Using Pacific Ocean climatic variability to improve hydrologic reconstructions

Anderson, SallyRose; Aziz, Oubeid; Tootle, Glenn A.; Grissino-Mayer, Henri D.; Barnett, A. P.

doi:10.1016/j.jhydrol.2012.02.037

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…4 shows that the skill improvement is due to the signal in the SSTs and not due to the addition of parameters. This is consistent with other studies that have used SSTs to improve reconstructions in snow water equivalent 15 , 16 and wildfires 17 , in demonstrating the utility of SST in reconstruction. This hybrid approach of using SSTs could potentially improve upon other reconstruction efforts such as precipitation 31 , extreme precipitation 32 and drought indices 33 – 35 .…”

Section: Discussionsupporting

confidence: 91%

“…Due to the upper bound on water holding capacity of soil, trees’ metabolic growth limits and increased cloud cover during wet years, high flows are not properly estimated using tree-ring chronologies 3 , 12 – 14 . Studies have shown that incorporating sea surface temperature (SSTs) along with tree-ring chronologies resulted in improved reconstruction of streamflow and wildfire synchrony 15 – 17 . Recent progress in understanding ocean-atmosphere interactions and the ensued teleconnection shows that there are well organized modes of interannual and interdecadal variability in climate that modulate these dominant moisture delivery pathways and has significant projections on continental scale rainfall and streamflow patterns 18 , 19 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Role of Pacific SSTs in improving reconstructed streamflow over the coterminous US

Mukhopadhyay

Patskoski

Sankarasubramanian

2018

Sci Rep

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Reconstructed annual streamflows using tree-ring chronologies provide useful information on moisture availability during the pre-historic period, but they have limitations in estimating high flows due to the upper bound on soil water holding capacity and trees’ metabolic growth limits. We propose a hybrid approach that uses tree-ring chronologies and climatic indices for improving high flows in 301 basins whose annual streamflows are modulated by ENSO and/or PDO. The hybrid decomposition approach relies on separating the moisture supply into the basin as outside-the-region moisture and within-the-region moisture with the former being estimated by SST indices and the latter being estimated by tree-ring chronologies. Analyses over the 301 stations from coterminous US show that the proposed approach improves the high flows and improves the overall error in the reconstructed streamflows. Potential utility of the improved reconstructed annual streamflows with improved high flows is also discussed.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Role of Pacific SSTs in improving reconstructed streamflow over the coterminous US

Mukhopadhyay

Patskoski

Sankarasubramanian

2018

Sci Rep

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“…The streamflow and SWE reconstructions created in this study cover five centuries. Similar to Anderson et al (2012b), this study found a strong relationship between streamflow and SWE. Both reconstructions indicate that the years 1460, 1510, 1590, 1670, and 1850 were extremely dry and the years 1490, 1620 and 1680 were extremely wet (Fig.…”

Section: Comparison With Observed Hydrologysupporting

confidence: 84%

Reconstructions of Hydrologic Variables in the North Platte River Basin

Anderson

Bowen

Tootle

et al. 2020

Int. J. Eng. Tech. Mgmt. Res.

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Reconstructions of hydrologic variables are commonly created using tree-ring chronologies (TRCs) to generate information about historic climate and potential future variability. This study used TRCs to reconstruct annual streamflow, April 1st Snow Water Equivalent (SWE), and soil moisture in the North Platte River Basin (NPRB). Stepwise linear regression was performed to determine which of the 55 moisture sensitive TRCs were the best predictors of hydrologic variation. The regressions explained 63% of the variability in streamflow, 55% of the variability in SWE, and 66% of the variability in soil moisture. This study then maximized the overlapping period of records which resulted in a decrease in the percent of variability explained but indicated that the regression models were stable for long reconstruction periods. This study successfully reconstructed all three hydrologic variables for NPRB to 1438 or earlier. Temporal wet and dry periods for streamflow and SWE were closely aligned while soil moisture did not follow similar temporal patterns. This was likely due to a natural “lag” between soil moisture and streamflow / SWE given soil moisture tends to retain antecedent signals. The availability of reconstructed hydrologic data in NPRB allows for a better understanding of the long-term hydrologic variability in the region.

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“…Thus, re-dimensioning the input data to provide a robust calibration, and rescaling them to avoid losing the value of a certain variable are crucial prerequisites (Araghinejad 2014). Various techniques involving principal component analysis (PCA), wavelet analysis (WA), singular value decomposition (SVD), singular spectrum analysis (SSA), for example, have been applied by hydrologists for pre-possessing large volumes of data (Marques et al 2006, Hu et al 2007, Partal and Kisi 2007, Sivapragasam et al 2007, Wu et al 2009, Chen et al 2009, Aziz et al 2010, Martinez and Jones 2011, Anderson et al 2012, Córdoba-Machado et al 2015. Among the pre- A c c e p t e d M a n u s c r i p t 4 processing techniques, PCA is a common mathematical tool used to reduce the number of variables, whilst keeping their variation as far as possible .…”

Section: Introductionmentioning

confidence: 99%

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Chitsaz

Azarnivand

Araghinejad

2016

Hydrological Sciences Journal

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An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfallrunoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis, and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in Downloaded by [Rutgers University] at 15:43 27 August 2015A c c e p t e d M a n u s c r i p t 2 forecasting accuracy. The results reveal that SVD was highly effective in preprocessing of data-driven evaluations.

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Using Pacific Ocean climatic variability to improve hydrologic reconstructions

Cited by 11 publications

References 33 publications

Role of Pacific SSTs in improving reconstructed streamflow over the coterminous US

Role of Pacific SSTs in improving reconstructed streamflow over the coterminous US

Reconstructions of Hydrologic Variables in the North Platte River Basin

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

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