scholarly journals ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction

2016 ◽  
Vol 20 (8) ◽  
pp. 3277-3287 ◽  
Author(s):  
Joost V. L. Beckers ◽  
Albrecht H. Weerts ◽  
Erik Tijdeman ◽  
Edwin Welles
Keyword(s):  
Pacific Northwest ◽  
Southern Oscillation ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
The Us ◽  
Resampling Method ◽  
Streamflow Regime ◽  
Ensemble Streamflow Prediction ◽  
Index Value ◽  
Climate Mode

Abstract. Oceanic–atmospheric climate modes, such as El Niño–Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A new method is proposed to incorporate climate mode information into the well-known ensemble streamflow prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of skill at forecasting stations that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal hindcasts of streamflows at three test stations in the Columbia River basin in the US Pacific Northwest. An improvement in forecast skill of 5 to 10 % is found for two test stations. The streamflows at the third station are less affected by ENSO and no change in forecast skill is found here.

scholarly journals ENSO-Conditioned Weather Resampling Method for Seasonal Ensemble Streamflow Prediction

2016 ◽  
Author(s):  
Joost V. L. Beckers ◽  
Albrecht H. Weerts ◽  
Erik Tijdeman ◽  
Edwin Welles
Keyword(s):  
Pacific Northwest ◽  
Southern Oscillation ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
The Pacific ◽  
Resampling Method ◽  
Streamflow Regime ◽  
Ensemble Streamflow Prediction ◽  
Index Value ◽  
Climate Mode

Abstract. Oceanic-atmospheric climate modes, such as El Niño Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A method is proposed to incorporate climate mode information into the Ensemble Streamflow Prediction (ESP) method for seasonal forecasting. The ESP is conditioned on an ENSO index in two steps. First, a number of original historical ESP traces are selected based on similarity between the index value in the historical year and the index value at the time of forecast. In the second step, additional ensemble traces are generated by a stochastic ENSO-conditioned weather resampler. These resampled traces compensate for the reduction of ensemble size in the first step and prevent degradation of forecast skill in sub-basins that are less affected by ENSO. The skill of the ENSO-conditioned ESP is evaluated over 50 years of seasonal reforecasts of streamflows in three sub-basins of the Columbia River basin in the Pacific Northwest. An improvement in forecast skill up to 10% is found.

scholarly journals An intercomparison of approaches for improving operational seasonal streamflow forecasts

2017 ◽  
Vol 21 (7) ◽  
pp. 3915-3935 ◽  
Author(s):  
Pablo A. Mendoza ◽  
Andrew W. Wood ◽  
Elizabeth Clark ◽  
Eric Rothwell ◽  
Martyn P. Clark ◽  
...  
Keyword(s):  
Water Supply ◽  
Pacific Northwest ◽  
Land Surface ◽  
Forecast Skill ◽  
Climate Indices ◽  
Statistical Regression ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Ensemble Streamflow Prediction ◽  
Seasonal Streamflow

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.

scholarly journals An intercomparison of approaches for improving predictability in operational seasonal streamflow forecasting

2017 ◽  
Author(s):  
Pablo A. Mendoza ◽  
Andrew W. Wood ◽  
Elizabeth Clark ◽  
Eric Rothwell ◽  
Martyn P. Clark ◽  
...  
Keyword(s):  
Water Supply ◽  
Pacific Northwest ◽  
Land Surface ◽  
Forecast Skill ◽  
Climate Indices ◽  
Statistical Regression ◽  
Climate Information ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Streamflow

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 IHC and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the U.S. 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 inter-compare 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 (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.

scholarly journals Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill

2011 ◽  
Vol 15 (11) ◽  
pp. 3529-3538 ◽  
Author(s):  
S. Shukla ◽  
D. P. Lettenmaier
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Forecast Skill ◽  
Climate Forecast ◽  
Streamflow Prediction ◽  
Seasonal Climate ◽  
Western Us ◽  
Ensemble Streamflow Prediction ◽  
Hydrologic Forecast ◽  
Hydrologic Conditions

Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.

scholarly journals Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times

2021 ◽  
Vol 25 (7) ◽  
pp. 4159-4183
Author(s):  
Seán Donegan ◽  
Conor Murphy ◽  
Shaun Harrigan ◽  
Ciaran Broderick ◽  
Dáire Foran Quinn ◽  
...  
Keyword(s):  
Water Resources ◽  
North Atlantic Oscillation ◽  
Water Resources Management ◽  
North Atlantic ◽  
Lead Time ◽  
Forecast Skill ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Resources Management ◽  
Ensemble Streamflow Prediction

Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 catchments using the GR4J (Génie Rural à 4 paramètres Journalier) hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 d to 12 months for each of the 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with a continuous ranked probability skill score (CRPSS) of 0.8 (1 d), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ=0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and the East, being those where slowly responding, high-storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low- and high-flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7 %–18 % were possible over lead times of 1 to 3 months and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

scholarly journals Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

2020 ◽  
Author(s):  
Marc Girons Lopez ◽  
Louise Crochemore ◽  
Ilias G. Pechlivanidis
Keyword(s):  
Hydrological Model ◽  
Forecast Skill ◽  
Added Value ◽  
Spatiotemporal Variations ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
The Future ◽  
Ensemble Streamflow Prediction ◽  
Forecasting And Warning

Abstract. Probabilistic seasonal forecasts are important for many water-intensive activities requiring long-term planning. Among the different techniques used for seasonal forecasting, the Ensemble Streamflow Prediction (ESP) approach has long been employed due to the singular dependence on past meteorological records. The Swedish Meteorological and Hydrological Institute is currently extending the use of long-range forecasts within its operational warning service, which requires a thorough analysis of the suitability and applicability of different methods with the national S-HYPE hydrological model. To this end, we aim to evaluate the skill of ESP forecasts over 39,493 catchments in Sweden, understand their spatiotemporal patterns, and explore the main hydrological processes driving forecast skill. We found that ESP forecasts are generally skilful for most of the country up to 3 months into the future but that large spatiotemporal variations exist. Forecasts are most skilful during the winter months in northern Sweden, except for the highly-regulated hydropower-producing rivers. The relationships between forecast skill and 15 different hydrological signatures show that forecasts are most skilful for slowly-reacting, baseflow-dominated catchments and least skilful for flashy catchments. Finally, we show that forecast skill patterns can be spatially clustered in 7 unique regions with similar hydrological behaviour. Overall, these results contribute to identify in which areas, seasons, and how long into the future ESP hydrological forecasts provide an added value, not only for the national forecasting and warning service but, most importantly, to guide decision-making in critical services such as hydropower management and risk reduction.

scholarly journals Inaccuracies in survey reporting of alcohol consumption

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Conor Gilligan ◽  
Kristen G. Anderson ◽  
Benjamin O. Ladd ◽  
Yun Ming Yong ◽  
Michael David
Keyword(s):  
Public Health ◽  
College Students ◽  
Alcohol Consumption ◽  
Survey Data ◽  
Pacific Northwest ◽  
Accurate Calculation ◽  
Standard Drink ◽  
Web Based ◽  
The Us ◽  
Serving Sizes

Abstract Background Alcohol consumption estimates in public health predominantly rely on self-reported survey data which is likely to underestimate consumption volume. Surveys tend to ask specifically about standard drinks and provide a definition or guide in an effort to gather accurate estimates. This study aimed to investigate whether the inclusion of the term standard drinks with pictorial guide is associated with an adjustment in self-reported alcohol volume. Methods A web-based survey was administered with AUDIT-C questions repeated at the beginning and end of the survey with and without the standard drink term and guide. The order in which respondents were presented with the different question types was randomised. Two cohorts of university/college students in NSW Australia (n = 122) and the US Pacific Northwest (n = 285) completed the survey online. Results Australian students did not adjust their responses to questions with and without the standard drink term and pictorial guide. The US students were more likely to adjust their responses based on the detail of the question asked. Those US students who drank more frequently and in greater volume were less likely to adjust/apply a conversion to their consumption. Conclusions This study supports previous findings of the inaccuracy of alcohol consumption volume in surveys, but also demonstrates that an assumption of underestimation cannot be applied to all individual reports of consumption. Using additional questions to better understand drink types and serving sizes is a potential approach to enable accurate calculation of underestimation in survey data.

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