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LLM - Digital Humanities 16

ERROR: type should be string, got "https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. The degree and depth limitation of deep soil desiccation and its \nimpact on xylem hydraulic conductivity in dryland tree plantations \nNana He1, Xiaodong Gao2,3,4*, Dagang Guo1, Yabiao Wu1, Dong Ge1, Lianhao Zhao2, Lei Tian5, Xining \nZhao2,3,4 \n1College of Water Resources and Architectural Engineering, Northwest A&F University, 712100 Yangling, Shannxi Province, \n5 \nChina \n2Institute of Soil and Water Conservation, Chinese Academy of Science & Ministry of Water Resources, 712100, Yangling, \nShaanxi Province, China \n3Institute of Soil and Water Conservation, Northwest A&F University, 712100, Yangling, Shaanxi Province, China \n4National Engineering Research Center of Water Saving and Irrigation Technology at Yangling, 712100, Shaanxi Province, \n10 \nChina \n5Institute of Green Development Yellow River Drainage Basin, Lanzhou University, 730000, Lanzhou, Gansu Province, \nChina \nCorrespondence to: Xiaodong Gao (gao_xiaodong@nwafu.edu.cn) 1 \nIntroduction The degree and depth limitation of deep soil desiccation and its \nimpact on xylem hydraulic conductivity in dryland tree plantations \nNana He1, Xiaodong Gao2,3,4*, Dagang Guo1, Yabiao Wu1, Dong Ge1, Lianhao Zhao2, Lei Tian5, Xining \nZhao2,3,4 na He1, Xiaodong Gao2,3,4*, Dagang Guo1, Yabiao Wu1, Dong Ge1, Lianhao Zhao2, Lei Tian5, Xining \nao2,3,4 1College of Water Resources and Architectural Engineering, Northwest A&F University, 712100 Yangling, Shannxi Province, \n5 \nChina \n2Institute of Soil and Water Conservation, Chinese Academy of Science & Ministry of Water Resources, 712100, Yangling, \nShaanxi Province, China \n3Institute of Soil and Water Conservation, Northwest A&F University, 712100, Yangling, Shaanxi Province, China \n4National Engineering Research Center of Water Saving and Irrigation Technology at Yangling, 712100, Shaanxi Province, \n10 \nChina \n5Institute of Green Development Yellow River Drainage Basin, Lanzhou University, 730000, Lanzhou, Gansu Province, \nChina \nCorrespondence to: Xiaodong Gao (gao_xiaodong@nwafu.edu.cn) Abstract. In water-limited areas, planted trees can abstract substantial amounts of soil water from deep layers (>200 cm) to \n15 \nmeet their high water demand, resulting in deep soil desiccation, which influences not only regional water cycling but also \nthe sustainability of trees per se in drylands. However, the limitation in relation to both degree and depth of deep-layer soil \nmoisture (DSM) beyond which root water uptake ceases remains unclear. Whether limitation depends on tree species and \nhow it will affect tree’s xylem hydraulic conductivity are also unclear, restricting our ability to predict the fate of dryland tree Abstract. In water-limited areas, planted trees can abstract substantial amounts of soil water from deep layers (>200 cm) to \n15 \nmeet their high water demand, resulting in deep soil desiccation, which influences not only regional water cycling but also \nthe sustainability of trees per se in drylands. However, the limitation in relation to both degree and depth of deep-layer soil \nmoisture (DSM) beyond which root water uptake ceases remains unclear. Whether limitation depends on tree species and \nhow it will affect tree’s xylem hydraulic conductivity are also unclear, restricting our ability to predict the fate of dryland tree \nplantations. We, therefore, studied the spatiotemporal distribution of deep soil moisture deficit (DSMD, the relative \n20 \ndifference in soil moisture for given trees and referenced cropland or grassland, to minimize the effect of climate differences \nin various sampling years) for two typical planted trees, apple (Malus pumila Mill.) and black locust (Robinia pseudoacacia \nL.), based on the published data and multiple field samplings on China’s Loess Plateau. The results indicated that the lowest \nDSM occurred under the planted trees aged 24-28 years at all sites. The degree and depth limitation of deep soil desiccation and its \nimpact on xylem hydraulic conductivity in dryland tree plantations \nNana He1, Xiaodong Gao2,3,4*, Dagang Guo1, Yabiao Wu1, Dong Ge1, Lianhao Zhao2, Lei Tian5, Xining \nZhao2,3,4 The lowest DSMD fluctuated around -0.6, which close to \nthe DSMD at the permanent wilting point (PWP) irrespective of tree species and site, although shallow (<200 cm) soil \n25 \nmoisture was not reduced to the point of limitation. This suggests that PWP is a good indicator of the degree limitation of \nDSM for the tree species examined. The corresponding maximum depth of soil moisture use reached 18.0-22.0 m for these \nold planted trees at different sites while it was more than 25 m for black locust in the drier site of Mizhi. Furthermore, when \nthe degree and depth limitations were reached, the percent loss of xylem hydraulic conductivity of planted tree’s branches \nreached 74.9-96.5%, indicating that tree mortality may occur. The findings will help to predict the sustainability of planted \n30 \ntrees in semiarid regions with thick vadose zone. 1 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Worldwide, tree planting is extensively advocated as a nature-based solution (NbS) to facilitate the realization of Sustainable \nDevelopment Goals (Griscom et al., 2020). China’s Loess Plateau (LOP), the greatest loess deposits in the world with a \nthickness of 50-200 m loess (Shao et al., 2018), has been one of the most active regions for tree planting during the last four \n35 \ndecades, with the aim of controlling severe soil erosion and improving rural lives there (Fu et al., 2016). Many trees of a \nvariety of species (mostly exotic) have been planted during implementation of two large ecological projects: the Three-North \nShelter Forest Program and the Grain-For-Green Program (Gao et al., 2021). So far, the area of tree plantations on the LOP \nhas reached 7.47 million ha; the vegetation cover for this region increased from 31.6% in 1999 to 65.0% in 2017 (official \ndata from the National Forestry and Grassland Administration, http://www.forestry.gov.cn). As a result, a number of \n40 \necosystem services, including soil and water conservation, have been greatly improved on the LOP (Fu et al., 2016; Gao et \nal., 2021). In fact, the success of ecological restoration through tree planting on the LOP greatly depends on the presence of \nan abundant “soil reservoir” deposited in deep loess deposits (200-2000 cm) (Zhu, 2006). The degree and depth limitation of deep soil desiccation and its \nimpact on xylem hydraulic conductivity in dryland tree plantations \nNana He1, Xiaodong Gao2,3,4*, Dagang Guo1, Yabiao Wu1, Dong Ge1, Lianhao Zhao2, Lei Tian5, Xining \nZhao2,3,4 They found that the canopy transpiration and the net photosynthetic \n70 \nrate of apple trees was reduced, respectively, by 36% and 20% on average compared with the trees with deep roots in a \nsemiarid site on the LOP. Moreover, embolism resistance increases with a decrease in soil water availability, leading to a \nreduction in xylem hydraulic conductivity and even hydraulic failure of the plantation (Liu et al., 2020; Fuchs et al., 2021). However, it remains unclear on the effect of deep-layer soil desiccation on xylem hydraulic conductivity of planted trees \nwhen degree limitation and depth limitation of DSM are reached. 75 \nTo address the gaps, we built a dataset, including 11980 observations of 380 soil profiles from 34 peer-reviewed publications \nand 4200 observations of 72 soil profiles in-situ sampling across the LOP. The main objectives of this study were (i) to \nidentify the degree and depth limitation of DSM for different tree species and the influencing factors, and (ii) to disentangle \ntheir impact on xylem hydraulic conductivity of planted trees in different climatic zones on the LOP. Our hypotheses were \nthat the degree and depth limitation of deep-layer soil desiccation depends on tree species because of their different \n80 \ndrought-resistance capacity; and that the xylem hydraulic conductivity of planted trees is greatly reduced when degree and \ndepth limitation are reached due to low soil water availability in deep layers. The occurrence of soil desiccation in deep layers can greatly affect the growth and eco-physiological traits of the \naboveground parts of planted trees. It has been widely observed that the aboveground growth of planted trees is greatly \n65 \nconstrained due to deep soil drying, resulting in so-called “dwarf aged trees” on the LOP (Fang et al., 2016). Recently, Li et \nal. (2021) found that when deep soil below 200 cm is dry, the canopy transpiration of apple trees with a rooting depth beyond \n2000 cm is reduced by around 40% relative to that without deep soil desiccation on the LOP. Furthermore, Yang et al. (2022) \nconducted a deep-soil-and-root-partitioning experiment to quantify the effect of deep-layer (>200 cm) root uptake on the \ntranspiration and physiological features of apple tree. They found that the canopy transpiration and the net photosynthetic \n70 \nrate of apple trees was reduced, respectively, by 36% and 20% on average compared with the trees with deep roots in a \nsemiarid site on the LOP. The degree and depth limitation of deep soil desiccation and its \nimpact on xylem hydraulic conductivity in dryland tree plantations \nNana He1, Xiaodong Gao2,3,4*, Dagang Guo1, Yabiao Wu1, Dong Ge1, Lianhao Zhao2, Lei Tian5, Xining \nZhao2,3,4 Moreover, embolism resistance increases with a decrease in soil water availability, leading to a \nreduction in xylem hydraulic conductivity and even hydraulic failure of the plantation (Liu et al., 2020; Fuchs et al., 2021). However, it remains unclear on the effect of deep-layer soil desiccation on xylem hydraulic conductivity of planted trees \nwhen degree limitation and depth limitation of DSM are reached. 75 To address the gaps, we built a dataset, including 11980 observations of 380 soil profiles from 34 peer-reviewed publications \nand 4200 observations of 72 soil profiles in-situ sampling across the LOP. The main objectives of this study were (i) to \nidentify the degree and depth limitation of DSM for different tree species and the influencing factors, and (ii) to disentangle \ntheir impact on xylem hydraulic conductivity of planted trees in different climatic zones on the LOP. Our hypotheses were \nthat the degree and depth limitation of deep-layer soil desiccation depends on tree species because of their different \n80 \ndrought-resistance capacity; and that the xylem hydraulic conductivity of planted trees is greatly reduced when degree and \ndepth limitation are reached due to low soil water availability in deep layers. To address the gaps, we built a dataset, including 11980 observations of 380 soil profiles from 34 peer-reviewed publications \nand 4200 observations of 72 soil profiles in-situ sampling across the LOP. The main objectives of this study were (i) to \nidentify the degree and depth limitation of DSM for different tree species and the influencing factors, and (ii) to disentangle \ntheir impact on xylem hydraulic conductivity of planted trees in different climatic zones on the LOP. Our hypotheses were \nthat the degree and depth limitation of deep-layer soil desiccation depends on tree species because of their different \n80 \ndrought-resistance capacity; and that the xylem hydraulic conductivity of planted trees is greatly reduced when degree and \ndepth limitation are reached due to low soil water availability in deep layers. The degree and depth limitation of deep soil desiccation and its \nimpact on xylem hydraulic conductivity in dryland tree plantations \nNana He1, Xiaodong Gao2,3,4*, Dagang Guo1, Yabiao Wu1, Dong Ge1, Lianhao Zhao2, Lei Tian5, Xining \nZhao2,3,4 However, inappropriate tree planting can incur other ecological issues, such as soil desiccation due to excessive water \nconsumption in drylands (Wang et al., 2010; Jia et al., 2017; Wu et al., 2021), preventing the sustainable development of \n45 \nrevegetation and may even result in tree mortality (Breshears et al., 2013; Choat et al., 2018; Stovall et al., 2019). Considerable researches have shown that planted trees abstract substantial deep-layer soil moisture (DSM) by developing \ndeep roots to maintain its normal growth on the LOP (e.g., Gao et al., 2018; Su and Shangguan, 2019; Shi et al., 2021). However, continuous use of DSM over a number of years leads to severe soil desiccation (Wang et al., 2011; Gao et al., 2018; Tao et al., 2021), because DSM below 200 cm is hardly recharged by precipitation under tree plantations. In fact, deep-layer \n50 \nsoil desiccation under tree plantations can occur at multiple degrees depending on tree age and local climate (Fang et al., \n2016; Jia et al., 2017). Although soil desiccation of different degrees has been widely reported in the above citations, \nknowledge remains restricted about variation in the lowest amount of soil moisture that limits growth (hereafter “degree \nlimitation”) and/or the maximum depth from which trees are able to extract water (hereafter “depth limitation”) for different 2 2 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. The occurrence of soil desiccation in deep layers can greatly affect the growth and eco-physiological traits of the \naboveground parts of planted trees. It has been widely observed that the aboveground growth of planted trees is greatly \n65 \nconstrained due to deep soil drying, resulting in so-called “dwarf aged trees” on the LOP (Fang et al., 2016). Recently, Li et \nal. (2021) found that when deep soil below 200 cm is dry, the canopy transpiration of apple trees with a rooting depth beyond \n2000 cm is reduced by around 40% relative to that without deep soil desiccation on the LOP. Furthermore, Yang et al. (2022) \nconducted a deep-soil-and-root-partitioning experiment to quantify the effect of deep-layer (>200 cm) root uptake on the \ntranspiration and physiological features of apple tree. 2.1.1 Data sources \n85 When analyzing the degree limitation of DSM under apple tree i\n110 \nLuochuan, the extracted data were divided into tree age range for analysis to avoid random errors. Simultan\ncollected basic data from each publication, including data source, geographic coordinates (longitude, latitude\nprecipitation (MAP), tree species, and soil texture. Since there was relatively little published information on\nlocust forests, we also performed supplementary experiments to obtain relevant data, as described in Section 2\nhttps://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. (2) Soil moisture was the gravimetric or volumetric moisture content measured by drying methods or moisture sensors. The volumetric moisture content was uniformly converted to gravimetric moisture content by means of the \naverage soil bulk density in each study area. (3) Experiments with only shrub and grass communities or experiments with irrigation, mulching, pruning and/or \nother measures were excluded. (4) Experiments yielded paired data for DSM in apple orchards or black locust forests and that in farmland or \ngrassland (control). (5) L\ni\nl\ni i\ni\n(\n) f\nd\ni\nl\nl\nd (4) Experiments yielded paired data for DSM in apple orchards or black locust forests and that in farmland or \ngrassland (control). (5) Location, mean annual precipitation (mm), forest age, and vegetation types were clearly presented. 2.1.1 Data sources \n85 Peer-reviewed research papers, published between 1999 and 2021 were extracted from the Web of Science (United States) \nand CNKI (China Knowledge Resource Integrated Database, China; 2000-2021) using the search terms (“Loess plateau” OR \n“Loess hilly region”) AND (“artificial forest” OR “afforestation” OR “plantation”) AND (“soil moisture” OR “soil water \ncontent” OR “soil water deficit”). To avoid publication bias, the following criteria were set to filtrate 718 articles obtained: (1) Soil profiles for moisture extraction under deep-rooted artificial forests must exceed a depth of 500 cm at dense \n90 \nsampling intervals (≤100 cm). The reason for this criteria is that 0-200 cm soil layer is greatly affected by rainfall, \nwhile the phenomenon of 200-500 cm dry soil layer is common (Jia et al., 2020), thus, data from a sampling depth \ngreater than 500 cm were considered in this study in order to explore the state of DSM. 3 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. (2) Soil moisture was the gravimetric or volumetric moisture content measured by drying methods or m\nThe volumetric moisture content was uniformly converted to gravimetric moisture content by\n95 \naverage soil bulk density in each study area. (3) Experiments with only shrub and grass communities or experiments with irrigation, mulching, \nother measures were excluded. (4) Experiments yielded paired data for DSM in apple orchards or black locust forests and that \ngrassland (control). 100 \n(5) Location, mean annual precipitation (mm), forest age, and vegetation types were clearly presented. 2.1.2 Data extraction and classification \nBased on the above criteria, a total of 11980 observations from 380 soil profile, which covered 83 sites in\nmunicipalities (Fig. 1) and extracted from 34 peer-reviewed publications were retrieved, and the specific\nshown in Table 1. The original data were either clearly obtained from tables in the selected papers or intui\n105 \nfrom figures in those papers using GetData Graph Digitizer (version 2.24, http://getdata-graph-digitizer.com\nsoil layers were divided into 200 cm intervals, when more than one soil moisture data sample was obtained\n(e.g., with an interval of 20 cm or 10 cm), we averaged the values of all soil layers to convert them into a 2\nSoil moisture data for the same stand age collected in different sampling years were treated independently \nthe influence of forest age on soil moisture. 