CSIRO, Land and Water, Waite Campus, Adelaide, South Australia, Australia.
CSIRO, Land and Water, Waite Campus, Adelaide, South Australia, Australia; University of Canberra, Canberra, Australian Capital Territory, Australia.
J Environ Manage. 2023 Apr 15;332:117393. doi: 10.1016/j.jenvman.2023.117393. Epub 2023 Feb 3.
Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT (Australia-wide Machine Learning ET for Trees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R of 0.94 (RMSE 6.63 mm period) and 0.92 (RMSE 6.89 mm period), respectively. In comparison, correlation between in-situ ET and a national ET product returned R of 0.44 (RMSE 34.08 mm period) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function.
由于水文过度抽取与气候变化相结合，干旱和半干旱流域的生态状况在全球范围内持续恶化。虽然为环境提供水资源一直是缓解生态恶化的主要方法，但如何准确监测河流树木在精细时空尺度上的变化，仍然是一个巨大的挑战。这在具有不同气候带的广阔流域中，由于不断变化的可用水量而变得更加复杂。在本文中，我们结合了罕见的、精细的、高频的实地采集数据，以及机器学习和遥感技术，提供了一个稳健的模型，通过实际蒸散量 (ET) 来监测河流树木对环境变化的生理水分胁迫响应。使用 sap 流传感器，在澳大利亚南部墨累-达令流域的 10 个研究地点，对桉树（红桉树）和桉树（黑盒子）的生理变化进行了瞬间捕获，大大减少了监测树冠变化时遇到的树木响应滞后。利用实地连续 4 年的时间序列数据，对两种树种的实际 ET 进行了偏置校正，建立了一个随机森林模型。利用土壤湿度、地表温度、辐射和 EVI 和 NDVI 等额外的遥感层，将随机森林模型纳入其中，得出了 2012 年至 2021 年分辨率为 30 米的精确月度 AMLETT（全澳树木机器学习 ET）ET 输出。通过对 AMLETT ET 和实地 ET 进行 Landsat 和 Sentinal-2 相关性分析，得出了 R 值分别为 0.94（RMSE 为 6.63 毫米/期）和 0.92（RMSE 为 6.89 毫米/期）。相比之下，实地 ET 与国家 ET 产品的相关性仅为 0.44（RMSE 为 34.08 毫米/期），这突出了需要进行偏置校正以生成准确的绝对 ET 值。本文提出的 AMLETT 方法增强了全球流域的环境管理。这种稳健的大范围监测可以为水核算提供信息，重要的是，有助于决定在哪里优先为环境分配水资源，以恢复和保护关键生态资产，并保护洪泛区和河岸生态功能。
