Soil erosion poses a formidable challenge to the Indian economy, particularly affecting approximately 34% of its land situated in semi-arid regions. This pervasive issue demands careful consideration and strategic intervention to mitigate its adverse impacts. Ramarao et al., 2019). Many agricultural practices have deep historical roots and rely heavily on the natural rain cycle 1. Assessing and monitoring watersheds at the sub-watershed (SWS) level can provide valuable insights into the state of vegetation health and its intricate connection with soil dynamics. This examination holds considerable significance for understanding and enhancing agricultural sustainability in the given context 1 .

Morphometric parameters provide crucial insights into soil characteristics within watersheds and sub-watershed (SWS) regions. Analyzing these parameters is pivotal for uncovering the nuanced soil properties, thereby advancing our comprehension of watershed dynamics and informing sustainable land management practices 2. Morphometric parameter values serve as invaluable resources for hydrological modeling and hold immense significance in natural resource management and watershed management endeavors. Their utilization plays a pivotal role in informing strategic decisions aimed at sustainable land and water resource management 3. Morphometric parameters are best for assessing flood risk mapping 4, 5, 6 and watershed management 1, 7. This study emphasizes the analysis of morphometric parameters within the watershed, highlighting their substantial influence on large-scale soil erosion dynamics. This research holds significance for understanding the intricate relationship between morphometric characteristics and soil erosion processes, thus contributing to informed watershed management strategies.

Numerous researchers worldwide have developed various methodologies to identify a dependable approach for prioritizing sub-watersheds (SWS) in spatial decision-making. This endeavor is crucial for enhancing the effectiveness of watershed management strategies and ensuring optimal resource allocation 1, 8. In recent times, the Analytical Hierarchy Process (AHP) has become a widely adopted and reliable multi-criteria decision-making system, garnering substantial trust among decision-makers9. This study employs the AHP methodology to pinpoint sub-watersheds susceptible to soil erosion, with the objective of delineating erosion-prone zones. The findings demonstrate the effectiveness of AHP in generating reliable insights for watershed management and monitoring at the sub-watershed level.

The Kumaradhara river, a tributary of the Nethravathi in Dakshina Kannada, spans from coordinates 12°29'4" to 12°58'33" N and 75°9'58" to 75°47'48" E, covering an area of 1776 sq. km. Originating in the central Western Ghats, its streams converge with the Nethravathi river near Uppinangadi town. The basin encompasses regions across Dakshina Kannada, Hassan, and Kodagu districts, hosting significant Hindu temples along its course. Agriculture serves as the primary livelihood in the region, though it faces substantial threats from soil erosion and sporadic flooding events.

Linear parameters, aerial parameters, and relief factors are utilized for the analysis of morphometric parameters. The extraction of watershed, sub-watershed, and streams is performed using ArcGIS 10.8.2 software and Arc SWAT extensions. These factors are outlined in Table 1 and elaborated upon. The study incorporates ten morphometric parameters, contributing to its comprehensive analysis and interpretation.

The streams within the Kumaradhara River basin are classified using the Strahler ordering technique, ranging from first to sixth order. Table 3 presents values associated with streams of different orders, including the total number of streams (Nu), stream length (Lu), mean stream length ratio (LSM), stream length ratio (RL), and bifurcation ratio (Rb). Analysis reveals that first-order streams exhibit the highest values, while sixth-order streams display the lowest. Additionally, Lu is greatest for first-order streams and least for sixth-order streams. Parameters such as LSM, RL, and Rb are notably smaller for sixth and second-order streams, with the highest values observed in second and first-order streams, respectively. This analysis offers insights into the spatial distribution and characteristics of streams within the basin, aiding in watershed management and hydrological studies.

Table 4 displays relief and aerial parameters for 20 sub-watersheds (SWS). Sub-watershed 10 exhibits the highest drainage density, while sub-watershed 9 has the lowest. The mean bifurcation ratio is minimal in SWS 13 and maximal in SWS 5. SWS 13 has the lowest stream frequency at 0.886, whereas SWS 10 records the highest at 1.756. These findings offer crucial insights into the hydrological characteristics and variability among sub-watersheds, aiding in watershed management and planning endeavors. SWS 14 exhibits the lowest drainage texture value at 0.514, while SWS 4 has the highest. The average length of overland flow is minimal in SWS 9 and maximal in SWS 10. Form factor is lowest in SWS 14 and highest in SWS 10. SWS 10 has the lowest shape factor at 1.965, whereas it is highest at 7.353 in SWS 14. Circularity ratio is lowest in SWS 14 and highest in SWS 19. SWS 14 has the lowest elongation ratio, whereas SWS 10 records the highest. Circularity ratio is lowest in SWS 19 and highest in SWS 14. These observations provide valuable insights into the morphological characteristics and variability among the sub-watersheds, contributing to informed watershed management strategies and decision-making processes.

In Table 4, certain parameters have a direct impact on soil erosion, while others have an inverse relationship. Linear parameters such as drainage density, mean bifurcation ratio, stream frequency, and drainage texture directly influence soil erosion. Conversely, all shape factors and Lg inversely affect soil erosion. These insights are crucial for understanding the factors contributing to soil erosion within the watershed, informing effective erosion control and management strategies.

Table 5 comprises a pairwise comparison matrix for various parameters, with drainage density identified as the parameter most likely to significantly impact soil erosion, while Lg is expected to have the least influence. The remaining parameters fall between these extremes and are ranked accordingly based on their weightage. Subsequently, in Table 6, the values from the comparison matrix are normalized. Utilizing the eigenvector value of the matrix along with the total from the comparison matrix, the Maximum eigenvector is calculated. The consistency index (CI) is then determined with the help of the maximum eigenvector value, and the consistency ratio is computed using the random consistency index (RI) value, with the obtained ratio found to be less than 10%, indicating acceptable consistency. These calculations provide a robust basis for the prioritization and assessment of parameters influencing soil erosion within the watershed, facilitating informed decision-making in erosion control and management strategies.

A comparison matrix is formulated to evaluate each parameter's pairwise comparison across the sub-watersheds (SWS). Eigenvectors are computed subsequent to normalization. The resulting consistency ratio, well below 10%, indicates satisfactory consistency and validates further analysis. These steps provide a rigorous framework for assessing the relative importance of parameters and their impact on soil erosion within the watershed, facilitating informed decision-making in erosion control and management strategies.

Eigenvector values are computed for all 20 sub-watersheds (SWS) across the ten parameters. The sum product of these eigenvectors from both the primary comparison matrix and the comparison matrix for each parameter is calculated. Results indicate that SWS 10, along with SWS 11, 8, and 14, are most susceptible to soil erosion. Conversely, SWS 18, 16, and 10 are identified as the least prone to soil erosion. These findings offer valuable insights into the spatial variability of soil erosion susceptibility across the watershed, informing targeted erosion control and management efforts.

Figure 4 depicts the classification of the entire area into five zones based on soil erosion susceptibility: Shallow (<30%), Low (30% - 45%), Moderate (45% - 60%), High (60% - 75%), and Very High (>75%). The eastern middle region exhibits a higher susceptibility to soil erosion compared to other areas. These findings provide crucial spatial insights into soil erosion vulnerability across the study area, guiding targeted conservation and management strategies for sustainable land use.

Table 7

SWS	DD	RBM	FS	DT	SF	RC	RE	FF	CC	LG	Weightage	Priority
SWS1	0.06	0.03	0.09	0.07	0.02	0.02	0.06	0.05	0.06	0.02	0.05	5%
SWS2	0.03	0.05	0.03	0.03	0.09	0.01	0.01	0.01	0.11	0.07	0.04	4%
SWS3	0.02	0.13	0.04	0.03	0.11	0.01	0.01	0.01	0.11	0.11	0.06	6%
SWS4	0.02	0.03	0.07	0.01	0.01	0.06	0.11	0.11	0.02	0.08	0.04	4%
SWS5	0.07	0.01	0.03	0.11	0.01	0.02	0.09	0.09	0.05	0.02	0.05	5%
SWS6	0.07	0.04	0.02	0.08	0.07	0.03	0.02	0.04	0.05	0.04	0.05	5%
SWS7	0.02	0.07	0.02	0.01	0.02	0.08	0.06	0.07	0.01	0.11	0.04	4%
SWS8	0.12	0.05	0.07	0.03	0.01	0.07	0.12	0.11	0.02	0.01	0.07	7%
SWS9	0.10	0.01	0.11	0.10	0.07	0.01	0.02	0.01	0.08	0.01	0.07	7%
SWS10	0.01	0.04	0.01	0.01	0.01	0.04	0.15	0.15	0.02	0.15	0.03	3%
SWS11	0.10	0.08	0.07	0.07	0.01	0.12	0.07	0.07	0.01	0.01	0.08	8%
SWS12	0.03	0.07	0.04	0.01	0.01	0.09	0.08	0.08	0.01	0.05	0.04	4%
SWS13	0.09	0.10	0.11	0.11	0.03	0.11	0.04	0.04	0.01	0.01	0.09	9%
SWS14	0.05	0.02	0.10	0.13	0.15	0.01	0.01	0.01	0.15	0.02	0.07	7%
SWS15	0.03	0.01	0.01	0.04	0.06	0.03	0.02	0.02	0.04	0.04	0.03	3%
SWS16	0.04	0.01	0.04	0.04	0.04	0.01	0.03	0.03	0.07	0.02	0.03	3%
SWS17	0.06	0.09	0.06	0.06	0.14	0.01	0.01	0.01	0.11	0.01	0.06	6%
SWS18	0.03	0.01	0.04	0.02	0.05	0.03	0.02	0.02	0.03	0.03	0.03	3%
SWS19	0.02	0.04	0.03	0.01	0.05	0.20	0.04	0.04	0.01	0.09	0.04	4%
SWS20	0.01	0.10	0.01	0.03	0.02	0.05	0.04	0.04	0.02	0.11	0.04	4%
Weight	0.24	0.18	0.16	0.16	0.07	0.06	0.06	0.03	0.02	0.02	1.00	100%

In the Kumaradhara River watershed, soil erosion poses a significant threat, prompting the need for targeted attention from decision-makers and government authorities. This study employs the Analytic Hierarchy Process (AHP) to identify priority areas within the watershed requiring immediate intervention. Through this method, the watershed is classified into categories ranging from Very Low to Very High susceptibility to soil erosion. These findings hold critical importance for guiding effective resource allocation and management strategies to mitigate soil erosion impacts in the watershed.