2.1.2 Data extraction and classification Based on the above criteria, a total of 11980 observations from 380 soil profile, which covered 83 sites in 4 provinces or \nmunicipalities (Fig. 1) and extracted from 34 peer-reviewed publications were retrieved, and the specific information is \nshown in Table 1. The original data were either clearly obtained from tables in the selected papers or intuitively extracted \n105 \nfrom figures in those papers using GetData Graph Digitizer (version 2.24, http://getdata-graph-digitizer.com). All sampled \nsoil layers were divided into 200 cm intervals, when more than one soil moisture data sample was obtained within 200 cm \n(e.g., with an interval of 20 cm or 10 cm), we averaged the values of all soil layers to convert them into a 200 cm interval. Soil moisture data for the same stand age collected in different sampling years were treated independently when analyzing \nthe influence of forest age on soil moisture. When analyzing the degree limitation of DSM under apple tree in Changwu and \n110 \nLuochuan, the extracted data were divided into tree age range for analysis to avoid random errors. Simultaneously, we also \ncollected basic data from each publication, including data source, geographic coordinates (longitude, latitude), mean annual \nprecipitation (MAP), tree species, and soil texture. Since there was relatively little published information on DSM in black \nlocust forests, we also performed supplementary experiments to obtain relevant data, as described in Section 2.2. 4 \n \nFigure 1: Distribution map of sampling sites on the Loess Plateau. The black dots represent the sampling sites extracted from the article, 115 4 \n \nFigure 1: Distribution map of sampling sites on the Loess Plateau. The black dots represent the sampling sites extracted from the art https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. and the red small crosses represent the sampling sites supplemented in the field. Table 1. Sample information used in this study. 2.1.2 Data extraction and classification Sites \nSpecies \nNEP (pair) \nNSP (pair) \nMAP(mm) \n2021-P (mm) \nClimate type zone \nLuochuan \nApple \n24 \n10 \n608 \n957 \nSemi-humid \nFufeng \nApple \n1 \n- \n606 \n- \nSemi-humid \nBlack locust \n4 \n- \n- \nYongshou \nBlack locust \n4 \n- \n600 \n- \nSemi-humid \nChangwu \nApple \n44 \n8 \n578 \n910 \nSemi-humid \nBlack locust \n6 \n10 \n \nBaishui \nApple \n2 \n- \n577 \n- \nSemi-humid \nJingchuan \nApple \n1 \n- \n571 \n- \nSemi-humid \nQingcheng \nApple \n6 \n- \n561 \n- \nSemi-humid \nYan’an \nApple \n7 \n- \n530 \n- \nSemi-humid \nBlack locust \n15 \n10 \n704 \nDaning \nApple \n2 \n- \n528 \n- \nSemi-humid \nZichang \nApple \n1 \n- \n480 \n- \nSemi-arid \nBlack locust \n1 \n- \n- \nZizhou \nApple \n1 \n- \n424 \n- \nSemi-arid \nMizhi \nApple \n9 \n10 \n421 \n441 \nSemi-arid \nBlack locust \n1 \n10 \n458 \nTotal \n- \n129 \n58 \n- \n- \n- Note: NEP and NSP indicate the number of data pairs from the literature and field sampling, respectively. A data pair refers to the moisture \ncontent of a soil profile from plantation and that of a soil profile from cropland/grassland. MAP is the mean annual precipitation; 250 \nmm<MAP<500 mm and 500 mm<MAP<800 mm are classified as the semi-arid and semi-humid climate zone, respectively (Wang et al., \n2011). The blue text indicates the supplementary sampling in this study with reference to the existing data, and these are representative \nplots for the different climatic regions upon which we focused. 2.2.1 Study area and sampling sites \n125 The LOP covers a total area of 640,000 km2, with an elevation range of 200-3000 m. It belongs to the continental monsoon \nregion where annual precipitation ranges from 150 mm in the northwest to 800 mm in the southeast, 55-78% of which falls \nfrom June to September (Wang et al., 2011). The location of sites from which we collected field data across the entire LOP \n(34°43′-41°16′ N, 100°54′-114°33′ E) are indicated by the red small crosses in Fig. 1 and the blue text in Table 1. 5 \n(34 43 -41 16 N, 100 54 -114 33 E) are indicated by the red small crosses in Fig. 1 and the blue text in Table 1. Geographic coordinates (altitude, longitude, and latitude) for each site were obtained using a GPS (Zhuolin A8, with an \n130 \naccuracy of 1 m). 2.2.2 Soil moisture content \nThe sampling events for soil moisture was done from April 9 to May 6 2022 (the early growing season of trees on the LOP) \nunder the plantations of apple tree and black locust at the ages of 20-30 years old as well as under the crop/grassland as the \ncontrol due to the intact DSM below 200 cm. At first, the sampling depth was set as 0-10 m under different tree plantations; \n135 Geographic coordinates (altitude, longitude, and latitude) for each site were obtained using a GPS (Zhuolin A8, with an \n130 \naccuracy of 1 m). 2.2.2 Soil moisture content The information of the tree plantations reaching the degree limitation was indicated in \nTable 2. The maximum sampling depth was set as 25 m because previous studies reported the maximum rooting depth less \nthan 25 m on the LOP (Wang et al., 2009; Li et al., 2019). In order to minimize the spatial variability in soil moisture caused \nby soil properties, the straight-line distance (SD) between the planted trees and their control was controlled at less than 500 \n140 \nm, and the average SD of the three replicates was shown in Table 2. All soil samples were collected by a hand auger (Φ=6 \ncm) at depth interval of 20 cm and 1.5 m horizontal to the trunk of trees; and three trees with similar growth status were \nselected for each age as repeats. Soil samples without roots was put into an aluminum specimen box and used to measure the \ngravimetric moisture content (g g-1), which was determined by drying method at 105℃ for constant weight. A total of 4200 \nsoil moisture data were collected at 72 soil profiles. Three repeats of soil moisture data under one given tree plantation were \n145 \naveraged when analyzing the degree and depth limitation of DSM under plantations. Moreover, the soil sampling under all \ntree plantations was done once because the temporal variation of DSM below 200 cm under tree plantations was negligible \nin one growing season (Figure S2; Gao et al., 2018; Li et al., 2019). Table 2. Basic information of the sampling trees that causes the lowest deep soil moisture deficit (DSMD). 2.2.2 Soil moisture content Sites \nPlant species \nLongitude \nLatitude \nElevation \n(m) \nSD \n(m) \nStand age \n(yr) \nPH \n(m) \nBD \n(cm) \nDBH \n(cm) \nCD \n(m) \nMizhi \nApple \n110°10′22″ \n37°52′13″ \n1135.1 \n430 \n27 \n3.5±0.25 \n5.8±0.53 \n4.9±0.21 5.73±0.35 \nCropland \n110°11′09″ \n37°52′21″ \n1139.6 \n- \n- \n- \n- \n- \nBlack locust \n110°13′58″ \n37°40′08″ \n1086.2 \n433 \n24 \n9.3±0.40 8.9±0.43 \n7.3±0.17 5.65±0.75 \nGrassland \n110°13′46″ \n37°40′16″ \n1076.4 \n- \n- \n- \n- \n- \nLuochuan \nApple \n109°21′59″ \n35°46′44″ \n1086.9 \n182 \n28 \n2.85±0.35 8.8±0.82 \n8.1±0.52 4.51±16.7 \nCropland \n109°21′58″ \n35°46′38″ \n1083.9 \n- \n- \n- \n- \n- \nYan’an \nBlack locust \n109°23′22″ \n36°40′02″ \n1325.1 \n412 \n25 \n14.35±0.25 9.3±0.19 \n7.9±0.86 5.81±0.77 \nGrassland \n109°22′29″ \n36°40′31″ \n1333.2 \n- \n- \n- \n- \n- \nChangwu \nApple \n107°41′01″ \n35°14′12″ \n1231.7 \n390 \n24 \n4.42±0.25 6.1±0.29 \n5.4±0.73 4.77±0.13 \nCropland \n107°40′43″ \n35°14′20″ \n1232.6 \n- \n- \n- \n- \n- \nBlack locust \n107°41′45″ \n35°12′50″ \n1215.2 \n212 \n28 \n13.35±0.25 9.4±0.66 \n8.1±0.51 5.96±0.01 \nGrassland \n107°42′12″ \n35°12′36″ \n1219.4 \n- \n- \n- \n- \n- \nNote: A±B, A represents the average of the three replicates of each parameter, and B represents the standard deviation. CD-Average crown \n150 \ndiameter; PH-Average plant height; BD-Average basal diameter; DBH-Average diameter at breast height; SD-Average of straight-line \ndistance between three trees and their control (cropland or grassland). Table 2. Basic information of the sampling trees that causes the lowest deep soil moisture deficit (DSMD). 2.2.2 Soil moisture content 5 \nThe sampling events for soil moisture was done from April 9 to May 6 2022 (the early growing season of trees on the LOP) \nunder the plantations of apple tree and black locust at the ages of 20-30 years old as well as under the crop/grassland as the \ncontrol due to the intact DSM below 200 cm. At first, the sampling depth was set as 0-10 m under different tree plantations; \n135 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. however, it was extended down to 25 m when the DSM reached the degree limitation under one given aged plantation as \nwell as under the paired control plots. The information of the tree plantations reaching the degree limitation was indicated in \nTable 2. The maximum sampling depth was set as 25 m because previous studies reported the maximum rooting depth less \nthan 25 m on the LOP (Wang et al., 2009; Li et al., 2019). In order to minimize the spatial variability in soil moisture caused \nby soil properties, the straight-line distance (SD) between the planted trees and their control was controlled at less than 500 \n140 \nm, and the average SD of the three replicates was shown in Table 2. All soil samples were collected by a hand auger (Φ=6 \ncm) at depth interval of 20 cm and 1.5 m horizontal to the trunk of trees; and three trees with similar growth status were \nselected for each age as repeats. Soil samples without roots was put into an aluminum specimen box and used to measure the \ngravimetric moisture content (g g-1), which was determined by drying method at 105℃ for constant weight. A total of 4200 \nsoil moisture data were collected at 72 soil profiles. Three repeats of soil moisture data under one given tree plantation were \n145 \naveraged when analyzing the degree and depth limitation of DSM under plantations. Moreover, the soil sampling under all \ntree plantations was done once because the temporal variation of DSM below 200 cm under tree plantations was negligible \nin one growing season (Figure S2; Gao et al., 2018; Li et al., 2019). however, it was extended down to 25 m when the DSM reached the degree limitation under one given aged plantation as \nwell as under the paired control plots. 