Abstract
Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.
Highlights
Climate change and intensive human activities extensively influence hydrological regimes and water resource systems
The nonlinearity of hydrological systems has been recognized for many years
Recent development of computational power and data acquisition us with tools and newThe methods development of computational power and data acquisition provided us with tools and new methods to study temporal and spatial variability in hydrological variables [20]
Summary
Climate change and intensive human activities extensively influence hydrological regimes and water resource systems. The early time series models assumed that the time series came from a stationary or a cyclostationary process [5]. These models performed well for hydrological data without signs of long-term memory or nonlinear dependence [6,7,8,9]. As records length increased, low-frequency structures of climate were associated with hydrologic time series. These structures became an essential feature in the hydrological analysis, especially in streamflow analysis, due to the extremely non-uniform temporal distribution of global runoff. Several studies have associated the changes in hydrological records with the effects of natural climate variability, from low-frequency climate indices such as the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation
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