Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

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.

A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)

“…SVM is a classic machine learning technique focused on mathematical learning theory and it has several benefits in the classification of massive data, feature identification, and regression analysis [46]. The aim of regression analysis with SVR is to estimate a function based on the given dataset (x, y), where x represents the input vector (in this case, the input vectors (x) refer to lagged precipitation and lagged inflow) and y represents the output (referring to forecasted values).…”

Vertical Differences in the Long-Term Trends and Breakpoints of NDVI and Climate Factors in Taiwan

“…The Break for Additive Seasonal and Trend (BFAST) method was developed by Verbesselt et al in 2010 [34], and has been successfully applied in many ecosystem-related studies on topics such as such as drought [38], hydrology [53], and climatology [54].…”