The document discusses how climate can trigger flash floods. It provides examples of indigenous communities in India and Tanzania using observations of clouds, wildlife behavior, wind patterns and other environmental indicators to predict weather events like droughts or heavy rains. Traditional knowledge is an important form of early warning for floods and seasonal rainfall that is passed down between community elders.

1. IKLIM SEBAGAI PEMICU BANJIR BANDANG
Halmar Halide
Jurusan Fisika FMIPA UNHASAS

2. Parodi et al. Predictability and predictive ability
of severe rainfall processes http://www.cimafoundation.org

3. http://www.popsugar.com/Flash-Flood-Boulder-Colorado-2013-Pictures-31755647
1 inch = 2,54 cm

5. http://www.nyc.gov/html/oem/html/hazards/weather_flashflood.shtml

6. http://pix11.com/2013/09/02/flash-flood-warning-in-effect-for-much-of-nycuntil-3-p-m/#axzz2fxDaGUar
Eastern Daylight Time
Summer time -4 jam UTC/GMT
1 inch = 2,54 cm

7. The tribals assess the probability of early warning of flood extent by observing color of
clouds, their location, intensity and frequency of rainfall. The unusual sounds and changes
in water flow, colour of water, direction of wind and the unusual behavior of wildlife like
ants, birds, rats and Snakes also helps in the assessment of climatic variations.
In the prediction that a particular year is likely to be a drought year, the nature of clouds is
described as follows: “There would neither be prosperity nor rain in the land should the
clouds be rough and small, tossed about by the wind have the shape of
camels, corms, dead bodies, monkeys or other inauspicious creatures, and be silent”
The tribals of Rajasthan also have faith in the sayings of elders about the prediction of
weather, for e.g “ Pawan baje Suryo,to hali halav kim puryo” if winds flow in the North –
west direction then farmer should not plough his field because it indicates heavy rains.

8. Indigenous knowledge in seasonal rainfall prediction in Tanzania: A case of the
South-western Highland of Tanzania
Ladislaus B. Chang’a, Pius Z. Yanda and James Ngana
Journal of Geography and Regional Planning Vol. 3(4), pp. 66-72, April 2010

9. Indigenous knowledge in seasonal rainfall prediction in Tanzania: A case of the
South-western Highland of Tanzania
Ladislaus B. Chang’a, Pius Z. Yanda and James Ngana
Journal of Geography and Regional Planning Vol. 3(4), pp. 66-72, April 2010

10. Indigenous knowledge in seasonal rainfall prediction in Tanzania: A case of the
South-western Highland of Tanzania
Ladislaus B. Chang’a, Pius Z. Yanda and James Ngana
Journal of Geography and Regional Planning Vol. 3(4), pp. 66-72, April 2010

11. http://bpkp-sidrap.blogspot.com/2011/05/petunjuk-lontara-allaorumang-adat-bugis.html

14. DESCRIPTION: Warm (red) and cold (blue) episodes based on a threshold of +/- 0.5oC for the
Oceanic Niño Index (ONI) [3 month running mean of ERSST.v3b SST anomalies in the Niño 3.4
region (5oN-5oS, 120o-170oW)], based oncentered 30-year base periods updated every 5 years.
For historical purposes cold and warm episodes (blue and red colored numbers) are defined
when the threshold is met for a minimum of 5 consecutive over-lapping seasons.

15. 2011

18. 2011

21. 2011

32. http://weather.unisys.com/upper_air/skew/details.php

33. http://weather.unisys.com/upper_air/skew/details.php

34. http://weather.unisys.com/upper_air/skew/details.php

36. Geostationary Operational Environmental Satellite
http://www.oso.noaa.gov/goes/
http://www.goes.noaa.gov/

44. RMSE is the root mean square error
MCC as the mean correlation coefficient
MCE is the mean coefficient of efficiency
MAE is the mean absolute error
MAPE is the mean absolute percentage error

53. El Nino
LaNina

60. Halide and Ridd, 2008

61. Bersama…..Kita bisa!
dan tentu saja…lebih cepat!