Abstract
Most large-scale evapotranspiration (ET) estimation methods require detailed information of land use, land cover, and/or soil type on top of various atmospheric measurements. The complementary relationship of evaporation (CR) takes advantage of the inherent dynamic feedback mechanisms found in the soil-vegetation-atmosphere interface for its estimation of ET rates without the need of such biogeophysical data. ET estimates over the conterminous United States by a new, globally calibrated, static scaling (GCR-stat) of the generalized complementary relationship (GCR) of evaporation were compared to similar estimates of an existing, calibration-free version (GCR-dyn) of the GCR that employs a temporally varying dynamic scaling. Simplified annual water balances of 327 medium and 18 large watersheds served as ground-truth ET values. With long-term monthly mean forcing, GCR-stat (also utilizing precipitation measurements) outperforms GCR-dyn as the latter cannot fully take advantage of its dynamic scaling with such data of reduced temporal variability. However, in a continuous monthly simulation, GCR-dyn is on a par with GCR-stat, and especially excels in reproducing long-term tendencies in annual catchment ET rates even though it does not require precipitation information. The same GCR-dyn estimates were also compared to similar estimates of eight other popular ET products and they generally outperform all of them. For this reason, a dynamic scaling of the GCR is recommended over a static one for modeling long-term behavior of terrestrial ET.
摘 要
区域或全球尺度的蒸散发模型大多需要翔实的土壤和植被信息作为输入. 蒸散发互补原理考虑了土壤-植被-大气界面内在的动态互馈机制, 故无需下垫面土壤和植被信息即可估算陆面蒸散发. 本文针对前人提出的静态标度广义互补模型(GCR-stat)和作者新提出的考虑了每个时间步长陆面干湿状态的动态标度广义互补模型(GCR-dyn), 利用美国大陆的 327 个中等流域和 18 个大河流域的水量平衡结果作为陆面蒸散发“实测值”, 对比评估了GCR-dyn和GCR-stat的蒸散发模拟效果. 结果发现, 当利用多年平均的逐月驱动时, GCR-dyn模拟陆面蒸散发的效果低于GCR-stat模型, 其原因是前者在多年平均尺度上不能充分利用其动态标度的优势. 然而, 当利用长时间尺度逐月实时驱动时, 两个模型在描述蒸散发多年均值上的效果几乎一致, 且即便无需降水作为输入, GCR-dyn模型在模拟蒸散发的长期变化趋势方面明显优于GCR-stat模型. 进一步地与其他8个主流蒸散发产品相比较, GCR-dyn在蒸散发多年均值和长期趋势上皆具有更好的模拟效果. 因此, 考虑了每个时间步长陆面干湿状态的动态标度广义互补蒸散发模型优于静态标度广义互补模型, 可更好地模拟地表蒸散发的长期变化特征.
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Article Highlights
• A temporally variable dynamic scaling of the GCR yields better long-term behavior than a static one.
• The dynamic scaling accounts for the aridity of the environment in each time step and thus improves land evaporation estimates.
• The dynamic scaling does not require precipitation information.
Acknowledgements
All data used in this study can be accessed from the following websites. NARR data: http://www.esrl.noaa.gov/psd/data/gridded/data.narr.html. PRISM temperature, humidity and precipitation: prism.oregonstate.edu/. USGS HUC2 and HUC6 runoff: waterwatch.usgs.gov/?id=wwds_runoff. Noah ET data: disc.gsfc.nasa.gov/datasets/NLDAS_NOAH0125_M_V002/summary?keywords=NLDAS. VIC ET data: disc.gsfc.xnasa.gov/datasets/NLDAS_VIC0125_M_V002/summary?keywords=NLDAS. Mosaic ET data: disc.gsfc.nasa.gov/datasets/NLDAS_MOS0125_M_V002/summary?keywords=NLDAS. NCEP-II ET data: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html. ERA-Interim ET data: http://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim. GLEAM ET data: http://gleam.eu/#home. PML ET data: data.csiro.au/dap/landingpage?pid=csiro:17375&v=%202&d=true. FLUXNET-MTE ET data: climatedataguide.ucar.edu/climate-data/fluxnet-mte-multi-tree-ensemble. The GCR-dyn modeled ET rates and the HUC2- and HUC6-averaged Ewb, P, Q, data are available from https://digitalcommons.unl.edu/natrespapers/986/. This research was supported by a BMEWater Sciences and Disaster Prevention FIKP grant of EMMI (BME FIKP-VIZ).
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Szilagyi, J., Crago, R. & Ma, N. Dynamic Scaling of the Generalized Complementary Relationship Improves Long-term Tendency Estimates in Land Evaporation. Adv. Atmos. Sci. 37, 975–986 (2020). https://doi.org/10.1007/s00376-020-0079-6
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DOI: https://doi.org/10.1007/s00376-020-0079-6
Key words
- land-atmosphere interactions
- land evaporation
- evapotranspiration
- complementary relationship of evaporation