## 3.2. Analysis of Hydrological Data

The discharges register a global decrease since 1970 (Table 4), or the probable ruptures are noted as well during rainy season and dry season (Table 4), which means that even the low water levels are slow, for a sudden drop, it indicates that the level of the groundwater has also abruptly fall.

<table><caption>**Table 4.** Detection of ruptures in the annual and monthly flow series and annual maximum ($Q_{maxd}$) and minimum ($Q_{mind}$) daily discharge (1968/1969–2006/2007). Dates of ruptures detected by three statistical tests (red: strong ruptures), by at least two statistical tests (dark gray: probable ruptures), by at least one statistical test (light gray: weak rupture) (on the catchment areas (in bold: the ruptures dates detected for more than one catchment area the same year or the same month) (empty box = no probable rupture detected).</caption><thead><tr><th>Station</th><th>S</th><th>O</th><th>N</th><th>D</th><th>J</th><th>F</th><th>M</th><th>A</th><th>M</th><th>J</th><th>J</th><th>A</th><th>Annual Discharge</th><th>Q<sub>maxd</sub> Annual Daily</th><th>Q<sub>mind</sub> Annual Daily</th></tr></thead><tbody><tr><td>Ain Hamara</td><td>72</td><td></td><td>99</td><td>71</td><td>74</td><td>06</td><td>81</td><td>84</td><td>80</td><td>70</td><td>00</td><td>79</td><td>72</td><td>92</td><td>72</td></tr><tr><td>Takhmert</td><td>93</td><td>75</td><td>96</td><td>89</td><td></td><td>95</td><td></td><td></td><td></td><td>02</td><td>97</td><td></td><td>94</td><td>85</td><td>94</td></tr><tr><td>Sidi Ali Ben Amar</td><td></td><td>74</td><td>82</td><td>82</td><td>83</td><td>88</td><td>87</td><td>87</td><td>87</td><td>89</td><td>90</td><td>90</td><td>81</td><td>70</td><td>81</td></tr><tr><td>Wadi El Abtal</td><td>73</td><td>73</td><td></td><td>70</td><td></td><td>95</td><td>72</td><td>81</td><td>80</td><td>76</td><td>79</td><td>75</td><td>74</td><td>84</td><td>72</td></tr><tr><td>Sidi Aek Djilali</td><td>70</td><td>75</td><td>90</td><td>70</td><td>90</td><td>87</td><td>91</td><td>75</td><td>76</td><td>76</td><td>75</td><td>74</td><td>75</td><td>95</td><td>74</td></tr><tr><td colspan="18">% annual discharge</td></tr><tr><td></td><td>1960</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0</td></tr><tr><td></td><td>1970</td><td>3</td><td>4</td><td></td><td>3</td><td>1</td><td></td><td>1</td><td>1</td><td>1</td><td>3</td><td>2</td><td>3</td><td>3</td><td>22</td></tr><tr><td>Number of ruptures</td><td>1980</td><td></td><td></td><td>1</td><td>2</td><td>1</td><td>2</td><td>2</td><td>3</td><td>3</td><td>1</td><td>1</td><td>2</td><td>1</td><td>15</td></tr><tr><td>in the decades</td><td>1990</td><td>1</td><td></td><td>3</td><td>1</td><td>2</td><td>1</td><td></td><td></td><td></td><td>2</td><td>1</td><td></td><td>1</td><td>11</td></tr><tr><td></td><td>2000</td><td></td><td></td><td></td><td></td><td>1</td><td></td><td></td><td></td><td></td><td>1</td><td>1</td><td></td><td></td><td>3</td></tr></tbody></table><table><thead><tr><th colspan="2">Total</th></tr></thead><tbody><tr><td></td><td>0</td></tr><tr><td></td><td>22</td></tr><tr><td></td><td>15</td></tr><tr><td></td><td>11</td></tr><tr><td></td><td>3</td></tr></tbody></table>

At annual level, the probable rupture dates are 1974, 1975, 1980 and 1994, which corresponds generally to the ruptures detected at month level. In addition, the ruptures on the series of annual daily maximum were for the most part noted in 1970, 1984 and 1995 and the ruptures dates for the annual daily minimum series are detected in 1972, 1981 and 1994. The rupture in the daily maximum flows series is thus never noted the same year as in the average annual flows series, while there are the same ruptures dates for the average annual flows and the minimum daily flows.

In Sahelian and non-Sahelian Africa, many authors have revealed, based on hydro-pluviometric series, a dry phase which started around 1970 [109,110] and which lasts until today [111,112], it was noted as well a decrease of rivers flows [109,113,114], lakes levels [113], as well as a shortening of the rainy season [115,116].

These results indicate a satisfactory concordance with the dates detected on the annual and monthly rainfall of late 1970s/early 1980s. We thus assume that the decrease of runoff would be caused by a global decrease in precipitations on the catchment area of Wadi Mina [47]. However, we must not rule out a possible impact of storages and uses on the drastic decline of low water levels compared to groundwater.

In addition, several authors, among which [117], have as well reported rain decreases for the Mediterranean perimeter: in Spain, Italy, Turkey and Cyprus from 1951 to 1995 [118], between 1950 and 2000 in October in Mediterranean Iberia, March in Atlantic Iberia, January and winter in Greece and winter in the Middle East. Hulme et al. [119] showed a strong decrease of rains for the Mediterranean basin between the start and the end of 20th century. Likewise, subsequent to an analysis of the evolution of pluviometric inputs in the plains of Ghriss (northwest Algeria) [10], it was shown a net decrease starting with 1973, estimated at more than 25%. We can quote as well other results that have indicated that rains simulation over north Algeria, with an annual decrease of 15%, would entail a reduction of the annual runoff of 40% [120]. In Morocco, a climatic rupture that starts around 1976 in certain regions and which extends largely around 1979–1980, to nearly the entire territory, was noted [69,121].

As a consequence of the decrease of rainfall after the 1970s rupture which was observed in most basins of western Algeria, river discharges generally decreased as well. Meddi and Hubert [6] showed that the decrease in river discharge varied between –37% and –70% from eastern Algeria to western
Algeria. In the Macta basin in northwest Algeria, runoff was estimated to be 28–36% lower in 1976–2002 as compared to 1949–1976 [11]. In the Tafna basin, also in northwest Algeria, research [122,123] showed that the decrease in precipitation after the rupture date was, on average, 29% over the whole basin and was accompanied by a decrease of 60% in river flow [42]. In Wadi Sebdou in west Algeria, Norrant and Douguédroit [118] showed that rainfall and discharge began to decrease in the 1970s. The diminution of rainfall by 24% between the period 1939–1975 (526 mm) and the period 1975–2004 (401 mm) induced a decrease by 55% of the yearly volume discharged in 1975–2004 as compared to 1939–1975. A similar variability was observed on Wadi Abd basin (Wadi Mina) [43].

Finally, we note that annual discharges have been decreasing overall since the 1970s, which generally corresponds to ruptures detected at the monthly time step. There is therefore a good agreement with the rupture dates in the annual rainfall series, which suggests a climatic cause, in the first place, the observed decline in discharges in the Wadi Mina basin [47].

The study of floods is a precondition for the choice of an adjustment distribution and thus the floods flows on the entire Wadi Mina basin are classified in category C and follow a GEV distribution (Table 5).

The results of stationary nature and independence hypotheses tests of the studied data concerning the annual floods of the stations of the sub-basins of Wadi Mina indicate the acceptance of the independence tests of Wald–Wolfowitz and the stationary nature tests of Kendall for all the stations (Table 5).

<table><caption>Table 5. Distribution of floods for each station of the Wadi Mina basin.</caption><thead><tr><th>Basin</th><th>Stations</th><th>Independence (Wald–Wolfowitz)</th><th>Stationary (Kendall)</th><th>Class Distributions</th></tr></thead><tbody><tr><td rowspan="3">Wadi El Abd Aval</td><td>Ain Hamara</td><td>Accepted to the 5% threshold</td><td>Accepted to the 1% threshold</td><td>C (GEV distribution)</td></tr><tr><td>Takhmert</td><td>Accepted to the 5% threshold</td><td>Accepted to the 5% threshold</td><td>C (GEV distribution)</td></tr><tr><td></td><td></td><td></td><td></td></tr><tr><td>Wadi Haddad</td><td>Sidi Abdel Kader Djilali</td><td>Accepted to the 1% threshold</td><td>Accepted to the 1% threshold</td><td>C (GEV distribution)</td></tr><tr><td rowspan="2">Upstream Wadi Mina</td><td>Wadi El Abtal</td><td>Accepted to the 5% threshold</td><td>Accepted to the 5% threshold</td><td>C (GEV distribution)</td></tr><tr><td>Sidi Ali Ben Amar</td><td>Rejected to the 1% threshold</td><td>Accepted to the 5% threshold</td><td>C (GEV distribution)</td></tr></tbody></table>

The study of the floods recurrence variability before and after the ruptures was performed on all the stations of the basin of Wadi Mina (Figure 6). For all the stations, the maximum discharges values decreased for the same return period with about 30 to 40%. For example, at the Ain Hamara station and before the rupture of 1992, the floods with intensity lower than 80 m³·s⁻¹ only return once in five years (Figure 6a). However, beyond this value, precisely starting with 99 m³·s⁻¹, the floods present longer return periods (20–100 years). However, after the same rupture date and for the same return periods, the intensities of the floods considerably decreased. For example, with a return period of 20 years before 1992, the flood with an intensity of 99 m³·s⁻¹ became centenary.

<figure><img src="image_45.png" /><figcaption>Figure 6. *Cont.*</figcaption></figure>
<figure><img src="image_46.png" /><figcaption>**Figure 6.** Impact of ruptures in the hydrometric series on the floods recurrence. (a) Ain Hamara station; (b) Sidi Ali Ben Amar station; (c) Sidi Abdelkader Djilali station; (d) Wadi El Abtal station; (e) Takhmert station.</figcaption></figure>

The change of the pluviometric regime of the 1970s had thus as an impact a variance of recurrence of floods on all the sub-basins of the catchment area of Wadi Mina.

The pluviometry reduction has, of course, consequences on the watercourses regimes and thus on the availability of water resources which is the key to the success of many development projects. Nevertheless, the consequences of this reduction of flown volumes are already obvious with regard to the water and environment resources exploitation. Agriculture and the alimentation of impoundments are largely sanctioned by this resource decrease. The consequences of this phenomenon are thus very worrying with regard to the good functioning and the cost-effectiveness of projects already performed or planned.

### 3.3. *Solids and Liquids Annual Transports*

The annual average concentrations of SPM are compared with annual average discharge (Figure 7). Despite year-to-year irregularities at the Sidi Aek Djilali station, concentrations and discharges have changed in opposite directions. Discharges increased from $14 \text{ m}^3\cdot\text{s}^{-1}$ in the late 1980s to less than 3 $\text{m}^3\cdot\text{s}^{-1}$ in the early 2000s, while concentrations increased from $44 \text{ g}\cdot\text{L}^{-1}$ in 1974 to nearly 78 g. In 2005, flows at the El Abtal Wadi station increased from more than $40 \text{ m}^3\cdot\text{s}^{-1}$ in 1974 to $0.48 \text{ m}^3\cdot\text{s}^{-1}$ in 1997, whereas the concentrations evolved by $12 \text{ g}\cdot\text{L}^{-1}$, in 1971, to almost more than $67 \text{ g}\cdot\text{L}^{-1}$ in 2005. However, ruptures were detected on the rainfall and discharges from the mid-1970s, but this result show that is no effect of ruptures in precipitation and discharge on the solid load response since the discharge and rainfall decrease in the 1970s and increase in the early 2000s while the sediment load is still increasing. Geomorphologists have noted the difficulty of predicting the impacts of climate change on sediment yield due to nonlinear effects [124–126]. In addition, Achite and Ouillon [42] in their study on Wadi Abd basin, found that the change in flow regime induced a fully nonlinear effect between river discharge and sediment yield. This must be considered in the forecasts, especially in small river basins in semi-arid areas.
<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /><figcaption>**Figure 7.** Evolution of annual average of discharges and concentrations of SPM in the Wadi Mina basin: (a) Wadi El Abtal station; (b) Sidi Aek Djilali station.</figcaption></figure>

Regression analysis was performed between the concentration in instant suspended sediment (c) and the instant discharge (Q) at Wadi Mina. Generally, the power model $Q = aC^b$, where a and b are regression coefficients, is the most used for the estimation of the sediment concentration (Figure 8) [77–79].

<figure><img src="image_49.png" /><figcaption>**Figure 8.** Relation between the liquids instantaneous flows and the concentrations of matters in suspension (Station of Wadi Abtal).</figcaption></figure>
As for the majority of watercourses, we found a power relation [127] for Wadi Mina, connecting the concentration of SPM in g·L⁻¹ to the liquid discharge in m³·s⁻¹ (Figure 8). A first graphic analysis of this figure shows a fairly strong dispersion around the regression line, with a very broad concentration range for low discharges below 10 m³·s⁻¹ and concentrations that become significantly higher, above 10 g·L⁻¹ for discharges greater than 50 m³·s⁻¹.

For studying the basin responses in terms of discharges and SPM during the hydrological year, it was considered useful to regroup the instantaneous values pursuant to the different seasons (Table 6).

<table><caption>**Table 6.** Seasonal models connecting the instantaneous flows with the concentration of matters in instant suspension.</caption><thead><tr><th>STATION</th><th>Correlation</th><th>Autumn</th><th>Winter</th><th>Spring</th><th>Summer</th></tr></thead><tbody><tr><td rowspan="2">W. El Abtal</td><td>Model</td><td>Q = 0.506C<sup>1.092</sup></td><td>Q = 1.940C<sup>0.719</sup></td><td>Q = 0.889C<sup>0.969</sup></td><td>Q = 0.483 C<sup>0.891</sup></td></tr><tr><td>Coefficient R<sup>2</sup></td><td>0.60</td><td>0.20</td><td>0.59</td><td>0.37</td></tr><tr><td rowspan="2">S.A.E.K. Djilali</td><td>Model</td><td>Q = 0.031C<sup>1.137</sup></td><td>Q = 0.125C<sup>0.561</sup></td><td>Q = 0.024C<sup>1.12</sup></td><td>Q = 0.161C<sup>0.584</sup></td></tr><tr><td>Coefficient R<sup>2</sup></td><td>0.60</td><td>0.20</td><td>0.63</td><td>0.30</td></tr><tr><td rowspan="2">A. Hamara</td><td>Model</td><td>Q = 1.038C<sup>0.854</sup></td><td>Q = 1.715<br>8C<sup>0.458</sup></td><td>Q = 0.893C<sup>0.867</sup></td><td>Q = 1.292C<sup>0.655</sup></td></tr><tr><td>Coefficient R<sup>2</sup></td><td>0.51</td><td>0.34</td><td>0.55</td><td>0.38</td></tr><tr><td rowspan="2">Takhmert</td><td>Model</td><td>Q = 0.8C<sup>1.022</sup></td><td>Q = 1.78 8C<sup>0.621</sup></td><td>Q = 1.13C<sup>0.934</sup></td><td>Q = 0.842C<sup>0.845</sup></td></tr><tr><td>Coefficient R<sup>2</sup></td><td>0.63</td><td>0.43</td><td>0.66</td><td>0.44</td></tr><tr><td rowspan="2">S.A.B. Amar</td><td>Model</td><td>Q = 0.799C<sup>0.733</sup></td><td>Q = 2.437C<sup>0.167</sup></td><td>Q = 0.67C<sup>0.712</sup></td><td>Q = 0.277C<sup>0.896</sup></td></tr><tr><td>Coefficient R<sup>2</sup></td><td>0.56</td><td>0.21</td><td>0.64</td><td>0.44</td></tr></tbody></table>

The analysis in Figure 9 shows that autumn and spring are distinguished by their strong liquid discharges generating an important flow of SPM. The maximum solid flow is of 42,365 kg·s⁻¹, achieved in September 1994, resulting from a liquid discharge of more than 351 m³·s⁻¹.

<figure><img src="image_50.png" /><figcaption>**Figure 9.** Seasonal variations (summer: June, July and August; autumn: September, October, November; winter: December, January, February; and spring: March, April, May) of the instantaneous values of discharges depending on the concentrations of suspended particles matters (SPM) (Station Wadi El Abtal). (a) Summer; (b) Autumn; (c) Spring; (d) Winter.</figcaption></figure>

In contrast, during winter and summer, we note a net regression of liquid discharge that do no longer exceed 84 m³·s⁻¹ in winter and 38 m³·s⁻¹ in summer. However, we note a few specificities for each season.
For autumn, the heavy charge is explained by the fact that rain often falls on a dried up soil and badly protected by vegetation [68].

The basin of Wadi Mina is bare in autumn and, after a long warm period, the autumn floods imply systematically suspended solids inputs in maximum number and this phenomenon is relatively reduced for the following season, in winter or when the lands forming the basin are saturated and the vegetation cover is developed [68].

On the one hand, the torrential and aggressive rains are the ones occurring in October and November and they tear off major quantities of SPM and, on the other hand, the weak vegetation cover of the preceding season (summer) favors the destruction of the soil aggregates that shall be subsequently transported in suspension by the watercourse.

In winter, the erosion of the bed and the banks along the watercourse becomes important because of the increase of discharges in Wadi, and as well runoffs are generated by rainy sequences which are relatively abundant but of weak intensity (0.4 mm) in Takhmert and (29.1 mm) in Ain Hamara. They find a loose soil with vegetation cover that confers soil roughness and favors water retention [128].

In winter, the runoffs remain important, although this season is a little rainy and cold transition period with a succession of freezing and thawing which is caused essentially by the important water reserves, stored in the soil, following the autumn rains.

In spring, the erosion of the bed and the banks along the watercourse becomes important as this is essentially related to the increase discharges in the Wadi and the herbaceous cover and cultures that considerably reduce the mobilization of thin materials on the slopes. We can add as well the reduction of rains of heavy intensity compared with the winter season.

At the end of spring, the soil is humid and the vegetation cover is well developed. On the slopes, the thin soils saturate rapidly and develop important erosion in the gully and mass movement [129,130]. In addition, in this period, the floods have an important role in the production of sediments in the drainage networks. In addition, Hallouz et al. [67], in their study on the Wadi Mina basin, found that the maximum values of the solid inputs, for all the sub-basins, are observed at the beginning of autumn and at the end of the spring, this variability is explained on the one hand by the variation of the vegetal cover (bare soils) during the year and the erosive nature of the autumn rains (high intensities) and on the other hand by the releases made by the Bakhadda Dam, so these two factors allow the first autumn floods to transport large quantities of sediment after a long dry season characterized by high temperatures and the destruction of soil aggregates. Rainfall erosivity is the potential ability of rainfall to cause soil loss [131]. The rainfall erosivity index represents the climate influence on water related soil erosion [132]. Rainfall erosivity is the impact of the kinetic energy of raindrops on soil. Higher velocity and larger size of the raindrops results in higher kinetic energy and higher soil loss. According to Yu [132], most soil erosion researchers and soil conservationists recognize the positive correlation between erosivity and rainfall intensity [133].

### 3.4. Annual Inputs

The calculation of the sediment average specific load (Ds) in tons is given by the product sum of three variables Q (m³·s⁻¹), C (g·L⁻¹) and T (duration in seconds). Table 7 resumes the average specific exportations in tons per km² and per year for each sub-basin.

<table><caption>**Table 7.** Results of the calculation of sediment charge in tons.</caption><thead><tr><th>Basins</th><th>W. Mina (I + II)</th><th>W. Haddad</th><th>W. Mina</th><th>W. Abd</th><th>W. Mina Amont</th></tr></thead><tbody><tr><td>Station name</td><td>(W. Abtal + S.A. Djilali)</td><td>S. Aek Djilali</td><td>W. El Abtal</td><td>A. Hamara</td><td>Takhmert</td></tr><tr><td>Average specific load (t·km<sup>-2</sup>·year<sup>-1</sup>)</td><td>860</td><td>965</td><td>762</td><td>502</td><td>220</td></tr><tr><td>Period of observation</td><td>1969/2010</td><td>1969/2010</td><td>1969/2010</td><td>1969/2010</td><td>1969/2007</td></tr></tbody></table>
The average specific sediment discharge of Wadi Mina at the Sidi M'Hamed Ben Aouda Dam is 860 $t·km^{-2}·year^{-1}$ (the sum of degradations measured at the stations Wadi El Abtal and Sidi Abdelkader El Djilali to which is added the load produced on the surface of the transitional basin VI). We note that this value is very moderated in relation to those published for other basins in the region, such as the basin of Wadi Mazafran (Algerian coastal basins), Wadi Isser (Lakhdaria), Wadi Sebdou (basin Tafna), Wadi Agrioum (east Algerian), whose specific degradations amount to 1610, 2300 and 1120 $t·km^{-2}·year^{-1}$ [134,135] and 5000 $t·km^{-2}·year^{-1}$ [136]. Meddi [68] already reported that Wadi Haddad à Sidi Abdelkader El Djilali has a high average specific load in relation to other catchment areas ($D_s = 965 t·km^{-2}·year^{-1}$), which means that this sub-basin continues to be the busiest supplier of sediments towards the dam. This catchment area is the most favorable for the runoff from the point of view of topography and vegetation cover [68]. It is characterized by a discontinuous and poor vegetation cover during warm season. The concentration of floods after the warm season in October and sometimes in May, with strong intensities, would generate strong concentrations in sediments during these months. This context favorable to the runoff and, consequently, to the solid transport explains this serious degradation of soils for this catchment area, in addition to its geological nature which is very favorable as well to this event (marl) [68].

The lowest degradation values are observed for Wadi Abd at Takhmert (220 $t·km^{-2}·year^{-1}$) and Wadi Mina upstream of Sidi Ali Ben Amar (239 $t·km^{-2}·year^{-1}$). These values can well be explained by the less favorable nature of the runoff of basins which are characterized by floods of average or light power [68].

### 3.5. Where from Does Originate the Sediments of the Dam?

Thus, the results of bathymetric surveys performed at the level of the SMBA Dam indicated that the capacity loss of the dam since its commissioning in 1978 and until 2004 is evaluated at $82 \times 10^6 m^3$ (Figure 10), equivalent to a third (1/3) of its initial capacity. Remini and Bensafia [64] estimated that, in 2010, the SMBA Dam registers a sediment volume equal to $109 \times 10^6 m^3$, namely an annual average loss of about $3.5 \times 10^6 m^3·year^{-1}$, equivalent to a sediment load equal to $5.6 \times 10^6 t·year^{-1}$ that deposits in the impoundment of the dam (considering an average density of the sediment of $1.6 t·m^{-3}$).

<figure><img src="image_51.png" /><figcaption>**Figure 10.** Evolution of the basin morphology and the surface in free water of the Sidi M'Hamed Ben Aouda Dam [14]. (a) Initial water volume (1978–1985); (b) Water volume (1985–2004).</figcaption></figure>
Indeed, according to bathymetric surveys conducted in 1978, 1985, 2000 and 2004, the SMBA Dam has lost much of its capacity (Figure 11), from 1978 to 1985 annual loss estimated to 1.35 million m³, from 1985 to 2000, an annual loss of about 4.41 million m³ and from 2000 to 2004 an annual loss was estimated to 1.42 × 10⁶ m³, over all these periods of bathymetric measurements, the highest loss rate was observed between 1985 and 2000, which is probably due to exceptional rains that hit the region during this period, thus in October 1993: "Twenty minutes of rain do 16 dead and a dozen of missing persons in Relizane" [137].

It is important to note that at the start of the dam exploitation, the reservoir filling with fine particles is carried out linearly according to weather conditions. Starting with 2000, there was a decrease in siltation, mainly caused by the dam exploitation mode (periodic shelving of fine particles by the outlet gates) [14].

<figure><img src="image_52.png" /><figcaption>Figure 11. Evolution of the accumulated loss of water from the Sidi M'Hamed Benaouda Dam [14].</figcaption></figure>

Conversely, the calculations made by the concentration measures for the basin of Wadi Mina at the level of the station of Wadi El Abtal and Wadi Haddad, at the Sidi AEK Djilali station level, and which control a surface of 5856 km², generated a load equal to 5.2 × 10⁶ t·year⁻¹ (Table 8). The difference of 400,000 t·year⁻¹ corresponds to the sedimentary inputs of the micro-basin surrounding the impoundment (192 km²) and the autochthon sedimentation in the impoundment, without being able to establish the parts of each source. To elaborate further on this topic, we should deploy specific measures as those presented by Maleval [138]. This difference of 400,000 t·year⁻¹ corresponds to a specific degradation of 21 t·ha⁻¹·year⁻¹, namely 2.4 times more than the specific degradation for the entire basin of 5856 km² (Table 8). This difference can be explained, on the one hand, by potential autochthon sedimentation, but which is not quantifiable for the moment, and, on the other side, by a specific degradation intrinsically higher because of geomorphology reasons, as the eroded products on the slopes arrive very rapidly at the impoundment, without the existence of an intermediary decantation area. Thus, this specific degradation value of 21 t·ha⁻¹·year⁻¹ is even higher than the "potential" erosion value of about 12 t·year⁻¹, calculated by Toumi et al. [66] for the entire basin, based on the USLE equation.