2.2.3 Percentage loss of hydraulic conductivity While collecting soil moisture, six current-year branches with 18-20 cm in length and 2-5 mm in diameter were cut off in the \nmorning from apple and black locust trees, age of which reached the degree limitation of DSM; and three trees with similar \n155 \ngrowth status were selected as repeats for each site. Sampled branches were sprayed with water and enclosed in humidified \nblack plastic bags before excising to minimize water loss. After brought these branches to laboratory, we quickly cut 3-5 cm While collecting soil moisture, six current-year branches with 18-20 cm in length and 2-5 mm in diameter were cut off in the \nmorning from apple and black locust trees, age of which reached the degree limitation of DSM; and three trees with similar \n155 \ngrowth status were selected as repeats for each site. Sampled branches were sprayed with water and enclosed in humidified \nblack plastic bags before excising to minimize water loss. After brought these branches to laboratory, we quickly cut 3-5 cm 6 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. from the middle of the branch in water and immediately measured xylem hydraulic conductivity of it by using the xylem \nembolism measurement system (Xylem embolism meter, Bronkhorst, Mon-tigny-les-Cormeilles, France). To prevent water \nspilt from the branch, the initial xylem hydraulic conductivity (Kini, kg s-1 MPa-1) was measured with 1 mmol·L-1 CaCl2 \n160 \nsolution (pH = 6) through a filter with a filter aperture of 0.22 μm at a low pressure of 0.5-1.0 kPa. And at a high pressure of \n0.2 MPa, the branch was flushed with 1 mmol·L-1 CaCl2 solution until there was no air bubble, then the xylem hydraulic \nconductivity was determined again at a low pressure of 0.5-1.0 kPa and the flushing was repeated until attaining a maximum \nconductivity (Kmax, kg s-1 MPa-1). The percentage loss of hydraulic conductivity (PLC, %) was calculated by, (1) \nmax\n1\n100\nini\nK\nPLC\nK\n\n\n\n\n\n\n\n\n\n \n165 max\n1\n100\nini\nK\nPLC\nK\n\n\n\n\n\n\n\n\n\n \n5 (1) 165 2.2.4 Basic parameters of plantation 2.2.4 Basic parameters of plantation In order to accurately target the sample plantation, tree age was determined on the basis of plant growth cones (Haglof, \nCO300-52, Sweden) before sampling, and the crown diameter, basal diameter and diameter at breast height were measured \nwith a tape (Table 2). The collection of plantation root samples was carried out simultaneously with the collection of soil \nmoisture samples, and soil samples with root was stored in a sealed sample bag to minimize the loss of fine roots (≤ 2 mm), and \n170 \nthen gently rinsed in a 100 mesh (0.15 mm) sieve, removing residual roots from the sieve with tweezers. Each fine root sample \nwas dried at 65℃ to constant weight and weighed using an electronic balance with a precision of 0.0001 g to determine root \ndry weight. Finally, the fine root dry weight density (FRDWD) was calculated using the formula given by Zhao et al. (2022). 2.2.5 Soil texture and permanent wilting point The sampling of soil properties was done simultaneously as soil moisture was sampled, and the data was showed in Table 3. 175 \nSoil samples without root were air-dried, ground, and then passed through a 16 mesh sieve for measuring soil particle \ncomposition (i.e., clay, silt and sand contents) and soil pH, and through a 100 mesh sieve for determining soil organic carbon \n(SOC), respectively. Soil particle composition was determined by laser diffraction using a Mastersizer-2000 Laser \nGranularity Analyzer (Malvern Instruments, Malvern, England) (Wang et al., 2008). Soil pH was determined by the DELTA The sampling of soil properties was done simultaneously as soil moisture was sampled, and the data was showed in Table 3. 175 \nSoil samples without root were air-dried, ground, and then passed through a 16 mesh sieve for measuring soil particle \ncomposition (i.e., clay, silt and sand contents) and soil pH, and through a 100 mesh sieve for determining soil organic carbon \n(SOC), respectively. Soil particle composition was determined by laser diffraction using a Mastersizer-2000 Laser \nGranularity Analyzer (Malvern Instruments, Malvern, England) (Wang et al., 2008). Soil pH was determined by the DELTA \n320 pH meter. SOC was measured by using the potassium dichromate volumetric method (Fu et al., 2010), and soil organic \n180 \nmatter (SOM) was converted from SOC (Eq. 4). 2.2.4 Basic parameters of plantation (3) \n(4) \n\n\n10\n10\n1\n1\n10\n2\n46.481\n4.757\n14.028 log (\n) 13.991 log (\n)\n \n42.261 log\n11.763\n19.198\n \n5.448\n0.044\n1.975\ni\ni\ni\ni\ni\ni\ni\ni\ni\ni\ni\nFC\nSOC\nclay\nsand\nSOC\nsand\nSOC\nBD\nSOC\nBD\nSOC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n5 \n1.724\ni\ni\nSOM\nSOC\n\n (3\n\n\n10\n2\ng\n \n5.448\n0.044\n1.975\ni\ni\ni\ni\ni\ni\ni\nBD\nSOC\nBD\nSOC\n\n\n\n\n\n\n (5) \n0.5\n10\n0.5\n2\n1.8284\n0.0429 log (\n)\n0.0205\n0.0125\n \ncos(\n)\n0.0061\n0.0001\n0.0098\n \n0.0071\n0.0505\n0.0002\ni\ni\ni\ni\ni\ni\ni\ni\ni\nBD\nclay\nclay\nclay\nsilt\nsilt\nSG\nSG\nSOC\nSOC\nSOC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n (5) \n0.5\n10\n0.5\n2\n1.8284\n0.0429 log (\n)\n0.0205\n0.0125\n \ncos(\n)\n0.0061\n0.0001\n0.0098\n \n0.0071\n0.0505\n0.0002\ni\ni\ni\ni\ni\ni\ni\ni\ni\nBD\nclay\nclay\nclay\nsilt\nsilt\nSG\nSG\nSOC\nSOC\nSOC\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n (5) where FC is field capacity, %; SOC and SOM are soil carbon content and soil organic matter, g kg-1; BD is the soil bulk \ndensity, g cm-3; clay, silt and sand are clay, silt and sand content, %, respectively; SG is slope gradient at each sample \nlocation, °; and i represents the ith soil depth. 190 Table 3. Basic soil properties of sampling sites. 2.2.6 Meteorological parameters The precipitation, air temperature, and relative humidity of different regions were from the European Centre for \nMedium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis (hereafter ERA5-Land) data, and the accuracy of \nwhich has been verified by Araújo et al. (2022). Based on the above meteorological data, vapor pressure deficit (VPD, kPa) \nwas calculated by using Eq.6. The precipitation, air temperature, and relative humidity of different regions were from the European Centre for (6) \nThe precipitation, air temperature, and relative humidity of different regions were from the European Centre for \nMedium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis (hereafter ERA5-Land) data, and the accuracy of \nwhich has been verified by Araújo et al. (2022). Based on the above meteorological data, vapor pressure deficit (VPD, kPa) \n195 \nwas calculated by using Eq.6. \n\n17.27\n237.3\n0.61078\n1\na\na\nT\nT\nVPD\ne\nRH\n\n\n\n\n\n＋\n \nWhere T is air temperature ℃; and RH is relative humidity % The precipitation, air temperature, and relative humidity of different regions were from the European Centre for \nMedium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis (hereafter ERA5-Land) data, and the accuracy of \nwhich has been verified by Araújo et al. (2022). Based on the above meteorological data, vapor pressure deficit (VPD, kPa) \n5 \nwas calculated by using Eq.6. 195 (6) \n\n\n17.27\n237.3\n0.61078\n1\na\na\nT\nT\nVPD\ne\nRH\n\n\n\n\n\n＋ (6) \n\n\n17.27\n237.3\n0.61078\n1\na\na\nT\nT\nVPD\ne\nRH\n\n\n\n\n\n＋ (6) Where Ta is air temperature, ℃; and RH is relative humidity, %. Where Ta is air temperature, ℃; and RH is relative humidity, %. 2.2.4 Basic parameters of plantation Sites \nPlant species \nSoil type \nClay \nSilt \nSand \nSoil organic metter (g kg-1) \nBulk density (g cm-3) \npH \nMizhi \nApple \nCultivated loessial soils \n15.5 \n24.3 \n60.2 \n2.94 \n1.28 \n7.64 \nCropland \nCultivated loessial soils \n14.9 \n25.1 \n60.0 \n2.33 \n1.29 \n8.44 \nBlack locust Cultivated loessial soils \n13.6 \n25.0 \n61.4 \n3.09 \n1.28 \n8.03 \nGrassland \nCultivated loessial soils \n13.8 \n25.4 \n60.8 \n2.35 \n1.29 \n8.36 \nLuochuan \nApple \nDark loessial soils \n21.5 \n37.1 \n41.4 \n4.62 \n1.31 \n7.54 \nCropland \nDark loessial soils \n22.2 \n38.5 \n39.3 \n4.38 \n1.33 \n8.17 \nYan’an \nBlack locust Cultivated loessial soils \n18.1 \n33.1 \n48.8 \n4.87 \n1.29 \n8.07 \nGrassland \nCultivated loessial soils \n19.6 \n31.5 \n48.9 \n4.43 \n1.30 \n8.53 \nChangwu \nApple \nDark loessial soils \n24.6 \n38.7 \n36.7 \n4.76 \n1.31 \n7.35 \nCropland \nDark loessial soils \n25.8 \n38.7 \n35.5 \n4.35 \n1.32 \n8.21 \nBlack locust \nDark loessial soils \n22.9 \n38.2 \n38.9 \n5.01 \n1.29 \n7.96 \nGrassland \nDark loessial soils \n22.7 \n38.4 \n38.9 \n4.26 \n1.31 \n8.23 2.2.4 Basic parameters of plantation Due to the challenges of collecting undisturbed soil cores within the 10 m \nprofile, bulk density (BD) and the PWP of each region were estimated using the pedo-transfer functions (PTFs) developed by \nWang et al. (2012) and Balland et al. (2008) based on the above indicators, as follows, 320 pH meter. SOC was measured by using the potassium dichromate volumetric method (Fu et al., 2010), and soil organic \n180 \nmatter (SOM) was converted from SOC (Eq. 4). Due to the challenges of collecting undisturbed soil cores within the 10 m \nprofile, bulk density (BD) and the PWP of each region were estimated using the pedo-transfer functions (PTFs) developed by \nWang et al. (2012) and Balland et al. (2008) based on the above indicators, as follows, (2) https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 2.3 Data analysis 2.3.1 Meta-analysis \n200 2.3.1 Meta-analysis \n200 2.3.1 Meta-analysis \n200 In this study, the differences between soil moisture and RR was considered significant if the 95% CI did not include zero, and \nthe differences were not considered significant if the 95% CI did not include zero. 2.3.2 Quantitative index for degree limitation of soil desiccation \n215 2.3.2 Quantitative index for degree limitation of soil desiccation \n215 \nThe effect of different deep-rooted planted trees RWU on soil moisture was expressed as soil moisture deficit (SMD, the \ncalculation method as shown in Eq. (10)), which could remove the interference of precipitation differences in different \nsampling years. In order to compare the difference between the degree limitation of soil desiccation and the PWP, the SMD \ncorresponding to PWP along the soil profile was calculated by Eq. (11). The effect of different deep-rooted planted trees RWU on soil moisture was expressed as soil moisture deficit (SMD, the \ncalculation method as shown in Eq. (10)), which could remove the interference of precipitation differences in different \nsampling years. In order to compare the difference between the degree limitation of soil desiccation and the PWP, the SMD \ncorresponding to PWP along the soil profile was calculated by Eq. (11). ,\n0,\n,\n0,\n \nj k\nk\nj k\nk\nSMC\nSMC\nSMD\nSMC\n\n\n220 \n0,\n,\n0,\n \nk\nk\npwp k\nk\nPWP\nSMC\nSMD\nSMC\n\n (10) (11) where SMCj,k and SMC0,k represent soil moisture content in the kth layer of the jth plantations and in the kth layer of the \ncontrol (cropland or grassland), %, respectively; and PWPk represents the PWP in the kth layer, %. 2.3.3 Quantitative index for depth limitation of soil desiccation 2.3.1 Meta-analysis \n200 In a traditional meta-analysis, the mean values of the treatment and control with their standard deviations or standard errors 8 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. are extracted from the publications. However, most published papers selected for this study reported their mean values \nwithout standard deviations. To maximize the number of observations, the following unweighted meta-analysis approach \nwas adopted (Li et al., 2021b). (7) \n(\n/\n) \ne\nC\nRR\nLn X\nX\n\n \n205 (7)\n(\n/\n) \ne\nC\nRR\nLn X\nX\n (7) (7) where RR is a unit-free index with positive and negative values indicating either increasing or decreasing soil moisture in \nresponse to changes in planted tree age, and Xe and Xc are values of soil moisture under trees and controls for each study, \nrespectively. To test the significance of differences between soil moisture and RR, a 95% confidence interval (CI) was used. A previously described method (Deng et al., 2016) was used to calculate the 95% CI, as shown in Eqs. (8) and (9), (8) \n(9) \n \ns\ntotal\nV\nSE\nN\n\n \n210 \n95%\n1.96\n \ntotal\nCI\nSE\n\n\n \nWhere SEtotal is the standard error of RR, and Vs and N are the variances of RR and the number of observations, respectively. In this study, the differences between soil moisture and RR was considered significant if the 95% CI did not include zero, and \nthe differences were not considered significant if the 95% CI did not include zero. (8) \n(9) \n \ns\ntotal\nV\nSE\nN\n\n \n210 \n95%\n1.96\n \ntotal\nCI\nSE\n\n\n \nWhere SEtotal is the standard error of RR, and Vs and N are the variances of RR and the number of observations, respectively. In this study, the differences between soil moisture and RR was considered significant if the 95% CI did not include zero, and \nthe differences were not considered significant if the 95% CI did not include zero. s\ntotal\nV\nSE\nN\n\n210 (8) 95%\n1.96\n \ntotal\nCI\nSE\n\n (9) (9) Where SEtotal is the standard error of RR, and Vs and N are the variances of RR and the number of observations, respectively. 3.1 Soil moisture under planted trees of the different ages \n235 The soil moisture under apple orchards (APO) and black locust forest (BLF) varied greatly with tree age across the entire \nsoil layer (Fig. 2). With an increase in tree age, the negative effect of APO (0-21 m soil layer) and BLF (0-10 m soil layer) \ngrowth on deep soil moisture (DSM) increased first and then tended to become stable. Specifically, the negative effect value \nof DSM (4-10 m) under BLF older 30 years decreased by 17.4% compared with 20-30 years. Thus, the tree age at which the \nplantation had an extreme effect on DSM appears to be between 20 and 30 years. 240 Figure 2: Mean effect sizes of soil moisture response to planted trees’ growth with different age classes on the Loess Plateau: (a) tree age \n<10 year, (b) 10 year ≤ tree age<20 year, (c) 20 year ≤ tree age<30 year, and (d) tree age ≥ 30 year. The vertical black dotted lines indicate \nRR=0. Blue and red dots represent black locust forests and apple orchard RRs, respectively, diamonds and circles represent the mean RR of \nblack locust and apple orchard, respectively, and the error bars represent 95% CI. 245 Figure 2: Mean effect sizes of soil moisture response to planted trees’ growth with different age classes on the Loess Plateau: (a) tree age \n<10 year, (b) 10 year ≤ tree age<20 year, (c) 20 year ≤ tree age<30 year, and (d) tree age ≥ 30 year. The vertical black dotted lines indicate \nRR=0. Blue and red dots represent black locust forests and apple orchard RRs, respectively, diamonds and circles represent the mean RR of \nblack locust and apple orchard, respectively, and the error bars represent 95% CI. Figure 2: Mean effect sizes of soil moisture response to planted trees’ growth with different age classes on the Loess Plateau: (a) tree age \n<10 year, (b) 10 year ≤ tree age<20 year, (c) 20 year ≤ tree age<30 year, and (d) tree age ≥ 30 year. The vertical black dotted lines indicate \nRR=0. Blue and red dots represent black locust forests and apple orchard RRs, respectively, diamonds and circles represent the mean RR of \nblack locust and apple orchard, respectively, and the error bars represent 95% CI. 2.3.3 Quantitative index for depth limitation of soil desiccation The paired sample t-test in SPSS was used to analyze the significance of soil moisture between plantations and their controls. 225 \nWhen the significant difference becomes insignificant (p>0.05) as the soil layer deepens, which is considered that the DSM \nconsumption of the plantation has reached the depth limitation. However, the difference is always significant (p<0.05) 9 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. indicates that the maximum water uptake depth has not been reached. indicates that the maximum water uptake depth has not been reached. 2.3.4 Statistical analysis \nRedundancy analysis (RDA) was performed to evaluate the contributions of environmental factors to SMD and soil moisture \n230 \nusing Canoco 5. Least significant difference (LSD) post-hoc tests in SPSS 22.0 software package (SPSS 22.0, SPSS Institute \nLtd., USA) was used to analyze the significant differences in PLC of current-year branches in planted trees, α = 0.05 was \nconsidered statistically significant. Origin 2021 software (OriginLab Corporation, USA) was used to draw the figures. 3.2 Limitation of DSM for planted trees 10 \nThe DSM under a given tree species can vary largely among different years even if at the same age due to varying annual \nprecipitation. Taken the 15-year-old APO in Changwu for example, the DSM varied greatly in different sampling years (Fig. 3a). Soil moisture deficit (SMD), defined as the relative difference of soil moisture under given trees and nearby controls, https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. was used to eliminate the effect of climate differences in various sampling years. Here, the DSM of control adjacent to the \n250 \nplantation served as the background soil moisture, which is not influenced by root water extraction due to the shallow (<100 \ncm) roots of annual crops; hence the DSM in adjacent control can be used to reflect local annual precipitation. We found that \nvariation coefficient (CV) of deep soil moisture deficit (DSMD) (Fig. 3b) was lower than that of DSM, thus, DSMD was \nused for the following in-depth analyses. was used to eliminate the effect of climate differences in various sampling years. Here, the DSM of control adjacent to the \n250 \nplantation served as the background soil moisture, which is not influenced by root water extraction due to the shallow (<100 \ncm) roots of annual crops; hence the DSM in adjacent control can be used to reflect local annual precipitation. We found that \nvariation coefficient (CV) of deep soil moisture deficit (DSMD) (Fig. 3b) was lower than that of DSM, thus, DSMD was \nused for the following in-depth analyses. 250 255 \nFigure 3: Distribution of soil moisture (a) and soil moisture deficit (b) in 15-year-old apple orchards in Changwu in 2002, 2010, 2016 and \n2019. The variation coefficient (CV) of soil moisture and soil moisture deficit below 2 m are shown in the small picture, and the dashed \nlines in the figure represent the mean values of the CV of deep soil moisture and deep soil moisture deficit, respectively. 255 Figure 3: Distribution of soil moisture (a) and soil moisture deficit (b) in 15-year-old apple orchards in Changwu in 2002, 2010, 2016 and \n2019. The variation coefficient (CV) of soil moisture and soil moisture deficit below 2 m are shown in the small picture, and the dashed \nlines in the figure represent the mean values of the CV of deep soil moisture and deep soil moisture deficit, respectively. 3.2.1 Degree limitation of DSM In order to reduce the randomness of the extracted data, the data for apple trees with similar ages in Changwu and \nLuochuan were combined. The gray line indicates SMD at permanent wilting point (SMDPWP) in each sampling site, and the red dashed \n275 \nline indicates the SMD equal to -0.6. Figure 4: Soil moisture deficit (SMD) distribution under apple orchards of different ages in Changwu (a), Luochuan (b) and Mizhi (c); and \nSMD under black locust forests of different ages in Changwu (d), Yan’an (e) and Mizhi (f). The capital letters A and B in the legend \nrepresent apple and black locust, respectively, and the numbers after the letters represent tree age, for example, A-22 represents \n22-year-old apple. In order to reduce the randomness of the extracted data, the data for apple trees with similar ages in Changwu and \nLuochuan were combined. The gray line indicates SMD at permanent wilting point (SMDPWP) in each sampling site, and the red dashed \n275 \nline indicates the SMD equal to -0.6. 3.2.1 Degree limitation of DSM The DSMD fluctuation in APO over 19 years was relatively stable in Changwu, Luochuan and Mizhi (Fig. 4a, b, c). 260 \nInterestingly, with the increase in tree age, there was the lowest DSMD of APO in different climate zones, and tree age \nbetween 24 and 28 years was associated with the lowest DSMD in different regions. After reaching the lowest value, apple \ntrees can no longer extract water from the deep soil. Notably, the lowest value of DSMD in three regions fluctuated around \n-0.6, which was very close to the DSMD value corresponding to the local PWP. In addition, DSMD of the BLF in the \nChangwu, Yan’an and Mizhi regions was similar to that in APO (Fig. 4d, e, f). The consumption of DSM reached the same \n265 \nlowest value in different regions. This suggests that PWP is a good indicator of the degree limitation of DSM, irrespective of \ntree species and site. It is worth noting that, compared with APO, the variation scope of DSMD in Changwu and Yan’an \ndecreased significantly with the age of the BLF, which may be due to the extreme rainfall in 2021, which recharges DSM in \nthe BLF (Table 1). 11 11 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 270 \nFigure 4: Soil moisture deficit (SMD) distribution under apple orchards of different ages in Changwu (a), Luochuan (b) and Mizhi (c); and \nSMD under black locust forests of different ages in Changwu (d), Yan’an (e) and Mizhi (f). The capital letters A and B in the legend \nrepresent apple and black locust, respectively, and the numbers after the letters represent tree age, for example, A-22 represents \n22-year-old apple. In order to reduce the randomness of the extracted data, the data for apple trees with similar ages in Changwu and \nLuochuan were combined. The gray line indicates SMD at permanent wilting point (SMDPWP) in each sampling site, and the red dashed \n275 \nline indicates the SMD equal to -0.6. 270 270 Figure 4: Soil moisture deficit (SMD) distribution under apple orchards of different ages in Changwu (a), Luochuan (b) and Mizhi (c); and \nSMD under black locust forests of different ages in Changwu (d), Yan’an (e) and Mizhi (f). The capital letters A and B in the legend \nrepresent apple and black locust, respectively, and the numbers after the letters represent tree age, for example, A-22 represents \n22-year-old apple. 3.2.2 Depth limitation of DSM ***, **, * and ns in the gray bands indicate the significance level of measured data \nobtained by the paired sample t-test for p< 0.001, p< 0.01, p< 0.05 and no significant differences, respectively. Figure 5: Dynamic distribution of maximum water absorption depth after reaching the degree limitation of deep soil desiccation. (a) 22- \n285 \n(literature data) and 24-year-old (measured data) apple in Changwu; (b) and (c) indicate 28- and 24-year-old apple in Luochuan and Mizhi, \nrespectively; (d), (e) and (f) indicate 28-, 25- and 24-year-old black locust in Changwu, Yan’an and Mizhi, respectively. Gray line \nrepresents soil moisture of grassland or farmland. ***, **, * and ns in the gray bands indicate the significance level of measured data \nobtained by the paired sample t-test for p< 0.001, p< 0.01, p< 0.05 and no significant differences, respectively. 3.2.2 Depth limitation of DSM Based on the measured and literature data, we analyzed the water consumption depth limit of plantations in different regions \nafter the soil desiccation degree limitation was reached (Fig. 5). With an increase in depth, the soil moisture of plantations \nwas close to or even higher than that of the control, except for the BLF in Mizhi. Thus, we calculated the difference \nsignificance of SM between the plantations and control using the paired sample t-test, treating the points with no significant \n(p>0.05) soil moisture difference as the maximum water uptake depth. And it reached 18.0-22.0 m for APO at different sites; \nand 18.4-18.8 m for BLF in Changwu and Yan’an while in Mizhi it was deeper than 25 m. 12 Figure 5: Dynamic distribution of maximum water absorption depth after reaching the degree limitation of deep soil desiccation. (a) 22- \n285 \nhttps://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. Figure 5: Dynamic distribution of maximum water absorption depth after reaching the degree limitation of deep soil desiccation. (a) 22- \n285 \n(literature data) and 24-year-old (measured data) apple in Changwu; (b) and (c) indicate 28- and 24-year-old apple in Luochuan and Mizhi, \nrespectively; (d), (e) and (f) indicate 28-, 25- and 24-year-old black locust in Changwu, Yan’an and Mizhi, respectively. Gray line \nrepresents soil moisture of grassland or farmland. ***, **, * and ns in the gray bands indicate the significance level of measured data \nobtained by the paired sample t-test for p< 0.001, p< 0.01, p< 0.05 and no significant differences, respectively. Figure 5: Dynamic distribution of maximum water absorption depth after reaching the degree limitation of deep soil desiccation. (a\n285 Figure 5: Dynamic distribution of maximum water absorption depth after reaching the degree limitation of deep soil desiccation. (a) 22- \n285 \n(literature data) and 24-year-old (measured data) apple in Changwu; (b) and (c) indicate 28- and 24-year-old apple in Luochuan and Mizhi, \nrespectively; (d), (e) and (f) indicate 28-, 25- and 24-year-old black locust in Changwu, Yan’an and Mizhi, respectively. Gray line \nrepresents soil moisture of grassland or farmland. 3.3 Effect of factors on the limitation \n290 13\nBased on all extracted and measured data, including SMD, soil moisture (SM), soil texture (clay, silt), and root biomass \n(FRDWD) along with ERA5-Land meteorological data (MAP, mean annual temperature (MAT), relative humidity (RH), \nvapor pressure deficit (VPD)), redundancy analysis (RDA) was used to analyze the key factors that determine the degree and \ndepth limitation of DSM (Fig. 6). The RDA showed that the explanatory variables (soil properties, meteorological variables \nand plant characteristic) account for 67.7% of the total variation of SM and SMD. SMD was significantly (p<0.01) positively \n295 \ncorrelated with VPD, and negatively correlated with clay, silt, RH, MAT, and MAP (Fig. 6a). The clay, VPD, silt, RH, and \nFRDWD provided statistically significant (p<0.01) explanations for the distribution of SMD and SM (Fig. 6b), and clay was 13 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. the most important. Thus, soil texture and climatic conditions were the main factors leading to the degree and depth \nlimitation of deep soil desiccation in different regions. However, the relationship between MAP and SMD was not significant, \nwhich may be due to the MAP of each region weakened the differences of precipitation in each sampling year. 300 the most important. Thus, soil texture and climatic conditions were the main factors leading to the degree and depth \nlimitation of deep soil desiccation in different regions. However, the relationship between MAP and SMD was not significant, \nwhich may be due to the MAP of each region weakened the differences of precipitation in each sampling year. 300 Figure 6: (a) redundancy analysis (RDA) of “response variable (soil moisture deficit (SMD), soil moisture (SM)) and environmental \nfactors”. Descriptors (red arrows) are soil properties (clay and silt), meteorological variables (vapor pressure deficit (VPD), mean annual \nprecipitation (MAP), relative humidity (RH), and mean annual temperature (MAT)), and plant characteristic (fine root dry weight density \n(FRDWD)). Axis 1 (65.67%, p<0.01) and Axis 2 (2.03%, p<0.01) are shown. (b) explanation provided by of each environmental variable \nfor the response variables. **, *, and ns following the bar chart indicate that p<0.01, p<0.05, and no significant, respectively. 3.4 Effect of the limitations on hydraulic conductivity In order to further explore how the limitation caused by deep soil desiccation affects the hydraulic conductivity of plants, we \ncompared hydraulic conductance of branches of plantation trees in relation to the degree and depth limitation of deep soil \ndesiccation at different sites (Fig. 7). PLC was higher than 50% for all tree species and regions. Furthermore, the PLC of \n310 \nblack locust in Mizhi was significantly higher than that in other places (p<0.05), which indicates that the risk of mortality of \nblack locust in Mizhi is greatest when deep soil desiccation reaches degree and depth limitation thresholds. Figure 7: Differences in percentage loss of hydraulic conductivity of annual branches of apple (a) and black locust (b) in different regions \n(Changwu, Luochuan, Yan’an, and Mizhi). * or ns indicate that the significance level is p<0.05 or there is no significant difference, \n315 \nrespectively. Figure 7: Differences in percentage loss of hydraulic conductivity of annual branches of apple (a) and black locust (b) in different regions \n(Changwu, Luochuan, Yan’an, and Mizhi). * or ns indicate that the significance level is p<0.05 or there is no significant difference, \n315 \nrespectively. Figure 7: Differences in percentage loss of hydraulic conductivity of annual branches of apple (a) and black locust (b) in different regions \n(Changwu, Luochuan, Yan’an, and Mizhi). * or ns indicate that the significance level is p<0.05 or there is no significant difference, \n315 \nrespectively. 14 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. 4 Discussion \n4.1 The mechanism underlying for the degree and depth limitation \nAlthough DSM (>200 cm) decreased with an increase of planted trees age, and even reached the lowest limitation, shallow \nsoil moisture (<200 cm) did not change significantly (Fig. 2 and 4). This is ascribed to surface or shallow layer soil moisture \n320 \nis usually greatly influenced by rainfall infiltration and evapotranspiration, while DSM is often in a negative balance, being \nconsumed by deep-rooted trees and unable to be replenished for a long time (Fang et al., 2016; Gao et al., 2020). As the \ncritical water resource for plants to cope with extreme droughts, DSM has reached its lowest limitation, seriously threatening \nthe sustainability of tree growth under future climate conditions. In our study, the lowest DSMD fluctuated around -0.6 irrespective of tree species and site (Fig. 4). 3.4 Effect of the limitations on hydraulic conductivity This contradicts the \n325 \nassumption that degree of deep-layer soil desiccation limitation is dependent on tree species, which is mainly because the \nclimate conditions and soil texture differ between regions (Fig. 6). The precipitation in the LOP showed a decreasing trend \nfrom southeast to northwest, the clay content also follows this trend (Wang et al., 2011). When the soil texture is coarser, it \nhas a larger matric potential or smaller suction under the same soil moisture conditions (Dexter, 2004), resulting in a larger \nmatric potential gradient around fine roots in areas with less rainfall (LRAs), thus effectively enhance soil water availability. 330 \nSoil water potential, when the soil hydraulic conductivity (Ks) drops to lower values than the root hydraulic conductivity (Kr), \ncan be regarded as the critical soil matric potential, at which the soil begins to limit the water flux in the soil-plant continuum \n(Fig. 8; Cai et al., 2022). Thus, the soil with coarser texture has higher Ks when soil starts limiting trees’ RWU, causing the \ncorresponding Kr in LRAs to be higher than that in areas with more rainfall (MRAs) by allocating more biomass to fine root \n(Fig. 8). In addition, VPD in the LRAs is relatively high, and the transpiration traction force increases (Will et al., 2013; \n335 \nHayat et al., 2019), causing a larger water potential gradient between roots and leaves under the same soil moisture, and thus \ndriving roots to absorb more moisture. Thus, although the physiological and growth characteristics of apple and black locust \nin different regions were obviously different (Table 2; Fig. 7), the lowest DSMD of all tree species at each site both \nfluctuated around-0.6. This indicates that the lowest amount of soil moisture absorbed by plants is determined by VPD and \nsoil water availability, which is mainly affected by the precipitation and soil texture. Notably, the lowest DSMD was very \n340 4.1 The mechanism underlying for the degree and depth limitation Our result shows that the maximum RWU depth \n350 \nreached 18.0-22.0 m for APO at different sites, and 18.4-18.8 m for BLF in Changwu and Yan’an while in Mizhi it was more \nthan 25 m (Fig. 5). The above- and under-ground biomass allocation mechanism of plantation determines its’ root: shoot \nratios (Mokany et al., 2005), thus, the FRDWD of the plantation indicates that their water absorption range can only reach \n18-22 m. Furthermore, roots can no longer extract moisture after reaching a certain depth, probably because the hydraulic \npath of water transport in the SPAC is too long and the water potential gradient is insufficient to drive water transport \n355 \nupwards (Ahmed et al., 2014). However, the water consumption depth of BLF in Mizhi was more than 25 m. Wang et al. (2009) also found that the maximum water consumption depth of the artificial Caragana microphylla forest in the semi-arid \nregion was 22.5 m, which indicates that chronic water shortages encourages planted trees to absorb water by growing roots \ndownwards in the semi-arid region. Thus, the RWU depth of plantations in other regions has reached its maximum depth, \neven though BLF in LRAs can obtain water through root extension. 360 Figure 8: Regulatory mechanism of the extreme influence of root water uptake (RWU) on deep soil moisture. The parameters of \nsoil-plant-atmosphere continuum (SPAC) in the white rectangle are quantized, the gray rectangle represents the intermediate variables that \nthe parameters of SPAC cause the result of minimum soil moisture profile (degree limitation), the blue rounded rectangle represents the \nlimitation of degree and maximum depth of RWU (depth limitation). The red arrow, the black solid arrow and the black dotted arrow \nindicate that the parameters of SPAC have a significant (p<0.05) effect on the degree limitation, have an influence on the depth limitation, \nand have an indirect effect on two results, respectively. 4.1 The mechanism underlying for the degree and depth limitation (Fig. 8). In addition, VPD in the LRAs is relatively high, and the transpiration traction force increases (Will et al., 2013; \n335 \nHayat et al., 2019), causing a larger water potential gradient between roots and leaves under the same soil moisture, and thus \ndriving roots to absorb more moisture. Thus, although the physiological and growth characteristics of apple and black locust \nin different regions were obviously different (Table 2; Fig. 7), the lowest DSMD of all tree species at each site both \nfluctuated around-0.6. This indicates that the lowest amount of soil moisture absorbed by plants is determined by VPD and 15 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. the LOP. After soil moisture use in the profile reaches the degree limitation, plants extend their roots into the deep soil to expand their \nwater hunting range during the dry season (Fig. 8; Ivanov et al., 2012). Our result shows that the maximum RWU depth \n350 \nreached 18.0-22.0 m for APO at different sites, and 18.4-18.8 m for BLF in Changwu and Yan’an while in Mizhi it was more \nthan 25 m (Fig. 5). The above- and under-ground biomass allocation mechanism of plantation determines its’ root: shoot \nratios (Mokany et al., 2005), thus, the FRDWD of the plantation indicates that their water absorption range can only reach \n18-22 m. Furthermore, roots can no longer extract moisture after reaching a certain depth, probably because the hydraulic \npath of water transport in the SPAC is too long and the water potential gradient is insufficient to drive water transport \n355 \nupwards (Ahmed et al., 2014). However, the water consumption depth of BLF in Mizhi was more than 25 m. Wang et al. (2009) also found that the maximum water consumption depth of the artificial Caragana microphylla forest in the semi-arid \nregion was 22.5 m, which indicates that chronic water shortages encourages planted trees to absorb water by growing roots \ndownwards in the semi-arid region. Thus, the RWU depth of plantations in other regions has reached its maximum depth, \neven though BLF in LRAs can obtain water through root extension. 360 the LOP. After soil moisture use in the profile reaches the degree limitation, plants extend their roots into the deep soil to expand their \nwater hunting range during the dry season (Fig. 8; Ivanov et al., 2012). 4.3 Limitations and practical implications \n380 4.3 Limitations and practical implications \n380 \nAffected by objective factors such as differences in the time of planting and vegetation succession in different regions, \nsampling of the same tree species in different regions cannot guarantee the same tree age. In addition, the collection of large \namounts of DSM data requires a great deal of resources, the number of samples available in this study is limited. Thus, the \nresults of this study need to be in-depth verified in different tree ages and in different regions to guide vegetation \nmanagement under local conditions. 385 \nLarge numbers of tree deaths caused by prolonged drought are of great concern because trees fulfil a critical ecological role \nand have the largest biomass and storage of carbon (Feng et al., 2016; Bai et al., 2021). Thus, it is essential to understand the \ndegree and depth limitation of deep-layer soil desiccation for maintaining the sustainability of vegetation restoration under \nfuture climatic drying. Our study shows that 24-28-year-old plantations, both APO and BLF, have reached the degree \nlimitation of deep soil desiccation; and the maximum DSMD is about -0.6, which corresponds to the DSMD at local PWP, \n390 \nirrespective of tree species and site. On the one hand, this confirms that PWP is a good indicator of the degree limitation of \ndeep-layer soil desiccation for the tree species examined; on the other hand, when the precise wilting point of deep soil in \ndifferent regions is difficult to determine, the lowest DSMD (0.6) can be used as an index for judging whether DSM can \nsustain the survival of the typical plantations on the LOP during prolonged drought. Furthermore, except for the BLF in \nMizhi, all plantations have reached the depth limitation of deep-layer soil desiccation, and after reaching the degree and \n395 \ndepth limitation thresholds, plantations of different sites suffer different degrees of embolism. Therefore, plantations of 24 \nyears and above face the greatest risk of dieback and death in drought years or prolonged dry periods, especially in semi-arid \nregions like Mizhi. This study provides practical insights into the sustainable development of vegetation restoration on the \nLOP in future extreme climate. 4.2 Effects of the limitations on the hydraulic conductivity of plantations 16 \nDuring prolonged dry periods, deep-rooted trees often rely on DSM to avoid drought stress (Ding et al., 2021; Wang et al., \n2021). However, deep soil desiccation reduces the moisture supply capacity of the soil, which can lead to xylem embolism, \nand even tissue dehydration and, consequently, hydraulic failure (Yang et al., 2022). At the same time, water stress leads to \nstomatal closure, and thus photosynthesis and other physiological processes are inhibited (McDowell et al., 2018). When the \nxylem hydraulic conductance drops below a certain threshold, plant may occurs dieback or even die completely due to the 16 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. combined occurrence of hydraulic failure and carbon starvation (Arend et al., 2021). Our results show that branches of \nsimilar aged trees that causes the DSM to reach the degree and depth limitation suffered embolism to different degrees (Fig. 375 \n7), which is consistent with the hypothesis about the hydraulic conductivity of planted trees. Furthermore, the embolism of \ntrees is more severe LRAs because higher VPD and lower water availability increase the risk of embolism (Breshears et al., \n2013). This indicates that mature (>24 year) plantations on the LOP are already facing a great risk of dieback and even death \ndue to embolism, especially in semi-arid areas. combined occurrence of hydraulic failure and carbon starvation (Arend et al., 2021). Our results show that branches of \nsimilar aged trees that causes the DSM to reach the degree and depth limitation suffered embolism to different degrees (Fig. 375 \n7), which is consistent with the hypothesis about the hydraulic conductivity of planted trees. Furthermore, the embolism of \ntrees is more severe LRAs because higher VPD and lower water availability increase the risk of embolism (Breshears et al., \n2013). This indicates that mature (>24 year) plantations on the LOP are already facing a great risk of dieback and even death \ndue to embolism, especially in semi-arid areas. 5 Conclusions \n400 Deep soil moisture plays an important role in maintaining the survival of deep-rooted plantations during prolonged dry \nperiods on the water-limited Loess Plateau. Our results show that the lowest deep soil moisture deficit (DSMD) was close to Deep soil moisture plays an important role in maintaining the survival of deep-rooted plantations during prolonged dry \nperiods on the water-limited Loess Plateau. Our results show that the lowest deep soil moisture deficit (DSMD) was close to 17 https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. https://doi.org/10.5194/hess-2023-12\nPreprint. Discussion started: 31 January 2023\nc⃝Author(s) 2023. CC BY 4.0 License. the DSMD at the permanent wilting point (PWP), irrespective of tree species and site. This confirms that PWP is a good \nindicator of the degree limitation of DSM for apple and black locust trees. The corresponding maximum water consumption \ndepth reached 18.0-22.0 m for apple trees at different sites, and 18.4-18.8 m for black locust in Changwu and Yan’an while it \n405 \nwas more than 25 m in the drier site of Mizhi, thus relying on the availability of DSM and the ability of deep roots to \ntransport water. Furthermore, when the degree and depth limitation thresholds were reached, the percentage loss of hydraulic \nconductivity of planted trees’ branches was between 74.9% and 96.5%, indicating that dieback and tree death may occur. Our \nstudy provides insights into the management and sustainable development of revegetated sites on the water-limited regions \nincluding the Loess Plateau. 410 the DSMD at the permanent wilting point (PWP), irrespective of tree species and site. This confirms that PWP is a good \nindicator of the degree limitation of DSM for apple and black locust trees. The corresponding maximum water consumption \ndepth reached 18.0-22.0 m for apple trees at different sites, and 18.4-18.8 m for black locust in Changwu and Yan’an while it \n405 \nwas more than 25 m in the drier site of Mizhi, thus relying on the availability of DSM and the ability of deep roots to \ntransport water. Furthermore, when the degree and depth limitation thresholds were reached, the percentage loss of hydraulic \nconductivity of planted trees’ branches was between 74.9% and 96.5%, indicating that dieback and tree death may occur. Our \nstudy provides insights into the management and sustainable development of revegetated sites on the water-limited regions \nincluding the Loess Plateau. 410 Data availability The basic data, including extracted and measured set of soil moisture, environmental factors, and percentage loss of \nhydraulic conductivity of trees’ branches, are available for download at https://doi.org/10.5281/zenodo.7412472. References \n425 \nAhmed, M. A., Kroener, E., Holz, M., Zarebanadkouki, M., and Carminati, A.: Mucilage exudation facilitates root water \nuptake in dry soils, Func. plant boil., 41(11), 1129-1137, 10.1071/FP13330, 2014. Acknowledgments \n420 Acknowledgments \n420 \nThe authors thank Min Yang and Shaofei Wang for their help. This work was jointly supported by the National Natural \nScience Foundation of China (42125705), Natural Science Basic Research Program of Shaanxi Province (2021JC-19), the \nCyrus Tang Foundation, the Shaanxi Key Research and Development Program (2020ZDLNY07-04), Shaanxi Province Key \nR&D Plan (2022NY-064) and Chinese Universities Scientific Fund (2452020242). The authors thank Min Yang and Shaofei Wang for their help. This work was jointly supported by the National Natural \nScience Foundation of China (42125705), Natural Science Basic Research Program of Shaanxi Province (2021JC-19), the \nCyrus Tang Foundation, the Shaanxi Key Research and Development Program (2020ZDLNY07-04), Shaanxi Province Key \nR&D Plan (2022NY-064) and Chinese Universities Scientific Fund (2452020242). References \n425 \nAhmed, M. A., Kroener, E., Holz, M., Zarebanadkouki, M., and Carminati, A.: Mucilage exudation facilitates root water \nuptake in dry soils, Func. plant boil., 41(11), 1129-1137, 10.1071/FP13330, 2014. Author contributions XG and XZ conceived the study; NH, DG, YW and DG performed field experiments and collected the data; NH performed \n415 \nthe analysis and prepared the first draft of the manuscript with the help of LT and LZ; XG edited and commented on the \nmanuscript. The authors declare that they have no conflict of interest. References \n425 18 https://doi.org/10.5194/hess-2023-12\nPreprint. 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