Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-10-28 DOI:10.1016/j.ecoinf.2024.102866

Jiawen Liu , Xianqi Zhang , Xiaoyan Wu , Yang Yang , Yupeng Zheng

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引用次数: 0

Abstract

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R² values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R² values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

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通过 SWAT 和 BiLSTM 耦合模型分析 LULC 变化对河流径流的影响：中国漳河流域案例研究

河流径流的变化对流域水资源的可持续利用具有重大影响，而这些变化与土地利用/土地覆被 (LULC) 的变化密切相关。本研究在中国漳河流域（ZRB）探索了一种创新方法，将基于概念的水文模型--水土评估工具（SWAT）与深度学习模型--双向长短期记忆网络（Bi-LSTM）相结合，以提高河流径流模拟的精度。通过分析 2002 年、2012 年和 2022 年 LULC 的变化，本研究开发了三个 SWAT 模型和三个 SWAT-BiLSTM 耦合模型，通过八种 LULC 情景定量评估了这些变化对河流径流的影响。研究结果表明，从 2002 年到 2022 年，LULC 发生了显著变化，耕地和草地面积减少，而森林和城市土地面积增加。草地、森林和耕地的总面积占流域面积的 93% 以上，表明土地类型的转换非常活跃。校准和验证结果表明，SWAT-BiLSTM 模型优于传统的 SWAT 模型，在径流模拟方面具有更高的精度。具体而言，在校准和验证过程中，SWAT-BiLSTM 模型的 R2 值分别为 0.89 和 0.90，而 SWAT 模型的 R2 值分别为 0.76 和 0.79。情景分析表明，农田、草地和城市地区的扩大与河流径流量的增加相关，而森林地区的扩大则导致径流量的减少。值得注意的是，城市土地变化对径流的影响最为明显，这强调了在城市规划中谨慎管理径流和降低洪水风险的必要性。通过结合 SWAT 和 Bi-LSTM 模型，本研究对土地利用、土地利用变化和土地利用变化对 ZRB 水资源的影响进行了创新性评估。研究结果为水资源管理、土地利用、土地利用变化（LULC）优化和洪水风险管理提供了有价值的见解，凸显了深度学习技术在水文模拟中的潜在应用。这项研究为 ZRB 和类似地区的政策制定和可持续土地利用规划提供了科学依据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.