Inter-Research > CR > Prepress Abstract

CR prepress abstract   -  DOI:

Climate change impact assessment on low streamflows using cross-entropy methods

Zahra Sheikh, Alireza Moghaddam Nia*, Dawei Han

*Corresponding author:

ABSTRACT: Climate change impacts on low streamflows provide a comprehensive picture of the state of surface and groundwater resources, particularly in arid and semi-arid regions. The objective of this study was to assess climate change impacts on low streamflow variations by detecting long-term spatiotemporal changes in climatic variables of rainfall and temperature, and their associations with low streamflow fluctuations. Seasonal variations in low streamflows (summer and winter) were examined at 18 hydrometric stations located in the Namak Lake Basin, Iran, over 1970–2015, using the modified Mann-Kendall and Sen’s slope estimator methods. Seasonal low streamflow has demonstrated a significant diminishing trend (more than 55% of the stations), while summer low streamflow has showed a more drastic decreasing trend. Long-term changes in seasonal and annual rainfall/temperature also revealed a dominant decreasing trend in winter and spring rainfall (82% and 58% of all stations, respectively) and a dominant increasing trend in all temperature time scales (90% of all stations). Climate variations effects on low streamflow are quantified by Spearman's rank correlation and Cross-SampEn methods. The results revealed that winter rainfall, annual and summer temperatures have the strongest association with seasonal low streamflows, especially according to on the entropy method. Although the relationships between low streamflows and temperature/rainfall are related to the different processes generating streamflows, particularly in heterogeneous locations, the results show that rainfall has a stronger influence on low streamflows in the study region than temperature does. The research findings indicate low streamflows are more nonlinearly related to climatic parameters, and Cross-SampEn robustness reflects degree of asynchrony for complex and non-stationary time series.