Parameters of selected runoff events for numerical simulations.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Numerical simulation is increasingly used for studying overland flows. Since runoff drives soil erosion and landscape evolution, the runoff models provide a foundation for modeling soil erosion, rill erosion, and related processes at the watershed scale [1, 2]. Models involving different levels of abstraction have been proposed [3, 4, 5]. Two commonly used models are the diffusion wave (DW) and kinematic wave (KW) models [6, 7, 8, 9]. The KW models set the friction slope to be equal to the bed slope and ignore the inertial terms [10]. The method has been successfully used to describe overland flows [11, 12, 13, 14]. The governing equations are highly nonlinear and do not have general analytical solutions, so one has to solve them numerically for practical cases [15]. The models based on full Saint-Venant (SV) equations have also been applied and produced better results.
Two-dimension models are generally used for cases with irregular domains. A distributed rainfall-runoff model using the KW approximation solved by an implicit finite difference scheme was developed [16], but channel flows are computed using a separate KW model. Fully two-dimensional shallow water equations are being utilized for modeling overland flows in late 1980s [17]. A two-dimensional finite difference (FD) runoff model was developed by solving 2D SV equations [18]. Shallow water equation-based 2D models [19] were used for runoff over an irregular topography of experimental scale with infiltration processes considered and in rural semiarid watersheds for overland flows generated by storms [20].
In addition to finite difference method (FDM), the two-dimensional finite element (FEM) and finite volume methods (FVM) have been used for overland flow simulations. A FEM KW model was developed by Liu et al. [21] for simulating runoff generation and concentration over an irregular bed and reproduced experimental results. Tests [15] indicated that the FVM-based 2D SV model performed better than that of FDM. Costabile et al. [22] solved the shallow water equations using the FVM and applied the resulting model to simulate a real event on a watershed of 40 km2. Nunoz-Carpena et al. [23] solved the KW equation using the Petrov-Galerkin method. Venkata et al. [24] developed a Galerkin DW FEM and applied it to a small watershed. Singh et al. [25] simulated runoff processes by solving the 2D shallow water equations with a shock-capturing scheme and the FVM. Shirmeen et al. [26] showed results of a validated, FEM 2D model in predicting runoff from a flat agricultural watershed.
In order to check numerical models’ mathematical correctness and physical applicability, the developed computational models have been tested with analytical solutions, experimental, and field data. Iwagaki [27] studied runoff using analytical methods and experimental data; several specific solutions were developed based on the characteristic method. Govindaraju et al. [28] developed analytical solutions using KW and DW approximations. Comparisons of analytical solutions, numerical solutions, and experimental data were discussed. Singh [29] detailed the KW model’s analytical and numerical solutions and their wide applications. Cea [30] tested FVM using an experimental watershed with a complex shape. These overland flow models use simplified equations and need to specify pre-existing channel networks, which make it difficult to simulate soil erosion cases with hill-slope evaluation and mixed sheet-channel flow conditions.
CCHE2D is a physically based model, which treats the entire watershed including the channels and ditches as one continuous domain. One does not need to differentiate overland sheet flow and channel flow calculation areas using grid cells and 1D channel networks as is done in GSSHA [31], WASH123D [32], NIKE-SHE [33], and SHETRAN [34]. It is also not necessary to employ arbitrarily shaped sub-watersheds and 1D channel networks as is done in the CCHE1D model [35]. In these models, 2D DW equations or KW equations are solved for the overland flow using finite difference methods, and the 1D SV equation is solved in the prescribed channel networks. In contrast to these models, in CCHE2D, hydrodynamics over the entire watershed is simulated using only 2D equations discretized on an irregular quadrilateral finite element mesh, which is generated using digital elevation model (DEM) data. The simulated overland sheet flow and channel flow are seamlessly connected everywhere in the domain and the channel network is formed automatically. This method may be more applicable when sediment transport, rill erosion, or gully erosion processes in watersheds are considered.
In this study, the CCHE2D model is modified and applied to simulate watershed hydrological processes. CCHE2D is a general hydrodynamic model for unsteady, turbulent free flows, sediment transport, and pollutant transport. It has been validated and applied widely to simulations of channel flow, flooding, coastal flow, bed topographic change, and chemical contamination in aquatic environments [36, 37, 38, 39, 40].
The major objectives of the present paper are to assess the accuracy and the effectiveness of this FEM in predicting overland runoff processes, and its applicability to practical agricultural watersheds with ditches and natural stream channels. The approach of the study followed the recommendations of [41] for quality assurance that numerical models have to be verified and validated using analytical solutions, physical experimental data, and field data. The validated numerical model was used to simulate and characterize the hydrological processes of an agricultural watershed in the Mississippi River alluvial plain where farm fields are drained and separated by ditches and stream channels. A limitation was found in the interpolation method when it is applied to the water surface elevation of the sheet runoff. A numerical scheme was developed and implemented for improving the bilinear interpolation. The present study focused on watershed surface flow processes over bare soils; interception, evapotranspiration, and infiltration were not considered.
Surface runoff due to precipitation is typically quite shallow and can be aptly represented by the 2D shallow water equations within the CCHE2D model [36, 38]. The water surface elevation of the runoff flow, η, is calculated by the continuity equation in a Cartesian coordinate system
in which h is the local water depth, t is time; R is rainfall intensity, which may vary in time and space, and u and v are depth-averaged velocity components in x and y directions, respectively. The depth-integrated 2D momentum equations for turbulent flows are as follows:
in which g is the gravitational acceleration, ρ is water density, τxx, τxy, τyx, and τyy are depth-averaged Reynolds stresses, and τbx, τby are bed shear stresses. In the overland runoff area, the Reynolds stress terms vanish, and Eqs. (2) and (3) become the shallow water equations. The Reynolds stress terms remain significant in the part of the domain with channel and concentrated flows. A special finite element method called the efficient element method is adopted in the model, in which a collocation approach is used to discretize the equations in a structured quadrilateral nonorthogonal mesh system. A partially staggered grid is used for solving these equations. A velocity correction method is used to couple the continuity equation and the momentum equations. More details about this model’s numerical methodology and techniques can be found in earlier publications [36, 38, 42].
The full Eqs. (1)–(3) are applicable for general flow conditions. In realistic cases where runoff and channel flow conditions coexist, a general flow model is necessary. Under the sheet flow condition, the advection and turbulence stress terms in the momentum equations vanish because the dominant forcing for the overland flow is the gravity and bed shear stress. The water depth is very small, and water surface slope and bed slope become almost the same:
in which b is the bed elevation. The general flow equations then become the KW equations. Under this condition, the flow is completely dominated by the bed slope. Shear stresses on the bed are evaluated in conjunction with the Manning equation as:
in which n is the Manning roughness coefficient and
CCHE2D uses a partially staggered method: the velocities are solved at collocation points and the pressure (water surface) is solved at cell centers [36]. A bilinear interpolation method is used to interpolate the water surface elevation solution to the collocation nodes where the momentum equations are solved. The bilinear interpolation works well for general channel flow simulations because the water depth is large in comparison with the variation of bed surface and the mesh size. When overland sheet runoff is simulated; however, the water depth is very small; it is often less than the microelevation variation of bed topography represented in an element. In this case, the interpolated water surface elevation may be lower than the bed if the bed is concave down and vice versa. This is a limitation of the interpolation method. In the concave down case, dry nodes are created artificially; in the concave up case, artificial masses of water could be erroneously created. Figure 1 illustrates this problem in one dimension. The problem occurs whenever irregular bed topographies are encountered. A correction is therefore necessary to the interpolation over the surface runoff area.
The error of underestimation and overestimation caused by linear interpolation of water surface elevation from cell centers to collocation nodes.
