1. Introduction
There exist macropores, cracks, fissures, and other structures in soil mass [1], which allow water to infiltrate deep into the soil along macroporous channels in a short time with a flow rate much greater than that of saturated flow. This phenomenon is referred to as preferential flow [2,3]. As early as the late nineteenth century, when researchers studied the relationship between rainfall and drainage, they found that a large portion of water infiltrating into the soil was rapidly transported through macropores, while the water held in the soil itself changed slightly. However, this finding did not attract much attention at the time. In the 1970s and 1980s, many “weird” incidents occurred. In 1980, the insecticide aldicarb, used to kill indigenous Colorado potato beetles, was found in approximately 1000 drinking wells in the east of Long Island in the United States, but people at that time understood that aldicarb spread very slowly in the soil and thought it might not cause groundwater pollution. Afterwards, groundwater pollution by chemicals was frequently reported, seriously threatening the survival and health of human beings. Since then, the preferential flow phenomenon has attracted great concern [4].
Researchers began to study the law of water seepage in soil using Darcy’s law, which is used to describe water seepage in saturated soil, based on which the Richard equation was developed. The Richard equation is currently the most widely used equation on water seepage in unsaturated soil and can well describe seepage with continuous wetting front under continuous water-flow conditions. However, it is difficult to describe the characteristics of preferential flow with the Richard equation, since continuous wetting fronts are not usually formed during preferential flow failing to meet continuous water-flow movement conditions. So, preferential flow cannot be described with the single-scale continuity equation of flow at the macro level.
Preferential flow is a widespread phenomenon in unsaturated soils in the vadose zone, representing a way in which water infiltrates from the surface into the ground and an important part of the global hydrological cycle [5], which is critical to ecological environment protection and human survival and development. The research of preferential flow involves many fields, such as environment [6,7,8], agriculture [9,10], disaster control [11,12], hydrology [13,14], and engineering [15,16]. Researchers have studied preferential flow phenomena from different professional perspectives focusing on different scientific problems and research points. However, there is still no consensus on preferential flow phenomena. Preferential flow exhibits different characteristics in different disciplines, and researchers from different disciplines have a different understanding of the preferential flow, which represents only an empirical knowledge of the phenomenon and a vague understanding of its mechanism. Suitable theoretical models not yet appeared and the research within a single discipline cannot fully reveal the nature of the phenomenon. Research frontiers and development trends are difficult to grasp through qualitative analysis in a single field, but bibliometric analysis with scientific knowledge map tools can help researchers quantitatively and objectively understand the research status and focuses of knowledge structure, as well as promote cross-integration between disciplines to develop new research ideas [17,18].
At present, the bibliometric analysis methods are relatively mature. The objects in bibliometrics include literature amount, author characteristics, publication source characteristics, and vocabulary quantity (document identifiers) of various publications in formal exchanges. Knowledge carriers are quantitatively analyzed using mathematics, statistics, etc. to objectively reveal the hotspots of discipline development. Dr. Chen from Drexel University developed the CiteSpace software, whose design and function realization are based on Kuhn’s theory of scientific development model, Price’s frontier theory of science, social network analysis theory, structural holes theory, etc., which can visualize bibliometric results, demonstrate knowledge maps of science development and structural relationships, and explore research status, research focuses, and development trends of disciplines through statistical analysis [19,20,21]. CiteSpace has promoted the development of bibliometrics, metrological data visualization, and other methods. Many research achievements have been made. Zhong et al. used CiteSpace to draw knowledge maps for carbon footprint research, then summarized and analyzed the knowledge structure and frontier terms [22]. Sun et al. quantitatively analyzed the literatures on supply chain risk retrieved from Web of Science (WoS) and analyzed the development trends of supply chain risk management based on CiteSpace [23]. Gao et al. conducted and visualized their analysis of literature on CRISPR gene editing technology with CiteSpace [24].
In order to find out the essential characteristics of preferential flow and unify its understanding in different disciplines, this paper quantitatively analyzes the literature published in the past 30 years regarding the preferential flow by means of bibliometrics, draws visualized scientific knowledge maps with the bibliometric software CiteSpace, clarifies the common characteristics of preferential flow studied in various disciplines, and summarizes the current research status and hotspots, and reveals the development trends of preferential flow research, so as to provide a knowledge structure reference and new ideas for research in related fields and help to promote cross-integration between disciplines.
2. Materials and Methods
The data samples in this paper were retrieved from the WoS core collection database by topic terms. By taking preferential flow and its different forms as topic terms, papers and review papers published between 1992 and 2021 were retrieved. A total of 4187 papers were retrieved by “preferential flow”, 546 papers were retrieved by “macropore flow”, 496 papers were retrieved by “non-uniform flow”, and a small number of papers were retrieved by other topic terms, which did not affect the statistical analysis results and were therefore neglected. Meanwhile, the term of “preferential flow” is involved in many other fields. For example, in the field of materials science, the flow of liquids or gases in certain materials of special structures represents preferential flow of fluids. In the medical field, the flow of blood in certain organs represents preferential blood flow. In the field of network information science, there exists preferential flow of information. The “preferential flow” mentioned in this paper refers to fast water flow in soil. In order to eliminate the interference of literatures from other fields, the topic term of “soil” is added.
The data samples provided in this paper come from the WoS core collection database. The topic terms of “preferential flow”, “macropore flow”, “non-uniform flow” and “soil” were retrieved from WoS according to the retrieval formula (((TS = (“Preferential flow”)) OR TS = (“Macropore flow”)) OR TS = (“Non-uniform flow”)) AND TS = (soil), with a time range from 1992 to 2021, and a document type of “paper” and “review paper”. Excluding other publications, 3315 papers related thereto were obtained upon screening and statistical analysis.
The literature data were analyzed by using the analytical function of the WoS database and CiteSpace to understand research trends, research hotspots, research questions from various fields, and cooperation between countries. The research methods are shown in Figure 1.
3. Results
3.1. Analysis of Literature Output Quantity
Figure 2 shows that 3315 papers on preferential flow published in the past 30 years (from 1992 to 2021) were retrieved from the WoS core collection database, showing an overall increasing trend. From 1992 to 2003, 844 papers were published, accounting for 25% of the total, with less than 100 papers published annually except in 2000 when 101 papers were published. From 2004 to 2011, 924 papers were published, accounting for 28% of the total, with 100–150 papers published annually. From 2012 to 2021, 1547 papers were published, accounting for 47% of the total, with more than 150 papers published annually, except in 2013 and 2015 when 117 and 143 papers were published, respectively. The number of papers published in the recent 10 years accounts for about half of that published in the past 30 years, indicating that countries have begun to pay more attention to the research on preferential flow.
3.2. Co-Occurrence Analysis of Keywords
Keywords refer to a refinement of topics proposed by authors. The bibliometric visualization analysis of keywords to a certain extent can explore research focuses in related fields. Keywords co-occurrence analysis is a method for multivariate statistical analysis involving counting the frequency of co-occurrence of keywords, as well as the analysis and prediction of hotspots according to affinities between words, so as to summarize contents, results, and development trends of research in the field.
The literature (1992 to 2021) on preferential flow retrieved from the WoS core collection database was imported into CiteSpace to carry out co-occurrence analysis of keywords from the literatures published in the past 30 years.
