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

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.

Probabilistic Streamflow forecast for Narmada River Basin

<p>Ensemble Streamflow Prediction (ESP) is a widely used method in forecasting streamflow, particularly for extremely low or high flows. However, the incorporation of reservoir operations in using ensemble streamflow prediction has not been investigated till yet. We calibrated Variable Infiltration Capacity (VIC) model for daily streamflow for Narmada river basin at four stations (Sandia, Handia, Mandleshwar and Garudeshwar) considering the effect of four reservoirs (Bargi, Tawa, Indira Sagar and Sardar Sarovar). The model is well-calibrated for the selected river basin (R2>0.55) at all locations. Further, routing of streamflow is done considering the reservoir storage dynamics and operating rules. Input data for ensemble prediction is taken from all 16 members of the Extended Range Forecast System (ERFS) developed by Indian Institute of Tropical Meteorology (IITM) and implemented by India Meteorological Department (IMD). Post-processing of the results gave us probabilities of uncertainties associated with streamflow prediction using ERFS members. This study provides key information in predictions of streamflow by incorporating the reservoirs based on the ERFS ensemble members, which can be used to effectively mitigate life and property losses associated with extreme flows in rivers.</p>

Conditioning Ensemble Streamflow Prediction with the North Atlantic Oscillation improves skill at longer lead times

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 hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 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 continuous ranked probability skill scores of 0.8 (1-day), 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 East, being those where slowly responding, high storage catchments are located. Results also highlight the potential utility of ESP for decision-making, as measured by its ability to forecast low and high flow events. In addition to our benchmarking experiment, we conditioned ESP on 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 of 7–18 % were possible over lead times of 1 to 3 months, and that NAO-conditioned ESP is 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.

Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden

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.

PREMHYCE : un outil opérationnel pour la prévision des étiages

De nombreux usages de l'eau peuvent être fortement impactés par les pénuries d'eau (eau potable, irrigation, hydroéléctricité...). Il est donc nécessaire d'anticiper les périodes d'étiage afin d'améliorer la gestion de l'eau. Ceci est renforcé par la perspective d'étiages futurs plus sévères dans le contexte du changement climatique. Cinq institutions françaises ont développé un outil opérationnel de prévision des bas débits, PREMHYCE. Il est testé en temps réel sur une centaine de bassins versants de France métropolitaine depuis 2017. PREMHYCE comprend cinq modèles hydrologiques qui peuvent être calés sur des bassins versants jaugés et assimilent les dernières observations de débit. Les prévisions de débits sont émises jusqu'à un horizon de 90 jours selon l'approche Ensemble Streamflow Prediction (ESP) (données météorologiques historiques utilisées comme ensemble de scénarios d'entrée). Ces données météorologiques (précipitations, évapotranspiration et température) sont issues de la réanalyse SAFRAN journalière de Météo-France, sur la période 1958–2018. Les performances de l'outil sont analysées sur les étiages 2017–2018 pour 38 bassins versants sur lesquels les prévisions sont disponibles pour tous les modèles. Ces derniers ont montré des capacités d'anticipation de l'ordre de 40 jours en moyenne. La plupart des modèles présentent une précision satisfaisante pour prévoir les bas débits à courte échéance (j + 7).

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

In order to enhance the streamflow forecast skill, seasonal/sub-seasonal streamflow forecasts can be post-processed by incorporating new information, such as climate signals. This study proposed a simple yet efficient approach, the “Bivar_update” model that utilizes bivariate climate forecast to update individual probabilities of the ensemble streamflow prediction. The Bayesian updating scheme is used to update the joint probability mass function derived from historic precipitation and temperature data sets. Thirty-five dam basins were used for the case study, and the modified Tank model was embedded into the ensemble streamflow prediction framework. The performance of the proposed approach was evaluated through a comparison with a reference streamflow forecast model, the “Univar_update” model, that reflects only precipitation forecast, in terms of deterministic and categorical streamflow forecast accuracy. For this purpose, multiple cases of probabilistic precipitation and temperature forecasts were synthetically generated. As a result, the Bivar_update model was able to decrease the errors in forecast under below-normal conditions. The improvements in forecasting skills were found for both measures; deterministic and categorical streamflow forecasts. Since the proposed Bivar_update model reflects both precipitation and temperature information, it can compensate low predictability especially under dry conditions in which the streamflow’s dependency on temperature increases.

The role of parameter estimation strategies on ensemble streamflow prediction results across extratropical Andean catchments

<p>In catchments with a highly variable flow regime, an accurate and reliable hydrological forecasting framework is critical to support water resources management. However, due to model structural deficiencies and changing climatic conditions, the parameter estimates during the calibration period are expected to vary with hydrological conditions. This work aims to test the added value of incorporating potential non-stationarities in hydrologic model parameters on seasonal streamflow forecasts in high-mountain environments, using the ensemble streamflow prediction (ESP) methodology. To this end, we apply the GR4J rainfall-runoff model coupled with the snow accumulation and ablation CemaNeige module in six basins located in Central Chile (30-36° S). We explore the effects of four parameter selection strategies on the quality of seasonal streamflow forecasts produced with the ESP method: (i) a single set of parameters for the entire hindcast period (our benchmark), (ii) using parameters calibrated with a ‘leave-one-year-out’ approach, (iii) using parameter sets based on expected hydroclimatic conditions, and (iv) dual data assimilation to improve the initial condition and parameters before the forecast initialization. Results show that parameters related to production store capacity in GR4J model, and degree-day melt coefficient and weighting coefficient for snow pack thermal state in the CemaNeige module have a high inter-annual variability, with variations of 50% with respect to the benchmark scenario.</p>

Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges

A novel multiscale postprocessor for ensemble streamflow prediction, MS-EnsPost, is described and comparatively evaluated with the existing postprocessor in the National Weather Service’s Hydrologic Ensemble Forecast Service, EnsPost. MS-EnsPost uses data-driven correction of magnitude-dependent bias in simulated flow, multiscale regression using observed and simulated flows over a range of temporal aggregation scales, and ensemble generation using parsimonious error modeling. For comparative evaluation, 139 basins in eight River Forecast Centers in the United States were used. Streamflow predictability in different hydroclimatological regions is assessed and characterized, and gains by MS-EnsPost over EnsPost are attributed. The ensemble mean and ensemble prediction results indicate that, compared to EnsPost, MS-EnsPost reduces the root-mean-square error and mean continuous ranked probability score of day-1 to day-7 predictions of mean daily flow by 5%–68% and by 2%–62%, respectively. The deterministic and probabilistic results indicate that for most basins the improvement by MS-EnsPost is due to both magnitude-dependent bias correction and full utilization of hydrologic memory through multiscale regression. Comparison of the continuous ranked probability skill score results with hydroclimatic indices indicates that the skill of ensemble streamflow prediction with post processing is modulated largely by the fraction of precipitation as snowfall and, for non-snow-driven basins, mean annual precipitation.