Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

Abstract

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.

Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

“…To the south of the study area there is a Lignite mine, where the Public Power Company of Greece is undergoing intensive mining activity. More information about the area can be also found in [34,35]. The area is characterized by heterogeneous terrain which is the reason for the high variability in the soil properties in terms of the carbonate and organic matter contents, which range from a very high carbonate content in the northeast to very high organic matter content in the southwest (Figure 2).…”

“…To the south of the study area there is a Lignite mine, where the Public Power Company of Greece is undergoing intensive mining activity. More information about the area can be also found in [34,35].…”

Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework

“…Additionally, soil erosion models, such as the Universal Soil Loss Equation (USLE) or the Revised Universal Soil Loss Equation (RUSLE) [48], simulate soil erosion processes and help identify areas prone to erosion, facilitating erosion control and soil conservation efforts. Moreover, hydrological models, such as the Soil and Water Assessment Tool (SWAT) [49], simulate water movement, infiltration, and runoff in landscapes, aiding in water resource management [50] and flood prediction. These advanced tools and simulations play a critical role in assessing the impacts of climate change, land management practices, and policy interventions on soil health, carbon dynamics, water resources, and overall ecosystem sustainability.…”

Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling

“…Since 2017, it has been demonstrated that deep learning (DL) networks (Shen, 2018; Shen et al., 2018), like long short‐term memory (LSTM; Hochreiter & Schmidhuber, 1997), can learn to predict environmental variables including stream temperature with exceptionally high accuracy (Rahmani, Lawson, et al., 2021; Rahmani, Shen, et al., 2021; Rehana & Rajesh, 2023; Sadler et al., 2022; Weierbach et al., 2022; S. Zhu & Piotrowski, 2020; Zwart et al., 2023). LSTM has shown its strength and versatility in simulating hydrologic variables such as soil moisture (Fang et al., 2017, 2019; J. Liu et al., 2022; O & Orth, 2021), streamflow (Feng et al., 2020, 2021; Khoshkalam et al., 2023; Xiang et al., 2020), dissolved oxygen (Heddam et al., 2022; Zhi et al., 2023), snow water equivalent (Broxton et al., 2019; Meyal et al., 2020), stream nitrate concentration (Saha et al., 2023; Samarinas et al., 2020), and radiation (Y. Liu et al., 2020; F. Zhu et al., 2021). However, mostly used as a forward simulator in hydrology, LSTM is also limited by its black‐box nature: it does not provide an interpretable explanation of internal processes, and its intermediate variables lack physical meaning and thus cannot be compared against observations to diagnose the model's internal logic (Appling et al., 2022).…”

“…Zhu & Piotrowski, 2020;Zwart et al, 2023). LSTM has shown its strength and versatility in simulating hydrologic variables such as soil moisture (Fang et al, , 2019O & Orth, 2021), streamflow (Feng et al, 2020(Feng et al, , 2021Khoshkalam et al, 2023;Xiang et al, 2020), dissolved oxygen (Heddam et al, 2022;Zhi et al, 2023), snow water equivalent (Broxton et al, 2019;Meyal et al, 2020), stream nitrate concentration (Saha et al, 2023;Samarinas et al, 2020), and radiation (Y. Liu et al, 2020;F.…”