Summer drought predictability over Europe: empirical versus dynamical forecasts (original) (raw)
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Hydrology and Earth System Sciences Discussions, 2015
Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from ECMWF in predicting droughts over the European 5 continent. The Standardized Precipitation Index (SPI) is used to quantify the onset and severity of droughts.
Ensemble projections of future streamflow droughts in Europe
Hydrol. Earth Syst. Sci. Discuss., 2013
There is growing concern in Europe about the possible rise in the severity and frequency of extreme drought events as a manifestation of global change. In order to plan suitable adaptation strategies it is important for decision makers to know how drought conditions will develop at regional scales. This paper therefore addresses the issue 5 of future developments in streamflow drought characteristics across Europe. Through off-line coupling of a hydrological model with an ensemble of bias-corrected climate simulations (IPCC SRES A1B) and a water use scenario (Economy First), long term (1961-2100) ensemble streamflow simulations are generated that account for changes in climate, and the uncertainty therein, and in water consumption. Using extreme value 10 analysis we derive minimum flow and deficit indices and evaluate how the magnitude and severity of low flow conditions may evolve throughout the 21st century. This analysis shows that streamflow droughts will become more severe and persistent in many parts of Europe due to climate change, except for northern and northeastern parts of Europe. Especially southern regions will face strong reductions in low flows. Future wa-15 ter use will aggravate the situation by 10-30 % in Southern Europe, whereas in some sub-regions in Western, Central and Eastern Europe a positive climate signal may be reversed due to intensive water use. The multi-model ensemble projections of more frequent and severe streamflow droughts in the south and decreasing drought hazard in the north are highly significant, while the projected changes are more dissonant in 20 a transition zone in between.
Drought forecasting: Application of ensemble and advanced machine learning approaches
IEEE Access, 2022
Depending on the severity and spatial-temporal variability, droughts can have a wide range of impacts such as crop failure, water shortages, and food insecurity. Accurate and timely forecasting is necessary to mitigate the hazards of extreme weather events, such as droughts, brought on by climate change. A district like Chitradurga in India, which typically receives around 450-600mm of annual rainfall, will require advanced drought mitigation strategies and plans before the onset of the drought. This research focuses on 1-step lead time forecasting of meteorological drought episodes making use of the 6-month Standardised Precipitation Index (SPI-6) as indicator. The fine resolution rainfall data (0.25°× 0.25°) obtained from the Indian Meteorological Department was used to derive the 6-month SPI data of 23 grid stations. The 1-step lead time SPI-6 time series was forecast considering the antecedent SPI-6 time series data as model input. The Mutual Information was used to determine the most relevant input features for drought forecasting. The standard artificial neural network, an advanced machine learning framework-multivariate adaptive regression splines, and the ensemble learning-based CatBoost regression and gradient tree boosting paradigms were employed to forecast drought episodes. Error and efficiency metrics were employed for performance evaluation of the simulated models. The multivariate adaptive regression splines and gradient tree boosting forecasts had slightly higher accuracy and lower error rates than the artificial neural network model, which suggests that they may be more reliable for drought forecasting. The root mean square error and normalized Nash-Sutcliffe efficiency ranges of the multivariate adaptive regression splines model (during test phase) were 0.37-0.54 and 0.78-0.87, respectively. The thematic maps that were created using spatial interpolation of model forecasts from all the stations also confirmed that the district as a whole experienced drought in April 2019.
Baseline Probabilities for the Seasonal Prediction of Meteorological Drought
Journal of Applied Meteorology and Climatology, 2012
The inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, movin...
Global Meteorological Drought Prediction Using the North American Multi-Model Ensemble
Journal of Hydrometeorology, 2015
Precipitation forecasts from six climate models in the North American Multi-Model Ensemble (NMME) are combined with observed precipitation data to generate forecasts of the standardized precipitation index (SPI) for global land areas, and their skill was evaluated over the period 1982–2010. The skill of monthly precipitation forecasts from the NMME is also assessed. The value-added utility in using the NMME models to predict the SPI is identified by comparing the skill of its forecasts with a baseline skill based solely on the inherent persistence characteristics of the SPI itself. As expected, skill of the NMME-generated SPI forecasts depends on the season, location, and specific index considered (the 3- and 6-month SPI were evaluated). In virtually all locations and seasons, statistically significant skill is found at lead times of 1–2 months, although the skill comes largely from initial conditions. Added skill from the NMME is primarily in regions exhibiting El Niño–Southern Osc...
Water Resources Research, 2013
1] Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1 resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km 2 ) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions. (2013), Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide, Water Resour.
Skill of a global forecasting system in seasonal ensemble streamflow prediction
Hydrology and Earth System Sciences Discussions, 2016
In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system FEWS-World which has been set up within the European Commission 7th Framework Programme project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the global hydrological model PCR-GLOBWB. We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with bias-corrected seasonal meteorological forecast ensembles from the ECMWF and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the skill from the ESP ensembles, which contain no actual information on weather, serves as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier Score to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75<sup>th</sup> and lowe...
Global meteorological drought – Part 2: Seasonal forecasts
Hydrology and Earth System Sciences, 2014
Global seasonal forecasts of meteorological drought using the standardized precipitation index (SPI) are produced using two data sets as initial conditions: the Global Precipitation Climatology Centre (GPCC) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (ERAI); and two seasonal forecasts of precipitation, the most recent ECMWF seasonal forecast system and climatologically based ensemble forecasts. The forecast evaluation focuses on the periods where precipitation deficits are likely to have higher drought impacts, and the results were summarized over different regions in the world. The verification of the forecasts with lead time indicated that generally for all regions the least reduction on skill was found for (i) long lead times using ERAI or GPCC for monitoring and (ii) short lead times using ECMWF or climatological seasonal forecasts. The memory effect of initial conditions was found to be 1 month of lead time for the SPI-3, 4 months for the SPI-6 and 6 (or more) months for the SPI-12. Results show that dynamical forecasts of precipitation provide added value with skills at least equal to and often above that of climatological forecasts. Furthermore, it is very difficult to improve on the use of climatological forecasts for long lead times. Our results also support recent questions of whether seasonal forecasting of global drought onset was essentially a stochastic forecasting problem. Results are presented regionally and globally, and our results point to several regions in the world where drought onset forecasting is feasible and skilful.
Skill assessment of a seasonal forecast model to predict drought events for water resource systems
Journal of Hydrology, 2018
Droughts cause significant socioeconomic and environmental impacts, so it has become an extremely important element in decision-making within water resource systems. For this reason, the research in this field has increased considerably over the last few decades. In order to be capable of making early decisions and reducing drought impacts, it is necessary to predict the occurrence of such events months or even years in advance. In this sense, various methods have been used to predict the occurrence of droughts. At present, seasonal forecast data can be used to forecast meteorological, hydrological, agricultural and operational droughts. However, the seasonal forecast data of these dynamical oceanatmosphere coupled models must be analyzed in an exhaustive way, since it is known that these models may not adequately represent the climatic variability at river basin scale. Hence, this paper presents a new methodology for assessing the skill of a climate forecasting system in order to predict the occurrence of droughts by using contingency tables. The indices obtained from the contingency tables are necessary to perform the analysis of the predictive ability of the model in a semi-distributed way. All this taking into account the intensity of droughts using different scenarios based on the threshold below which it is considered to be in drought. Finally, a single value is obtained to determine the predictive ability of the forecasting model for the entire basin. The proposed methodology is applied to the Júcar river basin in Spain. It has been found that the analyzed forecast model shows better results than those obtained using an autoregressive model. Further work is needed to enhance climate forecasting from the perspective of water resources management, however, it should be mentioned that this type of data could be used for drought forecasting, allowing possible mitigation measures.
Drought forecasting using the standardized precipitation index
Water resources …, 2007
Unlike other natural disasters, drought events evolve slowly in time and their impacts generally span a long period of time. Such features do make possible a more effective drought mitigation of the most adverse effects, provided a timely monitoring of an ...