A review on statistical postprocessing methods for hydrometeorological ensemble forecasting

Li, Wentao; Duan, Qingyun; Miao, Chiyuan; Gong, Wei; Di, Zhenhua

doi:10.1002/wat2.1246

Cited by 137 publications

(84 citation statements)

References 192 publications

(573 reference statements)

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“…While we have also attempted weighting ensemble members from all models equally, as in, for example, Becker and Van Den Dool (2016), the results are similar to the equal-model weighting approach used here. Furthermore, although different approaches have been developed for applying unequal weights to IMs based on historical skill (e.g., Raftery et al, 2005;Li et al, 2017), the application of such techniques is beyond the scope of the present study.…”

Section: Formulationsmentioning

confidence: 99%

A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

Dirkson

Denis

Merryfield

2019

Geophysical Research Letters

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We formulate seasonal probabilistic forecasts of Arctic sea ice concentration from a multimodel (MM) ensemble constructed from six state‐of‐the‐art climate models. Trend‐adjusted quantile mapping is applied to postprocess individual model forecasts prior to MM combination, and a comparison is made against two benchmark MM ensembles: one uncorrected and another where individual models are adjusted for mean and trend bias. Focusing on September hindcasts over 2000–2015 initialized monthly from April–August, calibration improves forecast skill for all models tested, but greatest skill is achieved by the calibrated MM ensemble. Compared against a climatology adjusted for trends, skill is seen over most of the Arctic for all MM formulations and at all lead times, with highest skill obtained by the calibrated MM ensemble. Furthermore, despite the overall skill for each MM formulation, we find evidence of the “spring predictability skill barrier” for forecasts initialized before June.

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Section: Formulationsmentioning

confidence: 99%

A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

Dirkson

Denis

Merryfield

2019

Geophysical Research Letters

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“…In other cases, original General Circulation Model data have been used to force a regional climate model specifically calibrated for the domain under consideration (e.g., Jacob et al, 2007). Both these methodologies are not free of criticism (Li et al, 2017;Chen et al, 2013;Ehret et al, 2012). In other applications-especially in case of short-term forecasts-the bias correction has been applied directly to the streamflow resulting from the hydrological analysis (e.g., Bogner & Pappenberger, 2011;Verkade et al, 2013;Yuan & Wood, 2012;Zalachori et al, 2012).…”

Section: Bias Correction Of Simulated Streamflowsmentioning

confidence: 99%

Future Climate and Land Use Change Impacts on River Flows in the Tapajós Basin in the Brazilian Amazon

et al. 2019

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Land conversion and changing climate are expected to significantly alter tropical forest hydrology. We used a land surface model integrated with a river routing scheme to analyze the hydrological alterations expected in the Tapajós River basin, a large portion of the Brazilian Amazon, caused by two environmental drivers: climate and land use. The model was forced with two future climate scenarios (years 2026-2045) from the Earth System Model HadGem2-ES with moderate (+4.5 W/m 2 radiative forcing value in the year 2100 with respect to preindustrial levels) and severe (+8.5 W/m 2 ) representative atmospheric carbon dioxide pathways (Representative Concentration Pathways). We tested the sensitivity of our results to the uncertainty in future climate projections by running simulations with IPSL-CM5 (wettest scenarios) and GISS-E2 (driest scenarios). Human land use effects on vegetation were evaluated using a limited and an extreme deforestation scenario. Our analysis indicates that climate change is predicted to reduce river flows across seasons (up to 20%) and bring a considerable shift in flow seasonality toward a later onset (nearly 1.5 months) and increase in interannual variability. While land use change partially counteracts the climate-driven diminishing trend in river flows, it is expected to contribute to a further increase in interannual and intraannual variability. From a water management perspective, the overall reduction of river flows and their increased variability, combined with the shift and the shortening of the wet season, could potentially affect the productivity of the large hydropower systems planned for the region and the growing demand for agricultural and transport expansion.Plain Language Summary Climate and land use change are expected to heavily modify the water cycle. This is particularly true in tropical areas, where human-driven changes may completely alter river discharge. The Amazon is the largest of the remaining tropical forests. Increasing agriculture and livestock production have significantly altered land cover in the region. This pattern is projected to continue in the coming decades. The change in the Amazon's future is highly uncertain: the direct effects of climate and land use change are not well understood and the response also depends on complex Earth-atmosphere exchanges. We used computer models representing water and energy cycles over time to understand how environmental changes are likely to alter the discharge of the Tapajós River system in southeastern Amazon. By combining two climate and two land use scenarios, we found that climate change is expected to reduce the river flows in the basin, bringing a delay in the flow seasonality and increasing the overall variability. Land use change, conversely, is expected to cause an increase in flow magnitude and variability. Besides the serious environmental consequences, these findings have important implications for the management and development of water in this strategic area for Brazil's energy and food production.

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“…Alternatively, numerous post-processors of deterministic or probabilistic models have been developed to account for the uncertainty from sources that are not modelled explicitly. They differ in several aspects (see a recent review by Li et al, 2017). Most approaches are conditional: the predictive uncertainty is modelled with respect to a predictor, which most often is the forecasted value (Todini, 2007(Todini, , 2009.…”

Section: Post-processing Approaches 25mentioning

confidence: 99%

A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

Berthet¹,

Bourgin²,

Perrin³

et al. 2019

Preprint

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An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment setup is based on i) a large set of catchments in France, ii) the GRP rainfall-runoff model designed for flood forecasting and used by the 5 French operational services and iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they used (log, Box-Cox and log-sinh) to account for heteroscedasticity. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality, were evaluated. Overall, the results highlight the challenge of 10 uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box-Cox transformation with a parameter between 0.1 and 0.3 can be a reasonable choice for flood forecasting. uncertainty into its two main components is now common: the input uncertainty (mainly the meteorological forecast uncertainty) and the modelling uncertainty, as proposed by Krzysztofowicz (1999). More generally, the predictive uncertainty due to various sources may be explicitly modelled and propagated through the modelling chain, while the "remaining" uncertainty (from the other sources) may then be assessed by statistical post-processing. Modelling each source of uncertainty 5A first approach intends to model each source of uncertainty separately and to propagate these uncertainties through the modelling chain . The heteroscedasticity of the predictive uncertainty distribution results from the separate modelling of each source of uncertainty and from the statistical model specification. While this approach is promising, operational application can be hindered by the challenge of making the hydrological modelling uncertainty explicit, as pointed out by Salamon and Feyen (2009).

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A review on statistical postprocessing methods for hydrometeorological ensemble forecasting

Cited by 137 publications

References 192 publications

A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

Future Climate and Land Use Change Impacts on River Flows in the Tapajós Basin in the Brazilian Amazon

A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

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