Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand.
Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand; Stockholm Environment Institute (SEI), Asia Centre, Bangkok, Thailand.
Sci Total Environ. 2020 Oct 20;740:140156. doi: 10.1016/j.scitotenv.2020.140156. Epub 2020 Jun 11.
Hydrological model parameters are important during representation of the hydrological characteristics of a watershed. The West Seti River Basin (WSRB), a prominent Himalayan Basin of Nepal, is a major source of fresh water in the western region of the country. We used the Soil and Water Assessment Tool (SWAT) for hydrological modelling and identified the most sensitive hydrological parameters, while the Sequential Uncertainty Fitting (SUFI-2) technique was employed for model calibration. The model was calibrated for the study period (1999-2005) with a three-year warm-up period (1996-1998). Subsequently, it was validated for three years (2006-2008). The results show that the large number of Hydrological Response Units (HRUs) for model simulation took a considerable time, without improving the performance statistics. Importantly, significant improvements were observed during both calibration and validation periods when elevation bands (EBs) were taken into consideration. The p-factor, r-factor, coefficient of determination (R), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), Root mean square error (RMSE)-observations, and standard deviation (STDEV) ratio (RSR) were used to measure the performance between observed and simulated values. The values of p-factor, r-factor, R, NSE, PBIAS, and RSR during the calibration were 0.82, 0.80, 0.84, 0.82, 7.2, and 0.42, respectively, whereas during validation they were 0.79, 0.72, 0.83, 0.82, 11.8, and 0.42, respectively. The calibrated model was then used to assess the anticipated river discharge. This study used four regional climate models (RCMs) for precipitation and six for temperature, together with their arithmetical average as multi-model ensembles (MMEs) under two representative concentration pathways (RCPs). We analysed the changes in precipitation, temperature, and river discharge for three future time frames: Future1 (F1: 2020-2044), Future2 (F2: 2045-2069), and Future3 (F3: 2075-2099) with respect to the baseline (1996-2005). The magnitude of changes varied according to the different climate models and warming scenarios. In general, the MMEs showed slightly increasing precipitation (higher during the F2 period), significantly increasing temperature (continuous rising trend), and moderately increasing river discharge (higher during the F2 period). Information such as the anticipated shift in the flow duration curve may be helpful to stakeholders across different water sectors for effective water resource management in the future. From the modelling perspective, the results show greater significance for EBs than HRUs during the modelling of high mountain basins with SWAT. This take-home message would be useful to hydrologists and other stakeholders in evaluating different scenarios over a short duration, without iteratively spending higher computational time.
水文模型参数在表示流域水文特征时非常重要。西塞蒂河流域（WSRB）是尼泊尔著名的喜马拉雅流域之一，是该国西部地区淡水的主要来源。我们使用土壤和水评估工具（SWAT）进行水文建模，并确定了最敏感的水文参数，同时使用顺序不确定性拟合（SUFI-2）技术进行模型校准。该模型在研究期间（1999-2005 年）进行了校准，其中包括三年的预热期（1996-1998 年）。随后，对其进行了三年的验证（2006-2008 年）。结果表明，大量的水文响应单元（HRU）用于模型模拟，这需要相当长的时间，而且没有提高性能统计数据。重要的是，在考虑海拔带（EBs）时，在校准和验证期间都观察到了显著的改进。p 因子、r 因子、决定系数（R）、纳什-苏特克里夫效率（NSE）、偏度百分比（PBIAS）、均方根误差（RMSE-观测值）和标准偏差比（RSR）用于衡量观测值和模拟值之间的性能。校准期间的 p 因子、r 因子、R、NSE、PBIAS 和 RSR 值分别为 0.82、0.80、0.84、0.82、7.2 和 0.42，而验证期间分别为 0.79、0.72、0.83、0.82、11.8 和 0.42。然后，使用校准后的模型评估预期的河川径流量。本研究使用了四个区域气候模型（RCMs）用于降水，六个用于温度，并将其算术平均值作为多模型集合（MMEs），在两种代表性浓度途径（RCPs）下使用。我们分析了未来三个时间框架内的降水、温度和河川径流量的变化：未来 1 期（F1：2020-2044 年）、未来 2 期（F2：2045-2069 年）和未来 3 期（F3：2075-2099 年），与基准期（1996-2005 年）相比。气候变化模型和变暖情景的不同导致了变化幅度的差异。一般来说，MMEs 显示出略微增加的降水（F2 期间更高）、显著增加的温度（持续上升趋势）和适度增加的河川径流量（F2 期间更高）。有关流量持续时间曲线预期变化的信息可能有助于不同水务部门的利益相关者，以便在未来进行有效的水资源管理。从建模的角度来看，在使用 SWAT 对高山流域进行建模时，EBs 比 HRUs 具有更大的意义。这一信息对水文学家和其他利益相关者评估短期内的不同情况非常有用，而无需反复花费更高的计算时间。
