Changes in F-wave under 10% MI condition.
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Motor imagery (MI) is defined as an active process during which a specific motor action is reproduced within working memory without any overt movement [1]. MI is considered a potential tool for improvement of motor function in rehabilitation. Indeed, MI has been shown to improve various motor functions. Yue and Cole [2] reported that muscle strength of little finger abduction was significantly increased after MI training for 4 weeks. Additionally, muscle strength of ankle dorsiflexion was significantly increased after MI training for 4 weeks [3]. Also, Guillot et al. [4] reported that muscle flexibility was improved after MI of stretching for 5 weeks.
Immediate enrollment in rehabilitation programs for functional reorganization should be important to obtain better outcomes [5]. Specifically, Motor-evoked potentials (MEPs) amplitude, an index of corticospinal excitability, was decreased in post-stroke [6]. However, MEPs amplitude was increased in patients who have functional motor recovery [7]. Additionally, spinal motor neuron excitability was significantly reduced in the post-stroke acute phase [8]. Thus, facilitating the corticospinal excitability, including the spinal motor neuron excitability, should be needed for post-stroke patients whom have motor impairment.
Numerous neurophysiological studies using positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and near infrared spectroscopy (NIRS) have demonstrated that MI and motor execution activate similar brain activation patterns [9, 10, 11, 12, 13]. Specifically, primary motor cortex, supplementary motor area, premotor area, somatosensory area, prefrontal cortex, parietal lobule, cingulate area, cerebellum, and basal ganglia were activated during MI and motor execution. Thus, MI shares common neural substrates with motor execution. When transcranial magnetic stimulation (TMS) was delivered over the primary motor cortex during MI, MEPs amplitude obtained from corresponding muscle was significantly increased relative to rest level [14, 15, 16]. The increase of MEPs amplitude during MI indicates that MI facilitates corticospinal excitability. Thus, MI can facilitate the central neural function.
However, previous studies have shown various patterns in the spinal motor neuron excitability during MI using the F-wave and H-reflex as indices of spinal reflex excitability [17, 18, 19]. Taniguchi et al. [17] reported that the F-wave amplitude was significantly decreased after volitional relaxation for 3 h. When subjects did MI of thumb abduction during volitional relaxation simultaneously, the F-wave amplitude was maintained at before volitional relaxation level. Whereas, Kasai et al. [18] reported that the H-reflex amplitude was unchanged during MI of wrist flexion movement. Oishi et al. [19] also reported that there was decline of H-reflex amplitude during MI of speed skating. Our laboratory previously investigated the spinal motor neuron excitability during MI of isometric thenar muscle activity at 50% maximal voluntary contraction (MVC) for 1 min using the F-wave [20]. The F-wave is a compound action potential resulting from re-excitation (“backfiring”) of an antidromic impulse following distal electrical stimulation of motor nerve fibers at the anterior horn cells [21, 22, 23]. The F-wave measured during MI at 50% MVC for 1 min was significantly increased than that at rest. Thus, we concluded that MI of isometric thenar muscle activity can increase the spinal motor neuron excitability.
We are aiming to find the way of MI obtained most beneficial effect. In order to do that, it is important to assess the spinal motor neuron excitability concurrent with the central nervous system. We think that facilitating the spinal motor neuron excitability will be required for improvement of motor function. Because, described above, the facilitation of the corticospinal excitability including the spinal motor neuron excitability is needed for recovery of motor function. In this chapter, we would like to introduce our previous works about the spinal motor neuron excitability during MI of isometric thenar muscle activity. In the first half of this chapter, we described about the spinal motor neuron excitability during MI of isometric thenar muscle activity at various imagined muscle contraction strengths. In the second half of this chapter, we described about the autonomic nervous system during MI. At the end of chapter, we discuss about how apply MI to neurorehabilitation using brain-machine interface (BMI).
We previously reported that when the subject performed MI of isomeric thenar muscle activity at 50% MVC, the spinal motor neuron excitability was significantly increased than at rest [20]. In actual motion, Suzuki et al. [27] reported the spinal motor neuron excitability was increased linearly with muscle contraction strength. If MI and motor execution share common neural networks, the spinal motor neuron excitability will be increased linearly with imagined muscle contraction strength. Then, we investigated the spinal motor neuron excitability during MI at various imagined muscle contraction strengths. Specifically, we adopted the 10, 30, 50, and 70% MVC for imagined muscle contraction strength. In this research, we assessed the spinal motor neuron excitability during MI by using the F-wave [24, 25, 26].
Ten healthy volunteers were participated in this research (5 males, 5 females; mean age = 28.7 ± 4.5 years). All participants provided informed consent before the study commenced. This research was approved by the Research Ethics Committee at Kansai University of Health Sciences. All recordings were conducted in accordance with the Declaration of Helsinki.
Participants were in supine position on a bed and instructed to fix one’s eye on a pinch meter (Digital indicator F304A, Unipulse Corp., Japan) display throughout the F-wave recording. A Viking Quest electromyography machine ver. 9.0 (Natus Medical Inc., USA) was used for the F-wave recordings. The room temperature was kept at 25°C. The skin was cleaned with an abrasive gel to keep impedance below 5 kΩ. F-waves were recorded from left thenar muscle after stimulating the left median nerve at the wrist. A pair of 10 mm silver EEG cup electrodes (Natus Medical Inc., USA) were placed over the ventral surface of the thumb and base of the first dorsal metacarpal bone. The simulating electrodes comprised a cathode placed over the left median nerve 3 cm proximal to the palmar crease and an anode was placed 2 cm more proximally. Before the F-wave recording, maximal intensity of electrical stimulation was determined by delivering 0.2-ms square-wave pulses of increasing intensity from 0 to 50 mA until eliciting the largest compound muscle action potential (M-wave). Supramaximal electrical stimuli (20% above maximal stimulus intensity) were delivered at 0.5 Hz in each trial. The sensitivity for the F-wave was set at 200 μV/division and a sweep of 5 ms/division. Filter bandwidth was ranged from 20 Hz to 3 kHz.
For the rest trial (rest), F-waves were recorded during relaxation for 1 min. Subsequently, for the motor task, participants learned the isometric thenar muscle activity at 50% MVC (i.e., participants press the sensor of pinch meter by left thumb and index finger at 50% MVC) for 1 min. They were instructed to keep the 50% MVC value (kgf) measured numerically on the display of pinch meter. For the MI trial, participants performed MI of isometric thenar muscle activity at 50% MVC for 1 min. F-waves were recorded during MI (50% MI). Immediately after 50% MI trial (post), F-waves were recorded during relaxation for 1 min. The above process was defined as the MI at 50% MVC condition (50% MI condition). This protocol was repeated for 10, 30, and 70% MI conditions. Each condition was performed randomly on different days.
All recorded F-wave data were analyzed for the persistence, F/M amplitude ratio, and latency in each trial. The minimum of F-wave peak-to-peak amplitude was at least 20 μV [21]. The persistence was defined as the number of detected F-wave responses divided by 30 supramaximal electrical stimuli. The F/M amplitude ratio was defined as the mean amplitude of all responses divided by the M-wave amplitude. The amplitude measured individually for each F-wave and then the mean calculated. The latency was defined as the mean latency from the time of electrical stimulation to onset of detected F-waves. The persistence reflects the number of backfiring spinal anterior horn cells [22, 23]. The F/M amplitude ratio reflects the number of backfiring spinal anterior horn cells and the individual cells excitability [22, 23]. Thus, these parameters are considered the indices of the spinal motor neuron excitability.
The normality of F-wave data was not confirmed by using the Kolmogorov-Smirnov and Shapiro-Wilk tests. We used a nonparametric method in this research. The persistence, F/M amplitude ratio, and latency among three trials (rest, MI, post) under each MI conditions (10% MI, 30% MI, 50% MI, and 70% MI conditions) were compared using the Friedman test and Scheffe’s post hoc test.
We also calculated the relative value obtained by dividing F-wave data during MI under four MI conditions by that at rest. The relative values among four MI conditions were compared using the Friedman test. We used SPSS statistics ver. 19 (IBM Corp., USA) for statistical analysis. The threshold for statistical significance was set to p = 0.05.
The persistence during MI under all MI conditions was significantly greater than that at rest (10% MI vs. Rest, 70% MI vs. Rest, **p < 0.01; 30% MI vs. Rest, 50% MI vs. Rest, *p < 0.05) (Tables 1, 2, 3, 4). The persistence immediately after MI under all MI conditions was reduced to rest level (Tables 1, 2, 3, 4).
Rest | 10% MI | post | |
---|---|---|---|
Persistence (%) | 61.8 ± 12.6 | 91.9 ± 9.70** | 73.1 ± 20.7 |
F/M amplitude ratio (%) | 0.90 ± 0.35 | 2.46 ± 2.61** | 1.18 ± 0.67 |
Latency (ms) | 25.3 ± 0.98 | 25.2 ± 1.25 | 25.5 ± 0.99 |
Changes in F-wave under 10% MI condition.
p < 0.01; significant difference between rest and 10% MI trial.
Rest | 30% MI | post | |
---|---|---|---|
Persistence (%) | 61.2 ± 19.5 | 88.0 ± 12.2** | 60.0 ± 18.7 |
F/M amplitude ratio (%) | 1.00 ± 0.94 | 2.92 ± 2.95** | 1.11 ± 0.52 |
Latency (ms) | 24.9 ± 1.16 | 24.6 ± 0.99 | 24.9 ± 1.14 |
Changes in F-wave under 30% MI condition.
p < 0.05; significant difference between rest and 30% MI trial.
Rest | 50% MI | post | |
---|---|---|---|
Persistence (%) | 62.7 ± 22.3 | 94.0 ± 9.40* | 65.5 ± 27.0 |
F/M amplitude ratio (%) | 1.08 ± 0.28 | 2.60 ± 2.30** | 0.98 ± 0.40 |
Latency (ms) | 24.5 ± 1.61 | 24.3 ± 1.82 | 24.5 ± 1.58 |
Changes in F-wave under 50% MI condition.
p < 0.05; significant difference between rest and 50% MI trial.
p < 0.01; significant difference between rest and 50% MI trial.
Rest | 70% MI | post | |
---|---|---|---|
Persistence (%) | 55.9 ± 17.6 | 88.1 ± 10.8** | 65.3 ± 19.9 |
F/M amplitude ratio (%) | 0.94 ± 0.33 | 1.79 ± 1.23 | 1.11 ± 0.44 |
Latency (ms) | 24.4 ± 1.37 | 24.1 ± 1.27 | 24.3 ± 1.15 |
Changes in F-wave under 70% MI condition.
p < 0.01; significant difference between rest and 70% MI trial.
The F/M amplitude ratio during MI under 10, 30, and 50% MI conditions was significantly greater than that at rest (10% MI vs. Rest, 50% MI vs. Rest, **p < 0.01; 30% MI vs. Rest, *p < 0.05) (Tables 1, 2, 3). The F/M amplitude ratio during MI under 70% MI condition was tended to be increased than that at rest (p ≒ 0.082) (Table 4). The F/M amplitude ratio immediately after MI under all MI conditions was reduced to rest level (Tables 1, 2, 3, 4).
No significantly differences in the latency were observed among three trials (rest, MI, post) under all MI conditions (Tables 1, 2, 3, 4).
The relative values of the persistence, F/M amplitude ratio, and latency did not exhibit significant differences among all MI conditions (Table 5).
10% MI condition | 30% MI condition | 50% MI condition | 70% MI condition | |
---|---|---|---|---|
Relative values of persistence | 1.53 ± 0.31 | 1.58 ± 0.61 | 1.78 ± 0.93 | 1.69 ± 0.45 |
Relative values of F/M amplitude ratio | 2.40 ± 1.38 | 3.31 ± 0.56 | 2.52 ± 1.96 | 2.10 ± 1.37 |
Relative values of latency | 0.99 ± 0.02 | 0.99 ± 0.02 | 0.99 ± 0.03 | 0.99 ± 0.02 |
Comparison of F-wave among 10% MI, 30% MI, 50% MI, and 70% MI condition.
Our previous works [24, 25, 26] suggested that MI of isometric thenar muscle activity at 10, 30, 50, and 70% MVC can facilitate the spinal motor neuron excitability. However, the imagined muscle contraction strength did not influence on change of the spinal motor neuron excitability. Whereas, Cowley et al. [29] previously reported that the amplitude of H-reflex during MI of ankle plantar flexion at 100% MVC was significantly greater than that at 50% MVC. Then, we hypothesized the MI of isometric thenar muscle activity at 100% MVC will be greater than that at 50% MVC. In this research, we compared the spinal motor neuron excitability between 50% MI and 100% MI condition [28].
Fifteen healthy volunteers were participated in this research (13 males, 2 females; mean age = 25.3 ± 5.0 years). All participants provided informed consent before the study commenced. This research was approved by the Research Ethics Committee at Kansai University of Health Sciences. All recordings were conducted in accordance with the Declaration of Helsinki.
The environment and F-wave recording condition was set as previous works [24, 25].
For the rest trial (rest), F-waves were recorded during relaxation for 1 min. Subsequently, for the motor task, participants learned the isometric thenar muscle activity at 50% MVC (i.e., participants press the sensor of pinch meter by left thumb and index finger at 50% MVC) for 1 min. They were instructed to keep the 50% MVC value (kgf) measured numerically on the display of pinch meter. For the MI trial, participants performed MI of isometric thenar muscle activity at 50% MVC for 1 min. F-waves were recorded during MI (50% MI) and immediately after 50% MI trial (post) for 1 min respectively. The above process was defined as the MI at 50% MVC condition (50% MI condition). F-wave recording under 100% MI condition was performed using the same protocol as 50% MI condition. These conditions were performed randomly on different days.
After all F-wave recordings, F-wave data was analyzed with respect to the persistence, F/M amplitude ratio, and latency.
The normality of F-wave data was not confirmed by using the Kolmogorov-Smirnov and Shapiro-Wilk tests. We used a nonparametric method in this research. The persistence, F/M amplitude ratio, and latency among three trials (rest, MI, post) under two MI conditions (50% MI and 100% MI conditions) were compared using the Friedman test and Scheffe’s post hoc test.
We also calculated the relative value obtained by dividing F-wave data during MI under four MI conditions by that at rest. The relative values among two MI conditions were compared using the Wilcoxon signed rank test. We used SPSS statistics ver. 19 (IBM Corp., USA) for statistical analysis. The threshold for statistical significance was set to p = 0.05.
The persistence during MI under two MI conditions was significantly greater than that at rest (50% MI vs. Rest, 100% MI vs. Rest, **p < 0.01) (Tables 6, 7). The persistence immediately after MI under two MI conditions was reduced to rest level (Tables 6, 7).
Rest | 50% MI | post | |
---|---|---|---|
Persistence (%) | 50.8 ± 21.7 | 88.2 ± 13.2** | 48.3 ± 19.9 |
F/M amplitude ratio (%) | 1.71 ± 0.89 | 3.96 ± 4.56** | 1.29 ± 0.56 |
Latency (ms) | 25.5 ± 1.40 | 24.9 ± 1.91 | 25.3 ± 1.29 |
Changes in F-wave parameters under 50% MI condition.
p < 0.01; significant difference between rest and 50% MI trial.
Rest | 100% MI | post | |
---|---|---|---|
Persistence (%) | 60.8 ± 24.9 | 91.9 ± 7.58** | 60.7 ± 21.5 |
F/M amplitude ratio (%) | 1.32 ± 1.12 | 3.57 ± 4.67** | 1.39 ± 1.25 |
Latency (ms) | 25.2 ± 1.32 | 24.8 ± 1.31 | 25.2 ± 1.40 |
Changes in F-wave parameters under 100% MI condition.
p < 0.01; significant difference between rest and 100% MI trial.
The F/M amplitude ratio during MI under two MI conditions was significantly greater than that at rest (50% MI vs. Rest, 100% MI vs. Rest, **p < 0.01) (Tables 6, 7). The F/M amplitude ratio immediately after MI under two MI conditions was reduced to rest level (Tables 6, 7).
No significantly differences in the latency were observed among three trials (rest, MI, post) under two MI conditions (Tables 6, 7).
The relative values of the persistence, F/M amplitude ratio, and latency did not exhibit significant differences between two MI conditions (Table 8).
50% MI condition | 100% MI condition | |
---|---|---|
Relative values of persistence | 2.04 ± 1.17 | 2.06 ± 1.71 |
Relative values of F/M amplitude ratio | 2.75 ± 2.04 | 2.53 ± 1.76 |
Relative values of latency | 0.98 ± 0.06 | 0.99 ± 0.03 |
Comparison of F-wave parameters between 50% MI and 100% MI condition.
From results of our previous works, it is suggested that MI of isometric thenar muscle activity at 10, 30, 50, 70, and 100% can facilitate the spinal motor neuron excitability. About this, it is considered to be influence of descending pathways corresponding to thenar muscle. Previous researches have demonstrated the activation of diverse brain area contribute to motor preparation and planning during MI [9, 10, 11, 12, 13]. The excitatory and inhibitory inputs modulate the spinal motor neuron excitability via the corticospinal and/or extrapyramidal tract [30]. Thus, it is plausibly that the activation of central nervous system contributes to motor preparation and planning during MI facilitated the spinal motor neuron excitability via the corticospinal and/or extrapyramidal tract.
Furthermore, all subjects participated in our previous works were instructed to perform MI with holding the sensor of a pinch meter. Mizuguchi et al. [31] reported that corticospinal excitability during MI utilizing an object was modulated by a combination of tactile and proprioceptive inputs while holding an object. We previously reported that the spinal motor neuron excitability during MI with holding the sensor of a pinch meter was significantly greater than that during MI without holding the sensor [20]. Consequently, it is suggested that tactile and proprioceptive perceptions during MI while holding the sensor facilitated the spinal motor neuron excitability cooperatively with MI-activated pathways.
In our previous works, the relative value of the persistence, F/M amplitude, and latency were similar among all MI conditions. It is suggested that the imagined muscle contraction strength may not affect the spinal motor neuron excitability. There are several previous researches investigated the spinal motor neuron excitability during MI at different imagined muscle contraction strengths. Bonnet et al. [32] reported that the amplitude of H-reflex was significantly greater during MI of ankle plantar flexion at 2 and 10% than that at rest. Additionally, the amplitude of H-reflex during MI was similar between 2% MI and 10% MI condition. Hale et al. [33] also reported that the amplitude of H-reflex during MI of ankle plantar flexion was similar among five (i.e., 20, 40, 60, 80, and 100% MVC) MI conditions. Similarly, Aoyama and Kaneko [34] reported that the amplitude of H-reflex during MI was similar between 50% MI and 100% MI condition. In actual motion, the spinal motor neuron excitability was increased linearly with the muscle contraction strength [27]. Described in the introduction, MI is the mental rehearsal of a movement without any overt movement [1]. One possibility is the contribution of neural mechanism which inhibits actual movement and muscle contraction during MI. Park and Li [35] reported that the amplitude of MEPs during MI of finger flexion and extension at 10, 20, 30, 40, 50, and 60% MVC was significantly greater than that at rest. However, the amplitude of MEPs during MI was similar among all six MI conditions. Further, in an event-related potential study, the magnitude of primary motor cortex activity during MI did not correlate with the imagined muscle contraction strength, although activities of the supplementary motor and premotor area during MI were strongly correlated with it [36]. The supplementary motor and premotor area have crucial roles in larger force generation [37], motor planning, preparation, and inhibition [38, 39]. Thus, the supplementary motor and premotor area may inhibit the actual muscle activity depending on the muscle contraction strength. Because these areas also are connected directly to primary motor cortex, inhibitory inputs from the supplementary motor and premotor area may suppress any additional excitation of primary motor cortex conferred by MI with high imagined contraction strength. Furthermore, the spinal motor neuron excitability during MI is thought to be affected by central nervous system via the corticospinal and/or extrapyramidal tract. The degree of the spinal motor neuron excitability during MI at various imagined muscle contraction strengths may be modulated by both excitatory and inhibitory inputs from the central nervous system.
Our previous woks showed significant increase of the spinal motor neuron excitability during MI of isometric thenar muscle activity. However, the imagined muscle contraction strength was not involved in change of the spinal motor neuron excitability.
We previously suggested that MI can facilitate the spinal motor neuron excitability. Sympathetic nerve activity was increased during actual isometric muscle contraction [41]. If MI shares common neural substrates with motor execution, it would be expected to observe the similar pattern in autonomic nervous system (ANS) activity during MI would be observed. In previous research, the heart rate during MI was significantly increased than that at rest [42]. Thus, MI can regulate sympathetic nerve activity without any overt movement. However, whether the imagined muscle contraction strength affects the ANS activity is still unclear. Then, this research aimed to investigate the ANS activity during MI of isometric thenar activity at 10 and 50% MVC [40].
Nine healthy volunteers were participated in this research (7 males, 2 females; mean age = 25.3 ± 5.3 years). All participants provided informed consent before the study commenced. This research was approved by the Research Ethics Committee at Kansai University of Health Sciences. All recordings were conducted in accordance with the Declaration of Helsinki.
The ANS activity was recorded using a heart rhythm scanner PE (Biocom Technologies, USA). The pulse wave from the photoplethysmography sensor attached on earlobe was measured. The low frequency/high frequency (LF/HF) ratio was calculated by analyzing measured the pulse wave. The LF/HF ratio is considered to be an index of the sympathetic nerve activity.
For the rest trial (rest), the ANS activity was recorded during relaxation for 5 min. The European Society of Cardiology and the North American Society of Pacing and Electrophysiology recommend 5 min recordings for heart rate variability analysis [43]. Subsequently, for the motor task, participants learned the isometric thenar muscle activity at 50% MVC (i.e., participants press the sensor of pinch meter by left thumb and index finger at 50% MVC) for 1 min. They were instructed to keep the 50% MVC value (kgf) measured numerically on the display of pinch meter. For the MI trial, participants performed MI of isometric thenar muscle activity at 10% MVC for 5 min. The ANS activity was recorded during MI (10% MI) and immediately after 10% MI trial (post) for 5 min respectively. The above process was defined as the MI at 10% MVC condition (10% MI condition). The ANS activity recording under 50% MI condition was performed using the same protocol as 10% MI condition. These conditions were performed randomly on different days.
The normality of the ANS activity data was not confirmed by using the Kolmogorov-Smirnov and Shapiro-Wilk tests. We used a nonparametric method in this research. The LF/HF ratio among three trials (rest, MI, post) under two MI conditions (10% MI and 50% MI conditions) were compared using the Friedman test and Scheffe’s post hoc test.
We also calculated the relative value obtained by dividing the LF/HF ratio during MI under four MI conditions by that at rest. The relative values among two MI conditions were compared using the Wilcoxon signed rank test. We used SPSS statistics ver. 19 (IBM Corp., USA) for statistical analysis. The threshold for statistical significance was set to p = 0.05.
The LF/HF ratio during MI under two MI conditions was greater than that at rest (50% MI vs. Rest, *p < 0.05) (Tables 9, 10). The LF/HF ratio immediately after MI under two MI conditions was reduced to rest level (Tables 9, 10).
Rest | 10% MI | post | |
---|---|---|---|
LF/HF ratio (%) | 1.23 ± 0.75 | 2.73 ± 3.68 | 1.54 ± 0.52 |
Changes in ANS activity under 10% MI condition.
Rest | 50% MI | post | |
---|---|---|---|
LF/HF ratio (%) | 1.74 ± 1.16 | 2.92 ± 2.17* | 2.07 ± 1.42 |
Changes in ANS activity under 50% MI condition.
p < 0.05; significant difference between rest and 50% MI trial.
The relative values of the LF/HF ratio did not exhibit significant differences between two MI conditions (Table 11).
50% MI condition | 10% MI condition | |
---|---|---|
Relative value of LF/HF ratio | 2.64 ± 3.35 | 1.75 ± 1.14 |
Comparison of ANS activity between 10% MI and 50% MI condition.
Firstly, about purpose, the ANS recording procedure, experimental protocol, and statistical analysis, please refer to our previous research [40].
Ten healthy volunteers were participated in this research (8 males, 2 females; mean age = 25.3 ± 5.3 years). All participants provided informed consent before the study commenced. This research was approved by the Research Ethics Committee at Kansai University of Health Sciences. All recordings were conducted in accordance with the Declaration of Helsinki.
The LF/HF ratio during MI under two MI conditions was significantly greater than that at rest (50% MI vs. Rest, 100% MI vs. Rest, *p < 0.05) (Tables 12, 13). The LF/HF ratio immediately after MI under two MI conditions was reduced to rest level (Tables 12, 13).
Rest | 50% MI | post | |
---|---|---|---|
LF/HF ratio (%) | 2.04 ± 1.44 | 3.40 ± 2.55* | 2.33 ± 1.58 |
Changes in LF/HF ratio under 50% MI condition.
p < 0.05; significant difference between rest and 50% MI trial.
rest | 100% MI | post | |
---|---|---|---|
LF/HF ratio (%) | 1.86 ± 1.21 | 4.60 ± 5.48* | 2.29 ± 1.12 |
Changes in LF/HF ratio under 100% MI condition.
p < 0.05; significant difference between rest and 50% MI trial.
The relative values of the LF/HF ratio did not exhibit significant differences between two MI conditions (Table 14).
50% MI condition | 100% MI condition | |
---|---|---|
Relative value of LF/HF ratio | 2.69 ± 3.32 | 2.14 ± 1.15 |
Comparison of ANS activity between 50% MI and 100% MI condition.
Our previous works demonstrated significant increase of the LF/HF ratio during MI at various imagined muscle contraction strengths (i.e., 10% MVC, 50% MVC, and 100% MVC) [40, 44]. Thus, MI of isometric thenar muscle activity can increase the sympathetic nerve activity as with previous researches [42]. The central command is defined as a feed-forward mechanism by which activation of cardiovascular and respiratory centers is accomplished by descending signals from central nervous system [45]. TMS delivered over the primary motor cortex increased the skin sympathetic nerve activity [46]. Furthermore, transcranial direct current stimulation (tDCS) delivered over the primary motor cortex increased the LF/HF ratio [47]. Thus, the corticospinal pathway including the primary motor cortex may affect the sympathetic nerve activity. The rostral ventromedial medulla is also part of the reticulospinal tract [48]. The activation of central nervous system during MI may increase the sympathetic nerve activity via the corticospinal and reticulospinal tracts.
