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Ghalesardi Gonjanak A, Elmizadeh H, Abbasi A, soleymani A. Evaluation of TWI index and TOPMODEL hydrological model in erosion and runoff (case study: Maroon basin in southwest Iran). E.E.R. 2024; 14 (3) :66-82
URL: http://magazine.hormozgan.ac.ir/article-1-800-en.html
Marin Geology Department, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran , Elmizadeh@kmsu.ac.ir
Abstract:   (1517 Views)
1- Introduction
The topography is, directly and indirectly, affects the geomorphic processes and hydrological behavior of the slope (Prancevic and Kirchner, 2019), therefore, quantitative correlation of topography features is possible due to deposition and runoff production and predicting the spatial distribution of soil and surface deposits (McKenzie and Ryan, 1999). different shapes and sides of slopes in the drainage basin affect the time of runoff movement on the slopes and drainage networks and the basin response, such as time, equilibrium, retardation time, concentration-time, and hydrograph peak time. These temporal features constitute an important part of the hydrological modeling of the watershed. Considering that most of rainfall-runoff models relate to runoff traverse times at the slopes surface, it is easy to study the topography effect on runoff features such as river discharge estimators, flood forecasting, peak flow, runoff volume, and water resource management (Merheb et al., 2016; Mlynski et al., 2019). In terms of morphodynamic conditions, the topography is directly related to erosion and sedimentation processes. Topographic wetness index theory (TWI) is designed as an important and all-purpose feature in the rainfall-runoff model to measure the effect and topography controlling on hydrological processes (Calogero et al., 2015; Jeziorska and Niedzielski, 2018; Xue et al., 2018). This model simulates the interaction of groundwater and surface water with topography to determine which regions are prone to saturation of the earth and thus have a high potential for surface accumulation water (Ballerine, 2017) and can be expressed quantitatively as a physical indicator the effect of the topography of watersheds slopes on the mechanism of substructure flow (groundwater), runoff production, the spatial distribution of soil moisture and the ability of soil moisture deficiency to saturation state at each point in the range and level of the basin. (Beven and Kirkby 1979; O'Loughlin 1986; Barling et al. 1994; Qiu et al., 2017).
2- Methodology
The Maroon River drainage basin, with an area of 7228 square kilometers and an area of 802 kilometers, covers the central part of the Jarahi-Zohreh basin. The Jarahi-Zohreh basin is itself a large part of the Persian Gulf-Oman Sea basin, which its drainage network pours into the northwest part of the Persian Gulf (Fig. 1). In this study, the based data obtained have formed 30 m SRTM DEM data, 1:50000 topographic maps, geological maps of 1:100000, aerial photos, Landsat satellite imagery, Google Earth, and field visits and ArcGIS10.3, QGIS, and SAGA software. To estimate topographic indices, first, it is processed the DEM data file and the stream network of the study basin in the SAGA software environment and then topographic basin indices based on DEM data were calculated and analyzed by the existing functions of this software. In the continuation, slopes of the Maroon basin were classified into nine different types based on two indices of plan shape (divergence, convergence, parallel) and profile (curvature) slopes profile (concave, convex, and flat) (Fig. 2) and the obtained amount of TWI index was analyzed in them. a: The hydrological model (TOPMODEL) is a semi-distributed model where the topography changes of the region and participating levels play a major role in the runoff, assuming that hydraulic gradient can be estimated using the land topography gradient (Ballerine, 2017). The topographic information used in this model is introduced as a topographic moisture indicator and can express the topography effect on runoff production and slope movements quantitatively. These values are calculated using the digital elevation model (DEM) of the studied area and by measuring the flow direction, current accumulation, gradient, and different geometrical characteristics obtained from Arc Hydro software. The final result is a Raster layer which shows pathways (regions) with drainage ditches where water is likely to accumulate there (Ballerine, 2017).
3- Results
The results of basin type stud show that the first-class waterways flow mostly in divergent basins with flat curvature. The second, third, and fourth classes of the basin flow in the concave and parallel basin, and the fifth and sixth classes flow in convex and convergent basins (Table 1). In divergent slopes of the basin, the topographic moisture index has been reduced and in convex and convergent basins, the topographic moisture index increases. Furthermore, the TWI index has a high inverse correlation with the degree gradient and average height of the river (-0.97). This index decreases with the increasing gradient and height of waterways (Table 2). In the trimming basins, the increasing general gradient has led to an acceleration of water flow, thus the time required for penetrating water flow and rainfalls is decreased and the concentration-time is decreased too, and correspondingly amount of erosion and water wasting is increased.
4- Discussion & Conclusions
The results of the study show that the topographic features of the basin-like the slope plan and longitudinal profile of the slope play a decisive role in hydrological processes and runoff time features and slope response time. These features not only directly affect geomorphological and hydrological conditions like annual runoff, flood volume, soil erosion intensity, and sediment production, but also indirectly effects the climate, ecological situation, and vegetation, as well as effects on the water situation in the basin. The results of the basin slope type study are such that the divergent slopes are the lowest amount in the topographic moisture index. Surface water and subsurface water is not concentrated, they spread, and pass slopes water rapidly. the results showed that there is a significant relationship between hydrological processes with the topography and geomorphology indices and can be used as a variable to simulate the moisture state of the Maroon basin area, which is an example of an indirect and low-cost approach to study the hydrological and geomorphological features of the region. the consistency of this index to local soil conditions is a certain advantage over the existing methods and in detailed application programs it needs to perform more activity and perform adaptive studies. Therefore, knowledge of these indices and features in terms of impact on the variability of soil hydraulic features and surface Sediment is necessary to achieve sustainable development and reorganization of the region.
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Received: 2023/06/11 | Published: 2024/10/1

References
1. Afshar Ardekani, A., Sabzevari, T. (2020). Effects of hillslope geometry on soil moisture deficit and base flow using an excess saturation model. Acta Geophys. 68, 773-782. https://doi:10.1007/s11600-020-00428-x [DOI:10.1007/s11600-020-00428-x]
2. Alexander, C., Deak, B., Heilmeier, H., (2016). Micro-topography driven vegetation patterns in open mosaic landscapes. Ecological Indicators. 60, 906-920. https://doi:10.1016/j.ecolind.2015.08.030 [DOI:10.1016/j.ecolind.2015.08.030]
3. Bader, M.Y., Ruijten, J.J.A., (2008). A topography-based model of forest cover at the alpine tree line in the tropical Andes. Journal of Biogeography. 35:711-723. https://doi:10.1111/j.1365-2699. 2007.01818.x [DOI:10.1111/j.1365-2699.2007.01818.x]
4. Balazs, B., Bíro, T., Gareth, Dyke, Singh, S.K. (2018). Extracting water-related features using reflectance data and principal component analysis of Landsat images. Hydrological Sciences Journal. 63(2): 269-284. [DOI:10.1080/02626667.2018.1425802]
5. Ballerine, C. (2017). Topographic Wetness Index Urban Flooding Awareness Act Action Support. Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, 22 p. http://hdl.handle.net/2142/98495
6. Barling, R. D., Moore, I. D., & Grayson, R. B. (1994). A quasi-dynamic wetness index for characterizing the spatial distribution of zones of surface saturation and soil water content. Water Resources Research, 30, 1029-1044. [DOI:10.1029/93WR03346]
7. Beven, K.J., Kirkby, M.J. (1979). A physically based variable contributing area model of basin hydrology. Hydrology Science Bulletin. 24: 43-69. [DOI:10.1080/02626667909491834]
8. Buchanan, B.P., Fleming, M., Schneider, R.L., Richards, B.K., Archibald, J., Qiu, Z., Walter, M.T., (2014). Evaluating topographic wetness indices across central New York agricultural landscapes. Hydrology and Earth System Sciences. 18 (8), 3279-3299. https://doi.org/10.5194/hess-18-3279-2014 [DOI:10.5194/hess-18-3279-2014, 2014.]
