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Advances in Forest Hydrology in Light of Human Intervention and...
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Advances in Forest Hydrology in Light of Human Intervention and Climate Change

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Hydrology".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2445

Special Issue Editors

Dr. André Ferreira Rodrigues

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Guest Editor
Department of Hydraulics and Water Resources, School of Engineering, Universidade Federal de Minas Gerais, CP 6627, Belo Horizonte 31270-901, MG, Brazil
Interests: canopy interception; soil water dynamics; nutrient and water cycles in forest systems; evapotranspiration; extreme events; forest resilience; urban forests
Dr. Chuan Yuan

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Guest Editor
School of Geographical Sciences, Southwest University, Chongqing 400715, China
Interests: ecohydrology; rainfall redistribution; critical zone; soil water dynamics; plant drought resistance
Special Issues, Collections and Topics in MDPI journals
Dr. Carlos Rogério Mello

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Guest Editor
Water Resources Department, Engineering School, Federal University of Lavras, Lavras 37200-000, MG, Brazil
Interests: hydrological modeling; water resources management; environmental science; soil physics; hydrology; environmental impact assessment; water balance; climate change impacts on hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest hydrology has been the subject of studies in recent decades focusing on canopy interception, the internal redistribution of precipitation (steamflow and throughfall), soil moisture spatiotemporal distribution, evapotranspiration, and recharge. Understanding water fate in forest systems goes beyond modeling as it can support forest management and planning to guarantee ecosystem services (e.g., water yield and nutrient cycles); however, most of these studies relied on short monitoring periods to draw conclusions, hampering the overall understanding of human and climate impacts on forest hydrology. In this sense, this Special Issue aims to improve our knowledge of forest hydrology, considering its interface with human intervention and climate change. Studies presenting new methods, knowledge, and models in the interface of human intervention, climate change, and forest hydrology are more than welcome. These include (but are not limited to) the following: (i) modeling forest hydrology under different climate change scenarios; (ii) case studies on forest hydrology response to human intervention; (iii) nutrient and hydrological cycles in urban forests; and (iv) forest resilience and adaptation to extreme events. Original research focused on understanding forest resilience and ecosystem services under climate extremes and human pressures is encouraged.

Dr. André Ferreira Rodrigues
Dr. Chuan Yuan
Dr. Carlos Rogério Mello
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • extreme events
  • anthropogenic pressure
  • ecosystem services
  • canopy interception
  • evapotranspiration
  • forest resilience

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Published Papers (3 papers)

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20 pages, 5597 KiB  
Article
Quantification of Soil Water Dynamics Response to Rainfall in Forested Hillslope Based on Soil Water Potential Measurement
by Ruxin Yang, Fei Wang, Xiangyu Tang, Junfang Cui, Genxu Wang, Li Guo and Han Zhang
Forests 2025, 16(1), 75; https://doi.org/10.3390/f16010075 - 5 Jan 2025
Viewed by 435
Abstract
Soil hydrological response is crucial for controlling water flow and biogeochemical processes on hillslopes. Understanding soil water dynamics in response to rainfall is essential for accurate hydrological modeling but remains challenging in humid mountainous regions characterized by high antecedent moisture and substantial heterogeneity. [...] Read more.
Soil hydrological response is crucial for controlling water flow and biogeochemical processes on hillslopes. Understanding soil water dynamics in response to rainfall is essential for accurate hydrological modeling but remains challenging in humid mountainous regions characterized by high antecedent moisture and substantial heterogeneity. We sought to elucidate soil water response patterns to rainfall by estimating lag time, wetting front velocity, rainfall threshold, and preferential flow (PF) frequency in 166 rainfall events across 36 sites on two hillslopes within the Hailuogou catchment, located on the eastern Qinghai–Tibet Plateau. Results indicated that over 90% of the events triggered rapid soil water potential (SWP) responses to depths of 100 cm, with faster responses observed at steeper upslope positions with thinner O horizons. Even light rainfall (2–3 mm) was sufficient to trigger SWP responses. PF was prevalent across the hillslopes, with higher occurrence frequencies at upslope and downslope positions due to steep terrain and consistently moist conditions, respectively. Using the Multivariate Adaptive Regression Splines (MARS) model, we found that site factors (e.g., soil properties and topography) had a greater influence on SWP responses than rainfall characteristics or antecedent soil wetness conditions. These findings highlighted the value of SWP in capturing soil water dynamics and enhancing the understanding and modeling of complex hillslope hydrological processes. Full article
(This article belongs to the Special Issue Advances in Forest Hydrology in Light of Human Intervention and Climate Change)
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Figure 1

