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\begin{document}

\title{Transpiration drives diurnal and seasonal streamflow in secondary
tropical montane forests of Eastern Himalaya}



\author[1]{Manish Kumar}%
\author[2]{Yangchenla Bhutia}%
\author[3]{Gladwin Joseph}%
\author[2]{Jagdish Krishnaswamy}%
\affil[1]{University of Birmingham Edgbaston Campus}%
\affil[2]{Ashoka Trust for Research in Ecology and the Environment}%
\affil[3]{Conservation Biology Institute}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Vegetation studies establishing direct mechanistic linkages between
stand transpiration and streamflow are rare from sub-tropical and
tropical montane forests (TMFs) like Himalaya. We quantified the impact
of diurnal and seasonal transpiration on lean season streamflow in a
broad-leaved evergreen secondary TMF in Eastern Himalaya. Whole-tree and
stand transpiration were measured using Granier's thermal dissipiation
sap flow probes at one of the wettest (4500 mm yr-1) and highest
elevation (2100 m) sites in the world to date. The observed daily and
annual transpiration rates were double of the reported values from TMFs
in relatively drier Central Himalaya, but at the lower bound of TMFs
globally. Solar radiation was the key driver of transpiration in
energy-limited winter under hydrated conditions. Vapour pressure deficit
(D) controlled transpiration in energy-abundant summer. We also found
that moderate precipitation events (10-30 mm) followed by clear skies
can induce significant increase (93\selectlanguage{ngerman}±110 \%) in stand transpiration. In
turn, transpiration was the main driver of lean season streamflow in dry
winter and to a lesser extent in wet summer. Thus, in winter, the
transpiration-driven abstraction induced corresponding diurnal cycles in
soil moisture and streamflow with an average lag of 1.3±1.8 hours and
2.9±2.5 hours, respectively, and strong negative correlations
(-0.8±0.1). Thus, changes in vegetation cover and precipitation patterns
are likely to impact local and regional moisture recycling by vegetation
and lean season flow, thereby affecting regional water security in the
Eastern Himalaya.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Manuscript Title:}

\textbf{Transpiration drives diurnal and seasonal streamflow in
secondary tropical montane forests of Eastern Himalaya}

Authors:

Manish Kumar\textsuperscript{a,b,c*}, Yangchenla
Bhutia\textsuperscript{b,c,d}, Gladwin Joseph\textsuperscript{b,c,e} and
Jagdish Krishnaswamy\textsuperscript{b,c,f}

Affiliations:

\textsuperscript{a}School of Geography, Earth and Environmental
Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT,
United Kingdom.

\textsuperscript{b}Ashoka Trust for Research in Ecology and the
Environment (ATREE), Royal enclave, Srirampura, Jakkur PO, Bangalore
560064, Karnataka, India.

\textsuperscript{c}Manipal Academy of Higher Education (MAHE), Manipal
576104, Karnataka, India.

\textsuperscript{d}Sikkim State Council of Science \& Technology,
Gangtok 737102, India.

\textsuperscript{e}Conservation Biology Institute, Oregon 97333, USA.

\textsuperscript{f}School of Environment and Sustainability, Indian
Institute for Human Settlements, Bangalore 560080, India.

*Corresponding author: Manish Kumar

\textbf{Corresponding author contact information}

Dr. Manish Kumar

Research Fellow,

School of Geography, Earth and Environmental Sciences

University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom

Mobile: +44 7927645279

Primary email id: manish.kumar@atree.org ; m.kumar.2@bham.ac.uk

ORCID: 0000-0002-6780-9336

\textbf{Running head}

Transpiration drives dry season streamflow in secondary Eastern
Himalayan Forests

\textbf{Keywords}

Eastern Himalaya; Ecohydrology; Plant-water relations; Sap flow;
Secondary forests; Streamflow; Transpiration; Tropical montane forests

\textbf{Abstract}

Vegetation studies establishing direct mechanistic linkages between
stand transpiration and streamflow are rare from sub-tropical and
tropical montane forests (TMFs) like Himalaya. We quantified the impact
of diurnal and seasonal transpiration on lean season streamflow in a
broad-leaved evergreen secondary TMF in Eastern Himalaya. Whole-tree and
stand transpiration were measured using Granier's thermal dissipiation
sap flow probes at one of the wettest (4500 mm yr\textsuperscript{-1})
and highest elevation (2100 m) sites in the world to date. The observed
daily and annual transpiration rates were double of the reported values
from TMFs in relatively drier Central Himalaya, but at the lower bound
of TMFs globally. Solar radiation was the key driver of transpiration in
energy-limited winter under hydrated conditions. Vapour pressure deficit
(D) controlled transpiration in energy-abundant summer. We also found
that moderate precipitation events (10-30 mm) followed by clear skies
can induce significant increase (93\selectlanguage{ngerman}±110 \%) in stand transpiration. In
turn, transpiration was the main driver of lean season streamflow in dry
winter and to a lesser extent in wet summer. Thus, in winter, the
transpiration-driven abstraction induced corresponding diurnal cycles in
soil moisture and streamflow with an average lag of 1.3±1.8 hours and
2.9±2.5 hours, respectively, and strong negative correlations
(-0.8±0.1). Thus, changes in vegetation cover and precipitation patterns
are likely to impact local and regional moisture recycling by vegetation
and lean season flow, thereby affecting regional water security in the
Eastern Himalaya.

\textbf{Graphical Abstract}

{[}Insert Graphical Abstract{]}



\section*{1. INTRODUCTION}

{\label{introduction}}

Moisture recycling by vegetation is a key ecohydrological process in
tropical forest hydrology (Wohl \emph{et al.} , 2012). While, the impact
of climate regimes on the evolution of distinct vegetation types is well
known, vegetation also significantly alters the availability of water in
their environment through ecohydrological processes such as
transpiration and infiltration of precipitation into the soil
(Peña-Arancibia \emph{et al.} , 2019). These two processes modulate soil
moisture distribution across both time and at local and regional scales,
and significantly influence hydrological services from vegetated
catchments such as streamflow (Bruijnzeel, 2004; Schlesinger and
Jasechko, 2014). The extent of vegetative influence on streamflow varies
considerably across vegetation types, dominant functional groups,
micro-climatic conditions (water or energy-limitations on plant
productivity) and the ability of vegetation to access deeper subsurface
moisture and/or groundwater. The literature on interactions between
water and vegetation has been largely focused on the ecophysiological
aspects of transpiration, while a few have extended it to interactions
between transpiration, subsurface moisture, and streamflow (Bond\emph{et
al.} , 2002; Moore \emph{et al.} , 2011). In terrestrial ecosystems,
diurnal and seasonal variations in transpiration can induce
corresponding patterns in soil moisture and streamflow (Bond \emph{et
al.} , 2002; Moore \emph{et al.} , 2011). Conversely, the transpiration
responses to changes in antecedent moisture and precipitation pulses is
another emerging area of research, especially in regions with extended
dry spells (Burgess, 2006; Zeppel\emph{et al.} , 2008; Chen \emph{et
al.} , 2014).

