Utilizing statistical and MCDM techniques in indexing morphometric parameters towards improved watershed management in the Nandhour-Kalish drainage system

Dheeraj Mohan Gururani, Divyesh Varade, Himanshu Joshi, Hemant Singh, Yogendra Kumar, Vinod Kumar
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Morphometric analysis plays a crucial role in the effective management of the surface as well as subsurface water resources. In the present study, ASTER DEM and Survey of India toposheets were utilized to characterize various morphological aspects of the Nandhour-Kalish watershed in Uttarakhand, India. In total, 12 sub-watersheds were determined using the SWAT 2012 in ArcGIS v10.4.1 and were coded as SW1–SW12. For each sub-watershed, 17 morphometric indices were estimated using Principal Component Analysis to determine the most influential indices or criteria of morphometric parameters in the context of the hydro-sedimentological response. The relative influence of identified criteria parameters was assessed using the Criteria Importance for Intercriteria Correlation (CRITIC) method. The developed sub-watersheds were then prioritized towards the prompt implementation of soil and water conservation measures. The results indicate that three sub-watersheds (SW2, SW3, and SW4) were categorized under a very high class of priority, while three (SW1, SW5, and SW6) were indexed under a high priority class. The study is an attempt to ease out the morphometry-based prioritization, which otherwise is relatively complex due to the availability of a large number of input parameters and ambiguity concerning parameter selection for the process of prioritization.

利用统计和 MCDM 技术确定形态计量参数指数,改善南都-卡利什排水系统的流域管理
查看大尺寸下载幻灯片查看大尺寸下载幻灯片 关闭模态形态分析在有效管理地表和地下水资源方面发挥着至关重要的作用。本研究利用 ASTER DEM 和印度测量局地形图来描述印度北阿坎德邦 Nandhour-Kalish 流域的各种形态特征。利用 ArcGIS v10.4.1 中的 SWAT 2012 确定了总共 12 个子流域,并将其编码为 SW1-SW12。对于每个子流域,使用主成分分析法估算了 17 个形态测量指数,以确定在水文沉积响应方面最具影响力的指数或形态测量参数标准。确定的标准参数的相对影响采用标准间相关性(CRITIC)方法进行评估。然后对制定的子流域进行优先排序,以便迅速实施水土保持措施。结果表明,三个子流域(SW2、SW3 和 SW4)被归类为极高优先级,而三个子流域(SW1、SW5 和 SW6)被归类为高优先级。这项研究试图简化基于形态测量的优先级排序,否则,由于存在大量输入参数以及优先级排序过程中参数选择的模糊性,优先级排序会相对复杂。
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