人工影响天气技术对廖内省森林和泥炭地火灾减灾的贡献

Tukiyat Tukiyat, A. E. Sakya, F. Widodo, Chandra Fadhillah
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摘要

泥炭和森林火灾已成为一种年度灾害,其中之一是由于降雨量少。因此,森林和泥炭地火灾最不安全的发生在旱季,那里降雨量很少,太阳强度很高。排放的烟雾和碳导致气温上升,导致全球变暖。有必要在它们发生之前采取缓解和控制措施。人工影响天气技术(WMT)是通过增加可能受影响地区的降雨量来控制森林火灾的技术解决方案之一。本研究旨在通过降雨强度、热点减少和土地水位增加来衡量2020年廖内省森林火灾的WMT绩效有效性水平。我们使用描述性和推断性统计方法,使用地下水位(GwL)测量数据作为森林和土地火灾缓解的参数。当水位低于泥炭地地表以下40cm时,泥炭地易燃指示灯亮。此外,我们还利用了降雨量、地表泥炭水位和热点。该研究于2020年7月24日至10月31日在廖内省进行。结果表明:WMT操作使同期降水较历史平均增加19.4%;WMT引发的降雨有助于维持零热点,置信度为80%。GwL = - 0.66 + 0.001 RF表示GwL对降雨量(RF)的回归分析表明两者呈正相关。因此,它证实了WMT可以用作减轻森林和陆地火灾灾害的技术。
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Contribution of Weather Modification Technology for Forest and Peatland Fire Mitigation in Riau Province
Peat and forest fire have become an annual disaster and one of which is due to low rainfall. The highest insecurity of forest and peatland fires thus occurs in the dry season, where rainfall is very low, and the intensity of the sun is high. The smoke and carbon emitted result in rising air temperatures and cause global warming. Mitigation and control measures before they happen are necessary. Weather Modification Technology (WMT) serves as one of the technological solutions to control forest fires by increasing rainfall in potentially affected locations. This study aims at examining the level of effectiveness of WMT performance in mitigating forest fires in Riau Province conducted in 2020 measured by rainfall intensity, hotspots decreased, and land water level increased. We used descriptive and inferential statistical approaches using Groundwater Level (GwL) measured data as the parameter for forest and land fire mitigation. The flammable peatland indicator is when the water level is lower than 40 cm below the surface of the peatland. In addition, we also utilized rainfall, surface peat water level, and hotspots. The study was conducted in Riau Province from July 24 – October 31, 2020. The results showed that the operation of WMT increased rainfall by 19.4% compared to the historical average in the same period. Rain triggered by WMT contributed to maintaining zero hotspots with a confidence level of 80%. The regression analysis of GwL to rainfall (RF) as depicted by Gwl = - 0.66 + 0.001 RF shows a positive correlation between the two. It thus confirms that WMT can be used as a technology to mitigate forest and land fire disasters.
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