Responses of soil water storage and crop water use efficiency to changing climatic conditions: A lysimeter-based space-for-time approach

. Future crop production will be affected by climatic changes. In several regions, the projected changes in total rainfall and seasonal rainfall patterns will lead to lower soil water storage (SWS) which in turn affects crop water uptake, crop yield, water use efficiency, grain quality and groundwater recharge. Effects of climate change on those variables depend on the soil properties and were often estimated based on model simulations. The objective of this study was to investigate the response of key variables in four different soils and for two different climates in Germany with different aridity index: 1.09 20 for the wetter (range: 0.82 to 1.29) and 1.57 for the drier climate (range: 1.19 to 1.77), by using high-precision weighable lysimeters. According to a “space-for-time”

more efficient crop water use under less optimal soil moisture conditions. Long-term effects of changing climatic conditions on the SWS and ecosystem productivity should be considered when trying to develop adaptation strategies in the agricultural 35 sector.

Introduction
The amount of water stored within the root zone of the soil and the vadose zone is a central and characteristic component of terrestrial ecosystems. Soil water storage (SWS) is important for provisioning (e.g., crop production, water balance, and plant available nutrients) as well as regulating and supporting ecosystem services (e.g. water, nutrients, climate, flood, 40 drought; Adhikari and Hartemink, 2016;Vereecken et al., 2016). The SWS capacity (SWSC) depends on soil texture, organic matter content, bulk density, and soil structure and is related to the effective field capacity, which can be derived from the soil water retention function (Vereecken et al., 2010). The knowledge on magnitude and temporal variation of the SWS is essential for understanding ecological and hydrological processes and to manage ecosystems (Cao et al., 2018).
Climate change will modify the temporal availability of soil water, increase the frequency and duration of droughts, affecting 45 the quantity and quality of aquifer recharge and might affect crop production. Thus future ecosystem productivity (e.g. crop yield) is expected to respond to changes in weather (short-term) and climate (long-term), because it will alter the crop water balance components, such as SWS, evapotranspiration (ET) and drainage (Yang et al., 2016). How to produce more crop yield with less water is a major challenge in agriculture, because i) water is a limiting factor for crop production in many regions of the world, and ii) predictions of future climate indicate an increasing water limitation for crop production caused 50 by reduced rainfall and changing seasonal rainfall distribution (Lobell and Gourdji, 2012).
Several studies have been conducted to investigate the impact of global climate change on crop water balance components (Sebastiá, 2007;Wu et al., 2015) and crop or grain yield (Ewert et al., 2002;Zhao et al., 2016;Schauberger et al., 2017;Asseng et al., 2019). Understanding the impact of weather signals on the agricultural productivity is of crucial importance for managing future crop production, since variations in weather conditions could explain much of the yield variability 55 . Temperature rise and changing seasonal rainfall patterns could alter the probability of droughts and affect freshwater resources (Gudmundsson and Seneviratne, 2016;Gudmundsson et al., 2017). Negative impacts of rising temperature on the yield of major crops at the global scale (Asseng et al., 2014;Zhao et al., 2017) are highlighting the potential vulnerability of agricultural productivity to climate change. Schauberger et al. (2017) showed a consistent negative response of US crops under rainfed conditions being mainly related to water stress induced by higher temperatures. In 60 addition to the direct effects of a temperature rise, an elevated atmospheric CO 2 -concentration, and changes in rainfall amounts on crop yield (Ewert et al., 2002;Asseng et al., 2014;Gammans et al., 2017;Scheelbeek et al., 2018), the higher temperatures could affect crop yields indirectly. Indirect effects caused by increasing the atmospheric water demand, limiting ET due to water stress and reducing the SWS, could in turn lead to a decrease in crop yield (Zhao et al., 2016;Zhao et al., https://doi.org/10.5194/hess-2019-411 Preprint. Discussion started: 27 August 2019 c Author(s) 2019. CC BY 4.0 License.
Previous studies reported estimates of crop water balance components and crop yield mostly based on either manipulative experiments or observational studies to predict the ecological response of crops to climate change (Yuan et al., 2017).  showed that the inter-annual variation of the SWS at northern middle and high latitudes increased under a warmer climate with higher values during the wetter and lower values of the SWS during the drier season. In this case, the frequency 70 of water logging events or soil crack formation will increase and probably alter soil properties such as macroporosity and SWSC and thus affect vadose zone hydrology at different scales (Robinson et al., 2016;Hirmas et al., 2018). Robinson et al. (2016) showed for a manipulative long-term experiment that intense summer droughts altered the soil water retention characteristic and lowered the SWSC.
