><td>0.05</td><td>7 &times; 10<sup>-6</sup></td><td>2 &times; 10<sup>-4</sup></td><td>0.64</td><td>0.02</td><td>20.2</td><td>453.5</td><td>1338</td></tr><tr><td>9.75 km<sup>2</sup></td><td>1 &times; 10<sup>-8</sup></td><td>8 &times; 10<sup>-5</sup></td><td>1 &times; 10<sup>-6</sup></td><td>2 &times; 10<sup>-6</sup></td><td>7 &times; 10<sup>-7</sup></td><td>2 &times; 10<sup>-6</sup></td><td>7 &times; 10<sup>-6</sup></td><td>2 &times; 10<sup>-7</sup></td><td>8.0</td><td>73.5</td><td>293</td></tr><tr><td>253 km<sup>2</sup></td><td>1 &times; 10<sup>-6</sup></td><td>6 &times; 10<sup>-4</sup></td><td>2 &times; 10<sup>-5</sup></td><td>2 &times; 10<sup>-5</sup></td><td>1 &times; 10<sup>-5</sup></td><td>3 &times; 10<sup>-5</sup></td><td>7 &times; 10<sup>-5</sup></td><td>3 &times; 10<sup>-5</sup></td><td>6.0</td><td>39.4</td><td>434</td></tr></tbody></table>
average, SC increased by 33% from 2.84 to 3.77 g L⁻¹, while SL increased by 1% only from 34.19 to 34.70 g m⁻² day⁻¹ (Table 1). This was followed by very sharp decrease of 96 and 99% for SC and SL respectively at microcatchment level. The rate of decrease in SL decrease between catchment outlets remained greater than that of SC in the downstream direction. For example, SC decreased by only 43% while SL decreased by 90% from microcatchment to sub-catchment outlet. The q, SC and SL results demonstrated greater magnitudes and dynamics within headwaters, especially the hill-slope, than at greater spatial scales downstream. Therefore, further exploration of the fluxes would be more important in the headwaters (i.e. scales from microplot to catchment level) than the

lower basin area.

### 3.3. Variability of daily water and sediment fluxes over time

The variability of q, SC and SL over time during the study period is shown in Figs. 4–6. The figures show that amplitudes of fluxes were greater with sharper peaks at microplot and plot scales; however the fluxes became more continuous and attenuated with increasing scale size. Despite being relatively dry (Fig. 2), the year 2011–2012 still recorded very high peaks of q, SC and SL at the hillslope scales (i.e. microplot to microcatchment). The winter rains recorded at Potshini in 2012–2013 and 2013–2014 (Fig. 2) did not

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Graphs showing variability of unit-area runoff fluxes (q, L m⁻² day⁻¹) at (a) microplot, (b) plot, (c) microcatchment, (d) subcatchment, (e) catchment, (f) sub-basin and (g) basin scale in Thukela basin over time during the study period 2010–2014.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Graphs showing variability of sediment concentrations (SC, g L⁻¹) at (a) microplot, (b) plot, (c) microcatchment, (d) subcatchment, (e) catchment, and (f) sub-basin scale in Thukela basin over time during the study period 2010–2014.</figcaption></figure>

have any significant effect on q, SC and SL at outlets on the stream channel (Fig. 4c–g, Fig. 5c–f and Fig. 6c–f). Fluxes at catchment outlet were negligible in comparison to other headwater scales due, most probably, to the effect of two dams immediately upstream of the outlet.

As expected, final cumulative q was greatest and least at each spatial scale in year with greatest and least annual rainfall, respectively (Table 2). However, in spite of less annual rainfall, the year 2012–2013 still had 7 and 1% greater cumulative q at microplot and plot scale respectively than in 2013–2014. The microcatchment scale exhibited greatest cumulative q in years when annual rainfall was greater than 800 mm yr⁻¹, but the annual q at microcatchment was less than at microplot in years when annual rainfall was less than 800 mm yr⁻¹. The difference of annual q between microplot and microcatchment was greatest in the wettest year. It was also apparent from the results that plot scale did not always have greater cumulative annual SL than microplot scale. For example, microplot had greater annual SL than plot scale

in 2012–2013 where the respective values were 1889 and 1853 g m² yr⁻¹ for microplot and plot scale, respectively (Table 2). It was also ironic that greater annual rainfall did not always produce greater annual SL. For example, the subcatchment level had 3 fold greater annual SL in 2012–2013 than 2013–2014, despite the fact that annual rainfall was lower in 2012–2013 than 2013–2014.

### 3.4. Factors controlling water and sediment fluxes

The Spearman correlation coefficients ($r_s$) shown in Table 3 indicate significant and positive correlations between the rainfall characteristics and fluxes at all scales in the headwater, except for q at the catchment outlet. Catchment scale q correlated significantly and positively with RainC only. The $r_s$ between the rainfall characteristics and fluxes decreased with spatial scale. However, the trends of $r_s$ for RainC were, again, exceptions. For instance, $r_s$ between q and RainC showed an increase from local scales (i.e. microplot and plot) to microcatchment and then decreased to
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Graphs showing variability of unit-area soil loss fluxes (SL, g m⁻² day⁻¹) at (a) microplot, (b) plot, (c) microcatchment, (d) subcatchment, (e) catchment, and (f) sub-basin scale in Thukela basin over time during the study period 2010–2014.</figcaption></figure>

catchment through the subcatchment level. A similar trend of r₅ was shown between RainC and SL; however the r₅ between RainC and SC decreased from local scales to microcatchment and then increased to catchment through the subcatchment level.

**4. Discussion**

The study results showed a general decrease of unit-area runoff with scale size (Table 1, Fig. 3a), which has also been widely reported in other studies (e.g. Van de Giesen et al., 2000; Joel et al., 2002; Asadzadeh et al., 2012; Thomaz and Vestena, 2012). This decrease of unit-area runoff with increasing area was mainly attributed to infiltration losses on hillslopes and stream channels. The infiltration losses increase with increasing surface area of contact and/or contact time; hence less surface flow volume per unit-area is expected to reach a designated outlet when these two increase. Greater infiltration on hillslopes, where flow is not fully channelized, is mainly promoted by physical barriers, such as

vegetation patches, (Cammeraat, 2004; Mayor et al., 2011), which retard flow velocity thereby increasing the contact time between flow and the infiltrating surface. Even when flow channelizes, Dunkerley (2010) explained that channel-associated plants may still modify flow conditions, thus reducing flow speeds and flow competence. The much lower runoff fluxes at outlets along the stream channel than hillslope scales (Fig. 3a), suggested further streamflow losses to infiltration in a downstream direction. Semi-arid streams are generally dominated infiltration losses of streamflow (Sorman and Abdulrazzak, 1993). The wider variability of the runoff fluxes at microcatchment and subcatchment than at catchment and other downstream outlets signified incidences of rapid storm-flows whose peaks attenuate with increasing stream channel length due to increasing flow resistance and impoundments. The existence of two dams immediately upstream of the catchment outlet may also explain the very low runoff fluxes observed.

The increase of sediment concentration and unit-area soil losses
<caption>**Table 2**</caption>

Annual cumulative unit-area runoff (q) and soil loss (SL) at different spatial scales (microplot: 1 m², plot: 10 m², microcatchment: 0.23 km², subcatchment: 1.20 km², catchment: 9.75 km², sub-basin: 253 km², and basin: 29,038 km²) in Thukela basin during the study period.

<table><thead><tr><th></th><th>2010–2011</th><th>2011–2012</th><th>2012–2013</th><th>2013–2014</th><th>Mean</th></tr></thead><tbody><tr><td>q (L m<sup>-2</sup> yr<sup>-1</sup>)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1 m<sup>2</sup></td><td>539</td><td>273</td><td>343</td><td>320</td><td><strong>369</strong></td></tr><tr><td>10 m<sup>2</sup></td><td>434</td><td>218</td><td>272</td><td>269</td><td><strong>298</strong></td></tr><tr><td>0.23 km<sup>2</sup></td><td>714</td><td>258</td><td>309</td><td>327</td><td><strong>402</strong></td></tr><tr><td>1.20 km<sup>2</sup></td><td>163</td><td>67</td><td>103</td><td>47</td><td><strong>95</strong></td></tr><tr><td>9.75 km<sup>2</sup></td><td>0.04</td><td>0.03</td><td>0.04</td><td>0.04</td><td><strong>0.04</strong></td></tr><tr><td>253 km<sup>2</sup></td><td>Nd</td><td>nd</td><td>nd</td><td>73</td><td><strong>73</strong></td></tr><tr><td>29,038 km<sup>2</sup></td><td>0.1</td><td>0.08</td><td>0.08</td><td>0.14</td><td><strong>0.1</strong></td></tr><tr><td>SL (g m<sup>-2</sup> yr<sup>-1</sup>)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1 m<sup>2</sup></td><td>3619</td><td>1608</td><td>1889</td><td>1803</td><td><strong>2230</strong></td></tr><tr><td>10 m<sup>2</sup></td><td>3646</td><td>1704</td><td>1853</td><td>1847</td><td><strong>2263</strong></td></tr><tr><td>0.23 km<sup>2</sup></td><td>316</td><td>182</td><td>337</td><td>389</td><td><strong>306</strong></td></tr><tr><td>1.20 km<sup>2</sup></td><td>57</td><td>6</td><td>13</td><td>4</td><td><strong>20</strong></td></tr><tr><td>9.75 km<sup>2</sup></td><td>4 × 10<sup>-4</sup></td><td>2 × 10<sup>-4</sup></td><td>0.28</td><td>0.29</td><td><strong>0.14</strong></td></tr><tr><td>253 km<sup>2</sup></td><td>Nd</td><td>nd</td><td>nd</td><td>0.06</td><td><strong>0.06</strong></td></tr><tr><td>29,038 km<sup>2</sup></td><td>Nd</td><td>nd</td><td>nd</td><td>nd</td><td><strong>nd</strong></td></tr></tbody></table>

nd not determined.

The bold values in Table 2 are annual average values for the four year study period.

from microplot to plot scales (Table 1; Fig. 3b–c) has also been observed at other hillslopes (e.g. Le Bissonnais et al., 1998; Parsons et al., 2006) and can be explained in terms of evolving erosion mechanisms. Soil erosion at microplots is dominated by the transport limited splash mechanism; while the longer slope at plot level enables flow velocities to mobilize adequate energy for lateral sheet wash. Therefore, sheet wash tends to be more competent than splash in terms of transportation of detached materials. However, the soil losses increase with slope length up to a peak before declining with further increase in slope length (Kinnell, 2009). The location of peak point varies with slope gradient, rainfall intensity and infiltrations conditions amongst many factors. On our study slope, the peak point is likely to be at a slope length greater than 5 m. The reduction of unit-area soil losses with further increase of area on hillslopes signifies redistribution of sediments within the slopes and deposition at footslopes (Chaplot et al., 2005). In our study, sediment concentrations and soil loss fluxes were much lower at microcatchment than local scales despite evidence of the operation of more erosive mechanisms (e.g. sheet, gully and

stream bank erosion). Such results can be explained by greater heterogeneity and variability of landscape features (e.g. slope, basal cover, complexity of erosion processes like increased detachment, transport and deposition cycles) at microcatchment than local scales which favoured greater sedimentation. The same appeared to occur at subcatchment level, where sediment fluxes were even lower. Several studies have also shown lower unit-area runoff and sediment fluxes on stream channels than hillslopes (Constantz, 1998; Feng and Li, 2008; Goransson et al., 2013; Xu, 2014) due to lower slope gradients which promote high infiltration and sediment deposition (Doble et al., 2012; Eder et al., 2014).

Results of cumulative runoff (Table 2) showed great annual runoff microcatchment level, which in some years (e.g. 2010–2011 and 2013–2014) was greater than at microplot level. High microcatchment level annual runoff has also been reported at this station by other studies (Chaplot and Ribolzi, 2014; Orchard et al., 2013) and at other footslopes (e.g. Castro dos Reis et al., 1999; Uhlenbrook et al., 2005; Van Tol et al., 2013). The main reason proffered by the other studies is downslope movement of soil water and exfiltration to join overland flow systems at footslopes and stream channels. The results of annual unit-area runoff in our study also suggest the existence of an annual rainfall threshold (of about 800 mm yr⁻¹) above which cumulative runoff at microcatchment would be greater than at microplot level. Number of rain-days and days of high intensity rainfall can explain this result. The years 2010–2011 and 2013–2014 (rainfall > 800 mm yr⁻¹) had greater number of rain-days and incidences of greater than 20 mm day⁻¹ rainfalls than 2011–2012 and 2012–2013 (<800 mm year⁻¹). Coincidentally, 2010–2011 and 2013–2014 had greater cumulative runoff at microcatchment than microplot level, and opposite in 2011–2012 and 2012–2013.

The results showing decline of correlation coefficients between rainfall characteristics and fluxes with scale size (Table 3) suggest rainfall effect on the fluxes was a local phenomenon whose significance would diminish with landscape area. This agrees with findings by Chamizo et al. (2012) who identified rainfall characteristics as major proponents of runoff generation at local scale only. Indices for rainfall amount (i.e. Rainfall and Dur) appeared to have greatest effect on the fluxes, followed by rainfall intensity indices (I and Max<sub>6min</sub>I) and lastly the moisture indices (Prerain-3 and RainC) at all headwater scales during the study. However, other environmental parameters need to be appraised.

<caption>**Table 3**</caption>

Spearman rank correlations between headwater rainfall characteristics (Rainfall: total rainfall amount per day, Dur: total duration of rainfall events per day, I: average rainfall intensity in a day, Max<sub>6min</sub>I: maximum 6-min rainfall intensity in a day, PreRain-3: antecedent 3-day rainfall and RainC: cumulative rainfall since the onset of the main rain season) and fluxes (q: unit-area runoff, SC: sediment concentration and SL: soil loss) at different scales (microplot: 1 m²; plot: 10 m², microcatchment: 0.23 km², subcatchment: 1.20 km², and catchment: 9.75 km²) in the headwater of Thukela basin.

<table><thead><tr><th rowspan="2">Fluxes</th><th rowspan="2">Spatial scale</th><th colspan="7">Rainfall characteristics</th></tr><tr><th>Rainfall (mm day<sup>-1</sup>)</th><th>Dur (mins)</th><th>I (mm h<sup>-1</sup>)</th><th>Max<sub>6min</sub>I (mm h<sup>-1</sup>)</th><th>PreRain-3 (mm)</th><th>RainC (mm)</th></tr></thead><tbody><tr><td rowspan="5">q (L m<sup>-2</sup> day<sup>-1</sup>)</td><td>1 m<sup>2</sup></td><td>0.80*</td><td>0.80*</td><td>0.71*</td><td>0.71*</td><td>0.66*</td><td>0.19*</td></tr><tr><td>10 m<sup>2</sup></td><td>0.80*</td><td>0.80*</td><td>0.71*</td><td>0.71*</td><td>0.66*</td><td>0.19*</td></tr><tr><td>0.23 km<sup>2</sup></td><td>0.57*</td><td>0.57*</td><td>0.55*</td><td>0.55*</td><td>0.57*</td><td>0.54*</td></tr><tr><td>1.20 km<sup>2</sup></td><td>0.26*</td><td>0.26*</td><td>0.26*</td><td>0.26*</td><td>0.31*</td><td>0.44*</td></tr><tr><td>9.75 km<sup>2</sup></td><td>-0.02</td><td>-0.02</td><td>-0.01</td><td>-0.01</td><td>0.00</td><td>0.36*</td></tr><tr><td rowspan="5">SC (g L<sup>-1</sup>)</td><td>1 m<sup>2</sup></td><td>0.79*</td><td>0.79*</td><td>0.71*</td><td>0.71*</td><td>0.66*</td><td>0.19*</td></tr><tr><td>10 m<sup>2</sup></td><td>0.79*</td><td>0.79*</td><td>0.71*</td><td>0.71*</td><td>0.66*</td><td>0.19*</td></tr><tr><td>0.23 km<sup>2</sup></td><td>0.52*</td><td>0.51*</td><td>0.46*</td><td>0.46*</td><td>0.30*</td><td>0.12*</td></tr><tr><td>1.20 km<sup>2</sup></td><td>0.45*</td><td>0.45*</td><td>0.41*</td><td>0.41*</td><td>0.28*</td><td>0.13*</td></tr><tr><td>9.75 km<sup>2</sup></td><td>0.44*</td><td>0.44*</td><td>0.40*</td><td>0.40*</td><td>0.28*</td><td>0.15*</td></tr><tr><td rowspan="5">SL (g m<sup>-2</sup> day<sup>-1</sup>)</td><td>1 m<sup>2</sup></td><td>0.80*</td><td>0.80*</td><td>0.71*</td><td>0.71*</td><td>0.66*</td><td>0.19*</td></tr><tr><td>10 m<sup>2</sup></td><td>0.80*</td><td>0.80*</td><td>0.71*</td><td>0.71*</td><td>0.66*</td><td>0.19*</td></tr><tr><td>0.23 km<sup>2</sup></td><td>0.61*</td><td>0.61*</td><td>0.58*</td><td>0.58*</td><td>0.57*</td><td>0.53*</td></tr><tr><td>1.20 km<sup>2</sup></td><td>0.35*</td><td>0.35*</td><td>0.34*</td><td>0.34*</td><td>0.35*</td><td>0.44*</td></tr><tr><td>9.75 km<sup>2</sup></td><td>0.12*</td><td>0.12*</td><td>0.11*</td><td>0.11*</td><td>0.07*</td><td>0.35*</td></tr></tbody></table>

<footnote>*Significant at p < 0.05.</footnote>
## 5. Conclusions

The objectives of this study conducted in Thukela Basin, South Africa, were to (i) evaluate water and sediment fluxes at different spatio-temporal scales, and (ii) interpret the results in terms of processes involved and factors of control. The results showed a decrease of unit-area runoff by 2.6 fold from microplot to plot scales followed by a sharp decrease at the o microcatchment level. Unit-area runoff decreased in downstream direction to very low values. Different soil erosion mechanisms, dependent on spatio-temporal scales, accompanied the changes in unit-area runoff culminating in 1% increase of average unit-area soil loss from microplot to plot scale and a very sharp (39 fold) decline at the microcatchment scale. The soil loss fluxes also decreased to very low values in the downstream direction. The change in sediment fluxes was associated with evolution of soil erosion types, from lateral to linear mechanisms. Splash erosion dominated at micro-plot scale, changing to the more transport competent lateral sheet erosion mechanism at plot scale. However, despite evidence of the operation of linear erosion mechanisms at microcatchment scale and beyond sediment fluxes were very low on the stream channel due to deposition on the slope and stream channel. In order to mitigate soil erosion at the study site, strategies to promote the recovery of vegetation and stabilisation of gullies are recommended.

## Acknowledgements

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007–2013) under the WHaTeR project (Water Harvesting Technologies Revisited) grant agreement n° 266360, Water Research Commission (WRC K5/2266) and African Conservation Trust (ACT).

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