Deforestation reduces the vegetation-accessible water storage in the unsaturated soil and affects catchment travel time distributions and young water fractions

Deforestation can considerably affect transpiration dynamics and magnitudes at the catchment-scale and thereby alter the partitioning between drainage and evaporative water fluxes released from terrestrial hydrological systems. However, 15 it has so far remained problematic to directly link reductions in transpiration to changes in the physical properties of the system and to quantify these changes of system properties at the catchment-scale. As a consequence, it is difficult to quantify the effect of deforestation on parameters of catchment-scale hydrological models. This in turn leads to substantial uncertainties in predictions of the hydrological response after deforestation but also to a poor understanding of how deforestation affects principal descriptors of catchment-scale transport, such as travel time distributions and young water 20 fractions. The objectives of this study are therefore to quantify the effects of deforestation in the Wüstebach experimental catchment on the partitioning of water fluxes and to directly associate these changes to changes in parameters of a hydrological model with integrated tracer routine based on the concept of storage age selection functions. Simultaneously modelling stream flow and stable water isotope dynamics using meaningfully adjusted model parameters both for the preand post-deforestation periods, respectively, the model is used to track fluxes through the system and to estimate the effects 25 of deforestation on catchment travel time distributions and young water fractions Fyw. It was found that deforestation led to a significant increase of stream flow, accompanied by corresponding reductions of evaporative fluxes. This is reflected by an increase of the runoff ratio from CR = 0.55 to 0.68 in the post-deforestation period despite similar climatic conditions. This reduction of evaporative fluxes could be linked to a reduction of the catchment-scale water storage volume in the unsaturated soil (SU,max) that is within the reach of active roots and thus accessible for vegetation 30 transpiration from ~225 mm in the pre-deforestation period to ~ 90 mm in the post-deforestations period. The hydrological model, reflecting the changes in the parameter SU,max indicated that in the post-deforestation period stream water was characterized by slightly higher mean fractions of young water (Fyw ~ 0.13) than in the pre-deforestation period (Fyw ~ 0.11). https://doi.org/10.5194/hess-2020-293 Preprint. Discussion started: 22 June 2020 c © Author(s) 2020. CC BY 4.0 License.

also mostly snapshots in time and therefore disregard the adaptive behaviour of plant communities, whose compositions, and thus characteristics, at ecosystem level continuously evolve over multiple scales in space and time in response to changes in ambient conditions (e.g. Laio et al., 2006;Brunner et al., 2015;Tron et al., 2015).
There is increasing evidence that vegetation does not only actively adapt to its (changing) environment, but that it does so in an efficient way (e.g. Guswa, 2008;Schymanski et al., 2008). The vegetation, i.e. a collective of individual different plants 70 within an area of interest, present at any given moment at any given location has survived. This in itself is a manifestation of the successful adaption of individual plants to their environment in the past. They have optimally allocated resources to balance sub-and above-surface growth to simultaneously meet water, nutrient and light requirements. This implies that these plants developed root-systems that, amongst other factors, ensure continuous access to sufficient water -but not more -to bridge dry periods. An individual plant that is not adapted to meet its water and nutrient requirements through its root-system 75 as well as its light requirements through its foliage system in competition with other plants will disappear and be replaced by a better adapted plant. The root-system of vegetation at ecosystem level, and the associated vegetation-accessible water storage capacity S U,max , is therefore at a dynamic equilibrium with and responding to the ever changing conditions of its environment. Similarly, any type of direct human interference with vegetation, such as deforestation, has an impact on transpiration water demand, the extent and structure of active root-systems and consequently on S U,max (Nijzink et al., 2016a). 80 For a meaningful quantification of S U,max at larger scales, such as the catchment-scale, it is therefore necessary to adopt a Darwinian perspective (Harman and Troch, 2014) and to estimate effective values of S U,max , reflecting the collective and adaptive behaviour of all individual plants within a catchment. Results from many previous studies suggest, broadly speaking, three methods to do so. The first is the use of inverse approaches that treat S U,max as model calibration parameter (Fenicia et al., 2008;Speich et al., 2018;Bouaziz et al., 2020;Knighton et al., 2020). Alternatively, the second type of 85 methods is based on optimality principles that maximize variables such as net primary production or carbon gain (Kleidon, 2004;Guswa, 2008;Hwang et al., 2009;Yang, et al., 2016;Speich et al., 2018), nitrogen uptake (McMurtrie et al., 2012) or transpiration rates (Collins and Bras, 2007;Sivandran and Bras, 2012). Lastly, S U,max and its evolution over time can be directly estimated through magnitudes of annual water deficits as determined from observed water balance data (Gentine et al., 2012;Donohue et al., 2012;Gao et al., 2014;DeBoer-Euser et al., 2016). 90 For transpiration, shallow-rooting plants extract pore water of unsaturated soils that is held against gravity, i.e. between field capacity and permanent wilting point, and within the reach of roots. Significant vertical or lateral drainage only occurs at water contents above field capacity. Transpiration extracting soil water below that therefore effectively generates a root-zone water storage reservoir between field capacity and permanent wilting point that is characterized by a storage capacity S U,max , i.e. a maximum vegetation-accessible storage volume, and that is at any given moment filled with a specific water volume 95 S U (t), depending on the past sequence of water inflow and release.
Storage volumes as S U,max , or others such as groundwater bodies, are key for hydrological functioning (Sprenger et al., 2019b) as they provide a buffer against hydrological extremes, such as floods and droughts. With increasing storage, the hydrological memory of a system increases as more water can be stored and held over longer periods of time (e.g. https://doi.org/10.5194/hess-2020-293 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. 100 residence time distributions (RTD) and travel time distributions (TTD; Soulsby et al., 2010). As fundamental descriptors of hydrological functioning RTDs and TTDs describe the age structure of water held in and released from catchments (Birkel et al., 2015;Rinaldo et al., 2015), which is critical for regulating solute transport and thus nutrient and contaminant dynamics .
However, neither the effects of land cover change (Blöschl et al., 2019) nor the individual roles of different storage 105 compartments in terrestrial hydrological systems are well understood Penna et al., 2018Penna et al., , 2020. This is mostly a consequence of the lack of suitable observational technology to directly observe their respective volumes at larger scales. It remains therefore also unclear how deforestation affects S U,max (e.g. due to a less developed and complex rooting system for subsequent younger vegetation) and how changes in S U,max may propagate to affect both, the partitioning of water fluxes as well as the age structure of water stored in and released from catchments as described by residence and 110 travel time distributions.
The objective of this study in the Wüstebach experimental catchment (Germany) is thus to analyse to which extent postdeforestation changes in the water balance can be attributed to changes in catchment-scale effective S U,max and to quantify the associated consequences on RTDs, TTDs and young water fractions F yw . Specifically we test the hypotheses that (1) deforestation affects water storage dynamics and the partitioning of water fluxes into transpiration and drainage, (2) the 115 changes in water storage dynamics and partitioning of water fluxes are a direct consequence of a reduction of the vegetationaccessible water storage capacity in the unsaturated root-zone (S U,max ) after deforestation and that (3) the deforestationinduced reduction of S U,max affects the shape of travel time distributions and results in shifts towards higher fractions of young water in the stream. 120

Study site
The experimental Wüstebach headwater catchment (0.39 km 2 ; Fig. 1a) is part of the Lower Rhine/Eifel Observatory of the Terrestrial Environmental Observatories network (TERENO; Bogena et al., 2018) located in the Eifel National Park in Germany (50 o 30'16"N, 06 o 20'00"E). The catchment is characterized by a humid, temperate climate with warm summers, mild winters and a mean annual temperature of around 7 o C (Zacharias et al., 2011). Mean annual precipitation is about 1200 125 mm yr -1 and mean annual runoff about 700 mm yr -1 (Fig. 2). Although most of the precipitation occurs in the winter months, the fraction that falls as snow is typically less than 10 % of the annual precipitation and snow cover is present for no more than 3-4 weeks per year.
The catchment is drained by a perennial 2 nd -order stream and extends from 595 to 630 m asl. The landscape is characterized by the gentle slopes of the surrounding hills and a flatter riparian area close to the stream, covering approximately 10 % of 130 the catchment (Fig. 1a). The underlying bedrock is largely Devonian shales with sandstone inclusions (Richter, 2008) https://doi.org/10.5194/hess-2020-293 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License. covered by periglacial layers (Borchardt, 2012). While cambisols dominate the hillslopes, gleysols and histosols characterize much of the riparian area . The average soil depth in the catchment reaches about 1.6 m with a maximum of 2 m (Graf et al., 2014). After the Second World War, the catchment was homogeneously and completely afforested ( Fig. 1) with Sitka spruce (Picea sitchensis) and Norway spruce (Picea abies; Etmann, 2009). The maximum 135 observed rooting depth of these spruce trees in the catchment is 50 cm and no roots were observed below this depth. In the course of the development of the area into a national park approximately 21 % of the catchment, including the entire riparian zone, were deforested in September 2013 and kept largely vegetation free since (Wiekenkamp et al., 2016;Fig. 1).

Hydro-meteorological data
Daily hydro-meteorological data were available for the period 01/10/2009 -30/09/2016 (Fig. 2a- and mean daily temperature T [ o C] were available from the Monschau-Kalterherberg meteorological station operated by the German Weather Service (Deutscher Wetterdienst DWD station 3339), located 9 km northwest of the Wüstebach catchment.
Stream discharge Q [mm d -1 ] at the outlet of the Wüstebach was observed with a V-notch weir for low flow measurements 145 and a Parshall flume for medium to high flows . Daily potential evaporation E P [mm d -1 ] was estimated using the Penman-Monteith equation.

Stable isotope data
Regular weekly δ 18 O data from bulk precipitation samples collected in a cooled wet deposition gauge at the meteorological station Schleiden-Schöneseiffen (Meteomedia station) 3 km northeast of the catchment, were available for the period 150 01/10/2010 -24/09/2012. After that, precipitation was sampled at half-daily intervals until 30/09/2016 using an automatic, cooled sampler (Eigenbrodt GmbH, Germany). The half-daily samples were precipitation volume-weighed to daily sampling intervals (Stockinger et al., 2016(Stockinger et al., , 2017. Weekly stream water grab samples for stable water isotope analysis were taken at the outlet of the Wüstebach catchment in the 01/10/2010 -30/09/2016 period (Fig. 2d).
Isotope analysis was carried out using laser-based cavity ringdown spectrometers (L2120-i/L2130-i, Picarro Inc.). Internal 155 standards calibrated against VSMOW, Greenland Ice Sheet Precipitation (GISP) and Standard Light Antarctic Precipitation (SLAP2) were used for calibration and to ensure long-term stability of analyses (Brand et al., 2014). The long-term precision of the analytical system was ≤ 0.1 ‰ for δ 18 O.

160
To quantify effects of deforestation on S U,max and, as a consequence of that, on the age structure of water as described by TTDs, RTDs and young water fractions F yw , the following stepwise experiment was designed: (1) quantify changes in the partitioning of annual water fluxes between the pre-and the post-deforestation periods based on observed water balance data; (2) estimate the effect of these changes on the magnitudes of pre-and post-deforestation S U,max , respectively, using the 165 same data; (3) calibrate a hydrological model to simultaneously reproduce stream flow and stream δ 18 O dynamics for the pre-deforestation period; (4) use the calibrated parameter sets to run the model in the post-deforestation period and evaluate the model's post-deforestation performance without further calibration; (5) re-calibrate the model for the post-deforestation period and evaluate if changes in calibrated S U,max (and other parameters) are plausible and reflect changes in S U,max directly estimated from water balance data in step (2); and finally (6) use the calibrated pre-and post-deforestation parameter sets, 170 respectively, to track modelled water fluxes through the system and quantify changes in TTDs, RTDs and F yw between the pre-and the post-deforestation periods.

Water balance-based estimation of SU,max
To survive, plants need continuous access to water to satisfy canopy water demand. The root-systems of vegetation are therefore adapted to provide access to water volumes that correspond to annual water deficits that result from the 175 combination of (1) the phase lag between and (2) the difference in the respective magnitudes of seasonal precipitation and solar radiation signals (Donohue et al., 2012;Gentine et al., 2012;Gao et al., 2014). On a daily basis, these water deficits The maximum deficit S D,j for a specific year j is then equivalent to the water volume that was accessible to vegetation through its root system over that period (deBoer-Euser et al., 2016;Nijzink et al., 2016a): 180 Daily effective precipitation P E , i.e. precipitation that actually reaches the soil, was estimated on basis of the water balance of a canopy interception storage (Nijzink et al., 2016a): Where E I [mm d -1 ] is daily interception evaporation and S I [mm] the canopy interception storage. For each time step, E I can then be computed as: This then further allows to estimate P E according to: where I max [mm] is the canopy interception capacity. In the absence of more detailed information P E was estimated with a range of different interception capacities, i.e. I max = 0, 1, 2, 3, and 4 mm, in a sensitivity analysis approach.
Note that the catchment average P E after deforestation was estimated as the areal weighted mean of P E in the deforested area (21% of catchment area) computed with an assumed I max = 0 mm and P E from the remaining area computed based on the above range of I max between 0 and 4 mm. In a next step, assuming negligible groundwater imports or exports (cf. Bouaziz et 210 al., 2018), data errors and storage changes, long-term mean transpiration was estimated according to the water balance: Where [mm d -1 ] is the long-term mean effective precipitation and [mm d -1 ] is the long-term mean observed stream 215 discharge. Daily transpiration E T [mm d -1 ] for use in Eq. (1) is then estimated by scaling the long-term mean transpiration to the signal of daily potential evaporation to approximate the seasonal fluctuation of energy input (Bouaziz et al., 2020): A range of previous studies provided evidence that mature forests develop root-systems that allow access to sufficiently large pore water storage volumes S U,max to bridge droughts with return periods T R ~ 20 years (Gao et al., 2014;deBoer-Euser et al., 2016;Nijzink et al., 2016a;Wang-Erlandsson et al., 2016). The maximum annual water deficits S D,j (Eq. 2) for all j years in the pre-deforestation study period were therefore used to fit a Gumbel extreme value distribution (Gumbel, 1941). 225 This subsequently allowed the estimation of a water deficit with a 20-year return period, which is for this study defined as vegetation-accessible water storage S U,max so that S U,max = S D,20yr .
Note that due to the limited length of the data series the S U,max estimates are rather uncertain and need to be understood as merely indicative approximations. This is in particular true for the post-deforestation period, where attempts to explicitly link S U,max to a specific return period are subject to additional uncertainty: as the catchment was not reforested and natural 230 recovery of vegetation is negligible (see aerial images in Figure 1), it is not implausible to assume that the development of the root-system after the disturbance is far from equilibrium and likely to be actively evolving over time. Also note that although E T is, for brevity, referred to as transpiration throughout this manuscript, it also contains soil evaporation. However, no explicit and quantitative distinction could be made between these two fluxes with the available data. A further critical assumption of the above method required that roots do not tap the groundwater and that water for transpiration is exclusively 235 extracted from the unsaturated soil. In contrast to other landscapes (Fan et al., 2017), it is likely that this assumption largely holds in the Wüstebach as throughout the catchment the groundwater levels, also in the riparian zone, remains largely below a depth of 50 cm during the relatively dry growing season  when storage deficits S D typically accumulate (~ May to October) and no roots have so far been observed for the dominant picea species below that depth in the Wüstebach catchment. This is also broadly consistent with the results of Evaristo and McDonnell (2017), who show 240 rather limited groundwater use by picea species.

Model architecture
A semi-distributed, process-based catchment model, iteratively customized and tested within the previously developed DYNAMITE modular modelling framework Fovet et al., 2015), was adapted with additional, hydrologically passive storage volumes to allow for simultaneous representation of water fluxes and tracer transport 245  based on the general concept of storage-age selection functions (SAS; Rinaldo et al., 2015).

Hydrological model
The model domain of the Wüstebach catchment was spatially discretized into two functionally distinct response units, i.e. hillslopes and riparian areas. These are represented in the model as two parallel suites of storage components, linked by a common groundwater body as shown in Figure 3 (e.g. Euser et al., 2015;Nijzink et al., 2016b). According to elevation data 250 and distribution of soil types (Fig.1)

Tracer transport model
The δ 18 O composition of water fluxes and storages was tracked through the model using the storage age selection approach [mm], which represents, "at any time t the cumulative volumes of water in a storage component as ranked by their age T" (Benettin et al., 2017). Similarly, decomposing each inflow and outflow of a storage component into their respective 275 cumulative, age-ranked volumes I T (T,t) and O T (T,t) [mm d -1 ], respectively, then allows to update the age-ranked storage S T (T,t) at each time step according to the general water age balance (Botter et al., 2011;van der Velde et al., 2012;Benettin et al., 2015aBenettin et al., , 2017Harman, 2015): where the term ∂S T /∂T represents the aging of water in storage. Reflecting the slightly more abstract approach by Rodriguez and Klaus (2019) and similar to previous studies based on the functionally equivalent mixing coefficient approach (e.g. Fenicia et al., 2010;McMillan et al., 2012;Birkel and Soulsby, 2016;Hrachowitz et al., 2015), the water age balance is here https://doi.org/10.5194/hess-2020-293 Preprint. Discussion started: 22 June 2020 c Author(s) 2020. CC BY 4.0 License.
individually formulated for each storage reservoir j (e.g. S I,H , S U,H , etc.), which each can have varying numbers N and M of 285 inflows I (e.g. P R , P M , R S,H , etc.) and outflows O (e.g. P M , R S,H , Q S , etc.), respectively (see Figure 3). It is assumed that the entire volume of a precipitation signal P(t) entering the system at t has an age T of zero so that the associated I T,P,j (T,t) = P T (T,t) = P(t) for all T. As all other inflows to any following storage component in the system are outflows of storage components prior in the sequence (see Figure  The outflow volume O m,j (t) is estimated via the hydrological model (see Section 4.2.1; Figure 3) and thus assumed to be known. In contrast, the cumulative age-distribution P o,m,j (T,t) can in general not be directly parametrized, as it depends on the temporally varying age distribution of water in the storage component j represented by S T,j (T,t) and thus on the history of past inflows and outflows (Botter et al., 2011;Harman, 2015). Instead, it is possible to define a SAS function  o,m,j (or  o,m,j in its 300 cumulative form) for each outflow m from each storage component j that describes how outflow is sampled (or selected) from the temporally varying water volumes of different age present in the age-ranked storage S T,j (T,t) at any time t: , , ( , ) = Ω , , , ( , ), (Eq.38) 305 From the cumulative age-distribution P o,m,j (T,t) the associated probability density function, which represents the outflow age distribution p o,m,j (T,t), frequently also referred to as backward travel time distribution of that outflow (TTD; e.g. Benettin et al., 2015a;Wilusz et al., 2017), can be obtained according to: Note that conservation of mass requires that any SAS function  O,m,j integrates to the total storage volume S j (t) present in j at any time t. To avoid the resulting need for rescaling  O,m,j at each time step, it is helpful to normalize the age-ranked storage to S T,norm,j (T,t) = S T,j (T,t)/S j (t) so that it remains bounded to the interval [0,1] and defines a residence time distribution (RTD).
For this study beta distributions, which are conveniently bound between the limits [0,1] and defined by two shape parameters  and , were used as SAS functions  o,m,j to sample water of different age for outflows from storage components. The and McDonnell, 2007;Beven, 2010;Beven and Germann, 2013;Klaus et al., 2013;Angermann et al., 2017;Loritz et al., 2017). Such preferential flow can, with increasing wetness, increasingly bypass water volumes stored in small pores with little exchange (Sprenger et al., 2016(Sprenger et al., , 2018(Sprenger et al., , 2019aCain et al., 2019;Evaristo et al., 2019;Knighton et al., 2019). This then 320 leads to an increasing preferential release of younger water as the system becomes wetter (Brooks et al., 2010). To mimic this, the shape parameters  of the preferential fluxes R F,H and R F,R released from the two unsaturated root-zone storage components S j = S U,H and S U,R (Figure 3), were allowed to vary as a function of the water volumes stored in S U,H and S U,R , respectively van der Velde et al., 2015): Where  0 is a calibration parameter representing a lower bound so that  m,j (t) can vary between  0 and 1. A value of  m,j = 1 indicates complete mixing in dry conditions. Any value below that entails incomplete mixing and thus increases the preference towards releasing younger water in wet conditions (Benettin et al., 2017). Although there is evidence for the 330 presence of preferential flow in other components of the system, such as in the groundwater (e.g. Berkowitz and Zehe, 2020), initial model testing suggested that the inclusion of the additional calibration parameters is not warranted by the available data. For simplicity and following the principle of model parsimony we assumed complete mixing for all other outflows from all other storage components (Figure 3; cf. Fenicia et al., 2010;Rodriguez et al., 2018). Parameter  was therefore fixed to value of 1 for these SAS functions. 335 The δ 18 O precipitation input signals had to be damped to the level of fluctuation observed in the stream. To achieve this a calibration parameter defining a hydrologically passive storage volume S S,p [mm] was added to the active groundwater storage S S,a . (Zuber, 1986;Hrachowitz et al., 2015Hrachowitz et al., , 2016, so that S S,tot = S S,a + S S,p ( Figure 3). While , ⁄ = 0, the ageranked groundwater storage was computed as S T,Ss,tot and the outflows from the groundwater component consequently sampled from the entire storage volume S S,tot . 340 Each individual volume with different age in I T,n,j (T,t) and, as a consequence, also in S T,j (T,t) is also characterized by a different tracer concentration C I,n,j (I T,n,j (T,t),t) and C S,j (S T,j (T,t),t), respectively. For a conservative tracer such as δ 18 O that is not significantly affected by decay, evapoconcentration, retention or any other biogeochemical transformation (e.g. Bertuzzo In contrast to other regions (e.g. Soulsby et al., 2017), isotopic fractionation in the Wüstebach was previously observed to mostly affect canopy interception evaporation (Stockinger et al., 2015). Fractionation was therefore here accounted for in the 350 two interception storage components (S I,H , S I,R ). Following the approach described by Birkel et al. (2014), the respective δ 18 O compositions , , and , , were accordingly updated for every time step.
Due to data availability, age tracking was here limited to 4 years in the pre-and 3 years in the post-deforestation period. For age beyond that it can only be said that water is older than these 4 and 3 years, respectively. The RTDs and TTDs reported hereafter are thus truncated at these ages. 355

Model calibration and post-calibration evaluation
The model was run with a daily time step and has a total of 14 free calibration parameters, which were calibrated for the model to simultaneously reproduce flow and δ 18 O dynamics in the stream. The uniform prior parameter distributions (Table   2) were sampled using a Monte Carlo approach with 10 6 realizations. To limit equifinality (Beven, 2006) and to ensure robust posterior parameter distributions for a meaningful process representation, an extensive multi-objective calibration 360 strategy was applied. Briefly, this was done using a total of 14 performance metrics that describe the model's skill to reproduce different signatures associated to streamflow ( ) and δ 18 O dynamics ( ) as shown in Table 3.
Combining these metrics into two equally weighted classes describing stream and δ 18 O dynamics, respectively, solutions with balanced overall model performances were then obtained using the mean Euclidean Distance D E [-] from the "perfect" model (i.e. D E = 1; Hulsman et al., 2020): 365 Where N is the number of different performance metrics describing streamflow and M the number of different performance metrics for δ 18 O. To construct the posterior parameter distributions and the corresponding model uncertainty intervals, the 370 retained parameter sets where then weighted according to a likelihood measure L = D E p (cf. Freer et al., 1996), where the exponent p was set to a value of 2 to emphasize models with good overall calibration performance.
In a first step, the model was calibrated for the pre-deforestation period 01/10/2009 -31/08/2013. Note that due to a lack of regular and weekly δ 18 O precipitation data before 01/10/2010, the performance metric describing the δ 18 O dynamics was computed from that date onwards only. The feasible parameter sets were then used to test the model without further 375 calibration in the post-deforestation period. In a second step, the model was re-calibrated for the 01/09/2013 -30/09/2016 post-deforestation period and the changes in the resulting model performance and posterior distributions compared to those from the pre-deforestation calibration. The estimation of the effects of deforestation on RTDs and TTDs is based on model parameter sets obtained from calibration in the pre-deforestation and post-deforestation periods, respectively.

Deforestation effects on the hydrological system
Initial analysis of water balance data suggests that the hydro-meteorological conditions as expressed by the aridity index = ⁄ , do not show significant differences between the pre-deforestation (I A = 0.50 ± 0.02) and the post-deforestation periods (I A = 0.51 ± 0.03), respectively (Figure 4a). However and in spite of these comparable climatic conditions, the data 385 show a shift in the partitioning of water fluxes between runoff Q and actual evaporation E A (note that E A = E I + E T ). While the fraction of precipitation that was released into the atmosphere as vapour was reduced ( ⁄ ; Figure 4a), the mean runoff ratio ( = 1 − ⁄ ) increased correspondingly from C R = 0.55 ± 0.04 to C R = 0.68 ± 0.03 after deforestation of 21 % of the catchment with p = 0.049 based on a Wilcoxon rank sum test. These results correspond well with the findings of an earlier study in the Wüstebach, based on a shorter study period (2011-2015Wiekenkamp et al., 2016), which estimated an 390 increase of C R from ~ 0.58 to ~ 0.66 during that period using eddy-covariance measurements. In absolute terms this entails that, notwithstanding rather stable mean annual precipitation P = 1269 ± 24 mm yr -1 and potential evaporation E P = 632 ± 9 mm yr -1 over the entire study period, the annual actual evaporation E A decreased from 576 ± 11 mm yr -1 to 401 ± 6 mm yr -1 whereas annual runoff Q increased by ~ 25 % from 694 ± 47 mm yr -1 to 870 ± 63 mm yr -1 .
The overall pattern found here also broadly reflect the effects of land cover/use change in many different environments 395 (Creed et al., 2014;Jaramillo and Destouni, 2014;Renner et al., 2014, van der Velde et al., 2014Moran-Tejada et al., 2015;Nijzink et al., 2016;Zhang et al., 2017;Jaramillo et al., 2018). The vast majority of these studies suggest that forest removal leads to an increase in the runoff ratio C R at the cost of reduced evaporation E A , although the magnitudes of these changes do substantially vary between individual catchments and studies, which is consistent with our physical understanding of the importance of forest for transpiration in hydrological systems. Under the assumption that reduction of E A is largely a direct 400 consequence of forest removal in the Wüstebach, a plausible hypothesis to directly attribute this shift in water partitioning from E A to Q to a physical process can be formulated as follows: the roots of harvested trees stopped extracting water for transpiration from the subsurface. In addition, the decrease of turbulent exchange of vapour with depth effectively limits soil evaporation to the first few centimetres of the soil (e.g. Brutsaert, 2014). Thus, the felling of trees led to a situation where under comparable atmospheric water demand E P , water volumes held at depths below that and previously within the reach of 405 active roots became largely unavailable for transpiration and evaporation after deforestation. This implies that the water volumes accessible to satisfy atmospheric water demand, i.e. S U,max and I max , are drastically reduced.
This becomes evident when comparing the catchment-scale maximum annual storage deficits S D,j (Eq. 2) of the pre-and post-deforestation periods, respectively, which are indicative of differences in soil depths affected by E A in the two periods.
In spite of similar climatic conditions, the mean annual maximum storage deficit in the pre-deforestation period is significantly higher (p = 0.047) than in the post-deforestation period. In the pre-deforestation period values between 105 ± 23 mm for I max = 0 mm and 95 ± 21 mm for I max = 4 mm, respectively, were found (Figure 4b). Whereas in the postdeforestation period the mean storage deficit only reached between 49 ± 10 mm and 33 ± 7 mm for the same values of I max (Figure 4b). Note that in both periods, S D,j is relatively insensitive to the magnitude of I max (cf. Gerrits et al., 2009). From the above maximum annual storage deficits S D,j , the corresponding catchment-scale vegetation-accessible water storage capacity, 415 assuming vegetation adaptation to dry conditions with 20-year return periods (see Section 4.1), was estimated at values of S U,max = 225 ± 62 mm for the pre-deforestation (R 2 = 0.91, p = 0.04; Figure 4c) and S U,max = 90 ± 149 mm for the postdeforestation period (R 2 = 0.83, p = 0.27; not shown). Note that in particular the estimates for the post-deforestation period need to be understood as merely indicative because of only 3 years of data, and a system that is likely to be far from equilibrium, because the deforested part cannot have adapted yet (e.g. Nijzink et al., 2016;Teuling and Hoek van Dijke, 420 2020). These considerable uncertainties are also reflected in the surprisingly low post-deforestation S U,max . Notwithstanding these limitations, the above results illustrate that here the reduction of transpiration due to deforestation is likely to go hand in hand with a considerably reduction of S U,max and thus the catchment-scale sub-surface pore volume (between field capacity and permanent wilting point) that is actively accessed by vegetation to satisfy the evaporative demand.

Model calibration for pre-deforestation period
The model parameter sets retained as feasible after calibration in the 2009-2013 pre-deforestation period reproduce the general features of the hydrograph in that period rather well (Figure 2c), similar to a previous modelling study (Cornelissen et al., 2014). This is true for both, the timing and magnitudes of high flows, with an associated Nash-Sutcliffe Efficiency catchment-wide relatively homogenous forest cover in the pre-deforestation period (Figure 1). Remarkably, these values also come close to the water balance-derived catchment-scale estimates of S U,max = 225 ± 62 mm, as described in 5.1 (Figure 4c).

Application of pre-deforestation model to post-deforestation period 445
In a next step, the parameter sets obtained from the above calibration in the pre-deforestation period were used to run the model without further re-calibration in the post-deforestation period. This entails the implicit and clearly wrong assumption that the physical characteristics of the system remained unaffected by deforestation. The consequence of that can be seen in  (Figure 4a). A likely explanation for the pattern produced by the model is that, in contrast to the real world, no reduction in E A due to the reduced forest cover is achieved because the model still relies on the catchment-scale vegetation-accessible storage volume S U,max that characterizes the extent of the catchment-scale active root-system before 460 deforestation. This S U,max falsely provides sufficient water supply to sustain E A at high levels comparatively close to E P throughout the year (see red line in Figure 2b), although, in the parts of the catchment where trees were removed, water stored at depths below a few centimetres is not available for significant evaporation anymore. Such an overestimation of S U,max implies also that in the model a more pronounced water storage deficit can and does develop throughout dry periods.
The model therefore assumes that soils dry out to deeper depths. Consequently, to establish connectivity and to eventually 465 generate flow during and after rainstorms, more water needs to be stored in the model than in the real world system to overcome this deficit. This water is then in the model held against gravity and thus only available for evaporation but not for drainage. Although it is reasonable to assume that groundwater recharge is affected in a similar way, the model can better reproduce low flows. The reason for this is that the draining groundwater body, which sustains summer low flows, is, due to limited recharge during these drier periods, largely disconnected from and thus largely unaffected by subsurface -vegetation 470 interaction in shallower parts of the subsurface. In the parts of the catchment where trees were removed a similar reasoning also holds for the interception capacity I max and the associated likely overestimation of interception evaporation E I , yet, due to the smaller magnitude of I max , to a lesser extent than for S U,max . The above described problems for the high flow periods are accompanied by the model's inability to describe the post-deforestation δ 18 O dynamics in stream water (E NS,δ18O < 0). While this is partly an effect of the above explained low signal-to-noise ratio of such a damped signal and thus of the chosen performance metric, the model also struggles to adequately reproduce the low-frequency fluctuations, such as between February and July 2014, when the model indicated rather stable δ 18 O values while the observed values show a slight yet clear decrease over the same period (not shown). This is also underlined by the more than doubling of the mean absolute errors of δ 18 O from MAE = 0.09 ‰ in the pre-deforestation period to MAE = 0.21 ‰ in the post-deforestation period. Together with the significant lower overall model performance metric D E (Figure 5a), these results illustrate that the pre-deforestation 480 model parameter sets provide an unsuitable characterization of the system characteristics in the post-deforestation period.

Recalibrate model for post-deforestation period
To estimate the effect of forest removal on the characteristics of the hydrological system and thus on the model parameters, the model was in a next step recalibrated for the post-deforestation period. This led to a significant improvement of the overall model performance from D E = 0.22 to 0.58 (Figure 5a). It can be observed that the recalibrated model can much 485 better reproduce the increased high flows in that period (Figure 2c), as reflected by improvements in the performance metrics associated with high flows (Figure 5a), but most notably E NS,Q = 0.69, E NS,PD = 0.93 (Figure 5e) or E NS,AC = 0.87 (Figure 5g).
In addition and perhaps most importantly, the runoff ratio also increased and was with a modelled value of C R = 0.58 (0.56 -0.61) closer to the observed C R (E R,CR = 0.84). This further implies that, in contrast to the initial model, the recalibrated model also features expected reductions of evaporative fluxes E A by about 10%, which can be seen in Figure 2b. Mirroring 490 the improvements in the reproduction of flows, recalibration also allowed the model to better capture the stream water δ 18 O dynamics (E NS,δ18O = 0.24; MAE = 0.10 ‰; Figure 5a). While there is little change in the model's ability to mimic the general level of damping of the δ 18 O signal and its low-frequency fluctuations, the more pronounced, albeit in absolute terms still small, high-frequency fluctuations, as short-term response to individual storms are better described (Figure 2d).
It is of course unsurprising that recalibration leads to an improved model performance in the post-deforestation period. 495 Without further analysis, such a mere model fitting exercise allows in the presence of model equifinality only little insight into the underlying processes (Beven, 2006;Kirchner, 2006). To gain more confidence that the improvements in the recalibrated model are at least partly due to the right reasons (Kirchner, 2006), the changes in the posterior parameter distributions resulting from the two calibration runs were thus analysed. It was hypothesized above that reductions in evaporative fluxes are directly linked to reduced water volumes accessible and available for evaporation and transpiration at 500 the catchment-scale. In the theoretical ideal case, the representations of the associated storage capacities in the model, i.e. the parameters S U,max and I max , should thus be the only ones to significantly change after deforestation. However, note that this is unlikely for two reasons. First, while it is plausible to assume that these storage capacities are significantly affected by forest removal, it is not unlikely that other system characteristics and their mutual interactions, thus far unknown and not considered, are similarly influenced, potentially causing considerable ontological uncertainty. Second, model parameter 505 interactions that arise as artefacts to compensate overly simplistic process representations and/or data uncertainty are also likely to affect parameters seemingly unrelated to deforestation.
Inspection of the posterior parameter distributions reveals that the catchment-scale S U,max experienced considerable reductions after recalibration. While in the hillslope parts of the catchment, which were less affected by deforestation (~ 10% of the hillslope area; Figure 1) an average decrease by ~ 75 mm to S U,max,H = 137 mm (118 -249 mm) can be seen (Figure  510 6a), the completely deforested riparian area exhibits an average decrease by ~ 130 mm to S U,max,R = 67 mm (53 -126 mm; Figure 6b). As an indicative value, the area-weighted catchment-average S U,max = 120 mm of the best performing parameter set falls remarkably well into the plausible range of S U,max = 90 ± 149 mm as described in Section 5.1. Similarly, significant reductions in I max can be observed, which are with average reduction of ~ 2 mm not as pronounced on the less deforested hillslopes (Figure 6d) than in the riparian areas where I max,R decreased on average by ~ 3 mm (Figure 6e). Comparing to the 515 posterior distributions of other parameters, the results illustrate that the storage parameters S U,max and I max of the riparian zone, and to a lesser extent of the hillslope, were subject to the most pronounced changes. In contrast, for most other parameters, the 5/95 th interquantile range of the pre-and post-deforestation posterior distributions largely remained overlapping ( Figure 6). However, it can also be observed that the individual parameter values associated with the best model solutions in the pre-and post-deforestation periods, respectively, do vary to a stronger degree for most parameters. 520 Notwithstanding the distinct overall effects of forest removal on the individual posterior distributions, this clearly highlights the influence of parameter compensation effects and related uncertainties. After preliminary unsuccessful testing, no further attempts were thus made to re-calibrate only the above discussed four storage parameters, i.e. S U,max,H , S U,max,R ., I max,H and I max,R .
Overall the results suggest that the model formulation together with the multi-objective calibration strategy ensured the 525 identification of solutions that provide a robust description of the system and allow a simultaneous representation of flow and isotope dynamics in the stream. There are indications that at least some processes and parameters can be directly linked to real world quantities. In particular, the results provide supporting evidence that the parameters S U,max,H and S U,max,R are not merely abstract quantities, but that it is not implausible to assume that they, taken together, provide a catchment-scale representation of vegetation-accessible and -accessed water volumes as defined by Equation 2. 530

Deforestation effects on travel time distributions, SAS-functions and young water fractions
While the volume weighted mean δ 18 O contents of observed precipitation with -7.9 ‰ and stream water with -8.2 ‰ are comparable, a substantial difference in their respective fluctuations, with standard deviations of 3.6 ‰ and 0.2 ‰, respectively, is evident (Figure 2d). This difference suggests a remarkably elevated degree of damping rarely found elsewhere (e.g. Speed et al., 2010), indicative of the importance of old water contributions to the stream in the study 535 catchment. No significant difference in damping ratios was observed between the pre-and post-deforestation period, which further corroborates the prevalence of old water.
Tracking the δ 18 O signals through the model then allowed to estimate residence (RTD) and travel time distributions (TTD).
Note that any results reported hereafter are necessarily conditional on the assumptions made in and the uncertainties arising from the modelling process.
In general and consistent with the observed high degree of damping, it was found that pre-deforestation the system was characterized by rather old water. On average only ~ 16% of the water stored in the system is younger than three years, as illustrated by the range of the truncated marginal RTDs that combine water stored in all model storage components ( Figure   7b). The young water fractions (F yw ), here defined as the fractions of water younger than three months (Kirchner, 2016), are similarly low but exhibit somewhat more pronounced variations from < 0.01 to 0.04 between dry and wet periods. In 545 contrast, the range of truncated TTDs of stream water not only shows more variability in response to changing wetness conditions but also somewhat younger water, with on average about 24 % of the discharge younger than 3 years (Figure 7b).
Stream water can contain F yw of up to ~ 0.30 for individual storm events in the wet period, while frequently dropping to < 0.01 during elongated summer dry periods (Figures 7c, 8a), similar to what has been reported elsewhere (e.g. Gallart et al., 2020b). It can also be observed that the age composition of stream water (Figure 7c) and the associated F yw (Figure 8a) do 550 considerably vary throughout wet periods. Dry periods are characterized by considerably less variability and more stable stream water TTDs. This is a consequence of increased bypass flow that has little interaction with resident water as the system gets wetter and which may reach the stream over preferential flow paths and increased contributions from the riparian zone with its shorter flow paths. In other words, in a wet system where little additional water can be stored, the precipitation volumes of individual storm events control the shape of TTDs (Heidbüchel et al, 2020). In the summer dry season, however, 555 precipitation is to a higher degree buffered in the root-zone and used for transpiration (Stockinger et al., 2014). Conversely, stream flow is then mostly sustained by groundwater which is characterized by large volumes of older water. This effectively attenuates fluctuations by the proportionally much lower volumes of younger precipitation water that cannot be stored and is thus quickly released to the stream. This is further corroborated by the significantly higher sensitivity of F yw to changes in stream flow in wet periods as compared to dry periods (Figure 8c). In spite of the low mean F yw ~ 0.11 (Figure 8a), the above 560 also entails that very fast switches towards higher young water fractions can be observed when the system is wetting up after dry periods as well as for storm events throughout the wet season. In general, the above observations are also encapsulated in the catchment-overall storage age selection functions ω, that represent the ratio of stream water TTD over the combined RTD of all model storage elements (Benettin et al., 2015a). While for dry periods under-sampling of young water ages with relatively little variability is evident, it can also be seen that in particular during wet-up and wet periods a considerable, yet 565 highly variable preference for very young water can be seen (Figure 9a).
The overall picture did not change in the post-deforestation period. Water stored in the subsurface remains to be characterized by RTDs with very low young water fractions F yw between < 0.01 and 0.04 with limited sensitivity to changes in wetness conditions (Figure 7i). Similar to the pre-deforestation period, the TTDs exhibit much more variability compared to RTDs, depending on the wetness conditions. However, in contrast to the pre-deforestation period and irrespective of the 570 wetness conditions, partly considerable shifts towards younger water can be observed for the TTDs (Figure 7d-g). While individual summer storms led to increases of almost exclusively very young water <10 -20 days in the stream (Figure 7d), considerable shifts towards younger water can be observed throughout the entire spectrum of tracked ages during wet-up and wet conditions (Figure 7e-f). During the wet period ~ 28 % of the stream water are on average younger than the tracked three years (Figure 7i). The mean F yw only slightly increased to 0.13 (Figure 8b), compared to 0.11 in the pre-deforestation period 575 ( Figure 8a). For individual winter storm events, however, F yw increased to up to ~ 0.40 (Figures 7j, 8b) compared to F yw of up to ~ 0.30 in the pre-deforestation period (Figures 7c, 8a). Besides the generally higher F yw during wet periods, the F yw became more sensitive to flow during wet-up and wet conditions, with dF yw /dQ ~ 0.27 and 0.43, respectively (Figure 8d), similar to what has been previously reported by von Freyberg et al. (2018) and Gallart et al. (2020a). At the end of dry periods and the beginning of the wet period, elsewhere also referred to as "autumn flush" (e.g. Dawson et al., 2011), the 580 switches towards younger water at given flow levels occur considerably faster in the post-deforestation period than in the pre-deforestation period. Therefore, where, at the same discharge, previously relatively little young water reached the stream, a much higher fraction of young water can now be observed in the stream. This is a direct effect of the reduced evaporative removal of relatively young near-surface water, which in turn is intimately linked to the reduced water supply for evaporative fluxes, i.e. smaller storage volumes S U,max and I max . This relatively young, surface-near water, not taken up by 585 vegetation anymore is thus to a higher degree flushed from the system mostly via fast responding flow paths to the stream.
Once connectivity and the associated higher degree of bypass flow are established in the wet period, the peak sensitivity of dF yw /dQ to flow increased to ~ 0.43, as under these conditions when little additional water can be stored in the shallow subsurface, F yw is largely controlled by magnitude of the individual precipitation signals and to a lesser extent by the footprint of the pre-storm history of evaporative fluxes in the shallow subsurface storage. In contrast, no significant changes 590 could be observed for the sensitivity of F yw to discharge during dry periods, as during that period, the composition of water ages is controlled by large volumes of old water. The above described post-deforestation changes are also manifest in the corresponding storage age selection function ω (Figure 9b) for that period. While the degree of under-sampling of young water during dry periods significantly decreased, a substantially higher preference for young water during wet-up and wet periods can be observed than during the pre-deforestation period, with a clear overall shift towards younger water for all 595 wetness conditions.