Figure 1
<p>The location of the study site on the eastern slope of Gongga Mountain, which is situated at the east end of the Qinghai–Tibet Plateau (<b>a</b>); an overview of the Hailuogou Valley catchment (<b>b</b>); conceptualization diagram of the study hillslopes and monitoring point locations (<b>c</b>); the vegetations and soil profiles of HS1 and HS2 (<b>d</b>–<b>g</b>).</p> Full article ">Figure 2
<p>Conceptual diagram of soil water potential response processes to rainfall. <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> represents the initial time of the rainfall event; <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> denotes the time of soil water potential response at a specific depth (<span class="html-italic">n</span> = 1, 2, 3, corresponding to depths of 10 cm, 50 cm, and 100 cm, respectively); <math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> is the response lag time at a specific depth, calculated as <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>; ASWP refers to the antecedent soil water potential, measured one hour prior to <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>. <span class="html-italic">S</span> is the cumulative rainfall amount required to trigger a measurable SWP response at a specific soil depth.</p> Full article ">Figure 3
<p>Depth (<span class="html-italic">RD</span>), period (<span class="html-italic">RP</span>), peak intensities (<span class="html-italic">In<sub>peak</sub></span>), and average intensities (<span class="html-italic">In<sub>aver</sub></span>) of all rainfall events on HS1 (<b>a</b>) and HS2 (<b>b</b>) hillslopes, respectively.</p> Full article ">Figure 4
<p>Temporal variations in rainfall and corresponding average soil water potential (SWP) at 10 cm, 50 cm, and 100 cm of different positions on HS1 ((<b>a</b>)—rainfall, (<b>b</b>)—upslope, (<b>c</b>)—mid-slope, (<b>d</b>)—downslope) and HS2 ((<b>e</b>)—rainfall, (<b>f</b>)—upslope, (<b>g</b>)—mid-slope, (<b>h</b>)—downslope).</p> Full article ">Figure 5
<p>Response lag time (<math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> across hillslopes, HS1 (<b>a1</b>,<b>a2</b>) and HS2 (<b>b1</b>,<b>b2</b>), with rainfall depth (blue bars) and peak intensity (grey filled circles) of each rainfall event (<b>a3</b>,<b>b3</b>). The box plot depicts the variation in <math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> across soil depths and hillslope positions (grey open circles are outliers). Symbol color represents the antecedent soil wetness condition (ASWP, hPa), and diameter represents the magnitude of <math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>. US, upslope; MS, mid-slope; DS, downslope. 10, 50, and 100 indicate the soil depths of 10 cm, 50 cm, and 100 cm.</p> Full article ">Figure 6
<p>Wetting front velocity (<math display="inline"><semantics> <mrow> <mi>v</mi> </mrow> </semantics></math>) across hillslopes, HS1 (<b>a1</b>,<b>a2</b>) and HS2 (<b>b1</b>,<b>b2</b>), with rainfall depth (blue bars) and peak intensity (grey filled circles) of each rainfall event (<b>a3</b>,<b>b3</b>). The box plot depicts the variation in <math display="inline"><semantics> <mrow> <mi>v</mi> </mrow> </semantics></math> across soil depths and hillslope positions (grey open circles are outliers). Symbol color represents the antecedent soil wetness condition (ASWP, hPa), and diameter represents the magnitude of <math display="inline"><semantics> <mrow> <mi>v</mi> </mrow> </semantics></math>. US, upslope; MS, mid-slope; DS, downslope. 10, 50, and 100 indicate the soil depths of 10 cm, 50 cm, and 100 cm.</p> Full article ">Figure 7
<p>Rainfall thresholds (<span class="html-italic">S</span>) to trigger soil water potential response at different hillslope positions of HS1 (<b>a</b>) and HS2 (<b>b</b>).</p> Full article ">Figure 8
<p>The occurrence (<b>a1</b>,<b>b1</b>) and frequencies of preferential flow (<b>a2</b>,<b>b2</b>) across HS1 and HS2, with rainfall depth (blue bars) and peak intensity (grey filled circles) of each rainfall event (<b>a3</b>,<b>b3</b>). Black circles indicate non-preferential flow; green filled circles indicate the occurrence of preferential flow; black crosses indicate non-response.</p> Full article ">Figure 9
<p>Relative importance of the temporal (<b>a</b>) and site (<b>b</b>) factors for soil water potential response metrics (response lag time, wetting front velocity, and preferential flow frequency) from Multivariate Adaptive Regression Spline models. Wider links indicate greater importance. <span class="html-italic">Ks</span>, hydraulic conductivity; <span class="html-italic">Silt</span>%, silt content of soil; <span class="html-italic">Clay</span>%, clay content of soil.</p> Full article ">
23 pages, 28901 KiB  
Article
Runoff Change Characteristics and Response to Climate Variability and Human Activities Under a Typical Basin of Natural Tropical Rainforest Converted to Monoculture Rubber Plantations
by Shiyu Xue, Lirong Zhu, Yanhu He, Dan Li and Changqing Ye
Forests 2024, 15(11), 1918; https://doi.org/10.3390/f15111918 - 30 Oct 2024
Viewed by 628
Abstract
Climate variability and human activities are major influences on the hydrological cycle. However, the driving characteristics of hydrological cycle changes and the potential impact on runoff in areas where natural forests have been converted to rubber plantations on a long-term scale remain unclear. [...] Read more.
Climate variability and human activities are major influences on the hydrological cycle. However, the driving characteristics of hydrological cycle changes and the potential impact on runoff in areas where natural forests have been converted to rubber plantations on a long-term scale remain unclear. Based on this, the Mann–Kendall (MK) and Pettitt breakpoint tests and the Double Mass Curve method were employed to identify the variation characteristics and breakpoints of precipitation (P), potential evapotranspiration (ET0), and runoff depth (R) in the Wanquan River Basin (WQRB) during the 1970–2016 period. The changes in runoff attributed to P, ET0, and the catchment characteristics parameter (n) were quantified using the elastic coefficient method based on the Budyko hypothesis. The results revealed that the P and R in the WQRB exhibited statistically insignificant decreasing trends, while ET0 displayed a significant increasing trend (p < 0.05). The breakpoint of runoff changes in the Jiabao and the Jiaji stations occurred in 1991 and 1983, respectively. The runoff changes show a negative correlation with both the n and ET0, while exhibiting a positive correlation with P. Moreover, it is observed that P and ET0 display higher sensitivity towards runoff changes compared to n. The decomposition analysis reveals that in the Dingan River Basin (DARB), human activities account for 53.54% of the runoff changes, while climate variability contributes to 46.46%. In the Main Wanquan River Basin (MWQRB), human activities contribute to 46.11%, whereas climate variability accounts for 53.89%. The research findings suggest that runoff is directly reduced by climate variability (due to decreased P and increased ET0), while human activities indirectly contribute to changes in runoff through n, exacerbating its effects. Rubber forest stands as the prevailing artificial vegetation community within the WQRB. The transformation of natural forests into rubber plantations constitutes the primary catalyst for the alteration of n in the WQRB. The research findings provide important reference for quantifying the driving force of hydrological changes caused by deforestation, which is of great significance for sustainable management of forests and water resources. Full article
(This article belongs to the Special Issue Advances in Forest Hydrology in Light of Human Intervention and Climate Change)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The locations of DARB, MWQRB, WQRB, and hydro-meteorological stations.</p> Full article ">Figure 2
<p>The temporal variation in <span class="html-italic">P</span>, <span class="html-italic">ET</span><sub>0</sub>, and <span class="html-italic">R</span> (Jiabao station) between 1970 and 2016 in the DARB.</p> Full article ">Figure 3
<p>The temporal variation in <span class="html-italic">P</span>, <span class="html-italic">ET</span><sub>0</sub>, and <span class="html-italic">R</span> (Jiaji station) between 1970 and 2016 in the MWQRB.</p> Full article ">Figure 4
<p>Sequential Mann–Kendall test for <span class="html-italic">P</span>, <span class="html-italic">ET</span><sub>0</sub>, and <span class="html-italic">R</span> of the DARB and MWQRB from 1970 to 2016. (<b>A</b>) Precipitation in the DARB; (<b>B</b>) Precipitation in the MWQRB; (<b>C</b>) Potential evapotranspiration in the DARB; (<b>D</b>) Potential evapotranspiration in the MWQRB; (<b>E</b>) Runoff depth in the DARB; and (<b>F</b>) Runoff depth in the MWQRB. When the calculated UF-value in the MK test falls below −1.96 or exceeds 1.96, it signifies a statistically significant decreasing or increasing trend at a confidence level of 95%.</p> Full article ">Figure 5
<p>The temporal dynamic characteristics of (<b>a</b>) <span class="html-italic">P</span> elasticity coefficient to runoff change, (<b>b</b>) <span class="html-italic">ET</span><sub>0</sub> elasticity coefficient to runoff change, and (<b>c</b>) <span class="html-italic">n</span> elasticity coefficient to runoff change in the (A) DARB and (B) MWQRB from 1970 to 2016.</p> Full article ">Figure 6
<p>Sequential Pettitt breakpoint test for annual runoff depth of (<b>A</b>) Jiabao and (<b>B</b>) Jiaji hydrological stations.</p> Full article ">Figure 7
<p>Precipitation–runoff double mass curves for the Jiabao (<b>A</b>) and Jiaji (<b>B</b>) stations from 1970 to 2016.</p> Full article ">Figure 8
<p>Land use/cover maps in the DARB and MWQRB in 1990 and 2010.</p> Full article ">
19 pages, 6503 KiB  
Article
The Effects and Contributions of Ecological Factors on Soil Carbon, Water and Nutrient Storages Under Long-Term Vegetation Restoration on the Eastern Loess Plateau
by Yingnan Xiong, Yufei Zhang, Zhuo Zhang, Tianjiao Feng, Ping Wang and Saskia Keesstra
Forests 2024, 15(11), 1898; https://doi.org/10.3390/f15111898 - 28 Oct 2024
Viewed by 954
Abstract
Vegetation restoration plays a crucial role in conserving soil and water, as well as rehabilitating ecosystems, by enhancing soil properties and vegetation attributes. The evaluation of the ecological consequences among different vegetation restoration types can be achieved by clarifying the impacts on carbon, [...] Read more.
Vegetation restoration plays a crucial role in conserving soil and water, as well as rehabilitating ecosystems, by enhancing soil properties and vegetation attributes. The evaluation of the ecological consequences among different vegetation restoration types can be achieved by clarifying the impacts on carbon, water and nutrient storages. In this study, we selected four typical vegetation restoration types (Pinus tabuliformis forest (PTF), Platycladus orientalis forest (POF) and Robinia pseudoacacia forest (RPF) as typical planted forests, and the natural secondary forest (NSF) as the control treatment) in the eastern Loess Plateau of China. The soil properties (at 0–200 cm depth) and vegetation attributes (including arborous, shrubs and herbaceous plants) were measured, as well as calculated soil carbon, water and nutrient storages, with a total of 1600 soil samples and 180 vegetation survey plots. The partial redundancy analysis (pRDA) and correlation analysis were also used to analyze the contributions and relationships among environmental factors, soil eco-hydrology and nutrient supplement services in different forestry ecosystems. The results indicate the following: (1) NSF has the lowest soil bulk density (1.21 ± 0.184 g·cm−3). Soil properties varied significantly at vertical scales, and had obvious surface accumulation. (2) Soil moisture storages were better in natural forests than those in planted forests, with more drastic changes in soil moisture dynamics. (3) The soil carbon, nitrogen, and phosphorus storages significantly differed among four vegetation types, with the highest carbon storages in PTF (207.75 ± 0.674 t·ha−1), the highest nitrogen storages in POF (5.54 t·ha−1), and the highest phosphorus storages in RPF (4.33 t·ha−1), respectively. (4) Soil carbon storages depend primarily on the coupling effect of soil properties and precipitation, while nutrient storage is mainly influenced by soil properties. The results quantify the significant differences in soil water, carbon, and nutrient storage across various vegetation restoration types, and reveal the individual and combined contributions of environmental factors, providing new insights into the mechanisms driving these differences. These findings offer practical guidance for the sustainable management of forest ecosystems and the optimization of ecological restoration strategies on the Loess Plateau. Full article
(This article belongs to the Special Issue Advances in Forest Hydrology in Light of Human Intervention and Climate Change)
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Figure 1

Figure 1
<p>Location of the study area and experimental sites (<b>a</b>) and the schematic of the sample plots (<b>b</b>). Note: NSF, natural secondary forests; POF, <span class="html-italic">Pl. orientalis</span> forests; PTF, <span class="html-italic">P. tabulaeformis</span> forests; RPF, <span class="html-italic">R. pseudoacacia</span> forests.</p> Full article ">Figure 2
<p>Soil properties of soil BD (<b>a</b>), soil pH (<b>b</b>), and soil cation exchange capacity (<b>c</b>) under different vegetation restoration types (NSF, natural secondary forests; POF, <span class="html-italic">Pl. orientalis</span> forests; PTF, <span class="html-italic">P. tabulaeformis</span> forests; RPF, <span class="html-italic">R. pseudoacacia</span> forests). Note: The difference in the lowercase letters for vegetation types at the different soil depths was significant, and the difference in the capital letters for different vegetation types in the same soil depth was significant (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>The distribution of soil sand, silt, and clay under different vegetation restoration types (NSF, natural secondary forests; POF, <span class="html-italic">Pl. orientalis</span> forests; PTF, <span class="html-italic">P. tabulaeformis</span> forests; RPF, <span class="html-italic">R. pseudoacacia</span> forests).</p> Full article ">Figure 4
<p>Temporal dynamics of soil moisture under different vegetation restoration types and daily precipitation in 2017–2022 (NSF, natural secondary forests; POF, <span class="html-italic">Pl. orientalis</span> forests; PTF, <span class="html-italic">P. tabu-laeformis</span> forests; RPF, <span class="html-italic">R. pseudoacacia</span> forests).</p> Full article ">Figure 5
<p>Comparison of soil water content in each soil layer under different vegetation types during 2017–2022 (NSF, natural secondary forests; POF, <span class="html-italic">Pl. orientalis</span> forests; PTF, <span class="html-italic">P. tabulaeformis</span> forests; RPF, <span class="html-italic">R. pseudoacacia</span> forests).</p> Full article ">Figure 6
<p>Comparisons of soil nutrient storages in 0–200 cm soil layer under different vegetation types. Note: (<b>a</b>) soil moisture storage; (<b>b</b>) soil nitrogen storage; (<b>c</b>) soil phosphorus storage; (<b>d</b>) soil carbon storage. Different capital letters indicate significant differences among each vegetation type at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 7
<p>Differences in vegetation attributes of stand density (<b>a</b>), DBH (<b>b</b>), plant height (<b>c</b>), branch diameter (<b>d</b>), height below branch (<b>e</b>), and crown width (<b>f</b>) for different vegetation types (NSF, natural secondary forests; POF, <span class="html-italic">Pl. orientalis</span> forests; PTF, <span class="html-italic">P. tabulaeformis</span> forests; RPF, <span class="html-italic">R. pseudoacacia</span> forests). Note: DBH means the diameter at breast height. Different capital letters indicate significant differences among different vegetation types at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 8
<p>Differences in understory vegetation attributes of shrub plant heights (<b>a</b>), shrub branch diameters (<b>b</b>), shrub crown widths (<b>c</b>), herbaceous plant heights (<b>d</b>), herbaceous base diameters (<b>e</b>), and herbaceous crown width (<b>f</b>) in different vegetation types (NSF, natural secondary forests; POF, <span class="html-italic">Pl. orientalis</span> forests; PTF, <span class="html-italic">P. tabulaeformis</span> forests; RPF, <span class="html-italic">R. pseudoacacia</span> forests). Different capital letters indicate significant differences among each vegetation types at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 9
<p>Variation partitioning analysis differentiated the influences of soil properties, vegetation attributes and precipitation on (<b>a</b>) soil nitrogen storage, (<b>b</b>) soil phosphorus storage, (<b>c</b>) soil moisture storage, and (<b>d</b>) soil carbon storage. The data represent percentages of variation explained by the factors. Note: REs means residuals, which indicate the unexplained part of the variation.</p> Full article ">Figure 10
<p>Correlation matrix analysis vegetation attributes and soil properties. Red means positive correlation, blue means negative correlation. The width of the ellipse represents significance level: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 (Oneway ANOVA Test). BD, bulk density; CEC, cation exchange capacity; TP, total phosphorus; TN, total nitrogen; SOC, soil organic content; DBH, diameter at breast height; PRE, precipitation; SNS, soil nitrogen storage; SPS, soil phosphorus storage; SWS, soil moisture storage; SCS, soil carbon storage.</p> Full article ">
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Driving Forces on Throughfall Spatial Heterogeneity and Temporal Stability of Multi-stemmed Shrubs
Authors: Feng Xiong; Jiayu Zhou; Chuan Yuan; Yanting Hu; Yafeng Zhang; Li Guo; Qin Liu; Zhiyun Jiang; Yianghao Gao; Wenhua Xiang; Delphis F. Levia
Affiliation: 1 Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China 2 College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, China 3 Faculty of Life Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China 4 Shapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China 5 State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China 6 School of Geographic Sciences, Nanjing University of Information Sciences & Technology, Nanjing 210044, China 7 School of Geography, South China Normal University, Guangzhou 510631, China 8 Department of Geography & Spatial Sciences, University of Delaware, Newark, DE, USA 9 Department of Plant & Soil Sciences, University of Delaware, Newark, DE, USA

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