Hydrological Functioning of Forested Catchments, Western Himalayan Region, India.

Background Forests, being the largest ecosystem and primary consumer of water, significantly affect the hydrological cycle at both global and local level and provide innumerable ecosystem services to humans. Western Himalayan forested catchments provide fresh water supply to millions of people. Hence, the understanding of linkages between forests and water is very crucial to recognize for availability and quality of water at catchment scale. Therefore, present study aims to understand hydrological response of two forested catchments (namely, Arnigad and Bansigad) in the Western Himalayan Region. Methods Three-year data (March, 2008 to February, 2011) were collected from meteorological and hydrological stations installed at Arnigad and Bansigad catchments. The present paper displays average hydrological response of forested catchments through detailed field investigation. Results The annual hyetograph analysis reveals that the rainfall at both the catchments were highly seasonal, and wet-period (June-September) plays a key role in catchment functioning. Exceedance of rainfall threshold of ~288 mm (~10% of annual rainfall) significantly increased streamflow generation at both the catchments. At Arnigad, stream was perennial with a mean baseflow of ~80 mm per month (~5% of annual streamflow) whereas, Bansigad had greater seasonality due to lack of flow during the pre-wet-period (March-May). Separation of hydrographs at Arnigad and Bansigad catchments i.e. stormflow (6% and 31%, respectively) and baseflow (50% and 32%, respectively) helps to understand the probability of flooding during wet-period and drought during dry-period. Forest ecosystem at Arnigad improves the hydrological functioning by: reducing stormflow (82%), and enhancing baseflow (52%), soil moisture (13%), steady infiltration rate (22%) and by increasing lag time (~15 minutes) relative to Bansigad. These enhanced values indicated potential for soil to store water at forested catchment (Arnigad) and helps to understand the volume of water that is available during dry-period. The decrease of denudation rate (at Arnigad) by 41% resulting in decrease of suspended sediment (18%) and bed load (75%) compared to Bansigad. Further, the enhancement Conclusion This confirms the crucial role of


Conclusion
This study confirms the crucial role of forests in maintaining hydrological balances at catchment scale in the Western Himalayan region.

Introduction
Catchments, as environmental systems, are characteristically complex and heterogeneous (Kirchner, 2016), consisting of wide range of processes (natural / anthropogenic) which may function simultaneously, affects spatial and temporal variability of the system (Zabaleta and Antiguedad, 2013). This is particularly evident for mountain headwater catchments where interactions between geology, geomorphology, vegetation and harsh topography coupled with climatic forcing and multiple water inputs beyond rainfall (meltwater from snowpack, glaciers and permafrost), makes the hydrological response highly complex. Understanding those processes is crucial in order to manage the runoff (qualitatively and quantitatively), particularly when climate or landuse are changing (Naef et al., 2002;Negley and Eshleman, 2006;Stewart and Fahey, 2010). The change of landuse especially forest loss or forest degradation interrupts the hydrologic cycle, disturbs food chain and habitat (Thompson et al., 2011;Jones, 2013), which in turn leads to serious damage in several ecological functioning of the ecosystem (Bond et al., 2008;Blumenfeld et al., 2009;Wei and Zhang 2010;Brandon 2014;Pereira et al., 2014;Poirier and Nguyen, 2017).
Substantial advancements have been made in forest hydrological research all over the globe, nevertheless, studies in India are in their infancy. Large number of headwater catchments in the Western Himalayan Region (WHR) in India are covered with dense forests (Tiyagi et al., 2013), which provide numerous ecosystem services to millions of people living in these regions (Tiwari et al., 2017). However, these services have not gained much attention in national economic decision-making (Pandey 2012). Studies (Qazi et al., 2012, Tiyagi et al., 2013, Chauhan et al., 2017and Qazi et al., 2017 suggests that forest plays significant role in hydrological functioning of catchments in WHR. Unfortunately, these forests are under severe stress due to dam construction, deforestation, overgrazing, tunneling, and other anthropogenic activities as well as climate change (Chaturvedi et al., 2011;Gopalkrishnan et al., 2011;Tiwari et al., 2017), which disrupts hydrological services at local or catchment scale in WHR. Further, long term field-based data, which is key for forest and water managers to understand and predict the spatial and temporal variability of hydrology, is also lacking in these regions. Therefore, in the present study, long term field-based data has been used in order to understand (i) how forests offer services to regulate hydrological processes, specifically: streamflow, soil moisture and sediments and (ii) how spatial and temporal variability affects hydrological functioning at catchment in WHR. The hydrological response of forested watersheds was studied for three consecutive years (March, 2008to February, 2011 and the paper displays average hydrological scenario of catchments. Such understanding is necessary to improve our ability to manage multiple resources at catchment-scale, and to meet needs of local people without adversely affecting the environment. were selected in the Mussoorie area, WHR (Fig. 1). Both catchments were located near (∼1.5 km areal distance) to each other, have similar slopes (21.86 o , Arnigad and 23.61 o , Bansigad). and are southfacing. The morphometric characteristics of both the catchments were also almost same (Table 1).

