key: cord-264152-0jr5nlli
authors: Bhattacharya, Raj Kumar; Chatterjee, Nilanjana Das; Das, Kousik
title: Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models
date: 2020-05-16
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.139474
sha: 
doc_id: 264152
cord_uid: 0jr5nlli

Abstract Kangsabati basin located in tropical plateau region faces multiple problems of soil erosion susceptibility (SES), soil fertility deterioration, and sedimentation in reservoirs. Hence, identification of SES zones in thirty-eight sub-basins (SB) for basin prioritization is necessary. The present research addressed the issue by using four multi-criteria decision-making (MCDM) models: VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), technique for order preference by similarity to ideal solution (TOPSIS), simple additive weighing (SAW), compound factor (CF). To determine the best fitted method from MCDM for erosion susceptibility (ES), a comparison has been made with Soil and Water Assessment Tool (SWAT), where fifteen morphometric parameters were considered for MCDM, and meteorological data, soil, slope and land use land cover (LULC) were considered for SWAT model. Two validation indices of percentage change and intensity change were used for evaluation and comparison of MCDM results. With SWAT model performance, SWAT calibration and uncertainty analysis programs (CUP) was used for sensitive analysis of SWAT parameters on flow discharge and sediment load simulation. The results showed that 23, 16, 18 SB have high ES; therefore they were given 1 to 3 ranks, whereas 31, 37, 21SB have low ES, hence given 38 to 36 rank as predicted by MCDM methods and SWAT. MCDM validation results depict that VIKOR and CF methods are more acceptable than TOPSIS and SAW. Calibration (flow discharge R2 0.86, NSE 0.75; sediment load R2 0.87, NSE 0.69) and validation (flow discharge R2 0.79, NSE 0.55; sediment load R2 0.79, NSE 0.76) of SWAT model indicated that simulated results are well fitted with observed data. Therefore, VIKOR reflects the significant role of morphometric parameters on ES, whereas SWAT reflects the significant role of LULC, slope, and soil on ES. However, it could be concluded that VIKOR is more effective MCDM method in comparison to SWAT prediction.

Soil is the most sustaining natural resource for all living organism, and it determines geomorphic processes widely (Keesstra et al., 2016; Ameri et al., 2018; Hembram and Saha, 2018) . Sustainable agricultural development and natural resource utilization both are largely determined by soil erosion; furthermore, this erosion is controlled by several hydrological functions in the watersheds worldwide (Molla and Sisheber, 2017;  of morphometric parameters at sub-basin level. In SWAT model, various weather datasets including temperature, humidity, wind speed, solar radiation at 12 grid points in Kangsabati basin were automatically collected from climate forecasting system reanalysis (CFSR) world climatic database (http://globalweather.tamu.edu/). While rainfall database has been modified with thirty five years average dataset of sixteen local rain gauge stations in this basin as provided by Indian Meteorological Department (IMD), for getting better accuracy of model. Average rainfall datasets were further fed into SWAT database through the incorporation with nearest CFSN grid points. In term of spatial resolution scale, temperature data was only available with coarser resolution scale of 1"×1" (approximately 110km×110km), but other weather databases were available with finer resolution scale of 0.25"×0.25" (approximately 27.5km×27.5km), respectively (Himanshu et al. 2019) . 

Data processing phase involved with two major steps, where first step comprises on scanning georeferences and image rectification. After that, rectified images were converted into resembled sheet, passes with masking and mosaic processes, derived from Universal Transverse Mercator Projection of WGS 1984 (45°N) using Arc GIS 10.2. Second step comprises of boundary demarcation for extraction of stream number and its contributing area of specific outlets, flow direction, and flow accumulation in each DEM pixel, using ArcSWAT tools. In order to attain high accuracy value, SRTM DEM was used for comparison and clarification of sub-basin boundary with topographical maps, and extracted three dominant morphometric aspects i.e. basin shape, linear and landscape. Table 1 presents the measuring techniques of nineteen major morphometric parameters following such literature. After extracting morphometric parameters, four MCDM methods of TOPSIS, VIKOR, SAW, and CF were applied to predict the sub-basins priority for estimating ES in Kangsabati basin at sub-basin level, retrieved from their validated results of percentage change and intensity change. In contrary, SWAT model was applied to estimate the flow discharge and sediment load, as well sediment yield for the assignment of actual sub-basin priority rank, and then make a comparison between effective MCDM methods and SWAT model.

Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is used for comprising of alternative conflicting criteria based on ranking sets under optimizing complex systems, introduced by Opricovic and Tzeng (2004) . Performance of VIKOR method as closeness of ideal is entirely depended on well organization following nine steps (Huang et al., 2009; Ameri et al., 2018) . Where Pij means element of normalized decision matrix and Rij mean i-th alternative sets in j-th criteria.

(iii) Third stage comprises of weight assignment for each requiring criteria using Analytical Hierarchical Process (AHP). VIKOR method also helps to re-evaluate all criteria, makes pair wise comparison, using Saaty's method (1977) in Expert Choice software.

(iv) After that, weighted normalized matrix has been computed through the multiplying normal matrix in each criterion weight, given by Huang et al. (2009) and Sanayei et al. (2010) as Eq. (2):

Where T ij means element of the weighted normalized decision matrix, P ij means element of normalized decision matrix and W j mean computed weight criteria given by AHP model.

(v) Fifth stage mainly involved with the determination of best value (Xj ) and worst value (X ̶ j ) of all standard criteria functions; if j=1, 2...n: where j-th criterion as most benefited criterion for the causes of maximum j-th value. This criterion becomes more relevant for this purpose. Then Xj =max ƒ ij , ƒ̶ ij = min ƒ ij .

(vi) Purpose of this stage is to compute maximum group utility index (Si) and minimum individual regret index of opponent group values (R i ) following respective Eq. (3) and (4):

Where w i mean jth criterion weight that means relative importance of criteria, V * j mean maximum X ij , and V * j mean minimum Vj . (vii) Computation the value of Q i following i=1, 2...m as Eq. (7) (El-Santawy, 2012):

Where S * mean minimum S j , S mean maximum S j , R * mean minimum of R i , R means minimum of R i , and V as introductory weight strategy of maximum group utility (S j ) and maximum criteria (R j ). Q value generally ranges from 0-1; in terms of range value, v> 0.5 denotes that trend value of Q index reaches in majority rule.

(viii) Alternative rank order has considered as sorting measurement from S, R and Q values, where suitable alternative values are assigned from least value of three major parameters.

(ix) Two conditions are used to determine the best alternative value from highest rank order of Q values as follow Eq.6 (El-Santawy, 2012; Ameri et al., 2018): C1 or Acceptable advantage:

J o u r n a l P r e -p r o o f 7 Where A 1 , A 2 means first and second alternative position in the ranking list; N is the number of alternative criteria.

C2 or Acceptable stability in decision making: In terms of satisfaction level, alternative rank values are assigned as rank 1 in Q, S and R or both of them. On the contrary or unsatisfactory level, ranking order of alternative value would be assigned as A 1 , A 2 ...Am where Am measured by following Eq. 7:

According to Tzeng (2004, 2007) , A 1 and A 2 must be selected for best solution, where outcome of C1 does not satisfy.

Technique for order preference by similarity to an ideal solution (TOPSIS) is a distance-based method, presented by Hwang and Yoon (2012) . Principle of TOPSIS indicates that measurement of alternative choice has been computed by Euclidean distance from shortest distance of positive ideal solution (PIS) and largest distance of negative ideal solution (NIS). In this context, closeness coefficient is used to express the results of PIS and NIS distances. In order to closeness coefficient value, preferred alternative value is taken as a higher coefficient value from a set of alternatives (Liou and Wang, 1992; Kannan et al., 2009) . This model has detected rating option following some calculation steps (Hwang and Yoon, 1981) :

(i) This step estimated normalized decision matrix (r ij ) as Eq. (8):

Where r ij presents normalized decision matrix element and a ij mean alternative performance of i-th under j-th criteria.

(ii) This step computed as weighted normalized decision matrix (V ij ) following Eq.9:

Where r ij mean normalized matrix elements and W j mean weight of j-th criteria (iii) This step computed the PIS and NIS using Eq. 10 and 11 (Ameri et al., 2018)

Where j mean benefitted criteria and J mean cost criteria, respectively.

(iv) This step computed as separation measures from ideal solution using Euclidean distance (n-dimension).

Separation from ideal distance of PIS and NIS measured as following Eq. 12 and 13

J o u r n a l P r e -p r o o f

(v) Purpose of the final step, is detect closeness coefficient of alternatives from ideal solution ranges 0 to 1 using Eq. 14. Superior values are estimated from higher relative closeness alternative values.

Where Cl i+ mean closeness coefficient, d i+ mean positive ideal solution (PIS) and d i-mean negative ideal solution (NIS).

