Multi-scale investigation on streamflow temporal variability and its connection to global climate indices for unregulated rivers in India

• Published in: Journal of water and climate change Vol. 13; no. 2; pp. 735 - 757
• Main Authors: Yeditha, Pavan Kumar, Pant, Tarun, Rathinasamy, Maheswaran, Agarwal, Ankit
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.02.2022
• Subjects: Analysis; Annual; Annual precipitation; Basins; Catchment area; Catchments; Climate; Climatic indexes; Coherence analysis; Continuous wavelet transform; cross wavelet spectrum; Datasets; Developing countries; Drought; El Nino; Global climate; Hydrologic cycle; Hydrologic data; Hydrology; Influence; Investigations; LDCs; Oscillations; Phase relationships; Precipitation; Rivers; Statistical analysis; Stream discharge; Stream flow; streamflow; Streamflow data; Temporal variability; Temporal variations; Time; time-frequency characterization; Trends; Variability; Water resources; Water shortages; wavelet; Wavelet analysis; wavelet coherence; Wavelet transforms; Canada; Indian Ocean; Indian subcontinent; Quebec Canada; India
• ISSN: 2040-2244; 2408-9354
• DOI: 10.2166/wcc.2021.189

Abstract With the increasing stress on water resources for a developing country like India, it is pertinent to understand the dominant streamflow patterns for effective planning and management activities. This study investigates the spatiotemporal characterization of streamflow of six unregulated catchments in India. Firstly, Mann Kendall (MK) and Changepoint analysis were carried out to detect the presence of trends and any abrupt changes in hydroclimatic variables in the chosen streamflows. To unravel the relationships between the temporal variability of streamflow and its association with precipitation and global climate indices, namely, Niño 3.4, IOD, PDO, and NAO, continuous wavelet transform is used. Cross-wavelet transform and wavelet coherence analysis were also used to capture the coherent and phase relationships between streamflow and climate indices. The continuous wavelet transforms of streamflow data revealed that intra-annual (0.5 years), annual (1 year), and inter-annual (2–4 year) oscillations are statistically significant. Furthermore, a better understanding of the in-phase relationship between the streamflow and precipitation at intra-annual and annual time scales were well-captured using wavelet coherence analysis compared to cross wavelet transform. Furthermore, our analysis also revealed that streamflow observed an in-phase relationship with IOD and NAO, whereas there was a lag correlation with Niño 3.4 and PDO indices at intra-annual, annual and interannual time scales.