Long Lead-Time Forecasting of U.S. Streamflow Using Partial Least Squares Regression

Tootle, Glenn A.; Singh, Ashok K.; Piechota, Thomas C.; Farnham, Irene

doi:10.1061/(asce)1084-0699(2007)12:5(442)

Cited by 46 publications

(30 citation statements)

References 28 publications

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“…The joint influence of ENSO and PDO on North American climate conditions, snowpack, and spring/summer runoff has been also well recognized and documented (e.g., Hamlet and Lettenmaier, 1999). As a consequence, many authors have explored the incorporation of large-scale climate information for seasonal streamflow forecasting in the PNW -using either standard indices (e.g., Hamlet and Lettenmaier, 1999;Maurer et al, 2004), custom indices from reanalysis fields (e.g., Opitz-Stapleton et al, 2007;Tootle et al, 2007), both (e.g., Moradkhani and Meier, 2010), or downscaled climate forecasts (e.g., Wood et al, 2005) -finding improved predictability for lead times longer than 2 months and particularly in years of strong anomalies in climate oscillations such as ENSO.…”

Section: Study Domainmentioning

confidence: 99%

“…Therefore, we also assess the potential of custom climate predictor indices derived from reanalysis data. Following Tootle et al (2007), we use partial least squares regression (PLSR; Wold, 1966) to extract information from climate fields. PLSR decomposes a set of independent variables X and dependent variables Y into a small Saha et al, 2014), aggregating the latter product to 2.0 • × 2.0 • horizontal resolution.…”

Section: Partial Least Squares Regression Using Reanalysis Fields (Stmentioning

confidence: 99%

“…On the other hand, statistical or purely data-driven methods rely on empirical relationships between seasonal streamflow volumes, and large-scale climate variables and/or in situ watershed observations. Several statistical approaches can be found in the literature, encompassing different degrees of complexity (e.g., Garen, 1992;Piechota et al, 1998;Grantz et al, 2005;Tootle et al, 2007;Pagano et al, 2009;Moradkhani and Meier, 2010). Other studies have tested multi-model combination techniques for purely statistical seasonal forecasts, using objective performance criteria (e.g., Regonda et al, 2006), both performance and predictor state information (Devineni et al, 2008), and Bayesian model averaging (e.g., Mendoza et al, 2014), among others.…”

Section: Introductionmentioning

confidence: 99%

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An intercomparison of approaches for improving operational seasonal streamflow forecasts

Mendoza

Wood

Clark

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches -statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) -and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction -HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

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Section: Study Domainmentioning

confidence: 99%

Section: Partial Least Squares Regression Using Reanalysis Fields (Stmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An intercomparison of approaches for improving operational seasonal streamflow forecasts

Mendoza

Wood

Clark

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Future research may focus on the use of a non-parametric or regression-based long lead-time streamflow forecast model , Tootle and Piechota 2004, Tootle et al 2007, Soukup et al 2009) for Adour-Garonne basin streamflow. Additionally, future research efforts may focus on determining a physical explanation of the results (Atlantic SST regions identified) found in this study by examining z500 geopotential height winds as well as global circulation phenomena.…”

Section: Discussionmentioning

confidence: 99%

“…The results led to the use of climatic variables as a predictor in long lead-time streamflow forecast models (Uvo et al 2002, Tootle and Piechota 2004, Grantz et al 2005, Pagano and Garen 2006, Tootle et al 2007, Soukup et al 2009). Previous studies evaluating the relationship between the Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO) climate indices and hydrological response neighbouring regions to the Adour-Garonne basin were inconclusive.…”

Section: Introductionmentioning

confidence: 99%

Atlantic Ocean sea-surface temperatures and regional streamflow variability in the Adour-Garonne basin, France

Oubeidillah

Tootle

Anderson

2012

Hydrological Sciences Journal

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The identification of Atlantic Ocean (AO) climatic drivers may prove valuable in long lead-time forecasting of streamflow in the Adour-Garonne basin in southwestern France. Previous studies have identified the Atlantic Multidecadal Oscillation (AMO) and the North Atlantic Oscillation (NAO) as drivers of European hydrology. The current research applied the singular value decomposition (SVD) statistical method to AO sea-surface temperatures (SSTs) to identify the primary AO climatic drivers of the Adour-Garonne basin streamflow. Annual and seasonal streamflow volumes were selected as the hydrological response, while average AO SSTs were calculated for three different 6-month averages (January-June, April-September and July-December) for the year preceding streamflow. The results identified a region along the Equator as the probable driver of the basin streamflow. Additional analysis evaluated the influence of the AMO and NAO on Adour-Garonne basin streamflow.

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Long‐range reservoir inflow forecasts using large‐scale climate predictors

Moradi

Dariane

Yang

et al. 2020

Intl Journal of Climatology

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Identifying significant large‐scale climate indicators has the potential to improve long‐range streamflow forecasts. In this research, we develop streamflow forecasts for Lake Urmia basin, Iran, specifically for inflow into the Boukan and Mahabad reservoirs. In doing so, two types of inflow forecast models are considered: a single site univariate model ignoring the cross correlation between streamflow at different stations, and a multi‐site multivariate forecast model which takes into consideration the cross correlations among stations. Predictor selection is performed through a principal component analysis and an adaptive‐network‐based fuzzy inference system is used to forecast streamflow. Forecast performance is investigated by employing different combinations of large‐scale climatic information and hydrologic data. We found that gridded ocean‐atmospheric circulation variables, including surface precipitation rate and omega (pressure vertical velocity), have the highest correlations (about 0.7) with annual streamflow. In general, multivariate models are able to better preserve the annual cross‐correlations between streamflow at different stations, as expected, without sacrificing forecast skill as compared to the univariate forecast model approach. Additionally, as compared with the baseline feed‐forward artificial neural network‐ and traditional multiple linear regression‐forecast models, the results were approximately the same. This similarity in the forecast performance between the linear and nonlinear models is likely due to the short of data (44‐sample record).

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Long Lead-Time Forecasting of U.S. Streamflow Using Partial Least Squares Regression

Cited by 46 publications

References 28 publications

An intercomparison of approaches for improving operational seasonal streamflow forecasts

An intercomparison of approaches for improving operational seasonal streamflow forecasts

Atlantic Ocean sea-surface temperatures and regional streamflow variability in the Adour-Garonne basin, France

Long‐range reservoir inflow forecasts using large‐scale climate predictors

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