Description Of Study Area 2.1 Morphometric characteristics of Catchments
Both the catchments are drained by second-order streams at the gauging site. The Arnigad subsidizes to the Rispana River (Ganga River Basin) whereas the Bansigad subsidizes to the Tones River (Yamuna River). Both catchments were protected under private ownership and management, therefore, no forest cover change occurred during the study period. Self Scanning Sensor, LISS-III satellite imagery, resolution 23.5 m) was taken from the Bhuvan website in 2008. Landuse / Land cover maps were developed from LISS-III imagery with the help of software (ERDAS Imagine 9.2). Tree Density (TD) was measured by laying out the quadrates. Six representative sites (three in each watershed) were selected and five quadrates (10 x 10 m) were laid down at each site of both the catchments. TD was higher at Arnigad catchment (487 trees/ha ± 210) as compared to Bansigad catchment (380 trees/ha ± 194). Diameter at Breast Height (DBH) was measured at each quadrate (at 1.73 m height) by using inch tape. Average DBH was also higher at Arnigad (30.57 cm ± 8) as compared to Bansigad (16 cm ± 7). At Bansigad, out of 15 quadrates selected at three sites, species composition was found to be 64% of Oak,17% of Cupressus,and 19% of others (which includes Bhimal,Parang,Kail,Khadki,Terbara,Jungli Nashpati), whereas at Arnigad, the species composition was 98% of Oak and 2% were others. Different forest coverage was the only differences between two catchments i.e. FCC (91%), TD (28%) and DBH (98%) were higher at Arnigad as compared to Bansigad. The percentage differences of FCC, TD and DBH were calculated as ((A-B)/B*100).

Climatology And Geography Of The Region
The climate of the studied region is warm-temperate and monsoonal. Mean annual rainfall is 2243 mm (1869-2010), majority of which (80%) is received during summer months (June to September) while 30% is received during the winter months (Sharma et al., 2012). Maximum annual temperature is 28 o C and occur in May, and minimum annual temperature is 6 o C and occur in January (Sharma et al., 2012).
The study area lies in Mussoorie hills, a sensitive, fragile zone, affected by the landslides and limestone quarrying (Yadav and Sri Ram, 2014). Geologically, the area is lying towards south of Main Boundary Thrust (MBT), refers to the litho-tectonic zone. The stratigraphy of the Mussoorie syncline consists largely of sedimentary rocks of Krol belt. It is separated from the Siwaliks by the MBT in the Lesser Himalaya at 1500-2500 m height (Chauhan et al., 2017). 3. Methodology 3.1. Rainfall measurements: In order to measure rainfall, two types of rain gauges were used: a tipping bucket rain gauge (Rainwise, USA; 325 cm 2 orifice, 0.25 mm per tip) and manual rain gauge (RK Engineering, India; 2000 cm 2 orifice). The data from the tipping bucket rain gauges were cross-checked with the manually rain gauges (1-day temporal resolution), which were installed at the same measurement site. Both types of rain gauges were installed at two elevations (at ~ 1700 m and ~ 1900 m a.m.s.l.) at each catchment. There was no significant difference between rainfall sums measured at two different stations at both Arnigad and Bansigad. Apparently, the elevation difference between the two stations was inadequate, while, the slope, aspect and other morphometric characteristics which are vital factors affecting rainfall catch in the WHR (Katiyar and Striffler, 1984), were almost similar at both the catchments (Table 1).

Discharge Measurements:
A rectangular weirs with a sharp-crested 120 o V-notch were constructed for the measurement of discharge at both the catchments (Fig. 2). The width of the weirs were 4.8 m and 6.6 m at Arnigad and Bansigad, respectively. Water levels were measured by using Automatic Water Level Recorder (AWLR) at 15-minute intervals and converted into discharge. Hydrograph separation was done with the help of physically based filter technique given by Furey and Gupta, 2001. Furthermore, baseflow recessions were examined by using the method described by De Zeeuw 1973

Soil Moisture measurements:
In the present study, Watermark Sensors (Irrometer) were used for the measurement of soil moisture.
Three sites were selected at both the catchments for measuring the soil potential ( Figure 1). The sensors at each site were installed at 25 cm, 50 cm and 80 cm depths, respectively. The soil matric potential from all the sensors were monitored twice in a month throughout the study period. During sensor installation, undisturbed soil samples were collected from all three depths at each site and soil moisture retention curves were developed by using pressure plate apparatus for the pressure 0.1, 0.33, 0.5, 0.7, 1, 3, 5, 7, 10 and 15 bar. The soil moisture retention curves were used to convert the observed soil matric potential values into equivalent values of volumetric soil moisture content.

Sediment Measurements
The water samples of 1-liter bottles were collected at the gauging sites of the mainstream (Fig. 2).
The collected water samples were analyzed by following a grab sample method (International Atomic Energy Agency, 2005). Whatman-72 filter paper were used for the separation of suspended sediments from the water samples. During the wet-period (June-September), the sampling was done three times in a day: 8:00 AM, 2:00 PM and 8:00 PM whereas during the dry-period (October-May), sampling frequency was done on daily basis (8:00 AM). The suspended sediment concentration (mg l − l ) was converted into suspended sediment load, SSL (t km − 2 ) by using conversion factor, discharge and area of the catchments.
Total Dissolved Solids were measured by using TDS meter. During wet-period, the frequency of TDS measurement was on daily basis, however, during rest of the year the frequency was once in every two weeks as there was no significant change in TDS. The concentration of TDS (ppm) was converted into total dissolved load, TDL (t km − 2 ) by using conversion factor, discharge and area of the catchments.
Bed load (BL) was estimated by following Hedrick et al., 2013. The pond like structure (6 × 4.8 m for Arnigad and 12 × 6.62 m for Bansigad) at gauging sites were constructed so that sediments could accumulate within them. We presumed that most of the BL material gets deposited in these structures. The volumes of BL were derived by measuring various depths/heights of deposited material at these structures. A bulk density of 1.4 t m − 3 (BBMB, 1997) was used for the conversion of these volumes into mass. The measurements of accumulated BL followed by mechanical cleaning were done every month, however, during wet-period, the frequency of measurements followed by cleaning was 5-6 times in a month to avoid flushing of bed material during peak events. Total sediment budget is the sum of SSL, TDL and BL.
In the Himalayan region, high relief and high intensity monsoonal rainfall provides favorable conditions for mass wasting (Korup and Weidinger, 2011). Long-term mass wastage or denudation rates were estimated by following Gregory and Walling, 1973:(

Files.)
Denudation (D) rates are expressed in mm year − 1 which is equivalent to m 3 km − 2 year − 1 , total load is in tonnes year − 1 , area is in km 2 and the average density of rock or soil was considered to be 2.67 gcm − 3 (Lupker et al., 2012;Chauhan et al., 2017).

Infiltration Measurements:
Infiltration tests were conducted with the help of double-ring infiltrometers in March 2010 when the soil profile has dried out. The inner ring was 30 cm in diameter and 15 cm high, while the outer ring was 60 cm in diameter and 15 cm high, respectively. Eight infiltration tests (4 at each) were conducted at both catchments.

Soil Properties:
To evaluate the soil properties, soil samples were collected from predetermined depths of 0-15, 15-30, 30-60, 60-90 and 90-120 cm by using Auger. All soil samples were collected at six representative sites (three at each catchment) (Fig. 1). The 3 sites spanned a gradient from ridgeline to catchment outlet. The soil samples were analyzed for Organic matter (Walkley and Black, 1934), texture and porosity (Black, 1965).

Soil properties:
To evaluate the soil properties, soil samples were collected from predetermined depths of 0-15, 15-30, 30-60, 60-90 and 90-120 cm by using Auger. All soil samples were collected at six representative sites (three at each catchment) ( Figure 1). The 3 sites spanned a gradient from ridgeline to catchment outlet. The soil samples were analyzed for Organic matter (Walkley and Black, 1934), texture and porosity (Black, 1965).

Statistical Analysis:
t-tests were used in order to calculate statistical differences. The percentage differences of all parameters between Arnigad and Bansigad were calculated as ((A-B)/B*100).

Lag Time Analysis:
In order to understand the response of catchments after rainfall, around 40 hydrographs (during wetperiod) were analyzed to determine lag time between rainfall and discharge. The lag time was analyzed by calculating the delay between the maximum rainfall amount and the peak discharge. Out of 40 hydrographs, 3 hydrographs along with corresponding hyetographs were analyzed in order to calculate the volume of water released from catchments after rainfall events.

Temporal variations of Rainfall:
Wet-period is the core season, when hydrological processes in catchments are the most active, whereas it is inactive during dry-period (October to May). The wet-period plays a substantial role at both the catchment's functioning by providing ~78% (for each catchment) of the annual precipitation (2922 mm), Figure 3. Patterns and amount of monthly rainfall observed (during three years) over both the catchments were quite similar and did not differ significantly (p < 0.05) from each other.
Minimum and maximum values of mean monthly rainfall ranges from 11 to 909 mm at both the catchments. Winter precipitation in the form of snow was negligible at either location. May and June were transition months/stage between dry and wet periods. During this transition period, rainfall exceeds thresholds (~10% of annual rainfall) and hyetograph starts rising. July and August were the peak months whereas September, the falling limb of the hyetograph (Figure 4).

Streamflow Behavior:
Temporal variations of streamflow (stormflow and baseflow) for both the catchments clearly reflect the seasonal patterns of streamflow and are in coherence with dry and wet periods ( Figure 3).
Generally, during last week of June, streamflow of Arnigad and Bansigad reached values of 48 mm and 14 mm (~3% and 1% of annual flow) and after that streamflow start rising instantly ( Figure 4).
Bansigad had greater seasonality due to lack of flow during the pre-wet-period (March-May), whereas streamflow was maintained year the round at Arnigad. Seasonal (wet period) and annual runoffs (total streamflow) at Arnigad were lower by ~34% and 13%, respectively relative to Bansigad.
Mean stormflow production at the Arnigad was modest with the sum of 167 mm yr -1 (10% of annual streamflow) and occurred during the main wet-period (90%). Conversely, stormflow was much higher for the degraded catchment 914 mm yr -1 (49% of annual streamflow) with 78% contribution from the wet-period. In addition, post-wet-period (October-November) stormflows were important at Bansigad, contributing 18% of the annual totals whereas it was just 2% at Arnigad. Annually, stormflow at Arnigad was lower by ~82% as compared to Bansigad.
Mean annual baseflow at Arnigad and Bansigad was 1446 mm yr -1 and 949 mm yr -1 (90% and 51% of annual flow) whereas seasonal (wet-period) contribution was 784 mm (54%) and 712 mm (75%), respectively. Baseflow was ~52% (annually) higher at Arnigad and became important contributor for making stream perennial during dry-period as compared to Bansigad. Recession rates of the baseflow for the Bansigad catchment during the dry period were much faster, with reservoir response factor of 0.028 day -1 , whereas it was ~0.0083 day -1 for the dense forest at Arnigad ( Figure 5). The exponential recession curve of the outflow from groundwater reservoirs in either catchment ( Figure 5) did not deviate from linear reservoir theory, indicating negligible leakage losses and hence letting direct comparison between the two catchments. The contribution of stormflow and baseflow significantly varies from January to December and its temporal variation is presented in Figure 6A and the water flux of both the catchments is shown in Figure 6B.

Soil moisture behavior:
Temporal behavior of soil moisture (SM) at different depths is presented in Figure 7A. Mean annual volumetric SM at Arnigad and Bansigad was higher (41% and 39%) during wet-period, however it was lower (28% and 24%) during dry-period. This shows that Arnigad was having 4% (wet-period) and 16% (dry-period) higher SM as compared to Bansigad, respectively. At annual scale (at Arnigad) mean SM was lower by 4% at upper surface and higher by 13% and 31% at deeper layers as compared to Bansigad ( Figure 7B). At Arnigad, SM at 80 cm depth holds maximum soil moisture than 50 cm.

Infiltration:
Variations of initial and steady infiltration rate at Bansigad were smaller (50-64 cm hr -1 and 13-32 cm hr -1 ) as compared to Arnigad (20-134 cm hr -1 and 8-30 cm hr -1 ). Initial infiltration rate was lower by 29%, however, steady infiltration rate was higher by 21% at Arnigad relative to Bansigad (Table 2). 4.5. Physical properties of soil: Soil texture at Arnigad was having higher amount of the Silt and clay (13% and 23%) fractions as compared to Bansigad. Organic matter (OM) and porosity were also higher (35% and 8%) at Arnigad as compared to Bansigad. The results reveal that soil texture was better at forested catchment (Arnigad) as compared to degraded forest (Bansigad) catchment. Figure 8 shows the variations of soil properties along depth.
− 1 − 1 4.6. Sediment Budget: Temporal variations of different types of sediments in streamflow including SSL, TDL and BL is presented in Fig. 9A, B and C). There was a huge temporal variation (monthly) in SSL ranging 0.28-738 t km − 2 and 0 -1265 t km − 2 at Arnigad and Bansigad, respectively. Arnigad experiences the lowest SSL from March to May, whereas the stream remained dry during these months at Bansigad.
The wet-period contributes 95% (of the annual load) of SSL, which substantially affects annual sediment behavior at both the catchments. The average annual budget of SSL was 1112 t km − 2 (Arnigad) and 2143 t km − 2 (Bansigad) respectively, resulting that suspended sediment budget was two-fold higher at Bansigad as compared to Arnigad (Fig. 9D).
Mean monthly TDL at Arnigad ranged between 21-153 t km − 2 and at Bansigad it ranged between 0.2-177 t km − 2 . The TDL was consistently found higher than SSL during drier months. Mean annual yield of TDL at Arnigad and Bansigad was 698 t km − 2 and 488 t km − 2 , respectively (Fig. 9D), resulting higher TDL at Arnigad.
The volume of BL (monthly) flowing in the Arnigad stream was in the range of 0.03-17.28 m 3 , whereas it was 74.64 m 3 at Bansigad and from March-mid June, no bedload material was observed.
Mass wastage has been considered the dominant erosional processes on hillslopes and the long-term mass wastage (denudation rate) was calculated for both the catchments. The average denudation rates were 0.68 mm year − 1 (Arnigad) and 1.02 mm year − 1 (Bansigad), respectively, resulting that Bansigad losses its mass (1.5 fold) higher as compared to Arnigad.

Hydrological Thresholds of catchments:
During the study period, intra-annual variation of rainfall was high and represented both dry and wet-periods (Fig. 4), thus allowed to study baseflow and stormflow conditions of the catchments. Dryperiod represents the greater part of annual hyetograph. Mean monthly rainfall of ~ 288 mm (~ 10% of annual rainfall) acts as threshold and when this threshold exceeds, streamflow generation increased significantly at both the catchments. The low magnitude rainfall (below 288 mm per month) during dry-period potentially contributes to satisfies several hydrological processes e.g. Interception, initial infiltration, depression storage and soil moisture stress (Tarboton, 2003) at both the catchments. The stream was perennial (at Arnigad) with a baseflow of ~ 80 mm (~ 3% of annual streamflow), however, at Bansigad baseflow was not available during dry-period (Fig. 6). The study indicates that both streams were dependent on rainfall for streamflow generation, but the rainfall at Arnigad sustained baseflow during dry-period and was greatly enhanced during wet-period relative to Bansigad. This behaviour at Arnigad was potentially due to two combined factors: (a) slow baseflow recession rate (Fig. 5) and (b) potential of forest soil to store maximum water (Figs. 6 and 7) and released subsequently during dry-period. Therefore, the rainfall threshold values of both catchments be helpful to predict streamflow generation (Kirkby et al., 2005, Gioia et al., 2008Kampf et al., 2018) which are vital for sustenance of streams and regulation of numerous ecological processes (Poff, et al., 1997;Doll et al., 2015).
Non-linear relationships between streamflow and SM ( Fig. 10) allowed the identification of a SM threshold value (~ 35%). When the SM threshold was exceeded, baseflow was activated, increased significantly and became a major contributor to stormflow. A clear threshold (~ 35%) between SM and streamflow, reveals the importance of initial moisture conditions, which determines the extent of the saturation and controls the streamflow production of the entire catchment (Penna et al., 2010). The threshold value (0.35) was very close to mean field capacity (0.35 and 0.33) at Arnigad and Bansigad, respectively. This further confirms that the activation of streamflow occurred only after soil attained threshold SM value of 35%. Other studies have observed threshold dynamics as: 45% (Penna et al., 2011;Song and Wang, 2019), 26% (Farrick and Branfireun, 2014) and 23% (James and Roulet, 2007) supported the importance of initial moisture conditions and above the SM threshold, streamflow activation indicates the occurrence of streamflow from the hillslope. The difference in threshold values might be due to difference in topography, climate, land use characteristics and sampling designs. Therefore, our results show that two factors: soil moisture and rainfall were responsible for streamflow activation and generation. Figure 4, indicates that June is a transition period, when hydrological functioning (streamflow activation and generation) of the catchments begins to activate and October is again a transition period when hydrological functioning begins to inactivate. These results are very much helpful to agriculturists, land managers and policy developers for the conversation and sustainable development of forest, soil and water resources, main important in this region.

Forest Cover impacts on Stream flows
Studies concerning the impact of forest cover changes on the magnitude of streamflow in Himalayan regions are rare Ashraf et al., 2013;Tiyagi et al., 2014); however, studies relating forest cover changes on the magnitude of components of streamflow (baseflow and stormflow) in Himalayan regions are even rarer. In the WHR where rainfall is highly seasonal, streamflow at the catchments showed distinctive behavior during dry and wet-period (Fig. 3).
Separation of hydrographs (Arnigad and Bansigad) into stormflow (6% and 31%) and baseflow (50% and 32%), Fig. 6A and B, vastly improves our understanding of streamflow regulation at catchment scale and surely will be helpful for water resource management (Nepal et al., 2014) in the WHR. The baseflow and stormflow at Bansigad showed larger variations as compared to Arnigad (Fig. 6A), the large variation was due to the faster recession rates at Bansigad catchment during the dry-period, with reaction/response factors of 0.028 day − 1 compared to Arnigad catchment (0.0083 day − 1 ). The faster recession rate at Bansigad, diminished streamflow completely during dry-period, however, at dense forest (Arnigad) the baseflow was higher by ~ 52% annually (Fig. 6B), helps to maintain streamflow year the round. Further, the higher proportion of the stormflow at Bansigad, indicated higher probability of water resources problems such as flooding in the wet-period and drought in the dry-period. Baseflow recessions are important for the management of both ground water and surface water resources during dry-period (Miller et al., 2016).
The response of catchments after rainfall exhibited that the lag time generally increased for small and early wet-period storms and decreased for larger storms. Lag time of both the catchments ranged between 0:15 to 0:45 hour. If the time gap between two consecutive rainfalls were larger, lag time of hydrographs also became larger and during wet-period when the catchments were fully saturated, few rainfall storms took zero time to become runoff/stream discharge. In order to compare the volume of water stored at Arnigad and Bansigad, out of 40 selected hydrographs, three hydrographs along with corresponding hyetographs at same time period from 29.07.08 to 31.07.08 and at same interval (15-minute interval) were analyzed in detail (Fig. 11). There was no significant difference (p = 0.05) in rainfall events between Arnigad (36-109 mm) and Bansigad (47-118 mm), however, there was significant difference in discharge between Arnigad (0.60-0.81 m 3 s − 1 ) and Bansigad (0.81-1.32 m 3 s − 1 ), respectively. Further, lag time of these three events were: 0:45, 0:45 and 0:30 hr (Arnigad) and 0:30, 0:30 and 0:15 hr (Bansigad), respectively (Fig. 11). The shape of the different hydrographs varied with each individual rainfall storm event. The analysis reveals that during wet-period, Arnigad releases lower volume of water and took averagely 15 minutes extra (compared to Bansigad) to reach to gauging site and potentially this behaviour of hydrographs (at Arnigad) may possibly be because of many combined factors: (i) slow recession rate of baseflow at Arnigad (Fig. 5), (ii) higher potential of forest soil (Fig. 6) to store water at Arnigad and (iii) higher infiltration rate (Table 2). Therefore, the volume of water that was stored (at Arnigad) during storm events and longer lag time supported the flow to release during a recession, helped in maintaining baseflow during dry-period, which is important ecosystem function of the catchment. Hence, the study indicates that forest cover at Arnigad showed significant and positive relationship with both baseflow and stormflow and results are needed to effectively manage current and future land use and water resources problems in WHR.

Soil moisture functioning at different soil profiles:
Temporal variations of SM at different depths under different forest covers are shown in Fig. 7A. It is observed that SM at all different profiles is responsive to rainfall events, though few events might have been missed as the data was measured at two-week intervals. The SM reached its maximum during wet periods, when ~ 78% of annual rainfall takes place. It is observed from Fig. 7A and B, that during the wet-period, the surface layers at both the catchments were wetter than other deeper layers. This was because when rainfall amount was too small, most of them were retained on the surface layer only (Li et al., 2016). The difference in SM at surface layer was even more distinct at Bansigad catchment, shows low interception losses due to degraded forest at Bansigad, resulting large volume of rainfall could reach to the ground surface (Liu et al., 2018;Venkatraman and Ashwath, 2016) and therefore, the Bansigad catchment shows higher (4%, annually) moisture regimes at surface layer than Arnigad (Fig. 7B). At Arnigad catchment, SM was maximum at deeper layer (80 cm) than at 50 cm depth. This was possibly due to lower rate of water movement to the next soil layer or may be influence of lateral flow (within the soil layer) from the upslope due to change in the saturated hydraulic conductivity properties (Venkatesh et al., 2011). Many studies (Gutiérrez-Jurado et al., 2007;Toro-Guerrero et al., 2018) from hillslopes or areas having steep slopes supported active response of lateral flow to deeper soil layers, thus efficiently bypassing the shallower soils which are more exposed to evapotranspiration. Therefore, SM in the hillslopes varies both in the vertical and lateral direction (Venkatesh et al., 2011). Annually, SM at Arnigad at 50 cm and 80 cm was enhanced by 13% and 31% in comparison with Bansigad (Fig. 7B). These enhanced values indicated potential for soil water storage at forested catchment (Arnigad) and release the water slowly during the subsequent dry-period, which consequently helps in regulation of sustained stream flows in the Himalayan region. This can be further supported by the Fig. 12, which shows that Arnigad had higher OM (21-89%) and higher porosity (3-11%) than Bansigad which helps Arnigad in retaining SM and upholding sponge characteristics (Qazi et al., 2017). The lowest values of volumetric SM (mean monthly) were recorded as 25% (Arnigad) and 21% (Bansigad), indicating low (19%) storage deficit at Bansigad relative to Arnigad. Therefore, water retention / flow regulation at dense forested catchment (Arnigad) was better as compared to the degraded forested catchment (Bansigad). Therefore, the present study suggests that forests plays positive role in SM functioning at local sites (Bruijnzeel, 2004) and provides hydrological service in different ways at catchment scale.
However, further research work is required to understand the dynamics and transport of soil water content from shallow to deeper soil layers for potential ground water recharge.

Dynamic Relationships Of Soil Moisture
Soil moisture showed non-linear positive relationship with streamflow ( Fig. 10), which indicates that with the increase in SM, streamflow also increased. This relationship allowed the identification of a SM threshold value (~ 35%), below which streamflow cannot increase. Non-linear behavior is common in hydrological systems (Zuecco et al., 2018) and this thresholds can be used as a classification tool to better conceptualize runoff response behavior under a range of weather conditions (Ali et al., 2013;. OM showed direct positive linear relationship with tree density (Fig. 12A). Higher tree density means higher OM in soil, which helps in binding soil particles together into stable aggregates, increasing porosity (Zuazo and Pleguezuelo, 2008;Tobella et al., 2014), and finally leads to higher infiltration ( Fig. 12B). Both SM and vegetation are closely linked with each other. SM positively influence vegetation growth (Wang et al., 2007), whereas vegetation displays complex relationship with SM.
More vegetation either conserve more water, causing retention of SM or consumption of water itself, causing the depletion of SM (Pielke et al., 1998;Wang et al., 2006). Hence, more vegetation may correspond either to increase (Bounoua et al., 2000;Buermann et al., 2001) or to decrease SM (Pielke et al., 1998;Wang et al., 2006). Hence, the present study supported the fact that forests/vegetation leads strong bond with SM and interestingly SM also showed positive and direct impact on infiltration rate (Fig. 12C). Further work is required in future to understand these relationships at different spatial and temporal scale in WHR.

Dynamic role of forest in sediment transportation / Erosion behavior
Sediment transport is a function of several interacting factors including vegetation, climate, topography, parent material, and soil. Vegetation is the most important factor controlling sediment transport activity in Himalayan catchments by different forest components (forest cover, understory, tree roots, and woody debris). The reduction (18%) in suspended sediment production at Arnigad compared to Bansigad, are probably due to dense forest cover at Arnigad catchment. The roots of trees hold soil particles tightly and doesn't allow natural forces (wind and water) to take away the upper-most layer of the soil. Moreover, the understory (shrubs, herbs, leaf litter etc.) at Arnigad also helps in decrease of surface erosion by reduction of kinetic energy of raindrops (Fukuyama et al., 2010;Nanko et al., 2015). On the other hand, degraded forest along with high intensity rainfall trigger loosened material and debris (Fuller et al., 2003), leads to landslides (Struck et al., 2015), and further to higher sediment production at Bansigad stream (Tyagi et al., 2014), continuously disturbing the natural system (Mukherjee, 2013) of the Bansigad catchment. The lower (75%) deposited BL material at Arnigad catchment was because of the standing trees, felled logs and understory of dense forest, which slow down the movement of big boulders, gravel and debris (Qazi et al., 2018). Moreover, the strong tree root system supports slope stability, decreases landslides and debris flows frequency (Imaizumi et al., 2008;Goetz et al., 2015), hence BL material couldn't reach to the Arnigad stream relative to Bansigad stream. Hartanto et al., 2003;Imaizumi et al., 2019 also supports that large amount of sediments are captured by woody debris on hillslopes. Further, forest controls erosion dynamics through their strong root system and organic humus layer (Nepal et al., 2014). Therefore, the present study ensures that forest plays important roles in controlling erosion and can be considered as an important way to improve the environment.
Interestingly, the concentration of dissolved material in streams at Arnigad was also enhanced by 114% (annually) as compared to Bansigad. As both the catchments were located near to each other, the rock types and their erodibility are assumed to be the same. Apparently, the landuse or forest was the only element to account for higher dissolved solids at Arnigad catchment. Large quantity of OM are generated in the forest floor at Arnigad catchment, which decompose, percolate through rain water (Krishna and Mohan, 2017), and reach to streams in dissolved form (Markewitz et al., 2004;Andrade et al., 2011;Cost et al., 2017). Hence, the dissolved OM effects TDS in the stream. Dryperiod has significant impact on wide range of TDS at Bansigad, because TDS becomes more concentrated with decrease in discharge (Tipper et al., 2006;Calmels et al., 2011). TDS at both the catchments was the permissible limit according to WHO, 2003;BIS, 2012. In the Himalayan region, high relief coupled with intensive rainfall during monsoon provide favorable conditions for mass wasting (Korup and Weidinger, 2011), which cause serious long-term problems e.g. functioning of hydropower plants, dam and river management, environmental flow, biological diversity, reservoir siltation, landslides etc. (Zokaib and Naser, 2011;Hedrick et al., 2013;Sudhishri et al., 2014;Iwuoha et al., 2016). Reduction of long-term mass wastage (denudation rate) by 41% at Arnigad compared to Bansigad, further confirms the crucial role of trees and forests in preventing mass wastage which in turn maintains balances ecological functioning, biological diversity, landslides etc. at long term scale.

Conclusion
During the study period, rainfall was quite variable and comprised both dry-period and wet-period, thus allowed to study baseflow and stormflow conditions of the catchments. The annual hyetograph analysis reveals that the rainfall at both the catchments was highly seasonal, and wet-period plays a key role in catchment functioning.
The Arnigad maintains its baseflow of ~ 80 mm per month (~ 5% of annual streamflow) during dry-period and makes stream perennial, however, baseflow was not available at Bansigad during dry-period and makes stream intermittent. The analysis reveals that both streams were dependent on rainfall for streamflow generation, but the timescale over which precipitation at Arnigad can sustain baseflow is greatly enhanced relative to Bansigad.
Present study also highlights the strong control exerted by SM on streamflow. A sharp threshold (~ 35%) existed between SM and streamflow, above which baseflow was activated, increased significantly and become major contributor to stormflow. Therefore, the study estimates the threshold, responsible for streamflow activation and generation, and may serve as a foundation for future studies that predict streamflow response to climate and anthropogenic change in the WHR. Further, the continuous faster recession rates of baseflow, low potential of forest soil to store water and lower infiltration rates were responsible factors for the diminishing stream flows during a dry-period at Bansigad catchment. In comparison to Bansigad, the decrease in sediment budget, confirms the crucial role of forests in controlling and maintaining erosion dynamics at Arnigad catchment.
Therefore, the present study suggests that forests provide substantial services by regulating water balance, SM and sediment budget at Arnigad catchments. Moreover, forest also helps in maintaining soil properties and infiltration rate by adding OM to soil. Based on the findings, the paper concludes that our understanding of hydrological functioning at catchment scale advances our ability to improve water resource management in WHR and meets needs of local people without adversely affecting the environment.

WHR:
Western Himalayan Region

FCC:
Forest Canopy Cover

TD:
Tree Density

LISS:
The Linear Imaging Self Scanning Sensor

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