Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed
Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed
复制标题
DOI：
10.1029/2021wr030993
复制
发表时间：
2022-03
影响因子：
5.4
通讯作者：
Tianfang Xu;Q. Longyang;C. Tyson;R. Zeng;B. Neilson
中科院分区：
文献类型：
--
作者：
Tianfang Xu;Q. Longyang;C. Tyson;R. Zeng;B. Neilson
以积雪为主的山地喀斯特流域是美国西部和世界各地许多地区的主要供水来源。这些流域的典型特征是复杂的地形、时空变化的积雪和融化过程，以及由于基质（微孔和小裂缝）和岩溶管道并置而导致的流动和存储动态的二元性。因此，由于基于物理或概念的水文模型无法代表这些独特的特征，根据气象输入预测水流一直具有挑战性。我们提出了一种混合建模方法，它将基于物理的空间分布雪模型与深度学习喀斯特模型相结合。更具体地说，高分辨率雪模型捕获融雪的时空变化，深度学习模型模拟受复杂地表和地下特性影响的水流的相应响应。深度学习模型基于卷积长短期记忆（ConvLSTM）架构，能够处理时空补给模式和流域存储动态。混合建模方法在犹他州北部的一个流域进行了测试，该流域具有季节性积雪和不同程度的喀斯特碳酸盐岩基岩。混合模型能够高精度模拟流域出口处的水流。然后检查 ConvLSTM 模型学习到的空间和时间补给和排放模式，并与已知的水文地质信息进行比较。结果表明，与研究区域的参考模型相比，ConvLSTM 模拟水流的精度更高，并提供了对空间影响的水文响应的深入了解，而这些在集总建模方法中是无法实现的。
Snow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches.