Abstract

Prioritizing watersheds based on erodibility holds significant importance, particularly in regions like the Tlawng basin, where the landscape is characterized by undulating hills and varying slopes along with annual heavy monsoon rains. This study involved conducting morphometric analysis and prioritizing 23 sub-basins within the Tlawng basin in Mizoram, utilizing Geographical Information System (GIS) techniques. ArcGIS software was employed to determine some fundamental morphometric parameters within each sub-watershed such as its basin area, perimeter, length of the basin, stream number, stream order, and stream length. Furthermore, the morphometric parameters pertaining to aerial and relief features were computed utilizing a range of established formulae. The findings reveal that the basin is characterized by a 5th order stream, with stream order 1 being the most prevalent. Subsequently, the Principal Component Analysis (PCA) was performed using the Statistical Package for the Social Sciences (SPSS) software to identify the principal components for prioritizing the sub-watersheds. Three parameters, namely stream frequency, compactness constant, and relative relief were identified as the principal components. Based on their ranking values, the compound parameter was computed, which was then used to assign the final rank to each sub-watershed in terms of erodibility. According to the findings, Sub-watershed 3, which had the lowest compound parameter value at 3.333, was assigned the top rank of 1, indicating its highest level of priority. Conversely, Sub-watershed 9 was given the lowest rank of 23, owing to its relatively higher compound parameter value of 22.333. The findings suggest that soil conservation efforts should initially be targeted at sub-watersheds with higher rankings, as they are relatively more susceptible to erosion and its related risks, and then implemented in the remaining watershed in the order of their priority. This prioritized approach will ensure the effective management and mitigation of soil erosion issues within the watershed.

1 Introduction

The susceptibility of soil to erosion is known as erodibility. Soil erosion involves the dislodgment, conveyance, and settling of the soil particles, as well as the segregation and replacement of rock aggregates to a new location through the agency of natural forces such as water and wind (Masselink et al. 2017). The process of soil erosion has been accelerated by anthropogenic actions such as deforestation and improper land management. Globally, soil erosion emerges as a significant ecological and agricultural predicament, with far-reaching economic, political, and social repercussions. India also faces significant challenges with soil erosion and land degradation, impacting a vast area of approximately 175 million hectares. This comprises 66% of the country's overall land area and includes both ongoing erosion and various forms of degradation such as ravines, gullies, shifting cultivation, salinity, and waterlogging (Gajbhiye et al. 2014). Moreover, with the increasing population growth in the country, and the land and water resources being finitely limited, the demand pressure on available land is ever increasing (Biswas et al. 1999). This makes it imperative to emphasize watershed management planning that employs effective techniques for conserving soil and water to mitigate erosion within the river catchments (Sharma et al. 2014).

Assessing morphometric parameters is crucial in watershed management planning. This analysis offers significant insights into soil properties, erosion characteristics, and land surface processes (Shekar and Mathew 2024). Such parameters are frequently used to understand erosion dynamics in watersheds where there is no direct measurement or insufficient data (Javed et al. 2011; Chowdary et al. 2013; Meshram and Sharma 2015). The process of morphometric analysis involves measuring and mathematically evaluating the structure, dimensions, and characteristics of Earth’s surface features (Clarke 1966). It quantitatively represents the drainage system by assessing various linear, aerial, and relief characteristics within a watershed (Horton 1945). The conventional approach to estimating these parameters is arduous and time intensive. In response, the remote sensing (RS) and geographical information system (GIS) approaches have gained widespread acceptance over the conventional method due to their flexibility and powerful capabilities in accurately assessing basins through the processing and evaluation of geospatial data. Numerous researchers have successfully showcased the application of RS and GIS approach in the morphometric assessment of watersheds (Chopra et al. 2005; Gajbhiye et al. 2014; Mangan et al. 2019).

