Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin

IF 2.6 Q2 WATER RESOURCES
María José Merizalde, Paul Muñoz, Gerald Corzo, David F. Muñoz, Esteban Samaniego, Rolando Célleri
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Abstract

Introduction In complex mountain basins, hydrological forecasting poses a formidable challenge due to the intricacies of runoff generation processes and the limitations of available data. This study explores the enhancement of short-term runoff forecasting models through the utilization of long short-term memory (LSTM) networks. Methods To achieve this, we employed feature engineering (FE) strategies, focusing on geographic data and the Soil Conservation Service Curve Number (SCS-CN) method. Our investigation was conducted in a 3,390 km 2 basin, employing the GSMaP-NRT satellite precipitation product (SPP) to develop forecasting models with lead times of 1, 6, and 11 h. These lead times were selected to address the needs of near-real-time forecasting, flash flood prediction, and basin concentration time assessment, respectively. Results and discussion Our findings demonstrate an improvement in the efficiency of LSTM forecasting models across all lead times, as indicated by Nash-Sutcliffe efficiency values of 0.93 (1 h), 0.77 (6 h), and 0.67 (11 h). Notably, these results are on par with studies relying on ground-based precipitation data. This methodology not only showcases the potential for advanced data-driven runoff models but also underscores the importance of incorporating available geographic information into precipitation-ungauged hydrological systems. The insights derived from this study offer valuable tools for hydrologists and researchers seeking to enhance the accuracy of hydrological forecasting in complex mountain basins.
将地理数据和SCS-CN方法与LSTM网络相结合,增强复杂山地流域径流预测
在复杂的山地流域,由于径流生成过程的复杂性和可用数据的局限性,水文预报提出了一个巨大的挑战。本研究探讨利用长短期记忆(LSTM)网络增强短期径流预测模型。为了实现这一目标,我们采用特征工程(FE)策略,重点关注地理数据和土壤保持服务曲线数(SCS-CN)方法。利用GSMaP-NRT卫星降水产品(SPP)在一个3390 km 2的流域进行了调查,建立了提前期分别为1、6和11 h的预报模型。这些提前期分别用于近实时预报、山洪预报和流域集中时间评估。我们的研究结果表明,LSTM预测模型在所有提前期内的效率都有所提高,Nash-Sutcliffe效率值分别为0.93 (1 h)、0.77 (6 h)和0.67 (11 h)。值得注意的是,这些结果与基于地面降水数据的研究结果相当。这种方法不仅展示了先进的数据驱动径流模型的潜力,而且强调了将现有地理信息纳入未测量降水的水文系统的重要性。从这项研究中获得的见解为寻求提高复杂山区盆地水文预报准确性的水文学家和研究人员提供了有价值的工具。
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来源期刊
Frontiers in Water
Frontiers in Water WATER RESOURCES-
CiteScore
4.00
自引率
6.90%
发文量
224
审稿时长
13 weeks
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