9. Cairns, D.M., (2001). A comparison of methods for predicting vegetation type. Plant Ecology, 156:3-18. https://doi:10.1023/A:1011975321668 [DOI:10.1023/A:1011975321668]
10. Calogero Schillaci1, Andreas Braun1 & Jan Kropacek., (2015). Terrain analysis and landform recognition. Geomorphological Techniques,4. 2 :1-18. https://doi:10.13140/RG.2.1.3895.2802
11. Chaplot, V., Walter, C., (2003). Subsurface topography to enhance the prediction of the spatial distribution of soil wetness. Hydrological Processes. 17 (13), 2567-2580. https://doi:10.5772/intechopen.86109 [DOI:10.5772/intechopen.86109]
12. Chen, C.Y., Chen, L.K., Yu, F.C., Lin, S.C., Lin, Y.C., Lee, C.L., Wang, Y.T., and Cheung, K.W. (2008). Characteristics analysis for the flash flood-induced debris flows. Journal of Natural Hazards. 47(1): 245-261. https://doi:10.1007/s11069-008-9217-7 [DOI:10.1007/s11069-008-9217-7]
13. DaSilva, J.M.F., Santos, L.J.C. & Oka-Fiori, C. (2019). Spatial correlation analysis between topographic parameters for defining the geomorphometric diversity index: application in the environmental protection area of the Serra da Esperança (state of Paraná, Brazil). Environmental Earth Sciences, 78(12):356. https://doi:10.1007/s12665-019-8357-2 [DOI:10.1007/s12665-019-8357-2]
14. Dirnbock, T., Hobbs, R.J., Lambeck, R.J., Caccetta, P.A., (2002). Vegetation distribution in relation to topographically driven processes in south western Australia. Applied Vegetation Science, 5:147-158. https://doi:10.1111/j.1654-109X.2002.tb00544.x [DOI:10.1111/j.1654-109X.2002.tb00544.x]
15. Dobrowski, S.Z., Safford, H.D., Cheng, Y.B., Ustin, S.L., (2008). Mapping mountain vegetation using species distribution modeling, imagebased texture analysis, and object-based classification. Applied Vegetation Science, 11:499-508. https://doi:10.3170/2008-7-18560 [DOI:10.3170/2008-7-18560]
16. Endreny, T.A., Wood, E.F. (2003). Maximizing spatial congruence of observed and DEM-delineated overland flow networks. International Journal of Geographical Information Science. 17(7): 699-713. https://doi:10.1080/1365881031000135483 [DOI:10.1080/1365881031000135483]
17. Esper Angillieri, M.Y., Perucca, L.P., (2014), Geomorphology and morphometry of the de La Flecha river basin, San Juan, Argentina: Environmental Earth Sciences, 72, 3227-3237. https://doi:10.1007/s12665-014-3227-4. [DOI:10.1007/s12665-014-3227-4]
18. Evans, J.S., Cushman, S.A., (2009). Gradient modeling of conifer species using random forests. Landscape Ecology, 24:673-683. https://doi:10.1007/s10980-009-9341-0 [DOI:10.1007/s10980-009-9341-0]
19. Fitterer, J.L., Nelson, T.A., Coops, N.C., Wulder, M.A., (2012). Modelling the ecosystem indicators of British Columbia using Earth observation data and terrain indices. Ecological Indicators, 20:151-162. https://doi:10.3390/d5020352 [DOI:10.3390/d5020352]
20. Franklin, J., (2002). Enhancing a regional vegetation map with predictive models of dominant plant species in chaparral. Applied Vegetation Science, 5(1):135 - 146. https://doi:10.1111/j.1654-109X.2002.tb00543.x [DOI:10.1111/j.1654-109X.2002.tb00543.x]
21. Gessler P.E., Chadwick O.A., Chamran F., Althouse L., and Holmes K. (2000). Modeling soil landscape and ecosystem properties using terrain attributes. Soil Science Society of American Journal, 64: 2046- 2056. [DOI:10.2136/sssaj2000.6462046x]
22. Gruber, S. & Peckham, S. (2008). Land-surface parameters and objects in hydrology. In: Hengl, T. & Reuter, H.I. Geomorphometry: concepts, software, applications. pp. 171-194. Elsevier, Amsterdam, NL. https://doi:10.1016/S0166-2481(08)00007-X [DOI:10.1016/S0166-2481(08)00007-X]
23. Gumindoga, W., Rwasokab, D.T. and Murwirac, A. (2011). Simulation of streamflow using TOPMODEL in the Upper Save River catchment of Zimbabwe. Physics and Chemistry of the Earth, 36, 806-813. https://doi:10.1016/j.pce.2011.07.054 [DOI:10.1016/j.pce.2011.07.054]
24. Guntner, A., Uhlenbrook, S., Leibundgut, C., Siebert, J., (1999). Estimation of saturation excess overland flow areas: comparison of topographic index calculations with field mapping. Int. Assoc. Water Resources Research. 254, 203-210 IAHS Publication. https://doi:10.1029/2003WR002864 [DOI:10.1029/2003WR002864]
25. Guo, P.T., Liu, H.B., and Wu, W. (2019). Spatial prediction of soil organic matter using terrain attributes in a hilly area. International Conference on Environmental Science and Information Application Technology. Wuhan, China. 3: 759-762. https://doi:10.1109/ESIAT.2009.330 [DOI:10.1109/ESIAT.2009.330]
26. Haring, T., Reger, B., Ewald, J., Hothorn, T., Schröder, B., (2019). Predicting Ellenberg's soil moisture indicator value in the Bavarian Alps using additive georegression. Applied Vegetation Science. 16 (1), 110-121. https://doi:10.1111/j.1654-109X.2012.01210.x [DOI:10.1111/j.1654-109X.2012.01210.x]
27. Iverson, L.R., Dale, M.E., Scott, C.T. & Prasad, A. (1997). A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.). Landscape Ecology, 12: 331-348. [DOI:10.1023/A:1007989813501]
28. Jeziorska, J., Niedzielski, T. (2018). Applicability of TOPMODEL in the mountainous catchments in the upper Nysa Kłodzka river basin (SW Poland). Acta Geophys. 66, 203-222. [DOI:10.1007/s11600-018-0121-6]
29. Kadirhodjaev, A., Kadavi, P.R., Lee, C. (2018). Analysis of the relationships between topographic factors and landslide occurrence and their application to landslide susceptibility mapping: a case study of Mingchukur, Uzbekistan. Geosciences Journal, 22, 1053-1067. https://doi:10.1007/s12303-018-0052-x [DOI:10.1007/s12303-018-0052-x]
30. Kaliraj, S., Chandrasekar, N., Magesh, N.S. (2015). Morphometric analysis of the River Thamirabarani subbasin in Kanyakumari district, South west coast of Tamil Nadu, India, using remote sensing and GIS. Environmental Earth Sciences, 73, 7375-7401. https://doi:10.1007/s12665-014-3914-1 [DOI:10.1007/s12665-014-3914-1]
31. Kopecky, M., Cizkova, S. (2010). Using topographic wetness index in vegetation ecology: does the algorithm matter. Applied Vegetation Science, 13: 450-459. [DOI:10.1111/j.1654-109X.2010.01083.x]
32. Liu, J.; Engel, B.A.; Wang, Y.; Wu, Y.; Zhang, Z.; Zhang, M. (2019). Runoff Response to Soil Moisture and Micro-Topographic Structure on the Plot Scale. Scientific Reports. 9, 2532. [DOI:10.1038/s41598-019-39409-6]
33. Luca, C., Si, B.C., and Farrell, R.E. (2007). Upslope length improves spatial estimation of soil organic carbon content. Canada Journal of Soil Science. 87: 291-300. https://doi:10.4141/CJSS06012 [DOI:10.4141/CJSS06012]
34. Marques da Silva, J. R., & Alexandre, C. (2005). Spatial variability of irrigated corn yield in relation to field topography and soil chemical characteristics. Precision Agriculture, 6, 453-466. [DOI:10.1007/s11119-005-3679-3]
35. Mattivi, P., Franci, F., Lambertini, A. (2019). TWI computation: a comparison of different GISs. Open geospatial data, 4, 6. [DOI:10.1186/s40965-019-0066-y]
36. McKenzie, N.J., Ryan, P.J., (1999). Spatial prediction of soil properties using environmental correlation. Geoderma, 89 (1), 67-94. [DOI:10.1016/S0016-7061(98)00137-2]
37. Merheb, M., Moussa, R.., Abdallah, C., Colin, F., Perrin, C., Baghdadi, N. (2016) Hydrological response characteristics of Mediterranean catchments at different time scales: a meta-analysis, Hydrological Sciences Journal, 61:14, 2520-2539. [DOI:10.1080/02626667.2016.1140174]
38. Mlynski, D.; Wałega, A.; Petroselli, A.; Tauro, F.; Cebulska, M. (2019). Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland. Atmosphere. 10, 43. [DOI:10.3390/atmos10020043]
39. Moeslund, J.E., Arge, L., Bøcher, P.K., Dalgaard, T., Odgaard, M.V., Nygaard, B., Svenning, J.C., (2013). Topographically controlled soil moisture is the primary driver of local vegetation patterns across a lowland region. Ecosphere, 4 (7), 1-26. https://doi:10.1890/ES13-00134.1 [DOI:10.1890/ES13-00134.1]
40. Moore, I., Gessler, P., Nielsen, G., & Peterson, G. (1993). Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57, 443-452. https://doi:10.2136/sssaj1993.572NPb [DOI:10.2136/sssaj1993.572NPb]
41. Moore, I.D., and Grayson, R.B. (1991). Landson. Digital terrain Modeling: A review of hydrological, Geomorphological and Biological application. Modelling in Hydrology. 5: 3-30. [DOI:10.1002/hyp.3360050103]
42. Muad, A.M., Foody, G.M., (2012). Super-resolution mapping of lakes from imagery with a coarse spatial and fine temporal resolution. Journal of Applied Earth Observation Geo information. (12) 1: 79-91. [DOI:10.1016/j.jag.2011.06.002]
43. O'Loughlin E. M. (1986). Prediction of surface saturation zones in natural catchments by topographic analysis. Water Resources Research journal. 22(5): 794-804. [DOI:10.1029/WR022i005p00794]
44. Pan, F., Peters-Lidard, C.D., Sale, M.J., and King, A.W. (2004). A comparison of geographical information system-based algorithms for computing the TOPMODEL topographic index. Water Resources Research. 40: 1-11. https://doi:10.1029/2004WR003069 [DOI:10.1029/2004WR003069]
45. Parolo, G., Rossi, G., Ferrarini, A., (2008). Toward improved species niche modelling: Arnica montana in the Alps as a case study. Journal of Applied Ecology, 45(5):1410-1418. https://doi:10.1111/j.1365-2664.2008.01516.x [DOI:10.1111/j.1365-2664.2008.01516.x]
46. Petroselli, A., Vessella, F., Cavagnuolo, L., Piovesan, G., Schirone, B. (2013). Ecological behavior of Quercus suber and Quercus ilex inferred by topographic wetness index (TWI). Trees, 27:1201-1215. https://doi:10.1007/s00468-013-0869-x [DOI:10.1007/s00468-013-0869-x]
47. Petroselli, A.; Grimaldi, S. (2018). Design hydrograph estimation in small and fully ungauged basins: A preliminary assessment of the EBA4SUB framework. Flood risk management. 2018, 8, 1-14. DOI:10.1111/jfr3.12193 [DOI:10.1111/jfr3.12193]
48. Pielech, R., Anioł-Kwiatkowska, J., Szczęśniak, E., (2015). Landscape-scale factors driving plant species composition in mountain streamside and spring riparian forests. Forest Ecology and Management. 347, 217-227. [DOI:10.1016/j.foreco.2015.03.038]