However, when it comes to soil conservation efforts in a watershed, it is not viable to develop and manage the entire basin area all at once, owing to certain geo-environmental or economic factors, thereby necessitating the division of the basin into sub-basins or micro-basins according to its drainage characteristics (Meshram and Sharma 2015). Therefore, it is essential to check the vulnerability of all the sub-basins and prioritize accordingly through their morphometric parameters (Abdeta et al. 2020). The process of sub-watershed prioritization involves assigning distinct ranks and establishing a sequential order for various sub-watersheds, thereby determining their priority in carrying out certain soil conservation efforts (Uniyal and Gupta 2013). The ranking and prioritization processes through morphometric parameters have been successfully carried out using fuzzy analytical hierarchical processes (Rahaman et al. 2015), factor analysis (Praus 2005), cluster analysis, and Principal Component Analysis (PCA). Among these, PCA is the most extensively employed technique, known for its ease in identifying key components, which are instrumental in performing watershed prioritization (Kamel and Bachir 2017). It aids in simplifying the complexity of the variables by identifying the most significant components that explain most of the variance within the data (Ouyang et al. 2006; Shrestha and Kazama 2007). Numerous studies reported the successful application of PCA in prioritizing sub-basins based on erodibility through morphometric parameters in different basins (Javed et al. 2011; Meshram and Sharma 2015; Nunchhani et al. 2020; Singh et al. 2021; Govarthanambikai and Sathyanarayan 2024).

The Tlawng River serves as the primary water source for Aizawl, the capital city of Mizoram, India. Implementing soil and water conservation measures in its basin is crucial, given that hundreds of thousands of urban residents depend entirely on it. The quality of water in the Tlawng River has been affected by sedimentation caused by erosion, leading to clogging of water pumps and resulting in an inconsistent water supply in the city, especially during the monsoon season. Therefore, understanding the basin’s features and assessing its erodibility becomes crucial. Lalramchulloa et al. (2021) conducted a morphometric analysis of the Tlawng River basin and highlighted that erosion continues to occur in the region. However, there have been no reports of morphometric analysis at the sub-watershed scale or erodibility assessment for the Tlawng River basin to date. Therefore, taking all the above-mentioned facts into account, the present research was conducted to analyse the basin’s geomorphological characteristics at the sub-watershed level and perform an erodibility-based, sub-basin prioritization through morphometric parameters. The GIS techniques, in conjunction with the PCA approach, were adopted to facilitate the achievement of these objectives. Further, prioritizing erosion-prone zones within this basin will aid in focusing soil conservation efforts on regions that are more susceptible to erosion and its related hazards. This strategic approach will allow for a more targeted and efficient use of resources, ultimately resulting in the maximization of the impact of conservation efforts.

2 Data and Methodology

2.1 Study area

The Tlawng River basin is situated within the geographical boundaries of Mizoram state, covering an area of 3,519.076 km². It is bound by the latitudes 22°49' 53.84"N and 24°14' 37.43"N, as well as the longitudes 92°21'12.27"E and 92°50'21.07"E (Figure 1). The basin encompasses the administrative districts of Lunglei, Aizawl, Serchhip, Mamit, and Kolasib. The landscape is characterized by undulating topography, with altitudes varying from 25 to 1,602 meters above sea level. A tropical and humid climate prevails in this region, and it experiences substantial monsoon rainfall, which contributes to an average yearly rainfall of around 2,450 mm within the study area. Additionally, the Tlawng River, stretching across 187 kilometres, holds the distinction of being the state’s longest river. It originates from the eastern slopes of Zopui Hill, located east of Lunglei town. Flowing northward, it is joined by the Tut and Teirei tributaries before entering the Aizawl district near Khawlek. This river serves as the primary water source for Aizawl City.

Figure 1 Location of Tlawng Basin.

2.2 Acquisition of data

A high-resolution Digital Elevation Model (DEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with a resolution of 30 metres, was acquired from NASA's website (http://search.earthdata.nasa.gov/). This DEM was used for watershed delineation and the generation of watershed parameters.

2.3 Watershed delineation

Watershed delineation, a crucial step for model parameterization, was carried out using ArcGIS. This process involved creating flow direction and accumulation grids derived from the DEM, modeling the stream network using a threshold value of 5,000 pixels, and selecting an appropriate outlet point (92.5323°E, 24.1863°N) to delineate the boundary. Subsequently, ArcSWAT was used to subdivide the watershed into 23 small watersheds, as depicted in Figure 2.

Figure 2 Sub-divided watersheds of Tlawng Basin.

2.4 Determination of morphometric parameters

The fundamental parameters (stream order, stream length, area, perimeter, and elevation) for each sub-watershed were independently computed using ArcGIS. Additionally, established formulae proposed by Horton (1945), Strahler (1964), Schumm (1956), Nookaratnam et al. (2005), and Miller (1953) were applied to calculate other morphometric parameters such as bifurcation ratio (R_b), drainage density (D_d), stream frequency (F_s), texture ratio (T), form factor (R_f), circulatory ratio (R_c), elongation ratio (R_e), and compactness constant (C_c). The various methods and formulae adopted are listed in Table 1.

Table 1 Formulae used for estimating some morphometric parameters.

Sl. No.	Parameter	Formula/Method	Reference	Description
1	Stream order (u)	Hierarchical order	Strahler (1964)
2	Stream length (Lu)	Length of the stream	Horton (1945)	Lu = total length of streams across all orders (km)
3	Bifurcation ratio (Rb)		Schumm (1956)	Nu = total stream number of order u Nu+1 = total stream number of next higher order
4	Mean bifurcation ratio (Rbm)	Average of Rb of all orders	Strahler (1964)
5	Basin length (Lb)		Nookaratnam et al. (2005)	Lb = length of basin (km) A = basin surface area (km2)
6	Drainage density (Dd)		Horton (1945)
7	Stream frequency (Fs)		Horton (1945)
8	Texture ratio (T)		Horton (1945)	P = basin perimeter (km)
9	Form factor (Rf)		Horton (1945)
10	Circulatory ratio (Rc)		Miller (1953)
11	Elongation ratio (Re)		Schumm (1956)
12	Compactness constant (Cc)		Horton (1945)
13	Relief ratio (Rh)			H = maximum watershed relief (m) Lch = Longest dimension of the basin (km)
14	Relative relief (Rr)
16	Ruggedness number (RN)

2.5 Principal component analysis (PCA)

PCA technique is employed to determine the correlation matrix and the key parameters that most significantly affect the watershed. This process ultimately leads to a new, reduced set of morphometric parameters. The PCA was carried out in Statistical Package for Social Sciences (SPSS 14.0) and the steps involved in the generation of principal components are shown in Figure 3. The initial step involved obtaining a correlation matrix of the input parameters, which holds significant importance. Based on this correlation matrix, the initial factor-loading matrix was derived. Then a variance table was generated, which demonstrates the distribution of variance among various factors. Factors with eigenvalues exceeding 1 were considered useful and were selected for further interpretation. Conversely, factors with eigenvalues less than 1 were deemed less significant, for they provide less information or explain less variability in the data compared to an individual item on its own. Therefore, only selected factors were then subjected to orthogonal rotation. The unrotated factor matrix of the selected factors was then generated, which was subsequently transformed into a rotated factor matrix.

Figure 3 Systematic process for Principal Component Analysis (PCA) carried out using SPSS.

From the resulting rotated matrix table, the parameters exhibiting the strongest correlation with the selected components were then used for assigning erosion-based rankings to each sub-watershed. In cases where a parameter was directly or positively correlated to erodibility, its highest value was given the 1^st rank, and accordingly, its lowest value was assigned with the lowest rank (23^rd rank). Conversely, if a parameter has an indirect relationship, the ranking order is reversed. Studies have shown that the linear parameters are positively correlated with erodibility, while shape parameters exhibit a negative correlation (Javed et al. 2009; Nookaratnam et al. 2005; Singh et al. 2013). These allocated rankings were then averaged to compute compound parameter (C_p) values for each sub-watershed. Subsequently, the prioritization process involves assigning higher ranks to sub-watersheds having lower C_p values and then assigning the following ranks to sub-watersheds with progressively higher C_p values, ultimately resulting in the final prioritization of all sub-watersheds.

3 Results

3.1 Basic parameters of the watershed

The computed basic watershed parameters for each sub-watershed are shown in Table 2. Sub-watershed 8 has the largest basin area (624.429 km²) and longest basin length (50.688 km), while Sub-watershed 23 has the smallest basin area (3.979 km²) and shortest basin length (2.874 km).

Table 2 Basic parameters for 23 sub-watersheds in the Tlawng Basin.

Sub-watershed	Basin area (km2)	Perimeter (km)	Basin length (km)
1	75.192	58.628	15.261
2	149.621	96.485	22.559
3	61.099	48.213	13.564
4	62.820	49.617	13.780
5	92.919	71.442	17.211
6	148.724	100.054	22.482
7	112.259	89.229	19.16
8	624.429	226.907	50.788
9	423.160	198.060	40.718
10	425.854	147.156	40.865
11	6.384	14.938	3.760
12	235.368	113.102	29.180
13	4.945	16.441	3.252
14	239.585	122.113	29.475
15	172.547	99.586	24.462
16	94.004	60.442	17.325
17	44.034	51.840	11.261
18	39.272	40.021	10.552
19	17.994	28.612	6.774
20	429.863	143.645	41.083
21	48.367	47.043	11.878
22	6.657	18.665	3.850
23	3.979	15.505	2.874
Total	3,519.076	1,857.744	452.114

3.2 Linear morphometric parameters

The stream orders (u) identified for each sub-watershed, along with the stream number (N_u), stream length (L_u), and mean bifurcation ratio (R_b) are displayed in Table 3. As per the observations, 5^th order streams were identified in the region, leading to their classification as a 5^th order basin. Specifically, Sub-watersheds 21, 22, and 23 are noted to contain streams of this order. The analysis also highlighted the prevalence of 1st order streams across the basin. Notably, Sub-watershed 8 stood out with the highest count of 1^st order streams, totaling 46, and an overall stream length extending 110.964 km.

Table 3 Linear parameters of the 23 sub-watersheds.

Sub-water shed	Stream order
	1		2		3		4		5
	No. of streams	Stream length (km)	No. of strms	Strm length (km)	No. of strms	Stream length (km)	No. of strms	Stream length (km)	No. of strms	Stream length (km)	Mean bifurcation ratio
1	6	14.428	1	11.552							6
2	13	23.621	4	18.796	2	9.537					2.62
3	7	9.532	2	12.195	1	1.748					2.75
4	3	18.404	1	3.063							3
5	7	16.726	3	12.311	1	4.503					2.66
6	14	24.197	2	22.458	1	3.737					4.50
7	8	18.652	2	14.107	1	7.431					3
8	46	110.964	8	52.530	2	39.715	1	22.343			3.92
9	18	68.239	4	39.601	1	38.009					4.25
10	40	81.471	8	30.204	1	51.144					6.50
11					1	2.147					1
12	13	54.700	1	1.568	2	0.078	1	27.203			5.17
13			2	0.621	1	3.820					2
14	17	43.638	3	12.706			1	36.424			4.33
15	11	28.220	4	13.801			1	16.756			3.37
16	7	15.583	2	3.443			1	11.135			2.75
17	3	13.026					1	7.7440			3.00
18	4	6.563			1	0.0024	1	6.583			2.50
19	1	1.988					1	3.857			1
20	29	80.420	7	28.722	1	0.164	1	55.776			4.04
21	3	3.928					2	0.535	1	12.934	1.75
22					1	0.140			1	5.050	1
23					1	0.188			1	0.753	1
Total	250	634.3	54	277.678	18	162.175	11	188.356	3	18.737

Stream numbers directly influence a watershed's erodibility by affecting surface runoff and sediment transport. The basin's total stream count across all orders is 336, out of which 250 streams are designated as 1st order, 54 streams as 2^nd order, 18 streams as 3^rd order, 11 streams as 4^th order, and 3 streams as 5^th order. This distribution corresponds with Horton's (1945) observation that stream numbers tend to decrease with higher stream orders.

The basin has a cumulative stream length of 1,281.46 km across all stream orders. Notably, the 1^st order streams throughout the entire basin extend over a length of 634.31 km, while 2nd order streams cover 277.68 km. Furthermore, 3rd order streams measure 162.37 km in length, 4th order streams span 188.36 km, and 5^th order streams measure a total of 18.74 km. The substantially greater length of first-order streams compared to higher orders reflects the typical pattern where increasing stream order correlates with decreasing stream length.

The bifurcation ratio in the basin ranges between 1 and 6.5, having an average value of 3.14 for the entire watershed. This parameter provides significant insights into the basin’s geological and tectonic features, with lower R_b values suggesting a more stable basin with minimal structural disturbances, resulting in a preserved drainage pattern.

3.3 Aerial morphometric parameters

The aerial parameters computed for each sub-watershed are displayed in Table 4. The drainage density (D_d) values range between 0.237 km/km² and 0.898 km/km², indicating the prevalence of low D_d in the region. This suggests that the basin exhibits a moderate to thick layer of vegetative cover and subsurface elements with low permeability.

Table 4 Aerial parameters of the 23 sub-watersheds.

Sub-basin	Drainage density (Dd)	Stream frequency (Fs)	Circulatory ratio (Rc)	Form factor (Rf)	Elongation ratio (Re)	Texture ratio (T)	Compactness constant (Cc)	Ruggedness number (RN)
1	0.346	0.146	0.275	0.323	0.641	0.188	1.907	1.945
2	0.347	0.174	0.202	0.294	0.612	0.269	2.225	3.702
3	0.384	0.213	0.330	0.332	0.650	0.270	1.740	5.041
4	0.342	0.080	0.321	0.331	0.649	0.101	1.766	2.888
5	0.361	0.140	0.229	0.314	0.632	0.182	2.091	5.018
6	0.339	0.188	0.187	0.294	0.612	0.280	2.314	3.941
7	0.358	0.134	0.177	0.306	0.624	0.168	2.376	4.654
8	0.361	0.146	0.152	0.242	0.555	0.401	2.562	4.613
9	0.345	0.083	0.136	0.255	0.570	0.177	2.716	4.436
10	0.382	0.195	0.247	0.255	0.570	0.564	2.012	4.653
11	0.336	0.157	0.360	0.451	0.758	0.067	1.668	2.065
12	0.355	0.123	0.231	0.276	0.593	0.256	2.080	5.002
13	0.898	0.607	0.230	0.467	0.771	0.182	2.086	1.563
14	0.387	0.150	0.202	0.276	0.593	0.295	2.226	4.724
15	0.341	0.133	0.219	0.288	0.606	0.231	2.139	3.948
16	0.321	0.160	0.323	0.313	0.631	0.248	1.759	4.309
17	0.472	0.182	0.206	0.347	0.665	0.154	2.204	6.539
18	0.335	0.280	0.308	0.353	0.670	0.275	1.802	3.027
19	0.330	0.222	0.276	0.392	0.707	0.140	1.903	1.926
20	0.384	0.142	0.262	0.255	0.569	0.425	1.954	5.707
21	0.360	0.186	0.275	0.343	0.661	0.191	1.908	2.809
22	0.780	0.451	0.240	0.449	0.756	0.161	2.041	2.737
23	0.237	0.754	0.208	0.481	0.783	0.193	2.193	0.869

Stream frequency correlates directly with drainage density in watersheds, indicating that those with higher stream frequency tend to have a greater abundance of smaller streams and tributaries, thereby increasing runoff potential during heavy precipitation events. The Tlawng basin exhibit a low F_s, having a mean value of 0.219 streams/km². Sub-watershed 23 records the highest value at 0.754 streams/km², while Sub-watershed 4 has the lowest value at 0.080 streams/km². The form factor (R_f) of the basin varies between 0.242 and 0.481, with an average value of 0.332. Sub-watershed 23 has the highest R_f and Sub-watershed 8 has the lowest R_f value.

The circulatory ratio (R_c) varies within the study area, ranging from a low value of 0.136 in Sub-watershed 9 to the highest value of 0.360 in Sub-watershed 11. Sub-watersheds with a more circular shape (lower R_c) tend to exhibit greater peak flows due to shorter concentration time and consequently greater susceptibility to erosion as well.

The elongation ratio (R_e) provides information on the basin’s geometry, typically ranging from 0.6 to 1.0 across diverse climatic and geological conditions. According to Strahler (1964), regions with low relief typically have ratios close to 1.0, whereas regions with high relief terrain are linked to ratios between 0.6 and 0.8. The basin has an average elongation ratio of 0.647, varying from 0.555 in Sub-watershed 8 to 0.783 in Sub-watershed 23, reflecting the region’s high relief topography.

The texture ratio (T ) serves as a vital factor in morphometric analysis, with the highest value of 0.564 in Sub-watershed 10 and the lowest of 0.067 in Sub-watershed 11. On average, T is 0.236, categorizing the basin as having a "very coarse (< 2)" drainage texture according to Smith (1950). According to Horton (1945), watersheds with a coarse texture tend to experience greater dissection and increased erosion in the region.

The compactness constant (C_c) varies between a low of 1.667 and a high of 2.716. Sub-watersheds 11, 3, and 6 demonstrate lower C_c values, while Sub-watersheds 7, 8, and 9 exhibit relatively higher C_c values. A higher compactness constant value in a region signifies a basin characterized by more of a compact and circular form, whereas a lesser compactness constant suggests a greater elongated and irregularly shaped basin.

The ruggedness number (R_N) reveals the landscape's unevenness and irregularity in a region. Sub-watershed 17 has the highest R_N at 6.539, while Sub-watershed 23 has the lowest value in the basin at 0.869. Higher R_N values suggest a more uneven topography and potentially steeper slopes, factors that contribute to erosion and sedimentation.

3.4 Relief morphometric parameters

These parameters provide insights into the gradient of a basin and reflect the degree of erosion activity occurring on its slopes. The relief parameters computed for all the sub-watersheds are presented in Table 5.

Table 5 Relief parameters of the 23 sub-watersheds.

Sub-watershed	Relief ratio (Rh)	Relative relief (Rr)	Average slope (Sa)
1	0.027	0.010	11.772
2	0.036	0.011	13.772
3	0.099	0.027	24.405
4	0.051	0.017	18.378
5	0.069	0.019	23.038
6	0.032	0.012	17.564
7	0.044	0.015	21.016
8	0.013	0.006	22.573
9	0.018	0.006	22.580
10	0.021	0.008	15.947
11	0.152	0.041	21.840
12	0.043	0.012	10.259
13	0.036	0.011	11.042
14	0.024	0.010	21.955
15	0.039	0.012	24.743
16	0.087	0.022	23.952
17	0.081	0.027	19.952
18	0.074	0.023	14.414
19	0.064	0.020	12.043
20	0.023	0.010	17.148
21	0.045	0.017	19.203
22	0.062	0.019	20.427
23	0.070	0.024	16.865

Sub-watershed 11 has the highest R_h at 0.152, while Sub-watershed 8 has the lowest value at 0.013. Sub-watersheds 16, 3, and 11 in our study region exhibit higher relief ratios, indicating steeper slopes and more erodible soils, likely leading to increased rates of erosion and sedimentation. These effects can have adverse repercussions on downstream water quality, aquatic ecosystems, and infrastructure integrity.

The maximum relative relief (R_r) of 0.041 is observed in Sub-watershed 11, while both Sub-watersheds 8 and 9 have a minimum value of 0.006 in the basin.

The slope (S_a) in the entire watershed varies from 10.259% to 24.743%, with an average slope of 18.473%. Sub-watersheds 3, 15, and 16 stand out with the highest slope percentages in the region, suggesting a relatively greater susceptibility to soil erosion in these areas.

3.5 Prioritization of sub-watersheds based on PCA

The correlation matrix of the geomorphic parameters was generated and subjected to PCA using SPSS 14.0. A variance table was generated (Table 6), and it is observed that the eigenvalues of the first three components are greater than 1 and collectively explains about 81% of the total variance. This implies that these three components capture a significant portion of the variability present in the geomorphic parameters being analyzed and thereby used for generating the initial unrotated factor-loading matrix presented in Table 7. To enhance interpretability and clarity of the relationships between variables and principal components, a rotated matrix (Table 8) was subsequently generated. Parameters having the strongest correlation with each component, such as stream frequency (F_s) with component 1, compactness constant (C_c) with component 2, and relative relief (R_r) with component 3 were identified as the most crucial parameters and subsequently used for prioritization.

Table 6 Total variance explained for Tlawng basin.

Component	Initial eigenvalues				Extraction sums of squared loadings			Rotation sums of squared loadings
	Total	% of Variance		Cumulative %	Total	% of Variance	Cum %	Total	% of Variance	Cum %
1	5.09		46.277	46.277	5.09	46.277	46.277	3.638	33.069	33.069
2	2.493		22.666	68.943	2.493	22.666	68.943	2.875	26.135	59.204
3	1.345		12.226	81.169	1.345	12.226	81.169	2.416	21.965	81.169
4	0.871		7.918	89.086
5	0.646		5.874	94.961
6	0.41		3.727	98.687
7	0.103		0.934	99.621
8	0.028		0.252	99.873
9	0.01		0.095	99.968
10	0.003		0.029	99.997
11	0		0.003	100

Table 7 Unrotated factor matrix.

