DataSheet1_Investigating Anzali Wetland Sediment Estimation Using the MPSIAC Model.pdf
The adverse effects of upland erosion impact the Anzali Wetland in Iran. The Modified Pacific South-west Inter Agency Committee model (MPSIAC) was used to estimate the sediment yield in the watershed. The watershed was divided into twelve sub-watersheds based on the geomorphologic features and waterway orientations (Sw0-Sw11). To investigate the effect of different factors on erosion and sedimentation, data were digitized using ArcGIS software. The effective factor weights were determined using the MPSIAC model, and the total sediment yield was calculated for each sub-watershed. Results showed that the amount of particulate sediment in the critical sub-watersheds Sw6 and Sw9 was 777.9 and 730.2 t km−2. yr−1, respectively. Based on erosion and sedimentation results, the sub-watershed erosion was prioritized as Sw6> Sw9> Sw4> Sw1> Sw0> Sw5> Sw2> Sw8> Sw3> Sw11 > Sw7> Sw10. Both model inputs (precipitation) and outputs (sediment) at different parts of the watershed were assessed via point observations data. Comparison of correlation values reveals that the correlation between the simulated and sampling values was strong in sub-watershed 1 (R2 < 0.8). EF, RMSE, nRMSE, CRM, and MAE were 0.23, 16.74 tons per year, 5.05%, 0.55, and −3.6, respectively, which indicates the model’s high performance in Sw0. Areas with insufficient cover and bare soil showed a high correlation with the final erosion model. Thus, land-use classes, such as dense vegetation and good pastures, correspond to areas with low erosion. Conversely, bare soils and poor pastures were located on the eroded flats.
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References
- https://doi.org//10.1007/s10967-019-06739-8
- https://doi.org//10.1007/s10661-017-5784-y
- https://doi.org//10.4137/aswr.s3427
- https://doi.org//10.29252/jsaeh.7.3.1
- https://doi.org//10.33899/regs.2011.6426
- https://doi.org//10.17707/AgricultForest.64.2.12
- https://doi.org//10.1007/s11707-011-0189-7
- https://doi.org//10.1016/j.ecss.2019.03.002
- https://doi.org//10.1016/j.geoderma.2011.12.028
- https://doi.org//10.1016/j.ijforecast.2006.03.001
- https://doi.org//10.1016/0378-4290%2891%2990040-3
- https://doi.org//10.1007/s11629-012-2301-1
- https://doi.org//10.15666/aeer/1701_13371347
- https://doi.org//10.1016/0169-7722%2891%2990038-3
- https://doi.org//10.1086/629606
- https://doi.org//10.1007/s12665-018-7908-2
- https://doi.org//10.11648/j.earth.20130201.13
- https://doi.org//10.4236/ijg.2013.47104
- https://doi.org//10.1016/j.iswcr.2015.06.008
- https://doi.org//10.1007/s11069-017-3123-9
- https://doi.org//10.1016/j.iswcr.2016.06.001
- https://doi.org//10.1007/s11069-017-3123-910.13031/2013.36572
- https://doi.org//10.19026/rjees.5.5709
- https://doi.org//10.17707/AgricultForest.63.4.21
- https://doi.org//10.22111/gdij.2010.1125
- https://doi.org//10.1016/0304-3800%2880%2990042-3
- https://doi.org//10.1016/j.jseaes.2005.06.002
- https://doi.org//10.1016/0022-1694%2875%2990080-3
- https://doi.org//10.14358/PERS.69.8.889
- https://doi.org//10.3354/cr030079
- https://doi.org//10.22099/iar.2017.4035
- https://doi.org//10.1504/IJGENVI.2019.098890
- https://doi.org//10.1016/S1001-6279%2810%2960045-5
- https://doi.org//10.1016/S2095-6339%2815%2930002-2
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