Verification tools for probabilistic forecasts of continuous hydrological variables

Laio, Francesco; Tamea, Stefania

doi:10.5194/hess-11-1267-2007

Cited by 308 publications

(227 citation statements)

References 33 publications

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“…Most current diagnostic efforts rely on evaluation of flow time series (Maurer et al, 2007;Moss, 1979a, b), which represent only a small component of the information within a model. New metrics are needed that can evaluate the ability of models to represent non-stationary water systems (Laio and Tamea, 2007;Montanari and Koutsoyiannis, 2012;Sikorska et al, 2013). Finally, observations will be essential to identify the presence of events and trends that were not predicted.…”

Section: Challenge 3: Uncertainty Predictability and Observations Ofmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

129

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Abstract.Globally, many different kinds of water resources management issues call for policy-and infrastructure-based responses. Yet responsible decision-making about water resources management raises a fundamental challenge for hydrologists: making predictions about water resources on decadal-to century-long timescales. Obtaining insight into hydrologic futures over 100 yr timescales forces researchers to address internal and exogenous changes in the properties of hydrologic systems. To do this, new hydrologic research must identify, describe and model feedbacks between water and other changing, coupled environmental subsystems. These models must be constrained to yield useful insights, despite the many likely sources of uncertainty in their predictions. Chief among these uncertainties are the impacts of the increasing role of human intervention in the global water cycle -a defining challenge for hydrology in the Anthropocene. Here we present a research agenda that proposes a suite of strategies to address these challenges from the perspectives of hydrologic science research. The research agenda focuses on the development of co-evolutionary hydrologic modeling to explore coupling across systems, and to address the implications of this coupling on the long-time behavior of the coupled systems. Three research directions support the development of these models: hydrologic reconstruction, comparative hydrology and model-data learning. These strategies focus on understanding hydrologic processes and feedbacks over long timescales, across many locations, and through strategic coupling of observational and model data in specific systems. We highlight the value of use-inspired and team-based science that is motivated by real-world hydrologic problems but targets improvements in fundamental understanding to support decision-making and management. Fully realizing the potential of this approach will ultimately require detailed integration of social science and physical science understanding of water systems, and is a priority for the developing field of sociohydrology.

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Section: Challenge 3: Uncertainty Predictability and Observations Ofmentioning

confidence: 99%

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Thompson

Sivapalan

Harman

et al. 2013

Hydrol. Earth Syst. Sci.

129

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“…Reliability was evaluated with the probability integral transform (PIT; Gneiting et al, 2007;Laio and Tamea, 2007) diagram. The PIT diagram represents the cumulative distribution of the positions of the observation within the distribution of forecast values.…”

Section: Evaluation Of Forecast Attributesmentioning

confidence: 99%

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Crochemore¹,

Ramos²,

Pappenberger³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in longrange streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

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“…The PIT represents the non-exceedance probability of observed streamflow obtained from the CDF of the ensemble forecast. If the forecast ensemble spread is 5 appropriate and free of bias then observations will be contained within the forecast ensemble spread, with reliable forecasts having PIT values that follow a uniform distribution between 0 and 1 (Laio and Tamea, 2007).…”

Section: Verificationmentioning

confidence: 99%

“…We use PIT plots for verification of the reliability and robustness of the forecast probability distributions, to assess whether there are biases in the forecasts, or whether the forecast probability distributions are too wide or too narrow (Laio and Tamea, 2007) Robustness is also assessed by plotting forecast quantile ranges and observed flows against the forecast median (Figure 3d and Figure 4d) and chronologically (Figure 3e and Figure 4e, for Jhelum at Mangla and Indus at Tarbela respectively). These show that BJP forecasts reasonably account for the range of observed variability for both locations.…”

Section: Performance Diagnosticsmentioning

confidence: 99%

Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

Charles

Wang

Ahmad

et al. 2017

Preprint

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Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the 15 BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April-September) streamflow forecasts for inflow to Pakistan's two largest Upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESP) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM 20 for this location. Cross validation of the streamflow forecasts show that the BJP approach using two predictors (March flow and an ENSO climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April-September), early Kharif (April-June) and late Kharif (July-September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early 25Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Verification tools for probabilistic forecasts of continuous hydrological variables

Cited by 308 publications

References 33 publications

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene

Seasonal streamflow forecasting by conditioning climatology with precipitation indices

Assessment of methods for seasonal streamflow forecasting in the Upper Indus Basin of Pakistan

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