The case for probabilistic forecasting in hydrology

By Roman Krzysztofowicz. 2001

415 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/25cd9082e21615354b5ca4a03d53e7413cc462e4

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In Atmospheric Science

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

A review on the applications of wavelet transform in hydrology time series analysis (2013, background): “The optimal results do not take uncertainty into account effectively, so they cannot meet the practical needs adequately (Krzysztofowicz, 2001).”
Long‐lead probabilistic forecasting of streamflow using ocean‐atmospheric and hydrological predictors (2006, background): “quantiles, probabilities of categories, or an ensemble of equiprobable realizations [Krzysztofowicz, 2001].”
Judgmental forecasting: A review of progress over the last 25 years (2006, background): “Irrespective of the contextual contingencies, it is argued that the bprobabilistic forecasts mollify the potential for misperception of responsibilities and misattribution of decisionsQ (Krzysztofowicz, 2001, p. 5) by decoupling the forecasting and the decisionmaking tasks.”
Development of decision support products based on ensemble forecasts in the European flood alert system (2007, background): “The use of probabilistic flood forecasting for risk assessment and decision making in flood warning is a challenge for the scientific community (Krzysztofowicz, 2001), which stresses the need of efficient communication of probabilistic forecasts to end users.”
Comparison of ensemble‐MOS methods in the Lorenz '96 setting (2006, background): “Accordingly, the dispersion of a forecast ensemble can at best only approximate the PDF of forecast uncertainty (Krzysztofowicz 2001; Smith 2001; Hansen 2002).”
Artificial neural network technique for rainfall forecasting applied to the São Paulo region (2005, background): “The probabilistic forecast is one way to quantify these weather uncertainties (Krzysztofowicsz, 2001).”
Toward a Relational Concept of Uncertainty: about Knowing Too Little, Knowing Too Differently, and Accepting Not to Know (2008, background): “…successful results in many fields, such as probabilistic estimates of flood events used to inform the flood management activity (Toth et al. 2000, Krzysztofowicz 2001, Chen and Yu 2007), it fails to adequately function in cases where uncertainty cannot be captured by probabilistic approaches.”
Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: A comparison (2014, background): “A number of research studies suggest that enclosing predictive uncertainty estimates indeed leads to benefits for end users (Krzysztofowicz, 2001; Collier et al., 2005; Verkade and Werner, 2011; Ramos et al., 2013; Dale et al., 2014).”
Stochasticity, nonlinearity and forecasting of streamflow processes (2006, neutral): “Probabilistic forecasts are scientifically more honest (Krzysztofowicz, 2001).”
Why hydrological predictions should be evaluated using information theory (2010, background): “Note that the example of Grand Forks in (Krzysztofowicz, 2001) shows that not all users do this.”
Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles (2005, background): “The importance of using probabilistic information has been noted for both hydrological (Krzysztofowicz, 2001) and crop modelling (Hansen and Indeje, 2004) studies.”
Verification tools for probabilistic forecasts of continuous hydrological variables (2006, neutral): “Probabilistic forecasts of hydrological variables are nowadays commonly used to quantify the prediction uncertainty and to supplement the information provided by point-value predictions ( Krzysztofowicz, 2001; Ferraris et al. , 2002; Todini, 2004; Montanari and Brath , 2004; Siccardi et al. , 2005; ”
Machine learning approaches for estimation of prediction interval for the model output (2006, neutral): “The first approach is to forecast the model outputs probabilistically and it is often used in hydrological modeling (Krzysztofowicz, 2000).”