Exploring the potential of GIS-based integrated agroforestry cum soil conservation measure on spring discharge in data scarce region, Kumaon Himalaya, Uttarakhand

Springs in Kumaon region represent one of the largest and most precious sources of fresh water, necessary to meet the drinking water of the population. Understanding the temporal variability of spring discharge is therefore crucial for sustainable water management. Knowledge of the discharge characteristics, organized in a coherent framework, is essential for protecting spring water and preventing shortages. Sentinel 2A and SRTM DEM data were used to study topography, delineate springshed boundary, analyze structural setting, and surface water flow pattern. This study uses a novel approach to assess the impact of agroforestry cum soil conservation measure at selected sites in springshed on spring discharge. The result indicated that average discharge of the period 2020–2023 is 11.49 m³/day, which is almost twice the average value of 2019–2020 (6.93 m³/day) before intervention. Statistical analyses indicated a clear improvement in spring discharge after intervention, with linear regression showing an increase in R² from 0.27 (p = 0.16) before intervention to 0.43 (p = 0.0000012) after intervention. One-way ANOVA (F = 14.813, p = 0.0003) and Spearman’s correlation (ρ increased from 0.250 to 0.521) further confirmed a statistically significant enhancement in spring discharge following the integrated agroforestry cum conservation measures. The spring discharge dynamics was accessed using recession curve analysis. It was found that the fitting of recession-curve (of the Attadhar spring under study) with one exponential component gives accurate results. The value of exponential coefficient (i.e., 0.0206) represents the major contribution to drainage from the spring-catchment’s portion with highest permeability. We conclude that the quantitative insights gained from this analysis offer a valuable addition to conventional spring classification methods, which typically rely on qualitative assessments. Our proposed approach refines these traditional schemes by increasing objectivity and reproducibility, thereby fostering greater consistency across hydrological disciplines.

1 Introduction

Historically, Himalayan mountain communities have relied on springs as their main source of water for both domestic use and agricultural purposes (Rawat et al., 2022; Sikdar, 2019; Vashisht and Sharma, 2007; Verma and Jamwal, 2022; Kumari et al., 2021). It is one of the most important sources for inhabitants of Himalayan region (Fiorillo et al., 2021). Springs are ubiquitous throughout the region and play a crucial role in sustaining the livelihoods of Himalayan communities (Rana and Gupta, 2009). A report by NITI Aayog (2018), the Government of India’s think tank, highlights that 60% of the country’s five million springs are situated in the Indian Himalayan Region (IHR), with nearly half of them either drying up or turning seasonal. Over the past two decades, several Himalayan springs have experienced a sharp reduction in discharge due to a combination of factors, including climate change, human-induced activities, and inadequate scientific watershed management (Valdiya and Bartarya, 1991; Agarwal et al., 2012; Negi and Joshi, 2004; Jeelani, 2008; Dumka et al., 2025). Most settlements in the region are situated remotely at the top of ridges, making springs essential for their survival. Since rivers flow through the valleys and are inaccessible to hilltop communities, they cannot fulfill the domestic and agricultural water needs of these populations. In the study area, villagers rely on spring water as their primary source for drinking, washing, cattle care, and irrigating agricultural fields. The springs in the mid-Himalayas of Nepal are also experiencing depletion, attributed to the combined effects of biophysical and socio-economic factors (ICIMOD, 2015). Additionally, a survey in Sikkim found that all the state’s springs have diminished by half, presenting a dire scenario for a Himalayan region that depends heavily on springs for water supply (Tambe et al., 2011; Kumar M. et al., 2023). A spring’s discharge depends largely on the characteristics of the recharge area and rainfall patterns (Weiss and Gvirtzman, 2007; Peleg and Gvirtzman, 2010). However, in recent years, there has been a noticeable surge in the drying up of springs or their conversion to seasonal sources across the world such as in Italy, Picentini Mountain, China, Shanxi Province, Plateau of Colardo (Leone et al., 2021; Fan et al., 2023; Weiss and Gvirtzman, 2007; Tiwari, 2000: Pandit et al., 2024; Panwar, 2020).

The drying of spring water sources has been attributed to multiple interacting factors, including erratic rainfall patterns (Macchi et al., 2014) and a decline in winter precipitation (Tambe et al., 2011), rising temperatures (Pandey et al., 2018), deforestation (Valdiya and Bartarya, 1991), land-use changes (Joshi et al., 2014), and forest degradation (Rautela, 2015), particularly shifts in forest composition and type (Ghimire et al., 2012; Naudiyal and Schmerbeck, 2015). These disturbances have greatly destabilized the Himalayan biodiversity, disrupting the natural equilibrium and raising concerns for the sustainability of springs and streams across the region (Rai and Khawas, 2023; Ghimire et al., 2019). Irregular rainfall patterns hinder water infiltration, thereby affecting the hydrological balance. Additionally, spring discharge is influenced by factors such as rock fractures, the size of the groundwater drainage area, and the hydraulic head.

