Barman Swapnali, Singh Waikhom Rahul, Tyagi Jaivir, Sharma Sanjay Kumar
National Institute of Hydrology, North Eastern Regional Centre, Guwahati, Assam, India 781006.
National Institute of Hydrology, North Eastern Regional Centre, Guwahati, Assam, India 781006.
J Environ Manage. 2024 Aug;365:121538. doi: 10.1016/j.jenvman.2024.121538. Epub 2024 Jun 20.
The current study focuses on analyzing the impacts of climate change and land use/land cover (LULC) changes on sediment yield in the Puthimari basin, an Eastern Himalayan sub-watershed of the Brahmaputra, using a hybrid SWAT-ANN model approach. The analysis was meticulously segmented into three distinct time spans: 2025-2049, 2050-2074, and 2075-2099. This innovative method integrates insights from multiple climate models under two Representative Concentration Pathways (RCP4.5 and RCP8.5), along with LULC projections generated through the Cellular Automata Markov model. By combining the strengths of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) techniques, the study aims to improve the accuracy of sediment yield simulations in response to changing environmental conditions. The non-linear autoregressive with external input (NARX) method was adopted for the ANN component of the hybrid model. The adoption of the hybrid SWAT-ANN approach appears to be particularly effective in improving the accuracy of sediment yield simulation compared to using the SWAT model alone, as evidenced by the higher coefficient of determination value of 0.74 for the hybrid model compared to 0.35 for the standalone SWAT model. In the context of the RCP4.5 scenario, during 2075-99, the study noted a 29.34% increase in sediment yield, accompanied by simultaneous rises of 42.74% in discharge and 27.43% in rainfall during the Indian monsoon season, spanning from June to September. In contrast, under the RCP8.5 scenario, for the same period, the increases in sediment yield, discharge, and rainfall for the monsoon season were determined to be 116.56%, 103.28%, and 64.72%, respectively. The present study's comprehensive analysis of the factors influencing sediment supply in the Puthimari River basin fills an important knowledge gap and provides valuable insights for designing proactive flood and erosion management strategies. The findings from this research are crucial for understanding the vulnerability of the Puthimari basin to climate and land use changes, and by incorporating these findings into policy and decision-making processes, stakeholders can work towards enhancing resilience and sustainability in the face of future hydrological and environmental challenges.
本研究聚焦于运用SWAT-ANN混合模型方法，分析气候变化和土地利用/土地覆盖（LULC）变化对布拉马普特拉河东部喜马拉雅子流域普蒂马里河流域产沙量的影响。分析被细致地划分为三个不同的时间跨度：2025 - 2049年、2050 - 2074年和2075 - 2099年。这种创新方法整合了两种代表性浓度路径（RCP4.5和RCP8.5）下多个气候模型的见解，以及通过元胞自动机马尔可夫模型生成的LULC预测。通过结合土壤和水资源评估工具（SWAT）与人工神经网络（ANN）技术的优势，该研究旨在提高产沙量模拟在应对不断变化的环境条件时的准确性。混合模型的ANN部分采用了带外部输入的非线性自回归（NARX）方法。与单独使用SWAT模型相比，采用SWAT-ANN混合方法在提高产沙量模拟准确性方面似乎特别有效，混合模型的决定系数值为0.74，而单独的SWAT模型为0.35，这证明了这一点。在RCP4.5情景下，在2075 - 99年期间，该研究指出产沙量增加了29.34%，同时在6月至9月的印度季风季节，径流量增加了42.74%，降雨量增加了27.43%。相比之下，在RCP8.5情景下，同一时期，季风季节产沙量、径流量和降雨量的增加分别为116.56%、103.28%和64.72%。本研究对普蒂马里河流域影响泥沙供应因素的全面分析填补了重要的知识空白，并为设计积极的洪水和侵蚀管理策略提供了有价值的见解。这项研究的结果对于理解普蒂马里河流域对气候和土地利用变化的脆弱性至关重要，通过将这些结果纳入政策和决策过程，利益相关者可以努力提高面对未来水文和环境挑战时的恢复力和可持续性。
