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Soil Dynamics and Water Resource Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Soil and Water".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 9108

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Soils Department, Universidade Federal de Santa Maria, Santa Maria 97105-900, RS, Brazil
Interests: soil physics; soil mechanics; soil hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Studies are welcomed for submission to this Special Issue on the general topic of soil dynamics and water resource management. Studies should have a focus on the understanding of processes and practical applications by stakeholders in the field.

Combining soil and water at different landscape scales will enrich the scientific contribution. Research on soil quality, structure, and compaction studied at different scales (microscopic to macroscopic), with the use of traditional and modern technologies, coupled or not to soil, landscape, and watershed hydrology affecting the amount and quality of water are most welcome, as well as those on the interaction between soil and plants (native species or agricultural, forest and grassland crops), especially coupled with soil type and quality and hydrological behavior.

These are some research topics for this Special Issue on soil dynamics and water resource management, but the scope is not limited to those, as myriad topic combinations are possible when studying soils and practices for sustainable land use and water management. The scientific contributions may be original research papers, new study methods, perspectives and opinions, reviews (traditional or narrative, systematic, meta-analysis, or meta-synthesis), or modeling approaches.

We look forward to receiving your contributions.

Prof. Dr. José Miguel Reichert
Guest Editor

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Keywords

  • soil structure
  • soils and plants
  • watershed hydrology
  • water balance
  • water availability to plants

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Published Papers (8 papers)

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12 pages, 2439 KiB  
Article
Preparation of Slow-Release Potassium Persulfate Microcapsules and Application in Degradation of PAH-Contaminated Soil
by Hao Wu, Yuting Yang, Lina Sun, Yinggang Wang, Hui Wang and Xiaoxu Wang
Water 2024, 16(21), 3045; https://doi.org/10.3390/w16213045 - 24 Oct 2024
Viewed by 899
Abstract
Due to potassium persulfate’s excessive reaction speed and severe impact on the soil environment, slowing down the reaction rate and reducing its environmental impact is an important but challenging matter. Hence, microencapsulation technology was taken to modify potassium persulfate, and potassium persulfate microcapsules [...] Read more.
Due to potassium persulfate’s excessive reaction speed and severe impact on the soil environment, slowing down the reaction rate and reducing its environmental impact is an important but challenging matter. Hence, microencapsulation technology was taken to modify potassium persulfate, and potassium persulfate microcapsules were used to remediate the PAHs-contaminated soil. The results of XRD and an infrared spectrum identified that the core material (potassium persulfate) exists after being encapsulated by the wall material (stearic acid), and there was no chemical reaction between the core material and wall material. The results of the sustained release effect and kinetic equation showed that the release rate of the potassium persulfate microcapsules was close to 60% after 48 h, and it had a good sustained-release effect compared with previous studies. The results of the radical probe revealed that the free radicals produced from potassium persulfate microcapsules activated by Fe2+ were the main reasons for the degradation of PAHs, and SO4· played the most important major role in the degradation of PAHs, followed by ·OH, and the reducing substances also played an auxiliary role. The results also suggested that potassium persulfate microcapsules not only degraded PAHs in soil (53.6% after 72 h) but also had fewer negative effects on the environment, and they even promoted the growth and development of microorganisms and increased the germination rate of seeds due to the slow-release effect of the microcapsules. This work reveals the degradation mechanism of potassium persulfate microcapsules and provides a new amendment of potassium persulfate in the remediation of PAHs-contaminated soil. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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Figure 1

Figure 1
<p>XRD patterns before and after microencapsulation of potassium persulfate. (<b>a</b>) Potassium persulfate before microencapsulation, (<b>b</b>) 1:3 microcapsules, (<b>c</b>) 1:2 microcapsules, and (<b>d</b>) 1:1 microcapsules.</p>
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<p>Infrared spectrogram before and after microencapsulation: (<b>a</b>) 1:1 microcapsules, (<b>b</b>) 1:2 microcapsules, (<b>c</b>) 1:3 microcapsules, and (<b>d</b>) stearic acid before microencapsulation.</p>
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<p>Scanning electron microscopy before and after sustained release of the 1:2 microcapsule. (<b>a</b>) 2 μm before sustained release, (<b>b</b>) 2 μm after sustained release, (<b>c</b>) 10 μm before sustained release, and (<b>d</b>) 10 μm after sustained release.</p>
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<p>Release curves of potassium persulfate microcapsules.</p>
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<p>Degradation rate of PAHs in soil by potassium persulfate microcapsules.</p>
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<p>Degradation mechanism study on PAHs by potassium persulfate microcapsules. (<b>a</b>) Degradation effect of PAHs in deionized water, (<b>b</b>) effects of removing free radicals, (<b>c</b>) effect of removing ·OH, and (<b>d</b>) effects of removing reducing substances. The different letters indicate statistical differences between the groups (one-way ANOVA test) and represent a 5% significance level.</p>
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<p>Effects on environments after degradation by microcapsule. (<b>a</b>) Soil pH, (<b>b</b>) seed germination rate, (<b>c</b>) the number of microorganisms in the soil with time, and (<b>d</b>) a comparison of the number of microorganisms in the soil during different treatments. The different letters indicate statistical differences between the groups (one-way ANOVA test) and represent a 5% significance level.</p>
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22 pages, 2487 KiB  
Article
Applying a Comprehensive Model for Single-Ring Infiltration: Assessment of Temporal Changes in Saturated Hydraulic Conductivity and Physical Soil Properties
by Mirko Castellini, Simone Di Prima, Luisa Giglio, Rita Leogrande, Vincenzo Alagna, Dario Autovino, Michele Rinaldi and Massimo Iovino
Water 2024, 16(20), 2950; https://doi.org/10.3390/w16202950 - 16 Oct 2024
Cited by 1 | Viewed by 795
Abstract
Modeling agricultural systems, from the point of view of saving and optimizing water, is a challenging task, because it may require multiple soil physical and hydraulic measurements to investigate the entire crop cycle. The Beerkan method was proposed as a quick and easy [...] Read more.
Modeling agricultural systems, from the point of view of saving and optimizing water, is a challenging task, because it may require multiple soil physical and hydraulic measurements to investigate the entire crop cycle. The Beerkan method was proposed as a quick and easy approach to estimate the saturated soil hydraulic conductivity, Ks. In this study, a new complete three-dimensional model for Beerkan experiments recently proposed was used. It consists of thirteen different calculation approaches that differ in estimating the macroscopic capillary length, initial (θi) and saturated (θs) soil water contents, use transient or steady-state infiltration data, and different fitting methods to transient data. A steady-state version of the simplified method based on a Beerkan infiltration run (SSBI) was used as the benchmark. Measurements were carried out on five sampling dates during a single growing season (from November to June) in a long-term experiment in which two soil management systems were compared, i.e., minimum tillage (MT) and no tillage (NT). The objectives of this work were (i) to test the proposed new model and calculation approaches under real field conditions, (ii) investigate the impact of MT and NT on soil properties, and (iii) obtain information on the seasonal variability of Ks and other main soil physical properties (θi, soil bulk density, ρb, and water retention curve) under MT and NT. The results showed that the model always overestimated Ks compared to SSBI. Indeed, the estimated Ks differed by a factor of 11 when the most data demanding (A1) approach was considered by a factor of 4–8, depending on the transient or steady-state phase use, when A3 was considered and by a practically negligible factor of 1.0–1.9 with A4. A relatively higher seasonal variability was detected for θi at the MT than NT system. Under both MT and NT, ρb did not change between November and April but increased significantly until the end of the season. The selected calculation approaches provided substantially coherent information on Ks seasonal evolution. Regardless of the approach, the results showed a temporal stability of Ks at least from early April to June under NT; conversely, the MT system was, overall, more affected by temporal changes with a relative stability at the beginning and middle of the season. These findings suggest that a common sampling time for determining Ks could be set at early spring. Soil management affected the soil properties, because the NT system was significantly wetter and more compact than MT on four out of five dates. However, only NT showed a significantly increasing correlation between Ks and the modal pore diameter, suggesting the presence of a relatively smaller and better interconnected pore network in the no-tilled soil. This study confirms the need to test infiltration models under real field conditions to evaluate their pros and cons. The Beerkan method was effective for intensive soil sampling and accurate field investigations on the temporal variability of Ks. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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<p>Timeline of the field measurements (i.e., Beerkan infiltration tests and soil sampling) carried out under minimum tillage (MT) and no tillage (NT) plots in the 5 sampling dates (i.e., from 1 to 5). Numbers marked with lowercase or uppercase green letters represent the number of days elapsed between the beginning and the end of a single sampling date (d) and the time between two successive sampling dates (D). The daily rainfall was reported as a black continuous line.</p>
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<p>Box plots of soil water content at the time of sampling (θ<span class="html-italic"><sub>i</sub></span>) and soil bulk density (ρ<sub>b</sub>) carried out for each sampling date (1 to 5) under minimum tillage (MT) and no tillage (NT). The thick red–green line within each box represents the mean value (the fine black line, the median); for improved interpretation, mean values are also reported by numbers. Circles represent outliers. For a given soil management, inferences of the THSD-test between dates (i.e., x vs. y) are summarized on the right (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; n.s. not significant). For a given sampling date, inferences of the two tailed <span class="html-italic">t</span>-test between MT and NT were reported under the NT boxes (* <span class="html-italic">p</span> &lt; 0.05; n.s. not significant).</p>
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<p>Mean values of the measured soil water retention data (Obs) for each sampling time (1 to 5) for minimum tillage (MT) and no tillage (NT) systems. The Brooks and Corey (BC) fitting curve (lines) are also reported (sample size, <span class="html-italic">N,</span> was between 5 and 12).</p>
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<p>Cumulative infiltration carried out under minimum tillage and no tillage plots (MT and NT) during the five sampling dates. Note that mean curves were represented with black-red dashed lines.</p>
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<p>Success percentage of saturated hydraulic conductivity estimation obtained with the five calculation approaches (A1 to A5). The acronyms CI, CL, and DL refer to fitting methods used to analyze the transient-state data (i.e., cumulative infiltration, cumulative linearization, and differential linearization, respectively), while SS refers to steady-state data. A1 to A4 refer to the Stewart and Abou Najm [<a href="#B35-water-16-02950" class="html-bibr">35</a>] model, while A5 refers to the SSBI method (Bagarello et al. [<a href="#B51-water-16-02950" class="html-bibr">51</a>]). Note that, for each soil management, the sample size N = 34 refers to the sum of the five sampling dates.</p>
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<p>Empirical cumulative frequency distribution of the saturated hydraulic conductivity (<span class="html-italic">K<sub>s</sub></span>) obtained from different calculation criteria and considering the minimum dataset (N = 44).</p>
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<p>Comparison between estimated <span class="html-italic">K<sub>s</sub></span> values obtained with A5 criterion (<span class="html-italic">K<sub>s</sub></span>–A5) against the calculation criteria A1, A3, and A4 and different fitting methods CI, CL, and DL (<span class="html-italic">K<sub>s</sub></span>–A<sub>n</sub>) using the minimum dataset (N = 44).</p>
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<p>Box plots of saturated hydraulic conductivity (<span class="html-italic">K<sub>s</sub></span>) at different sampling dates for minimum tillage (MT) and no tillage (NT) management systems conducted using the A1, A3<sub>SS</sub>, and A5 (SSBI) approaches. For a given soil management, inferences of the THSD test between dates (i.e., x vs. y) are summarized on the right (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; n.s. not significant). For a given sampling date, inferences of the two tailed <span class="html-italic">t</span>-test between MT and NT were reported near NT boxes (* <span class="html-italic">p</span> &lt; 0.05; n.s. not significant). For the general interpretation on box plots, please refer to the captions in <a href="#water-16-02950-f002" class="html-fig">Figure 2</a>. Note that the discrepancies regarding the statistical significances among the three calculation criteria are shown with red character.</p>
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<p>Ratio of saturated hydraulic conductivity obtained with approaches A1 and A5 (SSBI) against the relative error of the fitting of the functional relationships to the experimental data (<b>a</b>), and examples of fitting accuracy for the minimum (Er<sub>FIT</sub> = 1.8%; experiment MT1-SD3) (<b>b</b>), intermediate (Er<sub>FIT</sub> = 15.8%; NT1-SD3) (<b>c</b>), and maximum (Er<sub>FIT</sub> = 31.7%; NT5-SD5) (<b>d</b>) values, as labeled in subpanel (<b>a</b>) (black-edged points). The black continuous regression line corresponds to the whole set of data.</p>
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<p>Normalized pore volume distributions and corresponding modal diameters (continuous and dotted lines, respectively) for the first (1) and last (5) sampling dates under no tillage, NT, and minimum tillage, MT (<b>a</b>), and a correlation between the saturated hydraulic conductivity (<span class="html-italic">K<sub>s</sub></span>) and modal pores diameter (<span class="html-italic">d<sub>mode</sub></span>) for all sampling dates (<b>b</b>). Note that the <span class="html-italic">K<sub>s</sub></span> values refer to the medians obtained with Approach 5 (SSBI).</p>
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15 pages, 8921 KiB  
Article
Surface and Subsurface Water Impacts of Forestry and Grassland Land Use in Paired Watersheds: Electrical Resistivity Tomography and Water Balance Analysis
by Éricklis Edson Boito de Souza, Franciele de Bastos, Pedro Daniel da Cunha Kemerich, Marieli Machado Zago, Éderson Diniz Ebling, Elias Frank de Araujo, Antonio Celso Dantas Antonino and José Miguel Reichert
Water 2024, 16(15), 2191; https://doi.org/10.3390/w16152191 - 2 Aug 2024
Viewed by 901
Abstract
Global forest plantations are expanding, causing land-use changes and impacting the water cycle. This study assesses whether eucalyptus plantations reduce groundwater levels compared to grasslands in paired subtropical watersheds. The hydrological dynamics of surface and subsurface water were compared in three small watersheds [...] Read more.
Global forest plantations are expanding, causing land-use changes and impacting the water cycle. This study assesses whether eucalyptus plantations reduce groundwater levels compared to grasslands in paired subtropical watersheds. The hydrological dynamics of surface and subsurface water were compared in three small watersheds in southern Brazil, mainly occupied by Eucalyptus saligna (Es-W, 79.9 ha), Eucalyptus benthamii (Eb-W, 82.1 ha), and degraded anthropized natural grassland (G-W, 109.4 ha). Rainfall, flow, and piezometric levels were monitored. Runoff, evapotranspiration, and water balance in the soil profile were estimated, and the subsurface environment was characterized using electrical resistivity tomography. During higher accumulated rainfall, water surplus increased for all watersheds. In the wet period (accumulated rainfall of 1098.0 mm), evapotranspiration was higher for eucalyptus (624.3 mm for Eb-W and 512.5 mm for Es-W) than for the grassland watershed (299.5 mm), resulting in the highest runoff in G-W (649.6 mm). During the dry period (accumulated rainfall of 478.5 mm), water deficit and withdrawal were mainly observed in forested watersheds, decreasing groundwater. Combining water balance and electrical resistivity tomography estimations results in a better understanding of the hydrological dynamics in paired watersheds with different land uses. This information is useful for developing best-practice management strategies for sustainable water resource use and forest production. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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Figure 1
<p>Study area with three watersheds predominantly under <span class="html-italic">Eucalyptus benthamii</span> (Eb-W), <span class="html-italic">Eucalyptus saligna</span> (Es-W), and grassland (G-W), showing the transects in the watersheds: G-W (L1 to L3), Es-W (L4, L5 and L7), and Eb-W (L6, L8, and L9) for electrical resistivity measurements, where each transect had a length of about 90 m and a dipole–dipole arrangement with an electrode spacing of 5 m.</p>
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<p>Sequential water balance, adapted from Thornthwaite and Mather (1955), and monitoring flow levels for Eb-W (<b>left</b>), Es-W (<b>center</b>), and G-C (<b>right</b>). The lower piezometers were located at elevations of 243.5, 236.4, and 260.8 m a.s.l. for Eb-W, Es-W, and G-C, respectively, while the upper ones were at elevations of 298.3, 266.3, and 291.8 m a.s.l., respectively. Data used until 15 June 2020.</p>
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<p>Electrical resistivity values (Ω·m) for the transects evaluated at the end of the wet period on 19 November 2019 (<b>left</b>) and at the end of the dry period on 04 June 2020 (<b>right</b>).</p>
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<p>Zoning of transects evaluated at the end of the wet (<b>left</b>) and dry periods (<b>right</b>).</p>
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<p>Zoning of transect 9 (L9) evaluated at the end of the wet (<b>a</b>) and dry periods (<b>b</b>).</p>
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<p>Zoning of transect 4 (L4) evaluated at the end of the wet (<b>a</b>) and dry periods (<b>b</b>).</p>
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12 pages, 558 KiB  
Article
Estimation of the Soil–Water Characteristic Curve from Index Properties for Sandy Soil in China
by Shijun Wang, Xing Guo, Feng You, Zhong Zhang, Tianlun Shen, Yuhui Chen and Qian Zhai
Water 2024, 16(14), 2044; https://doi.org/10.3390/w16142044 - 19 Jul 2024
Viewed by 744
Abstract
The soil–water characteristic curve (SWCC) is an important parameter of unsaturated soil, and almost all the engineering characteristics of unsaturated soil are more or less related to the SWCC. The SWCC contains important information for geotechnical engineering, water engineering, hydrogeology modelling and climate [...] Read more.
The soil–water characteristic curve (SWCC) is an important parameter of unsaturated soil, and almost all the engineering characteristics of unsaturated soil are more or less related to the SWCC. The SWCC contains important information for geotechnical engineering, water engineering, hydrogeology modelling and climate modelling. It is noted that the experimental measurement of SWCC is costly and time consuming, which limits the implementation of principles of unsaturated soil mechanics in practical engineering. The indirect method, which estimates the SWCC from the index properties of soil, can provide the SWCC with the errors which are within tolerance in practical engineering. In addition, the indirect method can determine SWCC very fast and almost with no cost. In this paper, the domestic sandy soils are selected and the index properties of those sands are used to correlate the SWCC fitting parameters. Consequently, mathematical equations are proposed to estimate SWCC from index properties of domestic sands. The proposed models are trained from 44 sets of experimental data and verified with another independent 8 sets of experimental data from published literature. It is observed that the results from the proposed model agree well with the experimental data from literature. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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<p>Comparison between the predicted and measured SWCCs of the sandy soil in China. (<b>a</b>) Clay gravel; (<b>b</b>) riddled sand sand II; (<b>c</b>) medium sand; (<b>d</b>) fine sand; (<b>e</b>) sandy soil; (<b>f</b>) sandy soil; (<b>g</b>) Hunan sandy soil; (<b>h</b>) coarse sand.</p>
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15 pages, 5248 KiB  
Article
Determination of Heavy Metal Sources in an Agricultural Catchment (Poland) Using the Fingerprinting Method
by Damian Bojanowski
Water 2024, 16(9), 1209; https://doi.org/10.3390/w16091209 - 24 Apr 2024
Cited by 1 | Viewed by 1095
Abstract
This study investigates the heavy metal contamination of soils and suspended sediments in the Nurzec River catchment (Eastern Poland), focusing in particular on the effects of catchment land use. The fingerprinting technique has been combined with the classic, Igeo, and EF [...] Read more.
This study investigates the heavy metal contamination of soils and suspended sediments in the Nurzec River catchment (Eastern Poland), focusing in particular on the effects of catchment land use. The fingerprinting technique has been combined with the classic, Igeo, and EF index-supported contamination analysis to identify heavy metals sources. A wide range of elements (31 chemical elements including heavy metals) allowed the author to analyse the pathways and to identify the pressures of heavy metal contamination. The developed statistical models of heavy metal source distribution displayed results at a statistically significant level. The results have revealed the significant impact of land use connected with urban areas (URBAN) and pastures (PAST), which together constitute less than 20% of the river catchment area. These results are relevant to the local authorities and stakeholders, as they highlight the significant impact of low-density urban areas that are not locally considered as the major sources of heavy metal pollution. These results will contribute to sustainable decisions in the field of contaminated catchment area remediation. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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<p>Nurzec River catchment with land use (<b>a</b>), soil structure (<b>b</b>), sampling points locations (<b>c</b>), location on the map of Poland (<b>d</b>), and suspended sediment sampler [<a href="#B14-water-16-01209" class="html-bibr">14</a>] (<b>e</b>). Sources of background layers: Landuse—Corine Land Cover EEA, 2018; Soils—Institute of Cultivation, Fertilization and Soil Science, 2019; Orthophoto—Head Office of Geodesy and Cartography, 2023; Country borders—Head Office of Geodesy and Cartography, 2023.</p>
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<p>Classification of I<sub>geo</sub> and EF indices of soils samples within the Nurzec River catchment. The values in the chart represent the number of samples classified into each class of both indices.</p>
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<p>Maximum I<sub>geo</sub> and EF classes observed for each analysed land-use category, in comparison with suspended sediment (SAMPLER).</p>
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<p>Comparison between a simulation based on the fingerprinting model and an analysis of the suspended sediment (log<sub>10</sub> transformed).</p>
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<p>Visualisation of the I<sub>geo</sub> and EF classes across the analysed sampling points. Pie charts represent the number of samples in each point and classes of I<sub>geo</sub>/EF. The tables show the percentage of samples in each class of each index. n = 396.</p>
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22 pages, 2533 KiB  
Article
Soil Moisture Forecast Using Transfer Learning: An Application in the High Tropical Andes
by Diego Escobar-González, Marcos Villacís, Sebastián Páez-Bimos, Gabriel Jácome, Juan González-Vergara, Claudia Encalada and Veerle Vanacker
Water 2024, 16(6), 832; https://doi.org/10.3390/w16060832 - 13 Mar 2024
Viewed by 1517
Abstract
Soil moisture is a critical variable in the hydrological cycle and the climate system, significantly impacting water resources, ecosystem functioning, and the occurrence of extreme events. However, soil moisture data are often scarce, and soil water dynamics are not fully understood in mountainous [...] Read more.
Soil moisture is a critical variable in the hydrological cycle and the climate system, significantly impacting water resources, ecosystem functioning, and the occurrence of extreme events. However, soil moisture data are often scarce, and soil water dynamics are not fully understood in mountainous regions such as the tropical Andes of Ecuador. This study aims to model and predict soil moisture dynamics using in situ-collected hydrometeorological data for training and data-driven machine-learning techniques. Our results highlight the fundamental role of vegetation in controlling soil moisture dynamics and significant differences in soil water balance related to vegetation types and topography. A baseline model was developed to predict soil moisture dynamics using neural network techniques. Subsequently, by employing transfer-learning techniques, this model was effectively applied to different soil horizons and profiles, demonstrating its generalization capacity and adaptability. The use of neural network schemes and knowledge transfer techniques allowed us to develop predictive models for soil moisture trained on in situ-collected hydrometeorological data. The transfer-learning technique, which leveraged the knowledge from a pre-trained model to a model with a similar domain, yielded results with errors on the order of 1×106<ϵ<1×103. For the training data, the forecast of the base network demonstrated excellent results, with the lowest magnitude error metric RMSE equal to 4.77×106, and NSE and KGE both equal to 0.97. These models show promising potential to accurately predict short-term soil moisture dynamics with potential applications for natural hazard monitoring in mountainous regions. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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<p>Location of the study area in the northern Ecuadorian Andes. (<b>a</b>) Study area with location of the soil profiles. (<b>b</b>) Location of the study site in the Metropolitan District of Quito. (<b>c</b>) Location of the study area, JTU_01, within the Jatunhuayco catchment.</p>
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<p>Flowchart for soil moisture forecasting using neural networks.</p>
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<p>Basic representation of a neural network.</p>
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<p>Two-dimensional representation of a convolution. The kernel’s sliding or <span class="html-italic">stride K</span> is one step over the input matrix <span class="html-italic">I</span>. The result <span class="html-italic">I*K</span> is the <span class="html-italic">feature map</span>, i.e., the element-wise product of elements.</p>
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<p>Neural network architecture: soil moisture forecast for the next 48 h based on information of the preceding 7 days. Base network developed for soil profile under cushion-forming plants in the footslope position of the soil catena (<math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>).</p>
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<p>Transfer learning for similar domains showing the transfer of knowledge for forecasting soil moisture under 15 different conditions. The codes refer to different types of vegetation cover (CU = cushion-forming plants; TU = tussock grasses), topographic position (LO = footslope; MI = mid-slope; UP = top slope; UPR = replica at top slope position) and soil horizon (A and 2A).</p>
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<p>Violin plots of meteorological variables at the JTU_AWS station and soil variables at Jatunhuayco JTU_01.</p>
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<p>Loss function of the network forecasting 48 h of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <msub> <mi>U</mi> <mi>L</mi> </msub> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math> given hourly information of <span class="html-italic">P</span>, <span class="html-italic">T</span>, <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>R</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <msub> <mi>U</mi> <mi>L</mi> </msub> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>C</mi> <msub> <mi>U</mi> <mi>L</mi> </msub> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math> for seven days. (<b>a</b>) Loss function value during training. (<b>b</b>) Zoom of the loss function value after 127 epochs.</p>
Full article ">Figure 9
<p>Forecast of 48 h of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math> for the evaluation dataset consisting of 7-day hourly data of <span class="html-italic">P</span>, <span class="html-italic">T</span>, <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>R</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>S</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>a</b>) Forecast for the evaluation dataset. (<b>b</b>) Zoom of subfigure (<b>a</b>) from 22 January 2022 to 4 March 2022.</p>
Full article ">Figure 10
<p>Forecast for different time points of the evaluation series <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <msub> <mi>U</mi> <mrow> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </mrow> </msub> </semantics></math>. (<b>a</b>) Forecast of the evaluation series as of 14 January 2022. (<b>b</b>) Forecast of the evaluation series as of 8 February 2022. (<b>c</b>) Forecast of the evaluation series as of 26 March 2022. (<b>d</b>). Forecast of the evaluation series as of 9 March 2022.</p>
Full article ">Figure 10 Cont.
<p>Forecast for different time points of the evaluation series <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <msub> <mi>U</mi> <mrow> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </mrow> </msub> </semantics></math>. (<b>a</b>) Forecast of the evaluation series as of 14 January 2022. (<b>b</b>) Forecast of the evaluation series as of 8 February 2022. (<b>c</b>) Forecast of the evaluation series as of 26 March 2022. (<b>d</b>). Forecast of the evaluation series as of 9 March 2022.</p>
Full article ">Figure 11
<p>Forecasts of soil moisture under tussock grasses <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> </mrow> </msub> </semantics></math> for different topographic positions and soil horizons. (<b>a</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>P</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>b</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>P</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>c</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>R</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>d</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>R</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>e</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>M</mi> <mi>I</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>f</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>M</mi> <mi>I</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>g</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>h</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 11 Cont.
<p>Forecasts of soil moisture under tussock grasses <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> </mrow> </msub> </semantics></math> for different topographic positions and soil horizons. (<b>a</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>P</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>b</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>P</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>c</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>R</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>d</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>R</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>e</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>M</mi> <mi>I</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>f</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>M</mi> <mi>I</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>g</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>h</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 12
<p>Forecasts at different moments of soil moisture in tussock grass <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>T</mi> <mi>U</mi> </mrow> </msub> </semantics></math> across all profiles and horizons analyzed. (<b>a</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>P</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from the base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>b</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>P</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from the base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>c</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>R</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from the base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>d</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>U</mi> <mi>R</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from the base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>e</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>M</mi> <mi>I</mi> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from the base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>f</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>M</mi> <mi>I</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from the base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>. (<b>g</b>) Forecast of <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mn>2</mn> <mi>A</mi> </mrow> </msub> </semantics></math> using transfer learning from the base network <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>C</mi> <mi>U</mi> <mo>_</mo> <mi>L</mi> <mi>O</mi> <mi>A</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">
16 pages, 3151 KiB  
Article
Soil Macropore and Hydraulic Conductivity Dynamics of Different Land Uses in the Dry–Hot Valley Region of China
by Yi Wang, Jingru Ruan, Yongkang Li, Yaping Kong, Longxi Cao and Wei He
Water 2023, 15(17), 3036; https://doi.org/10.3390/w15173036 - 24 Aug 2023
Cited by 2 | Viewed by 1954
Abstract
Soil macropores and hydraulic conductivity are important indexes used to describe soil hydrology. In the dry-hot valley region of Southwest China, with its dramatic seasonal dry–wet rhythm, soil properties and hydraulic conductivity can reflect unique dynamics as determined by the interaction between land [...] Read more.
