Response of deep soil drought to precipitation, land use and topography across a semiarid watershed

Agricultural and Forest Meteorology 282–283 (2020) 107866

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Response of deep soil drought to precipitation, land use and topography across a semiarid watershed

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Yunqiang Wanga,b,f,g, , Ming'an Shaoc, Hui Suna, Zihuan Fua, Jun Fand, Wei Hue, Linchuan Fangd a

State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, Shaanxi 710061, China Interdisciplinary Research Center of Earth Science Frontier, Beijing Normal University, Beijing 100875, China c Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China d State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China e New Zealand Institute for Plant & Food Research Limited, Private Bag 4704, Christchurch 8140, New Zealand f Interdisciplinary Research Center of Earth Science Frontier, Beijing Normal University, Beijing, 100875, China g Department of Earth and Environmental Sciences, Xi’an Jiaotong University, Xi’an 710049, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Deep soils Land use Meteorological variables Soil texture Topography The Loess Plateau

Soil drought caused by climatic change and/or poor land management in arid and semi-arid regions are seldom recognised due to a lack of comparative data on soil moisture (SM) in soil profiles. This lack of information endangers the sustainability of these fragile ecosystems. The current study assessed spatial-temporal variations of soil drought, as indicated by dried soil layers (DSL), at a watershed scale, and tested the hypothesis that soil drought in deep profiles is controlled by the combined effects of meteorological processes, land use, and topography. We measured SM to a depth of 500 cm on 20 occasions at 73 locations from 2013 to 2016 at a small watershed on the Chinese Loess Plateau (CLP). We also collected data on possible environmental factors including meteorological variables, land use, topographical elements, and soil properties. The DSLs occurred at > 90% of the sampling sites within the watershed, and the spatially and temporally averaged DSL formation depth (DSLFD), DSL thickness (DSLT) and soil water content within the DSL (DSL-SWC) were 125 cm, 257 cm, and 10.4%, respectively. This suggests that 51.4% of the 500-cm-profile is drying out below 125 cm. The DSLFD, DSLT and DSL-SWC demonstrated a moderate degree of variability (20% < CV < 84%) in space, and showed a moderate, moderate and weak temporal variability, in time, respectively. The temporal series of the mean spatial DSLT and DSLFD were significantly correlated with climatic variables. The spatial variation of the mean temporal DSL-SWC differed significantly among the land uses and between shaded and sunlit aspects. We found that plan curvature, slope gradient, clay and silt content regulated DSLs in both space and time. This result verified our hypothesis that meteorological processes, land use, and topography play an essential role in shaping DSL variation and distribution pattern. Taking DSL reclamation into account in the study area, grassland would be the optimum land use type. Understanding this information is helpful for watershed soil and water conservation, and soil drought meditation via the best management practices in the CLP and other water-limited regions with deep soils.

1. Introduction Soil moisture (SM) plays an important role in energy partitioning between sensible and latent heat fluxes in the terrestrial ecosystems (Guillod et al., 2015; Lee et al., 2005; Wang and Wang, 2017). It also affects a range of other processes including precipitation (D'Odorico and Porporato, 2004; Guillod et al., 2015), soil erosion, solute transport (Seeger et al., 2004; Ziadat and Taimeh, 2013), carbon

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sequestration, enzyme activities (Duan et al., 2018; Peng et al., 2013; Zhang and Shangguan, 2016), plant growth, and vegetation succession (Feng et al., 2016; Granier et al., 2007). In some water-limited areas, either persistently (i.e., semiarid and arid regions) or ephemerally (i.e., dry seasons in humid regions), such as the Chinese Loess Plateau (CLP) (Wang et al., 2008), the Western-High Plains region in the US (Mo et al., 2011; Salley et al., 2016), the Mediterranean (Padilla and Pugnaire, 2007) and Western Australia (Robinson et al., 2006), the

Corresponding author. E-mail address: [email protected] (Y. Wang).

https://doi.org/10.1016/j.agrformet.2019.107866 Received 22 March 2018; Received in revised form 29 July 2019; Accepted 2 December 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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Appendix: Nomenclature BD CLP CR CV DSL DSLFD

DSL-SWC FC PlanCur PTF SD SM SOC

Bulk density The Chinese Loess Plateau Counting rate Coefficient of variation Dried soil layer The formation depth of the DSL

The mean soil water content within the DSL Field capacity Plan (horizontal) curvature Pedotransfer function Standard deviation Soil moisture Soil organic carbon content

dynamics in the field in relation to meteorological conditions, land uses and topographical elements at a watershed scale. This is attributed to the lack of relevant data such as long-term in-situ SM data in deep soil profiles across a watershed. Determining this information is important in water-limited ecosystems and under global change scenarios where longer drought spells are predicted (Jia et al., 2017; Padilla and Pugnaire, 2007; Wang et al., 2011c). Although precipitation and air-temperature projections under climate scenarios are variable and uncertain, climate models (i.e., general circulation models) project consistent increases in air temperature and thus increases in evaporation in many water-limited ecosystems around the world (Anderegg et al., 2015; Seager et al., 2007; Zavaleta et al., 2003). This may lead to more intense and frequent droughts in already drought-prone regions, and may result in further decreases in SM and various patterns of DSLs. Increased intensity and duration of droughts, and/or high-rainfall events, have been associated with DSL-dynamics in many parts of the world (Dai et al., 2004; Fischer et al., 2007; Taylor et al., 2012; Wang et al., 2011a). These events are expected to become more common under future climates (Calanca, 2007; Jipp et al., 1998; Liu et al., 2010). However, the response of DSL-patterns to rainfall processes or drought events at a watershed scale remains poorly understood. Beyond the impact of meteorological conditions, the dynamics of DSLs are also affected by (1) land use patterns and plants traits (i.e., species, transpiration, photosynthetic capacity, and rooting distribution) (Jipp et al., 1998; Wang et al., 2008, 2011c), and (2) topographical elements (i.e., altitude, slope aspect, and slope position) (Chen et al., 2008; Wang et al., 2012b). Whereas the former can be mediated by policy makers and used to determine which species are suitable for a region, the latter are usually static but could be used to help determine which locations are well-suited for specific plant species. However, little information is available about the influence of land use and topography on DSLs spatial and temporal variability at the watershed scale. Such data would help to establish the best management practices by taking DSL reclamation into account. In this study, we hypothesize that the belowground response of soil drought (as indicated by DSL indices) is controlled by the combined effects of meteorological processes, land use, and topography at a watershed scale. We then expect that land use patterns can be optimized at the watershed scale. To test this hypothesis, we selected a typical watershed in the CLP and performed in-situ monitoring of the DSLs in deep soil profiles for four years. The specific objectives of this study were to (1) investigate the spatial and temporal variability of DSLs to a depth of 500 cm at a watershed scale, (2) evaluate the impacts of meteorological conditions, land uses, topographies on DSL dynamics at a watershed scale in a water-limited ecosystem, and (3) assess the best management practices related to optimum land use pattern by taking DSL reclamation into account. A better understanding of DSL dynamics and factors that influences their formation and distribution (e.g., land use and topography) is essential for assessing the impacts of ecological restoration projects on soil water cycles and for developing sustainable land management policies.

