Long-range hydrometeorological ensemble predictions of drought parameters

Fundel, Felix; Jörg-Hess, Stefanie; Zappa, Massimiliano

doi:10.5194/hessd-9-6857-2012

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…high and low flow. Evaluating high flow is important for flood forecasting systems, and low flow, which tends to have a longer predictability is useful in hydrological drought forecasting systems, and is less often investigated than high flows (Fundel et al, 2012;Pushpalatha et al, 2012). Both of these functions are performed by EFAS and so are investigated here.…”

Section: High and Low Flowsmentioning

confidence: 98%

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

Pappenberger

Ramos²,

Cloke

et al. 2015

Journal of Hydrology

164

152

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s u m m a r yThe skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are 'toughest to beat' and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon.Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better.

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Section: High and Low Flowsmentioning

confidence: 98%

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

Pappenberger

Ramos²,

Cloke

et al. 2015

Journal of Hydrology

164

152

View full text Add to dashboard Cite

show abstract

“…A number of case studies using experimental and (pre) operational systems, however, have demonstrated their potential benefits (see, e.g., Zappa et al 2010 for references). Recent verification studies of hydrologie ensemble forecasts or hindcasts (i.e., forecasts that are retroactively generated using a fixed forecasting system) over long time periods include Bartholmes et al (2009), Jaun and Ahrens (2009), Renner et al (2009), Hopson and Webster (2010), Demargne et al (2010), Thirel et al (2010), Van den Bergh and Roulin (2010), Addor et al (2011), and Zappa et al (2012) for short-to medium-range hydrologie forecasts and Kang et al (2010), Wood et al (2011), Fundel et al (2012, Singla et al (2012), and Yuan et al (2013) for monthly to seasonal hydrologie ensembles. Objective veriflcation analysis of ensemble forecasts or hindcasts over multiple years should improve not only the science of hydrologie ensemble forecasting but also the utility…”

mentioning

confidence: 99%

The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

Demargne¹,

Wu²,

Regonda³

et al. 2014

Bull. Amer. Meteor. Soc.

256

164

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“…Fundel et al . () forecasted an 18‐year (1991–2008) hydrological drought in Thur River, Switzerland, simulated from the Variable Resolution Ensemble Prediction System, a semi‐distributed hydrological model employing a lead time of 1 month. But none of them have considered ensemble prediction of droughts at different lead times, incorporating climatic information.…”

Section: Introductionmentioning

confidence: 99%

“…Only few studies have addressed this issue in the context of drought prediction: Hwang and Carbone (2009) developed autoregressive drought prediction models and quantified associated forecast uncertainty using residual sampling based on modified K-NN (where K denotes the number of neighbours and NN refers to the nearest neighbour) algorithm. Fundel et al (2012) forecasted an 18-year (1991-2008) hydrological drought in Thur River, Switzerland, simulated from the Variable Resolution Ensemble Prediction System, a semi-distributed hydrological model employing a lead time of 1 month. But none of them have considered ensemble prediction of droughts at different lead times, incorporating climatic information.…”

Section: Introductionmentioning

confidence: 99%

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

Ganguli

Reddy

2013

Hydrological Processes

100

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In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.

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Long-range hydrometeorological ensemble predictions of drought parameters

Cited by 4 publications

References 52 publications

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction

The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

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