According to Chinnasamy and Prathapar (2016), the understanding of Himalayan spring hydro-geology remains insufficient and has been sparsely explored. Bruijnzeel and Bremmer (1989) highlighted the pressing need to enhance our knowledge of Himalayan hydrological processes to create effective management plans, as the current understanding falls short and may not address the growing water scarcity issues. Despite the urgency and far-reaching impacts of this crisis, increasing water demand, ecological degradation, and rapid urbanization will continue to impact spring flow rates. In the Indian Himalayan Region (IHR), natural springs and their sustainable development receive insufficient attention at both policy and implementation levels, even though they are crucial for water security. This is largely due to significant gaps in both hydrological and socio-economic data, including the lack of understanding of community dependence on springs and their contributions to socio-cultural services. Bridging these knowledge gaps is critical to understand the interconnectedness of biophysical attributes, lithology, and spring flow dynamics. This understanding will be essential for expanding springshed development practices and implementing innovative management strategies. In planning long-term water resource management, it is vital to assess current conditions, water demand, and availability in advance (Sharma and Shakya, 2006). A scientific understanding of water resource system responses to evolving trends requires insight into both present conditions and future projections (McCarthy et al., 2002). Additionally, it is essential to engage local communities and raise awareness, encouraging cooperative efforts to ensure the equity and sustainability of water security plans (Planning Commission India, 2007). Therefore, extensive field research is crucial for developing a new mechanistic understanding of how spring systems function.

Although numerous studies have been published on spring discharge dynamics (Dass et al., 2021; Hao et al., 2006; Sati, 2005), none of the previous research has used an integrated approach combining mechanical measures, agroforestry interventions, GIS and remote-sensing–based analysis to evaluate their collective impact on spring discharge. This lack of a holistic, interdisciplinary methodology represents the key research gap that the present study addresses, particularly in quantifying temporal variability in response to combined watershed treatments in the Himalayan region. Nearly 50% of perennial springs in the Indian Himalayas have either dried up or turned seasonal in nature (Rana and Gupta, 2009). In the light of aforementioned studies Attadhar spring in Almora was selected because it is representative of typical mid-Himalayan springs that are experiencing declining discharge, making it suitable for assessing the effectiveness of spring shed treatments. Additionally, the site offered practical advantages such as easy accessibility for repeated monitoring and the availability of prior hydrological and land-use data, which enabled a more robust analysis of temporal variability and treatment impacts.

The present study is structured as follows: First, the daily spring discharge series was examined, including an analysis of its monthly variation. The spring discharge for the Attadhar spring of ICAR VPKAS experimental farm Hawalbagh, Almora (29°37′50.19′′N and 79°37′55.10′′E) was assessed using recession curve analysis. Second, the recession Curve (RC) was developed using continuous daily discharge data collected over three water years (2020–21), (2021–22) and (2022–23). Finally, a Flow Duration Curve (FDC) was developed pre and post intervention, and its implications for spring flow utilization were discussed. Such a comprehensive investigation has not been previously conducted in this region. It is anticipated that the findings of this study will provide valuable insights to engineers, water resource planners, and policymakers for the effective management and utilization of spring discharge.

The study aims to: (i) investigate the effectiveness of time series analysis in providing a mechanistic understanding of hydrological processes at the springshed scale, (ii) examine the hydro geology of springsheds to better comprehend water flow processes, (iii) evaluate spring flow characteristics through flow duration curves and master recession analysis, and (iv) develop a flow duration curve (FDC) of the Attadhar spring. The findings of this research are expected to enhance the conceptual understanding of Himalayan spring systems and contribute to more effective water security planning and design.

2 Experimental site and description

2.1 Study area

One micro-watershed of experimental farm (Hawalbagh) ICAR-Vivekananda Parvatiya Krishi Anusandhan Sansthan, Almora located in Almora district of Uttarakhand state, India (Figure 1) was evaluated. The altitude of experimental farm is 1,250 m above mean sea level (MSL). The state of Uttarakhand in India is divided into two sub regions, Kumaon and Garhwal, which together consist of 13 districts. The majority of the region is mountainous, with the exception of a few valleys and the Tarai area—located near the foothills of the Himalayas—found in the districts of Nainital, Dehradun, and Haridwar. The northern section of these mountains is characterized by snow-capped peaks, meandering rivers, and scenic valleys, all supporting a rich diversity of flora and fauna within various ecosystems. In contrast, the southern section features undulating, rugged terrain with sparse to almost no vegetation cover. About 60% of Uttarakhand’s total area is richly endowed with water resources and forest cover (Chauhan, 2010). The region benefits from heavy snowfall and perennial rivers in its catchments, which serve as the foundation for a complete hydrological cycle. Additionally, over 60% of the state’s population relies primarily on mountain agriculture for their livelihood and main source of income. The watershed in the study area drains into the Kosi River, located in the Almora district of Uttarakhand. The specific spring being studied is situated at 29°37′50.19′′N and 79°37′55.10′′E at an elevation of 1200 meters above MSL and agroforestry block of experimental farm. For the study, daily data on spring discharge, rainfall, evaporation, and temperature were collected over four years (2019–2023).

Figure 1

Figure 1. Geographical setting and hydrological characteristics of the study area showing location of the study region within India and Uttarakhand, and detailed watershed map delineated using GIS. The map displays the watershed boundary (black), drainage network (blue), and the identified spring location.

2.2 Conceptual diagram of Attadhar spring

Attadhar spring is classified as a gravitational fractured spring, emerging from a sequence of surface rocks underlain by a layer of colluvial sediments. Figure 2 illustrating the lithology map of the Almora district encompassing the watershed area. The upper rock formations at higher elevations, consisting mainly of phyllite and quartzite, are highly fractured, allowing for enhanced infiltration of rainfall into the springshed. These rocks dip in the northeast direction and contain numerous vertical and inclined fractures that facilitate rapid percolation of rainwater down to the base layer (Figure 3a). The base of the springshed comprises a mixture of grains with varying sizes, shapes, and porosities (ACWADAM, 2009), which influences drainage depending on the level of recharge and the hydraulic conductivity of the medium.

Figure 2

Figure 2. Lithological map of the study area showing the spatial distribution of major rock units and geological formations within the watershed boundary (outlined in red). The map highlights diverse lithologies, including quartzite, schist, gneiss, limestone, phyllite, slate, granite, and associated metamorphic and sedimentary units.

Figure 3

Figure 3. Conceptual and field-scale representation of Attadhar spring showing recharge processes: (a) hydrogeological conceptual model illustrating lithology, recharge areas, and spring discharge, and (b) field photograph and interpretive sketch showing infiltration of rainfall through the soil zone sandwiched between upper and lower stone layers facilitating subsurface flow and recharge.

The geological conditions enable two distinct flow types within the aquifer system: quick flow and matrix flow. Initially, rainfall is absorbed by the topsoil, which typically starts with a high moisture deficit. With continued rainfall, soil moisture gradually increases, allowing water to percolate deeper into the fractured rock layers. Water entering through vertical fractures contributes to increased storage depth and fluctuates based on rainfall intensity and distribution—offering valuable insights into recharge dynamics. The typical conceptual diagram of geological crossection of Attadhar spring is shown in Figure 3b.

Hergarten and Birk (2007) modeled spring behavior using a power law in fractal conduit systems, which is applicable here. Following rainfall events, surface runoff water moves rapidly through fractures as part of quick flow. However, this speed diminishes as water migrates into smaller pores, which then govern the recession phase. As the hydraulic head declines within the fracture-conduit system after recharge, spring discharge correspondingly decreases.

3 Methodology

The integrated framework of geospatial technology (Remote sensing (RS) and Geographical information system (GIS)) was employed for investigating spring characteristics and discharge augmentation for Attadhar spring (ICAR-VPKAS, Hawalbagh, Almora) in Kumaon region of Uttarakhand. The steps of the framework is presented as flow chart in Figure 4.

Figure 4

Figure 4. Work flow for modeling the impact of integrated geospatial, mechanical structure, and agroforestry measure approach on spring discharge dynamics.

3.1 Integrated framework of remote sensing and GIS based approach for demarcating the topographical feature in the study area

A study was conducted to ascertain the potential of GIS based integrated agroforestry and soil conservation measure on spring discharge augmentation at research experimental farm of ICAR VPKAS Almora.

3.1.1 Watershed delineation

The geographical location of Attadhar spring is determined using Gramin 30 GPS. The shuttle radar topography mission (STRM) 1 arc-second global digital elevation model (DEM) of 30 m spatial resolution was downloaded from the USGS Earth explorer website¹ to delineate the watershed. A hydrologically corrected digital elevation model (DEM) was generated using the hydrological toolset in QGIS to accurately analyze the drainage characteristics of the terrain. The delineated watershed was further subdivided into micro-watersheds to identify potential recharge zone of the spring.

3.1.2 Land use land cover mapping

Land use and land cover (LULC) is one of the important parameter that affect the surface and sub-surface hydrology of the catchment (Panikkar et al., 2023; Srivastava et al., 2020). Land use–land cover (LULC) maps were prepared using Google Earth Engine by classifying multi-temporal satellite imagery with the Classification and Regression Tree (CART) algorithm. Training samples representing major LULC classes were used to develop the decision-tree model, enabling accurate discrimination of land-cover types based on spectral characteristics.

3.1.3 Normalized difference vegetation index

Sentinel 2A multispectral imagery was used for thorough vegetation analysis in the catchment. The Normalized Difference Vegetation Index (NDVI) is a widely used spectral index and an effective proxy for vegetation greenness and health (Gupta et al., 2023; Kumar et al., 2021, Kumar U. et al.,2023; Kumari et al., 2019; Adamala et al., 2025; Dutra et al., 2025). It is computed as a normalized ratio of reflectance values in the near-infrared (NIR) and red (R) bands, expressed as:

$NDVI=\frac{\left(NIR-R\right)}{\left(NIR+R\right)}$

For Sentinel-2A imagery, NDVI was calculated using the near-infrared (Band 8) and red (Band 4) bands.

3.2 Process for selection of sites for integrated agroforestry cum soil conservation measure

The methodological selection process for implementing integrated soil conservation and agroforestry measures in the springshed followed a systematic, GIS-driven approach combined with field validation.

a. Springshed Delineation

The process began with delineating the springshed boundary using hydrological modeling tools in ArcGIS, focusing on contributing watersheds to target springs. Stream order (Figure 5) describes the hierarchical arrangement and connectivity of streams within a drainage network and is commonly used as an index of stream development (Horton, 1945; Strahler, 1957). It represents a fundamental step in morphometric analysis of a river basin, as the overall drainage characteristics are strongly influenced by stream order. The widely adopted stream-ordering system proposed by Strahler (1957) provides a systematic approach for classifying streams based on their branching structure. Drainage shapefiles were overlaid to delineate the stream network, and second- and third-order streams were prioritized for identifying intervention sites, as these stream orders are often associated with higher erosion susceptibility and play a significant role in groundwater recharge and sediment transport processes (Horton, 1945; Strahler, 1957; Schumm, 1956).

a. Overlay analysis of thematic layers and site Identification

Figure 5

Figure 5. Stream order map of watershed derived from SRTM DEM using Strahler’s classification, showing the hierarchical stream network (1st to 3rd order) within the watershed boundary.

Overlay analysis was performed to integrate all thematic layers. An additive overlay approach was applied using the Intersect tool and other spatial analysis functions to identify areas where all relevant parameters spatially coincided. In this study, stream order was considered the primary criterion for site selection; therefore, thematic layers including geomorphology, NDVI, and LULC map were overlaid specifically on stream order network map. The intersect operation retained only those subclasses of each thematic layer that overlapped with stream order. Following overlay analysis, identification of suitable site was carried out using a multi-criteria evaluation (MCE) approach by integrating stream network, slope map, LULC, geomorphology, and NDVI variables through the weighted linear combination (WLC) method. Several objective and subjective approaches are commonly employed to determine the weights of each variable or criterion according to their relative importance in the analysis (Ivanco et al., 2017; Wotlolan et al., 2021; Singh et al., 2022; Mushtaq et al., 2024; Dhawale et al., 2025). All input variables were first standardized and then combined within a GIS environment to identify the site identification. Approximately 15 locations were selected at stream confluences or high-runoff zones within the springshed,. These sites offered optimal placement for terraces to intercept sheet erosion without disrupting natural flow paths.

a. Field Survey and Validation

Pre-monsoon 2020 field surveys verified GIS-identified spots, assessing soil type, accessibility, and community consent (Figure 6). Structures were designed as 2 m x 0.5 m x 1 m terraces, with Napier grass selected for its deep roots and rapid establishment to enhance infiltration time on both sides. This integrated mechanical-vegetative method ensured scalability and long-term stability.

Figure 6

Figure 6. (a) Field survey and validation during the pre-monsoon season (2020) illustrating site assessment and (b) construction of 2 m × 0.5 m × 1 m trench integrated with Napier grass along both side of terrace.

3.3 Spring discharge measurement and uncertainty analysis

The daily discharge data from July 2019 to December 2023 were recorded using volume time method (Thapa et al., 2020). The discharge of springs was measured using the volumetric method, as described by Stevens et al. (2011). This technique is typically applied to springs with low to moderate flow rates, especially in cases where water exits freely from a pipe and can be easily collected in a container. The discharge (Q) was determined using the formula

$\mathrm{Q}=\frac{\mathrm{V}}{\mathrm{T}}$

where V represents the volume of the container and T is the time required to fill it. For each spring discharge data, measurements were taken three to five times over a 1-min period, and the average discharge value (in liters per second) was calculated and recorded (Figure 7).

Figure 7

Figure 7. Spring discharge measurement and validation procedure: (a) collection of discharge in collection chamber for Attadhar spring; (b) spring discharge measurement using volumetric method; (c) validation of spring discharge measurement using flow probe meter.

The discharge was quantified with a variable frequency of four to five times per week (Monday to Friday). For each day discharge was measured three times and mean value was recorded the actual discharge of that day. Filling time was recorded using a digital stopwatch (resolution 0.01 s). The flow probe meter was used to check the accuracy of data measured by volumetric method. The spring discharge datasets were subjected to stringent quality control, including test for abnormally larger or physically improbable daily spring discharge value, unusually long sets of dry spells (Blenkinsop et al., 2017; Einfalt and Michaelides, 2008). The uncertainty in discharge (ΔQ) was estimated using standard error propagation:

$\frac{\Delta Q}{Q}=\sqrt{{\left(\frac{\Delta V}{V}\right)}^2+{\left(\frac{\Delta T}{T}\right)}^2}$

where ΔV represents volume uncertainty and ΔT represents timing uncertainty. Based on repeated measurements and instrument precision, the overall uncertainty in discharge estimates was within ±5%. Periodic cross-checking with a portable flow probe meter showed close agreement, confirming the reliability of the volumetric measurements.

3.4 Meteorological data

The monthly cumulative rainfall and monthly air temperature within the same duration (2019–2023) covered by spring discharge recording for the catchment area were collected from weather observatory located in experimental farm Hawalbagh of VPKAS Almora (Figure 8). The climatology of the study area is sub-temperate (Kumar and Singh 2023). The distribution of rainfall over the year is uneven, with most precipitation falling during the monsoon season (June to September). During these months, about 75% of the yearly rainfall occurs, resulting in a pronounced concentration of rain during this period. The overall mean temperature across these years is 13.0 °C.

Figure 8

Figure 8. Temporal variation of rainfall, Tmax, and Tmin at Hawalbagh experimental farm during the study period (2019–2023).

3.5 Discharge variability analysis

Three indicators were used to quantify seasonal discharge variability: the Flow Duration Curve (FDC), coefficient of variation (Cv) and Meinzer’s variability index (MV), (Bartnik and Moniewski, 2019; Kritz, 1973). The FDC represents the frequency distribution of discharge over different exceedance probabilities, typically derived using the Weibull plotting position. It illustrates the capacity of a catchment to generate low, medium, and high flows and provides valuable insights into springshed behavior and aquifer characteristics (Dass et al., 2021; Kumar and Sen, 2017; Vogel and Fennessey, 1996). The coefficient of variation (CV, %) is commonly used as a measure of flow variability, calculated as the ratio of the standard deviation to the mean hourly discharge. CV provides a dimensionless measure of dispersion relative to the mean discharge and therefore reflects the overall temporal variability of the spring. However, CV alone does not indicate how strongly the discharge fluctuates between wet and dry periods in a hydro-geological sense. Due to the limited availability of long-term data for Himalayan springs, it is difficult to establish region-specific empirical thresholds of spring behavior. Therefore, CV thresholds proposed by Kritz (1973) and later applied by Bartnik and Moniewski (2019) were adopted here as reference values to assess spring flow characteristics. According to this classification, CV < 20% represents stable flows, 20–40% indicates moderately variable flows, 40–100% reflects high variability, and >100% corresponds to unstable flows. The CV for Attadhar spring is 33%, which indicates that it is moderately variable flow. In contrast, Meinzer’s variability index ( $\mathrm{Iv}$ ), which is typically derived from annual maximum and minimum discharges, is more susceptible to measurement errors at both ends of the discharge spectrum (Meinzer, 1923). Iv captures the seasonal amplitude and sensitivity of the spring to recharge conditions. Iv therefore indicates the reliability and perennial nature of the spring, which is a key parameter for evaluating the effectiveness of conservation measures in maintaining minimum flows. The index of variability of the spring is calculated by the following formula as suggested by

$\mathrm{Iv}=\left[\left(\mathrm{Qmax}-\mathrm{Qmin}\right)/\mathrm{Qav}\right]\times 100\%$

Where $\mathrm{Qmax}$ = maximum spring discharge, $\mathrm{Qmin}$ = minimum spring discharge and $\mathrm{Qav}$ =average spring discharge. Based on Meinzer’s variability index ( $\mathrm{Iv}$ ) it is found to be 134% which indicates that it is a variable spring. Taken together, CV and Iv provide a more balanced interpretation of spring.

3.6 Development of spring discharge hydrograph

Spring hydrographs, which plot spring discharge against time, display a series of individual peaks that reflect the aquifer’s response to recharge events (see Figure 9). Each peak on the hydrograph includes both, a rising limb, where discharge increases, and a falling limb, where discharge decreases. The inflection point on the falling limb marks the end of direct recharge. The steep portion of the falling limb that precedes this inflection point is known as the flood recession, whereas the flatter portion that follows is referred to as the baseflow recession. Quickflow, responsible for the prominent hydrograph peaks, results from rapid recharge through features like sinkholes, vertical shafts, and the epikarst, which channel water from sinking streams. The extent and timing of quickflow are affected by factors such as topography, land use, soil properties, epikarst behavior, and the depth of the saturated zone, all of which can alter the proportion and timing of quickflow relative to rainfall. In contrast, baseflow is sustained by the slow release of water stored in low-permeability matrix blocks and is less sensitive to changes in rainfall patterns. This baseflow is replenished through diffuse surface infiltration and by gradient inversion processes between matrix blocks and adjacent conduits (bank storage).

Figure 9

Figure 9. Characteristic feature of a typical spring hydrograph.

3.7 Recession curve analysis

The recession rate of discharge is a key factor in estimating flow behavior within an aquifer system. To analyze the recession characteristics of Attadhar spring, daily discharge observations were utilized. The mathematical models of base flow recession were provided by Boussinesq and later by Maillet (1905):

$Q_{\left(t\right)}=Q_0{e}^{-\alpha t}$

Where $Q_t$ is the discharge [L³T⁻¹] at time t; $Q_0$ is the initial discharge[L³T⁻¹]; and $\propto$ is the recession coefficient [T⁻¹] expressed in days. When plotted on a semi-logarithmic graph, this function appears as a straight line with a slope of α. This equation is typically suitable for characterizing the behavior of karst systems during low-flow or recession stages. Forkasiewicz and Paloc (1967) realized that the decreasing limb of hydrograph peaks can usually be decomposed into several (usually three) exponential segments.

$Q_{\left(t\right)}=Q_1{e}^{-{\overline{\alpha }}_{1}t}+Q_2{e}^{-a_{2}t}+Q_3{e}^{-a_{3}t}$

3.8 Flow duration curve (FDC)

The flow duration curve is a graphical representation of the frequency distribution of total flow from a watershed. It represents the percentage of duration in which discharge is equaled or exceeded from an aquifer. The FDC for a particular spring can be constructed from daily or at monthly time series discharge data. The information derived from the FDC can be applied in water resource assessment which includes the design of an irrigation system, water supply, hydropower, evaluation of flow for ecological balance. Weibull equation is

$p_m=p\left[Q\ge q\left(m\right)\right]=\frac{m}{n+1}$

In this context, pₘ represents the probability of exceedance for q(m), where q(m) is the daily observed spring discharge ranked in descending order by magnitude. Here, m is the rank of the discharge value, and n denotes the total number of observations. In a Flow Duration Curve (FDC), the x-axis indicates the percentage of time a specific discharge value is equaled or exceeded, whereas the y-axis shows the corresponding discharge rate. On the FDC, an x-axis value of 0% corresponds to the maximum observed discharge, whereas 100% represents the minimum recorded discharge. For instance, a flow duration interval of 20% corresponding to a spring discharge of 17.6 liters per minute (lpm) indicates that 20% of the recorded daily average discharge values are equal to or greater than 17.6 lpm.

3.9 Statistical analysis

To test the hypothesis of impact of conservation measure on spring discharge trends, three statistical tests were applied namely, linear regression, one-way analysis of variance (ANOVA) and Spearman’s rank correlation. Together, these complementary methods provided a robust framework for assessing the effectiveness of conservation measures on spring discharge behavior.

3.9.1 Linear regression

A simple linear regression analysis of independent variable (spring discharge) (slope and p values) against time was done to determine using following equation:

$y=a+bt$

Where: y = spring discharge (m³/day) a = the constant, t = year, and b = constant.

3.9.2 Analysis of variance

One-way analysis of variance (ANOVA) was performed to test whether there was a statistically significant difference in mean spring discharge between the pre-intervention and post-intervention periods. The null hypothesis assumes no difference in mean discharge between the two periods.

3.9.3 Spearman’s rank correlation

Spearman’s rank correlation analysis was applied to evaluate the monotonic relationship between time and spring discharge for the pre- and post-intervention periods. This non-parametric method was selected because it does not assume normality and is suitable for detecting temporal trends in hydrological data. The analysis was conducted separately for pre- and post-intervention periods to enable comparison of discharge behavior before and after the intervention. Correlation coefficients (ρ) and corresponding p-values were used to assess the strength and statistical significance of temporal trends in discharge.

4 Results

4.1 Meteorological parameter in the catchment

Table 1 presents descriptive statistics of hydrological (discharge) and meteorological (rainfall, Tmax, Tmin) parameters for 2019–2023, including mean, standard deviation (SD), minimum (Max.), maximum (Min.), and coefficient of variation (CV). These statistics highlight pronounced variability in rainfall (CV ~ 105%) and moderate variability in discharge (CV ~ 34%), while temperature parameters are relatively less variable.

Table 1

Table 1. Descriptive statistics of hydrological and meteorological statistics, including discharge, rainfall, and temperature during (2019–2023).

4.2 Development of spring hydrograph

A spring hydrograph is a graphical illustration that depicts the changes in spring discharge over time. The spring hydrograph of the hydrologic year 2019–20 shows a rising limb during August and reached peak in September. This is followed by a depletion limb down to a minimum of 6.93 m³/day at the end of February 2021. The average yearly discharge is 6.93 m³/day. Noticeable differences between the two complete yearly time series of 2021–22 and 2022–23 are observed in the maximum discharge values and yearly averages, showing an increase of 10 m³/day and 6 m³/day from 2019 to 20, respectively. The difference is less pronounced (3 m³/day) for minimum discharge values. The distinct discharge patterns observed in 2020–21 (higher flow) and 2022–23 (lower flow) are in line with the precipitation trend of those two years (Figure 10). The discharge fluctuates annually, largely influenced by atmospheric precipitation. Specifically, from January to June and November to December, the discharge generally decreases, while from July to October it tends to rise. This pattern is due to most of the rainfall in the study area occurring from June to September, which significantly recharges the spring and raises the local water table, resulting in both an increase in the number of springs and higher discharge rates during this period. As precipitation decreases, the groundwater level drops, causing a reduction in spring discharge It should be noted that limited monitoring period may be considered as limitation of this study. There is a noticeable lag between the period of maximum precipitation and the peak spring discharge. The lowest discharge during the June to September (wet season) usually occurs in June and July, while the highest is observed in October and November, as depicted in Figure 10.

Figure 10

Figure 10. Hydrograph of the total discharge of Attadhar Spring (including uptake and overflow) from July 2019 to December 2023. The point of intervention is indicated by red solid lines. Monthly precipitation from the Hawalbagh experimental weather station is included for comparison.

4.3 Identification of sites for implementation of mechanical structure and agroforestry measure in the springshed using geospatial techniques

4.3.1 Digital elevation model

The Digital Elevation Model (DEM) of the watershed (Figure 11a) reveals noticeable altitudinal variation, with elevation ranging from approximately 1,139 m to 1,916 m above mean sea level. Higher elevations are predominantly observed in the western and north-western parts of the watershed, while comparatively lower elevations occur towards the eastern and south-eastern regions. This elevation gradient controls surface runoff patterns, drainage development, and influences soil moisture distribution, thereby playing an important role in agroforestry suitability and water conservation planning within the watershed.

Figure 11

Figure 11. Thematic maps of the study watershed showing (a) Elevation derived from DEM, (b) Slope classification, (c) Sentinel 2 a false color composite, (d) NDVI indicating vegetation density, (e) Geomorphological units (ridge, valley, slope, and plain), and (f) Land use/land cover distribution.

