The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale.

中文翻译：

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基于SWAT模拟和深度学习分类算法的年度更新土地覆盖产品的改进的硝酸盐和沉积物浓度估算

农业部门和自然资源高度依赖，包括一个连贯但复杂的系统。土壤和水评估工具（SWAT）被广泛用于评估区域流域管理的这些相互依赖性。但是，对农业流域进行长期模拟被认为是不现实的，因为它们通常是在假设土地长期使用不变的情况下进行的，并且是基于现有全球或国家数据的粗分辨率进行的。这项工作提出了SWAT模型与深度学习分类算法之间协同作用的初步见解，以提供每年更新和现实的模型参数化和仿真。提出的混合建模方法将物理过程SWAT模型与地球观测数据驱动的非线性深度学习算法的多功能性相结合，以进行土地利用分类（总精度（OA）= 79.58％和Kappa = 0.79），为决策者进行有效的管理计划。提供了位于希腊北部一个农业流域的验证案例，以证明其协同作用来估算Zazari湖中的硝酸盐和沉积物浓度。在两种不同的模拟下实施了SWAT模型。一种是使用静态粗略土地利用图，另一种是使用连续三年（2017-2019年）的年度更新土地利用图。结果表明，将年度土地利用图纳入SWAT模拟后，土地利用变化会影响最终估算，从而使沉积物和硝酸盐的预测性能分别提高1％和2％。在这种情况下，混合方法可进一步有助于应对挑战，并支持以数据为中心的计划，以便在流域范围内就环境和农业问题做出明智的决策。