Water Erosion Monitoring and Prediction in Response to the Effects of Climate Change Using RUSLE and SWAT Equations: Case of R’Dom Watershed in Morocco
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Used and Methodology
Data | Spatial Resolution | Coordinate System | Source |
---|---|---|---|
Digital Elevation Model (DEM) | 30 m | UTM/WGS84 1, Zone 30 N | [47] |
Land cover Map | 10 m | UTM/WGS84 1, Zone 30 N | [47] |
Soil data | 30 m | UTM/WGS84 1, Zone 30 N | Field collection/reports |
Climate data | 30 m | UTM/WGS84 1, Zone 30 N | Sebou Hydraulic Basin Agency (SHBA) |
2.2.1. Sentinel-2 Images
2.2.2. Digital Elevation Model (DEM)
2.2.3. Soil Data
2.2.4. Climatic Data
2.3. Methodology Adopted
2.3.1. RUSLE Equation
- A is the computed average soil loss over a period selected for R, usually on yearly basis (t ha−1 y−1);
- R is the rainfall-runoff erosivity factor (MJ mm ha−1 h −1 y−1);
- K is the soil erodibility factor (t ha h ha−1 MJ−1 mm−1);
- LS is the slope length (L) and slope gradient (S) factor (dimensionless);
- C is the cropping management factor (dimensionless, ranging between 0 and 1);
- P is the supporting conservation practice factor (dimensionless, ranging between 0 and 1).
- A.
- Rainfall and Runoff erosivity factor (R)
- E: kinetic energy of rains (MJ/ha)
- I30: maximum rainfall intensity in 30 min mm/h
- I: rain intensity
- R is the rainfall-runoff erosivity factor (MJ mm ha−1 h −1 y−1)
- P is the average annual rainfall (mm)
- B.
- Soil erodibility factor (K)
- C.
- Slope Length and Steepness factor (LS)
- D.
- Cover management factor (C)
- E.
- Support practice factor (P)
2.3.2. SWAT Model
- SWt is the final soil water content (mm),
- SW0 is the initial soil water content (mm),
- t is the time (days),
- Rday is the amount of precipitation on day i (mm),
- Qsurf is the amount of surface runoff on day i (mm),
- Ea is the amount of evapotranspiration on day i (mm),
- Wseep is the amount of water entering the vadose zone from the soil profile on day i (mm),
- Qlat is the amount of return flow on day i (mm).
- Sed is the sediment yield on a given day (tons),
- Qsurf is the surface runof volume (mm H2O/ha),
- qpeak is the peak runof rate (m3 /s),
- Areahru is the area of the HRU (ha),
- KUSLE is the soil erodability factor
- CUSLE is the cover and management factor,
- PUSLE is the support practice factor,
- LSUSLE is the topographic factor and CFRG is the coarse fragment factor
2.3.3. Spatial Autocorrelation Analysis
3. Results and Discussion
3.1. Stream Flow
3.2. Factors Maps Analysis
3.3. Spatial Distribution of Soil Erosion Rate
3.3.1. Soil Erosion Rate Using RUSLE Method
3.3.2. Soil Erosion Rate Using SWAT Model
3.4. Soil Erosion Rate in Relation to Land Use
3.5. Spatial Autocorrelation of Soil Erosion Rate
4. Conclusions
- -
- Standard calibration statistics were used to assess the performance of SWAT model. Comparison of modeled and observed monthly streamflow datasets resulted from R2, NSE, and PBIAS values of 0.75, 0.70, and −1.1 respectively, which indicated that the hydrological cycle of the R’Dom watershed could be accurately simulated using the SWAT model.
- -
- The erosion rate values vary from 0 to 8 t ha−1 for both periods. For the 2000/2013 period, the RUSLE showed that 71.90% of the total area of the watershed is exposed to a very low erosion, whereas only 0.02% of the area was exposed to a very high erosion, while the SWAT showed that 35.57% is exposed to a very low erosion, whereas only 9% of the area was exposed to a very high erosion. For the period of 2014/2027 it is expected to have a very low erosion risk for a portion of 58.33% and 36.97% of the total area, and a very high risk for 0.27% and 9.24% for RUSLE method and SWAT respectively.
- -
- It was verified that erosion loss was found mainly in agricultural lands (AGRL) with an area of 48.1% and 59.58%, Followed by bare lands (BARR) with 38.27% and 25.26% in RUSLE equation and SWAT model respectively.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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2000/2013 | 2014/2027 | |||||
---|---|---|---|---|---|---|
Station Name | Coordinate (m) | Average Rainfall | R | Average Rainfall | R | |
X | Y | |||||
P3401 | 237,745 | 3,772,098 | 395.30 | 732.82 | 497.69 | 1061.82 |
P6170 | 234,726 | 3,726,750 | 488.72 | 1031.18 | 543.69 | 1224.24 |
P6405 | 301,106 | 3,741,223 | 529.02 | 1171.49 | 695.13 | 1818.33 |
P6769 | 283,324 | 3,775,316 | 467.92 | 961.44 | 501.92 | 1076.38 |
P6924 | 285,738 | 3,705,901 | 615.66 | 1495.50 | 792.65 | 2246.3 |
P7188 | 242,214 | 3,802,127 | 373.58 | 669.09 | 390.67 | 719.06 |
Soil Type | Sand (%) (0.05–2 mm) | Silt (%) (0.02–0.05 mm) | Clay (%) (<0.02 mm) | Organic Matter (%) | K-Factor |
---|---|---|---|---|---|
Calcimagnesic Soils | 7.3 | 37.7 | 55 | 2.16 | 0.031 |
Isohumic Soils | 55.7 | 21.1 | 24 | 1.07 | 0.062 |
Raw Mineral Soils | 8.8 | 34.5 | 56.1 | 2.62 | 0.025 |
Vertisols | 53.8 | 19.3 | 27.1 | 1.62 | 0.052 |
Poorly developed Soils | 63.7 | 20.8 | 15.5 | 2.51 | 0.062 |
Fersiallitic Soils | 18.6 | 36.2 | 45.2 | 1.63 | 0.045 |
Land Use Class | Slope (%) | P Factor Values |
---|---|---|
Agricultural area | 0–4 | 0.5 |
4–12 | 0.6 | |
12–20 | 0.7 | |
20–25 | 0.8 | |
>52 | 0.9 | |
Other land | All | 1.0 |
WATER | FRST | RNGE | WETN | AGRL | BARR | URHD | UIDU | User Accuracy | |
---|---|---|---|---|---|---|---|---|---|
WATER | 127 | 2 | 1 | 23 | 2 | 0 | 0 | 0 | 81.93 |
FRST | 1 | 230 | 13 | 8 | 2 | 1 | 8 | 2 | 86.79 |
RNGE | 0 | 35 | 159 | 3 | 19 | 2 | 0 | 8 | 70.35 |
WETN | 8 | 0 | 2 | 56 | 0 | 0 | 2 | 82.35 | |
AGRL | 1 | 6 | 0 | 0 | 72 | 7 | 4 | 2 | 78.26 |
BARR | 2 | 0 | 0 | 0 | 1 | 66 | 3 | 9 | 81.48 |
URHD | 5 | 0 | 0 | 0 | 0 | 0 | 276 | 11 | 94.52 |
UIDU | 2 | 7 | 0 | 3 | 5 | 18 | 26 | 54 | 46.95 |
Producer Accuracy | 86.98 | 82.14 | 90.85 | 60.21 | 71.28 | 70.21 | 86.52 | 62.79 | OA = 0.80% Kappa index = 76.8 |
2000–2013 | 2014–2027 | |||
---|---|---|---|---|
Soil Erosion Severity Class | Soil Loss (t ha−1) | Area (%) | Soil Loss (t ha−1) | Area (%) |
Very low | <10 | 71.90 | <10 | 58.33 |
Low | 10–20 | 20.20 | 10–20 | 27.71 |
Moderate | 20–30 | 6.25 | 20–30 | 11.94 |
High | 30–40 | 1.42 | 30–40 | 1.73 |
Very high | >40 | 0.20 | >40 | 0.27 |
2000–2013 | 2014–2027 | |||
---|---|---|---|---|
Soil Erosion Severity Class | Soil Loss (t ha−1) | Area (%) | Soil Loss (t ha−1) | Area (%) |
Very low | <10 | 35.57 | 0–10 | 36.97 |
Low | 10–20 | 28.28 | 10–20 | 25.22 |
Moderate | 20–30 | 18.45 | 20–30 | 18.35 |
High | 30–40 | 8.7 | 30–40 | 10.21 |
Very high | >40 | 9.00 | >40 | 9.24 |
Area (%) | Very Low | Low | Moderate | High | Very High | |
---|---|---|---|---|---|---|
Water | 0.15 | 0.09 | 0.36 | 0.17 | 0.10 | 0.00 |
forest | 8.01 | 7.66 | 5.53 | 8.58 | 10.74 | 7.53 |
Range-Grasses | 0.1 | 0.19 | 0.14 | 0.13 | 0.06 | 0.00 |
Wetlands-Non-Forested | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Agricultural Land-Generic | 48.1 | 67.45 | 61.06 | 48.45 | 35.89 | 27.65 |
Barren | 38.27 | 15.25 | 26.19 | 38.01 | 50.12 | 61.80 |
Residential-High Density | 4.83 | 8.83 | 5.90 | 3.85 | 2.55 | 3.01 |
Industrial | 0.51 | 0.48 | 0.79 | 0.78 | 0.51 | 0.00 |
Area (%) | Very Low | Low | Moderate | Very High | High | |
---|---|---|---|---|---|---|
Water | 0.12 | 0.05 | 0.01 | 0.41 | 0.05 | 0.09 |
Forest | 7.33 | 4.67 | 14.95 | 3.93 | 5.82 | 7.30 |
Range-Grasses | 0.06 | 0.11 | 0.04 | 0.15 | 0.01 | 0.01 |
Wetlands-Non-Forested | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Agricultural Land-Generic | 59.58 | 75.47 | 68.26 | 66.08 | 56.03 | 32.05 |
Barren | 25.26 | 11.01 | 8.43 | 19.18 | 34.35 | 53.32 |
Residential-High Density | 7.30 | 8.39 | 8.01 | 10.09 | 2.90 | 7.03 |
Industrial | 0.33 | 0.27 | 0.27 | 0.12 | 0.82 | 0.17 |
Moran’s I | RUSLE Equation | SWAT Model |
---|---|---|
Moran’s Index | 0.12 | 0.32 |
Variance | 0.00 | 0.00 |
z-score | 125.37 | 731.57 |
p-value | 0 | 0 |
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Alitane, A.; Essahlaoui, A.; El Hafyani, M.; El Hmaidi, A.; El Ouali, A.; Kassou, A.; El Yousfi, Y.; van Griensven, A.; Chawanda, C.J.; Van Rompaey, A. Water Erosion Monitoring and Prediction in Response to the Effects of Climate Change Using RUSLE and SWAT Equations: Case of R’Dom Watershed in Morocco. Land 2022, 11, 93. https://doi.org/10.3390/land11010093
Alitane A, Essahlaoui A, El Hafyani M, El Hmaidi A, El Ouali A, Kassou A, El Yousfi Y, van Griensven A, Chawanda CJ, Van Rompaey A. Water Erosion Monitoring and Prediction in Response to the Effects of Climate Change Using RUSLE and SWAT Equations: Case of R’Dom Watershed in Morocco. Land. 2022; 11(1):93. https://doi.org/10.3390/land11010093
Chicago/Turabian StyleAlitane, Abdennabi, Ali Essahlaoui, Mohammed El Hafyani, Abdellah El Hmaidi, Anas El Ouali, Amina Kassou, Yassine El Yousfi, Ann van Griensven, Celray James Chawanda, and Anton Van Rompaey. 2022. "Water Erosion Monitoring and Prediction in Response to the Effects of Climate Change Using RUSLE and SWAT Equations: Case of R’Dom Watershed in Morocco" Land 11, no. 1: 93. https://doi.org/10.3390/land11010093
APA StyleAlitane, A., Essahlaoui, A., El Hafyani, M., El Hmaidi, A., El Ouali, A., Kassou, A., El Yousfi, Y., van Griensven, A., Chawanda, C. J., & Van Rompaey, A. (2022). Water Erosion Monitoring and Prediction in Response to the Effects of Climate Change Using RUSLE and SWAT Equations: Case of R’Dom Watershed in Morocco. Land, 11(1), 93. https://doi.org/10.3390/land11010093