Department of Civil Engineering, Birla Institute of Technology and Science, Pilani-Hyderabad Campus, Hyderabad, 500078, India.
Environ Monit Assess. 2020 Sep 15;192(10):643. doi: 10.1007/s10661-020-08490-0.
Hydrological models apply different methods to estimate runoff and route flows. Suitability of these methods is not unique, but varies with catchment conditions. This study aims to find the suitable overland runoff and flow routing methods for a catchment in Hyderabad, India, using customised Storm Water Management Model (SWMM-C). Currently, SWMM adapts only non-linear reservoir (NLR) method to estimate overland runoff. Linear reservoir (LR) and kinematic wave overland flow (KWO) have been incorporated as additional overland runoff methods. For flow routing, SWMM currently has kinematic wave (KW) and dynamic wave (DW) methods. Muskingum, Muskingum Cunge (MC) and lag methods have been included as additional methods in this customised version. SWMM-C was calibrated with four event rainfalls and tested with six event rainfalls using all possible combinations of overland runoff and flow routing methods. Efficiency of SWMM-C in simulating runoff was evaluated using performance indices. Results showed that for low magnitude event rainfalls, NLR, LR and KWO simulated runoff with a maximum deviation of 50%, 60% and 40% from observed runoff, respectively. In high magnitude event rainfalls, NLR, LR and KWO simulated runoff with maximum deviations of 20%, 40% and 20%, respectively, from the observed runoff. It was inferred from model outputs that NLR method could simulate runoff reasonably well for rainfalls that have duration greater than the time of concentration of catchment. LR method could simulate peak runoff better. KWO method was found to be suitable for chosen catchment for all rainfall durations. Flow routing methods KW, DW and MC are found to have minor influences on the runoff.
水文模型应用不同的方法来估算径流量和河道流量。这些方法的适用性不是唯一的,而是随集水区条件而变化。本研究旨在使用定制的 Storm Water Management Model (SWMM-C) 为印度海得拉巴的一个集水区找到合适的坡面径流和水流路径方法。目前,SWMM 仅采用非线性水库 (NLR) 方法来估算坡面径流。线性水库 (LR) 和运动波坡面流 (KWO) 已被纳入作为额外的坡面径流方法。对于水流路径,SWMM 目前具有运动波 (KW) 和动力波 (DW) 方法。马斯京根、马斯京根康格 (MC) 和滞后方法已被纳入这个定制版本的附加方法。SWMM-C 采用了四次事件降雨进行校准,并使用所有可能的坡面径流和水流路径方法组合进行了六次事件降雨测试。采用性能指标评估了 SWMM-C 模拟径流的效率。结果表明,对于小幅度事件降雨,NLR、LR 和 KWO 分别从观测径流中模拟出最大偏差为 50%、60%和 40%的径流。在大降雨量事件中,NLR、LR 和 KWO 分别从观测径流中模拟出最大偏差为 20%、40%和 20%的径流。从模型输出推断,NLR 方法可以合理地模拟降雨持续时间大于集水区汇流时间的径流。LR 方法可以更好地模拟峰值径流。KWO 方法被发现适用于所选集水区的所有降雨持续时间。水流路径方法 KW、DW 和 MC 被发现对径流的影响较小。