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

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 spatio-temporal 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 demonstrated a significant diminishing trend (more than 55% of the stations), while summer low streamflow 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). The effects of climate variations on low streamflow were quantified by the Spearman’s rank correlation and Cross-SampEn methods. The results revealed that winter rainfall, and annual and summer temperatures have the strongest association with seasonal low streamflows, especially according to 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.

Multiscale Analysis of Runoff Complexity in the Yanhe Watershed

“…Entropy methods have been widely used in evaluating the complexity of nonlinear and overall hydrological dynamics [ 11 , 16 , 17 ]. Sample entropy (SE) [ 18 ] quantifies the degree of regularity of a time series by evaluating the appearance of repetitive patterns, and has excellent stability and reliability in detecting the randomness and complexity of runoff [ 19 , 20 ]. Complexity is associated with ‘meaningful structural richness’ [ 21 ], but SE essentially comprises the statistical analysis of regularity, without detecting the nonlinear characteristics or quantifying the fractal behaviors of signals [ 22 ].…”