Factor	Principal component
	1	2	3
Dd	0.262	-0.577	0.065
Fs	0.562	-0.674	0.047
Rc	0.642	0.563	-0.468
Rf	0.932	-0.328	0.123
Re	0.942	-0.305	0.12
T	-0.632	-0.019	-0.428
Cc	-0.644	-0.48	0.541
RN	-0.675	0.434	0.213
Rh	0.779	0.53	0.214
Rr	0.801	0.488	0.224
Sa	-0.138	0.502	0.688

Table 8 Rotated factor matrix.

Factor	Principal component
	1	2	3
Dd	0.603	-0.198	-0.053
Fs	0.875	-0.068	0.04
Rc	0.021	0.958	0.175
Rf	0.861	0.277	0.418
Re	0.85	0.298	0.429
T	-0.393	-0.099	-0.647
Cc	-0.086	-0.960	-0.086
RN	-0.779	-0.287	0.011
Rh	0.115	0.588	0.757
Rr	0.16	0.572	0.759
Sa	-0.488	-0.245	0.668

Prior to the final prioritization of sub-watersheds, rankings were assigned to all the selected principal parameters, and a final rank or priority was allocated to each sub-watershed according to their respective C_p values (Table 9). Here, Sub-watershed 3 receives the highest priority (rank 1), due to its lowest C_p value of 3.333, while the least priority (rank 23) is assigned to Sub-watershed 9 owing to its maximum C_p of 22.333. Based on this, a prioritized rank map for the Tlawng basin was created, as shown in Figure 4. The higher priority rank assigned to a sub-watershed indicates its greater susceptibility to erosion and necessitates a prioritized allocation of resources for implementing various conservation and management strategies. Consequently, our findings suggest that soil conservation measures should be initially targeted at Sub-watershed 3 followed by subsequent implementation in the remaining watershed. This prioritized approach will ensure the effective management and mitigation of soil erosion issues within the watershed.

Table 9 Prioritization of sub-watersheds and their rankings.

Sub-watershed	Fs value	Fs rank	Cc value	Cc rank	Rr value	Rr rank	Compound parameter (Cp)	Final priority
1	0.146	15	1.907	7	0.010	20	14.000	14
2	0.174	11	2.225	18	0.011	16	15.000	17
3	0.213	6	1.740	2	0.027	2	3.333	1
4	0.080	23	1.766	4	0.017	10	12.333	11
5	0.140	18	2.091	14	0.019	8	13.333	13
6	0.188	8	2.314	20	0.012	15	14.333	15
7	0.134	19	2.376	21	0.015	12	17.333	20
8	0.146	16	2.562	22	0.006	23	20.333	22
9	0.083	22	2.716	23	0.006	22	22.333	23
10	0.195	7	2.012	10	0.008	21	12.667	12
11	0.157	13	1.668	1	0.041	1	5.000	3
12	0.123	21	2.080	12	0.012	13	15.333	18
13	0.607	2	2.086	13	0.011	17	10.667	10
14	0.150	14	2.226	19	0.010	19	17.333	20
15	0.133	20	2.139	15	0.012	14	16.333	19
16	0.160	12	1.759	3	0.022	6	7.000	5
17	0.182	10	2.204	17	0.027	3	10.000	9
18	0.280	4	1.802	5	0.023	5	4.667	2
19	0.222	5	1.903	6	0.020	7	6.000	4
20	0.142	17	1.954	9	0.010	18	14.667	16
21	0.186	9	1.908	8	0.017	11	9.333	8
22	0.451	3	2.041	11	0.019	9	7.667	7
23	0.754	1	2.193	16	0.024	4	7.000	5

Figure 4 Prioritized rank map of the Tlawng Basin.

4 Discussion

The morphometric analysis of the study region provides valuable insights into its hydrological and geomorphic characteristics. The computation of basic parameters highlights significant variations across the basin. This variation in basin size and length influences the hydrological response of each sub-watershed, with larger basins like Sub-watershed 8 potentially experiencing longer times of concentration and lower peak discharges compared to smaller basins.

In terms of linear morphometric parameters, the stream orders identified for each sub-watershed range up to the 5th order. The dominance of 1st order streams across the basin highlights the prevalence of smaller tributaries contributing to the main river system. Sub-watersheds 8 and 20, with relatively large numbers of 1st order streams, suggests a dense network of tributaries which can lead to quick runoff responses during rainfall events. However, owing to their low stream frequency (F_s) and relative relief (R_r) values, the erodibility in the region is relatively very low, as reflected in the final priority map. Accordingly, it is interesting to note that smaller sub-watersheds (viz., 3, 16, 18, 19, and 23), having relatively lesser numbers of streams are more vulnerable to erosion, owing to the higher values of F_s and R_r, and lower compactness constant (C_c) values.