Nevertheless, current knowledge on changes of SWS are still limited mostly to the analysis of soil moisture observations 75 related to restricted soil volumes and soil moisture ranges (Mei et al., 2019;Yost et al., 2019). As an alternative method, weighable lysimeters allow the direct observation of SWS by monitoring the temporal changes of the total soil mass in mostly cylindrical containers. However, the use of weighable lysimeters was often limited in the past to the quantitative determination of the water balance components of precipitation (P), evapotranspiration (ET), and subsurface inflow (Q in ) and outflow (Q out ; e.g. drainage); the change of SWS was obtained as residual of the water balance components (e.g. Herbrich et 80 al., 2017;Groh et al., 2018b). This approach accumulated all possible errors introduced by other components into the SWS, causing a relatively low precision. The direct derivation of SWS from lysimeter mass changes could provide a new perspective on the use of lysimeter data as an additional model calibration variable and for lysimeters that are large enough to fully capture the complete soil profile with the relevant soil horizons and intact soil structures to be representative for the pedon scale. 85 Crop water use efficiency (WUE), being the ratio between grain yield or total biomass and the water lost to the atmosphere by ET, is one of the possible ways to quantify the impact of changes in the environmental conditions and of management decisions (e.g. irrigation) on agricultural productivity. The WUE provides insights to better manage and understand the productivity and ecological functioning of agricultural ecosystems (Zhang et al., 2015). The prevailing general hypothesis for WUE is that plant productivity increases with increasing water use (ET; Hatfield and Dold, 2019), which implies that 90 WUE efficiency is a linear function of the water used by a crop to produce grain yield or the total above ground biomass. But several studies have shown that crop WUE was negatively correlated with annual rainfall and plants achieved their maximum crop WUE under less favourable soil water availability (Zhang et al., 2010;Ponce-Campos et al., 2013;Xiao et al., 2013;Zhang et al., 2015). The last statement might imply that plants are able to adapt their water use during drought conditions by improving their WUE or that there is simply less non-productive water losses by evaporation. Nevertheless, 95 temperature above a certain threshold (extremely high temperature) especially during the reproductive period (Gourdji et al., 2013) or due to drought and heat stress reduce yield. However, such investigations are often focused on one specific environmental variable (e.g. P or temperature) in manipulation experiments. This basically ignores joint effects of several climate variables on the crop WUE in climate impact research studies. The impact of altered climatic conditions on different agricultural ecosystems within manipulative experiments has not been thoroughly studied yet; due to problems to either 100 realistically manipulate the climatic conditions at a specific site or to move an intact soil to another site with contrasting climatic conditions.
Here, we hypothesize that WUE will not increase for drier climate; because a change in plant productivity will simultaneously alter the water use (ET) and thus describe WUE as a linear function between both variables. In addition we wanted to test if observed lysimeter mass changes can be used to monitor the long-term change of SWS, which might be in 105 addition to water flux observation a useful dataset for the calibration of vadose zone models. We used observations from a German soil-climate crossed factorial experiment (TERENO-SOILCan; Pütz et al., 2016). The lysimeter network of TERENO-SOILCan has been initiated to assess effects of climatic changes on arable and grassland soil ecosystems including the water balance components (ET, SWS, net drainage) and crop characteristics including yield, yield quality and WUE. As part of this project, arable-land lysimeters filled with four different soils were transferred within and between 110 TERENO observatories (space-for-time; see details in Pütz et al., 2016) to expose soils from originals sites to other climatic conditions. The space-for-time approach means that soils are translocated in space instead of waiting at the same location for changes in climatic conditions in time. The concept initially intended to evaluate the impact of climate on agricultural ecosystems (Pütz et al., 2016). It represents basically a crossed soil type and climate experimental setup that could allow quantifying changes in the soil water balance and the crop production as response to imposed variations in climatic 115 conditions. Results from this experimental setup can primarily be used to evaluate models that predict changes in response to possible future climatic conditions. Our objectives were: i) to develop an approach to obtain time series of changes in SWS directly from lysimeter data , ii) to determine the other soil water balance components (P, ET, inflow and drainage) of soils each exposed to two different 120 climates, iii) to compare the net flux (inflow and drainage)/SWS dynamics for the same soils in relatively dry and wet climates and iv) to test the hypothesis that WUE of crops remains constant under changing climatic conditions in these crossed soil type and climate experiment. The analysis was based on lysimeter data from April 2011 until December 2017.