In the WoS database, “Time Slicing” was set to January 1992–December 2021, “Term Source” was set to “Title”, “Abstract”, and “Author Keywords”, and the keywords were analyzed by “Pathfinder” and “Pruning sliced networks” to obtain the keyword knowledge maps, as shown in Figure 3.
In Figure 3, each circle node represents a keyword, and the connections between nodes represent the co-occurrence relationship between keywords. A node with a purple ring at its outermost layer indicates that the keyword has a high betweenness centrality.
Figure 3 displays 841 network nodes and 1196 connecting lines with a network density of 0.0034, among which the largest node is “preferential flow” with a frequency of 620, followed by “macropore flow” and “solute transport” with a frequency of 87. Among the 841 keywords, the top five keywords with the highest frequency are listed in Table 1 and the top five keywords with highest betweenness centrality are listed in Table 2.
In Table 1 and Table 2, “preferential flow” and “macropore flow” are search terms showing the highest frequency of occurrence and the highest betweenness centrality, which cannot reflect the statistical characteristics of the data. Except the search terms, the terms of “solute transport”, “vadose zone”, “dye tracer”, and “hydraulic conductivity” occur with higher frequency or have higher betweenness centrality, indicating that these terms currently occur as keywords in quite a number of literatures. “Vadose zone” reveals the places where preferential flow occurs. “Hydraulic conductivity” and “solute transport” represent the contents of research in different aspects. Therefore, the research content of preferential flow can be divided into two categories: research on the law of water flow in vadose zone, and research on the law of solute transport in vadose zone. “Dye tracer” refers to an experimental technique. The high frequency of occurrence of the term reflects the rapid development of experimental techniques in recent years, and technological development promotes the research of preferential flow.
3.3. Cluster Analysis of Keywords
By employing the clustering algorithm (LLR algorithm) provided by CiteSpace, closely related keywords were aggregated into a set, which is called a cluster of keywords, and named by the most representative keyword. Different clusters are sorted by ID number. A smaller ID number indicates that more data are involved in the statistics of this cluster, and this cluster is more representative.
The keywords from the WoS database were clustered into 17 categories on the keyword clustering knowledge map based on CiteSpace, with a module value of 0.6579, and an average contour value of 0.9302, indicating that the clustering structure is significant and that the clustering effect is convincing. The top 10 clusters are selected for analysis, as shown in Figure 4. These clusters and their representative keywords are shown in Table 3.
From Table 3, eight clusters were analyzed except two clusters whose names were used as search terms. The clusters of “soil moisture” and “vadose zone” refer to physical properties of soil and places where preferential flow occurs, respectively, both of which appear in many literatures and are generic terms. The clusters of “hydraulic conductivity” and “solute transport” represent the two categories of research on preferential flow—the law of water flow and the law of solute transport, which is consistent with the analysis results in Section 3.2. The clusters of “numerical modeling” and “dual-permeability model” represent a large number of research studies on computational methods and theoretical models, indicating that the development of computational methods and theoretical models promotes the research of preferential flow. The cluster of “soil water repellency” shows that many results of research on soil water repellency have been achieved. The cluster of “soil structure” represents the influence of soil structure on preferential flow, indicating that preferential flow is closely related to soil structure.
Table 3 presents that some representative keywords of each cluster are of certain peculiarity and can reflect the research content of different studies. By analyzing these keywords from the perspective of soil structure, the research content represented by these keywords can be divided into three categories by research scale, as shown in Table 4.
From Table 4, “X-ray CT” refers to a technical method for measuring pore structure. Both “soil macropore” and “soil matrix” reflect the structural characteristics of soil at the pore scale. These keywords represent that some literatures have researched the formation mechanisms and theoretical models of preferential flow at the pore scale. “Dye tracer experiment” and “breakthrough curves” represent experimental methods for studying preferential flows at the field scale and reflect the occurrence of preferential flow in soil in different regions described in other studies at the field scale. “Hillslope hydrology”, “runoff generation”, “hillslope discharge”, and other keywords indicate that the law of runoff yielding and concentrating was studied in some studies at the hillslope or catchment scale.
Preferential flow exhibits different seepage characteristics at different research scales. The characteristics of preferential flow that occur at the pore scale are determined by the complex structure of soil and the spatiotemporal scale of research. At a larger scale (macro level), seepage parameters exhibit strong spatial variability. There are still many unsolved problems in the further mechanisms of preferential flow production, e.g., the structure for connection of pores and the connection between preferential flows at different scales need to be quantitatively described.
3.4. Analysis of Burst Terms
Burst terms refer to keywords that appear frequently or are used more frequently in a relatively short period of time. CiteSpace burst detection generally includes the detection of intensity distribution and year distribution, and keywords that appear suddenly in a certain period of time can reflect hotspots, frontiers, and development trends of research in this period of time.
The top 16 burst terms were detected from the WoS database using the burst detection function of CiteSpace, as shown in Table 5. Among them, “water repellency” appeared most frequently, with a burst intensity of 8.31, followed by “macropore flow” with a burst intensity of 4.97, “soil water repellency” appeared up to the date with the longest time of nine years, and “unsaturated flow”, “subsurface flow”, and “dual-permeability model” appeared for seven years.
The burst terms of “soil water repellency”, “stable isotope”, “saturated hydraulic conductivity”, “slope stability”, and “unsaturated soil” have been used to the present and are current research hotspots.
3.5. Distribution of Countries and Cooperative Relationship between Them
A statistical tool that comes with the WoS database was used to count the number of papers published by countries in the past 30 years, as shown in Figure 5. Figure 5 shows top 10 countries with the most publications on preferential flow from 1992 to 2021, among which the United States ranks first with 1163 published papers, accounting for 35% of the total, Germany and China rank second and third with 463 and 415 published papers, respectively, followed by the remaining countries, with about 200 published papers.
CiteSpace was used to draw a diagram of partnership network between countries, as shown in Figure 6. In the figure, each circle node represents a country, and the connections between nodes represent the partnerships between countries. The more connections from one node to another, the more cooperation it has with other countries. A node with a purple ring at its outermost layer indicates that the country at the node has a high betweenness centrality.
Figure 6 displays 94 network nodes and 459 connecting lines with a network density of 0.105. Analysis of Figure 5 and Figure 6 indicates that a number of countries in America, Europe, Asia, Oceania, and Africa have carried out research on preferential flow. The United States started early in preferential flow research since the phenomenon attracted the attention of the scientific community in the 1970s and has since achieved the most publications and a higher degree of success. Germany, Canada, the United Kingdom, Switzerland, Australia, France, Sweden, The Netherlands, Japan, and other countries also started early in preferential flow research and have attained a relatively large number of achievements. China started preferential flow research around 2000 and accelerated the research in recent years with the deepening of reform and opening up as well as global cooperation. Figure 6 shows that the pink rings on the nodes of countries with more publications are thick, indicating they have high betweenness centrality and cooperate more with other countries. From this, international cooperation is one of remarkable features of modern scientific research. Giving full play to the wisdom of scientists around the world and jointly overcoming difficulties will contribute to scientific progress and improvement in human survival and quality of life.
3.6. Knowledge Map Analysis of Research Institutions
CiteSpace was used to draw a diagram of the partnership network between research institutions, as shown in Figure 7. In the figure, each circle node represents a research institution, and the connections between nodes represent the partnerships between research institutions. The more connections from one node to another, the more cooperation it has with other institutions. If a node has a purple ring in its outermost layer, the betweenness centrality of the research institution at this node is high. As can be seen from Figure 7, research institutions with larger number of published papers have higher betweenness centrality. This demonstrates that exchanges and cooperation between research institutions can better contribute to research.
4. Discussion
4.1. Research Trend of Preferential Flow
Figure 2 shows that the increasing trend in the number of published papers indicates a gradual increase of interest in preferential flow research, which can be attributed to the following three reasons.
-
(1). Increased understanding of influence of preferential flow
In the past 10 years, people have gradually realized the importance of preferential flow to human production and life. Research has shown that preferential flow has a great impact on the global ecosystem, valued at approximately $304 billion per year [25].
-
(2). Development of experimental techniques
The keyword of “dye tracer” in Section 3.2 has a high frequency of occurrence and that of “stable isotope” in Section 3.4 has been a hot term since its outbreak in 2016, indicating that, in the past 10 years, tracing technology, CT scanning technology, geophysical probing technique, and other techniques have been used in experiments for research on preferential flow.
-
(3). Development of computational methods
The clusters of “numerical modeling” and “dual-permeability model” in Section 3.3 show many studies have reported results on computational methods and theories. In the past 10 years, the computational speed and accuracy have been greatly improved under the background of the rapid development of global big data technology and computer artificial intelligence technology. With the improvement of computing power, more computational methods are available for research of preferential flow at larger scales.
4.2. Fields Involved in Preferential Flow Research
The analysis of the literature (published in the past 30 years) retrieved from the WoS database by using its own analysis function shows that preferential flow research is involved in many fields, among which the top five are water resources, environmental science, soil science, earth science, and civil engineering, as shown in Figure 8.
In the past 10 years, during which there has been a spike in publications on preferential flow, the top five disciplines with the most publications on preferential flow are still water resources, environmental science, soil science, earth science, and civil engineering. Preferential flow research is involved in these fields is closely related to human survival and development. From Table 3, “water balance”, “hillslope discharge”, “runoff generation”, and “hillslope hydrology” reflect that preferential flow research involves disciplines, such as water resources and earth science. In these disciplines, preferential flow poses a significant influence on runoff generation control and water regulation. “Contaminant transport” and “solute transport” reflect that researchers from environmental science have noticed that the waste carried by preferential water flow can result in extensive impacts on drinking water systems. “Cracking clay soil”, “soil properties”, “forest soil”, and “soil water repellency” reflect that the preferential flow research involves soil science. The researchers have noticed the inconsistent (positive/negative) role of preferential flow in soil science. Preferential flow can improve soil physicochemical properties and nutrient cycling in root zone, but it poses negative effects on water supply in root zone, which could prevent crop growth and decrease food production. From Table 5, the burst term of “slope stability” reflects that the preferential flow research involves civil engineering. The researchers from civil engineering have noticed that preferential flow pathways treated as conduits of subsurface water flow in the slope can prompt slope instability.
Researchers from different disciplines carry out research on the influence of preferential flow at different scales.
At the field scale, research on the influence of preferential flow involves disciplines, such as water resources, environmental science, and soil science. Water infiltrates deep into the ground along preferential flow paths, shortening the time for crops to absorb water and fertilizer, and causing leakage loss of agricultural irrigation and fertilization, which is not conducive to water resource utilization and farmland irrigation management [26]. In the meantime, the pesticides, domestic waste, “three industrial wastes”, and other pollutants in the soil would be carried by preferential flow deep into the ground, increasing the risk of groundwater pollution [27,28]. The research of preferential flow enables people to clearly understand the law of water or pollutants transmission and provides a basis for developing farming and irrigation systems and disposing of waste in a reasonable manner.
At the hillslope scale, the research on the influence of preferential flow on the stability of the soil slope involves environmental science, earth science, and other disciplines. During rainy seasons, a large amount of rainwater quickly penetrates deep into the soil through preferential flow and causes geological disasters, such as landslides and debris flows [29]. Researchers from industrial and civil construction and other engineering fields have noticed that preferential flow threatens the stability of building foundations, foundation pit slopes, etc. [30], so the research on the preferential infiltration law enables people to accurately understand the soil-water interaction mechanism, rationally develop disaster prevention and mitigation measures to protect the safety of human life and property, and formulate engineering design plans to ensure the safety and stability of building structures.
At the catchment scale or even larger region scale, the research on the influence of preferential flow involves earth science, environmental science, water resources, and other disciplines. As an important part of the global hydrological cycle, preferential flow promotes the interactions among hydrosphere, lithosphere, atmosphere, and biosphere, and is also affected by temperature, precipitation, soil structure, etc. Preferential flow changes the ratio between surface water and groundwater [31,32]. Research on the infiltration law of preferential flow can improve the prediction accuracy of runoff yield and concentration and play an important role in promoting the rational development and utilization of surface water and groundwater resources.
4.3. Two Categories of Research on Preferential Flow
According to the clustering and representative keywords in Table 3, the research on preferential flow mainly involves runoff and water quality from the perspective of solving scientific problems, as shown in Table 6. The conclusions of the two categories of research are consistent with the analysis results in Section 3.2 and Section 3.3.
The studies on runoff mainly involve slope runoff, rainfall infiltration, flow and velocity during groundwater seepage, and their spatial distribution, etc. [33,34,35]. The studies on water quality mainly involve the solute transport law, e.g., the research on groundwater pollution focuses on the change of pollutant concentrations to reveal the change law of concentration of solute in water and its transport law [36,37].
At the pore scale, the mechanisms and theoretical computational models of water flow and solute transport were studied. Such research involves soil physics, soil chemistry, particle surface science, saturated/unsaturated soil mechanics, and other disciplines.
The dual-porosity model [38] and dual-permeability model [39] are the main theoretical calculation models. The dual-porosity model divides the soil volume into two regions, i.e., the matrix region with lower permeability, where water is assumed to be relatively immobile, and the preferential flow region with higher permeability, where the motion of water flow is described by the Richard equation or the wave equation. In a partial differential equation, the exchange of water or solutes between the two regions is represented by the coupling between “source” and “concentration” terms. The dual-permeability model [40] is proposed by regarding water in the matrix region as flowing water based on the dual-porosity model. Different equations are used for describing the two regions. In general, the matrix region is described by the Richard equation and the preferential flow region is described by the kinematic wave equation. For the dual-permeability model, soil-water characteristic parameters of the two regions are required for model calculation, so it is relatively complicated. The theoretical model of solute transport is established based on the dual-porosity model, the convection-dispersion equation, and the mass balance equation. The establishment and solution of the model are achieved using numerical solutions [41,42].
The research on runoff involves water resource management, engineering science, earth science, and other disciplines at the macro scale, such as field scale, hillslope scale, catchment scale, and even larger region scale, in addition to pore scale in the aspects of basin water resources planning, water distribution in river basins, laws of hydrological cycles, groundwater exploitation and utilization, water and soil loss, stability of building foundations, and the prevention and control of geological disasters. The research on water quality involves environmental science, soil science and other disciplines regarding groundwater pollution, soil pollution, crop planting, rational irrigation and fertilization, soil improvement, ecosystem stability, etc.
The influence of preferential flow is manifested at different macro scales, and the common issues studied by researchers from various disciplines at different scales are related to preferential water flow and quality.
4.4. Analysis of Research Hotspots
According to the continuing-to-present burst terms in Table 5, the three research hotspots are summarized as shown in Figure 9.
4.4.1. New Experimental Testing Methods of Preferential Flow
This research hotspot is proposed according to the burst term of “stable isotope”. The topic term of “dye tracer” in Section 3.2 has a high frequency of occurrence, which also indicates that the development of techniques is a research hotspot.
The development of experimental techniques also provides technological means for research on preferential flow, as shown in Figure 10. Common experimental testing methods for preferential flow research include CT scanning [43,44,45], MRI [46], dye tracing [47,48,49], ion tracing [50], and isotope tracing [51,52], as well as geophysical detection methods such as electrical resistivity tomography at a large scale [53]. Such methods are constantly developing, but limited in many aspects:
(1). Image interpretation is only used.
(2). A great difference between sampling and measurement brings difficulty in comparison.
(3). It is difficult to directly measure parameters at pore-scale.
4.4.2. Preferential Flow in Water-Repellent Soil
This research hotspot is concluded according to the burst term of “soil water repellency”, and the analysis in Section 3.3 also reflects that soil water repellency is currently a hot research subject. Table 5 shows the outbreak of the keyword “water repellency” in 2005, indicating that the research on water repellent soil started early and continues to the present.
As the surface of water-repellent soil particles is difficult to wet by water, its repellency is usually caused by forest fires, low water content, variation of organic matter content, etc. [54,55]. Preferential flow is prone to occur in water-repellent soil and affects food crop production and the ecological environment, resulting in problems, such as unreasonable utilization of water resources, as shown in Figure 11.
The research on soil water repellency started about a century earlier than that on preferential flow and suggested that it was related to the nature of organic matter in soil. Soil water repellency severely affects crop yields and has therefore been studied by researchers. Since the 1970s, preferential flow has attracted the attention of academia. Preferential flow is used to describe unbalanced flow phenomenon in soil and pollutant transport pathways, which can well explain the reduction in crop yield on water repellent soil [56]. Such knowledge exchanges and references among disciplines provide a new idea for research of water repellent soil. Therefore, preferential flow in water repellent soil has become a research hotspot.
In recent years, many scholars have studied water-repellent soils. Wang et al. conducted water infiltration experiments in water-repellent clay loam and sandy loam soils and quantitatively studied the infiltration path morphology and permeability parameters of preferential flow [57]. Mirbabaei et al. observed the preferential flow phenomena in water-repellent soils through rainfall infiltration experiments and compared them with simulation results by SWAP model. It was found that the water content was overestimated and that the runoff was underestimated in water-repellent soil by using a uniform infiltration model, and the calculated results were closer to actual observations considering soil water repellency by SWAP model [58].
Previous studies have shown the importance of the preferential flow phenomenon in water repellent soil. However, as its production mechanism is complex, most of the studies focused on describing properties of water repellent soil without recognized computational models [3]. Research results were highly dependent on specific locations, it was difficult to compare those obtained in different locations due to differences in geological conditions, measurement methods, etc., and quantitative theoretical models need to be further studied in the future.
4.4.3. Influence of Preferential Flow on Slope Stability
This research hotspot is proposed according to the burst terms of “saturated hydraulic conductivity”, “unsaturated soil”, and “slope stability”. The outbreak of these terms occurred in 2017, indicating that the influence of preferential flow on slope stability has become a research hotspot in recent years.
The stability of slopes or landslides is closely related to human production and even the safety of people’s lives and property. For landslides, especially deep-seated landslides, their structural features, such as macropores, fissures, wormholes, plant roots, and soil water repellency, will lead to preferential infiltration, which is a more extensive and common form of infiltration [59]. Preferential flow uplifts the groundwater level, reduces the shear strength of rock and soil mass, and causes the landslides, as shown in Figure 12. The preferential flow is an important form of water infiltration and a key factor that triggers the instability of slopes or landslides [60].
Slope stability is an important subject in soil mechanics research. It has been recognized in engineering practice that infiltrated rainfall does not completely saturate slip mass due to the pore structure of slope, but passes through the slip mass to the depth. This form of infiltration has a great impact on slope stability. Principles of soil mechanics did not involve preferential flow. However, with the deepening study of preferential flow, computational models of preferential flow can better reflect the infiltration process of rainfall passing through slip mass and can better analyze the influence of rainfall on slope stability in combination with the computation of slope stability involved in soil mechanics. Such exchanges, references, and integration among disciplines can promote the emergence of new ideas and methods.
In recent years, researchers have achieved some results. Shao et al. analyzed the influence of preferential flow on slope stability and discussed that when the rainfall intensity is small, preferential flow favors the drainage of slopes, resulting in smaller areas of slope failure simulated by the dual-permeability model than those of the single-permeability model. When the rainfall intensity increases, preferential flow accelerates the infiltration of rainwater and instability of slopes [61,62]. Luo et al. studied the landslide triggering by preferential flow in fractured soil under rainfall conditions and established a preferential flow infiltration prediction model reflecting this mechanism [63].
At present, the influence of preferential flow on slope stability is preliminarily studied focusing on the structure of slope with fractures in simplified form. The macro structure of pores in slope is very complex, and a more general slope structure needs to be further studied in the future.
4.5. Future Research Directions
The research hotspots reflect the current and future research directions of preferential flow. These three research hotspots reflect two development directions.
New experimental testing methods of preferential flow indicate the development direction of experimental techniques and computational methods. In the past 10 years, better knowledge of the modes of appearance of preferential flows, enhanced by experimental techniques and computational methods, has resulted in a huge increase in our understanding of preferential flow mechanisms. The complexity of preferential flow determines that the acquisition of measurable parameters depends on the development of experimental techniques. Meanwhile, mathematical models that are more in line with such characteristics, as well as theories that describe the relationship between microstructure and macro phenomena are required. Therefore, experimental techniques and computational methods represent a future research direction with regard to preferential flow.
The other two hotspots represent the development direction of Interdisciplinary cross-integration. Preferential flow phenomenon is studied by researchers from different disciplines. Exchanges and references among disciplines on research theories, research methods, testing, and experimental techniques promote interdisciplinary cross-integration. The approach incorporating the propagation law of preferential flow into knowledge systems of other disciplines to solve specific problems therein can promote the emergence of new ideas, such as preferential flow in water-repellent soil and the influence of preferential flow on slope stability. Therefore, interdisciplinary cross-integration is another future research direction of preferential flow.
4.6. Comparison with other Literature Analysis Methods
Bibliometrics methods are related to and different from other literature analysis methods, among which review is a common literature analysis method. The paper [3] reviews some important achievements made in the study of preferential flow in the vadose zone from 2007 to 2016, and presents some research needs for the coming years. The review method used in the paper [3] and methods used herein are compared and analyzed from the following three aspects.
4.6.1. Comparison of Literature Selection
The author of the paper [3] selected the topic of preferential flow based on his experience, read a large number of relevant studies published within a specific time period, then summarized and arranged literatures based on his own knowledge and understanding of professional issues. Selected studies are relevant to the issues analyzed. The literatures selected for analysis in this paper were searched from WoS database by specific topic terms according to certain rules, and at the same time screened with a statistical tool that comes with the WoS database. Compared with the paper [3], this paper uses a larger number of studies for analysis, including all studies from the database that meet the search rules. A large number of studies searched according to certain rules have a strong statistical significance for reflecting the characteristics of topic terms.
