Seasonality shift and streamflow flow variability trends in central India

Acta Geophysica - 2020
Alban Kuriqi1, Rawshan Ali2,3, Quoc Bao Pham4,5, Julio Montenegro Gambini6, Vivek Gupta7, Anurag Malik8, Nguyen Thi Thuy Linh9,10, Yogesh Joshi7, Duong Tran Anh11, Van Thai Nam11, Xiaohua Dong12,3
1CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
2Department of Petroleum, Koya Technical Institute, Erbil Polytechnic University, Erbil, Iraq
3College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, China
4Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
6Group of Mathematical Modelling and Numerical Simulation (GMMNS), National University of Engineering (UNI), Lima, Peru
7Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India
8Punjab Agricultural University, Regional Research Station, Bathinda- 151001, India
9Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam
10Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang 550000, Vietnam
11Ho Chi Minh City University of Technology (HUTECH) 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Vietnam
12Hubei Provincial Collaborative Innovation Center for Water Security, Wuhan, China

Tóm tắt

A better understanding of intra/inter-annual streamflow variability and trends enables more effective water resources planning and management for current and future needs. This paper investigates the variability and trends of streamflow data from five stations (i.e. Ashti, Chindnar, Pathgudem, Polavaram, and Tekra) in Godavari river basin, India. The streamflow data were obtained from the Indian Central Water Commission and cover more than 30 years of mean daily records (i.e. 1972–2011). The streamflow data were statistically assessed using Gamma, Generalised Extreme Value and Normal distributions to understand the probability distribution features of data at inter-annual time-scale. Quantifiable changes in observed streamflow data were identified by Sen’s slope method. Two other nonparametric, Mann–Kendall and Innovative Trend Analysis methods were also applied to validate findings from Sen’s slope trend analysis. The mean flow discharge for each month (i.e. January to December), seasonal variation (i.e. Spring, Summer, Autumn, and Winter) as well as an annual mean, annual maximum and minimum flows were analysed for each station. The results show that three stations (i.e. Ashti, Tekra, and Polavaram) demonstrate an increasing trend, notably during Winter and Spring. In contrast, two other stations (i.e. Pathgudem, Chindnar) revealed a decreasing trend almost at all seasons. A significant decreasing trend was observed at all station over Summer and Autumn seasons. Notably, all stations showed a decreasing trend in maximum flows; remarkably, Tekra station revealed the highest decreasing magnitude. Significant decrease in minimum flows was observed in two stations only, Chindnar and Pathgudem. Findings resulted from this study might be useful for water managers and decision-makers to propose more sustainable water management recommendations and practices.

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