Wavelet and Hidden Markov-Based Stochastic Simulation Methods Comparison on Colorado River Streamflow

Erkyihun, Solomon Tassew; Zagona, Edith; Rajagopalan, Balaji

doi:10.1061/(asce)he.1943-5584.0001538

Cited by 10 publications

(7 citation statements)

References 48 publications

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“…Three scenarios of projection were modeled and evaluated using three statistical performance evaluation measures (RMSE, MAE, and R). The forecasts for 5-and 10-year periods presented remarkably high values of R. Although the value for the 15-year period was low, the result is significant when compared with the results provided in [7], whose authors used a climate hidden Markov model and presented an R 2 of approximately 0.26 for a lead time of 15 years. The model presents an improvement in the information of the dry period.…”

Section: Discussionmentioning

confidence: 59%

“…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]. However, as records length increased, low-frequency structures of climate were associated with hydrologic time series.…”

Section: Introductionmentioning

confidence: 99%

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

Rolim

Filho

2020

Water

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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.

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Section: Discussionmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

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

Rolim

Filho

2020

Water

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“…Thyer and Kuczera (2003a,b) extended the approach to multiple sites with the premise that multiyear persistence of large-scale modes of climate variability would influence annual variability. Applications using annual data have also been extended to analyzing and reconstructing annual-resolution paleoclimate data (Prairie et al 2008;Bracken et al 2016;Erkyihun et al 2017). HMMs have also been applied across large spatial scales using spatially correlated gridded data to model precipitation across India (Greene et al 2011) and the Asian summer monsoon (Yoo et al 2010).…”

Section: Methods and Background A Hmms For Characterizing Continentamentioning

confidence: 99%

How Wet and Dry Spells Evolve across the Conterminous United States Based on 555 Years of Paleoclimate Data

Lall

Cook

2018

Journal of Climate

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Evolving patterns of droughts and wet spells in the conterminous United States (CONUS) are examined over 555 years using a tree-ring-based paleoclimate reconstruction of the modified Palmer drought severity index (PDSI). A hidden Markov model is used as an unsupervised method of classifying climate states and quantifying the temporal evolution from one state to another. Modeling temporal variability in spatial patterns of drought and wet spells provides the ability to objectively assess and simulate historical persistence and recurrence of similar patterns. The Viterbi algorithm reveals the probable sequence of states through time, enabling an examination of temporal and spatial features and associated large-scale climate forcing. Distinct patterns of sea surface temperature that are known to enhance or inhibit rainfall are associated with some states. Using the current CONUS PDSI field the model can be used to simulate the space–time PDSI pattern over the next few years, or unconditional simulations can be used to derive estimates of spatially concurrent PDSI patterns and their persistence and intensity across the CONUS.

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“…2005; Bracken and Rajagopalan 2014; Beckers et al. 2016; Erkyihun and Zagona 2017). Increasingly, the advancing skill of dynamical climate models argues for using climate forecasts to drive pre‐ESP methods.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi‐Model Climate Forecasts

Baker

Rajagopalan

Wood

2021

J American Water Resour Assoc

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In the Colorado River Basin (CRB), ensemble streamflow prediction (ESP) forecasts drive operational planning models that project future reservoir system conditions. CRB operational seasonal streamflow forecasts are produced using ESP, which represents climate using an ensemble of meteorological sequences of historical temperature and precipitation, but do not typically leverage additional real‐time subseasonal‐to‐seasonal climate forecasts. Any improvements to streamflow forecasts would help stakeholders who depend on operational projections for decision making. We explore incorporating climate forecasts into ESP through variations on an ESP trace weighting approach, focusing on Colorado River unregulated inflows forecasts to Lake Powell. The k‐nearest neighbors (kNN) technique is employed using North American Multi‐Model Ensemble one‐ and three‐month temperature and precipitation forecasts, and preceding three‐month historical streamflow, as weighting factors. The benefit of disaggregated climate forecast information is assessed through the comparison of two kNN weighting strategies; a basin‐wide kNN uses the same ESP weights over the entire basin, and a disaggregated‐basin kNN applies ESP weights separately to four subbasins. We find in general that climate‐informed forecasts add greater marginal skill in late winter and early spring, and that more spatially granular disaggregated‐basin use of climate forecasts slightly improves skill over the basin‐wide method at most lead times.

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Wavelet and Hidden Markov-Based Stochastic Simulation Methods Comparison on Colorado River Streamflow

Cited by 10 publications

References 48 publications

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

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

How Wet and Dry Spells Evolve across the Conterminous United States Based on 555 Years of Paleoclimate Data

Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi‐Model Climate Forecasts

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