This is another leading MCDM method, which assigned score in each option, and obtained by aggregating values in different criteria. Relative weights are taken for assigning each criteria score using decision maker (Hwang and Yoon, 1981; Sargaokar et al., 2010) . Simple Additive Weighting (SAW) model estimated rating option following consecutive steps as (i) Decision matrix is used for the determination of normalized initial matrix (R ij ) of j-th criterion as Eq.15

(Ameri et al., 2018):

Where X ij mean initial weights and M means criteria number (ii) This step involved mainly determined the relation as R ij = R ij min or R ij = R ij max Two different relation can be obtained from normalized weight, where minimum value in efficiency index represent as R ij =R ij min , and maximum value in efficiency index represent as R ij =R ij max .

(iii) Consistency index helps to determine as normalized value following Eqs. 16 (maximum efficiency index) and 17 (minimum efficiency index):

(iv) The purpose in this step is determining the weighted normalized decision matrix (V ij ) following Eq.18:

Where W j mean criteria weight of Analytical Hierarchical Process (AHP) model

(v) Final step of SAW model involved mainly data integration, acquired from final score value in each option (A i ) following Eq.19 (Ma et al., 1999; Ameri et al., 2018) :

Compound factor (CF) is an important MCDM methods, which is used for subject base conversion phenomena obtaining from two estimation of logical learning estimation and knowledge-driven framework codes based numerical estimation (Todorovski and Dzeroski, 2006; Ameri et al., 2018; Hembram and Saha, 2018) . In this study, CF method was applied for assignment of rank order in each estimated morphometric parameters that are Where R denote parameters rank value, pn denote watershed rank from each parameter CF is estimated rank order or erosion priority scale of sub-basin level from fifteen morphometric parameters in thirty-eight sub-basins of Kangsabati basin. According to Patel et al. (2012) and Balasubramanian et al. (2017) , high ES has corresponded with maximum compound value of areal and linear aspect, which is given rank 1 and second highest compound value is given rank 2 and others in same assigning manner. While high ES has corresponded with minimum compound value of shape aspect, which is given rank 1 and so on.

Percentage change and intensity change are uses for the assessment of validation performance and comparison among four MCDM methods results that are predicted SES at sub-basin level (Badri, 2003; Ameri et al., 2018) .

Percentage of changes is expressed as Eq. 21:

Where ΔP denote percentage of changes, N denote alternative numbers, and N constant denote alternative numbers of same rank

Intensity of changes is expressed as Eq.22:

Where ΔI denote intensity of changes, r1 denote first method of alternative rank, and r2 denote second method of alternative rank

Several predominant parameters like weather condition, topographic characteristic, soil properties, and land use land cover (LULC) were required for successful set-up and run the SWAT model on a monthly time-scale (1.1.2010-31.12.2015) using the ArcSWAT interface (Neitsch et al., 2005; Markhi et al., 2019) . Database of slope, soil and LULC distribution map over the thirty-eight sub-basins including 12387 HRUs are presented in Fig.3a , b, c. The unique combinations of LULC and soil type were separately represented by two different HRU.

5% of slope, 5% of soil and 5% of LULC were assigned as user threshold values for the determination of in HRU tool box. Other important data sets like weather databases including rainfall, temperature, and evaporation etc. were extracted from IMD and CFSR World weather gridded database. On the other hand, hydrological data sets of monthly flow discharge or outflow data and sediment load were collected from Mohanpur station, only available monthly flow discharge data set was and collected from Irrigation Office of Paschim Medinipur, but there is no record of any observed data on sediment load. In this context, total sediment load (Q T ) was computed from field observation database of nine fluvial and sediment hydraulic variables (average flow velocity, water depth, weight of specific water, kinematic viscosity of water, gravity acceleration, sediment diameter, weight of specific sediment, shear velocity, bed shear stress) in Mohanpur station. Total sediment load was calculated in ton unit using fluid weight of Ackers and White (1973) method (Bhattacharya et al., 2019a) ; furthermore, those results were comparing with the SWAT simulation results. Moreover, sediment load data sets helped in SWAT sensitivity analysis, including calibration and validation test on observed and simulated sediment load. Total Sediment load (Q T ) has measured from Eq. 23:

J o u r n a l P r e -p r o o f = .

(23)

Where Q denote water discharge, X denote sediment concentration

Sensitive analysis of SWAT parameters must be needed for the determination of significant contribution on model outputs that are measured by ranking method ( parameters are selected for the analysis of calibration and validation performance in SWAT model using p-value (Mittal et al., 2015) . P-value means fraction values of observed discharge data under 95 Percent Prediction Uncertainty (95PPU) band, which ranges from 0 to 1 (Vaghefi et al. 2013; Mittal et al., 2015) . In this study, Pvalue of <0.05 was considered for selection of sensitive parameters in calibration and validation analysis. To determine the calibration and validation of results, iteration steps were required to find out the optimum values for two different times (Yang et al., 2008) . Therefore, the calibration (1.1.2010-31.12.2013) and validation

(1.1.2014-31.12.2015) tests between observation and simulation data were applied to determine the model performance. In this context, Mohanpur gauge station was taken for this purpose to make a comparison between observed data and SWAT simulated results for both flow discharge and sediment load estimation.

Nash-Sutcliffe efficiency criterion (NSE) was used for the determination of calibration result in observation data (Nash and Sutcliffe, 1970) . This well-known statistical criterion provided the coefficient determination (R 2 ), Nash-Sutcliffe efficiency criterion (NSE), and percent bias (PBIAS) values that were obtained from Eq.24, 25, 26, respectively.

Where Y sin denote simulated value, and Y obs denote observed value; Z obs refer to mean of observed value as n;

and Z sin refer to mean of simulated values as n

J o u r n a l P r e -p r o o f

Basin morphometric parameters play significant role to determine the earth system processes in surface, incorporating geomorphology, geology and hydrological sets-up (Ifabiyi and Eniolorunda, 2012; Balasubramanian et al., 2017) . Moreover, relief and drainage system in the entire basin, have great influence on soil erosion vulnerability, whereas three major morphometric aspects like linear, relief and shape, have control the runoff volume and infiltration capacity (Sharma et al., 1985; Hembram and Saha, 2018) . This basin area is extended to 6480 km 2 with perimeter of 3306 km 2 , and texture with 6971 stream number, including 833 km 2 stream lengths. 5321 first stream order were identified in high erosion prone sites in the entire catchment area.

According to erosion rate, fifteen morphometric parameters were taken from thirty-eight sub-basins to assign the (2012) denoted that all shape parameters are inversely correlated with ES. In order of erosion priority rank, this study considered maximum value of mean bifurcation ratio and relief ratio that were assigned as highest rank and minimum value of those parameters assigned as lowest rank.

In this research, weight assignment in each criterion was computed from final matrix table that was prepared from fifteen morphometric parameters using AHP method. According to Saaty (1977) , weight assignment becomes accepted; if incompatibility rate in a final matrix is lower than 0.1. In this study, consistency index (CI) and randomness index (RI) in comparison matrix table is 0.08 and 1.59, respectively; however, incompatibility rate of final matrix become 0.054; therefore, weight assignment of effective morphometric parameters on ES are within acceptable range (Table 2 ). In addition, weight assignment of fifteen morphometric parameter in matrix 

After the weight assignment, all morphometric parameters were calculated for the preparation of decision matrix and data normalization that are essential for priority rank of VIKOR, TOPSIS, SAW, and CF models. In this study, fifteen morphometric parameters were used for the computation of priority rank in each sub-basin, where all data sets were normalized to prepare the rank value in four different MCDM methods. Linear vector method J o u r n a l P r e -p r o o f was used for the computation of data normalization in TOPSIS model , whereas data normalization of VIKOR and SAW models were computed following Eqs. 1-7, 15-19 (Ameri et al., 2018) . In VIKOR model, best and worst values in each criterion helps to normalization of regret levels; however, based on perspective of regret theory, only best values in each criterion play dominant role to determine the regret levels, and worst values in each criterion play dominant role to determine the effective role of normalized S and R values on regret levels (Huang et al., 2009) . In this study, best and worst values are calculated in Table 3 using Eqs. 1-2, whereas utility index, regret index both were determined the priority rank as ascending order in the thirty-eight sub-basins using Eqs. 3-5 (Table 3 ). In TOPSIS model, positive value, negative value and Euclidean distance between them were calculated using Eqs. 8-13 and is presented in (Fig. 5a ). In respectively. It is a point that sub-basins with more complex score have high sensitivity to erosion, and subbasins with least final score have low sensitivity to erosion (Fig.5c) (Fig. 5a, b, c) . On the other hand, based on compound values from selected morphometric parameters, CF model has classified into four categories i.e. very high (>100), high (50-75), medium (25-50) and low (>25). In this study, only two CF categories of moderate and low are identified in the thirty-eight sub-basins (Fig.5 d) 

In general, SWAT model performance is evaluated by three different analysis of sensitivity, calibration, and validation from assigned parameters (Welde, 2016) . In this study, sensitive analysis was done in a Latin hypercube sampling at 12 intervals, where twenty parameters were considered for flow discharge and sediment load estimation. 1000 iterations were used for the determination of following parameters, where 500 iterations for flow parameters (10×50 per iteration), and 500 iterations for sediment parameters (10×50 per iteration) were required with the help of SWAT-CUP software. Table 7 denotes maximum, minimum and fitted values of ten sensitive parameters for flow discharge simulation. Table 8 denotes maximum, minimum and fitted values of ten parameters for sediment load simulation. Sensitivity analysis reveals that CN2.mgt (number of SCS runoff curve in moisture condition II) is more sensitive followed by GWQMN.gw (occurring of return flow for threshold depth of water) in flow discharge calibration and validation. While R_SPEXP.bsn (exponent parameter for calculating sediment recent rained in channel sediment) is more sensitive followed by R__SLSUBBSN.hru (average slope length) in sediment load calibration, but HRU_SLP.hru (average slope steepness) is more sensitive followed by R_SPEXP.bsn in sediment load validation. After the successful run of calibration procedure on sensible parameters, Table 7 Therefore, based on all statistical indices of calibration and validation, simulated results are considered as goodness of fit with observed data.

Suspended sediment load and its movement in stream has entirely depended on several hydraulic variables like stream discharge, watershed slope, including flow and sediment regime characteristics (Sridhar et al., 2018; Bhattacharya et al., 2020a) . Huge sediment load helps to accumulation of sediment yield at sub-basin level where SWAT model is applied to estimate the amount of yield following some considerable hydrological parameters (Welde, 2016 Figure 7 showed that most of the basin area come under low to medium sediment yield priority class, whereas high and very high sediment yield classes are mainly concentrated at outlets or confluence points. Generally, absences of flow discharge, low drainage density and lower catchment area helps to generate low sediment yield, while presence of confluence points, high drainage density and large catchment area helps to accumulate of high sediment yield (Bhattacharya et al., 2020a) . In terms of ES at sub-basin level, monthly sediment yield priority is classified into four classes i.e. According to assigned rank of simulated sediment yield, very high erosion priority class mainly concentrated in SB 13, 16, 18, 19, 23 , while high erosion priority class concentrated in SB 2, 6, 14, 17, 20, 22, 25, 26, 28 and 31, respectively (Fig. 8 ). In addition, SB 1, 3, 4, 5, 7, 8, 9, 10, 12, 15, 24, 29, 30, 32 and 34 having monthly sediment yield of 0.70-0.93 m ton/ha comes under moderate erosion priority class, and SB 1, 3, 4, 5, 7, 8, 9, 10, 12, 15, 24, 29, 30, 32 and 34 having monthly sediment yield of 0-0.69 m ton/ha comes under low erosion priority class. Therefore, it is pointed that amount of sediment yield are fully dependent with ES level throughout the basin. classes throughout the basin. These susceptible classes are major sediment source sites where huge sediment load supply helps to vast accumulation of sediment yield. Therefore, VIKOR method is an effective MCDM model, which helps to identify the erosion-prone sites, sediment load supply, and sediment yield accumulation throughout the basin.

Morphometric parameters are not considered as only key factors for the determination of ES, but other dominant geo-environmental parameters such as LULC, soil erodibility, slope length-steepness, lithology, geomorphic setup, etc. are also important (Chauhan et al.,2016; Bhattacharya et al., 2019b) . In comparison between MCDM methods and SWAT model, effective MCDM methods are helpful to identify the significant role of morphometric parameters on ES; furthermore, linear and aerial aspects are positively correlated with ES (Nooka Ratnam et al., 2005; Bhattacharya et al., 2019b) , but shape aspects are inversely correlated with ES (Patel, 2012 (Patel, , 2013 . In contrary, when preparing sediment yield distribution map from seven considerable factors: LULC, slope, soil, ground water depth, run off volume, precipitation lapse rate and channel hydro-morphogenetic

properties (Markhi et al., 2019; Himanshu et al., 2019) , SWAT model also helps to assess ES in each sub basin.