4.6.2. Comparison between Analysis Methods
The analysis method proposed in the paper [3] is used to directly analyze and summarize collected literatures based on the author’s experience and knowledge in the professional field. With this analysis method, the author has a unique insight into current research state and future trends in the areas which he is familiar with and specializes in. The paper [3] analyzes the development of experimental technical methods and theoretical models with an emphasis on improving the understanding and knowledge of preferential flow. The academic standing or personal influence of the author in this field also makes the analysis results more convincing and influential.
This paper presents a co-occurrence analysis, cluster analysis, and burst analysis of keywords and other information in selected studies with a statistical tool provided by the database and CiteSpace. Statistical results are quantitative data, which are further analyzed and interpreted based on the author’s own expertise. This method features that the data are obtained based on statistical theories and algorithms rather than the author’s experience, and the data-based analysis is to some extent objective and credible.
4.6.3. Comparison between Analysis Results
The comparison between their analysis results shows that this paper has the same understanding of preferential flow as the paper [3]. The analysis in Section 4.1 and Section 4.4.1 of this paper indicates that people continue to research and understand preferential flow with the development of new technologies. The same result was obtained in the paper [3].
The bibliometrics method has its own characteristics. The contents analyzed with this method are relatively fixed, including quantitatively analyzed keyword co-occurrence characteristics, clustering characteristics, outbreak intensity and time of burst terms, and cooperative relationships between states and institutions, which cannot be analyzed by reviewing.
The preferential flow phenomenon involves many disciplines. Studies are retrieved by topic terms and counted using the bibliometrics method. Quantitative data results obtained cover multiple disciplines, indicating that statistics on research contents crossing many disciplines are comprehensive, while such multidisciplinary contents are not easy to analyze by reviewing. Most scholars do not specialize in multiple disciplines, such as repellent soil and landslide areas with wide distance between them.
To sum up, both the bibliometrics method and review method are used for literature analysis, but there is a big difference between them, i.e., the review method is used to conduct a qualitative review and summary of research progress, and it has its own advantages in the fields in which the author specializes. The bibliometrics method is used for quantitative statistical analysis. All studies are retrieved from specific database according to certain rules and undergo quantitative statistics. Then, statistical results are analyzed and interpreted. The statistical indicators of the bibliometrics method are not available using the review method, and statistical data have certain objectivity. Both the methods have their own strengths, and it is advisable to use them together. Statistical data represent a nice addition and support to experience and knowledge.
5. Conclusions
By retrieving studies on preferential flow (1992–2021) from the WoS core collection database, this paper analyzes the research frontiers and development trends of preferential flow research with bibliometric methods and draws the following conclusions:
(1). Preferential flow is a multi-scale phenomenon in essence. The future research on preferential flow should focus on establishing appropriate computational models, linking the production mechanism of preferential flow at pore scale to macro phenomena, and explaining the causes of macro phenomena.
(2). Researchers from different disciplines carry out the same research on preferential water flow and pollution at different scales. A clearer and more common understanding of preferential flow will be reached due to multi-disciplinary cross-integration in the future.
(3). The current research trends indicate that researchers in different fields are paying more attention to preferential flow phenomena and will gradually increase their research efforts. International cooperation can better promote scientific research. The research hotspots are mainly concentrated in two dimensions: new experimental techniques and computational methods, and new ideas brought about by multi-disciplinary cross-integration. This provides future research directions for researchers in related fields.
(4). Bibliometrics methods are based on statistical theory and used for quantitative analysis and statistics of literature data indicators, which are not available in other literature analysis methods. The bibliometrics methods involve a comprehensive range of disciplines when used for retrieving studies to analyze cross-disciplinary issues.
In summary, this paper summarizes the essential characteristics of preferential flow, unifies its understanding in different disciplines, and reveals its research hotspots and future research directions, which can promote the research of preferential flow and the cross-integration between disciplines.
Conceptualization, C.L.; methodology, C.L.; software, Y.Y.; validation, C.L., Y.Y. and A.Z.; formal analysis, C.L. and L.G.; investigation, H.Z. and X.L.; resources, C.L.; data curation, C.L. and A.Z.; writing—original draft preparation, C.L.; writing—review and editing, Y.Y. and A.Z.; visualization, L.G., H.Z. and X.L.; supervision, C.L.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.
The authors would like to thank the editors and reviewers for their insightful comments and efforts towards improving this manuscript.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 2. Quantity of literatures on preferential flow retrieved from WoS core collection database (1992 to 2021).
Enlarge this image.Figure 5. Quantity of literatures on preferential flow published by different countries in the past 30 years.
Enlarge this image.Figure 6. Cooperative network of countries in preferential flow research in the past 30 years.
Enlarge this image.Figure 7. Cooperative network of institutions in preferential flow research in the past 30 years.
Enlarge this image.Figure 8. Top 5 fields with the most publications on preferential flow from WoS database.
Enlarge this image.Figure 10. New experimental testing methods of preferential flow. (a) shows dye tracing method; (b) shows isotope tracing method.
Frequency of keywords from WoS database.
| Ranking | Keywords | Frequency |
|---|---|---|
| 1 | preferential flow | 620 |
| 2 | macropore flow | 87 |
| 3 | solute transport | 87 |
| 4 | vadose zone | 59 |
| 5 | dye tracer | 53 |
Betweenness centrality of keywords from WoS database.
| Ranking | Keywords | Centrality |
|---|---|---|
| 1 | preferential flow | 0.73 |
| 2 | macropore flow | 0.15 |
| 3 | hydraulic conductivity | 0.11 |
| 4 | solute transport | 0.1 |
| 5 | vadose zone | 0.08 |
Preferential flow clustering and representative keywords from WoS database.
| Cluster ID | Clusters | Representative Keywords (LLR) |
|---|---|---|
| #0 | preferential flow | macropore flow; critical soil water content; Richards equation; hydraulic conductivity |
| #1 | solute transport | soil water; water repellency; water flow; piston flow |
| #2 | macropore flow | water balance; model; preferential flow; dye tracer experiment |
| #3 | numerical modeling | dye tracer; unsaturated zone; breakthrough curves; dye tracing |
| #4 | soil moisture | hillslope hydrology; subsurface flow; runoff generation; subsurface stormflow |
| #5 | vadose zone | unsaturated flow; contaminant transport; capillary barrier; bromide tracer |
| #6 | hydraulic conductivity | X-ray CT; bulk density; soil macropore; preferential flow |
| #7 | soil structure | bypass flow; air permeability; saturated hydraulic conductivity; cracking clay soil |
| #8 | soil water repellency | soil water content; soil matrix; soil hydrology; soil properties |
| #9 | dual-permeability model | hillslope discharge; forest soil; soil pore structure; single-porosity model |
Research scales and representative keywords.
| No. | Research Scales | Representative Keywords |
|---|---|---|
| 1 | pore scale | X-ray CT; soil macropore; soil matrix |
| 2 | field scale | dye tracer experiment; dye tracer; breakthrough curves; dye tracing |
| 3 | hillslope or catchment scale | hillslope hydrology; subsurface flow; runoff generation; subsurface stormflow; hillslope discharge |
Analysis of burst terms from WoS database.
| Burst Terms | Strength | Begin Year | End Year | 1992–2021 |
|---|---|---|---|---|
| macropore flow | 4.97 | 1997 | 1998 | ☐☐☐☐☐■■☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ |
| unsaturated flow | 3.88 | 1998 | 2004 | ☐☐☐☐☐☐■■■■■■■☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ |
| unsaturated zone | 4.00 | 2000 | 2005 | ☐☐☐☐☐☐☐☐■■■■■■☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐ |
| water repellency | 8.31 | 2005 | 2010 | ☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■■☐☐☐☐☐☐☐☐☐☐☐ |
| pesticide leaching | 3.94 | 2005 | 2007 | ☐☐☐☐☐☐☐☐☐☐☐☐☐■■■☐☐☐☐☐☐☐☐☐☐☐☐☐☐ |
| water balance | 3.87 | 2005 | 2010 | ☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■■☐☐☐☐☐☐☐☐☐☐☐ |
| subsurface flow | 4.44 | 2010 | 2016 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■■■☐☐☐☐☐ |
| soil moisture | 3.93 | 2011 | 2015 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■☐☐☐☐☐☐ |
| dual-permeability model | 4.24 | 2012 | 2018 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■■■☐☐☐ |
| soil water repellency | 4.35 | 2013 | 2021 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■■■■■ |
| hydraulic conductivity | 3.76 | 2013 | 2017 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■☐☐☐☐ |
| stable isotope | 4.84 | 2016 | 2021 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■■ |
| runoff generation | 3.67 | 2016 | 2017 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■☐☐☐☐ |
| saturated hydraulic conductivity | 4.48 | 2017 | 2021 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■ |
| slope stability | 3.61 | 2017 | 2021 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■■ |
| unsaturated soil | 3.58 | 2018 | 2021 | ☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐☐■■■■ |
Two categories of research.
| No. | Research Content | Clusters and Representative Keywords |
|---|---|---|
| 1 | runoff | hydraulic conductivity; piston flow; water balance; runoff generation; |
| 2 | water quality | solute transport; contaminant transport |
References
1. Yan, J.; Chen, X.; Cai, Y.; Cheng, F.; Fan, T. A review of genetic classification and characteristics of soil cracks. Open Geosci.; 2021; 13, pp. 1509-1522. [DOI: https://dx.doi.org/10.1515/geo-2020-0315]
2. Nimmo, J.R. The processes of preferential flow in the unsaturated zone. Soil Sci. Soc. Am. J.; 2021; 85, pp. 1-27. [DOI: https://dx.doi.org/10.1002/saj2.20143]
3. Jarvis, N.; Koestel, J.; Larsbo, M. Understanding preferential flow in the vadose zone: Recent advances and future prospects. Vadose Zone J.; 2016; 15, pp. 1-11. [DOI: https://dx.doi.org/10.2136/vzj2016.09.0075]
4. Beven, K. A century of denial: Preferential and nonequilibrium water flow in soils, 1864–1984. Vadose Zone J.; 2018; 17, pp. 1-17. [DOI: https://dx.doi.org/10.2136/vzj2018.08.0153]
5. Zhang, Y.; Zhang, Z.; Ma, Z.; Chen, J.; Akbar, J.; Zhang, S.; Che, C.; Zhang, M.; Cerdà, A. A review of preferential water flow in soil science. Can. J. Soil Sci.; 2018; 98, pp. 604-618. [DOI: https://dx.doi.org/10.1139/cjss-2018-0046]
6. Zhang, W.; Jiang, F.; Sun, W. Investigating colloid-associated transport of cadmium and lead in a clayey soil under preferential flow conditions. Water Sci. Technol.; 2021; 84, pp. 2486-2498. [DOI: https://dx.doi.org/10.2166/wst.2021.441] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34810326]
7. Franklin, S.M.; Kravchenko, A.N.; Vargas, R.; Vasilas, B.; Fuhrmann, J.J.; Jin, Y. The unexplored role of preferential flow in soil carbon dynamics. Soil Biol. Biochem.; 2021; 161, 108398. [DOI: https://dx.doi.org/10.1016/j.soilbio.2021.108398]
8. Zhen, Q.; Ma, W.; Li, M.; He, H.; Zhang, X.; Wang, Y. Effects of vegetation and physicochemical properties on solute transport in reclaimed soil at an opencast coal mine site on the Loess Plateau, China. Catena; 2015; 133, pp. 403-411. [DOI: https://dx.doi.org/10.1016/j.catena.2015.06.009]
9. Zhang, Z.; Si, B.; Li, H.; Li, M. Quantify piston and preferential water flow in deep soil using Cl− and soil water profiles in deforested apple orchards on the Loess Plateau, China. Water; 2019; 11, 2183. [DOI: https://dx.doi.org/10.3390/w11102183]
10. King, K.W.; Williams, M.R.; Macrae, M.L.; Fausey, N.R.; Frankenberger, J.; Smith, D.R.; Kleinman, P.J.A.; Brown, L.C. Phosphorus transport in agricultural subsurface drainage: A review. J. Environ. Qual.; 2015; 44, pp. 467-485. [DOI: https://dx.doi.org/10.2134/jeq2014.04.0163]
11. Ronchetti, F.; Piccinini, L.; Deiana, M.; Ciccarese, G.; Vincenzi, V.; Aguzzoli, A.; Malavasi, G.; Fabbri, P.; Corsini, A. Tracer test to assess flow and transport parameters of an earth slide: The Montecagno landslide case study (Italy). Eng. Geol.; 2020; 275, 105749. [DOI: https://dx.doi.org/10.1016/j.enggeo.2020.105749]
12. Zhang, J.; Luo, Y.; Zhou, Z.; Chong, L.; Victor, C.; Zhang, Y. Effects of preferential flow induced by desiccation cracks on slope stability. Eng. Geol.; 2021; 288, 106164. [DOI: https://dx.doi.org/10.1016/j.enggeo.2021.106164]
13. Li, M.; Yao, J.; Cheng, J. Study on the preferential flow characteristics under different precipitation amounts in Simian mountain grassland of China. Water; 2020; 12, 3489. [DOI: https://dx.doi.org/10.3390/w12123489]
14. Jiang, X.J.; Chen, C.; Zhu, X.; Zakari, S.; Singh, A.K.; Zhang, W.; Zeng, H.; Yuan, Z.; He, C.; Yu, S. et al. Use of dye infiltration experiments and HYDRUS-3D to interpret preferential flow in soil in a rubber-based agroforestry systems in Xishuangbanna, China. Catena; 2019; 178, pp. 120-131. [DOI: https://dx.doi.org/10.1016/j.catena.2019.03.015]
15. Di Prima, S.; Marrosu, R.; Lassabatere, L.; Angulo-Jaramillo, R.; Pirastru, M. In situ characterization of preferential flow by combining plot- and point-scale infiltration experiments on a hillslope. J. Hydrol.; 2018; 563, pp. 633-642. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2018.06.033]
16. Krzeminska, D.M.; Bogaard, T.A.; Malet, J.P.; van Beek, L.P.H. A model of hydrological and mechanical feedbacks of preferential fissure flow in a slow-moving landslide. Hydrol. Earth Syst. Sci.; 2013; 17, pp. 947-959. [DOI: https://dx.doi.org/10.5194/hess-17-947-2013]
17. Shan, W.; Liu, C.; Yu, J. Features of the discipline knowledge network: Evidence from China. Technol. Econ. Dev. Econ.; 2014; 20, pp. 45-64. [DOI: https://dx.doi.org/10.3846/20294913.2014.825460]
18. Mokhnacheva, Y.V.; Tsvetkova, V.A. Bibliometric analysis of soil science as a scientific area. Eurasian Soil Sci.; 2020; 53, pp. 838-844. [DOI: https://dx.doi.org/10.1134/S1064229320060095]
19. Liu, Z.; Yin, Y.; Liu, W.; Dunford, M. Visualizing the intellectual structure and evolution of innovation systems research: A bibliometric analysis. Scientometrics; 2015; 103, pp. 135-158. [DOI: https://dx.doi.org/10.1007/s11192-014-1517-y]
20. Koondhar, M.A.; Shahbaz, M.; Memon, K.A.; Ozturk, I.; Kong, R. A visualization review analysis of the last two decades for environmental Kuznets curve “EKC” based on co-citation analysis theory and pathfinder network scaling algorithms. Environ. Sci. Pollut. Res.; 2021; 28, pp. 16690-16706. [DOI: https://dx.doi.org/10.1007/s11356-020-12199-5]
21. Wu, J.; Wu, X.; Zhang, J. Development trend and frontier of stormwater management (1980–2019): A bibliometric overview based on CiteSpace. Water; 2019; 11, 1908. [DOI: https://dx.doi.org/10.3390/w11091908]
22. Zhong, S.; Chen, R.; Song, F.; Xu, Y. Knowledge mapping of carbon footprint research in a LCA perspective: A visual analysis using CiteSpace. Processes; 2019; 7, 818. [DOI: https://dx.doi.org/10.3390/pr7110818]
23. Sun, L.; Xu, X.; Yang, Y.; Liu, W.; Jin, J. Knowledge mapping of supply chain risk research based on CiteSpace. Comput. Intell.; 2020; 36, pp. 1686-1703. [DOI: https://dx.doi.org/10.1111/coin.12306]
24. Gao, C.; Wang, R.; Zhang, L.; Yue, C. Visualization analysis of CRISPR gene-editing knowledge map based on CiteSpace. Biol. Bull.; 2021; 48, pp. 705-720. [DOI: https://dx.doi.org/10.1134/S1062359021060108]
25. Clothier, B.E.; Green, S.R.; Deurer, M. Preferential flow and transport in soil: Progress and prognosis. Eur. J. Soil Sci.; 2008; 59, pp. 2-13. [DOI: https://dx.doi.org/10.1111/j.1365-2389.2007.00991.x]
26. Pluer, W.T.; Macrae, M.; Buckley, A.; Reid, K. Contribution of preferential flow to tile drainage varies spatially and temporally. Vadose Zone J.; 2020; 19, pp. 1-15. [DOI: https://dx.doi.org/10.1002/vzj2.20043]
27. Gassmann, M. Modelling the fate of pesticide transformation products from plot to catchment scale—state of knowledge and future challenges. Front. Environ. Sci.; 2021; 9, 717738. [DOI: https://dx.doi.org/10.3389/fenvs.2021.717738]
28. Willkommen, S.; Lange, J.; Ulrich, U.; Pfannerstill, M.; Fohrer, N. Field insights into leaching and transformation of pesticides and fluorescent tracers in agricultural soil. Sci. Total Environ.; 2021; 751, 141658. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.141658]
29. Kukemilks, K.; Wagner, J.F.; Saks, T.; Brunner, P. Conceptualization of preferential flow for hillslope stability assessment. Hydrogeol. J.; 2018; 26, pp. 439-450. [DOI: https://dx.doi.org/10.1007/s10040-017-1667-0]
30. González-Arteaga, J.; Moya, M.; Yustres, Á.; Alonso, J.; Merlo, O.; Navarro, V. Characterisation of the water content distribution beneath building foundations. Measurement; 2019; 136, pp. 82-92. [DOI: https://dx.doi.org/10.1016/j.measurement.2018.12.054]
31. Que, Y.; Lin, P.; Lin, D. Integrative analysis of surface runoff and macropore flow for slopes under rainfall conditions. Math. Probl. Eng.; 2018; 2018, 9458410. [DOI: https://dx.doi.org/10.1155/2018/9458410]
32. Zhu, L.; Fan, D.; Ma, R.; Zhang, Y.; Zha, Y. Experimental and numerical investigations of influence on overland flow and water infiltration by fracture networks in soil. Geofluids; 2018; 2018, 7056858. [DOI: https://dx.doi.org/10.1155/2018/7056858]
33. Laurenson, S.; Cichota, R.; Reese, P.; Breneger, S. Irrigation runoff from a rolling landscape with slowly permeable subsoils in New Zealand. Irrig. Sci.; 2018; 36, pp. 121-131. [DOI: https://dx.doi.org/10.1007/s00271-018-0570-3]
34. Glaser, B.; Jackisch, C.; Hopp, L.; Klaus, J. How meaningful are plot-scale observations and simulations of preferential flow for catchment models?. Vadose Zone J.; 2019; 18, pp. 1-18. [DOI: https://dx.doi.org/10.2136/vzj2018.08.0146]
35. Dusek, J.; Vogel, T. Modeling travel time distributions of preferential subsurface runoff, deep percolation and transpiration at a montane forest hillslope site. Water; 2019; 11, 2396. [DOI: https://dx.doi.org/10.3390/w11112396]
36. Karan, S.; Badawi, N.; Jensen, A.M.D.; Rosenbom, A.n.E. Impact of fate properties, groundwater fluctuations and the presence of worm burrows on pesticide leaching assessments through golf areas. Environ. Pollut.; 2021; 289, 117822. [DOI: https://dx.doi.org/10.1016/j.envpol.2021.117822]
37. Li, M.; Yao, J.; Yan, R.; Cheng, J. Effects of infiltration amounts on preferential flow characteristics and solute transport in the protection forest soil of southwestern China. Water; 2021; 13, 1301. [DOI: https://dx.doi.org/10.3390/w13091301]
38. Rahbeh, M.; Srinivasan, R.; Mohtar, R. Numerical and conceptual evaluation of preferential flow in Zarqa River Basin, Jordan. Ecohydrol. Hydrobiol.; 2019; 19, pp. 224-237. [DOI: https://dx.doi.org/10.1016/j.ecohyd.2019.04.001]
39. Aguilar-López, J.P.; Bogaard, T.; Gerke, H.H. Dual-permeability model improvements for representation of preferential flow in fractured clays. Water Resour. Res.; 2020; 56, e2020WR027304. [DOI: https://dx.doi.org/10.1029/2020WR027304]
40. Lassabatere, L.; Di Prima, S.; Bouarafa, S.; Iovino, M.; Bagarello, V.; Angulo-Jaramillo, R. BEST-2K method for characterizing dual-permeability unsaturated soils with ponded and tension infiltrometers. Vadose Zone J.; 2019; 18, pp. 1-20. [DOI: https://dx.doi.org/10.2136/vzj2018.06.0124]
41. Fox, G.A.; Heeren, D.M.; Miller, R.B.; Mittelstet, A.R.; Storm, D.E. Flow and transport experiments for a streambank seep originating from a preferential flow pathway. J. Hydrol.; 2011; 403, pp. 360-366. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2011.04.014]
42. Di Pietro, L.; Ruy, S.; Capowiez, Y. Predicting preferential water flow in soils by traveling-dispersive waves. J. Hydrol.; 2003; 278, pp. 64-75. [DOI: https://dx.doi.org/10.1016/S0022-1694(03)00124-0]
43. Lissy, A.S.; Sammartino, S.; Ruy, S. Can structure data obtained from CT images substitute for parameters of a preferential flow model?. Geoderma; 2020; 380, 114643. [DOI: https://dx.doi.org/10.1016/j.geoderma.2020.114643]