The areal morphometric parameters provide insights into the drainage characteristics and shape of the sub-watersheds. Varying drainage densities reflect differences in infiltration capacities and surface runoff potential. High drainage densities in sub-watersheds such as 13 and 22 indicate less permeable surfaces, leading to higher surface runoff and consequently a higher potential for erosion. Overall, the prevalence of low drainage density in the basin, observed in the present study, aligns with findings by Lalramchulloa et al. (2021), who reported a comparable finding of low drainage density within the Tlawng basin, indicating the basin river to be a consistent and stable channel with nearly constant flow rates and minimal variation in the stream pattern over time. They also reported the existence of permeable rocks like sandstone in the region, which further explains the low stream frequency observed in our study. However, since the stream frequency was identified as one of the principal components affecting erosion, it was a key factor in ranking the sub-watersheds.  The form factor and elongation ratio values suggest that the sub-watersheds are generally elongated, which is typical for regions with longer flow paths and delayed peak flows. Circularity ratio values further support this, as most sub-watersheds exhibit fewer circular shapes, indicative of prolonged flood durations with lower peak flows. The ruggedness parameter values further reveal an uneven and steep topography in the region. This shape characteristic, combined with the region's high relief, suggests a significant influence on the hydrological response of the sub-watersheds.

The relief aspects results reveal significant variation in relief ratios, relative relief and slope percentages among the sub-watersheds. Sub-watersheds 11, 3, and 16 exhibit higher relief ratios and steeper slopes, indicating greater susceptibility to erosion. This can potentially lead to increased sedimentation and adversely affect water quality and ecosystems downstream.

Finally, the PCA tool proved to be efficacious in simplifying the analysis by reducing the 11 parameters into 3 principal components, which were used to identify the varying degree of susceptibility of each sub-basin to soil erosion. The discussion therefore underscores the importance of all these findings in understanding basin dynamics, guiding sustainable developmental practices, and promoting cost-effective erosion mitigation measures.

5 Conclusions

The study area, the Tlawng basin, was sub-divided into 23 sub-watersheds using ArcSWAT. Subsequently, morphometric parameters were computed for each sub-watershed utilizing GIS techniques and established mathematical formulas. Erodibility-based prioritization of these sub-watersheds was achieved by Principal Component Analysis (PCA) using SPSS 14.0 software. The PCA results highlighted stream frequency (F_s), compactness constant (C_c), and relative relief (R_r) as the most important parameters for ranking the sub-watersheds. Accordingly, Sub-watershed 3 emerged with the highest priority (1^st rank), indicating its increased vulnerability to soil erosion, whereas Sub-watershed 9 was assigned the lowest priority (23^rd rank). This prioritization approach suggests implementing soil conservation measures initially in Sub-watershed 3 to mitigate erosion risks effectively. Subsequent interventions can then be adapted to address erosion concerns in other sub-watersheds based on their priority rankings. This strategic approach facilitates targeted and efficient resource allocation, thereby enhancing the efficacy of conservation efforts. This research underscores the efficacy of integrating GIS techniques with PCA methodology for comprehensive morphometric analysis and prioritization based on erodibility. These findings provide critical insights for informed decision-making in natural resource management, particularly in promoting sustainable development and climate change adaptation strategies. Future research could further investigate the spatial patterns and intensity of erosion within the Tlawng basin, along with evaluating the effectiveness of various soil conservation techniques in erosion mitigation.

Acknowledgements

The authors sincerely thank the officials and personnel of the Irrigation and Water Resources Department (Mizoram), Central Water Commission (Mizoram), Agriculture Department of Mizoram, and Mizoram State Meteorological Centre for their kind assistance rendered in the form of data, logistic support, and related matters. Authors also take this opportunity to thank Ms. P.C. Vanlalnunchhani for liaising with Mizoram Govt. Officials.

This study was financially supported by the Irrigation and Water Resources Department (Mizoram) via National Hydrology Project, Govt. of India.

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