利用并行方法对水文建模进行SWAT模型校准,以卢旺达尼罗尼亚巴龙戈河流域为例

IF 2.6 Q2 WATER RESOURCES
Aboubakar Gasirabo, Chen Xi, Alishir Kurban, Tie Liu, Hamad R. Baligira, Jeanine Umuhoza, Adeline Umugwaneza, Umwali Dufatanye Edovia
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引用次数: 0

摘要

尼亚巴龙戈尼罗河是卢旺达最大的河流,正面临着人类活动和气候变化的双重压力。这些因素对水的过程有很大的影响,使有效管理水资源变得困难。为了解决这个问题,找到最准确的模拟水文过程的技术是很重要的。本研究旨在利用GLUE、ParaSol和SUFI-2等多种算法对SWAT模型进行校正,用于尼罗尼亚巴隆戈河流域的水文模拟。利用不同的数据源,如DEM、Landsat图像、土壤数据和日常气象数据,将信息输入SWAT建模过程。为了有效划分流域面积,在充分考虑土壤特征和土地覆被多样性的情况下,划分了25个子流域。结果表明,SUFI-2在SWAT标定方面优于其他算法,需要较少的计算模型运行并产生最佳结果。ParaSol建立了驻留效率最低的算法。在用SUFI-2进行校准后,发现建模最敏感的参数是(1)有效通道水力导电性(CH K2),测量水在通道中的流动情况,值越大表明导电性越好;(2)曼宁n值(CH N2)代表通道内的粗糙度或流动阻力,值越小表明通道越光滑。(3)地表径流滞后时间(SURLAG),用于量化降雨与地表径流发生之间的滞后时间,SURLAG值越短表明径流响应越快;(4)通用土壤流失方程(USLE P),用于估算土壤流失量。流域内平均蒸散量为559.5 mm -1。这些校准结果对流域水平衡管理的决策和更新政策具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SWAT model calibration for hydrological modeling using concurrent methods, a case of the Nile Nyabarongo River basin in Rwanda
The Nile Nyabarongo, which is Rwanda's largest river, is facing stress from both human activities and climate change. These factors have a substantial contribution to the water processes, making it difficult to effectively manage water resources. To address this issue, it is important to find out the most accurate techniques for simulating hydrological processes. This study aimed to calibrate the SWAT model employing various algorithms such as GLUE, ParaSol, and SUFI-2 for the simulation of hydrology in the basin of the Nile Nyabarongo River. Different data sources, such as DEM, Landsat images, soil data, and daily meteorological data, were utilized to input information into the SWAT modeling process. To divide the basin area effectively, 25 sub-basins were created, with due consideration of soil characteristics and the diverse land cover. The outcomes point out that SUFI-2 outperformed the other algorithms for SWAT calibration, requiring fewer computing model runs and producing the best results. ParaSol established residing the least effective algorithm. After calibration with SUFI-2, the most sensitive parameters for modeling were revealed to be (1) the Effective Channel Hydraulic Conductivity (CH K2) measuring how well water can flow through a channel, with higher values indicating better conductivity, (2) Manning's n value (CH N2) representing the roughness or resistance to flow within the channel, with smaller values suggesting a smoother channel, (3) Surface Runoff Lag Time (SURLAG) quantifying the delay between rainfall and the occurrence of surface runoff, with shorter values indicating faster runoff response, (4) the Universal Soil-Loss Equation (USLE P) estimating the amount of soil loss. The average evapotranspiration within the basin was calculated to be 559.5 mma-1. These calibration results are important for decision-making and updating policies related to water balance management in the basin.
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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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