Ecohydrological studies have explored the relative impact of
transpiration on hydrological fluxes at different spatiotemporal scales
and across different functional groups (Asbjornsen \emph{et al.} ,
2011). For example, shallow-rooted trees may tap into the subsurface
moisture, whereas deep-rooted tree species can access the water table
directly, and releases it into the atmosphere through transpiration
(Maeght \emph{et al.} , 2013; Kumar \emph{et al.} , 2022). Deng \emph{et
al.} , (2021) report groundwater table modulating sap flow in karst
aquifer systems, along with meteorological drivers. The loss of soil
moisture or baseflow to the atmosphere, which otherwise could have
contributed to streamflow, can significantly alter the local water
balance and hydrological services from a forest (Barbeta and Peñuelas,
2017; Perry and Jones, 2017). In contrast, the ``two water worlds''
hypothesis suggests that the water available for trees is potentially
disconnected from the moisture stock that contributes to streamflow and
that trees have limited access to groundwater (Berry \emph{et al.} ,
2018). However, the literature remains inconclusive on the presence of
two moisture movement pathways with research evidence on trees accessing
stream/groundwater in the valley or riparian zones, especially in
topical montane forests (TMFs) with shallow soil strata, highly
fractured geology, and significant dry season as seen in the Himalaya
and Western Ghats of India (Krishnaswamy \emph{et al.} , 2013; Barbeta
and Peñuelas, 2017). However, the literature on interlinkages between
vegetation and streamflow remains scarce from pan-tropical mountains
such as Himalaya (Célleri and Feyen, 2009; Bruijnzeel \emph{et al.} ,
2011).

Previous research from Himalaya does highlight the important role of
broad-leaved TMFs in precipitation partitioning, runoff generation, and
sediment transport in the Himalaya, where evapotranspiration can go up
to 40 \% of the annual water budget (Sharma \emph{et al.} , 2007;
Ghimire \emph{et al.} , 2014; Qazi \emph{et al.} , 2017). At basin
scales, studies on stream hydrology have tried to understand the
relative contribution of glacial melt, snowmelt, runoff, and baseflow to
the total discharge and quantify the impact of climate change in
Himalaya (Singh and Bengtsson, 2005; Singh and Kumar, 2010). At
catchment scales, most of such studies have come from the Western and
Central (Nepal) Himalaya and have focused on establishing linkages
between stream characteristics and land-use forms, including the role of
TMFs (Sharma \emph{et al.} , 2007; Krishnaswamy, 2017). The region is
projected to experience significant warming (0.8-1.2 °C
decade\textsuperscript{-1}), increasingly drier winters and wetter
monsoon and summer seasons trends under climate change (Krishnan
\emph{et al.} , 2019; Kumar \emph{et al.} , 2021). Broad-leaved
secondary forests form a significant portion (32.5 \%) of total area
under TMFs in Sikkim and are likely to have distinctly altered carbon
and water cycles than primary forests (Kanade and John, 2018; Bhutia
\emph{et al.} , 2019). The high water demand from these regenerating
secondary forests dominated by pioneer species can exert significant
pressures on limited soil moisture reserves and in turn on lean season
streamflows (Wright \emph{et al.} , 2018; Kumar \emph{et al.} , 2022).
However, to the best of our knowledge, ecohydrological controls of
vegetation-water use on streamflow have not been quantified and
mechanistically explained in TMFs of Himalaya. Thus, in the first
attempt from Himalaya, the relative controls of vegetation-driven
transpiration on lean season streamflow were investigated in Sikkim, a
representative region of Eastern Himalaya. Thus, the key questions
addressed in the study are: (a) How does micro-climate and soil moisture
changes affect diurnal and seasonal variability in stand transpiration?,
(b) What is the impact of precipitation events size on stand
transpiration?, and (c) How diurnal and seasonal variability in stand
transpiration impacts lean season streamflow dynamics in an East
Himalayan secondary TMF?

\section*{2. MATERIAL AND METHODS}

{\label{material-and-methods}}

\subsection*{2.1. Study site and forest stand
description}

{\label{study-site-and-forest-stand-description}}

The study site is located in the Fambong-Lho Wildlife sanctuary (FWS,
N27.583, E88.933) draining into Teesta river) in East Sikkim, which is
part of the Teesta river basin (Figure 1). The instrumented
micro-watershed has a catchment area of 0.016 km\textsuperscript{2} and
is situated at an elevation of 2100 - 2400 meters above sea level
(masl). The mean annual precipitation is 4650±120 mm with temperature
fluctuating between an annual maximum of 24\selectlanguage{english}0C to minima of -20\selectlanguage{english}C. Three
distinct seasons in the study area are winter (November - February) with
sunny days, freezing nights, sporadic snowfall and low
evapotranspiration, summer (March-May) characterized by warm and cloudy
days, high evapotranspiration, and substantial pre-monsoon
precipitation, and monsoon (June - October) with high humidity, low
evapotranspiration and concentrated precipitation (Pandey \emph{et al.}
, 2016; Kumar \emph{et al.} , 2021). The winter (December 2013 --
February 2014) marked the dry season with sunny but cold days and
sub-zero night temperatures. The summer (March 2014 -- May 2014) saw
increased day-length, predominantly afternoon precipitation, abundant
moisture, warmer temperatures, and higher plant productivity (Kumar
\emph{et al.} , 2022).

{[}Insert Figure 1{]}

Figure 1. Study area map showing (a) Digital elevation model (DEM) map
of Himalaya and Sikkim within India; (b) the location of Fambong-Lho
wildlife sanctuary (FWS) and the instrumented watershed within Sikkim;
and (c) DEM of the delineated watershed of the first-order stream in FWS
with the location of the sap flow instrumentation site, and rain and
stream gauging stations.

The forest soil was well-drained sandy-loam with an average soil depth
of 70 cm. The stand was moderately sloped (10\selectlanguage{ngerman}°-35°) and dominated by
north-east aspect. The site showed much lower soil hydraulic
conductivity than similar elevation forests in Central Himalaya, a known
characteristic of secondary forests (Wright \emph{et al.} , 2018). The
forest stand represents early successional secondary East Himalayan
broad-leaved wet montane forests (Sudhakar \emph{et al.} , 2008; Kanade
and John, 2018) and as a mid-altitude forests dominated
by\emph{Castanopsis hystrix} (Bhutia \emph{et al.} , 2019). The stand is
characterized by (a) the dominance of broad-leaved evergreen species and
(b) sub-tropical montane climate with high precipitation and strong
diurnal and seasonal temperature gradients. The vegetation data were
derived from five 1000 m\textsuperscript{2} (100 m X 10 m) vegetation
survey plots (Bhutia \emph{et al.} , 2019). The species composition is
dominated by the three species: \emph{Symplocos racemosa} , \emph{Eurya
acuminata,} and \emph{Castanopsis hystrix} , which form 58.3 \% of total
stand trees and 38.3 \% of the total basal area (Bhutia \emph{et al.} ,
2019; Kumar \emph{et al.} , 2022). The short-statured canopy (3-8 m
tall) is dominated by pioneer species like \emph{S. racemosa} and
\emph{E. acuminata} , while the older remnant trees \emph{C. hystrix}
stand out as emergent (Bhutia \emph{et al.} , 2019; Kumar \emph{et al.}
, 2022).

\subsection*{2.2. Data collection}

{\label{data-collection}}

\subsubsection*{2.2.1. Sap flux
measurements}

{\label{sap-flux-measurements}}

Sap flux density was measured using Granier's thermal dissipation method
(TDP) probes in 13 trees of the three dominant species, \emph{S.
racemosa} , \emph{E. acuminata,} and \emph{C. hystrix} (Granier, 1987;
Lu\emph{et al.} , 2004). The thermal dissipation method applies to a
wide range of species and easy to manufacture locally at low-costs
(Davis\emph{et al.} , 2012; Flo \emph{et al.} , 2019). The probes were
installed to cover both radial and azimuthal variabilities with in an
individual tree to estimate volumetric sap flow rates (V, mm
h\textsuperscript{-1}). The detailed methodology for TDP probe
manufacturing, instrumentation, and scaling to whole-tree sap flow (V in
kg h\textsuperscript{-1}) can be referred at Kumar \emph{et al.} (2022).

\subsubsection*{2.2.2. Scaling from whole-tree sap flow to stand
transpiration}

{\label{scaling-from-whole-tree-sap-flow-to-stand-transpiration}}

The volumetric sap flow data from the 13 trees was scaled to stand
transpiration (T, mm h\textsuperscript{-1}) using the data from
vegetation plots and sapwood area as the vegetative scalar. A five-step
approach was adopted to scale sap flow (V in kg h\textsuperscript{-1})
from the instrumented trees to the stand-level using tree DBH as a
scalar (Chiu \emph{et al.} , 2016). First, a non-linear least squared
(NLS) regression model (intercept = 0.112, power coefficient = 0.857, P
\textless{} 0.001) was fitted between tree diameter (DBH in m) and
sapwood area (A\textsubscript{total} in m\textsuperscript{2}) of the 13
trees (see supplementary data Figure S1). The NLS regression model
coefficients were used to compute total sapwood are
(A\textsubscript{total}) from DBH for each tree (above 10 cm DBH)
sampled in the vegetation plots in the next step. Third, the
instrumented trees were categorized into four DBH classes \textless{}
0.15 m, 0.15-0.19 m, 0.20-0.24 m, and \textgreater{} 0.25 m (see
supplementary data Figure S2a). The average sap flux density per class
(J\textsubscript{avg}) was computed by normalizing the V of each
instrumented tree by its A\textsubscript{total} and averaging for each
class. Fourth, all trees above 10 cm DBH sampled in the vegetation plots
were categorized into the above-mentioned DBH classes (see supplementary
data Figure S2b). Whole-tree sap flow (V) was estimated for each tree in
the forest stand by multiplying the corresponding J\textsubscript{avg}of
the diameter class with its A\textsubscript{total}. Fifth, stand
transpiration (T in mm h\textsuperscript{-1}) was computed by
aggregating hourly V for all trees in the vegetation plots and dividing
it by the total plot area (5000 m\textsuperscript{2}). Gap-filling of
the missing data for sap flow and environmental variables was
consciously avoided due to the strong variability in the raw data.

\subsubsection*{2.2.3. Environmental and hydrological
measurements}

{\label{environmental-and-hydrological-measurements}}

An automated stilling well fitted with a capacitance water level
recorder (Dataflows Systems LTD, New Zealand) was installed on the
first-order stream draining the micro-watershed. Streamflow (Q, mm
h\textsuperscript{-1}) was computed from the water-level using a
stream-specific rating curve and catchment area. On-site Precipitation
(P, mm h\textsuperscript{-1}) was recorded using an automated
tipping-bucket rain gauge. Microclimate observations were used to
understand the drivers of stand transpiration. Air temperature (°C),
relative humidity (R\textsubscript{h}, \%), wind speed (U, m
s\textsuperscript{-1}), and incoming short-wave radiation
(R\textsubscript{s}, kW m\textsuperscript{-2}) were recorded using an
automatic weather station (Vantage-pro Davis Net, USA. Hourly vapour
pressure deficit (D, kPa), which is an important parameter of
atmospheric dryness, was computed from air temperature (°C) and relative
humidity data from the weather stations and hygrochrons (iButton
Hygrochrons, Maxim Int., USA). Reference evapotranspiration
(E\textsubscript{0}, mm h\textsuperscript{-1}) was computed the
meteorological data following FAO's Penman-Monteith method (equation 6
of FAO56PM and Allen et al. 1998). Soil water potential was recorded at
10 cm incremental depths from the topsoil to up to 30 cm depth using
granular matrix-based (watermark) sensors (Virtual Electronics, Roorkee)
and converted to volumetric water content using the site-specific van
Genuchten water retention curve developed using Rosetta software
(Schaap\emph{et al.} , 2001). Total soil moisture (S, mm) was computed
for the topsoil (0-30 cm depth) using the trapezoidal method and stray
missing values were gap-filled through 3-step moving-average window
(Nachabe\emph{et al.} , 2005).

\subsection*{2.3. Data analysis}

{\label{data-analysis}}

After quality checks and clean-up, a total of 114 days (November 2013 to
May 2014) of data for T, including 72 days data available for T and
environmental variables (S, P, D, Q and E\textsubscript{0}), was used
for final analysis (Figure 2). The four main analytical methods used
were: (a) Seasonal assessment of diurnal cycles in SPFAC variables, (b)
Comparison of transpiration responses to precipitation pulses, (c) Lag
correlation analysis to explore the shifting lags between Transpiration,
soil moisture and streamflow, and (d) Step-wise lag regression models to
quantify the relative influences of transpiration and micro-climate on
streamflow. The data processing, analysis, and visualization were done
in the R programming software (version 4.1.3) (R Core Team, 2022).

\subsubsection*{2.3.1. Understanding the environmental drivers of stand
transpiration}

{\label{understanding-the-environmental-drivers-of-stand-transpiration}}

Seasonal changes in diurnal cycles of sap flow and environmental drivers
were plotted to illustrate the shifts in their inter-relationships. Both
Q and S were de-trended to remove excessive noise
(\emph{stlplus}function, package \emph{stlplus} ) and the filtered
diurnal signals, S\textsubscript{diu} and Q\textsubscript{diu},
respectively, were extracted for understanding diurnal patterns (Moore
\emph{et al.} , 2011). Contiguous subsets of Q longer than 5 days were
subset and Q\textsubscript{daily.amp} was computed as the difference
between highest and lowest discharge in a day for the rainless periods.
The capacitance water-level recorders used in the study are known to
suffer from measurement error induced by diurnal changes in ambient
temperature (Larson and Runyan, 2009). We empirically computed the
measurement error (0.008 m) for the capacitance probe at the field site
using a standing water column experiment with a fixed water level, and
corrected for the measurement error in the estimation of
Q\textsubscript{daily.amp}. The ratio of the variance of the diurnal
component of streamflow (Q\textsubscript{diu}) to the variance of
streamflow (Q) and the amplitude of diurnal variation in Q
(Q\textsubscript{daily.amp}) was computed for contiguous rainless
periods longer than 5 days to know the seasonal variability in the
significance of diurnal cycle of Q to overall Q. Hourly binned averages
of P, T, S\textsubscript{diu,} and Q\textsubscript{diu} were estimated
and plotted for each month to visually assess the seasonal shifts in
their respective diurnal cycles (Kumar \emph{et al.} , 2022).

\subsubsection*{2.3.2. Impact of precipitation pulses on stand
transpiration}

{\label{impact-of-precipitation-pulses-on-stand-transpiration}}

The daily precipitation time-series was analyzed to understand the
impact of precipitation pulses on plant water-use. Precipitation event
volume denotes the extent of moisture input to a system and thus was
preferred over intensity. Precipitation events were separated with a
threshold of 2.5 mm d\textsuperscript{-1} and interevent time of 1-day.
All events with duration longer than 2 days and total precipitation
volume above 2.5 mm were considered as a pulse (Zeppel \emph{et al.} ,
2008). The percentage changes stand transpiration (T), Q,
E\textsubscript{0}, R\textsubscript{s} and D were estimated as
difference between their respective values on the day of the
precipitation event and the day after (Chen \emph{et al.} , 2014).

\subsubsection*{2.3.3. Lag correlation analysis between Transpiration,
soil moisture and
streamflow}

{\label{lag-correlation-analysis-between-transpiration-soil-moisture-and-streamflow}}

Lag correlation analysis allows the quantification of correlation
between variables that are at lag with each other in a physical
environment and was preferred due to the observations of strong
autocorrelations (Moore \emph{et al.} , 2011; Kumar \emph{et al.} ,
2022). The lag (in hours) between following combinations:
S\textsubscript{diu}vs. Q\textsubscript{diu}, T vs. Q\textsubscript{diu}
and P vs. Q\textsubscript{diu} was computed for each day using the
cross-correlation function (\emph{ccf} function in R) and filtered for
auto-correlation coefficients (ACF) \textgreater{}=
\textbar{}±0.4\textbar{} (Moore \emph{et al.} , 2011). Positive
(negative) autocorrelation coefficient (ACF) values signified that a
high (low) value of the driver variable was followed by a high (low)
value of the response variable after the corresponding lag-hours
(Kumar\emph{et al.} , 2022).

\subsubsection*{2.3.4. Step-wise lag regression models for
streamflow}

{\label{step-wise-lag-regression-models-for-streamflow}}

In order to assess the relative influences of transpiration and rainfall
on streamflow, a combination of a temporal lag model and the Generalized
least squares (GLS) regression model (\emph{gls} function,
package\emph{nlme} ) with the suitable correlational structure were used
in a step-wise manner (Kumar \emph{et al.} , 2022). The temporal lag
model was necessitated by the observed lag between the different SPFAC
variables, which also showed significant temporal autocorrelations. The
GLS regression method estimates the maximum likelihood of the regression
coefficients using generalized least-squares and is most suitable for
analyzing time-series data with autocorrelational structures
(Krishnaswamy \emph{et al.} , 2012; Kumar \emph{et al.} , 2022). The
streamflow GLS model used Q as the response variable and T, S,
Antecedent moisture index (M\textsubscript{a})\textsubscript{,} and P as
predictor variables. M\textsubscript{a} was used as a proxy for the
antecedent state of moisture in the system and estimated using equation
1, where, P\textsubscript{cum}, T\textsubscript{cum,} and
Q\textsubscript{cum} are cumulative sums of P, T, and Q, respectively
(Potts \emph{et al.} , 2006).

\begin{equation}
M_{a}=P_{\text{cum}}-\ T_{\text{cum}}-\ Q_{\text{cum}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (1)\nonumber \\
 \end{equation}

The streamflow GLS models were separately run for the rainless (December
--February) and rainy (March-May) periods with first-order
autoregressive structure (corAR1), which is considered suitable and
parsimonious for streamflow modeling (Krishnaswamy \emph{et al.} ,
2012). The interaction term between S and T was the predictor variable
in the rainless period, whereas P, M\textsubscript{a}, and the
interaction term between S and T were the predictor variables in the
rainy period. Before running the models, S and M\textsubscript{a} were
normalised by subtracting the daily mean, tested for collinearity and
then contiguous subsets longer than 5 days and without any missing
values were extracted.

Owing to the observed lag between the driver variables, the GLS model
was run using a reconstituted time-series dataset, where each driver
variable was first tested for lag in a stepwise manner. For each driver
variable, firstly, the highest significant lag (L in hours, at ACF
\textgreater{}= \textbar{}\selectlanguage{ngerman}±0.4\textbar{}) between the driver and
response (Q) variable was calculated for each contiguous subset
using\emph{ccf} function. Then the driver variable was lagged from zero
lag to L hours to develop multiple lagged time-series for each driver
variable. Multiple linear regression models (MLRs) were fitted between Q
and the lagged time-series the driver variable and the timeseries with
the lowest significant lag (\emph{p} \selectlanguage{english}[?] 0.05) was chosen. The procedure
was repeated for the other driver variables and the final timeseries set
was used for running the GLS model (Kumar \emph{et al.} , 2022). We also
tested for the impact of adding correlational structure to the GLS model
by running the GLS model separately with and without correlations
structures and comparing the results using the Analysis of variance
(ANOVA) test. The GLS model with the lowest Akaike Information Criterion
(AIC), higher likelihood ratio, and significance were chosen for
interpretations of variables with significant coefficients (\emph{p} \selectlanguage{english}[?]
0.05). The Gupta-Kling Efficiency (KGE) score used to assess the model
performances by comparing the observed and predicted time-series of Q to
(Gupta and Kling, 2011).

\section*{3. RESULTS}

{\label{results}}

\subsection*{3.1. Stand transpiration
characteristics}

{\label{stand-transpiration-characteristics}}

Average stand transpiration (T) was 1.45+-1.1 mm
d\textsuperscript{-1}(mean+-standard deviation) approaching a maximum
rate of 5.28 mm d\textsuperscript{-1} and ranged from 0.02-5.28 mm
d\textsuperscript{-1}. T exceeded 3\textsuperscript{rd} quartile (1.9 mm
d\textsuperscript{-1}) of all values on 25.5 \% (29 days) of the total
days (114 days) of sap flow observations. The average T in summer
(1.76+-1.3 mm d\textsuperscript{-1}) was 34 \% higher than in winter
(1.16+-0.8 mm d\textsuperscript{-1}). The nocturnal transpiration (1800
-- 0500 h) was a significant fraction of (13.8 +- 6 \%) of the daily T
with a marginally higher proportion of evening (1800 -- 0000 h) than
pre-dawn flux (0000 -- 0500 h). Small and medium-sized trees (DBH
\textless{} 0.2 m), dominated by \emph{S. racemosa} and \emph{E.
acuminata} , contributed to 46 \% of T, whereas large-girthed trees (DBH
\textgreater{} 0.25 m) contributed up to 40 \%.

{[}Insert Figure 2{]}

Figure 2. Time-series plots of the raw daily data of the SPAC variables
from winters (December) to summers (May): (a) Precipitation (P, mm
d\textsuperscript{-1}), (b) Total Soil moisture (S, mm), (c) Stand
Transpiration (T, mm d\textsuperscript{-1}) and (d) Streamflow (Q in mm
d\textsuperscript{-1}).

\subsection*{3.2. Environmental drivers of stand
transpiration}

{\label{environmental-drivers-of-stand-transpiration}}

\subsubsection*{3.2.1. Shifting roles of sunlight and VPD in driving
stand
transpiration}

{\label{shifting-roles-of-sunlight-and-vpd-in-driving-stand-transpiration}}

R\textsubscript{s} and D are two key drivers of transpiration when not
constrained by moisture limitations. Under moisture-abundant and
energy-deficient conditions, as in our case, the relative effect of
R\textsubscript{s} and D changed with season. In winter,
R\textsubscript{s} had relatively higher impact on both high and low
values of T (Figure 3a), highlighted by the affinity of the scatterplot
towards the x-axis. In summer, the scatter shifted to the center of the
field suggesting an interactive effect of R\textsubscript{s} and D on
high values of T (Figure 3b).

{[}Insert Figure 3{]}

Figure 3. Three-dimensional scatterplot showing hourly Stand
transpiration (T, z-axis) as the response variable against Incoming
short-wave radiation (R\textsubscript{s}, x-axis) and Vapour pressure
deficit (D, y-axis) plotted for (a) winter and (b) summer seasons. The
values are for shown for day period (0600 -- 1700 h) only.

The changing dynamics can be closely observed in Figure 4, which shows
continuous time-series plots of R\textsubscript{s}, D, T, S,
S\textsubscript{diu}, Q, and Q\textsubscript{diu} for a long rainless
period in the winter. The days are consistently sunny (Figure 4a),
whereas D showed strong fluctuations with initial period of
high-moderate humidity followed by atmospherically drier days (Figure
4b). In response, T was high on the initial days with high
R\textsubscript{s} and moderate D, with an unexplained spike on the day
with lowest D (January 26\textsuperscript{th} 2014) (Figure 4c). T
increases again with another spike (4\textsuperscript{th} February 2014)
under high D and R\textsubscript{s} conditions. S declined linearly over
the period, whereas Q showed an abrupt decline in the middle (Figure 4d
and 4f). S\textsubscript{diu} had early morning troughs and afternoon
peaks symptomatic of T-induced controls (Figure 4d). Conversely,
Q\textsubscript{diu} had bi-modal peaks in the morning and midnight, and
troughs in the pre-dawn and mid-day periods (Figure 4e). In the latter
half of the period, the second peak and trough smoothened out to give a
unimodal pattern with a peak in the morning and trough in mid-day. The
first troughs and rising limbs of S\textsubscript{diu} and
Q\textsubscript{diu} were in sync until Q\textsubscript{diu} started
declining rapidly while S\textsubscript{diu} declined gradually.
Overall, T increased from winter to summer peaking in April.

{[}Insert Figure 4{]}

Figure 4. Time-series plots of SPAFC variables for a rainless 17-day
contiguous period in winters: (a) Incoming short-wave radiation
(R\textsubscript{s}), (b) Vapour pressure deficit (D), (c) Stand
transpiration (T), (d) Soil moisture (S), (f) Streamflow (Q), and (e)
S\textsubscript{diu} and (h) Q\textsubscript{diu} are the diurnal
components of soil moisture and streamflow, respectively. The vertical
dashed lines mark 0000 h time for each day.

\subsubsection*{3.2.2. Impact of Precipitation pulses on Stand
transpiration}

{\label{impact-of-precipitation-pulses-on-stand-transpiration-1}}

A total of nine precipitation pulses were identified during the study
period with total volumes ranging from 5.3--82.2 mm and mean
precipitation volume of 35.9+-29.1 mm. On average, T almost doubled
(93+-110 \%) after the three medium-sized precipitation pulses (10-30
mm), and increased by one-third (29+-28 \%) after the four large-sized
pulses (\selectlanguage{english}[?]30 mm) (Table 1). Incidentally, the days with unusually higher
values of T after the medium-sized precipitation pulse (10-30 mm) was
accompanied by clear skies and high evaporative demand with increasing
R\textsubscript{s} (48+-41 \%), D (14+-39 \%), and
E\textsubscript{0}(51+-44 \%). The days showing moderate increase in T
after the four very large precipitation pulses (\selectlanguage{english}[?]30 mm) had
proportionate increase in evaporative demand with increasing
R\textsubscript{s} (79+-45 \%) and E\textsubscript{0} (67+-58 \%), and
minor reduction in D (-13+-32). Conversely, T declined significantly
-64+-7\% after the two small precipitation pulses (2.5-10 mm), likely due
to stomatal closure caused by the significant increase in D (74+-118 \%)
on the following day. Expectedly, the increased in size of precipitation
pulses led to exponential increase in Q, but had little effect on S.

{[}Insert Table 1{]}

Table 1. Percentage changes in transpiration and environmental variables
after precipitation pulses of varying sizes (a) Incoming short-wave
radiation (R\textsubscript{s}), (b) Vapour Pressure Deficit (D), (c)
Reference Evapotranspiration (E\textsubscript{0}), (d) Stand
Transpiration (T), (e) Soil moisture and (f) Streamflow (Q) (SE =
standard error, N = Number of events).

\subsubsection*{3.2.3. Transpiration affecting diurnal cycles of
streamflow}

{\label{transpiration-affecting-diurnal-cycles-of-streamflow}}

The seasonal changes in T are also reflected in changing diurnal cycles
of S\textsubscript{diu} and Q\textsubscript{diu} (Supplementary Data
Figure S3). The diurnal peaks in S\textsubscript{diu} shifted from
afternoon to evening with increasing amplitude from winter to summer.
Q\textsubscript{diu} showed similar patterns with increasing diurnal
amplitude from the winter to summer. The summer peaks in
Q\textsubscript{diu} followed the afternoon peaks in P with perceptible
lag. We also checked for the relative size of diurnal cycles of
streamflow (Q) to the overall Q over 12 contiguous rainless periods
(longer than 5 days) to understand if the diurnal cycles were
significant (Figure 5). The ratio of the variance of the diurnal
component of Q (Q\textsubscript{diu}) to the variance of Q increased
from winter to summer, peaking in March when the system was driest
(Figure 5a). The low value in the April month was preceded by a large
precipitation event. Similarly, the ratio of the diurnal amplitude of Q
(Q\textsubscript{daily.amp}) to daily Q (Q\textsubscript{daily}) also
peaked in March and November during extended rainless periods (Figure
5b).

{[}Insert Figure 5{]}

Figure 5. Barplots showing (a) mean ratio between variance of the
diurnal component of streamflow (Q\textsubscript{diu}) and streamflow
(Q), and (b) mean ratio of the diurnal amplitude of Q
(Q\textsubscript{daily.amp}) to daily Q (Q\textsubscript{daily}),
computed for contiguous rainless periods in the hydrological year
2013-2014. The error bars represent standard error.

\subsection*{3.4. Lag correlation between Transpiration, Soil Moisture
and
Streamflow}

{\label{lag-correlation-between-transpiration-soil-moisture-and-streamflow}}

The simple correlation tests between the SPAFC variables yielded low
correlations due to temporal lags between the diurnal cycles of
different variables. Here, the cross-correlation analysis explored the
shifting lags between T, S, P and Q across distinctly separated winter
(dry) and summer (wet) periods (Figure 6). T led
S\textsubscript{diu}with higher lag in winter (4.5+-4 h) with strong
positive correlations (0.7+-0.1). With the advent of rains in March, T
led S\textsubscript{diu}than summer (1.3+-1.8 h) but with strong negative
correlations (-0.8+-0.1). Similarly, in the winter, S\textsubscript{diu}
led Q\textsubscript{diu} (2.8+-2.9 h) and with strong negative
correlations (-0.7+-0.1). However, in the wet season,
Q\textsubscript{diu} led S\textsubscript{diu} (2.8+-1 h) and strong
positive correlations (0.6+-0.1). In the dry period, T led
Q\textsubscript{diu} (2.9+-2.5 h) and strong negative correlations
(-0.7+-0.1). In the summer, the lag between T and Q\textsubscript{diu}
(7.4+-2.5 h) was confounded by precipitation and showed strong positive
correlations (0.8+-0.1). Incidentally, T lagged Q\textsubscript{diu} on
certain days in winter (1.4+-0.6 h) and summer (1.3+-0.5 h) with strong
negative correlations (-0.8+-0.1), which were characterized by
significant pre-dawn sap flux movement (for details see Kumar \emph{et
al.} (2022)). In rainy periods, Q\textsubscript{diu} lagged P (4.4+-5.4
h) strong positive correlations.

{[}Insert Figure 6{]}

Figure 6. Boxplots showing diurnal lag hours at maximum auto-correlation
coefficients (ACF) for the combinations: (a) T vs. S\textsubscript{diu},
(b) T vs. Q\textsubscript{diu}, (c) P vs. Q\textsubscript{diu,} and (d)
S\textsubscript{diu} vs. Q\textsubscript{diu},. The variables are Stand
transpiration (T, mm h\textsuperscript{-1}), Precipitation (P, mm
h\textsuperscript{-1}), and diurnal components of Soil moisture
(S\textsubscript{diu}, mm h\textsuperscript{-1}) and Streamflow
(Q\textsubscript{diu}, mm h\textsuperscript{-1}).

\subsection*{3.5. Step-wise Lag regression model for
streamflow}

{\label{step-wise-lag-regression-model-for-streamflow}}

The streamflow GLS model was fitted to four rain-free periods from
November to May, including three in winter (6-17 days in length) and one
in summer (27 days in length) (Table 2). The streamflow GLS model with
corAR1 correlation structure had significantly lower AIC than the GLS
model without any correlational structure and was chosen for model
interpretation. In summer, P was lagged by an hour as per the
observations of lag regression results. The streamflow GLS model showed
seasonal changes in the influence of the predictors of Q with moderate
concurrence (r\textsuperscript{2} = 0.37, \emph{p} \textless{} 0.001,
NSE = 0.27) (Table 2). The streamflow GLS model performed better in
winters with higher concurrence between predicted and observed Q
(r\textsuperscript{2} = 0.56, \emph{p} \textless{} 0.001, NSE = 0.56)
than summers (r\textsuperscript{2} = 0.31, \emph{p} \textless{} 0.001,
NSE = 0.27). After a precipitation event, the hydrographs of predicted Q
matched well with observed Q in the rising limb but failed to correspond
to the falling limb (see supplementary Figure S4).

{[}Insert Table 2{]}

Table 2. Results from Generalized least squares (GLS) linear regression
model with corAR1 correlational structure for streamflow
(*\emph{p}\textless{} 0.05, ** \emph{p} \textless{} 0.01, *** \emph{p}
\textless{} 0.001). Predictor variables include Incoming short-wave
radiation (R\textsubscript{s}, kW m\textsuperscript{-2}), Vapour
pressure deficit (D, kPa), transpiration (T, mm h\textsuperscript{-1}),
soil moisture (S, mm h\textsuperscript{-1}), Precipitation (P, mm
h\textsuperscript{-1}), Antecedent moisture index (M\textsubscript{a},
mm h\textsuperscript{-1}), and the response variable is Streamflow (Q,
mm h\textsuperscript{-1}).

At the start of the dry season, with abundant soil moisture conditions
in the Event 1, the interaction term between S and T (S*T) was a
moderately significant (P = 0.069) predictor of Q with negative
coefficient suggesting soil moisture and transpiration are modulating
streamflow in combination. As the dry season progressed, in the Event 2
(\emph{p} = 0.09) and Event 3 (\emph{p} = 0.01), T became the stronger
predictor of Q with negative coefficients and under average S
conditions. However, with the advent of precipitation in summer in the
Event 4, P (\emph{p} \textless{} 0.001) was the strongest predictor
followed by M\textsubscript{a} (\emph{p} = 0.004), whereas the effect of
the interaction between S and T weakened considerably (\emph{p} = 0.12).
Incidentally, T had negative slope coefficients across the dry and wet
periods indicating loss of potential Q to T.

\section*{4. DISCUSSION}

{\label{discussion}}

The study site is unique in being the wettest high-elevation TMF site in
the world, where direct transpiration, streamflow, and micro-climate
measurements have been carried out so far (see Supplementary Figure S5)
(McJannet \emph{et al.} , 2007; Bruijnzeel \emph{et al.} , 2011). The
site falls above the 90\textsuperscript{th} percent quantile of the
elevations and 80\textsuperscript{th} percent quantile of precipitation
among global studies from TMFs. At the site, the low latitudinal
position (27deg-28deg) and proximity to south-west Indian monsoon favor
tropical climatology with the bulk of the precipitation occurring in the
late-night to early morning period, a phenomenon unique to Himalaya
(Barros and Lang, 2003). The studied broad-leaved montane forests in
Eastern Himalaya are the most species-rich in the world and have
experienced considerable human pressures (Sudhakar \emph{et al.} , 2008;
Kanade and John, 2018). The combination of high moisture availability,
tropical climatology, and regenerating secondary forests provides unique
conditions for observing plant-water relations and hydrological
services, hitherto unstudied in the Himalaya. The study provides the
first mechanistic understanding of vegetation-streamflow linkages in a
very wet high elevation tropical broad-leaved evergreen wet montane
forest in Eastern Himalaya. The observed vegetation-streamflow linkages
are compared with results and mechanisms from other wet tropical montane
forests (TMFs).

\subsection*{4.1. Transpiration in wet TMF of Eastern
Himalaya}

{\label{transpiration-in-wet-tmf-of-eastern-himalaya}}

The rate of transpiration by a forest stand provides crucial information
regarding the vegetation's capacity to recirculate moisture, which
varies considerably with the age of the stand, species-specific
water-use efficiency, and availability of moisture and energy. The
observed mean daily transpiration (1.45+-1 mm d\textsuperscript{-1}) in
wetter Sikkim was found to be double of the relatively drier Central
Himalayan oak forests (MAP = 1331 mm) in Nepal (Ghimire \emph{et al.} ,
2014), but comparable with similarly wet low elevation (450- 650 masl)
TMFs (MAP = 4200-5000 mm) in Costa Rica (Aparecido \emph{et al.} , 2016;
Moore \emph{et al.} , 2018) and similar elevation TMF (MAP = 2067 mm) in
Southern Andes (Motzer \emph{et al.} , 2010). The maximum daily stand
transpiration (5.3 mm) is 14 \% higher than the highest rates
(\textasciitilde{}4.6 mm) reported from tropical montane or lowland
forests (Bruijnzeel et al., 2011; McJannet et al., 2007). The observed
nocturnal transpiration (T\textsubscript{night}) was similar to studies
(\textasciitilde{}12 \%) from China (Siddiq and Cao, 2018) and other
parts of the globe (Forster, 2014). The thermal dissipation method used
in the study is known to underestimate sap flow making our estimates of
T conservative in nature (Flo \emph{et al.} , 2019). The relatively
higher transpiration rates, despite being at a higher elevation,
possibly fueled by faster growth rates and higher interception losses
from the secondary forest vegetation in a very wet environment. The high
transpiration rates could also indicate evolution under the relatively
wetter climate of Eastern Himalaya and merit further investigations on
water-use efficiencies with changing climate (Panthi \emph{et al.} ,
2020).

Along similar lines, Pandey \emph{et al.} (2020) have suggested that
climate warming and increased summer precipitation are likely to remove
moisture constraints on photosynthesis on treeline conifer and
broad-leaved species in wetter parts of eastern Nepal Himalaya. This is
further bolstered by faster transpiration recovery after low-mid-sized
precipitation pulses. Such behaviour is usually accompanied by high
evaporative demand (high E\textsubscript{0} and D), as seen in the
Australian woodland (Burgess, 2006; Zeppel \emph{et al.} , 2008). It is
also common to forest stands with deep-rooted species as seen at the
study site (Kumar \emph{et al.} , 2022) and in the semi-arid parts of
China (Chen \emph{et al.} , 2014). The observations adds empirical
evidences to the discussion on the ``two worlds hypothesis'' by
suggesting that deep-rooted trees can access sub-surface soil moisture
to fuel transpiration demands (Berry \emph{et al.} , 2018). Here, the
role of low-moderate precipitation pulses after prolonged dry period
becomes crucial for the shallow-rooted pioneer species and less so for
the deep-rooted late-successional species. Thus, with climate change,
the predicted decline in winter precipitation and small precipitation
events may have significant impact on the structure and productivity of
these regenerating secondary forests (Krishnaswamy \emph{et al.} , 2014;
Krishnan \emph{et al.} , 2019).

\subsection*{4.2. Environmental driver of stand transpiration in
TMFs}

{\label{environmental-driver-of-stand-transpiration-in-tmfs}}

Similar to observations from TMFs, we observed strong seasonal
fluctuations in moisture and energy conditions between winter and summer
seasons (Bruijnzeel and Veneklaas, 1998; Aparecido \emph{et al.} ,
2018). The relative influence of R\textsubscript{s} and D on T changed
with season (Moore \emph{et al.} , 2018). The observation of solar
radiation driving T in winters was congruent with reports from Andes and
Alps (Fiora and Cescatti, 2006; Motzer \emph{et al.} , 2010), whereas
increasing effect of D on summer T was consistent with warmer Central
Himalaya (Ghimire \emph{et al.} , 2014). Our observations of 2-3 h of
lag between R\textsubscript{s} and VPD and T were seasonally consistent
and comparable with reports from TMFs in Costa Rica (Moore \emph{et al.}
, 2018) and marginally higher than reports from Tibet
(\textasciitilde{}1 hour lag) (Wang \emph{et al.} , 2017). The lags may
represent delays due to low temperature in winters and leaf wetness in
the summers due to night rains or dew (Aparecido \emph{et al.} , 2016;
Moore \emph{et al.} , 2018).

\subsection*{4.3. Role of transpiration in catchment
ecohydrology}

{\label{role-of-transpiration-in-catchment-ecohydrology}}

Few studies have explored the interactions between vegetation water use,
soil moisture, and streamflow at fine temporal resolution (Moore\emph{et
al.} , 2011; Graham and Barnard, 2013). Low soil infiltration rates, as
observed at the study site, coupled with high precipitation intensities
may lead to higher runoff generation in the wet season. Conversely,
higher moisture uptake by vegetation may lead to relatively lower
streamflow output in the dry season, in comparison to a primary forest
(Wright \emph{et al.} , 2018). The GLS model results showed that, at the
study site, transpiration exerted significant controls on diurnal and
seasonal streamflow with soil moisture acting as the mediator in winter.
T and its interaction term with S were significant predictors of Q with
significant negative coefficients indicating that high T is leading to
decline in Q (Bond \emph{et al.} , 2002; Thomas \emph{et al.} , 2012).
At the study site, Kumar \emph{et al.} , (2022) observe that the
deep-rooted Fagaceae species \emph{Castanopsis hystrix} are able to
access deeper, which otherwise would have contributed to the stream as
baseflow. In the summers, precipitation, and antecedent moisture
(M\textsubscript{a}) overtook as the drivers of streamflow, although the
interaction term between T and S remained a significant predictor
signaling continued water-uptake by vegetation.

The observed afternoon peaks in streamflow were similar to the
vegetation-induced diurnal patterns in streamflow reported globally
(Barnard \emph{et al.} , 2010; Moore \emph{et al.} , 2011; Graham and
Barnard, 2013). The diurnal cycles in streamflow gained significance
with the progression of dry periods and synchronized increase in
vegetation activity, highlighting the controls exerted by
evapotranspiration on streamflow (Bond \emph{et al.} , 2002; Moore
\emph{et al.} , 2011). The increasing amplitudes of diurnal streamflow
from winters to summers signified the growing abstraction by vegetation
to support primary productivity in the dry season (Graham and Barnard,
2013; Barbeta and Penuelas, 2017). Referring back to the literature on
the isotopic separation of water that contributes to transpiration and
streamflow (Barbeta and Penuelas, 2017; Berry \emph{et al.} , 2018), we
suggest that in these broad-leaved Himalayan TMFs, with steep slopes and
shallow soils, trees can access deeper sub-surface water for
transpiration. Evidence to this are shown by Kumar \emph{et al.} ,
(2022) at the study site, where groundwater is well within the reach of
deep-rooted tree species like \emph{C. hystrix}allowing them to continue
transpiring at the peak of the dry season, while shallow-rooted
fast-growing pioneer species like \emph{S. racemosa} and \emph{E.
acuminata} propelled vegetation water use in moisture abundant
conditions in summer. Thus, in a regenerating secondary forest, stand
transpiration can have a significant impact on streamflow (Wright
\emph{et al.} , 2018) and thus, can influence diurnal precipitation
patterns through active local moisture recycling, an underexplored area
of investigation in vegetation-water interaction in the Himalaya (Barros
and Lang, 2003).

Studies on climate change in Eastern Himalaya have predicted an increase
in summer precipitation, declining winter precipitation; and increasing
summer and winter temperatures (Krishnan \emph{et al.} , 2019;
Kumar\emph{et al.} , 2021). In summers, precipitation provides enough
moisture to ensure peak vegetation productivity in April. However,
increased summer precipitation in the future could result in higher
cloud cover negatively impacting both vegetation productivity and
transpiration leading to increased streamflow (Donohue \emph{et al.} ,
2017). The overall effect of changes in temperature and precipitation on
biodiversity in the region remains complex and requires ecohydrological
models specific to the East Himalayan TMFs (Asbjornsen \emph{et al.} ,
2011).

\section*{5. CONCLUSIONS}

{\label{conclusions}}

Globally, the effect of vegetation water-use on streamflow varies
greatly due to species-specific, environmental and geomorphological
peculiarities (Asbjornsen \emph{et al.} , 2011; Wright \emph{et al.} ,
2018). These are the first empirical observations of mechanistic control
of transpiration on streamflow under the SPAFC framework from Himalaya
and add to the observations drawn by previous studies from Himalaya.
Annual transpiration is double of relatively drier Central Himalaya
(Ghimire \emph{et al.} , 2014), but at the lower bound of the values
reported from tropical montane cloud forests globally
(Bruijnzeel\emph{et al.} , 2011). It indicates the interactive role of
precipitation and elevation in modulating the available energy,
moisture, which in turn modulates the plant water-use and productivity.
The time lag between streamflow and transpiration increased with
seasonal dryness, until the advent of rains when precipitation was the
primary driver of streamflow. Transpiration was a significant predictor
of streamflow in the dry season and to a lesser extent in the wet
season. The study shows that moderate precipitation pulses followed by
clear skies can induce significant increase in stand transpiration.
Thus, changes in vegetation cover and precipitation patterns with
climate change may have a significant impact on the
vegetation-streamflow linkages and local and regional moisture recycling
by vegetation in the secondary broad-leaved tropical montane forests in
the Eastern Himalaya. The TMFs provide protective catchments for the
principal water resources, the springs, and streams, and thus better
understanding their SPACF processes is critical to the quantifying
ecosystem services, addressing regional water security issues and
ecosystem modeling efforts. More regional studies inclusive of diurnal
and seasonal variability in transpiration will be critical to increasing
the accuracy of land-surface interaction models, and predicting the
impact of climate change on Himalayan ecohydrology.

\section*{6. CONFLICT OF INTERESTS}

{\label{conflict-of-interests}}

The authors have no conflict of interests to declare.

\section*{7. ACKNOWLEDGMENTS}

{\label{acknowledgments}}

The authors are thankful to Manipal Academy of Higher Education (MAHE),
Manipal, India for institutional support. The authors are grateful to
the Department of Biotechnology, Govt. of India (GoI) for supporting the
research through the sanctioned project titled ``Technological
Innovations and Ecological Research for the Sustainable Use of
Bio-resources in the Eastern Himalaya'' (Grant No. BT/01/NE/PS/NCBS/09).
The authors thank the Department of Forests, Environment and Wildlife
Management, Rural Management and Development Department, and Home
Department, Govt. of Sikkim, India, and the Indian Army for research
permits and field support. The study would not have been possible
without the able field support from Passang Tamang, Girish Varma, Naresh
Rai, the staff of Fambong-Loh Wildlife Sanctuary. The authors thank
Nachiket Kelkar and Dr. Aniruddha Marathe for their useful comments on
statistical analysis. The authors are indebted to Prof. Nathan G.
Phillips at Boston University, USA, Prof. Frederick Meinzer at Oregon
State University, USA, and Forests Science Laboratory (Corvallis), the
USA for generously sharing the original thermal dissipation probe
assembly design. The authors acknowledge the logistical support provided
by ATREE Regional Office, Gangtok, and National Center for Biological
Sciences, Bangalore, India during the study.

\section*{8. FUNDING SUPPORT}

{\label{funding-support}}

The study was funded by the Department of Biotechnology, Govt. of India
(GoI) as part of the research project titled ``Technological Innovations
and Ecological Research for the Sustainable Use of Bio-resources in the
Eastern Himalaya'' (Grant No. BT/01/NE/PS/NCBS/09). The authors are also
grateful to the fellowship support provided by the National Mission for
Himalayan Studies (NMHS) under the Ministry of Environment, Forest and
Climate Change, GoI for further fellowship support during the writing
period (Grant No: GBPI/NMHS/HF/RA/2015-16/).

\section*{9. AUTHOR's CONTRIBUTIONS}

{\label{authors-contributions}}

\textbf{Manish Kumar:} Conceptualization, Methodology, Manufacturing,
Testing and modification of the sap flow probes, Data collection, Formal
analysis, Visualization, Writing -- Original draft
preparations\textbf{.} \textbf{Yangchenla Bhutia:} Resources, Data
Curation, Writing -- Review \& Editing. \textbf{Gladwin
Joseph:}Conceptualization, Methodology, Testing and modification of the
sap flow probes, Writing -- Review \& Editing. \textbf{Jagdish
Krishnaswamy:}Conceptualization, Methodology, Testing and modification
of the sap flow probes, Writing -- Review \& Editing, Supervision,
Project Administration, Funding acquisition.

\section*{10. DATA SHARING}

{\label{data-sharing}}

The data that support the findings of this study are available on
request from the corresponding author. The data are not publicly
available due to privacy or ethical restrictions. A sample subset of the
data that support the findings of this study is available in Dryad at
https://doi.org/10.5061/dryad.47d7wm3cg under the citation Kumar (2021).
The sample dataset is available for preview at the following url:

https://datadryad.org/stash/share/bkDWLqnM4S2C3w0wgri1AcXlyDz3eM2Sgs7prOqLZqk

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