The imagined muscle contraction did not affect the change of the sympathetic nerve activity. This is very similar with the result of the spinal motor neuron excitability during MI at various imagined muscle contraction strengths [24, 25, 26, 28]. If central command during MI affects the sympathetic nerve activity via the corticospinal pathway, the imagined muscle contraction strength may affect the sympathetic nerve activity. Park and Li [35] reported that the imagined muscle contraction strength did not affect the corticospinal excitability. Thus, it considered that the imagined muscle contraction strength might not be involved in change of the sympathetic nerve activity.
Our previous woks showed significant increase of the sympathetic nerve activity during MI of isometric thenar muscle activity. However, the imagined muscle contraction strength was not involved in change of the sympathetic nerve activity.
30–60% of patients have difficulty in using their affected upper limb after stroke [49]. Nakayama et al. [50] reported that recovery of upper limb function related activity of daily living mainly took place within the first 2 months after stroke. Further they reported that 79% of patients with mild upper limb paresis could achieve full upper limb function, whereas, in case with severe upper limb paresis, only 18% of patients who could achieve full upper limb function.
Depending on alteration of peripheral and central inputs, cortical connections and responses are continuously reorganized [51]. Motor cortex excitability will be decreased in post-stroke due to the damage of neural substrates, loss of sensory inputs, and disuse of the affected limb [52]. Described in introduction of this chapter, various brain areas including primary motor cortex corresponding to motor planning, preparation and execution were activated during MI [9, 10, 11, 12, 13]. Pascual-Leon et al. [53] employed TMS in the healthy subjects to map the primary motor cortex targeting the contralateral hand muscles pre- and post-MI training. Cortical representation of hand muscles in contralateral the primary motor cortex increased after MI training. Similarly, MI induced an enhancement of hand muscle cortical representation in post-stroke [54]. Thus, MI can induce the cortical plasticity after neural damage. Additionally, Wrigley et al. [55] reported that the corticospinal excitability was decreased following the significant decline of both size and number of the corticospinal neurons. Also, the spinal motor neuron excitability was significantly reduced in the post-stroke acute phase [8]. Ruffino et al. [56] indicated that neural adaptation following MI training, such as cortical reorganization, the reinforcement of synapse conductivity, and the decline of pre-synaptic inhibition, would be occurred at cortical and spinal level. Thus, in post-stroke patients, facilitating the corticospinal excitability, including the spinal motor neuron excitability should be important for improvement motor function. MI can increase the corticospinal excitability [14, 15, 16]. Further, Grosprêtre et al. [57] reported that during MI, the amplitude of cervico-medullar-evoked potentials (CMEPs) can measure directly pyramido-motoneuronal junction was significantly increased. The H-reflex amplitude, however, was unchanged. Conversely, the H-reflex amplitude was increased during MI [29]. Further, we showed significant increase the F-wave during MI [24, 25, 26, 28]. In regard to difference between two techniques, the H-reflex size can be influenced by pre-synaptic interneuron, whereas the F-wave is solely dependent on the spinal motor neuron excitability [58]. Although effect of MI on the spinal motor neuron excitability is still under debate, MI can be considered to be an effective method for improvement upper limb function in post-stroke.
Brain-machine interface (BMI) is thought to be a potentially useful technology in neurorehabilitation. BMI can supplement for the lost motor function by bypassing disabled neuromuscular system, and improve brain plasticity and restoration of motor function by using external feedback [59, 60]. Various neurophysiological technologies, such as electroencephalography (EEG), magnetencephalography (MEG), and NIRS, have been used to measure and analyze brain activities. Among, the mu (μ) rhythm (ranged from 10-12 Hz) has been commonly used to monitor brain activities [61]. The event-related desynchronization (ERD) of the μ-rhythm was observed during MI. MI plays an important role in neurorehabilitation using EEG triggered-BMI. However, many people have difficulty in performing MI. Especially MI ability was significantly decreased in post-stroke patients [62]. They have no feedback about whether MI did perform correctly, because MI is a mental rehearsal of movement without any overt motor outputs [1]. Thus, MI training should be needed with providing appropriate feedback. Actually, kinesthetic feedback provided better hand motor recovery in MI-based BCI combined with exoskeleton [63].
From the result of our previous works [24, 25, 26, 28], we propose the spinal motor neuron excitability may be one of useful index of MI training effect, because Takemi et al. [64] suggested that the degree of ERD was significantly correlated with the spinal motor neuron excitability. Actually, Hale et al. [33] reported that the spinal motor neuron excitability was more facilitated with each MI practice. Thus, the spinal motor neuron excitability during MI may be altered depending on MI learning status. However, Oishi et al. [19] also reported that the spinal motor neuron excitability was decreased during MI in athlete of speed skating. About alteration of the spinal motor neuron excitability during MI in various learning status, further research will be required.
None declared.
Throughout the northern and southern Pacific Oceans, lay many remote islands. These islands are prone to extreme waves, tectonic activity, and climate change which results in storm surges, shoreline change, tsunamis, and sea level rise. The remoteness of these islands, which allows these regions to capture fully-developed seas, and their lack of a continental shelf, puts them at particular risk to ocean hazards. The Hawaiian Islands are among the most remote islands in the world. Seven natural phenomena have been identified as posing significant threat to coastal areas of the Hawaiian Islands which include: coastal erosion, sea level rise, major storms, volcanic and seismic activity, tsunami inundation, coastal stream flooding, and extreme seasonal high wave events [1]. Coastal slope, distance to shoreline and geologic setting are also important factors when considering coastal infrastructure exposure and vulnerability.
\nWe consider ocean hazards on coastal infrastructure, in this case, road infrastructure. Our previous study [2] used ocean hazard values which include: historical sea level rise, historical significant wave height, tides, and historical shoreline change (without sea level rise). A methodology was developed to quantify historical ocean hazards at critical road locations that have particularly large CRESI (Coastal Road Erosion Susceptibility Index) values and where the Department of Transportation is concerned about road collapse. Note, that although tides is an important ocean variable that should correctly be considered around much of the world, we have omitted it now, in this study, since the Hawaiian Islands have a low mean tidal range of about 2 feet [3].
\nHere, we propose that using projected ocean hazards may give a more accurate representation when planning for future climate change on infrastructure. The ocean hazards we use include historical and projected sea level rise; projected shoreline change with sea level rise; projected storm surge; historical and projected tsunamis, and historical extreme seasonal high wave events. We develop a quantitative Ocean Hazards Classification Scheme (OHCS) based on the Ocean Hazards Database (OHD) [4] of 302 mileposts across coastal state routes in Hawaii (Figure 1). These mileposts are identified as vulnerable in [2] due to: road distance to the shoreline, road elevation, and historical road degradation due to coastal processes.
\nStudy location area, State of Hawaii, USA. The red squares show the location on each island. The white circles (with inner black dots) indicate the milepost (MP) locations where each measurement was taken.
Although probability risks assessments (PRAs) are used widely to give predictions of storminess or shoreline change in a region, it is a time consuming method requiring historical data for twenty years or more, in order to create accurate projections. Also, it is often limited to a localized area due to long computational times.
\nThe aim of this study is to obtain the projected ocean hazards vulnerability rankings using projected rates and projected inundation and the CVI method [5]. The data comes from various governmental and academic sources, which are used and put into the OHCS equation to develop one number, ranging from 0 to 100. With these rankings, we are not only able to produce an overall vulnerability ranking for the five hazards, but we are also able to identify which of the five hazards most affects the coastal road section in a region.
\nIn the next section, we discuss our methodology and the development of the five ocean hazard variables (sea level rise, waves, shoreline change, tsunamis, storm surge) that we use. In Section 3, we give the results by evaluating which of the five ocean hazards most affect vulnerable highway sections in the State of Hawaii and show the overall vulnerability rankings. In the subsequent sections, Sections 4, 5, 6, 7, and 8, we give the conclusions, acknowledgements, references, figures and tables, respectively.
\nThe use of historical and projected values is important towards the development of an Ocean Hazards Classification Scheme (OHCS) for projecting future scenario vulnerability ranking on coastal built infrastructure. Our variables we consider: (1) sea level rise rate, (2) wave height, (3) shoreline change rate, (4) tsunami inundation, and (5) storm surge inundation, are described here.
\nVariable (1), sea level rise, is the sea level rise rate (1905–2050, extreme scenario) (in/yr). Local sea level rise is the result of both global sea level rise and local factors. Global sea level rise is due to warmer ocean temperatures and melting land ice, both caused by climate change. Local factors include land motions and tides, currents, and winds. Local sea levels can rise faster than the average global rate.
\nVariable (2), maximum annually recurring waves, is the significant wave height (2010–2018) (ft). This includes all forecasted wind-waves from 2010 to 2018, which was modeled in the wind-driven Simulating WAves Nearshore (SWAN) wave model.
\nVariable (3), shoreline change, is the mean projected shoreline change rate (2008–2100) (ft/yr); and CRESI – armoring ranking (1–5) [6]. Variable 3, shoreline change, determines the seaward encroachment of the beach towards the road and how protected the road is, whether there is existing armoring or not. Shoreline change is seasonal, where erosion and accretion are present during different times of the year. The most significant shoreline change is influenced by wave action, particularly storm surge events, which occur almost annually, transporting much of the coastline away during one event.
\nVariable (4), tsunamis, considers the historical and hypothetical inundation (ft). Variable 4, tsunamis, are seismic ocean waves causing coastal inundation caused by earthquakes, underwater landslides, volcanic eruptions, or meteorites.
\nVariable (5), storm surge, is Category 1, 2, 3, and 4 storm inundation (ft). Variable 5, storm surge, is a rapid rise in sea level causing coastal inundation due to low pressure, high winds, and high waves associated with hurricanes.
\nUsing the ranking, from 1 to 5, for each of the five variables, we input these variables into one equation (Eq. (1)), which we call the Ocean Hazard Classification Scheme (OHCS), to obtain a value between 1 to 100, where the higher the values, the more vulnerable the region.
\nwhere Variable 1 is 2050 sea level rise rate ranking (extreme scenario) (1 to 5), Variable 2 is significant wave height ranking (1 to 5), Variable 3 is mean shoreline change rate ranking (1 to 5), Variable 4 is tsunami inundation ranking (1 to 5), and Variable 5 is storm surge inundation ranking (1 to 5).
\n\nEq. (1) is taken as the square root of the geometric mean of the ranking variables, with the addition of a power scalar to adjust the range of theoretical OHCS rankings to maximize at a value of 100. Therefore as the number of variables change, so does the scalar power. When considering five input variables, each with a maximum ranked value of 5, a power scalar value of 1.345 results in a potential maximum OHCS value of 100. Our method is similar to that used in Chapter 1 of [2] for calculating Coastal Road Erosion Susceptibility Index (CRESI) values, in [7, 8] who was the first to use the coastal vulnerability index (CVI) for the entire Hawaiian Islands to assess coastal vulnerability, and that described by [5] for finding the coastal vulnerability index (CVI) rankings.
\nHistorical rates of sea level rise are estimated from observed data, and future sea level rise rates are estimated from projected data. For both historical and future scenarios, it is essential to take the spatial variation into consideration when determining the rate of sea level rise. For this reason, we divide each island into a certain number of segments and derive the historical and future sea level rise rates for each segment, respectively. Currently, there are two types of data used to estimate the historical sea level rise rate: tide gauge and satellite altimetry data. Tide gauges are usually placed on piers and measure the sea level relative to a nearby geodetic benchmark, known as relative sea level (RSL). Satellite altimetry measures the sea level relative to a reference ellipsoid, known as absolute sea level (ASL). Here, we study how the sea level rise affects the coastal infrastructure (i.e. roads) in the Hawaiian Islands. Therefore, we focus on the trend estimates of RSL. There are six tide gauge stations in operation in the Hawaiian Islands: NAWI is located in Nawiliwili Bay, Kauai Island with data spanning 1955–2016; MOKU is located in Mokuoloe Island, Oahu Island with data spanning 1957–2016; HONO is located in Honolulu, Oahu Island with data spanning 1905–2016; KAHA is located in Kahului Harbor, Maui Island with data spanning 1947–2016; KAWA is located in Kawaihae, Hawaii Island with data spanning 1988–2016; and HIHA is located in Hilo, Hawaii Island with data spanning 1927–2016. The RSL data of the six available stations in the Hawaiian Islands are downloaded from the Permanent Service for Mean Sea Level (PSMSL) [9, 10]. We make use of all available RSL data from the six tide gauge stations to estimate the RSL trends, respectively. Before estimating the RSL trends, the following process is applied. First, the seasonal signal is removed from the RSL time series using the Seasonal Trend Decomposition using Loess (STL) procedure [11]. Second, we remove the common-mode-oceanographic signals from each RSL time series. The common-mode-oceanographic signals can be derived by averaging the monthly detrended and de-seasoned RSL time series of the all six available tide-gauge stations in the Hawaiian Islands. Finally, the linear trends of the RSL are estimated. However, tide gauge stations are sparsely distributed and not all the segments are covered. For those segments not covered by the tide gauge stations, an indirect way is applied to derive the relative sea level rise trend (RSLT). The RSL variation is comprised of two components: ASL variation and vertical land motion (VLM). Eq. (2) indicates the relationship of the three components:
\nwhere ASLT represents ASL trend, RSLT represents RSL trend, and VLMR represents VLM rate. Therefore, the RSLT of the segments without tide gauge stations can be estimated by combining the ASLT and VLMR. In this paper, we use the reprocessed and merged-gridded sea-level-anomaly heights for global areas processed by Ssalto/Duacs [12] to derive the ASLT. The satellite altimetry data spans 1993–2017 and has a resolution of 0.25 arc degrees. If there is more than one satellite altimetry grid point near the study segment, the time series are averaged to derive the ASLT. Before estimating the ASLT, the Dynamic Atmospheric Correction (DAC) is downloaded and added back to the satellite altimetry data to keep in accordance with the tide gauge data which do not use the barometric pressure correction. The DAC data are produced by Collecte Localisation Satellites (CLS) using the Mog2D model from Legos and distributed by Aviso+, with support from CNES (
Several future sea level rise scenario products have been developed to help planning and decision-making stakeholders analyze and understand vulnerabilities and future risks under scientific uncertainty. We use [17, 18] to estimate the future sea level rise rate for each segment. Sea levels under different scenarios of [17, 18] are projected to tide gauge stations and grid points, which have a resolution of 1 arc degree. If a tide gauge station exists in the segment, we use the data projected to the tide gauge station. If no tide gauge station exists in the segment, the projected grid points nearby the segment will be used. If there is more than one grid point nearby a segment, the mean value is derived and used to represent the projected sea level rise of the segment. Detailed information on the projected sea level rise data for each segment is available in [14]. In this paper, we consider the projected sea level rise under extreme scenario for 2050. For segments with tide gauge stations, the tide gauge data are integrated with the projected sea level rise data to obtain the future sea level rise rate. For segments without tide gauge stations, the combined satellite altimetry and GNSS data are integrated with the projected sea level rise data to obtain the future sea level rise rate.
\nAfter deriving the historical and future sea level rise rates, we rank them according to the percentile of the observed maximum rates, respectively. If a value falls within the highest 80 to 100th percentile, it is ranked 5 (very high). Similarly, values falling within the 60 to 80th percentile are ranked 4 (high), 40 to 60th percentile are ranked 3 (moderate), 20 to 40th percentile are ranked 2 (low), and 0 to 20th percentile are ranked 1 (very low).
\nDue to the sparse distribution of buoy stations in the Hawaiian Islands region, there is not enough coverage to provide wave information at a local level, i.e., for each milepost. Therefore, we use modeled wave output downloaded from Pacific Islands Ocean Observing System (PacIOOS) [19] to understand the wave conditions at each milepost. PacIOOS provides 5-day hourly wave forecasts that are calibrated using local wave buoys for the Hawaiian Islands region. Wave forecasts are simulated using WaveWatch III (WW3), surrounding the main Hawaiian Islands at an approximate resolution of 0.05 degrees, and the SWAN model, surrounding each main island at an approximate resolution of 0.31 mile (500 m) [19]. In this study, we use the wave forecasts simulated by the SWAN model, which has a finer resolution. The time span of wave data for each island varies, i.e., Oahu: 2010–2019, Maui: 2016–2019, Molokai: 2016–2019, Kauai: 2010–2019, Hawaii: 2016–2019. For each milepost, a ‘virtual buoy’, that is, the closest point offshore and perpendicular to the road at each milepost, is selected to obtain wave data. In this study, significant wave height was used, which is estimated as four times the square root to the zeroth order moment of the wave spectrum [19].
\nWe extract the maximum annually recurring wave information using the method presented in [20, 21]. The process of deriving maximum annually recurring wave information is as follows. First, we identify the local peaks from the time series of significant wave heights with a time interval greater than 24 hours. Second, the peaks are divided into different bins according to incoming directions. Here, we select a 30-degree bin window, which shifts by 15-degree increments. Therefore, a maximum of 24 bins can be obtained, and there are overlaps between bins. Third, we select the three highest significant wave heights from each year and perform the generalized extreme value (GEV) fit for each bin. Then, the maximum annually recurring significant wave height (MARSWH) for each bin are derived. Finally, the wave information triplet with maximum MARSWH among all bins is selected as the annually recurring maximum wave information. We repeat this process to obtain wave information at each milepost.
\nAfter deriving the wave information triplet for each milepost, we rank the two index variables, MARSWH and corresponding peak period, according to the percentile of the observed maximum value, respectively. If a value falls within the highest 80 to 100th percentile, it is ranked 5 (very high). Similarly, values falling within the 60 to 80th percentile is ranked 4 (high), 40 to 60th percentile is ranked 3 (moderate), 20 to 40th percentile is ranked 2 (low), and 0 to 20th percentile is ranked 1 (very low).
\nErosion and weakening shorelines are a direct threat to coastal roads and infrastructure. Through the course of this study, we have observed both damages and an increased failure potential of nearshore state roads induced by coastal erosion.
\nSeasonal and storm-driven shifts in the directional transportation of sand, as well as the projected effects of sea level rise (SLR), limit the long-term numerical modeling of Hawaiian shoreline evolution. To assess the potential impact of an acceleration of shoreline change in response to rising sea levels, we interpret relative rates of shoreline change from erosion exposure forecasts developed by [20]. In [22], they describe the probabilistic method by which erosion exposure areas are determined. In [22], they use an equation for shoreline change similar to that of [23], while substituting in the geometric sediment transport model for shoreline equilibrium proposed by [24], to forecast the evolution of sandy shores on the islands of Oahu, Maui, and Kauai. Hindcast and study area limits for the model in [20] are identified from historical shorelines produced by [25]. Hindcast timespans vary between islands and study areas. Complete hindcast timespans for each island are: 1910–2007 on Oahu, 1899–2007 on Maui, and 1926–2008 on Kauai [25]. Acceleration of SLR used by [22] are taken from the Intergovernmental Panel on Climate Change (IPCC) 2013 report, AR5 high-end representative concentration pathway (RCP) 8.5 scenario – the “business as usual” scenario [26].
\nShoreline change is shown in ArcGIS by digitizing the nearshore vegetation line over different periods [20]. Digitized vegetation lines (polylines), which we refer to as “Shoreline Vegetation Lines (SVLs)”, are determined in [20] as the 80th percentile of the probability density function for change due to SLR of the present SVL defined during a 2006–2008 study. Projected shoreline change rates (ft/yr) are determined by dividing the length between the SVLs at the milepost, from the present vegetation line to future projected vegetation lines for SLR of 0.5, 1.1, 2.0, and 3.2 feet, by the number of years within the respective period. We assess the shoreline change at each milepost along a new polyline perpendicular to the road and extending through the SVLs, which we identify as the “measurement axis”. Projected occurrence for SLR of 0.5, 1.1, 2.0, and 3.2 feet is identified by [20] using the IPCC 2013 report AR5 RCP 8.5 scenario, for the years 2030, 2050, 2075, and 2100, respectively [26]. We average the rates of shoreline erosion and accretion at each milepost over the four time periods (i.e. 2030, 2050, 2075, 2100).
\nRates of interpreted averaged shoreline change are ranked into five classes according to their percentile ranges, from no change and accretion to the maximum observed averaged rate. Erosion values roughly within the highest 80 to100th percentile, are ranked 5 (very high). Similarly, erosion rates falling near the 60 to 80th percentile are ranked 4 (high), the 40 to 60th percentile is ranked 3 (moderate), and the 20 to 40th percentile is ranked 2 (low). Shoreline change values representing accretion or no change, fall roughly within the 0 to 20th percentile of maximum observed values are ranked 1 (very low). Mileposts outside of [20] are ranked based on armoring observations made in CRESI [6]. Mileposts with shoreline change values of N/A and hard armoring, where the CRESI armor ranking is greater than 3, are ranked 2 (low). Mileposts with shoreline change values of N/A and no armoring, where CRESI armor ranking [6] is less than or equal to 3, are ranked 3 (moderate).
\nTsunami, which is commonly caused by an earthquake in subduction zones, is one of the most devastating coastal hazards. The Hawaiian Islands region, located in the center of the Pacific Ocean, is circled by the ‘Ring of Fire’, a region of subduction zone volcanism. Therefore, the Hawaiian Islands region is significantly threatened by tsunamis, which result from earthquakes along the ‘Ring of Fire’ [27, 28]. For this reason, we take into account tsunami hazard in our assessment. We use modeled tsunami flow depth data, provided by [29], to create inundation for each milepost which in turn helps us understand how tsunami hazards affect the coastal roads in the Hawaiian Islands. The term tsunami flow depth refers to the height of tsunami water surface above ground, which can be derived by subtracting ground elevation from tsunami water level. In this study, we use two types of tsunami flow depth data: one is modeled according to historical earthquake events, and the other is based on hypothetical earthquake events. Both types of data were simulated using the model Non-hydrostatic Evolution of Ocean Wave (NEOWAVE), which is a community model developed and maintained at the University of Hawaii [30, 31]. Historical tsunami scenarios are based on the five most destructive far-field or trans-Pacific tsunamis, which were generated by the 1946 Aleutian, the 1952 Kamchatkan, the 1957 Aleutian, the 1960 Chilean, and the 1964 Alaskan earthquakes. NEOWAVE model parameters are calibrated by comparing results with well-documented runup records for those tsunamis on Hawaii shores [32, 33, 34, 35]. The NEOWAVE model applied nested grids with increasing resolution, from 2 arcminutes (~2.3 miles) for open ocean to 0.3 arcseconds (~29.53 ft) for coastlines [32, 33, 34, 35]. Hypothetical tsunami scenarios are based on two extreme tsunamis which apply the seismic source parameters of two hypothetical great Aleutian earthquakes. Tectonic parameters of the two great Aleutian earthquakes, with moment magnitudes of (Mw) 9.3 and 9.6, are compiled by NOAA Pacific Marine Environmental Laboratory (PMEL) and both hypothetical earthquakes are identified by a seismological study as potential sources of devastating tsunamis to Hawaii [27, 28, 29]. The model also applies nested grids with increasing resolution from 2 arcminutes (~2.3 miles) for open ocean to 0.3 arcseconds (~29.53 ft) for coastlines [29].
\nWe use the Geographical Information System (GIS) software ArcGIS to create tsunami inundation and extract tsunami flow depth values for each milepost. Tsunami flow depths are ranked for each milepost as follows. First, mileposts are classified into three categories: Category 1 has values in the historical scenario, Category 2 has no values in the historical scenario, but has values in the hypothetical scenario, and Category 3 has no values in both historical and hypothetical scenarios. For Category 1, if a value falls within the highest 67 to 100th percentile of the observed maximum value in the historical scenario, it is ranked 5 (very high). Similarly, if a value falls within the 33 to 67th percentile, it is ranked 4 (high), and within the 0 to 33rd percentile, it is ranked 3 (moderate). For Category 2, because the tsunami flow depth in the hypothetical scenario for milepost 6 (MP 6) on Route 83, North Shore, Oahu exceeds three standard deviations of the mean, we rank it 2 and remove it from the list when searching the maximum value of Category 2. Therefore, if a value falls within the highest 50 to 100th percentile of the observed maximum value in the hypothetical scenario, it is ranked 2 (low), within the 0 to 50th percentile, it is ranked 1 (very low). All mileposts in Category 3 are ranked 1 (very low).
\nPredicting and preparing for hurricanes is a top priority for the residents and city managers of Hawaii. To assess the “worst case scenario” of inundation from storm surge, we utilize the most recent national storm surge hazard maps produced by the Storm Surge Unit (SSU) of the National Hurricane Center (NHC), National Oceanic and Atmospheric Administration (NOAA) [36].
\nVersion 1 of the national storm surge hazard maps are published by [36] and include inundation model results for flooding caused by storm surge along the East and Gulf Coasts of the United States. Version 2, also by [36], became available in November 2018 and includes storm surge inundation estimates for the U.S. Virgin Islands, Hawaii, and Hispaniola. Measures of storm surge inundation height reflect the extents of flooding caused by storm driven uplift of the ocean surface. Estimates of storm surge inundation in this assessment are based on GIS datasets obtained through personal communication with members of the SSU and NOAA affiliates. Internal SSU issues, beyond the control of our team, have prevented a complete handover and description of the Hawaii storm surge data. As a result of the incomplete handover, there are minor errors in the projection of the data, as well as a limited understanding of the model hindcast. However, despite the shortcomings, the data still remains the best and most complete storm surge inundation data for the Hawaiian Islands. Storm surge hazard data presents hypothetical inundations found using a composite deterministic and probabilistic approach with the Sea, Lake and Overland Surges from Hurricanes (SLOSH) numerical model, developed by the National Weather Service (NWS). In the Hawaiian Islands, where steep offshore bathymetry can produce an increase in mean water level due to wave dissipation, or wave setup, the SLOSH model is loosely coupled to the third generation of the SWAN model to account for storm-related increases in mean water levels. SLOSH model forecasts consider historical atmospheric and hurricane track data, to produce a model of the wind field which drives hypothetical storm surge. However, as we mention, internal SSU issues prevents us from describing the time period for the historical atmospheric data, as well as the number and distribution of historical storm tracks.
\nHawaii SLOSH model estimates include inundation scenarios for category 1 through 4 hurricanes and a broad range of storm tracks and landfall locations, consisting of hundreds of thousands of hypothetical hurricanes. Assessed storm surge inundation heights are determined as the maximum of the maximum envelops of water (MOMs), relative to a DEM of Hawaii from NOAA Office for Coastal Management (OCM) high-resolution raster elevation datasets. DEMs for each island are reoriented and divided to optimize SLOSH operation, resulting in polar oriented cells of various sizes, as small as roughly 24 ft (9 m), on each side. Within each cell, MOM values are determined in feet as a combination of all simulated inundation scenarios, with the MOM identifying the greatest observed inundation height from all simulations. Milepost assessments of storm surge inundation are sampled from the individual category of storm surge datasets, within a circular buffer centered on the milepost with a radius of 82 ft (25 m). Ranked values of storm surge inundation are determined as the percent coverage-area-weighted mean of the MOM values within the milepost buffer area. Percent coverage for each milepost buffer area is determined by first using the ArcGIS zonal statistics tool to find the buffer area overlapping with the storm surge dataset. Then, the inundation, or overlapping of the buffer area, is divided by the known total buffer area of roughly 21,000 square ft, to determine the percentage of the buffer inundated. Mean inundation height within the milepost buffer areas is also determined using the ArcGIS zonal statistics tool. Ranked values of storm surge inundation are finally calculated as the mean inundation height multiplied by the percent coverage.
\nPercent coverage-area-weighted mean storm surge inundation heights are ranked based on their observed distribution within the maximum observed value of each category of storm, respectively. Mileposts with inundation heights within the 50 to 100th percentile of Category 1 storm surge are ranked 5 (very high). Inundation heights greater than zero and within the 0 to 50th percentile of Category 1 storm surge, as well as the 50 to 100th percentile of Category 2 storm surge, are ranked 4 (high). If milepost inundation heights for Category 2 storm surge are greater than zero and within the 0 to 50th percentile, or within the 50 to 100th percentile for Category 3 storm surge, they are ranked 3 (moderate). Storm surge inundation heights within the 50 to 100th percentile for Category 4 storm surge, or within the 0 to 50th percentile for Category 3 storm surge are ranked 2 (low). Milepost assessments with no inundation, or with inundation heights in the 0 to 50th percentile for Category 4 storm surge are ranked 1 (very low).
\nThere are twelve regions in the State of Hawaii where coastal roads, are owned by the State, and selected due to their location to shoreline, elevation and road condition from previous ocean hazards. Of these twelve regions, four are on Oahu, two are on Molokai, three are on Maui, three are on Kauai, and one is on Hawaii. Oahu includes Waianae Coast (WC), North Shore (NS), East Shore (ES), and East Oahu (EO). Molokai includes Molokai West (KW) and Molokai East (KE). Maui includes West Maui (WM), East Maui (EM), and Central Maui (CM). Kauai includes West Kauai (W), North Kauai (N), and East Kauai (E). Hawaii includes Hilo (HILO).
\nHere, we present our results and how five ocean hazards: sea level rise, waves, shoreline change, tsunami, and storm surge are collectively used to rank the vulnerability of coastal highways in the State of Hawaii. A list of ocean hazards data and their associated references (superscripted) used for the Ocean Hazards Classification Scheme (OHCS) in Eq. (1) is shown in Table 1.
\nVariable | \nClassification | \nDescription [units] | \n
---|---|---|
1 | \nSea Level Rise | \n2050 Sea Level Rise Rate [9, 10, 12, 14, 17, 18] (1905–2050, extreme scenario) [in/yr] | \n
2 | \nMaximum Annually Recurring Waves | \nSignificant Wave Height [19, 20] (2010–2018) [ft] | \n
3 | \nShoreline Change | \nMean Shoreline Change Rate [6, 20] (2008–2100) [ft/yr] | \n
4 | \nTsunami | \nInundation Depth (Historical and Hypothetical) [29, 32, 33, 34, 35] [ft] | \n
5 | \nStorm Surge | \nCategory 1–4 Storm Inundation Depth [36] (Hypothetical) [ft] | \n
Historical and projected ocean hazards variables used in the Ocean Hazards Classification Scheme (OHCS) for State coastal roads in the State of Hawaii. For more detailed explanation of each, refer to [4]. 12 inches = 1 foot = 0.3048 meters.
\nTable 2 is the Ocean Hazards Classification Scheme (OHCS) for historical and projected ocean hazards developed from the Ocean Hazards Database (OHD) [4] for state coastal roads in the State of Hawaii. In the first column is the vulnerability rank, 1 to 5, where 1 is low vulnerability and 5 is high vulnerability. The remaining columns are the associated Variables and their resulting rates, heights or depths according to the methodology described in Section 2, using 302 mileposts across the State from [2]. Using Table 2, we rank each Variable (1 to 5) and apply it to Eq. (1) to retrieve the OHCS ranking, that is, a combined ranking of vulnerability for sea level rise, significant wave height, shoreline change, tsunami and storm surge. Our results are listed as follows.
\nVunerability Rank | \nVariable 1 | \nVariable 2 | \nVariable 3 | \nVariable 4 | \nVariable 5 | \n
---|---|---|---|---|---|
Sea Level Rise | \nMaximum Annually Recurring Waves | \nShoreline Change | \nTsunami | \nStorm Surge | \n|
2050 Sea Level Rise Rate [9, 10, 12, 14, 17, 18] (1905–2050, extreme scenario) | \nSignificant Wave Height [19, 20] (2010–2018) | \nMean Shoreline Change Rate [6, 20] (2008–2100) | \nTsunami Inundation [29, 32, 33, 34, 35] (Historical and Hypothetical) | \nWeighted Mean Storm Surge Inundation [36] (Hypothetical) | \n|
1 | \n<0.1 in/yr | \n<7 ft | \n<0 ft/yr | \nNo inundation or Hypothetical inundation <16 ft with no Historical Inundation | \nNo Inundation or Category 4 Inundation <4 ft | \n
2 | \n0.1 to 0.2 in/yr | \n7 to 14 ft | \n0 to 2 ft/yr & “N/A” with >3 Armoring Ranking | \nHypothetical inundation ≥16 ft with no Historical Inundation | \nCategory 3 Inundation <4 ft or Category 4 Inundation of 4 to 8 ft | \n
3 | \n0.2 to 0.3 in/yr | \n14 to 21 ft | \n2 to 5 ft/yr & “N/A” with ≤3 Armoring Ranking | \nHistorical inundation <6 ft | \nCategory 3 Inundation of 4 to 7 ft or Category 2 Inundation <1 ft | \n
4 | \n0.3 to 0.4 in/yr | \n21 to 29 ft | \n5 to 7 ft/yr | \nHistorical inundation of 6 to 12 ft | \nCategory 2 Inundation of 1 to 6 ft or Category 1 Inundation <1 ft | \n
5 | \n> 0.4 in/yr | \n> 29 ft | \n> 7 ft/yr | \nHistorical inundation ≥12 ft | \nCategory 1 Inundation of 1 to 4 ft | \n
Ocean Hazards Classification Scheme (OHCS) for historical and projected ocean hazards developed from [34] for State coastal roads in the State of Hawaii. 12 inches = 1 foot = 0.3048 meters.
Oahu Waianae Coast (WC), Figures 2-4: Includes 39 mileposts. The OHCS vulnerability ranking ranges from 1 to 3, with a few higher ranking outliers of 5, 5, and 6 at MPs 19 + 0.55, 16 + 0.41 and 10 + 0.25, respectively. In this region, sea level rise ranges from 1 to 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 4. The outliers, i.e. the OHCS rankings of 5 and 6 at MP 19 + 0.55, 16 + 0.41 and 10 + 0.25, are a result from the increased tsunami and storm surge rankings, due to proximity and elevation of the road to the shoreline at those particular locations.
\nOcean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 13 + 0.1 to 19 + 0.55. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 7 + 0.67_13 + 0.1. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Oahu Waianae Coast (WC) MP 3 to 7 + 0.67. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Oahu North Shore (NS), Figure 5: Includes 19 mileposts. The OHCS vulnerability ranking ranges from 2 to 15. In this region, sea level rise is 2, significant wave height ranges from 1 to 3, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge is 1 with a three MPs ranked at 4. Although most of the OHCS values range from 7 or below, the three MPs worth noting, i.e. MP 3 + 0.66, 4 + 0.49 and 6, with a ranking of 15, 15, and 10, respectively, are the MPs with a storm surge ranking of 4, compared to the other MPs with a storm surge ranking of 1.
\nOcean Hazards Classification Scheme (OHCS) ranking for Oahu North Shore (NS) MP 2 to 10 + 0.58. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Oahu East Shore (ES), Figures 6-8: Includes 44 mileposts. The OHCS vulnerability ranking ranges from 1 to 12. In this region, sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 1 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. We see particularly high OHCS rankings of 9 to 12 at certain MPs. These regions with OHCS values of 9 to 12, is a result from the increased tsunami and storm surge rankings.
\nOcean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 32 to 38. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 23 to 32. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Shore (ES) MP 17 to 23. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Oahu East Oahu (EO), Figures 9 and 10: Includes 20 mileposts. The OHCS vulnerability ranking ranges from 1 to 10. In this region, sea level rise ranges from 1 to 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. High OHCS rankings of 7 to 10, is a result from the increased tsunami and storm surge rankings.
\nOcean Hazards Classification Scheme (OHCS) ranking for Oahu East Oahu (EO) MP 9 to 17 + 0.18. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Oahu East Oahu (EO) MP 4 to 9. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Molokai Molokai West (KW), Figure 11: Includes 5 mileposts. The OHCS vulnerability ranking ranges from 13 to 17, with a low OHCS ranking outlier of 3 at MP 2. In this region for OHCS rankings of 13 to 17, the sea level rise is 5, significant wave height is 1, shoreline change ranges from 2 to 3, tsunami is 3, and storm surge ranges from 4 to 5. High OHCS rankings of 13 to 17 is a result of higher rankings for sea level rise, storm surge and tsunami inundation in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai West (KW) MP 2 to East 4. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Molokai Molokai East (KE), Figures 12-14: Includes 49 mileposts. The OHCS vulnerability ranking ranges from 3 to 33. In this region, the sea level rise is 5, significant wave height ranges from 1 to 2, shoreline change is 3, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for sea level rise, storm surge and tsunami inundation in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 17 to 21 + 0.32. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 10 + 0.06 to 17. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Molokai Molokai East (KE) MP 4 to 10 + 0.06. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Maui West Maui (WM), Figures 15-17: Includes 48 mileposts. The OHCS vulnerability ranking ranges from 1 to 14. In this region, the sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 1 to 3, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 20 to 29. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 15 to 20. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Maui West Maui (WM) MP 9 to 15. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Maui East Maui (EM), Figure 18: Includes 11 mileposts. The OHCS vulnerability ranking ranges from 6 to 10. In this region, the sea level rise is 2, significant wave height is 1, shoreline change ranges from 2 to 3, tsunami ranges from 4 to 5, and storm surge ranges from 2 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Maui East Maui (EM) MP 1 to 3 + 0.14. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Maui Central Maui (CM), Figures 19 and 20: Includes 13 mileposts. The OHCS vulnerability ranking ranges from 3 to 16. In this region, the sea level rise ranges from 2 to 5, significant wave height ranges from 1 to 5, shoreline change ranges from 2 to 5, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is generally a result of high rankings for sea level rise, storm surge and tsunami inundation in this region. However, significant wave height contributes to high OHCS rankings at MPs 8 + 0.42 and 8 + 0.63 and shoreline change at MP 0 + 0.05.
\nOcean Hazards Classification Scheme (OHCS) ranking for Maui Central Maui (CM) MP 6 to 9. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Ocean Hazards Classification Scheme (OHCS) ranking for Maui Central Maui (CM) MP 0 to 0 + 0.71. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Kauai West Kauai (W), Figure 21: Includes 11 mileposts. The OHCS vulnerability ranking ranges from 4 to 11, with a low OHCS outlier of 1 at MP 24 + 0.91. In this region, the sea level rise is 2, significant wave height is 1, shoreline change ranges from 1 to 4, tsunami ranges from 3 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of high rankings for storm surge and tsunami inundation in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Kauai West Kauai (W) MP 24 to 28. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Kauai North Kauai (N), Figure 22: Includes 8 mileposts. The OHCS vulnerability ranking ranges from 3 to 11. In this region, the sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 5, tsunami ranges from 3 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher rankings for storm surge and shoreline change in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Kauai North Kauai (N) MP 2 + 0.5 to 4 + 0.51. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Kauai East Kauai (E), Figure 23: Includes 13 mileposts. The OHCS vulnerability ranking ranges from 2 to 9. In this region, the sea level rise is 2, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 4, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher rankings for shoreline change, tsunami and storm surge in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Kauai East Kauai (E) MP 5 to 11. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
Hawaii Hilo (HILO), Figure 24: Includes 12 mileposts. The OHCS vulnerability ranking ranges from 4 to 18. In this region, the sea level rise is 4, significant wave height ranges from 1 to 2, shoreline change ranges from 2 to 3, tsunami ranges from 1 to 5, and storm surge ranges from 1 to 5. High OHCS rankings is a result of higher rankings for sea level rise, tsunami and storm surge in this region.
\nOcean Hazards Classification Scheme (OHCS) ranking for Hawaii Hilo (HILO) MP 0 to 5. The OHCS consists of five variables: (i) sea level rise 1905–2050, (ii) maximum annually recurring significant wave height 2010–2018, (iii) shoreline change 2008–2100 and CRESI Armoring, (iv) historical and hypothetical tsunami, and (v) Category 1,2,3,4 hypothetical storm surge. Rankings of ocean hazard increase from 1 to a theoretical maximum of 100.
In summary from our results, sea level rise ranges from 1 to 5, waves ranges from 1 to 5, and the OHCS ranges from 1 to 33. Although the OHCS equation allows a value up to 100, OHCS only went up to 33, showing that no locations have all Variables at high vulnerability (i.e. 5), but rather a one or two Variables may be at rank 5 while the other Variables remain low (i.e. 1 or 2).
\nAnother result shows that the island of Molokai has the highest OHCS overall. The Variables that contribute to the high OHCS includes sea level rise, tsunami and storm surge, all of which were nearly ranked at 5.
\nA third result is that the Variable, storm surge, is consistently the largest contributor in coastal vulnerability on state roads for all islands. This is shown in the ranking of all Variables which largely show a storm surge of rank 5 at most locations, where the other Variables remain at 1 or 2. Tsunamis are the second largest contributor in our results. Although sea level rise was not one of the highest contributors, it should be considered a main contributor since the sea level rise inundation amplifies storm surge and tsunami inundation.
\nThe high rankings of storm surge inundation and tsunami inundation are due to lower road elevation, which puts the road at greater risk. Road relocation inland is recommended, if possible. Where road relocation is not possible, and usually not an option for state roads in Hawaii, elevating the road infrastructure (and therefore other surrounding infrastructure) should be taken into consideration in community planning and development. To reinforce the elevated road, hardening should be included also.
\nAlthough our Variables we consider: (1) sea level rise, (2) waves, (3) shoreline change, (4) tsunamis, and (5) storm surge, work for our study region, i.e. the Hawaiian Islands, one should be aware that assessing vulnerability is “location specific”. This means that natural hazards affecting an area depend on many factors such as the geology, oceanic, bathymetric, and climate trends in a location. These factors differ region to region. Each coastal region should develop their own vulnerability ranking method to include or not include Variables which most likely affect their region.
\nWhile natural hazard exposure to infrastructure is important, other multiple indicators should also be considered. For roadways this may include traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations [37]. However, this type of data changes frequently as land use develops at a rapid pace or additionally roads may be added. Also adding these additional indicators may change the CVI (or OHCS).
\nCoastal hazard and risk not only comes in the form of the physical processes on the ecosystem or built infrastructure, but also through social perceptions, as well. Perceptions of coastal hazards and risks and community support for engineered adaptation methods are important for implementation among different stakeholder groups (experts, businesses, and community members) [38].
\nBy understanding the vulnerability of a region, we may assign what adaptation method to use in vulnerable coastal regions dealing with climate change, in particular, inundation. These engineered adaptation methods include offshore barriers, coastal armoring, elevated development, floating development, floodable development, living shorelines, and managed retreat [39]. In the future, if we want to continue to live on coast, we must adapt.
\nFunding provided by the State of Hawaii Department of Transportation Highways Division, under Project Number HWY-06-16. We would like to also acknowledge our affiliations to the following at the University of Hawaii at Manoa: Department of Civil and Environmental Engineering, Sea Grant College Program, and the Coastal Hydraulics Engineering Resilience (CHER) Lab. Publications fees provided by Research & Training Revolving Fund from the Civil and Environmental Engineering Department at the University of Hawaii at Manoa.
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The Corresponding Author (acting on behalf of all Authors) and INTECHOPEN LIMITED, incorporated and registered in England and Wales with company number 11086078 and a registered office at 5 Princes Gate Court, London, United Kingdom, SW7 2QJ conclude the following Agreement regarding the publication of a Book Chapter:
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\n\n6.1 Unless prevented from doing so by events outside its reasonable control, IntechOpen, in its discretion, agrees to publish the Chapter attributing it to the Corresponding Author and any Co-Author.
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\n\n6.3 IntechOpen is granted the authority to enforce the rights from this Publication Agreement, on behalf of the Corresponding Author and any Co-Author, against third parties (for example in cases of plagiarism or copyright infringements). In respect of any such infringement or suspected infringement of the copyright in the Chapter, IntechOpen shall have absolute discretion in addressing any such infringement which is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
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\n\n7.1 Further Assurance: The Corresponding Author shall and will ensure that any relevant third party (including any Co-Author) shall, execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\n7.2 Third Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\n7.3 Entire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces and extinguishes all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by or on behalf of the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (together "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of its pre-contract fraudulent misrepresentation or fraudulent concealment.
\n\n7.4 Waiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\n7.5 Variation: No variation of this Publication Agreement shall be effective unless it is in writing and signed by the parties (or their duly authorized representatives).
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\n\nAny modification to or deletion of a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\n\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
\n\nLast updated: 2020-11-27
\n\n\n\n
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