Opeyemi Olorunleke Faseyiku, Obinna Anthony Obiora-Okeke, Ayodeji Stanley Olowoselu, Oluwatosin Raphael Olafusi, James Rotimi Adewumi
{"title":"Validation of selected gridded potential evapotranspiration datasets with ground-based observations over the Ogun-Osun River Basin, Nigeria","authors":"Opeyemi Olorunleke Faseyiku, Obinna Anthony Obiora-Okeke, Ayodeji Stanley Olowoselu, Oluwatosin Raphael Olafusi, James Rotimi Adewumi","doi":"10.1007/s12517-024-11962-z","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":null,"pages":null},"PeriodicalIF":1.8270,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arabian Journal of Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12517-024-11962-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.