State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
State Key Laboratory of Water Resources and Hydropower Engineering Sciences, Wuhan University, Wuhan 430072, China; Institute for Environmental Genomics, University of Oklahoma, Norman, OK 73019, USA.
Water Res. 2020 Oct 15;185:116221. doi: 10.1016/j.watres.2020.116221. Epub 2020 Jul 23.
River algal blooms have become a challenging environmental problem worldwide due to strong interference of human activities and megaprojects (e.g., big dams and large-scale water transfer projects). Previous studies on algal blooms were mainly focused on relatively static water bodies (i.e., lakes and reservoirs), but less on the large rivers. As the largest tributary of the Yangtze River of China and the main freshwater source of the South-to-North Water Diversion Project (SNWDP), the Han River has experienced frequent algal blooms in recent decades. Here we investigated the algal blooms during a decade (2003-2014) in the Han River by two gradient boosting machine (GBM) models with k-fold cross validation, which used explanatory variables from current 10-day (GBMc model) or previous 10-day period (GBMp model). Our results advocate the use of GBMp due to its higher accuracy (median Kappa = 0.9) and practical predictability (using antecedent observations) compared to GBMc. We also revealed that the algal blooms in the Han River were significantly modulated by antecedent water levels in the Han River and the Yangtze River and water level variation in the Han River, whereas the nutrient concentrations in the Han River were usually above thresholds and not limiting algal blooms. This machine-learning-based study potentially provides scientific guidance for preemptive warning and risk management of river algal blooms through comprehensive regulation of water levels during the dry season by making use of water conservancy measures in large rivers.
由于人类活动和大型工程（如大坝和大型调水工程）的强烈干扰，河流藻类水华已成为全球具有挑战性的环境问题。以前的藻类水华研究主要集中在相对静态的水体（如湖泊和水库）上，但对大河的研究较少。作为中国长江最大的支流和南水北调工程（SNWDP）的主要淡水来源，近年来汉江频繁发生藻类水华。在这里，我们通过两个具有 k 折交叉验证的梯度提升机（GBM）模型，使用当前 10 天（GBMc 模型）或前 10 天（GBMp 模型）的解释变量，调查了汉江过去十年（2003-2014 年）的藻类水华。结果表明，与 GBMc 相比，GBMp 的准确性更高（中位数 Kappa = 0.9），实用性预测更好（使用前序观测），因此推荐使用 GBMp。我们还揭示了汉江藻类水华受汉江和长江前期水位以及汉江水位变化的显著调节，而汉江的营养盐浓度通常高于阈值，对藻类水华没有限制作用。这项基于机器学习的研究为通过利用大河的水利措施，在旱季全面调节水位，对河流藻类水华进行预警和风险管理提供了科学指导。
