基于实测雨量、流量和SWAT模型模拟的克拉尼恒河流域径流气候弹性评价

IF 0.4 Q4 ENGINEERING, MULTIDISCIPLINARY Engineer-Journal of the Institution of Engineers Sri Lanka Pub Date : 2021-11-24 DOI:10.4038/engineer.v54i3.7455
K. Siriwardena, R. Rajapakse
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引用次数: 2

摘要

克拉尼恒河流域是斯里兰卡第七大流域,横跨2292公里,每年向大海排放4225亿立方米的流量。该流域目前拥有该国19%以上的人口,是大科伦坡400多万人的主要饮用水来源。基于此,本研究利用SWAT模型对克拉尼恒河流域的实测降雨、径流数据和模拟未来径流进行了气候弹性评估,旨在为未来流域水资源的可持续管理提供依据。采用两种方法评估了当前和2040年情景下的径流弹性(ε)。1980 - 2016年,所选的三个水文测量站均呈现明显的下降趋势。根据创新趋势分析(ITA), 1980 - 2016年,流域中上游80%的雨量计显示雅拉季节降水总量从高到低呈显著下降趋势。以质量平衡性能误差(Er)、Nash-Sutcliffe效率(NSE)和决定系数(r2)为多目标函数,SWAT模型1970 ~ 1980年和1982 ~ 1992年定标期和验证期的上述目标函数分别为8.90%、0.65、0.72和9.10%、0.69、0.69。根据Zheng等人([24])和Sankarasubramanian等人([22])提出的方法,温度升高1⁰C会导致当前情景下径流减少6.9%和7.4%,而在未来悲观气候变化情景下,温度升高1⁰C会导致径流增加0.4%和减少1.5%。两种方法估算的当前情景下,降雨量每增加1%,径流量分别增加0.002%和0.70%,2040年径流量分别增加0.005%和0.36%。建议在今后的研究中考虑到1995年以后确定的趋势,进一步分析水资源分配模式,以便在实际实施中取得更好的结果,以便今后更好地规划和管理水资源。
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Evaluation of Climate Elasticity of Runoff based on Observed Rainfall, Streamflow and Simulated Future Streamflow using SWAT Model in Kelani Ganga Basin
: Kelani Ganga basin is the 7 th largest watershed in Sri Lanka, spanning over 2,292 km 2 and annually discharging 4,225 MCM flow to the sea. The basin currently hosts over 19% of the country’s population and is the primary source of drinking water to over 4 million people living in Greater Colombo. Hence, the present study was undertaken to evaluate the Climate Elasticity of Runoff based on observed rainfall, streamflow data and simulated future streamflow using the SWAT Model in the Kelani Ganga basin, targeting sustainable management of basin water resources in future. The runoff elasticity ( ε ) is assessed by two methods for the present and 2040 scenarios. The selected three hydrometric gauging stations exhibit significant downward trends for the period of 1980 to 2016. An 80% of the rain gauges in the middle and upper basin show significant decreasing trends for high to low rainfall totals for Yala season as per Innovative Trend Analysis (ITA) for the period of 1980 to 2016. Mass balance performance error ( Er ), Nash–Sutcliffe Efficiency ( NSE ) and Coefficient of determination ( R 2 ) are used as multi-objective functions and 8.90%, 0.65, 0.72 and 9.10%, 0.69, 0.69 are obtained for the above objective functions in SWAT model for the calibration and validation periods of 1970 to 1980 and 1982 to 1992, respectively. A 1 ⁰ C of temperature increase causes a 6.9% and 7.4% runoff decrease for the current scenario and it causes 0.4% increase and 1.5% decrease of runoff for Future Pessimistic Climate change Scenario as evaluated by the methods proposed by Zheng et al. [24] and Sankarasubramanian et al. [22], respectively. A 1% of rainfall increase causes a runoff increase of 0.002% and 0.370% for the current scenario and a runoff increase of 0.005% and 0.360% for 2040 as evaluated by the two methods, respectively. It is recommended to further analyse the water allocation model for better results with practical implementations by considering the identified trend after 1995 in future research for better planning and management of water resources in future.
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