Basin‐scale stream‐flow forecasting using the information of large‐scale atmospheric circulation phenomena
Basin‐scale stream‐flow forecasting using the information of large‐scale atmospheric circulation phenomena
复制标题
利用大尺度大气环流现象信息进行流域尺度径流预报
DOI：
10.1002/hyp.6630
复制
发表时间：
2008
影响因子：
3.2
通讯作者：
D. Nagesh Kumar
中科院分区：
文献类型：
--
作者：
R. Maity;D. Nagesh Kumar
众所周知，降雨量和径流量等水文变量的时间序列受到各种大尺度大气环流模式的显著影响。厄尔尼诺-南方振荡(ENSO)通过水文气候遥相关对水文变量的影响是全世界公认的。印度夏季风降水(ISMR)已被证明受到ENSO的显著影响。最近，人们发现ISMR和ENSO之间的关系受到印度洋地区大气环流模式的影响。最近的研究确定了印度洋偶极子(IOD)模和赤道印度洋振荡(EQUINOO)对ISMR的影响。因此，对于印度次大陆，水文时间序列受到ENSO和EQUINOO的显著影响。虽然研究了这些大尺度大气环流对大尺度雨型的影响，但它们对流域尺度水系径流的影响还有待研究。本文利用来自热带太平洋的ENSO和来自热带印度洋的EQUINOO的信息，根据它们对应的指数，对印度奥里萨邦的马哈纳迪河进行径流预报。为了模拟流域尺度径流与这种大尺度大气环流信息之间的复杂非线性关系，选择了人工神经网络(ANN)方法。利用基于遗传算法的进化优化器对ANN结构进行了有效的优化。这项研究证明，这种大规模大气环流信息的使用潜在地改善了每月流域尺度径流预测的性能，而这反过来又有助于更好地管理水资源。版权所有©2007 John Wiley&Sons，Ltd.
It is well recognized that the time series of hydrologic variables, such as rainfall and streamflow are significantly influenced by various large‐scale atmospheric circulation patterns. The influence of El Niño‐southern oscillation (ENSO) on hydrologic variables, through hydroclimatic teleconnection, is recognized throughout the world. Indian summer monsoon rainfall (ISMR) has been proved to be significantly influenced by ENSO. Recently, it was established that the relationship between ISMR and ENSO is modulated by the influence of atmospheric circulation patterns over the Indian Ocean region. The influences of Indian Ocean dipole (IOD) mode and equatorial Indian Ocean oscillation (EQUINOO) on ISMR have been established in recent research. Thus, for the Indian subcontinent, hydrologic time series are significantly influenced by ENSO along with EQUINOO. Though the influence of these large‐scale atmospheric circulations on large‐scale rainfall patterns was investigated, their influence on basin‐scale stream‐flow is yet to be investigated. In this paper, information of ENSO from the tropical Pacific Ocean and EQUINOO from the tropical Indian Ocean is used in terms of their corresponding indices for stream‐flow forecasting of the Mahanadi River in the state of Orissa, India. To model the complex non‐linear relationship between basin‐scale stream‐flow and such large‐scale atmospheric circulation information, artificial neural network (ANN) methodology has been opted for the present study. Efficient optimization of ANN architecture is obtained by using an evolutionary optimizer based on a genetic algorithm. This study proves that use of such large‐scale atmospheric circulation information potentially improves the performance of monthly basin‐scale stream‐flow prediction which, in turn, helps in better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd.