Erosion susceptibility mapping of a loess-covered region using Analytic Hierarchy Process – A case study: Kalat-e-Naderi, northeast Iran 339Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.DOI: 10.15201/hungeobull.72.4.2 Hungarian Geographical Bulletin 72 2023 (4) 339–364. Introduction Soil erosion stands as a critical global envi- ronmental challenge, exerting far-reaching impacts on agricultural productivity, natural resources, and socio-economic development (Jebur, M.N. et al. 2014; Zhao, J. et al. 2016; Li, Y. et al. 2020; Vanmaercke, M. et al. 2021; Cen, Y. et al. 2022). Soil erosion is speeded up by a complex interplay of environmen- tal factors, such as topography, soil charac- teristics, climate, and vegetation, as well as human activities like deforestation, farming, and construction (Lukić, T. et al. 2018, 2019; Thomas, J. et al. 2018; Abbas, S. et al. 2022). To mitigate soil erosion, diverse strategies, ranging from conservation tillage to the im- plementation of hydraulic structures, have been developed (Morgan, R. 1995; Barbera, V. et al. 2012; Lukić, T. et al. 2016). Due to the importance of soil erosion, researchers have been investigating soil erosion using differ- ent models, including Agricultural Non-point Source Pollution Model (AGNPS) (Young, R. et al. 1989; Zhu, K.-W. et al. 2020; Zou, L. et al. 2020; Huang, C. et al. 2022), Soil and Water 1 Department of Physical Geography, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, H-1117, Budapest, Hungary. E-mails: fatimanooshin@gmail.com, kevingh70@gmail.com, erzsebet.horvath@ttk.elte.hu Erosion susceptibility mapping of a loess-covered region using Analytic Hierarchy Process – A case study: Kalat-e-Naderi, northeast Iran Fatemeh NOOSHIN NOKHANDAN 1, Kaveh GHAHRAMAN 1 and Erzsébet HORVÁTH1 Abstract In this study, the Analytic Hierarchy Process (AHP) is applied to generate erosion susceptibility maps in four basins of Kalat-e-Naderi county, namely Archangan, Kalat, Qaratigan, and Chahchaheh basins, situated in northeast Iran. The Kalat-e-Naderi region is characterized by a partial coverage of loess. Given the agricultural significance of loess and its susceptibility to erosion, this research focuses specifically on regions covered by loess. Geographic Information System (GIS) tools, including ArcMap and Quantum Geographic Information System (QGIS), were utilized to facilitate the creation of erosion susceptibility maps. Seven factors, including slope, aspect, elevation, drainage density, lithology, the Normalized Difference Vegetation Index (NDVI), and precipitation were selected for consideration. Recognizing the variability of precipitation and vegetation cover across different seasons, seasonal data for the specified factors were employed. Consequently, erosion susceptibility maps were generated on a seasonal basis. Pairwise comparison tables revealed that precipitation, lithology, and slope emerged as the dominant factors contributing to erosion susceptibility in this region. The resultant maps distinctly delineate basins with higher precipitation values, unresistant lithology (such as loess, characterized by high porosity and permeability), and steeper slopes, exhibiting heightened susceptibility to erosion (Archangan and Kalat basins). The credibility of the research findings was examined through on-site observations. The outcomes of this study may provide pertinent insights for decision-makers and planners. This information can be effectively employed in formulating strategies aimed at conserving soil quality in areas vulnerable to erosion hazards. Keywords: Analytic Hierarchy Process (AHP), erosion susceptibility on loess, GIS, Kalat-e-Naderi Received July 2023, accepted November 2023. mailto:fatimanooshin@gmail.com mailto:kevingh70@gmail.com Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.340 Assessment Tool (SWAT) (Neitsch, S.L. et al. 2011; Bhattacharya, R.K. et al. 2020; Echog- dali, F.Z. et al. 2022), European Soil Erosion Model (EUROSEM) (Morgan, R. et al. 1998; Pandey, S. et al. 2021; Raza, A. et al. 2021; Shen, N. et al. 2023), Erosion Potential Model (EPM) (Ahmadi, M. et al. 2020; Ennaji, N. et al. 2022; Aleksova, B. et al. 2023), and Revised Univer- sal Soil Loss Equation (RUSLE) (Kebede, Y.S. et al. 2021; Aswathi, J. et al. 2022; Micić Ponjiger, T. et al. 2023). Besides the mentioned methods, the AHP (Saaty, T.L. 1980) is widely used to investigate soil erosion and to map the erosion susceptible areas (Saha, S. et al. 2019; Das, B. et al. 2020; Kucuker, D.M. and Giraldo, D.C. 2022). However, among these advance- ments, challenges persist in predicting the spatial distribution of soil erosion, including constraints related to time, cost, facilities, and result accuracy (Kucuker, D.M. and Giraldo, D.C. 2022). Recent years have witnessed the integration of remote sensing techniques and Geographic Information Systems (GIS), over- coming these limitations and enabling the prediction of soil erosion in larger areas with reasonable cost and accuracy (Aslam, B. et al. 2021; Alam, N.M. et al. 2022; Alizadeh, M. et al. 2022; Kucuker, D.M. and Giraldo, D.C. 2022; Hayatzadeh, M. et al. 2023). The investigated area, Kalat-e-Naderi county, located in a partially loess-covered region in northeast Iran, becomes the focal point of our study. Some areas of the coun- ty are dedicated to agriculture, while crop production is active on less steep slopes, em- phasizing the need to address soil erosion, particularly in loess-covered regions, for sustainable food production. Loess is an eo- lian (windblown) pale yellow sediment (Pye, K. and Tsoar, H. 1987; Fenn, K. et al. 2022), which besides its agricultural importance, serves as a crucial repository of Quaternary climate changes (Xu, J. et al. 2022), offering a comprehensive terrestrial record of inter- glacial-glacial cycles. It stands out as a sig- nificant geological formation that captures the dynamic shifts in environmental condi- tions over time (Markovič, S.B. et al. 2014). Defined as sediment entrained, transported, and deposited by the wind, and diagenetised in situ, loess is characterized by the predom- inance of silt-sized particles (Wang, X. et al. 2017), ranging from 2 µm to 50 µm in diameter (Smalley, I. et al. 2011). While most loess de- posits exhibit a composition that includes mea- surable amounts of sand (> 50 µm) and clay (< 2 µm), the distinctive feature of loess lies in its prevalent content of silt-sized particles, typically ranging from 60 to 90 percent (Muhs, D.R. 2007). Due to its elevated porosity and silt content, loess is acknowledged as one of the most fertile forms of unconsolidated sedimentary rock. The inherent porosity of loess facilitates the absorption of gases containing carbon and nitrogen, enabling the provision of water and dissolved nutri- ents to plants through capillary rise during dry periods (Richthofen, F. 1872; Emerson, W.W. and McGarry, D. 2003). However, the susceptibility of loess to erosion is evident, as it can be easily eroded by surface water, making it prone to the formation of subcuta- neous hollow landforms (Pécsi, M. 1990; Wu, Q. et al. 2019). Consequently, the sensitivity of loess landforms to erosion highlights their significance in the context of natural hazards and related issues. While numerous studies have delved into the well-known northern loess regions of Iran (e.g., Aqband, Neka, Maraveh Tappeh, etc.) (Khormali, F. et al. 2009; Asadi, S. et al. 2013; Ghafarpour, A. et al. 2016, 2023; Gharibreza, M. et al. 2020; Sharifigarmdareh, J. et al. 2020) few investigations have specifically focused on the Kalat-e-Naderi loess region (Okhravi, R. and Amini, A. 2001; Karimi, A. et al. 2011), particularly in terms of erosion and with a geomorphological approach. Against the backdrop of the unique charac- teristics and challenges posed by loess-cov- ered areas, this study has two aims: 1) to gen- erate soil erosion susceptibility maps in the less known, and less investigated loess-cov- ered region of northeast Iran using the AHP method, and 2) to analyse the role of soil ero- sion in shaping various geomorphological landforms within the study area, employing a geomorphological approach. 341Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. Material and methods Study area The study area is located near the Iran-Turk- menistan border and is recognized as one of the loess-covered regions within the Kho- rasan-e-Razavi province. This region encom- passes four basins including Archangan, Kalat, Qaratigan, and Chahchaheh basins, arranged from northwest to southeast (Figure 1). Loess regions in the study area share a common chronological relationship with the loesses in the Caspian Lowlands, despite a more than 500 km between the two areas (Karimi, A. et al. 2011). In the Kalat-e-Naderi region, the loess occurs in a patchy distribution with a thick- ness of up to 12 metres ((Karimi, A. et al. 2011), which is notably less than the thickness of loesses in the Caspian Lowlands in the north- ern part of Iran (Frechen, M. et al. 2009; Kehl, M. et al. 2021; Feizi, V. et al. 2023). As reported by Karimi, A. et al. (2011), the sand, silt, clay, gypsum, and carbonate contents of Kalat-e- Naderi sections are 10–18, 67–86, 4–16, 11–25, and 2–12 percent, respectively, showing the typical loess characteristics (Pécsi, M. 1990). Loess deposits are predominantly distributed on the north-eastern slopes of the Kopeh Dagh mountain range, covering a plateau-like geo- morphic surface within a synclinal structure (Karimi, A. et al. 2011). The Kopeh Dagh mountain range demon- strates a northwest-southeast orientation. From a structural geological standpoint, the region showcases numerous folds, faults, and fis- sures, with the primary fault direction oriented northwest-southeast. The formation of syn- clines and anticlines in this area can be attrib- uted to the predominant northwest-southeast directional pressure. According to the Digital Elevation Model (DEM) of the study area, the Fig. 1. The location of the studied basins in northeast Iran. The green rectangles indicate the positions of the visiting sites, as depicted in photos 1 through 5. Source: Authors’ own elaboration. Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.342 highest and lowest elevations are recorded at 460 m and 2,780 m, respectively. According to Köppen’s cli- mate classification, Kalat-e- Naderi falls into the cold semi- arid (BSk) category (Köppen, W. 1900). Climatic data from the Qaratigan watershed indi- cate a mean annual precipita- tion of 287 mm and a tempera- ture of 12.2 °C (Amini, A. 1995). Historical climatic data for the area and its surroundings reveal significant seasonal var- iations in precipitation. The highest amounts (averaging 46.6 mm) occur between late January and the end of April, while the low- est precipitation values are observed between June and October (0.2 mm in August). Due to the arid and semi-arid climate, precipita- tion characteristics differ from more humid regions. In such arid and semi-arid regions, precipitation typically manifests as short but intense rainfall events (Ghahraman, K. and Nagy, B. 2023). Overall, in pursuit of our objectives, we em- ployed the AHP methodology, coupled with remotely sensed data (e.g., digital elevation models and optical satellite imagery), GIS tools (e.g., ArcGIS and QGIS), and field surveys. Analytic Hierarchy Process The AHP is a suitable technique for iden- tifying and mapping erosion-prone areas (Belkendil, A. et al. 2018; Belloula, M. et al. 2020; Aslam, B. et al. 2021; Sinshaw, B.G. et al. 2021; Ebhuoma, O. et al. 2022). AHP is a method that compares qualitative factors and expresses them as numerical values. This re- search used AHP due to its advantages, such as the availability of input factors, the capa- bility of comparing multiple parameters, and ease of use (Rajesh, C. et al. 2016; Belkendil, A. et al. 2018; Tairi, A. et al. 2019). Table 1 shows the numerical scale (by Saaty, R.W. 1987) proposed to be used as a source for the pairwise comparison. Depending on the im- portance of the selected factors, AHP assigns a value of 1 to 9 to each factor to decide the significance of the factor in association with the objective. The AHP method comprises three primary steps. The initial step involves selecting the pertinent criteria for erosion. Criterion selec- tion is contingent upon the impact of each factor on the occurrence of the phenomenon, our knowledge about the study area, in- sights gleaned from related researches, and crucially, the availability of data for each re- gion (Arabameri, A. et al. 2018; Azareh, A. et al. 2019; Neji, N. et al. 2021; Kucuker, D.M. and Giraldo, D.C. 2022). Considering these critical considerations, we selected 7 factors including slope, aspect, elevation, drainage density, lithology, normalized difference vegetation index (NDVI), and precipitation. The flowchart and factors utilized in the AHP method for our study are depicted in Figure 2. Maps corresponding to each factor were generated using ArcMap and QGIS soft- ware (Figure 3). One of the key geomorphological pa- rameters influencing erosion is topography (Rahmati, O. et al. 2016). Topographic fac- tors, including slope, aspect, and elevation, were derived from the SRTM (1 Arc sec) Digital Elevation Model. The substantial in- fluence of slope gradient on soil erosion is widely acknowledged (Saini, S.S. et al. 2015; Meshram, S.G. et al. 2022; Olii, M.R. et al. 2023). Hence, it is imperative to recognize slope as a pivotal factor in studies pertaining to soil erosion, given its profound impact on the phenomenon (Aslam, B. et al. 2021). Table 1. Pairwise comparison scale* Rating scale Numerical Reciprocal Extremely preferred Very strongly to extremely preferred Very strongly preferred Strongly to very strongly preferred Strongly preferred Moderately to strongly preferred Moderately preferred Equally to moderately preferred Equally preferred 9 8 7 6 5 4 3 2 1 1/9 1/8 1/7 1/6 1/5 1/4 1/3 1/2 1 *Proposed by Saaty, R.W. 1987. 343Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. The slope map in our study was catego- rized into nine classes: 0°–5°, 5°–10°, 10°–15°, 15°–20°, 20°–25°, 25°–30°, 30°–35°, 35°–40°, and > 40° (see Figure 3, a). Considering the impracticality of agricultural expansion on steep slopes, the effective slope limit for ero- sion was set at 40°. Aspect influences ero- sion by regulating vegetation type, moisture, evaporation and transpiration, and sunlight exposure duration (Jaafari, A. et al. 2014). The aspect map encompasses ten direction- al classes: flat (–1°), north (0°–22.5°), north- east (22.5°–67.5°), east (67.5°–112.5°), south- east (112.5°–157.5°), south (157.5°–202.5°), southwest (202.5°–247.5°), west (247.5°– 292.5°), northwest (292.5°–337.5°), and north (337.5°–360°) (see Figure 3, b). Elevation, by impacting vegetation type and precipitation, can affect erosion and gully development (Gómez-Gutiérrez, Á. et al. 2015). Given the mountainous nature of the area, this study adopted an eight- category elevation map with 290-metre elevation intervals to allow for more pre- cise weight assignments for each category. The elevation categories include 460–750 m, 750–1,040 m, 1,040–1,330 m, 1,330–1,620 m, 1,620–1,910 m, 1,910–2,200 m, 2,200–2,490 m, and 2,490–2,780 m (see Figure 3, c). The drainage density map, extracted from SRTM-DEM using the line density tool in ArcGIS 10.3, was divided into seven classes to construct the AHP comparison matrix with higher precision. The drainage density classes include 0.02–0.52, 0.52–1.02, 1.02–1.52, 1.52–2.02, 2.02–2.52, 2.52–3.02, and 3.02–3.70 (see Figure 3, d). The lithology raster layer was prepared based on the 1:250,000 scale geologic map of the study area (see Figure 3, e). The study area, including 16 major lithological units, was di- vided into two categories, namely loess and solid rocks, given the focus on loess and ero- sion in loess-covered regions. It is important to note that the number of classes for factors such as slope, elevation, and drainage density may vary depending on the specific character- istics and conditions of each study area. Numerous studies have highlighted the significance of precipitation and NDVI as influential factors in erosion susceptibility mapping using AHP (Alexakis, D.D. et al. Fig. 2. The flowchart depicting the AHP method and the input data utilized for soil erosion susceptibility mapping. Source: Authors’ own elaboration. Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.344 Fig. 3. Erosion contributing factor layers of the study area: slope (a), aspect (b), elevation (c), line density (d), lithology (e), and precipitation for spring (f). Source: Authors’ own elaboration. 345Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. Fig. 3. Continued – Erosion contributing factor layers of the study area: precipitation for autumn, and winter (g, h), and NDVI for spring, autumn, and winter (i, j, k). Source: Authors’ own elaboration. Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.346 2013; Kachouri, S. et al. 2015; Tairi, A. et al. 2019; Aslam, B. et al. 2021; Bouamrane, A. et al. 2021; Sandeep, P. et al. 2021; Mushtaq, F. et al. 2023). However, it has been observed that many researchers have relied on mean annual precipitation data (Kachouri, S. et al. 2015; Boufeldja, S. et al. 2020; Neji, N. et al. 2021), disregarding the uneven distribution of precipitation across different seasons. To overcome this limitation and improve the ac- curacy of the erosion susceptibility maps, it is essential to consider seasonal precipitation data. This is crucial because varying precipi- tation values have an impact on other factors and, more broadly, on erosional processes. By incorporating seasonal variability, our study can provide more accurate and nu- anced insights into the spatiotemporal dy- namics of soil erosion and inform more effec- tive soil conservation strategies, particularly given the significance of agricultural activi- ties in the study area. In our study, we encountered the un- availability of meteorological station data for the study area. As a solution, we ob- tained precipitation data from the Center of Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine, data portal (https://chrsdata.eng.uci.edu). To gene- rate a comprehensive precipitation map for the year 2021, we utilized ArcMap and employed the Inverse Distance Weighted (IDW) interpola- tion method specifically for the spring, autumn, and winter seasons (see Figure 3, f, g, h). It is worth noting that since there was no recorded precipitation during the summer of 2021, the summer map depicting precipitation and NDVI was excluded from our analysis. The NDVI maps were extracted from Sentinel-2 images using ArcMap. NDVI values range from -1 to +1 and are calculated using the following equation (Farah, A. et al. 2021; Parsian, S. et al. 2021; Durlević, U. et al. 2022): NDVI = (NIR–RED)/(NIR+RED) In Equation (1) NIR represents the near- infrared (band 8), and RED corresponds to the red band (band 4) of the Sentinel-2 im- agery. NDVI values less than zero indicate the presence of water bodies and moisture, while values near zero (0–0.2) signify bare surfaces, rocks, sand, and snow. Values from 0.2 to 0.4 indicate areas covered by shrubs, grassland, and crops, while values exceeding 0.4 signify the presence of dense vegetation, such as orchards and, in certain regions, rice fields (see Figure 3, f, g, h). The second step in the AHP involves as- signing weights to the chosen criteria and conducting pairwise comparisons, a process that must also be extended to the sub-classes of each criterion. As presented in Table 1, each factor is assigned a value ranging from 1 to 9 based on its perceived importance or impact on erosion. The final step entails construct- ing a pairwise comparison matrix using the values assigned to the factors in the previ- ous step. In AHP, the acceptable limit for the consistency ratio is equal to or less than 10 percent (Saaty, T.L. 1988; Alonso, J.A. and Lamata, M.T. 2006). The consistency ratio (CR) and consistency index (CI) are deter- mined using the following equations: where CI is the consistency index, and RI is the random consistency index that is ob- tained from Table 2 (Saaty, T.L. and Vargas, L.G. 2001). CI is calculated using the follow- ing equation: where βmax is the largest eigenvalue of the comparison matrix, and n is the comparison matrix size. The process of determining the priority or weight for each factor involves calculating the eigenvalue (Costa, C.A.B. and Vansnick, J.-C. 2008). The eigenvalue is obtained by summing the products of each element in the eigenvector and the sum of the reciprocal matrix. To affirm the consistency of the given values, the consistency ratio (CR) must be calculated for each factor’s pairwise comparison, as well as for the pairwise com- parison of each factor’s subclasses. (1) (2) (3) 347Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. The erosion susceptibility maps for the study area were generated using the weighted overlay tool in ArcMap. The values assigned in the tool were selected based on the weight or importance of each class. Subsequently, the validation process was conducted through field surveys on visiting sites (see Figure 1). Field surveys allowed us to assess the mod- el’s success in identifying susceptible areas. Results As mentioned earlier, AHP pairwise compari- son was drawn during the AHP preparation stages. Subsequently, the pairwise comparison tables and erosion susceptibility maps for each season are presented. The spring season In pairwise comparison, a score of 1 indicates equal importance, while a score of 9 signi- fies very high importance of one factor over the other (Saaty, T.L. 1980) (see Table 1). The pairwise comparison table for the spring sea- son (Table 3) reveals that precipitation, lithol- ogy, slope, and drainage density carry the highest weights among the factors, with val- ues of 0.34, 0.22, 0.13, and 0.10, respectively. Conversely, elevation (0.06), aspect (0.07), and NDVI (0.08) have the least impact on erosion in the spring season. Slope, with a score value of 3, is moderately preferred over aspect and elevation. Over 716 km2 of the area falls within slope categories between 15° and 40°. Field observations high- lighted that these slopes, due to the presence of soil and agricultural development, are particularly prone to erosion. Consequently, a value of 8 is assigned to these sub-criteria in the weighted overlay tool. Lithology is moder- ately preferred over all factors except precipi- tation. Field observations confirmed various forms of erosion on loess-covered surfaces, in- dicating that loess is highly susceptible to ero- sion even with minimal rainfall. Thus, loess is assigned a value of 8 as a sub-criterion of lithology. In the spring season, precipitation is moderately to strongly preferred over as- pect, NDVI, and drainage density (see Table 3). The highest precipitation values in the spring season (90–100 mm, and >100 mm) receive a score value of 9 in the weighted overlay tool, indicating the highest importance in erosion. To validate the assigned values, the consist- ency ratio was computed, yielding a spring season consistency ratio of 0.06, affirming the correct assignment of weights to the factors. The autumn season According to Table 4, precipitation (0.26), li- thology (0.23), and slope (0.14) emerge as the most influential factors contributing to ero- sion in the autumn season. Conversely, NDVI Table 2. Random consistency index (RI) values n RI n RI n RI 1 2 3 0.00 0.00 0.58 4 5 6 0.90 1.12 1.24 7 8 9 1.32 1.41 1.45 Table 3. Pairwise comparison table of the spring season Pairwise comparison Slope Aspect Lithology Elevation NDVI Precipitation Drainage density Weight Slope Aspect Lithology Elevation NDVI Precipitation Drainage density 1 1/3 3 1/3 1 3 1/2 3 1 3 1 1/2 4 2 1/3 1/3 1 1/3 1/3 3 1/3 3 1 3 1 2 3 2 1 2 3 1/2 1 4 2 1/3 1/4 1/3 1/3 1/4 1 1/4 2 1/2 3 1/2 1/2 4 1 0.13 0.07 0.22 0.06 0.08 0.34 0.10 CR: 0.06 Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.348 and drainage density, with weight values of 0.07 and 0.08, respectively, exhibit the least in- fluence on erosion during the autumn season. Aspect and elevation, carrying weight values of 0.12 and 0.10, prove to be more impactful than NDVI and drainage density, yet less sig- nificant than precipitation, lithology, and slope. In terms of preference, lithology and precipita- tion are moderately favoured over aspect and NDVI. Notably, slope, with a score value of 3, is moderately preferred in comparison to drain- age density. Given the semi-arid climate of Kalat-e-Naderi region, the erosion in the region can be influenced even by minimal rainfall. The primary agents of erosion during the autumn season are the areas covered by loess, coupled with precipitation and steep slopes. The con- sistency ratio for the autumn season (0.05) as- sures the reliability of the assigned weights. The winter season Precipitation, lithology, slope, and drainage density emerge as the most impactful factors on erosion during the winter season, with re- spective weight values of 0.33, 0.23, 0.14, and 0.10 (Table 5). Elevation, aspect, and NDVI carry lower weights of 0.07, 0.07, and 0.06, indicating their relatively lesser influence on erosion. Pre- cipitation, with score values of 3 and 4, is mod- erately to strongly preferred over aspect and NDVI, and moderately preferred over slope, lithology, elevation, and drainage density. As depicted previously, the Archangan and Kalat basins receive the highest amount of precipita- tion during the winter season (see Figure 3, h), while the spring season precipitation is distrib- uted almost evenly across the four basins (see Figure 3, f), with Archangan, Kalat, and Chah- chaheh being more prominent. For the weighted overlay tool, the assigned value for the high- est amount of precipitation (80–90 mm) is 8 (Table 6). Furthermore, lithology, scoring 3, is moderately preferred over slope, aspect, eleva- tion, NDVI, and drainage density. Recognizing the susceptibility of loess to erosion, it is assigned a value of 8 in the weighted overlay tool (see Table 6). The winter season’s consistency ratio of 0.04 falls within an acceptable range, affirming the reliability of the assigned weights. The calculated consistency ratio for each factor’s sub-classes, as presented in Table 6, is below 10 percent (< 0.1), meeting the accept- able threshold. Subsequently, erosion sus- ceptibility maps were generated in ArcGIS 10.3 using the weighted overlay tool. Values ranging from 1 to 9 were assigned in the tool based on the weight assigned to each sub- class. The Weighted Overlay Tool operates according to Equation (4) (Feizizadeh, B. et al. 2014; Arabameri, A. et al. 2018; Kahsay, A. et al. 2018; Tairi, A. et al. 2019; Boufeldja, S. et al. 2020; Aslam, B. et al. 2021), wherein the dataset is multiplied by its weight, and the sum of all results yields the erosion sus- ceptibility (ES) value for each pixel. ES = [(Sl·W) + (As·W) + (Li·W) + (El·W) + (NDVI·W) + (Pr·W) + (Drd·W)], where Sl is the slope, As is the aspect, Li is the lithology, El is the elevation, Pr is the pre- cipitation, Drd is the drainage density, and W Table 4. Pairwise comparison table of the autumn season Pairwise comparison Slope Aspect Lithology Elevation NDVI Precipitation Drainage density Weight Slope Aspect Lithology Elevation NDVI Precipitation Drainage density 1 2 2 1/2 1/2 2 1/3 1/2 1 3 1 1/2 3 1 1/2 1/3 1 1/3 1/3 2 1/3 2 1 3 1 1/2 2 1/2 2 2 3 2 1 3 1 1/2 1/3 1/2 1/2 1/3 1 1/2 3 1 3 2 1 2 1 0.14 0.12 0.23 0.10 0.07 0.26 0.08 CR: 0.05 (4) 349Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. Table 5. Pairwise comparison table of the winter season Pairwise comparison Slope Aspect Lithology Elevation NDVI Precipitation Drainage density Weight Slope Aspect Lithology Elevation NDVI Precipitation Drainage density 1 1/3 3 1/3 1/2 3 1/2 3 1 3 1 1/2 4 2 1/3 1/3 1 1/3 1/3 3 1/3 3 1 3 1 1 3 2 2 2 3 1 1 4 2 1/3 1/4 1/3 1/3 1/4 1 1/3 2 1/2 3 1/2 1/2 3 1 0.14 0.07 0.23 0.07 0.06 0.33 0.10 CR: 0.04 Table 6. Weight, consistency ratio (CR), and assigned values to the weighted overlay tool of all sub-classes of each factor Criteria CR Sub-criteria Weights Assigned values to weighted overlay tool Slope 0.007 0–5° 5–10° 10–15° 15–20° 20–25° 25–30° 30–35° 35–40° >40° 0.020 0.031 0.044 0.177 0.177 0.177 0.177 0.177 0.020 1 2 3 8 8 8 8 8 1 Aspect 0.030 Flat North Northeast East Southeast South Southwest West Northwest 0.021 0.074 0.074 0.035 0.222 0.222 0.222 0.035 0.095 1 5 5 2 7 7 7 2 5 Lithology 0.000 Solid rocks Loess 0.111 0.889 4 8 Elevation 0.006 460–750 750–1,040 1,040–1,330 1,330–1,620 1,620–1,910 1,910–2,200 2,200–2,490 2,490–2,780 0.030 0.135 0.135 0.135 0.231 0.231 0.051 0.051 1 5 5 5 6 6 2 2 Drainage density 0.010 0.02–0.52 0.52–1.02 1.02–1.52 1.52–2.02 2.02–2.52 2.52–3.02 3.02–3.70 0.041 0.061 0.101 0.113 0.169 0.258 0.258 1 2 3 3 4 5 5 Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.350 Table 6. Continued Criteria CR Sub-criteria Weights Assigned values to weighted overlay tool NDVI (spring) 0.003 -0.31–0.0 0.0–0.1 0.1–0.2 0.2–0.3 0.3–0.4 0.4–0.5 0.5–0.6 0.6–0.7 0.7–0.83 0.043 0.223 0.223 0.137 0.075 0.075 0.075 0.075 0.075 1 4 4 3 2 2 2 2 2 NDVI (autumn) 0.006 -0.56–0.0 0.0–0.1 0.1–0.2 0.2–0.3 0.3–0.4 0.4–0.5 0.5–0.6 0.6–0.7 0.7–0.86 0.075 0.140 0.140 0.271 0.075 0.075 0.075 0.075 0.075 1 2 2 3 1 1 1 1 1 NDVI (winter) 0.004 -0.37–0.0 0.0–0.1 0.1–0.2 0.2–0.3 0.3–0.4 0.4–0.5 0.5–0.6 0.6–0.7 0.047 0.241 0.241 0.146 0.081 0.081 0.081 0.081 1 4 4 3 2 2 2 2 Precipitation (spring) 0.090 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 >100 0.015 0.021 0.031 0.047 0.071 0.107 0.162 0.239 0.307 2 3 4 5 6 7 8 9 9 Precipitation (autumn) 0.000 0–10 10–20 20–30 0.250 0.250 0.500 1 1 2 Precipitation (winter) 0.030 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 0.024 0.033 0.048 0.071 0.106 0.157 0.231 0.331 1 2 3 4 5 6 7 8 represents the weight value of each factor. In this study, Equation 4 is applied three times as each factor’s weight has three different values associated with three distinct seasons. Figures 4, 5, and 6 depict the final erosion sus- ceptibility maps for each season. Employing the natural break method (Shahabi, H. and Hashim, M. 2015; Saha, S. et al. 2019), the maps 351Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. Fig. 4. AHP-based erosion susceptibility map of the spring season. Source: Authors’ own elaboration. Fig. 5. AHP-based erosion susceptibility map of the autumn season. Source: Authors’ own elaboration. Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.352 were reclassified into six categories: insignifi- cant, very low, low, moderate, high, and very high (Ebhuoma, O. et al. 2022). It is noteworthy that, owing to the seasonal variation in the im- pact of each factor, the erosion susceptibility maps encompass different categories. The very high susceptibility class in the spring map (see Figure 4) is predominantly distributed in Chahchaheh and Kalat basins, covering an area of 2.511 km2 in the study area. In contrast, the areas with very low erosion levels (259.59 km2) are mainly lo- cated in the central and north-eastern parts of the Qaratigan basin. The distribution of each erosion susceptibility class in the spring season is detailed in Table 6. The low (1,023.09 km2), and moderate (869.37 km2) erosion susceptibility classes cover the ma- jority of the studied basins in the spring season. High erosion susceptibility areas are minor in the Qaratigan basin during the spring season. In Kalat and Chahchaheh basins, the high erosion susceptibility area is primarily in the central section, while in the Archangan basin, the south-western part is mainly in the high erosion susceptibility class. Overall, 105.615 km2 of the study area is classified as having high erosion suscep- tibility in the spring season. The spring season erosion susceptibility map highlights that regions with the highest susceptibility to erosion are mainly associated with high precipitation values, loess cover, and steep slopes. Conversely, regions with the lowest susceptibility to erosion correspond to gentle slopes and lower precipitation values. The winter season’s erosion susceptibil- ity map (see Figure 6) was categorized into five classes, ranging from insignificant to a high level. The Chahchaheh and Qaratigan basins are primarily covered with the very low (1,077.80 km2) and low (743.38 km2) classes, while the other two basins (Kalat and Archangan) are dominated by moder- ate (382.28 km2) and high (36.73 km2) classes (see Table 7). As shown in Figure 6, the areas Fig. 6. AHP-based erosion susceptibility map of the winter season. Source: Authors’ own elaboration. 353Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. with moderate and high erosion potential in winter coincide with the region covered by loess, which is very sensitive to water ero- sion. Thus, precipitation and lithology can be considered as the most dominant factors in the overall erosion process. Discussion Based on the pairwise comparisons (see Tables 3, 4, and 5), erosion has been more significantly influenced by precipitation and lithology than any other factors, as they con- sistently exhibit higher weights in all three pairwise comparison tables for each studied season. The increased impact of precipita- tion and lithology can be attributed to the relatively intense yet short downpours in the study area, leading to flash floods, rill and sheet erosion, especially on unresistant and unvegetated surfaces such as loess. Ghahra- man, K. and Nagy, B. (2023) have also report- ed intense rainfall in short time periods in arid and semi-arid regions of northeast Iran. Field observations indicated that both ac- tive and inactive agricultural lands have been undergoing erosion (Photo 1, a, b). This ob- servation aligns with our soil erosion maps, where high-susceptibility areas correspond to the location of agricultural lands. The in- creased vulnerability of agricultural lands to erosion can be attributed to their location on loess deposits, as well as human activities like cultivation and plowing, making the sur- face more prone to erosion (Beniston, J.W. et al. 2015; Zhang, J. et al. 2019). According to the erosion susceptibil- ity maps, the region around Gerow village falls into the high erosion susceptibility cat- egory in winter and spring seasons, and the moderate category in the autumn season (see Figures 4, 5, and 6). Field observations confirm the presence of significant erosion- al features near Gerow village, including loess sinkholes, gully erosion, and suffusion (Photo 1, a, d, and e). The occurrence of sink- holes, even in agricultural lands, can be pri- marily attributed to the presence of water, especially in the form of precipitation, and li- thology. In loess-covered regions, subsurface cracks allow rainfall to penetrate, extending existing fissures and washing away mate- rial, leading to the formation of sinkholes, gullies, and suffusion. It is noteworthy that field observations indicate the presence of small and occasionally large pebbles in the loess around Gerow. The existence of these materials in the loess suggests that, in this area, sediment/material transportation is not solely occurring through wind processes; water transportation has also been active in these areas. In other areas classified as moderately to highly erosion-susceptible, such as the moun- tain slopes upstream of the abandoned agri- cultural lands near Gerow village, erosion has led to the exposure of bedrock (Photo 1, c). The presence of exposed bedrock highlights the significance of erosion in the study area. Large- scale erosional processes, including landslides, were also observed during field surveys (not in loess-covered sections) (Photo 1, b), under- scoring the importance of mass movements in the area. Authorities and planners need to take these factors into consideration when planning in these areas. Another visited site was Idelik village (see Figure 1), also classified in high and moderate erosion-susceptible classes on our maps. The predominant form of erosion in this area is the selective erosion of the folded structure Table 7. Area and percentage of each class of erosion Season Insignificant Very low Low Moderate High Very high km2 % km2 % km2 % km2 % km2 % km2 % Spring Autumn Winter – 12.00 0.88 259.59 1,370.45 1,077.80 259.59 1,370.45 1,077.80 11.48 60.65 47.69 1,023.09 591.79 743.38 45.27 26.19 32.90 869.37 26.29 382.28 38.47 1.16 16.91 105.61 – 36.73 4.67 – 1.62 2.51 – – 0.11 – – Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.354 Photo 1. Validation sites around Gerow village. Sinkhole in an abandoned agricultural land (a), landslides in the vicinity of active agricultural fields (b), exposed bedrock on the slopes and in fallow agricultural fields (c), gully development on the abandoned agricultural fields (d), and suffusion on loess walls caused by CaCO3 dissolution (e). Photos taken by the authors. 355Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. of the mountains by water and mass move- ments, resulting in steep walls from the more resistant layers and less steep slopes on the less resistant layers (Photo 2, a). Although this type of erosion does not directly impact farmlands in the study area, it holds signifi- cance from a hazard perspective. Our field observations revealed gully erosion, as well as topples and slides, especially around the rice fields located on loess-covered lands (Photo 2, b). Additional erosional processes, such as solifluction and landslides, were observed on the slopes, exposing the un- derlying materials by removing the topsoil. This type of erosion resembles a minor mass movement, evident on the slopes. According to our maps, the regions around the “Charam now” village are classified as Photo 2. Validation sites around the Idelik village showing erodible layers on the erosion resistant underlying layer on the slopes (a), and columnar blocks being detached from the loess mass (topples) on the loess walls (b). Photos taken by the authors. E Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.356 very low and low classes, while field ob- servations reveal substantial soil erosion in this area. This discrepancy can be primarily attributed to the scale of the lithological map, as some parts of this area were not classified as loess. However, field observations showed strong erosion on the loess near this village. According to the lithological input, the model considered the lithology as resistant, classify- ing this area in very low and low soil erosion susceptible classes. It is noteworthy that we have systematically checked the entire basins for this issue, and the “Charam now” vil- lage and its surrounding area were the only instances where the lithological map did not accurately indicate the units. In general, fallen loess walls, gully development, fallen riverbanks, and small-scale landslides were observed in this area. To examine remote or inaccessible areas of the “Charam now” village, we utilized Google Earth satellite imagery. Erosional fea- tures such as landslides and gullies near the village are illustrated in Photo 3. Local roads have been constructed to facilitate access to agricultural lands situated on the top of the hills. Instability of the slopes and increased chance of landslides in this area can also be related to anthropogenic activities such as road construction. The majority of agricultural lands in the Kalat basin are located on slopes, with some fields occupying less inclined gradients. Ploughing these agricultural fields, in con- junction with the loess composition of the land and steeper slopes, has exacerbated erosion in this region, as evidenced by our soil erosion maps. With respect to cultivation, erosional processes in these loess-covered areas have the potential to adversely impact crop production. Harris, H.L. and Drew, W.B. (1943) have demonstrated that unerod- ed loess fields provide an environment that is 50–100 percent more favourable for plant growth than eroded loess fields. Observations of gully and sheet erosion, subsurface ero- sional features, and landslides in the Kalat basin indicate the prevalence of strong ero- sional processes in this area (Photo 4). Considering the inherent limitations of the availability of relevant lithological map, namely its 1:250,000 scale and lack of de- tailed information and given our research focus on loess-covered regions, we opted to categorize the lithological map into two classes: loess and solid rocks. Solid rocks en- compass both erodible and resistant rocks, such as limestone, shale, sandstone, and marl. Consequently, in the weighted overlay tool, loess was assigned a score of 8 due to its erosivity and prevalence in the study area, while solid rocks were assigned a score of 4, reflecting their lower susceptibility to ero- sion owing to the presence of resistant rocks (~ 740 km2). This approach aligns with other studies that have employed different weights for resistant and erodible rocks in their AHP models (Arabameri, A. et al. 2018; El Jazouli, A. et al. 2019; Bozali, N. 2020). In addition to precipitation and lithology, the influence of slope on soil erosion is sig- nificant. This is primarily attributed to the impact of slope on flow accumulation, run- off velocity, and surface instability (Rahmati, O. et al. 2017). Multiple studies have demon- strated that even gentle slopes can be vul- nerable to erosion and gully development (Le Roux, J.J. and Sumner, P. 2012; Lukić, T. et al. 2018; Arabameri, A. et al. 2019; Phinzi, K. et al. 2021). Vegetation cover in the study area has been subjected to overgrazing by livestock, such as goats and sheep, which can exacerbate soil erosion by removing protective vegetation cover. However, in our study area, we ob- served that animal footpaths have created micro-terrace structures on the slopes, while other slopes have remained intact from this process (Photo 5). This observation is con- sistent with the findings of Afrah, H. et al. (2010), who reported that micro-terraces in the Golestan Province loesses have remained unchanged in terms of morphological struc- ture for an extended period. This preserva- tion is primarily attributed to the fact that animals have consistently used the old paths for grazing, leaving the vegetation cover in- tact in other parts of the rangeland. 357Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. Photo 3. The Google Earth image showing erosional features such as gullies and landslides as well as anthro- pogenic features such as agricultural lands and a road in the vicinity of the “Charam now” village. Authors’ own elaboration based on the Google Earth image. Photo 4. Validation sites in the vicinity of the Kalat city showing landslide (a), deep gully erosion (b), agricultural lands developed on loess-covered areas (c), and sinkholes created by the subsurface erosion on loess-covered areas impacting urban infrastructure (d). Photos taken by the authors. Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364.358 Conclusions This research comprehensively investigated erosion-susceptible areas within the major basins of Kalat-e-Naderi county, situated in northeast Iran. The study’s focal point was the dual nature of loess, serving as fertile ground for agriculture while presenting vulnerability to erosion. Leveraging the AHP method with 7 key parameters, includ- ing slope, aspect, elevation, lithology, NDVI, drainage density, and precipitation, we suc- cessfully delineated the spatial distribution of erosion-susceptible regions. The integra- tion of seasonal data, accounting for varia- tions in precipitation and vegetation cover, allowed for the creation of detailed erosion susceptibility maps. Key factors influencing water erosion, as identified through pair- wise comparison tables, include precipita- tion, lithology, and slope. These findings have been visually represented on erosion susceptibility maps, highlighting areas prone to erosion during different seasons. Notably, these vulnerable regions exhibit a discernible correlation with the three primary factors – precipitation, lithology, and slope – while the convergence of the additional four factors amplifies erosion. Given the semi-arid climate of the region and the loess’s heightened erodibility in wet seasons with higher precipitation, the research focused specifically on water ero- sion. Validation through field observations affirmed the accuracy of the erosion suscep- tibility maps where erosion-susceptible areas in the map correspond to the observed areas in the field. Despite limitations, such as the absence of comparable studies in neighbour- ing areas for validation, challenges in reach- ing erosion sites, and data constraints, the results of this method are invaluable. They offer actionable insights for policymakers and planners, facilitating effective damage mitigation and the formulation of preventa- tive strategies. Photo 5. Micro-terraces created by livestocks on the slopes of the study area. Photo taken by Nooshin Nokhandan, F. 359Nooshin Nokhandan, F. et al. Hungarian Geographical Bulletin 72 (2023) (4) 339–364. Acknowledgements: We express our gratitude to the Stipendium Hungaricom program for their support. Additionally, we extend our thanks to the Erasmus + 20% project (2021-1-HU01-KA131-HED-000003804) for their financial assistance towards our fieldwork expenses. We would also like to acknowledge Professor Farhad Khormali for his invaluable scientific support and his dedication in facilitating our field observations. We also would like to thank the reviewers for their comments, which improved the quality of our work. REFERENCES Abbas, S., Dastgeer, G., Yaseen, M. and Latif, Y. 2022. 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