water supply is stressed if the area experiences population growth or requires re-vegetation (Jia et al., 2017; Scanlon et al., 2005; Wang et al., 2011c; Zhu and Shao, 2008). The accompanying soil drought can trigger a dried soil layer (DSL) in the soil profile in some areas (Jipp et al., 1998; Li, 1983; Yang, 2001). Dried soil layers have three indices that can be used to quantify soil drought: (1) the formation depth of the DSL (DSLFD, in cm or m), (2) the thickness of the DSL (DSLT, in cm or m), and (3) the mean soil water content within the DSL (DSL-SWC, in% or mm) (Cheng and Liu, 2014; Jia et al., 2015; Li et al., 2008; Liu et al., 2010; Robinson et al., 2006). The higher the value of DSLT, or the lower the value of DSLFD and DSL-SWC, the more severe the soil drought. Dried soil layers were first discovered in 1893 in Russia (Bысоцкий, 1960; Wang et al., 2004), and then were formally defined by Li (1983) in the water-limited ecosystems on the CLP. The CLP is a typical waterlimit ecosystem which varies both spatially and temporally (Feng et al., 2016). The water stress is related to long-term effects of natural environmental evolutions and human activities (Chen et al., 2008; Wang et al., 2010a). Currently, the CLP is affected by climate change (e.g., decreased precipitation and increased temperature) (Liu et al., 2016) and intensive land management (e.g., the “Grain for Green” and “Gully land consolidation” projects) (Chen et al., 2015; Feng et al., 2016). The vegetation coverage within the CLP has been increasing, and soils on slopes have been stabilised by banning cultivation and establishing grassland and forests using perennial plants (Chen et al., 2015; Feng et al., 2016). Some of the planted perennial species such as alfalfa (Medicago sativa), Korshinsk peashrub (Caragana korshinskii), pine (Pinus tabuliformis), and apple tree (Malus domestica) can extract water and nutrients from deep soils due to their capability to establish deep roots (Li et al., 2019a, 2019b; Wang et al., 2009). Recently established deep rooting vegetation can alter the cycling of water and thus profoundly affect the patterns of DSLs. Therefore, the CLP is an ideal region to investigate the response of belowground soil drought to aboveground changes in climate, land use, and other environmental conditions. Since the definition of DSLs, it has been found that as a direct result of serious soil drought, DSLs may (1) alter vertical water cycling by preventing the exchange of water between upper soil layers and groundwater (Chen et al., 2008; Wang et al., 2008), (2) lead to soil quality degradation which can have a negative effect on crop yields (Li and Huang, 2008; Shangguan, 2007), and (3) result in poor vegetation growth, forest renewal, and rotations (e.g. Eucalyptus globulus) with decreased biological carbon sequestration (Goulden and Bales, 2019; Meissner et al., 2012; Mendham et al., 2011; Nepstad et al., 2004; Wang et al., 2008). Related studies have been focusing on DSL formation under different environment conditions (Li et al., 2008; Li and Huang, 2008; Wang et al., 2010b), the spatial variability of DSLs at slope, watershed and regional scales (Jia et al., 2015; Wang et al., 2010a), and the factors influencing DSLs at different spatial scales and site conditions (Wang et al., 2012b; Yan et al., 2015). Efforts have been also made to use field observations to develop predictive models for mediating DSLs (Huang and Gallichand, 2006; Wang et al., 2012a). The basic characteristics, spatial distribution, and factors that influence DSLs have long been acknowledged (Wang et al., 2015; Yan et al., 2015). However, there is less information about DSL 2

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2. Material and methods

high rainfall and wind speed, low humidity) on DSL dynamics. This area is typical for the ‘wind-water erosion crisscross region’ of the CLP, which covers an area of 17.8 × 104 km2 and accounts for 28.5% of the total CLP area (62.4 × 104 km2) (Tang, 2000). The ecosystem is fragile and characterized by severe soil erosion in rainy season and strong wind erosion in non-rainy season (Wang et al., 2016). The soils are mainly Aeolian sand soil (Calcaric Arenosol, FAO, 1988) and Loessial Mein soil (Calcaric Regosol, FAO, 1988) (Hu et al., 2012). The types of land use in the watershed are mainly abandoned farmland (i.e., cropland abandoned in autumn, 2012), grassland (dominated by Stipa bungeana), shrubland (dominated by Caragana korshinskii, Salix psammophila, and Artemisia desertorum), orchard (dominated by Armeniaca vulgaris) and forest (dominated by Populus Simonii). Fig. 1b presents the distribution of the plant species across the LaoYeManQu watershed based on our field investigation.

2.1. Study area The study was conducted from 2013 to 2016 in the LaoYeManQu watershed, which is located in the northern CLP (Fig. 1a). The watershed has an area of 20 ha, an elevation of 1056–1130 m above sea level, and is characterized by a temperate semiarid climate. The annual mean precipitation of the area is 437 mm with significant seasonal variations. More than 70% of the precipitation falls between June and September, and more than 50% occurs in the form of storms. The annual mean temperature is 8.4 °C, and the lowest and highest monthly temperatures are −9.7 and 23.7 °C in January and July, respectively. The annual mean wind speed is 2.2 m s−1. The aridity index is 1.8 and the area has 135 annual mean frost-free days (Hu et al., 2010). Fig. 2 presents the distribution of daily precipitation in the study area from 2013 to 01–01 to 2016–12–31, and shows that 2013 and 2016 were wet years, and 2014 and 2015 were normal years. Table 2 lists the basis statistics of the seven meteorological variables during the four experiment years. The precipitation was highly variable among the four years, while humidity gradually increased from 2013 to 2016, and the highest wind speed was observed in 2013. This provided us an ideal opportunity to verify the effect of varying meteorological variables (i.e.,

2.2. Soil sampling and data collection We designed an intensive soil sampling scheme that covered the entire watershed to investigate in detail the spatial and temporal variations of DSLs. A 50 m × 50 m grid was superimposed on a digital topographic map of the watershed. Soil samples were then collected from the centres of the grid units, and the locations for each sampling

Fig. 1. The location of the study area (a), the spatial distribution of land use and plant species across the LaoYeManQu watershed (b), and the distribution of soil sampling points (n = 73) and the watershed gully systems that were identified using changes in elevation (c). CLP represents Chinese Loess Plateau. 3

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at the 73 points (about 0.5 m away from the neutron tubes). The BD and saturated hydraulic conductivity were determined by using the volume–mass relationship for each oven-dried core sample (Wang et al., 2008) and the constant head method (Klute and Dirksen, 1986), respectively. For the 584 disturbed soil samples, we measured soil organic-carbon content (SOC) by using the Walkley-Black method (Nelson and Sommers, 1982), and clay, silt, and sand contents by laser diffraction using a Mastersizer 2000 (Malvern Instruments, Malvern, England). Soil pH (1:5 H2O) was also determined by using a pH meter with a glass electrode (McLean, 1982). Since a DSL has a range of soil water contents between permanent wilting point and stable field capacity - which is generally considered to be equivalent to 60% of field capacity (FC) for soils in the study area (Wang et al., 2011c) - it is necessary to determine the value of FC in a soil profile. However, it is a challenge to collect a large numbers of undisturbed soil cores to measure FC directly in the 0–500 cm profile. Instead, the values for FC were indirectly derived from a pedotransfer function (PTF) which was selected from five established PTFs based on their performance. The detailed selection of the PTF of FC, which contained soil texture, SOC and slope gradient, can be found in Wang et al. (2013). To evaluate the factors influencing DSLs, we collected data on 10 topographic features. The geographic coordinates altitude, longitude, and latitude were determined using the GPS receiver. Slope gradient and aspect were measured using a geological compass at each site. Other topographic factors were derived from a 1 m × 1 m digital elevation model (in Albers coordinates) (Hu et al., 2010; Wang et al., 2012b). These factors included total curvature, profile (vertical) curvature, plan (horizontal) curvature (PlanCur), slope length, distance to river, and topographic wetness index. We used three indices (DSLFD, DSLT, and DSL-SWC) to quantify the DSLs (Wang et al., 2018). 2.3. Data analysis Fig. 2. Distribution of daily precipitation (a) and air temperature (b) in the LaoYeManQu watershed, from 2013 to 01–01 to 2016–12–31.

Soil texture of all samples both in vertical and horizontal directions were classified according to the relative proportions of clay, silt, and sand by using the textural triangle. Combing soil texture with the SOC, we calculated the value of FC for each sample and then obtained corresponding values of stable field capacity. We then computed the values of three DSL indices (DSLFD, DSLT and DSL-SWC) for each profile, and then calculated the basic statistical parameters, including minimum, maximum, mean, standard deviation (SD), and coefficients of variation for the measured spatial and temporal DSL data across the watershed. To evaluate the differences in each DSL index among the land-use types and topographical properties, we conducted an ANOVA and least significant difference tests post hoc for the entire watershed and for the different measurement times. We also calculated Pearson correlation coefficients to determine the strength of correlations between DSL indices and measured soil and environmental variables, and their changes with time. Multiple linear regression (MLR) analysis was also conducted to generate a minimum data set of factors that significantly affect DSL indices (Zornoza et al., 2007). All statistical analyses were performed using Microsoft Excel (version 2010), SPSS (version 13.0), R (version 3.3.3) or SigmaPlot (version 12.0). A map of the sampling sites was generated by GIS software (ESRI® ArcMap™ 9.3).

point were marked using a GPS receiver (eTrex venture, 5 m precision in the horizontal direction). It should be noted that (1) a square dissected by the study area boundary line was regarded as an individual unit if more than half of it lay inside the study area; otherwise it was merged with a neighbouring square; and (2) some sampling points within certain grid units were inaccessible due to the separation of deep erosion gullies. A total of 73 points were sampled in the watershed (Fig. 1c). At each sampling point, we first collected disturbed soil samples using a soil auger (5 cm in diameter) to a depth of 500 cm in eight layers: 0–10, 10–20, 20–60, 60–100, 100–200, 200–300, 300–400, and 400–500 cm. A total of 584 disturbed soil samples were collected across the watershed. Then, we installed aluminium neutron probe access tubes at the sampling point to a depth of 500 cm. From May 8, 2013 to May 23, 2016, we obtained 20 slow neutron counting rate (CR) measurements at intervals of 10 and 20 cm for the 0–100 cm and 100–500 cm layers, respectively. To calibrate the neutron probes, we excavated eight 100-cm deep pits (about 0.5 m away from a corresponding neutron tube), and measured gravimetric soil water content (g H2O/100 g dry soil,%) and bulk densities (BD, g cm−3 dry soil) during the monitoring period. The geographical locations of the eight pits covered contrasting soil, land use and vegetation types of the watershed. After we obtained the paired data of CR, gravimetric soil water content, and BD, we built a function for changing CR value into volumetric soil water content. The detailed calibration methods can be seen in Wang et al. (2015). To explore the possible effects of soil hydraulic properties on DSL variation, we collected intact soil samples from the surface 0–5 cm layer

3. Results 3.1. Basic characteristics of DSLs across the semiarid watershed We found DSLs occurred frequently in the watershed during the 20 measurements from 2013 to 2016 (Fig. 3a). Dried soil layers occurred at > 90% of the measured sites (Fig. 3b). The common appearance of DSLs and their overall static nature indicates a persistent negative 4

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Fig. 3. Records of the sampling sites with (orange) and without (blue) the occurrence of a dried soil layer (DSL) at each measurement (a) and the ratio between sites with DSL (orange color) and total sites from 2013 to 2016 (b), in the LaoYeManQu watershed on Chinese Loess Plateau. Sites marked in yellow represent missing data due to damage of the aluminium neutron probe access tubes caused by geological activities in the area.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

balance in the deep soil water cycle within the study area. The few sites without DSLs were mainly distributed in abandoned land and grassland (Fig. 3a), where soil water in 0–500 cm profile was greater than that of shrubland and forest. Interestingly, some sites (e.g., grassland sites 47, 48 and 73) had wet conditions without the occurrence of soil drought while other sites (e.g., forest sites 26 and 34, shrubland site 22, and grassland site 44) showed intermittently occurring DSLs. This phenomenon can be explained by the rapid soil exchange processes at these sites, or at certain times, which are termed “hot spots” and “hot moments”, respectively (McClain et al., 2003). The DSL and non-DSL sites generally showed an organized distribution in space and time. Only the data containing DSL occurrences were used for further analyses with the aim to focus on the spatial and temporal variations of DSLs at the watershed scale as DSLs represent a majority of the watershed's eco-hydrological processes. The spatially and temporally averaged DSLFD, DSLT and DSL-SWC in the 0–500 cm profile were 125 cm, 257 cm, and 10.4%, respectively (Table 1), which indicated a serious degree of soil drought; 51.4% of the 500-cm-profile was drying out below 125 cm. At the watershed scale, all three DSL indices demonstrated a moderate degree of variability, with the mean values of CV ranging from 20 to 84%. From 2013 to 2016, however, the CVT values of spatially-averaged (across the entire watershed) DSLFD (= 32%), DSLT (= 12%), and DSL-SWC (= 2%) indicated moderate, moderate and weak temporal variability, respectively. The standard deviation (SD) and SDT in both spatial and temporal dimensions (Table 1) also supported the variation characteristics of DSL indices, which were consistent with the CV data.

Table 1 Basic statistics of dried soil layers in the LaoYeManQu watershed (n = 73) from 2013 to 2016 in 0–500 cm profiles, and associated standard deviations (SD) and coefficients of variation (CV). Spatial variables

Temporal statistics

DSLFD (cm)

DSLT (cm)

DSL-SWC (%)

Mean of DSLs

Minimum Maximum Mean Median SDT CVT Minimum Maximum Mean Median SDT CVT Minimum Maximum Mean Median SDT CVT

77.7 218.5 125.1 110.3 40.2 32 29.3 115.4 78.7 80.9 20.9 27 38 84 64 64 12 19

148.5 289.0 257.2 263.1 31.8 12 109.0 148.7 137.9 139.1 9.5 7 47 73 54 54 6 11

10.0 11.2 10.4 10.4 0.3 2 2.2 2.7 2.5 2.5 0.1 5 20 26 24 24 2 7

SD of DSLs

CV of DSLs

Note: DSLFD, DSL formation depth, cm; DSLT, DSL thickness, cm; DSL-SWC, mean soil-water content within a DSL,%. The SD of DSL represents the standard deviation of the spatial DSL, the CV of DSL represents the coefficient of variation of the spatial DSL, the SDT represents the standard deviation of the time series based on the mean spatial DSL, and the CVT represents the coefficient of variation for the time series based on mean spatial DSL. 5

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Table 2 Basic statistics of meteorological variables from 2013 to 2016 in the LaoYeManQu watershed. Year

Minimum daily temperature (°C)

Mean daily temperature (°C)

Maximum daily temperature (°C)

Daily humidity (%)

Daily wind speed (m/s)

Daily solar radiation (MJ/m2/d)

Precipitation (mm)

2013 2014 2015 2016

4.5 4.4 4.4 4.1

9.6 9.6 9.7 9.3

15.3 15.5 15.5 15.1

44.91 47.16 49.36 50.74

2.80 2.25 2.33 2.27

16.35 15.99 16.03 16.00

669.4 439.2 371.1 704.3

3.2. DSLs in response to meteorological forcings

Table 3 Pearson correlation coefficients between dried soil layer thickness (DSLT), forming depth (DSLFD) and mean soil-water content (DSL-SWC), and meteorological forcings factors in the LaoYeManQu watershed.

Fig. 4 shows that DSLT increased, DSLFD had a significant decreasing trend, and DSL-SWC had an undulating trend with time. During the rainy season in 2013 (a wet year), all the three indices responded to rainfall. By contrast, in the 2014 and 2015 rainy seasons, DSLs did not show responses to meteorological conditions. This may be attributed to the differences in rainfall amount and patterns. The amount of rainfall during some events that occurred over the 2013 rainy season were several times higher than normal, which may have recharged deep soil moisture via infiltration, thus reclaiming the DSLs. The rainwater during the normal rainy season had limited infiltration depth, and thus failed to replenish the deep soil layer. Correlation analysis showed that among the three DSL indices, spatially-averaged DSLT was negatively correlated with spatially-averaged DSLFD (r = −0.819, P < 0.001) and DSL-SWC (r = −0.704, P < 0.001) (Table 3). The three DSL indices was not significantly correlated with corresponding meteorological forcings data, with the exception of the relationship between spatially-averaged DSL-SWC and humidity (r = −0.462, P < 0.05). By contrast, spatially-averaged DSLT and DSLFD were significantly correlated with all of the accumulated meteorological forcings data over the four year measurement period (from 2013 to 01–01 onward) at a level of 0.05 and 0.01, respectively. Unexpectedly, the negative correlations between spatially-averaged DSL-SWC and the meteorological data were all not significant (Table 3). Such correlations imply that (1) the accumulated effects of meteorological forcings have a great impact on DSL dynamics as represented

Variables

DSLFD

DSLT

DSL-SWC

DSLFD DSLT DSL-SWC Tavg (°C) Tmax (°C) Tmin (°C) Daily humidity (%) Daily wind speed (m/s) Daily Rs (MJ/m2/d) Daily precipitation (mm) Accumulated Tavg Accumulated Tmax Accumulated Tmin Accumulated Humidity Accumulated WS Accumulated Rs Accumulated Precipitation

1.000

−0.819** 1.000

0.149 0.112 0.161 −0.057 0.070 0.039 0.059 −0.753** −0.756** −0.725** −0.744** −0.750** −0.757** −0.743**

−0.13 −0.089 −0.152 0.058 0.026 0.058 −0.098 0.525* 0.537* 0.477* 0.538* 0.543* 0.545* 0.482*

0.678** −0.704** 1.000 0.177 0.162 0.142 −0.462* 0.000 0.250 −0.296 −0.361 −0.352 −0.378 −0.330 −0.333 −0.341 −0.382

Note: Tavg, mean daily temperature; Tmax, maximum daily temperature; Tmin, minimum daily temperature; WS, wind speed; Rs, Solar radiation. * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

Fig. 4. Changes of the three evaluation indices of dried soil layers (DSLs): thickness (DSLT), formation depth (DSLFD), and mean soil-water content (DSL-SWC) from May 8, 2013 to May 16, 2016, under all land uses in the LaoYeManQu watershed. Different lowercase letters indicate significant differences for each DSL index from different measurements (least significant difference test: *, P < 0.05; **, P < 0.01). 6

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DSL-SWC under forest was the smallest among the five land uses. Before 2013–10–31, DSL-SWC under forest was significantly lower than the other four land uses. After 2013–10–31, the DSL-SWC under shrubland also significantly decreased. Unexpectedly, the orchard land use had the highest DSL-SWC among the five land uses.

by DSLT and DSLFD, but not DSL-SWC, and (2) an individual rainfall event has a weak influence on the DSL variation. From Fig. 4 and Table 3 together, we can infer that meteorological conditions, especially the data reflecting the accumulative climate forcings over the four years, are a predominant factor controlling the dynamics of DSLs, as represented by DSLT and DSLFD, at the watershed scale.

3.3.2. Comparison of DSLs between shaded and sunlit aspects The DSLFD differed significantly (P < 0.05) between shaded and sunlit aspects in nine of the 20 measurements (Fig. 6). More interestingly, the nine cases occurred before the 2014 rainy season implying that such a significant difference depends on the amount of precipitation. Perhaps surpassing a threshold amount of precipitation is needed to invoke differences. The more precipitation, the more likely it is that there will be a difference in DSLFD between measurements. We detected three cases with significantly different DSLT between shaded and sunlit aspects, and attributed these differences to random effects of environment conditions on DSLT, as well as to the intrinsic variation in DSLT. Similar to the differences among different land use types, slope aspect significantly affected the DSL-SWC (Fig. 6), implying that the total amount of DSL-SWC was greater in the shaded slopes than in the sunlit slopes.

3.3. DSLs dynamics under different land uses and topography 3.3.1. Variations of DSLs under different land use types The impacts of land use on DSLs variation was evaluated. At the beginning of the measurements, several heavy rainfall events between May 8 and August 17, 2013, caused subsequent increases in the DSLFD value, and there were no significant differences of DSLFD between the five land uses. However, after the rainy season in 2013, we detected a significant difference of DSLFD among the five land uses at two measurement dates (2013–10–31 and 2014–06–01) (Fig. 5). By contrast, in the normal years from 2014 to 2016, DSLFD was usually stable and not significantly different between land uses. Differing from the DSLFD, DSLTs under the five land uses seemed to have a certain immunity to rainfall events (including a wet year in 2013), and were stable with no significant differences detected in our study. It should be noted that we detected decreasing DSLT-values under the five land uses, which were not significantly different, due to recharge of rainfall in 2013 (the wet year). Interestingly, significant differences in DSLT among the five land uses were found on two dates: 2014–09–01 and 2015–08–31. These differences may have been caused by limited rainfall and higher air temperatures. The DSL-SWC among the five land uses differed significantly (P < 0.05) across the 20 measurements from 2013 to 2016, and the value of

3.4. Factors affecting DSLs at a watershed scale Factors that significantly influence DSLs were different between the three DSL indices (Table 4), indicating that different models are needed for each DSL index. This was mainly related to differences in the physical meaning of the indices — the unit of DSLFD and DSLT is length as “cm”, while for DSL-SWC is “%”. Moreover, for each DSL index, the dominant factors obtained with MLR differed at some occasions. This may be caused by aboveground processes (i.e., meteorological

Fig. 5. Changes of the three evaluation indices of dried soil layers (DSLs): thickness (DSLT), formation depth (DSLFD), and mean soil-water content (DSL-SWC) from May 8, 2013 to May 16, 2016, under five land use types in the LaoYeManQu watershed. Different lowercase letters indicate significant differences (P < 0.05) for each measured time. 7

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Fig. 6. Changes of the three evaluation indices of dried soil layers (DSLs): thickness (DSLT), formation depth (DSLFD), and mean soil-water content (DSL-SWC) from May 8, 2013 to May 16, 2016, under shaded and sunlit slopes, respectively, in the LaoYeManQu watershed. Different lowercase letters indicate significant differences (P < 0.05) for each measured time.

comparing the factors that strongly contributed to DSL variation, we found that plan curvature, slope gradient, clay and silt content were the dominant factors for the three DSL indices, although the respective influence of each on the individual DSL indices were different. In total, the number of occurrences selected by MLR for the three DSL indices was 35, 33, 30 and 25 for plan curvature, slope gradient, clay and silt content, respectively.

conditions, human activities, and topographical elements) and belowground processes (i.e., soil water redistribution, preferential flow, and root uptakes), as discussed in Sections 3.2 and 3.3. For DSLFD, the variables selected in the equation by MLR at least five times were slope gradient which occurred most often, followed by silt, clay, distance to river, profile (vertical) curvature, and PlanCur. For DSLT, the variables selected were clay, PlanCur, saturated hydraulic conductivity, altitude, and slope gradient. By contrast, only four variables including PlanCur, silt, slope gradient and clay highly contributed to the variation of DSL-SWC at a watershed scale (Table 4). By

Table 4 Controlling factors of dried soil layers (DSLs) indices—thickness (DSLT), formation depth (DSLFD), and mean soil-water content (DSL-SWC) for each measured time on the LaoYeManQu watershed, selected by using the multiple linear regression method. Time

2013–05–08 2013–06–23 2013–07–05 2013–07–20 2013–08–03 2013–08–17 2013–10–31 2014–06–01 2014–07–01 2014–08–01 2014–09–01 2014–09–16 2014–10–05 2014–10–20 2014–12–27 2015–05–17 2015–08–31 2015–09–14 2015–10–24 2016–05–16

DSLFD Controlling factors

P

DSLT Controlling factors

P

DSL-SWC Controlling factors

P

SG, Clay, ProCur, PlanCur SG, Silt, Clay, BD, SOC Silt, Clay, Dis_River SG, Silt, Clay, ProCur SG, ProCur, PlanCur ProCur, PlanCur, Ks SG, PlanCur, SOC, Altitude SG, Silt, Ks Silt, BD / SG, Dis_River SG, Dis_River, BD, SL SG, Dis_River, SOC SG, Silt, Clay, Dis_River SG SG SG, PlanCur, SOC, Altitude Silt, Dis_River, BD SG, ProCur, Ks, pH SG, Silt, Clay

0.001 0.011 0.097 0.003 0.009 0.001 0.002 0.084 0.107 / 0.036 0.015 0.021 0.044 0.118 0.019 0.122 0.055 0.011 0.006

Clay, PlanCur, Ks, pH Clay, Ks, Altitude, SG Clay, Ks, Altitude, SG Clay, PlanCur, Ks Clay, Ks, Altitude, SG PlanCur, Ks, TWI Clay, PlanCur, TWI Clay, PlanCur Clay, PlanCur Clay, PlanCur, Altitude, pH Clay, PlanCur, Ks Clay, PlanCur, Ks Clay, Ks, Altitude, SG Clay, PlanCur, Ks Clay, Altitude, SG, Dis_River Clay, PlanCur Clay, Altitude, Dis_River Clay, Altitude Clay, PlanCur Clay, Ks, Altitude, TWI, Dis_River, ProCur

0.004 0.000 0.006 0.001 0.003 0.063 0.001 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.001 0.001 0.000 0.001 0.003 0.004

PlanCur, Silt, SG PlanCur, Silt, SG PlanCur, Silt, SG PlanCur, Silt, SG PlanCur, Silt SG, Clay, SL, Dis_River, TWI PlanCur, Silt, SL, Dis_River PlanCur, Silt PlanCur, Silt, SG PlanCur, Clay, Ks PlanCur, Silt, SG, Clay PlanCur, Silt, SG PlanCur, Silt, Clay PlanCur, Silt, SG PlanCur, Silt, SG PlanCur, Silt PlanCur, Silt SG, Clay, Ks PlanCur, Silt, SG PlanCur, Silt, SG, BD

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Note: Factors in bold affect DSL variation during the 20 measurements. BD, bulk density (g cm−3); SOC, soil organic carbon content (g kg−3); Ks, saturated hydraulic conductivity (cm min−1); TWI, topographic wetness index; ProCur, profile (vertical) curvature; PlanCur, plan (horizontal) curvature; Dis_River, distance to river (m); SL, Slope length (m); SG, Slope gradient. 8

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4. Discussion

DSLs and its intrinsic mechanisms is crucial for developing land use management and planning strategies at the watershed scale (Wang et al., 2010b). Our results showed that the DSL-SWC among the abandoned land, grassland, shrubland, orchard and forest sites differed significantly during the 20 measurements from 2013 to 2016 (Fig. 5). The DSL-SWC under forest was significantly lower than the other four land uses, which means that planting trees in this area is not sustainable due to the limited water resources combined with higher evapotranspiration rates by trees. By contrast, orchards, which had the highest DSL-SWC, only showed evidence of weak soil desiccation, which may have resulted from proper management practices by the local farmers such as controlling plant density, fertilizing, thinning, and micro-land-projects for soil and water conservation. In fact, the DSL-SWC did not differ significantly between abandoned land, grassland and orchards (Fig. 5), which suggests that the three land use types result in similar DSL regimes. Combined with previous studies conducted on the CLP (Jia et al., 2015; Wang et al., 2011c), we recommend that grassland would be the best land use type in the study area characterized by semiarid climate and coarser soil texture, since grasslands do not require intensive management as orchards do. In addition to land use, best management practices may be further developed by considering the influence of topographical elments and soil properties on DSLs. Since the DSL-SWC on the shaded slopes were significantly higher than those on the sunlit slopes (Fig. 6), biological or engineering measures can be applied more efficiently to reclaim DSLs. The relationships involving soil texture or other soil properties may help select plant species as we can choose plant species whose root systems are suited for the soil properties. For example, plants with deep rooting systems are more suitable for sandy soils as more water is stored in deep soil layer due to the high infiltration rate. It should be noted that the relative importance of the factors influencing DSLs depends on the dynamic processes of a series of physical, chemical, and biological processes, and their interactions, at a wide range of scales that occur at different rates (Wang et al., 2013). However, a change in climate may stimulate or inactivate the impacts of these factors since a DSL is directly related to soil moisture content within a soil profile (Chen et al., 2008; D'Odorico and Porporato, 2004; Wang et al., 2012a; Yan et al., 2015). Moreover, understanding the spatial-temporal variations of soil drought and its related factors is important for evaluating the impacts of climate change (Anderegg et al., 2015). For example, future climate change scenarios characterized by different vegetation patterns and/or climatic conditions may yield changes in belowground root allocation, biomass and carbon storage, which would lead to further changes in the vertical soil moisture patterns and thus DSLs. This should be explored in the near future due to the rapid pace of climate change and increasing pressure from human activities. The soil–vegetation–atmosphere transfer system is a mutually interacting system where soil water can be transported to the atmosphere via evapotranspiration under the drive of the vapor pressure deficit (Mencuccini and Binks, 2015). We verified that the formation and variability of DSLs is a comprehensive result of precipitation, land use and topography. We then inferred that grassland is the optimum land use type in the study area. Our results provide new insights regarding deep soil drought and have implications for sustainable management of land use and soil water resource, and also for dryland farming on the ‘wind-water erosion crisscross region’ of the CLP. These findings can be applied to throughout the CLP because of the homogeneity of the source of loessial soil, the meteorological traits of monsoon climate, and similar human activities occurring across the whole area. Moreover, this information can also be applicable to other arid and semi-arid areas of the world covered with deep soils.

4.1. Sources of variation in DSLs at different scales The occurrence of a DSL is a comprehensive symptom of soil drought that occurs as a result of a negative water balance in soil–vegetation–atmosphere transfer system, which may be influenced by regional-scale-factors and local-scale-factors, and their interactions (Chen et al., 2008; Li, 1983; Wang et al., 2011c). At the regional scale, factors such as regional precipitation (Jia et al., 2015; Wilson et al., 2005), regional evapotranspiration (Perry and Niemann, 2007), soil type (Pollen, 2007; Wang et al., 2011c), vegetation zone (Bosch et al., 2006; Li et al., 2008), and spatial distribution of soil nutrition (Liu et al., 2013; Yoo and Kim, 2004) may affect DSL patterns and thus should be considered to improve the accuracy of DSL research in the future. At the local scale, factors such as soil texture and soil water holding capacity (Chen et al., 2008; Li, 1983), land use type (Wang et al., 2010b), plant growth age and root water uptake (Liu et al., 2010; Wang et al., 2008), slope gradient and aspect (Chen et al., 2008; Shangguan, 2007), and related human activities (i.e. irrigation, fertilizer, and thinning) (Yan et al., 2015) have been reported to be strongly related to the development of DSLs. Studies on the CLP by Wang et al. (2010b; 2011c) confirmed that combining regional (e.g. soil type, climatic condition) and local scale factors (e.g., land use, plant species, soil texture, micro-topography) could consistently determine the spatial distribution of DSLs. In the current study, we found that meteorological variables, land use, and topography significantly affect watershed scale DSLs both in time and space (Figs. 4–6 and Table 4) as DSLs are related to vertical and horizontal soil water balance processes. Meteorological variables control the input of soil water via precipitation and humidity, and also the output of soil water by affecting the flux of evapotranspiration via the changes of temperature and wind speed (Wang et al., 2017). Different land uses are usually associated with different types of vegetation which will influence the quantity and rate of soil water loss by transpiration due to differences in plant photosynthesis, root structures and life cycles (Wang et al., 2008, 2011c). The effects of topographical features such as slope aspect and gradient were strongly related to the distribution of solar radiation (Wang et al., 2011b), and they also indirectly affect soil water movement and related hydrological processes along slopes, thus affecting the level of a DSL. Chen et al. (2008) and Yan et al. (2015) have reported similar findings. For DSLT, at some occasions, the effects of altitude on DSL were significant in our study (Table 4). This may be because altitude represents the integrated results of meteorological forcings, topographies, and their interactions, although the changes in altitude were relatively small (75 m) in the watershed. Moreover, the heterogeneity of soil textures greatly affected the DSL distribution patterns. Coarser soils usually have relatively low degrees of soil water holding capacity due to weak pore structure which results in higher hydraulic conductivity. In coarse soil, this led to the leaching of soil water, especially in wet years, resulting in a high possibility for rainwater infiltration. The significant influence of soil texture on soil moisture has also been reported in other studies (Markewitz et al., 2010; Wang et al., 2017). It should be noted that the impacts of these factors on DSLs at a watershed scale depend on their level, status, flux, and interactions. For example, land use is certainly a key factor of influencing DSLs, however, it ceases to be a factor when precipitation exceeds the actual evapotranspiration of the vegetation, and therefore, there is no water deficit, and hence no DSLs formation (Wang et al., 2011c). 4.2. Implications for land use management and vegetation recovery Among the factors controlling DSLs patterns at different scales, only one factor - land use type - can be easily managed via changes to policy (Wang et al., 2013). Thus, understanding the impact of land use on 9

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5. Conclusion

395–412. Klute, A., Dirksen, C., 1986. Hydraulic conductivity of saturated soils. In: Klute, A. (Ed.), Methods of Soil Analysis. ASA and SSSA, Madison, Wisconsin, USA, pp. 694–700. Lee, J.E., Oliveira, R.S., Dawson, T.E., et al., 2005. Root functioning modifies seasonal climate. In: Proceedings of the National Academy of Sciences of the United States of America. 102. pp. 17576–17581. Li, H.J., Si, B.C., Ma, X.J., et al., 2019a. Deep soil water extraction by apple sequesters organic carbon via root biomass rather than altering soil organic carbon content. Sci. Total Environ. 670, 662–671. Li, H.J., Si, B.C., Wu, P.T., et al., 2019b. Water mining from the deep critical zone by apple trees growing on loess. Hydrol. Process. 33 (2), 320–327. Li, J., Chen, B., Li, X.F., et al., 2008. Effects of deep soil desiccation on artificial forestlands in different vegetation zones on the Loess Plateau of China. Acta Ecologica Sinica 28 (4), 1429–1445. Li, Y.S., 1983. The properties of water cycle in soil and their effect on water cycle for land in the Loess Plateau. Acta Ecologica Sinica 3 (2), 91–101 in Chinese with English abstract. Li, Y.S., Huang, M.B., 2008. Pasture yield and soil water depletion of continuous growing alfalfa in the Loess Plateau of China. Agric. Ecosyst. Environ. 124 (1–2), 3–12. Liu, W.Z., Zhang, X.C., Dang, T.H., et al., 2010. Soil water dynamics and deep soil recharge in a record wet year in the southern Loess Plateau of China. Agric. Water Manage. 97 (8), 1133–1138. Liu, Z.P., Shao, M.A., Wang, Y.Q., 2013. Scale-dependent correlations between soil properties and environmental factors across the Loess Plateau of China. Soil Res. 51 (2), 112–123. Liu, Z.P., Wang, Y.Q., Shao, M.A., et al., 2016. Spatiotemporal analysis of multiscalar drought characteristics across the Loess Plateau of China. J. Hydrol. (Amst.) 534, 281–299. Markewitz, D., Devine, S., Davidson, E.A., et al., 2010. Soil moisture depletion under simulated drought in the Amazon: impacts on deep root uptake. New Phytol. 187 (3), 592–607. McClain, M.E., Boyer, E.W., Dent, C.L., et al., 2003. Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems 6 (4), 301–312. McLean, E.O., 1982. Soil pH and lime requirement. In Methods of Soil Analysis Part II. Chemical and Microbiological Properties. Madison, WI, USA ASA-SSSA. Meissner, M., Kohler, M., Schwendenmann, L., et al., 2012. Partitioning of soil water among canopy trees during a soil desiccation period in a temperate mixed forest. Biogeosciences 9 (8), 3465–3474. Mencuccini, M., Binks, O., 2015. Forest ecology: tall, leafy conifers lose out. Nature Clim. Change 5 (7), 625–626. Mendham, D.S., White, D.A., Battaglia, M., et al., 2011. Soil water depletion and replenishment during first- and early second-rotation Eucalyptus globulus plantations with deep soil profiles. Agric. For. Meteorol. 151 (12), 1568–1579. Mo, K.C., Long, L.N., Xia, Y.L., et al., 2011. Drought indices based on the climate forecast system reanalysis and ensemble NLDAS. J. Hydrometeorol. 12 (2), 181–205. Nelson, D.W., Sommers, L.E., 1982. Total carbon, organic carbon and organic matter. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis, Part 2, 2nd Edition, Agronomy Monograph 9. Madison, WI, pp. 534–580 ASA and SSSA. Nepstad, D., Lefebvre, P., Da Silva, U.L., et al., 2004. Amazon drought and its implications for forest flammability and tree growth: a basin-wide analysis. Glob. Chang. Biol. 10 (5), 704–717. Padilla, F.M., Pugnaire, F.I., 2007. Rooting depth and soil moisture control Mediterranean woody seedling survival during drought. Funct. Ecol. 21 (3), 489–495. Peng, S.S., Piao, S.L., Shen, Z.H., et al., 2013. Precipitation amount, seasonality and frequency regulate carbon cycling of a semi-arid grassland ecosystem in Inner Mongolia, China: a modeling analysis. Agricult. For. Meteorol. 178–179 (4), 46–55. Perry, M.A., Niemann, J.D., 2007. Analysis and estimation of soil moisture at the catchment scale using EOFs. J. Hydrol. (Amst.) 334 (3–4), 388–404. Pollen, N., 2007. Temporal and spatial variability in root reinforcement of streambanks: accounting for soil shear strength and moisture. Catena 69 (3), 197–205. Robinson, N., Harper, R., Smettem, K., 2006. Soil water depletion by eucalyptus spp. Integrated into dryland agricultural systems. Plant and Soil 286 (1), 141–151. Salley, S.W., Sleezer, R.O., Bergstrom, R.M., et al., 2016. A long-term analysis of the historical dry boundary for the great plains of North America: implications of climatic variability and climatic change on temporal and spatial patterns in soil moisture. Geoderma 274, 104–113. Scanlon, B.R., Levitt, D.G., Reedy, R.C., et al., 2005. Ecological controls on water-cycle response to climate variability in deserts. In: Proceedings of the National Academy of Sciences of the United States of America. 102. pp. 6033–6038. Seager, R., Ting, M.F., Held, I., et al., 2007. Model projections of an imminent transition to a more arid climate in southwestern North America. Science 316 (5828), 1181–1184. Seeger, M., Errea, M.P., Beguerı́a, S., et al., 2004. Catchment soil moisture and rainfall characteristics as determinant factors for discharge/suspended sediment hysteretic loops in a small headwater catchment in the Spanish pyrenees. J. Hydrol. (Amst.) 288 (3), 299–311. Shangguan, Z.P., 2007. Soil desiccation occurrence and its impact on forest vegetation in the Loess Plateau of China. Int. J. Sustain. Dev. World Ecol. 14 (3), 299–306. Tang, K., 2000. Importance and urgency of the management in the wind-water erosion crosscriss zone on the Loess Plateau of China. Soil Water Conserv. Chin. 11 (5), 11–17 in Chinese with English abstract. Taylor, C.M., de Jeu, R.A.M., Guichard, F., et al., 2012. Afternoon rain more likely over drier soils. Nature 489, 423. Wang, A., Lettenmaier, D.P., Sheffield, J., 2011a. Soil moisture drought in China, 1950–2006. J. Clim. 24 (13), 3257–3271.

Across a small watershed on the northern Loess Plateau of China, DSLs occurred at more than 90% of sampling sites. We verified that the belowground response of soil drought (as indicated by DSL indices) is controlled by the combined effects of meteorological processes, land use, and topography at the watershed scale. The three DSL indices DSLFD, DSLT and DSL-SWC - exhibited a moderate degree of spatial variability, while they showed moderate, moderate and weak temporal variability, respectively. The DSL indices, particularly the DSLFD, were more sensitive to rainfall in wet years relative to normal years. Temporal variation of mean spatial DSLT and DSLFD were significantly correlated with climatic variables. The spatial variation of mean temporal DSL-SWC was highly sensitive to land use and topography. Plan curvature, slope gradient, clay and silt content also affected DSLs in the watershed. Grassland may be the optimum land use type to minimize the occurrence of DSL. This information is important for evaluating the soil water regime, estimating soil drought, and mediating water cycling in water-limited regions with deep soils around the world. Acknowledgements This research was supported by the National Natural Science Foundation of China (Nos. 41722106, 41530854, 41971045, and 41471189), and the Youth Innovation Promotion Association CAS. References Anderegg, W.R.L., Flint, A., Huang, C.Y., et al., 2015. Tree mortality predicted from drought-induced vascular damage. Nat. Geosci. 8 (5), 367–371. Bosch, D.D., Lakshmi, V., Jackson, T.J., et al., 2006. Large scale measurements of soil moisture for validation of remotely sensed data: georgia soil moisture experiment of 2003. J. Hydrol. (Amst.) 323 (1–4), 120–137. Bысоцкий, Γ.H., 1960. Избранные tpyды Изд. No304. Москва:Сельхозгиз 31–67. Calanca, P., 2007. Climate change and drought occurrence in the Alpine region: how severe are becoming the extremes? Glob. Planet. Change 57 (1), 151–160. Chen, H.S., Shao, M.A., Li, Y.Y., 2008. Soil desiccation in the Loess Plateau of China. Geoderma 143, 91–100. Chen, Y., Wang, K., Lin, Y., et al., 2015. Balancing green and grain trade. Nat. Geosci. 8 (10), 739–741. Cheng, L.P., Liu, W.Z., 2014. Long term effects of farming system on soil water content and dry soil layer in deep loess profile of Loess Tableland in China. J. Integr. Agric. 13 (6), 1382–1392. D'Odorico, P., Porporato, A., 2004. Preferential states in soil moisture and climate dynamics. In: Proceedings of the National Academy of Sciences of the United States of America. 101. pp. 8848–8851. Dai, A., Trenberth, K.E., Qian, T., 2004. A global dataset of palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. J. Hydrometeorol. 5 (6), 1117–1130. Duan, C., Fang, L., Yang, C., et al., 2018. Reveal the response of enzyme activities to heavy metals through in situ zymography. Ecotoxicol. Environ. Saf. 156, 106–115. Feng, X.M., Fu, B.J., Piao, S.L., et al., 2016. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang 6, 1019–1022. Fischer, E.M., Seneviratne, S.I., Vidale, P.L., et al., 2007. Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J. Clim. 20 (20), 5081–5099. Goulden, M.L., Bales, R.C., 2019. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. Granier, A., Reichstein, M., Bréa, N., et al., 2007. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agric. For. Meteorol. 143 (1–2), 123–145. Guillod, B.P., Orlowsky, B., Miralles, D.G., et al., 2015. Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nat. Commun. 6, 6443–6448. Hu, W., Shao, M.A., Han, F.P., et al., 2010. Watershed scale temporal stability of soil water content. Geoderma 158 (3–4), 181–198. Hu, W., Shao, M.A., Si, B.C., 2012. Seasonal changes in surface bulk density and saturated hydraulic conductivity of natural landscapes. Eur. J. Soil Sci. 63 (6), 820–830. Huang, M.B., Gallichand, J., 2006. Use of the SHAW model to assess soil water recovery after apple trees in the gully region of the Loess Plateau, China. Agricult. Water Manage. 85 (1–2), 67–76. Jia, X.X., Shao, M.A., Zhang, C.C., et al., 2015. Regional temporal persistence of dried soil layer along south–north transect of the Loess Plateau, China. J. Hydrol. (Amst.) 528, 152–160. Jia, X.X., Shao, M.A., Zhu, Y.J., et al., 2017. Soil moisture decline due to afforestation across the Loess Plateau, China. J. Hydrol. (Amst.) 546, 113–122. Jipp, P.H., Nepstad, D.C., Cassel, D.K., et al., 1998. Deep soil moisture storage and transpiration in forests and pastures of seasonally-dry Amazonia. Clim. Change 39,

10

Agricultural and Forest Meteorology 282–283 (2020) 107866

Y. Wang, et al.

Ecol. Eng. 74, 213–222. Wang, Y.Q., Shao, M.A., Zhu, Y.J., et al., 2011c. Impacts of land use and plant characteristics on dried soil layers in different climatic regions on the Loess Plateau of China. Agric. For. Meteorol. 151 (4), 437–448. Wang, Y.Q., Shao, M.A., Zhu, Y.J., et al., 2018. A new index to quantify dried soil layers in water-limited ecosystems: a case study on the Chinese Loess Plateau. Geoderma 322, 1–11. Wang, Z.Q., Liu, B.Y., Liu, G., et al., 2009. Soil water depletion depth by planted vegetation on the Loess Plateau. Sci. Chin. Series D Earth Sci. 52 (6), 835–842. Wilson, D.J., Western, A.W., Grayson, R.B., 2005. A terrain and data-based method for generating the spatial distribution of soil moisture. Adv. Water Resour. 28 (1), 43–54. Yan, W.M., Deng, L., Zhong, Y., et al., 2015. The characters of dry soil layer on the Loess Plateau in China and their influencing factors. PLoS ONE 10 (8), e0134902. Yang, W.Z., 2001. Soil water resources and afforestation in Loess Plateau. J. Natur. Resour. 16 (5), 433–438 in Chinese with English abstract. Yoo, C., Kim, S., 2004. EOF analysis of surface soil moisture field variability. Adv. Water Resour. 27 (8), 831–842. Zavaleta, E.S., Thomas, B.D., Chiariello, N.R., et al., 2003. Plants reverse warming effect on ecosystem water balance. In: Proceedings of the National Academy of Sciences of the United States of America. 100. pp. 9892–9893. Zhang, Y.W., Shangguan, Z.P., 2016. The coupling interaction of soil water and organic carbon storage in the long vegetation restoration on the Loess Plateau. Ecol. Eng. 91, 574–581. Zhu, Y.J., Shao, M.A., 2008. Variability and pattern of surface moisture on a small-scale hillslope in Liudaogou catchment on the northern Loess Plateau of China. Geoderma 147 (3–4), 185–191. Ziadat, F.M., Taimeh, A.Y., 2013. Effect of rainfall intensity, slope, land use and antecedent soil moisture on soil erosion in an arid environment. Land Degrad. Develop. 24 (6), 582–590. Zornoza, R., Mataix-Solera, J., Guerrero, C., et al., 2007. Evaluation of soil quality using multiple lineal regression based on physical, chemical and biochemical properties. Sci. Total Environ. 378 (1–2), 233–237.

Wang, D., Wang, L., 2017. Dynamics of evapotranspiration partitioning for apple trees of different ages in a semiarid region of northwest China. Agric. Water Manage. 191, 1–15. Wang, L., Shao, M.A., Wang, Q.J., et al., 2004. Review of research on soil desication in the Loess Plateau. Trans. CSAE 20 (5), 27–31 in Chinese with English abstract. Wang, L., Wang, Q.J., Wei, S.P., et al., 2008. Soil desiccation for Loess soils on natural and regrown areas. For. Ecol. Manage. 255 (7), 2467–2477. Wang, L., Wei, S.P., Horton, R., et al., 2011b. Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China. Catena 87 (1), 90–100. Wang, T.J., Franz, T.E., Li, R.P., et al., 2017. Evaluating climate and soil effects on regional soil moisture spatial variability using EOFs. Water Resour. Res. 53 (5), 4022–4035. Wang, X.C., Li, J., Tahir, M.N., et al., 2012a. Validation of the EPIC model and its utilization to research the sustainable recovery of soil desiccation after alfalfa (Medicago sativa L.) by grain crop rotation system in the semi-humid region of the Loess Plateau. Agricult. Ecosyst. Environ. 161, 152–160. Wang, Y.Q., Han, X.W., Jin, Z., et al., 2016. Soil organic carbon stocks in deep soils at a watershed scale on the Chinese Loess Plateau. Soil Sci. Soc. Am. J. 80 (1), 157–167. Wang, Y.Q., Shao, M.A., Liu, Z.P., 2010a. Large-scale spatial variability of dried soil layers and related factors across the entire Loess Plateau of China. Geoderma 159 (1), 99–108. Wang, Y.Q., Shao, M.A., Liu, Z.P., 2013. Vertical distribution and influencing factors of soil water content within 21-m profile on the Chinese Loess Plateau. Geoderma 193, 300–310. Wang, Y.Q., Shao, M.A., Liu, Z.P., et al., 2012b. Investigation of factors controlling the regional-scale distribution of dried soil layers under forestland on the Loess Plateau, China. Surv. Geophys. 33 (2), 311–330. Wang, Y.Q., Shao, M.A., Shao, H.B., 2010b. A preliminary investigation of the dynamic characteristics of dried soil layers on the Loess Plateau of China. J. Hydrol. (Amst.) 381 (1), 9–17. Wang, Y.Q., Shao, M.A., Zhang, C.C., et al., 2015. Choosing an optimal land-use pattern for restoring eco-environments in a semiarid region of the Chinese Loess Plateau.

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