基于TOPMODEL和遥感的莫桑比克林波波河流域水文气象模拟

Tomásio Eduardo Januário, A. P. Pereira Filho, Marcos Figueiredo Salviano
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引用次数: 0

摘要

林波波河流域(LRB)以易受洪水、高蒸散率和干旱的影响而闻名,这给当地社区造成了重大损失。利用TOPMODEL和卫星遥感数据,对2000 - 2015年莫桑比克LRB两个子流域Chókwè和Xai-Xai的洪水事件进行了模拟研究。作为TOPMODEL的输入,使用了两个高分辨率全球卫星降水产品的数据:气候预测中心MORPHing技术(CMORPH)和全球降水任务(GPM)综合多卫星检索算法(IMERG)、8天MOD16蒸散发产品和全球土地数据同化系统(GLDAS)估算的地表径流数据。采用蒙特卡罗模拟方法对TOPMODEL参数进行灵敏度测试。采用shuffed Complex Evolution (SCE-UA)方法对模型进行校正和验证,并用KGE指数对模型进行评价。结果表明,GPM-IMERG模拟结果(KGE分别为0.59和0.65)往往低估了河流流量,而CMORPH模拟结果(KGE分别为0.66和0.77)在两个子流域的模拟结果要好得多。因此,TOPMODEL可以帮助在莫桑比克类似地区开发利用卫星遥感数据的洪水监测系统。
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Hydrometeorological Modeling of Limpopo River Basin in Mozambique with TOPMODEL and Remote Sensing
The Limpopo River basin (LRB) is known for its vulnerability to floods, high rates of evapotranspiration, and droughts that cause significant losses to the local community. The present study aimed to perform simulations of flood events occurring in two Mozambican sub-basins of LRB, namely Chókwè and Xai-Xai from 2000 to 2015 with TOPography-based hydrological MODEL (TOPMODEL) and satellite remote sensing data. As input in TOPMODEL, data from two high-resolution global satellite-based precipitation products: Climate Prediction Center MORPHing technique (CMORPH) and Integrated Multi-Satellite Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), 8-day MOD16 evapotranspiration product and surface runoff data estimated by Global Land Data Assimilation System (GLDAS) were used. The sensitivity tests of TOPMODEL parameters were applied using the Monte Carlo simulation. Calibration and validation of the model were performed by the Shuffled Complex Evolution (SCE-UA) method and were evaluated with the Kling-Gupta Efficiency (KGE) index. The results indicated that simulations with the GPM-IMERG (KGE: 0.59 and 0.65) tended to un-derestimate the stream flows, while with the CMORPH product the performance was much better (KGE: 0.66 and 0.77) in both sub-basins. Thus, TOPMODEL can help to develop flood monitoring systems from satellite remotely sensed data in similar regions of Mozambique.
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