Next Article in Journal
Enhanced Early Warning Threshold Setting for Dam Safety Monitoring Based on M-Estimation and Confidence Interval Method
Previous Article in Journal
Algal Community Dynamics in Three Water Intakes of Poyang Lake: Implications for Drinking Water Safety and Management Strategies
Previous Article in Special Issue
Perceptions and Adaptive Behaviors of Farmers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 17
Issue 13
10.3390/w17132039
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Watershed Prioritization with Respect to Flood Susceptibility in the Indian Himalayan Region (IHR) Using Geospatial Techniques for Sustainable Water Resource Management

by
Ashish Mani
1,2,
Ruchi Badola
2,
Maya Kumari
1,*,
Varun Narayan Mishra
3,
Kgabo Humphrey Thamaga
4,
Fahdah Falah Ben Hasher
5 and
Mohamed Zhran
6,*
1
Amity School of Natural Resources & Sustainable Development (ASNRSD), Amity University, Sector-125, Noida 201313, Uttar Pradesh, India
2
Wildlife Institute of India, Post Box 18, Chandrabani, Dehradun 248001, Uttarakhand, India
3
Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University, Sector-125, Noida 201313, Uttar Pradesh, India
4
Department of Geography, University of South Africa, Florida 1709, South Africa
5
Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
6
Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
*
Authors to whom correspondence should be addressed.
Water 2025, 17(13), 2039; https://doi.org/10.3390/w17132039
Submission received: 25 April 2025 / Revised: 4 July 2025 / Accepted: 5 July 2025 / Published: 7 July 2025
(This article belongs to the Special Issue The Interrelationship Between Climate Change, Human Activities and Hydrological Processes (3rd Edition))
Browse Figures
Versions Notes

Abstract

The rising demand for freshwater, driven by population growth, economic development, and climate change, necessitates proactive watershed management. This study focuses on prioritizing the watersheds of the Doon Valley in the Indian Himalayan Region (IHR) using geospatial techniques. It involves a detailed morphometric analysis incorporating hydrological and topographical parameters, ranking the watersheds using the compound factor value (CFV), and prioritizing them based on the given CFV. The Doon Valley watersheds exhibit dendritic to parallel drainage patterns and moderate relief. The study identifies the Suswa watershed as the most susceptible, necessitating urgent conservation attempts to mitigate soil erosion and ensure sustainable land use. In contrast, the Song watershed, characterized by steep slopes and high relief, requires targeted management strategies to control rapid runoff and prevent potential flooding. The Asan watershed, with a medium priority classification, also requires intervention to prevent ecological degradation. Prioritization based on the CFV provides a strategic framework for targeted management, offering valuable insights for policymakers and planners. This research supports sustainable watershed management by guiding effective conservation practices and addressing the specific needs of each watershed.

1. Introduction

The management and availability of freshwater resources are essential for the sustainable well-being of human populations [1]. The Ramsar Convention’s emphasis on conservation aligns perfectly with the urgent need to protect water resources and ensure environmental sustainability [2]. Climate change, extreme weather events, and human activities like urbanization and land use changes can significantly impact water resources [3,4,5]. Therefore, it is crucial to understand the intricate relationship between land, soil, and water. The repercussions of these impacts are evident in densely populated countries like India, where livelihoods heavily rely on land and water resources. Understanding the local drainage morphometry and its environmental consequences is crucial in promoting comprehensive watershed planning and enhancing water resource management [6].
Morphometric analysis is a mathematical method used to measure terrain dimensions and describe the shape of the Earth’s surface, which is essential for watershed planning. This method is vital in analyzing the development of topographical features over time and their effects on river and stream flow dynamics [7,8]. Within this analytical framework, the compound factor value (CFV) is an essential metric used to rank and prioritize watersheds based on multiple morphometric parameters [9]. The CFV is derived by evaluating and ranking individual morphometric parameters that are indicative of watershed characteristics—for instance, the drainage density, bifurcation ratio, elongation ratio, circularity ratio, form factor, and many other parameters. These parameters collectively influence the watershed’s response to precipitation, runoff generation, soil erosion potential, and overall hydrological behavior. By assigning ranks to these factors and computing their composite impacts, the CFV serves as an effective tool for categorizing watersheds into priority classes—high, medium, or low. As remote sensing data and GIS tools become more widely available, CFV-based watershed prioritization is emerging as a crucial tool for land use planning and ecological conservation.
Since the mid-1990s, the morphometric analysis method has been utilized widely to assess linear, aerial, and relief aspects and their relationships with vegetation, soil, and water management, with significant advancements in techniques driven by researchers refining and developing methods for a better understanding of drainage basin characteristics and the modeling of erosion-prone areas [10,11,12,13,14]. Jhariya et al. [15] used remote sensing and the GIS method integrated with the analytic hierarchy process (AHP) to prioritize Bindra’s sub-watersheds for soil and water hazard modeling. Mundetia et al. [9] utilized the ranking and CFV methods, supported by geospatial techniques, to assess the groundwater potential in the Khari river basin’s sub-watersheds. Similarly, Abdekareem et al. [16] applied the AHP-weighted overlay method with GIS–remote sensing techniques to identify groundwater sustainability regions in arid landscapes. In another study, Das et al. [17] employed morphometric and land use land cover (LULC) analysis, integrated with remote sensing and GIS techniques, to prioritize the sub-basins of the Gomti River for soil and water conservation practices. Bashir and Alsalman [18] evaluated the erosion vulnerability of sub-basins based on RS-GIS techniques and morphometric parameters in the Rabigh area along the Eastern Red Sea coastal plain, Saudi Arabia. Pramanik [19] utilized remote sensing and GIS techniques to evaluate the morphometric characteristics of the Tista river basin, providing critical insights for water resource planning and erosion mitigation strategies. Woldesenbet et al. [20] investigated the hydrological impacts of land use and land cover changes in the source region of the Upper Blue Nile Basin, Ethiopia, highlighting their influence on runoff, erosion, and watershed dynamics. Arabameri et al. [21] conducted a morphometric analysis combined with a novel GIS-based ensemble modeling approach to map the soil erosion susceptibility, enhancing watershed management and erosion risk prediction. Sampath and Radhakrishnan [22] prioritized sub-watersheds in an ungauged river basin for soil erosion susceptibility by applying various combinations of objective weighting and multi-criteria decision-making (MCDM) techniques, improving erosion risk assessment and management strategies. In another study, Govarthanambikai and Sathyanarayan [23] employed morphometric analysis integrated with geospatial tools and principal component analysis (PCA) to prioritize sub-watersheds in the Noyyal river basin, Tamil Nadu, enhancing decision-making for watershed management and erosion control. Kumar et al. [24] applied morphometric analysis along with PCA and the weighted sum approach (WSA) using GIS techniques to prioritize watersheds in the Lesser Himalayan region of India for soil erosion risk mapping and management. Shaikh, M. [25] employed quantitative morphometric analysis using the compound factor method to prioritize sub-watersheds based on their runoff potential, enabling effective planning for watershed management and erosion control. Similarly, Godif and Manjunatha [26] conducted a study on the Geba river basin in Tigray, Ethiopia, using morphometric analysis combined with the weighted sum approach (WSA) to prioritize sub-watersheds for soil and water conservation, facilitating targeted erosion control and sustainable watershed management strategies. Pastor [27] conducted a morphometric analysis and the prioritization of sub-watersheds in the Buzău river basin, Romania, which features heterogeneous geographical conditions. The study employed geospatial techniques to evaluate the drainage characteristics and rank the sub-watersheds based on their erosion susceptibility and resource management needs, offering a valuable framework for sustainable watershed planning. Therefore, the present study is consistent with international research in using morphometric indices and geographic methods to prioritize sub-watersheds according to their vulnerability.
GIS and remote sensing technologies have revolutionized the understanding and simulation of topography and hydrology. Traditionally, watershed planning has relied on manual data collection, analysis, and predictions from printed aerial maps, which were labor-intensive and often inaccurate. GIS and remote sensing techniques have transformed this process, utilizing digital elevation models (DEMs) for enhanced precision and accurate data representation [28]. These advanced methods offer public and private organizations essential datasets for more effective watershed management. DEMs are particularly valuable in modeling the water flow direction and velocity across landscapes, significantly improving the accuracy of hydrological assessments [29].
This study prioritizes the three watersheds of the Doon Valley in the Indian Himalayan Region (IHR)—Asan, Song, and Suswa—as outlined in Table 1. The CFV-based watershed prioritization method, utilizing morphometric analysis, assesses the topographical and hydrological characteristics of these watersheds. This scientific database will be a valuable resource for future hydrological research and aid decision-makers across various sectors by enhancing their understanding of water resource management. However, this method has some limitations, such as neglecting soil, land use, and climate influences. It also does not represent hydrogeological processes and ecological assessments.

