可微分、基于物理的机器学习水文模型对未测量地区和气候变化影响评估的适用性
Hydrology and Earth System Sciences
(
IF
5.7
)
Pub Date : 2023-06-30
, DOI:
10.5194/hess-27-2357-2023
Dapeng Feng
,
Hylke Beck
,
Kathryn Lawson
,
Chaopeng Shen
摘要。作为物理信息机器学习的一种类型，产后康复13825404095具有基于区域化深度网络的参数化管道的基于可微过程的水文模型（缩写为 δ 或 Delta 模型）最近被证明可以提供接近状态的每日径流预测性能。最先进的长短期记忆（LSTM）深度网络。同时，δ 模型提供了一整套诊断物理变量并保证质量守恒。在这里，我们进行了实验来测试（1）它们推断到远离流量计的区域的能力，以及（2）它们对长期（十年尺度）变化趋势做出可靠预测的能力。我们根据每日水位线指标（纳什-萨特克利夫模型效率系数等）评估模型并预测十年水流趋势。对于未计量流域（PUB；代表空间插值的随机采样未计量流域）的预测，δ 模型在日常水文指标中接近或超过 LSTM 的性能，具体取决于所使用的气象强迫数据。他们在年平均流量和高流量方面提供了与 LSTM 相当的趋势性能，但在低流量方面表现更差。对于未测量区域的预测（PUR；代表数据高度稀疏场景中的空间外推的区域保留测试），δ 模型在日常水文指标上超越了 LSTM，并且其在平均和高流量趋势方面的优势变得突出。此外，未经训练的变量蒸散量即使对于外推情况也保留了良好的季节性。δ 模型基于深度网络的参数化管道产生的参数场即使在数据高度稀缺的情况下也能保持非常稳定的空间模式，这解释了它们的鲁棒性。结合其可解释性和同化多源观测的能力，δ 模型是区域和全球规模水文模拟和气候变化影响评估的有力候选者。
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Abstract. As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.