4.3.2 Slope map

The slope map of the study watershed reveals considerable spatial variability in terrain steepness (Figure 11b). Slope values range from gentle to very steep gradients, with the watershed predominantly characterized by low to moderate slope classes (≤ 49.34%), which occupy the central and southern portions of the basin. These areas exhibit relatively smooth terrain with gradual elevation changes. Moderately steep slopes (49.34–74.01%) are distributed across the mid-slope regions, forming transitional zones between valley bottoms and upper slopes. Steep to very steep slope classes (74.01–98.69% and > 98.69%) are mainly concentrated along the upper reaches and ridge zones, particularly toward the northern and northeastern parts of the watershed. These areas represent rugged terrain with higher relief and increased susceptibility to surface runoff and erosion. Slope is a key factor in agroforestry planning as it controls soil erosion, runoff, and moisture retention (Kumari et al., 2020; Srivastava et al., 2019, 2021, 2022, 2024). Gentle slopes are most suitable because they allow better water infiltration, stable soil conditions, and easier management of tree–crop systems. Moderate slopes can support agroforestry with appropriate soil and water conservation measures, whereas steep slopes are generally less suitable due to high erosion risk and shallow soils, limiting sustainable tree growth.

4.3.3 NDVI

The NDVI map indicates clear spatial variability in vegetation vigor across the watershed. Higher NDVI values (≈0.37–0.50), shown in dark green, dominate the central and northern parts, reflecting dense and healthy vegetation cover (Figure 11c), which is highly suitable for agroforestry interventions due to better biomass productivity and soil moisture conditions (Figure 11d). Moderate NDVI zones (≈0.24–0.37) represent areas with sparse to moderate vegetation and offer good potential for agroforestry improvement through enrichment planting. In contrast, low NDVI values (≤0.24), mainly distributed along the peripheral and southern sections, indicate poor vegetation cover or degraded lands, which may require soil and water conservation measures prior to agroforestry implementation. Overall, the NDVI analysis highlights priority zones for agroforestry planning based on existing vegetation health.

4.3.4 Geomorphology

The geomorphological landform map of the study watershed is presented in Figure 11e. The classification delineates four major geomorphological units, namely ridges, valleys, slopes, and plains, derived from DEM-based terrain analysis. The watershed is predominantly characterized by slope and ridge units, indicating moderately undulating to gently sloping terrain across large portions of the basin. Ridge areas are mainly distributed along the upper reaches and watershed boundaries, representing elevated landforms with relatively steep gradients. These zones are typically associated with shallow soil depth and rapid surface runoff, making them less suitable for intensive land-use interventions. In contrast, valley units occur along drainage lines and lower topographic positions, where deeper soils and higher moisture availability are observed, favoring agricultural and agroforestry activities. Slope units occupy a significant proportion of the watershed and act as transitional zones between ridges and valleys. These areas require appropriate soil and water conservation measures to minimize erosion and enhance land productivity. Plain areas, though limited in extent, are relatively flat and stable, offering favorable conditions for sustainable agroforestry practices and water retention.

4.3.5 Land use land cover map

The land use/land cover (LULC) map was generated using Google Earth Engine (GEE) by applying the Classification and Regression Tree (CART) algorithm to multispectral satellite imagery (Sentinel 2A) (Figure 11f). The classification delineates major classes including cropland, forest, built-up areas, roads, and barren land, capturing the heterogeneous landscape of the study area. Accuracy assessment indicated a strong agreement between classified and reference data, with a Kappa coefficient of 0.86, reflecting high classification reliability. This LULC information is essential for understanding land-use dynamics and for supporting hydrological analysis, spring recharge assessment, and agroforestry planning. In the study area, agricultural land with minimal disturbance (Dhawale et al., 2025) and open scrub were identified as highly suitable for agroforestry due to their accessibility, soil exposure, and potential for tree–crop integration. Forested areas were assigned lower suitability to avoid disturbance to existing natural vegetation, while built-up areas and water bodies were considered unsuitable owing to land-use constraints. Thus, LULC classification provides an essential baseline for identifying areas where agroforestry interventions can be implemented with minimal ecological conflict.

After delineation of the springshed, the drainage line shapefile was overlaid using Google Earth and QGIS environment. The second order and third order stream was marked in the springshed area. About 15 locations were identified for integrated soil conservation cum agroforestry measure. The field survey was conducted in the springshed area for implementing the structure before the pre monsoon season in 2020 as shown in Figure 6. Soil conservation measure consist of terrace of size 2 m length, 0.5 m width and 1 m depth. Agroforestry measure includes planting Napier grass along the length of the terrace on both side of the trench. The purpose of planting Napier grass on both sides is to increase the opportunity time for infiltration. The typical integrated soil conservation cum agroforestry measure adopted in this study is shown in Figure 12.

Figure 12

Figure 12. Integrated agroforestry cum soil conservation measure on a hill slope: (a) Newly constructed trenches during the initial establishment phase, and (b) mature trenches with well-established grass cover showing improved slope stabilization and enhanced infiltration.

4.4 Impact of conservation measure on spring discharge

To assess the impact of conservation measure on spring discharge Attadhar spring is monitored for one hydrological year in natural condition (2019–20). The integrated agroforestry cum soil conservation measure was implemented in the spring catchment before monsoon season of next year. The spring discharge in monitored for three years (2020–2023) after the intervention. To assess the impact of conservation measure linear regression, one-way ANOVA and Spearman rank correlation analysis were performed before and after intervention. The coefficient of determination (R²) and p values before intervention were 0.27 and 0.16 whereas that of after intervention wasa 0.43 and 0.0000012 using simple linear regression. The p value showed that impact of integrated agroforestry cum conservation measure on spring discharge was statistically significant as shown in Figure 13. Further, the one-way ANOVA revealed a statistically significant difference in mean spring discharge between the pre- and post-intervention periods (F = 14.813, p = 0.0003), indicating that the conservation measures led to a measurable change in discharge characteristics. Spearman’s rank correlation analysis further highlighted contrasting discharge–time relationships across the two periods, with pre-intervention period showing a weak and statistically insignificant correlation (ρ = 0.250, p = 0.5165) and post-intervention period exhibiting a moderate, statistically significant positive correlation (ρ = 0.521, p = 0.0003). Together, these results demonstrated a significant improvement and increasing trend in spring discharge following the intervention, supporting the effectiveness of the implemented measures. Figure 14 illustrates the average monthly spring discharge during 2020–2023, showing clear seasonal variability in spring flow. Discharge increases from the monsoon period, peaking during August–September, and declines during the pre-monsoon and winter months, reflecting rainfall-controlled recharge dynamics.

Figure 13

Figure 13. Linear regression modelling of spring discharge before and after intervention.

Figure 14

Figure 14. Observed spring flow hydrograph for period 2020–2023.

4.5 Flow duration analysis

A flow duration curve (FDC) is constructed by arranging the daily spring discharge values in descending order and plotting them against the percentage of time the flow is equaled or exceeded. This representation offers a comprehensive view of the flow characteristics of a spring or stream over a specified period. Typically, the discharge values are plotted on a logarithmic scale versus the exceedance probability, which highlights variations across different flow magnitudes (Smakhtin 2001). For the Attadhar spring, Figure 15a presents the flow duration curves based on daily discharge data for study period (2019-2023), while Figure 15b shows FDC before pre and post intervention. It clearly indicates the impact of implemented measures on spring flow sustainability.

Figure 15

Figure 15. Flow duration curves (FDCs) for Attadhar spring: (a) FDC for the entire study period derived from daily observed spring discharge data, and (b) comparison of FDCs representing pre- and post-intervention conditions.

It is observed that discharge data of Attadhar spring, the average Q₉₀ (discharge equaled or exceeded 90% of the time) is less than 30.6 liters per minute (lpm). This Q₉₀ value can be considered a characteristic minimum flow, which is crucial for water management policies aimed at addressing low flow situations in similar aquifer systems.

5 Conclusion

In a first-of-its-kind study from the Kumaon Himalaya using high-resolution discharge data, we analyzed the potential of integrated agroforestry cum soil conservation measure to understand its impact on hydrological behavior of spring discharge. The spring behavior in this study is assessed using recession curve analysis. The analyzed monthly cumulative spring discharge during the monsoon period (July–September) is about 73% of the total spring discharge. The integrated agroforestry cum soil conservation measure has a significant impact on spring discharge as supported by three statistical analysis. The flow duration curve (FDC) for different periods highlights significant seasonal variability in spring discharge. The statistical analyses clearly demonstrate the effectiveness of the integrated agroforestry–cum–soil conservation measures in enhancing spring discharge. Linear regression results showed a marked improvement in the strength and significance of the discharge trend after intervention, with the coefficient of determination increasing from 0.27 to 0.43 and the p-value decreasing from a non-significant level to a highly significant level. One-way ANOVA further confirmed a statistically significant difference in mean spring discharge between the pre- and post-intervention periods, indicating a measurable change in discharge characteristics due to the conservation measures. Spearman’s rank correlation analysis revealed a shift from a weak and insignificant discharge–time relationship before intervention to a moderate and statistically significant positive relationship after intervention.

The study further highlights the advantages of high-frequency spring monitoring using observed data for dhara-type springs. When integrated with microclimatic and precipitation datasets, such monitoring provides valuable insights into aquifer properties and their responses to intervention made in the catchment and precipitation variability. We adopt an integrated framework combining agroforestry cum soil conservation measure to improve understanding of spring, which is essential for achieving SDG 6 (clean water access) through springshed management strategies aligned with SDG 15 (life on land) and adaptive to future climate change impacts (SDG 13). This study also serves as a baseline for evaluating springshed management programmes and for developing springshed- and regional-scale ecosystem service models to assess future water security challenges in the Eastern Himalaya. The entire results of this study can guide future investigations, researchers, govern-mental decisions, and efforts to manage spring water resources in the Himalayan region.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

UK: Writing – original draft, Writing – review & editing, Investigation, Methodology, Software, Supervision, Conceptualization, Data curation, Formal analysis, Project administration, Resources, Validation, Visualization. RM: Writing – review & editing, Investigation, Software. LK: Writing – original draft, Writing – review & editing, Supervision. AS: Methodology, Conceptualization, Writing – original draft, Writing – review & editing, Visualization.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The Research was supported by ICAR-DARE, Government of India.

Acknowledgments

The authors would like to thank Indian Council of Agricultural Research (ICAR), Director, ICAR-Vivekananda Parvatiya Krishi Anusandhan Sansthan Almora, and AICRP on PEASEM for providing facilities to carry the study.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Footnotes

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