Soil macropores and hydraulic conductivity are important indexes used to describe soil hydrology. In the dry-hot valley region of Southwest China, with its dramatic seasonal dry–wet rhythm, soil properties and hydraulic conductivity can reflect unique dynamics as determined by the interaction between land use and the seasonal dry–wet cycle. In this study, the soil macropore characteristics and hydraulic conductivity of five land uses (traditional corn, plum orchard, pine forest, grassland, and abandoned cropland) in a dry–hot valley region were quantified using X-ray computed tomography (CT) and a mini disk infiltrometer in the rainy season (July) and dry season (November), respectively. The results showed that the soil macropore indexes (soil macroporosity, mean diameter of macropores, connectivity, hydraulic radius and compactness) in the rainy season were, on average, 1.26 times higher than those in the dry season. Correspondingly, the hydraulic conductivity of different land uses in the rainy season was significantly higher than those in the dry season (2.10 times higher, on average). Correlation analysis and principal component analysis (PCA) indicated that the hydraulic conductivity was mainly determined by soil macropore parameters rather than by general soil properties, such as organic matter (OM) and bulk density (BD). The hydraulic conductivity for the five land uses followed the order of PF > GL > TC > PO > AC in both the rainy and the dry seasons. This ranking order reflects the protective effect of vegetation in reducing raindrop splash and soil crust formation processes. The above results can help guide soil water conservation and vegetation restoration in the dry-hot valley region of Southwest China. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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Figure 1

Figure 1
<p>Study area and the location of the experimental sites. (<b>Left</b>) the location of the study area; (<b>Middle</b>) the positions of field experiment sites; (<b>Right</b>) scene of plots of five land use types.</p>
Full article ">Figure 2
<p>Soil column sampling (<b>a</b>) and procedures of CT scanning image processing (<b>b</b>).</p>
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<p>The soil texture for five experimental sites of different land use types. TC: traditional corn; PO: plum orchard; PF: pine forest; GL: grassland; AC: abandoned cropland.</p>
Full article ">Figure 4
<p>Seasonal variation in macroporosity distribution along the ROI depth. TC: traditional corn; PO: plum orchard; PF: pine forest; GL: grassland; AC: abandoned cropland.</p>
Full article ">Figure 5
<p>Variation characteristics of hydraulic conductivity in different land uses. TC: traditional corn; PO: plum orchard; PF: pine forest; GL: grassland; AC: abandoned cropland.</p>
Full article ">Figure 6
<p>Correlation analysis between soil hydraulic conductivity and soil properties. Note: MWD: mean weight diameter; WSA0.25: percentage content of water-stable aggregates greater than 0.25; SOM: soil organic matter; BD: bulk density; MP: macroporosity; MD: mean diameter of macropore; HD: hydraulic radius; Γ: global connectivity; CP: compactness; K(h): hydraulic conductivity; CI: cumulative infiltration.</p>
Full article ">Figure 7
<p>Factor loading and eigenvalues of extracted components using PCA.</p>
Full article ">

Review

Jump to: Research

22 pages, 1007 KiB  
Review
Interactions Between Forest Cover and Watershed Hydrology: A Conceptual Meta-Analysis
by Mathurin François, Terencio Rebello de Aguiar Junior, Marcelo Schramm Mielke, Alain N. Rousseau, Deborah Faria and Eduardo Mariano-Neto
Water 2024, 16(23), 3350; https://doi.org/10.3390/w16233350 - 21 Nov 2024
Abstract
The role of trees in watershed hydrology is governed by many environmental factors along with their inherent characteristics and not surprisingly has generated diverse debates in the literature. Herein, this conceptual meta-analysis provides an opportunity to propose a conceptual model for understanding the [...] Read more.
The role of trees in watershed hydrology is governed by many environmental factors along with their inherent characteristics and not surprisingly has generated diverse debates in the literature. Herein, this conceptual meta-analysis provides an opportunity to propose a conceptual model for understanding the role of trees in watershed hydrology and examine the conditions under which they can be an element that increases or decreases water supply in a watershed. To achieve this goal, this conceptual meta-analysis addressed the interaction of forest cover with climatic conditions, soil types, infiltration, siltation and erosion, water availability, and the diversity of ecological features. The novelty of the proposed conceptual model highlights that tree species and densities, climate, precipitation, type of aquifer, and topography are important factors affecting the relationships between trees and water availability. This suggests that forests can be used as a nature-based solution for conserving and managing natural resources, including water, soil, and air. To sum up, forests can reduce people’s footprint, thanks to their role in improving water and air quality, conserving soil, and other ecosystem services. The outcomes of this study should be valuable for decision-makers in understanding the types of forests that can be used in an area, following an approach of environmental sustainability and conservation aiming at restoring hydrological services, mitigating the costs of environmental services, promoting sustainable land use, managing water resources, and preserving and restoring soil water availability (SWA) when investing in reforestation for watershed hydrology, which is important for the human population and other activities. Full article
(This article belongs to the Special Issue Soil Dynamics and Water Resource Management)
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