利用地理空间技术评估尼日利亚西南部奥帕流域减少灾害风险的径流潜力

IF 0.3 Q4 REMOTE SENSING South African Journal of Geomatics Pub Date : 2022-09-04 DOI:10.4314/sajg.v10i2.8
Orewole Maruf Oladotun
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引用次数: 1

摘要

洪水预报在土地和水资源管理中非常重要。如果做好充分的准备,许多洪水灾害都可以得到缓解,尤其是在城市流域。本研究使用遥感和土壤保护局或SCS曲线数(CN)技术评估了尼日利亚西南部Opa流域的径流潜力。2007年的2007年NigSat图像被划分为不同的土地覆盖类别,并与其水文土壤组相结合,以确定每个子流域的曲线数。曲线数较低的子流域被认为具有较低的径流潜力,而曲线数较高的子流域则被认为具有较高的径流潜力。CN还用于估计每个子流域的潜在最大蓄水量(S)和潜在径流量(Q),使用流域内2年重现期的降雨事件。加权径流用于确定具有最高和最低径流潜力的子流域。研究表明,平均CN值为85.93的城市子流域9具有最高的加权径流潜力(5.53mm),而平均CN值69.46的植被子流域10具有最低的加权径流潜能(0.34mm)。该研究得出结论,利用现有的地理空间技术和适当的水文评估技术是减少灾害风险和可持续城市流域管理的有效洪水预测方法。
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Assessment of runoff potential for disaster risk reduction using geospatial technology in Opa watershed, Southwestern Nigeria
Flood prediction is very important in land and water resources management. Many flood disasters could be mitigated with adequate preparedness especially in urban watershed. This study assessed the runoff potential of Opa watershed in Southwest Nigeria using remote sensing and the Soil Conservation Service or SCS curve number (CN) techniques. The 2007 NigSat image of the year 2007 was classified into different land cover classes and combined with its hydrological soil groups to determine the curve number of each sub-watershed. The sub-watershed with low curve number is considered to have lower runoff potential while the one with higher curve number is considered to have higher runoff potential. The CN was also used to estimate the potential maximum retention, (S) and potential runoff, (Q) for each sub-watershed using a rainfall event of 2-year return period in the watershed. The weighted runoff was used to determine sub-watersheds with highest and lowest runoff potential. The study showed that urban sub-watershed 9 with average CN value of 85.93 has highest weighted runoff potential (5.53 mm) while the vegetated sub-watershed 10 with average CN value of 69.46 has the lowest weighted runoff potential (0.34 mm). The study concluded that using available geospatial technology and appropriate hydrologic assessment techniques constitute an effective flood prediction method for disaster risk reduction and sustainable urban watershed management.
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