<table><caption>Table 8. Sedimentary load and specific degradation in the basin of Wadi Mina.</caption><thead><tr><th>Designation</th><th>SMBA Dam</th></tr></thead><tbody><tr><td>Sedimentary load deposited in the dam impoundment (bathymetry).</td><td>5.6 × 10<sup>6</sup> t·year<sup>-1</sup></td></tr><tr><td>Sedimentary load by concentration measure on Wadi Haddad and Wadi Mina upstream.</td><td>5.2 × 10<sup>6</sup> t·year<sup>-1</sup></td></tr><tr><td>Sedimentary load coming from the dam slopes.</td><td>4 × 10<sup>5</sup> t·year<sup>-1</sup></td></tr><tr><td>Specific degradation (dam slopes).</td><td>21 t·ha<sup>-1</sup>·year<sup>-1</sup></td></tr><tr><td>Specific degradation (Stations: W.El Abtal + Sidi Abdelkader Djilali).</td><td>8.6 t·ha<sup>-1</sup>·year<sup>-1</sup></td></tr></tbody></table>
For concluding this part, we can note that, in accordance with the digits in Table 8, over 90% of sediments of the SMBA Dam appears to come from the contributions of the Wadi Haddad and Wadi Mina upstream of the dam.

Thus, Touaibia [45] showed that the USLE equation allowed to map each parameter of this equation for the entire basin of Wadi Mina and superposition of layers (for each parameter) in a GIS and resulted in values of degradation in $t·ha^{-1}·year^{-1}$ (Table 9).

Indeed, the results of the concentration measures as well as those found by the erosive potentials equation (USLE) show that the basin upstream of the SMBA (1B) Dam (Figure 12) presents an important erosive risk, because it produces a specific degradation equal to $13.36 t·ha^{-1}·year^{-1}$ and $19.64 t·ha^{-1}·year^{-1}$, respectively, this because of the predominance of tertiary marls in this part of the basin and which are covered in northeast by calcareous sandstone and dolomites and the slopes, which are from 12% to 30%, generating important corrosive risks with occurrence of the signs caused by diffuse streaming, without forgetting the sparse vegetation cover [139]. In addition, the specific degradations vary from $11.54 to $12.64 t·ha^{-1}·year^{-1}$ at the basin level (5B) upstream of the Bakhadda Dam and, respectively, the basin of Wadi Haddad (2B), calculated by the USLE equation [66] against $2.39 to $9.64 t·ha^{-1}·year^{-1}$ on the same sub-basins, these values remaining high, considering that the basin of Wadi Haddad presents a lithology constituted by the Continental Quaternary (this formation is represented by alluvia, regs and terraces), the Continental Pliocene (represented by lacustrine limestone) and the marine Lower Miocene (represented by a marly formation). The basin vegetation is formed essentially of scrubland, representing almost 30% from the entire basin (degraded forest). The vegetation is overgrazed and is discontinuous in the space [68] and the basin (5B) upstream of the Bakhadda Dam is dominated by limestone and dolomites alternating with marls and an average dense vegetation and slopes varying from 5% to 12% that present weak erosion risks, but can be strong with the presence of agricultural activities, mainly grain farming that dominates soil occupation [139].

The values of specific degradation are low in the southwest part of the basin (basin of Wadi El Abd) and thus the fields in this regional are quasi-flat (slope lower than 10%) [139], and thus the specific degradations are equal to $3.86 t·ha^{-1}·year^{-1}$ at the level of the Takhmert station and $8.82 t·ha^{-1}·year^{-1}$ at the Ain Hamara station [66], in accordance with the calculations generated using the USLE equation, and varying from $2.20 t·ha^{-1}·year^{-1}$ at the level of the Takhmert station and $5.02 t·ha^{-1}·year^{-1}$ at the Ain Hamara station (Table 9), this basin marking the existence of marls which are friable rocks, thus a favorable factor for degradation and an insufficiency of forest vegetation and the surfaces are often used for pasture [128]. The climatic conditions semiarid are associated with a high degree of rainfall irregularity with average precipitations of 266 mm in Ain Hamara and 238 mm in Takhmert.

The maximum value of losses in soil is registered at the level of the dam basin ($400,000 t·ha^{-1}$), this value representing the sum of the solid transport by suspension and that of the bed load which is estimated between 15% and 25% of the suspension [140], namely a loss in soil equal to $392,157 t·ha^{-1}$ and by bed load equal to 78,431 $t·ha^{-1}$, estimating a load transport equal to 20% of the suspension.

Compared to the found values, we observe that the basin upstream of the SMBA Dam (1B) is the greatest producer of sediments towards the dam because it shows a specific degradation equal to $13.36 t·ha^{-1}·year^{-1}$, value found after the elimination of release of water from the Bakhadda Dam, estimated at $1.32 t·ha^{-1}·year^{-1}$ on average, considering that this dam registers an average loss amounting to $0.27 × 10^6 m^3$ since its launching in 1936; this result corroborates with that found by Touaibia [45] as well as with the works of GTZ [141], because this area is dominated by a marly lithological formation situated often on the piedmonts of a slope of 10 to 20% (Table 9), favouring soil erosion.
<table><caption>**Table 9.** Values of specific degradation per sub-basin.</caption><thead><tr><th rowspan="2">Sub-Basin</th><th rowspan="2">Wadi Name</th><th colspan="3">Specific Degradation (t·ha<sup>-1</sup>·year<sup>-1</sup>)</th></tr><tr><th>USLE Equation</th><th>Concentrations Measure</th><th>Difference (USLE, Concentrations Measures)</th></tr></thead><tbody><tr><td>1B</td><td rowspan="3">Wadi Mina</td><td>19.64</td><td>13.36</td><td>6.28/32%</td></tr><tr><td>5B</td><td>11.54</td><td>2.39</td><td>9.15/79%</td></tr><tr><td>6B</td><td>/</td><td>21</td><td>/</td></tr><tr><td>2B</td><td rowspan="3">Wadi Haddad</td><td>12.64</td><td>9.64</td><td>3.00/24%</td></tr><tr><td>3B</td><td>8.82</td><td>5.02</td><td>3.80/43%</td></tr><tr><td>4B</td><td>3.86</td><td>2.20</td><td>1.66/43%</td></tr></tbody></table>

In addition, the other sub-basins record specific degradation varying from 2.2 to 9.64 t·ha⁻¹·year⁻¹, and these values are close to those found by [66] using the USLE equation, except for the basin of Wadi Mina upstream at the level of the station of Sidi Ali Ben Amar, where the calculated value using the USLE equation is 4.83 times higher than the one calculated by the direct concentration measures (Figure 12). The value of the basin 5B using the concentrations measure considers the solids quantities sedimented in the dam impoundment in contrast to that generated by the USLE equation which does not consider the volumes of sediments caught in the Bakhadda dam but it only models potential soil erosion taking place. This calculation approach generated the difference between the estimated values by the two techniques (Table 9).

<figure><img src="image_53.png" /><figcaption>**Figure 12.** Specific degradation per sub-basin.</figcaption></figure>

In fact, we only measured the sediment that actually reached the stream system, so the difference between what is measured in the current and what is predicted by USLE is largely due to the deposits of halfway that do not reach the watercourse. This does not prevent lake like Bakhadda dam also playing a role of intermediate trapping on the watercourse.

Soil erosion is such a highly variable process that one should avoid establishing rules for larger catchments without measuring at least a few of the processes taking place directly. Sediment concentration may point towards the real processes, but they are still an indirect value for material eroded. In this study, the purpose of the comparison of the different sources of information that we use is to evaluate the validity of the observed and modelled values: are the fluxes measured in the rivers (SPM) compatible with the rates of erosivity provided by USLE? Does the sedimentation rate in the dam reflect the orders of magnitude of SPM observed in streams? Thus, sub-basins can be used to locate a potential source of sediment in small sub-basins close to the reservoir, for which the erosivity rate could be higher, and/or the proximity to the reservoir allows for greater fraction of SPM carried to reach the reservoir.
The analysis of the photo of the slopes of the dam basin (Figure 13) indicates a favorable environment (inclined slopes and formed of slightly resistant soil, little or no vegetation cover, aggressive climate and human activities), for causing important inputs in sediments that shall deposit directly in the lake. This would explain largely the very serious specific degradation calculated for the small catchment area downstream with 192 km² that surrounds the dam (6B) after the last station of hydrologic measurement, obtained by the difference between the equivalent in SPM, deducted from the dam silt volume and the concentration measures in SPM at the upstream hydrological station.

Indeed, the average annual streaming can reach 15–30%, varying with the years and depending on the fallow land silting by weeds between two weedings, but as well with the distribution of showers depending on the agricultural works and the prior humidity of the soil [142]. Average showers falling on the bare, saturated and crusted surface soil can cause a streaming exceeding 60–80%. Here lies the greatest risk for the dams silting because, gathering in gullies and Wadis, the floods peaks move very important quantities of materials [142].

We consider that the matters in suspension, measured at the catchment area outlet, do not reflect the current dynamics of all the slopes, but they reflect the transport capacity of streaming and runoff waters in the gullies. There exists a stock of fine sediments which is progressively evacuated towards the SMBA Dam and in a rhythm that depends on the available force: that of floods, related in particular to the quantity of rains received and the streaming coefficient [143].

<figure><img src="image_54.png" /><figcaption>Figure 13. Bare banks of the SMBA Dam (Shot Hallouz, August 2013).</figcaption></figure>

Thus, depending on the gullied lands and the type of gully, the input to silting of an impoundment can reach and even exceed 30% [144].

**4. Discussion**

In this study, we could use the maximum of information available which allowed us, on the one hand, to calculate the quantities of solid transport on the whole of the watershed, and, on the other hand, to propose the hypotheses on the origin of the material sedimented in the SMBA Dam.

The rain, the discharge, and concentrations of SPM are fairly well known on the watershed of the Wadi Mina and its main tributaries.

Rainfall series for which a rupture could be detected are more to the north than to the south. The recorded rainfall deficits frequently are around 19–20%. This rainfall deficit is thus felt for over three decades and seems to have spread over the basin during the 1970s. Indeed, 13 stations time series show a significant rupture during the 1970s across the entire watershed of Wadi Mina which represents 50% of rainfall stations used in this study.
The largest number of monthly ruptures was detected during the 1970s especially during the months of January and April with, respectively, 18 and 15 ruptures whose average monthly rainfall accounts for about 12% of annual rainfall.

This decline of rains has resulted in a decrease in discharge, the latter, know an overall decrease since the 1970s, or the probable ruptures are observed both in the rainy season and in the dry season. At the annual time step, probable rupture dates are 1974, 1975, 1980 and 1994 which generally correspond to years where most of the ruptures detected at the monthly time step took place.

These results show a satisfactory agreement with the dates detected on the series of annual and monthly rainfall of late 1970s/early 1980s. Thus, we suppose that reduced flows would be due to an overall decrease in precipitation over the watershed of Wadi Mina [47]. Small changes in precipitation can have significant impact on the flow of surface water [145]. In fact, climate is recognized to be the main factor in semi-arid Mediterranean areas of Algeria which experience short and intense rain episodes, high evaporating power of wind, prolonged droughts and freezing and thawing cycles [42,45,46].

The recent increase of rainfall represented by a significant occurrence of positive ruptures in time series during the 2000s is not followed by an increase in discharges, which might be explained by an increasing impact of dams and small hydraulic structures over the basins that mitigate the effect of rainfall on the hydrological regime of sub basins.

With the aid of HYFRAN-PLUS software used in the second part, the assumptions of stationary and independence are verified on all the floods of the Wadi Mina basin and these are classified in Category C, following the GEV distribution.

In addition, the dates of rupture detected on each sub basin showed a decrease of floods from 1970 onward. The flood values change around the rupture date, and the study concluded that, on the entire study area, a flood, for a twenty-year return period before the rupture date, became centennial after.

The two parts of this study have shown that the feeling of local population of a decrease in rainfall regime in the region is absolutely founded.

The last part of the study concerns the relationship between the suspended sediment transport on the Wadi Mina and siltation of the SMBA Dam.

Thus, the annual average concentrations of SPM were compared with annual average discharge. Despite year-to-year irregularities at the Sidi Aek Djilali station, concentrations and discharges have changed in opposite directions [68]. We found that ruptures were detected on the rainfall and discharges from the mid-1970s, this result show that is no effect of ruptures in precipitation and discharge on the solid load response since the discharge and rainfall decrease in the 1970s and increase in the early 2000s while the sediment load is still increasing.

The watershed of Wadi Haddad in Sidi Abdelkader El Djilali has a specific charge very high compared to other basins (Ds = 965 t·km⁻²·year⁻¹) which means that even after the rupture in the rainfall time series, and the development of agriculture and dams, this sub-basin is still the provider of the largest amount of sediment to the reservoir. The lowest degradation values are observed on the Wadi in Abd Takhmert (220 t·km⁻²·year⁻¹) and the Wadi Mina upstream in Sidi Ali Ben Amar (239 t·km⁻²·year⁻¹). Wadi Mina basin annually provides 38 million m³ of water with a flow of 2.6 million tons of suspended sediment, which therefore corresponds to a relatively low specific degradation of about 860 t·km⁻²·year⁻¹, a value significantly below published estimates for neighboring basins in Algeria and North Africa, This value indicates an increase in recent decades, which could be related to an increased susceptibility to soil erosion in the basin, under the combined effect of climatic and anthropogenic forcing [51]. However, the quantities of sediment exported by the Wadi are highly variable from one year to another. Note that the year 1994/95 alone represents a contribution of 8.15 million tons from the Wadi Abd, i.e., a specific degradation of more than 1500 t·km⁻²·year⁻¹, a value that corresponds to estimates found for Maghreb watersheds as a whole.

We found that the basin upstream of the SMBA Dam (1B) is the greatest producer of sediments towards the dam because it shows a specific degradation equal to 13.36 t·ha⁻¹·year⁻¹, a value found
after the elimination of release of water from the Bekhadda Dam, considering that this dam registers an average loss amounting to $0.27 \times 10^6 \text{ m}^3$ since its launching in 1936. In addition, the other sub-basins record specific values varying from 2.2 to $9.64 \text{ t·ha}^{-1}·\text{year}^{-1}$, and these values are close to those found by Toumi et al. [66] using the USLE equation, except for the basin of Wadi Mina upstream at the level of the station of Sidi Ali Ben Amar, where the calculated value using the USLE equation is 4.83 times higher than the one calculated by the direct concentration measures. The value of the basin 5B using the concentrations measure considers the solids quantities sedimented in the dam impoundment in contrast to that generated by the USLE equation which does not consider the volumes of sediments caught in the Bekhadda Dam but only models potential soil erosion taking place. In fact, we only measured the sediment that actually reached the stream system, so the difference between what is measured in the current and what is predicted by USLE is largely due to the deposits of halfway that do not reach the watercourse. This does not prevent lake-like Bekhadda Dam also playing a role of intermediate trapping on the watercourse.

Soil erosion is such a highly variable process that one should avoid establishing rules for larger catchments without measuring at least a few of the processes taking place directly. Sediment concentration may point towards the real processes, but they are still an indirect value for material eroded. In this study, the purpose of the comparison of the different sources of information that we use is to evaluate the validity of the observed and modeled values. Thus, sub-basins can be used to locate a potential source of sediment in small sub-basins close to the reservoir, for which the erosivity rate could be higher, and/or the proximity to the reservoir allows for greater fraction of SPM carried to reach the reservoir.

According to the bathymetric surveys carried out at the SMBA Dam, loss in water capacity of this dam since its commissioning in 1978 until 2004 is estimated at 82 million $\text{m}^3$, equivalent to a third of its original capacity. According to Remini and Bensafia [63], in 2010 the SMBA Dam contains a volume of sediments equal to $109 \times 10^6 \text{ m}^3$ or an annual average loss of about $3.5 \times 10^6 \text{ m}^3·\text{year}^{-1}$.

Based on these results, we concluded that over 90% of the sediments of the SMBA Dam come from the contributions of the Wadi Haddad and Wadi Mina upstream of the dam, but the specific degradation around the dam is higher than over the rest of the basin. Currently the dam filling by sediments is about 48%, so it should be completely silted up by 2048, without dynamic change.

Despite all the efforts made in recent years, the dam silting phenomenon has increased due to the increase in anthropogenic influence and erosion.

Definitively, the decrease in rainfall, which hit the region since the mid-1970s, induced a progressive degradation of the vegetation cover combined with the increase of summer thunderstorms, and induces changes in the functioning of the watershed with a greater susceptibility to erosion. For specific years, moderate rainfall and runoff generated considerable sedimentary flow.

## 5. Conclusions

This article presents a study of erosion, sedimentation and climate change on Wadi Mina, at the Sidi M'Hamed Ben Aouda Dam in the northeast of Algeria, in a semiarid area. It is important to fallow the evolution of water resources available for this dam in a variable climatic environment.

We have noted a rupture corresponding to a decrease of precipitations during the mid-1970s over the whole Wadi Mina basin which 58% series present a rupture in 1976. These results confirm as well the occurrence in Morocco of a pluviometric deficit starting with 1970 and its continuation during the 1980s, an extremely severe and long drought episode [106]. At the level of North Africa, the results obtained by Meddi et al. [11] in Algeria and by GIEC [107] in Tunisia indicate the same rupture period and show the spatial extension of the drought, accompanied by a net pluviometric reduction.

In addition, the discharges register a global decrease since 1970. At annual level, the probable rupture dates are 1974, 1975, 1980 and 1994, which corresponds generally to the ruptures detected at month level. In addition, the ruptures on the series of annual daily maximum were for the most part noted in 1970, 1984 and 1995 and the ruptures dates for the annual daily minimum series are detected
in 1972, 1981 and 1994. The rupture in the daily maximum flows series is thus never noted the same year as in the average annual flows series, while there are the same ruptures dates for the average annual flows and the minimum daily flows.

As for the majority of watercourses, we found a power relation [127] for Wadi Mina, connecting the concentration of suspended matters in g·L⁻¹ to the liquid discharge in m³·s⁻¹. At seasonal scale, the analysis shows that autumn and spring are distinguished by their strong liquid discharges generating an important flow of suspended particles matters (SPM). The maximum solid flow is of 42,365 kg·s⁻¹, achieved in September 1994, resulting from a liquid discharge of more than 351 m³·s⁻¹.

In contrast, during winter and summer, we note a net regression of liquid discharge that no longer exceed 84 m³·s⁻¹ in winter and 38 m³·s⁻¹ in summer.

The average specific sediment discharge of Wadi Mina at the Sidi M'hamed Ben Aouda Dam is 860 t·km⁻²·year⁻¹. We can note that over 90% of sediments of the SMBA Dam appears to come from the contributions of the Wadi Haddad and Wadi Mina upstream of the dam.

In fact, starting in the late 1980s, annual production of sediments became seven times larger than the previous period, with an increase four times greater of contribution of the dry season rates [146].

**Author Contributions:** F.H. wrote the main paper, and F.H., M.M., G.M., S.T., S.A.R. discussed the results and implications and commented on the manuscript at all stages. F.H., M.M., G.M., S.T., S.A.R. contributed extensively to the work presented in this paper.

**Funding:** This study was supported by funding from the AUF through the program SIGMED (MeRSI No. 6313PS005) Bureau Western Europe and North Africa.

**Acknowledgments:** Thanks to ANRH for the hydrological and rainfall data.

**Conflicts of Interest:** The authors declare no conflict of interest.

## References

1. Laborde, J.P. *Carte Pluviométrique de L'ALGÉRIE du Nord à L'échelle du 1/500000*; Projet PNUD/ALG/88/021, Une Carte Avec Notice Explicative; Agence Nationale des Ressources Hydrauliques: Bir Mourad Rais, Algeria, 1993; 44p. (In French)

2. Rossel, F.; Le Goulven, P.; Cadier, E. Répartition spatiale de l'influence de l'ENSO sur les précipitations annuelles de l'Équateur. *Rev. Sci. L'eau J. Water Sci.* **1999**, *12*, 183–200. (In French) [CrossRef]

3. Touazi, M.; Laborde, J.P. Cartographie des pluies annuelles en Algérie du Nord. *Assoc. Int. Climatol.* **2000**, *13*, 192–198. (In French)

4. Meddi, H. *Quantification des Précipitations: Application au Nord-Ouest Algérien Méthodologie Pluvia*; Mémoire de Magister, Centre Universitaire de Mascara: Mascara, Algeria, 2001; 160p. (In French)

5. El Mahi, A. *Déficit Pluviométrique des Dernières Décennies en Algérie du Nord and Son Impact sur les Ressources en Eau*; Mémoire de Magister, Centre Universitaire de Mascara: Mascara, Algeria, 2002; 120p. (In French)

6. Meddi, M.; Hubert, P. Impact de la modification du régime pluviométrique sur les ressources en eau du Nord-ouest de l'Algérie. In *Proceedings of the International Symposium on Hydrology of the Mediterranean and Semi Arid Regions, Montpellier, France, 1-4 April 2003*; IAHS Publication: Wallingford, UK, 2003; pp. 229–235. (In French)

7. El Mahi, A.; Meddi, M.; Matari, A.; Kattrouci, K. Etat de la pluviométrie en période de sécheresse en Algérie du Nord et sa relation avec le phénomène ENSO. In *Acte du Colloque 'Terre and Eau'*, Algerian Journal of Arid Areas: Annaba, Algeria, 2004; pp. 420–423. (In French)

8. Talia, A.; Meddi, M. La pluvio-variabilité dans le Nord de l'Algérie. In *Actes du Colloque 'Terre and Eau'*, Algerian Journal of Arid Areas: Annaba, Algeria, 2004; pp. 477–480. (In French)

9. Meddi, H.; Meddi, M. Variabilité spatiale and temporelle des précipitations du Nord-Ouest de l'Algérie. *Geogr. Tech.* **2007**, *2*, 49–55. (In French)

10. Bekoussa, B.; Meddi, M.; Jourde, H. Forçage climatique and anthropique sur la ressource en eau souterraine d'une région semi-aride: Le cas de la plaine de Ghriss (Nord-Ouest algérien). *Sécheresse* **2008**, *19*, 173–184. (In French)
11. Meddi, M.; Talia, A.; Martin, C. Evolution récente des conditions climatiques et des écoulements sur le bassin versant de La Macta Nord-Ouest de l'Algérie. *Géogr. Phys. Environ. Physio-Géo* **2009**, *3*, 61–84. (In French) [CrossRef]

12. Meddi, M.; Meddi, H.; Assani, A. Variabilité temporelle des précipitations dans les bassins de la Macta et la Tafna, Nord-Ouest d'Algérie. *Water Ressour. Manag.* **2010**, *24*, 3817. (In French) [CrossRef]

13. Niazi, S.; Snoussi, M. Evaluation des risques d'inondation à une élévation accélérée du niveau de la mer sur le littoral de Tétouan Méditerranée occidentale marocaine. In Proceedings of the 3ème Journée Internationales de Géosciences de l'environnement, El Jadida, Morocco, 8–10 June 2005. (In French)

14. Agence Nationale des Barrages et Transferts (ANBT). *Rapport Annuel D'activité sur L'état des Barrages Algériens*; ANBT: Kouba, Algeria, 2014. (In French)

15. Williams, J.R.; Berndt, H.D. Sediment yield computed with universal equation. *J. Hydraul. Div.* **1972**, *98*, 2087–2098.