44. Li, Z.; Hu, X.; Li, X.; Huang, Y.; Wu, X.; Wang, P.; Liu, L. Quantification of soil macropores at different slope positions under alpine meadow using computed tomography in the Qinghai Lake watershed, NE Qinghai–Tibet. Eurasian Soil Sci.; 2019; 52, pp. 1391-1401. [DOI: https://dx.doi.org/10.1134/S1064229319110152]
45. Sammartino, S.; Lissy, A.S.; Bogner, C.; Van Den Bogaert, R.; Capowiez, Y.; Ruy, S.; Cornu, S. Identifying the functional macropore network related to preferential flow in structured soils. Vadose Zone J.; 2015; 14, pp. 1-16. [DOI: https://dx.doi.org/10.2136/vzj2015.05.0070]
46. Zhang, Q.; Dong, Y.; Deng, H.; Elsworth, D. High-resolution characterization of nanoparticle transport in heterogeneous porous media via low-field nuclear magnetic resonance. J. Hydrol.; 2020; 583, 124558. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2020.124558]
47. Liu, M.; Guo, L.; Yi, J.; Lin, H.; Lou, S.; Zhang, H.; Li, T. Characterising preferential flow and its interaction with the soil matrix using dye tracing in the Three Gorges Reservoir Area of China. Soil Res.; 2018; 56, pp. 588-600. [DOI: https://dx.doi.org/10.1071/SR17238]
48. Fuentes, I.; Casanova, M.; Seguel, O.; Padarian, J.; Nájera, F.; Salazar, O. Preferential flow paths in two alluvial soils with long-term additions of pig slurry in the Mediterranean zone of Chile. Soil Res.; 2015; 53, pp. 433-447. [DOI: https://dx.doi.org/10.1071/SR14264]
49. Haas, C.; Horn, R.; Ellerbrock, R.H.; Gerke, H.H. Fluorescence imaging for mm-scale observation of macropore-matrix mass transfer: Calibration experiments. Geoderma; 2020; 360, 114002. [DOI: https://dx.doi.org/10.1016/j.geoderma.2019.114002]
50. Luo, Z.; Niu, J.; Xie, B.; Zhang, L.; Chen, X.; Berndtsson, R.; Du, J.; Ao, J.; Yang, L.; Zhu, S. Influence of root distribution on preferential flow in deciduous and coniferous forest soils. Forests; 2019; 10, 986. [DOI: https://dx.doi.org/10.3390/f10110986]
51. Jiang, B.; Cui, B.; Wang, Y.; Wang, Y.; Li, D.; Wang, L.; Li, X. Stable-isotope tracing of vadose-zone water transport in Achnatherum splendens grassland of the Qinghai Lake Basin, NE Qinghai–Tibet Plateau, China. Catena; 2021; 200, 105088. [DOI: https://dx.doi.org/10.1016/j.catena.2020.105088]
52. Mueller, M.H.; Alaoui, A.; Kuells, C.; Leistert, H.; Meusburger, K.; Stumpp, C.; Weiler, M.; Alewell, C. Tracking water pathways in steep hillslopes by δ18O depth profiles of soil water. J. Hydrol.; 2014; 519, pp. 340-352. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2014.07.031]
53. Robert, T.; Caterina, D.; Deceuster, J.; Kaufmann, O.; Nguyen, F. A salt tracer test monitored with surface ERT to detect preferential flow and transport paths in fractured/karstified limestones. Geophysics; 2012; 77, pp. B55-B67. [DOI: https://dx.doi.org/10.1190/geo2011-0313.1]
54. Smettem, K.R.J.; Rye, C.; Henry, D.J.; Sochacki, S.J.; Harper, R.J. Soil water repellency and the five spheres of influence: A review of mechanisms, measurement and ecological implications. Sci. Total Environ.; 2021; 787, 147429. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2021.147429]
55. Mao, J.; Nierop, K.G.J.; Dekker, S.C.; Dekker, L.W.; Chen, B. Understanding the mechanisms of soil water repellency from nanoscale to ecosystem scale: A review. J. Soils Sediments; 2019; 19, pp. 171-185. [DOI: https://dx.doi.org/10.1007/s11368-018-2195-9]
56. DeBano, L.F. Water repellency in soils: A historical overview. J. Hydrol.; 2000; 231–232, pp. 4-32. [DOI: https://dx.doi.org/10.1016/S0022-1694(00)00180-3]
57. Wang, Y.; Wang, X.; Chau, H.W.; Si, B.; Yao, N.; Li, Y. Water movement and finger flow characterization in homogeneous water-repellent soils. Vadose Zone J.; 2018; 17, pp. 1-12. [DOI: https://dx.doi.org/10.2136/vzj2018.01.0021]
58. Mirbabaei, S.M.; Shabanpour, M.; van Dam, J.; Ritsema, C.; Zolfaghari, A.; Khaledian, M. Observation and simulation of water movement and runoff in a coarse texture water repellent soil. Catena; 2021; 207, 105637. [DOI: https://dx.doi.org/10.1016/j.catena.2021.105637]
59. Cueto-Felgueroso, L.; Suarez-Navarro, M.J.; Fu, X.; Juanes, R. Numerical simulation of unstable preferential flow during water infiltration into heterogeneous dry soil. Water; 2020; 12, 909. [DOI: https://dx.doi.org/10.3390/w12030909]
60. Shao, W.; Yang, Z.; Ni, J.; Su, Y.; Nie, W.; Ma, X. Comparison of single- and dual-permeability models in simulating the unsaturated hydro-mechanical behavior in a rainfall-triggered landslide. Landslides; 2018; 15, pp. 2449-2464. [DOI: https://dx.doi.org/10.1007/s10346-018-1059-0]
61. Shao, W.; Bogaard, T.A.; Bakker, M.; Greco, R. Quantification of the influence of preferential flow on slope stability using a numerical modelling approach. Hydrol. Earth Syst. Sci.; 2015; 19, pp. 2197-2212. [DOI: https://dx.doi.org/10.5194/hess-19-2197-2015]
62. Valis, D.; Hasilová, K.; Forbelská, M.; Pietrucha-Urbanik, K. Modelling water distribution network failures and deterioration. Proceedings of 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM); Singapore, 10–13 December 2017; pp. 924-928. [DOI: https://dx.doi.org/10.1109/IEEM.2017.8290027]
63. Luo, Y.; Zhang, J.; Zhou, Z.; Shen, Z.; Chong, L.; Victor, C. Investigation and prediction of water infiltration process in cracked soils based on a full-scale model test. Geoderma; 2021; 400, 115111. [DOI: https://dx.doi.org/10.1016/j.geoderma.2021.115111]
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Yuan, Ying 2 ; Zhou, Aihong 2 ; Guo, Lefan 3 ; Zhang, Hongrui 3 ; Liu, Xuedi 3 1 School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China; Hebei Center for Ecological and Environmental Geology Research, Shijiazhuang 050031, China; Hebei Technology Innovation Center for Intelligent Development and Control of Underground Built Environment, Shijiazhuang 050031, China; Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources, Shijiazhuang 050031, China
2 School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China; Hebei Technology Innovation Center for Intelligent Development and Control of Underground Built Environment, Shijiazhuang 050031, China; Key Laboratory of Intelligent Detection and Equipment for Underground Space of Beijing-Tianjin-Hebei Urban Agglomeration, Ministry of Natural Resources, Shijiazhuang 050031, China
3 School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China