Site descriptions 125
The study was conducted at the experimental field sites Selhausen (50°52´7´´N, 6°26´58´´E) and Bad Lauchstädt  Table A1 (see appendix). The transferred eroded Luvisol soil monoliths from Dedelow have a varying soil depth to the clay illuviation horizon (B t ) and to the marly, illitic glacial till (C-horizon).
They represent part of the erosion gradient typically observed in agricultural landscapes of hummocky ground moraines (Sommer et al., 2008;Rieckh et al., 2012;Herbrich et al., 2017). Detailed information about the lysimeter design and general 140 experimental-set up of TERENO-SOILCan can be found in Pütz et al. (2016). The climatic conditions of the central sites from 1 January 2012 to 31 December 2017 (complete years) are shown in Fig. 1 according to Walter and Lieth (1967).
Although the patterns in average monthly temperature values are relatively similar at both sites (Fig. 1), a more pronounced amplitude of the temperature variations over the year could be found in Bad Lauchstädt (representing a more continental climate) as compared to the more temperate and humid climate (sub-oceanic or sub-Atlantic) in Selhausen (Fig. 1). The 145 average annual grass reference evapotranspiration (ET 0 ) calculated with the FAO56 Penman-Monteith method (Allen et al., 2006) is slightly higher at Bad Lauchstädt (710 mm) than at Selhausen (694 mm). Larger differences are shown in the annual rainfall and the rainfall distribution over the year (Fig. 1). The lower annual P in Bad Lauchstädt (458 mm) than in Selhausen (644 mm) corresponds with a higher aridity index (AI = ET 0 P -1 , see data repository) of 1.57 for Bad Lauchstädt than for Selhausen (1.09). The rainfall distribution over the year was more uniform in Selhausen whereas the probability of 150 relatively dry periods in spring (April) and late summer (September) was higher in Bad Lauchstädt. Thus, the climatic conditions at the SOILCan experimental sites can be defined as drier for Bad Lauchstädt and wetter at Selhausen, which corresponds well to long-term weather station data reported by Groh et al. (2016)

Soil water storage (SWS)
Monthly changes in SWS (ΔSWS) were calculated from lysimeter observations as: 160 where ΔW is the monthly lysimeter mass change, and ΔL yscor corresponds to mass changes by maintenance, harvesting, or other disturbances that occur accidently (e.g. erroneous load cells) or naturally (e.g., animals). The variable ΔW was directly obtained by analysing lysimeter mass data (average value: 12°AM until 2°AM) defined as: where W is the lysimeter mass at the beginning of month i. The variable ΔL yscor was determined from monthly changes of lysimeter mass during maintenance work. Less than 0.6 % of ΔSWS values could not be calculated, because lysimeter mass data at the beginning of the corresponding month were missing. A linear regression model obtained for the entire time series between ΔSWS of the soils was used for interpolation to fill the gaps. This was first based on ΔSWS from surrounding lysimeters of the same soil type and if not available, then the average values of ΔSWS obtained from all available lysimeters 170 at the respective station were used.

Crop water use efficiency (WUE), grain yield and yield quality
In total 12 arable land lysimeters (three replicates of four soil types) with a surface area of 1 m 2 and a depth of 1.5 m were embedded within larger fields at the respective central experimental site at Selhausen (250 m²) and Bad Lauchstädt (720 m²). The following Eq. (3) was used to calculate the crop WUE (kg m -3 ): where Y is the grain yield (kg m -2 ), and ET (m 3 m -2 ) is the measure of the consumed water during the growing season of the corresponding crop (Katerji et al., 2008). The growing periods of the crops were defined as the time between sowing and harvest (see appendix Table A2). The required ET during the growing season was estimated based on the monthly water 190 balance equation and observed precipitation (P) in mm per month as: where Q net is the monthly sum of net water flux across the lysimeter bottom (Q net > 0: drainage; Q net < 0: capillary rise) and ΔL ysvol is mass change determined from monthly soil water sampling volume. P was measured with a tipping bucket rain gauge (15189, Lambrecht, Göttingen, Germany) at Bad Lauchstädt (experimental station Bad Lauchstädt), and with a 195 weighing rain gauge (Ott Pluvio2 L, Ott, Kempten, Germany) at Selhausen (Se_BDK_002). Data of the latter station is available at TERENO data portal (http://teodoor.icg.kfa-juelich.de/ddp/index.jsp). The Ott rain gauge was installed in April 2013; data before April 2013 was estimated by linear regression models and P data from surrounding climate stations of the TERENO data portal (station names: SE_BDK_002; RU_BCK_003; RU_K_001; ME_BCK_001), which can be used to interpolate between the given data points. We used the R software (R-Core-Team, 2016) and the function lm of the package 200 stats (R-Core-Team, 2016) to set-up linear regressions. The coefficient of determination (R 2 ) was used to determine the goodness of fit of the linear regression. A stepwise gap-filling approach was used to gap-fill missing P data after April 2013, which consisted of an analysis of data from other meteorological stations that were operating and missing values, were filled based on the observation which had the highest R 2 . Monthly Q net values were obtained from mass changes of the leachate from the lysimeters, collected with a weighable reservoir tank. The lysimeter bottom boundary pressure head condition was 205 imposed by a pumping mechanism, which enabled either outflow or inflow according to differences in pressure head values upward and downward water fluxes and representation of ET processes in lysimeters (Groh et al., 2016) more realistically and comparable to the intact soil profile. More technical details can be found in Pütz et al. (2016). Missing data in the time series of Q net were filled for small gaps of about one minute by linear interpolation and for gaps between >1 and 10 minutes 210 by using a moving average with a window width of 30 minutes. Larger gaps in the time series were filled by average water flux values from other lysimeters of the same soil type. Nearly 5% of monthly ET values were found not plausible perhaps due to water loss by leaking during periods with water-saturated conditions at the lysimeter bottom. These conditions occurred mainly in winter, when monthly ET fluxes were in general relatively low as compared to summer conditions, so that potential error was low and easily detectable. A linear regression based on either single or average ET values from other 215 non-affected lysimeters with similar soils were used for interpolation to fill the gaps. Detailed information on the monthly water balance data and missing data can be taken from the TERENO data portal (see section Data availability). Sauerbach (Sb; Fig. 2d) as compared to that of the other two soils from Dedelow (Dd; Fig. 2f) and Selhausen (Se; Fig. 2h).

Soil water storage change
The Sb and BL soils were strongly desiccated by the winter wheat crop in 2015, which can be seen from ET June 2015 for 230 BL and Sb of about 125 -175 mm/month (Figs. 2a and 2c) was larger than for Dd and Se soils of about 100 -125 mm/month (Figs. 2e and 2g) even for the soils exposed to the drier climate in Bad Lauchstädt. For the BL (Fig. 2b) and Sb (Fig. 2d) soils, the amount of rainfall after the growing season of 327 mm (August 2015 -April 2016) in Bad Lauchstädt was not sufficient to compensate for ET and drainage such that the soil profile did not return to a SWS capacity (i.e., typical spring   (Robinson et al., 2016), biochemical e.g. enhanced organic matter mineralization, due to increasingly oxidation of the organic horizons during dry periods (Robinson et al., 2016), which will consequently result in a degradation of organic soil structure, or change in the soil wettability (Ellerbrock et al., 2005).

Net drainage
The water fluxes across the suction rake system at the lysimeter bottom in 1.5 m depth were cumulated to monthly net drainage fluxes (Q Mnet ). The time series' of Q Mnet for all soils at Se, the site with relatively wet climate, were in general directed downward during the winter months and upward (capillary rise) during spring and summer (Fig. 3). However, the magnitude of monthly fluxes Q Mnet differed between the soil types (e.g. soils in Se for 2012 or 2013 see Fig.3); Q Mnet for 270 lysimeters with the coarser-textured soils from Dd ( Fig. 3c) was mostly larger (e.g., drainage during bare fallow 2014) than for those with the finer-textured soils from BL (Fig. 3a), Sb (Fig. 3b), and Se (Fig. 3d). For the same soils under the relatively dry climate in BL, time series' of Q Mnet were rather similar, with the largest values of upward fluxes for the soil from Dd (Fig. 3c). The magnitude of Q Mnet for soils under BL climate was mostly smaller for drainage and larger for upward directed fluxes as compared to the Q Mnet values for the soils under the wet climate in Selhausen. 275

280
Error bars indicate the variability of storage changes between individual lysimeters of each soil group. The background colour corresponds to different crops lysimeter cover types: bare soil (white) and different crops (green). The Q Mnet time series' (Fig. 3) demonstrate that weather conditions in 2015 impacted the soil water fluxes in the following years: Under the dry climate in BL, hardly any drainage was observed for all soils after 2015. This indicates that the soils 285 remained so dry during the winter period that downward water percolation or groundwater drainage was limited. The lack of water recharge during winter also affected the upward directed Q Mnet flux rates in the following years, which generally decreased after 2015, especially for soils from BL and Sb. The nearly unchanged Q Mnet values for the soils at BL after 2015 indicate that soil water saturation and dynamics is limited throughout the soil profile. This range could be explained by variation in soil water storage capacities between Dd soils, which depended on the thickness of the upper soil horizons that were modified by soil erosion (Herbrich et al., 2017). The long-term average values of Q Anet for all soils in the dry climate were negative and varied only in a small range (from -18 mm to -28 mm; see appendix Table A1). Long term negative groundwater recharge is only possible at sites where groundwater can be replenished, for 300 instance, by lateral subsurface water flow. Whether the Q Anet flux under the BL climate will continue to be negative for all soils would require a longer time series. Nevertheless, a low and even negative groundwater recharge has not only an impact on the groundwater quantity, but it will also affect the groundwater quality. In case of a small net recharge, the concentrations of solutes from agricultural fertilizers, pesticides, and those of dissolved minerals and salts in the water-filled soil pores will become relatively high, and soil water movement still remains negligibly small. Thus under conditions of 305 relatively small leaching rates, solutes including plant nutrients will largely be retained within the soil's root zone. Under long term conditions of net negative leaching, soils and soil horizons may accumulate carbonates (e.g., BL soil Haplic Chernozems), or if leaching is small such that the carbonates from the topsoil horizons precipitate already in the subsoil within the 1.5 m soil monoliths like in the Ccv horizons in Dd subsoil of Calcic Luvisols (see soil profile descriptions in Herbrich and Gerke, 2017) and eventually salts. 310 Q Anet values under a relatively wet climate (in Se) were for all soils positive, indicating in general downward directed drainage fluxes (Fig. 4). The long-term average Q Anet values ranged between 49 to 119 mm (see appendix Table A1) depended on the soil type. The Q Anet value was larger for the coarser-textured soil from Dd (Fig. 4c) as compared to the other soils. For 2013 (Winter Canola crop), the Q Anet fluxes were negative for all finer-textured soils (i.e. Bad Lauchstädt, Sauerbach, and Selhausen, Fig. 4a, b, d), which might be related to the deeper reaching root system of the crop canola 315 (Breuer et al., 2003)

Crop yield and Water Use Efficiency 325
The grain yields were in general larger for a dry climate at Bad Lauchstädt than for a wet climate at Selhausen except for the peas (Fig. 5a). The pea crop had in comparison to the other cereal crops a relatively short vegetation period and depends more on conditions during germination in early spring than on differences in climatic conditions in late spring and summer.
For the other crops the spread of fungal pathogens under a more humid climate (Talley et al., 2002;Agam and Berliner, 2006) and frequent occurrence of dew formation (Xiao et al., 2009;Groh et al., 2018a;Brunke et al., 2019;Groh et al., 2019) 330 could explain the generally lower yield of grain crops for soils under a wet climate in Selhausen. However, an appropriate crop management with one to three applications of fungicides during the growing season (see appendix Table A2) impact on crop yield such that other reasons have to be considered. The yield varied for the most crops among the soil replicates at a certain site, which can be described by the coefficient of variation (CV), below a CV value of 28%, except for 335 pea, which showed for all soils a high value, for winter canola grown on finer-textured soils in Se (BL, Se see appendix Table A3), and for winter barley (Dd and Sb in 2012, Sb in 2016) cropped at Se. For winter canola this might be related to a higher loss of rapeseeds during manual harvesting, natural pod shattering, cleaning and threshing (Alizadeh et al., 2007;Kuai et al., 2015). The CV value of the observed yield variability between each soil type corresponds to values reported between 5 to 27 % by Joernsgaard and Halmoe (2003) and Wallor et al. (2018). The yield of winter wheat (7.8 t ha -1 see 340 appendix Table A3) for the soil from BL at BL agreed well with observations on yields from a long term fertilization experiment at the BL site (Merbach and Schulz, 2013), which demonstrates the high yield potential of the soil from BL.

350
The scatterplot of the total biomass (Fig. 5b) shows that most crops produced relatively similar amounts of total above climate than under a wet climate (Fig. 5c). This means that crops under a dry climate were more productive with respect to crop yield than under a wet climate. The crop ET (i.e., ET related to the vegetation period) was larger under the wet than under the dry climate (Fig. 5d), and the corresponding crop water use efficiency (WUE) was larger at the site with the relatively dry (BL) as compared to the wet (Se) climate (Fig. 5e). These results demonstrated that plants were more efficient 360 to produce yield at a site with a suboptimal water supply. The present results are in line with earlier findings from Zhang et al. (2015), who showed that the WUE reached a maximum under warm and dry and a stable minimum under warm-wet climatic conditions. Also when WUE was calculated based on the total aboveground biomass, a higher WUE was observed for the corresponding crop under a dry than under a wet climate (Fig. 5f), which demonstrated that climatic conditions were not only beneficial for the grain yield but also for that of the straw. However, differences in fertilizer application (see 365 appendix Table A2) with lower nitrate application in the wet site could be another reason for the differences in yield and biomass production.
The lower WUE under a wet climate might be related to a higher soil evaporation and plant canopy interception evaporation. Kunrath et al. (2018) found for the crop tall fescue that limiting nitrogen-supply conditions negatively affected WUE values by a reduced leaf area index, leaf photosynthesis and radiation efficiency, which hence increased the ratio of soil evaporation 370 to transpiration. Thus, we further compared the ET during periods when ET was either transpiration (ET T ) or evaporation (ET E ) dominated. The transpiration-dominated period was defined from the beginning of April, which corresponds well with the temporal increase of the monthly ET, until the time when plants reached the growth stage of ripening /maturity of their fruit or seeds about a month before harvest (see appendix Table A2). The rest of the vegetation period was defined as the evaporation-dominated period. Evaporation was considered to be non-productive water use. The cumulative values of ET, 375 ET T and ET E during the observation period are shown in Table 1. The differences for ET E between all soils in the dry and wet climate from 359 mm to 576 mm was larger than the differences for ET T (range: -72 mm to 199 mm). Especially the larger soil evaporation (ET E ) at Selhausen contributed to the lower WUE under wet climate.
The relationship between yield and ET was reported to correspond with the productivity function of crops (grain yield vs. ET) and often assumed to be linear (Tolk and Howell, 2009;Wichelns, 2014). However, for our present data, a quadratic 380 productivity function (Fan et al., 2018) of the winter barley and pea crops (Fig. 5g) rather than a linear one could explain the observed larger WUE of soils under a dry climate at Bad Lauchstädt. The crop winter canola could be best described by a linear productivity function with a negative slope (Fig. 5g). The other crops, winter rye and winter wheat, could neither be described by a linear nor a quadratic function. Longer time series' with more crop yield observations under different climatic conditions would be necessary to confirm the assumed quadratic productivity function for these crops. 385 Grain yield quality in terms of the nitrogen content of the grains is an additional important variable to characterize the quality of legume and cereal crops (Kemanian et al., 2007). The scatterplot of the nitrogen content in the yield compares results from the same soils in the dry and wet climate (Fig. 5h). The comparison showed no effect of climatic conditions or of the fertilization on the crop grain quality. Larger deviations from the 1:1 line were only visible for the soils from Dedelow and the crop pea under a dry climate and for soils from Bad Lauchstädt and crop winter rye under a wet climate (Fig. 5h). Nuttall et al. (2017) remarked that heat stress during the time of flowering and higher temperatures during the post-anthesis period of crops impact grain-size and milling yield. The impact of rising temperatures and increasing CO 2 concentrations in the atmosphere on yield quality could affect the nutritional quality and end-use value (Asseng et al., 2019). The grain yield quality was reported to be influenced mainly by genetics, crop management, and environmental conditions (Nuttall et al., 2017). Since in the present study, the crop management was similar and the same cultivars were used, the altered climatic 395 conditions seemed not to affect the quality of the yield in our crossed soil-climate experiment.

Conclusion
Lysimeter data from a German-wide lysimeter network (TERENO-SOILCan), where intact soil monoliths were moved to 405 sites with contrasting climatic conditions, were used to analyse effects of soil and climate on agricultural ecosystems in a soil-climate crossed factorial design. In the wet climate, there was a net drainage which was larger for the coarser-than for the finer-textured soils. In the dry climate, a small negative net drainage (upward flux) was obtained when observing the long-term average for the whole period 2011-2017. In the wet climate, drainage dominated for all soils. When looking at shorter periods, negative values of monthly net fluxes observed during the summer months at both sites. 410 During winter months, the soil water storage (SWS) returned to a typical value and drainage occurred when this value was reached. In the dry climate, this critical SWS was not reached in two soils after the growing season of 2015 in which the SWS was strongly depleted. The resulting insufficient refilling of the soil water storage capacity after a drought suggests that the precipitation during the following winter months was not sufficient to refill the soil so that no drainage took place. This ecosystem water balances and crop development should consider the long lasting impact of droughts on the soil water balance and soil water fluxes that are carried over to following years. Results indicate that direct observation on SWS will become increasingly important in environmental climate change studies, where changing climatic conditions could affect the SWSC. Longer term monitoring data are needed to observe effects of impacts on soil properties. 420 Crops were more productive in terms of grain yield and used less water under drier climatic conditions. Plant development and a higher crop water use efficiency demonstrated that less plant available soil water did not go along with a decline of grain yield, because plants used the available soil water resources under such conditions more efficiently (e.g. by reduced soil evaporation). Results revealed in contrast to our hypothesis of a linear productivity function for some crops a quadratic productivity function and thus showed that plants can maximize their grain yield under an intermediate ET range in rainfed 425 agriculture. However, longer time series are necessary to confirm the latter hypothesis of a quadratic productivity function of the corresponding crop. Our results suggest that despite the higher grain yield (quantity) climatic conditions seemed not to affect the quality of the yield, which might reflect a positive effect of the regional drier climatic conditions for crop production. The results of this study so far confirmed that typical soil water balance components, crop water use and especially the soil water storage dynamics undergo a substantial change when exposed to different climatic conditions. 430 We could show that: 1) The result further suggests that a new approach based on lysimeter mass data can enable the long-term monitoring of SWS changes at the pedon scale.
2) SWS dynamics were vulnerable to droughts and led to an insufficient refilling of the soil water storage capacity. The results herald the need to account for potential changes in soil water storage and plant reactions due to changes in climatic conditions and variability when trying to develop adaptation strategies in the agricultural sector. 440
Climate data for the experimental station Bad Lauchstädt can be acquired upon request from Ralf Gründling. The processed data to support the findings of this study can be acquired also from the TERENO data portal 445 (https://hdl.handle.net/20.500.11952/butt.metadata.handle/00000010).

Author contribution
TP conceived the experiments. JG, JV, HHG, and TP had the idea and designed the study. JG and RG provided the data for the corresponding lysimeter stations. JG performed the data analysis and wrote the manuscript with equal contributions from all co-authors. 450 Appendix data: