Quantitative Evaluation of Soil Water and Wind Erosion Rates in Pakistan
<p>The spatial distribution of elevation and meteorological stations across Pakistan (source “political_map_pakistan 5th 2020” downloaded from <a href="https://www.pakistan.gov.pk" target="_blank">https://www.pakistan.gov.pk</a>, (accessed on 22 April 2022)).</p> "> Figure 2
<p>The spatial pattern of land use and sampling survey units in Pakistan (source: Climate Change Initiative—Land Cover 2000 (CCI-LC 2000), European Space Agency, ESA).</p> "> Figure 3
<p>The procedure for predicting the SAER in Pakistan.</p> "> Figure 4
<p>Map showing the flowchart for evaluating the SIER in Pakistan.</p> "> Figure 5
<p>Trend in the model error (<b>a</b>) and ROC curve (<b>b</b>).</p> "> Figure 6
<p>Spatial pattern of the SAER in Pakistan (S1, S2 represent sample 1 and sample 2, respectively).</p> "> Figure 7
<p>Spatial pattern of the SIER in Pakistan (S1, S2 represent sample 1 and sample 2, respectively).</p> "> Figure 8
<p>Geographical distribution of the soil erosion rates in Pakistan (S1, S2 represent sample 1 and sample 2, respectively).</p> "> Figure 9
<p>Comparative map of the monthly SIER with the FVC and WF−factor in Pakistan.</p> "> Figure 10
<p>Spatial comparison of the SAER over the Potohar Plateau: (<b>a</b>) Ullah et al., (2018) study [<a href="#B25-remotesensing-15-02404" class="html-bibr">25</a>] and (<b>b</b>) current study.</p> "> Figure 11
<p>Spatial comparison of the SAER between Gilani et al., (2021) [<a href="#B19-remotesensing-15-02404" class="html-bibr">19</a>] (<b>a</b>) and current study (<b>b</b>) for Pakistan.</p> "> Figure 12
<p>Spatial comparison of the SAER in Borrelli et al., (2017) [<a href="#B6-remotesensing-15-02404" class="html-bibr">6</a>] (<b>a</b>) and current research (<b>b</b>) across Pakistan.</p> "> Figure 13
<p>Correlation of the spatial prediction result with Borrelli et al., (2017) [<a href="#B6-remotesensing-15-02404" class="html-bibr">6</a>] for Pakistan.</p> "> Figure 14
<p>Correlation of the spatial prediction result with Borrelli et al., (2017) [<a href="#B6-remotesensing-15-02404" class="html-bibr">6</a>] for each land use type.</p> "> Figure 15
<p>Spatial comparison of the SIER between Yang et al., (2021) [<a href="#B20-remotesensing-15-02404" class="html-bibr">20</a>] (<b>a</b>) and the current study (<b>b</b>) for Pakistan (the sample3 (S3) with a large variation of wind speeds).</p> ">
Abstract
:1. Introduction
2. Method
2.1. Study Area
2.2. Materials
2.3. Spatial Prediction of Soil Water Erosion
2.3.1. Rainfall Erosivity Factor (R)
2.3.2. Soil Erodibility Factor (K)
2.3.3. Terrain Factor (LS)
2.3.4. Biological Practices Factor (B)
2.3.5. Engineering Practices Factor (E)
2.3.6. Tillage Practices Factor (T)
2.4. Revised Wind Erosion Equation (RWEQ) Model
2.4.1. Weather Factor (WF)
2.4.2. Soil Wind Erodible Fraction (EF) and Soil Crust Factor (SCF)
2.4.3. Soil Roughness Factor (K′)
2.4.4. Vegetation Factor (C)
3. Result
3.1. Spatial Distribution of Soil Water Erosion
3.2. Spatial Distribution of Soil Wind Erosion
3.3. Soil Erosion in Relation to Socio-Economics
4. Discussion
4.1. Spatial Patterns of Soil Erosion Risks
4.2. Plausibility of Soil Erosion
4.2.1. Plausibility of Soil Water Erosion
4.2.2. Plausibility of Soil Wind Erosion
4.3. Suggestions for Improving Soil Erosion
4.4. Limitation and Future Research Prospects
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Input Parameters | Data Sources | Spatio-Temporal Resolution | Data Period |
---|---|---|---|
Rainfall erosivity (R) | National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/zh-hans/data) (accessing date: 12 May 2022) | 1 km | 1986–2015 |
Soil erodibility (K) | From the team led by the corresponding author of this manuscript | 250 m | 2018 |
Terrain factor (LS) | From the team led by the corresponding author of this manuscript | 30 m | 2018 |
Tc (percent Tree_cover, percent NonTree_vegetate) data | http://ladsweb.modaps.eosidis.nasa.gov/search (accessing date: 12 May 2022) | 250 m | 2018 |
Climate Change Initiative—Land Cover 2000 | http://maps.elie.ucl.ac.be/CCI/viewer (accessing date: 12 May 2022) | 300 m | 2015 |
Cropping rotation system resource and T-factor attribution table | From the team led by the corresponding author of this manuscrip | N/A | 2018 |
Vector data of 475 sampling survey units | Provided by Chinese Academy of Sciences (CAS) | N/A | 2018 |
Wind speed (m·s−1) | https://www.ncdc.noaa.gov/ (accessing date: 12 May 2022) | Daily | 2018 |
Precipitation (mm) | https://www.ncdc.noaa.gov/ (accessing date: 12 May 2022) | Daily | 2018 |
Temperature (°C) | https://www.ncdc.noaa.gov/ (accessing date: 12 May 2022) | Daily | 2018 |
Snow depth (mm) | https://www.ncdc.noaa.gov/ (accessing date: 12 May 2022) | Daily | 2018 |
Digital elevation model (DEM) | https://search.earthdata.nasa.gov/ (accessing date: 23 March 2021) | 30 m | 2018 |
Evapotranspiration (mm) | https://crudata.uea.ac.uk/cru/data/hrg/ (accessing date: 12 May 2022) | Monthly | 2018 |
Normalized Difference Vegetation Index (NDVI) | https://search.earthdata.nasa.gov/ (accessing date: 5 January 2022) | 250 m | 2018 |
Soil sand content (%), soil silt content (%), soil clay content (%), soil organic matter content (%) | https://www.isric.org (accessing date: 25 December 2022) | 250 m | 2020 |
CaCO3 | http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (accessing date: 8 November 2021) | 1 km | 2009 |
GDP | Dryad database, Dryad Home-Publish, and Preserve your Data (datadryad.org) | 10 km | 2018 |
Population density | Google Earth Engine (GEE): GPWv4 (Gridded Population of the World, Version 4) | 1 km | 2018 |
Land Use Type | MINb–MAXb | Land Use Type | MINb–MAXb |
---|---|---|---|
Cropland | 1 | Urban land | 0 |
Forest | 0.0001–0.003 | Desert sparse | 0.01–0.15 |
Grassland | 0.01–0.15 | Tundra | 0.01–0.15 |
Shrub | 0.01–0.15 | Bare land | 0.1–0.5 |
Wetland-water | 0 | Glacier | 0 |
Cropland Type | Slope | E Value |
---|---|---|
Rainfed cropland | ≤5° | 0.1025 |
5–20° | 0.414 | |
>20° | 0.828 | |
Post-flooding or irrigated cropland | —— | 0.1025 |
Administrative Unit Name | Soil Water Erosion Rates (t·km−2·a−1) | Soil Water Erosion Amount (×107 t·a−1) | Soil Wind Erosion Rates (t·km−2·a−1) | Soil Wind Erosion Amount (×108 t·a−1) | GDP (Billion Dollars) | Population Density (cap·km−2) |
---|---|---|---|---|---|---|
Azad Jammu and Kashmir | 2942.28 | 3.22 | 0.12 | 0.00 | 8.66 | 300.02 |
Balochistan | 299.51 | 10.25 | 3751.07 | 12.67 | 20.38 | 22.84 |
Gilgit-Baltistan | 576.59 | 3.96 | 0.09 | 0.00 | 2.29 | 16.25 |
Khyber Pakhtunkhwa | 1053.15 | 10.65 | 12.01 | 0.02 | 44.17 | 331.39 |
Punjab | 377.36 | 7.73 | 1.15 | 1.15 | 167.77 | 569.62 |
Sindh | 212.97 | 2.97 | 3.87 | 3.87 | 83.45 | 316.87 |
Administrative Unit | Borrelli Study (t·km−2·a−1) | Gilani Study (t·km−2·a−1) | Current Study (t·km−2·a−1) |
---|---|---|---|
National scale | 1251.79 | 259 | 552.65 |
Azad Jammu and Kashmir | 4800.13 | 2225 | 3058.31 |
Balochistan | 1215.59 | 41 | 477.71 |
Gilgit-Baltistan | 1668.39 | 872 | 732.26 |
Khyber Pakhtunkhwa | 3020.49 | 1178 | 1117.31 |
Punjab | 629.19 | 35 | 400.81 |
Sindh | 602.30 | 2 | 203.67 |
Study Area | Method | Study Period | SIER (×102 t·km−2·a−1) | References | |||
---|---|---|---|---|---|---|---|
Bare Land | Forest | Cropland | Desert Sparse | ||||
Pakistan | RWEQ | 2018 | 38.64 | 1.05 | 3.52 | 38.82 | Current study |
Central Asia | RWEQ | 1986–2005 | 43.08 | 3.44 | 4.74 | N/A | Li et al., 2020 [49] |
Tibet Plateau | RWEQ | 1980–2015 | 38.73 | 2.66 | 11.57 | 36.6 | Teng et al., 2021 [50] |
Northern China | RWEQ | 2000–2010 | 50.21 | 0.78–5.11 | 5.02 | 8.67–20.89 | Gong et al., 2014 [69] |
Zhundong, Xinjiang, China | 137Cs | 2014–2015 | 36.44 | N/A | 7.40 | 14.37 | Ding et al., 2018 [70] |
Korla, Xinjiang, China | 137Cs | 1998 | 59.87 | N/A | 35.37 | 31.71 | Pu et al., 1998 [71] |
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Yang, X.; Yang, Q.; Zhu, H.; Wang, L.; Wang, C.; Pang, G.; Du, C.; Mubeen, M.; Waleed, M.; Hussain, S. Quantitative Evaluation of Soil Water and Wind Erosion Rates in Pakistan. Remote Sens. 2023, 15, 2404. https://doi.org/10.3390/rs15092404
Yang X, Yang Q, Zhu H, Wang L, Wang C, Pang G, Du C, Mubeen M, Waleed M, Hussain S. Quantitative Evaluation of Soil Water and Wind Erosion Rates in Pakistan. Remote Sensing. 2023; 15(9):2404. https://doi.org/10.3390/rs15092404
Chicago/Turabian StyleYang, Xuyan, Qinke Yang, Haonan Zhu, Lei Wang, Chunmei Wang, Guowei Pang, Chaozheng Du, Muhammad Mubeen, Mirza Waleed, and Sajjad Hussain. 2023. "Quantitative Evaluation of Soil Water and Wind Erosion Rates in Pakistan" Remote Sensing 15, no. 9: 2404. https://doi.org/10.3390/rs15092404
APA StyleYang, X., Yang, Q., Zhu, H., Wang, L., Wang, C., Pang, G., Du, C., Mubeen, M., Waleed, M., & Hussain, S. (2023). Quantitative Evaluation of Soil Water and Wind Erosion Rates in Pakistan. Remote Sensing, 15(9), 2404. https://doi.org/10.3390/rs15092404