The study examined extreme wind characteristics of the coastal communities in Bayelsa State, Nigeria for possible community planning and development. To achieve this aim, data on wind speed were sourced from the Nigerian Meteorological Agency (NIMET). Personal interview and focused group discussions were done with the aid of well structured questionnaire in the various coastal communities sampled to identify impact and coping strategies from extreme winds. The Beaufort Winds Scale and regression analysis were the statistical tools used for the analysis of the data in order to achieve the objectives of the study. The results indicate that, the return period to obtain maximum 1-year wind speed value of 29.3 m/s (violent storm), 27.8 m/s (storm), 24.3 m/s (strong Gale) and 20.6 m/s (fresh Gale) will be 17 years, 5.7 years, 2.8 years and 1.4 years respectively. Fresh Gale characterized the extreme wind events in the area. Result further showed that out of 19 occurrences of wind events, 11 were extreme cases, while 2 occurred as violent storm of 29.3 m/s. Findings also showed that extreme winds occurred more (8 cases) during the early part (March-April) of the raining season when the area is under the influence of maritime moisture laden air mass than the dry season which is dried and dusty. Using a combination of return period of the magnitude of extreme wind and the log of wind speed for the 16 years a model predicting the incidence of extreme wind was done. Awareness on the dangers of wind hazard increases while early warning systems are advocated to mitigate the associated dangers with extreme wind events in the study area.
Bayelsa’s coastal areas are dynamic environments that are susceptible to a broad range of processes that can create and generate potentially climatic related hazardous conditions. Much of Bayelsa’s populated coast is vulnerable to the effects of coastal storms (high winds, wave action, overwash, and storm surge), flooding, sea-level rise, and both episodic and chronic shoreline erosion. In coastal areas, particularly of developing countries, where rapid growth is the pattern of global environmental changes that has been identified as a key precursor of risks. A major manifestation of global climate change is the increase in occurrence and intensity of natural hazards with devastating effect on humans; buildings and infrastructure [
Studies of climate event impacts for developing countries (Tropical Africa) emphasize mostly drought and floods [
Wind related hazards have not been adequately acknowledged as environmental problem, like flooding and gully erosion. This is in-spite of the fact that wind hazards claims lives, destroys buildings and social infrastructure annually. Globally much attention is given to major extreme weather events such as droughts, floods and tropical storms, yet small hazard events, which are mostly neglected in the policy arena, have strategic importance to development planning as aggregate loss resulting from their occurrence is usually high. These are outcomes of mesoscale climatic circumstances which may be affected by climate changes. As small hazard events, the occurrence of local windstorms is becoming frequent and increasingly important to human security as they have the capacity to facilitate large events by eroding people’s assets and the integrity of critical infrastructure subsequently lowering society’s threshold of resilience in local communities [
The study area is Yenagoa LGA of Bayelsa State (
Weather systems particularly rainfall in Nigeria are primarily as result of the interplay between two major pressure and wind systems. These are two dynamically generated sub-tropical high pressure cells entered over the Azores Achipelago (off the coast of North Africa) and St. Hellena Islands (off the coast of Namibia). These high pressure centers (or anticyclones) which are permanently generated and drive respectively the North-East trade wind and South-east trade wind of the South Atlantic Ocean. Both air streams blow over Bayelsa coastal
communities at various seasons of the year. It is important to note that these air streams or masses follow the apparent movement of the sun which passes the region twice on its way to and from the tropic of cancer [
Data on daily windspeed was obtained from the Nigerian Meteorological Agency (NIMET) in the study area from the period of five years (1997-2012) making a total of 5475 days and the effects of extreme wind events in the study area was collected with the aid of the questionnaire and focused group discussion with the residents of the sampled coastal communities in Bayelsa. They were selected based on a population criterion of 1200 persons and above. They include Oporoma, Brass, Akassa, Amasoma and Kaiama. Similarly wind cup anemometers were installed in the selected communities to validate the data on wind speed for six months. The data was analysed using Regression technique. Probability of occurrence, Return Period and Trend Analysis were similarly done to determine the recurrence interval of extreme wind events of specific magnitude in the study area. A return period, also known as a recurrence interval (sometimes repeat interval) is an estimate of the likelihood of an event, such as an extreme wind to occur [
In a given period of n years, the probability of a given number r of extreme wind events of a return period µ is given by the binomial distribution as follows:
In general, if the random variable X follows the binomial distribution with parameters
In the limit of:
Then,
Take,
where;
T = is return interval;
N = is number of years on record;
m = is the number of recorded occurrences of the event being considered.
Findings showed that extreme wind event occurred more during the early part of rainy season (March and April) and the later part of the season (December through January) (see
Findings showed that the violent storms 29.3 m/s and 28.5 m/s occurred in June 2012, and march 2005 respectively (
Year | Wind speed (m/s) | Wind speed range (m/s) | Extreme wind types | Frequency |
---|---|---|---|---|
2012 | 29.3 | 28.4 - 32.6 | Violent storm | 2 |
2005 | 28.5 | 28.4 - 32.6 | Violent storm | 1 |
2000 | 27.8 | 24.5 - 28.4 | Storm | 3 |
2004 | 26.5 | 24.5 - 28.4 | Storm | 1 |
1999 | 24.7 | 24.5 - 28.4 | Storm | 1 |
2008 | 24.3 | 20.7 - 24.5 | Strong Gale | 6 |
2011 | 24.2 | 20.7 - 24.5 | Strong Gale | 1 |
2006 | 23.5 | 20.7 - 24.5 | Strong Gale | 1 |
2002 | 22.8 | 20.7 - 24.5 | Strong Gale | 1 |
2007 | 22.6 | 20.7 - 24.5 | Strong Gale | 1 |
2009 | 21.9 | 20.7 - 24.5 | Strong Gale | 1 |
Total | 276.1 | 19 |
Year | Month | Extreme wind | Wind speed (m/s) | Wind speed range (m/s) |
---|---|---|---|---|
2012 | June | Violent storm | 29.3 | 28.4 - 32.6 |
2005 | March | Violent storm | 28.5 | 28.4 - 32.6 |
2000 | April | Storm | 27.8 | 24.5 - 28.4 |
2004 | April | Storm | 26.5 | 24.5 - 28.4 |
1999 | March | Storm | 24.7 | 24.5 - 28.4 |
2008 | January | Strong Gale | 24.3 | 20.7 - 24.5 |
2011 | April | Strong Gale | 24.2 | 20.7 - 24.5 |
2006 | April | Strong Gale | 23.5 | 20.7 - 24.5 |
2002 | March | Strong Gale | 22.8 | 20.7 - 24.5 |
2007 | April | Strong Gale | 22.6 | 20.7 - 24.5 |
2009 | January | Strong Gale | 21.9 | 20.7 - 24.5 |
next is March, followed by January and finally June, with the remaining months having no records of extreme winds.
Result showed that the magnitude of extreme wind events have changed significantly over time in the study area. Using a combination of return period of the magnitude of extreme wind and the log of wind speed for the 16 years a model predicting the incidence of extreme wind was done. This is expressed as:
The computed regression coefficient (β) = 0.012, while the base intercept (a) = −21.77. Fitting appropriately to the linear regression equation form y = a + bx. This implies that as wind speed increases, the return period of a magnitude of extreme wind also increase by a unit factor of 0.012, implying a cause and effect, direct relationship. Where return period is time and y is wind speed the forecast of extreme wind events for the next return period can be computed using the above obtained equation in deriving the magnitude of the forecasted wind speed over time.