Therefore, in this study, LULC and morphometric parameters are considered for ES assessment following the validation performance of MCDM and SWAT models, respectively. In terms of response to erosion priority, land covers like laterite with barren land, wasteland, and settlement are positively related to ES, while agricultural land, dense forest, pasture land are inversely related to ES (Altaf et al., 2014) .

Based on MCDM validation performances; TOPSIS, SAW and CF does not give satisfactory results due to disadvantages of unavailable data sets for all decision-making problems and assumption based relative weight assignment in each variables (Khosravi et al., 2019) . In contrast, VIKOR method has given better results than other MCDM methods, which are validated by SWAT model for its advantages: hierarchical formulating issue, pair wise comparison using expert quantitative and qualitative knowledge, and assessment of compatibility and incompatibility decision (Saaty, 1980; Arabameri et al., 2019) .

SWAT and VIKOR helps to understand the role of LULC and morphometric properties on ES in Kangsabati basin. In spite of dense vegetation cover and pasture land, SB 13, 16, 18, 23 are more susceptible to erosion due to maximum values of linear morphometric parameters (mean bifurcation ratio, drainage density, drainage texture, stream frequency) and relief morphometric parameters (slope, ruggedness index, relief ratio, basin relief) as well as minimum values of basin form parameters (form factor, shape factor, compact coefficient and circular ratio) (Fig.9 ). In contrast, despite the presence of barren land, double crop practice and dense settlement, of 0.8073 m ton/ha. Thus, based on the above discussion, it can be said that among all the available MCDM methods for ES, VIKOR model is more pragmatic for ES in respect to simulated sediment yield rank as predicted by SWAT. In order to ES and sediment yield deposition at sub-basin level, in this study demonstrated that VIKOR is fittest MCDM model for suitable selection of best and worst values from morphometric parameters using normalize decision matrix of AHP, maximum group utility index, minimum regret index of opposite group value, as well as assessment of compatibility and incompatibility decision among effective morphometric parameters. Therefore, VIKOR model is useful MCDM method to prepare rational sub-basin prioritization from linear normalize ranking of all morphometric parameters. Moreover, VIKOR method helps to identify the five critical sub-basins that are most sensible to erosion due to presence of responsible morphometric parameters in line with the results of Bhattacharya et al. (2020a) . On the other hand, SWAT parameters helps to determine the significant role of morphometric parameters on ES in response to sediment yield deposition throughout the basin. Therefore, it can be stated that morphometric parameters are considered as crucial contributing parameters for ES generation in this plateau fringe basin, followed by LULC patterns, climate, and soil characteristic. These agreements are validated with the findings of Biswas et al. (1999) , Hembram and Saha (2018) , Sridhar et al. (2018) . Based on literature review in introduction, previous researchers have presented sub-basin prioritization in order to ES using MCDM methods; however, there is no hydrological model to measure the significant role of hydrological parameters like runoff volume, sediment load, sediment concentration, etc. on sub-basin prioritization. In this context, there is research gap in previous studies of sub-basin prioritization. Present study tried to address this research gap using a comparison between MCDM and SWAT models. In term of ES, present study humbly argues that effective morphometric and hydrological parameters are prerequisite for sub-basin prioritization in any region in the world as assigned by VIKOR and SWAT models. Moreover, both models reveal that all morphometric and hydrological parameters are not equally significant in every sub-basin as they have own characteristics.

The findings in this research might help to identify critical sub-basins where ES has been found most severe with the presence of responsible factors, thus VIKOR and SWAT could provide important tools for planner or policy maker to take rational strategies for soil and water conservation in watershed management.

The present study demonstrated that ES is a sensitive criterion to determine the sub-basin prioritization using a and suggestions which were immensely benefitted to improve our manuscript during revision process.

Abbaspour 

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Evolution of drainage systems and slopes in badlands at Perth Amboy

Progress in the application of landform analysis in studies of semiarid erosion

Increasing farmer's income and reducing soil erosion using intercropping in rainfed maize-wheat rotation of Himalaya

Assessment and Planning for Integrated River Basin Management using Remote Sensing, SWAT Model and Morphometric Analysis (Case study: Kaddam River Basin, India)

Dynamic basis of geomorphology

Quantitative geomorphology of drainage basin and channel networks. Handbook of applied hydrology

Integrating knowledge-driven and data-driven approaches to modeling

Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions:Karkheh River Basin in Iran

Modelling sediment fluxes in the Danube River Basin with SWAT

Identification and prioritization of subwatersheds for land and water management in Tekeze dam watershed