49. Prancevic, J.P.; Kirchner, J.W. (2019). Topographic Controls on the Extension and Retraction of Flowing Streams. Geophysical Research Letters. 46, 2084-2092. [DOI:10.1029/2018GL081799]
50. Qin, C.Z., Zhu, A.X., Pei, T., Li, B.L., Scholten, T., Behrens, T., and Zhou, C.H. (2011). An approach to computing topographic wetness index based on maximum downslope gradient. Precision Agriculture. 12: 32-43. DOI:10.1007/s11119-009-9152-y [DOI:10.1007/s11119-009-9152-y]
51. Qiu, Z., Pennock, A., Giri, S. (2017). Assessing Soil Moisture Patterns Using a Soil Topographic Index in a Humid Region. Water Resour Manage, 31, 2243-2255. DOI: 10.1007/s11269-017-1640-7 [DOI:10.1007/s11269-017-1640-7]
52. Raduła, M.W., Szymura, T.H., Szymura, M. (2018). Topographic wetness index explains soil moisture better than bioindication with Ellenberg's indicator values. Ecological Indicators, 85: 172-179. [DOI:10.1016/j.ecolind.2017.10.011]
53. Rinderer, M., van Meerveld, H. J. & Seibert, J., (2014). Topographic controls on shallow groundwater levels in a steep, prealpine catchment: When are the TWI assumptions valid, Water Resources Research. 50, 7. 6067-6080. [DOI:10.1002/2013WR015009]
54. Sabzevari T, Noroozpour S, Pishvaei M (2015) Effects of geometry on runoff time characteristics and time-area histogram of hillslopes. Journal of Hydrology. 531:638-648. https://doi:10.1016/j.jhydrol.2015.10.063 [DOI:10.1016/j.jhydrol.2015.10.063]
55. Schmidt, S., Tresch, S., Meusburger, K., (2019). Modification of the RUSLE slope length and steepness factor (LS-factor) based on rainfall experiments at steep alpine grasslands. MethodsX, 6, 219-229. [DOI:10.1016/j.mex.2019.01.004]
56. Shanableh, A.; Al-Ruzouq, R.; Yilmaz, A.; Siddique, M.; Merabtene, T.; Imteaz, M. (2018). Effects of Land Cover Change on Urban Floods and Rainwater Harvesting: A Case Study in Sharjah. UAE. Water, 10(5), 631; [DOI:10.3390/w10050631]
57. Si, C.B. and Farrell, R.E. (2004). Scale-dependent relationship between wheat yield and topographic indices: A wavelet approach. Soil Science Society of American Journal, 68: 577-587. DOI: 10.1080/01431160600794621 [DOI:10.1080/01431160600794621]
58. Sorensen, R., Zinko, U., and Seibert, J. (2005). On the calculation of the topographic wetness index: evaluation of different methods based on field observation. Hydrology and Earth System Sciences. 10: 101-112. [DOI:10.5194/hess-10-101-2006]
59. Svetlitchnyi, A.A., Plotnitskiy, S.V., and Stepovaya, O.Y. (2003). Spatial distribution of soil moisture content within catchments and its modeling on the basis topographic data. Journal of Hydrology. 277: 50-60. DOI:10.1016/S0022-1694(03)00083-0 [DOI:10.1016/S0022-1694(03)00083-0]
60. Taverna, K., Urban, D.L., McDonald, R.I., (2005). Modeling landscape vegetation pattern in response to historic land-use: a hypothesisdriven approach for the North Carolina Piedmont, USA. Landscape Ecology, 20:689-702. [DOI:10.1007/s10980-004-5652-3]
61. Troch P. A. van Loon A. and Hilberts H. (2002). Analytical solutions to a hillslope storage kinematic wave equation for subsurface flow. Advance in Water Resource journal. 25(6): 637- 649. https://doi:10.1016/S0309-1708(02)00017-9 [DOI:10.1016/S0309-1708(02)00017-9]
62. Van Niel, K.P., Laffan, S.W., Lees, B.G., (2004). Effect of error in the DEM on environmental variables for predictive vegetation modelling. Journal of Vegetation Science. 15:747-756. https://doi:10.1111/j.1654-1103.2004.tb02317.x [DOI:10.1111/j.1654-1103.2004.tb02317.x]
63. Wang G. Hapuarachchi H. A. P. Takeuchi K. and Ishidaira H. (2010). Grid-based distribution model for simulating runoff and soil erosion from a large-scale river basin. Hydrologic Process. 24: 641-653. https://doi:10.1002/hyp.7558 [DOI:10.1002/hyp.7558]
64. Wang, Q.M., Wang, D.F., (2011). Sub-pixel mapping based on sub-pixel to sub-pixel spatial attraction model. In: Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. 593-596. https://doi:10.1109/IGARSS.2011.6049198 [DOI:10.1109/IGARSS.2011.6049198]
65. Welsch, D.L., Kroll, C.N., Mc Donnell, J.J., and Burns, D.A. (2001). Topographic controls on the chemistry of subsurface stormflow. Hydrological Processes. 15: 10. 1925-1938. https://doi:10.1002/hyp.247 [DOI:10.1002/hyp.247]
66. Western, A.W, (2004). Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology. 286: 1-4. 113-134. [DOI:10.1016/j.jhydrol.2003.09.014]
67. Western, A.W., Grayson, R.B., Blöschl, G., Willgoose, G.R., McMahon, T.A., (1999). Observed spatial organization of soil moisture and its relation to terrain indices. Water Resources Research. 35 (3), 797-810. [DOI:10.1029/1998WR900065]
68. Whelan, M.J., and Gandolfi, C. (2002). Modelling of spatial controls on denitrification at the landscape scale. Hydrology Journal. 16: 7. 1437-1450. [DOI:10.1002/hyp.354]
69. Wolock, D. M., and G. J. McCabe. (1995). Comparison of single and multiple flow direction algorithms for computing topographic parameters in TOPMODEL. Water Resources, 31(5):1315-1324. [DOI:10.1029/95WR00471]
70. Wysocki, D.A., Schoeneberger, P.J., LaGarry, H.E., (2000). Geomorphology of soil landscapes. In: Sumner, M. (Ed.), CRC handbook of soil science. CSC Press, New York, pp. E1-E39.
71. Xue, L., Yang, F., Yang, C. (2018). Hydrological simulation and uncertainty analysis using the improved TOPMODEL in the arid Manas River basin, China. Scientific Reports, 8, 452. [DOI:10.1038/s41598-017-18982-8]
72. Yousefzadeh, A., Zeynali, B., Valizadeh Kamran, Kh., Asghari Sar Eskanrood, S. (2019). The Extraction of Flood Potential of Simineh River Basin Applying Satellite Images, Topographic Wetness Index and Morphological Features. Geography and Sustainability of Environment, 9 (3), 49-61. doi: 10.22126/GES.2019.4294.2071
73. Zhao, B., Dai, Q., Han, D. (2020). Application of hydrological model simulations in landslide predictions. Landslides, 17, 877-891. [DOI:10.1007/s10346-019-01296-3]
74. Zhu, A.-X., Yang, L., Li, B.-L., Qin, C.-Z., Pei, T., & Liu, B.-Y. (2009). Construction of membership functions for predictive soil mapping under fuzzy logic. Geoderma. 155(3-4):164-174. https://doi:10.1016/j.geoderma.2009.05.024.3 [DOI:10.1016/j.geoderma.2009.05.024]
75. Zhu, H.D., Shi, Z.H., Fang, N.F., Wu, G.L., Guo, Z.L., Zhang, Y., (2014). Soil moisture response to environmental factors following precipitation events in a small catchment. Catena, 120, 73-78. https://doi:10.1016/j.catena.2014.04.003 [DOI:10.1016/j.catena.2014.04.003]

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