A numerical scheme has been developed and implemented in CCHE2D to correct the interpolation error [43]. Figure 2 illustrates how the scheme is formulated in one dimension with an exaggerated vertical scale. Eq. (7) is the formulation to compute the correction value Δb for nonuniform meshes, and it is simplified to Eq. (8) if the mesh is uniform. It is straightforward to extend Eqs. (7) and (8) to two dimension. Water depth at the cell centers is positive, without this correction, the depth at the middle point would become negative because the interpolated water surface elevation is below the bed. This scheme is necessary and effective when cases with irregular topography are simulated
Definition sketch for the formulation of the correction (Eqs. (7) and (8)) to linear water surface elevation interpolation. b1, b2, and b3 are bed elevation. Δb is the interpolation correction and Δx1 and Δx2 are mesh spacing.
where b1, b2, and b3 are bed elevation, Δb is the interpolation correction,
Two analytical solutions were obtained by solving a one-dimensional kinematic equation analytically for rain-generated runoff by [44, 45]. The solution of sustained rains for the runoff to reach a steady state [44] and the solution for rainfall that stops before the runoff becomes steady [45], including the tailing stage solution after rainfall stops, were provided. The governing one-dimensional kinematic equation for deriving these solutions is:
in which q is the discharge of water per unit width (m2/s), k is an exponent (=5/3), and α (=5) is a coefficient (m2−k/s). These analytical solutions were realized for a few simple cases: runoff due to steady rainfall intensity on a uniform planar area of 200 × 1 m with a slope of 1.0%. The rainfall intensity was R = 2.7 × 10−5 m/s, and the Manning’s coefficient was n = 0.02 m−1/3s. For comparison, the same case was simulated using CCHE2D and a 10 × 100 point 2D mesh with uniform spacing. The solutions were recorded at cross sections located at 50, 100, 150, and 200 m, from the upstream end of the plane.
Figure 3 shows the comparisons of the simulated runoff and the analytical solutions for the sustained rain collected at the four cross sections. Hydrographs at each cross section indicate that equilibrium runoff (steady state) is reached before the rain stops at T = 1000 s. The runoff is always nonuniform, and the peak discharge increases in the downstream direction. At first, the flow is unsteady (rising limb), then becomes steady until T = 1000 s, and finally becomes unsteady in the falling limb. The runoff reaches equilibrium earlier at locations closer to upstream. The simulation is a little less than the analytical solution at the time approaching the peak discharge, particularly near the downstream. The solution can be improved by reducing the local mesh size effectively.
Comparisons of the simulated runoff hydrographs and analytical solutions. The sustained rain stopped at T = 1000 s after the steady states have reached everywhere on the slope. Comparisons at four cross sections are shown. Δx is the mesh spacing in the runoff direction.
Figure 4 shows a case in which the rainfall stops before runoff reaches steady state (T = 200 s); the hydrographs, thus, have a different pattern. The peak discharge is reached at the time the rain stopped and is the same for all cross sections. The peak discharge for the lower cross sections lasts longer because the flows at the lower locations are sustained by upstream contributions. The runoff recession is earlier for upstream locations. The shape of the two sets of simulated hydrographs at all cross-section locations corresponded well with the analytical solutions.
Comparisons of the simulated runoff hydrographs and analytical solutions. The rain stopped at T = 200 s, before the flow at any of the four cross sections reached steady state. Δx is the mesh spacing in the runoff direction.
CCHE2D model was validated using experimental data sets collected from the literature. All of these cases were carried out on impervious overland flow planes. The only quantity measured in these experiments was the downstream runoff discharge.
Morgali and Linsley [46] obtained two sets of experimental runoff data. Their tests were carried out over a straight turf surface of 21.95 m long with a constant slope (0.04) and width. The Manning’s coefficient, n, was found to be 0.5 m−1/3s. The rains had two different intensities and were uniform along the slope for 1200 s (20 min). Figure 5 compares the experimental data and the numerical simulations. The analytical solution for this test condition [44] is also presented in Figure 5. It was found that these runoff experiments fit well with the analytical solution. A 110 × 10 points uniform mesh and 0.01 s time step were used for the numerical simulation. The CCHE2D numerical results showed good agreements with the analytical solution as well as the experimental results (Figure 5). The rising limb of the discharge hydrograph and the peak discharge were captured very well by the simulations. The processes of the two experiments, 1A (R = 92.96 mm/h) and 1B (R = 48.01 mm/h), look similar because the only difference in the experiments was rainfall intensity. The peak discharges of the experiments occurred at approximately 850 and 1100 s, respectively, for Case 1A and Case 1B. The numerical solutions of CCHE2D agreed well with the experimental data. The peak discharges for Case 1A and Case 1B are 5.67×10−4 and 2.93 × 10−4 m3/s, respectively. The times to peak discharge for Case 1A resulted from the analytical solution, CCHE2D and the experiment, are 760, 850 and 950 s, respectively. The differences among the three are less for the Case 1B (Figure 5).
Comparisons of measured data with analytical solution, results of CCHE2D, and other numerical models.
Figure 5 also compares the simulation results of CCHE2D and the model results by Govindaraju et al. [28]; the two numerical solutions agree well for the case with the higher rain (1A), but the fit of their solutions based on the SV equations does not correspond well for the case with the smaller rainfall (1B). The results of CCHE2D also outperform the analytical solution of the DW approximation [28].
Cea et al. [30] conducted three runoff experiments of complex topography and simulated these cases using a 2D unstructured FVM. The experimental watershed was a rectangle (2 × 2.5 m) made by three planes of stainless steel, each of them with a slope of 0.05 (Figure 6). Two dikes (1.86 and 1.01 m in length) were placed in the watershed to vary the topography. Rainfall intensity, duration, and runoff hydrographs were measured. As a result, the runoff direction, distribution, and pattern of the hydrograph were affected. The runoff was accumulated and became channel flows along intercepting lines of slopes and dikes. Since both overland flow and channel flow are involved, faithful simulation requires solving full governing Eqs. (1)–(3). The rainfall applied to each test case was different. In the first test (2A), the rainfall intensity was 317 mm/h for 45 s. In the second test (2B), the rainfall intensity was 320 mm/h for 25 s; then it was stopped for 4 s and restarted for an additional 25 s with the same intensity. In the third test (2C), rainfall intensity was 328 mm/h. The rainfall was applied for 25 s; then it was stopped for 7 s and then restarted for another 25 s.
Topography of the experimental watershed [30].
In this study, CCHE2D was applied and the numerical results were compared with experimental data. The watershed was modeled using an irregular structured mesh with the cell size ranging from 0.034 to 0.009 m; the mesh was refined near the main channel and the outlet for improving results. The Manning’s roughness coefficient was set equal to 0.009 m−1/3s. The simulation time was 120 s for each case. The channel flow and runoff sheet flow coexisted: the runoff from the watershed surface was accumulated in the bottom of the watershed channel with a triangle-shaped cross section formed by the side slopes. Results of cases 2A and 2C are shown in Figures 7 and 8, respectively.
Comparison of measured and simulated hydrographs using rainfall with one peak (Case 2A).
Comparison of measured and simulated hydrographs using rainfall with two peaks (Case 2C).
Figure 7 shows the comparison between the numerical solution and experimentally observed runoff hydrograph of Case 2A. The solution of the CCHE2D model agrees very well with the experimental results. The flow discharge increased continuously once the rain started. The peak discharge occurred at the time the rainfall stopped (at 45 s). Although the rising and the falling limbs of the hydrograph were slightly overestimated, the shape of the hydrograph and the peak discharge were aptly predicted.
Figure 8 shows the comparison between the numerical and experimental runoff hydrographs of Case 2C. The shape of the hydrograph was successfully predicted. The interval between the two rainfall peaks was 7 s. The first runoff peak discharge occurred at the time the rainfall stopped, at 25 s. The runoff discharge decreased for approximately 10 s and then increased. The second runoff peak discharge occurred at approximately 57 s. The simulated processes and the observed physical processes showed a good general agreement; it also matched well with the model results of [30].
Figure 9 shows the simulation results at t = 54 s (the peak of the second rainfall) for Case 2C: (a) simulated water depth contour distribution, (b) simulated flow unit discharge pattern and (c) velocity vector distribution in the watershed. The distributions indicate how the overland sheet flow, under the influence of dikes and topography, concentrates into channels and flows out of the watershed. The flows over the slopes are sheet runoff, but complex recirculations are developed in the main channel. The water surface is no longer parallel to the bed surface. These flows cannot be represented by KW, DW, and SV models.
Distributions of simulated (a) water depth contours (b) flow (unit discharge) distribution and (c) velocity vectors at t = 54 s for test Case 2C.
This section presents the application of CCHE2D to a sub-watershed of the Howden Lake watershed, an 18 km2 agricultural watershed in the Mississippi River alluvial plain (Figure 10). In this region of low relief, watersheds are configured by farm fields drained by culverts, ditches, and intermittently flowing streams called bayous. During periods between runoff events, the channels contain standing water. The studied sub-watershed was upstream of a gaging station on an intermittently flowing bayou. The average annual precipitation in this region is about 1440 mm. Precipitation occurs as intense thunderstorms or low-intensity rains associated with major frontal movements. The latter type of events may stretch over several days of drizzle and sporadic showers. During growing seasons, channels experience some flow and stage fluctuation due to irrigation withdrawals and return flows.
Location and topography of the Howden Lake watershed. Dashed curve encloses the runoff simulation area, and the dark closed curve is the gaged watershed.
Watershed topography was surveyed by airborne LiDAR with a 1.5 m horizontal resolution. The vertical accuracy was 15.0 cm RMSE or better. The watershed elevation ranges from approximately 43.89–48.99 m. A nearly uniform fine mesh (mesh spacing = 3.76–4.98 m) was generated for the simulation with the ditches and small streams between the plots further refined locally. Cultivated fields are connected to the streams and ditches with drainage culverts, which often convey water from one sub-watershed to another. The locations of culverts in the study watershed were identified in a field survey and incorporated in the numerical mesh.
Soil data were obtained from the Soil Survey Geographic (SSURGO) database [47]. The watershed is covered mostly by soils with high clay content, which is typical of the region [48]. Infiltration is, therefore, negligible and was not considered in the simulation. Precipitation and flow stage data were measured by field instrumentation. Because the stream instrumented with the gage station has complex conditions, it was difficult to collect reliable velocity data during a rain event. Only stage data were available. As a result, the gage station does not have a discharge-stage rating curve. Development of a rating curve using simulations and measured data for this site would be helpful for understanding the hydrologic processes in these watersheds.
Because the Howden Lake watershed is of low-relief, it was often difficult to determine the boundaries between sub-watersheds in field surveys or on topographic contour maps. For example, the runoff from a piece of field may flow in two directions into two sub-watersheds, and the location of the divide line might be identified only from the runoff flow distribution during a simulation. Normally, the outline of a watershed is a given condition for a hydrology study. In this study, the exact boundary outline was not firmly established even after field surveys. A larger area containing the studied watershed was simulated, and the watershed boundary and area were finally defined by the simulated runoff and channel flow patterns. The boundary outline of the studied watershed (Figure 11) contributing to the gage was identified by visually checking the simulated overland flow directions of CCHE2D.
Numerical simulation identified watershed for the gage station. Simulation results in the dashed rectangle area are shown in Figure 12.
In the simulations, the streams and ditches between farming plots were represented using DEM elevations like flat surface areas. No channel networks were prescribed, but the simulated surface runoff flowed logically to the ditches and to the stream channel. No other watershed analysis tools were needed. Although the study results presented later are for this identified watershed, the spatial domain of numerical simulations was several times larger (Figure 10). The northern side of the stream channel had been blocked by farmers, so the overland flow from the watershed entered the stream in the middle and flowed in a southwesterly direction (Figure 11). The water from this identified watershed pasted the gage, while runoff from the region outside this watershed was discharged from the simulation domain via other ditches and streams. The area of this watershed, including cultivated land, drainage ditches and a stream segment, was found to be 973,700 m2. In this area, the topographic elevation ranges from approximately 46.77–47.49 m in one plot and from 47.27 to 48.09 m in another. The mean slope of the fields is 0.0097 and 0.0098, respectively.
Several observed storm events were selected for the model application. To reduce minor losses of water due to evaporation, soil wetting and infiltration, etc., only large rain events were considered. The rainfall event in April 2011 (Table 1) was first used for simulation. Figure 12a shows the detailed ground elevation contour of a small simulation area (dashed rectangle area in Figure 11). The elevation of this area ranges from about 46.8 to about 47.4 m. Figure 12b shows the direction vectors of the runoff near the end of the simulation. Because the water is very shallow, the flow direction is highly affected by the ground topography. Figure 12c and d shows the direction vectors and water depth distribution at the peak time of the rainfall.
Event | Measured rainfall (mm) | Runoff volume* (m3) | z | r | L0 (m) |
---|---|---|---|---|---|
4/27–4/28/2011 | 88.39 | 85,817 | 2.4 | 1.223 | 0.45 |
10/30–11/4/2013 | 53.59 | 52,182 | 1.9 | 4.24 | 0.78 |
11/21–25/2011 | 62.99 | 61,333 | 1.4 | 1.613 | 0.45 |
5/20–24/2013 | 48.77 | 47,483 | 1.0 | 1.436 | 0.59 |
9/25–27/2011 | 52.32 | 50,946 | 1.8 | 5.211 | 0.48 |
Parameters of selected runoff events for numerical simulations.
Computed from the main bulk of the rain event.
Information and simulation results in an area indicated in Figure 11 in a dashed rectangle: (a) bed elevation contours, (b) velocity direction distribution near the end of the simulation, (c) velocity direction, and (d) the water depth distribution at the peak of the April 2011 rainfall.
Although the variation of the bed surface topography is very small, the simulation shows how the runoff is controlled by microtopography (Figure 12a). At the peak time of the rainfall, the overall water depth in this area (Figure 12d) is much deeper, and the flow directions (Figure 12c) are less affected by the local microtopographic features. The flow on the right side of the domain is still sheet runoff under the KW condition; while on the left side, the water depth is more than 0.2 m, and the flow is no longer governed by the KW condition. This model provides the outflow hydrograph as well as the temporal and spatial distribution of the water depth and flow velocity, which can be used for studying soil erosion, agro-pollutant transport, and water quality.
The gage station (Figure 11) recorded the channel water surface elevation at regular time intervals, but velocities were generally too low for accurate measurement, and therefore, water discharge was not measured. In order to better understand the watershed hydrology, a rating curve of the form:
was developed using simulated discharge, in which L is the measured water surface elevation, r and z are parameters, and L0 is the initial water surface elevation prior to a rainfall event. Eq. (11) has two unknown parameters, but there is only one relationship available for determining their values. The total volume of runoff, obtained by numerically integrating Eq. (11) in time, is equal to the rain volume, VR:
in which Li is the measured water surface elevation at the gage station. With Eq. (12) satisfied, values of r and z that best fit the shape of the discharge hydrograph computed using Eq. (11), and that of the numerical simulation, were determined for each event by trial and error.
Attempts were made to fit all simulated curves using a single set of values for r, z and a mean base stage L0, but the result showed unacceptable discrepancies. L0 varied due to antecedent precipitation, downstream discharge control, sedimentation, and water usage between events. The range of L0 for the studied events is 0.33 m (Table 1). Given the complexities of the hydraulic regime in the water body, varying from standing to moving state and with varying downstream controls, variable rating curve parameters are sensible. Event-specific rating curve parameters are not ideal but are useful in a research context.
Manning’s roughness coefficient (n) is a major factor in the determination of watershed runoff characteristics and generally reflects ground cover and management. The event on April 27–28, 2011 was used for initial calibration of Manning’s coefficient. The studied watershed is cultivated with soybeans (Glycine max L. Merr.), corn (Zea mays L.), cotton (Gossypium hirsutum L.), and rice (Oryza sativa L.). The sensitivity of the CCHE2D model in Howden Lake watershed to Manning’s n was examined using a wide range of values from 0.030 to 0.30 m−1/3s. Smaller Manning’s n results in a higher runoff peak discharge and an earlier peak flow arrival time. A visual comparison of discharge hydrographs based on stage measurements and numerical simulation (Figure 13) indicates that n = 0.3 m−1/3s is the most appropriate choice for the overland runoff area because the peak times of these runoff events are consistent. Considering that the depths of the sheet runoff are much smaller than the microtopographic irregularities over the fields, the calibrated n represents not only the bed resistance but also form drags due to the microbed forms, crop residue, and vegetation. This n value agrees with the recent runoff studies [25, 31, 49] in cases of overland flows, including those in the Goodwin Creek Experimental Watershed in Northern Mississippi. There are numerous trees, bushes, and weeds growing along and within the channel, thus, n = 0.16 m−1/3s was used for the channel and kept unchanged for other rain event cases.
Sensitivity of simulated hydrograph to Manning’s coefficient.
The total observed rainfall volume for the April 27–28, 2011 event (Figure 13) was approximately 86,000 m3 (88.32 mm). The total simulated runoff volume is about 80,600 m3 (83.78 mm), which is reasonable because the hydrograph recession limb extended past the simulation termination at 47 h. There were several small rain events that occurred before the event shown in Figure 13, so the runoff volume based on the observed water surface elevation may include recession of the earlier events.
Figure 14 compares the discharge hydrographs of several additional runoff events computed using Eq. (12) and that of the numerical simulations. The identified parameters for these events, r and z, are listed in Table 1. Events 9/2011 and 10/2013 have one major peak, while those of 11/2011 and 5/2013 each have two major peaks. The simulated hydrographs fit well with those computed using Eq. (11). The two rain peaks of the 5/2013 event were separated by about 2 h, but those of the 11/2011 event were separated by 15 h. The runoff of the 5/2013 event showed only one peak because the two rain peaks were very close, and the runoff peaks were superimposed. However, the temporal separation of the two peaks of the 11/2011 event was much longer. Therefore, the superimposed hydrologic response also displayed two peaks. These watershed responses were reproduced by the numerical simulations.
