利用尼日利亚奥贡河-奥孙河流域的地面观测数据验证选定的网格潜在蒸散量数据集

IF 1.827 Q2 Earth and Planetary Sciences Arabian Journal of Geosciences Pub Date : 2024-04-15 DOI:10.1007/s12517-024-11962-z
Opeyemi Olorunleke Faseyiku, Obinna Anthony Obiora-Okeke, Ayodeji Stanley Olowoselu, Oluwatosin Raphael Olafusi, James Rotimi Adewumi
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引用次数: 0

摘要

气候变化对水文循环的影响引发了广泛的研究,特别是关于潜在蒸散量(PET)的研究,这是一个连接水、能源、碳循环和生态系统服务的重要变量。潜在蒸散量的估算通常依赖于实地气象站数据,但尼日利亚奥贡-奥孙盆地等地区的数据匮乏带来了挑战。由于原地蒸散发监测站较少,研究人员转而使用卫星和再分析产品等其他 PET 来源。在这项研究中,我们评估了奥贡奥孙盆地的四种 PET 产品:全球陆地蒸发阿姆斯特丹模型(GLEAM)、每小时潜在蒸散量(hPET)、胺预警系统网络(NET)陆地数据同化系统(FLDAS)和全球陆地数据同化系统(GLDAS)。我们使用皮尔逊相关系数 (PCC/r)、平均绝对误差 (M.A.E.)、均方根误差 (RMSE)、纳什-苏克里夫效率 (NSE) 和偏差百分比 (PBIAS) 等统计指标对月度和年度时间尺度进行了评估。结果表明,hPET 在月尺度上的表现优于其他 PET 数据集,相关性最高,误差最小,偏差值最小(P.C.C. = 0.80,RMSE = 25.55,PBIAS = 13.62%)。GLDAS 数据集的性能更接近 hPET 数据集(P.C.C. = 0.61,RMSE = 94.76,PBIAS = 71.1%)和 GLEAM(P.C.C. = 0.12,RMSE = 64.67,PBIAS = 73.52%)。此外,与其他已评估的 PET 数据集相比,FLDAS 数据集的性能最差。hPET 的整体性能更好,这在年度规模上得到了进一步证明,在所有性能指标上再次优于其他产品(PCC = 0.34,M.A.E. = 258.10,RMSE = 263.05)。其他产品的性能相当差,但 GLEAM 产品与其他被评估产品相比最接近 hPET(P.C.C. = - 0.20,M.A.E. - 711.57,RMSE = 716.97)。总之,在两个时间尺度上,hPET 在所有统计指标上都占优势,是本研究评估的 PET 产品中最好的。研究结果表明,hPET 是 PET 数据的可靠替代来源,可为奥贡奥孙盆地未来的水文研究和建模提供极大支持。
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Validation of selected gridded potential evapotranspiration datasets with ground-based observations over the Ogun-Osun River Basin, Nigeria

The impact of climate change on the hydrological cycle has spurred extensive research, particularly regarding potential evapotranspiration (PET), a crucial variable linking water, energy, carbon cycles, and ecosystem services. PET estimation usually relies on in situ weather station data, but data scarcity in regions like Nigeria’s Ogun-Osun Basin poses challenges. With few in situ ET monitoring stations, researchers have turned to alternative PET sources, such as satellite and reanalysis products. In this study, we evaluated four PET products in the Ogun-Osun Basin: Global Land Evaporation Amsterdam Model (GLEAM), hourly potential evapotranspiration (hPET), amine early warning systems network (NET) Land Data Assimilation System (FLDAS), and Global Land Data Assimilation System (GLDAS). We assessed monthly and annual timescales using statistical indicators such as the Pearson correlation coefficient (PCC/r), mean absolute error (M.A.E.), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). The results showed that hPET outperformed other PET datasets at the monthly scale, with the highest correlation, lowest errors, and minimal bias values (P.C.C. = 0.80, RMSE = 25.55, PBIAS = 13.62%). GLDAS dataset showed closer performance to the hPET dataset (P.C.C. = 0.61, RMSE = 94.76, PBIAS = 71.1%) and GLEAM (P.C.C. = 0.12, RMSE = 64.67, PBIAS = 73.52%). Moreover, the FLDAS dataset performed least compared to other assessed PET datasets. hPET’s overall better performance was further certified at the annual scale, again outperforming the other products across all performance indicators (PCC = 0.34, M.A.E. = 258.10, RMSE = 263.05). The performance of the other products was quite poor, but the GLEAM product came closest to hPET compared to the other assessed products (P.C.C. = − 0.20, M.A.E. – 711.57, RMSE = 716.97). Overall, the hPET dominated all statistical indicators at both timescales, making it the best PET product among the ones evaluated by this study. The findings indicate that hPET is a reliable alternative source of PET data, which can greatly support future hydrological research and modelling in the Ogun-Osun Basin.

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来源期刊
Arabian Journal of Geosciences
Arabian Journal of Geosciences GEOSCIENCES, MULTIDISCIPLINARY-
自引率
0.00%
发文量
1587
审稿时长
6.7 months
期刊介绍: The Arabian Journal of Geosciences is the official journal of the Saudi Society for Geosciences and publishes peer-reviewed original and review articles on the entire range of Earth Science themes, focused on, but not limited to, those that have regional significance to the Middle East and the Euro-Mediterranean Zone. Key topics therefore include; geology, hydrogeology, earth system science, petroleum sciences, geophysics, seismology and crustal structures, tectonics, sedimentology, palaeontology, metamorphic and igneous petrology, natural hazards, environmental sciences and sustainable development, geoarchaeology, geomorphology, paleo-environment studies, oceanography, atmospheric sciences, GIS and remote sensing, geodesy, mineralogy, volcanology, geochemistry and metallogenesis.
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