The polynomial trendline (
The scatter gram and the polynomial trendline between logarithm of extreme winds and their return period (
Rank | Years | Wind speed (m/s) | P-m/n + 1 | RT = 1/p | Log of wind speed |
---|---|---|---|---|---|
13 | 1997 | 20.5 | 13/17 = 0.7647 | 1.3077 | 1.3118 |
12 | 1998 | 20.6 | 12/17 = 0.7059 | 1.4166 | 1.3139 |
5 | 1999 | 24.7 | 5/17 = 0.2941 | 3.4002 | 1.3927 |
3 | 2000 | 27.8 | 3/17 = 0.1765 | 5.6657 | 1.4440 |
14 | 2001 | 19.3 | 14/17 = 0.8235 | 1.2143 | 1.2856 |
9 | 2002 | 22.8 | 9/17 = 0.5294 | 1.8889 | 1.3579 |
16 | 2003 | 18.5 | 16/17 = 0.9412 | 10625 | 1.2672 |
4 | 2004 | 26.5 | 4/17 = 0.2353 | 4.2499 | 1.4232 |
2 | 2005 | 28.5 | 2/17 = 0.1176 | 8.5034 | 1.4548 |
8 | 2006 | 23.5 | 8/17 = 0.4706 | 2.1249 | 1.3711 |
10 | 2007 | 22.6 | 10/17 = 0.5882 | 1.7001 | 1.3541 |
6 | 2008 | 24.3 | 6/17 = 0.3529 | 2.8337 | 1.3856 |
11 | 2009 | 21.9 | 11/17 = 0.6471 | 1.5454 | 1.3404 |
15 | 2010 | 18.8 | 15/17 = 0.8823 | 1.1334 | 1.2742 |
7 | 2011 | 24.2 | 7/17 = 0.4118 | 2.4284 | 1.3838 |
1 | 2012 | 29.3 | 1/17 = 0.0588 | 17.0068 | 1.4669 |
Total | 16 | 373.3 | 7.89406 | 57.4819 | 21.8272 |
The associated destruction caused by wind hazard in the study area include but are not limited to displacement from dwelling, damage to property, economic hardship, restriction of movement, depression and death (
From responses gathered from the study area with the aid of questionnaire and focused group discussion, the perceived factors responsible for increasing vulnerability/exposure to extreme wind hazards are proximity to the sea, obsolete structures, use of substandard construction materials and insufficient trees to serve as natural wind breakers as shown on
Impact | Coping Strategies |
---|---|
Displacement from dwellings | Relocated to neighbours residence |
Damage to property | Repair/replace damaged property |
Economic hardship | Cut down of financial expenses |
Restriction of movement | Rescheduled appointment |
Depression | Borrowing to effect repairs |
Death |
Factor | Percentage Responses |
---|---|
Proximity to sea | 20 |
Obsolete structures | 5 |
Use of poor construction materials | 34 |
Insufficient trees to serve as wind breakers | 41 |
Total | 100 |
If coastal hazards are not considered in the process of community planning and development, properties, local residents and businesses, traditional indigenous owners, the insurance, property development and real estate industries, fishing and agricultural industries and their consumers, tourist operators and visitors, environmental organisations and recreational groups may be subject to unnecessary and increased risks from extreme wind and their associated hazards. Strong governance arrangements are critical to developing an effective response to extreme winds in the highly vulnerable coastal communities of the state. Unfortunately, the complicated layers of legislation and policy, and overlapping Commonwealth, state and local responsibilities for aspects of the coastal zone, represent a major challenge for effective governance within Bayelsans coastal amenity communities.
This study has shown that, the return period to obtain maximum 1-year wind speed value of 29.3 m/s (violent storm), 27.8 m/s (storm), 24.3 m/s (strong Gale) and 20.6 m/s (fresh Gale) will be 17 years, 5.7 years, 2.8 years and 1.4 years respectively. Violent storm, Storm, and Strong Gale characterized the extreme wind events in the area. More so, extreme winds occurred more (8 cases) during the early part (March-April) of the raining season when the area is under the influence of maritime moisture laden air mass than the dry season which is dried and dusty. Using a combination of return period of the magnitude of extreme wind and the log of wind speed for the 16 years a model predicting the incidence of extreme wind was done.
A coordinated federal effort, in cooperation with other levels of government, academia and the private sector should build on existing efforts and should include but not limited to:
1) Assessing individual and community capability to respond to wind events, including vulnerability analyses, risk perception, risk communication, and sharing; installation and communication of early wind warnings and public response, evacuation capability, and knowledge of appropriate actions for wind events, especially among vulnerable populations (the aged and children and women).
2) Evaluating the response of the built environment and critical infrastructure to wind events by investigating aerodynamic response, load path, ultimate capacity and the performance of the building envelope.
3) Assessing the impact of wind and wind-blown debris on wind and water.
4) Examining the interaction between wind and storm surge to determine the impact on building foundations and critical infrastructure.
5) Exploring the near-ground and channeling/shielding effects of winds on buildings through testing and instrumentation.
6) Developing new technologies on ground, and satellite based observing systems to improve predictability, knowledge and understanding of windstorms and the wind variability within those storms.
Vincent Ezikornwor Weli,Jimmy O. Adegoke,Douye Pere-Ere Wodu, (2016) Extreme Wind Characteristics of Coastal Communities in Bayelsa State: Implications for Community Planning and Development in Nigeria. Atmospheric and Climate Sciences,06,180-189. doi: 10.4236/acs.2016.62016