Comparisons of simulated runoff and Eq. (11).
As noted above, the watershed has multiple field ditches that convey runoff into the channel (Figures 10 and 11). Ditch and channel flow were simulated together with the overland sheet runoff. Figure 15 shows the simulated flows in the channel network of the watershed. The contours represent the distribution of the unit flow discharge. The vectors in the ditches and in the stream formed a channel network indicated by the large velocity vectors; those in the runoff area are too small to be seen. The flows in the stream are turbulent when the rainfalls are large. Because no velocity data were acquired, the simulated velocity results in the channel were not validated.
Simulated flow in the network of drainage ditches and the stream in the watershed.
The numerical model CCHE2D was used to model sheet runoff from watersheds, large and complex enough to include both overland and channel flow processes. The model was systematically verified and validated using analytical solutions and experimental data due to steady and unsteady rainfall intensity, and applied to a real world watershed. Good agreement between the analytical solutions, experimental data, and numerical simulations were obtained. For the experimental cases involving complex watershed shapes, the numerical model has the ability to simulate runoff over the slope surfaces and the channel flows.
A numerical scheme was developed to correct the bilinear interpolation of the water surface elevation from its solutions at the staggered cell centers to the collocation nodes. The scheme was necessary and effective for obtaining good sheet runoff simulation results in watersheds with irregular topography. One would have to smooth the ground topography if a model requires the interpolation of water surface solution under this condition.
The model was applied to an agricultural watershed in the Mississippi River alluvial plain. It was useful to identify the boundary of the monitored watershed and develop the rating curve at the gage station of the watershed. Several significant runoff events were selected for simulation. Each of the simulated runoff hydrographs and the rating curves agreed well with those observed in the field. The sensitivity of the model to overland sheet flow friction was studied. An increase in the bed surface friction coefficient significantly diminishes the peak of runoff discharge, delaying its time of arrival. Values of n = 0.2–0.3 m−1/3s for overland flow were found to be adequate to best fit the numerical simulations and the observed data in the studied watershed. With a high-resolution mesh, the model can predict the complex surface runoff pattern over the agricultural land. Ditch and stream channels in the domain are a connected channel network. The model is able to simulate sheet runoff, turbulent channel flow, and their transitions seamlessly. The simulated hydrological processes for several storm events fit well to those observed at the gage station. The capability would be useful for studies related to soil erosion and agro-pollutant transport. The model is currently used for watershed applications without considering interception, evapotranspiration, and infiltration. Additional work is needed to further extend the research in these areas.
This work is supported in part by USDA Agriculture Research Service under the Research Project No. 6060-13000-025-00D (NCCHE) monitored by the USDA-ARS National Sedimentation Laboratory (NSL). Support is also in part by the Southeast Region Research Initiative (SERRI) project and the University of Mississippi (UM).
Pandemics and epidemics of infectious origin are large-scale outbreaks that can greatly increase morbidity and mortality globally or over a wide geographic area, respectively [1]. Pandemics have occurred throughout history and appear to be increasing in frequency in the last centuries. Noteworthy examples include the Black Death at the end of the Middle Ages, Spanish flu in 1918, the 2014 West Africa Ebola epidemic or the current COVID-19 pandemic. The direct impact of pandemics on health can be dramatic. These large outbreaks can disproportionally affect younger or active workers, but vulnerable populations such as the elderly are at a particular high-risk. Pandemics can cause acute, short-term as well as longer-term damage to economic growth due to public health efforts, health system expenditures, and aid to affected sectors. Evidence suggests that epidemics and pandemics can have significant social and political consequences too, by debilitating institutions, amplifying political tensions, stigmatizing minority populations, or encouraging sharp differences between social classes [2].
Outbreaks by respiratory ribonucleic acid (RNA) viruses such as influenza or coronaviruses entail the principal threat due to their ease of spreading among humans, their potential severity and recurrence. However, other RNA viruses such as flaviviruses (Zika) or filoviruses (Ebola) must be taken into consideration due to a great overall burden of morbidity and mortality [3]. Antiviral drugs can help mitigate a viral outbreak by reducing the disease in infected patients or their infectiousness. While these drugs can be very successful against some viruses (e.g. hepatitis C virus [HCV]) [4], they are not universally effective as exemplified in the current SARS-CoV-2 pandemic [5]. Nowadays, having effective vaccines may be the only tool to reduce susceptibility to infection and thus, prevent the rate of virus spread [2].
Vaccination has dramatically decreased the burden of infectious diseases. Vaccines have saved hundreds of millions of lives over the years [6]. It has been estimated that approximately 103 million cases of childhood diseases were prevented in the United States through vaccination between 1924 and 2010 [7]. The eradication of smallpox in 1980 through vaccination is considered one of the crown accomplishments of medicine. Despite these achievements, effective vaccines have been developed against just over 30 pathogens among bacteria and viruses. There are many pathogens, including viruses such as human immunodeficiency virus (HIV) or respiratory syncytial virus (RSV), for which all efforts for vaccine development have failed so far. In addition, current available vaccines for worldwide important viral diseases like influenza are suboptimal, especially in the elderly, resulting in vulnerability among billions of at-risk populations [6]. On the other hand, having a new effective and safe vaccine in time to control highly contagious emerging viruses that cause epidemic or pandemic threats is an almost impossible task considering the timeframes for vaccine development. This includes preclinical and clinical research, its approval by the regulatory authorities, as well as its production and distribution [3].
Altogether, it has been postulated that one possibility of filling the gap between the appearance of a viral outbreak by an emerging pathogen and the availability of a specific vaccine is to take advantage of the heterologous protection of some existing vaccines, in order to increase the non-specific resistance of the host through trained immunity [8, 9].
Conventional (specific) anti-infectious vaccines are biological preparations containing live-attenuated or dead microorganisms, their antigens or nucleic acids encoding for them, designed for specific pathogens. The purpose of vaccination is to induce a long lasting adaptive immune response against key antigens able to confer host resistance for future encounters with the corresponding pathogen. Either the production of antibodies, generation of T helper/effector cells, or both, may play a critical role in such a resistance, which greatly depends on the type of pathogen, the route of entrance and the host-pathogen relationship (e.g., extracellular and/or intracellular) [10]. Successful vaccines are highly effective not only in inducing long-lasting immunity against disease-causing pathogens, but also in providing herd immunity to the community that substantially restricts the spread of infection [6].
Most of the vaccines available today have been developed empirically and used successfully long before their mechanism of action on the immune system was understood. Early protection is associated to induction of antigen-specific antibodies, being their quality (avidity, specificity, or neutralizing capacity) key factors for their efficacy. Long-term protection relies on the persistence of vaccine antibodies and availability of immune memory cells capable of rapid and effective reactivation with subsequent microbial exposure. On the other hand, T cells have a critical role in the induction of high affinity antibodies and immune memory. Furthermore, T cells have a direct role in protection conferred by some vaccines, including the tuberculosis Bacille Calmette-Guérin (BCG) vaccine [11].
Vaccines using whole pathogens have been classically classified as either live attenuated or inactivated (killed). Subunit vaccines contain just selected antigens (e.g., proteins, polysaccharides). Recently, due to a growing availability of bioinformatics and sequencing tools, there has been an increase interest on so-called “rational” vaccine design approaches for subunit vaccines, such as the reverse vaccinology [12]. In this regard, modern vaccines include recombinant proteins or nucleic acids [13]. Rather than administering the antigen itself, DNA and mRNA vaccines targeting dendritic cells (DCs) encode the antigen of interest that will be produced by the vaccinated host, representing a new era in vaccinology [14]. In fact, the first RNA vaccine licensed for humans in Western countries has been recently developed for SARS-CoV-2.
As commented before, a vaccine response is linked to the induction of T and B cell specific responses to the antigens contained in the vaccine. This requires lymphocyte activation, proliferation and differentiation on specialized lymphoid tissues (e.g lymph nodes), where antigen presenting cells, like DCs for T cells or follicular dendritic cells (FDCs) for B cells, are present. Mature DCs are recruited into the T cell areas of lymph nodes from the periphery, e.g., at the site of injection of the vaccine. DCs express pattern recognition receptors (PRR) that recognize evolutionary conserved pathogen-associated molecular patterns (PAMPs) that are not contained in self-antigens and are identified as “danger signals” [15]. When immature DCs are exposed to the vaccine-derived antigens at the site of vaccination, they uptake them and become activated [16]. This activation will lead to their maturation with the expression of homing receptors at their surface, triggering DC migration to the draining lymph node through afferent lymphatic vessels, where the activation of T and B lymphocytes will occur. Mature DCs process the up-taken antigens and present them to naïve T cells associated to molecules of the major histocompatibility complex (MHC) within the T cell areas of lymph nodes. On the other hand, unprocessed native antigens, either free or complexed with antibodies or complement, access the B cell areas of lymph nodes (lymphoid follicles) where they are captured by FDCs and displayed from their cell surface to the B cells. Antigen-specific B cells will rapidly proliferate forming a germinal center and differentiate into plasma cells producing low-affinity immunoglobulin (Ig) M antibodies. The B cells will then receive additional signals from activated T cells, undergoing isotype antibody switch from IgM to IgG or IgA and affinity maturation of the antibodies produced.
For a vaccine to be immunogenic enough, DC activation, that can be achieved by adjuvants, is essential. Live attenuated and inactivated whole-cell vaccines are considered “self-adjuvanted” as they naturally present sufficient PAMPs to activate innate immune cells, including DCs; thus, promoting a robust antigen-specific immune response. In contrast, subunit vaccines generally require different types of adjuvants to enhance and/or drive the immune response in the desired direction [15, 17].
Viral outbreaks appear when there is a sufficient number of susceptible individuals within a nearby population. Although susceptibility is a balance between host factors (high/low resistance) and pathogens (high/low virulence), in many cases it reflects a lack of prior contact with a given pathogen. In general, this is related to the emergence of new viruses or the lack of effective vaccines against known viruses. As pointed above, the development of effective vaccines is not an easy task against certain viruses. We are still lacking vaccines for some of the most lethal viral infections, including HIV and MERS-CoV, among others. These pathogens are difficult to tackle, as we do not fully understand their mechanisms to evade the immune system or how to elicit protective immunity against them [13]. However, encouraging progress is being made against these pathogens and there are currently several “pipeline vaccines” in development, such as RSV, universal influenza vaccine, and SARS-CoV-2 [18, 19, 20]. Apart of SARS-CoV-2 for obvious reasons in the current pandemic, there is an urgency to have a universal influenza vaccine that provides a broad and durable protection from influenza virus infection. Yet, the high level of antigenic diversity and variability, and antigenic drift in the surface antigens, enable these viruses to escape antibody-mediating neutralization [21]. On the other hand, there is a number of vaccines currently licensed, including the influenza A virus vaccine, that provide incomplete protection, especially in high-risk groups [22]. Mumps outbreaks observed in Ireland, United Kingdom and United States in vaccinated subjects with Measles Mumps Rubella (MMR) vaccine is another example [23]. Different factors have been postulated to contribute to mumps outbreak, including waning immunity and primary and secondary vaccine failure. Yet, their actual contribution is not fully understood [23].
Vaccine efficacy must consider different target populations as well. Adaptive immune response to vaccines may be limited in newborn and the elderly. Early in life, immune responses are dampened compared to adults [24, 25]. Neonates have underdeveloped germinal centers in lymph nodes and the spleen, and low expression of B cell receptors which in turn results in low levels of primary IgG responses to infections and vaccines [26]. As we age, our immune system undergoes age-related changes that lead to progressive deterioration of the innate and the adaptive immune responses, this is termed immunosenescence. The most common features of immunosenescence are short-lived memory responses, impaired response to new antigens, increased predisposition to autoimmune diseases and low-grade systemic inflammation (inflammaging) [27, 28]. Immunosenesence results in increased susceptibility to infections and deficient response to vaccination causing high hospitalization and mortality rates. For example, influenza vaccine efficiency has been reported to be 17–53% in the elderly, compared with the 70–90% efficacy in young adults [29]; and vaccination with Varicella zoster virus (VZV), also an important pathogen in elderly people, only partially prevents reactivation of herpes zoster [27].
If the difficulties listed above are outlined for existing or developing vaccines, quickly obtaining an effective vaccine to urgently control a new virus outbreak is almost an impossible task in the short-term as pointed above. This is well exemplified by the SARS-CoV-2 vaccine race pushed by the devastating COVID-19, with more than 100 vaccine candidates in the running. It is considered that no less than 1 year will last the time until the first licensed vaccine can provide protection in the best scenario [30]. This, in spite of greatly shortening the usual clinical development time and regulatory obstacles for a new vaccine and, therefore, without knowing its true performance and/or safety in the medium term compared to other authorized vaccines [31].
It has become evident from epidemiological, clinical and experimental data that some conventional whole-cell vaccines, like BCG and others, also provide resistance to infectious diseases not related with the specific pathogen targeted by the vaccine [32, 33, 34]. Much of these non-specific “heterologous” effects appear to depend on the activation of innate immune cells by the PAMPs contained naturally in these vaccines [10], although other mechanisms such as cross-reactive epitopes between different pathogens could also account for this protection in some cases [35].
Immunological memory, understood as the ability to “remember” past encounters with pathogens, has been classically attributed to the adaptive branch of the immune system exclusively, by virtue of the antigen-driven clonal expansion of T and B lymphocytes and exemplified by the mechanism of conventional specific vaccines pointed above. However, the notion that innate immunity was unable to induce immunological memory has been challenged in recent years, particularly from studies in organisms that lack adaptive immunity, such as plants or invertebrates, as well as early studies in mice lacking the adaptive immune system [8, 36]. Altogether, the term ‘trained immunity’ was coined to define an innate immune memory that lead the innate immune system to an enhanced response to secondary challenges [37]. Importantly, trained immunity seems to be underlying the heterologous effects of an increasing number of vaccines [38, 39, 40].
What is trained immunity? - Trained immunity is defined as the memory of the innate immune system, where an encounter with a first stimulus (e.g. a microbial insult) results in a subsequent long-term adaptation and enhanced non-specific response by innate immune cells against a secondary challenge (the same or unrelated), thus providing non-specific, broad-spectrum, long-term protection in case of infection [8, 9, 37, 41].
Which cells can be trained? - Trained immunity properties have been defined for distinct cell subsets of the innate immune system [9, 42], including natural killer (NK) cells and innate lymphoid cells [43]. Of note, training of myeloid cells [42], particularly monocytes and macrophages [44, 45], and more recently DCs [46, 47] and hematopoietic stem cells [48], have been extensively studied. Finally, the acquisition of this immunological memory has also been demonstrated to a lesser extent for non-immune cells [49].
How to get trained? - A wide variety of stimuli can train innate immune cells, particularly when considering monocytes and macrophages [9, 50]. Among infectious agents, live microorganisms such as the tuberculosis vaccine BCG [51], Candida spp [52] or viruses [53, 54]; bacterial components, such as flagellin, lipopolysaccharide, muramyl dipeptide [55], fungal components as β-glucan [52] or even helminth products [56]. In general, microbial ligands engaging some PRR, like C-type lectin receptors (CLRs), nucleotide-binding oligomerization domain-like receptors (NLRs) are well established training inducers, whereas those engaging toll-like receptors (TLRs) may have opposite effects depending on the TLR type and concentration [55, 57]. Intriguingly, not only infectious agents but also endogenous inducers and metabolites such as oxidized low-density lipoprotein or mevalonate can induce trained immunity [50].
What hallmarks define trained immunity? - In contrast to adaptive immune responses, epigenetic reprogramming of transcriptional pathways — rather than gene recombination — mediates trained immunity. This training phenomenon comprises three key hallmarks that occur at the intracellular level: increased cytokine production upon rechallenge, changes in the metabolism and epigenetic reprogramming [9, 58, 59], which eventually support increased protection upon infection.
Among those cytokines whose production is augmented after re-exposure in trained cells, proinflammatory molecules such as tumor necrosis factor α (TNF-α), interleukin (IL)-6, IL-1β and interferon γ (IFN-γ) are fairly constant [45, 52, 55, 60, 61]. Modulation of IL-10 varies between studies [45, 52, 56, 62, 63]. A noted shift from oxidative phosphorylation to aerobic glycolysis (Warburg effect) is the main change in cellular metabolism during the induction trained immunity [64]. Moreover, glutaminolysis, cholesterol synthesis and the tricarboxylic acid cycle are non-redundant pathways required for trained immunity to take place [64, 65]. Epigenetic reprogramming, mainly mediated by histone modifications, is one of the bases for the long-lasting effect of trained immunity [8, 66, 67, 68]. Immune pathway activation and changes in metabolism serve as basis for epigenetic rewiring [65]. As a result, epigenetic modifications have been found at the level of important promoters for the training process, which makes chromatin more accessible and conditions gene expression patterns of trained cells upon stimulation with a secondary challenge [69].
As a result of the whole process, enhanced, broad-spectrum, non-specific protection mediated by innate immune cells is found upon infection. This cross-protection has been observed for a wide range of human pathogens including fungi [51, 52], parasites [70, 71] and different bacterial infections [72, 73, 74, 75]. Importantly, induction of trained immunity has been proved to be effective against viral infections including yellow fever [76], influenza A virus [77] and others [78, 79]. In this line, the induction of this phenomenon has been also proposed as a tool for reducing susceptibility to emergent SARS-CoV-2 infection, as will be described at the end of the chapter [78, 80].
How long does trained immunity last? – Trained immunity phenotypes have been observed for months and up to one year after the training insult. This was initially controversial, as trained immunity properties had been attributed to short-lived myeloid cells such as monocytes or DCs [38]. In this regard, several studies have shown that modulation of bone marrow progenitors is also an integral component of trained immunity, supporting the long-lasting effect of this phenomenon [9, 81]. In this way, trained immunity inducers [82, 83, 84, 85] would be able to reprogram and induce expansion of hematopoietic progenitors with a particular bias to the myeloid lineage. Thus, bone marrow-derived mature cells would be also trained [86], showing improved clearance of infection [83].
Complementary to progenitor reprogramming, peripheral trained immunity induction would take place in tissue-resident cells [9]. This is especially relevant at the mucosal level, where cells encounter most of the infectious training inducers. Alveolar macrophage (AM) memory was demonstrated following viral infection [87, 88]. Training of these long-living cells led to increase antimicrobial properties, independently of systemic immunity [87, 89]. This local training of AM was further reproduced following respiratory mucosal administration of tuberculosis vaccine, being crucial for Mycobacterium tuberculosis clearance [90]. On the other hand, training of NK cells lead to long-lived, self-renewing, stable expanded cells with memory-like properties, both in an antigen-dependent or independent manner [91, 92, 93]. Finally, it was also reported that self-renewing long-living skin epithelial stem cells exhibited local trained immunity, providing faster wound healing in primed mice than in naïve mice [94, 95].
Non-specific effects of vaccines have been extensively studied and reported over the last decades. Although trained innate cells could partially account for these effects, involvement of adaptive immunity has also been suggested [96]. An adaptive immune mechanism of non-specific effects could be heterologous immunity; vaccine antigens can give rise to T cell cross-reactivity against other antigens that may confer some protection against unrelated pathogens [96, 97].
However, innate immune cells constitute the bridge between the intrusion of microbial threats and the activation of adaptive immunity. As said before, following sensing of pathogens by PRRs, activated innate immune cells secrete different factors and act as antigen-presenting cells (APCs) to initiate activation of adaptive immunity [98]. Thus, it would not be unexpected that trained innate immune cells, within their acquired enhanced properties, would be able to induce stronger adaptive immune responses [39]. In this regard, BCG vaccine, a well-known trained immunity inducer, has shown to enhance the antibody titer and alter heterologous T cell responses against a wide range of vaccines and unrelated infections [99, 100, 101]. In different experimental models, BCG-mediated protection against viral and Plasmodium infections was abrogated in the absence of T cells. In these models, BCG vaccination has been mainly associated with modulation of CD4+ T helper (Th) 1 responses. Similar observations have been found in different clinical studies [99]. Of note, BCG vaccinated human volunteers displayed a long-lasting heterologous Th1 and Th17 response upon stimulation with unrelated pathogens and TLR-ligands [38]. To some extent, similar observations have been found in other vaccines such as diphtheria-tetanus-pertussis (DTP) or measles vaccine [99].
As said before, trained immunity properties have been recently described also for DCs. As being the most professional APCs, they emerge as crucial bridge for potentiating adaptive immune responses. In this sense, DCs with high immunostimulatory properties that enhance adaptive immune responses via IL-1β release had been described [102]. More recently, programmed memory DCs have shown to increase Th1/Th17 immunity and confer protection during cryptococcosis [46]. Finally, different polybacterial preparations of whole-cell inactivated bacteria, have shown to prime DCs and induce enhanced Th1, Th17 and IL-10 T cell responses against related and unrelated stimuli [103, 104]. This capability of modulating heterologous T cell responses by APCs have been also described to suppress pathogenic T cell immunity in experimental models of autoimmune encephalomyelitis [56].
As noted above, a hallmark of trained innate immune cells is the enhancement of some effector functions leading to increased non-specific resistance against a variety of pathogens. In this regard, β-glucan-trained monocytes show enhanced candidacidal activity and efficiently inhibit the C. albicans outgrowth [52]. Production of reactive oxygen species (ROS) has shown to be also affected by the induction of training. Thus, BCG-trained monocytes [45], β-glucan-trained macrophages [105] or β-glucan-trained neutrophils [106] produced increased amount of ROS following different challenges. Finally, increased phagocytosis and production of microbicidal molecules have been observed in β-glucan-trained macrophages [70, 105]. Mechanisms underlying this enhanced effector function could be an intrinsic cell reprogramming as consequence of the training, as well as be supported increased expression of different PRRs and surface molecules [45, 60, 87]. Altogether, these enhanced effector responses could improve pathogen clearance by increasing host resistance.
On the other hand, a substantial part of the adaptive immune response is directed at recruiting other effector cells from the innate immune system to eventually resolve an infection. Both T helper and B responding cells release cytokines, antibodies, and other mediators that activate monocytes, macrophages, NK cells or neutrophils to clear extracellular and intracellular pathogens [107]. Multiple studies have demonstrated the importance of IFN-γ-mediated priming in the activation of macrophages [108, 109], produced by CD4+ Th1 and CD8+ T cells [107]. In this sense, it has been previously demonstrated that adaptive T cells render innate macrophage memory via IFN-γ-dependent priming [87, 89]. Furthermore, a deep crosstalk between Th17 and neutrophils have been widely demonstrated, via production of IL-17 and other related cytokines [110].
Taken into account the potential role of trained innate cells in both the induction of adaptive and effector responses, a notable amplification loop in the global immune response could be considered (Figure 1).
Effect of trained immunity on ongoing immune responses. Induction of trained immunity allows trained cells to enhance adaptive immune responses and vice versa, final effector functions of trained cells can be further potentiated by enhanced adaptive responses.
Based on trained immunity pillars, a next generation of anti-infectious vaccines has been postulated, coined as ‘Trained Immunity-based Vaccines’ (TIbVs). TIbVs would be conceived to confer a broad protection far beyond the antigens they contain. By proper targeting of innate immune cells to promote trained immunity, a TIbV may confer non-specific resistance to unrelated pathogens while trained immunity memory is still present, in addition to the specific response given by intrinsic antigens [39].
A bona fide TIbV would consist of two main components: the trained immunity inducer(s) and the specific antigen(s). The antigen(s) mission is to generate an adaptive (specific) immune response as any conventional vaccine. The trained immunity inducers aim to promote the training of innate immune cells. This innate immune training would confer non-specific resistance against unrelated pathogens for a window of time (months) plus an enhanced adaptive immune response to the antigens present in the vaccine itself or from other sources (e.g., coming from eventual infections or bystander pathogens) [39].
Two additional concepts arise under the TIbV umbrella: i) trained immunity-based immunostimulants (TIbIs) and ii) trained-immunity-based adjuvants (TIbAs). The former (TIbIs) would induce the training of innate immune cells, so they would be ready-to-act against upcoming infections conferring broad non-specific protection while trained immunity is present, still enhancing adaptive immune responses following any eventual natural infection. The latter (TIbAs) would enhance adaptive responses against specific antigens incorporated either to the trained inducers as in bona fide TIbVs, or in a separated but combined vaccine [39] (Figure 2).
Different possibilities of trained immunity-based vaccines (TIbVs). Under the umbrella of trained immunity-based vaccines (TIbVs) different possibilities exist depending on their design and purpose. Bona fide TIbVs are those containing both trained immunity inducers and antigens in the same vaccine as occurs in conventional vaccines with trained immunity inducing properties. These vaccines show heterologous protection in addition to the specific response to the target antigen. TIbIs are intended just to confer non-specific protection by means of trained immunity induction beyond the intrinsic antigens they may contain. TIbAs are intended to enhance the specific response of other vaccines that are administered later, once trained immunity has been induced, or specific antigens combined in the same vaccine as any other adjuvant.
Following the above features, the TIbV concept can be applied to existing anti-infectious vaccines composed of microorganisms that show heterologous protection ascribed to trained immunity.
During the last decades, robust epidemiological data has demonstrated the role of certain vaccines leading to protection against heterologous infection with a high impact on overall mortality in children [111, 112, 113]. This protection could not only be explained by protection achieved by the target disease. Studies on MMR vaccination in high-income settings have also evidenced a reduction in non-target infections, particularly in respiratory infections [114]. A limitation for most of these epidemiological studies is that they do not identify the agent (viral, bacterium or parasite) responsible for the infection. These heterologous effects of certain vaccines conferring non-specific protection for a quite long time are believed to be largely due to non-specific stimulation of the innate immune system. It is not yet clear whether this is a direct reflection of trained immunity induction (i.e., acting as TIbVs) in every case. The fact that most of these vaccines use live-attenuated microorganisms, i.e., self-replicating agents, may suggest that a continuous stimulation of innate immune cells is necessary to obtain protection and/or to achieve a proper trained immunity for this purpose.
The BCG-Denmark strain was tested in randomized-controlled trials (RCT) in infants who normally did not receive the BCG vaccine at birth. These studies carried out in Guinea-Bissau demonstrated that vaccination at birth was associated with lower neonatal mortality, especially due to neonatal sepsis, respiratory infections, and fever [111, 115]. In these lines, a meta-analysis commissioned by the WHO concluded that BCG administration during the first month of life reduces all-cause mortality by 30% [116]. In these studies authors did not discriminate the etiology of infection (bacterial vs. virus); therefore, a reduction in viral infections may explain, to some extent, this result. However, in two studied carried out in India in neonates with BCG-Russian strain no such effect was observed [117]; suggesting that different immunological effect of diverse BCG strains may account for these discrepancies. A study carried out in infants to assess the impact of BCG vaccination on the incidence of RSV infection suggested a possible protective role for BCG vaccination against acute lower respiratory tract infection [118]. Other clinical studies have provided evidence suggesting a protective role for BCG on secondary viral infections [79]. In this regard, the impact of BCG vaccination on viral infection in human healthy volunteers has been assessed using the live attenuated yellow fever vaccine (YFV) as a model of viral human infection [76]. BCG vaccination induced epigenetic reprogramming in human monocytes, and these modifications correlated with IL-1β upregulation and the reduction of viremia, all these features being the hallmarks of trained immunity [76].
Similar protective effect of BCG was observed in several studies in elderly people regarding respiratory tract infections. BCG vaccination in subjects of 60–75 years old once a month for three consecutive months resulted in reduction of acute upper respiratory tract infection, concomitant to significant increase in IFN-γ and IL-10 compared with those receiving placebo [119]. A recent randomized trial of BCG vaccination was carried out in elderly patients (age ≥ 65 years) returning home from hospital admission, these subjects are at high risk to develop infections. The BCG vaccination increased the time to first infection (primary outcome) and decreased the incidence of a new infection [120]. Besides, results demonstrated that BCG vaccination resulted in lower number of infections of all causes, especially respiratory tract infections of probable viral origin, although no discrimination was made between respiratory tract infections caused by bacteria or viruses.
BCG has also been shown to enhance the response to vaccines directed against viral infections [79]. A clinical study in healthy volunteers demonstrated that BCG administration prior to influenza vaccination increases antibody titers against the 2009 pandemic influenza A (H1N1) vaccine strain, concomitantly with an enhanced IFN-γ production to influenza antigens compared with the control group [121].
The cold-adapted, live attenuated influenza vaccine (CAIV) has been shown to provide non-specific cross-protection against RSV in an experimental model of infection [122].
In a randomized pilot study conducted in healthy volunteers receiving a trivalent influenza vaccine, cytokine responses against unrelated pathogens were observed [121]. During the 2003–2004 influenza A (H3N2) outbreak, an open-labeled, nonrandomized vaccine trial was carried out in children 5 to 18 years old. Subjects received either trivalent live attenuated or inactivated influenza vaccine. Live attenuated influenza vaccine but not trivalent inactivated vaccine was effective in children administered during influenza outbreak, despite the dominant circulating influenza virus was antigenically different from the vaccine strain [123].
Measles vaccine (MV) is among the live vaccines that have been shown to have beneficial effects reducing all-cause mortality [124]. Randomized clinical trials and observational studies from low-income countries have concluded that measles vaccination is associated with decreased overall mortality and morbidity [100]. However, a systematic review carried out by Higgins and colleagues has pointed out that most of these studies were considered at high risk of bias [116]. Nevertheless, MV seems to induce a transient suppressive effect on both the lymphoproliferative and innate response evaluated in peripheral blood mononuclear cells (PBMCs) from children, with slight increase in innate immune response, measured by non-specific cytokine production [100]. It has been reported that following measles vaccination, the ex vivo production of both innate (IL-6 and TNF-α) and adaptive (IFN-γ and IL-2) cytokines decreases for 2 weeks, but levels of IL-2, IL-6 and IFN-γ are increased at day 30 post vaccination compared with baseline [125]. Differences in males and females have been reported, where girls seem to receive stronger beneficial effects. In this regard, a study of MV-specific innate responses following MMR vaccination found higher TNFα, IL-6 and IFN-α secretion, cytokines associated to trained immunity, in adolescent girls than boys [126].
There are currently only three countries where polio remains endemic. Thus, polio-free, high income countries are introducing the use of the inactivated polio vaccine (IPV). However, there are still many countries that use the live-attenuated oral polio vaccine (OPV). Despite current WHO policy to replace OPV by IPV, there is epidemiological evidence that supports that replacing OPV by IPV might have an impact on overall mortality [96], since OPV has shown strong non-specific beneficial effects even in settings where the incidence of the targeted infection is low. In this regard, campaigns to eliminate polio in West Africa have been associated with lower child mortality rates [127].
As pointed above, most of the vaccines described so far showing non-specific heterologous effects contain live-attenuated microorganisms. Nevertheless, fully inactivated bacterial vaccines have also been described conferring protection against viral infections, and some of them for a fairly long period of time. Interestingly, these vaccines are mucosal preparations that are administered daily for long periods of time (weeks/months) rather than single, or seldom, doses used in live attenuated vaccines. Thus, it seems that the much longer administration of these inactivated mucosal vaccines resembles the effect achieved by live vaccines on heterologous protection associated to trained immunity (Figure 3).
Trained immunity window by self-replicating and inactivated TIbVs. Trained immunity-based vaccines (TIbVs) containing live-attenuated self-replicating microorganisms (e.g. BCG) may require fewer administrations to induce an adequate trained immunity window of sufficient intensity, quality and/or duration than vaccines with dead microorganisms. Fully-inactivated TIbVs can be enhanced to induce trained immunity with a multiple dose schedule (e.g. MV130).
These vaccines are used for the prevention of recurrent infections in susceptible subjects, mainly associated to the respiratory and urogenital tracts [128, 129, 130, 131, 132, 133, 134]. Since they target infections occurring in these tracts, their administration is generally through mucosal tissues to obtain a better mucosal response [135, 136].
MV130 is a sublingual vaccine used to prevent recurrent respiratory tract infections [128, 129] containing inactivated whole-cell bacteria that are common pathogens in the airways. Its ability immunomodulating DCs has been addressed experimentally in vitro and in vivo. MV130 triggers the release of cytokines ascribed to trained immunity in different setting, including TNF-α, IL-1β and IL-6 [103, 137, 138]. Sublingual immunization of mice with MV130 induces a systemic Th1/Th17 and IL-10 enhanced responses against unrelated antigens [103]. Similar enhancement was shown in patients treated with MV130 where an increased T cell response to flu antigens were described [128]. MV130 was successfully used in infants with recurrent wheezing, a condition triggered in most cases by viral infections. It is noteworthy that the protective effect was also shown 6 months after discontinuation of treatment, which points to a long-lasting effect that fits with the memory ascribed to trained immunity (Nieto et al., under review). In this regard, MV130 has been shown to induce trained immunity and to confer protection against experimental virus infections (Brandi et al., under review). Recent studies have assessed the clinical benefit of MV130 as a TIbV in the context of recurrent respiratory infections in vulnerable populations such as patients with different primary and secondary immunodeficiencies showing a reduced rate of respiratory infections [130, 139] (Ochoa-Grullón et al., in press).
Although not considered vaccines but immunostimulants, these bacterial preparations are, like MV130, used for the prevention of recurrent respiratory infections. OM-85, one of the best studied, is composed of chemically treated bacterial lysates for oral administration, acting through the gastro-intestinal mucosa. OM-85 has been shown to be effective in experimental viral infections [140] and in children with recurrent wheezing [141], a condition triggered by viruses as noted above. OM-85 stimulates the release of proinflammatory cytokines such as IL-1β, TNF-α and IL-6 by macrophages [142], typical of trained immunity induction, as well as Th1 cytokines including IFN-γ [143]. It is not known, however, the role of trained immunity in their mechanism of protection. A recent study conducted in infants, the observed protection against respiratory infections under OM-85 treatment stopped when treatment was discontinued [144], which may point against the memory ascribed to trained immunity.
The non-specific mechanism of TIbVs against widely differing pathogens associated to the induction of trained immunity can be exploited clinically. This makes TIbVs as a ready-to-act tool to tackle disease outbreaks from different angles where conventional specific vaccines have proven their limitations:
Newly emerging disease outbreaks, with no conventional vaccines available. Even in the presence of therapeutic options, vaccines are the best tool to prevent infections. However, even with worldwide efforts, getting a vaccine to the public takes time. In addition, side effects, dosing issues, and manufacturing problems can all cause delays [3]. Herein, using available TIbVs could mitigate the devastating consequences of emergent outbreaks by means of non-specific protection, until a suitable specific vaccine is available.
Newly emerging disease outbreaks, first coming vaccines with partial efficacy. Even if a vaccine gets available to the market, conventional strategies might raise some issues. The unpredictable identity of largely unknown emerging pathogens, the lack of appropriate experimental animal models, and the time for faster developing may give raise to an upcoming vaccine with no full efficacy [3]. On the other hand, limitations of current vaccines, such as mumps, also include a low efficacy resulting from an unacceptable drop in the immune response over time, requiring re-immunization [145]. In these contexts, the administration of a TIbV prior to the specific vaccine may enhance the response to the latter (111).
Re-emerging disease outbreaks, pathogens with high mutation rates and loss of vaccine efficacy. Mutations are the building blocks of evolution in any organism. Viruses are among the fastest evolving entities, especially RNA viruses such as influenza. Implications in conventional vaccine design are numerous, as a high mutation rate makes it hard to design a vaccine that is universally effective across many years. As a result, this makes a vaccine effective for shorter and raises the need for yearly vaccination programs [22, 146]. Since the underlying mechanism of TIbVs extend well beyond their nominal antigens and have a broad-spectrum of protection, TIbVs could overcome the troublesome of highly specific vaccines that promote antigen variant switching [147].
Disease outbreaks in vulnerable populations. During infectious disease outbreaks, vulnerable populations are usually disproportionately affected due to an interplay of immunological, epidemiological, and medical factors, which leads to sub-optimal or even under-vaccination [148]. This is well exemplified in the elderly population, where successful vaccination against important infectious pathogens which cause high morbidity and mortality represents a growing public health priority. Age-related immunosenescence and ‘inflammaging’ have been postulated as underlying mechanisms responsible for decreased response and high mortality, including during COVID-19 pandemic or influenza season [80, 149]. Therefore, more potent vaccines are needed. In this regard, the induction of trained immunity by the use of TIbVs is proposed to overcome the immune dysfunction found in these individuals [28]. Thus, elderly has been proposed as one of the groups to benefit from the use of TIbVs, including severe COVID-19 disease, with the aim of potentiating the immunogenicity of their vaccination [80]. Moreover, some types of immunodeficiencies or immunosuppression may benefit from TIbVs. These formulations, by means of tackling both branches of immunity, especially the innate compartment, may be an achievable alternative to reinforce protection or optimize immunogenicity of vaccination in this population [130, 139].
Altogether, harnessing the TIbV concept has been suggested as a crucial step in future vaccine development and implementation, because a wide range of clinical applications may benefit to some extent from their use [150].
Despite the tremendous financial and scientific effort invested to rapidly obtain a prophylactic vaccine against SARS-CoV-2, only the first one has been licensed in December 2020. Although this means less than a year since the declaration of the pandemic by the WHO, which is an unprecedented achievement, in the meantime, two pandemic waves of COVID-19 and more than 1.5 million deaths have been declared worldwide. Therefore, alternative strategies have been considered to fill the gap until a safe and effective vaccine is available. As noted earlier in this chapter, TIbVs can play an important role for this purpose by increasing host resistance to other pathogens, including viruses.
A bunch of recent studies have been published supporting the role of certain vaccines, including BCG, OPV and measles, as a possible successful strategy to reduce susceptibility and severity to SARS-CoV-2 through trained immunity induction [80, 151, 152]. Thus, clinical trials are currently being conducted to find out the contribution of trained immunity as a preventive tool in the context of COVID-19 pandemics [153]. In a prospective observational trial, 255 MMR vaccinated subjects were followed searching for COVID-19 cases, thirty-six presented COVID-19 but all with a remarkable mild course [154]. Recent studies have also suggested a potential benefit of influenza vaccine on the susceptibility and the outcome of certain infections including SARS-CoV-2. In this sense, a particular attention has been focused on a high-risk population, the elderly. In a study conducted in Italy, influenza vaccination in people aged 65 and over was associated with a reduced spread and a less severe clinical expression of COVID-19 [155].
Finally, in addition to the potential role of TIbV conferring resistance against SARS-CoV-2 infection, they can eventually be used to increase efficacy of specific anti-COVID-19 vaccines, when available, especially in vulnerable population. In this sense, implications of vaccination route and mucosal immunity have also been raised as a key aspect in the development of safe and effective prophylaxis interventions against SARS-CoV-2. Most formulations in development are parentally administered; only a few COVID-19 vaccine candidates are administered by mucosal routes. Still, studies indicate that even if mucosal immunization against coronavirus does not confer sterilizing immunity, the ability to induce anti-SARS-CoV-2 IgA responses in the airways may prevent virus spread to the lung and avoid respiratory distress [156]. In this regard, mucosal TIbVs could enhance the mucosal response of specific COVID-19 vaccines acting as TIbAs by combining them as pointed above in those especially vulnerable subjects.
Viral outbreaks can cause epidemics and pandemics if the route of transmission allows for the rapid virus spread. Given the ease of travel and the global exchange of potential transmitting agents, these situations will be increasingly frequent in the future. Preventing the spread of a virus outbreak caused by a highly contagious agent is not easy in the absence of effective therapies or preventive measures. Although the development of effective prophylactic vaccines specific for the threatening virus is the final goal when possible, this requires a minimum time of almost a year in the best possible scenario. Meanwhile, the consequences of the spread of a deadly virus can be devastating, as it is exemplified during the COVID-19 pandemic. This scenario may also take place in the case of re-emerging viruses tackled by partial efficacious vaccines. In such situations, harnessing the heterologous non-specific protection of some existing vaccines with a known safety track record is an interesting possibility. This protection may be critical for vulnerable subjects and/or for highly exposed individuals, like healthcare workers.
Non-specific protection of some vaccines is thought to be mainly dependent on their effect on the innate immune system. Increasing evidence gathered over the past few years points that innate immune cells show memory-like features when properly activated. This memory termed “trained immunity” has been associated with the non-specific protection of vaccines. The concept of “trained immunity-based vaccine” (TIbV) has been drawn to exploit the potential of trained immunity in designing novel vaccines or to redefine bacterial-derived preparations conferring broad protection against widely differing pathogens. As trained immunity may have implications on the adaptive immune response and vice-versa, its potential to provide enhanced immune responses is quite broad whether considering natural infections or following vaccination.
Taken advantage of the current COVID-19 pandemic, a number of clinical trials have been launched with putative TIbVs in order to address protection in highly exposed subjects. The results are eagerly expected as these initiatives may be considered as a proof-of-concept supporting their use in future epidemics/pandemics to fill the gap until a specific vaccine is available. Nevertheless, as trained immunity can be achieved by different inducers, it is unlikely to obtain the same degree of protection, duration, etc. for all of them, which may also depend on the biological behavior and the route of transmission of the threatening pathogen. As in most instances rapidly spreading viruses are airborne and primarily infect the mucosa of the airway tract, induction of trained immunity at the local mucosal level can confer a more adequate protection. This may be an opportunity for mucosal TIbVs as compared to those given parenterally.
Trained immunity may justify heterologous protection of vaccines, help to explain their underlying mechanisms, open avenues for next generation of vaccines, or be proposed to tackle outbreaks by new pathogens as described here. However, this is an emerging field that requires more clinical data before being a reality in the clinical practice; not only to be used against infectious outbreaks, but to fight against recurrent infections in vulnerable subjects for whom no effective vaccines are yet available.
JLS is the founder and CEO of Inmunotek SL, Spain, a pharmaceutical company that manufactures bacterial vaccines. LC and PS-L are employees of Inmunotek.
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