Uncertainties, unresolved questions and limitations
As emphasized above, all results are conditional on the assumptions taken throughout the modelling process. These assumptions, present in model structure, parameterization and parameters, can lead to uncertainties. Yet, notwithstanding 600 these potential uncertainties, extensive preliminary model testing together with the use of multiple model calibration and evaluation criteria suggest there is relatively strong evidence to support the main results in this study: the post-deforestation reduction of evaporative fluxes can, at least partially, be linked to a relatively clear reduction in the catchment-scale storage capacities S U,max and I max , which in turn triggered a shift towards younger water ages in the stream, particularly during wet-up and wet conditions. 605 One specific interesting observation was that the results suggest that a passive mixing volume S S,p of at least ~ 10.000 mm is necessary for the model to attenuate the amplitudes of the precipitation δ 18 O signals to those in the stream water. Although, S S,p is rather well constrained (Figure 6h), there has in the past been no hydrogeological evidence for the presence of such a surprisingly large groundwater volume nor for its hydrological relevance in the study catchment. Indeed, the authors are not aware of any catchment-scale study that reported similarly high values for S S,p or functionally equivalent parameters (e.g. 610 Birkel et al., 2011a,b;Hrachowitz et al., 2013Hrachowitz et al., ,2015Benettin et al., 2013Benettin et al., ,2015aHarman, 2015;van der Velde et al., 2015).
Yet, to achieve the degree of damping observed in the stream water, such a volume is necessary, if the current understanding of conservative tracer dynamics holds. Reflecting our insufficient knowledge to which depths exchange with surface water occurs (e.g. Condon et al., 2020), a potential explanation for this observation is that the frequently layered and fractured structure of the Devonian shale bedrock may provide relatively high-permeability pathways for the circulation of and 615 exchange with water at depth. Another, yet, given the current understanding of the Wüstebach (e.g. Graf et al., 2014), less likely hypothesis is the presence of significant lateral groundwater exchange (e.g. Bouaziz et al., 2018). In other words the possibility that the subsurface catchment does not match with the surface catchment ( Figure 1) and that older groundwater is imported from "outside" the surface catchment, while an equivalent volume of younger groundwater is exported, maintaining the mass balance. These are hypotheses to be tested in future studies, as the currently available data do not allow 620 a conclusive answer to this question.

Conclusions
The small Wüstebach catchment experienced significant deforestation in 2013. Analyzing the effects of this deforestation on the hydrology and stable isotope circulation dynamics in the study catchment our main findings are: (1) Water balance data suggest that deforestation led to a significant increase of stream flow, accompanied by corresponding 625 reductions of evaporative fluxes. This is reflected by an increase of the runoff ratio from C R = 0.55 to 0.68 in the postdeforestation period despite similar climatic conditions, supporting previous results based on eddy covariance measurements (Wiekenkamp et al., 2016).
(2) Based on water balance data, this reduction of evaporative fluxes, as a consequence of reduced vegetation water uptake, could at least partly be linked to a reduction of the catchment-scale water storage volume in the unsaturated soil (S U,max ) 630 that is within the reach of active roots and thus accessible for vegetation transpiration from ~225 mm in the predeforestation period to ~ 90 mm in the post-deforestations period.
(3) Estimating S U,max as calibration parameter of a process-based hydrological model led to similar conclusions. The catchment-average calibrated model parameters representing S U,max for both, the pre-and deforestation periods, respectively, correspond with ~ 240 mm and ~ 120 mm remarkably well with S U,max directly estimated from water 635 balance data. Other model parameters, assumed to have a less direct link to vegetation, exhibited much lower levels of systematic change following deforestation.
(4) Using the model to track the age composition of stream water suggested that, in general, water reaching the stream in the pre-deforestation period was rather old with a mean young water fraction F yw ~ 0.11. In spite of the overall low F yw , clear shifts in the shape of travel time distributions towards younger water can be seen under wet conditions with young water 640 fractions increasing up to F yw ~ 0.30.
(5) Deforestation and the associated reduction of S U,max led to shifts in travel time distributions towards younger water. Under wet conditions, this resulted in increases of young water fractions to up to F yw ~ 0.40 for individual storms. In contrast, dry period travel time distributions exhibited only minor changes. Overall the mean fraction of young water in the stream increased to F yw ~ 0.13. 645 (6) Deforestation resulted in a considerable increase of the sensitivity of young water fractions to discharge under wet conditions from dF yw /dQ = 0.25 to 0.43. This implies faster switches towards younger water and thus faster routing of solutes during and shortly after storm events and thus faster routing of solutes with increasing wetness.
Overall, this study demonstrates that deforestation has not only the potential to affect the partitioning between drainage and evaporation, and thus the fundamental hydrological response characteristics of catchments, but also catchment-scale tracer 650 circulation dynamics. In particular for wet and wet-up conditions, sometimes also referred to as "autumn flush", deforestation caused higher proportions of younger water to reach the stream, implying faster routing of water and plausibly also solutes through the subsurface.

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Author contributions. MH and MS designed the experiment. MH did the analysis and wrote the first draft. All authors discussed the design, results and the first draft and contributed to writing the final manuscript.
Competing interests. The authors declare that they have no conflict of interest.