Analysis of trends in streamflow and its linkages with rainfall and anthropogenic factors in Gomti River basin of North India
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Trend analysis of hydro-climatic variables such as streamflow, rainfall, and temperature provides useful information for effective water resources planning, designing, and management. Trends in observed streamflow at four gauging stations in the Gomti River basin of North India were assessed using the Mann–Kendall and Sen’s slope for the 1982 to 2012 period. The relationships between trends in streamflow and rainfall were studied by correlation analyses. There was a gradual decreasing trend of annual, monsoonal, and winter seasonal streamflow (p < 0.05) from the midstream to the downstream of the river and also a decreasing trend of annual streamflow for the 5-year moving averaged standardized anomalies of streamflow for the entire basin. The declining trend in the streamflow was attributed partly to the increased water withdrawal, to increased air temperature, to higher population, and partly to significant reducing trend of post monsoon rainfall especially at downstream. Upstream gauging station showed a significant increasing trend of streamflow (1.6 m3/s/year) at annual scale, and this trend was attributed to the significant increasing trend of catchment rainfall (9.54 mm/year). It was further evident in the significant coefficient of positive correlation (ρ = 0.8) between streamflow and catchment rainfall. The decreasing trend in streamflow and post-monsoon rainfall especially towards downstream area with concurrent increasing trend of temperature indicates a drying tendency of the Gomti River basin over the study period. The results of this study may help stakeholders to design streamflow restoration strategies for sustainable water management planning of the Gomti River basin.
KeywordsStreamflow Monsoon Season Entire Basin Annual Streamflow Monthly Streamflow
We wish to thank the CWC Government of India for providing streamflow data and to the Department of Agriculture, Uttar Pradesh, for providing crop production data. We wish to acknowledge geospatial modeling and DSS laboratory in WTC, IARI, for providing the MATLAB code for performing the trend analysis reported in this study. This study was funded in part by “National Initiative on Climate Resilient Agriculture” (NICRA) project.
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