16. Lahlou, A. Modèles de prédiction de la sédimentation des retenues de barrages des pays du Grand Maghreb. In Proceedings of the Atelier International UNESCO-AISH-ENIT sur L'Application des Modèles Mathématiques à L'évaluation des Modifications de la Qualité Des Eaux, Tunis, Tunisia, 7–12 May 1990; pp. 312–324. (In French)

17. Wicks, J.M.; Bathurst, J.C. SHE-SED: A physically based, distributed erosion and sediment yield component for the SHE hydrological modeling system. *J. Hydrol.* **1996**, *175*, 213–238. [CrossRef]

18. Kassoul, M.; Abdelgader, A.; Belorgey, M. Caractérisation de la sédimentation des barrages en Algérie. *Rev. Sci. L'eau J. Water Sci.* **1997**, *3*, 339–358. (In French) [CrossRef]

19. Terfous, A.; Meghnounif, A.; Bouanani, A. Etude du transport solide en suspension dans l'oued Mouillah (Nord-Ouest Algérien). *Rev. Sci. L'eau J. Water Sci.* **2001**, *14*, 173–185. (In French) [CrossRef]

20. Abdellaoui, B.; Merzouk, A.; Aberkan, M.; Albergel, J. Bilan hydrologique et envasement du barrage Saboun (Maroc). *Rev. Sci. L'eau J. Water Sci.* **2002**, *15*, 737–748. (In French) [CrossRef]

21. Marzougui, A.; Ben Mammou, A. Le barrage de l'Oued Sejnane: Quantification de l'alluvionnement et évaluation de l'érosion spécifique de son bassin versant. *Geo-Eco-Trop* **2006**, *30*, 57–68. (In French)

22. Remini, B.; Hallouche, W. Prévision de l'envasement dans les barrages Du Maghreb. *Larhyss J.* **2005**, *4*, 69–80. (In French)

23. Lahlou, A. *Envasement des Barrages au Maroc* [Siltation of Dams in Morocco]; Wallada: Casablanca, Morocco, 1994; p. 286. ISBN 9981823074. (In French)

24. Saadaoui, M. *Érosion et Transport Solide en Tunisie*. Mesure et Prévision du Transport Solide Dans les Bassins Versants et de L'envasement Dans les Retenues des Barrages; Direction Générale des Barrages et des Grands Travaux Hydrauliques (DGBGTH), Ministère de L'Agriculture, Direction Générale des Ressources en Eau, Direction des Eaux de Surface: Tunis, Tunisie, 1995; 30p. (In French)

25. Abid, M. *Envasement des Barrages en Tunisie*; Direction Générale des Barrages et des Grands Travaux Hydrauliques (DGBGTH); Ministère de L'Agriculture: Tunis, Tunisie, 1998; 69p. (In French)

26. Bourouba, M. Contribution à l'étude de l'érosion et des transports solides de l'Oued Medjerda supérieur (Algérie orientale). *Bull. Rés. Eros.* **1998**, *18*, 76–97. (In French)

27. Colombani, J.; Olivry, J.C.; Kallel, R. Phénomènes exceptionnels d'érosion et de transport solide en Afrique aride et semi-aride. In *Challenges in African Hydrology and Water, Ressources, Proceedings of the Harare Symposium, Harare, Zimbabwe*, 23–27 July 1984; IAHS Publication: Oxfordshire, UK, 1984; Volume 144, pp. 295–300. (In French)

28. Kettab, A. Les ressources en eau en Algérie: Stratégies, enjeux et vision. *Desalination* **2001**, *136*, 25–33. (In French) [CrossRef]

29. Remini, B. *La Problématique de L'eau en Algérie*; Office des Publications Universitaires: Alger, Algérie, 2005; p. 162. (In French)

30. Boithias, L.; Acuña, V.; Vergoñós, L.; Ziv, G.; Marcé, R.; Sabater, S. Assessment of the water supply: Demand ratios in a Mediterranean basin under different global change scenarios and mitigation alternatives. *Sci. Total Environ.* **2014**, *470*, 567–577. [CrossRef] [PubMed]

31. Surface Water Resources Mobilization. Document of Algerian Ministry of Water Resources. Available online: http://www.mree.gov.dz (accessed on 24 April 2018).

32. Hamiche, A.; Boudghene Stambouli, A.; Flazi, S. A review on the water and energy sectors in Algeria: Current forecasts, scenario and sustainability issues. *Renew. Sustain. Energy Rev.* **2015**, *41*, 261–276. [CrossRef]
33. Bouzid-Lagha, S.; Djelita, B. Study of eutrophication in the Hamman Boughrara Reservoir (Wilaya de Tlemcen, Algeria). *Hydrol. Sci. J.* **2012**, *57*, 186–201. [CrossRef]

34. Tidjani, A.E.; Yebdri, D.; Cherif, E.A. *Ampleur de L'envasement Dans les Barrages Algériens*; Documents Techniques en Hydrologie; UNESCO: Paris, France, 2000; Volume 29, pp. 121–128. (In French)

35. Bourouba, M. Phénomène de transport solide dans les Hauts Plateaux Orientaux. Cas de l'Oued Logmane et oued Leham dans le bassin de la Hodna. *Rev. Sci. Technol.* **1998**, *9*, 5–11.

36. Megnounif, A.; Terfous, A.; Bouanani, A. Production et transport des matières solides en suspension dans le bassin versant de la Haute-Tafna (Nord-Ouest algérien). *Rev. Sci. L'eau J. Water Sci.* **2003**, *16*, 369–380. [CrossRef]

37. Achite, M.; Meddi, M. Variabilité spatio-temporelle des apports liquide et solide en zone semi-aride. Cas du bassin versant de l'oued Mina (nord-ouest algérien). *Rev. Sci. L'eau J. Water Sci.* **2005**, *18*, 37–56. [CrossRef]

38. Achite, M.; Ouillon, S. Suspended sediment transport in semiarid watershed, Wadi abd, Algeria (1973–1995). *J. Hydrol.* **2007**, *343*, 187–202. [CrossRef]

39. Ghenim, A.; Terfous, A.; Seddini, A. Étude du transport solide en suspension dans les régions semiarides méditerranéennes: Cas du bassin versant de l'Oued Sebdou (Nord-Ouest Algérien). *Sécheresse* **2007**, *18*, 39–44. (In French)

40. Ghenim, A. Étude des Écoulements et des Transports Solides Dans les Régions Semi-Arides Méditerranéennes. Ph.D. Thesis, Université de Tlemcen, Chetouane, Algérie, 2008.

41. Zettam, A.; Taleb, A.; Sauvage, S.; Boithias, L.; Belaidi, N.; Sánchez-Pérez, J.M. Modeling Hydrology and Sediment Transport in a Semi-Arid and Anthropized Catchment Using the SWAT Model: The Case of the Tafna River (Northwest Algeria). *Water* **2017**, *9*, 216. [CrossRef]

42. Achite, M.; Ouillon, S. Recent changes in climate, hydrology and sediment load in the Wadi Abd, Algeria (1970–2010). *Hydrol. Earth Syst. Sci.* **2016**, *20*, 1355–1372. [CrossRef]

43. Abdelkader Benkhaled & Boualem Remini. Temporal variability of sediment concentration and hysteresis phenomena in the Wadi Wahrane basin, Algeria. *Hydrol. Sci. J.* **2003**, *48*, 243–255. [CrossRef]

44. Khanchoul, K.; Jansson, M.B.; Lange, Y. Comparison of suspended sediment yield in two catchments, northeast, Algeria. *Z. Geomorphol.* **2007**, *51*, 63–94. [CrossRef]

45. Touaibia, B. Problématique de l'érosion et du transport solide en Algérie septentrionale. *Sécheresse* **2010**, *21*, 333–335. (In French)

46. Houyou, Z.; Bielders, C.L.; Benhorma, H.A.; Dellal, A.; Boutemdjet, A. Evidence of strong land degradation by wind erosion as a result of rainfed cropping in the Algerian steppe: A case study at Laghouat. *Land Degrad. Dev.* **2014**, *27*, 1788–1796. [CrossRef]

47. Hallouz, F.; Meddi, M.; Mahé, G. Modification du régime hydroclimatique dans le basin de l'oued Mina (Nord-Ouest d'Algérie). *Rev. Sci. L'eau J. Water Sci.* **2013**, *26*, 33–38. (In French) [CrossRef]

48. Khomsi, K.; Mahe, G.; Sinan, M.; Snoussi, M.; Cherifi, R.; Nait Said, Z. Evolution des évènements chauds rares et très rares dans les bassins versants du Tensift et du Bouregreg (Maroc) et identification des types de temps synoptiques associés. In *From Prediction to Prevention of Hydrological Risk in Mediterranean Countries*; Ferrari, E., Versace, P., Eds.; University of Calabria: Cosenza, Italy, 2012; pp. 169–182.

49. Mahé, G.; Aksoy, H.; Brou, Y.T.; Meddi, M.; Roosevelt, E. Relationships between man, environment and sediment transport: A spatial approach. *Rev. Sci. L'eau J. Water Sci.* **2013**, *26*, 235–244. [CrossRef]

50. Laouina, A.; Aderghal, M.; Al Karkouri, J.; Chaker, M.; Machmachi, I.; Machouri, N.; Sfa, M. Utilisation des sols, ruissellement et dégradation des terres, le cas du secteur Sehoul, région atlantique, Maroc. *Sécheresse* **2010**, *21*, 309–316. (In French)

51. Mahè, G.; Benabdelfadel, H.; Dieulin, C.; El Baraka, M.; Ezzaouini, M.; Khomsi, K.; Rouche, N.; Sinan, M.; Snoussi, M.; Tra Bi, A.; et al. Evolution des débits liquides et solides du Bouregreg, 2015. In *Gestion Durable des Terres, Proceedings of the Réunion Multi-Acteurs sur le Bassin du Bouregreg, Rabat, Morocco, 28 May 2013*; ARGDT: Rabat, Morocco, 2014; pp. 53–62. ISBN 978-9954-33-482-9. (In French)

52. Touaïbia, B.; Gomer, D.; Ouffar, F.; Geyer, F. Approche Quantitative de l'Erosion Hydrique essais de simulation de pluies sur micro-bassins expérimentaux bassin versant de l'oued mina. *W. Relizane. Bull. Rés. Eros.* **1992**, *12*, 401–407. (In French)

53. Touaïbia, B.; Dautrebande, S.; Gomer, D.; Mostefaoui, M. Quantification de l'érosion à partir d'implantation de quatre retenues collinaires dans la zone des marnes. *W. Relizane Algérie. Bull. Rés. Eros.* **1995**, *15*, 408–416. (In French)
54. Touaïbia, B.; Dautrebande, S.; Gomer, D.; Aidaoui, A. Approche quantitative de l'érosion hydrique à différentes échelles spatiales: Bassin versant de l'Oued Mina. *J. Sci. Hydrol.* **1999**, *44*, 408–416. (In French) [CrossRef]

55. Touaïbia, B.; Gomer, D.; Aidaoui, A. Estimation de l'index d'érosion de wischmeïer dans les micros bassins expérimentaux de l'oued mina en Algérie du Nord. *Bull. Rés. Eros.* **2000**, *20*, 478–484. (In French)

56. Touaïbia, B.; Gomer, D.; Aïdaoui, A.; Achite, M. Quantification et variabilité temporelles de l'écoulement solide en zone semi-aride, de l'Algérie du Nord. *Hydrol. Sci. J.* **2001**, *46*, 41–53. (In French) [CrossRef]

57. Gomer, D. Programme Hydrométrique: Projet Pilote D'aménagement Intégré; Rapport D'évaluation: Sidi-M'Hamed Ben Aouda, Algérie, 1990. (In French)

58. Gomer, D.; Vogt, T. Determination of runoff and soil erosion under semiarid conditions using GIS to integrate Landsat TM, DEH und hydrological field data from the Wadi Mina project Algeria. In Proceedings of the International Meeting "Soil Erosion in Semi-Arid Mediterranean Arcas", Taormina, Italy, 28–30 October 1993; European Society for Soil Conservation: Catania, Italy, 1993; pp. 19–93.

59. Kouri, L.; Vogt, H. Détermination de la Sensibilité des Terrains Marneux au Ravinement au Moyen de Système D'information Géographique. B.V de L'oued Mina; Tell Oranais Algérie. *Bull Rés. Eros.* **1997**, *17*, 64–73. (In French)

60. Kouri, L.; Vogt, H.; Gomer, D. Analyse des processus d'érosion hydrique linéaire en terrain marneux. Bassin-versant de l'oued Mina, Tell Oranais, Algérie. *Bull. Rés. Eros.* **1997**, *17*, 64–73. (In French)

61. Gomer, D. Ecoulement et Érosion Dans les Petits Bassins Versants à sols Marneux Sous Climat Semi-Méditerranéen. Ph.D. Thesis, Wasserbau, Université de Kaj, Karlsruhe, Germany, 1994.

62. Touaïbia, B.; Achite, M. Contribution à la cartographie de l'érosion spécifique du bassin versant de l'Oued Mina en zone semi-aride de l'Algérie septentrionale. *Hydrol. Sci. J.* **2003**, *48*, 235–242. (In French) [CrossRef]

63. Remini, B.; Bensafia, D. Envasement du Barrage de SMBBA Algérie. Comité Scientifique Projet SIGMED Rabat Maroc. 2011. Available online: http://armspark.msem.univ-montp2.fr/SigMED/index.asp?menu=ComiteScientifique (accessed on 12 September 2012). (In French)

64. Bekhti, B.; Errih, M.; Sidi Adda, M. Modélisation de la sédimentation dans les retenues de barrages en Algérie (barrage Es-Saada). *Sécheresse* **2012**, *23*, 38–47. (In French)

65. Remini, B. Evolution spatio-temporelle de l'envasement dans le barrage S.M.B.A. In Proceedings of the Séminaire International, Relizane, Algérie, 2–5 June 2012; pp. 75–89. (In French)

66. Toumi, S.; Meddi, M.; Mahé, G.; Brou, Y.T. Cartographie de l'érosion dans le bassin versant de l'Oued Mina en Algérie par télédétection et SIG. *Hydrol. Sci. J.* **2013**, *58*, 1–17. (In French) [CrossRef]

67. Hallouz, F.; Meddi, M.; Mahe, G. Régimes des matières en suspension dans le bassin versant de l'oued Mina sur l'oued Cheliff (Nord-Ouest algérien). *La Houille Blanche* **2017**, *4*, 61–71. (In French) [CrossRef]

68. Meddi, M. Hydro-Pluviométrie and Transport Solide Dans le Bassin Versant de L'oued Mina (Algérie). Ph.D. Thesis, University Louis Pasteur, Strasbourg, France, 1992. (In French)

69. Dubreuil, P.; Guiscafre, J. La planification du réseau hydrométrique minimal. *Cah. ORSTOM Sér. Hydrol.* **1971**, *8*, 3–38. (In French)

70. Mahieddine, M. Quantification and Variabilité Parcellaire Sous Simulation de Pluie Dans le Bassin Versant de L'Oued Mina. Ph.D. Thesis, Ecole Nationale Supérieure des Sciences Agronomiques, Alger, Algeria, 1997.

71. Walling, D.E. Assessing the accuracy of suspended sediment rating curves for a small basin. *Water Resour. Res.* **1977**, *13*, 531–538. [CrossRef]

72. Asselman, N.E.M. Fitting and interpretation of sediment rating curves. *J. Hydrol.* **2000**, *234*, 228–248. [CrossRef]

73. El Mahi, A.; Meddi, M.; Bravard, J.P. Analyse du transport solide en suspension dans le bassin versant de l'Oued El Hammam (Algérie du Nord). *Hydrol. Sci. J.* **2012**, *57*, 1642–1661. (In French) [CrossRef]

74. Tebbi, F.Z.; Dridi, H.; Morris, G.L. Optimization of cumulative trapped sediment curve for an arid zone reservoir: Foum El Kherza (Biskra, Algeria). *Hydrol. Sci. J.* **2012**, *57*, 1368–1377. [CrossRef]

75. Louamri, A.; Mebarki, A.; Laignel, B. Variabilité interannuelle et intra-annuelle des transports solides de l'Oued Bouhamdane, à l'amont du barrage Hammam Debagh (Algérie orientale). *Hydrol. Sci. J.* **2013**, *58*, 1559–1572. (In French) [CrossRef]

76. Hallouz, F. Transport Solide Dans le Bassin D'El Oued Mina et Sédimentation du Barrage SMBBA. Ph.D. Thesis, Es-Sciences, Ecole Nationale Supérieure d'Hydraulique (ENSH), Soumaâ, Algeria, 2014.
77. Ferguson, R.I. Accuracy and precision of mandhods for estimating river loads. *Earth Surf. Process. Landf.* **1987**, *12*, 95–104. (In French) [CrossRef]

78. Cordova, J.R.; Gonzalez, M. Sediment yield estimation in small watersheds based on stream flow and suspended sediment discharge measurements. *Soil Technol.* **1997**, *11*, 57–69. [CrossRef]

79. Jansson, M.B. Estimating a sediment rating curve of the Reventazon river at Palomo using logged mean loads within discharge classes. *J. Hydrol.* **1996**, *183*, 227–241. [CrossRef]

80. Kendall, S.M.; Stuart, A. *The Advanced Theory of Statistics*, 1969–1976 ed.; Charles Griffin: London, UK, 1943.

81. Lubes-Niel, H.; Masson, J.; Paturel, J.; Servat, E. Variabilité climatique et statistiques. Etude par simulation de la puissance et de la robustesse de quelques tests utilisés pour vérifier l'homogénéité de chroniques. *Rev. Sci. L'eau J. Water Sci.* **1998**, *11*, 383–408. (In French) [CrossRef]

82. Maftei, C.; Bărbulescu, A.; Buta, C.; Şerban, C. Change points detection on variability analysis of some precipitation series, MAMECTIS/NOLASC/CONTROL/WAMUS’11. In Proceedings of the 13th WSEAS International Conference on Mathematical Methods, Computational Techniques and Intelligent Systems, Iasi, Romania, 1–3 July 2011; pp. 232–237.

83. Khomsi, K.; Mahé, G.; Sinan, M.; Snoussi, M. Hydro-climatic variability in two Moroccan basins: Comparative analysis of temperature, rainfall and runoff regimes. In *Climate and Land Surface Changes in Hydrology, Proceedings of the H01, IAHS-IAPSO-IASPEI Assembly, Gothenburg, Sweden, 22–26 July 2013*; IAHS Publication: Oxfordshire, UK, 2013; pp. 193–190, 359.

84. Bonneaud, S. *Méthodes de Détection des Ruptures Dans les Séries Chronologiques*; Rapport de Stage de fin D'études; ISIM: Montpellier, France, 1994. (In French)

85. KHRONOSTAT. *Logiciel D'analyse Statistique de Séries Chronologiques*; IRD Ex: Paris, France, 1998; Available online: http://www.hydrosciences.org/spip.php?article239 (accessed on 14 October 2011). (In French)

86. Lubes, H.; Masson, J.M.; Servat, E.; Paturel, J.-E.; Kouame, B.; Boyer, J.F. ICCARE: Rapport n 3: Caractérisation de Fluctuations Dans Une Série Chronologique par Applications de Tests Statistiques: Étude Bibliographique; IRD Ex: Montpellier, France, 1994; 22p. (In French)

87. Meddi, M.; Humbert, J. Etude des potentialités de l'écoulement fluvial dans le Nord de l'Algérie en vue d'une réalimentation des aquifères. In *Eaux Sauvages, Eaux Domestiquées, Hommage à L. DAVY*; University of Provence: Marseille, France, 2000; pp. 177–190.

88. Singla, S.; Mahé, G.; Dieulin, C.; Driouech, F.; Milano, M.; El Guelai, F.Z.; Ardoin-Bardin, S. Evolution des relations pluie-débit sur des bassins versants du Maroc. In *Global Change: Facing Risks and Threats to Water Resources, Proceedings of the Sixth World FRIEND Conference, Fez, Morocco, 25–29 October 2010*; IAHS Publication: Oxfordshire, UK, 2010; Volume 340, pp. 679–687.

89. Hubert, P.; Carbonnel, J.P.; Chaouche, A. Segmentation des séries hydrométéorologiques. Application à des séries de précipitations and de débits de l'Afrique de l'Ouest. *J. Hydrol.* **1989**, *110*, 349–367. (In French) [CrossRef]

90. Chaouche, A. Structure de la Saison des Pluies en Afrique Soudano-Sahélienne. Ph.D. Thesis, J'Ecole des Mines de Paris, Paris, France, 1988. (In French)

91. Moron, V. Variabilité spatio-temporelle des précipitations en Afrique sahélienne and guinéenne de 1933 à 1990. *Météorologie* **1992**, *43–44*, 24–30. (In French)

92. Kebaili Bargaoui, Z. Occurrence des sécheresses dans le bassin de la Medjerda (Tunisie). *Rev. Sci. L'eau J. Water Sci.* **1989**, *2*, 429–447. (In French) [CrossRef]

93. Bobée, B. Extreme flood events valuation using frequency analysis: A critical review. *La Houille Blanche* **1999**, *100–105*. [CrossRef]

94. El Adlouni, S.; Ouarda, T.B.M.J. Étude de la loi conjointe débit-niveau par les copules: Cas de la rivière Châteauguay. *Can. J. Civ. Eng.* **2008**, *35*, 1128–1137. (In French) [CrossRef]

95. Fisher, R.A. On the 'Probable Error' of a Coefficient of Correlation deduced from a Small Sample. *Metron* **1921**, *1*, 3–32. (In French)

96. Gumbel, E.J. *Statistics of Extremes*; Columbia University Press: New York, NY, USA, 1958.

97. El Adlouni, S.; Ouarda, T.B. Frequency Analysis of Extreme Rainfall Events. In *Rainfall: State of the Science*; Testik, F.Y., Gebremichael, M., Eds.; American Geophysical Union: Washington, DC, USA, 2010. [CrossRef]
98. El Adlouni, S.; Bobée, B. Système d'Aide à la Décision pour l'estimation du risque hydrologique. In *Numéro Spécial du Journal des Sciences Hydrologiques, Proceedings of the Conference FRIEND, Fès, Morocco, 25-29 October 2010*; IASH Publication: Edinburgh, UK, 2010. (In French)

99. El Adlouni, S.; Bobée, B. Delta diagram based test for the Halphen (A and B) and the Gamma distributions. In Proceedings of the EGU General Assembly Conference, Vienna, Austria, 27 April–2 May 2014; Volume 16.

100. Beirlant, J.; Goegebeur, Y.; Segers, J.; Teugels, J. *Statistics of Extremes: Theory and Applications*; Wiley Series in Probability and Statistics; John Wiley & Sons, Ltd.; Chichester, UK, 2004; 522p, ISBN 13 978-0-471-97647-9.

101. Von Mises, R. *La distribution de la plus grande de n valeurs*; American Mathematical Society: Providence, RI, USA, 1954; Volume II, pp. 271–294.

102. Fisher, R.; Tippett, L. Limiting forms of the frequency distribution of the largest and smallest member of a sample. *Proc. Camb. Philos. Soc.* **1928**, *24*, 180-190. [CrossRef]

103. Jenkinson, A.F. The frequency distribution of the annual maximum (or minimum) values of meteorological elements. *Q. J. R. Meteorol. Soc.* **1955**, *81*, 58-171. [CrossRef]

104. Wischmeier, W.H.; Smith, D.D. *Predicting Rainfall Erosion Losses: A Guide to Conservation Planning*; U.S. Department of Agriculture: Washington, DC, USA, 1978.

105. National Agency for Hydraulic Resources (ANRH). Les changements climatiques et leur impact sur les ressources en eau en Algérie. In *Proceedings of the Assises Nationales sur L'Eau, Fez, Morocco, 25-29 October 2010; ANRH: Alger, Algeria, 2010*; p. 41. (In French)

106. Bouzaiane, S.; Laforgue, A. *Monographie Hydrologique des oueds Zéroud et Merguellil*; DGRE-ORSTOM: Tunis, Tunisie, 1986. (In French)

107. Le Groupe d'experts intergouvernemental sur l'évolution du climat (GIEC). Impacts Adaptation and Vulnerability. Summary for Policymakers. In *Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change*; Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E., Eds.; Cambridge University Press: Cambridge, UK, 2007; Available online: www.ipcc.ch (accessed on 2 July 2018).

108. Abdellali, S.A.; Fougrach, H.; Hsain, M.; Saloui, A.; Badri, W. Andude de la variabilité du régime pluviométrique au Maroc septentrional (1935-2004). *Sécheresse* **2011**, *22*, 139-148. (In French)

109. Mahé, G.; Olivry, J.C. Variations des précipitations and des écoulements en Afrique de l'Ouest and centrale de 1951 à 1989. *Sécheresse* **1995**, *6*, 109-117. (In French)

110. Wotling, G.; Mahé, G.; L'Hote, Y.; Lebarbe, L. Analyse par les vecteurs régionaux de la variabilité spatiotemporelle des précipitations annuelles liées à la Mousson africaine. *Veill. Clim. Satell.* **1995**, *52*, 58-73.

111. Kingumbi, A.; Bergaoui, Z.; Bourges, J.; Hubert, P.; Kalled, R. *Étude de L'évolution des Séries Pluviométriques de la Tunisie Centrale*; Documents Techniques en Hydrologie, No. 51 ("Hydrologie des Régions Méditerranéennes" Actes du Séminaire de Montpellier); UNESCO: Paris, France, 2000; pp. 341-345.

112. L'Hôte, Y.; Mahé, G.; Somé, B.; Triboulet, J.P. Analysis of a Sahelian annual rainfall index updated from 1896 to 2000; the drought still goes on. *Hydrol. Sci. J.* **2002**, *47*, 563-572.

113. Sircoulon, J. Variation des débits des cours d'eau and des niveaux des lacs en Afrique de l'ouest depuis le début du vingtième siècle. In *The Influence of Climate Change and Climatic Variability on the Hydrology Regime and Water Resources, Proceedings of the Vancouver Symposium, Vancouver, BC, Canada, 9-22 August 1987*; Solomon, S.I., Beran, M., Hogg, W., Eds.; IAHS Publication: Oxfordshire, UK, 1987; pp. 13-25.

114. Opoku-Ankomah, Y.; Amisigo, B.A. Rainfall and runoff variability in the south western river system of Ghana. In *Proceedings of the Abidjan'98 Conference on the Water Resources Variability in Africa during the XXth Century, Abidjan, Cote d'Ivoire, 30 November 1998*; IAHS Publication: Oxfordshire, UK, 1998; pp. 307-321.

115. Bello, N.J. Evidence of climate change based on rainfall records in Nigeria. *Weather* **1998**, *52*, 412-418. [CrossRef]

116. Servat, E.; Paturel, J.E.; Lubès-Niel, H.; Kouamé, B.; Masson, J.M.; Travaglio, M.; Marieu, B. De différents aspects de la variabilité de la pluviométrie en Afrique de l'ouest et centrale non sahélienne. *Rev. Sci. L'eau J. Water Sci.* **1999**, *12*, 363-387. (In French) [CrossRef]

117. Alpert, P.; Ben-Gai, T.; Bahard, A.; Benjamini, Y.; Yekutieli, D.; Colacino, M.; Diodato, L.; Ramis, C.; Homar, V.; Romero, R.; et al. The paradoxical increase of Mediterranean extreme daily rainfall in spite of decrease in total values. *Geophys. Res. Lett.* **2002**, *29*, 31-1-31-4. [CrossRef]
118. Norrant, C.; Douguédroit, A. Monthly and daily precipitation trends in the Mediterranean (1950–2000). *Theor. Appl. Climatol.* **2006**, *83*, 89–106. [CrossRef]

119. Hulme, M.; Osborn, T.J.; Johns, T.C. Precipitation sensitivity to global warming: Comparisons of observations with HadCM2 simulations. *Geophys. Res. Lett.* **1998**, *25*, 3379–3382. [CrossRef]

120. Laborde, J.P.; Goubesville, P.; Assaba, M.; Demmak, A.; Belhouli, L. Climate evolution and possible effects on surface water resources of North Algeria. *Curr. Sci.* **2010**, *98*, 1056–1062.

121. Mahé, G.; Singla, S.; Driouech, F.; Khomsi, K. Analyse de la persistance de ruptures dans des séries pluviométriques au Maroc en fonction de l'échelle spatiale et de la reconstitution des données. In *Proceedings of the Conférence CIREDD4*, Blida, Algeria, 22–23 February 2011; pp. 22–23.

122. Ghenim, A.N.; Megnounif, A. Ampleur de la sécheresse dans le bassin d'alimentation du barrage Meffrouche (Nord-Ouest de l'Algérie). *Géogr. Phys. Environ. Physio-Géo* **2013**, *7*, 35–49. (In French) [CrossRef]

123. Ghenim, A.N.; Megnounif, A. Analyse des précipitations dans le Nord-Ouest Algérien. *Sécheresse* **2013**, *24*, 107–114. (In French) [CrossRef]

124. Jerolmack, D.J.; Paola, C. Shredding of environmental signals by sediment transport. *Geophys. Res. Lett.* **2010**, *37*, L19401. [CrossRef]

125. Coulthard, T.J.; Ramirez, J.; Fowler, H.J.; Glenis, V. Using the UKCPO9 probabilistic scenarios to model the amplified impact of climate change on drainage basin sediment yield. *Hydrol. Earth Syst. Sci.* **2012**, *16*, 4401–4416. [CrossRef]

126. Knight, J.; Harrison, S. The impacts of climate change on terrestrial Earth surface systems. *Nat. Clim. Chang.* **2013**, *3*, 24–29. [CrossRef]

127. Probst, J.L.; Bazerbach, A. Transports en solution et en suspension par la Garonne Supérieure. *Sci. Géol. Bull.* **1986**, *39*, 79–98. (In French)

128. Asnouni, F. Etude du Transport Solide en Suspension Dans le Bassin Versant D'oued AL ABD. Master's Thesis, Hydraulique Université de Tlemcen, Tlemcen, Algeria, 2014.

129. Roosevelt, E. *Introduction à la Gestion Conservatoire de l'eau, de la Biomasse et de la Fertilité des Sols (GCES)*; Bulletin Pédologique de la FAO No. 70; United Nations Food and Agriculture Organization: Rome, Italy, 1999; Available online: http://www.fao.org/docrep/T1765F/t1765f00.htm (accessed on 22 July 2016).

130. Roosevelt, É.; Chebbani, R.; Bourougaa, L. Ravinement en Algérie: Typologie, facteurs de contrôle, quantification et réhabilitation. *Sécheresse* **2000**, *11*, 317–326.

131. Silva, A.M. Rainfall Erosivity Map for Brazil. *Catena* **2004**, *57*, 251–259. [CrossRef]

132. Yu, B. Rainfall Erosivity and its Estimation for Australia's Tropics. *Aust. J. Soil Res.* **1998**, *36*, 143–166. [CrossRef]

133. Isikwue, M.O.; Ocheme, J.O.; Aho, M.I. Evaluation of Rainfall Erosivity Index for Index for Abuja Lombardi Method. *Niger. J. Technol.* **2015**, *34*, 56–63. [CrossRef]

134. Bourouba, M. Les variations de la turbidité et leurs relations avec les précipitations et les débits des oueds semi arides de l'Algérie orientale. *Bull. ORSTOM* **1997**, *17*, 345–360.

135. Megnounif, A. Étude du Transport des Sédiments en Suspension Dans les Écoulements de Surface. Ph.D. Thesis, Université de Tlemcen, Tlemcen, Algérie, 2007; p. 164.

136. Demmak, A. Contribution à l'étude de L'érosion et des Transports Solides en Algérie Septentrionale. Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 1982. (In French)

137. La CROIX. Inondations Meurtrières en Algérie, Parue le 12/11/2001 à 12h00, L'explication Météorologique. Available online: http://www.la-croix.com/Archives/2001-11-12/INondations-meurtrieres-en-Algerie-NP_2001-11-12-144826 (accessed on 3 March 2018).

138. Maleval, V. Premiers résultats des mesures d'érosion ravinaire sur les versants lacustres du barrage Sidi Mohammed Ben Abdellah (Maroc) et perspectives de recherche. In *Gestion Durable des Terres, Proceedings of the Réunion Multi-Acteurs sur le Bassin du Bouregreg CERGéo, Rabat, Morocco, 28 May 2013*; Laouina, A., Mahe, G., Eds.; ARGDT: Rabat, Morocco, 2014; pp. 53–62. ISBN 978-9954-33-482-9. (In French)

139. Toumi, S. Application des Techniques Nucléaires et de la Télédétection à L'étude de L'érosion Hydrique Dans le Bassin Versant de L'oued Mina. Ph.D. Thesis, Es-Sciences, Ecole Nationale Supérieure d'Hydraulique (ENSH), Soumaa, Algeria, 2014; p. 189. (In French)

140. Larfi, B.; Remini, B. Le transport solide dans le bassin versant de l'oued Isser. Impact sur l'envasement fu barrage de Beni Amrane (Algérie). *LARHYSS J.* **2006**, *5*, 63–73. (In French)
141. Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ). *Carte du Danger D'érosion. Projet D'aménagement Intégré du Bassin Versant de L'oued Mina. Schéma Directeur D'aménagement*; Office Allemand de la Coopération Technique, S.A.R.L (GTZ), IFG, Institut des Géosciences Appliquées: Offenbach, Germany, 1987. (In French)

142. Kouidri, R. *Premiers Résultats de Quantification de Ruissellement et de L'érosion en Nappe sur Jachère en Algérie (Région de Médéa)*; Annales de la Recherche Forestière en Algérie; INRF: Alger, Algérie, 1993. (In French)

143. Diallo, D. Erosion des sols en Zone Soudanienne du Mali Transfert des Matériaux Érodés Dans le Bassin Versant de Djitiko (Haut Niger). Ph.D. Thesis, L'université Joseph Fourier, Grenoble, France, 2000; p. 212. (In French)

144. Naimi, M.; Tayaa, M.; Ouzizi, S.; Choukra-Ilha, R.; Kerby, M. Dynamique de l'érosion par ravinement dans un bassin versant du Rif occidental au Maroc. *Sécheresse* **2003**, *14*, 95-100. (In French)

145. Lienou, G.; Mahé, G.; Paturel, J.E.; Servat, E.; Sighomnou, D.; Ecodeck, G.E.; Dezeter, A.; Dieulin, C. Changements des régimes hydrologiques en région équatoriale camerouaise: Un impact du changement climatique en Afrique équatoriale? *Hydrol. Sci. J.* **2008**, *53*, 789-801. (In French) [CrossRef]

146. Megnounif, A.; Nekkache Ghenim, A. Influence des fluctuations hydro-pluviométriques sur la production des sédiments: Cas du bassin de la Haute Tafna. *Rev. Sci. L'eau J. Water Sci.* **2013**, *26*, 53-62. [CrossRef]

<figure><img src="image_55.png" /></figure>

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Article

# Anthropogenic Reservoirs of Various Sizes Trap Most of the Sediment in the Mediterranean Maghreb Basin

Mahrez Sadaoui<sup>1,2,*</sup>, Wolfgang Ludwig<sup>1,2 ID</sup>, François Bourrin<sup>1,2</sup>, Yves Le Bissonnais<sup>3</sup>
and Estela Romero<sup>2,4 ID</sup>

Received: 16 June 2018; Accepted: 10 July 2018; Published: 12 July 2018

<figure><img src="image_56.png" /></figure>

check for
updates

**Abstract:** The purpose of this study is to obtain a spatially explicit assessment of the impact of reservoirs on natural river sediment fluxes to the sea in the Mediterranean Maghreb Basin (MMB), a region where both mechanical erosion rates and the anthropogenic pressure on surface water resources are high. We combined modeling of riverine sediment yields (sediment fluxes divided by the drainage basin area) and water drainage intensities in a 5' × 5' grid point resolution (~10 km × 10 km) with a compilation of existing reservoirs in the area, and calculated sediment trapping based on average water residence time in these reservoirs. A total number of 670 reservoirs could be assembled from various sources (including digitization from Google maps), comprising large-scale, small-scale and hillside reservoirs. 450 of them could be implemented in our modeling approach. Our results confirm that natural sediment yields are clearly above the world average, with the greatest values for Morocco (506 t km⁻<fnref n="2" /> year⁻<fnref n="1" />), followed by Algeria (328 t km⁻<fnref n="2" /> year⁻<fnref n="1" />) and by Tunisia (250 t km⁻<fnref n="2" /> year⁻<fnref n="1" />). Including dams in the downstream routing of suspended sediments to the sea reduces the natural sediment flux in the entire MMB to the sea from 96 to 36 Mt km⁻<fnref n="1" /> year⁻<fnref n="1" />, which corresponds to an average sediment retention of 62%. Trapping rates are highest in the Tunisian basin part, with about 72%, followed by the Algerian (63%) and the Moroccan basin parts (55%). Small reservoirs and hillside reservoirs are quantitatively important in the interception of these sediments compared to large reservoirs. If we only considered the dams included in the widely used Global Reservoir and Dam (GRanD) database which comprises mainly large reservoirs sediment retention behind dams would account for 36% of the natural suspended particulate matter (SPM) flux to the Mediterranean Sea. Our data reveal negative correlation between sediment retention and natural erosion rates among the three Maghreb countries, which can be explained by the greater difficulties to build dams in steep terrains where natural sediment yields are high. Although the lowest sediment retention rates are found in the Moroccan part of the MMB, it is probably here where riverine sediment starvation has the greatest impacts on coastline dynamics. Understanding the impact of dams and related water infrastructures on riverine sediment dynamics is key in arid zones such as the MMB, where global warming is predicted to cause important changes in the climatic conditions and the water availability.

**Keywords:** Mediterranean Maghreb Basin; water fluxes; sediment fluxes; reservoirs; hillside reservoirs; sediment retention

<footnote n="1">¹ Centre de Formation et de Recherche sur les Environnements Méditerranéens, Université de Perpignan Via Domitia, UMR 5110, 52 Avenue Paul Alduy, F-66860 Perpignan CEDEX, France; ludwig@univ-perp.fr (W.L.), francois.bourrin@univ-perp.fr (F.B.)</footnote>

<footnote n="2">² Centre de Formation et de Recherche sur les Environnements Méditerranéens, CNRS, UMR 5110, 52 Avenue Paul Alduy, F-66860 Perpignan CEDEX, France; estela.romero@univ-perp.fr</footnote>

<footnote n="3">³ Laboratoire d'Étude des Interactions Sol-Agrosystème-Hydrosystème, INRA, UMR LISAH, INRA-IRD-SupAgro-Univ. Montpellier, 2 Place Viala, F-34060 Montpellier, France; y.le-bissonnais@orange.fr</footnote>

<footnote n="4">⁴ Global Ecology Unit, Center for Ecological Research and Forestry Applications, CREAF-Universitat Autònoma de Barcelona, Edifici C, Campus UAB, 08193 Bellaterra, Spain</footnote>

<footnote>* Correspondence: mahrez.sadaoui@univ-perp.fr; Tel.: +33-68-66-20-93</footnote>
# 1. Introduction

Because of the strong seasonal and inter-annual variability of flow regimes in inland waters, water stress is endemic in most parts of the Mediterranean drainage basin. Water is relatively scarce throughout most of the year, whereas high flows threaten lives and property on a small number of days per year or decade. Under such conditions, the rising demand for water is met by steadily increasing reservoir constructions to store seasonal or annual water surpluses [1]. These reservoirs impact the natural transfer and cycling of water, sediments and associated elements in the corresponding drainage basins. They retain considerable amounts of riverine nutrients and suspended sediments [2,3], preventing them to be supplied to the coastal waters in order to sustain biological productivity and to allow geomorphological edification of beaches and deltaic systems.

Sediment starvation in rivers through damming has been recognized as a major environmental problem in the Mediterranean area, as it exacerbates coastal erosion and sea level rise in relation to global warming [4,5]. In Mediterranean rim countries most of the population is concentrated in the coastal areas, which also represent the dominant economic resources via tourism and fishery. Erosion in these areas is hence a major threat. Rapid sediment accumulation in the reservoirs has also negative economic impacts as it reduces their performance and lifetime. Detailed knowledge on the riverine sediment dynamics in relation to the natural and anthropogenic forcings is therefore mandatory for sustainable development strategies. This is particularly true in the Mediterranean area because natural riverine sediment fluxes are high in its drainage basins [6] and environmental conditions respond rapidly to anthropogenic alterations.

The impact of reservoir construction on riverine sediment fluxes to the Mediterranean Sea was examined in a recent study [7]. By combining spatially explicit modeling of natural sediment and water fluxes with the precise locations and properties of dams as they are documented in the GRanD database (Global Reservoir and Dam, [8]), they calculated that about 35% of the natural sediment discharges should nowadays be retained in reservoirs. This underlines their significant impact on the basin-wide budgets, although the percentage is comparable to what has been estimated at the global scale [9,10], which questions the idea of the Mediterranean being a hot spot area for anthropogenic perturbations. Nevertheless, Mediterranean rivers are often small in size [11] and GRanD is far from being complete in this area.

We therefore attempted to go a step further and to examine the role of the “missing dams”. We selected the Maghreb region of Northern Africa as a regional focus and intended to assemble a much more complete database from various sources, also including small-scale and hillside reservoirs. This allowed us to produce for the first time a detailed regional assessment on dams in the area, which is not only useful for general budget considerations, but which may also serve as a management tool for the evaluation of potential environmental risks in terms of coastal erosion and/or reservoir silting. Our method may further be applied to other regions of the Mediterranean drainage basin in order to put together a more complete picture of human perturbations in this key environment for global change studies.

# 2. Data and Methods

## 2.1. *Study Area*

The Maghreb in northwestern Africa, also known as the small Maghreb, is a mountainous region including the countries of Morocco, Algeria and Tunisia. In particular, the Mediterranean Maghreb basin (MMB) covers the northern part of Morocco excluding the terrains that are drained to the Atlantic, Algeria, and the northern and eastern parts of Tunisia (Figure 1). The Maghreb is among the regions that are most vulnerable to erosion in the world. Several researchers [12–14] have attempted to explain the mechanisms of solid transport in this region, and Probst and Suchet proposed an overall estimate for the riverine sediment fluxes in the Maghreb basin of 100 Mt year⁻¹ [15], corresponding to a specific
sediment flux (sediment yield) of about 400 t km⁻² year⁻¹. These are probably among the highest values in Africa.

<figure><img src="image_57.png" /><figcaption>**Figure 1.** Location map of the Mediterranean Maghreb Basin (MMB) and delimitation of its drainage basins at a spatial resolution of 5 arc-minutes. Topography was extracted from the digital elevation model of [16]. All rivers and locations which are mentioned in the text have been included in this figure. Abbreviations: ALB: Alboran sub-basin, CEN: Central sub-basin, SWE: Southwestern sub-basin, TY: Tyearrhenian sub-basin.</figcaption></figure>

These average values, however, cannot be transposed locally because their spatial variability can be significant. For example, rather moderate sediment yields of 165 t km⁻² year⁻¹ have been reported for the Mouilah basin [17] in Algeria; intermediate values of about 600 t km⁻² year⁻¹ for the Medjerda basin [15] in Tunisia and 884 t km⁻² year⁻¹ for the Upper Kebir basin in Algeria [18]; high values of 1500 and 2000 t km⁻² year⁻¹ for the Kamech basin in Tunisia [19] and the Martil basin in Morocco [20], respectively; and peak values of 5000 to 5900 t km⁻² year⁻¹ for the Agrioum basin [21] in Algeria and the Nekor basin [22] in Morocco. Several factors combine in order to explain these high erosion rates. The Maghreb is a fragile mountainous area. The mountains extend over large areas and are characterized by high altitudes as well as steep slopes. Lithology is often made of soft sedimentary rocks susceptible to erosion and the Maghreb is subject to contrasted climatic conditions. In summer, climate is dominated by Saharan aridity with high temperature and low precipitation. In winter, climate is colder and wetter due to the oceanic influence [23]. Hence, climate is irregular and characterized by a strong contrast between dry and wet years, with often intense and devastating rains [24].

Due to the aridity of the climate, water is an important factor for economic development and the construction of dams represents a major issue for the Maghreb countries. North Africa (here considered as including the totality of these three countries: Morocco, Algeria and Tunisia) has currently over 230 large dams with a total storage capacity of 23 km³ [25]. Large dams serve in general to store drinking water and for industrial and electrical energy production. In addition, many small dams and hillside reservoirs exist and serve to mitigate floods, contribute to water table recharge and improve water storage for irrigation and livestock watering and domestic uses. A detailed census of the different reservoir categories is nevertheless difficult. The only existing database is the GRanD database [8], which includes 53 dams in the Maghreb basin with a total storage capacity of about 6.5 km³ [7].

Anthropogenic degradation in MMB is very high, increasing erosion and enhancing early silting of reservoirs in Morocco [20,26], in Algeria [24,27] and in Tunisia [28]. According to [25], who monitored
and evaluated silting in the 230 dams of North Africa, the average capacity loss in these dams can be estimated to about 0.54% year⁻¹. Silting of these dams poses many problems at the level of the reservoir itself, but also in the basin parts upstream and downstream the reservoir. The authors of [25,29] cite several dams in North Africa where major degradations occurred, such as the reduction of the dam capacity, the destabilization of the structure (e.g., Oued El Fodda, Algeria), the filling of drainage ditches (e.g., Zardézas dams, Algeria), the silting of irrigation channels (e.g., bypass channel, Morocco) and the degradation of water quality (e.g., El Khattabi Dam, Morocco).

## 2.2. Modeling Framework

The modeling framework we used to calculate river sediment fluxes in the MMB and their retention in dams was set up in the study by [7]. It is based on the numerical definition of the drainage basin delineations and hydrological networks around the Mediterranean Sea in a 5' × 5' grid point resolution (~10 km × 10 km), and was produced by aggregation of the Hydrosheds digital elevation model of [30] with manual corrections of the resulting hydrological networks (for further details, see [7]). A total of 549 river basins could be distinguished for the entire Mediterranean drainage basin. In each basin all internal grid points are linked to one single mouth point that represents the river estuary according to a realistic river routing scheme (multi-branch estuaries that often exist for large river systems cannot be reproduced this way).

Quantification of specific riverine suspended matter (SPM) and specific water (Q) fluxes was obtained through empirical modeling relating these parameters at the grid point scale to a series of environmental factors that are considered to represent their major controls. The corresponding multi-regression models could be established on the basis of the average long-term basin characteristics from a large data set of 126 (Q) and 80 (SPM) Mediterranean rivers [7]. In short, specific water discharge (Q) was produced as a combination of annual precipitation, potential evapotranspiration, seasonal variability of precipitation (all climatic parameters were taken from [31]) and grid point slope [30], whereas specific SPM fluxes could be linked to specific water discharge, grid point slope, the percentage of sedimentary rocks in altitudes above 600 m [32] and the percentage of erodible land use classes (agriculture, grasslands and bare soils—[33]).

Both the produced Q and SPM fluxes represent average long-term values that do not account for temporal fluctuations in relation to climate variability and/or recent climate change, although we are aware that seasonal and inter-annual variability of SPM fluxes can be very important in the Mediterranean area and short and violent floods may represent more than 90% of the annual sediment load [34]. Moreover, the SPM fluxes are considered to represent natural (i.e., “pre-damming”) values, since most of the literature estimates that were used to fit the regressions correspond to the pre-1950–1980 period, when human impoundments on rivers were not as frequent as today. This allowed simulating the impact of the dams constructed along the river courses through conservative routing of the grid-point SPM fluxes into the reservoirs and retaining them as a function of the average water residence time according to the equation initially proposed by [35]. The whole method has been described with more detail in [7].

## 2.3. Inventory of Dams

We started our inventory with the geo-referenced dams included in the GRanD database of [8]. It contains 53 dams in the Maghreb basin (6 for Morocco, 28 for Algeria and 19 for Tunisia). To this, 48 dams could be added from repertoires from various national agencies in the Maghreb, including 36 dams from the ANBT (National Agency for Dams and Transfers, [36]) in Algeria, 7 dams from the ABHL (Loukkos Hydraulic Basin Agency, [37]) in Morocco and 5 dams from the DGBGTH (General Directorate for Dams and Major Hydraulic Works, [38]) in Tunisia. Generally, these agencies provide information on storage capacity, water discharge and the year of reservoir construction, but no digitized files in a GIS format for the location of the dams. Manual location of these dams from Google satellite maps was necessary (consulted in June 2016). A total of 101 dams were consequently assembled
(Table 1). The majority of these dams were constructed in recent decades. More than 30% were built after 2000, and 60% between 1950 and 2000. Less than 10% were erected over the period from 1860 to 1950.

<table><caption>**Table 1.** Characteristics of the 101 dams of the Mediterranean Maghreb Basin (MMB) considered in this study.</caption><thead><tr><th>NB</th><th>Dams</th><th>Country</th><th>Year of Construction</th><th>CAP (Mm³)</th><th>S (km²)</th><th>H (m)</th><th>Source</th></tr></thead><tbody><tr><td>1</td><td>Ain Dalia</td><td>Algeria</td><td>1987</td><td>82</td><td>3.4</td><td>24</td><td>GRanD</td></tr><tr><td>2</td><td>Ain zada</td><td>Algeria</td><td>1986</td><td>125</td><td>6.9</td><td>18</td><td>GRanD</td></tr><tr><td>3</td><td>Ajras</td><td>Morocco</td><td>1969</td><td>3</td><td>0.4</td><td>8</td><td>GRanD</td></tr><tr><td>4</td><td>Al Thelat-Laou</td><td>Morocco</td><td>1934</td><td>30</td><td>0.9</td><td>33</td><td>GRanD</td></tr><tr><td>5</td><td>Asmir</td><td>Morocco</td><td>1991</td><td>43</td><td>2.6</td><td>17</td><td>ABHL</td></tr><tr><td>6</td><td>Bakhada</td><td>Algeria</td><td>1935</td><td>56</td><td>3.5</td><td>16</td><td>GRanD</td></tr><tr><td>7</td><td>Barbara-Zoutina</td><td>Tunisia</td><td>1999</td><td>58</td><td>3.9</td><td>15</td><td>GRanD</td></tr><tr><td>8</td><td>Beni Amrane</td><td>Algeria</td><td>1988</td><td>12</td><td>1.0</td><td>12</td><td>ANBT</td></tr><tr><td>9</td><td>Beni Bahdel</td><td>Algeria</td><td>1946</td><td>63</td><td>1.2</td><td>53</td><td>GRanD</td></tr><tr><td>10</td><td>Beni Boussaid</td><td>Algeria</td><td>2007</td><td>1</td><td>0.2</td><td>7</td><td>ANBT</td></tr><tr><td>11</td><td>Beni Haroune</td><td>Algeria</td><td>2003</td><td>998</td><td>44.4</td><td>22</td><td>ANBT</td></tr><tr><td>12</td><td>Beni metir</td><td>Tunisia</td><td>1954</td><td>73</td><td>1.8</td><td>42</td><td>GRanD</td></tr><tr><td>13</td><td>Beni zid</td><td>Algeria</td><td>2000</td><td>40</td><td>3.5</td><td>12</td><td>ANBT</td></tr><tr><td>14</td><td>Bezrik</td><td>Tunisia</td><td>1959</td><td>7</td><td>0.7</td><td>9</td><td>GRanD</td></tr><tr><td>15</td><td>Bir M'Cherga</td><td>Tunisia</td><td>1971</td><td>160</td><td>4.3</td><td>37</td><td>GRanD</td></tr><tr><td>16</td><td>Bou Hanifa</td><td>Algeria</td><td>1948</td><td>73</td><td>2.7</td><td>27</td><td>GRanD</td></tr><tr><td>17</td><td>bou heurtna</td><td>Tunisia</td><td>1976</td><td>118</td><td>7.1</td><td>17</td><td>GRanD</td></tr><tr><td>18</td><td>Bou Roumi</td><td>Algeria</td><td>1985</td><td>188</td><td>1.7</td><td>111</td><td>GRanD</td></tr><tr><td>19</td><td>Boughzoul</td><td>Algeria</td><td>1934</td><td>55</td><td>16.3</td><td>3</td><td>GRanD</td></tr><tr><td>20</td><td>Bougous</td><td>Algeria</td><td>2010</td><td>65</td><td>2.8</td><td>24</td><td>ANBT</td></tr><tr><td>21</td><td>Boukourdene</td><td>Algeria</td><td>1992</td><td>110</td><td>3.1</td><td>36</td><td>ANBT</td></tr><tr><td>22</td><td>Boussiaba</td><td>Algeria</td><td>2000</td><td>120</td><td>10.3</td><td>12</td><td>ANBT</td></tr><tr><td>23</td><td>Cap Djinet</td><td>Algeria</td><td>1990</td><td>13</td><td>0.4</td><td>34</td><td>ANBT</td></tr><tr><td>24</td><td>Cheffia</td><td>Algeria</td><td>1965</td><td>171</td><td>7.0</td><td>24</td><td>GRanD</td></tr><tr><td>25</td><td>Cheelif</td><td>Algeria</td><td>2009</td><td>130</td><td>7.2</td><td>18</td><td>ANBT</td></tr><tr><td>26</td><td>Cheufra</td><td>Algeria</td><td>1935</td><td>14</td><td>1.3</td><td>11</td><td>GRanD</td></tr><tr><td>27</td><td>Chiba</td><td>Tunisia</td><td>1963</td><td>8</td><td>1.1</td><td>7</td><td>GRanD</td></tr><tr><td>28</td><td>Colonel bougara</td><td>Algeria</td><td>1988</td><td>13</td><td>2.3</td><td>6</td><td>GRanD</td></tr><tr><td>29</td><td>Dahmouni</td><td>Algeria</td><td>1987</td><td>41</td><td>3.4</td><td>12</td><td>GRanD</td></tr><tr><td>30</td><td>Deurdeur</td><td>Algeria</td><td>1984</td><td>115</td><td>4.8</td><td>24</td><td>ANBT</td></tr><tr><td>31</td><td>Djoumine</td><td>Tunisia</td><td>1983</td><td>130</td><td>5.4</td><td>24</td><td>GRanD</td></tr><tr><td>32</td><td>Douera</td><td>Algeria</td><td>2014</td><td>87</td><td>1.3</td><td>65</td><td>ANBT</td></tr><tr><td>33</td><td>Draa Diss</td><td>Algeria</td><td>2016</td><td>151</td><td>0.5</td><td>80</td><td>ANBT</td></tr><tr><td>34</td><td>El Abid</td><td>Tunisia</td><td>2002</td><td>10</td><td>1.4</td><td>7</td><td>DGBGTH</td></tr><tr><td>35</td><td>El Agrem</td><td>Algeria</td><td>2002</td><td>34</td><td>2.3</td><td>15</td><td>ANBT</td></tr><tr><td>36</td><td>El Djomoaa</td><td>Morocco</td><td>1992</td><td>7</td><td>0.5</td><td>12</td><td>ABHL</td></tr><tr><td>37</td><td>El habib-ouareb</td><td>Tunisia</td><td>1988</td><td>110</td><td>5.0</td><td>22</td><td>GRanD</td></tr><tr><td>38</td><td>El Hamiz</td><td>Algeria</td><td>1990</td><td>16</td><td>0.9</td><td>17</td><td>ANBT</td></tr><tr><td>39</td><td>El hamma</td><td>Tunisia</td><td>2002</td><td>12</td><td>0.6</td><td>21</td><td>DGBGTH</td></tr><tr><td>40</td><td>El Masri</td><td>Tunisia</td><td>1968</td><td>6</td><td>0.7</td><td>8</td><td>ANBT</td></tr><tr><td>41</td><td>Ennakhela</td><td>Morocco</td><td>1961</td><td>13</td><td>0.6</td><td>22</td><td>GRanD</td></tr><tr><td>42</td><td>Erraguen</td><td>Algeria</td><td>1961</td><td>200</td><td>11.0</td><td>18</td><td>ANBT</td></tr><tr><td>43</td><td>Fergoug</td><td>Algeria</td><td>1970</td><td>18</td><td>0.7</td><td>26</td><td>GRanD</td></tr><tr><td>44</td><td>Gargar</td><td>Algeria</td><td>1988</td><td>450</td><td>19.0</td><td>24</td><td>GRanD</td></tr><tr><td>45</td><td>GHrib</td><td>Algeria</td><td>1938</td><td>280</td><td>7.1</td><td>39</td><td>GRanD</td></tr><tr><td>46</td><td>GuenitraOumToub</td><td>Algeria</td><td>1984</td><td>125</td><td>1.3</td><td>96</td><td>GRanD</td></tr><tr><td>47</td><td>H.M.Bouhemdene</td><td>Algeria</td><td>1987</td><td>220</td><td>5.1</td><td>43</td><td>GRanD</td></tr><tr><td>48</td><td>Hammam Grouz</td><td>Algeria</td><td>1987</td><td>45</td><td>2.3</td><td>20</td><td>GRanD</td></tr><tr><td>49</td><td>Harreza</td><td>Algeria</td><td>1984</td><td>70</td><td>1.0</td><td>70</td><td>GRanD</td></tr><tr><td>50</td><td>Hassan II</td><td>Morocco</td><td>2000</td><td>400</td><td>11.0</td><td>36</td><td>ABHL</td></tr><tr><td>51</td><td>Hassan II Midelt</td><td>Morocco</td><td>2006</td><td>400</td><td>11.8</td><td>34</td><td>ABHL</td></tr><tr><td>52</td><td>Ighil Emda</td><td>Algeria</td><td>1953</td><td>156</td><td>2.9</td><td>53</td><td>ANBT</td></tr><tr><td>53</td><td>kasseb</td><td>Tunisia</td><td>1969</td><td>81</td><td>4.0</td><td>20</td><td>GRanD</td></tr><tr><td>54</td><td>Kebir</td><td>Tunisia</td><td>1925</td><td>26</td><td>1.6</td><td>16</td><td>GRanD</td></tr><tr><td>55</td><td>Keddara</td><td>Algeria</td><td>1990</td><td>142</td><td>5.6</td><td>26</td><td>ANBT</td></tr><tr><td>56</td><td>Kef Eddir</td><td>Algeria</td><td>2015</td><td>150</td><td>0.5</td><td>77</td><td>ANBT</td></tr><tr><td>57</td><td>Kerrada</td><td>Algeria</td><td>2010</td><td>70</td><td>3.6</td><td>19</td><td>ANBT</td></tr><tr><td>58</td><td>Kissir</td><td>Algeria</td><td>2010</td><td>68</td><td>3.6</td><td>19</td><td>ANBT</td></tr><tr><td>59</td><td>Koudiat Acerdoun</td><td>Algeria</td><td>2009</td><td>400</td><td>16.5</td><td>24</td><td>ANBT</td></tr><tr><td>60</td><td>Koudiat resfa</td><td>Algeria</td><td>2007</td><td>75</td><td>3.7</td><td>20</td><td>ANBT</td></tr><tr><td>61</td><td>Kramis</td><td>Algeria</td><td>2005</td><td>45</td><td>2.1</td><td>21</td><td>ANBT</td></tr><tr><td>62</td><td>Ladrat</td><td>Algeria</td><td>1988</td><td>10</td><td>0.5</td><td>20</td><td>GRanD</td></tr><tr><td>63</td><td>Lakhmess</td><td>Tunisia</td><td>1966</td><td>8</td><td>0.7</td><td>11</td><td>GRanD</td></tr></tbody></table>
<table><caption>Table 1. *Cont.*</caption><thead><tr><th>NB</th><th>Dams</th><th>Country</th><th>Year of Construction</th><th>CAP (Mm³)</th><th>S (km²)</th><th>H (m)</th><th>Source</th></tr></thead><tbody><tr><td>64</td><td>Lekhal</td><td>Algeria</td><td>1985</td><td>30</td><td>0.9</td><td>33</td><td>GRanD</td></tr><tr><td>65</td><td>Iepna</td><td>Tunisia</td><td>1986</td><td>30</td><td>6.6</td><td>5</td><td>GRanD</td></tr><tr><td>66</td><td>M.B.A.L.khattabi</td><td>Morocco</td><td>1981</td><td>43</td><td>2.1</td><td>20</td><td>GRanD</td></tr><tr><td>67</td><td>Mahouane</td><td>Algeria</td><td>2014</td><td>148</td><td>0.3</td><td>76</td><td>ANBT</td></tr><tr><td>68</td><td>Martil</td><td>Morocco</td><td>2014</td><td>100</td><td>0.8</td><td>118</td><td>ABHL</td></tr><tr><td>69</td><td>Mechra Hammadi</td><td>Morocco</td><td>1955</td><td>42</td><td>1.2</td><td>35</td><td>GRanD</td></tr><tr><td>70</td><td>Mefrouch</td><td>Algeria</td><td>1960</td><td>15</td><td>1.8</td><td>8</td><td>ANBT</td></tr><tr><td>71</td><td>Merdja sidi abed</td><td>Algeria</td><td>1984</td><td>55</td><td>7.0</td><td>8</td><td>GRanD</td></tr><tr><td>72</td><td>Meurad</td><td>Algeria</td><td>1860</td><td>0.2</td><td>0.1</td><td>26</td><td>ANBT</td></tr><tr><td>73</td><td>Mexa</td><td>Algeria</td><td>1998</td><td>47</td><td>5.4</td><td>9</td><td>ANBT</td></tr><tr><td>74</td><td>Mlaabi</td><td>Tunisia</td><td>1967</td><td>4</td><td>0.8</td><td>4</td><td>DGBGTH</td></tr><tr><td>75</td><td>Mohammed V</td><td>Morocco</td><td>1967</td><td>730</td><td>16.3</td><td>45</td><td>GRanD</td></tr><tr><td>76</td><td>M. Bouchta</td><td>Morocco</td><td>2014</td><td>13</td><td>0.7</td><td>18</td><td>ABHL</td></tr><tr><td>77</td><td>Nabeur-Mellegue</td><td>Tunisia</td><td>1954</td><td>300</td><td>8.5</td><td>24</td><td>GRanD</td></tr><tr><td>78</td><td>Nebhana</td><td>Tunisia</td><td>1965</td><td>86</td><td>2.6</td><td>33</td><td>GRanD</td></tr><tr><td>79</td><td>Oued Cherf</td><td>Algeria</td><td>1995</td><td>157</td><td>10.2</td><td>15</td><td>ANBT</td></tr><tr><td>80</td><td>Oued el hajar</td><td>Tunisia</td><td>1999</td><td>6</td><td>2.0</td><td>3</td><td>DGBGTH</td></tr><tr><td>81</td><td>Oueder mel tunis</td><td>Tunisia</td><td>1999</td><td>22</td><td>7.8</td><td>3</td><td>DGBGTH</td></tr><tr><td>82</td><td>Oued Fouda</td><td>Algeria</td><td>1932</td><td>228</td><td>2.9</td><td>79</td><td>GRanD</td></tr><tr><td>83</td><td>Oued Rmel</td><td>Morocco</td><td>2008</td><td>25</td><td>1.1</td><td>22</td><td>ABHL</td></tr><tr><td>84</td><td>Quizert</td><td>Algeria</td><td>1985</td><td>100</td><td>2.0</td><td>50</td><td>GRanD</td></tr><tr><td>85</td><td>S.M.B.Aouda</td><td>Algeria</td><td>1978</td><td>235</td><td>8.0</td><td>29</td><td>GRanD</td></tr><tr><td>86</td><td>Sarno</td><td>Algeria</td><td>1953</td><td>22</td><td>0.2</td><td>110</td><td>GRanD</td></tr><tr><td>87</td><td>Sejnane</td><td>Tunisia</td><td>1990</td><td>130</td><td>5.8</td><td>22</td><td>GRanD</td></tr><tr><td>88</td><td>Sekkak</td><td>Algeria</td><td>2004</td><td>30</td><td>2.0</td><td>15</td><td>ANBT</td></tr><tr><td>89</td><td>Sidi abdelli</td><td>Algeria</td><td>1988</td><td>110</td><td>0.8</td><td>138</td><td>GRanD</td></tr><tr><td>90</td><td>Sidi el barek</td><td>Tunisia</td><td>2002</td><td>275</td><td>9.2</td><td>30</td><td>GRanD</td></tr><tr><td>91</td><td>Sidi saad</td><td>Tunisia</td><td>1981</td><td>209</td><td>9.8</td><td>21</td><td>GRanD</td></tr><tr><td>92</td><td>Sidi salem</td><td>Tunisia</td><td>1981</td><td>555</td><td>40.1</td><td>14</td><td>GRanD</td></tr><tr><td>93</td><td>Sidi Yakoub</td><td>Algeria</td><td>1983</td><td>286</td><td>6.4</td><td>45</td><td>GRanD</td></tr><tr><td>94</td><td>Siliana</td><td>Tunisia</td><td>1987</td><td>70</td><td>4.7</td><td>15</td><td>GRanD</td></tr><tr><td>95</td><td>SMB taiba</td><td>Algeria</td><td>2005</td><td>75</td><td>2.9</td><td>25</td><td>ANBT</td></tr><tr><td>96</td><td>Souani</td><td>Algeria</td><td>1988</td><td>14</td><td>2.0</td><td>7</td><td>GRanD</td></tr><tr><td>97</td><td>Teksebt</td><td>Algeria</td><td>2001</td><td>180</td><td>4.6</td><td>39</td><td>ANBT</td></tr><tr><td>98</td><td>Tichy haff</td><td>Algeria</td><td>2009</td><td>80</td><td>4.6</td><td>18</td><td>ANBT</td></tr><tr><td>99</td><td>Tilsedit</td><td>Algeria</td><td>2009</td><td>83</td><td>9.7</td><td>9</td><td>ANBT</td></tr><tr><td>100</td><td>Zardezaz</td><td>Algeria</td><td>1973</td><td>31</td><td>1.3</td><td>24</td><td>ANBT</td></tr><tr><td>101</td><td>Zit Emba</td><td>Algeria</td><td>2002</td><td>117</td><td>7.7</td><td>15</td><td>ANBT</td></tr></tbody></table>

Note: NB: number of dams; CAP: reservoir storage capacity; S: reservoir surface; H: height of dike; ABHL: Loukkos Hydraulic Basin Agency, Morocco; ANBT: National Agency for Dams and Transfers, Algeria; DGBGTH: General Directorate for Dams and Major Hydraulic Works, Tunisia; GRanD: Global Reservoir and Dam database.

In a second step, we enlarged our database through manual digitization of additional reservoirs from Google satellite maps. In this approach, we mainly focused on the inclusion of small and hillside reservoirs, but also a few large reservoirs previously not identified could be added that way. To facilitate our work, we first imported our river network map and the previously collected dams (GRanD and Maghreb agencies) in order to avoid the re-digitization of existing reservoirs. The maps were imported in kml-format at a resolution of 5 arc-minutes and consisted of polygons, vectors and points.

We then created our own layer on which we digitized the rest of the reservoirs by zooming in on the Google maps to their maximum resolution and following the river network from upstream to downstream in each river basin. Reservoirs were identified initially by the blue color of the waters and the presence of a dike downstream. Two layers were created, the first one to digitize the surface of the reservoirs with polygons and the second one to locate the dams by fixing a geographical reference point downstream the reservoir (near the dike), which represents the outlet of the dam. It was sometimes difficult to distinguish between small dams, hillside reservoirs, and natural lakes. Besides checking the legend of reservoir names provided by Google maps, we used the shape of the water surfaces as a distinguishing criterion. Reservoirs are generally characterized by their longitudinal shapes and the existence of a dike downstream the flow direction, whereas natural lakes are usually characterized by a round shape and the absence of the dike. Hillside reservoirs are not implemented on major river courses and typically found in agricultural fields.
For the manually-digitized reservoirs we had no information on the storage capacity. In order to produce an estimate for this parameter too, we calculated a linear regression between the storage capacity (CAP) and the surface (S) of the reservoir. In fact, two regression models were established, one for the GRanD database reservoirs (Figure 2A)

$$ CAP = 34.79 S \qquad (1) $$
$$ n = 323, r^2 = 0.82, p < 0.001 $$

And one for the hillside reservoirs (Figure 2B)

$$ CAP = 9.29 S \qquad (2) $$
$$ n = 25, r^2 = 0.89, p < 0.001 $$

<figure><img src="image_58.png" /><figcaption>**Figure 2.** Linear relationships between storage capacity and reservoir surface (A) using the Mediterranean reservoirs of the GRanD database [8] and (B) using the hillside reservoir database of [39].</figcaption></figure>

Both regressions were forced to pass through the origin (i.e.; no intercept). Equation (1) is based on information for all Mediterranean reservoirs in the GRanD database, and Equation (2) on the compilation of hillside reservoirs in the Tizi-Ouzou region (Algeria) produced by [39], which is the only available study in the area (Table 2).

<table><caption>**Table 2.** Characteristics of the hillside reservoirs of the Tizi-Ouzou region (Algeria) described by the authors of [39] and considered in this study.</caption><thead><tr><th>NB</th><th>Name of Hillside Reservoir</th><th>S (10<sup>3</sup> m<sup>2</sup>)</th><th>H (m)</th><th>CAP (10<sup>-3</sup> Mm<sup>3</sup>)</th><th>Year of Construction</th></tr></thead><tbody><tr><td>1</td><td>Bouzeguene N° 2</td><td>4.0</td><td>13</td><td>52</td><td>1999</td></tr><tr><td>2</td><td>Bouzeguene N° 3</td><td>7.5</td><td>10</td><td>75</td><td>1999</td></tr><tr><td>3</td><td>Iloula oumalou N° 1</td><td>3.6</td><td>11</td><td>40</td><td>2000</td></tr><tr><td>4</td><td>Iloula oumalou N° 399</td><td>8.0</td><td>10</td><td>80</td><td>2000</td></tr><tr><td>5</td><td>Timizart N° 201</td><td>6.7</td><td>9</td><td>60</td><td>1989</td></tr><tr><td>6</td><td>Timizart N° 360</td><td>5.0</td><td>10</td><td>50</td><td>1988</td></tr><tr><td>7</td><td>Timizart N° 363</td><td>7.8</td><td>9</td><td>70</td><td>1986</td></tr><tr><td>8</td><td>Timizart N° 366</td><td>13.5</td><td>10</td><td>135</td><td>1985</td></tr><tr><td>9</td><td>Timizart N° 388</td><td>7.5</td><td>8</td><td>60</td><td>1985</td></tr><tr><td>10</td><td>Freha N° 389</td><td>6.3</td><td>8</td><td>50</td><td>2000</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><th>NB</th><th>Name of Hillside Reservoir</th><th>S (10<sup>3</sup> m<sup>2</sup>)</th><th>H (m)</th><th>CAP (10<sup>-3</sup> Mm<sup>3</sup>)</th><th>Year of Construction</th></tr></thead><tbody><tr><td>11</td><td>Freha N° 395</td><td>5.8</td><td>9.5</td><td>55</td><td>1985</td></tr><tr><td>12</td><td>Freha N° 396</td><td>12.5</td><td>8</td><td>100</td><td>1987</td></tr><tr><td>13</td><td>Freha N° 397</td><td>4.7</td><td>9.5</td><td>45</td><td>1986</td></tr><tr><td>14</td><td>Freha N° 398</td><td>10.0</td><td>10</td><td>100</td><td>1989</td></tr><tr><td>15</td><td>Mekla N° 367</td><td>1.2</td><td>8.5</td><td>10</td><td>1985</td></tr><tr><td>16</td><td>Mekla N° 368</td><td>8.2</td><td>8.5</td><td>70</td><td>1986</td></tr><tr><td>17</td><td>Mekla N° 391</td><td>4.0</td><td>10</td><td>40</td><td>1989</td></tr><tr><td>18</td><td>Mekla N° 393</td><td>6.9</td><td>7</td><td>48</td><td>1985</td></tr><tr><td>19</td><td>Mekla N° 394</td><td>5.7</td><td>7</td><td>40</td><td>1986</td></tr><tr><td>20</td><td>Souomaâ N° 1</td><td>5.5</td><td>11</td><td>60</td><td>1999</td></tr><tr><td>21</td><td>Azzefoun N° 10</td><td>4.2</td><td>9.5</td><td>40</td><td>1986</td></tr><tr><td>22</td><td>Azzefoun N° 11</td><td>5.0</td><td>12</td><td>60</td><td>1997</td></tr><tr><td>23</td><td>Azzefoun N° 12</td><td>7.0</td><td>10</td><td>70</td><td>1988</td></tr><tr><td>24</td><td>Azzefoun N° 14</td><td>3.6</td><td>11</td><td>40</td><td>1994</td></tr><tr><td>25</td><td>Azzefoun N° 15</td><td>2.9</td><td>10.5</td><td>30</td><td>1995</td></tr></tbody></table>

Note: S: hillside reservoir surface; H: Dike height; CAP: reservoir storage capacity.

It should be noted that the database produced by [39] was only used to determine Equation (2). Since the database does not include the geographic coordinates of the reservoirs, it is difficult to differentiate them from other newly digitalized hillside reservoirs. To avoid confusion and duplications, we kept this database only to determine Equation (2).

The storage capacity of the reservoirs can be strongly influenced by their morphology, and notably by the slope of the reservoir surface. The slope of hillside reservoirs is generally very low compared to that of the dams, in particular large dams. Two reservoirs that have the same surface but a different slope can have a very different storage capacity; this is the reason why we kept two different regression models (Figure 2A,B).

Overall, we were able to digitize the surface of 569 additional reservoirs, mostly consisting of small dams and hillside reservoirs. This expanded our database to 670 reservoirs with areas larger than one hectare. On the basis of Equations (1) and (2), we then calculated the storage capacity of these additional reservoirs (i.e., when the newly digitized reservoirs were identified as hillside reservoirs, we applied Equation (2); elsewhere, we used Equation (1). All the reservoirs were then classified according to their storage capacities into seven classes: C₁ (CAP > 200 Mm³), C₂ (100 < CAP < 200 Mm³), C₃ (50 < CAP < 100 Mm³), C₄ (20 < CAP < 50 Mm³), C₅ (5 < CAP < 20 Mm³), C₆ (1 < CAP < 5 Mm³), and C₇ (CAP < 1 Mm³). It should be noted that hillside reservoirs never exceeded a storage capacity of 1 Mm³, and almost 90% of them had a storage capacity lower than 0.8 Mm³. For small dams, we considered that the maximum capacity limit was 20 Mm³ (see Section 3.2) and found that only 21% of these dams had a storage capacity lower than 1 Mm³. Although there is some overlap with the maximum size of hillside reservoirs, we considered this value to be a reasonable lower limit for small dams.

The integration of all these reservoirs in our modeling framework suffered however from the limited spatial resolution of this framework. In many cases several reservoirs fell within one single 5 arc-minutes grid cell. For the calculation of sediment retention, in addition to the storage capacity we need the amount of water entering and leaving the reservoirs annually. This parameter can only be calculated at the grid point level from our empirical modeling of mean annual water discharge (see Section 2.2). We therefore decided to retain for our modeling of sediment retention only the largest reservoir in a given grid point cell, to which we attributed the corresponding average annual water discharge following the assumption that this reservoir has been constructed along the principal grid point drainage direction. This reduced the total number of reservoirs which could be properly used for modeling to 450.
# 3. Results and Discussion

## 3.1. Natural Water and Sediment Fluxes

Average water fluxes in the MMB are rather low (Figure 3A). Drainage intensity (water discharge divided by the basin area) for the entire region is only 59 mm per year (Table 3), with little differences between the three countries (52, 62 and 62 mm year⁻¹ for Morocco, Algeria and Tunisia, respectively). More humid conditions are restricted to mountainous areas in the vicinity of the coast and exist in the NW part of Morocco (e.g., the Martil and Laou rivers with Q values of 332 mm year⁻¹ and 252 mm year⁻¹, respectively) and along the central and NE coast of Algeria (e.g., the Sebaou and Bou-Namoussa rivers with Q values of 387 mm year⁻¹ and 290 mm year⁻¹, respectively). The driest conditions are found in the Saharan part of Tunisia and the drainage basins and basin parts which stretch far to the South. All MMB rivers together discharge about 16.2 × 10³ Mm³ year⁻¹ of water to the Mediterranean Sea, which corresponds to less than 4% of the total freshwater flux exported by the Mediterranean drainage basin [7]. About 48% of the MMB water flux stems from the rivers in Algeria. The mean annual water discharge for individual rivers ranges from 1 Mm³ for the Tunisian Saharan rivers to 1720 Mm³ for the Moulouya in Morocco, the largest river in the MMB (55,713 km²). Only about 7% of all rivers have an average water discharge greater than 1000 Mm³ year⁻¹, but they provide more than 32% of the total water flux. On the other hand, 50% of the rivers have a water flux which does not exceed 100 Mm³ year⁻¹ and represent only 7% of the total water flux.

<table><caption>**Table 3.** Summary of average water and sediment fluxes in the Mediterranean Maghreb Basin (MMB) before and after dam construction, and total number of reservoirs considered in this study.</caption><thead><tr><th>Parameters</th><th>Morocco</th><th>Algeria</th><th>Tunisia</th><th>Maghreb</th></tr></thead><tbody><tr><td>A (10³ km²)</td><td>69</td><td>126</td><td>78</td><td>274</td></tr><tr><td>A-drained by dams (10³ km²)</td><td>59</td><td>110</td><td>40</td><td>209</td></tr><tr><td>Q (mm year⁻¹)</td><td>52</td><td>62</td><td>62</td><td>59</td></tr><tr><td>FSPMs pre-damming (t km⁻² year⁻¹)</td><td>506</td><td>328</td><td>250</td><td>351</td></tr><tr><td>FSPMs post-damming (t km⁻² year⁻¹)</td><td>229</td><td>120</td><td>69</td><td>133</td></tr><tr><td>NB LD tot</td><td>12</td><td>62</td><td>25</td><td>99</td></tr><tr><td>NB SD tot</td><td>18</td><td>126</td><td>80</td><td>224</td></tr><tr><td>NB Hil tot</td><td>26</td><td>190</td><td>131</td><td>347</td></tr><tr><td>NB Dams tot</td><td>56</td><td>378</td><td>236</td><td>670</td></tr><tr><td>NB LD mod</td><td>12</td><td>62</td><td>25</td><td>99</td></tr><tr><td>NB SD mod</td><td>18</td><td>126</td><td>80</td><td>224</td></tr><tr><td>NB Hil mod</td><td>8</td><td>87</td><td>32</td><td>127</td></tr><tr><td>NB Dams mod</td><td>38</td><td>275</td><td>137</td><td>450</td></tr></tbody></table>

Note: A: Basin area; Q: Drainage intensity; FSPMs pre-damming: specific natural suspended particulate matter flux; FSPMs post-damming: specific anthropogenic suspended particulate matter flux; NB LD tot: Total number of large dams; NB SD tot: Total number of small dams; NB Hil tot: Total number of hillside dams; NB Dams tot: Total number of dams; NB LD mod: number of large dams used for modeling; NB SD mod: number of small dams used for modeling; NB Hil mod: number of hillside dams used for modeling; NB Dams mod: number of dams used for modeling.

Mountainous regions have also a dominant role for the average sediment fluxes (Figure 3B). High sediment yields (specific suspended particulate matter fluxes, FSPMs) are associated with high Q values, although peak values can also stretch further to the South to lower discharge regions in combination with higher elevations (e.g., in the larger basins such as the Moulouya and Medjerda basins). On average, sediment yields (Table 3) are greatest in Morocco (506 t km⁻² year⁻¹), followed by Algeria (328 t km⁻² year⁻¹) and by Tunisia (250 t km⁻² year⁻¹). Peak values are obtained for the Agrioum River in Algeria (4001 t km⁻² year⁻¹) and the Nekor River in Morocco (2956 t km⁻² year⁻¹), whereas the lowest values are found for the Bou-Namoussa River in Algeria (15 t km⁻² year⁻¹), which is located east of the Seybouse River, and for the Ighzar n-Thyaouth River in Morocco (21 t km⁻² year⁻¹), which lies between the Moulouya and Kert rivers. Notice that for the former
two rivers, which are probably the most erosive rivers in Africa, our empirical modeling resulted in somewhat lower values than those found in the literature (see Section 2.1). This is due to the fact that we excluded from our regressions a few rivers (notably the Agrioum and Nekor rivers) that had average SPM concentrations greater than 15 g L⁻¹. We considered that such extreme values might indicate possible errors in the literature citations or refer to highly peculiar erosion processes that cannot be reproduced by general regression models (for a more detailed discussion, see [7]).

<figure><img src="image_59.png" /><figcaption>**Figure 3.** Spatial distribution of (A) specific riverine water discharge (Q-mod) and (B) specific suspended particulate matter fluxes (FSPMs pre-damming) in the Mediterranean Maghreb Basin according to the study of [7].</figcaption></figure>

We estimate that the specific natural suspended particulate matter flux (FSPMs pre-damming) for the entire MMB is 351 t km⁻² year⁻¹ and corresponds to an overall sediment delivery to the Mediterranean Sea of 96 Mt year⁻¹. This value is very close to the estimate of 100 Mt year⁻¹ presented by [15] for the same study area. About 60% of this value is exported by only seven rivers (Moulouya, Medjerda, Agrioum, Kert, Nekor, Chelif and Seybousse), and Algerian rivers alone account for about 43% of it. Compared to the world average FSPMs of 150 t km⁻² year⁻¹ [40], it becomes evident that the
Mediterranean Maghreb basin average erosion rates are higher than those in other regions of the world. This is also apparent when compared to other key environments in the Mediterranean Sea. Taking the Gulf of Lions (GoL) drainage basin as an example (which includes the Rhone River, nowadays the largest Mediterranean river in terms of its average water discharge and also characterized by remarkable sediment yields—[34]), we calculate that, despite the fact that the GoL rivers discharge into the Mediterranean a water flux that is four times greater than the water flux of the MMB rivers, their SPM flux represents only 40% of the SPM flux from the MMB rivers.

## 3.2. Sediment Retention by Dams

The values cited above represent average long-term means that do not take into account sediment retention by recent damming of the MMB rivers. In order to quantify the latter at the scale of our study region, we combined our inventory of dams (Figure 4A and Table 3) with our modeling of water and SPM fluxes according to the method described by [7]. Dams of various sizes stretch over the entire study region (Figure 4A), except in the Saharan part of Tunisia, where conditions are too dry for the sustainable exploitation of surface water resources. Consequently, about 76% of the MMB area is drained into dams (87% for Algeria, 86% for Morocco and 51% for Tunisia). The average density of all types of dams is about 2.5 dams per $10^3$ km$^2$, with increasing densities as the size of the dams decreases (0.4, 0.8 and 1.3 dams per $10^3$ km$^2$ for large dams, small dams and hillside reservoirs, respectively). When relating the densities to the areas which are drained into the dams (hence omitting to some extent the Saharan part of Tunisia), it is clear that the average dam densities increase from Morocco to Tunisia (1.0 dams per $10^3$ km$^2$ for Morocco, 3.4 for Algeria and 5.9 for Tunisia), which is opposite to the average sediment yields in these countries (see Section 3.1). Construction of dams is obviously constrained by the availability of existing water resources and the natural erodibilities of the corresponding land surfaces. Notice also from Table 3 that while all large and small dams in our inventory could be included in the modeling approach, we could only include 37% of the hillside reservoirs (46%, 31% and 24% for Algeria, Morocco and Tunisia, respectively) due to the model limited resolution (see Section 2.3). Sediment retention by hillside reservoirs may therefore be somewhat underestimated in our study.

Including dams in the routing of suspended sediments to the sea reduces the average SPM flux from the MMB to the sea to $36 \text{ Mt year}^{-1}$, which corresponds to a post-damming FSPMs value of $133 \text{ t km}^{-2} \text{ year}^{-1}$. In other words, 62% of the natural SPM flux (Section 3.1) is retained behind dams. Retention rates are highest in the Tunisian basin part (72%), followed by the Algerian (63%) and the Moroccan basin parts (55%). This is in agreement with the density of dams, which is highest in Tunisia and decreases westward toward Morocco. In terms of the absolute SPM fluxes, the greatest retention is observed for Algeria ($26 \text{ Mt year}^{-1}$), followed by Morocco ($19 \text{ Mt year}^{-1}$) and Tunisia ($14 \text{ Mt year}^{-1}$). Interestingly, when only considering the dams included in the GRanD database (as was the case in the study by [7]), sediment retention behind the dams accounts for only 36% of the natural SPM flux to the Mediterranean Sea. The present results therefore demonstrate that small reservoirs are quantitatively crucial for the interception of sediments in river basins compared to large reservoirs. The majority of the MMB reservoirs (~85%) in the GRanD database have a storage capacity greater than or equal to $20 \text{ Mm}^3$. If we consider this value as an appropriate threshold between small and large reservoirs, we can make the following observations: small reservoirs have very limited capacities (cumulative capacity of $1.1 \text{ versus } 12.8 \text{ km}^3$ for large dams), intercept annually less water discharge (cumulative interception of $8.4 \text{ versus } 20.1 \text{ km}^3 \text{ year}^{-1}$ for large dams), drain smaller areas (mean area of 151 versus 1525 km$^2$ for large dams), and have shorter water residence time in the reservoir (mean of 0.13 versus 0.63 years for large dams). They nevertheless assign a significant anthropogenic signature to the global sediment flux in the MMB (Figure 5) and enhance mean retention by 13% (from 49 to 62%) as compared to large reservoirs. It can also be seen from Figure 5 that the different size classes we created increase about linearly the average sediment retention in the MMB from large to small basins (except for very
large reservoirs which are underrepresented in the MMB), whereas the total number of these reservoirs
increase more or less exponentially.

<figure><img src="image_60.png" /><figcaption>(A) Average reservoir storage capacity (Mm³)</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>(B) Average life span (LS, years).</figcaption></figure>

**Figure 4.** Geographical distribution of the 450 dams and hillsides implemented in the modeling part of this study.
<figure><img src="image_62.png" /><figcaption>**Figure 5.** Cumulative reservoir storage capacity (blue line) and sediment retention (orange line) according to various classes of reservoir storage capacities. The cumulative number of dams in each class is shown as well (purple bars). For further explanations, see text.</figcaption></figure>

### 3.3. Economic and Environmental Implications

Our combination of a spatially explicit mapping of natural sediment yields with an inventory of reservoirs in the MMB also allows evaluating the loss of storage capacity and the life spans of the reservoirs. On the one hand, this can be compared with observed accumulation rates behind dams for validation purposes. On the other hand, it may also help to assess the loss of performance and economic value of existing dams and to evaluate the feasibility of future damming projects.

For this purpose, we followed the approach of [7] and converted the calculated mass accumulation of sediments into volumetric values by assuming an average density of 0.96 t m⁻³, as proposed by [41]. The reservoirs of the MMB consequently retain a volume of 54 Mm³ of sediment each year, which corresponds to an annual loss of storage capacity of about 0.39%. This volume can be further split into 22 Mm³ for the Algerian part (annual loss of 0.27%), 14 Mm³ for the Tunisian part (annual loss of 0.39%) and 18 Mm³ for the Moroccan part (annual loss of 0.89%). These values match well with other estimates from the literature. Remini estimated the sediment retention in reservoirs behind Algerian dams to be 20 Mm³ [42], which accounts for a reduction of their capacity (6.2 Mm³ according to [43]) of 0.3% each year. This value is identical to our estimate. Mammou and Louati proposed a capacity loss for Tunisian dams between 0.5 and 1% each year [28], compared to the 0.4% we found. Due to its greater relief and higher natural sediment yields, the values proposed for the Moroccan part of the MMB generally exceed 0.7% [20,22]. We estimate an annual capacity loss of about 0.9%. At larger scales, Remini, W. and Remini, B. evaluated the annual capacity loss of the 230 dams in North Africa to be 0.54% per year [25], which is only slightly higher than our results for the MMB. But many of the reservoirs in their study are located outside our study region, especially for Morocco. Notice that only the three Moroccan dams Eddahabi, El Massira and Ben El Ouidane in the Atlantic drainage basin of the Maghreb retain annually 18.3 Mm³ of sediment, which is equivalent to one third of the overall sediment retention in the entire MMB.

The life span of reservoirs (LS, year) can consequently be estimated via the ratio between the reservoir capacity and the annual volume of sediments that is retained behind the reservoir dams. Figure 4B shows the average LS for the 450 reservoirs considered in our modeling approach. It varies from less than 10 years to 143 × 10³ years, with a median of 150 years and lower and upper quartiles
of 50 and 550 years. About 35% of these reservoirs have a LS of less than 50 years in relation to reduced storage capacities (90% have a storage capacity of less than 2.5 Mm³). The majority is in the Algerian basin part (62%), followed by the Tunisian (28%) and the Moroccan basin parts (10%). On the other side, 25% of all reservoirs have LS between 50 and 200 years, and 23% between 200 and 1000 years. Only for 17% of the reservoirs LS exceeds 1000 years.

For the 101 dams in our database which were collected from the GRanD database and the national agencies of the Maghreb countries (see Section 2.3), the precise construction dates were known (Table 1) and therefore we could predict their filling. In 2016, the total sediment volume accumulated in these dams was 1.5 km³, equivalent to a fill of 12% of the total storage capacity (11.7 km³). The average silting is thus 37 Mm³ year⁻¹, and if nothing changes, about 30% of their initial capacity would be lost by 2060. Interestingly, with our modeling tools we can also calculate that, if these were the only dams in the MMB and no smaller and hillside dams existed to catch part of the sediment fluxes further upstream, the average silting would increase to 42 Mm³ year⁻¹ and the 30% capacity loss would arrive by the year 2050. These results confirm the findings of [24], who suggested that the delay of siltation of large dams is possible by installing smaller structures further upstream.

Table 4 lists the siltation rates for the 11 dams in the MMB that are the most affected by rapid filling. Two examples are worth being cited more in detail. One is the Mohamed Ben Abd Lekrim El Khattabi dam in the Nekor basin of Morocco, with a storage capacity of 43 Mm³. Our study shows that average annual silting is here 3.1 Mm³, which corresponds to a storage capacity loss of 7.2%. These figures are well confirmed by the study of [26], who estimated an average silting rate of 2.7 Mm³ year⁻¹ and an annual storage capacity loss of 6.3%. The accelerated siltation of this dam is closely linked to the extremely high erosion rates in the Nekor basin, one of the highest in the world (see Section 2.1). Without measures to prevent siltation, this dam would have been completely filled only 15 years after its construction in 1981.
<table><caption>**Table 4.** Calculated volume of sediments retained in some Mediterranean reservoirs that are threatened by rapid silting in comparison with other literature estimates.</caption><thead><tr><th>Dams</th><th>Count</th><th>Source</th><th>Year-Cons</th><th>CAP (Mm³)</th><th>Vsed (Mm³ year⁻¹)</th><th>Vsed (% year⁻¹)</th><th>Vsed 2016 (Mm³)</th><th>Vsed 2016 (%)</th><th>LS (Years)</th><th>Vsed-lit (%)</th><th>Source-lit</th></tr></thead><tbody><tr><td>Bakhada</td><td>ALG</td><td>GRanD</td><td>1935</td><td>56</td><td>0.1</td><td>0.1</td><td>4</td><td>8</td><td>1033</td><td>20</td><td>2</td></tr><tr><td>Beni Amrane</td><td>ALG</td><td>ANBT</td><td>1988</td><td>12</td><td>0.3</td><td>2.3</td><td>8</td><td>64</td><td>44</td><td>80</td><td>2</td></tr><tr><td>Beni Bahdel</td><td>ALG</td><td>GRanD</td><td>1946</td><td>63</td><td>0.1</td><td>0.2</td><td>9</td><td>15</td><td>469</td><td>17</td><td>2</td></tr><tr><td>Bou Hanifa</td><td>ALG</td><td>GRanD</td><td>1948</td><td>73</td><td>0.5</td><td>0.7</td><td>36</td><td>50</td><td>137</td><td>57</td><td>2</td></tr><tr><td>Boughizoul</td><td>ALG</td><td>GRanD</td><td>1934</td><td>55</td><td>0.5</td><td>1.0</td><td>43</td><td>78</td><td>105</td><td>56</td><td>2</td></tr><tr><td>Fergoug</td><td>ALG</td><td>GRanD</td><td>1970</td><td>18</td><td>0.3</td><td>1.7</td><td>14</td><td>77</td><td>60</td><td>100</td><td>2</td></tr><tr><td>Ighil Emda</td><td>ALG</td><td>ANBT</td><td>1953</td><td>156</td><td>1.0</td><td>0.7</td><td>66</td><td>42</td><td>149</td><td>54</td><td>2</td></tr><tr><td>M.B.A.L.khattabi</td><td>MAR</td><td>GRanD</td><td>1981</td><td>43</td><td>3.1</td><td>7.2</td><td>108</td><td>251</td><td>15</td><td>59</td><td>3</td></tr><tr><td>MohammedV</td><td>MAR</td><td>GRanD</td><td>1967</td><td>730</td><td>8.2</td><td>1.1</td><td>403</td><td>55</td><td>89</td><td>56</td><td>1</td></tr><tr><td>Mellegue</td><td>TUN</td><td>GRanD</td><td>1954</td><td>300</td><td>3.3</td><td>1.1</td><td>203</td><td>68</td><td>92</td><td>85</td><td>1</td></tr><tr><td>Sidi Salem</td><td>TUN</td><td>GRanD</td><td>1981</td><td>555</td><td>2.9</td><td>0.5</td><td>100</td><td>18</td><td>194</td><td>21</td><td>1</td></tr></tbody></table>

Note: Count: Country; Year-Cons: Year of Construction; CAP: reservoir storage capacity; Vsed: volume of sediment trapped by dams; Vsed 2016: volume of sediment trapped by dams until 2016; LS: life span of the dams; Vsed-lit: volume of sediment trapped by dams from the literature; Source-lit: source according to the literature. Source-lit: 1: [25] silting until 2002]; 2: [24] (silting until 2006); 3: [26] (silting until 1990). For the Source abbreviations, see Table 1.
The second example is the Ighil Emda dam in Algeria, located in a drainage basin with the same name. We calculated here an average sediment filling of 1.1 Mm³ year⁻¹ and an average capacity loss of 0.67% year⁻¹. Since its commissioning in 1953, the Ighil Emda dam has accumulated nearly 66 Mm³ of sediments and lost 42% of its capacity. These results are very close to those presented in the study by [44], who estimated the cumulative silting during the period 1953–1993 from bathymetric surveys to be 52 Mm³. During this 40-year period, the dam has lost nearly 35% of its initial capacity. This corresponds to an average capacity loss of 0.87% year⁻¹. The main reason for this rapid silting is the nature of its drainage basin, formed by soft rocks, in combination with a thin plant cover and a predominance of the agricultural domain [44].

It is finally also interesting to look at the theoretical sediment starvation in the coastal areas, which can be estimated from the difference between the pre-damming river fluxes of SPM and the present-day SPM fluxes normalized to the coastline length. The latter is about 422 km, 1210 km, and 1225 km for Morocco, Algeria and Tunisia, respectively. Although this information does not take into account the integration of sediment starvation over time (which depends on the precise construction date of dams), neither the existence of littoral drifts which redistribute the sediments into the coastal domain, it is nevertheless indicative for the potential risk of coastal erosion in relation to decreasing river sediment supply. Maximum values of $45 \times 10^3 \text{ t km}^{-1} \text{ year}^{-1}$ are obtained for the coast of Morocco, which should consequently be the most threatened by beach erosion and sea level rise. Note that this value per kilometer of coastline corresponds to about the average SPM flux produced annually within the whole basin of a typical coastal river in the Gulf of Lions—like the Tet River, with a basin area of 1400 km² [34,45]. In other words, the sediments produced elsewhere in a hinterland area of 1400 km² are equivalent to the amount of sediments missing every km along the Moroccan coast. The corresponding values for Algeria and Tunisia are also rather high: $21 \times 10^3 \text{ t km}^{-1} \text{ year}^{-1}$ and $12 \times 10^3 \text{ t km}^{-1} \text{ year}^{-1}$, respectively.

## 4. Conclusions

For the first time a spatially explicit assessment of the impact of river damming on the natural sediment fluxes to the sea could be produced for the Mediterranean Maghreb basin (MMB), a region which is characterized by a critical state of the available surface water resources and by strong mechanical erosion rates. Our data are based on modeling, which often represents simplifications with respect to reality and which is always constraint by the validity of the basic assumptions leading to the model formulations. Modeling on a grid point scale is limited by the quality of the input data and the adequacy of the regression models used to predict the riverine sediment fluxes. It is furthermore difficult to assess the quality of the literature estimates we used to adjust our regressions and which define the natural (pre-damming) state of the river systems, because anthropogenic perturbations can have a long history in the Mediterranean area. Nevertheless, the values we produced fit very well with other literature estimates, both in terms of the general sediment budgets and with regard to the measured reservoir silting rates, which makes us confident on their reliability.

Our results clearly demonstrate that, because of damming, nowadays only slightly more than one third of the natural river sediment fluxes reach the coastal Mediterranean waters of the Maghreb. Contrary to other hot spot areas of river damming, such as the East, Southern and Southeast Asian region where most of the sediment retention is related to the existence of mega dams [46], a considerable part of the sediment retention is here related to the existence of small dams and hillside reservoirs. Consequently, when only considering the large dams as those documented in the GRanD database, sediment retention is strongly underestimated. Using the latter database alone results in an overall sediment retention of about 36%, while the supplementary dams and hillside reservoirs we collected increase this value to 62%.

Large and small reservoirs in regulated basins trap 30% and 23% of the global riverine sediment fluxes according to [9]. Our retention estimates are therefore substantially higher than those given at the global scale, but the differences are not as striking as one may expect. In the [9] estimate, large
reservoirs correspond to CAP > 500 Mm³, which are rare in the MMB, and the 62% retention we calculated should rather be compared to the 23% retention that [9] found for small dams. The major difference is that small dams have shorter life spans than large dams, and the economic exploitation of these structures is limited in time. Notice that according to our assessment, 35% of the MMB reservoirs have life spans of less than 50 years in relation to reduced storage capacities, which requires frequent renewal and replacement of these structures for further development. It should be also mentioned that sediment retention rates alone are not necessarily an appropriate measure of the potential impact of damming on natural systems. Our data reveal some negative correlation between sediment retention and natural erosion rates in the three Maghreb countries (see Section 3.2), which could be explained by the greater difficulties to build dams in terrains with steep slopes and high natural sediment yields. Although sediment retention rates are “only” 55% in the Moroccan part of MMB, it is probably here where riverine sediment starvation may have the greatest impacts on the coastline dynamics.

**Author Contributions:** M.S. conducted the data analysis and wrote the draft of the manuscript. M.S. and W.L. worked on subsequent drafts of the manuscript. M.S., W.L., F.B., Y.L. and E.R. contributed to the final version of the manuscript.

**Funding:** This work was supported by the National Centre of Scientific Research—France (Centre National de la Recherche Scientifique—France, CNRS) through the attribution of a PhD grant to M.S., and by the DEMEAUX project funded by the regional council Occitania and the FEDER.

**Acknowledgments:** We are grateful to three anonymous reviewers for their valuable and useful comments during the review process.

**Conflicts of Interest:** The authors have no conflict of interest to declare.

## References and Note

1. García-Ruiz, J.M.; Ignacio López-Moreno, J.I.; Vicente-Serrano, S.M.; Lasanta-Martinez, T.; Beguería, S. Mediterranean water resources in a global change scenario. *Earth Sci. Rev.* **2011**, *105*, 121–139.

2. Van Cappellen, P.; Maavara, T. Rivers in the Anthropocene: Global scale modifications of riverine nutrient fluxes by damming. *Ecohydrol. Hydrobiol.* **2016**, *16*, 106–111. [CrossRef]

3. Walling, D.E.; Fang, D. Recent trends in the suspended sediment loads of the World's rivers. *Glob. Planet. Chang.* **2003**, *39*, 111–126. [CrossRef]

4. Brunel, C.; Certain, R.; Sabatier, F.; Robin, N.; Barusseau, J.P.; Alemant, N.; Raynal, O. 20th century sediment budget trends on the Western Gulf of Lions shoreface (France): An application of an integrated method for the study of sediment coastal reservoirs. *Geomorphology* **2014**, *204*, 625–637. [CrossRef]

5. Besset, M.; Anthony, E.J.; Sabatier, F. River delta shoreline reworking and erosion in the Mediterranean and Black Seas: the potential roles of fluvial sediment starvation and other factors. *Elem. Sci. Anth.* **2017**, *5*, 54. [CrossRef]

6. Vanmaercke, M.; Poesen, J.; Verstraeten, G.; de Vente, J.; Ocakoglu, F. Sediment yield in Europe: Spatial patterns and scale dependency. *Geomorphology* **2011**, *130*, 142–161. [CrossRef]

7. Sadaoui, M.; Ludwig, W.; Bourrin, F.; Romero, E. The impact of reservoir construction on riverine sediment and carbon fluxes to the Mediterranean Sea. *Prog. Oceanogr.* **2018**, *163*, 94–111. [CrossRef]

8. Lehner, B.; Reidy, Liermann, C.; Revenga, C.; Vorosmosty, C.; Fekete, B.; Crouzet, P.; Doll, P.; Endejan, M.; Frenken, K.; et al. *Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01*; NASA Socioeconomic Data and Applications Center (SEDAC): Palisades, NY, USA, 2011. [CrossRef]

9. Vörösmarty, C.J.; Meybeck, M.; Fekete, B.; Sharma, K.; Green, P.; Syvitski, J.P.M. Anthropogenic sediment retention: major global impact from registered river impoundments. *Glob. Planet. Chang.* **2003**, *39*, 169–190. [CrossRef]

10. Syvitski, J.P.M.; Vörösmarty, C.; Kettner, A.J.; Green, P. Impact of humans on the flux of terrestrial sediment to the global coastal ocean. *Science* **2005**, *308*, 376–380. [CrossRef] [PubMed]

11. Ludwig, W.; Meybeck, M.; Abousamra, F. *Riverine Transport of Water, Sediments, and Pollutants to the Mediterranean Sea*; UNEP/MAP: Athens, Greece, 2003; Available online: http://web.unep.org/unepmap/mts-141-riverine-transport-water-sediments-and-pollutants-mediterranean-sea (accessed on 12 July 2018).

12. Fournier, F. *Climat et Erosion*; Presses Universitaires de France: Paris, France, 1960; p. 201. (In French)
13. Dedkhov, A.P.; Mozzherin, D.I. Erosiya I stok nanosov na zemle, Izdatelstvo Kasagskogo Universsita. 1984.

14. Walling, D.E.; Webb, B.W. Material Transport by the World's Rivers: Evolving Perspectives. Available online: http://hydrologie.org/redbooks/a164/iahs_164_0313.pdf (accessed on 1 May 2016).

15. Probst, J.L.; Suchet, P.A. Fluvial suspended sediment transport and mechanical erosion in the Maghreb (North Africa). *Hydrol. Sci. J.* **1992**, *37*, 621–636. [CrossRef]

16. Hijmans, R.J.; Cameron, S.E.; Parra, J.L.; Jones, P.G.; Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. *Int. J. Climatol.* **2005**, *25*, 1965–1978. [CrossRef]

17. Ghenim, A.; Seddini, A.; Terfous, A. Variation temporelle de la dégradation spécifique du bassin versant de l'Oued Mouilah (nord-ouest algérien). *Hydrol. Sci. J.* **2008**, *53*, 448–456. (In French) [CrossRef]

18. Tourki, M.; Khanchoul, K.; Le Bissonnais, Y.; Belala, F. Sediment yield assessment in the Upper Wadi Kebir catchment, Kébir Rhumel River, Northeast of Algeria (1973–2006). *Rev. Sci. Technol.* **2017**, *34*, 122–133.

19. Inoubli, N.; Raclot, D.; Mekki, I.; Moussa, R.; Le Bissonnais, Y. A spatiotemporal multiscale analysis of runoff and erosion in a Mediterranean marly catchment. *Vadose Zone J.* **2017**, *16*. [CrossRef]

20. Badraoui, A.; Hajji, A. Envasement des retenues de barrages. *La Houille Blanche* **2001**, *6–7*, 72–75. [CrossRef]

21. Demmak, A. Contribution à l'Étude de l'Érosion et du Transport Solide en Algérie Septentrionale. Thèse de Docteur-Ingénieur, Université Paris-VI, Paris, France, 1982. (In French)

22. Lahlou, A. Etude actualisée de l'envasement des barrages au Maroc. *Rev. Sci. Eau.* **1988**, *6*, 337–356. (In French)

23. Estienne, P.; Godard, A. *Climatologie*; Librairie Armand Colin: Paris, France, 1970. (In French)

24. Remini, B.; Leduc, C.; Hallouche, W. Évolution des grands barrages en régions arides: Quelques exemples algériens. *Sécheresse* **2009**, *20*, 96–103. (In French)

25. Remini, W.; Remini, B. La sédimentation dans les barrages de l'Afrique du Nord. *Larhyss* **2003**, *2*, 45–54. (In French)

26. Lahlou, A. Envasement du barrage Mohamed Ben Abdelkrim Al Khattabi et lutte anti-érosive du bassin versant montagneux situe à l'amont. *IAHS Publ.* **1990**, *194*, 243–252. (In French)

27. Kassoul, M.; Abdelkader, A.; Belorgey, M. Caractérisation de la sédimentation des barrages en Algérie. *Revue des Sciences de l'Éau* **1997**, *10*, 339–358. (In French) [CrossRef]

28. Mammou, B.A.; Louati, M.H. Evolution temporelle de l'envasement des retenues de barrages de Tunisie. *Rev. Sci. Eau* **2007**, *20*, 201–210. (In French) [CrossRef]

29. Seklaoui-Oukid, O. Valorisation des Sédiments du Barrage d'El Merdja Sidi Abed: Etude Technoéconomique. Available online: http://www.secheresse.info/spip.php?article60431 (accessed on 1 April 2016). (In French)

30. Lehner, B.; Verdin, K.; Jarvis, A. New global hydrography derived from spaceborne elevation data. *Eos Trans. AGU* **2008**, *89*, 93–94. [CrossRef]

31. Harris, I.; Jones, P.D.; Osborn, T.J.; Lister, D.H. Updated high resolution grids of monthly climatic observations—The CRU TS3.10 Dataset. *Int. J. Climatol.* **2014**, *34*, 623–642. [CrossRef]

32. Dürr, H.; Meybeck, M.; Dürr, S.H. Lithologie composition of the earth's continental surfaces derived from a new digital map emphasizing riverine material transfer. *Glob. Biochem. Cycles* **2005**, *19*. [CrossRef]

33. Mayaux, P.; Bartholomé, E.; Cabral, A.; Cherlet, M.; Defourny, P.; Di Gregorio, A.; Diallo, O.; Massart, M.; Nonguierma, A.; Pekel, J.F.; et al. A new land-cover map of Africa for the year 2000. *J. Biogeogr.* **2004**, *31*, 861–877. [CrossRef]

34. Sadaoui, M.; Ludwig, W.; Bourrin, F.; Raimbault, P. Controls, budgets and variability of riverine sediment fluxes to the Gulf of Lions (NW Mediterranean Sea). *J. Hydrol.* **2016**, *540*, 1002–1005. [CrossRef]

35. Vörösmarty, C.J.; Sharma, K.; Fekete, B.; Copeland, A.H.; Holden, J.; Marble, J.; Lough, J.A. The storage and aging of continental runoff in large reservoir systems of the world. *Ambio* **1997**, *26*, 210–219.

36. ANBT. National Agency for Dams and Transfers. 2016. Available online: http://www.soudoud-dzair.com (accessed on 1 June 2016).

37. ABHL. Loukkos Hydraulic Basin Agency/Tétouan. 2016. Available online: http://www.abhloukkos.ma/abhl/index.php/fr/ (accessed on 1 June 2016).

38. DGBGTH. General Directorate for Dams and Major Hydraulic Works. 2016. http://www semide.tn/ (accessed on 1 June 2016).

39. Saradouni, F. Contribution à l'Etude de la Vulnérabilité des Retenues Collinaires vis-à-vis des Aléas Naturels, dans un Système d'Information Géographique (SIG). Available online: https://dl.ummto.dz/handle/ummto/910 (accessed on 11 July 2018). (In French)

40. Ludwig, W.; Probst, J.L. River sediment discharge to the oceans: Present-day controls and global budgets. *Am. J. Sci.* **1998**, *296*, 265–295. [CrossRef]
41. Minear, J.T.; Kondolf, G.M. Estimating reservoir sedimentation rates at large spatial and temporal scales: A case study of California. *Water Resour. Res.* **2009**, *45*. [CrossRef]

42. Remini, B. L'envasement des Barrages: Quelques Exemples Algériens. Revue Techniques Sciences Méthodes. 1999. Available online: http://www.beep.ird.fr/collect/bre/index/assoc/HASH6487.dir/20-165-171.pdf (accessed on 1 April 2016).

43. Riad, S.; Salih, A. Options for future water security in the Arab Countries. In Proceeding of the First International Conférence on the Geology of Africa, Assiut, Egypt, 23–25 November 1999; pp. 459–466.

44. Remini, B.; Kettab, A.; Hmat, H. Envasement du Barrage d'IGHIL EMDA (Algérie). 1995. Available online: https://www.shf-lhb.org/articles/lhb/pdf/1995/02/lhb1995008.pdf (accessed on 1 March 2016).

45. Serrat, P.; Ludwig, W.; Navarro, B.; Blazi, J.L. Variabilité spatio-temporelle des flux de matières en suspension d'un fleuve côtier méditerranéen: la têt (France). *Earth Planet. Sci. Lett.* **2001**, *333*, 389–397. [CrossRef]

46. Gupta, H.; Kao, S.-J.; Dai, M. The rôle of mega-dams in reducing sediment fluxes: A case study of large Asian Rivers. *J. Hydrol.* **2012**, *464–465*, 447–458. [CrossRef]

<figure><img src="image_63.png" /></figure>

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
<figure><img src="image_64.png" /></figure>

Article

# Evaluating the Erosion Process from a Single-Stripe Laser-Scanned Topography: A Laboratory Case Study

Yung-Chieh Wang* and Chun-Chen Lai

Department of Soil and Water Conservation, National Chung Hsing University, Taichung 402, Taiwan;
final898y@gmail.com

* Correspondence: wangyc@nchu.edu.tw; Tel.: +886-4-2284-0831 (ext. 105)

Received: 30 May 2018; Accepted: 17 July 2018; Published: 19 July 2018

<figure><img src="image_65.png" /></figure>

check for
updates

**Abstract:** Topographies during the erosion process obtained from the single-stripe laser-scanning method may provide an accurate, but affordable, soil loss estimation based on high-precision digital elevation model (DEM) data. In this study, we used laboratory erosion experiments with a sloping flume, a rainfall simulator, and a stripe laser apparatus to evaluate topographic changes of soil surface and the erosion process. In the experiments, six slope gradients of the flume (5° to 30° with an increment of 5°) were used and the rainfall simulator generated a 30-min rainfall with the kinetic energy equivalent to 80 mm/h on average. The laser-scanned topography and sediment yield were collected every 5 min in each test. The difference between the DEMs from laser scans of different time steps was used to obtain the eroded soil volumes and the corresponding estimates of soil loss in mass. The results suggest that the collected sediment yield and eroded soil volume increased with rainfall duration and slope, and quantified equations are proposed for soil loss prediction using rainfall duration and slope. This study shows the applicability of the stripe laser-scanning method in soil loss prediction and erosion evaluation in a laboratory case study.

**Keywords:** soil erosion; rill development; erosion topography; sloping flume experiments

## 1. Introduction

Rill and interrill erosion are the major intermediate processes between sheet and gully erosion on hillslopes. As the main source that causes soil loss on sloping croplands and rangelands, rill erosion provides 76% of the total sediment load eroded from hillslopes [1]. In an eroding rill, the flow concentrates and results in hydrodynamic characteristics with an increasing scouring power, and thus leads to a significant increase in soil erosion [2]. Meanwhile, the development of a rill network results in a threefold soil loss increment [3], and the eroding rills become the primary channels for sediment transport. It is reported that more than 80% of the eroded soil is transported through rills [4,5]. Therefore, rill development not only has its significance in erosion process evaluation, but also shows an implication for drainage network evolution on hillslopes.

The evolution of rill networks and rill morphology progresses in time and space [6]. Rill networks develop with varying complexity, leading to flow concentration and increasing runoff connectivity along the networks [7]. Variations in rill morphology interact with soil erosion processes, including detachment, transport, and deposition, which are dependent on the hydrodynamic features and transport capacity of the flow in rills [8]. Complex feedback exists in rill erosion mechanics, since the flow erodes soil and changes the rill bed surface, which in turn alters the hydrodynamic features and transport capacity of the flow [9]. In general, a rill flow with a higher velocity and deeper depth provides a larger transport capacity for carrying sediments, leading to more variations in rill morphology.
In erosion process evaluation, the evolution of a rill network and rill morphology has been a focus to develop and improve process-based erosion models and prediction [5,10]. While the topography caused by rill erosion is often complicated and irregular, a rill-by-rill survey is difficult and even impractical in the field [1], and field-scale experiments are limited to qualitative or semi-quantitative descriptions [11]. Therefore, quantification of the rill erosion process has become an essential but challenging issue. At the laboratory scale, rainfall simulators are widely applied in research on infiltration and soil erosion processes for mimicking the process and characteristics of natural rainfall [12]. In order to replicate rill erosion processes, rainfall simulation has its advantages in producing rainfall events at an arbitrary intensity and duration on demand; thereby, the erratic and unpredictable variability of natural rains can be eliminated [12]. Most of the previous studies on erosion process evaluation have developed quantification methods for rill erosion and rill network development using rainfall simulators to produce rainfall events on laboratory flumes or field plots.

Among the rill erosion research, many types of rill erosion quantification methods have been applied, such as collecting sediment outflow at the flume end [12], a volumetric or volume replacement method [2,13,14], manual measurements of rills using photo images [1,15], photogrammetry methods [16–19], a terrestrial laser scanner (TLS) [5,20–23], an airborne laser scanner (ALS) [24,25], and other remote-sensing technologies. In [12], laboratory erosion experiments were carried out in a two-dimensional tilting flume with a pre-forming rill before the rainfall application on the soil surface; then, rill and interrill erosion were assessed by flow measurements taken from the two outlets, corresponding to the rill and interrill area, at the end of the flume.

The volumetric method measures and calculates the cross-sectional areas along the eroded rills and estimates the eroded soil volume [14]. The volume replacement method uses other materials, such as soil, tiny foam particles, rice grains, and water, to refill rills and measure the rill volume by the filling materials to estimate the erosion volume [2,13]. Specifically, water is used to refill the eroded rills in order to minimize the measurement errors due to the difficulty of rill identification in the cases of not-well-confined rills and various rill sizes [2]. However, sealing of the soil surface, which prevents infiltration and further erosion, is the key factor for precise measurements of the rill volume and may become the major source of error in practice. Manual measurements of rills using photo images are applied in laboratory studies [1,15] for obtaining each rill's width, depth, and location coordinates along with rainfall duration from photographs taken of the soil surface in 1- or 2-min intervals throughout each rainfall event. The manual measurement method is usually time- and force-consuming and can be dependent on subjective judgement of rill boundaries.

Methods of photogrammetry are applied to assess soil erosion in laboratory-scale experiments [16–19]. Estimation of soil erosion using small-scale laboratory erosion experiments was carried out by the method of structure-from-motion (SfM) photogrammetry [16]. Digital elevation models (DEMs) of a 0.5 m² soil box were built by the photogrammetric SfM technique, and the computed sediment yield was obtained by the DEM of difference (DoD) technique [16]. They proposed that the choice of DoD threshold was a key point that affects the computed sediment yield; the results show that the computed sediment yield was 13% greater than the measured sediment yield [16]. Rill development and soil erosion was studied using a gravity-type rainfall simulator, a tilting box filled with sand and soil material, and photogrammetric equipment [17]. In this research, stereo imagery was taken from two positions to generate DEMs of the soil surface, which were used to analyze soil surface and rill network changes between the time steps of rainfall events. The results suggest that rill density and energy expenditure decreased with time [17]. The application of a close-range photogrammetry method for soil erosion assessment and a comparison between photogrammetry and laser-scanner technology in producing DEMs and soil surface elevation differences are shown in previous studies [18,19]. While both of the studies were carried out using laboratory flumes, they suggest the potential of the photogrammetry method in field applications provided the limitations of the technique are considered [18,19].

The application of the TLS technique in soil erosion and rill development evaluation can be found in laboratory, plot, and field studies [5,20–23]. The study of the morphological characteristics of rill
evolution was performed using laboratory soil pan rainfall simulations [5] and the TLS technique
following [20]. They proposed quantitative descriptions of a rill network using the fractal dimension,
which was analyzed from the TLS data and the resulting DEMs. To evaluate soil erosion at the
plot scale, uses three types of TLS are used to measure the topography before and after the manual
removal of some soil volume (to imitate soil erosion) of a field plot about 30 m × 30 m in size with
a 20° slope [21]. The results show that soil erosion measured with TLS varies considerably when
different data processing software is used, and the laser-scanning technique is applicable in measuring
soil erosion at the plot scale when adequate calibration and spatial resolution are performed [21].
In field investigations of soil erosion using TLS [20,22,23], rill morphology and soil loss evaluation at
the Masse experimental station in Italy were studied [20]. The triangulated irregular network model
was used to quantify the eroded volume and rill morphological characteristics; corresponding manual
measurements were also carried out using a profilometer [20]. A basin-scale assessment of riverbank
erosion in Northern Italy was monitored, and the eroded and deposited volume in the surveyed
area were measured using TLS [22]. The effects of TLS to analyze the intensity of soil erosion in a
mountainous forest area with sufficient measurement stations are shown [23]. As the results indicate,
choices of the coordinate system for the object or the scanner significantly influence the analysis of
erosion phenomena when applying TLS in forested areas [23].

The technique of ALS has also been applied in field studies to assess riverbank erosion [24] and characterize land degradation processes [25]. ALS was used to obtain detailed topographic data for characterizing sediment and phosphorous contributions from riverbank erosion of the Blue Earth River in southern Minnesota, USA [24]. Since the source of error and the uniformity within and between the scans were difficult to determine, no elevation or planimetric corrections of the laser data were made in the study. Nevertheless, the soil loss estimates obtained by ALS locate in the range of the reported literature values for the same study site. The land degradation processes, including soil erosion, channel incision, and collapse sinkholes development in the Dead Sea region, are quantified using ALS [25]. The results demonstrate the ability of ALS to detect sub-metric geomorphic features, such as gullies, headcuts, and embryonic sinkholes, which can occur in land degradation processes [25]. Other remote-sensing technologies, such as an Unmanned Aerial Vehicle (UAV), Time-of-Flight (ToF) cameras (or range cameras), and airborne and terrestrial Light Detection and Ranging (LiDAR) [20], have been applied to investigate topographic evolution in the field, and have their potential and applicability in soil erosion and land degradation process evaluation, but are usually expensive in cost and/or require delicate operating and data-processing skills.

In this study, we took advantage of the high resolution provided by the laser-scanning technique but used a more affordable stripe laser apparatus, compared to a three-dimensional (3D) laser scanner, to construct the erosion topography for erosion process evaluation. At the laboratory scale, erosion processes on slopes were reproduced in an adjustable slope flume with a rainfall simulator, and a series of laser-scanned topographies during the erosion process was obtained by the stripe laser apparatus for evaluating the rill development, erosion progress, and the eroded soil volume. The effects of rainfall duration and slope on the eroded soil volume and sediment yield were quantified using a multiple regression analysis. By comparing the eroded volume with the outflow sediments collected from the flume outlet, a quantified relation was proposed between the eroded soil volume and the sediment yield at the outlet of the sloping flume. The results of this study present a quantified evaluation of the influences of rainfall duration and slope on the erosion process and a prediction of the sediment yield using single-stripe laser-scanned topography in a laboratory flume case study.

## 2. Materials and Methods

### 2.1. Soil Specimens and Geotechnical Tests

The soils used for the erosion experiments were collected from the watershed of Agongdian Reservoir in Kaohsiung city, which is located in southwestern Taiwan (Figure 1). The parent rock in
the watershed belongs to green-gray mudstone, which melts easily when absorbing water; most of the top soils are weathered loam or sandy loam based on the USDA (U.S. Department of Agriculture) soil classification system. Due to the high percentage of fine sediments and the melting characteristic of mudstone, the soils are hardened when dry but easily eroded when subjected to continuous rainfall during storm events. In addition, the high content of salts of mudstone soils is not suitable for vegetation, and thus soil loss from hillslopes has led to environmental issues in the watershed, such as reservoir siltation.

<figure><img src="image_66.png" /><figcaption>**Figure 1.** Map of the Agongdian Reservoir watershed in Kaohsiung city, Taiwan.</figcaption></figure>

After the soils were collected from the field, preliminary treatments, including air drying, gravels and plants picking, peds crumbling, and sieving through a 2-mm sieve, were carried out before the conventional geotechnical tests. As background information, the specific gravity and grain size distribution of the mudstone soils were determined before the erosion experiments by following procedures outlined in American Society for Testing and Materials (ASTM) standards, including ASTM D854-14 [26], ASTM C136/C136M-14 [27], ASTM D1140-14 [28], and ASTM D422-63-07 [29], referring to the pycnometer test, dry sieve analysis, wet sieve analysis, and hydrometer test, respectively. As the results indicate, the mudstone soils have an average specific gravity of 2.69 g/cm³ and consist of 52.15% of sand, 45.75% of silt, and 5.91% of clay on average, and belong to sandy loam based on the USDA soil classification system. The moisture content shows the ratio of pore water mass to the mass of dry solids, and was conducted following the ASTM D2216-10 [30] procedure. Before each trial of the erosion experiments, 24 soil specimens were sampled to estimate the spatial variation of moisture contents of the soils filled in the flume to determine the antecedent soil moisture condition. The soil specimens for soil moisture content measurements were sampled following the method for group B in the ASTM D4220/D4220M-14 [31] procedure. Based on [1,15], the soils were filled to the flume as three 0.05-m-thick layers, and eight soil specimens were sampled at an equal distance for each soil layer.
## 2.2. Sloping Flume Erosion Experiment

The experimental setup of the erosion experiment, including the rainfall simulator, the sloping flume, and the stripe laser apparatus, is shown in Figure 2. The flume is 0.75 m in length, 0.25 m in width, and 0.25 m in depth, and with an adjustable slope gradient range from 0° to 30°. Below the flume bottom, a plastic container was used to collect the infiltrated outflow and sediments during the erosion process. At the outlet of the flume, a funnel-shape opening was designed for collecting the outflow water and sediment yields. To minimize experimental uncertainty and obtain good controls on experimental factors, we carried out the erosion experiments and recorded the erosion topography using a small-scale rainfall simulator and a flume with adjustable slopes. The rainfall simulator, stripe laser apparatus, soil specimen preparation, and test procedure and measurements of the laboratory erosion experiment are described in the following subsections.

<figure><img src="image_67.png" /><figcaption>**Figure 2.** Setup of the sloping flume erosion experiment.</figcaption></figure>

### 2.2.1. Rainfall Simulator

The mini rainfall simulator used for the erosion test is manufactured by Eijkelmamp Soil & Water (EM Giesbeek, the Netherlands) [32], with a rainfall area of 0.25 m × 0.25 m produced by 49 probes with a constant water head. Compared to natural precipitation, simulated rainfalls usually have a shorter falling distance, which results in a lower terminal velocity of rain drops and thus a smaller kinetic energy. This concept was described in the previous study, in which the same rainfall simulator of this study was applied [33]. Therefore, to compensate for the short falling distance of the simulated rainfall (ranging from 0.61 m to 0.69 m with the slope gradient ranging from 5° to 30°, which covered most of the slopes in the study area), the high intensity of the simulated rainfall was applied. From the equation of time-specific kinetic energy [34], the time-specific kinetic energy produced by the simulated rainfalls equals that of natural rainfall events with the intensity ranging from 76 mm/h to 84 mm/h when the slope varies from 5° to 30°. The rainfall simulator was located at the upstream side of the flume and generated simulated rainfalls covering the range of 0.25 m × 0.25 m. The remaining downstream region of the soil surface was used to observe rill development during the erosion process.

### 2.2.2. Stripe Laser Apparatus

The stripe laser apparatus used for obtaining erosion topographies consists of a 100-mW diode laser (FLEXPOINT® Machine Vision Lasers Mvnano Series, Olching, Germany), an electric slide rail (Igus drylin® ZLW, Köln, Germany), and a digital camera (Nikon D7200, Tokyo, Japan). By applying
pattern projection methods [35], the projected single stripe of light onto the soil surface, which was produced by the diode laser moving with a constant speed along the flume on the electric slide rail, was recorded by the digital camera to reconstruct a whole surface from digital camera images. Detailed discussions of the stripe laser apparatus, the basic principles of the scan processing method, and scan resolution and error estimation are provided in previous studies [35,36]. The reconstruction of a scanned surface was achieved using the images of the stripe laser rays captured by the digital camera for each cross-section of the soil surface apart by 1 mm. The images were processed in the MATLAB (2017a, MathWorks Inc., Natick, MA, USA) program developed the previous study [36] and the erosion topographies were obtained.

### 2.2.3. Soil Specimen Preparation

For the soil specimen preparation of each erosion test, the air-dried and pretreated soils were mixed with tap water thoroughly by human force; then, the moisturized soil was placed in the storage container and the soil surface was covered and allowed to stand for at least 16 h as suggested by the wet preparation method in the ASTM D4318-10 procedure [37]. To target an antecedent soil moisture content around 20% and a bulk density of 1400 kg/m³, 39.375 kg of dried soil and 1.875 kg of tap water were used. The moisturized soils were filled by three layers, with each layer 0.05-m thick [1,15], and a total of 0.15 m in soil depth. After a layer was filled, soils sampled at the eight sites distributed in equal spaces were used to determine the actual antecedent soil moisture conditions. Therefore, a total of 24 soil specimens for the 3 filled soil layers were sampled to determine the average antecedent soil moisture content for each test of the erosion experiments. After the soils were filled in the flume, an initial laser scan was carried out through the soil surface to obtain the topography before the simulated rainfall [36]. Then, the flume was tilted by a screwing jack to a designed slope and the simulated rainfall began.

### 2.2.4. Test Procedure and Measurements

After a rainfall simulation began, the outflows of water and sediments were collected by the bottom container and the outlet bucket every 5 min until the end of the test. To collect infiltrations during experiments [38,39], the bottom container collected the infiltrated outflow in this study, while the outlet bucket collected the surface outflow. Then, the total sediment yield can be obtained as the summation of the sediments transported by the infiltrated and surface outflows. The collected outflows were dried in an oven at 105 °C for 24 h [1,15,40], and the remnant soils were weighed as the measurements of sediment yields. In this study, the collected sediment yields are considered as the “measured” soil loss. Meanwhile, to record the soil surface topography for each 5-min increment of the rainfall, the simulated rainfall was temporarily stopped for 20 min [17], the flume was lowered to the horizontal position, and the laser scan was carried out. The duration of each rainfall event was 30 min [15]. Therefore, seven laser-scanned topographies were obtained for a single rainfall event. To account for the effect of steepness on hillslope erosion, six slope gradients (5°, 10°, 15°, 20°, 25°, and 30°) were used in the experiments and two replicates [15] were applied for each gradient.

## 2.3. Erosion Volume Generation and Data Treatment

By applying the stripe laser-scanning technique, we obtained a DEM of the soil surface through each test of the scanning procedure at each time step. Then, the DEMs were subtracted on a cell-by-cell basis to obtain the difference of depths throughout the whole soil surface. The soil volume differences among the erosion topographies were obtained; then, the mass of soil loss can be estimated by the soil volume differences, the moisture content, and the bulk density of the soil. The resultant soil loss in mass is considered to be the “estimated” soil loss. Since the computation of the difference of depths from two DEMs can propagate the errors of the initial DEMs [16,35,36], we applied an error analysis by comparing the elevation precision using check points on the soil surface of the laser-scanned DEMs to determine the minimum level of detection for the depth difference. The error in depth difference of
two DEMs was estimated as the quadratic composition of errors [35,36] in each of the original DEMs being compared [16], and it can be calculated as:

$$E_{dod\_i} = \sqrt{E_{DEM\_i}^2 + E_{DEM\_{i+1}}^2} \qquad (1)$$

where $E_{dod\_i}$ means the error of depth difference between two DEMs of the erosion experiments; $E_{DEM\_i}$ means the error of the DEM at time step $i$ calculated from the check points calibration; and $E_{DEM\_{i+1}}$ means the error of the DEM at time step $i+1$ calculated from the check points calibration. For the erosion experiments, the detection threshold for the depth differences between two DEMs was set as the $E_{dod}$ value. From the calibrations and error analyses of the check points, the minimum value of $E_{dod}$ [16] was 1.28 mm. Therefore, a difference of depths was accounted for in the calculation of a soil volume difference between two DEMs when its absolute value is ≥1.28 mm.

## 3. Results

### 3.1. Soil Loss Measured by the Sediment Yields

During a test of a 30-min simulated rainfall event, the sediment yield was collected every 5 min until the end of the test, and the cumulative sediment yields are shown in Table 1. The cumulative sediment yields increase with the rainfall duration, and the total sediment yields at the end of the test range from 1.228 kg to 8.300 kg of oven-dried soil mass. Based on the infiltrated outflow collected by the plastic container below the flume bottom during the erosion experiments, outflows of infiltrated sediments occurred in the tests with 5°, 10°, and 15° of slopes but not in the tests with steeper slopes. On average, the cumulative infiltrated sediment outflows account for 18.4% of the total cumulative sediment yields after the 30-min rainfall events.

<table><caption>Table 1. Sediment yield measurements collected at the outlet of the flume.</caption><thead><tr><th rowspan="3">Slope</th><th colspan="6">Cumulative Sediment Yield <sup>b</sup> (kg)</th></tr><tr><th colspan="6">Rainfall Duration (min)</th></tr><tr><th>5</th><th>10</th><th>15</th><th>20</th><th>25</th><th>30</th></tr></thead><tbody><tr><td>30° (1) <sup>a</sup></td><td>1.802</td><td>3.335</td><td>4.404</td><td>5.546</td><td>6.923</td><td>8.3</td></tr><tr><td>30° (2) <sup>a</sup></td><td>1.471</td><td>2.815</td><td>4.06</td><td>5.149</td><td>5.948</td><td>6.766</td></tr><tr><td>25° (1)</td><td>1.829</td><td>2.98</td><td>4.052</td><td>4.946</td><td>5.714</td><td>6.439</td></tr><tr><td>25° (2)</td><td>2.287</td><td>3.422</td><td>4.687</td><td>5.975</td><td>7.382</td><td>8.919</td></tr><tr><td>20° (1)</td><td>1.018</td><td>1.937</td><td>2.816</td><td>3.799</td><td>4.484</td><td>5.376</td></tr><tr><td>20° (2)</td><td>0.802</td><td>1.604</td><td>2.315</td><td>3.396</td><td>4.289</td><td>5.086</td></tr><tr><td>15° (1)</td><td>0.173</td><td>0.695</td><td>1.195</td><td>1.68</td><td>2.099</td><td>2.46</td></tr><tr><td>15° (2)</td><td>0.285</td><td>0.596</td><td>0.836</td><td>1.062</td><td>1.334</td><td>1.646</td></tr><tr><td>10° (1)</td><td>0.167</td><td>0.675</td><td>1.108</td><td>1.395</td><td>1.75</td><td>2.071</td></tr><tr><td>10° (2)</td><td>0.467</td><td>1.116</td><td>1.678</td><td>2.15</td><td>2.708</td><td>3.1</td></tr><tr><td>5° (1)</td><td>0.088</td><td>0.303</td><td>0.504</td><td>0.903</td><td>1.213</td><td>1.507</td></tr><tr><td>5° (2)</td><td>0.118</td><td>0.374</td><td>0.646</td><td>0.807</td><td>1.065</td><td>1.228</td></tr></tbody></table>

Note: <sup>a</sup> (1) and (2) refers to the first and the second erosion test of each slope gradient, respectively. <sup>b</sup> Sediment yield = sediment outflow collected by the outlet bucket + infiltrated sediment outflow collected by the bottom container.

### 3.2. Soil Loss Estimated by the Laser-Scanned Soil Volume Differences

From the differences of depths generated by the subtraction of any two DEMs (with a spatial resolution of 1 mm × 1 mm), the soil volume differences were calculated by summing up all the depth difference values exceeding the detection threshold. Then, the estimated soil loss in mass, the volume-difference soil loss, was the product of the soil volume difference and the soil bulk density resulting from the average soil moisture contents. During a test of a 30-min simulated rainfall event, the volume-difference soil loss was obtained every 5 min until the end of the test, and the cumulative
volume-difference soil loss is shown in Table 2. The cumulative volume-difference soil loss increases with the rainfall duration, and the total volume-difference soil loss at the end of the test ranges from 1.833 kg to 10.775 kg. From observations during the experiments, the depression of the soil surface under rain drop compaction and collapse along the side-wall of rills were the main factors for overestimates in the soil loss, especially during the first 5 min of the rainfalls.

<table><caption>**Table 2.** Estimates of soil loss using laser-scanned topographies.</caption><thead><tr><th rowspan="3">Index</th><th colspan="6">Cumulative Volume-Difference Soil Loss (kg)</th></tr><tr><th colspan="6">Rainfall Duration (min)</th></tr><tr><th>5</th><th>10</th><th>15</th><th>20</th><th>25</th><th>30</th></tr></thead><tbody><tr><td>Slope</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>30° (1) <sup>a</sup></td><td>3.100</td><td>5.058</td><td>6.087</td><td>7.367</td><td>8.540</td><td>9.518</td></tr><tr><td>30° (2)</td><td>1.981</td><td>4.706</td><td>4.990</td><td>5.443</td><td>6.880</td><td>6.992</td></tr><tr><td>25° (1)</td><td>3.836</td><td>5.012</td><td>5.820</td><td>6.813</td><td>7.081</td><td>7.725</td></tr><tr><td>25° (2)</td><td>3.982</td><td>6.120</td><td>7.407</td><td>8.123</td><td>9.594</td><td>10.775</td></tr><tr><td>20° (1)</td><td>3.080</td><td>4.414</td><td>5.074</td><td>5.652</td><td>6.822</td><td>6.798</td></tr><tr><td>20° (2)</td><td>3.113</td><td>3.854</td><td>4.397</td><td>5.176</td><td>6.057</td><td>6.593</td></tr><tr><td>15° (1)</td><td>2.630</td><td>3.206</td><td>3.553</td><td>4.025</td><td>4.114</td><td>4.552</td></tr><tr><td>15° (2)</td><td>1.831</td><td>1.947</td><td>2.418</td><td>2.630</td><td>2.661</td><td>2.917</td></tr><tr><td>10° (1)</td><td>2.937</td><td>3.432</td><td>4.097</td><td>4.106</td><td>4.568</td><td>4.740</td></tr><tr><td>10° (2)</td><td>2.327</td><td>3.143</td><td>3.471</td><td>3.877</td><td>4.216</td><td>4.403</td></tr><tr><td>5° (1)</td><td>0.979</td><td>1.394</td><td>1.374</td><td>1.861</td><td>2.030</td><td>2.082</td></tr><tr><td>5° (2)</td><td>0.858</td><td>1.382</td><td>1.520</td><td>1.698</td><td>1.862</td><td>1.833</td></tr></tbody></table>

Note: <sup>a</sup> (1) and (2) refers to the first and the second erosion test of each slope gradient, respectively.

### 3.3. Laser-Scanned Topographies during Erosion

For each test of the simulated rainfall erosion experiment, seven laser-scanned topographies, including the initial soil surface topography, were carried out. As an example, the laser scanner topographies of the erosion test with a 30° slope are shown in Figure 3. From the soil surface topographies, the propagation of erosion processes can be described qualitatively. In Figure 3, an increase in the soil surface depression areas suggested soil erosion propagation caused by rain drop compaction/rain splash erosion, sheet flow erosion, and sediment entrainment of concentrated flows. Specifically, rain drop compaction occurred in the rainfall region at 45 cm to 70 cm from the outlet; erosion of the overland sheet flow occurred in the rainfall region and the middle stream area around $x = 30$ cm to $x = 45$ cm during the first 10 min of the rainfall, and then the formation of rills was observed as the effect of rill erosion. In the area with a distance from the outlet less than 30 cm, the main erosion process was sediment entrainment of the concentrated flows. The depths of the eroded channel for the concentrated flow in $x = 0$ to $x = 30$ increased with the rainfall duration, which are shown in Figure 3b–f. However, comparison between Figure 3f,g suggests that deposition occurred in the region of $x = 0$ to $x = 15$ during the last 5 min of the rainfall event, which may be caused by collapse of the rill sides during sediment entrainments or the outflow being too small and plugged.
<figure><img src="image_68.png" /><figcaption>Figure 3. *Cont.*</figcaption></figure>
<figure><img src="image_69.png" /><figcaption>**Figure 3.** The laser-scanned topographies during the simulated rainfall erosion test of a 30° slope: (a) the initial topography before the rainfall started; the erosion topographies after (b) 5 min; (c) 10 min; (d) 15 min; (e) 20 min; (f) 25 min; and (g) 30 min of rainfall.</figcaption></figure>

# 4. Discussion

## 4.1. Effects of Rainfall Duration and Slope on Soil Loss

Effects of rainfall duration and slope on the soil loss mass are illustrated in Figure 4. From the figure, the mass of the soil loss, both measured from the sediment yields and estimated from the soil volume differences, increased with the rainfall duration and the slope. From previous studies [41,42], similar results have been proposed regarding the positive correlation between rainfall or constant flow duration and the eroded soil volume quantified by rill dimensions. Under a constant rainfall intensity, the increase in rainfall duration suggests the increase of cumulative kinetic energy provided
by rain drop compaction and the surface flow, which thus results in a larger eroded soil volume and mass of soil loss [41,42]. The increase of soil loss with slope has also been presented in many previous studies [2,15,43], which may be attributed to the decrease of soil particle stability as the slope increases [44].

In Figure 4, the slopes of the trend lines decreased from 0.22 to 0.04 as the slope gradient of the flume decreased from 30° to 5°, indicating that the effect of the topography on soil loss became less significant in cases of mild slopes. In other words, the effect of rainfall duration on the soil loss mass can be influenced by the slope, and thus a collective effect of the rainfall duration and the slope on the soil loss should be considered. We applied a multiple regression analysis to quantify the effects of rainfall duration, slope, and their product term (to account for the collective effect of rainfall duration and slope) on the measured/estimated soil loss in mass, and the equations are as follows:

$$SL_m = -0.023t + 0.018S + 0.009(S \times t) \qquad R^2 = 0.96 \qquad (2)$$

$$SL_e = 0.048t + 0.112S + 0.005(S \times t) \qquad R^2 = 0.96 \qquad (3)$$

where $SL_m$ = measured soil loss in mass (kg); $SL_e$ = estimated soil loss in mass (kg); $S$ = slope of the flume (°); $t$ = rainfall duration (min). In Equations (2) and (3), positive coefficients before the product terms ($S \times t$) suggest that the positive effects of rainfall duration on soil loss manifested as the slope increased.

<figure><img src="image_70.png" /><figcaption>**Figure 4.** Cumulative mass of soil erosion obtained by: (a) the cumulative sediment yields as the measured soil loss; (b) the cumulative volume-differences soil loss as the estimated soil loss.</figcaption></figure>
## 4.2. Relation between the Estimated and Measured Soil Loss

In Figure 5, data of the estimated soil loss, calculated based on the soil volume difference between two DEMs, are plotted versus the measured soil loss, which comprised the sediment yields collected during the experiments. The data points located above the 1:1 line suggest a systematic overestimation, which might occur within the first 5 min of rainfalls for all of the cases. Soil compaction and surface depression caused by the impact of rain drops might be the main reason for the overestimated soil loss. After the compaction of the surface soil stopped when the large soil pores collapsed, the soil volume difference increased as soil erosion propagated and more soil particles were eroded and transported to the flume outlet. The linear relation between the estimated soil loss ($SL_e$) and the measured soil loss ($SL_m$) shown in Equation (4) suggests a similar increasing rate in $SL_e$ with $SL_m$ by the trend line slope close to unity. The positive intercept in Equation (4) indicates an overestimation in soil loss resulting from soil compaction and effects of heterogeneity in soil structure and soil density or moisture content.

$$SL_e = 0.98SL_m + 1.73 \qquad R^2 = 0.91 \qquad (4)$$

<figure><img src="image_71.png" /><figcaption>Figure 5. Estimated soil loss versus measured soil loss.</figcaption></figure>

For application, the best-fit least square trend line is proposed by plotting the measured cumulative soil loss versus the soil volume differences subtracted from two DEMs, which were obtained by the stripe laser-scanned topographies, and the equation is:

$$SL_m = 1281.9V - 1.36 \qquad R^2 = 0.91 \qquad (5)$$

where V means the eroded soil volume (m³) calculated as the soil volume differences subtracted from two DEMs. From Equation (5), the overestimation of soil volume differences due to soil compaction by rain drops was accounted for and offset by the negative intercept. Therefore, once an eroded soil volume can be obtained, the soil loss in mass may be predicted using Equation (5).

## 4.3. Erosion Processes and Rill Development

Different types of soil erosion were reviewed, including splash erosion, sheet flow (interrill) erosion and rill erosion, and sediment transport of flow [45]. Based on the descriptions and characteristics of the soil erosion types [45], we classified the erosion processes for the case of the 30° slope erosion test shown in Figure 3. From the soil surface topographies in Figure 3, we quantified
the estimated soil loss from soil volume differences for three cross sections in the flume following the descriptions of the laser-scanned topographies in Section 3.3, which are 45–70 cm (the rainfall region), 30–45 cm, and 0–30 cm from the outlet, to show the percentages of soil loss attributed to different erosion processes. From Table 3, the rain drop compaction and rain splash erosion that occurred in the rainfall area (45 cm to 70 cm from the outlet) accounted for 23% to 33% of the estimated total soil loss as the rainfall duration increased. However, the soil surface depression observed during the experiments suggested that the larger volumetric soil differences resulted from soil surface depression and compaction by the rain drops in addition to the actual soil loss by rain splash erosion; this is considered to be the main source of error for overestimations of the measured soil loss. The region of 30 cm to 45 cm from the outlet provided 28% to 33% of the estimated total soil loss. In this region, sheet erosion and interill erosion of the overland flow were observed as the dominant erosion processes during the first 10 min. Afterwards, rill erosion became more significant as rills developed. In the region close to the outlet (x = 0 cm to x = 30 cm), sediment entrainment by the concentrated flow in the rills was the dominant erosion process, and it provided 34% to 49% of the estimated total soil loss. While the transitions among different erosion processes were observed during the experiments and judged based on the laser-scanned topographies, more experiments designed for observing each erosion type and relative further studies are desired to validate the quantitative descriptions of the erosion process propagation.

<table><caption>**Table 3.** Soil loss percentage of regions with different distances from the outlet: results of the erosion test with a 30° slope.</caption><thead><tr><th rowspan="3">Index</th><th colspan="3">Soil Loss Percentage to the Total Soil Loss</th></tr><tr><th colspan="3">Distance from the Outlet</th></tr><tr><th>45–70 cm</th><th>30–45 cm</th><th>0–30 cm</th></tr></thead><tbody><tr><td>Rainfall Duration (min)</td><td></td><td></td><td></td></tr><tr><td>5</td><td>23%</td><td>28%</td><td>49%</td></tr><tr><td>10</td><td>25%</td><td>32%</td><td>43%</td></tr><tr><td>15</td><td>29%</td><td>32%</td><td>39%</td></tr><tr><td>20</td><td>32%</td><td>31%</td><td>37%</td></tr><tr><td>25</td><td>33%</td><td>31%</td><td>36%</td></tr><tr><td>30</td><td>33%</td><td>33%</td><td>34%</td></tr></tbody></table>

From Figure 3, the development of a rill can be observed with the propagation of erosion processes and be described qualitatively. Specifically, the areas with dense contours suggest an incision in the soil surface and the development of rills. For the case of the 30° slope erosion test (Figure 3b), the initiation of the rill occurred at $x = 20$ cm to $x = 30$ cm where the concentrated flow was formed on the soil surface, which was around 20 cm apart from the lower boundary of the rainfall region (i.e., $x = 45$ cm), after 5 min of rainfall. Afterwards, the development of the dendritic structure indicated by the dense contours in Figure 3c–g suggested that the rill continuously developed upstream by headcutting towards the rainfall region as the rainfall duration increased. Meanwhile, the concentrated flow eroded the soil surface continuously towards the outlet and resulted in a deep and wide main stream in the region of $x = 0$ cm to $x = 30$ cm. Critical conditions for rill initiation were studied [46]. They proposed that headcutting occurred following rill formation and thus the upper ends of the rills were not the exact positions of rill initiation. While the rill development process observed in this study was similar to that reported in the literature [46], further research is required to obtain quantified evaluations of rill network development and the effects of topographic factors, such as slope.

## 5. Conclusions

In this study, erosion processes on a small-scale soil surface were observed and evaluated by erosion experiments using an adjustable slope flume, a rainfall simulator, and a stripe laser-scanning apparatus. From the results, the effects of rainfall duration, slope, and the combined influences of
the two factors on the measured/estimated soil loss mass were quantified by Equations (2) and (3), suggesting that the positive effects on soil loss of rainfall duration were manifested as the slope increased. By comparing the measured and the estimated soil loss, Equation (4), with the slope near to unity and the $R^2$ value of 0.91, indicates good agreements between the soil loss measurements and the estimations. The stripe laser-scanning method was applied successfully to evaluate soil loss by subtracting the DEMs generated from scanned topographies. In practice, Equation (5) suggests that soil loss can be predicted by the soil volume differences between two DEMs given information of soil bulk density/moisture content and the soil compaction by rain drops.

From the laser-scanned soil surface topographies, the propagation of erosion processes and development of rills can be described qualitatively. From the case of the 30° slope erosion experiment, erosion processes, including rain drop compaction and rain splash erosion, sheet erosion, interrill erosion, and rill erosion, were observed. Based on the observation, the initiation of a rill occurred around 20 cm apart from the lower boundary of the rainfall region after 5 min of rainfall and progressed both upstream and downstream as the rainfall continued. This phenomenon coincides with the headcutting reported in the literature [46]. While more experiments and further studies are desired to obtain a whole picture of erosion process propagation and rill development during rainfall events, the results in this study provide: (1) quantified equations to predict soil loss by rainfall duration and slope and by eroded soil volume from DEM data; and (2) a potential method to quantitatively evaluate an erosion process in laboratory-scale experiments.

**Author Contributions:** Y.-C.W. conceived and designed the experiments; C.-C.L. performed the experiments; Y.-C.W. and C.-C.L. analyzed the data; Y.-C.W. wrote the paper.

**Funding:** The authors would like to thank the Ministry of Science and Technology, and the Soil and Water Conservation Bureau of the Republic of China, Taiwan, for financially supporting this research under the Contract No. 106-2625-M-005-013 and the Report No. SWCB-107-042, respectively.

**Acknowledgments:** The authors would like to thank Chi-Yao Hung, who is currently a faculty member in the Department of Soil and Water Conservation, National Chung Hsing University, Taiwan, and provided professional advice and technical support for the stripe laser-scanning method and DEM data transformation.

**Conflicts of Interest:** The authors declare no conflict of interest.

## References

1. Shen, H.; Zheng, F.; Wen, L.; Lu, J.; Jiang, Y. An experimental study of rill erosion and morphology. *Geomorphology* **2015**, *231*, 193–201. [CrossRef]

2. Chen, X.-Y.; Zhao, Y.; Mi, H.-X.; Mo, B. Estimating rill erosion process from eroded morphology in flume experiments by volume replacement method. *Catena* **2016**, *136*, 135–140. [CrossRef]

3. Meyer, L.D.; Foster, G.R.; Romkens, M.J.M. Source of soil eroded by water from upland slopes. In *Present and Prospective Technology for Predicting Sediment Yields and Sources, Proceedings of the Sediment-Yield Workshop, Oxford, MS, USA, 28–30 November 1972*; pp. 177–189.

4. Mutchler, C.K.; Young, R.A. Soil detachment by raindrops. In *Present and Prospective Technology for Predicting Sediment Yields and Sources, Proceedings of the Sediment-Yield Workshop, Oxford, MS, USA, 28–30 November 1972*; pp. 113–117.

5. Zhang, P.; Tang, H.; Yao, W.; Zhang, N.; Xizhi, L.V. Experimental investigation of morphological characteristics of rill evolution on loess slope. *Catena* **2016**, *137*, 536–544. [CrossRef]

6. Boardman, J. Soil erosion science: Reflections on the limitations of current approaches. *Catena* **2006**, *68*, 73–86. [CrossRef]

7. Moreno-de Las Heras, M.; Espigares, T.; Merino-Martín, L.; Nicolau, J.M. Water-related ecological impacts of rill erosion processes in Mediterranean-dry reclaimed slopes. *Catena* **2011**, *84*, 114–124. [CrossRef]

8. Lei, T.W.; Zhang, Q.; Zhao, J.; Tang, Z. A laboratory study of sediment transport capacity in the dynamic process of rill erosion. *Am. Soc. Agric. Eng.* **2001**, *44*, 1537–1542.

9. Lawrence, W.; Gatto, L.W. Soil freeze-thaw-induced changes to a simulated rill: Potential impacts on soil erosion. *Geomorphology* **2000**, *32*, 147–160.