2. Materials and Methods

2.1. Study Area

The Doon Valley, located between the Lesser Himalayas and the Shiwalik ranges in Uttarakhand, extends from longitudes 77°38′ E to 78°20′ E and latitudes 30°01′ N to 30°28′ N. It encompasses two primary watersheds: Asan, covering 701.15 km2 with a westward flow, and Song, covering 1040.49 km2 with an eastward flow [14]. The Suswa watershed, covering an area of 310.98 km2 [8], is part of the larger Song watershed. As a result, the area of the Song watershed is adjusted to 729.51 km2. The Asan watershed flows into the Yamuna river, whereas the Song and Suswa watersheds drains into the Ganga river. Together, these watersheds form the Doon Valley, with an area of 1741.64 km2 and elevation from 303 m to 2764 m [30]. The region receives approximately 2000 mm of average annual rainfall and experiences hot summers with temperatures up to 45 °C and cold winters with temperatures dropping to −2 °C. Key towns within the study area include Dehradun, Mussoorie, Vikasnagar, Doiwala, and Rishikesh. The study area map is shown in Figure 1.

2.2. Data and Methods

This study utilized SRTM DEM data with a 30 m spatial resolution to develop a detailed database and assess various topographical factors. Watershed boundaries and drainage networks were delineated using the Spatial Analyst Tools (SATs) in the ArcGIS Desktop software 10.6.1. SATs were employed to calculate the slope, identify the flow direction and accumulation, define the drainage network, segment streams, and fill depressions in the DEM [7]. The morphometric analysis parameters and formulas are outlined in Table 2, and the flowchart of the methodology is shown in Figure 2.
The CFV method was used in this research for the prioritization of watersheds because it is methodologically simple, transparent, and efficient in combining several morphometric parameters in a GIS environment. The CFV has found extensive global application in similar uses in previous studies [17,31,32,33,34] and hence can be trusted and compared. Although the AHP is widely used as a multi-criteria decision-making method, the CFV was opted to be consistent with standard methodologies used in morphometric analysis and to allow for the more straightforward and interpretable ranking of the sub-watersheds according to their erosion susceptibility. This choice offers a formal yet understandable method of spatial decision-making in watershed management.
The morphometric analysis method was employed to prioritize watersheds by assessing ranks and the CFV based on various topographic and hydrologic parameters [17]. These parameters included the elongation ratio, stream frequency, relief ratio, bifurcation ratio, compactness coefficient, texture ratio, drainage density, circularity ratio, length of overland flow, form factor, and drainage texture [31]. The parameters were ranked according to real-world scenarios for each watershed. The CFV is derived by averaging the ranks of various parameters for each watershed, providing a systematic approach to prioritizing watersheds based on their vulnerability or importance [32,33,34]. Together, the above parameters affect the watershed’s response to precipitation, runoff characteristics, topography, land use, and overall hydrological dynamics. By averaging the ranks, the CFV creates a composite score that reflects the overall priority of each watershed in relation to the specific goals of the study, such as effective watershed management or conservation. Watersheds are then ranked according to their CFV values, where a lower CFV indicates higher priority, meaning that the watershed is more susceptible or requires more immediate attention. Conversely, a higher CFV value signals lower priority. This method ensures that resources and interventions are focused on the most critical watersheds, supporting sustainable watershed management and policy decision-making. This scientific methodology provided a robust foundation for watershed prioritization and was utilized in previous studies.
Table 2. Formulas for morphometric analysis.
Table 2. Formulas for morphometric analysis.
S. No.ParameterFormulaReference
1Stream order (w)Hierarchical rank[35]
2Stream length (Lu)Length of the stream[36]
3Sinuosity index (SI)SI = actual length of the river/straight-line distance between source and mouth[36]
4Mean stream length (Lsm)Lsm = Lu/Nu[35]
5Stream length ratio (RL)RL= Lu/(Lu − 1)[36]
6Bifurcation ration (Rb)(Rb) = Nu/Nu + 1[37]
7Mean bifurcation ratio (Rbm)Rbm = average of bifurcation ratios of all orders[38]
8Drainage density (Dd)Dd = Lu/A[36]
9Drainage texture (Td)Td = Nu/P[36]
10Texture ratio (Rt)Rt= N1/P[36]
11Stream frequency (Fs)Fs = Nu/A[36]
12Elongation ratio (Re)Re = 2√(A/π)/Lb[37]
13Circularity ratio (Rc)Rc = 4 π A/P2[39]
14Form factor (Ff)Ff = A/L2[36]
15Basin relief (Rb)Rb = H − h[40]
16Relief ratio (Rr)Rr = R/L[37]
17Length of overland flow (Lo)Lo = 2/Dd[36]
18Compactness coefficient (Cc)Cc = 0.2821 × P/(A)0.5[36]
Notes: Nu: number of streams, N1: number of streams of order 1, Lb: basin length, A: area of watershed/basin, P: perimeter of watershed/basin, H: maximum height of watershed/basin, h: minimum height of watershed/basin.

3. Results

3.1. Morphometric Analysis

The findings from the morphometric analysis are categorized and presented in distinct sections. Linear factors focus on the watershed/basin’s topographical features; areal aspects highlight geological and climatic characteristics; and relief factors address erosional properties [8]. Linear and relief parameters demonstrate a direct relationship, while areal parameters generally show an inverse correlation with landslides and erosion.

3.1.1. Linear Aspects

Linear aspects provide the one-dimensional details of the watersheds. The following are some of the one-dimensional features of a watershed that are revealed by linear aspects: stream order (w), stream number (Nu), bifurcation ratio (Rb), length ratio (RL), and stream length (Lu). These are discussed below (Table 3).
  • Stream Order (w)
The river and stream networks are classified into three order types based on their positions from the source to the confluence. Type 1 encompasses headwaters, including stream orders 1–3. Type 2 comprises medium-sized streams with stream orders ranging from 4 to 6, while Type 3 consists of large rivers with stream orders larger than 6 [41]. The stream orders in the Doon Valley watersheds range from 1 to 7 (Figure 3).
  • Stream Number (Nu)
The stream number is the overall number of stream segments over different stream orders and is generally inversely related to the stream order, as shown in Figure 4. According to Horton’s first law, the stream number declines with a rise in the stream order [8]—a trend that was consistently observed in all studied watersheds.
  • Stream Length (Ls)
The stream length measures the distance from the origin of a stream channel to its endpoint, serving as a critical indicator of surface runoff within a watershed. Longer streams typically correspond to flatter gradients, whereas shorter streams are usually associated with steeper slopes and finer textures. The stream length is also used to calculate the river’s sinuosity. The relation between the stream order and stream length is shown in Figure 5.
  • Sinuosity index (SI)
The sinuosity index (SI) calculates the degree of a river meandering from a straight line. It is found by dividing the actual river length by the straight-line distance from the source to the mouth of the river [36]. A greater SI indicates a more sinuous, curving route, while a smaller SI indicates a straight river course. This measure is crucial in understanding the river’s behavior, sediment transport, and impacts on the surrounding environment. The results indicate that the Asan river exhibits the highest SI value, at 1.11, followed by the Song river, at 1.07, and then the Suswa river, at 1.02.
  • Length ratio (RL)
The length ratio (RL), introduced by Horton [36], is defined as the ratio of the length of a stream of a higher order to that of the stream in the next, lower order. In this study, the RL value varies from 0.18 to 2.84. This ratio plays a crucial role in understanding the hydrogeology of a watershed, as it reflects variations in the watershed/basin slope, terrain, and decay periods across different stream orders [42].
  • Bifurcation ratio (Rb)
The bifurcation ratio (Rb), proposed by Schumm [37], is the ratio of the number of streams in a particular order to the number of streams in the next, higher order. In common dendritic drainage systems, the average bifurcation ratio tends to range between 3 and 5 [43,44]. A higher Rb generally indicates more rugged terrain with an increased flooding risk. At the same time, a lower ratio suggests a flatter topography with high permeability, more significant infiltration, and enhanced groundwater storage. Watersheds with a circular shape tend to have higher Rb values, whereas elongated watersheds exhibit lower Rb values. In this study, the mean bifurcation ratio is the lowest in the Song watershed, with a value of 4.06, and highest in the Asan watershed, with a value of 5.45.
  • Length of overland flow (Lo)
Horton [26] described the length of overland flow (Lo) as the distance that surface water covers across the land before it becomes concentrated into distinct stream channels. It provides insights into the erosion potential and flow dynamics of a watershed. A shorter overland flow is typically associated with increased channel erosion, while a longer overland flow is linked to more significant sheet erosion [17]. The Lo value varies from 0.70 to 0.79 in the Doon Valley watersheds. The lower Lo value in the Doon Valley suggests higher potential for surface runoff and erosion since water is less likely to infiltrate and more likely to be transported rapidly to stream channels [9].

3.1.2. Areal Aspects

The areal aspects of a watershed highlight its two-dimensional characteristics, which are critical for watershed management. They help to identify the potential for surface runoff, infiltration, and evapotranspiration across different watershed regions. This information is crucial in pinpointing areas susceptible to waterlogging, erosion, or flooding. By analyzing the areal characteristics, researchers and water resource managers gain insights into the watershed’s overall environmental significance, aiding in making informed decisions about watershed management and sustainable water use. Key components such as the watershed’s area and perimeter are integral in evaluating other factors. For detailed information on the areal aspects of all watersheds, see Table 4.
  • Basin length
The basin length refers to the maximum linear distance within a watershed, measured along the main drainage line from the source to the outlet. In this study, the basin lengths of the Doon Valley watersheds were determined using the ArcGIS Desktop software 10.6.1 by measuring the greatest length parallel to the main drainage line. The Song watershed had the longest basin length, at 50.35 km, while the Suswa watershed had the shortest, at 40.50 km.
  • Form factor (Ff)
The form factor (Ff) measures how elongated or circular a watershed/basin is. It is calculated by dividing the area of the watershed/basin by the square of its length, with values ranging from 1 to 0—values closer to one indicate a circular shape and those closer to zero indicate an elongated shape [45]. More circular-shaped watersheds/basins have greater form factor (Ff) values, and these are likely to produce more acute peak flows over shorter times. Elongated basins have smaller Ff values, and these are likely to produce lower peak flows over longer periods. In this study, the Asan watershed has the highest Ff value (0.38), while the Suswa watershed has the lowest value (0.19). Therefore, the Suswa watershed is at a greater erosion risk and has a smaller infiltration capacity.
  • Elongation ratio (Re)
The elongation ratio (Re), as defined by Schumm [37], is determined by dividing the diameter of a circle having the same area as the watershed/basin by the basin’s maximum length. An elongation ratio value close to one indicates a circular watershed/basin shape. According to the classifications by Schumm [37] and Strahler [35], the elongation ratio falls into four categories: elongated (<0.7), less elongated (0.7–0.8), oval (0.8–0.9), and circular (>0.9). The Suswa watershed has the lowest Re value (0.49), while the Asan watershed has the highest value (0.70). A higher Re value indicates a watershed/basin with a gentle slope, leading to low runoff and significant groundwater potential due to the higher infiltration capacity of the sub-soil. Conversely, a lower Re value indicates a watershed/basin with a steep slope, resulting in high runoff and limited groundwater potential due to the lower sub-soil infiltration capacity [17].
  • Circularity ratio (Rc)
The circularity ratio (Rc) is a dimensionless quantitative metric. It is the ratio of the watershed/basin area to the area of a circle whose diameter equals the watershed/basin’s perimeter [39]. This ratio, which ranges from 0 to 1, indicates the growth stage of a watershed/basin: young, mature, and old. Lower values near zero signify elongated watershed/basins, whereas values near one indicate circular watershed/basins [9,46]. Several factors influence the circularity ratio, including the stream length and density, land use changes, geological formations, climatic conditions, and watershed/basin topography. These factors collectively impact the circularity ratio value [7]. The Suswa watershed has the lowest Rc value (0.26) among the selected watersheds, whereas the Asan watershed has the highest Rc value (0.47).
  • Stream frequency (Fs)
The stream frequency (Fs), as defined by Horton [45], refers to the total number of stream segments of all orders per unit area and is closely associated with the drainage density of the watershed/basin [8]. A low stream frequency suggests a gentle watershed/basin slope with good sub-surface material infiltration or permeability, while a high stream frequency indicates a steep watershed/basin slope with limited sub-surface material infiltration or permeability [17,47]. Among the selected watersheds, the Suswa watershed has the lowest Fs value (3.51), while the Song watershed has the highest Fs value (3.87).
  • Drainage density (Dd)
The drainage density (Dd) is the sum of the lengths of all stream segments of different orders within a watershed/basin, expressed as a ratio per unit area of the watershed/basin [36,39]. It is a significant indicator of watershed/basin characteristics, determined by the slope gradient and relative relief [7]. A high drainage density typically indicates a coarse drainage texture, gentle relief, low surface runoff, permeable sub-soil, high infiltration potential, and dense vegetation. In contrast, a low drainage density reflects steep relief, increased surface runoff, impermeable sub-soil, poor infiltration, and sparse vegetation cover [48,49]. In the Doon Valley watersheds, the highest Dd value is observed in the Asan watershed (2.86), while the lowest Dd value is found in the Song watershed (2.52).
  • Drainage texture (Td)
Horton [36] defined the drainage texture (Td) as the sum of all stream segments of all orders divided by the watershed/basin perimeter. It is controlled by the drainage density and stream frequency. Smith [50] categorized the drainage texture into five types: very coarse (<2), coarse (2–4), moderate (4–6), fine (6–8), and very fine (>8). For the selected watersheds, the drainage texture value varies from 9.74 in the Song watershed to 10.67 in the Asan watershed, indicating that all watersheds exhibit very fine drainage textures.
  • Texture ratio (Rt)
The texture ratio (Rt) is a significant variable in the morphometric analysis of a watershed. It is primarily influenced by sub-surface lithological features, the soil infiltration capacity, and the topographical gradient [51]. Horton [36] defined Rt as the total number of first-order streams per unit perimeter of a watershed/basin. In this study, the Rt values range from 7.06 in the Suswa watershed to 14.58 in the Asan watershed. The fine Rt values observed in the Doon Valley watersheds suggest the presence of a permeable lithology with a high infiltration capacity in regions with gentle to moderate slopes and an impermeable lithology with a low infiltration capacity in regions with steep slopes [17].
  • Compactness coefficient (Cc)
Horton [36] introduced the compactness coefficient (Cc) as a measure of a watershed’s shape, indicating how compact or elongated it is. A value of one signifies a perfectly circular watershed, while values greater than one denote more elongated shapes. Cc is closely associated with the soil erosion susceptibility. A lower Cc indicates a reduced erosion risk, whereas a higher value suggests increased susceptibility to erosion [52]. In this study, the Suswa watershed, with a compactness coefficient (Cc) of 1.96, is more prone to erosion. In contrast, the Asan watershed, with a Cc value of 1.46, demonstrates lower susceptibility to erosion.

3.1.3. Relief Aspects

Relief aspects provide a three-dimensional perspective on the watershed/basin, focusing on its relief and relief ratio. These factors are crucial for topographical and hydrological studies of watersheds. Table 5 provides detailed information on the relief aspects for the selected watersheds.
  • Basin relief (Rb)
Basin relief (Rb) is the elevation difference between the highest and lowest points [40]. In this study, the highest Rb value of 2764 m is observed in the Song watershed, while the lowest Rb value of 303 is also found in the Song watershed. Higher Rb values are associated with steep stream slopes, leading to low infiltration and high runoff due to the pronounced slope. Conversely, lower Rb values indicate gentler stream slopes, resulting in high infiltration and a low runoff capacity.
  • Relief ratio (Rr)
The relief ratio, defined by Schumm [37], is the ratio of the basin relief to the basin length. This ratio is used to determine the severity of erosion processes within a watershed/basin and the overall steepness of the watershed/basin. The Song watershed has the highest (48.88), while the Asan watershed has the shortest (42.51).

3.2. Other Significant Topographical Factors for Watershed Management

In addition to morphometric analysis, factors such as elevation, slope, and aspect are crucial in understanding the hydrology and topography of a watershed. These interdependent topographical elements influence the water flow across the landscape, playing a vital role in hydrological models and research and contributing to flood modeling, erosion assessment, and overall water resource management.

3.2.1. Elevation

The elevation directly affects various hydrological and geomorphological processes within a watershed, making it an essential factor in morphometric analysis and management. In this study, the elevation ranges for the three watersheds are as follows: the Asan watershed spans 390 m to 2218 m, the Song watershed extends from 303 m to 2764 m, and the Suswa watershed varies between 405 m and 2276 m (Figure 6a) [8,14]. All three watersheds fall within an overall elevation range of 303 m to 2764 m. High-elevation areas typically contribute to steeper slopes, leading to faster surface runoff, reduced infiltration, and increased erosion potential. Lower elevation areas, on the other hand, typically have gentler slopes, which lead to higher infiltration rates and a lesser risk of erosion [8,30]. Understanding elevation is crucial to watershed management because it helps to identify regions susceptible to erosion, landslides, or flooding and helps to develop efficient water conservation measures [30]. It also helps to forecast sediment migration and choose appropriate sites for construction, such as check dams. Elevation is crucial in understanding watershed characteristics and implementing sustainable watershed management techniques.

3.2.2. Slope and Aspect

Slope and aspect are critical factors in morphometric analysis and watershed management, as they significantly influence hydrological processes and landscape development. In this study, the Song watershed exhibits the steepest slope, reaching up to 75°, followed by the Asan and Suswa watersheds, which have maximum slopes of 66° (Figure 6b) [8,14]. The direction and speed of water flow in a watershed/basin are determined by its slope; steeper slopes result in more runoff, decreased infiltration, and increased rates of erosion. Conversely, gentle slopes promote increased water penetration and sediment deposition, which support soil stability and groundwater recharge [7]. Aspect, or the slope’s direction, impacts microclimatic factors such as the temperature, moisture content, and exposure to sunshine. Aspect values range from 0° to 360°, with 0–22.5° representing north and 22.5–67.5° representing northeast, continuing clockwise [20]. In this research, the slope orientation is mostly west-facing and southwest-to-south-facing, as shown in Figure 6c. South-facing slopes in the Northern Hemisphere receive more sunlight, causing higher evaporation and drier conditions, while north-facing slopes tend to be cooler and have more moisture [53]. Understanding the slope and aspect helps to design soil conservation measures, identify erosion-prone locations, and maximize land use in watershed management. These elements are necessary for sustainable development inside a watershed, flood control, and the efficient management of water resources.

3.2.3. Drainage Density

The drainage density is another topographical parameter that indicates the closeness of the spacing of the channels within a watershed and has significant implications for runoff and erosion processes. In this study, the largest drainage density range, from >9 km/km2 to 10.6 km/km2, is found in the lower region (Figure 6d) [30]. The higher drainage density in the Asan and Suswa watersheds suggests a less permeable sub-surface and reduced runoff, making the region less prone to immediate flooding but more vulnerable to soil erosion over time. Conversely, the low drainage density in the Song watershed reflects a more dissected landscape with steep slopes, facilitating rapid surface runoff and increasing the erosion potential [8,30].

3.3. Prioritization of Doon Valley Watersheds

Prioritization is a crucial strategy in determining critical watersheds where preservation efforts are necessary. It is a simple and essential tool enabling prioritization at the watershed level using a statistical ranking of morphometric parameters, being especially beneficial considering the substantial investment required for traditional techniques in terms of time and resources [9,17,54,55,56].
The three morphometric aspects (linear, areal, and relief) were used to prioritize the watersheds in the Doon Valley. Each watershed’s rank was determined based on the values assigned to each parameter. Furthermore, these watersheds were assigned a CFV based on their average rank. Watersheds with a CFV of 1.80 or less are classified as high priority, those with a CFV between 1.81 and 2.10 are considered medium priority, and those with a CFV greater than 2.10 are designated as low priority (Figure 7). The CFV for the selected watersheds ranges from 1.75 to 2.17 (Table 6). The Suswa watershed exhibits the lowest CFV (1.75) and highest priority, whereas the Song watershed exhibits the highest CFV (2.17) and lowest priority. Among the three watersheds, the Suswa watershed demonstrates exceptional susceptibility to erosion. The prioritization approach based on morphometric parameters allows us to identify optimal soil conservation strategies within the context of integrated water resource management and planning [57,58].

3.4. Correlations Among Morphometric Parameters

Morphometric parameters exhibit interdependence, influencing watershed characteristics. Their correlation helps to assess hydrological behavior, erosion susceptibility, and drainage efficiency, aiding in watershed prioritization for effective management and conservation planning (Table 7).

3.4.1. Positive Correlations (Strong Direct Relationships)

  • Mean bifurcation ratio, drainage texture, and drainage density: These parameters are highly correlated (1.00), indicating that, as the bifurcation ratio increases, the drainage density and drainage texture also increase. This suggests that areas with a higher number of stream segments tend to have more closely spaced channels.
  • Circularity ratio, form factor, and elongation ratio: These parameters show a strong correlation (1.00), meaning that watersheds that are elongated tend to have higher form factor values.
  • Texture ratio and compactness coefficient: These are strongly correlated with the elongation ratio, form factor, and circularity ratio (1.00), indicating that watersheds/basins with higher texture ratios also tend to have higher compactness coefficients and are susceptible to soil erosion, with steep slopes and higher runoff.

3.4.2. Negative Correlations (Inverse Relationships)

  • Mean bifurcation ratio vs. circularity ratio, form factor, and elongation ratio (−0.5): A higher mean bifurcation ratio is directly associated with a circular watershed/basin with a gentle slope and indirectly with an elongated watershed/basin with a steep slope.
  • Relief ratio vs. texture ratio, drainage density, and mean bifurcation ratio (−1.0): This suggests that areas with a high texture ratio, drainage density, and mean bifurcation ratio tend to have lower relief ratios, indicating a more mature landscape with well-developed drainage networks.
  • Sinuosity index vs. circularity ratio, form factor, and elongation ratio (−1.0): Higher sinuosity (indicating the meandering nature of rivers) is associated with less elongated and circular watersheds.
  • Length of overland flow vs. circularity ratio, form factor, and elongation ratio (−0.5): The length of overland flow shows a negative correlation with the circularity ratio, form factor, and elongation ratio, indicating that more elongated watersheds/basins with steep slopes have shorter overland flow paths, leading to faster runoff and lower infiltration rates.

3.4.3. Neutral and Moderate Correlations

  • Stream frequency vs. most parameters (−0.5 to 0.5): The stream frequency shows moderate negative correlations with most parameters, indicating that its influence is relatively balanced across multiple morphometric characteristics.
  • Relief ratio: The relief ratio shows a neutral correlation with the elongation ratio, form factor, and circularity ratio (0.5), which indicates that a watershed/basin’s steepness does not directly determine its shape due to geomorphic influences.

4. Discussion

The integration of GIS and remote sensing approaches in this research has demonstrated their vast applicability towards the comprehension of watershed dynamics and in the guidance of sustainable management practices. Through morphometric analysis and CFV-based prioritization, this research identifies spatial differences in watershed vulnerability within the Doon Valley, specifically among the Asan, Suswa, and Song river basins. This strategy is consistent with research works like Rao et al. [48] and Javed et al. [32], which emphasized morphometric parameters in the evaluation of erosion risks and watershed integrity.
Among the three, the Suswa watershed was the most susceptible, largely because it had the highest compactness coefficient (Cc = 1.96) and lowest elongation ratio (Re = 0.49), which imply a greater tendency to be concentrated under runoff and soil erosion. These results align with the erosion-prone state of elongated watersheds reported by Nag and Chakraborty [11], emphasizing that measures must be taken promptly. Furthermore, the Suswa river passes through Dehradun’s urban center, subjecting it to higher levels of biotic stress, such as the release of domestic sewage, disposal of solid waste, and encroachment—a situation also noted in recent Himalayan urban hydrology research [8,59]. Against these physical and human-induced stresses, the Suswa watershed’s prioritization is both statistically and situationally warranted. Its hydrological vulnerability—coupled with city stress—highlights the need for bioengineering treatments such as vegetative buffers, managed city drainage, and stream restoration initiatives. In addition to sediment yield reductions, they foster ecosystem integrity, as argued in the research of Liu et al. [60] and Prabhakar et al. [61] on the urbanization of watersheds.
The Asan watershed, despite having a medium-priority rating based on morphometric parameters like the drainage density and bifurcation ratio, still requires strong management. The area includes the Asan Conservation Reserve—Uttarakhand’s sole Ramsar-designated wetland—where avian diversity relies on hydrological integrity. Previous research by Mishra et al. [62], Mandal et al. [63], and Singh et al. [64] emphasizes the Asan river’s ecological vulnerability, and this research similarly underscores the need for wetland protection, buffer zone demarcation, and upstream land use control, which could influence the water inflow and water quality.
Furthermore, the moderate shape parameters of the Asan watershed (e.g., Cc = 1.46, Re = 0.70) suggest a more stable morphology compared to Suswa but one that could become more susceptible if left unmanaged due to development pressures and climate change [65,66]. Therefore, this study adds to existing knowledge by identifying areas of concern that may be neglected in conventional planning, as suggested by the findings of Magesh et al. [13] regarding the predictive value of morphometric analysis in prioritizing watersheds.
Conversely, the Song watershed, while identified as the lowest in vulnerability by CFV analysis, has challenges of its own because of its rugged nature and high slope gradients. These enhance surface runoff and tend to increase the flood susceptibility, as confirmed in comparable mountainous landscapes by Khanduri [67] and Dwivedi et al. [68]. Despite the reduced erosion potential according to compactness and elongation measures, the elevated relief ratio (Rr = 48.88) and stream frequency (Fs = 3.87) indicate probable flash floods, particularly from intense rainfall episodes. Thus, the Song watershed must not be deprioritized entirely but rather dealt with through focused interventions involving afforestation, check dams, and slope stabilization.
The most significant contribution of this research is its combination of geospatial data and morphometric parameters to develop a tiered watershed management framework for a data-scarce Himalayan region. This research provides the first comparative evaluation for multiple Doon Valley watersheds based on high-resolution satellite datasets. The detailed prioritization matrix developed in this study enhances watershed planning and supports decentralized conservation efforts, which is a significant step forward in local-scale hydrological modeling in India.
In addition, the CFV approach adopted in this work combines several morphometric parameters—the stream order, drainage density, form factor, bifurcation ratio, and relief ratio—into a composite score that can drive prioritization. This multi-pronged approach is a better alternative to the univariate measures employed in previous studies, as indicated by Meshram et al. [34], and better captures watershed behavior under changing physiographic and land use conditions. The research by Singh & Kansal [69] in the Alaknanda basin employed the RUSLE model within a GIS-based framework to quantify soil erosion in twelve sub-basins. Their results identified a significant variability in erosion rates—ranging from 6 to 27.9 t/ha/year—attributable to flash floods, steep slopes, and poor land management. This enabled the identification of priority sub-basins in need of urgent conservation interventions. Similarly, Kumar et al. [70] used the Sediment Yield Index (SYI) approach in the Chambal basin, which is characterized by intense gully erosion. Their work computed sediment yields of 560-2625 ton/ha/year and identified high-priority catchments (e.g., Lower Chambal subbasin) which require immediate gully control measures. Both works highlight the important role of erosion modeling in informing watershed management options.
Collectively, the studies illustrate the applicability of erosion-centric modeling strategies in the sub-watershed prioritization for soil and water conservation. However, while RUSLE and SYI concentrate on the outputs of erosion, the CFV model employed in this study incorporates morphometric parameters to estimate erosion susceptibility without empirical erosion data. This makes CFV a feasible and scalable method, particularly in data-deficient mountainous areas such as the Doon Valley, which allows efficient proactive planning for conservation.
Overall, the findings highlight the need for a differentiated approach to watershed management in the Doon Valley. Each watershed exhibits distinct drainage characteristics and susceptibility to erosion, necessitating conservation measures for effective management and conservation [71,72]. Although Suswa demands immediate restoration and pollution mitigation, Asan requires wetland conservation and eco-sensitive land use planning, and Song requires slope-specific runoff mitigation measures. These situation-specific interventions, with support from morphometric data and spatial analysis, provide an imitable strategy for sustainable watershed management in other Himalayan and urban foothill catchments. This comprehensive framework is crucial in addressing the specific environmental challenges of each watershed and ensuring sustainable land use practices in the region [73,74].

5. Conclusions

In this study, remote sensing and GIS technologies have been used to comprehensively understand the Doon Valley watersheds in the IHR, revealing significant differences in drainage characteristics and erosion risks. The Suswa watershed, with its low compactness and high erosion susceptibility, is the most susceptible and requires urgent conservation efforts to prevent soil erosion and ensure sustainable land use. The Asan watershed, while less critical, still needs medium-priority conservation to maintain ecological balance. Despite having the lowest priority, the Song watershed faces challenges due to its steep slopes and high relief, leading to rapid runoff and increased flood risks. To enhance watershed sustainability, future efforts should focus on integrated conservation strategies such as afforestation and soil conservation in high-risk areas, as well as sustainable land use planning, flood mitigation measures, community engagement in conservation programs, and long-term monitoring using advanced remote sensing and GIS techniques. These initiatives will ensure effective watershed management, minimizing environmental degradation and enhancing the resilience against erosion and flooding in the IHR. Additionally, this research aligns with the United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land), by promoting sustainable watershed management, reducing environmental degradation, and enhancing resilience against climate-induced disasters.

Author Contributions

Conceptualization, A.M. and M.Z.; methodology, A.M. and M.K.; software, A.M.; validation, A.M., M.K. and R.B.; formal analysis, A.M.; investigation, A.M. and M.Z.; resources, A.M.; data curation, A.M.; visualization, A.M.; writing—original draft preparation, A.M.; writing—review and editing, A.M., M.K., R.B., V.N.M., K.H.T., F.F.B.H. and M.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R675), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Data Availability Statement

Data are contained within the article. All data used in this study were collected by the authors.

Acknowledgments

The authors extend their appreciation to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R675), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to express their sincere gratitude to their organizations for their valuable support and guidance.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Grill, G.; Lehner, B.; Thieme, M.; Geenen, B.; Tickner, D.; Antonelli, F.; Babu, S.; Borrelli, P.; Cheng, L.; Crochetiere, H.; et al. Mapping the world’s free-flowing rivers. Nature 2019, 569, 215–221. [Google Scholar] [CrossRef] [PubMed]
  2. Braga, B.; Chartres, C.; Cosgrove, W.J.; da Cunha, L.V.; Gleick, P.H.; Kabat, P.; Kadi, M.A.; Loucks, D.P.; Lundqvist, J.; Narain, S.; et al. Water and the Future of Humanity: Revisiting Water Security, 1st ed.; Calouste Gulbenkian Foundation; Springer International: Berlin/Heidelberg, Germany, 2014; pp. 1–241. [Google Scholar]
  3. McGrane, S.J. Impacts of Urbanisation on Hydrological and Water Quality Dynamics, and Urbanwater Management: A Review. Hydrol. Sci. J. 2016, 61, 2295–2311. [Google Scholar] [CrossRef]
  4. Mani, A.; Kumari, M.; Badola, R. Urban Watershed Management in the Doon Valley: A Geospatial Assessment of Himalayan Watersheds. J. Landsc. Ecol. 2025, 18, 127–149. [Google Scholar] [CrossRef]
  5. Li, R.; Zhu, G.; Lu, S.; Meng, G.; Chen, L.; Wang, Y.; Huang, E.; Jiao, Y.; Wang, Q. Effects of cascade hydropower stations on hydrologic cycle in Xiying river basin, a runoff in Qilian mountain. J. Hydrol. 2025, 646, 132342. [Google Scholar] [CrossRef]
  6. Cui, Z.; Chen, Q.; Luo, J.; Ma, X.; Liu, G. Characterizing Subsurface Structures from Hard and Soft Data with Multiple-Condition Fusion Neural Network. Water Resour. Res. 2024, 60, e2024WR038170. [Google Scholar] [CrossRef]
  7. Singh, P.; Gupta, A.; Singh, M. Hydrological inferences from watershed analysis for water resource management using remote sensing and GIS techniques. Egypt. J. Remote Sens. Space Sci. 2014, 17, 111–121. [Google Scholar] [CrossRef]
  8. Mani, A.; Kumari, M.; Badola, R. Morphometric Analysis of Suswa River Basin Using Geospatial Techniques. Eng. Proc. 2022, 27, 65. [Google Scholar]
  9. Mundetia, N.; Sharma, D.; Dubey, S.K. Morphometric assessment and sub-watershed prioritization of Khari River basin in semi-arid region of Rajasthan, India. Arab. J. Geosci. 2018, 11, 530. [Google Scholar] [CrossRef]
  10. Breyer, S.P.; Scott, S.R. Drainage basin perimeters: A fractal significance. Geomorphology 1992, 5, 143–157. [Google Scholar] [CrossRef]
  11. Nag, S.K.; Chakraborty, S. Influence of rock types and structures in the development of drainage network in hard rock area. J. Indian Soc. Remote Sens. 2003, 31, 26–35. [Google Scholar] [CrossRef]
  12. Das, A.K.; Mukherjee, S. Drainage morphometry using satellite data and GIS in Raigad district, Maharashtra. J. Geol. Soc. India 2005, 65, 577–586. [Google Scholar]
  13. Magesh, N.S.; Chandrasekar, N.; Soundranayagam, J.P. Morphometric evaluation of Papanasam and Manimuthar watersheds, parts of Western Ghats, Tirunelveli district, Tamil Nadu, India: A GIS approach. Environ. Earth Sci. 2011, 64, 373–381. [Google Scholar] [CrossRef]
  14. Sreedevi, P.D.; Owais, S.H.H.K.; Khan, H.H.; Ahmed, S. Morphometric analysis of a watershed of South India using SRTM data and GIS. J. Geol. Soc. India 2009, 73, 543–552. [Google Scholar] [CrossRef]
  15. Jhariya, D.C.; Kumar, T.; Pandey, H.K. Watershed Prioritization Based on Soil and Water Hazard Model Using Remote Sensing, Geographical Information System and Multi-Criteria Decision Analysis Approach. Geocarto Int. 2018, 35, 188–208. [Google Scholar] [CrossRef]
  16. Abdekareem, M.; Al-Arifi, N.; Abdalla, F.; Mansour, A.; El-Baz, F. Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions. Sustainability 2022, 14, 7871. [Google Scholar] [CrossRef]
  17. Das, B.; Singh, S.; Jain, S.K.; Thakur, P.K. Prioritization of Sub-Basins of Gomti River for Soil and Water Conservation Through Morphometric and LULC Analysis Using Remote Sensing and GIS. J. Indian. Soc. Remote Sens. 2021, 49, 2503–2522. [Google Scholar] [CrossRef]
  18. Bashir, B.; Alsalman, A. Morphometric and Soil Erosion Characterization Based on Geospatial Analysis and Drainage Basin Prioritization of the Rabigh Area Along the Eastern Red Sea Coastal Plain, Saudi Arabia. Sustainability 2024, 16, 9008. [Google Scholar] [CrossRef]
  19. Pramanik, M.K. Morphometric Characteristics and Water Resource Management of Tista River Basin Using Remote Sensing and GIS Techniques. J. Hydrogeol. Hydrol. Eng. 2016, 5, 17–19. [Google Scholar] [CrossRef]
  20. Woldesenbet, T.A.; Elagib, N.A.; Ribbe, L.; Heinrich, J. Hydrological responses to land use/cover changes in the source region of the Upper Blue Nile Basin, Ethiopia. Sci. Total Environ. 2017, 575, 724–741. [Google Scholar] [CrossRef]
  21. Arabameri, A.; Tiefenbacher, J.P.; Blaschke, T.; Pradhan, B.; Tien Bui, D. Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model. Remote Sens. 2020, 12, 874. [Google Scholar] [CrossRef]
  22. Sampath, V.K.; Radhakrishnan, N. Prioritization of Sub-Watersheds Susceptible to Soil Erosion using Different Combinations of Objective Weighting and MCDM Techniques in an Ungauged River Basin. Water Resour. Manag. 2024, 38, 3447–3469. [Google Scholar] [CrossRef]
  23. Govarthanambikai, K.; Sathyanarayan Sridhar, R. Prioritization of watershed using morphometric parameters through geospatial and PCA technique for Noyyal River Basin, Tamil Nadu, India. J. Water Clim. Change 2024, 15, 1218–1231. [Google Scholar]
  24. Kumar, A.; Singh, S.; Pramanik, M.; Chaudhary, S.; Maurya, A.K.; Kumar, M. Watershed prioritization for soil erosion mapping in the Lesser Himalayan Indian basin using PCA and WSA methods in conjunction with morphometric parameters and GIS-based approach. Environ. Dev. Sustain. 2022, 24, 3723–3761. [Google Scholar] [CrossRef]
  25. Shaikh, M.; Yadav, S.; Manekar, V. Application of the compound factor for runoff potential in sub-watersheds prioritisation based on quantitative morphometric analysis. J. Geol. Soc. India 2022, 98, 687–695. [Google Scholar] [CrossRef]
  26. Godif, G.; Manjunatha, B.R. Prioritizing sub-watersheds for soil and water conservation via morphometric analysis and the weighted sum approach: A case study of the Geba river basin in Tigray, Ethiopia. Heliyon 2022, 8, e12261. [Google Scholar] [CrossRef]
  27. Pastor, I.; Tanislav, D.; Nedelea, A.; Dunea, D.; Serban, G.; Haghighi, A.T.; Sabau, D.; Bretcan, P. Morphometric Analysis and Prioritization of Sub-Watersheds Located in Heterogeneous Geographical Units—Case Study: The Buzău River Basin. Sustainability 2024, 16, 7567. [Google Scholar] [CrossRef]
  28. Shekar, P.R.; Mathew, A.; Hasher, F.F.B.; Mehmood, K.; Zhran, M. Towards Sustainable Development: Ranking of Soil Erosion-Prone Areas Using Morphometric Analysis and Multi-Criteria Decision-Making Techniques. Sustainability 2025, 17, 2124. [Google Scholar] [CrossRef]
  29. Jones, K.L.; Poole, G.C.; O’Daniel, S.J.; Mertes, L.A.K.; Stanford, J.A. Surface hydrology of low-relief landscapes: Assessing surface water flow impedance using LIDAR-derived digital elevation models. Remote Sens. Environ. 2008, 112, 4148–4158. [Google Scholar] [CrossRef]
  30. Mani, A.; Kumari, M.; Badola, R. Landslide hazard zonation (LHZ) mapping of Doon Valley using multi-criteria analysis method based on remote sensing and GIS techniques. Discov. Geosci. 2024, 2, 35. [Google Scholar] [CrossRef]
  31. Rekha, B.V.; George, A.V.; Rita, M.J.E.R. Morphometric analysis and micro-watershed prioritization of Peruvanthanam sub-watershed, the Manimala River Basin, Kerala, South India. Environ. Res. Eng. Manag. 2011, 57, 6–14. [Google Scholar]
  32. Javed, A.; Khanday, M.Y.; Rais, S. Watershed prioritization using morphometric and land use/land cover parameters: A remote sensing and GIS based approach. J. Geol. Soc. India 2011, 78, 63–75. [Google Scholar] [CrossRef]
  33. Chandniha, S.K.; Kansal, M.L. Prioritization of subwatersheds based on morphometric analysis using geospatial technique in Piperiya watershed India. Appl. Water Sci. 2014, 7, 329–338. [Google Scholar] [CrossRef]
  34. Meshram, S.G.; Sharma, S.K. Prioritization of watershed through morphometric parameters: A PCA-based approach. Appl. Water Sci. 2017, 7, 1505–1519. [Google Scholar] [CrossRef]
  35. Strahler, A.N. Quantative Geomorphology of Drainage Basins and Channel Networks. In Hand Book of Applied Hydrology, 1st ed.; Te Chow, V., Ed.; McGraw Hill Book Company: New York, NY, USA, 1964; pp. 439–476. [Google Scholar]
  36. Horton, R.E. Erosional Development of Streams and Their Drainage Basins: Hydrophysical Approach to Quantitative Morphology. Geol. Soc. Am. Bull. 1945, 56, 275–370. [Google Scholar] [CrossRef]
  37. Schumm, S.A. Evolution of Drainage Systems and Slopes in Badlands at Perth Amboy, New Jersey. Geol. Soc. Am. Bull. 1956, 67, 597–646. [Google Scholar] [CrossRef]
  38. Strahler, A.N. Quantitative Analysis of Watershed Geomorphology. Trans. Am. Geophys. Union 1957, 38, 913–920. [Google Scholar]
  39. Miller, V.C. A Quantitative Geomorphic Study of Drainage Basin Characteristics in the Clinch Mountain Area, Virginia and Tennessee; Columbia University: New York, NY, USA, 1953; Volume 3, pp. 389–402. [Google Scholar]
  40. Strahler, A.N. Dynamic Basis of Geomorphology. Geol. Soc. Am. Bull. 1952, 63, 923–938. [Google Scholar] [CrossRef]
  41. Vannote, R.L.; Minshall, G.W.; Cummins, K.W.; Sedell, J.R.; Cushing, C.E. The River Continuum Concept. Can. J. Fish. Aquat. Sci. 1980, 37, 130–137. [Google Scholar] [CrossRef]
  42. Rao, G.T.; Rao, V.G.; Dakate, R.; Rao, S.M.; Rao, B.R. Remote sensing and GIS based comparative morphometric study of two sub-watershed of different physiographic conditions, West Godavari District, AP. J. Geol. Soc. India 2012, 79, 383–390. [Google Scholar] [CrossRef]
  43. Kuchay, N.A.; Bhat, M.S. Automated drainage characterization of Dudganga watershed in western Himalayas. Eur. Sci. J. 2013, 9, 126–138. [Google Scholar]
  44. Verstappen, H. The Applied Geomorphology; International Institute for Aerial Survey and Earth Science (ITC): Enschede, The Netherlands, 1983. [Google Scholar]
  45. Horton, R.E. Drainage Basin Characteristics. Trans. Am. Geophys. Union 1932, 13, 350–361. [Google Scholar]
  46. Shaikh, M.; Birajdar, F. Analysis of watershed characteristics using remote sensing and gis techniques. Int. J. Innov. Res. Sci. Eng. Technol. 2015, 4, 1971–1976. [Google Scholar]
  47. Farhan, Y.; Anbar, A.; Al-Shaikh, N.; Mousa, R. Prioritization of semi-arid agricultural watershed using morphometric and principal component analysis, remote sensing, and GIS techniques, the Zerqa River Watershed, Northern Jordan. Agric. Sci. 2016, 8, 113–148. [Google Scholar] [CrossRef]
  48. Nautiyal, M.D. Morphometric Analysis of a Drainage Basin Using Aerial Photographs: A Case Study of Khairkuli Basin, District Dehradun, U.P. J. Indian Soc. Remote Sens. 1994, 22, 251–261. [Google Scholar] [CrossRef]
  49. Choudhari, P.P.; Nigam, G.K.; Singh, S.K.; Thakur, S. Morphometric based prioritization of watershed for groundwater potential of Mula river basin, Maharashtra, India. Geol. Ecol. Landsc. 2018, 2, 256–267. [Google Scholar] [CrossRef]
  50. Smith, K.G. Standards for Grading Texture of Erosional Topography. Am. J. Sci. 1950, 248, 655–668. [Google Scholar] [CrossRef]
  51. Rai, P.K.; Chandel, R.C.; Mishra, V.N.; Singh, P. Hydrological inferences through morphometric analysis of lower Kosi River Basin of India for water resource management based on remote sensing data. Appl. Water Sci. 2018, 8, 15. [Google Scholar] [CrossRef]
  52. Sinha, R.K.; Eldho, T.I. Assessment of soil erosion susceptibility based on morphometric and landcover analysis: A case study of Netravati River Basin, India. J. Indian Soc. Remote Sens. 2021, 49, 1709–1725. [Google Scholar] [CrossRef]
  53. Smith, T.; Bookhagen, B. Climatic and biotic controls on topographic asymmetry at the global scale. J. Geophys. Res. Earth Surf. 2021, 126, e2020JF005692. [Google Scholar] [CrossRef]
  54. Zende, A.M.; Nagarajan, R.; Atal, K.R. Prioritization of sub-watersheds in semi arid region, Western Maharashtra, India using Geographical Information System. Am. J. Eng. Res. 2013, 2, 128–135. [Google Scholar]
  55. Balasubramanian, A.; Duraisamy, K.; Thirumalaisamy, S.; Krishnaraj, S.; Yatheendradasan, R.K. Prioritization of subwatersheds based on quantitative morphometric analysis in lower Bhavani basin, Tamil Nadu, India using DEM and GIS techniques. Arab. J. Geosci. 2017, 10, 1–18. [Google Scholar] [CrossRef]
  56. Bagwan, W.A.; Gavali, R.S. An integrated approach for the prioritization of subwatersheds in the Urmodi River catchment (India) for soil conservation using morphometric and land use land cover (LULC) factors. J. Sediment. Environ. 2021, 6, 39–56. [Google Scholar] [CrossRef]
  57. Das, D. Identification of erosion prone areas by morphometric analysis using GIS. J. Inst. Eng. India A 2014, 95, 61–74. [Google Scholar] [CrossRef]
  58. Singh, O.; Singh, J. Soil erosion susceptibility assessment of the lower Himachal Himalayan Watershed. J. Geol. Soc. India 2018, 92, 157–165. [Google Scholar] [CrossRef]
  59. Subudhi, S.P.; Kansal, A.; Ghoshal, T.; Singhal, N.; Rana, D. Recent Trends of Temporal and Spatial Variation in Waste Generation and Its Impacts on River Water Quality: Special Emphasis on Suswa and Song Rivers of Uttarakhand. In International Conference on Advances and Innovations in Recycling Engineering; Springer Nature: Singapore, 2023; pp. 129–140. [Google Scholar]
  60. Liu, J.; Wang, Y.; Li, Y.; Peñuelas, J.; Zhao, Y.; Sardans, J.; Tetzlaff, D.; Liu, J.; Liu, X.; Yuan, H.; et al. Soil ecological stoichiometry synchronously regulates stream nitrogen and phosphorus concentrations and ratios. CATENA 2023, 231, 107357. [Google Scholar] [CrossRef]
  61. Prabhakar, A.K.; Singh, K.K.; Lohani, A.K.; Chandniha, S.K. Study of Champua watershed for management of resources by using morphometric analysis and satellite imagery. Appl. Water Sci. 2019, 9, 127. [Google Scholar] [CrossRef]
  62. Mishra, A.P.; Singh, S.; Jani, M.; Singh, K.A.; Pande, C.B.; Varade, A.M. Assessment of water quality index using Analytic Hierarchy Process (AHP) and GIS: A case study of a struggling Asan River. Int. J. Environ. Anal. Chem. 2022, 104, 1159–1171. [Google Scholar] [CrossRef]
  63. Mandal, S.; Mani, A.; Lall, A.R.; Kumar, D. Slope stability assessment and landslide susceptibility mapping in the Lesser Himalaya, Mussoorie, Uttarakhand. Discov. Geosci. 2024, 2, 51. [Google Scholar] [CrossRef]
  64. Singh, S.; Naik, R.; Bahuguna, A.; Kumar, A.; Sharma, N. Habitat Quality Characterization and Management of Asan Wetland Biodiversity. Int. J. Ecol. Environ. Sci. 2024, 50, 557–575. [Google Scholar] [CrossRef]
  65. Efraimidou, E.; Spiliotis, M. A GIS-Based Flood Risk Assessment Using the Decision-Making Trial and Evaluation Laboratory Approach at a Regional Scale. Environ. Process. 2024, 11, 9. [Google Scholar] [CrossRef]
  66. Shrestha, S.; Dahal, D.; Poudel, B.; Banjara, M.; Kalra, A. Flood Susceptibility Analysis with Integrated Geographic Information System and Analytical Hierarchy Process: A Multi-Criteria Framework for Risk Assessment and Mitigation. Water 2025, 17, 937. [Google Scholar] [CrossRef]
  67. Khanduri, S. Cloudbursts Strike over Foothills Himalaya of Uttarakhand, India: A Case Study from Maldeota, Dehradun District. Acad. Platf. J. Nat. Hazards Disaster Manag. 2024, 5, 30–45. [Google Scholar] [CrossRef]
  68. Dwivedi, S.K.; Thakur, P.K.; Dhote, P.R.; Kruczkiewicz, A.; Upadhyay, M.; Moothedan, A.J.; Bisht, M.; Singh, R.P. Unravelling flash flood dynamics of Song watershed, Doon Valley: Key insights for floodplain management. Geomat. Nat. Hazards Risk 2024, 15, 2378979. [Google Scholar] [CrossRef]
  69. Singh, S.; Kansal, M.L. Sub-basin prioritisation using RUSLE in a Mountainous River Basin of Uttarakhand (India). Environ. Dev. Sustain. 2024, 26, 6851–6877. [Google Scholar] [CrossRef]
  70. Kumar, R.; Naqvi, H.R.; Devrani, R.; Deshmukh, B.; Huang, J.C. Sediment yield assessment, prioritization and control practices in Chambal River basin employing SYI model. J. Geol. Soc. India 2022, 98, 1585–1594. [Google Scholar] [CrossRef]
  71. Chen, L.; Zhu, G.; Lin, X.; Li, R.; Lu, S.; Jiao, Y.; Qiu, D.; Meng, G.; Wang, Q. The Complexity of Moisture Sources Affects the Altitude Effect of Stable Isotopes of Precipitation in Inland Mountainous Regions. Water Resour. Res. 2024, 60, e2023WR036084. [Google Scholar] [CrossRef]
  72. Zhao, Y.; Wang, H.; Song, B.; Xue, P.; Zhang, W.; Peth, S.; Hill, R.L.; Horn, R. Characterizing uncertainty in process-based hydraulic modeling, exemplified in a semiarid Inner Mongolia steppe. Geoderma 2023, 440, 116713. [Google Scholar] [CrossRef]
  73. Baidauletova, G.; Yessimzhanova, S.; Beisenova, M.; Yesbolova, Y.; Taizhanov, L. Modern Problems of Water Resources and Ways to Solve Them: The Case of Kazakhstan. Water Conserv. Manag. 2024, 8, 150–155. [Google Scholar] [CrossRef]
  74. Misra, A.K.; Dolui, S.; Dutta, K.; Baruah, B.; Ranjan, R.K.; Wanjari, N. Sustainable water management in Sikkim Himalayan region: Innovative solutions for rainwater harvesting reservoirs. Geol. Ecol. Landsc. 2024, 1–14. [Google Scholar] [CrossRef]
Water 17 02039 g001
Figure 1. Study area map.
Figure 1. Study area map.
Water 17 02039 g001
Water 17 02039 g002
Figure 2. Flowchart of the methodology for watershed prioritization.
Figure 2. Flowchart of the methodology for watershed prioritization.
Water 17 02039 g002
Water 17 02039 g003
Figure 3. Drainage map of watersheds in Doon Valley.
Figure 3. Drainage map of watersheds in Doon Valley.
Water 17 02039 g003
Water 17 02039 g004
Figure 4. Graphical representation of stream order and number of streams.
Figure 4. Graphical representation of stream order and number of streams.
Water 17 02039 g004
Water 17 02039 g005
Figure 5. Graphical representation of stream order and stream length.
Figure 5. Graphical representation of stream order and stream length.
Water 17 02039 g005
Water 17 02039 g006
Figure 6. (a) Elevation map, (b) slope map, (c) aspect map, and (d) drainage density map of watersheds in Doon Valley.
Figure 6. (a) Elevation map, (b) slope map, (c) aspect map, and (d) drainage density map of watersheds in Doon Valley.
Water 17 02039 g006
Water 17 02039 g007
Figure 7. Map of prioritization of watersheds in Doon Valley.
Figure 7. Map of prioritization of watersheds in Doon Valley.
Water 17 02039 g007
Table 1. Doon Valley watershed area statistics.
Table 1. Doon Valley watershed area statistics.
S. No.WatershedRiverLocation in Doon ValleyArea in km2Area in %
1Asan watershedAsanWestern Doon Valley701.1540.25
2Song watershedSongEastern Doon Valley729.5141.89
3Suswa watershedSuswaCentral Doon Valley310.9817.86
Total1741.64100
Table 3. Linear aspects of watersheds in Doon Valley.
Table 3. Linear aspects of watersheds in Doon Valley.
WatershedStream Order (w)No. of Streams (Nu)Bifurcation Ratio (Rb)Mean Bifurcation Ratio (Rbm)Total Length of Streams (km)Mean Stream Length (Lsm)Length Ratio (RL)Sinuosity Index
(SI)		Length of Overland Flow (Lo)
Asan12003 973.75 1.110.70
24944.05 511.67 0.53
3925.375.45303.090.770.59
4243.83 155.62 0.51
5212.00 29.31 0.19
612.00 32.41 1.11
Total2616 Total2005.85
Song12197 910.8 1.070.79
24884.50 457.13 0.50
31054.65 207.82 0.45
4264.044.06133.080.650.64
555.20 75.63 0.57
615.00 34.03 0.45
711.00 17.87 0.53
Total2823 Total1836.36
Suswa1864 421.07 1.020.70
21744.97 253.43 0.60
3404.355.13127.30.810.50
4123.33 48.56 0.38
5112.00 8.54 0.18
611.00 24.22 2.84
Total1092 Total883.12
Table 4. Areal aspects of watersheds in Doon Valley.
Table 4. Areal aspects of watersheds in Doon Valley.
WatershedBasin Area (km2)Perimeter (km)Basin Length (km)Form Factor (Ff)Elongation Ratio (Re)Circularity Ratio (Rc)Drainage Density (Dd) (km/km2)Stream Frequency (Fs)Texture Ratio (Rt)Drainage Texture (Td)Compactness Coefficient (Cc)
Asan701.15137.35430.380.700.472.863.7314.5810.671.46
Song729.51169.1950.350.290.610.322.523.8712.999.741.77
Suswa310.98122.4540.500.190.490.262.843.517.069.971.96
Table 5. Relief aspects of watersheds in Doon Valley.
Table 5. Relief aspects of watersheds in Doon Valley.
WatershedHeight of Basin Mouth (h), mMaximum Height of Basin (H), mBasin Relief (Rb), mRelief Ratio (Rr)
Asan3902218182842.51
Song3032764246148.88
Suswa4052278187346.25
Table 6. Watershed prioritization table.
Table 6. Watershed prioritization table.
WatershedMean Bifurcation Ratio (Rbm)Drainage Density (Dd)Drainage Texture (Td)Form Factor (Ff)Elongation Ratio (Re)Circularity Ratio (Rc)Stream Frequency (Fs)Texture Ratio (Rt)Length of Overland Flow (Lo)Compactness Coefficient (Cc)Relief Ratio (Rr)Sinuosity IndexCompound Factor Value (CFV)Priority
Asan1113332313312.08Medium
Song3332221232122.17Low
Suswa2221113121231.75High
Table 7. Correlation matrix table of morphometric parameters.
Table 7. Correlation matrix table of morphometric parameters.
Morphometric ParameterMean Bifurcation Ratio (Rbm)Drainage Density (Dd)Drainage Texture (Td)Form Factor (Ff)Elongation Ratio (Re)Circularity Ratio (Rc)Stream Frequency (Fs)Texture Ratio (Rt)Length of Overland Flow (Lo)Compactness Coefficient (Cc)Relief Ratio (Rr)Sinuosity Index (SI)
Mean Bifurcation Ratio (Rbm)111−0.5−0.5−0.5−0.5−0.51−0.5−10.5
Drainage Density (Dd)111−0.5−0.5−0.5−0.5−0.51−0.5−10.5
Drainage Texture (Td)111−0.5−0.5−0.5−0.5−0.51−0.5−10.5
Form Factor (Ff)−0.5−0.5−0.5111−0.51−0.510.5−1
Elongation Ratio (Re)−0.5−0.5−0.5111−0.51−0.510.5−1
Circularity Ratio (Rc)−0.5−0.5−0.5111−0.51−0.510.5−1
Stream Frequency (Fs)−0.5−0.5−0.5−0.5−0.5−0.51−0.5−0.5−0.50.50.5
Texture Ratio (Rt)−0.5−0.5−0.5111−0.51−0.510.5−1
Length of Overland Flow (Lo)111−0.5−0.5−0.5−0.5−0.51−0.5−10.5
Compactness Coefficient (Cc)−0.5−0.5−0.5111−0.51−0.510.5−1
Relief Ratio (Rr)−1−1−10.50.50.50.50.5−10.51−0.5
Sinuosity Index (SI)0.50.50.5−1−1−10.5−10.5−1−0.51
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mani, A.; Badola, R.; Kumari, M.; Mishra, V.N.; Thamaga, K.H.; Hasher, F.F.B.; Zhran, M. Watershed Prioritization with Respect to Flood Susceptibility in the Indian Himalayan Region (IHR) Using Geospatial Techniques for Sustainable Water Resource Management. Water 2025, 17, 2039. https://doi.org/10.3390/w17132039

AMA Style

Mani A, Badola R, Kumari M, Mishra VN, Thamaga KH, Hasher FFB, Zhran M. Watershed Prioritization with Respect to Flood Susceptibility in the Indian Himalayan Region (IHR) Using Geospatial Techniques for Sustainable Water Resource Management. Water. 2025; 17(13):2039. https://doi.org/10.3390/w17132039

Chicago/Turabian Style

Mani, Ashish, Ruchi Badola, Maya Kumari, Varun Narayan Mishra, Kgabo Humphrey Thamaga, Fahdah Falah Ben Hasher, and Mohamed Zhran. 2025. "Watershed Prioritization with Respect to Flood Susceptibility in the Indian Himalayan Region (IHR) Using Geospatial Techniques for Sustainable Water Resource Management" Water 17, no. 13: 2039. https://doi.org/10.3390/w17132039

APA Style

Mani, A., Badola, R., Kumari, M., Mishra, V. N., Thamaga, K. H., Hasher, F. F. B., & Zhran, M. (2025). Watershed Prioritization with Respect to Flood Susceptibility in the Indian Himalayan Region (IHR) Using Geospatial Techniques for Sustainable Water Resource Management. Water, 17(13), 2039. https://doi.org/10.3390/w17132039

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop